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| author | leshe4ka46 <alex9102naid1@ya.ru> | 2025-10-18 12:25:53 +0300 |
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| committer | leshe4ka46 <alex9102naid1@ya.ru> | 2025-10-18 12:25:53 +0300 |
| commit | 910a222fa60ce6ea0831f2956470b8a0b9f62670 (patch) | |
| tree | 1d6bbccafb667731ad127f93390761100fc11b53 | |
| parent | 35b9040e4104b0e79bf243a2c9769c589f96e2c4 (diff) | |
nvidia2
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diff --git a/Fundamentals_of_Accelerated_Data_Science/1-02_data_manipulation.ipynb b/Fundamentals_of_Accelerated_Data_Science/1-02_data_manipulation.ipynb new file mode 100644 index 0000000..37ffbe6 --- /dev/null +++ b/Fundamentals_of_Accelerated_Data_Science/1-02_data_manipulation.ipynb @@ -0,0 +1,2151 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "b53a7b12-538d-4459-b82a-a35c8c417849", + "metadata": {}, + "source": [ + "<img src=\"./images/DLI_Header.png\" width=400/>" + ] + }, + { + "cell_type": "markdown", + "id": "ae497b71-bc43-471e-8970-88a1878e7cf9", + "metadata": {}, + "source": [ + "# Fundamentals of Accelerated Data Science # " + ] + }, + { + "cell_type": "markdown", + "id": "a149b6d1-1880-4a5d-9d71-f963d3097aa4", + "metadata": {}, + "source": [ + "## 02 - Data Manipulation ##\n", + "\n", + "**Table of Contents**\n", + "<br>\n", + "This notebook explores the fundamentals of data acquisition and manipulation using DataFrame APIs, covering essential techniques for handling and processing datasets. This notebook covers the below sections: \n", + "1. [Data Background](#Data-Background)\n", + "1. [cuDF and pandas](#cuDF-and-pandas)\n", + " * [pandas](#pandas)\n", + " * [cuDF](#cuDF)\n", + "3. [Data Acquisition](#Data-Acquisition)\n", + "4. [Initial Data Exploration](#Initial-Data-Exploration)\n", + "5. [Indexing and Data Selection with `.loc` Accessor](#Indexing-and-Data-Selection-with-.loc-Accessor)\n", + "6. [Basic Operations](#Basic-Operations)\n", + " * [Exercise #1 - Convert `county` Column to Title Case](#Exercise-#1---Convert-county-Column-to-Title-Case)\n", + "7. [Aggregation](#Aggregation)\n", + "8. [Applying User-Defined Functions (UDFs) with `.map()` and `.apply()`](#Applying-User-Defined-Functions-(UDFs)-with-.map()-and-.apply())\n", + "9. [Filtering with `.loc` and Boolean Mask](#Filtering-with-.loc-and-Boolean-Mask)\n", + " * [Exercise #2 - Counties North of Sunderland](#Exercise-#2---Counties-North-of-Sunderland)\n", + "10. [Creating New Columns](#Creating-New-Columns)\n", + "11. [pandas vs. cuDF](#pandas-vs.-cuDF)\n", + "12. [cuDF pandas](#cuDF-pandas)\n", + " * [Exercise #3 - Automatic Acceleration](#Exercise-#3---Automatic-Acceleration)" + ] + }, + { + "cell_type": "markdown", + "id": "8b739635-4883-40b2-94e9-7a08f853871c", + "metadata": {}, + "source": [ + "## Data Background ##\n", + "For this workshop, we will be reading almost 60 million records (corresponding to the entire population of England and Wales) which were synthesized from official UK census data. " + ] + }, + { + "cell_type": "markdown", + "id": "95e6bbed-1c08-4002-837c-392d5a12658f", + "metadata": {}, + "source": [ + "## cuDF and pandas ##" + ] + }, + { + "cell_type": "markdown", + "id": "050926cb-1dee-447a-9da8-49ebb1292d55", + "metadata": {}, + "source": [ + "### pandas ###\n", + "[pandas](https://pandas.pydata.org/) is a widely-used open-source library for data manipulation and analysis in Python. It provides high-performance, easy-to-use data structures and tools for working with structured data. It popularized the term DataFrame as a data structure for statistical computing. In data science, pandas is used for: \n", + "* **Data loading and writing**: reads from and writes to various file formats like CSV, Excel, JSON, and SQL databases\n", + "* **Data cleaning and processing/preprocessing**: helps users with handling missing data, merging datasets, and reshaping data\n", + "* **Data analysis**: performs grouping, aggregating, and statistical operations\n", + "\n", + "**Note**: Data preprocessing refers to the process of transforming raw data into a format that is more suitable for analysis and other downstream tasks. " + ] + }, + { + "cell_type": "markdown", + "id": "79e09f10-be1d-4ffe-9247-1e605e3f450f", + "metadata": {}, + "source": [ + "### cuDF ###\n", + "Similarly, [cuDF](https://docs.rapids.ai/api/cudf/stable/) is a Python GPU DataFrame library for loading, joining, aggregating, filtering, and otherwise manipulating data. cuDF is designed to accelerate data science workflows by utilizing the parallel processing power of GPUs, potentially offering significant speed improvements over CPU-based alternatives for large datasets. The key features of cuDF include: \n", + "* **GPU Acceleration**: leverages NVIDIA GPUs for fast data processing and analysis\n", + "* **pandas-like API**: provides users a familiar interface and transition to GPU-based computing\n", + "* **Integration with other RAPIDS libraries**: works seamlessly with other GPU-accelerated tools in the RAPIDS ecosystem" + ] + }, + { + "cell_type": "markdown", + "id": "ff5519e2-f77f-4160-b362-979301705733", + "metadata": {}, + "source": [ + "**Note**: Both Pandas and cuDF serve similar purposes in data manipulation and analysis, but cuDF is specifically optimized for GPU acceleration, making it particularly useful for working with large datasets where performance is critical." + ] + }, + { + "cell_type": "markdown", + "id": "770fb1d8-73c5-4c45-a1e4-599f66e6b833", + "metadata": {}, + "source": [ + "## Data Acquisition ##\n", + "In our context, data acquisition refers to the process of collecting and importing data from various sources into a Python environment for analysis, processing, and manipulation. Data can come from a variety of sources: \n", + "* Local file in various formats\n", + "* Databases\n", + "* APIs\n", + "* Web scraping\n", + "\n", + "It's worth noting that Python's rich ecosystem of libraries makes it versatile for acquiring data from various sources, allowing data scientists to work with diverse datasets efficiently. CPU processing will be responsible for acquiring data from APIs or Web Scraping. In most cases, network bandwidth will likely be the bottleneck. Furthermore, cuDF doesn't have a way to get transactions from SQL data bases directly into GPU memory. The recommended approach for reading data from a database is to first use CPU-based methods (i.e. pandas), then convert to cuDF for GPU-accelerated processing. \n", + "\n", + "Below we use the `head` linux command to display the beginning of the data file. This allows us to understand how to read the data correctly. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "247d2b96-1bce-4e26-89bd-d659df3528d7", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\"age\",\"sex\",\"county\",\"lat\",\"long\",\"name\"\n", + "0,\"m\",\"DARLINGTON\",54.53364379,-1.524400639,\"FRANCIS\"\n", + "0,\"m\",\"DARLINGTON\",54.42625551,-1.465313919,\"EDWARD\"\n", + "0,\"m\",\"DARLINGTON\",54.55520036,-1.496417277,\"TEDDY\"\n", + "0,\"m\",\"DARLINGTON\",54.54790635,-1.572341399,\"ANGUS\"\n" + ] + } + ], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "!head -n 5 data/uk_pop.csv" + ] + }, + { + "cell_type": "markdown", + "id": "be7bd168-eb68-45cc-8009-64569974a187", + "metadata": {}, + "source": [ + "One row will represent one person. We have information about their `age`, `sex`, `county`, location, and `name`. Using cuDF, the RAPIDS API providing a GPU-accelerated DataFrame, we can read data from [a variety of formats](https://rapidsai.github.io/projects/cudf/en/0.10.0/api.html#module-cudf.io.csv), including csv, json, parquet, feather, orc, and pandas DataFrames, among others. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "89c435b1-35d5-4971-ade1-549ae77d22db", + "metadata": {}, + "outputs": [], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "import cudf\n", + "import cupy as cp\n", + "import numpy as np\n", + "\n", + "from datetime import datetime\n", + "import random\n", + "import time" + ] + }, + { + "cell_type": "markdown", + "id": "9cb9faf3-4dc9-42bf-b481-98fb4155033e", + "metadata": {}, + "source": [ + "Below we read the data from a local csv file directly into GPU memory with the `read_csv()` function. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "a343a943-fd64-45f6-abd5-a991810cf5f3", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Duration: 3.72 seconds\n" + ] + } + ], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "start=time.time()\n", + "df=cudf.read_csv('./data/uk_pop.csv')\n", + "print(f'Duration: {round(time.time()-start, 2)} seconds')" + ] + }, + { + "cell_type": "markdown", + "id": "c406541c-884a-49c3-b5cb-7aaf21b60403", + "metadata": {}, + "source": [ + "**Note**: Because of the sophisticated GPU memory management behind the scenes in cuDF, the first data load into a fresh RAPIDS memory environment is sometimes substantially slower than subsequent loads. The [RAPIDS Memory Manager](https://github.com/rapidsai/rmm) is preparing additional memory to accommodate the array of data science operations that we may be interested in using on the data, rather than allocating and deallocating the memory repeatedly throughout the workflow. \n", + "\n", + "Below we get the general information about the DataFrame with the `DataFrame.info()` method. " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "18cd5602-9129-4809-a95f-1e30940558c5", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "<class 'cudf.core.dataframe.DataFrame'>\n", + "RangeIndex: 58479894 entries, 0 to 58479893\n", + "Data columns (total 6 columns):\n", + " # Column Dtype\n", + "--- ------ -----\n", + " 0 age int64\n", + " 1 sex object\n", + " 2 county object\n", + " 3 lat float64\n", + " 4 long float64\n", + " 5 name object\n", + "dtypes: float64(2), int64(1), object(3)\n", + "memory usage: 2.9 GB\n" + ] + } + ], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "df.info(memory_usage='deep')" + ] + }, + { + "cell_type": "markdown", + "id": "a8289385-f1ac-4ccd-8ba4-6b127b200b42", + "metadata": {}, + "source": [ + "The **DataFrame** is a two-dimensional labeled data structure. It's organized in rows and columns, similar to a spreadsheet or SQL table. Both rows and columns have labels. Rows are typically labeled with an index, while columns have column names. Data is aligned based on row and column labels when performing operations. This is useful for enabling highly efficient vectorized operations across columns or rows. A **Series** refers to a one-dimensional array and is typically associated with a single column of data with an index. \n", + "\n", + "There are ~60MM records across 6 columns. cuDF is able to read data from local files directly into the GPU very efficiently. By default, cuDF samples the dataset to infer the most appropriate data types for each columns. \n", + "\n", + "**Note**: The DataFrame has `.dtypes` and `.columns` attributes that can be used to get similar information. " + ] + }, + { + "cell_type": "markdown", + "id": "af6127ab-437e-4a60-b9bd-5f9671c10602", + "metadata": {}, + "source": [ + "## Initial Data Exploration ##\n", + "Now that we have some data loaded, let's do some initial exploration. \n", + "\n", + "Below we preview the DataFrame with the `DataFrame.head()` method. " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "7faf372e-644c-4120-8080-779f3a23a152", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>age</th>\n", + " <th>sex</th>\n", + " <th>county</th>\n", + " <th>lat</th>\n", + " <th>long</th>\n", + " <th>name</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.533644</td>\n", + " <td>-1.524401</td>\n", + " <td>FRANCIS</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.426256</td>\n", + " <td>-1.465314</td>\n", + " <td>EDWARD</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.555200</td>\n", + " <td>-1.496417</td>\n", + " <td>TEDDY</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.547906</td>\n", + " <td>-1.572341</td>\n", + " <td>ANGUS</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.477639</td>\n", + " <td>-1.605995</td>\n", + " <td>CHARLIE</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " age sex county lat long name\n", + "0 0 m DARLINGTON 54.533644 -1.524401 FRANCIS\n", + "1 0 m DARLINGTON 54.426256 -1.465314 EDWARD\n", + "2 0 m DARLINGTON 54.555200 -1.496417 TEDDY\n", + "3 0 m DARLINGTON 54.547906 -1.572341 ANGUS\n", + "4 0 m DARLINGTON 54.477639 -1.605995 CHARLIE" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "id": "ee4649b1-2730-47e9-a9d7-331fe7514241", + "metadata": {}, + "source": [ + "## Indexing and Data Selection with `.loc` Accessor ##\n", + "The `.loc` accessor in cuDF DataFrames is used for label-based indexing and selection of data. It allows us to access and manipulate data in a DataFrame based on row and column labels. We can use `DataFrame.loc[row_label(s), column_label(s)]` to access a group of rows and columns. When selecting multiple labels, a list (`[]`) is used. Furthermore, we can use the slicing operator (`:`, i.e. `start:end`) to specify a range of elements. 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" <td>DARLINGTON</td>\n", + " <td>54.533644</td>\n", + " <td>-1.524401</td>\n", + " <td>FRANCIS</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.426256</td>\n", + " <td>-1.465314</td>\n", + " <td>EDWARD</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.555200</td>\n", + " <td>-1.496417</td>\n", + " <td>TEDDY</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.547906</td>\n", + " <td>-1.572341</td>\n", + " <td>ANGUS</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.477639</td>\n", + " <td>-1.605995</td>\n", + " <td>CHARLIE</td>\n", + " </tr>\n", + " <tr>\n", + " <th>5</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.522900</td>\n", + " <td>-1.599255</td>\n", + " <td>VICTOR</td>\n", + " </tr>\n", + " <tr>\n", + " <th>6</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.501872</td>\n", + " <td>-1.667874</td>\n", + " <td>EAMONN</td>\n", + " </tr>\n", + " <tr>\n", + " <th>7</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.554709</td>\n", + " <td>-1.494506</td>\n", + " <td>HARRY</td>\n", + " </tr>\n", + " <tr>\n", + " <th>8</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.602288</td>\n", + " <td>-1.586457</td>\n", + " <td>HECTOR</td>\n", + " </tr>\n", + " <tr>\n", + " <th>9</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.489992</td>\n", + " <td>-1.652537</td>\n", + " <td>THEODORE</td>\n", + " </tr>\n", + " <tr>\n", + " <th>10</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.551315</td>\n", + " <td>-1.519593</td>\n", + " <td>MUHAMMAD</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " age sex county lat long name\n", + "0 0 m DARLINGTON 54.533644 -1.524401 FRANCIS\n", + "1 0 m DARLINGTON 54.426256 -1.465314 EDWARD\n", + "2 0 m DARLINGTON 54.555200 -1.496417 TEDDY\n", + "3 0 m DARLINGTON 54.547906 -1.572341 ANGUS\n", + "4 0 m DARLINGTON 54.477639 -1.605995 CHARLIE\n", + "5 0 m DARLINGTON 54.522900 -1.599255 VICTOR\n", + "6 0 m DARLINGTON 54.501872 -1.667874 EAMONN\n", + "7 0 m DARLINGTON 54.554709 -1.494506 HARRY\n", + "8 0 m DARLINGTON 54.602288 -1.586457 HECTOR\n", + "9 0 m DARLINGTON 54.489992 -1.652537 THEODORE\n", + "10 0 m DARLINGTON 54.551315 -1.519593 MUHAMMAD" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "# get first cell\n", + "display(df.loc[0, 'age'])\n", + "print('-'*40)\n", + "\n", + "# get multiple rows and columns\n", + "display(df.loc[[0, 1, 2], ['age', 'sex', 'county']])\n", + "print('-'*40)\n", + "\n", + "# slice a range of rows and columns\n", + "display(df.loc[0:5, 'age':'county'])\n", + "print('-'*40)\n", + "\n", + "# slice a range of rows and columns\n", + "display(df.loc[:10, :'name'])" + ] + }, + { + "cell_type": "markdown", + "id": "a451118f-b986-49b6-ae03-2526e44007a7", + "metadata": {}, + "source": [ + "**Note**: `df[column_label(s)]` is another way to access specific columns, similar to `df.loc[:, column_labels]`. " + ] + }, + { + "cell_type": "markdown", + "id": "055f8828-db5b-419a-aab5-bf149b9fd829", + "metadata": {}, + "source": [ + "## Basic Operations ##\n", + "cuDF support a wide range of operations for numerical data. Although strings are not a data type traditionally associated with GPUs, cuDF supports powerful accelerated string operations.\n", + "* Numerical operations:\n", + " * Arithmetic operations: addition, subtraction, multiplication, division\n", + "* String operations:\n", + " * Case conversion: `.upper()`, `.lower()`, `.title()`\n", + " * String manipulation: concatenation, substring, extraction, padding\n", + " * Pattern matching: `contains()`\n", + " * Splitting: `.split()`\n", + "* Comparison operations: greater than, less than, equal to, etc.\n", + "\n", + "These operations will be performed element-wise for each row. This allows for efficient **vectorized operations** across entire columns. These operations are implemented as vector operations instead of iteration because vector operations can be applied to entire arrays of data, instead of iterating through each element individually. Vectorization is significantly faster than iterating over elements, especially for large datasets. When operating on multiple columns, operations are aligned by index, ensuring that calculations are performed on the correct corresponding elements across columns. These element-wise operations are typically highly optimized and can be much faster than explicit loops, especially for large datasets. We can get the underlying array of data with the `.values` attribute. This is useful when we want to perform operations on the underlying data. \n", + "\n", + "**Note**: Iterating over a cuDF Series, DataFrame or Index is not supported. This is because iterating over data that resides on the GPU will yield extremely poor performance, as GPUs are optimized for highly parallel operations rather than sequential operations. \n", + "\n", + "Below we calculate the birth year for each person. " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "d4286299-3a43-4e53-a9fb-04e1f20a40a4", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 2025\n", + "1 2025\n", + "2 2025\n", + "3 2025\n", + "4 2025\n", + " ... \n", + "58479889 1935\n", + "58479890 1935\n", + "58479891 1935\n", + "58479892 1935\n", + "58479893 1935\n", + "Name: age, Length: 58479894, dtype: int64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "array([2025, 2025, 2025, ..., 1935, 1935, 1935])" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "# get current year\n", + "current_year=datetime.now().year\n", + "\n", + "# derive the birth year\n", + "display(current_year-df.loc[:, 'age'])\n", + "\n", + "# get the age array (CuPy for cuDF)\n", + "age_ary=df.loc[:, 'age'].values\n", + "\n", + "# derive the birth year\n", + "current_year-age_ary" + ] + }, + { + "cell_type": "markdown", + "id": "00213228-e32e-4a88-853e-eef53fad4da8", + "metadata": {}, + "source": [ + "When performing operations between a DataFrame and a scalar value, the scalar is \"broadcast\" to match the shape of the DataFrame, effectively applying it to each element. \n", + "\n", + "```\n", + "current_year - df.loc[:, 'age']\n", + "-------------------------------\n", + " (scalar) (array) \n", + " 2024, - 0\n", + " 2024, - 0\n", + " 2024, - 0\n", + " 2024, - 0\n", + " 2024, - 0\n", + " ... - ...\n", + "```\n", + "\n", + "This partially explains why cuDF provides significant performance improvements over pandas, especially for large datasets. The parallel processing architecture of GPUs are designed with thousands of small, specialized cores that can execute many operations simultaneously. This architecture is ideal for vectorized operations, which perform the same instruction on multiple data elements in parallel. " + ] + }, + { + "cell_type": "markdown", + "id": "760a2729-9c7a-4602-83ac-5e171cc4f5f9", + "metadata": {}, + "source": [ + "<a name='s4.1'></a>\n", + "### Exercise #1 - Convert `county` Column to Title Case ###\n", + "As it stands, all of the counties are UPPERCASE. We want to convert the `county` column to title case. \n", + "\n", + "**Instructions**: <br>\n", + "* Modify the `<FIXME>` only and execute the below cell to convert the `county` column to title case. " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "5365b5b6-6a00-47e7-8839-0cc8b183c6d7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 Darlington\n", + "1 Darlington\n", + "2 Darlington\n", + "3 Darlington\n", + "4 Darlington\n", + " ... \n", + "58479889 Newport\n", + "58479890 Newport\n", + "58479891 Newport\n", + "58479892 Newport\n", + "58479893 Newport\n", + "Name: county, Length: 58479894, dtype: object" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['county'].str.title()" + ] + }, + { + "cell_type": "raw", + "id": "738892d3-4bab-4404-af83-b8623804ca5d", + "metadata": {}, + "source": [ + "\n", + "df['county'].str.title()" + ] + }, + { + "cell_type": "markdown", + "id": "35a2520b-5eec-4d59-a956-3f31b43a98b2", + "metadata": {}, + "source": [ + "Click ... for solution. " + ] + }, + { + "cell_type": "markdown", + "id": "d163438b-9993-41e7-856c-76101135a9ad", + "metadata": {}, + "source": [ + "Performing comparison operations or applying conditions create boolean values (`True`/`False`) that corresponds element-wise. \n", + "\n", + "Below we check if each person is an adult. " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "481218f1-ec09-4776-bda6-b039ccc190ab", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 False\n", + "1 False\n", + "2 False\n", + "3 False\n", + "4 False\n", + " ... \n", + "58479889 True\n", + "58479890 True\n", + "58479891 True\n", + "58479892 True\n", + "58479893 True\n", + "Name: age, Length: 58479894, dtype: bool" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "df['age']>=18" + ] + }, + { + "cell_type": "markdown", + "id": "0b98c380-828a-404b-9fb2-1d215337eff0", + "metadata": {}, + "source": [ + "## Aggregation ##\n", + "Aggregation is an important operation for data science tasks, allowing us to summarize and analyze grouped data. It's commonly used for tasks like calculating totals, averages, counts, etc. cuDF supports common aggregations like `.sum()`, `.mean()`, `.min()`, `.max()`, `.count()`, `.std()`(standard deviation), etc. It also supports more advanced aggregations like `.quantile()` and `.corr()` (correlation). With the `axis` parameter, aggregation operations can be applied column-wise (`0`) or row-wise (`1`). \n", + "\n", + "When the aggregation is implemented as a vector operation, specifically a reduction operation, it is very efficient on the GPU becasue a large number of data elements can be processed simultaneously and in parallel. Column-wise operations also benefit from the [Apache Arrow columnar memory format](https://arrow.apache.org/docs/format/Columnar.html). \n", + "\n", + "<p><img src='images/sum_reduction.png' width=720></p>\n", + "\n", + "Below we calculate the arithmetic mean of `lat` and `long` to get an approximate center. " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "bee8be82-8631-4863-a1fa-e46eed47e334", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "lat 52.350600\n", + "long -1.304956\n", + "dtype: float64" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "df[['lat', 'long']].mean()" + ] + }, + { + "cell_type": "markdown", + "id": "8a53f0f4-7dc5-40fd-af07-3e82d6556393", + "metadata": {}, + "source": [ + "## Applying User-Defined Functions (UDFs) with `.map()` and `.apply()` ##\n", + "The `.map()` and `.apply()` methods are the primary ways of applying user-defined functions element-wise, and row or column-wise, respectively. We can pass a callable function (built-in or user-defined) as the argument, which is then applied to the entire data structure. Not all operations can be vectorized, especially complex custom logic. In such cases, methods like `.apply()` or custom UDFs might be necessary.\n", + "\n", + "Below we use `.apply()` to check if each person is an adult. " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "c65e80f8-1cc3-453c-85f2-910dab451228", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 0\n", + "1 0\n", + "2 0\n", + "3 0\n", + "4 0\n", + " ..\n", + "58479889 1\n", + "58479890 1\n", + "58479891 1\n", + "58479892 1\n", + "58479893 1\n", + "Length: 58479894, dtype: int64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Duration: 2.19 seconds\n" + ] + } + ], + "source": [ + "# DO NOT CHNAGE THIS CELL\n", + "# define a function to check if age is greater than or equal to 18\n", + "start=time.time()\n", + "def is_adult(row): \n", + " if row['age']>=18: \n", + " return 1\n", + " else: \n", + " return 0\n", + "\n", + "# derive the birth year\n", + "display(df.apply(is_adult, axis=1))\n", + "print(f'Duration: {round(time.time()-start, 2)} seconds')" + ] + }, + { + "cell_type": "markdown", + "id": "02828781-6f5b-49d5-87a5-aa5ef08adf15", + "metadata": {}, + "source": [ + "We can also use a [**lambda function**](https://docs.python.org/3/glossary.html#term-lambda) when the function is simple. Lambda functions are limited to a single expression but can include a conditional statement and mulitple arguments. " + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "31d6e7fb-f435-4b1f-8e74-e732cc406b51", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 0\n", + "1 0\n", + "2 0\n", + "3 0\n", + "4 0\n", + " ..\n", + "58479889 1\n", + "58479890 1\n", + "58479891 1\n", + "58479892 1\n", + "58479893 1\n", + "Length: 58479894, dtype: int64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Duration: 0.05 seconds\n" + ] + } + ], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "# derive the birth year\n", + "start=time.time()\n", + "display(df.apply(lambda x: 1 if x['age']>=18 else 0, axis=1))\n", + "print(f'Duration: {round(time.time()-start, 2)} seconds')" + ] + }, + { + "cell_type": "markdown", + "id": "475cc2f1-4dc9-4492-aab5-9e51ebf54ebb", + "metadata": {}, + "source": [ + "**Note**: The `.apply()` function in pandas accepts any user-defined function that can include arbitrary operations that are applied to each value of a Series and DataFrame. cuDF also supports `.apply()`, but it relies on Numba to JIT compile the UDF (not in scope) and execute it on the GPU. This can be extremely fast, but imposes a few limitations on what operations are allowed in the UDF. See the docs on [UDFs](https://docs.rapids.ai/api/cudf/stable/user_guide/guide-to-udfs/) for details." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "ecadefaa-380c-412c-87af-05c63d3f7871", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 0\n", + "1 0\n", + "2 0\n", + "3 0\n", + "4 0\n", + " ..\n", + "58479889 1\n", + "58479890 1\n", + "58479891 1\n", + "58479892 1\n", + "58479893 1\n", + "Name: age, Length: 58479894, dtype: int64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Duration: 0.02 seconds\n" + ] + } + ], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "# derive the birth year\n", + "start=time.time()\n", + "display((df['age']>=18).astype('int'))\n", + "print(f'Duration: {round(time.time()-start, 2)} seconds')" + ] + }, + { + "cell_type": "markdown", + "id": "84f0c41b-ad56-4cc7-b074-f2390f41cc70", + "metadata": {}, + "source": [ + "Below we use `Series.map()` to determine the number of characters in each person's name. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c4a3e4e1-fd83-4024-bcbf-29216c11016f", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "df['name'].map(lambda x: len(x))" + ] + }, + { + "cell_type": "markdown", + "id": "7c717ada-69b2-4982-b81b-8594af6d9bf1", + "metadata": {}, + "source": [ + "## Filtering with `.loc` and Boolean Mask ##\n", + "A boolean mask is an array of `True`/`False` values that corresponds element-wise to another array or data structure. It's used for filtering and selecting data based on certain conditions. In this context, the mask can be used to index or filter a DataFrame with `.loc`, selecting only the elements where the mask is `True`. \n", + "\n", + "**Note**: Boolean masking is often more efficient than iterative approaches, especially for large datasets, as it leverages vectorized operations. \n", + "\n", + "Below we use the `.loc` accessor and a boolean mask to filter people whose names start with an `E`. " + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "cf9cc540-1de6-4e50-986a-5bf9bd9056a6", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>age</th>\n", + " <th>sex</th>\n", + " <th>county</th>\n", + " <th>lat</th>\n", + " <th>long</th>\n", + " <th>name</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.426256</td>\n", + " <td>-1.465314</td>\n", + " <td>EDWARD</td>\n", + " </tr>\n", + " <tr>\n", + " <th>6</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.501872</td>\n", + " <td>-1.667874</td>\n", + " <td>EAMONN</td>\n", + " </tr>\n", + " <tr>\n", + " <th>34</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.483065</td>\n", + " <td>-1.501312</td>\n", + " <td>ETHAN</td>\n", + " </tr>\n", + " <tr>\n", + " <th>45</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.640205</td>\n", + " <td>-1.558986</td>\n", + " <td>ELVIN</td>\n", + " </tr>\n", + " <tr>\n", + " <th>49</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.575450</td>\n", + " <td>-1.600592</td>\n", + " <td>EDWARD</td>\n", + " </tr>\n", + " <tr>\n", + " <th>...</th>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " </tr>\n", + " <tr>\n", + " <th>58479859</th>\n", + " <td>90</td>\n", + " <td>f</td>\n", + " <td>NEWPORT</td>\n", + " <td>51.576452</td>\n", + " <td>-2.891774</td>\n", + " <td>EDIE</td>\n", + " </tr>\n", + " <tr>\n", + " <th>58479867</th>\n", + " <td>90</td>\n", + " <td>f</td>\n", + " <td>NEWPORT</td>\n", + " <td>51.555083</td>\n", + " <td>-3.080259</td>\n", + " <td>ELEANOR</td>\n", + " </tr>\n", + " <tr>\n", + " <th>58479871</th>\n", + " <td>90</td>\n", + " <td>f</td>\n", + " <td>NEWPORT</td>\n", + " <td>51.515820</td>\n", + " <td>-2.839532</td>\n", + " <td>EMERSON</td>\n", + " </tr>\n", + " <tr>\n", + " <th>58479875</th>\n", + " <td>90</td>\n", + " <td>f</td>\n", + " <td>NEWPORT</td>\n", + " <td>51.510140</td>\n", + " <td>-3.004406</td>\n", + " <td>ELLA</td>\n", + " </tr>\n", + " <tr>\n", + " <th>58479885</th>\n", + " <td>90</td>\n", + " <td>f</td>\n", + " <td>NEWPORT</td>\n", + " <td>51.586575</td>\n", + " <td>-2.799302</td>\n", + " <td>ELIN</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "<p>5081794 rows × 6 columns</p>\n", + "</div>" + ], + "text/plain": [ + " age sex county lat long name\n", + "1 0 m DARLINGTON 54.426256 -1.465314 EDWARD\n", + "6 0 m DARLINGTON 54.501872 -1.667874 EAMONN\n", + "34 0 m DARLINGTON 54.483065 -1.501312 ETHAN\n", + "45 0 m DARLINGTON 54.640205 -1.558986 ELVIN\n", + "49 0 m DARLINGTON 54.575450 -1.600592 EDWARD\n", + "... ... .. ... ... ... ...\n", + "58479859 90 f NEWPORT 51.576452 -2.891774 EDIE\n", + "58479867 90 f NEWPORT 51.555083 -3.080259 ELEANOR\n", + "58479871 90 f NEWPORT 51.515820 -2.839532 EMERSON\n", + "58479875 90 f NEWPORT 51.510140 -3.004406 ELLA\n", + "58479885 90 f NEWPORT 51.586575 -2.799302 ELIN\n", + "\n", + "[5081794 rows x 6 columns]" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "boolean_mask=df['name'].str.startswith('E')\n", + "df.loc[boolean_mask]" + ] + }, + { + "cell_type": "markdown", + "id": "5d76a6dd-5d0b-4fd2-868a-235255375af0", + "metadata": {}, + "source": [ + "Multiple conditions can be combined using logical operators (`&` and `|`). \n", + "\n", + "**Note**: When using multiple conditions, it's important to wrap each condition in parentheses (`(` and `)`) to ensure correct order of operations. \n", + "\n", + "Below we use the `.loc` accessor and multiple conditions to filter adults whose names start with an `E`. " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "03713403-6575-437d-99f0-c7f8ec3cb13b", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>age</th>\n", + " <th>sex</th>\n", + " <th>county</th>\n", + " <th>lat</th>\n", + " <th>long</th>\n", + " <th>name</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.426256</td>\n", + " <td>-1.465314</td>\n", + " <td>EDWARD</td>\n", + " </tr>\n", + " <tr>\n", + " <th>6</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.501872</td>\n", + " <td>-1.667874</td>\n", + " <td>EAMONN</td>\n", + " </tr>\n", + " <tr>\n", + " <th>34</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.483065</td>\n", + " <td>-1.501312</td>\n", + " <td>ETHAN</td>\n", + " </tr>\n", + " <tr>\n", + " <th>45</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.640205</td>\n", + " <td>-1.558986</td>\n", + " <td>ELVIN</td>\n", + " </tr>\n", + " <tr>\n", + " <th>49</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.575450</td>\n", + " <td>-1.600592</td>\n", + " <td>EDWARD</td>\n", + " </tr>\n", + " <tr>\n", + " <th>...</th>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " </tr>\n", + " <tr>\n", + " <th>58479889</th>\n", + " <td>90</td>\n", + " <td>f</td>\n", + " <td>NEWPORT</td>\n", + " <td>51.626744</td>\n", + " <td>-2.859381</td>\n", + " <td>FREYA</td>\n", + " </tr>\n", + " <tr>\n", + " <th>58479890</th>\n", + " <td>90</td>\n", + " <td>f</td>\n", + " <td>NEWPORT</td>\n", + " <td>51.546043</td>\n", + " <td>-2.897815</td>\n", + " <td>GEORGINA</td>\n", + " </tr>\n", + " <tr>\n", + " <th>58479891</th>\n", + " <td>90</td>\n", + " <td>f</td>\n", + " <td>NEWPORT</td>\n", + " <td>51.605268</td>\n", + " <td>-2.849656</td>\n", + " <td>REBECCA</td>\n", + " </tr>\n", + " <tr>\n", + " <th>58479892</th>\n", + " <td>90</td>\n", + " <td>f</td>\n", + " <td>NEWPORT</td>\n", + " <td>51.554649</td>\n", + " <td>-2.934364</td>\n", + " <td>JESSICA</td>\n", + " </tr>\n", + " <tr>\n", + " <th>58479893</th>\n", + " <td>90</td>\n", + " <td>f</td>\n", + " <td>NEWPORT</td>\n", + " <td>51.578787</td>\n", + " <td>-2.827954</td>\n", + " <td>FLORENCE</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "<p>47085782 rows × 6 columns</p>\n", + "</div>" + ], + "text/plain": [ + " age sex county lat long name\n", + "1 0 m DARLINGTON 54.426256 -1.465314 EDWARD\n", + "6 0 m DARLINGTON 54.501872 -1.667874 EAMONN\n", + "34 0 m DARLINGTON 54.483065 -1.501312 ETHAN\n", + "45 0 m DARLINGTON 54.640205 -1.558986 ELVIN\n", + "49 0 m DARLINGTON 54.575450 -1.600592 EDWARD\n", + "... ... .. ... ... ... ...\n", + "58479889 90 f NEWPORT 51.626744 -2.859381 FREYA\n", + "58479890 90 f NEWPORT 51.546043 -2.897815 GEORGINA\n", + "58479891 90 f NEWPORT 51.605268 -2.849656 REBECCA\n", + "58479892 90 f NEWPORT 51.554649 -2.934364 JESSICA\n", + "58479893 90 f NEWPORT 51.578787 -2.827954 FLORENCE\n", + "\n", + "[47085782 rows x 6 columns]" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "df[(df['age']>=18) | (df['name'].str.startswith('E'))]" + ] + }, + { + "cell_type": "markdown", + "id": "69b7f7ef-b270-4eae-8852-d6f48bf83086", + "metadata": {}, + "source": [ + "<a name='s4.1'></a>\n", + "### Exercise #2 - Counties North of Sunderland ###\n", + "This exercise will require to use the `.loc` accessor, and several of the techniques described above. We want to identify the latitude of the northernmost resident of Sunderland county (the person with the maximum `lat` value), and then determine which counties have any residents north of this resident. Use the `Series.unique()` method of to de-duplicate the result.\n", + "\n", + "**Instructions**: <br>\n", + "* Modify the `<FIXME>` only and execute the below cell to identify counties north of Sunderland. " + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "8c1394db-6bca-473b-a053-61a6066bd835", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 COUNTY DURHAM\n", + "1 NORTHUMBERLAND\n", + "2 GATESHEAD\n", + "3 NEWCASTLE UPON TYNE\n", + "4 NORTH TYNESIDE\n", + "5 SOUTH TYNESIDE\n", + "6 CUMBRIA\n", + "7 NORTH YORKSHIRE\n", + "Name: county, dtype: object" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sunderland_residents=df.loc[df['county'].str.upper() == 'SUNDERLAND']\n", + "northmost_sunderland_lat=sunderland_residents['lat'].max()\n", + "df.loc[df['lat'] > northmost_sunderland_lat]['county'].unique()" + ] + }, + { + "cell_type": "raw", + "id": "0cf99881-1a27-409a-822b-7e62b5953f3a", + "metadata": {}, + "source": [ + "\n", + "sunderland_residents=df.loc[df['county'] == 'SUNDERLAND']\n", + "northmost_sunderland_lat=sunderland_residents['lat'].max()\n", + "df.loc[df['lat'] > northmost_sunderland_lat]['county'].unique()" + ] + }, + { + "cell_type": "markdown", + "id": "0594efe7-97d4-4884-bffb-a26f5144ad54", + "metadata": {}, + "source": [ + "Click ... for solution. " + ] + }, + { + "cell_type": "markdown", + "id": "d5ead43e-37bc-4a3d-a64f-bb82ad01ad99", + "metadata": {}, + "source": [ + "## Creating New Columns ##" + ] + }, + { + "cell_type": "markdown", + "id": "45ada779-bc14-4fba-88d8-62c282345a63", + "metadata": {}, + "source": [ + "We can create new columns by assigning values to the column label. The new column should have the same number of rows as the existing DataFrame. Typically, we create new columns by performing operations on existing columns. \n", + "\n", + "Below we create a few additional columns. " + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "977fdb2b-dbf1-4842-ab0f-31b9af65e0d1", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>age</th>\n", + " <th>sex</th>\n", + " <th>county</th>\n", + " <th>lat</th>\n", + " <th>long</th>\n", + " <th>name</th>\n", + " <th>birth_year</th>\n", + " <th>sex_normalize</th>\n", + " <th>county_normalize</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.533644</td>\n", + " <td>-1.524401</td>\n", + " <td>Francis</td>\n", + " <td>2025</td>\n", + " <td>M</td>\n", + " <td>Darlington</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.426256</td>\n", + " <td>-1.465314</td>\n", + " <td>Edward</td>\n", + " <td>2025</td>\n", + " <td>M</td>\n", + " <td>Darlington</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.555200</td>\n", + " <td>-1.496417</td>\n", + " <td>Teddy</td>\n", + " <td>2025</td>\n", + " <td>M</td>\n", + " <td>Darlington</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.547906</td>\n", + " <td>-1.572341</td>\n", + " <td>Angus</td>\n", + " <td>2025</td>\n", + " <td>M</td>\n", + " <td>Darlington</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.477639</td>\n", + " <td>-1.605995</td>\n", + " <td>Charlie</td>\n", + " <td>2025</td>\n", + " <td>M</td>\n", + " <td>Darlington</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " age sex county lat long name birth_year \\\n", + "0 0 m DARLINGTON 54.533644 -1.524401 Francis 2025 \n", + "1 0 m DARLINGTON 54.426256 -1.465314 Edward 2025 \n", + "2 0 m DARLINGTON 54.555200 -1.496417 Teddy 2025 \n", + "3 0 m DARLINGTON 54.547906 -1.572341 Angus 2025 \n", + "4 0 m DARLINGTON 54.477639 -1.605995 Charlie 2025 \n", + "\n", + " sex_normalize county_normalize \n", + "0 M Darlington \n", + "1 M Darlington \n", + "2 M Darlington \n", + "3 M Darlington \n", + "4 M Darlington " + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "# get current year\n", + "current_year=datetime.now().year\n", + "\n", + "# numerical operations\n", + "df['birth_year']=current_year-df['age']\n", + "\n", + "# string operations\n", + "df['sex_normalize']=df['sex'].str.upper()\n", + "df['county_normalize']=df['county'].str.title().str.replace(' ', '_')\n", + "df['name']=df['name'].str.title()\n", + "\n", + "# preview\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "66c5d332-a6ef-4f8d-9560-fa860ea1679a", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'status': 'ok', 'restart': True}" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "import IPython\n", + "app = IPython.Application.instance()\n", + "app.kernel.do_shutdown(True)" + ] + }, + { + "cell_type": "markdown", + "id": "cd7bed0a-d46d-402a-884e-e2e17c98738c", + "metadata": {}, + "source": [ + "## pandas vs. cuDF ##\n", + "Except for being much more performant with large datasets, cuDF looks and feels a lot like Pandas. By way of review, cuDF and pandas share the below similarities: \n", + "* **API similarity**: cuDF provides a pandas-like API that is familiar to data engineers and data scientists. It aims to implement many of the same functions and operations as pandas, allowing users to easily accelerate their existing pandas workflows.\n", + "* **Similar operations**: cuDF implements many common pandas operations such as filtering, joining, aggregating, and groupby.\n", + "\n", + "<p><img src='images/pandas_vs_cudf.png' width=1080></p>\n", + "\n", + "Comparing to pandas, cuDF tends to perform better for large datasets because of the follow features: \n", + "* GPUs excel at parallel computation, which is advantageous for many data science and machine learning tasks.\n", + "* GPUs typically have much higher memory bandwidth than CPUs, allowing for faster data access in memory-bound operations.\n", + "* cuDF leverages GPU's ability to perform vectorized operations efficiently, which is particularly beneficial for large datasets.\n", + "* cuDF uses a columnar data format, which can lead to more efficient memory access patterns on GPUs. When performing data operations on cuDF Dataframes, column operations are typically much more performant than row-wise operations.\n", + "\n", + "**Note**: It's important to note that the performance advantage of cuDF over pandas can vary depending on the specific operation, data size, and hardware configuration. For smaller datasets or simpler operations, the overhead of GPU initialization might make pandas on CPU faster." + ] + }, + { + "cell_type": "markdown", + "id": "c9c00b32-f5f2-46de-a50a-fc54f3244dab", + "metadata": {}, + "source": [ + "## cuDF pandas ##\n", + "Starting with version `23.10.01`, cuDF introduced a **pandas accelerator mode** (`cudf.pandas`) that supports 100% of the pandas API. This mode allows users to accelerate pandas code on the GPU without requiring any code changes. Not all operations can be performed on the GPU. When using `cudf.pandas`, operations that can be accelerated will run on the GPU, while unsupported operations will automatically fall back to pandas on the CPU. For example, `.read_sql()`. this will first read sql with pandas and move the data to cuDF. \n", + "\n", + "There are two ways to activate cuDF pandas:\n", + "- Jupyter Magic Command\n", + "```\n", + "%load_ext cudf.pandas\n", + "import pandas\n", + "...\n", + "```\n", + "- Python Import\n", + "```\n", + "import cudf.pandas\n", + "cudf.pandas.install()\n", + "\n", + "import pandas as pd\n", + "...\n", + "```\n", + "\n", + "**Note**: There are no other changes required - this is useful to quickly accelerate existing workloads with minimum code change. More information about cuDF pandas can be found [here](https://docs.rapids.ai/api/cudf/stable/cudf_pandas/). \n", + "\n", + "cuDF pandas is a no code change accelerator for pandas for automatic acceleration of any supported pandas call. \n", + "\n", + "Below we run some basic DataFrame operations with pandas, before demonstrating how cudf pandas is enabled. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "5fed82ae-0ecb-4471-bb8f-060b1bf4542f", + "metadata": {}, + "outputs": [], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "%load_ext cudf.pandas" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "7671791e-c491-4831-bd1b-956de6b455e5", + "metadata": {}, + "outputs": [], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "import pandas as pd\n", + "import time\n", + "from datetime import datetime" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "47c87c9f-5b97-4a0d-bfa7-a26c1369314f", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Duration: 1.99 seconds\n" + ] + }, + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>age</th>\n", + " <th>sex</th>\n", + " <th>county</th>\n", + " <th>lat</th>\n", + " <th>long</th>\n", + " <th>name</th>\n", + " <th>birth_year</th>\n", + " <th>sex_normalize</th>\n", + " <th>county_normalize</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.533644</td>\n", + " <td>-1.524401</td>\n", + " <td>Francis</td>\n", + " <td>2025</td>\n", + " <td>M</td>\n", + " <td>Darlington</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.426256</td>\n", + " <td>-1.465314</td>\n", + " <td>Edward</td>\n", + " <td>2025</td>\n", + " <td>M</td>\n", + " <td>Darlington</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.555200</td>\n", + " <td>-1.496417</td>\n", + " <td>Teddy</td>\n", + " <td>2025</td>\n", + " <td>M</td>\n", + " <td>Darlington</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.547906</td>\n", + " <td>-1.572341</td>\n", + " <td>Angus</td>\n", + " <td>2025</td>\n", + " <td>M</td>\n", + " <td>Darlington</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.477639</td>\n", + " <td>-1.605995</td>\n", + " <td>Charlie</td>\n", + " <td>2025</td>\n", + " <td>M</td>\n", + " <td>Darlington</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " age sex county lat long name birth_year \\\n", + "0 0 m DARLINGTON 54.533644 -1.524401 Francis 2025 \n", + "1 0 m DARLINGTON 54.426256 -1.465314 Edward 2025 \n", + "2 0 m DARLINGTON 54.555200 -1.496417 Teddy 2025 \n", + "3 0 m DARLINGTON 54.547906 -1.572341 Angus 2025 \n", + "4 0 m DARLINGTON 54.477639 -1.605995 Charlie 2025 \n", + "\n", + " sex_normalize county_normalize \n", + "0 M Darlington \n", + "1 M Darlington \n", + "2 M Darlington \n", + "3 M Darlington \n", + "4 M Darlington " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\"> </span>\n", + "<span style=\"font-style: italic\"> Total time elapsed: 3.759 seconds </span>\n", + "<span style=\"font-style: italic\"> </span>\n", + "<span style=\"font-style: italic\"> Stats </span>\n", + "<span style=\"font-style: italic\"> </span>\n", + "┏━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━┓\n", + "┃<span style=\"font-weight: bold\"> Line no. </span>┃<span style=\"font-weight: bold\"> Line </span>┃<span style=\"font-weight: bold\"> GPU TIME(s) </span>┃<span style=\"font-weight: bold\"> CPU TIME(s) </span>┃\n", + "┡━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━┩\n", + "│ 2 │ <span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> start</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">=</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">time</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">.</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">time()</span><span style=\"background-color: #272822\"> </span> │ │ │\n", + "│ │ <span style=\"background-color: #272822\"> </span> │ │ │\n", + "│ 4 │ <span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> df</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">=</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">pd</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">.</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">read_csv(</span><span style=\"color: #e6db74; text-decoration-color: #e6db74; background-color: #272822\">'./data/uk_pop.csv'</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">)</span><span style=\"background-color: #272822\"> </span> │ 1.540004319 │ │\n", + "│ │ <span style=\"background-color: #272822\"> </span> │ │ │\n", + "│ 5 │ <span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> current_year</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">=</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">datetime</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">.</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">now()</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">.</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">year</span><span style=\"background-color: #272822\"> </span> │ │ │\n", + "│ │ <span style=\"background-color: #272822\"> </span> │ │ │\n", + "│ 7 │ <span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> df[</span><span style=\"color: #e6db74; text-decoration-color: #e6db74; background-color: #272822\">'birth_year'</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">]</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">=</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">current_year</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">-</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">df[</span><span style=\"color: #e6db74; text-decoration-color: #e6db74; background-color: #272822\">'age'</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">]</span><span style=\"background-color: #272822\"> </span> │ 0.011366894 │ │\n", + "│ │ <span style=\"background-color: #272822\"> </span> │ │ │\n", + "│ 9 │ <span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> df[</span><span style=\"color: #e6db74; text-decoration-color: #e6db74; background-color: #272822\">'sex_normalize'</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">]</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">=</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">df[</span><span style=\"color: #e6db74; text-decoration-color: #e6db74; background-color: #272822\">'sex'</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">]</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">.</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">str</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">.</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">upper()</span><span style=\"background-color: #272822\"> </span> │ 0.015399261 │ │\n", + "│ │ <span style=\"background-color: #272822\"> </span> │ │ │\n", + "│ 10 │ <span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> df[</span><span style=\"color: #e6db74; text-decoration-color: #e6db74; background-color: #272822\">'county_normalize'</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">]</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">=</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">df[</span><span style=\"color: #e6db74; text-decoration-color: #e6db74; background-color: #272822\">'county'</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">]</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">.</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">str</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">.</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">title()</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">.</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">str</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">.</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">replace(</span><span style=\"color: #e6db74; text-decoration-color: #e6db74; background-color: #272822\">' '</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">, </span><span style=\"color: #e6db74; text-decoration-color: #e6db74; background-color: #272822\">'_…</span> │ 0.066522101 │ │\n", + "│ │ <span style=\"background-color: #272822\"> </span> │ │ │\n", + "│ 11 │ <span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> df[</span><span style=\"color: #e6db74; text-decoration-color: #e6db74; background-color: #272822\">'name'</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">]</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">=</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">df[</span><span style=\"color: #e6db74; text-decoration-color: #e6db74; background-color: #272822\">'name'</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">]</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">.</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">str</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">.</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">title()</span><span style=\"background-color: #272822\"> </span> │ 0.037128624 │ │\n", + "│ │ <span style=\"background-color: #272822\"> </span> │ │ │\n", + "│ 13 │ <span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> print(</span><span style=\"color: #e6db74; text-decoration-color: #e6db74; background-color: #272822\">f'Duration: {</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">round(time</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">.</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">time()</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">-</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">start, </span><span style=\"color: #ae81ff; text-decoration-color: #ae81ff; background-color: #272822\">2</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">)</span><span style=\"color: #e6db74; text-decoration-color: #e6db74; background-color: #272822\">} seconds'</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">)</span><span style=\"background-color: #272822\"> </span> │ │ │\n", + "│ │ <span style=\"background-color: #272822\"> </span> │ │ │\n", + "│ 15 │ <span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> display(df</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">.</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">head())</span><span style=\"background-color: #272822\"> </span> │ 1.463330139 │ │\n", + "│ │ <span style=\"background-color: #272822\"> </span> │ │ │\n", + "└──────────┴──────────────────────────────────────────────────────────────────────────┴─────────────┴─────────────┘\n", + "</pre>\n" + ], + "text/plain": [ + "\u001b[3m \u001b[0m\n", + "\u001b[3m Total time elapsed: 3.759 seconds \u001b[0m\n", + "\u001b[3m \u001b[0m\n", + "\u001b[3m Stats \u001b[0m\n", + "\u001b[3m \u001b[0m\n", + "┏━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1mLine no.\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mLine \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mGPU TIME(s)\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mCPU TIME(s)\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━┩\n", + "│ 2 │ \u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mstart\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mtime\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mtime\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m │ │ │\n", + "│ │ \u001b[48;2;39;40;34m \u001b[0m │ │ │\n", + "│ 4 │ \u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mdf\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mpd\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mread_csv\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m./data/uk_pop.csv\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m │ 1.540004319 │ │\n", + "│ │ \u001b[48;2;39;40;34m \u001b[0m │ │ │\n", + "│ 5 │ \u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mcurrent_year\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mdatetime\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mnow\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34myear\u001b[0m\u001b[48;2;39;40;34m \u001b[0m │ │ │\n", + "│ │ \u001b[48;2;39;40;34m \u001b[0m │ │ │\n", + "│ 7 │ \u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mdf\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m[\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mbirth_year\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m]\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mcurrent_year\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m-\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mdf\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m[\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mage\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m]\u001b[0m\u001b[48;2;39;40;34m \u001b[0m │ 0.011366894 │ │\n", + "│ │ \u001b[48;2;39;40;34m \u001b[0m │ │ │\n", + "│ 9 │ \u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mdf\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m[\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34msex_normalize\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m]\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mdf\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m[\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34msex\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m]\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mstr\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mupper\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m │ 0.015399261 │ │\n", + "│ │ \u001b[48;2;39;40;34m \u001b[0m │ │ │\n", + "│ 10 │ \u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mdf\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m[\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mcounty_normalize\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m]\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mdf\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m[\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mcounty\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m]\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mstr\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mtitle\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mstr\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mreplace\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m_\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m…\u001b[0m │ 0.066522101 │ │\n", + "│ │ \u001b[48;2;39;40;34m \u001b[0m │ │ │\n", + "│ 11 │ \u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mdf\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m[\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mname\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m]\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mdf\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m[\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mname\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m]\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mstr\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mtitle\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m │ 0.037128624 │ │\n", + "│ │ \u001b[48;2;39;40;34m \u001b[0m │ │ │\n", + "│ 13 │ \u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mprint\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mf\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mDuration: \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m{\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mround\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mtime\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mtime\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m-\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mstart\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m2\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m}\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m seconds\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m │ │ │\n", + "│ │ \u001b[48;2;39;40;34m \u001b[0m │ │ │\n", + "│ 15 │ \u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mdisplay\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mdf\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mhead\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m │ 1.463330139 │ │\n", + "│ │ \u001b[48;2;39;40;34m \u001b[0m │ │ │\n", + "└──────────┴──────────────────────────────────────────────────────────────────────────┴─────────────┴─────────────┘\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%cudf.pandas.line_profile\n", + "# DO NOT CHANGE THIS CELL\n", + "start=time.time()\n", + "\n", + "df=pd.read_csv('./data/uk_pop.csv')\n", + "current_year=datetime.now().year\n", + "\n", + "df['birth_year']=current_year-df['age']\n", + "\n", + "df['sex_normalize']=df['sex'].str.upper()\n", + "df['county_normalize']=df['county'].str.title().str.replace(' ', '_')\n", + "df['name']=df['name'].str.title()\n", + "\n", + "print(f'Duration: {round(time.time()-start, 2)} seconds')\n", + "\n", + "display(df.head())" + ] + }, + { + "cell_type": "markdown", + "id": "7ebe113c-9fc0-4da5-932c-0a68af0d5a31", + "metadata": {}, + "source": [ + "<a name='s4.1'></a>\n", + "### Exercise #3 - Automatic Acceleration ###\n", + "**Instructions**: <br>\n", + "* Go back to the top of this subsection, re-execute the cells and uncomment the `%load_ext` magic command to accelerate with cuDF pandas. \n", + "* Observe the acceleration. \n", + "* Go back to the top of this subsection, re-execute the cells and uncomment the `%%cudf.pandas.line_profile` magic command to use the line profiler. \n", + "* Observe the output from the line profiler. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "f1688462-783c-4fea-ae18-5d37524d26d8", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'status': 'ok', 'restart': True}" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "import IPython\n", + "app = IPython.Application.instance()\n", + "app.kernel.do_shutdown(True)" + ] + }, + { + "cell_type": "markdown", + "id": "0a691784-dac5-4485-89fb-5e405f10c05c", + "metadata": {}, + "source": [ + "**Well Done!** Let's move to the [next notebook](1-03_memory_management.ipynb). " + ] + }, + { + "cell_type": "markdown", + "id": "81e47f0a-547e-4714-878d-34eb9b75c835", + "metadata": {}, + "source": [ + "<img src=\"./images/DLI_Header.png\" width=400/>" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.15" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Fundamentals_of_Accelerated_Data_Science/1-03_memory_management.ipynb b/Fundamentals_of_Accelerated_Data_Science/1-03_memory_management.ipynb new file mode 100644 index 0000000..cae5373 --- /dev/null +++ b/Fundamentals_of_Accelerated_Data_Science/1-03_memory_management.ipynb @@ -0,0 +1,1135 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "def31b0f-921a-43eb-9807-8b9b31eb7b32", + "metadata": {}, + "source": [ + "<img src=\"./images/DLI_Header.png\" width=400/>" + ] + }, + { + "cell_type": "markdown", + "id": "4a0fd4dd-f7be-4c90-8ddd-384a760ac04f", + "metadata": {}, + "source": [ + "# Fundamentals of Accelerated Data Science # " + ] + }, + { + "cell_type": "markdown", + "id": "6a8fdf2e-a481-455e-8a52-8be8472b63bf", + "metadata": {}, + "source": [ + "## 03 - Memory Management ##\n", + "\n", + "**Table of Contents**\n", + "<br>\n", + "This notebook explores the dynamics between data and memory. This notebook covers the below sections: \n", + "1. [Memory Management](#Memory-Management)\n", + " * [Memory Usage](#Memory-Usage)\n", + "2. [Data Types](#Data-Types)\n", + " * [Convert Data Types](#Convert-Data-Types)\n", + " * [Exercise #1 - Modify `dtypes`](#Exercise-#1---Modify-dtypes)\n", + " * [Categorical](#Categorical)\n", + "3. [Efficient Data Loading](#Efficient-Data-Loading)" + ] + }, + { + "cell_type": "markdown", + "id": "1b59367c-48bc-4c72-b1f4-4cfdfa5470cf", + "metadata": {}, + "source": [ + "## Memory Management ##\n", + "During the data acquisition process, data is transferred to memory in order to be operated on by the processor. Memory management is crucial for cuDF and GPU operations for several key reasons: \n", + "* **Limited GPU memory**: GPUs typically have less memory than CPUs, therefore efficient memory management is essential to maximize the use of available GPU memory, especially for large datasets.\n", + "* **Data transfer overhead**: Transferring data between CPU and GPU memory is relatively slow compared to GPU computation speed. Minimizing these transfers through smart memory management is critical for performance.\n", + "* **Performance tuning**: Understanding and optimizing memory usage is key to achieving peak performance in GPU-accelerated data processing tasks.\n", + "\n", + "When done correctly, keeping the data on the GPU can enable cuDF and the RAPIDS ecosystem to achieve significant performance improvements, handle larger datasets, and provide more efficient data processing capabilities. \n", + "\n", + "Below we import the data from the csv file. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "b7b8a623-f799-4dad-aca9-0e571bb6e527", + "metadata": {}, + "outputs": [], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "import pandas as pd\n", + "import random\n", + "import time" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "711d0a7f-8598-49fc-949c-5caf6029ce47", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>age</th>\n", + " <th>sex</th>\n", + " <th>county</th>\n", + " <th>lat</th>\n", + " <th>long</th>\n", + " <th>name</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.533644</td>\n", + " <td>-1.524401</td>\n", + " <td>FRANCIS</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.426256</td>\n", + " <td>-1.465314</td>\n", + " <td>EDWARD</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.555200</td>\n", + " <td>-1.496417</td>\n", + " <td>TEDDY</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.547906</td>\n", + " <td>-1.572341</td>\n", + " <td>ANGUS</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.477639</td>\n", + " <td>-1.605995</td>\n", + " <td>CHARLIE</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " age sex county lat long name\n", + "0 0 m DARLINGTON 54.533644 -1.524401 FRANCIS\n", + "1 0 m DARLINGTON 54.426256 -1.465314 EDWARD\n", + "2 0 m DARLINGTON 54.555200 -1.496417 TEDDY\n", + "3 0 m DARLINGTON 54.547906 -1.572341 ANGUS\n", + "4 0 m DARLINGTON 54.477639 -1.605995 CHARLIE" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "df=pd.read_csv('./data/uk_pop.csv')\n", + "\n", + "# preview\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "id": "36416fd0-7081-42aa-bf31-d1231b81ec0b", + "metadata": {}, + "source": [ + "### Memory Usage ###\n", + "Memory utilization of a DataFrame depends on the date types for each column.\n", + "\n", + "<p><img src='images/dtypes.png' width=720></p>\n", + "\n", + "We can use `DataFrame.memory_usage()` to see the memory usage for each column (in bytes). Most of the common data types have a fixed size in memory, such as `int`, `float`, `datetime`, and `bool`. Memory usage for these data types is the respective memory requirement multiplied by the number of data points. For `string` data type, the memory usage reported _for pandas_ is the number of elements times 8 bytes. This accounts for the 64-bit required for the pointer that points to an address in memory but not the memory used for the actual string values. The actual memory required for a string value is 49 bytes plus an additional byte for each character. The `deep` parameter provides a more accurate memory usage report that accounts for the system-level memory consumption of the contained `string` data type. \n", + "\n", + "Below we get the memory usage. " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "8378207b-2d9e-4102-8408-c2dddafc8a40", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "<class 'pandas.core.frame.DataFrame'>\n", + "RangeIndex: 58479894 entries, 0 to 58479893\n", + "Data columns (total 6 columns):\n", + " # Column Dtype \n", + "--- ------ ----- \n", + " 0 age int64 \n", + " 1 sex object \n", + " 2 county object \n", + " 3 lat float64\n", + " 4 long float64\n", + " 5 name object \n", + "dtypes: float64(2), int64(1), object(3)\n", + "memory usage: 11.5 GB\n" + ] + }, + { + "data": { + "text/plain": [ + "Index 128\n", + "age 467839152\n", + "sex 3391833852\n", + "county 3934985133\n", + "lat 467839152\n", + "long 467839152\n", + "name 3666922374\n", + "dtype: int64" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "# pandas memory utilization\n", + "df.info(memory_usage='deep')\n", + "mem_usage_df=df.memory_usage(deep=True)\n", + "mem_usage_df" + ] + }, + { + "cell_type": "markdown", + "id": "07c24bb1-c4f7-440c-a949-d4c57800ec61", + "metadata": {}, + "source": [ + "Below we define a `make_decimal()` function to convert memory size into units based on powers of 2. In contrast to units based on powers of 10, this customary convention is commonly used to report memory capacity. More information about the two definitions can be found [here](https://en.wikipedia.org/wiki/Byte#Multiple-byte_units). " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "5ae42218-1547-49fd-9123-ab508a2b03de", + "metadata": {}, + "outputs": [], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "suffixes = ['B', 'kB', 'MB', 'GB', 'TB', 'PB']\n", + "def make_decimal(nbytes):\n", + " i=0\n", + " while nbytes >= 1024 and i < len(suffixes)-1:\n", + " nbytes/=1024.\n", + " i+=1\n", + " f=('%.2f' % nbytes).rstrip('0').rstrip('.')\n", + " return '%s %s' % (f, suffixes[i])" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "e6d4a613-3eea-4dce-8e71-39593ff6f226", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'11.55 GB'" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "make_decimal(mem_usage_df.sum())" + ] + }, + { + "cell_type": "markdown", + "id": "a352c0b2-65aa-4231-b753-556aca46ff49", + "metadata": {}, + "source": [ + "Below we calculate the memory usage manually based on the data types. " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "630327b9-6dc1-4b70-9fdf-9f7763ec4d50", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Numerical columns use 467839152 bytes of memory\n" + ] + } + ], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "# get number of rows\n", + "num_rows=len(df)\n", + "\n", + "# 64-bit numbers uses 8 bytes of memory\n", + "print(f'Numerical columns use {num_rows*8} bytes of memory')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "bb22b5f4-e38f-438e-9426-61746b509e50", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "county column uses 3934985133 bytes of memory.\n" + ] + } + ], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "# check random string-typed column\n", + "string_cols=[col for col in df.columns if df[col].dtype=='object' ]\n", + "column_to_check=random.choice(string_cols)\n", + "\n", + "overhead=49\n", + "pointer_size=8\n", + "\n", + "# nan==nan when value is not a number\n", + "# nan uses 32 bytes of memory\n", + "string_col_mem_usage_df=df[column_to_check].map(lambda x: len(x)+overhead+pointer_size if x else 32)\n", + "string_col_mem_usage=string_col_mem_usage_df.sum()\n", + "print(f'{column_to_check} column uses {string_col_mem_usage} bytes of memory.')" + ] + }, + { + "cell_type": "markdown", + "id": "94e393c2-c0d0-40ee-82d2-730c4667e9b8", + "metadata": {}, + "source": [ + "**Note**: The `string` data type is stored differently in cuDF than it is in pandas. More information about `libcudf` stores string data using the [Arrow format](https://arrow.apache.org/docs/format/Columnar.html#variable-size-binary-layout) can be found [here](https://developer.nvidia.com/blog/mastering-string-transformations-in-rapids-libcudf/). " + ] + }, + { + "cell_type": "markdown", + "id": "737ff50b-9426-4e08-a00a-d7ee69f48b9f", + "metadata": {}, + "source": [ + "## Data Types ##\n", + "By default, pandas (and cuDF) uses 64-bit for numerical values. Using 64-bit numbers provides the highest precision but many applications do not require 64-bit precision when aggregating over a very large number of data points. When possible, using 32-bit numbers reduces storage and memory requirements in half, and also typically greatly speeds up computations because only half as much data needs to be accessed in memory. " + ] + }, + { + "cell_type": "markdown", + "id": "0b77d450-c415-44b8-87ac-20ce616ec809", + "metadata": {}, + "source": [ + "### Convert Data Types ###\n", + "The `.astype()` method can be used to convert numerical data types to use different bit-size containers. Here we convert the `age` column from `int64` to `int8`. " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "603f7c70-134e-4466-a790-8a18b9088ca6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "age int8\n", + "sex object\n", + "county object\n", + "lat float64\n", + "long float64\n", + "name object\n", + "dtype: object" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "df['age']=df['age'].astype('int8')\n", + "\n", + "df.dtypes" + ] + }, + { + "cell_type": "markdown", + "id": "973a6dd4-2aef-44d9-8b01-8853032eddae", + "metadata": {}, + "source": [ + "### Exercise #1 - Modify `dtypes` ###\n", + "**Instructions**: <br>\n", + "* Modify the `<FIXME>` only and execute the below cell to convert any 64-bit data types to their 32-bit counterparts." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "beb7d71b-6672-462e-b65c-a64dbe5f7a57", + "metadata": {}, + "outputs": [], + "source": [ + "df['lat']=df['lat'].astype('float32')\n", + "df['long']=df['long'].astype('float32')" + ] + }, + { + "cell_type": "raw", + "id": "3b44fb22-a0f1-4e43-a332-1ccbad50caee", + "metadata": {}, + "source": [ + "\n", + "df['lat']=df['lat'].astype('float32')\n", + "df['long']=df['long'].astype('float32')" + ] + }, + { + "cell_type": "markdown", + "id": "98b6542d-22cc-4926-b600-a3e052c37c96", + "metadata": {}, + "source": [ + "Click ... for solution. " + ] + }, + { + "cell_type": "markdown", + "id": "7b2cd622-977c-4915-a87f-2fe03c1793f5", + "metadata": {}, + "source": [ + "### Categorical ###\n", + "Categorical data is a type of data that represents discrete, distinct categories or groups. They can have a meaningful order or ranking but generally cannot be used for numerical operations. When appropriate, using the `categorical` data type can reduce memory usage and lead to faster operations. It can also be used to define and maintain a custom order of categories. \n", + "\n", + "Below we get the number of unique values in the string columns. " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "f249e4b8-5d7a-4b44-ac15-bd3360a43f2a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "sex 2\n", + "county 171\n", + "name 13212\n", + "dtype: int64" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "df.select_dtypes(include='object').nunique()" + ] + }, + { + "cell_type": "markdown", + "id": "f1d8bd88-b39b-4043-9039-d8bd75fe851a", + "metadata": {}, + "source": [ + "Below we convert columns with few discrete values to `category`. The `category` data type has `.categories` and `codes` properties that are accessed through `.cat`. " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "a99bebbf-2e5b-4720-96f9-9fd7d42d2fe8", + "metadata": {}, + "outputs": [], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "df['sex']=df['sex'].astype('category')\n", + "df['county']=df['county'].astype('category')" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "41b7b290-cfcf-4ff6-b6b4-454c19b44a62", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['BARKING AND DAGENHAM', 'BARNET', 'BARNSLEY',\n", + " 'BATH AND NORTH EAST SOMERSET', 'BEDFORD', 'BEXLEY', 'BIRMINGHAM',\n", + " 'BLACKBURN WITH DARWEN', 'BLACKPOOL', 'BLAENAU GWENT',\n", + " ...\n", + " 'WESTMINSTER', 'WIGAN', 'WILTSHIRE', 'WINDSOR AND MAIDENHEAD', 'WIRRAL',\n", + " 'WOKINGHAM', 'WOLVERHAMPTON', 'WORCESTERSHIRE', 'WREXHAM', 'YORK'],\n", + " dtype='object', length=171)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----------------------------------------\n" + ] + }, + { + "data": { + "text/plain": [ + "0 37\n", + "1 37\n", + "2 37\n", + "3 37\n", + "4 37\n", + " ..\n", + "58479889 96\n", + "58479890 96\n", + "58479891 96\n", + "58479892 96\n", + "58479893 96\n", + "Length: 58479894, dtype: int16" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "display(df['county'].cat.categories)\n", + "print('-'*40)\n", + "display(df['county'].cat.codes)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "01d12a78-5f70-4152-a708-68ee68046a1e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "age int8\n", + "sex category\n", + "county category\n", + "lat float32\n", + "long float32\n", + "name object\n", + "dtype: object" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "24138ffc-80b2-46ea-930d-1c1ab9706b10", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>age</th>\n", + " <th>sex</th>\n", + " <th>county</th>\n", + " <th>lat</th>\n", + " <th>long</th>\n", + " <th>name</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.533646</td>\n", + " <td>-1.524401</td>\n", + " <td>FRANCIS</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.426254</td>\n", + " <td>-1.465314</td>\n", + " <td>EDWARD</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.555199</td>\n", + " <td>-1.496417</td>\n", + " <td>TEDDY</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.547905</td>\n", + " <td>-1.572341</td>\n", + " <td>ANGUS</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.477638</td>\n", + " <td>-1.605994</td>\n", + " <td>CHARLIE</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " age sex county lat long name\n", + "0 0 m DARLINGTON 54.533646 -1.524401 FRANCIS\n", + "1 0 m DARLINGTON 54.426254 -1.465314 EDWARD\n", + "2 0 m DARLINGTON 54.555199 -1.496417 TEDDY\n", + "3 0 m DARLINGTON 54.547905 -1.572341 ANGUS\n", + "4 0 m DARLINGTON 54.477638 -1.605994 CHARLIE" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3d0addcc-c078-42f5-a66a-3bb9a969d7e8", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "737385ab-677c-4bef-a86a-10aa3119e29a", + "metadata": {}, + "source": [ + "**Note**: `.astype()` can also be used to convert data to `datetime` or `object` to enable datetime and string methods. " + ] + }, + { + "cell_type": "markdown", + "id": "552c47c2-0fbc-455e-8745-cb98fc777243", + "metadata": {}, + "source": [ + "## Efficient Data Loading ##\n", + "It is often advantageous to specify the most appropriate data types for each columns, based on range, precision requirement, and how they are used. " + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "c2b9f0c3-8598-4a28-9481-ce28fea7544b", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Index 128\n", + "age 467839152\n", + "sex 3391833852\n", + "county 3934985133\n", + "lat 467839152\n", + "long 467839152\n", + "name 3666922374\n", + "dtype: int64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading 11.55 GB took 33.87 seconds.\n" + ] + } + ], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "start=time.time()\n", + "df=pd.read_csv('./data/uk_pop.csv')\n", + "duration=time.time()-start\n", + "\n", + "mem_usage_df=df.memory_usage(deep=True)\n", + "display(mem_usage_df)\n", + "\n", + "print(f'Loading {make_decimal(mem_usage_df.sum())} took {round(duration, 2)} seconds.')" + ] + }, + { + "cell_type": "markdown", + "id": "5729520e-3ed8-4ec6-ae1f-ba46d642f48d", + "metadata": {}, + "source": [ + "Below we enable `cuda.pandas` to see the difference. " + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "99aa0f32-4d2a-43a7-bec1-f1b88bcc37c2", + "metadata": {}, + "outputs": [], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "%load_ext cudf.pandas\n", + "\n", + "import pandas as pd\n", + "import time" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "2b724201-9ad1-4e9b-b712-f3b31bdc4104", + "metadata": {}, + "outputs": [], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "suffixes = ['B', 'kB', 'MB', 'GB', 'TB', 'PB']\n", + "def make_decimal(nbytes):\n", + " i=0\n", + " while nbytes >= 1024 and i < len(suffixes)-1:\n", + " nbytes/=1024.\n", + " i+=1\n", + " f=('%.2f' % nbytes).rstrip('0').rstrip('.')\n", + " return '%s %s' % (f, suffixes[i])" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "99bdd7b0-8563-41db-bd8e-3a7279394ede", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "age 58479894\n", + "sex 58479908\n", + "county 58482446\n", + "lat 467839152\n", + "long 467839152\n", + "name 117096917\n", + "Index 0\n", + "dtype: int64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading 1.14 GB took 2.12 seconds.\n" + ] + }, + { + "data": { + "text/html": [ + "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\"> </span>\n", + "<span style=\"font-style: italic\"> Total time elapsed: 2.687 seconds </span>\n", + "<span style=\"font-style: italic\"> </span>\n", + "<span style=\"font-style: italic\"> Stats </span>\n", + "<span style=\"font-style: italic\"> </span>\n", + "┏━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━┓\n", + "┃<span style=\"font-weight: bold\"> Line no. </span>┃<span style=\"font-weight: bold\"> Line </span>┃<span style=\"font-weight: bold\"> GPU TIME(s) </span>┃<span style=\"font-weight: bold\"> CPU TIME(s) </span>┃\n", + "┡━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━┩\n", + "│ 2 │ <span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> start</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">=</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">time</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">.</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">time()</span><span style=\"background-color: #272822\"> </span> │ │ │\n", + "│ │ <span style=\"background-color: #272822\"> </span> │ │ │\n", + "│ 5 │ <span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> dtype_dict</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">=</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">{</span><span style=\"background-color: #272822\"> </span> │ │ │\n", + "│ │ <span style=\"background-color: #272822\"> </span> │ │ │\n", + "│ 6 │ <span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> </span><span style=\"color: #e6db74; text-decoration-color: #e6db74; background-color: #272822\">'age'</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">: </span><span style=\"color: #e6db74; text-decoration-color: #e6db74; background-color: #272822\">'int8'</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">, </span><span style=\"background-color: #272822\"> </span> │ │ │\n", + "│ │ <span style=\"background-color: #272822\"> </span> │ │ │\n", + "│ 7 │ <span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> </span><span style=\"color: #e6db74; text-decoration-color: #e6db74; background-color: #272822\">'sex'</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">: </span><span style=\"color: #e6db74; text-decoration-color: #e6db74; background-color: #272822\">'category'</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">, </span><span style=\"background-color: #272822\"> </span> │ │ │\n", + "│ │ <span style=\"background-color: #272822\"> </span> │ │ │\n", + "│ 8 │ <span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> </span><span style=\"color: #e6db74; text-decoration-color: #e6db74; background-color: #272822\">'county'</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">: </span><span style=\"color: #e6db74; text-decoration-color: #e6db74; background-color: #272822\">'category'</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">, </span><span style=\"background-color: #272822\"> </span> │ │ │\n", + "│ │ <span style=\"background-color: #272822\"> </span> │ │ │\n", + "│ 9 │ <span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> </span><span style=\"color: #e6db74; text-decoration-color: #e6db74; background-color: #272822\">'lat'</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">: </span><span style=\"color: #e6db74; text-decoration-color: #e6db74; background-color: #272822\">'float64'</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">, </span><span style=\"background-color: #272822\"> </span> │ │ │\n", + "│ │ <span style=\"background-color: #272822\"> </span> │ │ │\n", + "│ 10 │ <span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> </span><span style=\"color: #e6db74; text-decoration-color: #e6db74; background-color: #272822\">'long'</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">: </span><span style=\"color: #e6db74; text-decoration-color: #e6db74; background-color: #272822\">'float64'</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">, </span><span style=\"background-color: #272822\"> </span> │ │ │\n", + "│ │ <span style=\"background-color: #272822\"> </span> │ │ │\n", + "│ 11 │ <span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> </span><span style=\"color: #e6db74; text-decoration-color: #e6db74; background-color: #272822\">'name'</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">: </span><span style=\"color: #e6db74; text-decoration-color: #e6db74; background-color: #272822\">'category'</span><span style=\"background-color: #272822\"> </span> │ │ │\n", + "│ │ <span style=\"background-color: #272822\"> </span> │ │ │\n", + "│ 14 │ <span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> efficient_df</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">=</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">pd</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">.</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">read_csv(</span><span style=\"color: #e6db74; text-decoration-color: #e6db74; background-color: #272822\">'./data/uk_pop.csv'</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">, dtype</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">=</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">dtype_dict)</span><span style=\"background-color: #272822\"> </span> │ 1.718211215 │ │\n", + "│ │ <span style=\"background-color: #272822\"> </span> │ │ │\n", + "│ 15 │ <span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> duration</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">=</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">time</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">.</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">time()</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">-</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">start</span><span style=\"background-color: #272822\"> </span> │ │ │\n", + "│ │ <span style=\"background-color: #272822\"> </span> │ │ │\n", + "│ 17 │ <span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> mem_usage_df</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">=</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">efficient_df</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">.</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">memory_usage(</span><span style=\"color: #e6db74; text-decoration-color: #e6db74; background-color: #272822\">'deep'</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">)</span><span style=\"background-color: #272822\"> </span> │ 0.005751408 │ │\n", + "│ │ <span style=\"background-color: #272822\"> </span> │ │ │\n", + "│ 18 │ <span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> display(mem_usage_df)</span><span style=\"background-color: #272822\"> </span> │ 0.011270449 │ 0.007067286 │\n", + "│ │ <span style=\"background-color: #272822\"> </span> │ │ │\n", + "│ 20 │ <span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> print(</span><span style=\"color: #e6db74; text-decoration-color: #e6db74; background-color: #272822\">f'Loading {</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">make_decimal(mem_usage_df</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">.</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">sum())</span><span style=\"color: #e6db74; text-decoration-color: #e6db74; background-color: #272822\">} took {</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">round(dura…</span> │ 0.004789912 │ │\n", + "│ │ <span style=\"background-color: #272822\"> </span> │ │ │\n", + "└──────────┴──────────────────────────────────────────────────────────────────────────┴─────────────┴─────────────┘\n", + "</pre>\n" + ], + "text/plain": [ + "\u001b[3m \u001b[0m\n", + "\u001b[3m Total time elapsed: 2.687 seconds \u001b[0m\n", + "\u001b[3m \u001b[0m\n", + "\u001b[3m Stats \u001b[0m\n", + "\u001b[3m \u001b[0m\n", + "┏━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1mLine no.\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mLine \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mGPU TIME(s)\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mCPU TIME(s)\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━┩\n", + "│ 2 │ \u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mstart\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mtime\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mtime\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m │ │ │\n", + "│ │ \u001b[48;2;39;40;34m \u001b[0m │ │ │\n", + "│ 5 │ \u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mdtype_dict\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m{\u001b[0m\u001b[48;2;39;40;34m \u001b[0m │ │ │\n", + "│ │ \u001b[48;2;39;40;34m \u001b[0m │ │ │\n", + "│ 6 │ \u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mage\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m:\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mint8\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m │ │ │\n", + "│ │ \u001b[48;2;39;40;34m \u001b[0m │ │ │\n", + "│ 7 │ \u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34msex\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m:\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mcategory\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m │ │ │\n", + "│ │ \u001b[48;2;39;40;34m \u001b[0m │ │ │\n", + "│ 8 │ \u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mcounty\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m:\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mcategory\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m │ │ │\n", + "│ │ \u001b[48;2;39;40;34m \u001b[0m │ │ │\n", + "│ 9 │ \u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mlat\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m:\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mfloat64\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m │ │ │\n", + "│ │ \u001b[48;2;39;40;34m \u001b[0m │ │ │\n", + "│ 10 │ \u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mlong\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m:\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mfloat64\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[48;2;39;40;34m \u001b[0m │ │ │\n", + "│ │ \u001b[48;2;39;40;34m \u001b[0m │ │ │\n", + "│ 11 │ \u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mname\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m:\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mcategory\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[48;2;39;40;34m \u001b[0m │ │ │\n", + "│ │ \u001b[48;2;39;40;34m \u001b[0m │ │ │\n", + "│ 14 │ \u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mefficient_df\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mpd\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mread_csv\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m./data/uk_pop.csv\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mdtype\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mdtype_dict\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m │ 1.718211215 │ │\n", + "│ │ \u001b[48;2;39;40;34m \u001b[0m │ │ │\n", + "│ 15 │ \u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mduration\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mtime\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mtime\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m-\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mstart\u001b[0m\u001b[48;2;39;40;34m \u001b[0m │ │ │\n", + "│ │ \u001b[48;2;39;40;34m \u001b[0m │ │ │\n", + "│ 17 │ \u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mmem_usage_df\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mefficient_df\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mmemory_usage\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mdeep\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m │ 0.005751408 │ │\n", + "│ │ \u001b[48;2;39;40;34m \u001b[0m │ │ │\n", + "│ 18 │ \u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mdisplay\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mmem_usage_df\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m │ 0.011270449 │ 0.007067286 │\n", + "│ │ \u001b[48;2;39;40;34m \u001b[0m │ │ │\n", + "│ 20 │ \u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mprint\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mf\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mLoading \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m{\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mmake_decimal\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mmem_usage_df\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34msum\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m}\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m took \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m{\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mround\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mdura…\u001b[0m │ 0.004789912 │ │\n", + "│ │ \u001b[48;2;39;40;34m \u001b[0m │ │ │\n", + "└──────────┴──────────────────────────────────────────────────────────────────────────┴─────────────┴─────────────┘\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%cudf.pandas.line_profile\n", + "# DO NOT CHANGE THIS CELL\n", + "start=time.time()\n", + "\n", + "# define data types for each column\n", + "dtype_dict={\n", + " 'age': 'int8', \n", + " 'sex': 'category', \n", + " 'county': 'category', \n", + " 'lat': 'float64', \n", + " 'long': 'float64', \n", + " 'name': 'category'\n", + "}\n", + " \n", + "efficient_df=pd.read_csv('./data/uk_pop.csv', dtype=dtype_dict)\n", + "duration=time.time()-start\n", + "\n", + "mem_usage_df=efficient_df.memory_usage('deep')\n", + "display(mem_usage_df)\n", + "\n", + "print(f'Loading {make_decimal(mem_usage_df.sum())} took {round(duration, 2)} seconds.')" + ] + }, + { + "cell_type": "markdown", + "id": "0f4607d8-6de3-4b27-96d4-a9720d268333", + "metadata": {}, + "source": [ + "We were able to load data faster and more efficiently. \n", + "\n", + "**Note**: Notice that the memory utilized on the GPU is larger than the memory used by the DataFrame. This is expected because there are intermediary processes that use some memory during the data loading process, specifically related to parsing the csv file in this case. \n", + "\n", + "```\n", + "+-----------------------------------------------------------------------------+\n", + "| NVIDIA-SMI 525.60.13 Driver Version: 525.60.13 CUDA Version: 12.0 |\n", + "|-------------------------------+----------------------+----------------------+\n", + "| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n", + "| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n", + "| | | MIG M. |\n", + "|===============================+======================+======================|\n", + "| 0 Tesla T4 Off | 00000000:00:1B.0 Off | 0 |\n", + "| N/A 32C P0 26W / 70W | 1378MiB / 15360MiB | 0% Default |\n", + "| | | N/A |\n", + "+-------------------------------+----------------------+----------------------+\n", + "| 1 Tesla T4 Off | 00000000:00:1C.0 Off | 0 |\n", + "| N/A 31C P0 26W / 70W | 168MiB / 15360MiB | 0% Default |\n", + "| | | N/A |\n", + "+-------------------------------+----------------------+----------------------+\n", + "| 2 Tesla T4 Off | 00000000:00:1D.0 Off | 0 |\n", + "| N/A 30C P0 26W / 70W | 168MiB / 15360MiB | 0% Default |\n", + "| | | N/A |\n", + "+-------------------------------+----------------------+----------------------+\n", + "| 3 Tesla T4 Off | 00000000:00:1E.0 Off | 0 |\n", + "| N/A 30C P0 26W / 70W | 168MiB / 15360MiB | 0% Default |\n", + "| | | N/A |\n", + "+-------------------------------+----------------------+----------------------+\n", + " \n", + "+-----------------------------------------------------------------------------+\n", + "| Processes: |\n", + "| GPU GI CI PID Type Process name GPU Memory |\n", + "| ID ID Usage |\n", + "|=============================================================================|\n", + "+-----------------------------------------------------------------------------+\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "92f7ee37-4acb-46aa-bb73-4c0139d3f6b8", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sat Oct 11 16:44:59 2025 \n", + "+-----------------------------------------------------------------------------+\n", + "| NVIDIA-SMI 525.85.12 Driver Version: 525.85.12 CUDA Version: 12.0 |\n", + "|-------------------------------+----------------------+----------------------+\n", + "| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n", + "| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n", + "| | | MIG M. |\n", + "|===============================+======================+======================|\n", + "| 0 Tesla T4 On | 00000000:00:1B.0 Off | 0 |\n", + "| N/A 30C P0 25W / 70W | 11338MiB / 15360MiB | 0% Default |\n", + "| | | N/A |\n", + "+-------------------------------+----------------------+----------------------+\n", + "| 1 Tesla T4 On | 00000000:00:1C.0 Off | 0 |\n", + "| N/A 31C P0 25W / 70W | 168MiB / 15360MiB | 0% Default |\n", + "| | | N/A |\n", + "+-------------------------------+----------------------+----------------------+\n", + "| 2 Tesla T4 On | 00000000:00:1D.0 Off | 0 |\n", + "| N/A 31C P0 25W / 70W | 168MiB / 15360MiB | 0% Default |\n", + "| | | N/A |\n", + "+-------------------------------+----------------------+----------------------+\n", + "| 3 Tesla T4 On | 00000000:00:1E.0 Off | 0 |\n", + "| N/A 31C P0 25W / 70W | 168MiB / 15360MiB | 0% Default |\n", + "| | | N/A |\n", + "+-------------------------------+----------------------+----------------------+\n", + " \n", + "+-----------------------------------------------------------------------------+\n", + "| Processes: |\n", + "| GPU GI CI PID Type Process name GPU Memory |\n", + "| ID ID Usage |\n", + "|=============================================================================|\n", + "+-----------------------------------------------------------------------------+\n" + ] + } + ], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "!nvidia-smi" + ] + }, + { + "cell_type": "markdown", + "id": "c031d2c7-03cb-4ac7-a195-70fc25cb191d", + "metadata": {}, + "source": [ + "When loading data this way, we may be able to fit more data. The optimal dataset size depends on various factors including the specific operations being performed, the complexity of the workload, and the available GPU memory. To maximize acceleration, datasets should ideally fit within GPU memory, with ample space left for operations that can spike memory requirements. As a general rule of thumb, cuDF recommends data sets that are less than 50% of the GPU memory capacity. " + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "ec6cefea-dc64-4f13-815e-081cd35651b9", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "We can load 408997980 rows.\n" + ] + } + ], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "# 1 gigabytes = 1073741824 bytes\n", + "mem_capacity=16*1073741824\n", + "\n", + "mem_per_record=mem_usage_df.sum()/len(efficient_df)\n", + "\n", + "print(f'We can load {int(mem_capacity/2/mem_per_record)} rows.')" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "ddaaa1ac-66ec-4323-9842-2543c6d85e4e", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'status': 'ok', 'restart': True}" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "import IPython\n", + "app = IPython.Application.instance()\n", + "app.kernel.do_shutdown(True)" + ] + }, + { + "cell_type": "markdown", + "id": "658e9847-775f-4d12-af4e-8f896df4e6fe", + "metadata": {}, + "source": [ + "**Well Done!** Let's move to the [next notebook](1-04_interoperability.ipynb). " + ] + }, + { + "cell_type": "markdown", + "id": "b86451cf-60e6-4733-b431-1bc0bd586bc2", + "metadata": {}, + "source": [ + "<img src=\"./images/DLI_Header.png\" width=400/>" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.15" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Fundamentals_of_Accelerated_Data_Science/1-04_interoperability.ipynb b/Fundamentals_of_Accelerated_Data_Science/1-04_interoperability.ipynb new file mode 100644 index 0000000..cb725df --- /dev/null +++ b/Fundamentals_of_Accelerated_Data_Science/1-04_interoperability.ipynb @@ -0,0 +1,1186 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "b53a7b12-538d-4459-b82a-a35c8c417849", + "metadata": {}, + "source": [ + "<img src=\"./images/DLI_Header.png\" width=400/>" + ] + }, + { + "cell_type": "markdown", + "id": "ae497b71-bc43-471e-8970-88a1878e7cf9", + "metadata": {}, + "source": [ + "# Fundamentals of Accelerated Data Science # " + ] + }, + { + "cell_type": "markdown", + "id": "a149b6d1-1880-4a5d-9d71-f963d3097aa4", + "metadata": {}, + "source": [ + "## 04 - Interoperability of the GPU PyData Ecosystem ##\n", + "\n", + "**Table of Contents**\n", + "<br>\n", + "This notebook provides examples of how we can use cuDF and CuPy together to take advantage of CuPy array functionality (such as advanced linear algebra operations). This notebook covers the below sections: \n", + "1. [NumPy, SciPy, and CuPy](#NumPy,-SciPy,-and-CuPy)\n", + " * [cuDF vs. CuPy](#cuDF-vs.-CuPy)\n", + "2. [Working with CuPy](#Working-with-CuPy)\n", + "3. [Grid Converter](#Grid-Converter)\n", + " * [Lat/Long to OSGB Grid Converter with NumPy](#Lat/Long-to-OSGB-Grid-Converter-with-NumPy)\n", + " * [Lat/Long to OSGB Grid Converter with CuPy](#Lat/Long-to-OSGB-Grid-Converter-with-CuPy)\n", + " * [Exercise #1 - Adding Grid Coordinate Columns to Dataframe](#Exercise-#1---Adding-Grid-Coordinate-Columns-to-DataFrame)\n", + "4. [Boolean Array Indexing](#Boolean-Array-Indexing)" + ] + }, + { + "cell_type": "markdown", + "id": "3c0ac5b5-5cc1-4240-9375-297cf15c6fbc", + "metadata": {}, + "source": [ + "## NumPy, SciPy, and CuPy ##\n", + "Per it's own user guide, [NumPy](https://numpy.org/doc/stable/user/whatisnumpy.html) is the fundamental package for scientific computing in Python. It is a Python library that provides a **multidimensional array object**, various derived objects (such as masked arrays and matrices), and an assortment of routines for fast operations on arrays. These operations include mathematical, logical, shape manipulation, sorting, selecting, I/O, discrete Fourier transforms, basic linear algebra, basic statistical operations, random simulation and much more. While NumPy focuses on arrays, mathematical operations, and basic linear algebra, [SciPy](https://docs.scipy.org/doc/scipy-1.8.1/tutorial/general.html) builds on this foundation to provide additional functionality, especially in the domain of scientific computing and optimization. \n", + "\n", + "On the other hands, [CuPy](https://cupy.dev/) is an open-source array library for GPU-accelerated computing with Python. CuPy can be seen as a GPU-accelerated counterpart to NumPy, offering similar functionality and API with the added benefit of GPU acceleration for compatible workloads. While NumPy operates on CPU memory, CuPy primarily works with GPU memory, leveraging CUDA-enabled GPUs for computation. CuPy's interface is highly compatible with NumPy and SciPy. In most cases it can be used as a drop-in replacement. All we need to do is just replace `numpy` and `scipy` with `cupy` and `cupyx.scipy` in the Python code. This makes it easier for users familiar with NumPy to transition to GPU-accelerated computing. \n", + "\n", + "CuPy is designed to work seamlessly with other GPU-accelerated libraries in the RAPIDS ecosystem, similar to how NumPy works with pandas and other CPU-based libraries. By keeping data on the GPU throughout the workflow, we are able to reduce data transfer overhead between CPU and GPU memory. " + ] + }, + { + "cell_type": "markdown", + "id": "3e1483d0-c479-474a-a7fa-a31f4140268d", + "metadata": {}, + "source": [ + "### cuDF vs. CuPy ###\n", + "So far, the DataFrame we've worked with puts data in a structured, tabular format. This is useful when we need to perform DataFrame-like operations such as grouping, aggregating, filtering, and joining data. However, there might be use cases that requires working with multi-dimensional arrays or matrics, such as perfrming linear algebra operations or scientific computing tasks. For these instances, we would want to use libraries that are dedicated to performing these tasks, such as NumPy, SciPy, or CuPy. In other words, use cuDF when working with high-level (less abstract) data manipulation, and use CuPy when doing low-level numerical operations on multi-dimensional arrays. \n", + "\n", + "In practice, most data scientists work with both libraries, since most of their workflows involve DataFrame operations and array-based computations. For example, we might use cuDF for data loading and preprocessing, then convert to CuPy arrays for specific numerical computations, and convert back to cuDF for further analysis or output. cuDF and CuPy are designed to be interoperable, allowing us to easily convert between cuDF DataFrames/Series and CuPy arrays while keeping the data on the GPU. This enables us to create efficient workflows that take advntage of both libraries' strengths. " + ] + }, + { + "cell_type": "markdown", + "id": "2fb75cf7-2590-4638-8e92-6d90a8e34b63", + "metadata": {}, + "source": [ + "## Working with CuPy ##\n", + "There are several ways to use CuPy. From cuDF, the `DataFrame.values` property will return the CuPy representation of the data frame. Alternatively, we can also convert via the CUDA array interface using `DataFrame.to_cupy()`. In addition to these, we can also pass the Series to the `cupy.asarray()` function since cuDF Series exposes the CUDA array interface as the fastest approach. \n", + "\n", + "Below we demonstrate a **row-wise sum** on the DataFrame. cuDF’s support for row-wise operations isn’t mature, so we’d need to either transpose the DataFrame or write a UDF and explicitly calculate the sum across each row. Transposing could lead to hundreds of thousands of columns (which cuDF wouldn’t perform well with) depending on our data’s shape, and writing a UDF can be time intensive. By leveraging the interoperability of the GPU PyData ecosystem, this operation becomes very easy. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "4808029f-a5e6-4066-b125-84d63c3c6d43", + "metadata": {}, + "outputs": [], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "# import libraries\n", + "import cudf\n", + "import time" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "50653cc1-53a1-4141-b1fb-4ef3ef7994f4", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>a</th>\n", + " <th>b</th>\n", + " <th>c</th>\n", + " <th>d</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>0</td>\n", + " <td>10</td>\n", + " <td>100</td>\n", + " <td>1000</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>1</td>\n", + " <td>11</td>\n", + " <td>101</td>\n", + " <td>1001</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>2</td>\n", + " <td>12</td>\n", + " <td>102</td>\n", + " <td>1002</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>3</td>\n", + " <td>13</td>\n", + " <td>103</td>\n", + " <td>1003</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>4</td>\n", + " <td>14</td>\n", + " <td>104</td>\n", + " <td>1004</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " a b c d\n", + "0 0 10 100 1000\n", + "1 1 11 101 1001\n", + "2 2 12 102 1002\n", + "3 3 13 103 1003\n", + "4 4 14 104 1004" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "num_ele = 1000000\n", + "\n", + "df = cudf.DataFrame(\n", + " {\n", + " \"a\": range(num_ele),\n", + " \"b\": range(10, num_ele + 10),\n", + " \"c\": range(100, num_ele + 100),\n", + " \"d\": range(1000, num_ele + 1000)\n", + " }\n", + ")\n", + "\n", + "# preview\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b122f0a0-0354-43ed-9078-651d9761d6ed", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 1110\n", + "1 1114\n", + "2 1118\n", + "3 1122\n", + "4 1126\n", + " ... \n", + "999995 4001090\n", + "999996 4001094\n", + "999997 4001098\n", + "999998 4001102\n", + "999999 4001106\n", + "Length: 1000000, dtype: int64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0.31916284561157227" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "start=time.time()\n", + "display(df.sum(axis=1))\n", + "time.time()-start" + ] + }, + { + "cell_type": "markdown", + "id": "d49ebab0-bff0-4b7e-aabd-c46430799983", + "metadata": {}, + "source": [ + "The same operation runs faster with CuPy. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "9b618d3b-9b6d-423d-beb7-98334a8b3339", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1110, 1114, 1118, ..., 4001098, 4001102, 4001106])" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0.21457505226135254" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "arr=df.values\n", + "\n", + "start=time.time()\n", + "# alternative approach\n", + "# arr=df.to_cupy()\n", + "\n", + "display(arr.sum(axis=1))\n", + "time.time()-start" + ] + }, + { + "cell_type": "markdown", + "id": "ae656ac1-d4a2-499f-a904-6d1409c6ba79", + "metadata": {}, + "source": [ + "When using cuDF pandas, we can use the `.values` property as well as the `cupy.asarray()` function. \n", + "\n", + "**Note**: We can use the `.to_numpy()` method to convert cuDF DataFrames or Series to NumPy arrays. " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "84e218f5-b4ee-4d49-baff-eac5f1f76b3f", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'status': 'ok', 'restart': True}" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "import IPython\n", + "app = IPython.Application.instance()\n", + "app.kernel.do_shutdown(True)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "44200db8-d133-44c5-9f71-2796ec37df5c", + "metadata": {}, + "outputs": [], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "%load_ext cudf.pandas\n", + "import pandas as pd\n", + "\n", + "import numpy as np\n", + "import cupy as cp\n", + "import time" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "e8016e6d-829d-4117-a37c-bae82431626c", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>a</th>\n", + " <th>b</th>\n", + " <th>c</th>\n", + " <th>d</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>0</td>\n", + " <td>10</td>\n", + " <td>100</td>\n", + " <td>1000</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>1</td>\n", + " <td>11</td>\n", + " <td>101</td>\n", + " <td>1001</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>2</td>\n", + " <td>12</td>\n", + " <td>102</td>\n", + " <td>1002</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>3</td>\n", + " <td>13</td>\n", + " <td>103</td>\n", + " <td>1003</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>4</td>\n", + " <td>14</td>\n", + " <td>104</td>\n", + " <td>1004</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " a b c d\n", + "0 0 10 100 1000\n", + "1 1 11 101 1001\n", + "2 2 12 102 1002\n", + "3 3 13 103 1003\n", + "4 4 14 104 1004" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "num_ele = 1000000\n", + "\n", + "df = pd.DataFrame(\n", + " {\n", + " \"a\": range(num_ele),\n", + " \"b\": range(10, num_ele + 10),\n", + " \"c\": range(100, num_ele + 100),\n", + " \"d\": range(1000, num_ele + 1000)\n", + " }\n", + ")\n", + "\n", + "# preview\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "0d314943-69cb-4fcd-a01a-5fe4734ff0bf", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1110, 1114, 1118, ..., 4001098, 4001102, 4001106])" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\"> </span>\n", + "<span style=\"font-style: italic\"> Total time elapsed: 0.732 seconds </span>\n", + "<span style=\"font-style: italic\"> </span>\n", + "<span style=\"font-style: italic\"> Stats </span>\n", + "<span style=\"font-style: italic\"> </span>\n", + "┏━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━┓\n", + "┃<span style=\"font-weight: bold\"> Line no. </span>┃<span style=\"font-weight: bold\"> Line </span>┃<span style=\"font-weight: bold\"> GPU TIME(s) </span>┃<span style=\"font-weight: bold\"> CPU TIME(s) </span>┃\n", + "┡━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━┩\n", + "│ 2 │ <span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> arr</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">=</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">df</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">.</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">values</span><span style=\"background-color: #272822\"> </span> │ 0.328914536 │ │\n", + "│ │ <span style=\"background-color: #272822\"> </span> │ │ │\n", + "│ 6 │ <span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> start</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">=</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">time</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">.</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">time()</span><span style=\"background-color: #272822\"> </span> │ │ │\n", + "│ │ <span style=\"background-color: #272822\"> </span> │ │ │\n", + "│ 8 │ <span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> display(arr</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">.</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">sum(axis</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">=</span><span style=\"color: #ae81ff; text-decoration-color: #ae81ff; background-color: #272822\">1</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">))</span> │ 0.011681609 │ │\n", + "│ │ <span style=\"background-color: #272822\"> </span> │ │ │\n", + "│ 10 │ <span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> time</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">.</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">time()</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">-</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">start</span><span style=\"background-color: #272822\"> </span> │ │ │\n", + "│ │ <span style=\"background-color: #272822\"> </span> │ │ │\n", + "└──────────┴──────────────────────────────┴─────────────┴─────────────┘\n", + "</pre>\n" + ], + "text/plain": [ + "\u001b[3m \u001b[0m\n", + "\u001b[3m Total time elapsed: 0.732 seconds \u001b[0m\n", + "\u001b[3m \u001b[0m\n", + "\u001b[3m Stats \u001b[0m\n", + "\u001b[3m \u001b[0m\n", + "┏━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1mLine no.\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mLine \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mGPU TIME(s)\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mCPU TIME(s)\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━┩\n", + "│ 2 │ \u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34marr\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mdf\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mvalues\u001b[0m\u001b[48;2;39;40;34m \u001b[0m │ 0.328914536 │ │\n", + "│ │ \u001b[48;2;39;40;34m \u001b[0m │ │ │\n", + "│ 6 │ \u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mstart\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mtime\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mtime\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m │ │ │\n", + "│ │ \u001b[48;2;39;40;34m \u001b[0m │ │ │\n", + "│ 8 │ \u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mdisplay\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34marr\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34msum\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34maxis\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m=\u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m1\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m │ 0.011681609 │ │\n", + "│ │ \u001b[48;2;39;40;34m \u001b[0m │ │ │\n", + "│ 10 │ \u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mtime\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mtime\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m-\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mstart\u001b[0m\u001b[48;2;39;40;34m \u001b[0m │ │ │\n", + "│ │ \u001b[48;2;39;40;34m \u001b[0m │ │ │\n", + "└──────────┴──────────────────────────────┴─────────────┴─────────────┘\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%cudf.pandas.line_profile\n", + "# DO NOT CHANGE THIS CELL\n", + "arr=df.values\n", + "# alternative approach\n", + "# arr=cp.asarray(df)\n", + "\n", + "start=time.time()\n", + "\n", + "display(arr.sum(axis=1))\n", + "\n", + "time.time()-start" + ] + }, + { + "cell_type": "markdown", + "id": "5a97fac8-b555-4ec1-a555-9b5ab79c2fe1", + "metadata": {}, + "source": [ + "Just like we can do with NumPy and pandas, we can weave cuDF and CuPy together in the same workflow while keeping the data entirely on the GPU. We’re able to seamlessly move between data structures in this ecosystem, giving us enormous flexibility without sacrificing speed. If we’re working with RAPIDS cuDF but need a more linear-algebra oriented function that exists in CuPy, we can leverage the interoperability of the GPU PyData ecosystem to use that function. \n", + "\n", + "To convert a CuPy array to a cuDF DataFrame or Series, we can use their respective constructors. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "9cea340d-c9c4-468d-8431-015492682db9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>a</th>\n", + " <th>b</th>\n", + " <th>c</th>\n", + " <th>d</th>\n", + " <th>sum</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>0</td>\n", + " <td>10</td>\n", + " <td>100</td>\n", + " <td>1000</td>\n", + " <td>1110</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>1</td>\n", + " <td>11</td>\n", + " <td>101</td>\n", + " <td>1001</td>\n", + " <td>1114</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>2</td>\n", + " <td>12</td>\n", + " <td>102</td>\n", + " <td>1002</td>\n", + " <td>1118</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>3</td>\n", + " <td>13</td>\n", + " <td>103</td>\n", + " <td>1003</td>\n", + " <td>1122</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>4</td>\n", + " <td>14</td>\n", + " <td>104</td>\n", + " <td>1004</td>\n", + " <td>1126</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " a b c d sum\n", + "0 0 10 100 1000 1110\n", + "1 1 11 101 1001 1114\n", + "2 2 12 102 1002 1118\n", + "3 3 13 103 1003 1122\n", + "4 4 14 104 1004 1126" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "df['sum']=arr.sum(axis=1)\n", + "\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "id": "2cfe3955-2849-451b-945a-c37955b91235", + "metadata": {}, + "source": [ + "## Grid Converter ##\n", + "Much of our data is provided with latitude and longitude coordinates, but for some of our tasks involving distance - identifying geographically dense clusters of infected people, locating the nearest hospital or clinic from a given person - it is convenient to have Cartesian grid coordinates instead. By using a region-specific map projection - in this case, the [Ordnance Survey Great Britain 1936](https://en.wikipedia.org/wiki/Ordnance_Survey_National_Grid) - we can compute local distances efficiently and with good accuracy." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "8dc82f3c-f1cb-436b-becb-a97635ec5f6a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>age</th>\n", + " <th>sex</th>\n", + " <th>county</th>\n", + " <th>lat</th>\n", + " <th>long</th>\n", + " <th>name</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.533638</td>\n", + " <td>-1.524400</td>\n", + " <td>FRANCIS</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.426254</td>\n", + " <td>-1.465314</td>\n", + " <td>EDWARD</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.555199</td>\n", + " <td>-1.496417</td>\n", + " <td>TEDDY</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.547909</td>\n", + " <td>-1.572342</td>\n", + " <td>ANGUS</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.477638</td>\n", + " <td>-1.605995</td>\n", + " <td>CHARLIE</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " age sex county lat long name\n", + "0 0 m DARLINGTON 54.533638 -1.524400 FRANCIS\n", + "1 0 m DARLINGTON 54.426254 -1.465314 EDWARD\n", + "2 0 m DARLINGTON 54.555199 -1.496417 TEDDY\n", + "3 0 m DARLINGTON 54.547909 -1.572342 ANGUS\n", + "4 0 m DARLINGTON 54.477638 -1.605995 CHARLIE" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "dtype_dict={\n", + " 'age': 'int8', \n", + " 'sex': 'category', \n", + " 'county': 'category', \n", + " 'lat': 'float32', \n", + " 'long': 'float32', \n", + " 'name': 'category'\n", + "}\n", + " \n", + "df=pd.read_csv('./data/uk_pop.csv', dtype=dtype_dict)\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "id": "f25cfe6a-9f31-4a13-b06f-c85a95637a44", + "metadata": {}, + "source": [ + "### Lat/Long to OSGB Grid Converter with NumPy ###\n", + "To perform coordinate conversion, we will create a function `latlong2osgbgrid` which accepts latitude/longitude coordinates and converts them to [OSGB36 coordinates](https://en.wikipedia.org/wiki/Ordnance_Survey_National_Grid): \"northing\" and \"easting\" values representing the point's Cartesian coordinate distances from the southwest corner of the grid.\n", + "\n", + "Immediately below is `latlong2osgbgrid`, which relies heavily on NumPy:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "3e298328-a11c-4cda-ae15-eb292fed3a5c", + "metadata": {}, + "outputs": [], + "source": [ + "# https://www.ordnancesurvey.co.uk/docs/support/guide-coordinate-systems-great-britain.pdf\n", + "\n", + "def latlong2osgbgrid(lat, long, input_degrees=True):\n", + " '''\n", + " Converts latitude and longitude (ellipsoidal) coordinates into northing and easting (grid) coordinates, using a Transverse Mercator projection.\n", + " \n", + " Inputs:\n", + " lat: latitude coordinate (north)\n", + " long: longitude coordinate (east)\n", + " input_degrees: if True (default), interprets the coordinates as degrees; otherwise, interprets coordinates as radians\n", + " \n", + " Output:\n", + " (northing, easting)\n", + " '''\n", + " \n", + " if input_degrees:\n", + " lat = lat * np.pi/180\n", + " long = long * np.pi/180\n", + "\n", + " a = 6377563.396\n", + " b = 6356256.909\n", + " e2 = (a**2 - b**2) / a**2\n", + "\n", + " N0 = -100000 # northing of true origin\n", + " E0 = 400000 # easting of true origin\n", + " F0 = .9996012717 # scale factor on central meridian\n", + " phi0 = 49 * np.pi / 180 # latitude of true origin\n", + " lambda0 = -2 * np.pi / 180 # longitude of true origin and central meridian\n", + " \n", + " sinlat = np.sin(lat)\n", + " coslat = np.cos(lat)\n", + " tanlat = np.tan(lat)\n", + " \n", + " latdiff = lat-phi0\n", + " longdiff = long-lambda0\n", + "\n", + " n = (a-b) / (a+b)\n", + " nu = a * F0 * (1 - e2 * sinlat ** 2) ** -.5\n", + " rho = a * F0 * (1 - e2) * (1 - e2 * sinlat ** 2) ** -1.5\n", + " eta2 = nu / rho - 1\n", + " M = b * F0 * ((1 + n + 5/4 * (n**2 + n**3)) * latdiff - \n", + " (3*(n+n**2) + 21/8 * n**3) * np.sin(latdiff) * np.cos(lat+phi0) +\n", + " 15/8 * (n**2 + n**3) * np.sin(2*(latdiff)) * np.cos(2*(lat+phi0)) - \n", + " 35/24 * n**3 * np.sin(3*(latdiff)) * np.cos(3*(lat+phi0)))\n", + " I = M + N0\n", + " II = nu/2 * sinlat * coslat\n", + " III = nu/24 * sinlat * coslat ** 3 * (5 - tanlat ** 2 + 9 * eta2)\n", + " IIIA = nu/720 * sinlat * coslat ** 5 * (61-58 * tanlat**2 + tanlat**4)\n", + " IV = nu * coslat\n", + " V = nu / 6 * coslat**3 * (nu/rho - np.tan(lat)**2)\n", + " VI = nu / 120 * coslat ** 5 * (5 - 18 * tanlat**2 + tanlat**4 + 14 * eta2 - 58 * tanlat**2 * eta2)\n", + "\n", + " northing = I + II * longdiff**2 + III * longdiff**4 + IIIA * longdiff**6\n", + " easting = E0 + IV * longdiff + V * longdiff**3 + VI * longdiff**5\n", + "\n", + " return(northing, easting)" + ] + }, + { + "cell_type": "markdown", + "id": "a4ddcbe5-7524-4976-a94d-ff6753da3ad8", + "metadata": {}, + "source": [ + "### Lat/Long to OSGB Grid Converter with CuPy ###\n", + "In the following `latlong2osgbgrid_cupy`, we simply swap `cp` in for `np`. While CuPy supports a wide variety of powerful GPU-accelerated tasks, this simple technique of being able to swap in CuPy calls for NumPy calls makes it an incredibly powerful tool to have at our disposal." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "41ffa2a3-34ba-4664-9399-4b74cc28f623", + "metadata": {}, + "outputs": [], + "source": [ + "# https://www.ordnancesurvey.co.uk/docs/support/guide-coordinate-systems-great-britain.pdf\n", + "\n", + "def latlong2osgbgrid_cupy(lat, long, input_degrees=True):\n", + " '''\n", + " Converts latitude and longitude (ellipsoidal) coordinates into northing and easting (grid) coordinates, using a Transverse Mercator projection.\n", + " \n", + " Inputs:\n", + " lat: latitude coordinate (north)\n", + " long: longitude coordinate (east)\n", + " input_degrees: if True (default), interprets the coordinates as degrees; otherwise, interprets coordinates as radians\n", + " \n", + " Output:\n", + " (northing, easting)\n", + " '''\n", + " \n", + " if input_degrees:\n", + " lat = lat * cp.pi/180\n", + " long = long * cp.pi/180\n", + "\n", + " a = 6377563.396\n", + " b = 6356256.909\n", + " e2 = (a**2 - b**2) / a**2\n", + "\n", + " N0 = -100000 # northing of true origin\n", + " E0 = 400000 # easting of true origin\n", + " F0 = .9996012717 # scale factor on central meridian\n", + " phi0 = 49 * cp.pi / 180 # latitude of true origin\n", + " lambda0 = -2 * cp.pi / 180 # longitude of true origin and central meridian\n", + " \n", + " sinlat = cp.sin(lat)\n", + " coslat = cp.cos(lat)\n", + " tanlat = cp.tan(lat)\n", + " \n", + " latdiff = lat-phi0\n", + " longdiff = long-lambda0\n", + "\n", + " n = (a-b) / (a+b)\n", + " nu = a * F0 * (1 - e2 * sinlat ** 2) ** -.5\n", + " rho = a * F0 * (1 - e2) * (1 - e2 * sinlat ** 2) ** -1.5\n", + " eta2 = nu / rho - 1\n", + " M = b * F0 * ((1 + n + 5/4 * (n**2 + n**3)) * latdiff - \n", + " (3*(n+n**2) + 21/8 * n**3) * cp.sin(latdiff) * cp.cos(lat+phi0) +\n", + " 15/8 * (n**2 + n**3) * cp.sin(2*(latdiff)) * cp.cos(2*(lat+phi0)) - \n", + " 35/24 * n**3 * cp.sin(3*(latdiff)) * cp.cos(3*(lat+phi0)))\n", + " I = M + N0\n", + " II = nu/2 * sinlat * coslat\n", + " III = nu/24 * sinlat * coslat ** 3 * (5 - tanlat ** 2 + 9 * eta2)\n", + " IIIA = nu/720 * sinlat * coslat ** 5 * (61-58 * tanlat**2 + tanlat**4)\n", + " IV = nu * coslat\n", + " V = nu / 6 * coslat**3 * (nu/rho - cp.tan(lat)**2)\n", + " VI = nu / 120 * coslat ** 5 * (5 - 18 * tanlat**2 + tanlat**4 + 14 * eta2 - 58 * tanlat**2 * eta2)\n", + "\n", + " northing = I + II * longdiff**2 + III * longdiff**4 + IIIA * longdiff**6\n", + " easting = E0 + IV * longdiff + V * longdiff**3 + VI * longdiff**5\n", + "\n", + " return(northing, easting)" + ] + }, + { + "cell_type": "markdown", + "id": "8b9f1ea5-595d-4933-afb0-29fa75feea29", + "metadata": {}, + "source": [ + "Below we pass the latitude/longitude coordinates into the converter, which returns north and east values within the OSGB grid. " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "c35f24b6-0a70-4ad0-897b-fdbb9dee3cec", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 482 ms, sys: 562 ms, total: 1.04 s\n", + "Wall time: 1.04 s\n" + ] + } + ], + "source": [ + "%%time\n", + "# DO NOT CHANGE THIS CELL\n", + "numpy_lat = np.asarray(df['lat'])\n", + "numpy_long = np.asarray(df['long'])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "edc2feb1-29a8-420c-80ab-62fff620edaa", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 12.8 s, sys: 3.27 s, total: 16.1 s\n", + "Wall time: 16.1 s\n" + ] + } + ], + "source": [ + "%%time\n", + "# DO NOT CHANGE THIS CELL\n", + "n_numpy_array, e_numpy_array = latlong2osgbgrid(numpy_lat, numpy_long)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "5c520b09-41a5-4d59-ac24-1690fcc8bbcf", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 662 μs, sys: 206 μs, total: 868 μs\n", + "Wall time: 881 μs\n" + ] + } + ], + "source": [ + "%%time\n", + "# DO NOT CHANGE THIS CELL\n", + "cumpy_lat = cp.asarray(df['lat'])\n", + "cumpy_long = cp.asarray(df['long'])" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "46de3086-d8a9-4f0a-a593-3f68d2c4493e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 3.39 s, sys: 24.8 ms, total: 3.41 s\n", + "Wall time: 3.39 s\n" + ] + } + ], + "source": [ + "%%time\n", + "# DO NOT CHANGE THIS CELL\n", + "n_cumpy_array, e_cumpy_array = latlong2osgbgrid_cupy(cumpy_lat, cumpy_long)" + ] + }, + { + "cell_type": "markdown", + "id": "b3f10d6f-2044-4517-904e-c9bd46350f17", + "metadata": {}, + "source": [ + "### Exercise #1 - Adding Grid Coordinate Columns to DataFrame ###\n", + "Now we will utilize `latlong2osgbgrid_cupy` to add `northing` and `easting` columns to `df`. We start by converting the two columns we need, `lat` and `long`, to CuPy arrays with the `cp.asarray()` function. Because cuDF and CuPy interface directly via the `__cuda_array_interface__`, the conversion can happen in nanoseconds. \n", + "**Instructions**: <br>\n", + "* Execute the below cell to create CuPy arrays for the `lat` and `long` columns. \n", + "* Modify the `<FIXME>` only and execute the cell below to use `latlong2osgbgrid_cupy` with `cupy_lat` and `cupy_long`, followed by add them as the `northing` and `easting` columns with the dtype `float32`. " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "3febd9fb-fc44-41f6-9712-60902da18d22", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 875 μs, sys: 0 ns, total: 875 μs\n", + "Wall time: 889 μs\n" + ] + } + ], + "source": [ + "%%time\n", + "# DO NOT CHANGE THIS CELL\n", + "cupy_lat = cp.asarray(df['lat'])\n", + "cupy_long = cp.asarray(df['long'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "da67fb99-fdab-49c4-9480-a9b5546dbc95", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "%%time\n", + "n_cupy_array, e_cupy_array = <<<<FIXME>>>>\n", + "df['northing'] = <<<<FIXME>>>>\n", + "df['easting'] = <<<<FIXME>>>>\n", + "print(df.dtypes)\n", + "df.head()" + ] + }, + { + "cell_type": "raw", + "id": "4c65c727-18a4-47a6-9e43-acba85212834", + "metadata": { + "jupyter": { + "source_hidden": true + }, + "scrolled": true + }, + "source": [ + "\n", + "%%time\n", + "n_cupy_array, e_cupy_array = latlong2osgbgrid_cupy(cupy_lat, cupy_long)\n", + "df['northing'] = pd.Series(n_cupy_array).astype('float32')\n", + "df['easting'] = e_cupy_array.astype('float32')\n", + "print(df.dtypes)\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "id": "82640e5c-ae3a-495b-bc48-df9b46f5d4b1", + "metadata": {}, + "source": [ + "Click ... for solution. " + ] + }, + { + "cell_type": "markdown", + "id": "d0e92cb7-c138-49ae-9aa4-e55f76235b98", + "metadata": {}, + "source": [ + "## Boolean Array Indexing ##\n", + "Below we use `np.logical_and` for element-wise boolean selection." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "61d7946d-f239-4c5b-9c38-2057aa88a085", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "start=time.time()\n", + "display(df.loc[np.logical_and(df['name'].str.startswith('E'), df['name'].str.endswith('D'))].head())\n", + "print(f'Duration: {round(time.time()-start, 2)} seconds')" + ] + }, + { + "cell_type": "markdown", + "id": "3f768cdc-3412-4256-921e-ffccfdcd6058", + "metadata": {}, + "source": [ + "Below we use the CuPy for boolean selection. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "04ac5517-c69b-4355-81e0-1e94deddcb2e", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "%%cudf.pandas.line_profile\n", + "# DO NOT CHANGE THIS CELL\n", + "start=time.time()\n", + "display(df.loc[cp.logical_and(df['name'].str.startswith('E'), df['name'].str.endswith('D'))].head())\n", + "print(f'Duration: {round(time.time()-start, 2)} seconds')" + ] + }, + { + "cell_type": "markdown", + "id": "c2cab4f4-eff4-4b09-a68d-dc1c089bfa72", + "metadata": {}, + "source": [ + "**Note**: String array is not yet implemented in CuPy. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7321b4f6-61c6-4968-a77b-3d3ed4cde745", + "metadata": {}, + "outputs": [], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "import IPython\n", + "app = IPython.Application.instance()\n", + "app.kernel.do_shutdown(True)" + ] + }, + { + "cell_type": "markdown", + "id": "f9d91047-2067-42f7-89b5-e9915eefb1de", + "metadata": {}, + "source": [ + "**Well Done!** Let's move to the [next notebook](1-05_grouping.ipynb). " + ] + }, + { + "cell_type": "markdown", + "id": "ec10008d-0c52-4f35-ad4a-969a892c35b8", + "metadata": {}, + "source": [ + "<img src=\"./images/DLI_Header.png\" width=400/>" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.15" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Fundamentals_of_Accelerated_Data_Science/1-05_grouping.ipynb b/Fundamentals_of_Accelerated_Data_Science/1-05_grouping.ipynb new file mode 100644 index 0000000..0595f20 --- /dev/null +++ b/Fundamentals_of_Accelerated_Data_Science/1-05_grouping.ipynb @@ -0,0 +1,1350 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "b53a7b12-538d-4459-b82a-a35c8c417849", + "metadata": {}, + "source": [ + "<img src=\"./images/DLI_Header.png\" width=400/>" + ] + }, + { + "cell_type": "markdown", + "id": "ae497b71-bc43-471e-8970-88a1878e7cf9", + "metadata": {}, + "source": [ + "# Fundamentals of Accelerated Data Science # " + ] + }, + { + "cell_type": "markdown", + "id": "a149b6d1-1880-4a5d-9d71-f963d3097aa4", + "metadata": {}, + "source": [ + "## 05 - Grouping ##\n", + "\n", + "**Table of Contents**\n", + "<br>\n", + "This notebook discusses and demonstrates how grouping in used in data science. This notebook covers the below sections: \n", + "1. [Grouping](#Grouping)\n", + " * [Split, Apply, and Combine](#Split,-Apply,-and-Combine)\n", + " * [Exercise #1 - Average Age Per County](#Exercise-#1---Average-Age-Per-County)\n", + "2. [Binning](#Binning)\n", + " * [Exercise #2 - Using the Profiler](#Exercise-#2---Using-the-Profiler)\n", + "3. [Advanced Groupby Operations](#Advanced-Groupby-Operations)\n", + " * [`.apply()`](#.apply())\n", + " * [`.transform()`](#.transform())\n", + "4. [Pivot Table](#Pivot-Table)" + ] + }, + { + "cell_type": "markdown", + "id": "f4f2e800-7d61-4370-8a96-ebef3a6d0c0a", + "metadata": {}, + "source": [ + "## Grouping ##\n", + "In data science, we often would like to split data into groups and perform further analysis on them such as: \n", + "* Aggregate based on the grouping\n", + "* Compare metrics across different groups\n", + "* Understand patterns in data across different groups\n", + "* Remove duplicates or fill missing values based on group-level information\n", + "* Create new features based on group-level statistics\n", + "* Integrate with visualization" + ] + }, + { + "cell_type": "markdown", + "id": "93d943bf-aaad-42c8-9b2c-70c8b6c0bf44", + "metadata": {}, + "source": [ + "Below we load in our dataset. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "89c435b1-35d5-4971-ade1-549ae77d22db", + "metadata": {}, + "outputs": [], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "%load_ext cudf.pandas\n", + "import pandas as pd\n", + "import time" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "8dc82f3c-f1cb-436b-becb-a97635ec5f6a", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>age</th>\n", + " <th>sex</th>\n", + " <th>county</th>\n", + " <th>lat</th>\n", + " <th>long</th>\n", + " <th>name</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.533638</td>\n", + " <td>-1.524400</td>\n", + " <td>FRANCIS</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.426254</td>\n", + " <td>-1.465314</td>\n", + " <td>EDWARD</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.555199</td>\n", + " <td>-1.496417</td>\n", + " <td>TEDDY</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.547909</td>\n", + " <td>-1.572342</td>\n", + " <td>ANGUS</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.477638</td>\n", + " <td>-1.605995</td>\n", + " <td>CHARLIE</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " age sex county lat long name\n", + "0 0 m DARLINGTON 54.533638 -1.524400 FRANCIS\n", + "1 0 m DARLINGTON 54.426254 -1.465314 EDWARD\n", + "2 0 m DARLINGTON 54.555199 -1.496417 TEDDY\n", + "3 0 m DARLINGTON 54.547909 -1.572342 ANGUS\n", + "4 0 m DARLINGTON 54.477638 -1.605995 CHARLIE" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "dtype_dict={\n", + " 'age': 'int8', \n", + " 'sex': 'category', \n", + " 'county': 'category', \n", + " 'lat': 'float32', \n", + " 'long': 'float32', \n", + " 'name': 'category'\n", + "}\n", + " \n", + "df=pd.read_csv('./data/uk_pop.csv', dtype=dtype_dict)\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "id": "c8bbdeac-9134-443b-9b38-7a980b19decf", + "metadata": {}, + "source": [ + "## Split, Apply, and Combine ##\n", + "We use the `.groupby()` method to to group large amounts of data and compute operations on these groups. A groupby operation involves some combination of splitting the object, applying a function, and combining the results. cuDF implements record grouping in a manner comparable to Pandas, but with some notable differences. \n", + "\n", + "<p><img src='images/groupby.png' width=720></p>\n", + "\n", + "cuDF supports a number of common `DataFrameGroupBy` computations and descriptive statistics, such as `.size()`, `.mean()`, `.count()`, `.cov()`, `.cumprod()`, `.cumsum()`, `.max()`, `.min()`, `.nunique()`. \n", + "\n", + "**Note**: More information about how `.groupby()` behaves for pandas and how it differs from cuDF can be found in the links below: \n", + "* [pandas](https://pandas.pydata.org/docs/user_guide/groupby.html)\n", + "* [cuDF](https://docs.rapids.ai/api/cudf/stable/user_guide/groupby/)\n", + "\n", + "Below we find the number of people in each county. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "72b646d3-b175-4a4e-804f-43afcb6910a2", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "county\n", + "BARKING AND DAGENHAM 211998\n", + "BARNET 392140\n", + "BARNSLEY 245199\n", + "BATH AND NORTH EAST SOMERSET 192106\n", + "BEDFORD 171623\n", + " ... \n", + "WOKINGHAM 167979\n", + "WOLVERHAMPTON 262008\n", + "WORCESTERSHIRE 592057\n", + "WREXHAM 136126\n", + "YORK 209893\n", + "Length: 171, dtype: int64" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "df.groupby('county').size()" + ] + }, + { + "cell_type": "markdown", + "id": "a4775215-c6d4-49b0-ba8d-4d10a7e9f434", + "metadata": {}, + "source": [ + "**Note**: The results is unsorted. We can sort the output using the `.sort_index()` or `.sort_values()` method. \n", + "\n", + "Below we count the number of people with the most and least popular names. " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "6b22bb2b-8b86-45cb-ba26-3b845be5ac00", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "name\n", + "AKASHDEEP 213\n", + "DALHA 214\n", + "BOGOMIL 215\n", + "REMMY 217\n", + "KAIYAAN 219\n", + " ... \n", + "GEORGE 459096\n", + "HARRY 459346\n", + "AMELIA 460659\n", + "OLIVIA 483789\n", + "OLIVER 576135\n", + "Length: 13212, dtype: int64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "name\n", + "A 752\n", + "A'ISHA 889\n", + "A'NIYAH 298\n", + "A-JAY 785\n", + "AABAN 223\n", + " ... \n", + "ZYANA 857\n", + "ZYLA 285\n", + "ZYLAN 251\n", + "ZYON 501\n", + "ZYRAH 472\n", + "Length: 13212, dtype: int64" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "display(df.groupby('name').size().sort_values())\n", + "display(df.groupby('name').size().sort_index())" + ] + }, + { + "cell_type": "markdown", + "id": "fb83bfcd-d9b2-434d-a0b3-d8d435901ece", + "metadata": {}, + "source": [ + "Below we find the approximate centers of each county using `.groupby().mean()`. When performing groupby operations, we should **only** include columns that are being used. " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "9f0efddf-7c0a-4fbc-8c88-b0fad245b05f", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>lat</th>\n", + " <th>long</th>\n", + " </tr>\n", + " <tr>\n", + " <th>county</th>\n", + " <th></th>\n", + " <th></th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>BARKING AND DAGENHAM</th>\n", + " <td>51.621062</td>\n", + " <td>0.129583</td>\n", + " </tr>\n", + " <tr>\n", + " <th>BARNET</th>\n", + " <td>51.812557</td>\n", + " <td>-0.218212</td>\n", + " </tr>\n", + " <tr>\n", + " <th>BARNSLEY</th>\n", + " <td>53.571895</td>\n", + " <td>-1.548719</td>\n", + " </tr>\n", + " <tr>\n", + " <th>BATH AND NORTH EAST SOMERSET</th>\n", + " <td>51.354950</td>\n", + " <td>-2.486675</td>\n", + " </tr>\n", + " <tr>\n", + " <th>BEDFORD</th>\n", + " <td>52.145476</td>\n", + " <td>-0.454974</td>\n", + " </tr>\n", + " <tr>\n", + " <th>...</th>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " </tr>\n", + " <tr>\n", + " <th>WOKINGHAM</th>\n", + " <td>51.459659</td>\n", + " <td>-0.899371</td>\n", + " </tr>\n", + " <tr>\n", + " <th>WOLVERHAMPTON</th>\n", + " <td>52.716852</td>\n", + " <td>-2.127595</td>\n", + " </tr>\n", + " <tr>\n", + " <th>WORCESTERSHIRE</th>\n", + " <td>52.057964</td>\n", + " <td>-2.209184</td>\n", + " </tr>\n", + " <tr>\n", + " <th>WREXHAM</th>\n", + " <td>53.000793</td>\n", + " <td>-2.991957</td>\n", + " </tr>\n", + " <tr>\n", + " <th>YORK</th>\n", + " <td>53.992329</td>\n", + " <td>-1.073789</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "<p>171 rows × 2 columns</p>\n", + "</div>" + ], + "text/plain": [ + " lat long\n", + "county \n", + "BARKING AND DAGENHAM 51.621062 0.129583\n", + "BARNET 51.812557 -0.218212\n", + "BARNSLEY 53.571895 -1.548719\n", + "BATH AND NORTH EAST SOMERSET 51.354950 -2.486675\n", + "BEDFORD 52.145476 -0.454974\n", + "... ... ...\n", + "WOKINGHAM 51.459659 -0.899371\n", + "WOLVERHAMPTON 52.716852 -2.127595\n", + "WORCESTERSHIRE 52.057964 -2.209184\n", + "WREXHAM 53.000793 -2.991957\n", + "YORK 53.992329 -1.073789\n", + "\n", + "[171 rows x 2 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\"> </span>\n", + "<span style=\"font-style: italic\"> Total time elapsed: 0.588 seconds </span>\n", + "<span style=\"font-style: italic\"> </span>\n", + "<span style=\"font-style: italic\"> Stats </span>\n", + "<span style=\"font-style: italic\"> </span>\n", + "┏━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━┓\n", + "┃<span style=\"font-weight: bold\"> Line no. </span>┃<span style=\"font-weight: bold\"> Line </span>┃<span style=\"font-weight: bold\"> GPU TIME(s) </span>┃<span style=\"font-weight: bold\"> CPU TIME(s) </span>┃\n", + "┡━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━┩\n", + "│ 3 │ <span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> county_center_df</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">=</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">df[[</span><span style=\"color: #e6db74; text-decoration-color: #e6db74; background-color: #272822\">'county'</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">, </span><span style=\"color: #e6db74; text-decoration-color: #e6db74; background-color: #272822\">'lat'</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">, </span><span style=\"color: #e6db74; text-decoration-color: #e6db74; background-color: #272822\">'long'</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">]]</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">.</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">groupby(</span><span style=\"color: #e6db74; text-decoration-color: #e6db74; background-color: #272822\">'county'</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">)[[</span><span style=\"color: #e6db74; text-decoration-color: #e6db74; background-color: #272822\">'…</span> │ 0.096854766 │ │\n", + "│ │ <span style=\"background-color: #272822\"> </span> │ │ │\n", + "│ 4 │ <span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> display(county_center_df)</span><span style=\"background-color: #272822\"> </span> │ 0.177956740 │ │\n", + "│ │ <span style=\"background-color: #272822\"> </span> │ │ │\n", + "└──────────┴──────────────────────────────────────────────────────────────────────────┴─────────────┴─────────────┘\n", + "</pre>\n" + ], + "text/plain": [ + "\u001b[3m \u001b[0m\n", + "\u001b[3m Total time elapsed: 0.588 seconds \u001b[0m\n", + "\u001b[3m \u001b[0m\n", + "\u001b[3m Stats \u001b[0m\n", + "\u001b[3m \u001b[0m\n", + "┏━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1mLine no.\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mLine \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mGPU TIME(s)\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mCPU TIME(s)\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━┩\n", + "│ 3 │ \u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mcounty_center_df\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mdf\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m[\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m[\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mcounty\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mlat\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mlong\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m]\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m]\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mgroupby\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mcounty\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m[\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m[\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m…\u001b[0m │ 0.096854766 │ │\n", + "│ │ \u001b[48;2;39;40;34m \u001b[0m │ │ │\n", + "│ 4 │ \u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mdisplay\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mcounty_center_df\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m │ 0.177956740 │ │\n", + "│ │ \u001b[48;2;39;40;34m \u001b[0m │ │ │\n", + "└──────────┴──────────────────────────────────────────────────────────────────────────┴─────────────┴─────────────┘\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%cudf.pandas.line_profile\n", + "# DO NOT CHANGE THIS CELL\n", + "\n", + "county_center_df=df[['county', 'lat', 'long']].groupby('county')[['lat', 'long']].mean()\n", + "display(county_center_df)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b99556b3-b75d-4755-9b03-141d4addb023", + "metadata": {}, + "outputs": [], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "county_center_df.columns=['lat_county_center', 'long_county_center']\n", + "county_center_df.to_csv('county_centroid.csv')" + ] + }, + { + "cell_type": "markdown", + "id": "49a498f4-b298-446c-8255-60def6d3b0ed", + "metadata": {}, + "source": [ + "### Exercise #1 - Average Age Per County ###\n", + "We would like to find the average age for each county. We will need to use both `.groupby()` and `.sort_values()`. Using the `.mean()` method on the data grouped by `county`, identify the 5 counties with the highest average age. \n", + "\n", + "**Instructions**: <br>\n", + "* Modify the `<FIXME>` only and execute the below cell find the average age for each county. " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "0772c391-4ad6-4fcc-8754-97b575bca1c5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "county\n", + "DORSET 46.577193\n", + "ISLE OF WIGHT 46.149253\n", + "CONWY 45.854473\n", + "POWYS 45.849366\n", + "ISLES OF SCILLY 45.467440\n", + "Name: age, dtype: float64" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[['county', 'age']].groupby('county')['age']\\\n", + " .mean()\\\n", + " .sort_values(ascending=False)\\\n", + " .head()" + ] + }, + { + "cell_type": "raw", + "id": "cd43f771-0ccb-426d-8746-0c6ad63deff4", + "metadata": { + "jupyter": { + "source_hidden": true + }, + "scrolled": true + }, + "source": [ + "\n", + "%%cudf.pandas.line_profile\n", + "# DO NOT CHANGE THIS CELL\n", + "display(\n", + " df[['county', 'age']].groupby('county')['age']\\\n", + " .mean()\\\n", + " .sort_values(ascending=False)\\\n", + " .head()\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "0c4a3e80-c5cb-410f-a5ba-fde705594c66", + "metadata": {}, + "source": [ + "Click ... for solution. " + ] + }, + { + "cell_type": "markdown", + "id": "73fc5851-33dc-42a2-b086-6b5d7d2d65bb", + "metadata": {}, + "source": [ + "## Binning ##\n", + "When grouping continuous numerical data, it is sometimes helpful to bin numbers into discrete intervals or buckets. There are primarily two ways of binning: \n", + "* Equal-width binning: divide the range into equal-sized intervals\n", + "* Custom binning: define custom bins based on domain knowledge or specific criteria\n", + "\n", + "The `.cut()` function can be used to bin values into discrete intervals" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "ccf6ff71-40e5-46d6-9d1a-0e3d60fd0b51", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "age_bucket\n", + "0 7874941\n", + "1 6630853\n", + "2 7758863\n", + "3 7691036\n", + "4 7598003\n", + "5 7712976\n", + "6 6124213\n", + "7 4530946\n", + "8 2558063\n", + "dtype: int64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\"> </span>\n", + "<span style=\"font-style: italic\"> Total time elapsed: 0.532 seconds </span>\n", + "<span style=\"font-style: italic\"> </span>\n", + "<span style=\"font-style: italic\"> Stats </span>\n", + "<span style=\"font-style: italic\"> </span>\n", + "┏━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━┓\n", + "┃<span style=\"font-weight: bold\"> Line no. </span>┃<span style=\"font-weight: bold\"> Line </span>┃<span style=\"font-weight: bold\"> GPU TIME(s) </span>┃<span style=\"font-weight: bold\"> CPU TIME(s) </span>┃\n", + "┡━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━┩\n", + "│ 3 │ <span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> bins</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">=</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">[</span><span style=\"color: #ae81ff; text-decoration-color: #ae81ff; background-color: #272822\">0</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">, </span><span style=\"color: #ae81ff; text-decoration-color: #ae81ff; background-color: #272822\">10</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">, </span><span style=\"color: #ae81ff; text-decoration-color: #ae81ff; background-color: #272822\">20</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">, </span><span style=\"color: #ae81ff; text-decoration-color: #ae81ff; background-color: #272822\">30</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">, </span><span style=\"color: #ae81ff; text-decoration-color: #ae81ff; background-color: #272822\">40</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">, </span><span style=\"color: #ae81ff; text-decoration-color: #ae81ff; background-color: #272822\">50</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">, </span><span style=\"color: #ae81ff; text-decoration-color: #ae81ff; background-color: #272822\">60</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">, </span><span style=\"color: #ae81ff; text-decoration-color: #ae81ff; background-color: #272822\">70</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">, </span><span style=\"color: #ae81ff; text-decoration-color: #ae81ff; background-color: #272822\">80</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">, </span><span style=\"color: #ae81ff; text-decoration-color: #ae81ff; background-color: #272822\">90</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">, </span><span style=\"color: #ae81ff; text-decoration-color: #ae81ff; background-color: #272822\">100</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">]</span><span style=\"background-color: #272822\"> </span> │ │ │\n", + "│ │ <span style=\"background-color: #272822\"> </span> │ │ │\n", + "│ 5 │ <span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> df[</span><span style=\"color: #e6db74; text-decoration-color: #e6db74; background-color: #272822\">'age_bucket'</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">]</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">=</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">pd</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">.</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">cut(df[</span><span style=\"color: #e6db74; text-decoration-color: #e6db74; background-color: #272822\">'age'</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">]</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">.</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">values, bins</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">=</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">bins, right</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">=</span><span style=\"color: #66d9ef; text-decoration-color: #66d9ef; background-color: #272822\">True</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">, in…</span> │ 0.145499838 │ │\n", + "│ │ <span style=\"background-color: #272822\"> </span> │ │ │\n", + "│ 6 │ <span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> display(df</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">.</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">groupby(</span><span style=\"color: #e6db74; text-decoration-color: #e6db74; background-color: #272822\">'age_bucket'</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">)</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">.</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">size())</span><span style=\"background-color: #272822\"> </span> │ 0.049299458 │ 0.006484685 │\n", + "│ │ <span style=\"background-color: #272822\"> </span> │ │ │\n", + "└──────────┴──────────────────────────────────────────────────────────────────────────┴─────────────┴─────────────┘\n", + "</pre>\n" + ], + "text/plain": [ + "\u001b[3m \u001b[0m\n", + "\u001b[3m Total time elapsed: 0.532 seconds \u001b[0m\n", + "\u001b[3m \u001b[0m\n", + "\u001b[3m Stats \u001b[0m\n", + "\u001b[3m \u001b[0m\n", + "┏━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1mLine no.\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mLine \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mGPU TIME(s)\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mCPU TIME(s)\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━┩\n", + "│ 3 │ \u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mbins\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m[\u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m0\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m10\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m20\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m30\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m40\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m50\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m60\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m70\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m80\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m90\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m100\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m]\u001b[0m\u001b[48;2;39;40;34m \u001b[0m │ │ │\n", + "│ │ \u001b[48;2;39;40;34m \u001b[0m │ │ │\n", + "│ 5 │ \u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mdf\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m[\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mage_bucket\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m]\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mpd\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mcut\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mdf\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m[\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mage\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m]\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mvalues\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mbins\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mbins\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mright\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m=\u001b[0m\u001b[38;2;102;217;239;48;2;39;40;34mTrue\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34min…\u001b[0m │ 0.145499838 │ │\n", + "│ │ \u001b[48;2;39;40;34m \u001b[0m │ │ │\n", + "│ 6 │ \u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mdisplay\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mdf\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mgroupby\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mage_bucket\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34msize\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m │ 0.049299458 │ 0.006484685 │\n", + "│ │ \u001b[48;2;39;40;34m \u001b[0m │ │ │\n", + "└──────────┴──────────────────────────────────────────────────────────────────────────┴─────────────┴─────────────┘\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%cudf.pandas.line_profile\n", + "# DO NOT CHANGE THIS CELL\n", + "\n", + "bins=[0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100]\n", + "\n", + "df['age_bucket']=pd.cut(df['age'].values, bins=bins, right=True, include_lowest=True, labels=False)\n", + "display(df.groupby('age_bucket').size())" + ] + }, + { + "cell_type": "markdown", + "id": "db8ec7e5-f6f4-4041-8187-578a15c73808", + "metadata": {}, + "source": [ + "### Exercise #2 - Using the Profiler ###\n", + "cuDF pandas will attempt to use the GPU whenever possible and fall back to CPU for certain operations. Running the code with the `cudf.pandas.line_profile` magic command generates a report showing which operations used the GPU and which used the CPU. \n", + "\n", + "**Instructions**: <br>\n", + "* Notice that the below cell is a very similar operation as before, except that it uses the `range()` function for the `bins` parameter. As it stands, this is not supported in cuDF. \n", + "* Execute the cell below to run the binning operation on the CPU.\n", + "* Compare the time it takes to run the similar operation above. " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "991c437f-afee-408e-9144-006c4313f9f3", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "age_bucket\n", + "0 7874941\n", + "1 6630853\n", + "2 7758863\n", + "3 7691036\n", + "4 7598003\n", + "5 7712976\n", + "6 6124213\n", + "7 4530946\n", + "8 2558063\n", + "dtype: int64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\"> </span>\n", + "<span style=\"font-style: italic\"> Total time elapsed: 1.724 seconds </span>\n", + "<span style=\"font-style: italic\"> </span>\n", + "<span style=\"font-style: italic\"> Stats </span>\n", + "<span style=\"font-style: italic\"> </span>\n", + "┏━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━┓\n", + "┃<span style=\"font-weight: bold\"> Line no. </span>┃<span style=\"font-weight: bold\"> Line </span>┃<span style=\"font-weight: bold\"> GPU TIME(s) </span>┃<span style=\"font-weight: bold\"> CPU TIME(s) </span>┃\n", + "┡━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━┩\n", + "│ 3 │ <span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> df[</span><span style=\"color: #e6db74; text-decoration-color: #e6db74; background-color: #272822\">'age_bucket'</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">]</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">=</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">pd</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">.</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">cut(df[</span><span style=\"color: #e6db74; text-decoration-color: #e6db74; background-color: #272822\">'age'</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">]</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">.</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">values, bins</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">=</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">range(</span><span style=\"color: #ae81ff; text-decoration-color: #ae81ff; background-color: #272822\">0</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">, </span><span style=\"color: #ae81ff; text-decoration-color: #ae81ff; background-color: #272822\">100</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">, </span><span style=\"color: #ae81ff; text-decoration-color: #ae81ff; background-color: #272822\">10</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">), r…</span> │ 0.333567078 │ 1.021651556 │\n", + "│ │ <span style=\"background-color: #272822\"> </span> │ │ │\n", + "│ 4 │ <span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> display(df</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">.</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">groupby(</span><span style=\"color: #e6db74; text-decoration-color: #e6db74; background-color: #272822\">'age_bucket'</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">)</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">.</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">size())</span><span style=\"background-color: #272822\"> </span> │ 0.073766744 │ 0.006484282 │\n", + "│ │ <span style=\"background-color: #272822\"> </span> │ │ │\n", + "└──────────┴──────────────────────────────────────────────────────────────────────────┴─────────────┴─────────────┘\n", + "</pre>\n" + ], + "text/plain": [ + "\u001b[3m \u001b[0m\n", + "\u001b[3m Total time elapsed: 1.724 seconds \u001b[0m\n", + "\u001b[3m \u001b[0m\n", + "\u001b[3m Stats \u001b[0m\n", + "\u001b[3m \u001b[0m\n", + "┏━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1mLine no.\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mLine \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m 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\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mdf\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m[\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mage_bucket\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m]\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mpd\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mcut\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mdf\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m[\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mage\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m]\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mvalues\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mbins\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mrange\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m0\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m100\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m10\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mr…\u001b[0m │ 0.333567078 │ 1.021651556 │\n", + "│ │ \u001b[48;2;39;40;34m \u001b[0m │ │ │\n", + "│ 4 │ \u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mdisplay\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mdf\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mgroupby\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mage_bucket\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34msize\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m │ 0.073766744 │ 0.006484282 │\n", + "│ │ \u001b[48;2;39;40;34m \u001b[0m │ │ │\n", + "└──────────┴──────────────────────────────────────────────────────────────────────────┴─────────────┴─────────────┘\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%cudf.pandas.line_profile\n", + "# DO NOT CHANGE THIS CELL\n", + "\n", + "df['age_bucket']=pd.cut(df['age'].values, bins=range(0, 100, 10), right=True, include_lowest=True, labels=False)\n", + "display(df.groupby('age_bucket').size())" + ] + }, + { + "cell_type": "markdown", + "id": "95ef539b-6ab4-4f28-b84e-8b83f32d0c0c", + "metadata": {}, + "source": [ + "**Note**: The profiler can help us identify parts of our code that could be rewritten to be more GPU-friendly. " + ] + }, + { + "cell_type": "markdown", + "id": "7c76d718-693c-4550-8eec-3de742565a9d", + "metadata": {}, + "source": [ + "## Advanced Groupby Operations ##\n", + "We can also use function application helpers on `DataFrameGroupBy` instances: \n", + "* `DataFrameGroupby.aggregate()` / `Groupby.agg()`(alias): used when we have specific computation for different columns or more than one computation on the same column\n", + "* `DataFrameGroupby.apply()`: used when we want to perform a specific user-defined function to each group\n", + "* `DataFrameGroupby.transform()`: used when the resulting values should be broadcast across the whole group and return a same-indexed dataframe" + ] + }, + { + "cell_type": "markdown", + "id": "059bded6-fb78-4129-b723-6fa1de7ed8f0", + "metadata": {}, + "source": [ + "### `.apply()` ###\n", + "The `.apply()` method will **sequentially** apply the function group-wise and concatenate the results together. We can pass a callable function to be performed on the entire DataFrame for each group. \n", + "\n", + "Below we calculate the distance of each person from their respective county center. " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "6906dc46-32fd-45f4-8be1-ff30d944d226", + "metadata": {}, + "outputs": [], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "\n", + "# define distance function\n", + "def distance(lat_1, long_1, lat_2, long_2): \n", + " return ((lat_2-lat_1)**2+(long_2-long_1)**2)**0.5" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "63906fdc-10a5-4239-951e-e9551ff8d60b", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.10/site-packages/cudf/core/groupby/groupby.py:1449: RuntimeWarning: GroupBy.apply() performance scales poorly with number of groups. Got 171 groups. Some functions may perform better by passing engine='jit'\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "text/html": [ + "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\"> </span>\n", + "<span style=\"font-style: italic\"> Total time elapsed: 20.268 seconds </span>\n", + "<span style=\"font-style: italic\"> </span>\n", + "<span style=\"font-style: italic\"> Stats </span>\n", + "<span style=\"font-style: italic\"> </span>\n", + "┏━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━┓\n", + "┃<span style=\"font-weight: bold\"> Line no. </span>┃<span style=\"font-weight: bold\"> Line </span>┃<span style=\"font-weight: bold\"> GPU TIME(s) </span>┃<span style=\"font-weight: bold\"> CPU TIME(s) </span>┃\n", + "┡━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━┩\n", + "│ 3 │ <span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> distance_df</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">=</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">df</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">.</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">groupby(</span><span style=\"color: #e6db74; text-decoration-color: #e6db74; background-color: #272822\">'county'</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">)[[</span><span style=\"color: #e6db74; text-decoration-color: #e6db74; background-color: #272822\">'lat'</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">, </span><span style=\"color: #e6db74; text-decoration-color: #e6db74; background-color: #272822\">'long'</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">]]</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">.</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">apply(</span><span style=\"color: #66d9ef; text-decoration-color: #66d9ef; background-color: #272822\">lambda</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> x: d…</span> │ 3.190437470 │ │\n", + "│ │ <span style=\"background-color: #272822\"> </span> │ │ │\n", + "│ 4 │ <span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> df[</span><span style=\"color: #e6db74; text-decoration-color: #e6db74; background-color: #272822\">'R_1'</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">]</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">=</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">distance_df</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">.</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">reset_index(level</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">=</span><span style=\"color: #ae81ff; text-decoration-color: #ae81ff; background-color: #272822\">0</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">, drop</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">=</span><span style=\"color: #66d9ef; text-decoration-color: #66d9ef; background-color: #272822\">True</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">)</span><span style=\"background-color: #272822\"> </span> │ 0.613676883 │ │\n", + "│ │ <span style=\"background-color: #272822\"> </span> │ │ │\n", + "└──────────┴──────────────────────────────────────────────────────────────────────────┴─────────────┴─────────────┘\n", + "</pre>\n" + ], + "text/plain": [ + "\u001b[3m \u001b[0m\n", + "\u001b[3m Total time elapsed: 20.268 seconds \u001b[0m\n", + "\u001b[3m \u001b[0m\n", + "\u001b[3m Stats \u001b[0m\n", + "\u001b[3m \u001b[0m\n", + "┏━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1mLine no.\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mLine \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mGPU TIME(s)\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mCPU TIME(s)\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━┩\n", + "│ 3 │ \u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mdistance_df\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mdf\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mgroupby\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mcounty\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m[\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m[\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mlat\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mlong\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m]\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m]\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mapply\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;102;217;239;48;2;39;40;34mlambda\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mx\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m:\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34md…\u001b[0m │ 3.190437470 │ │\n", + "│ │ \u001b[48;2;39;40;34m \u001b[0m │ │ │\n", + "│ 4 │ \u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mdf\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m[\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mR_1\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m]\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mdistance_df\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mreset_index\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mlevel\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m=\u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m0\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mdrop\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m=\u001b[0m\u001b[38;2;102;217;239;48;2;39;40;34mTrue\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[48;2;39;40;34m \u001b[0m │ 0.613676883 │ │\n", + "│ │ \u001b[48;2;39;40;34m \u001b[0m │ │ │\n", + "└──────────┴──────────────────────────────────────────────────────────────────────────┴─────────────┴─────────────┘\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%cudf.pandas.line_profile\n", + "# DO NOT CHANGE THIS CELL\n", + "\n", + "distance_df=df.groupby('county')[['lat', 'long']].apply(lambda x: distance(x['lat'], x['long'], x['lat'].mean(), x['long'].mean()))\n", + "df['R_1']=distance_df.reset_index(level=0, drop=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "fc047fb9-8121-40ce-91ba-7156f5fb98c3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>lat</th>\n", + " <th>long</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>54.533638</td>\n", + " <td>-1.524400</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>54.426254</td>\n", + " <td>-1.465314</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>54.555199</td>\n", + " <td>-1.496417</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>54.547909</td>\n", + " <td>-1.572342</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>54.477638</td>\n", + " <td>-1.605995</td>\n", + " </tr>\n", + " <tr>\n", + " <th>...</th>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " </tr>\n", + " <tr>\n", + " <th>339338</th>\n", + " <td>51.624126</td>\n", + " <td>-2.814977</td>\n", + " </tr>\n", + " <tr>\n", + " <th>339339</th>\n", + " <td>51.616467</td>\n", + " <td>-2.859555</td>\n", + " </tr>\n", + " <tr>\n", + " <th>339340</th>\n", + " <td>51.604843</td>\n", + " <td>-2.915709</td>\n", + " </tr>\n", + " <tr>\n", + " <th>339341</th>\n", + " <td>51.584824</td>\n", + " <td>-2.848425</td>\n", + " </tr>\n", + " <tr>\n", + " <th>339342</th>\n", + " <td>51.623924</td>\n", + " <td>-2.901835</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "<p>855 rows × 2 columns</p>\n", + "</div>" + ], + "text/plain": [ + " lat long\n", + "0 54.533638 -1.524400\n", + "1 54.426254 -1.465314\n", + "2 54.555199 -1.496417\n", + "3 54.547909 -1.572342\n", + "4 54.477638 -1.605995\n", + "... ... ...\n", + "339338 51.624126 -2.814977\n", + "339339 51.616467 -2.859555\n", + "339340 51.604843 -2.915709\n", + "339341 51.584824 -2.848425\n", + "339342 51.623924 -2.901835\n", + "\n", + "[855 rows x 2 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(df.groupby('county')[['lat', 'long']].head())" + ] + }, + { + "cell_type": "markdown", + "id": "447dcea4-3fc7-4501-9f67-17801f914875", + "metadata": {}, + "source": [ + "We can also define the function in-line. " + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "97a963d9-e61c-4af6-bd32-89a5b892b09b", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.10/site-packages/cudf/core/groupby/groupby.py:1449: RuntimeWarning: GroupBy.apply() performance scales poorly with number of groups. Got 171 groups. Some functions may perform better by passing engine='jit'\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "text/html": [ + "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\"> </span>\n", + "<span style=\"font-style: italic\"> Total time elapsed: 19.596 seconds </span>\n", + "<span style=\"font-style: italic\"> </span>\n", + "<span style=\"font-style: italic\"> Stats </span>\n", + "<span style=\"font-style: italic\"> </span>\n", + "┏━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━┓\n", + "┃<span style=\"font-weight: bold\"> Line no. </span>┃<span style=\"font-weight: bold\"> Line </span>┃<span style=\"font-weight: bold\"> GPU TIME(s) </span>┃<span style=\"font-weight: bold\"> CPU TIME(s) </span>┃\n", + "┡━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━┩\n", + "│ 3 │ <span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> df[</span><span style=\"color: #e6db74; text-decoration-color: #e6db74; background-color: #272822\">'R_2'</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">]</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">=</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">df</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">.</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">groupby(</span><span style=\"color: #e6db74; text-decoration-color: #e6db74; background-color: #272822\">'county'</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">)[[</span><span style=\"color: #e6db74; text-decoration-color: #e6db74; background-color: #272822\">'lat'</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">, </span><span style=\"color: #e6db74; text-decoration-color: #e6db74; background-color: #272822\">'long'</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">]]</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">.</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">apply(</span><span style=\"color: #66d9ef; text-decoration-color: #66d9ef; background-color: #272822\">lambda</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> x: ((x…</span> │ 3.577081005 │ │\n", + "│ │ <span style=\"background-color: #272822\"> </span> │ │ │\n", + "└──────────┴──────────────────────────────────────────────────────────────────────────┴─────────────┴─────────────┘\n", + "</pre>\n" + ], + "text/plain": [ + "\u001b[3m \u001b[0m\n", + "\u001b[3m Total time elapsed: 19.596 seconds \u001b[0m\n", + "\u001b[3m \u001b[0m\n", + "\u001b[3m Stats \u001b[0m\n", + "\u001b[3m \u001b[0m\n", + "┏━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1mLine no.\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mLine \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mGPU TIME(s)\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mCPU TIME(s)\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━┩\n", + "│ 3 │ \u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mdf\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m[\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mR_2\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m]\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mdf\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mgroupby\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mcounty\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m[\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m[\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mlat\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mlong\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m]\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m]\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mapply\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;102;217;239;48;2;39;40;34mlambda\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mx\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m:\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mx\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m…\u001b[0m │ 3.577081005 │ │\n", + "│ │ \u001b[48;2;39;40;34m \u001b[0m │ │ │\n", + "└──────────┴──────────────────────────────────────────────────────────────────────────┴─────────────┴─────────────┘\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%cudf.pandas.line_profile\n", + "# DO NOT CHANGE THIS CELL\n", + "\n", + "df['R_2']=df.groupby('county')[['lat', 'long']].apply(lambda x: ((x['lat'].mean()-x['lat'])**2+(x['long'].mean()-x['long'])**2)**0.5).reset_index(level=0, drop=True)" + ] + }, + { + "cell_type": "markdown", + "id": "e15cfd17-9b05-4de4-9c9a-fa9167f2d05f", + "metadata": {}, + "source": [ + "**Note**: This is quite slow due to the iterative nature of the `.apply()` method. " + ] + }, + { + "cell_type": "markdown", + "id": "7b7dc9d6-8c1d-4e1f-a6c3-85d036c62c26", + "metadata": {}, + "source": [ + "### `.transform()` ###\n", + "The `.transform()` method aggregates each group, and broadcasts the result to the group size, resulting in a DataFrame that is the same size and index as the input DataFrame. Underneath the hood, the `.transform()` method passes each column individually as a Series to the function. \n", + "\n", + "Below we group the DataFrame by `county` and transform the columns `lat` and `long` using `mean`. We will subtract the transformed mean from the original columns, then apply the distance formula to calculate the resulting distance. By keeping the DataFrame the same shape, we can perform cuDF operations quickly, resulting in performance gain. " + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "b1a3b520-1da5-4486-b024-ff9c4d3e1df3", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "# make data types more precise\n", + "df[['lat', 'long']]=df[['lat', 'long']].astype('float64')" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "d6aa82c0-e7df-4f6a-837a-a9bdd3657787", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\"> </span>\n", + "<span style=\"font-style: italic\"> Total time elapsed: 1.283 seconds </span>\n", + "<span style=\"font-style: italic\"> </span>\n", + "<span style=\"font-style: italic\"> Stats </span>\n", + "<span style=\"font-style: italic\"> </span>\n", + "┏━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━┓\n", + "┃<span style=\"font-weight: bold\"> Line no. </span>┃<span style=\"font-weight: bold\"> Line </span>┃<span style=\"font-weight: bold\"> GPU TIME(s) </span>┃<span style=\"font-weight: bold\"> CPU TIME(s) </span>┃\n", + "┡━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━┩\n", + "│ 3 │ <span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> c</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">=</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">[</span><span style=\"color: #e6db74; text-decoration-color: #e6db74; background-color: #272822\">'lat'</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">, </span><span style=\"color: #e6db74; text-decoration-color: #e6db74; background-color: #272822\">'long'</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">]</span><span style=\"background-color: #272822\"> </span> │ │ │\n", + "│ │ <span style=\"background-color: #272822\"> </span> │ │ │\n", + "│ 4 │ <span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> df[</span><span style=\"color: #e6db74; text-decoration-color: #e6db74; background-color: #272822\">'R_3'</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">]</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">=</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">((df[c] </span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">-</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> df</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">.</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">groupby(</span><span style=\"color: #e6db74; text-decoration-color: #e6db74; background-color: #272822\">'county'</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">)[c]</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">.</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">transform(</span><span style=\"color: #e6db74; text-decoration-color: #e6db74; background-color: #272822\">'mean'</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">)) </span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">**</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> </span><span style=\"color: #ae81ff; text-decoration-color: #ae81ff; background-color: #272822\">2</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">…</span> │ 1.137889476 │ │\n", + "│ │ <span style=\"background-color: #272822\"> </span> │ │ │\n", + "└──────────┴──────────────────────────────────────────────────────────────────────────┴─────────────┴─────────────┘\n", + "</pre>\n" + ], + "text/plain": [ + "\u001b[3m \u001b[0m\n", + "\u001b[3m Total time elapsed: 1.283 seconds \u001b[0m\n", + "\u001b[3m \u001b[0m\n", + "\u001b[3m Stats \u001b[0m\n", + "\u001b[3m \u001b[0m\n", + "┏━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1mLine no.\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mLine \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mGPU TIME(s)\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mCPU TIME(s)\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━┩\n", + "│ 3 │ \u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mc\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m[\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mlat\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mlong\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m]\u001b[0m\u001b[48;2;39;40;34m \u001b[0m │ │ │\n", + "│ │ \u001b[48;2;39;40;34m \u001b[0m │ │ │\n", + "│ 4 │ \u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mdf\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m[\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mR_3\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m]\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mdf\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m[\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mc\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m]\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m-\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mdf\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mgroupby\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mcounty\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m[\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mc\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m]\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m.\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mtransform\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34mmean\u001b[0m\u001b[38;2;230;219;116;48;2;39;40;34m'\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m*\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m*\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m2\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m…\u001b[0m │ 1.137889476 │ │\n", + "│ │ \u001b[48;2;39;40;34m \u001b[0m │ │ │\n", + "└──────────┴──────────────────────────────────────────────────────────────────────────┴─────────────┴─────────────┘\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%cudf.pandas.line_profile\n", + "# DO NOT CHANGE THIS CELL\n", + "\n", + "c=['lat', 'long']\n", + "df['R_3']=((df[c] - df.groupby('county')[c].transform('mean')) ** 2).sum(axis=1) ** 0.5" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "05a40a5f-138b-4535-a86b-d07271d8fa1c", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>age</th>\n", + " <th>sex</th>\n", + " <th>county</th>\n", + " <th>lat</th>\n", + " <th>long</th>\n", + " <th>name</th>\n", + " <th>age_bucket</th>\n", + " <th>R_1</th>\n", + " <th>R_2</th>\n", + " <th>R_3</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.533638</td>\n", + " <td>-1.524400</td>\n", + " <td>FRANCIS</td>\n", + " <td>0</td>\n", + " <td>0.043671</td>\n", + " <td>0.043671</td>\n", + " <td>0.043671</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.426254</td>\n", + " <td>-1.465314</td>\n", + " <td>EDWARD</td>\n", + " <td>0</td>\n", + " <td>0.149973</td>\n", + " <td>0.149973</td>\n", + " <td>0.149973</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.555199</td>\n", + " <td>-1.496417</td>\n", + " <td>TEDDY</td>\n", + " <td>0</td>\n", + " <td>0.074265</td>\n", + " <td>0.074265</td>\n", + " <td>0.074265</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.547909</td>\n", + " <td>-1.572342</td>\n", + " <td>ANGUS</td>\n", + " <td>0</td>\n", + " <td>0.013108</td>\n", + " <td>0.013108</td>\n", + " <td>0.013108</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.477638</td>\n", + " <td>-1.605995</td>\n", + " <td>CHARLIE</td>\n", + " <td>0</td>\n", + " <td>0.069230</td>\n", + " <td>0.069230</td>\n", + " <td>0.069230</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " age sex county lat long name age_bucket R_1 \\\n", + "0 0 m DARLINGTON 54.533638 -1.524400 FRANCIS 0 0.043671 \n", + "1 0 m DARLINGTON 54.426254 -1.465314 EDWARD 0 0.149973 \n", + "2 0 m DARLINGTON 54.555199 -1.496417 TEDDY 0 0.074265 \n", + "3 0 m DARLINGTON 54.547909 -1.572342 ANGUS 0 0.013108 \n", + "4 0 m DARLINGTON 54.477638 -1.605995 CHARLIE 0 0.069230 \n", + "\n", + " R_2 R_3 \n", + "0 0.043671 0.043671 \n", + "1 0.149973 0.149973 \n", + "2 0.074265 0.074265 \n", + "3 0.013108 0.013108 \n", + "4 0.069230 0.069230 " + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "markdown", + "id": "858b309c-4533-4798-a22c-fe6858257cbc", + "metadata": {}, + "source": [ + "Although the `.apply()` method is more flexible and can handle complex operations, it is generally slower. On the other hand, the `.transform()` method can be much faster. When we design the procedures to use vector operations, we will realize significant performance benefits. \n", + "\n", + "**Note**: `Groupby.apply()` doesn't scale well with the number of groups, therefore this performance difference will be more pronounced with higher number of groups. " + ] + }, + { + "cell_type": "markdown", + "id": "7bc715eb-e357-4a92-8778-a144007a9697", + "metadata": {}, + "source": [ + "## Pivot Table ##\n", + "Pivot tables allow us to summarize and aggregate large datasets into a more manageable format for analysis. When using `DataFrame.pivot_table()`, we provide the `index`, `columns`, and `values` arguments, as well as `aggfunc`. This will group the data based on `index` and `columns`, and perform the aggregation on `values`. We can apply multiple aggregation functions, which is generally faster and more memory-efficient than manual grouping and aggregation for large datasets. \n", + "\n", + "Below we create a pivot table that counts the number of each sex in each county. Furthermore, we derive the percentage of the total for each county. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a6c8cd25-9892-458e-a9a0-8af10647f9c7", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "%%cudf.pandas.line_profile\n", + "# DO NOT CHANGE THIS CELL\n", + "\n", + "pvt_tbl=df[['county', 'sex', 'name']].pivot_table(index=['county'], columns=['sex'], values='name', aggfunc='count')\n", + "pvt_tbl=pvt_tbl.apply(lambda x: x/sum(x), axis=1)\n", + "display(pvt_tbl)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9ba52ec8-18bd-4f30-a3dd-ccbdface4bb4", + "metadata": {}, + "outputs": [], + "source": [ + "import IPython\n", + "app = IPython.Application.instance()\n", + "app.kernel.do_shutdown(True)" + ] + }, + { + "cell_type": "markdown", + "id": "9e99bf3f-be76-4ebf-aed3-624d4e8695ac", + "metadata": {}, + "source": [ + "**Well Done!** Let's move to the [next notebook](1-06_data_visualization.ipynb). " + ] + }, + { + "cell_type": "markdown", + "id": "81e47f0a-547e-4714-878d-34eb9b75c835", + "metadata": {}, + "source": [ + "<img src=\"./images/DLI_Header.png\" width=400/>" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.15" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Fundamentals_of_Accelerated_Data_Science/1-06_data_visualization.ipynb b/Fundamentals_of_Accelerated_Data_Science/1-06_data_visualization.ipynb new file mode 100644 index 0000000..4db2cfe --- /dev/null +++ b/Fundamentals_of_Accelerated_Data_Science/1-06_data_visualization.ipynb @@ -0,0 +1,2487 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "b53a7b12-538d-4459-b82a-a35c8c417849", + "metadata": {}, + "source": [ + "<img src=\"./images/DLI_Header.png\" width=400/>" + ] + }, + { + "cell_type": "markdown", + "id": "ae497b71-bc43-471e-8970-88a1878e7cf9", + "metadata": {}, + "source": [ + "# Fundamentals of Accelerated Data Science # " + ] + }, + { + "cell_type": "markdown", + "id": "a149b6d1-1880-4a5d-9d71-f963d3097aa4", + "metadata": {}, + "source": [ + "## 06 - Data Visualization ##\n", + "\n", + "**Table of Contents**\n", + "<br>\n", + "This notebook demonstrates the basics of data visualization for large datasets. This notebook covers the below sections: \n", + "1. [Data Visualization](#Data-Visualization)\n", + "2. [Bar Chart](#Bar-Chart)\n", + " * [Histogram](#Histogram)\n", + " * [Exercise #1 - Bar Chart](#Exercise-#1---Bar-Chart)\n", + "3. [Scatter Plot](#Scatter-Plot)\n", + "4. [Line Chart](#Line-Chart)\n", + "5. [Datashader](#Datashader)\n", + " * [Datashader Accelerated by GPU](#Datashader-Accelerated-by-GPU)\n", + "6. [Interactive Visualization](#Interactive-Visualization)\n", + " * [cuxfilter and Dashboard](#cuxfilter-and-Dashboard)\n", + "6. [Other Libraries](#Other-Libraries)" + ] + }, + { + "cell_type": "markdown", + "id": "39f0f08f-92a2-4bfc-b8bc-5904aa70b5fc", + "metadata": {}, + "source": [ + "## Data Visualization ##\n", + "Data visualization is an important part of data science for several reasons: \n", + "* **Data exploration**: enables data scientists to explore data and quickly identify patterns, trends, and outliers that may not be apparent when looking at raw data in tabular format\n", + "* **Interpretation**: transforms large and complex datasets into more digestible visual formats, making it easier to comprehend vast amounts of information\n", + "* **Communication**: helps data scientists communicate complex insights to stakeholders in an easy-to-understand visual format, making data more accessible to non-technical audiences\n", + "\n", + "Below is the simple dashboard we will create in this notebook: \n", + "\n", + "<p><img src='images/dashboard_1_sample.png' width=720></p>" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "3300e580-f39d-4147-8ad8-dfbf611ad323", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>age</th>\n", + " <th>sex</th>\n", + " <th>county</th>\n", + " <th>lat</th>\n", + " <th>long</th>\n", + " <th>name</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.533638</td>\n", + " <td>-1.524400</td>\n", + " <td>FRANCIS</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.426254</td>\n", + " <td>-1.465314</td>\n", + " <td>EDWARD</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.555199</td>\n", + " <td>-1.496417</td>\n", + " <td>TEDDY</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.547909</td>\n", + " <td>-1.572342</td>\n", + " <td>ANGUS</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.477638</td>\n", + " <td>-1.605995</td>\n", + " <td>CHARLIE</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " age sex county lat long name\n", + "0 0 m DARLINGTON 54.533638 -1.524400 FRANCIS\n", + "1 0 m DARLINGTON 54.426254 -1.465314 EDWARD\n", + "2 0 m DARLINGTON 54.555199 -1.496417 TEDDY\n", + "3 0 m DARLINGTON 54.547909 -1.572342 ANGUS\n", + "4 0 m DARLINGTON 54.477638 -1.605995 CHARLIE" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%load_ext cudf.pandas\n", + "# DO NOT CHANGE THIS CELL\n", + "import pandas as pd\n", + "\n", + "dtype_dict={\n", + " 'age': 'int8', \n", + " 'sex': 'object', \n", + " 'county': 'object', \n", + " 'lat': 'float32', \n", + " 'long': 'float32', \n", + " 'name': 'object'\n", + "}\n", + " \n", + "df=pd.read_csv('./data/uk_pop.csv', dtype=dtype_dict)\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "id": "db58461b-5877-4768-8586-a46765381b6b", + "metadata": {}, + "source": [ + "## Bar Chart ##\n", + "Bar charts are used to show and compare categorical data. It represent numercial values with rectangular bars where the length or height of each bar corresponds to the value it represents. \n", + "\n", + "Below we show the top 5 counties with the most people. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a0acbf39-b10c-4998-96d7-dde142d844e1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<Axes: xlabel='county'>" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 640x480 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "df.groupby('county').size().sort_values(ascending=False).head().plot(kind='bar')" + ] + }, + { + "cell_type": "markdown", + "id": "8b6d94bc-7006-4e73-accb-2649d7dec596", + "metadata": {}, + "source": [ + "### Histogram ###\n", + "Bar charts can also be used to show the distribution of data points across different subgroups. This is referred to as a historgram, which is done by counting the number of occurrences (frequency distribution) of each unique value in a dataset. It is used to visualize the shape, center, and spread of a dataset. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b804a51e-63b7-4389-8dd5-3beea5a5950b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<Axes: xlabel='age_bucket'>" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 640x480 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "bins=[0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100]\n", + "df['age_bucket']=pd.cut(df['age'], bins=bins, right=True, include_lowest=True, labels=False)\n", + "df.groupby('age_bucket').size().plot(kind='bar')" + ] + }, + { + "cell_type": "markdown", + "id": "88341062-8ecc-4264-8c42-bee7ae173c05", + "metadata": {}, + "source": [ + "### Exercise #1 - Bar Chart ###\n", + "We would like to find the distribution of sex in the population. \n", + "\n", + "**Instructions**: <br>\n", + "* Modify the `<FIXME>` only and execute the below cell plot the number of each sex in our dataset. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "d0d37093-e329-498d-b748-7fa3fb792303", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<Axes: xlabel='sex'>" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 640x480 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df.groupby('sex').size().plot(kind='bar')" + ] + }, + { + "cell_type": "raw", + "id": "b52410a7-777f-4b0a-862f-9fed419d9c79", + "metadata": {}, + "source": [ + "\n", + "df.groupby('sex').size().plot(kind='bar')" + ] + }, + { + "cell_type": "markdown", + "id": "5e49a376-90f6-4ae1-9a47-fae469a5d1da", + "metadata": {}, + "source": [ + "Click ... for solution. " + ] + }, + { + "cell_type": "markdown", + "id": "676bd687-b998-4724-bda5-e1d68307bb24", + "metadata": {}, + "source": [ + "## Scatter Plot ##\n", + "The scatter plot is used to show the relationship between two variables in a dataset. It can also be used to display coordinates of each data point to help identify outliers or clusters. " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "e170e8ba-5dbf-428d-b00a-0880d8bdcfad", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<Axes: xlabel='lat', ylabel='long'>" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 640x480 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "# sample a very small percentage of the data\n", + "small_df=df.sample(1000)\n", + "\n", + "small_df.plot(kind='scatter', x='lat', y='long')" + ] + }, + { + "cell_type": "markdown", + "id": "8e0dbf2c-9bbf-4621-bce6-661ede296af9", + "metadata": {}, + "source": [ + "## Line Chart ##\n", + "Line charts are good for connecting individual data points to show trends. It's useful for visualizing changes, trends, and patterns over time. \n", + "\n", + "The scatter plot doesn't scale well with the number of data points. When the data becomes large, the scatter plot take a long time to complete. Below is line chart of the compute time for different data sizes. \n", + "\n", + "<p><img src='images/scatter_time.png', width=480></p>\n", + "\n", + "**Note**: Below is the code used to produce this image. " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "ce18692d-b36f-4275-b056-fd1ec1170abc", + "metadata": {}, + "outputs": [], + "source": [ + "# import time\n", + "# import matplotlib.pyplot as plt\n", + "\n", + "# fig, ax=plt.subplots()\n", + "# exec_times={}\n", + "\n", + "# for size in (5*(10**i) for i in range(1, 8)): \n", + "# start=time.time()\n", + "# df.sample(size).plot(kind='scatter', x='long', y='lat', ax=ax)\n", + "# duration=time.time()-start\n", + "# exec_times[size]=duration\n", + "# ax.clear()\n", + "\n", + "# ax.plot(exec_times.keys(), exec_times.values(), marker='o')\n", + "# ax.set_xscale('log')\n", + "# ax.set_xlabel('Data Size')\n", + "# ax.set_ylabel('Execution Time')\n", + "# ax.set_title(\"Scatter Plot Doesn't Scale Well With Data Size\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "44e65ae7-f223-455f-a463-10f1e193ef64", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'status': 'ok', 'restart': True}" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "import IPython\n", + "app = IPython.Application.instance()\n", + "app.kernel.do_shutdown(True)" + ] + }, + { + "cell_type": "markdown", + "id": "b26686d7-fb05-49a0-9006-036810d86160", + "metadata": {}, + "source": [ + "## Datashader ##\n", + "[Datashader](https://datashader.org/#) is an open-source Python library for analyzing and visualizing large datasets. Specifically, Datashader is designed to \"rasterize\" or \"aggregate\" datasets into regular grids that can be analyzed further or viewed as images, making it simple and quick to see the properties and patterns of data. \n", + "\n", + "Plotting for big data is challenging because rendering a large number of points takes a long time. Datashader shifts the burden of visualization from rendering to computing. Underneath the hood, it turns a long list of (x, y) points into a 2D histogram instead of plotting each point individually. Furthermore, this aggregation can be accelerated through parallel computing. The resulting gridded data structure is then turn into an image, using color to show the magnitude, before being embedding into a plotting program. \n", + "\n", + "Datashader generates a plot using a five-step [pipeline](https://datashader.org/getting_started/Pipeline.html): \n", + "\n", + "<p><img src='images/ds_pipeline.png' width=720></p>\n", + "\n", + "Below we demonstrate how Datashader is used. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "a7bd466f-0e40-4b40-bd50-82c9fdc17e2f", + "metadata": {}, + "outputs": [], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "import time\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import datashader as ds\n", + "import datashader.transfer_functions as tf" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "813eb18b-5234-4a1f-ae05-58bcf8750e9f", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>age</th>\n", + " <th>sex</th>\n", + " <th>county</th>\n", + " <th>lat</th>\n", + " <th>long</th>\n", + " <th>name</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.533646</td>\n", + " <td>-1.524401</td>\n", + " <td>FRANCIS</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.426254</td>\n", + " <td>-1.465314</td>\n", + " <td>EDWARD</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.555199</td>\n", + " <td>-1.496417</td>\n", + " <td>TEDDY</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.547905</td>\n", + " <td>-1.572341</td>\n", + " <td>ANGUS</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.477638</td>\n", + " <td>-1.605994</td>\n", + " <td>CHARLIE</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " age sex county lat long name\n", + "0 0 m DARLINGTON 54.533646 -1.524401 FRANCIS\n", + "1 0 m DARLINGTON 54.426254 -1.465314 EDWARD\n", + "2 0 m DARLINGTON 54.555199 -1.496417 TEDDY\n", + "3 0 m DARLINGTON 54.547905 -1.572341 ANGUS\n", + "4 0 m DARLINGTON 54.477638 -1.605994 CHARLIE" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "import pandas as pd\n", + "\n", + "dtype_dict={\n", + " 'age': 'int8', \n", + " 'sex': 'object', \n", + " 'county': 'object', \n", + " 'lat': 'float32', \n", + " 'long': 'float32', \n", + " 'name': 'object'\n", + "}\n", + " \n", + "df=pd.read_csv('./data/uk_pop.csv', dtype=dtype_dict)\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "5f0085a4-97fa-494f-a525-792f2b65593e", 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color: var(--xr-disabled-color);\n", + "}\n", + "\n", + ".xr-section-summary-in:checked + label:before {\n", + " content: \"▼\";\n", + "}\n", + "\n", + ".xr-section-summary-in:checked + label > span {\n", + " display: none;\n", + "}\n", + "\n", + ".xr-section-summary,\n", + ".xr-section-inline-details {\n", + " padding-top: 4px;\n", + " padding-bottom: 4px;\n", + "}\n", + "\n", + ".xr-section-inline-details {\n", + " grid-column: 2 / -1;\n", + "}\n", + "\n", + ".xr-section-details {\n", + " display: none;\n", + " grid-column: 1 / -1;\n", + " margin-bottom: 5px;\n", + "}\n", + "\n", + ".xr-section-summary-in:checked ~ .xr-section-details {\n", + " display: contents;\n", + "}\n", + "\n", + ".xr-array-wrap {\n", + " grid-column: 1 / -1;\n", + " display: grid;\n", + " grid-template-columns: 20px auto;\n", + "}\n", + "\n", + ".xr-array-wrap > label {\n", + " grid-column: 1;\n", + " vertical-align: top;\n", + "}\n", + "\n", + ".xr-preview {\n", + " color: var(--xr-font-color3);\n", + "}\n", + "\n", + ".xr-array-preview,\n", + ".xr-array-data {\n", + " padding: 0 5px !important;\n", + " grid-column: 2;\n", + "}\n", + "\n", + ".xr-array-data,\n", + ".xr-array-in:checked ~ .xr-array-preview {\n", + " display: none;\n", + "}\n", + "\n", + ".xr-array-in:checked ~ .xr-array-data,\n", + ".xr-array-preview {\n", + " display: inline-block;\n", + "}\n", + "\n", + ".xr-dim-list {\n", + " display: inline-block !important;\n", + " list-style: none;\n", + " padding: 0 !important;\n", + " margin: 0;\n", + "}\n", + "\n", + ".xr-dim-list li {\n", + " display: inline-block;\n", + " padding: 0;\n", + " margin: 0;\n", + "}\n", + "\n", + ".xr-dim-list:before {\n", + " content: \"(\";\n", + "}\n", + "\n", + ".xr-dim-list:after {\n", + " content: \")\";\n", + "}\n", + "\n", + ".xr-dim-list li:not(:last-child):after {\n", + " content: \",\";\n", + " padding-right: 5px;\n", + "}\n", + "\n", + ".xr-has-index {\n", + " font-weight: bold;\n", + "}\n", + "\n", + ".xr-var-list,\n", + ".xr-var-item 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"}\n", + "\n", + ".xr-var-name,\n", + ".xr-var-dims,\n", + ".xr-var-dtype,\n", + ".xr-preview,\n", + ".xr-attrs dt {\n", + " white-space: nowrap;\n", + " overflow: hidden;\n", + " text-overflow: ellipsis;\n", + " padding-right: 10px;\n", + "}\n", + "\n", + ".xr-var-name:hover,\n", + ".xr-var-dims:hover,\n", + ".xr-var-dtype:hover,\n", + ".xr-attrs dt:hover {\n", + " overflow: visible;\n", + " width: auto;\n", + " z-index: 1;\n", + "}\n", + "\n", + ".xr-var-attrs,\n", + ".xr-var-data,\n", + ".xr-index-data {\n", + " display: none;\n", + " background-color: var(--xr-background-color) !important;\n", + " padding-bottom: 5px !important;\n", + "}\n", + "\n", + ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n", + ".xr-var-data-in:checked ~ .xr-var-data,\n", + ".xr-index-data-in:checked ~ .xr-index-data {\n", + " display: block;\n", + "}\n", + "\n", + ".xr-var-data > table {\n", + " float: right;\n", + "}\n", + "\n", + ".xr-var-name span,\n", + ".xr-var-data,\n", + ".xr-index-name div,\n", + ".xr-index-data,\n", + ".xr-attrs {\n", + " padding-left: 25px !important;\n", + "}\n", + "\n", + ".xr-attrs,\n", + ".xr-var-attrs,\n", + ".xr-var-data,\n", + ".xr-index-data {\n", + " grid-column: 1 / -1;\n", + "}\n", + "\n", + "dl.xr-attrs {\n", + " padding: 0;\n", + " margin: 0;\n", + " display: grid;\n", + " grid-template-columns: 125px auto;\n", + "}\n", + "\n", + ".xr-attrs dt,\n", + ".xr-attrs dd {\n", + " padding: 0;\n", + " margin: 0;\n", + " float: left;\n", + " padding-right: 10px;\n", + " width: auto;\n", + "}\n", + "\n", + ".xr-attrs dt {\n", + " font-weight: normal;\n", + " grid-column: 1;\n", + "}\n", + "\n", + ".xr-attrs dt:hover span {\n", + " display: inline-block;\n", + " background: var(--xr-background-color);\n", + " padding-right: 10px;\n", + "}\n", + "\n", + ".xr-attrs dd {\n", + " grid-column: 2;\n", + " white-space: pre-wrap;\n", + " word-break: break-all;\n", + "}\n", + "\n", + ".xr-icon-database,\n", + ".xr-icon-file-text2,\n", + ".xr-no-icon {\n", + " display: inline-block;\n", + " vertical-align: middle;\n", + " width: 1em;\n", + " height: 1.5em !important;\n", + " stroke-width: 0;\n", + " stroke: currentColor;\n", + " fill: currentColor;\n", + "}\n", + "</style><pre class='xr-text-repr-fallback'><xarray.DataArray (lat: 600, long: 600)> Size: 1MB\n", + "array([[0, 0, 0, ..., 0, 0, 0],\n", + " [0, 0, 0, ..., 0, 0, 0],\n", + " [0, 0, 0, ..., 0, 0, 0],\n", + " ...,\n", + " [0, 0, 0, ..., 0, 0, 0],\n", + " [0, 0, 0, ..., 0, 0, 0],\n", + " [0, 0, 0, ..., 0, 0, 0]], dtype=uint32)\n", + "Coordinates:\n", + " * long (long) float64 5kB -6.361 -6.346 -6.331 ... 2.662 2.677 2.693\n", + " * lat (lat) float64 5kB 49.52 49.54 49.55 49.56 ... 56.23 56.24 56.26\n", + "Attributes:\n", + " x_range: (-6.368374347686768, 2.7000913619995117)\n", + " y_range: (49.519046783447266, 56.261409759521484)</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 600</li><li><span class='xr-has-index'>long</span>: 600</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-ab1105ef-eafe-41c7-bc6a-79d74bd6a7ff' class='xr-array-in' type='checkbox' checked><label for='section-ab1105ef-eafe-41c7-bc6a-79d74bd6a7ff' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0</span></div><div class='xr-array-data'><pre>array([[0, 0, 0, ..., 0, 0, 0],\n", + " [0, 0, 0, ..., 0, 0, 0],\n", + " [0, 0, 0, ..., 0, 0, 0],\n", + " ...,\n", + " [0, 0, 0, ..., 0, 0, 0],\n", + " [0, 0, 0, ..., 0, 0, 0],\n", + " [0, 0, 0, ..., 0, 0, 0]], dtype=uint32)</pre></div></div></li><li class='xr-section-item'><input id='section-e4bf81dd-6bd1-4f37-b035-dcd5955c8d89' class='xr-section-summary-in' type='checkbox' checked><label for='section-e4bf81dd-6bd1-4f37-b035-dcd5955c8d89' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>long</span></div><div class='xr-var-dims'>(long)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-6.361 -6.346 ... 2.677 2.693</div><input id='attrs-d0d37d7a-024f-46c1-9339-a5692e087b80' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d0d37d7a-024f-46c1-9339-a5692e087b80' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c0c34d7d-d6a4-4d04-a3b7-1e9651914022' class='xr-var-data-in' type='checkbox'><label for='data-c0c34d7d-d6a4-4d04-a3b7-1e9651914022' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-6.360817, -6.345703, -6.330589, ..., 2.662306, 2.67742 , 2.692534])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>49.52 49.54 49.55 ... 56.24 56.26</div><input id='attrs-678197de-41bd-4948-95d0-2e6eece1e3d5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-678197de-41bd-4948-95d0-2e6eece1e3d5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-27c42c29-4881-4ccd-abc0-0bfb78ebcb87' class='xr-var-data-in' type='checkbox'><label for='data-27c42c29-4881-4ccd-abc0-0bfb78ebcb87' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([49.524665, 49.535903, 49.54714 , ..., 56.233317, 56.244554, 56.255791])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ec0c3110-afea-46d5-b969-049ddd123614' class='xr-section-summary-in' type='checkbox' ><label for='section-ec0c3110-afea-46d5-b969-049ddd123614' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>long</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-f71cc23b-3276-4428-a0c8-b80ce9f5b7b4' class='xr-index-data-in' type='checkbox'/><label for='index-f71cc23b-3276-4428-a0c8-b80ce9f5b7b4' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-6.3608172929286955, -6.345703183412552, -6.330589073896408,\n", + " -6.315474964380265, -6.30036085486412, -6.285246745347977,\n", + " -6.270132635831833, -6.255018526315689, -6.239904416799545,\n", + " -6.224790307283402,\n", + " ...\n", + " 2.5565073215961456, 2.5716214311122894, 2.586735540628433,\n", + " 2.601849650144577, 2.6169637596607207, 2.6320778691768645,\n", + " 2.6471919786930083, 2.6623060882091525, 2.6774201977252963,\n", + " 2.69253430724144],\n", + " dtype='float64', name='long', length=600))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-bc2b6bf5-57ab-4056-924a-861cf6e27491' class='xr-index-data-in' type='checkbox'/><label for='index-bc2b6bf5-57ab-4056-924a-861cf6e27491' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 49.52466541926066, 49.53590269088745, 49.54713996251424,\n", + " 49.558377234141034, 49.569614505767824, 49.580851777394614,\n", + " 49.5920890490214, 49.60332632064819, 49.61456359227498,\n", + " 49.62580086390177,\n", + " ...\n", + " 56.15465567906698, 56.16589295069377, 56.17713022232056,\n", + " 56.18836749394735, 56.199604765574136, 56.210842037200926,\n", + " 56.222079308827716, 56.233316580454506, 56.244553852081296,\n", + " 56.255791123708086],\n", + " dtype='float64', name='lat', length=600))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-299351c0-37bb-4b7f-aff9-01758ed71321' class='xr-section-summary-in' type='checkbox' checked><label for='section-299351c0-37bb-4b7f-aff9-01758ed71321' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>x_range :</span></dt><dd>(-6.368374347686768, 2.7000913619995117)</dd><dt><span>y_range :</span></dt><dd>(49.519046783447266, 56.261409759521484)</dd></dl></div></li></ul></div></div>" + ], + "text/plain": [ + "<xarray.DataArray (lat: 600, long: 600)> Size: 1MB\n", + "array([[0, 0, 0, ..., 0, 0, 0],\n", + " [0, 0, 0, ..., 0, 0, 0],\n", + " [0, 0, 0, ..., 0, 0, 0],\n", + " ...,\n", + " [0, 0, 0, ..., 0, 0, 0],\n", + " [0, 0, 0, ..., 0, 0, 0],\n", + " [0, 0, 0, ..., 0, 0, 0]], dtype=uint32)\n", + "Coordinates:\n", + " * long (long) float64 5kB -6.361 -6.346 -6.331 ... 2.662 2.677 2.693\n", + " * lat (lat) float64 5kB 49.52 49.54 49.55 49.56 ... 56.23 56.24 56.26\n", + "Attributes:\n", + " x_range: (-6.368374347686768, 2.7000913619995117)\n", + " y_range: (49.519046783447266, 56.261409759521484)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Duration: 1.25 seconds\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 640x480 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "start=time.time()\n", + "\n", + "# get points\n", + "ds_points_pandas=ds.Canvas().points(df,'long','lat')\n", + "display(ds_points_pandas)\n", + "\n", + "# plot points\n", + "plt.imshow(tf.shade(ds_points_pandas))\n", + "\n", + "print(f'Duration: {round(time.time()-start, 2)} seconds')" + ] + }, + { + "cell_type": "markdown", + "id": "09268b18-7e81-46e7-978b-964fe56cda2e", + "metadata": {}, + "source": [ + "### Datashader Accelerated by GPU ###\n", + "Datashader can be accelerated by assigning the computation to a GPU. As previously mentioned, the GPU typically has far more (though individually less powerful) cores available than a CPU does, and for highly parallelizable computations like those in Datashader a GPU can typically achieve much faster performance at a given price point than a CPU or distributed set of CPUs can. The DataFrame from cuDF can be used as a replacement for rasterization. The performance benefits are significant since the entire data-processing pipeline is executed on the GPU and there is no bottleneck from data transfer. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "557e70e8-9eaf-4d6b-9048-8a1b433943bd", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>age</th>\n", + " <th>sex</th>\n", + " <th>county</th>\n", + " <th>lat</th>\n", + " <th>long</th>\n", + " <th>name</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.533638</td>\n", + " <td>-1.524400</td>\n", + " <td>FRANCIS</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.426254</td>\n", + " <td>-1.465314</td>\n", + " <td>EDWARD</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.555199</td>\n", + " <td>-1.496417</td>\n", + " <td>TEDDY</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.547909</td>\n", + " <td>-1.572342</td>\n", + " <td>ANGUS</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.477638</td>\n", + " <td>-1.605995</td>\n", + " <td>CHARLIE</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " age sex county lat long name\n", + "0 0 m DARLINGTON 54.533638 -1.524400 FRANCIS\n", + "1 0 m DARLINGTON 54.426254 -1.465314 EDWARD\n", + "2 0 m DARLINGTON 54.555199 -1.496417 TEDDY\n", + "3 0 m DARLINGTON 54.547909 -1.572342 ANGUS\n", + "4 0 m DARLINGTON 54.477638 -1.605995 CHARLIE" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "import cudf\n", + "\n", + "dtype_dict={\n", + " 'age': 'int8', \n", + " 'sex': 'object', \n", + " 'county': 'object', \n", + " 'lat': 'float32', \n", + " 'long': 'float32', \n", + " 'name': 'object'\n", + "}\n", + " \n", + "gdf=cudf.read_csv('./data/uk_pop.csv', dtype=dtype_dict)\n", + "gdf.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "6a5b3eb3-40f2-46fb-a45b-5450f45ff398", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n", + "<defs>\n", + "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n", + "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n", + 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"body[data-theme=\"dark\"],\n", + "body.vscode-dark {\n", + " --xr-font-color0: rgba(255, 255, 255, 1);\n", + " --xr-font-color2: rgba(255, 255, 255, 0.54);\n", + " --xr-font-color3: rgba(255, 255, 255, 0.38);\n", + " --xr-border-color: #1f1f1f;\n", + " --xr-disabled-color: #515151;\n", + " --xr-background-color: #111111;\n", + " --xr-background-color-row-even: #111111;\n", + " --xr-background-color-row-odd: #313131;\n", + "}\n", + "\n", + ".xr-wrap {\n", + " display: block !important;\n", + " min-width: 300px;\n", + " max-width: 700px;\n", + "}\n", + "\n", + ".xr-text-repr-fallback {\n", + " /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n", + " display: none;\n", + "}\n", + "\n", + ".xr-header {\n", + " padding-top: 6px;\n", + " padding-bottom: 6px;\n", + " margin-bottom: 4px;\n", + " border-bottom: solid 1px var(--xr-border-color);\n", + "}\n", + "\n", + ".xr-header > div,\n", + ".xr-header > ul {\n", + " display: inline;\n", + " margin-top: 0;\n", + " margin-bottom: 0;\n", + "}\n", + "\n", + ".xr-obj-type,\n", + ".xr-array-name {\n", + " margin-left: 2px;\n", + " margin-right: 10px;\n", + "}\n", + "\n", + ".xr-obj-type {\n", + " color: var(--xr-font-color2);\n", + "}\n", + "\n", + ".xr-sections {\n", + " padding-left: 0 !important;\n", + " display: grid;\n", + " grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n", + "}\n", + "\n", + ".xr-section-item {\n", + " display: contents;\n", + "}\n", + "\n", + ".xr-section-item input {\n", + " display: inline-block;\n", + " opacity: 0;\n", + " height: 0;\n", + "}\n", + "\n", + ".xr-section-item input + label {\n", + " color: var(--xr-disabled-color);\n", + "}\n", + "\n", + ".xr-section-item input:enabled + label {\n", + " cursor: pointer;\n", + " color: var(--xr-font-color2);\n", + "}\n", + "\n", + ".xr-section-item input:focus + label {\n", + " border: 2px solid var(--xr-font-color0);\n", + "}\n", + "\n", + ".xr-section-item input:enabled + label:hover {\n", + " color: var(--xr-font-color0);\n", + "}\n", + "\n", + ".xr-section-summary {\n", + " grid-column: 1;\n", + " color: var(--xr-font-color2);\n", + " font-weight: 500;\n", + "}\n", + "\n", + ".xr-section-summary > span {\n", + " display: inline-block;\n", + " padding-left: 0.5em;\n", + "}\n", + "\n", + ".xr-section-summary-in:disabled + label {\n", + " color: var(--xr-font-color2);\n", + "}\n", + "\n", + ".xr-section-summary-in + label:before {\n", + " display: inline-block;\n", + " content: \"►\";\n", + " font-size: 11px;\n", + " width: 15px;\n", + " text-align: center;\n", + "}\n", + "\n", + ".xr-section-summary-in:disabled + label:before {\n", + " color: var(--xr-disabled-color);\n", + "}\n", + "\n", + ".xr-section-summary-in:checked + label:before {\n", + " content: \"▼\";\n", + "}\n", + "\n", + ".xr-section-summary-in:checked + label > span {\n", + " display: none;\n", + "}\n", + "\n", + ".xr-section-summary,\n", + ".xr-section-inline-details {\n", + " padding-top: 4px;\n", + " padding-bottom: 4px;\n", + "}\n", + "\n", + ".xr-section-inline-details {\n", + " grid-column: 2 / -1;\n", + "}\n", + "\n", + ".xr-section-details {\n", + " display: none;\n", + " grid-column: 1 / -1;\n", + " margin-bottom: 5px;\n", + "}\n", + "\n", + ".xr-section-summary-in:checked ~ .xr-section-details {\n", + " display: contents;\n", + "}\n", + "\n", + ".xr-array-wrap {\n", + " grid-column: 1 / -1;\n", + " display: grid;\n", + " grid-template-columns: 20px auto;\n", + "}\n", + "\n", + ".xr-array-wrap > label {\n", + " grid-column: 1;\n", + " vertical-align: top;\n", + "}\n", + "\n", + ".xr-preview {\n", + " color: var(--xr-font-color3);\n", + "}\n", + "\n", + ".xr-array-preview,\n", + ".xr-array-data {\n", + " padding: 0 5px !important;\n", + " grid-column: 2;\n", + "}\n", + "\n", + ".xr-array-data,\n", + ".xr-array-in:checked ~ .xr-array-preview {\n", + " display: none;\n", + "}\n", + "\n", + ".xr-array-in:checked ~ .xr-array-data,\n", + ".xr-array-preview {\n", + " display: inline-block;\n", + "}\n", + "\n", + ".xr-dim-list {\n", + " display: inline-block !important;\n", + " list-style: none;\n", + " padding: 0 !important;\n", + " margin: 0;\n", + "}\n", + "\n", + ".xr-dim-list li {\n", + " display: inline-block;\n", + " padding: 0;\n", + " margin: 0;\n", + "}\n", + "\n", + ".xr-dim-list:before {\n", + " content: \"(\";\n", + "}\n", + "\n", + ".xr-dim-list:after {\n", + " content: \")\";\n", + "}\n", + "\n", + ".xr-dim-list li:not(:last-child):after {\n", + " content: \",\";\n", + " padding-right: 5px;\n", + "}\n", + "\n", + ".xr-has-index {\n", + " font-weight: bold;\n", + "}\n", + "\n", + ".xr-var-list,\n", + ".xr-var-item {\n", + " display: contents;\n", + "}\n", + "\n", + ".xr-var-item > div,\n", + ".xr-var-item label,\n", + ".xr-var-item > .xr-var-name span {\n", + " background-color: var(--xr-background-color-row-even);\n", + " margin-bottom: 0;\n", + "}\n", + "\n", + ".xr-var-item > .xr-var-name:hover span {\n", + " padding-right: 5px;\n", + "}\n", + "\n", + ".xr-var-list > li:nth-child(odd) > div,\n", + ".xr-var-list > li:nth-child(odd) > label,\n", + ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n", + " background-color: var(--xr-background-color-row-odd);\n", + "}\n", + "\n", + ".xr-var-name {\n", + " grid-column: 1;\n", + "}\n", + "\n", + ".xr-var-dims {\n", + " grid-column: 2;\n", + "}\n", + "\n", + ".xr-var-dtype {\n", + " grid-column: 3;\n", + " text-align: right;\n", + " color: var(--xr-font-color2);\n", + "}\n", + "\n", + ".xr-var-preview {\n", + " grid-column: 4;\n", + "}\n", + "\n", + ".xr-index-preview {\n", + " grid-column: 2 / 5;\n", + " color: var(--xr-font-color2);\n", + "}\n", + "\n", + ".xr-var-name,\n", + ".xr-var-dims,\n", + ".xr-var-dtype,\n", + ".xr-preview,\n", + ".xr-attrs dt {\n", + " white-space: nowrap;\n", + " overflow: hidden;\n", + " text-overflow: ellipsis;\n", + " padding-right: 10px;\n", + "}\n", + "\n", + ".xr-var-name:hover,\n", + ".xr-var-dims:hover,\n", + ".xr-var-dtype:hover,\n", + ".xr-attrs dt:hover {\n", + " overflow: visible;\n", + " width: auto;\n", + " z-index: 1;\n", + "}\n", + "\n", + ".xr-var-attrs,\n", + ".xr-var-data,\n", + ".xr-index-data {\n", + " display: none;\n", + " background-color: var(--xr-background-color) !important;\n", + " padding-bottom: 5px !important;\n", + "}\n", + "\n", + ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n", + ".xr-var-data-in:checked ~ .xr-var-data,\n", + ".xr-index-data-in:checked ~ .xr-index-data {\n", + " display: block;\n", + "}\n", + "\n", + ".xr-var-data > table {\n", + " float: right;\n", + "}\n", + "\n", + ".xr-var-name span,\n", + ".xr-var-data,\n", + ".xr-index-name div,\n", + ".xr-index-data,\n", + ".xr-attrs {\n", + " padding-left: 25px !important;\n", + "}\n", + "\n", + ".xr-attrs,\n", + ".xr-var-attrs,\n", + ".xr-var-data,\n", + ".xr-index-data {\n", + " grid-column: 1 / -1;\n", + "}\n", + "\n", + "dl.xr-attrs {\n", + " padding: 0;\n", + " margin: 0;\n", + " display: grid;\n", + " grid-template-columns: 125px auto;\n", + "}\n", + "\n", + ".xr-attrs dt,\n", + ".xr-attrs dd {\n", + " padding: 0;\n", + " margin: 0;\n", + " float: left;\n", + " padding-right: 10px;\n", + " width: auto;\n", + "}\n", + "\n", + ".xr-attrs dt {\n", + " font-weight: normal;\n", + " grid-column: 1;\n", + "}\n", + "\n", + ".xr-attrs dt:hover span {\n", + " display: inline-block;\n", + " background: var(--xr-background-color);\n", + " padding-right: 10px;\n", + "}\n", + "\n", + ".xr-attrs dd {\n", + " grid-column: 2;\n", + " white-space: pre-wrap;\n", + " word-break: break-all;\n", + "}\n", + "\n", + ".xr-icon-database,\n", + ".xr-icon-file-text2,\n", + ".xr-no-icon {\n", + " display: inline-block;\n", + " vertical-align: middle;\n", + " width: 1em;\n", + " height: 1.5em !important;\n", + " stroke-width: 0;\n", + " stroke: currentColor;\n", + " fill: currentColor;\n", + "}\n", + "</style><pre class='xr-text-repr-fallback'><xarray.DataArray (lat: 600, long: 600)> Size: 1MB\n", + "array([[0, 0, 0, ..., 0, 0, 0],\n", + " [0, 0, 0, ..., 0, 0, 0],\n", + " [0, 0, 0, ..., 0, 0, 0],\n", + " ...,\n", + " [0, 0, 0, ..., 0, 0, 0],\n", + " [0, 0, 0, ..., 0, 0, 0],\n", + " [0, 0, 0, ..., 0, 0, 0]], dtype=uint32)\n", + "Coordinates:\n", + " * long (long) float64 5kB -6.361 -6.346 -6.331 ... 2.662 2.677 2.693\n", + " * lat (lat) float64 5kB 49.52 49.54 49.55 49.56 ... 56.23 56.24 56.26\n", + "Attributes:\n", + " x_range: (-6.368374, 2.7000911)\n", + " y_range: (49.51904, 56.26141)</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 600</li><li><span class='xr-has-index'>long</span>: 600</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-9d3128c0-09a0-463c-9c8f-5b2d332617f0' class='xr-array-in' type='checkbox' checked><label for='section-9d3128c0-09a0-463c-9c8f-5b2d332617f0' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>array([[0, 0, 0, ..., 0, 0, 0], [0, 0, 0, ..., 0, 0, 0], ...</span></div><div class='xr-array-data'><pre>array([[0, 0, 0, ..., 0, 0, 0],\n", + " [0, 0, 0, ..., 0, 0, 0],\n", + " [0, 0, 0, ..., 0, 0, 0],\n", + " ...,\n", + " [0, 0, 0, ..., 0, 0, 0],\n", + " [0, 0, 0, ..., 0, 0, 0],\n", + " [0, 0, 0, ..., 0, 0, 0]], dtype=uint32)</pre></div></div></li><li class='xr-section-item'><input id='section-1a032fa9-16a8-4eec-bdc8-3c2b970fefdf' class='xr-section-summary-in' type='checkbox' checked><label for='section-1a032fa9-16a8-4eec-bdc8-3c2b970fefdf' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>long</span></div><div class='xr-var-dims'>(long)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-6.361 -6.346 ... 2.677 2.693</div><input id='attrs-2db7fccd-31d0-4739-b522-9c9980919b6c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2db7fccd-31d0-4739-b522-9c9980919b6c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-412804e5-f6c2-42a0-b126-7449642525a7' class='xr-var-data-in' type='checkbox'><label for='data-412804e5-f6c2-42a0-b126-7449642525a7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-6.360817, -6.345703, -6.330589, ..., 2.662306, 2.67742 , 2.692534])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>49.52 49.54 49.55 ... 56.24 56.26</div><input id='attrs-f22cde25-c14d-4884-a446-220f7ec8e843' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f22cde25-c14d-4884-a446-220f7ec8e843' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c889db2b-2101-4859-bf90-0b5260bbba05' class='xr-var-data-in' type='checkbox'><label for='data-c889db2b-2101-4859-bf90-0b5260bbba05' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([49.524658, 49.535895, 49.547132, ..., 56.233317, 56.244554, 56.255791])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7d48608e-4b81-4190-bc09-acd3dd9d02a0' class='xr-section-summary-in' type='checkbox' ><label for='section-7d48608e-4b81-4190-bc09-acd3dd9d02a0' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>long</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-b926ff44-05cb-49cc-aa84-e65b2eec4834' class='xr-index-data-in' type='checkbox'/><label for='index-b926ff44-05cb-49cc-aa84-e65b2eec4834' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-6.360816816687584, -6.345702708363533, -6.330588600039482,\n", + " -6.315474491715431, -6.30036038339138, -6.28524627506733,\n", + " -6.270132166743279, -6.255018058419227, -6.239903950095177,\n", + " -6.224789841771126,\n", + " ...\n", + " 2.5565070945024484, 2.5716212028264995, 2.58673531115055,\n", + " 2.6018494194746014, 2.616963527798652, 2.632077636122703,\n", + " 2.647191744446754, 2.662305852770805, 2.6774199610948557,\n", + " 2.692534069418907],\n", + " dtype='float64', name='long', length=600))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-ea355aaf-027b-491f-8e1e-b7d4c4df8274' class='xr-index-data-in' type='checkbox'/><label for='index-ea355aaf-027b-491f-8e1e-b7d4c4df8274' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 49.52465779622396, 49.53589508056641, 49.547132364908855,\n", + " 49.558369649251304, 49.569606933593754, 49.5808442179362,\n", + " 49.592081502278646, 49.603318786621095, 49.614556070963545,\n", + " 49.625793355305994,\n", + " ...\n", + " 56.15465555826823, 56.16589284261068, 56.17713012695313,\n", + " 56.18836741129557, 56.19960469563802, 56.21084197998047,\n", + " 56.22207926432292, 56.233316548665364, 56.244553833007814,\n", + " 56.25579111735026],\n", + " dtype='float64', name='lat', length=600))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-dbc8d174-bfed-422e-a0c7-a9a9944057b5' class='xr-section-summary-in' type='checkbox' checked><label for='section-dbc8d174-bfed-422e-a0c7-a9a9944057b5' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>x_range :</span></dt><dd>(-6.368374, 2.7000911)</dd><dt><span>y_range :</span></dt><dd>(49.51904, 56.26141)</dd></dl></div></li></ul></div></div>" + ], + "text/plain": [ + "<xarray.DataArray (lat: 600, long: 600)> Size: 1MB\n", + "array([[0, 0, 0, ..., 0, 0, 0],\n", + " [0, 0, 0, ..., 0, 0, 0],\n", + " [0, 0, 0, ..., 0, 0, 0],\n", + " ...,\n", + " [0, 0, 0, ..., 0, 0, 0],\n", + " [0, 0, 0, ..., 0, 0, 0],\n", + " [0, 0, 0, ..., 0, 0, 0]], dtype=uint32)\n", + "Coordinates:\n", + " * long (long) float64 5kB -6.361 -6.346 -6.331 ... 2.662 2.677 2.693\n", + " * lat (lat) float64 5kB 49.52 49.54 49.55 49.56 ... 56.23 56.24 56.26\n", + "Attributes:\n", + " x_range: (-6.368374, 2.7000911)\n", + " y_range: (49.51904, 56.26141)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Duration: 14.53 seconds\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 640x480 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "start=time.time()\n", + "\n", + "# get points\n", + "ds_points_cudf=ds.Canvas().points(gdf,'long','lat')\n", + "display(ds_points_cudf)\n", + "\n", + "# plot points\n", + "plt.imshow(tf.shade(ds_points_cudf))\n", + "\n", + "print(f'Duration: {round(time.time()-start, 2)} seconds')" + ] + }, + { + "cell_type": "markdown", + "id": "98b1c4a7-9b60-4970-859f-b9ffedd4315c", + "metadata": {}, + "source": [ + "**Note**: Please re-execute the above cell if it took more than a few seconds for the more accurate compute time. " + ] + }, + { + "cell_type": "markdown", + "id": "2e664fb6-7482-49d0-bc60-efaefd2184d3", + "metadata": {}, + "source": [ + "## Interactive Visualization ##\n", + "Data visualization is crucial in data science as it bridges the gap between complex data and human understanding, making insights more accessible, actionable, and impactful throughout the data science process. Bringing interactivity in data visualization further enables: \n", + "* **Discovery**: enables discovery of hidden patterns, trends, and outliers that may not be apparant in static visualizations\n", + "* **Enhanced understanding**: allows users to view data from multiple perspective and levels of detail\n", + "* **Customization**: provides the ability to rapidly filter, sort, and aggregate data, leading to a more impactful presentation" + ] + }, + { + "cell_type": "markdown", + "id": "084911da-fb99-43e8-bff6-e11f032b4c9e", + "metadata": {}, + "source": [ + "### cuxfilter and Dashboard ###\n", + "cuxfilter enables GPU accelerated cross-filtering dashboards, which is ideal for multi-chart exploratory data analysis. Cross-filtering lets users interact with one chart and apply that interaction as a filter to other charts in the dashboard. \n", + "\n", + "cuxfilter acts as a connector library, which provides the connections between different visualization libraries and a GPU DataFrame without much hassle. This also allows users to use charts from different libraries in a single dashboard, while also providing the interaction. Currently, cuxfilter supports: \n", + "* [Bokeh](https://bokeh.org/) Charts\n", + " * Bar chart\n", + " * Line chart\n", + " * Choropleth\n", + "* [Datashader](https://datashader.org/) Charts\n", + " * Line\n", + " * Scatter\n", + "* [Panel Widgets](https://panel.holoviz.org/api/panel.widgets.html)\n", + " * Range\n", + " * Float\n", + " * Int\n", + " * Dropdown\n", + " * Multiselect" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "f1777e6a-a717-49d4-9b38-e855d987ca92", + "metadata": {}, + "outputs": [], + "source": [ + "import cuxfilter as cxf\n", + "\n", + "# factorize county for multiselect widget\n", + "gdf['county'], county_names = gdf['county'].factorize()\n", + "county_map = dict(zip(list(range(len(county_names))), county_names.to_arrow()))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "680c06ed-f8c6-4de5-b366-78f0c1092aeb", + "metadata": {}, + "outputs": [], + "source": [ + "# create cuxfilter DataFrame\n", + "cxf_data = cxf.DataFrame.from_dataframe(gdf)\n", + "\n", + "# create Datashader scatter plot\n", + "scatter_chart = cxf.charts.scatter(x='long', y='lat')" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "c2d8a372-6e14-442c-a61d-f5ea44fc7f6c", + "metadata": {}, + "outputs": [], + "source": [ + "# create Bokeh bar charts\n", + "chart_3=cxf.charts.bar('age')\n", + "chart_2=cxf.charts.bar('sex')" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "fb700802-45b8-4558-b58b-c2a96cfda78b", + "metadata": {}, + "outputs": [], + "source": [ + "# define layout\n", + "layout_array=[[1, 2, 2], \n", + " [3, 2, 2]]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a7964e08-1e77-4431-83b1-ef0e798d32f5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<script type=\"esms-options\">{\"shimMode\": true}</script><style>*[data-root-id],\n", + "*[data-root-id] > * {\n", + " box-sizing: border-box;\n", + " font-family: var(--jp-ui-font-family);\n", + " font-size: var(--jp-ui-font-size1);\n", + " color: var(--vscode-editor-foreground, var(--jp-ui-font-color1));\n", + "}\n", + "\n", + "/* Override VSCode background color */\n", + ".cell-output-ipywidget-background:has(\n", + " > .cell-output-ipywidget-background > .lm-Widget > *[data-root-id]\n", + " ),\n", + ".cell-output-ipywidget-background:has(> .lm-Widget > *[data-root-id]) {\n", + " background-color: transparent !important;\n", + "}\n", + "</style>" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", + "\n", + " const force = true;\n", + " const py_version = '3.6.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n", + " const reloading = false;\n", + " const Bokeh = root.Bokeh;\n", + "\n", + " // Set a timeout for this load but only if we are not already initializing\n", + " if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_failed_load = false;\n", + " }\n", + "\n", + " function run_callbacks() {\n", + " try {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) {\n", + " if (callback != null)\n", + " callback();\n", + " });\n", + " } finally {\n", + " delete root._bokeh_onload_callbacks;\n", + " }\n", + " console.debug(\"Bokeh: all callbacks have finished\");\n", + " }\n", + "\n", + " function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n", + " if (css_urls == null) css_urls = [];\n", + " if (js_urls == null) js_urls = [];\n", + " if (js_modules == null) js_modules = [];\n", + " if (js_exports == null) js_exports = {};\n", + "\n", + " root._bokeh_onload_callbacks.push(callback);\n", + "\n", + " if (root._bokeh_is_loading > 0) {\n", + " // Don't load bokeh if it is still initializing\n", + " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " return null;\n", + " } else if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n", + " // There is nothing to load\n", + " run_callbacks();\n", + " return null;\n", + " }\n", + "\n", + " function on_load() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", + " run_callbacks()\n", + " }\n", + " }\n", + " window._bokeh_on_load = on_load\n", + "\n", + " function on_error(e) {\n", + " const src_el = e.srcElement\n", + " console.error(\"failed to load \" + (src_el.href || src_el.src));\n", + " }\n", + "\n", + " const skip = [];\n", + " if (window.requirejs) {\n", + " window.requirejs.config({'packages': {}, 'paths': {'h3': 'https://cdn.jsdelivr.net/npm/h3-js@4.1.0/dist/h3-js.umd', 'deck-gl': 'https://cdn.jsdelivr.net/npm/deck.gl@9.0.20/dist.min', 'deck-json': 'https://cdn.jsdelivr.net/npm/@deck.gl/json@9.0.20/dist.min', 'loader-csv': 'https://cdn.jsdelivr.net/npm/@loaders.gl/csv@4.2.2/dist/dist.min', 'loader-json': 'https://cdn.jsdelivr.net/npm/@loaders.gl/json@4.2.2/dist/dist.min', 'loader-tiles': 'https://cdn.jsdelivr.net/npm/@loaders.gl/3d-tiles@4.2.2/dist/dist.min', 'mapbox-gl': 'https://api.mapbox.com/mapbox-gl-js/v3.0.1/mapbox-gl', 'carto': 'https://cdn.jsdelivr.net/npm/@deck.gl/carto@^9.0.20/dist.min'}, 'shim': {'deck-json': {'deps': ['deck-gl']}, 'deck-gl': {'deps': ['h3']}}});\n", + " require([\"h3\"], function(h3) {\n", + " window.h3 = h3\n", + " on_load()\n", + " })\n", + " require([\"deck-gl\"], function(deck) {\n", + " window.deck = deck\n", + " on_load()\n", + " })\n", + " require([\"deck-json\"], function() {\n", + " on_load()\n", + " })\n", + " require([\"loader-csv\"], function() {\n", + " on_load()\n", + " })\n", + " require([\"loader-json\"], function() {\n", + " on_load()\n", + " })\n", + " require([\"loader-tiles\"], function() {\n", + " on_load()\n", + " })\n", + " require([\"mapbox-gl\"], function(mapboxgl) {\n", + " window.mapboxgl = mapboxgl\n", + " on_load()\n", + " })\n", + " require([\"carto\"], function() {\n", + " on_load()\n", + " })\n", + " root._bokeh_is_loading = css_urls.length + 8;\n", + " } else {\n", + " root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n", + " }\n", + "\n", + " const existing_stylesheets = []\n", + " const links = document.getElementsByTagName('link')\n", + " for (let i = 0; i < links.length; i++) {\n", + " const link = links[i]\n", + " if (link.href != null) {\n", + " existing_stylesheets.push(link.href)\n", + " }\n", + " }\n", + " for (let i = 0; i < css_urls.length; i++) {\n", + " const url = css_urls[i];\n", + " const escaped = encodeURI(url)\n", + " if (existing_stylesheets.indexOf(escaped) !== -1) {\n", + " on_load()\n", + " continue;\n", + " }\n", + " const element = document.createElement(\"link\");\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.rel = \"stylesheet\";\n", + " element.type = \"text/css\";\n", + " element.href = url;\n", + " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", + " document.body.appendChild(element);\n", + " } if (((window.deck !== undefined) && (!(window.deck instanceof HTMLElement))) || window.requirejs) {\n", + " var urls = ['https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/h3-js@4.1.0/dist/h3-js.umd.js', 'https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/deck.gl@9.0.20/dist.min.js', 'https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/@deck.gl/json@9.0.20/dist.min.js', 'https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/@loaders.gl/csv@4.2.2/dist/dist.min.js', 'https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/@loaders.gl/json@4.2.2/dist/dist.min.js', 'https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/@loaders.gl/3d-tiles@4.2.2/dist/dist.min.js', 'https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/mapbox-gl-js/v3.0.1/mapbox-gl.js', 'https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/maplibre-gl/dist/maplibre-gl.js'];\n", + " for (var i = 0; i < urls.length; i++) {\n", + " skip.push(encodeURI(urls[i]))\n", + " }\n", + " } if (((window.mapboxgl !== undefined) && (!(window.mapboxgl instanceof HTMLElement))) || window.requirejs) {\n", + " var urls = ['https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/@deck.gl/carto@^9.0.20/dist.min.js'];\n", + " for (var i = 0; i < urls.length; i++) {\n", + " skip.push(encodeURI(urls[i]))\n", + " }\n", + " } var existing_scripts = []\n", + " const scripts = document.getElementsByTagName('script')\n", + " for (let i = 0; i < scripts.length; i++) {\n", + " var script = scripts[i]\n", + " if (script.src != null) {\n", + " existing_scripts.push(script.src)\n", + " }\n", + " }\n", + " for (let i = 0; i < js_urls.length; i++) {\n", + " const url = js_urls[i];\n", + " const escaped = encodeURI(url)\n", + " if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n", + " if (!window.requirejs) {\n", + " on_load();\n", + " }\n", + " continue;\n", + " }\n", + " const element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.src = url;\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " for (let i = 0; i < js_modules.length; i++) {\n", + " const url = js_modules[i];\n", + " const escaped = encodeURI(url)\n", + " if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n", + " if (!window.requirejs) {\n", + " on_load();\n", + " }\n", + " continue;\n", + " }\n", + " var element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.src = url;\n", + " element.type = \"module\";\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " for (const name in js_exports) {\n", + " const url = js_exports[name];\n", + " const escaped = encodeURI(url)\n", + " if (skip.indexOf(escaped) >= 0 || root[name] != null) {\n", + " if (!window.requirejs) {\n", + " on_load();\n", + " }\n", + " continue;\n", + " }\n", + " var element = document.createElement('script');\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.type = \"module\";\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " element.textContent = `\n", + " import ${name} from \"${url}\"\n", + " window.${name} = ${name}\n", + " window._bokeh_on_load()\n", + " `\n", + " document.head.appendChild(element);\n", + " }\n", + " if (!js_urls.length && !js_modules.length) {\n", + " on_load()\n", + " }\n", + " };\n", + "\n", + " function inject_raw_css(css) {\n", + " const element = document.createElement(\"style\");\n", + " element.appendChild(document.createTextNode(css));\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " const js_urls = [\"https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/h3-js@4.1.0/dist/h3-js.umd.js\", \"https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/deck.gl@9.0.20/dist.min.js\", \"https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/@deck.gl/json@9.0.20/dist.min.js\", \"https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/@loaders.gl/csv@4.2.2/dist/dist.min.js\", \"https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/@loaders.gl/json@4.2.2/dist/dist.min.js\", \"https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/@loaders.gl/3d-tiles@4.2.2/dist/dist.min.js\", \"https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/mapbox-gl-js/v3.0.1/mapbox-gl.js\", \"https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/maplibre-gl/dist/maplibre-gl.js\", \"https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/@deck.gl/carto@^9.0.20/dist.min.js\", \"https://cdn.holoviz.org/panel/1.5.4/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-3.6.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.6.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.6.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.6.2.min.js\", \"https://cdn.holoviz.org/panel/1.5.4/dist/panel.min.js\"];\n", + " const js_modules = [];\n", + " const js_exports = {};\n", + " const css_urls = [\"https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/mapbox-gl-js/v3.0.1/mapbox-gl.css?v=1.5.4\", \"https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/maplibre-gl@4.4.1/dist/maplibre-gl.css?v=1.5.4\"];\n", + " const inline_js = [ function(Bokeh) {\n", + " inject_raw_css(\"\\n.dataframe table{\\n border: none;\\n}\\n\\n.panel-df table{\\n width: 100%;\\n border-collapse: collapse;\\n border: none;\\n}\\n.panel-df td{\\n white-space: nowrap;\\n overflow: auto;\\n text-overflow: ellipsis;\\n}\\n\");\n", + " }, function(Bokeh) {\n", + " inject_raw_css(\"\\n.multi-select{\\n color: white;\\n z-index: 100;\\n background: rgba(44,43,43,0.5);\\n border-radius: 1px;\\n width: 120px !important;\\n height: 30px !important;\\n}\\n.multi-select > .bk {\\n padding: 5px;\\n width: 120px !important;\\n height: 30px !important;\\n}\\n\\n.deck-chart {\\n z-index: 10;\\n position: initial !important;\\n}\\n\");\n", + " }, function(Bokeh) {\n", + " inject_raw_css(\"\\n.center-header {\\n text-align: center\\n}\\n.bk-input-group {\\n padding: 10px;\\n}\\n#sidebar {\\n padding-top: 10px;\\n}\\n.custom-widget-box {\\n margin-top: 20px;\\n padding: 5px;\\n border: None !important;\\n}\\n.custom-widget-box > p {\\n margin: 0px;\\n}\\n.bk-input-group {\\n color: None !important;\\n}\\n.indicator {\\n text-align: center;\\n}\\n.widget-card {\\n margin: 5px 10px;\\n}\\n.number-card {\\n margin: 5px 10px;\\n text-align: center;\\n}\\n.number-card-value {\\n width: 100%;\\n margin: 0px;\\n}\\n\");\n", + " }, function(Bokeh) {\n", + " Bokeh.set_log_level(\"info\");\n", + " },\n", + " function(Bokeh) {\n", + " (function(root, factory) {\n", + " factory(root[\"Bokeh\"]);\n", + " })(this, function(Bokeh) {\n", + " let define;\n", + " return (function outer(modules, entry) {\n", + " if (Bokeh != null) {\n", + " return Bokeh.register_plugin(modules, entry);\n", + " } else {\n", + " throw new Error(\"Cannot find Bokeh. You have to load it prior to loading plugins.\");\n", + " }\n", + " })\n", + " ({\n", + " \"custom/main\": function(require, module, exports) {\n", + " const models = {\n", + " \"CustomInspectTool\": require(\"custom/cuxfilter.charts.datashader.custom_extensions.graph_inspect_widget.custom_inspect_tool\").CustomInspectTool\n", + " };\n", + " require(\"base\").register_models(models);\n", + " module.exports = models;\n", + " },\n", + " \"custom/cuxfilter.charts.datashader.custom_extensions.graph_inspect_widget.custom_inspect_tool\": function(require, module, exports) {\n", + " \"use strict\";\n", + " var _a;\n", + " Object.defineProperty(exports, \"__esModule\", { value: true });\n", + " exports.CustomInspectTool = exports.CustomInspectToolView = void 0;\n", + " const inspect_tool_1 = require(\"models/tools/inspectors/inspect_tool\");\n", + " class CustomInspectToolView extends inspect_tool_1.InspectToolView {\n", + " connect_signals() {\n", + " super.connect_signals();\n", + " this.on_change([this.model.properties.active], () => {\n", + " this.model._active = this.model.active;\n", + " });\n", + " }\n", + " }\n", + " exports.CustomInspectToolView = CustomInspectToolView;\n", + " CustomInspectToolView.__name__ = \"CustomInspectToolView\";\n", + " class CustomInspectTool extends inspect_tool_1.InspectTool {\n", + " constructor(attrs) {\n", + " super(attrs);\n", + " }\n", + " }\n", + " exports.CustomInspectTool = CustomInspectTool;\n", + " _a = CustomInspectTool;\n", + " CustomInspectTool.__name__ = \"CustomInspectTool\";\n", + " (() => {\n", + " _a.prototype.default_view = CustomInspectToolView;\n", + " _a.define(({ Boolean }) => ({\n", + " _active: [Boolean, true]\n", + " }));\n", + " _a.register_alias(\"customInspect\", () => new _a());\n", + " })();\n", + " //# sourceMappingURL=graph_inspect_widget.py:CustomInspectTool.js.map\n", + " }\n", + " }, \"custom/main\");\n", + " ;\n", + " });\n", + "\n", + " },\n", + "function(Bokeh) {} // ensure no trailing comma for IE\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " if ((root.Bokeh !== undefined) || (force === true)) {\n", + " for (let i = 0; i < inline_js.length; i++) {\n", + " try {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " } catch(e) {\n", + " if (!reloading) {\n", + " throw e;\n", + " }\n", + " }\n", + " }\n", + " // Cache old bokeh versions\n", + " if (Bokeh != undefined && !reloading) {\n", + " var NewBokeh = root.Bokeh;\n", + " if (Bokeh.versions === undefined) {\n", + " Bokeh.versions = new Map();\n", + " }\n", + " if (NewBokeh.version !== Bokeh.version) {\n", + " Bokeh.versions.set(NewBokeh.version, NewBokeh)\n", + " }\n", + " root.Bokeh = Bokeh;\n", + " }\n", + " } else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(run_inline_js, 100);\n", + " } else if (!root._bokeh_failed_load) {\n", + " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", + " root._bokeh_failed_load = true;\n", + " }\n", + " root._bokeh_is_initializing = false\n", + " }\n", + "\n", + " function load_or_wait() {\n", + " // Implement a backoff loop that tries to ensure we do not load multiple\n", + " // versions of Bokeh and its dependencies at the same time.\n", + " // In recent versions we use the root._bokeh_is_initializing flag\n", + " // to determine whether there is an ongoing attempt to initialize\n", + " // bokeh, however for backward compatibility we also try to ensure\n", + " // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n", + " // before older versions are fully initialized.\n", + " if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n", + " // If the timeout and bokeh was not successfully loaded we reset\n", + " // everything and try loading again\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_is_initializing = false;\n", + " root._bokeh_onload_callbacks = undefined;\n", + " root._bokeh_is_loading = 0\n", + " console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n", + " load_or_wait();\n", + " } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n", + " setTimeout(load_or_wait, 100);\n", + " } else {\n", + " root._bokeh_is_initializing = true\n", + " root._bokeh_onload_callbacks = []\n", + " const bokeh_loaded = root.Bokeh != null && (root.Bokeh.version === py_version || (root.Bokeh.versions !== undefined && root.Bokeh.versions.has(py_version)));\n", + " if (!reloading && !bokeh_loaded) {\n", + " if (root.Bokeh) {\n", + " root.Bokeh = undefined;\n", + " }\n", + " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " }\n", + " load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n", + " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", + " run_inline_js();\n", + " });\n", + " }\n", + " }\n", + " // Give older versions of the autoload script a head-start to ensure\n", + " // they initialize before we start loading newer version.\n", + " setTimeout(load_or_wait, 100)\n", + "}(window));" + ], + "application/vnd.holoviews_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n const py_version = '3.6.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n const reloading = false;\n const Bokeh = root.Bokeh;\n\n // Set a timeout for this load but only if we are not already initializing\n if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks;\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n if (js_modules == null) js_modules = [];\n if (js_exports == null) js_exports = {};\n\n root._bokeh_onload_callbacks.push(callback);\n\n if (root._bokeh_is_loading > 0) {\n // Don't load bokeh if it is still initializing\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n } else if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n // There is nothing to load\n run_callbacks();\n return null;\n }\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n window._bokeh_on_load = on_load\n\n function on_error(e) {\n const src_el = e.srcElement\n console.error(\"failed to load \" + (src_el.href || src_el.src));\n }\n\n const skip = [];\n if (window.requirejs) {\n window.requirejs.config({'packages': {}, 'paths': {'h3': 'https://cdn.jsdelivr.net/npm/h3-js@4.1.0/dist/h3-js.umd', 'deck-gl': 'https://cdn.jsdelivr.net/npm/deck.gl@9.0.20/dist.min', 'deck-json': 'https://cdn.jsdelivr.net/npm/@deck.gl/json@9.0.20/dist.min', 'loader-csv': 'https://cdn.jsdelivr.net/npm/@loaders.gl/csv@4.2.2/dist/dist.min', 'loader-json': 'https://cdn.jsdelivr.net/npm/@loaders.gl/json@4.2.2/dist/dist.min', 'loader-tiles': 'https://cdn.jsdelivr.net/npm/@loaders.gl/3d-tiles@4.2.2/dist/dist.min', 'mapbox-gl': 'https://api.mapbox.com/mapbox-gl-js/v3.0.1/mapbox-gl', 'carto': 'https://cdn.jsdelivr.net/npm/@deck.gl/carto@^9.0.20/dist.min'}, 'shim': {'deck-json': {'deps': ['deck-gl']}, 'deck-gl': {'deps': ['h3']}}});\n require([\"h3\"], function(h3) {\n window.h3 = h3\n on_load()\n })\n require([\"deck-gl\"], function(deck) {\n window.deck = deck\n on_load()\n })\n require([\"deck-json\"], function() {\n on_load()\n })\n require([\"loader-csv\"], function() {\n on_load()\n })\n require([\"loader-json\"], function() {\n on_load()\n })\n require([\"loader-tiles\"], function() {\n on_load()\n })\n require([\"mapbox-gl\"], function(mapboxgl) {\n window.mapboxgl = mapboxgl\n on_load()\n })\n require([\"carto\"], function() {\n on_load()\n })\n root._bokeh_is_loading = css_urls.length + 8;\n } else {\n root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n }\n\n const existing_stylesheets = []\n const links = document.getElementsByTagName('link')\n for (let i = 0; i < links.length; i++) {\n const link = links[i]\n if (link.href != null) {\n existing_stylesheets.push(link.href)\n }\n }\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const escaped = encodeURI(url)\n if (existing_stylesheets.indexOf(escaped) !== -1) {\n on_load()\n continue;\n }\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n } if (((window.deck !== undefined) && (!(window.deck instanceof HTMLElement))) || window.requirejs) {\n var urls = ['https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/h3-js@4.1.0/dist/h3-js.umd.js', 'https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/deck.gl@9.0.20/dist.min.js', 'https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/@deck.gl/json@9.0.20/dist.min.js', 'https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/@loaders.gl/csv@4.2.2/dist/dist.min.js', 'https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/@loaders.gl/json@4.2.2/dist/dist.min.js', 'https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/@loaders.gl/3d-tiles@4.2.2/dist/dist.min.js', 'https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/mapbox-gl-js/v3.0.1/mapbox-gl.js', 'https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/maplibre-gl/dist/maplibre-gl.js'];\n for (var i = 0; i < urls.length; i++) {\n skip.push(encodeURI(urls[i]))\n }\n } if (((window.mapboxgl !== undefined) && (!(window.mapboxgl instanceof HTMLElement))) || window.requirejs) {\n var urls = ['https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/@deck.gl/carto@^9.0.20/dist.min.js'];\n for (var i = 0; i < urls.length; i++) {\n skip.push(encodeURI(urls[i]))\n }\n } var existing_scripts = []\n const scripts = document.getElementsByTagName('script')\n for (let i = 0; i < scripts.length; i++) {\n var script = scripts[i]\n if (script.src != null) {\n existing_scripts.push(script.src)\n }\n }\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\n }\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (let i = 0; i < js_modules.length; i++) {\n const url = js_modules[i];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\n }\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (const name in js_exports) {\n const url = js_exports[name];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) >= 0 || root[name] != null) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\n }\n var element = document.createElement('script');\n element.onerror = on_error;\n element.async = false;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n element.textContent = `\n import ${name} from \"${url}\"\n window.${name} = ${name}\n window._bokeh_on_load()\n `\n document.head.appendChild(element);\n }\n if (!js_urls.length && !js_modules.length) {\n on_load()\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/h3-js@4.1.0/dist/h3-js.umd.js\", \"https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/deck.gl@9.0.20/dist.min.js\", \"https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/@deck.gl/json@9.0.20/dist.min.js\", \"https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/@loaders.gl/csv@4.2.2/dist/dist.min.js\", \"https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/@loaders.gl/json@4.2.2/dist/dist.min.js\", \"https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/@loaders.gl/3d-tiles@4.2.2/dist/dist.min.js\", \"https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/mapbox-gl-js/v3.0.1/mapbox-gl.js\", \"https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/maplibre-gl/dist/maplibre-gl.js\", \"https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/@deck.gl/carto@^9.0.20/dist.min.js\", \"https://cdn.holoviz.org/panel/1.5.4/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-3.6.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.6.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.6.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.6.2.min.js\", \"https://cdn.holoviz.org/panel/1.5.4/dist/panel.min.js\"];\n const js_modules = [];\n const js_exports = {};\n const css_urls = [\"https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/mapbox-gl-js/v3.0.1/mapbox-gl.css?v=1.5.4\", \"https://cdn.holoviz.org/panel/1.5.4/dist/bundled/deckglplot/maplibre-gl@4.4.1/dist/maplibre-gl.css?v=1.5.4\"];\n const inline_js = [ function(Bokeh) {\n inject_raw_css(\"\\n.dataframe table{\\n border: none;\\n}\\n\\n.panel-df table{\\n width: 100%;\\n border-collapse: collapse;\\n border: none;\\n}\\n.panel-df td{\\n white-space: nowrap;\\n overflow: auto;\\n text-overflow: ellipsis;\\n}\\n\");\n }, function(Bokeh) {\n inject_raw_css(\"\\n.multi-select{\\n color: white;\\n z-index: 100;\\n background: rgba(44,43,43,0.5);\\n border-radius: 1px;\\n width: 120px !important;\\n height: 30px !important;\\n}\\n.multi-select > .bk {\\n padding: 5px;\\n width: 120px !important;\\n height: 30px !important;\\n}\\n\\n.deck-chart {\\n z-index: 10;\\n position: initial !important;\\n}\\n\");\n }, function(Bokeh) {\n inject_raw_css(\"\\n.center-header {\\n text-align: center\\n}\\n.bk-input-group {\\n padding: 10px;\\n}\\n#sidebar {\\n padding-top: 10px;\\n}\\n.custom-widget-box {\\n margin-top: 20px;\\n padding: 5px;\\n border: None !important;\\n}\\n.custom-widget-box > p {\\n margin: 0px;\\n}\\n.bk-input-group {\\n color: None !important;\\n}\\n.indicator {\\n text-align: center;\\n}\\n.widget-card {\\n margin: 5px 10px;\\n}\\n.number-card {\\n margin: 5px 10px;\\n text-align: center;\\n}\\n.number-card-value {\\n width: 100%;\\n margin: 0px;\\n}\\n\");\n }, function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n function(Bokeh) {\n (function(root, factory) {\n factory(root[\"Bokeh\"]);\n })(this, function(Bokeh) {\n let define;\n return (function outer(modules, entry) {\n if (Bokeh != null) {\n return Bokeh.register_plugin(modules, entry);\n } else {\n throw new Error(\"Cannot find Bokeh. You have to load it prior to loading plugins.\");\n }\n })\n ({\n \"custom/main\": function(require, module, exports) {\n const models = {\n \"CustomInspectTool\": require(\"custom/cuxfilter.charts.datashader.custom_extensions.graph_inspect_widget.custom_inspect_tool\").CustomInspectTool\n };\n require(\"base\").register_models(models);\n module.exports = models;\n },\n \"custom/cuxfilter.charts.datashader.custom_extensions.graph_inspect_widget.custom_inspect_tool\": function(require, module, exports) {\n \"use strict\";\n var _a;\n Object.defineProperty(exports, \"__esModule\", { value: true });\n exports.CustomInspectTool = exports.CustomInspectToolView = void 0;\n const inspect_tool_1 = require(\"models/tools/inspectors/inspect_tool\");\n class CustomInspectToolView extends inspect_tool_1.InspectToolView {\n connect_signals() {\n super.connect_signals();\n this.on_change([this.model.properties.active], () => {\n this.model._active = this.model.active;\n });\n }\n }\n exports.CustomInspectToolView = CustomInspectToolView;\n CustomInspectToolView.__name__ = \"CustomInspectToolView\";\n class CustomInspectTool extends inspect_tool_1.InspectTool {\n constructor(attrs) {\n super(attrs);\n }\n }\n exports.CustomInspectTool = CustomInspectTool;\n _a = CustomInspectTool;\n CustomInspectTool.__name__ = \"CustomInspectTool\";\n (() => {\n _a.prototype.default_view = CustomInspectToolView;\n _a.define(({ Boolean }) => ({\n _active: [Boolean, true]\n }));\n _a.register_alias(\"customInspect\", () => new _a());\n })();\n //# sourceMappingURL=graph_inspect_widget.py:CustomInspectTool.js.map\n }\n }, \"custom/main\");\n ;\n });\n\n },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n ];\n\n function run_inline_js() {\n if ((root.Bokeh !== undefined) || (force === true)) {\n for (let i = 0; i < inline_js.length; i++) {\n try {\n inline_js[i].call(root, root.Bokeh);\n } catch(e) {\n if (!reloading) {\n throw e;\n }\n }\n }\n // Cache old bokeh versions\n if (Bokeh != undefined && !reloading) {\n var NewBokeh = root.Bokeh;\n if (Bokeh.versions === undefined) {\n Bokeh.versions = new Map();\n }\n if (NewBokeh.version !== Bokeh.version) {\n Bokeh.versions.set(NewBokeh.version, NewBokeh)\n }\n root.Bokeh = Bokeh;\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n }\n root._bokeh_is_initializing = false\n }\n\n function load_or_wait() {\n // Implement a backoff loop that tries to ensure we do not load multiple\n // versions of Bokeh and its dependencies at the same time.\n // In recent versions we use the root._bokeh_is_initializing flag\n // to determine whether there is an ongoing attempt to initialize\n // bokeh, however for backward compatibility we also try to ensure\n // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n // before older versions are fully initialized.\n if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n // If the timeout and bokeh was not successfully loaded we reset\n // everything and try loading again\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_is_initializing = false;\n root._bokeh_onload_callbacks = undefined;\n root._bokeh_is_loading = 0\n console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n load_or_wait();\n } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n setTimeout(load_or_wait, 100);\n } else {\n root._bokeh_is_initializing = true\n root._bokeh_onload_callbacks = []\n const bokeh_loaded = root.Bokeh != null && (root.Bokeh.version === py_version || (root.Bokeh.versions !== undefined && root.Bokeh.versions.has(py_version)));\n if (!reloading && !bokeh_loaded) {\n if (root.Bokeh) {\n root.Bokeh = undefined;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n }\n load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n }\n // Give older versions of the autoload script a head-start to ensure\n // they initialize before we start loading newer version.\n setTimeout(load_or_wait, 100)\n}(window));" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "\n", + "if ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n", + " window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n", + "}\n", + "\n", + "\n", + " function JupyterCommManager() {\n", + " }\n", + "\n", + " JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n", + " if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n", + " var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n", + " comm_manager.register_target(comm_id, function(comm) {\n", + " comm.on_msg(msg_handler);\n", + " });\n", + " } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n", + " window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n", + " comm.onMsg = msg_handler;\n", + " });\n", + " } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n", + " google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n", + " var messages = comm.messages[Symbol.asyncIterator]();\n", + " function processIteratorResult(result) {\n", + " var message = result.value;\n", + " console.log(message)\n", + " var content = {data: message.data, comm_id};\n", + " var buffers = []\n", + " for (var buffer of message.buffers || []) {\n", + " buffers.push(new DataView(buffer))\n", + " }\n", + " var metadata = message.metadata || {};\n", + " var msg = {content, buffers, metadata}\n", + " msg_handler(msg);\n", + " return messages.next().then(processIteratorResult);\n", + " }\n", + " return messages.next().then(processIteratorResult);\n", + " })\n", + " }\n", + " }\n", + "\n", + " JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n", + " if (comm_id in window.PyViz.comms) {\n", + " return window.PyViz.comms[comm_id];\n", + " } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n", + " var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n", + " var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n", + " if (msg_handler) {\n", + " comm.on_msg(msg_handler);\n", + " }\n", + " } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n", + " var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n", + " comm.open();\n", + " if (msg_handler) {\n", + " comm.onMsg = msg_handler;\n", + " }\n", + " } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n", + " var comm_promise = google.colab.kernel.comms.open(comm_id)\n", + " comm_promise.then((comm) => {\n", + " window.PyViz.comms[comm_id] = comm;\n", + " if (msg_handler) {\n", + " var messages = comm.messages[Symbol.asyncIterator]();\n", + " function processIteratorResult(result) {\n", + " var message = result.value;\n", + " var content = {data: message.data};\n", + " var metadata = message.metadata || {comm_id};\n", + " var msg = {content, metadata}\n", + " msg_handler(msg);\n", + " return messages.next().then(processIteratorResult);\n", + " }\n", + " return messages.next().then(processIteratorResult);\n", + " }\n", + " }) \n", + " var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n", + " return comm_promise.then((comm) => {\n", + " comm.send(data, metadata, buffers, disposeOnDone);\n", + " });\n", + " };\n", + " var comm = {\n", + " send: sendClosure\n", + " };\n", + " }\n", + " window.PyViz.comms[comm_id] = comm;\n", + " return comm;\n", + " }\n", + " window.PyViz.comm_manager = new JupyterCommManager();\n", + " \n", + "\n", + "\n", + "var JS_MIME_TYPE = 'application/javascript';\n", + "var HTML_MIME_TYPE = 'text/html';\n", + "var EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\n", + "var CLASS_NAME = 'output';\n", + "\n", + "/**\n", + " * Render data to the DOM node\n", + " */\n", + "function render(props, node) {\n", + " var div = document.createElement(\"div\");\n", + " var script = document.createElement(\"script\");\n", + " node.appendChild(div);\n", + " node.appendChild(script);\n", + "}\n", + "\n", + "/**\n", + " * Handle when a new output is added\n", + " */\n", + "function handle_add_output(event, handle) {\n", + " var output_area = handle.output_area;\n", + " var output = handle.output;\n", + " if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", + " return\n", + " }\n", + " var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", + " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", + " if (id !== undefined) {\n", + " var nchildren = toinsert.length;\n", + " var html_node = toinsert[nchildren-1].children[0];\n", + " html_node.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var scripts = [];\n", + " var nodelist = html_node.querySelectorAll(\"script\");\n", + " for (var i in nodelist) {\n", + " if (nodelist.hasOwnProperty(i)) {\n", + " scripts.push(nodelist[i])\n", + " }\n", + " }\n", + "\n", + " scripts.forEach( function (oldScript) {\n", + " var newScript = document.createElement(\"script\");\n", + " var attrs = [];\n", + " var nodemap = oldScript.attributes;\n", + " for (var j in nodemap) {\n", + " if (nodemap.hasOwnProperty(j)) {\n", + " attrs.push(nodemap[j])\n", + " }\n", + " }\n", + " attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n", + " newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n", + " oldScript.parentNode.replaceChild(newScript, oldScript);\n", + " });\n", + " if (JS_MIME_TYPE in output.data) {\n", + " toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n", + " }\n", + " output_area._hv_plot_id = id;\n", + " if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n", + " window.PyViz.plot_index[id] = Bokeh.index[id];\n", + " } else {\n", + " window.PyViz.plot_index[id] = null;\n", + " }\n", + " } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", + " var bk_div = document.createElement(\"div\");\n", + " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var script_attrs = bk_div.children[0].attributes;\n", + " for (var i = 0; i < script_attrs.length; i++) {\n", + " toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " }\n", + " // store reference to server id on output_area\n", + " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", + " }\n", + "}\n", + "\n", + "/**\n", + " * Handle when an output is cleared or removed\n", + " */\n", + "function handle_clear_output(event, handle) {\n", + " var id = handle.cell.output_area._hv_plot_id;\n", + " var server_id = handle.cell.output_area._bokeh_server_id;\n", + " if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n", + " var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n", + " if (server_id !== null) {\n", + " comm.send({event_type: 'server_delete', 'id': server_id});\n", + " return;\n", + " } else if (comm !== null) {\n", + " comm.send({event_type: 'delete', 'id': id});\n", + " }\n", + " delete PyViz.plot_index[id];\n", + " if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n", + " var doc = window.Bokeh.index[id].model.document\n", + " doc.clear();\n", + " const i = window.Bokeh.documents.indexOf(doc);\n", + " if (i > -1) {\n", + " window.Bokeh.documents.splice(i, 1);\n", + " }\n", + " }\n", + "}\n", + "\n", + "/**\n", + " * Handle kernel restart event\n", + " */\n", + "function handle_kernel_cleanup(event, handle) {\n", + " delete PyViz.comms[\"hv-extension-comm\"];\n", + " window.PyViz.plot_index = {}\n", + "}\n", + "\n", + "/**\n", + " * Handle update_display_data messages\n", + " */\n", + "function handle_update_output(event, handle) {\n", + " handle_clear_output(event, {cell: {output_area: handle.output_area}})\n", + " handle_add_output(event, handle)\n", + "}\n", + "\n", + "function register_renderer(events, OutputArea) {\n", + " function append_mime(data, metadata, element) {\n", + " // create a DOM node to render to\n", + " var toinsert = this.create_output_subarea(\n", + " metadata,\n", + " CLASS_NAME,\n", + " EXEC_MIME_TYPE\n", + " );\n", + " this.keyboard_manager.register_events(toinsert);\n", + " // Render to node\n", + " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", + " render(props, toinsert[0]);\n", + " element.append(toinsert);\n", + " return toinsert\n", + " }\n", + "\n", + " events.on('output_added.OutputArea', handle_add_output);\n", + " events.on('output_updated.OutputArea', handle_update_output);\n", + " events.on('clear_output.CodeCell', handle_clear_output);\n", + " events.on('delete.Cell', handle_clear_output);\n", + " events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n", + "\n", + " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", + " safe: true,\n", + " index: 0\n", + " });\n", + "}\n", + "\n", + "if (window.Jupyter !== undefined) {\n", + " try {\n", + " var events = require('base/js/events');\n", + " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", + " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", + " register_renderer(events, OutputArea);\n", + " }\n", + " } catch(err) {\n", + " }\n", + "}\n" + ], + "application/vnd.holoviews_load.v0+json": "\nif ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n}\n\n\n function JupyterCommManager() {\n }\n\n JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n comm_manager.register_target(comm_id, function(comm) {\n comm.on_msg(msg_handler);\n });\n } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n comm.onMsg = msg_handler;\n });\n } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n var messages = comm.messages[Symbol.asyncIterator]();\n function processIteratorResult(result) {\n var message = result.value;\n console.log(message)\n var content = {data: message.data, comm_id};\n var buffers = []\n for (var buffer of message.buffers || []) {\n buffers.push(new DataView(buffer))\n }\n var metadata = message.metadata || {};\n var msg = {content, buffers, metadata}\n msg_handler(msg);\n return messages.next().then(processIteratorResult);\n }\n return messages.next().then(processIteratorResult);\n })\n }\n }\n\n JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n if (comm_id in window.PyViz.comms) {\n return window.PyViz.comms[comm_id];\n } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n if (msg_handler) {\n comm.on_msg(msg_handler);\n }\n } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n comm.open();\n if (msg_handler) {\n comm.onMsg = msg_handler;\n }\n } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n var comm_promise = google.colab.kernel.comms.open(comm_id)\n comm_promise.then((comm) => {\n window.PyViz.comms[comm_id] = comm;\n if (msg_handler) {\n var messages = comm.messages[Symbol.asyncIterator]();\n function processIteratorResult(result) {\n var message = result.value;\n var content = {data: message.data};\n var metadata = message.metadata || {comm_id};\n var msg = {content, metadata}\n msg_handler(msg);\n return messages.next().then(processIteratorResult);\n }\n return messages.next().then(processIteratorResult);\n }\n }) \n var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n return comm_promise.then((comm) => {\n comm.send(data, metadata, buffers, disposeOnDone);\n });\n };\n var comm = {\n send: sendClosure\n };\n }\n window.PyViz.comms[comm_id] = comm;\n return comm;\n }\n window.PyViz.comm_manager = new JupyterCommManager();\n \n\n\nvar JS_MIME_TYPE = 'application/javascript';\nvar HTML_MIME_TYPE = 'text/html';\nvar EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\nvar CLASS_NAME = 'output';\n\n/**\n * Render data to the DOM node\n */\nfunction render(props, node) {\n var div = document.createElement(\"div\");\n var script = document.createElement(\"script\");\n node.appendChild(div);\n node.appendChild(script);\n}\n\n/**\n * Handle when a new output is added\n */\nfunction handle_add_output(event, handle) {\n var output_area = handle.output_area;\n var output = handle.output;\n if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n return\n }\n var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n if (id !== undefined) {\n var nchildren = toinsert.length;\n var html_node = toinsert[nchildren-1].children[0];\n html_node.innerHTML = output.data[HTML_MIME_TYPE];\n var scripts = [];\n var nodelist = html_node.querySelectorAll(\"script\");\n for (var i in nodelist) {\n if (nodelist.hasOwnProperty(i)) {\n scripts.push(nodelist[i])\n }\n }\n\n scripts.forEach( function (oldScript) {\n var newScript = document.createElement(\"script\");\n var attrs = [];\n var nodemap = oldScript.attributes;\n for (var j in nodemap) {\n if (nodemap.hasOwnProperty(j)) {\n attrs.push(nodemap[j])\n }\n }\n attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n oldScript.parentNode.replaceChild(newScript, oldScript);\n });\n if (JS_MIME_TYPE in output.data) {\n toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n }\n output_area._hv_plot_id = id;\n if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n window.PyViz.plot_index[id] = Bokeh.index[id];\n } else {\n window.PyViz.plot_index[id] = null;\n }\n } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n var bk_div = document.createElement(\"div\");\n bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n var script_attrs = bk_div.children[0].attributes;\n for (var i = 0; i < script_attrs.length; i++) {\n toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n }\n // store reference to server id on output_area\n output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n }\n}\n\n/**\n * Handle when an output is cleared or removed\n */\nfunction handle_clear_output(event, handle) {\n var id = handle.cell.output_area._hv_plot_id;\n var server_id = handle.cell.output_area._bokeh_server_id;\n if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n if (server_id !== null) {\n comm.send({event_type: 'server_delete', 'id': server_id});\n return;\n } else if (comm !== null) {\n comm.send({event_type: 'delete', 'id': id});\n }\n delete PyViz.plot_index[id];\n if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n var doc = window.Bokeh.index[id].model.document\n doc.clear();\n const i = window.Bokeh.documents.indexOf(doc);\n if (i > -1) {\n window.Bokeh.documents.splice(i, 1);\n }\n }\n}\n\n/**\n * Handle kernel restart event\n */\nfunction handle_kernel_cleanup(event, handle) {\n delete PyViz.comms[\"hv-extension-comm\"];\n window.PyViz.plot_index = {}\n}\n\n/**\n * Handle update_display_data messages\n */\nfunction handle_update_output(event, handle) {\n handle_clear_output(event, {cell: {output_area: handle.output_area}})\n handle_add_output(event, handle)\n}\n\nfunction register_renderer(events, OutputArea) {\n function append_mime(data, metadata, element) {\n // create a DOM node to render to\n var toinsert = this.create_output_subarea(\n metadata,\n CLASS_NAME,\n EXEC_MIME_TYPE\n );\n this.keyboard_manager.register_events(toinsert);\n // Render to node\n var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n render(props, toinsert[0]);\n element.append(toinsert);\n return toinsert\n }\n\n events.on('output_added.OutputArea', handle_add_output);\n events.on('output_updated.OutputArea', handle_update_output);\n events.on('clear_output.CodeCell', handle_clear_output);\n events.on('delete.Cell', handle_clear_output);\n events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n safe: true,\n index: 0\n });\n}\n\nif (window.Jupyter !== undefined) {\n try {\n var events = require('base/js/events');\n var OutputArea = require('notebook/js/outputarea').OutputArea;\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n } catch(err) {\n }\n}\n" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "<script type=\"esms-options\">{\"shimMode\": true}</script><style>*[data-root-id],\n", + "*[data-root-id] > * {\n", + " box-sizing: border-box;\n", + " font-family: var(--jp-ui-font-family);\n", + " font-size: var(--jp-ui-font-size1);\n", + " color: var(--vscode-editor-foreground, var(--jp-ui-font-color1));\n", + "}\n", + "\n", + "/* Override VSCode background color */\n", + ".cell-output-ipywidget-background:has(\n", + " > .cell-output-ipywidget-background > .lm-Widget > *[data-root-id]\n", + " ),\n", + ".cell-output-ipywidget-background:has(> .lm-Widget > *[data-root-id]) {\n", + " background-color: transparent !important;\n", + "}\n", + "</style>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# create multiselect widget\n", + "county_widget = cxf.charts.panel_widgets.multi_select('county', label_map=county_map)\n", + "\n", + "# define layout\n", + "dash = cxf_data.dashboard(charts=[chart_2, scatter_chart, chart_3],sidebar=[county_widget], theme=cxf.themes.dark, data_size_widget=True, layout_array=layout_array)\n", + "\n", + "dash.app()" + ] + }, + { + "cell_type": "markdown", + "id": "08b9ca80-2469-4f49-8781-1910bdce41bd", + "metadata": {}, + "source": [ + "## Other Libraries ##\n", + "* Plotly:\n", + " * https://dash.plotly.com/holoviews#gpu-accelerating-datashader-and-linked-selections-with-rapids\n", + " * https://developer.nvidia.com/blog/making-a-plotly-dash-census-viz-powered-by-rapids/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "e9d7f104-879d-4784-bf47-541fa4cda445", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'status': 'ok', 'restart': True}" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import IPython\n", + "app = IPython.Application.instance()\n", + "app.kernel.do_shutdown(True)" + ] + }, + { + "cell_type": "markdown", + "id": "9dc3b5a1-051f-4e4c-b830-1dbf066eb622", + "metadata": {}, + "source": [ + "**Well Done!** Let's move to the [next notebook](1-07_etl.ipynb). " + ] + }, + { + "cell_type": "markdown", + "id": "89ffb0bd-9cbf-4f75-affd-0f614e4074e3", + "metadata": {}, + "source": [ + "<img src=\"./images/DLI_Header.png\" width=400/>" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.15" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Fundamentals_of_Accelerated_Data_Science/1-07_etl.ipynb b/Fundamentals_of_Accelerated_Data_Science/1-07_etl.ipynb new file mode 100644 index 0000000..514b15e --- /dev/null +++ b/Fundamentals_of_Accelerated_Data_Science/1-07_etl.ipynb @@ -0,0 +1,573 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "4b2efdc2-313c-493b-9d6c-432ae77d342c", + "metadata": {}, + "source": [ + "<img src=\"./images/DLI_Header.png\" width=400/>" + ] + }, + { + "cell_type": "markdown", + "id": "b754f1a2-24e8-44d4-ae6a-257483573434", + "metadata": {}, + "source": [ + "# Fundamentals of Accelerated Data Science # " + ] + }, + { + "cell_type": "markdown", + "id": "881e48fa-78ac-4d92-a8bb-d419c14df9e9", + "metadata": {}, + "source": [ + "## 07 - Extract, Transform, and Load ##\n", + "\n", + "**Table of Contents**\n", + "<br>\n", + "In this notebook, we will go through the basics of extract, transform, and load. This notebook covers the below sections: \n", + "1. [Extract, Transform, and Load (ETL)](#Extract,-Transform,-and-Load-(ETL))\n", + " * [Extract](#Extract)\n", + " * [Transform](#Transform)\n", + " * [Load](#Load)\n", + "2. [Save to Parquet Format](#Save-to-Parquet-Format)\n", + " * [Reading from Parquet](#Reading-from-Parquet)\n", + "3. [Accelerated ETL for Downstream Tasks](#Accelerated-ETL-for-Downstream-Tasks)" + ] + }, + { + "cell_type": "markdown", + "id": "a083cd6e-73f4-42b8-be04-bdd2bdeb5185", + "metadata": {}, + "source": [ + "## Extract, Transform, and Load (ETL) ##\n", + "An important but perhaps not as highly glorified use case of RAPIDS is extract, transform, and load, or ETL for short. It is a data integration process used to combine data from multiple sources into a single, consistent data store. It's primary goals are: \n", + "* Consolidates data from multiple sources into a single, consistent format\n", + "* Improves data quality through cleaning and validation\n", + "* Enables more efficient data analysis and reporting\n", + "* Supports data-driven decision making" + ] + }, + { + "cell_type": "markdown", + "id": "a9d5d73e-1665-4706-8840-6e14c2fabaa7", + "metadata": {}, + "source": [ + "### Extract ###\n", + "**Extract** is the first step where data is collected from various source systems. These sources could include: \n", + "* Static files (csv, json)\n", + "* SQL RDBMS\n", + "* Webpages\n", + "* API\n", + "\n", + "**Note**: cuDF doesn't have a way to get transactions from external SQL databases directly to GPU. The workaround is reading with pandas and create cuDF dataframe with `cudf.from_pandas()`. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "17be68c6-49c0-429d-86d2-c2cb711a6dc3", + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext cudf.pandas\n", + "# DO NOT CHANGE THIS CELL\n", + "import pandas as pd\n", + "import time" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "37c75532-1bb6-4aab-9cee-3215d7137cee", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>age</th>\n", + " <th>sex</th>\n", + " <th>county</th>\n", + " <th>lat</th>\n", + " <th>long</th>\n", + " <th>name</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.533638</td>\n", + " <td>-1.524400</td>\n", + " <td>FRANCIS</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.426254</td>\n", + " <td>-1.465314</td>\n", + " <td>EDWARD</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.555199</td>\n", + " <td>-1.496417</td>\n", + " <td>TEDDY</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.547909</td>\n", + " <td>-1.572342</td>\n", + " <td>ANGUS</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>DARLINGTON</td>\n", + " <td>54.477638</td>\n", + " <td>-1.605995</td>\n", + " <td>CHARLIE</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " age sex county lat long name\n", + "0 0 m DARLINGTON 54.533638 -1.524400 FRANCIS\n", + "1 0 m DARLINGTON 54.426254 -1.465314 EDWARD\n", + "2 0 m DARLINGTON 54.555199 -1.496417 TEDDY\n", + "3 0 m DARLINGTON 54.547909 -1.572342 ANGUS\n", + "4 0 m DARLINGTON 54.477638 -1.605995 CHARLIE" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "dtype_dict={\n", + " 'age': 'int8', \n", + " 'sex': 'object', \n", + " 'county': 'object', \n", + " 'lat': 'float32', \n", + " 'long': 'float32', \n", + " 'name': 'object'\n", + "}\n", + " \n", + "df=pd.read_csv('./data/uk_pop.csv', dtype=dtype_dict)\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "id": "dd97bfdd-5ead-4cd7-bc17-0e1eeb4aa62b", + "metadata": {}, + "source": [ + "When importing data, it is important to only include columns that are relevant to reduce the memory and compute burden. \n", + "\n", + "Below we read in the county centroid data. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "d064ff45-4cf4-48cd-9cbe-3f3f177fb875", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: 'county_centroid.csv'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/cudf/pandas/fast_slow_proxy.py:988\u001b[0m, in \u001b[0;36m_fast_slow_function_call\u001b[0;34m(func, *args, **kwargs)\u001b[0m\n\u001b[1;32m 987\u001b[0m fast_args, fast_kwargs \u001b[38;5;241m=\u001b[39m _fast_arg(args), _fast_arg(kwargs)\n\u001b[0;32m--> 988\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mfast_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mfast_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 989\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m result \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28mNotImplemented\u001b[39m:\n\u001b[1;32m 990\u001b[0m \u001b[38;5;66;03m# try slow path\u001b[39;00m\n", + "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/cudf/pandas/fast_slow_proxy.py:28\u001b[0m, in \u001b[0;36mcall_operator\u001b[0;34m(fn, args, kwargs)\u001b[0m\n\u001b[1;32m 27\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcall_operator\u001b[39m(fn, args, kwargs):\n\u001b[0;32m---> 28\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/cudf/utils/performance_tracking.py:51\u001b[0m, in \u001b[0;36m_performance_tracking.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 44\u001b[0m stack\u001b[38;5;241m.\u001b[39menter_context(\n\u001b[1;32m 45\u001b[0m nvtx\u001b[38;5;241m.\u001b[39mannotate(\n\u001b[1;32m 46\u001b[0m message\u001b[38;5;241m=\u001b[39mfunc\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__qualname__\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 49\u001b[0m )\n\u001b[1;32m 50\u001b[0m )\n\u001b[0;32m---> 51\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/cudf/io/csv.py:67\u001b[0m, in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, prefix, mangle_dupe_cols, dtype, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, skip_blank_lines, parse_dates, dayfirst, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, comment, delim_whitespace, byte_range, storage_options, bytes_per_thread)\u001b[0m\n\u001b[1;32m 65\u001b[0m bytes_per_thread \u001b[38;5;241m=\u001b[39m ioutils\u001b[38;5;241m.\u001b[39m_BYTES_PER_THREAD_DEFAULT\n\u001b[0;32m---> 67\u001b[0m filepath_or_buffer \u001b[38;5;241m=\u001b[39m \u001b[43mioutils\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_reader_filepath_or_buffer\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 68\u001b[0m \u001b[43m \u001b[49m\u001b[43mpath_or_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 69\u001b[0m \u001b[43m \u001b[49m\u001b[43miotypes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mBytesIO\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mStringIO\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 70\u001b[0m \u001b[43m \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstorage_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 71\u001b[0m \u001b[43m \u001b[49m\u001b[43mbytes_per_thread\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbytes_per_thread\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 72\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 73\u001b[0m filepath_or_buffer \u001b[38;5;241m=\u001b[39m ioutils\u001b[38;5;241m.\u001b[39m_select_single_source(\n\u001b[1;32m 74\u001b[0m filepath_or_buffer, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mread_csv\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 75\u001b[0m )\n", + "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/cudf/utils/ioutils.py:1723\u001b[0m, in \u001b[0;36mget_reader_filepath_or_buffer\u001b[0;34m(path_or_data, mode, fs, iotypes, allow_raw_text_input, storage_options, bytes_per_thread, warn_on_raw_text_input, warn_meta, expand_dir_pattern, prefetch_options)\u001b[0m\n\u001b[1;32m 1720\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m allow_raw_text_input \u001b[38;5;129;01mor\u001b[39;00m paths[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mlower()\u001b[38;5;241m.\u001b[39mendswith(\n\u001b[1;32m 1721\u001b[0m \u001b[38;5;28mtuple\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.json\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mc\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m c \u001b[38;5;129;01min\u001b[39;00m compression_extensions)\n\u001b[1;32m 1722\u001b[0m ):\n\u001b[0;32m-> 1723\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mFileNotFoundError\u001b[39;00m(\n\u001b[1;32m 1724\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00minput_sources\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m could not be resolved to any files\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1725\u001b[0m )\n\u001b[1;32m 1726\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n", + "\u001b[0;31mFileNotFoundError\u001b[0m: ['county_centroid.csv'] could not be resolved to any files", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[3], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m centroid_df\u001b[38;5;241m=\u001b[39m\u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mcounty_centroid.csv\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2\u001b[0m centroid_df\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcounty\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlat_county_center\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlong_county_center\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m 3\u001b[0m centroid_df\u001b[38;5;241m.\u001b[39mhead()\n", + "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/cudf/pandas/fast_slow_proxy.py:713\u001b[0m, in \u001b[0;36m_CallableProxyMixin.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 712\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Any:\n\u001b[0;32m--> 713\u001b[0m result, _ \u001b[38;5;241m=\u001b[39m \u001b[43m_fast_slow_function_call\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 714\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# We cannot directly call self here because we need it to be\u001b[39;49;00m\n\u001b[1;32m 715\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# converted into either the fast or slow object (by\u001b[39;49;00m\n\u001b[1;32m 716\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# _fast_slow_function_call) to avoid infinite recursion.\u001b[39;49;00m\n\u001b[1;32m 717\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# TODO: When Python 3.11 is the minimum supported Python version\u001b[39;49;00m\n\u001b[1;32m 718\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# this can use operator.call\u001b[39;49;00m\n\u001b[1;32m 719\u001b[0m \u001b[43m \u001b[49m\u001b[43mcall_operator\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 720\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 721\u001b[0m \u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 722\u001b[0m \u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 723\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 724\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", + "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/cudf/pandas/fast_slow_proxy.py:1040\u001b[0m, in \u001b[0;36m_fast_slow_function_call\u001b[0;34m(func, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1038\u001b[0m _slow_function_call()\n\u001b[1;32m 1039\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m disable_module_accelerator():\n\u001b[0;32m-> 1040\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mslow_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mslow_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1041\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _maybe_wrap_result(result, func, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs), fast\n", + "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/cudf/pandas/fast_slow_proxy.py:28\u001b[0m, in \u001b[0;36mcall_operator\u001b[0;34m(fn, args, kwargs)\u001b[0m\n\u001b[1;32m 27\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcall_operator\u001b[39m(fn, args, kwargs):\n\u001b[0;32m---> 28\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/pandas/io/parsers/readers.py:1026\u001b[0m, in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[1;32m 1013\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[1;32m 1014\u001b[0m dialect,\n\u001b[1;32m 1015\u001b[0m delimiter,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1022\u001b[0m dtype_backend\u001b[38;5;241m=\u001b[39mdtype_backend,\n\u001b[1;32m 1023\u001b[0m )\n\u001b[1;32m 1024\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[0;32m-> 1026\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/pandas/io/parsers/readers.py:620\u001b[0m, in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m 617\u001b[0m _validate_names(kwds\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[1;32m 619\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[0;32m--> 620\u001b[0m parser \u001b[38;5;241m=\u001b[39m \u001b[43mTextFileReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 622\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[1;32m 623\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m parser\n", + "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/pandas/io/parsers/readers.py:1620\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m 1617\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 1619\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles: IOHandles \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m-> 1620\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_engine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/pandas/io/parsers/readers.py:1880\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[0;34m(self, f, engine)\u001b[0m\n\u001b[1;32m 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is to be opened in binary mode.\u001b[39;00m\n\u001b[1;32m 870\u001b[0m \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[1;32m 871\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mencoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mmode:\n\u001b[1;32m 872\u001b[0m \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[0;32m--> 873\u001b[0m handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[1;32m 874\u001b[0m \u001b[43m \u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 875\u001b[0m \u001b[43m \u001b[49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 876\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 877\u001b[0m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 878\u001b[0m \u001b[43m \u001b[49m\u001b[43mnewline\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 879\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 880\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 881\u001b[0m \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[1;32m 882\u001b[0m handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(handle, ioargs\u001b[38;5;241m.\u001b[39mmode)\n", + "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'county_centroid.csv'" + ] + } + ], + "source": [ + "centroid_df=pd.read_csv('county_centroid.csv')\n", + "centroid_df.columns=['county', 'lat_county_center', 'long_county_center']\n", + "centroid_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2dbf6f18-f28d-45ff-a4c4-1f50bfcf5860", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "%%cudf.pandas.line_profile\n", + "combined_df=df.merge(centroid_df, on='county')" + ] + }, + { + "cell_type": "markdown", + "id": "3294d6e0-00d9-4e33-91d8-2dc02db51512", + "metadata": {}, + "source": [ + "### Transform ###\n", + "During the **Transform** step, the extract data is cleaned, validated, and converted into a suitable format for analysis. \n", + "\n", + "Below we add a new column, representing each persons's distance from their respective county center. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1e6ce3e0-625b-49ec-a755-2bfddee41431", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "%%cudf.pandas.line_profile\n", + "c=['lat', 'long']\n", + "combined_df['R']=((combined_df[c] - combined_df.groupby('county')[c].transform('mean')) ** 2).sum(axis=1) ** 0.5" + ] + }, + { + "cell_type": "markdown", + "id": "7e676b1b-143d-4cf4-a534-8c25dafac1a6", + "metadata": {}, + "source": [ + "Using joins to get lookup values can be faster than deriving those. It is not uncommon to store group statistics for this purpose. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4339b0c7-c391-48f4-9ba3-00f79ba50b5b", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "%%cudf.pandas.line_profile\n", + "\n", + "# read in centroid data\n", + "centroid_df=pd.read_csv('county_centroid.csv')\n", + "\n", + "# merge \n", + "combined_df=df.merge(centroid_df, on='county', suffixes=['', '_county_center'])\n", + "\n", + "# calculate distance from county center\n", + "combined_df['R']=((combined_df['lat']-combined_df['lat_county_center'])**2+(combined_df['long']-combined_df['long_county_center'])**2)**0.5" + ] + }, + { + "cell_type": "markdown", + "id": "aa017ea0-4ad2-4184-9669-40747e31381a", + "metadata": {}, + "source": [ + "Below we filter the data to only include adults. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "917aa1ed-1778-40d4-bbbc-5892935ce7cd", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "%%cudf.pandas.line_profile\n", + "\n", + "senior_df_filter=combined_df['age'] >= 60\n", + "senior_df=combined_df.loc[senior_df_filter]\n", + "\n", + "display(senior_df.head())" + ] + }, + { + "cell_type": "markdown", + "id": "bb474531-389e-44aa-a6b3-8de205d9cb0b", + "metadata": {}, + "source": [ + "### Load ###\n", + "The final **Load** step is where the transformed data is loaded into a target system. The target system can be a database or a file. The key is to develop a system that is efficient for downstream tasks. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6f637229-1770-439b-9702-6356723c96f8", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "senior_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "348eac86-3d67-42e2-9c7f-e0acb14021cd", + "metadata": {}, + "outputs": [], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "senior_df.to_csv('senior_df.csv', index=False)" + ] + }, + { + "cell_type": "markdown", + "id": "e25ae20b-7eea-4775-9743-d43cbf6877a7", + "metadata": {}, + "source": [ + "**Note**: If the downstream task involves querying and analyzing the data further, the csv file format may not be the best choice. " + ] + }, + { + "cell_type": "markdown", + "id": "08413512-47f9-4543-a80c-442f1500b49a", + "metadata": {}, + "source": [ + "<a name='s1-6'></a>\n", + "## Save to Parquet Format ##\n", + "After processing the data, we persist it for later use. [Apache Parquet](https://parquet.apache.org/) is a columnar binary format and has become the de-facto standard for the storage of large volumes of tabular data. Converting to Parquet file format is important and csv files should generally be avoided in data products. While the csv file format is convenient and human-readable, importing csv files requires reading and parsing entire records, which can be a bottleneck. In fact, many developers will start their analysis by first converting csv files to the Parquet file format. There are many reasons to use Parquet format for analytics: \n", + "* The columnar nature of Parquet files allows for column pruning, which often yields big query performance gains. \n", + "* It uses metadata to store the schema and supports more advanced data types such as categorical, datetimes, and more. This means that importing data would not require schema inference or manual schema specification. \n", + "* It captures metadata related to row-group level statistics for each column. This enables predicate pushdown filtering, which is a form of query pushdown that allows computations to happen at the “database layer” instead of the “execution engine layer”. In this case, the database layer is Parquet files in a filesystem, and the execution engine is Dask. \n", + "* It supports flexible compression options, making it more compact to store and more portable than a database. \n", + "\n", + "We will use `.to_parquet(path)`[[doc]](https://docs.dask.org/en/stable/generated/dask.dataframe.to_parquet.html#dask-dataframe-to-parquet) to write to Parquet files. By default, files will be created in the specified output directory using the convention `part.0.parquet`, `part.1.parquet`, `part.2.parquet`, ... and so on for each partition in the DataFrame. This can be changed using the `name_function` parameter. Ouputting multiple files lets Dask write to multiple files in parallel, which is faster than writing to a single file. \n", + "\n", + "<p><img src='images/parquet.png' width=240></p>\n", + "\n", + "When working with large datasets, decoding and encoding is often an expensive task. This challenge tends to compound as the data size grows. A common pattern in data science is to subset the dataset by columns, row slices, or both. Moving these filtering operations to the read phase of the workflow can: 1) reduce I/O time, and 2) reduce the amount of memory required, which is important for GPUs where memory can be a limiting factor. Parquet file format enables filtered reading through **column pruning** and **statistic-based predicate filtering** to skip portions of the data that are irrelevant to the problem. Below are some tips for writing Parquet files: \n", + "* When writing data, sorting the data by the columns that expect the most filters to be applied or columns with the highest cardinality can lead to meaningful performance benefits. The metadata calculated for each row group will enable predicate pushdown filters to the fullest extent. \n", + "* Writing Parquet format, which requires reprocessing entire data sets, can be expensive. The format works remarkably well for read-intensive applications and low latency data storage and retrieval. \n", + "* Partitions in Dask DataFrame can write out files in parallel, so multiple Parquet files are written simultaneously.\n", + "\n", + "Below we write the data into Parquet format, after sorting by the county. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "35007914-f7af-4083-a42a-fc02048c7ec4", + "metadata": {}, + "outputs": [], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "senior_df=senior_df.sort_values('county')\n", + "\n", + "senior_df.to_parquet('senior_df.parquet', index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7e82b557-a2c2-4a42-96d0-35174370aaee", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "import IPython\n", + "app = IPython.Application.instance()\n", + "app.kernel.do_shutdown(True)" + ] + }, + { + "cell_type": "markdown", + "id": "2a3438a6-02d8-4ea0-824f-08c4bcac1063", + "metadata": {}, + "source": [ + "### Reading from Parquet ###\n", + "Querying data in Parquet format can be significantly more performant, especially as the size of the data increases. \n", + "\n", + "Below we read from both the csv format and Parquet format for comparison. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "11c2aab8-3f67-4a57-8eaa-b5d4b224a7b0", + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext cudf.pandas\n", + "import pandas as pd\n", + "import time" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3876ba06-ef43-4849-8a16-58ee2f3a8423", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "%%cudf.pandas.line_profile\n", + "\n", + "sel=[('county', '=', 'BLACKPOOL')]\n", + "parquet_df=pd.read_parquet('senior_df.parquet', columns=['age', 'sex', 'county', 'lat', 'long', 'name', 'R'], filters=sel)\n", + "parquet_df=parquet_df.loc[parquet_df['county']=='BLACKPOOL']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9c67aaf7-4dda-4bf5-9205-fd097d567ea4", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "parquet_df['county'].unique()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "61a225a7-3106-48a3-b100-24c829a6089c", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "%%cudf.pandas.line_profile\n", + "\n", + "df=pd.read_csv('./senior_df.csv', usecols=['age', 'sex', 'county', 'lat', 'long', 'name', 'R'])\n", + "df=df.loc[df['county']=='BLACKPOOL']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b1b5fff0-ccfa-47fe-b821-d68c5858f844", + "metadata": {}, + "outputs": [], + "source": [ + "df['county'].unique()" + ] + }, + { + "cell_type": "markdown", + "id": "8cdf4ff5-c182-41e5-b17d-3c0cd02dd9d5", + "metadata": {}, + "source": [ + "## Accelerated ETL for Downstream Tasks ##\n", + "Accelerating the ETL process is important for data science as it provides the below benefits: \n", + "* **Timely insights**: Faster ETL allows for more up-to-date data analysis, enabling data scientists to work with the most current information.\n", + "* **Increased productivity**: Reduced processing time means data scientists can spend more time on analysis and model development rather than waiting for data to be ready.\n", + "* **Handling larger datasets**: Accelerated ETL processes can manage larger volumes of data more efficiently.\n", + "* **Cost efficiency**: Accelerated ETL can reduce computational resources and time, leading to lower infrastructure costs.\n", + "\n", + "<p><img src='images/etl.png' width=720></p>" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5105158c-ce64-4b8c-9ee7-6d5d684ea5cf", + "metadata": {}, + "outputs": [], + "source": [ + "import IPython\n", + "app = IPython.Application.instance()\n", + "app.kernel.do_shutdown(True)" + ] + }, + { + "cell_type": "markdown", + "id": "c88554b0-23c6-4316-912e-f962b4c97456", + "metadata": {}, + "source": [ + "**Well Done!** Let's move to the [next notebook](1-08_dask-cudf.ipynb). " + ] + }, + { + "cell_type": "markdown", + "id": "391f35d9-6768-4bf3-8d96-b706351c2ad6", + "metadata": {}, + "source": [ + "<img src=\"./images/DLI_Header.png\" width=400/>" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.15" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Fundamentals_of_Accelerated_Data_Science/1-08_cudf-polars.ipynb b/Fundamentals_of_Accelerated_Data_Science/1-08_cudf-polars.ipynb new file mode 100644 index 0000000..c0f1115 --- /dev/null +++ b/Fundamentals_of_Accelerated_Data_Science/1-08_cudf-polars.ipynb @@ -0,0 +1,1357 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "734557aa-90fb-468f-9ed0-0e6f295bb9eb", + "metadata": {}, + "source": [ + "<img src=\"./images/DLI_Header.png\" width=400/>" + ] + }, + { + "cell_type": "markdown", + "id": "6dbb572c-1291-4011-9ed4-120eb2ec7b29", + "metadata": {}, + "source": [ + "# Fundamentals of Accelerated Data Science # " + ] + }, + { + "cell_type": "markdown", + "id": "377ba9f0-0acc-4574-9a43-c475cfd52dd5", + "metadata": {}, + "source": [ + "## 08 - Introduction to cuDF Polars ##\n", + "\n", + "**Table of Contents**\n", + "<br>\n", + "This notebook briefly introduces Polars and covers the new GPU engine. This notebook covers the below sections: \n", + "1. [Introduction to Polars](#Introduction-to-Polars)\n", + " * [Installation](#Installation)\n", + " * [Creating a DataFrame](#Creating-a-DataFrame)\n", + " * [Running Basic Operations](#Running-Basic-Operations)\n", + " * [Pandas Comparison](#Pandas-Comparison)\n", + " * [cuDF Pandas Comparison](#cuDF-Pandas-Comparison)\n", + "2. [Basic Polars Operations](#Basic-Polars-Operations)\n", + " * [Polars Eager Execution API Reference](#Polars-Eager-Execution-API-Reference)\n", + " * [Exercise #1 - Load Data](#Exercise-#1---Load-Data)\n", + " * [Exercise #2 - Calculate Average Age of Population](#Exercise-#2---Calculate-Average-Age-of-Population)\n", + " * [Exercise #3 - Group By and Aggregation](#Exercise-#3---Group-By-and-Aggregation)\n", + " * [Exercise #4 - Gender Distribution](#Exercise-#4---Gender-Distribution)\n", + "4. [Lazy Execution](#Lazy-Execution)\n", + " * [Polars Lazy Execution API Reference](#Polars-Lazy-Execution-API-Reference)\n", + " * [Execution Graph](#Execution-Graph)\n", + " * [Exercise #5 - Creating a Lazy Dataframe](#Exercise-#5---Creating-a-Lazy-Dataframe)\n", + " * [Exercise #6 - Query Creation](#Exercise-#6---Query-Creation)\n", + "5. [cuDF Polars](#cuDF-Polars)\n", + " * [Accelerate Previous Code](#Accelerate-Previous-Code)\n", + " * [Verify Results Across Engines](#Verify-Results-Across-Engines)\n", + " * [Fallback](#Fallback)\n", + " * [Exercise #7 - Enable GPU Engine](#Exercise-#7---Enable-GPU-Engine)" + ] + }, + { + "cell_type": "markdown", + "id": "e7dd978a-a785-4b67-822e-048d56e231e2", + "metadata": {}, + "source": [ + "## Introduction to Polars ##\n", + "Polars is a data analysis and manipulation library that is designed for large data processing (10-100GB) on a single GPU and is known for its speed and memory efficiency. While Pandas makes use of eager execution, Polars additionally has the capability for lazy execution through the built-in query optimizer and makes use of zero-copy optimization techniques. Due to these improvements, Polars typically performs common operations 5-10x faster than Pandas, and requires 2-4 times less RAM. NVIDIA brings hardware acceleration to Polars through a new GPU engine named cuDF Polars, which is available as a pip install." + ] + }, + { + "cell_type": "markdown", + "id": "a2187687-9a53-44f9-ba55-ba9e1081cac6", + "metadata": {}, + "source": [ + "### Creating a DataFrame ###\n", + "Now let's see how the syntax looks! We will create a dataframe to use within Polars." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "be96aa2e-f1fc-4521-bf11-89505a2981ac", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Time Taken: 0.7773 seconds\n" + ] + } + ], + "source": [ + "import polars as pl\n", + "import time\n", + "\n", + "start_time = time.time()\n", + "\n", + "polars_df = pl.read_csv('./data/uk_pop.csv')\n", + "\n", + "polars_time = time.time() - start_time\n", + "\n", + "print(f\"Time Taken: {polars_time:.4f} seconds\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "90dedd84-0792-4a54-9380-4b3a020bccd1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div><style>\n", + ".dataframe > thead > tr,\n", + ".dataframe > tbody > tr {\n", + " text-align: right;\n", + " white-space: pre-wrap;\n", + "}\n", + "</style>\n", + "<small>shape: (5, 6)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>age</th><th>sex</th><th>county</th><th>lat</th><th>long</th><th>name</th></tr><tr><td>i64</td><td>str</td><td>str</td><td>f64</td><td>f64</td><td>str</td></tr></thead><tbody><tr><td>0</td><td>"m"</td><td>"DARLINGTON"</td><td>54.533644</td><td>-1.524401</td><td>"FRANCIS"</td></tr><tr><td>0</td><td>"m"</td><td>"DARLINGTON"</td><td>54.426256</td><td>-1.465314</td><td>"EDWARD"</td></tr><tr><td>0</td><td>"m"</td><td>"DARLINGTON"</td><td>54.5552</td><td>-1.496417</td><td>"TEDDY"</td></tr><tr><td>0</td><td>"m"</td><td>"DARLINGTON"</td><td>54.547906</td><td>-1.572341</td><td>"ANGUS"</td></tr><tr><td>0</td><td>"m"</td><td>"DARLINGTON"</td><td>54.477639</td><td>-1.605995</td><td>"CHARLIE"</td></tr></tbody></table></div>" + ], + "text/plain": [ + "shape: (5, 6)\n", + "┌─────┬─────┬────────────┬───────────┬───────────┬─────────┐\n", + "│ age ┆ sex ┆ county ┆ lat ┆ long ┆ name │\n", + "│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n", + "│ i64 ┆ str ┆ str ┆ f64 ┆ f64 ┆ str │\n", + "╞═════╪═════╪════════════╪═══════════╪═══════════╪═════════╡\n", + "│ 0 ┆ m ┆ DARLINGTON ┆ 54.533644 ┆ -1.524401 ┆ FRANCIS │\n", + "│ 0 ┆ m ┆ DARLINGTON ┆ 54.426256 ┆ -1.465314 ┆ EDWARD │\n", + "│ 0 ┆ m ┆ DARLINGTON ┆ 54.5552 ┆ -1.496417 ┆ TEDDY │\n", + "│ 0 ┆ m ┆ DARLINGTON ┆ 54.547906 ┆ -1.572341 ┆ ANGUS │\n", + "│ 0 ┆ m ┆ DARLINGTON ┆ 54.477639 ┆ -1.605995 ┆ CHARLIE │\n", + "└─────┴─────┴────────────┴───────────┴───────────┴─────────┘" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "polars_df.head()" + ] + }, + { + "cell_type": "markdown", + "id": "12c66adc-5ac0-49a1-95fe-dc78fbb3950a", + "metadata": {}, + "source": [ + "### Running Basic Operations ###\n", + "That was simple- now let's try running a few operations on the dataset! We will be loading the dataset again for a fair comparison with Pandas later." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "7fc2280f-a89c-42dd-befc-39ffce2f01ec", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "shape: (5, 6)\n", + "┌─────┬─────┬──────────────────────────┬───────────┬───────────┬───────┐\n", + "│ age ┆ sex ┆ county ┆ lat ┆ long ┆ name │\n", + "│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n", + "│ i64 ┆ str ┆ str ┆ f64 ┆ f64 ┆ str │\n", + "╞═════╪═════╪══════════════════════════╪═══════════╪═══════════╪═══════╡\n", + "│ 1 ┆ f ┆ EAST RIDING OF YORKSHIRE ┆ 53.737344 ┆ -0.638535 ┆ ZYRAH │\n", + "│ 1 ┆ f ┆ SHEFFIELD ┆ 53.35529 ┆ -1.669447 ┆ ZYRAH │\n", + "│ 1 ┆ f ┆ LINCOLNSHIRE ┆ 53.164176 ┆ 0.015812 ┆ ZYRAH │\n", + "│ 1 ┆ f ┆ WORCESTERSHIRE ┆ 52.258629 ┆ -2.31696 ┆ ZYRAH │\n", + "│ 1 ┆ f ┆ HERTFORDSHIRE ┆ 51.731816 ┆ -0.377476 ┆ ZYRAH │\n", + "└─────┴─────┴──────────────────────────┴───────────┴───────────┴───────┘\n", + "Time Taken: 6.6932 seconds\n" + ] + } + ], + "source": [ + "start_time = time.time()\n", + "\n", + "#load data\n", + "polars_df = pl.read_csv('./data/uk_pop.csv')\n", + "\n", + "# Filter for ages above 0\n", + "filtered_df = polars_df.filter(pl.col('age') > 0.0)\n", + "\n", + "#Sort by name\n", + "sorted_df = filtered_df.sort('name', descending=True)\n", + "\n", + "print(sorted_df.head())\n", + "polars_time = time.time() - start_time\n", + "print(f\"Time Taken: {polars_time:.4f} seconds\")" + ] + }, + { + "cell_type": "markdown", + "id": "bf06893b-a772-4c15-a867-e4cf6aa0984b", + "metadata": {}, + "source": [ + "### Pandas Comparison ###\n", + "Let's see how long this would've taken in Pandas." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "99468ce0-ecb1-4da2-9e96-44c41c73605a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Time Taken: 147.3623 seconds\n", + "\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import time\n", + "start_time = time.time()\n", + "pandas_df = pd.read_csv('./data/uk_pop.csv')\n", + "\n", + "filtered_df = pandas_df[pandas_df['age'] > 0.0]\n", + "\n", + "sorted_df = filtered_df.sort_values(by=['name'], ascending=False)\n", + "\n", + "pandas_time = time.time() - start_time\n", + "print(f\"Time Taken: {pandas_time:.4f} seconds\\n\")" + ] + }, + { + "cell_type": "markdown", + "id": "d5f25cae-1ee8-462e-a487-68af7d9004ce", + "metadata": {}, + "source": [ + "### cuDF Pandas Comparison ###\n", + "Wow! That took quite some time to execute. Let's see if we can run it faster with cuDF Pandas." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "23484488-4d35-4359-804d-80bb60463d2e", + "metadata": {}, + "outputs": [], + "source": [ + "# Activate cuDF Pandas\n", + "%load_ext cudf.pandas\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "714b14dd-0ebd-4814-a341-75cbf8e44fd5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Time Taken for cuDF Pandas: 7.2949 seconds\n", + "\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import time\n", + "start_time = time.time()\n", + "pandas_df = pd.read_csv('./data/uk_pop.csv')\n", + "\n", + "filtered_df = pandas_df[pandas_df['age'] > 0.0]\n", + "\n", + "sorted_df = filtered_df.sort_values(by=['name'], ascending=False)\n", + "\n", + "pandas_time = time.time() - start_time\n", + "print(f\"Time Taken for cuDF Pandas: {pandas_time:.4f} seconds\\n\")" + ] + }, + { + "cell_type": "markdown", + "id": "d9a3692e-fe36-4bb2-bf1d-84ce5d4834f7", + "metadata": {}, + "source": [ + "**Note**: Even with cuDF Pandas, we sometimes notice that the performance can be slower than Polars." + ] + }, + { + "cell_type": "markdown", + "id": "dd499b82-9b82-472b-934c-5d7e34743458", + "metadata": {}, + "source": [ + "## Basic Polars Operations ##\n", + "Please refer to the following API reference guide to complete the exercises below.\n", + "\n", + "1. Load data\n", + "2. Calculate average age of population\n", + "3. Group By and Aggregation\n", + "4. Gender Distribution" + ] + }, + { + "cell_type": "markdown", + "id": "b3972070-b02e-4870-90c2-bcae0c43f82e", + "metadata": {}, + "source": [ + "### Polars Eager Execution API Reference ###\n", + "\n", + "**DataFrame**\n", + "\n", + "The main data structure for eager execution in Polars.\n", + "\n", + "- `pl.DataFrame(data)`: Create a DataFrame from data\n", + "- `pl.read_csv(file)`: Read CSV file into DataFrame\n", + "- `pl.read_parquet(file)`: Read Parquet file into DataFrame\n", + "\n", + "**Key Methods**\n", + "\n", + "- `filter(mask)`: Filter rows based on a boolean mask\n", + "- `select(columns)`: Select specific columns\n", + "- `with_columns(expressions)`: Add or modify columns\n", + "- `group_by(columns)`: Group by specified columns\n", + "- `agg(aggregations)`: Perform aggregations on grouped data\n", + "- `sort(columns)`: Sort the data by specified columns\n", + "- `join(other, on)`: Join with another DataFrame\n", + "\n", + "**Expressions**\n", + "\n", + "Used to define operations on columns:\n", + "\n", + "- `pl.col(\"column\")`: Reference a column\n", + "- `pl.lit(value)`: Create a literal value\n", + "- `pl.when(predicate).then(value).otherwise(other)`: Conditional expression\n", + "\n", + "**Series Operations**\n", + "\n", + "- `series.sum()`: Calculate sum of series\n", + "- `series.mean()`: Calculate mean of series\n", + "- `series.max()`: Find maximum value in series\n", + "- `series.min()`: Find minimum value in series\n", + "- `series.sort()`: Sort series values\n", + "\n", + "**Data Types**\n", + "\n", + "- `pl.Int64`: 64-bit integer\n", + "- `pl.Float64`: 64-bit float\n", + "- `pl.Utf8`: String\n", + "- `pl.Boolean`: Boolean\n", + "- `pl.Date`: Date\n", + "\n", + "**Utilities**\n", + "\n", + "- `pl.concat([df1, df2])`: Concatenate DataFrames\n", + "- `df.describe()`: Generate summary statistics\n", + "- `df.to_csv(file)`: Write DataFrame to CSV\n", + "- `df.to_parquet(file)`: Write DataFrame to Parquet\n", + "\n", + "The eager API executes operations immediately, providing direct access to results. It's suitable for interactive data exploration and smaller datasets." + ] + }, + { + "cell_type": "markdown", + "id": "d87e5c16-46df-4a5b-8454-64fc7bb761a4", + "metadata": {}, + "source": [ + "### Exercise #1 - Load Data ###\n", + "Load the csv file into a Dataframe using Polars." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "e138575b-8817-44e0-981c-128b07d6bf8f", + "metadata": {}, + "outputs": [], + "source": [ + "import polars as pl\n", + "\n", + "polars_df = pl.read_csv('./data/uk_pop.csv')" + ] + }, + { + "cell_type": "raw", + "id": "8e2c5846-9762-46f9-a440-b8fab6949b73", + "metadata": {}, + "source": [ + "\n", + "df = pl.read_csv('./data/uk_pop.csv')\n", + "\n", + "print(df.head())" + ] + }, + { + "cell_type": "markdown", + "id": "d7833215-ea20-4b6b-bcd1-be4c8ca983ca", + "metadata": {}, + "source": [ + "Click ... for solution. " + ] + }, + { + "cell_type": "markdown", + "id": "51b230dd-7c17-46fb-be96-1dd11dfbf90d", + "metadata": {}, + "source": [ + "### Exercise #2 - Calculate Average Age of Population ###\n", + "Now, filter for individuals aged 65 and above, and sort by ascending age." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "d22e37d3-a0c0-4b05-891a-c74086a79e59", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "shape: (5, 6)\n", + "┌─────┬─────┬──────────────────────────┬───────────┬───────────┬──────┐\n", + "│ age ┆ sex ┆ county ┆ lat ┆ long ┆ name │\n", + "│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n", + "│ i64 ┆ str ┆ str ┆ f64 ┆ f64 ┆ str │\n", + "╞═════╪═════╪══════════════════════════╪═══════════╪═══════════╪══════╡\n", + "│ 65 ┆ m ┆ CHESHIRE EAST ┆ 53.320049 ┆ -2.581048 ┆ A │\n", + "│ 65 ┆ m ┆ LANCASHIRE ┆ 53.835459 ┆ -2.6268 ┆ A │\n", + "│ 65 ┆ m ┆ EAST RIDING OF YORKSHIRE ┆ 53.846112 ┆ -0.731101 ┆ A │\n", + "│ 65 ┆ m ┆ NORTHAMPTONSHIRE ┆ 52.179982 ┆ -0.962304 ┆ A │\n", + "│ 65 ┆ m ┆ BRIGHTON AND HOVE ┆ 50.828285 ┆ -0.143481 ┆ A │\n", + "└─────┴─────┴──────────────────────────┴───────────┴───────────┴──────┘\n" + ] + } + ], + "source": [ + "filtered_df = polars_df.filter(pl.col('age') >= 65.0)\n", + "sorted_df = filtered_df.sort('name', descending=False)\n", + "print(sorted_df.head())" + ] + }, + { + "cell_type": "raw", + "id": "de1b9849-e5fe-43a7-b38e-68ec4aa20bce", + "metadata": {}, + "source": [ + "\n", + "filtered = (\n", + " df.filter(pl.col(\"age\") >= 65)\n", + " .sort(\"age\", descending=False)\n", + ")\n", + "\n", + "print(filtered)" + ] + }, + { + "cell_type": "markdown", + "id": "dd6a015f-d086-4856-972a-510d4073f614", + "metadata": {}, + "source": [ + "Click ... for solution. " + ] + }, + { + "cell_type": "markdown", + "id": "2af1bb9b-91bc-4665-a371-699b66067cd2", + "metadata": {}, + "source": [ + "### Exercise #3 - Group By and Aggregation ###\n", + "Next, group by county and calculate the total population and average age." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "191a03a2-f420-4669-96ed-ddb290ce09f7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div><style>\n", + ".dataframe > thead > tr,\n", + ".dataframe > tbody > tr {\n", + " text-align: right;\n", + " white-space: pre-wrap;\n", + "}\n", + "</style>\n", + "<small>shape: (5, 3)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>county</th><th>len</th><th>age</th></tr><tr><td>str</td><td>u32</td><td>f64</td></tr></thead><tbody><tr><td>"MIDDLESBROUGH"</td><td>140545</td><td>38.067509</td></tr><tr><td>"WARWICKSHIRE"</td><td>571010</td><td>41.794508</td></tr><tr><td>"NEATH PORT TALBOT"</td><td>142906</td><td>42.042343</td></tr><tr><td>"HOUNSLOW"</td><td>270782</td><td>36.310597</td></tr><tr><td>"SOMERSET"</td><td>559399</td><td>44.004834</td></tr></tbody></table></div>" + ], + "text/plain": [ + "shape: (5, 3)\n", + "┌───────────────────┬────────┬───────────┐\n", + "│ county ┆ len ┆ age │\n", + "│ --- ┆ --- ┆ --- │\n", + "│ str ┆ u32 ┆ f64 │\n", + "╞═══════════════════╪════════╪═══════════╡\n", + "│ MIDDLESBROUGH ┆ 140545 ┆ 38.067509 │\n", + "│ WARWICKSHIRE ┆ 571010 ┆ 41.794508 │\n", + "│ NEATH PORT TALBOT ┆ 142906 ┆ 42.042343 │\n", + "│ HOUNSLOW ┆ 270782 ┆ 36.310597 │\n", + "│ SOMERSET ┆ 559399 ┆ 44.004834 │\n", + "└───────────────────┴────────┴───────────┘" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "grouped = polars_df.group_by(\"county\")\n", + "aggregated = grouped.agg([pl.len(), pl.mean(\"age\")])\n", + "aggregated.head()" + ] + }, + { + "cell_type": "raw", + "id": "456a1ac3-a6dd-4c06-b2b4-7adaf59783fe", + "metadata": {}, + "source": [ + "\n", + "agg = (\n", + " df.group_by(\"county\")\n", + " .agg([\n", + " pl.len().alias(\"population\"),\n", + " pl.mean(\"age\").alias(\"average_age\")\n", + " ])\n", + " .sort(\"population\", descending=True)\n", + ")\n", + "\n", + "print(agg.head())" + ] + }, + { + "cell_type": "markdown", + "id": "59956d51-c6e1-4129-aa5f-11e29ab44a9a", + "metadata": {}, + "source": [ + "Click ... for solution. " + ] + }, + { + "cell_type": "markdown", + "id": "fb6e2475-3823-43a7-b75b-460e971da230", + "metadata": {}, + "source": [ + "### Exercise #4 - Gender Distribution ###\n", + "Lastly, let's calculate the percentage of males to females in the sample data." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "1fa57226-f174-4967-a75a-b11d9df8765f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div><style>\n", + ".dataframe > thead > tr,\n", + ".dataframe > tbody > tr {\n", + " text-align: right;\n", + " white-space: pre-wrap;\n", + "}\n", + "</style>\n", + "<small>shape: (2, 3)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>sex</th><th>count</th><th>percentage</th></tr><tr><td>str</td><td>u32</td><td>f64</td></tr></thead><tbody><tr><td>"m"</td><td>28900781</td><td>49.42003</td></tr><tr><td>"f"</td><td>29579113</td><td>50.57997</td></tr></tbody></table></div>" + ], + "text/plain": [ + "shape: (2, 3)\n", + "┌─────┬──────────┬────────────┐\n", + "│ sex ┆ count ┆ percentage │\n", + "│ --- ┆ --- ┆ --- │\n", + "│ str ┆ u32 ┆ f64 │\n", + "╞═════╪══════════╪════════════╡\n", + "│ m ┆ 28900781 ┆ 49.42003 │\n", + "│ f ┆ 29579113 ┆ 50.57997 │\n", + "└─────┴──────────┴────────────┘" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "grouped_age = polars_df.group_by(\"sex\")\n", + "aggregated_age = grouped_age.agg(pl.len().alias(\"count\")).with_columns(\n", + " (pl.col(\"count\") / polars_df.shape[0] * 100).alias(\"percentage\")\n", + " )\n", + "aggregated_age.head()" + ] + }, + { + "cell_type": "raw", + "id": "0a635638-1c6b-4752-b2c2-dadee0b73162", + "metadata": {}, + "source": [ + "\n", + "gender = (\n", + " df.group_by(\"sex\")\n", + " .agg(pl.len().alias(\"count\"))\n", + " .with_columns(\n", + " (pl.col(\"count\") / df.shape[0] * 100).alias(\"percentage\")\n", + " )\n", + ")\n", + "\n", + "print(gender)" + ] + }, + { + "cell_type": "markdown", + "id": "799cf9d3-1805-4a82-8d78-b780c73ab25e", + "metadata": {}, + "source": [ + "Click ... for solution. " + ] + }, + { + "cell_type": "markdown", + "id": "3b44f79a-a542-492d-ae01-b1ee6f9203dc", + "metadata": {}, + "source": [ + "## Lazy Execution ##\n", + "Polars utilizes a technique called lazy execution to perform operations. Unlike eager execution, where operations are performed immediately, Polars defines and stores operations in a computational graph that isn't executed until explicitly required. This allows Polars to optimize the sequence of operations to minimize computation overhead and apply optimization techniques such as: applying filters early (predicate pushdown), selecting only necessary columns (projection pushdown), and executing operations in parallel. To make use of lazy execution in polars, a \"LazyFrame\" data structure is used.\n", + "\n", + "Now, lets run the same operations with lazy execution and visualize the graph!" + ] + }, + { + "cell_type": "markdown", + "id": "0a709c71-6fae-4eca-8236-853e3d97e12a", + "metadata": {}, + "source": [ + "### Polars Lazy Execution API Reference ###\n", + "\n", + "**LazyFrame**\n", + "\n", + "The main entry point for lazy execution in Polars. Created from a DataFrame or data source.\n", + "\n", + "- `pl.LazyFrame(data)`: Create a LazyFrame from data.\n", + "- `df.lazy()`: Convert a DataFrame to LazyFrame.\n", + "\n", + "**Key Methods**\n", + "\n", + "- `filter(predicate)`: Filter rows based on a condition.\n", + "- `select(columns)`: Select specific columns.\n", + "- `with_columns(expressions)`: Add or modify columns.\n", + "- `group_by(columns)`: Group by specified columns.\n", + "- `agg(aggregations)`: Perform aggregations on grouped data.\n", + "- `sort(columns)`: Sort the data by specified columns.\n", + "- `join(other, on)`: Join with another LazyFrame.\n", + "- `collect()`: Execute the lazy query and return a DataFrame.\n", + "\n", + "**Expressions**\n", + "\n", + "Used to define operations on columns:\n", + "\n", + "- `pl.col(\"column\")`: Reference a column.\n", + "- `pl.lit(value)`: Create a literal value.\n", + "- `pl.when(predicate).then(value).otherwise(other)`: Define a conditional expression.\n", + "\n", + "**Execution**\n", + "\n", + "- `collect()`: Execute and return a DataFrame.\n", + "- `fetch(n)`: Execute and return the first n rows.\n", + "- `describe_plan()`: Show the query plan for optimization insights.\n", + "- `explain()`: Explain the query execution process.\n", + "\n", + "**Optimization**\n", + "\n", + "- `cache()`: Cache intermediate results for faster access.\n", + "- `optimize()`: Apply query optimizations to improve performance.\n", + "\n", + "The lazy API allows building complex queries that are optimized before execution, enabling better performance for large datasets." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "f183fa43-99e2-45dc-930c-44fee40d9135", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "shape: (5, 6)\n", + "┌─────┬─────┬──────────────────────────┬───────────┬───────────┬───────┐\n", + "│ age ┆ sex ┆ county ┆ lat ┆ long ┆ name │\n", + "│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n", + "│ i64 ┆ str ┆ str ┆ f64 ┆ f64 ┆ str │\n", + "╞═════╪═════╪══════════════════════════╪═══════════╪═══════════╪═══════╡\n", + "│ 1 ┆ f ┆ EAST RIDING OF YORKSHIRE ┆ 53.737344 ┆ -0.638535 ┆ ZYRAH │\n", + "│ 1 ┆ f ┆ SHEFFIELD ┆ 53.35529 ┆ -1.669447 ┆ ZYRAH │\n", + "│ 1 ┆ f ┆ LINCOLNSHIRE ┆ 53.164176 ┆ 0.015812 ┆ ZYRAH │\n", + "│ 1 ┆ f ┆ WORCESTERSHIRE ┆ 52.258629 ┆ -2.31696 ┆ ZYRAH │\n", + "│ 1 ┆ f ┆ HERTFORDSHIRE ┆ 51.731816 ┆ -0.377476 ┆ ZYRAH │\n", + "└─────┴─────┴──────────────────────────┴───────────┴───────────┴───────┘\n", + "Time Taken: 6.6778 seconds\n" + ] + } + ], + "source": [ + "import polars as pl\n", + "import time\n", + "\n", + "start_time = time.time()\n", + "\n", + "# Create a lazy DataFrame\n", + "lazy_df = pl.scan_csv('./data/uk_pop.csv')\n", + "\n", + "# Define the lazy operations\n", + "lazy_result = (\n", + " lazy_df\n", + " .filter(pl.col('age') > 0.0)\n", + " .sort('name', descending=True)\n", + ")\n", + "\n", + "# Execute the lazy query and collect the results\n", + "result = lazy_result.collect()\n", + "\n", + "print(result.head())\n", + "polars_time = time.time() - start_time\n", + "print(f\"Time Taken: {polars_time:.4f} seconds\")" + ] + }, + { + "cell_type": "markdown", + "id": "0ae1e01f-da85-453f-a708-37afca4753f6", + "metadata": {}, + "source": [ + "### Execution Graph ###" + ] + }, + { + "cell_type": "markdown", + "id": "4affd1a7-80b2-4f9b-88d8-26a7008f6dc9", + "metadata": {}, + "source": [ + "Let's see how the unoptimized execution graph looks." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "d43e9843-ef8e-48aa-b49c-482454d6e93f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"481pt\" height=\"190pt\" viewBox=\"0.00 0.00 481.00 190.00\">\n", + "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 186)\">\n", + "<title>polars_query</title>\n", + "<polygon fill=\"white\" stroke=\"transparent\" points=\"-4,4 -4,-186 477,-186 477,4 -4,4\"/>\n", + "<!-- p1 -->\n", + "<g id=\"node1\" class=\"node\">\n", + "<title>p1</title>\n", + "<polygon fill=\"none\" stroke=\"black\" points=\"326,-182 147,-182 147,-146 326,-146 326,-182\"/>\n", + "<text text-anchor=\"middle\" x=\"236.5\" y=\"-160.3\" font-family=\"Times,serif\" font-size=\"14.00\">SORT BY [col("name")]</text>\n", + "</g>\n", + "<!-- p2 -->\n", + "<g id=\"node2\" class=\"node\">\n", + "<title>p2</title>\n", + "<polygon fill=\"none\" stroke=\"black\" points=\"473,-110 0,-110 0,-74 473,-74 473,-110\"/>\n", + "<text text-anchor=\"middle\" x=\"236.5\" y=\"-88.3\" font-family=\"Times,serif\" font-size=\"14.00\">FILTER BY [(col("age").cast(Unknown(Float))) > (dyn float: 0.0)]</text>\n", + "</g>\n", + "<!-- p1--p2 -->\n", + "<g id=\"edge1\" class=\"edge\">\n", + "<title>p1--p2</title>\n", + "<path fill=\"none\" stroke=\"black\" d=\"M236.5,-145.7C236.5,-134.85 236.5,-120.92 236.5,-110.1\"/>\n", + "</g>\n", + "<!-- p3 -->\n", + "<g id=\"node3\" class=\"node\">\n", + "<title>p3</title>\n", + "<polygon fill=\"none\" stroke=\"black\" points=\"350,-38 123,-38 123,0 350,0 350,-38\"/>\n", + "<text text-anchor=\"middle\" x=\"236.5\" y=\"-22.8\" font-family=\"Times,serif\" font-size=\"14.00\">Csv SCAN [./data/uk_pop.csv]</text>\n", + "<text text-anchor=\"middle\" x=\"236.5\" y=\"-7.8\" font-family=\"Times,serif\" font-size=\"14.00\">π */6;</text>\n", + "</g>\n", + "<!-- p2--p3 -->\n", + "<g id=\"edge2\" class=\"edge\">\n", + "<title>p2--p3</title>\n", + "<path fill=\"none\" stroke=\"black\" d=\"M236.5,-73.81C236.5,-62.98 236.5,-49.01 236.5,-38.02\"/>\n", + "</g>\n", + "</g>\n", + "</svg>" + ], + "text/plain": [ + "<IPython.core.display.SVG object>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Show unoptimized Graph\n", + "lazy_result.show_graph(optimized=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "6f5883f3-6238-4f98-970b-f06adabfb50e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"414pt\" height=\"133pt\" viewBox=\"0.00 0.00 414.00 133.00\">\n", + "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 129)\">\n", + "<title>polars_query</title>\n", + "<polygon fill=\"white\" stroke=\"transparent\" points=\"-4,4 -4,-129 410,-129 410,4 -4,4\"/>\n", + "<!-- p1 -->\n", + "<g id=\"node1\" class=\"node\">\n", + "<title>p1</title>\n", + "<polygon fill=\"none\" stroke=\"black\" points=\"292.5,-125 113.5,-125 113.5,-89 292.5,-89 292.5,-125\"/>\n", + "<text text-anchor=\"middle\" x=\"203\" y=\"-103.3\" font-family=\"Times,serif\" font-size=\"14.00\">SORT BY [col("name")]</text>\n", + "</g>\n", + "<!-- p2 -->\n", + "<g id=\"node2\" class=\"node\">\n", + "<title>p2</title>\n", + "<polygon fill=\"none\" stroke=\"black\" points=\"406,-53 0,-53 0,0 406,0 406,-53\"/>\n", + "<text text-anchor=\"middle\" x=\"203\" y=\"-37.8\" font-family=\"Times,serif\" font-size=\"14.00\">Csv SCAN [./data/uk_pop.csv]</text>\n", + "<text text-anchor=\"middle\" x=\"203\" y=\"-22.8\" font-family=\"Times,serif\" font-size=\"14.00\">π */6;</text>\n", + "<text text-anchor=\"middle\" x=\"203\" y=\"-7.8\" font-family=\"Times,serif\" font-size=\"14.00\">σ [(col("age").cast(Unknown(Float))) > (dyn float: 0.0)]</text>\n", + "</g>\n", + "<!-- p1--p2 -->\n", + "<g id=\"edge1\" class=\"edge\">\n", + "<title>p1--p2</title>\n", + "<path fill=\"none\" stroke=\"black\" d=\"M203,-88.97C203,-78.51 203,-64.9 203,-53.16\"/>\n", + "</g>\n", + "</g>\n", + "</svg>" + ], + "text/plain": [ + "<IPython.core.display.SVG object>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Show optimized Graph\n", + "lazy_result.show_graph(optimized=True)" + ] + }, + { + "cell_type": "markdown", + "id": "7d0f6912-9b19-4473-80d3-4223271efe26", + "metadata": {}, + "source": [ + "As we can see, during execution, Polars ran the age filter in parallel with reading the csv to save time! These type of optimizations is part of the reason why Polars is such a powerful Data Science tool." + ] + }, + { + "cell_type": "markdown", + "id": "5301bcfb-634e-4659-b413-e8279c9bc2ce", + "metadata": {}, + "source": [ + "### Exercise #5 - Creating a Lazy Dataframe ###\n", + "First, let's load the csv as a lazy dataframe." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "f096b49a-1395-4ddb-bf28-f6e04fb8b4f1", + "metadata": {}, + "outputs": [], + "source": [ + "lazy_df = pl.scan_csv('./data/uk_pop.csv')" + ] + }, + { + "cell_type": "raw", + "id": "c43adefb-2176-4a9b-9217-76260033c29a", + "metadata": {}, + "source": [ + "\n", + "lazy_df = pl.scan_csv('./data/uk_pop.csv')" + ] + }, + { + "cell_type": "markdown", + "id": "5e287242-6d01-4848-a795-3f434ae1901a", + "metadata": {}, + "source": [ + "Click ... for solution. " + ] + }, + { + "cell_type": "markdown", + "id": "3581e118-7902-4d04-b4b4-9887c6ff73ba", + "metadata": {}, + "source": [ + "### Exercise #6 - Query Creation ###\n", + "Now, let's create a query to find the 5 most common names for individuals under 30. " + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "1615956e-c6b5-485c-85d3-c0cf89cedbca", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "shape: (5, 2)\n", + "┌────────┬────────┐\n", + "│ name ┆ count │\n", + "│ --- ┆ --- │\n", + "│ str ┆ u32 │\n", + "╞════════╪════════╡\n", + "│ OLIVER ┆ 218505 │\n", + "│ GEORGE ┆ 174261 │\n", + "│ HARRY ┆ 173862 │\n", + "│ OLIVIA ┆ 171424 │\n", + "│ AMELIA ┆ 163302 │\n", + "└────────┴────────┘\n" + ] + } + ], + "source": [ + "lazy_result = (\n", + " lazy_df\n", + " .filter(pl.col('age') < 30.0)\n", + " .group_by(\"name\")\n", + " .agg(pl.len().alias(\"count\"))\n", + " .sort('count', descending=True)\n", + " .limit(5)\n", + " .select([\"name\",\"count\"])\n", + ")\n", + "\n", + "top=lazy_result.collect()\n", + "print(top)" + ] + }, + { + "cell_type": "raw", + "id": "fef093ee-a9e5-44df-9c10-d7514dcc9eea", + "metadata": {}, + "source": [ + "\n", + "result = (\n", + " lazy_df.filter(pl.col(\"age\") < 30)\n", + " .group_by(\"name\")\n", + " .agg(pl.len().alias(\"count\"))\n", + " .sort(\"count\", descending=True)\n", + " .limit(5)\n", + " .select([\"name\", \"count\"])\n", + ")\n", + "\n", + "top_5_names=result.collect()\n", + "print(top_5_names)" + ] + }, + { + "cell_type": "markdown", + "id": "e2832dcd-9f2e-4970-932b-8d0732f9c4ab", + "metadata": {}, + "source": [ + "Click ... for solution. " + ] + }, + { + "cell_type": "markdown", + "id": "07cf331d-0e3d-407c-9990-c7c0873565da", + "metadata": {}, + "source": [ + "## cuDF Polars ##\n", + "cuDF Polars is built directly into the Polars Lazy API. The only requirement is to pass engine=\"gpu\" to the collect operation. Polars also allows defining an instance of the GPU engine for greater customization!" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "9c6506b1-5c96-4b43-9d53-5826b5a8a44d", + "metadata": {}, + "outputs": [], + "source": [ + "lazy_df = pl.scan_csv('./data/uk_pop.csv').collect(engine=\"gpu\")" + ] + }, + { + "cell_type": "markdown", + "id": "676d16d0-9da0-48a1-a9cc-dc60bde2b223", + "metadata": {}, + "source": [ + "Now let's try defining our own engine object!" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "66672414-a47d-48c4-9ad9-5329ae159765", + "metadata": {}, + "outputs": [], + "source": [ + "import polars as pl\n", + "import time\n", + "\n", + "gpu_engine = pl.GPUEngine(\n", + " device=0, # This is the default\n", + " raise_on_fail=True, # Fail loudly if we can't run on the GPU.\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "a939efd7-6c74-456b-baf0-8a2d9905956f", + "metadata": {}, + "outputs": [], + "source": [ + "lazy_df = pl.scan_csv('./data/uk_pop.csv').collect(engine=gpu_engine)" + ] + }, + { + "cell_type": "markdown", + "id": "7eafdfb6-92b0-4f0e-a2ea-90a2ee8a222c", + "metadata": {}, + "source": [ + "Now that the GPU is warmed up, let's try accelerating the same code as before! Notice that we added an engine parameter to the collect call." + ] + }, + { + "cell_type": "markdown", + "id": "52995c50-bf64-44be-a22e-ba41c778f509", + "metadata": {}, + "source": [ + "### Accelerate Previous Code ###" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "69e5074d-d15b-471a-a9dd-e1f7a52013a5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "shape: (5, 6)\n", + "┌─────┬─────┬──────────────────────────┬───────────┬───────────┬───────┐\n", + "│ age ┆ sex ┆ county ┆ lat ┆ long ┆ name │\n", + "│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n", + "│ i64 ┆ str ┆ str ┆ f64 ┆ f64 ┆ str │\n", + "╞═════╪═════╪══════════════════════════╪═══════════╪═══════════╪═══════╡\n", + "│ 1 ┆ f ┆ EAST RIDING OF YORKSHIRE ┆ 53.737344 ┆ -0.638535 ┆ ZYRAH │\n", + "│ 1 ┆ f ┆ SHEFFIELD ┆ 53.35529 ┆ -1.669447 ┆ ZYRAH │\n", + "│ 1 ┆ f ┆ LINCOLNSHIRE ┆ 53.164176 ┆ 0.015812 ┆ ZYRAH │\n", + "│ 1 ┆ f ┆ WORCESTERSHIRE ┆ 52.258629 ┆ -2.31696 ┆ ZYRAH │\n", + "│ 1 ┆ f ┆ HERTFORDSHIRE ┆ 51.731816 ┆ -0.377476 ┆ ZYRAH │\n", + "└─────┴─────┴──────────────────────────┴───────────┴───────────┴───────┘\n", + "Time Taken: 5.9006 seconds\n" + ] + } + ], + "source": [ + "start_time = time.time()\n", + "\n", + "# Create a lazy DataFrame\n", + "lazy_df = pl.scan_csv('./data/uk_pop.csv')\n", + "\n", + "# Define the lazy operations\n", + "lazy_result = (\n", + " lazy_df\n", + " .filter(pl.col('age') > 0.0)\n", + " .sort('name', descending=True)\n", + ")\n", + "\n", + "# Switch to gpu_engine\n", + "result = lazy_result.collect(engine=gpu_engine)\n", + "\n", + "print(result.head())\n", + "polars_time = time.time() - start_time\n", + "print(f\"Time Taken: {polars_time:.4f} seconds\")" + ] + }, + { + "cell_type": "markdown", + "id": "025606e3-9d16-416b-9501-b4cf702fb317", + "metadata": {}, + "source": [ + "### Verify Results Across Engines ###\n", + "How do we know the results are the same with both the CPU and GPU engine? Luckily with Polars, we can execute the same query across both and compare results using the built in testing module! " + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "759ad96e-6bc1-4c79-bc50-ab89430fdb42", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The test frames are equal\n" + ] + } + ], + "source": [ + "from polars.testing import assert_frame_equal\n", + "\n", + "# Run on the CPU\n", + "result_cpu = lazy_result.collect()\n", + "\n", + "# Run on the GPU\n", + "result_gpu = lazy_result.collect(engine=\"gpu\")\n", + "\n", + "# assert both result are equal - Will error if not equal, return None otherwise\n", + "if (assert_frame_equal(result_gpu, result_cpu) == None):\n", + " print(\"The test frames are equal\")" + ] + }, + { + "cell_type": "markdown", + "id": "5b4325cd-0e2d-4cae-8f71-83967708c7b9", + "metadata": {}, + "source": [ + "### Fallback ###\n", + "What happens when an operation isn't supported? " + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "30401f1d-45ad-4365-a00f-e5e79e01412f", + "metadata": {}, + "outputs": [ + { + "ename": "ComputeError", + "evalue": "'cuda' conversion failed: NotImplementedError: ('Query execution with GPU not possible: unsupported operations.\\nThe errors were:\\n- NotImplementedError: rolling mean', [NotImplementedError('rolling mean')])", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mComputeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[47], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m result \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m----> 2\u001b[0m \u001b[43mlazy_df\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwith_columns\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpl\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcol\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mage\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrolling_mean\u001b[49m\u001b[43m(\u001b[49m\u001b[43mwindow_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m7\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43malias\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mage_rolling_mean\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfilter\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpl\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcol\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mage\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m>\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0.0\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m 5\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcollect\u001b[49m\u001b[43m(\u001b[49m\u001b[43mengine\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgpu_engine\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 6\u001b[0m )\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28mprint\u001b[39m(result[::\u001b[38;5;241m7\u001b[39m])\n", + "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/polars/lazyframe/frame.py:2029\u001b[0m, in \u001b[0;36mLazyFrame.collect\u001b[0;34m(self, type_coercion, predicate_pushdown, projection_pushdown, simplify_expression, slice_pushdown, comm_subplan_elim, comm_subexpr_elim, cluster_with_columns, collapse_joins, no_optimization, streaming, engine, background, _eager, **_kwargs)\u001b[0m\n\u001b[1;32m 2027\u001b[0m \u001b[38;5;66;03m# Only for testing purposes\u001b[39;00m\n\u001b[1;32m 2028\u001b[0m callback \u001b[38;5;241m=\u001b[39m _kwargs\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpost_opt_callback\u001b[39m\u001b[38;5;124m\"\u001b[39m, callback)\n\u001b[0;32m-> 2029\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m wrap_df(\u001b[43mldf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcollect\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcallback\u001b[49m\u001b[43m)\u001b[49m)\n", + "\u001b[0;31mComputeError\u001b[0m: 'cuda' conversion failed: NotImplementedError: ('Query execution with GPU not possible: unsupported operations.\\nThe errors were:\\n- NotImplementedError: rolling mean', [NotImplementedError('rolling mean')])" + ] + } + ], + "source": [ + "result = (\n", + " lazy_df\n", + " .with_columns(pl.col('age').rolling_mean(window_size=7).alias('age_rolling_mean'))\n", + " .filter(pl.col('age') > 0.0) \n", + " .collect(engine=gpu_engine)\n", + ")\n", + "print(result[::7])" + ] + }, + { + "cell_type": "markdown", + "id": "bad531f3-ecfc-4214-b3c7-6506a3321e11", + "metadata": {}, + "source": [ + "We intially constructed the GPU engine with raise_on_fail=True to ensure all operations ran on GPU. But as we can see, the rolling mean operation is not currently supported, which results in the query not executing. To enable fallback, we can simply change the raise_on_fail parameter to False." + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "96b95c34-d3ea-494a-95b9-5d2fd8d49938", + "metadata": {}, + "outputs": [], + "source": [ + "gpu_engine_with_fallback = pl.GPUEngine(\n", + " device=0, # This is the default\n", + " raise_on_fail=False, # Fallback to CPU if we can't run on the GPU (this is the default)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "a7e15a86-dad3-4b71-8ccb-c03b61a35e3c", + "metadata": {}, + "source": [ + "Now let's try this query again." + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "73c30f3c-4075-44aa-8715-648b49fec1e9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "shape: (8_259_508, 7)\n", + "┌─────┬─────┬────────────┬───────────┬───────────┬───────────┬──────────────────┐\n", + "│ age ┆ sex ┆ county ┆ lat ┆ long ┆ name ┆ age_rolling_mean │\n", + "│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n", + "│ i64 ┆ str ┆ str ┆ f64 ┆ f64 ┆ str ┆ f64 │\n", + "╞═════╪═════╪════════════╪═══════════╪═══════════╪═══════════╪══════════════════╡\n", + "│ 1 ┆ m ┆ DARLINGTON ┆ 54.580675 ┆ -1.51359 ┆ PHILIP ┆ 0.142857 │\n", + "│ 1 ┆ m ┆ DARLINGTON ┆ 54.589555 ┆ -1.533749 ┆ SCOTT ┆ 1.0 │\n", + "│ 1 ┆ m ┆ DARLINGTON ┆ 54.526772 ┆ -1.557881 ┆ ISAAC ┆ 1.0 │\n", + "│ 1 ┆ m ┆ DARLINGTON ┆ 54.617086 ┆ -1.557996 ┆ SEBASTIAN ┆ 1.0 │\n", + "│ 1 ┆ m ┆ DARLINGTON ┆ 54.551081 ┆ -1.50386 ┆ FINN ┆ 1.0 │\n", + "│ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │\n", + "│ 90 ┆ f ┆ NEWPORT ┆ 51.589194 ┆ -2.825451 ┆ FREYA ┆ 90.0 │\n", + "│ 90 ┆ f ┆ NEWPORT ┆ 51.51582 ┆ -2.839532 ┆ EMERSON ┆ 90.0 │\n", + "│ 90 ┆ f ┆ NEWPORT ┆ 51.569834 ┆ -2.803327 ┆ THEA ┆ 90.0 │\n", + "│ 90 ┆ f ┆ NEWPORT ┆ 51.586575 ┆ -2.799302 ┆ ELIN ┆ 90.0 │\n", + "│ 90 ┆ f ┆ NEWPORT ┆ 51.554649 ┆ -2.934364 ┆ JESSICA ┆ 90.0 │\n", + "└─────┴─────┴────────────┴───────────┴───────────┴───────────┴──────────────────┘\n" + ] + } + ], + "source": [ + "result = (\n", + " lazy_df\n", + " .with_columns(pl.col('age').rolling_mean(window_size=7).alias('age_rolling_mean'))\n", + " .filter(pl.col('age') > 0.0) \n", + " .collect(engine=gpu_engine_with_fallback)\n", + ")\n", + "print(result[::7])" + ] + }, + { + "cell_type": "markdown", + "id": "19be5382-ba87-4bc3-8862-df73049598f7", + "metadata": {}, + "source": [ + "### Exercise #7 - Enable GPU Engine ###\n", + "The below code calculates the average latitude and longitude for each county. Let's try enabling the GPU Engine for this query!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "94243bde-3a3a-4ce9-887c-13d0b0875750", + "metadata": {}, + "outputs": [], + "source": [ + "# Create the lazy query with column pruning\n", + "gpu_engine = pl.GPUEngine(\n", + " device=0, # This is the default\n", + " raise_on_fail=True, # Fail loudly if we can't run on the GPU.\n", + ")\n", + "\n", + "\n", + "lazy_query = (\n", + " lazy_df\n", + " .select([\"county\", \"lat\", \"long\"]) # Column pruning: select only necessary columns\n", + " .group_by(\"county\")\n", + " .agg([\n", + " pl.col(\"lat\").mean().alias(\"avg_latitude\"),\n", + " pl.col(\"long\").mean().alias(\"avg_longitude\")\n", + " ])\n", + " .sort(\"county\")\n", + ")\n", + "\n", + "# Execute the query\n", + "result = lazy_query.collect(engine=gpu_engine)\n", + "\n", + "print(\"\\nAverage latitude and longitude for each county:\")\n", + "print(result.head()) # Display first few rows" + ] + }, + { + "cell_type": "raw", + "id": "42acb118-cb9d-478f-9636-694a5ddcb071", + "metadata": {}, + "source": [ + "\n", + "# Create the lazy query with column pruning\n", + "lazy_query = (\n", + " lazy_df\n", + " .select([\"county\", \"lat\", \"long\"]) # Column pruning: select only necessary columns\n", + " .group_by(\"county\")\n", + " .agg([\n", + " pl.col(\"lat\").mean().alias(\"avg_latitude\"),\n", + " pl.col(\"long\").mean().alias(\"avg_longitude\")\n", + " ])\n", + " .sort(\"county\")\n", + ")\n", + "\n", + "# Execute the query\n", + "result = lazy_query.collect(engine=\"gpu\")\n", + "\n", + "print(\"\\nAverage latitude and longitude for each county:\")\n", + "print(result.head()) # Display first few rows" + ] + }, + { + "cell_type": "markdown", + "id": "b942cf78-b3cc-4fa1-8d62-b355e20dd2ae", + "metadata": {}, + "source": [ + "Click ... for solution. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cf1158c3-8429-4637-a9c2-e8ca91a59965", + "metadata": {}, + "outputs": [], + "source": [ + "import IPython\n", + "app = IPython.Application.instance()\n", + "app.kernel.do_shutdown(True)" + ] + }, + { + "cell_type": "markdown", + "id": "cdecd196-8e0d-4b36-a428-4411bd480778", + "metadata": {}, + "source": [ + "**Well Done!**" + ] + }, + { + "cell_type": "markdown", + "id": "ed99fbcd-6e89-401f-b0cc-0b4fe3f112ad", + "metadata": {}, + "source": [ + "<img src=\"./images/DLI_Header.png\" width=400/>" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.15" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Fundamentals_of_Accelerated_Data_Science/1-09_dask-cudf.ipynb b/Fundamentals_of_Accelerated_Data_Science/1-09_dask-cudf.ipynb new file mode 100644 index 0000000..6fa9b0c --- /dev/null +++ b/Fundamentals_of_Accelerated_Data_Science/1-09_dask-cudf.ipynb @@ -0,0 +1,980 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<img src=\"./images/DLI_Header.png\" width=400/>" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Fundamentals of Accelerated Data Science # " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Transition Path: cuDF provides a way for users to scale their pandas workflows as data sizes grow, offering a middle ground between single-threaded pandas and distributed computing solutions like Dask or Apache Spark ." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 09 - Introduction to Dask cuDF ##\n", + "\n", + "**Table of Contents**\n", + "<br>\n", + "[Dask](https://dask.org/) cuDF can be used to distribute dataframe operations to multiple GPUs. In this notebook we will introduce some key Dask concepts, learn how to setup a Dask cluster for utilizing multiple GPUs, and see how to perform simple dataframe operations on distributed Dask dataframes. This notebook covers the below sections: \n", + "1. [An Introduction to Dask](#An-Introduction-to-Dask)\n", + "2. [Setting up a Dask Scheduler](#Setting-up-a-Dask-Scheduler)\n", + " * [Obtaining the Local IP Address](#Obtaining-the-Local-IP-Address)\n", + " * [Starting a `LocalCUDACluster`](#Starting-a-LocalCUDACluster)\n", + " * [Instantiating a Client Connection](#Instantiating-a-Client-Connection)\n", + " * [The Dask Dashboard](#The-Dask-Dashboard)\n", + "3. [Reading Data with Dask cuDF](#Reading-Data-with-Dask-cuDF)\n", + "4. [Computational Graph](#Computational-Graph)\n", + " * [Visualizing the Computational Graph](#Visualizing-the-Computational-Graph)\n", + " * [Extending the Computational Graph](#Extending-the-Computational-Graph)\n", + " * [Computing with the Computational Graph](#Computing-with-the-Computational-Graph)\n", + " * [Persisting Data in the Cluster](#Persisting-Data-in-the-Cluster)\n", + "6. [Initial Data Exploration with Dask cuDF](#Initial-Data-Exploration-with-Dask-cuDF)\n", + " * [Exercise #1 - Counties North of Sunderland with Dask](#Exercise-#1---Counties-North-of-Sunderland-with-Dask)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## An Introduction to Dask ##\n", + "[Dask](https://dask.org/) is a Python library for parallel computing. In Dask programming, we create computational graphs that define code we **would like** to execute, and then, give these computational graphs to a Dask scheduler which evaluates them lazily, and efficiently, in parallel. \n", + "\n", + "In addition to using multiple CPU cores or threads to execute computational graphs in parallel, Dask schedulers can also be configured to execute computational graphs on multiple CPUs, or, as we will do in this workshop, multiple GPUs. As a result, Dask programming facilitates operating on data sets that are larger than the memory of a single compute resource.\n", + "\n", + "Because Dask computational graphs can consist of arbitrary Python code, they provide [a level of control and flexibility superior to many other systems](https://docs.dask.org/en/latest/spark.html) that can operate on massive data sets. However, we will focus for this workshop primarily on the Dask DataFrame, one of several data structures whose operations and methods natively utilize Dask's parallel scheduling:\n", + "* Dask DataFrame, which closely resembles the Pandas DataFrame\n", + "* Dask Array, which closely resembles the NumPy ndarray\n", + "* Dask Bag, a set which allows duplicates and can hold heterogeneously-typed data\n", + "\n", + "In particular, we will use a Dask-cuDF dataframe, which combines the interface of Dask with the GPU power of cuDF for distributed dataframe operations on multiple GPUs. We will now turn our attention to utilizing all 4 NVIDIA V100 GPUs in this environment for operations on an 18GB UK population data set that would not fit into the memory of a single 16GB GPU." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setting up a Dask Scheduler ##\n", + "We begin by starting a Dask scheduler which will take care to distribute our work across the 4 available GPUs. In order to do this we need to start a `LocalCUDACluster` instance, using our host machine's IP, and then instantiate a client that can communicate with the cluster." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Obtaining the Local IP Address ###" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "b'172.18.0.3 \\n' 172.18.0.3\n" + ] + } + ], + "source": [ + "import subprocess # we will use this to obtain our local IP using the following command\n", + "cmd = \"hostname --all-ip-addresses\"\n", + "\n", + "process = subprocess.Popen(cmd.split(), stdout=subprocess.PIPE)\n", + "output, error = process.communicate()\n", + "IPADDR = str(output.decode()).split()[0]\n", + "\n", + "\n", + "print(output, IPADDR)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Starting a `LocalCUDACluster` ###\n", + "`dask_cuda` provides utilities for Dask and CUDA (the \"cu\" in cuDF) interactions." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-10-11 18:21:59,418 - distributed.shuffle._scheduler_plugin - WARNING - Shuffle 33ec4d90c48a979c353aeb71ce7b23a4 initialized by task ('shuffle-transfer-33ec4d90c48a979c353aeb71ce7b23a4', 9) executed on worker tcp://172.18.0.3:38431\n", + "2025-10-11 18:22:00,314 - distributed.shuffle._scheduler_plugin - WARNING - Shuffle 33ec4d90c48a979c353aeb71ce7b23a4 deactivated due to stimulus 'task-finished-1760206920.3121095'\n", + "2025-10-11 18:22:12,178 - distributed.shuffle._scheduler_plugin - WARNING - Shuffle 7c9f20b72e188f02f8e832adc1df3ce1 initialized by task ('shuffle-transfer-7c9f20b72e188f02f8e832adc1df3ce1', 9) executed on worker tcp://172.18.0.3:38431\n", + "2025-10-11 18:22:12,808 - distributed.shuffle._scheduler_plugin - WARNING - Shuffle 7c9f20b72e188f02f8e832adc1df3ce1 deactivated due to stimulus 'task-finished-1760206932.8072617'\n", + "2025-10-11 18:22:16,144 - distributed.shuffle._scheduler_plugin - WARNING - Shuffle 33ec4d90c48a979c353aeb71ce7b23a4 initialized by task ('shuffle-transfer-33ec4d90c48a979c353aeb71ce7b23a4', 9) executed on worker tcp://172.18.0.3:38431\n", + "2025-10-11 18:22:16,873 - distributed.shuffle._scheduler_plugin - WARNING - Shuffle 33ec4d90c48a979c353aeb71ce7b23a4 deactivated due to stimulus 'task-finished-1760206936.870322'\n" + ] + } + ], + "source": [ + "from dask_cuda import LocalCUDACluster\n", + "cluster = LocalCUDACluster(ip=IPADDR)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Instantiating a Client Connection ###\n", + "The `dask.distributed` library gives us distributed functionality, including the ability to connect to the CUDA Cluster we just created. The `progress` import will give us a handy progress bar we can utilize below." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "from dask.distributed import Client, progress\n", + "\n", + "client = Client(cluster)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### The Dask Dashboard" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Dask ships with a very helpful dashboard that in our case runs on port `8787`. Open a new browser tab now and copy this lab's URL into it, replacing `/lab/lab` with `:8787` (so it ends with `.com:8787`). This should open the Dask dashboard, currently idle." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Reading Data with Dask cuDF ##\n", + "With `dask_cudf` we can create a dataframe from several file formats (including from multiple files and directly from cloud storage like S3), from cuDF dataframes, from Pandas dataframes, and even from vanilla CPU Dask dataframes. Here we will create a Dask cuDF dataframe from the local csv file `pop5x_1-07.csv`, which has similar features to the `pop.csv` files you have already been using, except scaled up to 5 times larger (18GB), representing a population of almost 300 million, nearly the size of the entire United States." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "18G data/uk_pop5x.csv\n" + ] + } + ], + "source": [ + "# get the file size of `pop5x_1-07.csv` in GB\n", + "!ls -sh data/uk_pop5x.csv" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We import dask_cudf (and other RAPIDS components when necessary) after setting up the cluster to ensure that they establish correctly inside the CUDA context it creates." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "import dask_cudf" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "ddf = dask_cudf.read_csv('./data/uk_pop5x.csv', dtype=['float32', 'str', 'str', 'float32', 'float32', 'str'])" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "age float32\n", + "sex object\n", + "county object\n", + "lat float32\n", + "long float32\n", + "name object\n", + "dtype: object" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ddf.dtypes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Computational Graph ##\n", + "As mentioned above, when programming with Dask, we create computational graphs that we **would eventually like** to be executed. We can already observe this behavior in action: in calling `dask_cudf.read_csv` we have indicated that **would eventually like** to read the entire contents of `pop5x_1-07.csv`. However, Dask will not ask the scheduler execute this work until we explicitly indicate that we would like it do so.\n", + "\n", + "Observe the memory usage for each of the 4 GPUs by executing the following cell, and notice that the GPU memory usage is not nearly large enough to indicate that the entire 18GB file has been read into memory:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sat Oct 11 18:18:47 2025 \n", + "+-----------------------------------------------------------------------------+\n", + "| NVIDIA-SMI 525.85.12 Driver Version: 525.85.12 CUDA Version: 12.0 |\n", + "|-------------------------------+----------------------+----------------------+\n", + "| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n", + "| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n", + "| | | MIG M. |\n", + "|===============================+======================+======================|\n", + "| 0 Tesla T4 On | 00000000:00:1B.0 Off | 0 |\n", + "| N/A 31C P0 25W / 70W | 690MiB / 15360MiB | 0% Default |\n", + "| | | N/A |\n", + "+-------------------------------+----------------------+----------------------+\n", + "| 1 Tesla T4 On | 00000000:00:1C.0 Off | 0 |\n", + "| N/A 32C P0 25W / 70W | 168MiB / 15360MiB | 0% Default |\n", + "| | | N/A |\n", + "+-------------------------------+----------------------+----------------------+\n", + "| 2 Tesla T4 On | 00000000:00:1D.0 Off | 0 |\n", + "| N/A 32C P0 25W / 70W | 168MiB / 15360MiB | 0% Default |\n", + "| | | N/A |\n", + "+-------------------------------+----------------------+----------------------+\n", + "| 3 Tesla T4 On | 00000000:00:1E.0 Off | 0 |\n", + "| N/A 32C P0 25W / 70W | 168MiB / 15360MiB | 0% Default |\n", + "| | | N/A |\n", + "+-------------------------------+----------------------+----------------------+\n", + " \n", + "+-----------------------------------------------------------------------------+\n", + "| Processes: |\n", + "| GPU GI CI PID Type Process name GPU Memory |\n", + "| ID ID Usage |\n", + "|=============================================================================|\n", + "+-----------------------------------------------------------------------------+\n" + ] + } + ], + "source": [ + "!nvidia-smi" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Visualizing the Computational Graph ###\n", + "Computational graphs that have not yet been executed provide the `.visualize` method that, when used in a Jupyter environment such as this one, will display the computational graph, including how Dask intends to go about distributing the work. Thus, we can visualize how the `read_csv` operation will be distributed by Dask by executing the following cell:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n", + "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n", + " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n", + "<!-- Generated by graphviz version 2.43.0 (0)\n", + " -->\n", + "<!-- Title: %3 Pages: 1 -->\n", + "<svg width=\"115pt\" height=\"44pt\"\n", + " viewBox=\"0.00 0.00 115.00 44.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n", + "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 40)\">\n", + "<title>%3</title>\n", + "<polygon fill=\"white\" stroke=\"transparent\" points=\"-4,4 -4,-40 111,-40 111,4 -4,4\"/>\n", + "<!-- -8860210684066473962 -->\n", + "<g id=\"node1\" class=\"node\">\n", + "<title>-8860210684066473962</title>\n", + "<polygon fill=\"none\" stroke=\"black\" points=\"107,-36 0,-36 0,0 107,0 107,-36\"/>\n", + "<text text-anchor=\"middle\" x=\"53.5\" y=\"-13\" font-family=\"Helvetica,sans-Serif\" font-size=\"20.00\">ReadCSV</text>\n", + "</g>\n", + "</g>\n", + "</svg>\n" + ], + "text/plain": [ + "<graphviz.graphs.Digraph at 0x7fce7e72d570>" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ddf.visualize(format='svg') # This visualization is very large, and using `format='svg'` will make it easier to view." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As you can see, when we indicate for Dask to actually execute this operation, it will parallelize the work across the 4 GPUs in something like 69 parallel partitions. We can see the exact number of partitions with the `npartitions` property:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "69" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ddf.npartitions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Extending the Computational Graph ###\n", + "The concept of constructing computational graphs with arbitrary operations before executing them is a core part of Dask. Let's add some operations to the existing computational graph and visualize it again.\n", + "\n", + "After running the next cell, although it will take some scrolling to get a clear sense of it (the challenges of distributed data analytics!), you can see that the graph already constructed for `read_csv` now continues upward. It selects the `age` column across all partitions (visualized as `getitem`) and eventually performs the `.mean()` reduction (visualized as `series-sum-chunk`, `series-sum-agg`, `count-chunk`, `sum-agg` and `true-div`)." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n", + "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n", + " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n", + "<!-- Generated by graphviz version 2.43.0 (0)\n", + " -->\n", + "<!-- Title: %3 Pages: 1 -->\n", + "<svg width=\"296pt\" height=\"188pt\"\n", + " viewBox=\"0.00 0.00 296.00 188.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n", + "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 184)\">\n", + "<title>%3</title>\n", + "<polygon fill=\"white\" stroke=\"transparent\" points=\"-4,4 -4,-184 292,-184 292,4 -4,4\"/>\n", + "<!-- 5719483230307289854 -->\n", + "<g id=\"node1\" class=\"node\">\n", + "<title>5719483230307289854</title>\n", + "<polygon fill=\"none\" stroke=\"black\" points=\"235.5,-180 52.5,-180 52.5,-144 235.5,-144 235.5,-180\"/>\n", + "<text text-anchor=\"middle\" x=\"144\" y=\"-157\" font-family=\"Helvetica,sans-Serif\" font-size=\"20.00\">Mean(Projection)</text>\n", + "</g>\n", + "<!-- 1479189491105329378 -->\n", + "<g id=\"node2\" class=\"node\">\n", + "<title>1479189491105329378</title>\n", + "<polygon fill=\"none\" stroke=\"black\" points=\"288,-108 0,-108 0,-72 288,-72 288,-108\"/>\n", + "<text text-anchor=\"middle\" x=\"144\" y=\"-85\" font-family=\"Helvetica,sans-Serif\" font-size=\"20.00\">Projection(FromGraph, age)</text>\n", + "</g>\n", + "<!-- 1479189491105329378->5719483230307289854 -->\n", + "<g id=\"edge1\" class=\"edge\">\n", + "<title>1479189491105329378->5719483230307289854</title>\n", + "<path fill=\"none\" stroke=\"black\" d=\"M144,-108.3C144,-116.02 144,-125.29 144,-133.89\"/>\n", + "<polygon fill=\"black\" stroke=\"black\" points=\"140.5,-133.9 144,-143.9 147.5,-133.9 140.5,-133.9\"/>\n", + "</g>\n", + "<!-- -8624958272826520931 -->\n", + "<g id=\"node3\" class=\"node\">\n", + "<title>-8624958272826520931</title>\n", + "<polygon fill=\"none\" stroke=\"black\" points=\"207.5,-36 80.5,-36 80.5,0 207.5,0 207.5,-36\"/>\n", + "<text text-anchor=\"middle\" x=\"144\" y=\"-13\" font-family=\"Helvetica,sans-Serif\" font-size=\"20.00\">FromGraph</text>\n", + "</g>\n", + "<!-- -8624958272826520931->1479189491105329378 -->\n", + "<g id=\"edge2\" class=\"edge\">\n", + "<title>-8624958272826520931->1479189491105329378</title>\n", + "<path fill=\"none\" stroke=\"black\" d=\"M144,-36.3C144,-44.02 144,-53.29 144,-61.89\"/>\n", + "<polygon fill=\"black\" stroke=\"black\" points=\"140.5,-61.9 144,-71.9 147.5,-61.9 140.5,-61.9\"/>\n", + "</g>\n", + "</g>\n", + "</svg>\n" + ], + "text/plain": [ + "<graphviz.graphs.Digraph at 0x7fce98b92020>" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mean_age = ddf['age'].mean()\n", + "mean_age.visualize(format='svg')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Computing with the Computational Graph ###\n", + "There are several ways to indicate to Dask that we would like to perform the computations described in the computational graphs we have constructed. The first we will show is the `.compute` method, which will return the output of the computation as an object in one GPU's memory - no longer distributed across GPUs.\n", + "\n", + "**NOTE**: This value is actually a [*future*](https://docs.python.org/3/library/concurrent.futures.html) that it can be immediately used in code, even before it completes evaluating. While this can be tremendously useful in many scenarios, we will not need in this workshop to do anything fancy with the futures we generate except to wait for them to evaluate so we can visualize their values.\n", + "\n", + "Below we send the computational graph we have created to the Dask scheduler to be executed in parallel on our 4 GPUs. If you have the Dask Dashboard open on another tab from before, you can watch it while the operation completes. Because our graph involves reading the entire 18GB data set (as we declared when adding `read_csv` to the call graph), you can expect the operation to take a little time. If you closely watch the dashboard, you will see that Dask begins follow-on calculations for `mean` even while data is still being read into memory." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "40.1241924549316" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mean_age.compute()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Persisting Data in the Cluster ###\n", + "As you can see, the previous operation, which read the entire 18GB csv into the GPUs' memory, did not retain the data in memory after completing the computational graph:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sat Oct 11 18:19:55 2025 \n", + "+-----------------------------------------------------------------------------+\n", + "| NVIDIA-SMI 525.85.12 Driver Version: 525.85.12 CUDA Version: 12.0 |\n", + "|-------------------------------+----------------------+----------------------+\n", + "| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n", + "| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n", + "| | | MIG M. |\n", + "|===============================+======================+======================|\n", + "| 0 Tesla T4 On | 00000000:00:1B.0 Off | 0 |\n", + "| N/A 32C P0 25W / 70W | 1212MiB / 15360MiB | 0% Default |\n", + "| | | N/A |\n", + "+-------------------------------+----------------------+----------------------+\n", + "| 1 Tesla T4 On | 00000000:00:1C.0 Off | 0 |\n", + "| N/A 32C P0 25W / 70W | 690MiB / 15360MiB | 0% Default |\n", + "| | | N/A |\n", + "+-------------------------------+----------------------+----------------------+\n", + "| 2 Tesla T4 On | 00000000:00:1D.0 Off | 0 |\n", + "| N/A 32C P0 25W / 70W | 690MiB / 15360MiB | 0% Default |\n", + "| | | N/A |\n", + "+-------------------------------+----------------------+----------------------+\n", + "| 3 Tesla T4 On | 00000000:00:1E.0 Off | 0 |\n", + "| N/A 33C P0 25W / 70W | 690MiB / 15360MiB | 0% Default |\n", + "| | | N/A |\n", + "+-------------------------------+----------------------+----------------------+\n", + " \n", + "+-----------------------------------------------------------------------------+\n", + "| Processes: |\n", + "| GPU GI CI PID Type Process name GPU Memory |\n", + "| ID ID Usage |\n", + "|=============================================================================|\n", + "+-----------------------------------------------------------------------------+\n" + ] + } + ], + "source": [ + "!nvidia-smi" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A typical Dask workflow, which we will utilize, is to persist data we would like to work with to the cluster and then perform fast operations on that persisted data. We do this with the `.persist` method. From the [Dask documentation](https://distributed.dask.org/en/latest/manage-computation.html#client-persist):\n", + "\n", + ">The `.persist` method submits the task graph behind the Dask collection to the scheduler, obtaining Futures for all of the top-most tasks (for example one Future for each Pandas [*or cuDF*] DataFrame in a Dask[*-cudf*] DataFrame). It then returns a copy of the collection pointing to these futures instead of the previous graph. This new collection is semantically equivalent but now points to actively running data rather than a lazy graph.\n", + "\n", + "Below we persist `ddf` to the cluster so that it will reside in GPU memory for us to perform fast operations on. " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "ddf = ddf.persist()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As you can see by executing `nvidia-smi` (after letting the `persist` finish), each GPU now has parts of the distributed dataframe in its memory:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sat Oct 11 18:20:05 2025 \n", + "+-----------------------------------------------------------------------------+\n", + "| NVIDIA-SMI 525.85.12 Driver Version: 525.85.12 CUDA Version: 12.0 |\n", + "|-------------------------------+----------------------+----------------------+\n", + "| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n", + "| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n", + "| | | MIG M. |\n", + "|===============================+======================+======================|\n", + "| 0 Tesla T4 On | 00000000:00:1B.0 Off | 0 |\n", + "| N/A 33C P0 38W / 70W | 4240MiB / 15360MiB | 88% Default |\n", + "| | | N/A |\n", + "+-------------------------------+----------------------+----------------------+\n", + "| 1 Tesla T4 On | 00000000:00:1C.0 Off | 0 |\n", + "| N/A 34C P0 65W / 70W | 3792MiB / 15360MiB | 88% Default |\n", + "| | | N/A |\n", + "+-------------------------------+----------------------+----------------------+\n", + "| 2 Tesla T4 On | 00000000:00:1D.0 Off | 0 |\n", + "| N/A 34C P0 40W / 70W | 3868MiB / 15360MiB | 90% Default |\n", + "| | | N/A |\n", + "+-------------------------------+----------------------+----------------------+\n", + "| 3 Tesla T4 On | 00000000:00:1E.0 Off | 0 |\n", + "| N/A 34C P0 39W / 70W | 3960MiB / 15360MiB | 91% Default |\n", + "| | | N/A |\n", + "+-------------------------------+----------------------+----------------------+\n", + " \n", + "+-----------------------------------------------------------------------------+\n", + "| Processes: |\n", + "| GPU GI CI PID Type Process name GPU Memory |\n", + "| ID ID Usage |\n", + "|=============================================================================|\n", + "+-----------------------------------------------------------------------------+\n" + ] + } + ], + "source": [ + "!nvidia-smi" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Running `ddf.visualize` now shows that we no longer have operations in our task graph, only partitions of data, ready for us to perform operations:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n", + "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n", + " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n", + "<!-- Generated by graphviz version 2.43.0 (0)\n", + " -->\n", + "<!-- Title: %3 Pages: 1 -->\n", + "<svg width=\"135pt\" height=\"44pt\"\n", + " viewBox=\"0.00 0.00 135.00 44.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n", + "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 40)\">\n", + "<title>%3</title>\n", + "<polygon fill=\"white\" stroke=\"transparent\" points=\"-4,4 -4,-40 131,-40 131,4 -4,4\"/>\n", + "<!-- -8624958272826520931 -->\n", + "<g id=\"node1\" class=\"node\">\n", + "<title>-8624958272826520931</title>\n", + "<polygon fill=\"none\" stroke=\"black\" points=\"127,-36 0,-36 0,0 127,0 127,-36\"/>\n", + "<text text-anchor=\"middle\" x=\"63.5\" y=\"-13\" font-family=\"Helvetica,sans-Serif\" font-size=\"20.00\">FromGraph</text>\n", + "</g>\n", + "</g>\n", + "</svg>\n" + ], + "text/plain": [ + "<graphviz.graphs.Digraph at 0x7fce98c18250>" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ddf.visualize(format='svg')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Computing operations on this data will now be much faster:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "40.1241924549316" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ddf['age'].mean().compute()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Initial Data Exploration with Dask cuDF ##\n", + "The beauty of Dask is that working with your data, even though it is distributed and massive, is a lot like working with smaller in-memory data sets." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>age</th>\n", + " <th>sex</th>\n", + " <th>county</th>\n", + " <th>lat</th>\n", + " <th>long</th>\n", + " <th>name</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>0.0</td>\n", + " <td>m</td>\n", + " <td>Darlington</td>\n", + " <td>54.549641</td>\n", + " <td>-1.493884</td>\n", + " <td>HARRISON</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>0.0</td>\n", + " <td>m</td>\n", + " <td>Darlington</td>\n", + " <td>54.523945</td>\n", + " <td>-1.401142</td>\n", + " <td>LAKSH</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>0.0</td>\n", + " <td>m</td>\n", + " <td>Darlington</td>\n", + " <td>54.561127</td>\n", + " <td>-1.690068</td>\n", + " <td>MUHAMMAD</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>0.0</td>\n", + " <td>m</td>\n", + " <td>Darlington</td>\n", + " <td>54.542988</td>\n", + " <td>-1.543216</td>\n", + " <td>GRAYSON</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>0.0</td>\n", + " <td>m</td>\n", + " <td>Darlington</td>\n", + " <td>54.532101</td>\n", + " <td>-1.569116</td>\n", + " <td>FINLAY</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " age sex county lat long name\n", + "0 0.0 m Darlington 54.549641 -1.493884 HARRISON\n", + "1 0.0 m Darlington 54.523945 -1.401142 LAKSH\n", + "2 0.0 m Darlington 54.561127 -1.690068 MUHAMMAD\n", + "3 0.0 m Darlington 54.542988 -1.543216 GRAYSON\n", + "4 0.0 m Darlington 54.532101 -1.569116 FINLAY" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ddf.head() # As a convenience, no need to `.compute` the `head()` method" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "age 292399470\n", + "sex 292399470\n", + "county 292399470\n", + "lat 292399470\n", + "long 292399470\n", + "name 292399470\n", + "dtype: int64" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ddf.count().compute()" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "age float32\n", + "sex object\n", + "county object\n", + "lat float32\n", + "long float32\n", + "name object\n", + "dtype: object" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ddf.dtypes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exercise #1 - Counties North of Sunderland with Dask ###\n", + "Here we ask you to revisit an earlier exercise, but on the distributed data set. Hopefully, it's clear how similar the code is for single-GPU dataframes and distributed dataframes with Dask.\n", + "\n", + "Identify the latitude of the northernmost resident of Sunderland county (the person with the maximum `lat` value), and then determine which counties have any residents north of this resident. Use the `unique` method of a cudf `Series` to de-duplicate the result.\n", + "\n", + "**Instructions**: <br>\n", + "* Modify the `<FIXME>` only and execute the below cell to identify counties north of Sunderland. " + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 South Tyneside\n", + "0 Cumbria\n", + "0 County Durham\n", + "0 Gateshead\n", + "0 Northumberland\n", + "Name: county, dtype: object" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sunderland_residents = ddf.loc[ddf['county'].str.lower() == 'Sunderland'.lower()]\n", + "northmost_sunderland_lat = sunderland_residents['lat'].max()\n", + "counties_with_pop_north_of = ddf.loc[ddf['lat'] > northmost_sunderland_lat]['county'].unique()\n", + "results=counties_with_pop_north_of.compute()\n", + "results.head()" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "\n", + "sunderland_residents = ddf.loc[ddf['county'] == 'Sunderland']\n", + "northmost_sunderland_lat = sunderland_residents['lat'].max()\n", + "counties_with_pop_north_of = ddf.loc[ddf['lat'] > northmost_sunderland_lat]['county'].unique()\n", + "results=counties_with_pop_north_of.compute()\n", + "results.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Click ... for solution. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import IPython\n", + "app = IPython.Application.instance()\n", + "app.kernel.do_shutdown(True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Well Done!** Let's move to the [next notebook](1-09_cudf-polars.ipynb). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<img src=\"./images/DLI_Header.png\" width=400/>" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.15" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Fundamentals_of_Accelerated_Data_Science/2-02_prep_graph.ipynb b/Fundamentals_of_Accelerated_Data_Science/2-02_prep_graph.ipynb new file mode 100644 index 0000000..83e2049 --- /dev/null +++ b/Fundamentals_of_Accelerated_Data_Science/2-02_prep_graph.ipynb @@ -0,0 +1,1699 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "<img src=\"./images/DLI_Header.png\" width=400/>" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Fundamentals of Accelerated Data Science # " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 02 - Preparing Data for Graph Construction ##\n", + "\n", + "**Table of Contents**\n", + "<br>\n", + "This notebook introduces the basics of representing and constructing a graph. This notebook covers the below sections:\n", + "1. [Background](#Background)\n", + "2. [Environment](#Environment)\n", + "3. [Read Data](#Read-Data)\n", + " * [UK Road Nodes](#UK-Road-Nodes)\n", + " * [UK Road Edges](#UK-Road-Edges)\n", + " * [Exercise #1 - Make IDs Compatible](#Exercise-#1---Make-IDs-Compatible)\n", + "5. [Data Summary](#Data-Summary)\n", + "6. [Building the Road Network Graph](#Building-the-Road-Network-Graph)\n", + " * [Reindex `road_nodes`](#Reindex-road_nodes)\n", + " * [Analyzing the Graph](#Analyzing-the-Graph)\n", + "7. [Construct a Graph of Roads with Time Weights](#Construct-a-Graph-of-Roads-with-Time-Weights)\n", + " * [Road Type to Speed Conversion](#Road-Type-to-Speed-Conversion)\n", + " * [Step 1: Merge `speed_gdf` into `road_edges`](#Step-1:-Merge-speed_gdf-into-road_edges)\n", + " * [Exercse #2 - Step 2: Add Length in Seconds Column](#Exercse-#2---Step-2:-Add-Length-in-Seconds-Column)\n", + " * [Exercise #3 - Step 3: Construct the Graph](#Exercise-#3---Step-3:-Construct-the-Graph)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Background ##\n", + "As part of our larger data science goal for this workshop, we will be working with data reflecting the entire road network of Great Britain. We have as a starting point road data extracted into tabular csv format from official [GML](https://en.wikipedia.org/wiki/Geography_Markup_Language) files. Ultimately, we would like to use cuGraph to perform GPU-accelerated graph analytics on this data, but in order to do so, we need to do some preprocessing to get it ready for graph creation.\n", + "\n", + "In this notebook you will be learning additional cuDF data transformation techniques in a demonstration of prepping data for ingestion by cuGraph. Next, you will do a series of exercises to perform a similar transformation of the data for the creation of a graph with different edge weights." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Environment ##" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In addition to `cudf`, for this notebook we will also import `cugraph`, which we will use (after data preparation) to construct a GPU-accelerated graph. We also import `networkx` for a brief performance comparison later on." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "\n", + "import cudf\n", + "import cugraph as cg\n", + "\n", + "import networkx as nx" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Read Data ##" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this notebook we will be working with two data sources that will help us create a graph of the UK's road networks." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### UK Road Nodes ###" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The first data table describes the nodes in the road network: endpoints, junctions (including roundabouts), and points that break up a long stretch of curving road so that it can be mapped correctly (instead of as a straight line).\n", + "\n", + "The coordinates for each point are in the OSGB36 format we explored earlier in section 1-05." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>node_id</th>\n", + " <th>east</th>\n", + " <th>north</th>\n", + " <th>type</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>id02FE73D4-E88D-4119-8DC2-6E80DE6F6594</td>\n", + " <td>320608.0938</td>\n", + " <td>870994.0000</td>\n", + " <td>junction</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>id634D65C1-C38B-4868-9080-2E1E47F0935C</td>\n", + " <td>320628.5000</td>\n", + " <td>871103.8125</td>\n", + " <td>road end</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>idDC14D4D1-774E-487D-8EDE-60B129E5482C</td>\n", + " <td>320635.4688</td>\n", + " <td>870983.9375</td>\n", + " <td>junction</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>id51555819-1A39-4B41-B0C9-C6D2086D9921</td>\n", + " <td>320648.7188</td>\n", + " <td>871083.5625</td>\n", + " <td>junction</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>id9E362428-79D7-4EE3-B015-0CE3F6A78A69</td>\n", + " <td>320658.1875</td>\n", + " <td>871162.3750</td>\n", + " <td>junction</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " node_id east north type\n", + "0 id02FE73D4-E88D-4119-8DC2-6E80DE6F6594 320608.0938 870994.0000 junction\n", + "1 id634D65C1-C38B-4868-9080-2E1E47F0935C 320628.5000 871103.8125 road end\n", + "2 idDC14D4D1-774E-487D-8EDE-60B129E5482C 320635.4688 870983.9375 junction\n", + "3 id51555819-1A39-4B41-B0C9-C6D2086D9921 320648.7188 871083.5625 junction\n", + "4 id9E362428-79D7-4EE3-B015-0CE3F6A78A69 320658.1875 871162.3750 junction" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "road_nodes = cudf.read_csv('./data/road_nodes.csv')\n", + "road_nodes.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "node_id object\n", + "east float64\n", + "north float64\n", + "type object\n", + "dtype: object" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "road_nodes.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(3121148, 4)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "road_nodes.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 junction\n", + "1 road end\n", + "2 pseudo node\n", + "3 roundabout\n", + "Name: type, dtype: object" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "road_nodes['type'].unique()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### UK Road Edges ###" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The second data table describes road segments, including their start and end points, how long they are, and what kind of road they are." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>src_id</th>\n", + " <th>dst_id</th>\n", + " <th>length</th>\n", + " <th>type</th>\n", + " <th>form</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>#id138447A5-91D4-4642-BFAC-13F309705429</td>\n", + " <td>#id84C9DAD4-9243-4742-B582-E8CBC848E08A</td>\n", + " <td>314</td>\n", + " <td>Restricted Local Access Road</td>\n", + " <td>Single Carriageway</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>#idD615F9C5-5BE9-412D-9FED-F4928BAB4146</td>\n", + " <td>#idA1BB20B9-0751-4B42-9925-20607ABF5027</td>\n", + " <td>104</td>\n", + " <td>Restricted Local Access Road</td>\n", + " <td>Single Carriageway</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>#idDC14D4D1-774E-487D-8EDE-60B129E5482C</td>\n", + " <td>#id51555819-1A39-4B41-B0C9-C6D2086D9921</td>\n", + " <td>100</td>\n", + " <td>Restricted Local Access Road</td>\n", + " <td>Single Carriageway</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>#id626FC567-199C-41FB-9F29-1AB718874128</td>\n", + " <td>#idACD1B0A9-F870-4B46-88CF-C870A9EDAF8B</td>\n", + " <td>93</td>\n", + " <td>Restricted Local Access Road</td>\n", + " <td>Single Carriageway</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>#id03312900-B147-4CA3-A858-E2BF6AD1ECA7</td>\n", + " <td>#id02FE73D4-E88D-4119-8DC2-6E80DE6F6594</td>\n", + " <td>95</td>\n", + " <td>Restricted Local Access Road</td>\n", + " <td>Single Carriageway</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " src_id \\\n", + "0 #id138447A5-91D4-4642-BFAC-13F309705429 \n", + "1 #idD615F9C5-5BE9-412D-9FED-F4928BAB4146 \n", + "2 #idDC14D4D1-774E-487D-8EDE-60B129E5482C \n", + "3 #id626FC567-199C-41FB-9F29-1AB718874128 \n", + "4 #id03312900-B147-4CA3-A858-E2BF6AD1ECA7 \n", + "\n", + " dst_id length \\\n", + "0 #id84C9DAD4-9243-4742-B582-E8CBC848E08A 314 \n", + "1 #idA1BB20B9-0751-4B42-9925-20607ABF5027 104 \n", + "2 #id51555819-1A39-4B41-B0C9-C6D2086D9921 100 \n", + "3 #idACD1B0A9-F870-4B46-88CF-C870A9EDAF8B 93 \n", + "4 #id02FE73D4-E88D-4119-8DC2-6E80DE6F6594 95 \n", + "\n", + " type form \n", + "0 Restricted Local Access Road Single Carriageway \n", + "1 Restricted Local Access Road Single Carriageway \n", + "2 Restricted Local Access Road Single Carriageway \n", + "3 Restricted Local Access Road Single Carriageway \n", + "4 Restricted Local Access Road Single Carriageway " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "road_edges = cudf.read_csv('./data/road_edges.csv')\n", + "road_edges.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "src_id object\n", + "dst_id object\n", + "length int64\n", + "type object\n", + "form object\n", + "dtype: object" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "road_edges.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(3725531, 5)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "road_edges.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 Restricted Local Access Road\n", + "1 Local Road\n", + "2 B Road\n", + "3 Secondary Access Road\n", + "4 Minor Road\n", + "5 A Road\n", + "6 Local Access Road\n", + "7 Motorway\n", + "Name: type, dtype: object" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "road_edges['type'].unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 Single Carriageway\n", + "1 Collapsed Dual Carriageway\n", + "2 Roundabout\n", + "3 Slip Road\n", + "4 Shared Use Carriageway\n", + "5 Dual Carriageway\n", + "6 Guided Busway\n", + "Name: form, dtype: object" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "road_edges['form'].unique()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exercise #1 - Make IDs Compatible ###" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our csv files were derived from original [GML](https://en.wikipedia.org/wiki/Geography_Markup_Language) files, and as you can see from the above, both `road_edges['src_id']` and `road_edges['dst_id']` contain a leading `#` character that `road_nodes['node_id']` does not. To make the IDs compatible between the edges and nodes, use cuDF's [string method](https://docs.rapids.ai/api/nvstrings/stable/) `.str.lstrip` to replace the `src_id` and `dst_id` columns in `road_edges` with values stripped of the leading `#` characters." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "road_edges['src_id']=road_edges['src_id'].str.lstrip('#')\n", + "road_edges['dst_id']=road_edges['dst_id'].str.lstrip('#')" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>src_id</th>\n", + " <th>dst_id</th>\n", + " <th>length</th>\n", + " <th>type</th>\n", + " <th>form</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>id138447A5-91D4-4642-BFAC-13F309705429</td>\n", + " <td>id84C9DAD4-9243-4742-B582-E8CBC848E08A</td>\n", + " <td>314</td>\n", + " <td>Restricted Local Access Road</td>\n", + " <td>Single Carriageway</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>idD615F9C5-5BE9-412D-9FED-F4928BAB4146</td>\n", + " <td>idA1BB20B9-0751-4B42-9925-20607ABF5027</td>\n", + " <td>104</td>\n", + " <td>Restricted Local Access Road</td>\n", + " <td>Single Carriageway</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>idDC14D4D1-774E-487D-8EDE-60B129E5482C</td>\n", + " <td>id51555819-1A39-4B41-B0C9-C6D2086D9921</td>\n", + " <td>100</td>\n", + " <td>Restricted Local Access Road</td>\n", + " <td>Single Carriageway</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>id626FC567-199C-41FB-9F29-1AB718874128</td>\n", + " <td>idACD1B0A9-F870-4B46-88CF-C870A9EDAF8B</td>\n", + " <td>93</td>\n", + " <td>Restricted Local Access Road</td>\n", + " <td>Single Carriageway</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>id03312900-B147-4CA3-A858-E2BF6AD1ECA7</td>\n", + " <td>id02FE73D4-E88D-4119-8DC2-6E80DE6F6594</td>\n", + " <td>95</td>\n", + " <td>Restricted Local Access Road</td>\n", + " <td>Single Carriageway</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " src_id \\\n", + "0 id138447A5-91D4-4642-BFAC-13F309705429 \n", + "1 idD615F9C5-5BE9-412D-9FED-F4928BAB4146 \n", + "2 idDC14D4D1-774E-487D-8EDE-60B129E5482C \n", + "3 id626FC567-199C-41FB-9F29-1AB718874128 \n", + "4 id03312900-B147-4CA3-A858-E2BF6AD1ECA7 \n", + "\n", + " dst_id length \\\n", + "0 id84C9DAD4-9243-4742-B582-E8CBC848E08A 314 \n", + "1 idA1BB20B9-0751-4B42-9925-20607ABF5027 104 \n", + "2 id51555819-1A39-4B41-B0C9-C6D2086D9921 100 \n", + "3 idACD1B0A9-F870-4B46-88CF-C870A9EDAF8B 93 \n", + "4 id02FE73D4-E88D-4119-8DC2-6E80DE6F6594 95 \n", + "\n", + " type form \n", + "0 Restricted Local Access Road Single Carriageway \n", + "1 Restricted Local Access Road Single Carriageway \n", + "2 Restricted Local Access Road Single Carriageway \n", + "3 Restricted Local Access Road Single Carriageway \n", + "4 Restricted Local Access Road Single Carriageway " + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "road_edges.head()" + ] + }, + { + "cell_type": "raw", + "metadata": { + "jupyter": { + "source_hidden": true + } + }, + "source": [ + "\n", + "road_edges['src_id'] = road_edges['src_id'].str.lstrip('#')\n", + "road_edges['dst_id'] = road_edges['dst_id'].str.lstrip('#')\n", + "road_edges[['src_id', 'dst_id']].head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Click ... for solution. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Summary ##" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that the data is cleaned we can see just how many roads and endpoints/junctions/curve points we will be working with, as well as its memory footprint in our GPU. The GPUs we are using can hold and analyze much larger graphs than this one!" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3725531 edges, 3121148 nodes\n" + ] + } + ], + "source": [ + "print(f'{road_edges.shape[0]} edges, {road_nodes.shape[0]} nodes')" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Wed Oct 15 12:04:42 2025 \n", + "+-----------------------------------------------------------------------------+\n", + "| NVIDIA-SMI 525.85.12 Driver Version: 525.85.12 CUDA Version: 12.0 |\n", + "|-------------------------------+----------------------+----------------------+\n", + "| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n", + "| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n", + "| | | MIG M. |\n", + "|===============================+======================+======================|\n", + "| 0 Tesla T4 On | 00000000:00:1B.0 Off | 0 |\n", + "| N/A 29C P0 27W / 70W | 3416MiB / 15360MiB | 0% Default |\n", + "| | | N/A |\n", + "+-------------------------------+----------------------+----------------------+\n", + "| 1 Tesla T4 On | 00000000:00:1C.0 Off | 0 |\n", + "| N/A 30C P0 27W / 70W | 168MiB / 15360MiB | 0% Default |\n", + "| | | N/A |\n", + "+-------------------------------+----------------------+----------------------+\n", + "| 2 Tesla T4 On | 00000000:00:1D.0 Off | 0 |\n", + "| N/A 30C P0 27W / 70W | 168MiB / 15360MiB | 0% Default |\n", + "| | | N/A |\n", + "+-------------------------------+----------------------+----------------------+\n", + "| 3 Tesla T4 On | 00000000:00:1E.0 Off | 0 |\n", + "| N/A 29C P0 28W / 70W | 168MiB / 15360MiB | 0% Default |\n", + "| | | N/A |\n", + "+-------------------------------+----------------------+----------------------+\n", + " \n", + "+-----------------------------------------------------------------------------+\n", + "| Processes: |\n", + "| GPU GI CI PID Type Process name GPU Memory |\n", + "| ID ID Usage |\n", + "|=============================================================================|\n", + "+-----------------------------------------------------------------------------+\n" + ] + } + ], + "source": [ + "!nvidia-smi" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Building the Road Network Graph ##" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We don't have information on the direction of the roads (some of them are one-way), so we will assume all of them are two-way for simplicity. That makes the graph \"undirected,\" so we will build a cuGraph `Graph` rather than a directed graph or`DiGraph`.\n", + "\n", + "We initialize it with edge sources, destinations, and attributes, which for our data will be the length of the roads:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 399 ms, sys: 95.7 ms, total: 495 ms\n", + "Wall time: 494 ms\n" + ] + } + ], + "source": [ + "G = cg.Graph()\n", + "%time G.from_cudf_edgelist(road_edges, source='src_id', destination='dst_id', edge_attr='length')" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<cugraph.structure.graph_classes.Graph at 0x7f7bdc0adae0>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(G)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Just as a point of comparison, we also construct the equivalent graph in NetworkX from the equivalent cleaned and prepped Pandas dataframe." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 15.4 s, sys: 922 ms, total: 16.3 s\n", + "Wall time: 16.3 s\n" + ] + } + ], + "source": [ + "road_edges_cpu = road_edges.to_pandas()\n", + "%time G_cpu = nx.convert_matrix.from_pandas_edgelist(road_edges_cpu, source='src_id', target='dst_id', edge_attr='length')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Reindex `road_nodes` ###" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For efficient lookup later, we will reindex `road_nodes` to use the `node_id` as its index - remember, we will typically get results from the graph analytics in terms of `node_id`s, so this lets us easily pull other information about the nodes (like their locations). We then sort the dataframe on this new index:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 260 ms, sys: 12 ms, total: 272 ms\n", + "Wall time: 272 ms\n" + ] + }, + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>east</th>\n", + " <th>north</th>\n", + " <th>type</th>\n", + " </tr>\n", + " <tr>\n", + " <th>node_id</th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>id000000F5-5180-4C03-B05D-B01352C54F89</th>\n", + " <td>432920.250</td>\n", + " <td>572547.4375</td>\n", + " <td>road end</td>\n", + " </tr>\n", + " <tr>\n", + " <th>id000003F8-9E09-4829-AD87-6DA4438D22D8</th>\n", + " <td>526616.375</td>\n", + " <td>189678.3906</td>\n", + " <td>junction</td>\n", + " </tr>\n", + " <tr>\n", + " <th>id000010DA-C89A-4198-847A-6E62815E038A</th>\n", + " <td>336879.000</td>\n", + " <td>731824.0000</td>\n", + " <td>junction</td>\n", + " </tr>\n", + " <tr>\n", + " <th>id000017A0-1843-4BC7-BCF7-C943B6780839</th>\n", + " <td>380635.000</td>\n", + " <td>390153.0000</td>\n", + " <td>junction</td>\n", + " </tr>\n", + " <tr>\n", + " <th>id00001B2A-155F-4CD3-8E06-7677ADC6AF74</th>\n", + " <td>337481.000</td>\n", + " <td>350509.7188</td>\n", + " <td>junction</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " east north type\n", + "node_id \n", + "id000000F5-5180-4C03-B05D-B01352C54F89 432920.250 572547.4375 road end\n", + "id000003F8-9E09-4829-AD87-6DA4438D22D8 526616.375 189678.3906 junction\n", + "id000010DA-C89A-4198-847A-6E62815E038A 336879.000 731824.0000 junction\n", + "id000017A0-1843-4BC7-BCF7-C943B6780839 380635.000 390153.0000 junction\n", + "id00001B2A-155F-4CD3-8E06-7677ADC6AF74 337481.000 350509.7188 junction" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "road_nodes = road_nodes.set_index('node_id', drop=True)\n", + "%time road_nodes = road_nodes.sort_index()\n", + "road_nodes.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Analyzing the Graph ###" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have created the graph we can analyze the number of nodes and edges in it:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3078117" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "G.number_of_nodes()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1875790" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "G.number_of_edges()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that the number of edges is slightly smaller than the number of edges in `road_edges` printed above--the original data came from map tiles, and roads that passed over the edge of a tile were listed in both tiles, so cuGraph de-duplicated them. If we were creating a `MultiGraph` or `MultiDiGraph`--a graph that can have multiple edges in the same direction between nodes--then duplicates could be preserved." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also analyze the degrees of our graph nodes:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "deg_df = G.degree()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In an undirected graph, every edge entering a node is simultaneously an edge leaving the node, so we expect the nodes to have a minimum degree of 2:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "mean 4.689990\n", + "std 1.913452\n", + "min 2.000000\n", + "25% 2.000000\n", + "50% 6.000000\n", + "75% 6.000000\n", + "max 16.000000\n", + "Name: degree, dtype: float64" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "deg_df['degree'].describe()[1:]" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "count 3.078117e+06\n", + "mean 4.689990e+00\n", + "std 1.913452e+00\n", + "min 2.000000e+00\n", + "25% 2.000000e+00\n", + "50% 6.000000e+00\n", + "75% 6.000000e+00\n", + "max 1.600000e+01\n", + "Name: degree, dtype: float64" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "deg_df['degree'].describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You will spend more time using this GPU-accelerated graph later in the workshop." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Construct a Graph of Roads with Time Weights ##" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For this series of exercises, you are going to construct and analyze a new graph of Great Britain's roads using the techniques just demonstrated, but this time, instead of using raw distance for the edges' weights, you will be using the time it will take to travel between the two nodes at a notional speed limit.\n", + "\n", + "You will be beginning this exercise with the `road_edges` dataframe from earlier:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "src_id object\n", + "dst_id object\n", + "length int64\n", + "type object\n", + "form object\n", + "dtype: object" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "road_edges.dtypes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Road Type to Speed Conversion ###" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In order to calculate how long it should take to travel along a road, we need to know its speed limit. We will do this by utilizing `road_edges['type']`, along with rules for the speed limits for each type of road.\n", + "\n", + "Here are the unique types of roads in our data:" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 Restricted Local Access Road\n", + "1 Local Road\n", + "2 B Road\n", + "3 Secondary Access Road\n", + "4 Minor Road\n", + "5 A Road\n", + "6 Local Access Road\n", + "7 Motorway\n", + "Name: type, dtype: object" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "road_edges['type'].unique()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And here is a table with assumptions about speed limits we can use for our conversion:" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "# https://www.rac.co.uk/drive/advice/legal/speed-limits/\n", + "# Technically, speed limits depend on whether a road is in a built-up area and the form of carriageway,\n", + "# but we can use road type as a proxy for built-up areas.\n", + "# Values are in mph.\n", + "\n", + "speed_limits = {'Motorway': 70,\n", + " 'A Road': 60,\n", + " 'B Road': 60,\n", + " 'Local Road': 30,\n", + " 'Local Access Road': 30,\n", + " 'Minor Road': 30,\n", + " 'Restricted Local Access Road': 30,\n", + " 'Secondary Access Road': 30}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We begin by creating `speed_gdf` to store each road type with its speed limit:" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>type</th>\n", + " <th>limit_mph</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>Motorway</td>\n", + " <td>70</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>A Road</td>\n", + " <td>60</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>B Road</td>\n", + " <td>60</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>Local Road</td>\n", + " <td>30</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>Local Access Road</td>\n", + " <td>30</td>\n", + " </tr>\n", + " <tr>\n", + " <th>5</th>\n", + " <td>Minor Road</td>\n", + " <td>30</td>\n", + " </tr>\n", + " <tr>\n", + " <th>6</th>\n", + " <td>Restricted Local Access Road</td>\n", + " <td>30</td>\n", + " </tr>\n", + " <tr>\n", + " <th>7</th>\n", + " <td>Secondary Access Road</td>\n", + " <td>30</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " type limit_mph\n", + "0 Motorway 70\n", + "1 A Road 60\n", + "2 B Road 60\n", + "3 Local Road 30\n", + "4 Local Access Road 30\n", + "5 Minor Road 30\n", + "6 Restricted Local Access Road 30\n", + "7 Secondary Access Road 30" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "speed_gdf = cudf.DataFrame()\n", + "\n", + "speed_gdf['type'] = speed_limits.keys()\n", + "speed_gdf['limit_mph'] = [speed_limits[key] for key in speed_limits.keys()]\n", + "speed_gdf" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next we add an additional column, `limit_m/s`, which for each road type will give us a measure of how fast one can travel on it in meters / second." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>type</th>\n", + " <th>limit_mph</th>\n", + " <th>limit_m/s</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>Motorway</td>\n", + " <td>70</td>\n", + " <td>31.292722</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>A Road</td>\n", + " <td>60</td>\n", + " <td>26.822333</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>B Road</td>\n", + " <td>60</td>\n", + " <td>26.822333</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>Local Road</td>\n", + " <td>30</td>\n", + " <td>13.411167</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>Local Access Road</td>\n", + " <td>30</td>\n", + " <td>13.411167</td>\n", + " </tr>\n", + " <tr>\n", + " <th>5</th>\n", + " <td>Minor Road</td>\n", + " <td>30</td>\n", + " <td>13.411167</td>\n", + " </tr>\n", + " <tr>\n", + " <th>6</th>\n", + " <td>Restricted Local Access Road</td>\n", + " <td>30</td>\n", + " <td>13.411167</td>\n", + " </tr>\n", + " <tr>\n", + " <th>7</th>\n", + " <td>Secondary Access Road</td>\n", + " <td>30</td>\n", + " <td>13.411167</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " type limit_mph limit_m/s\n", + "0 Motorway 70 31.292722\n", + "1 A Road 60 26.822333\n", + "2 B Road 60 26.822333\n", + "3 Local Road 30 13.411167\n", + "4 Local Access Road 30 13.411167\n", + "5 Minor Road 30 13.411167\n", + "6 Restricted Local Access Road 30 13.411167\n", + "7 Secondary Access Road 30 13.411167" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# We will have road distances in meters (m), so to get road distances in seconds (s), we need to multiply by meters/mile and divide by seconds/hour\n", + "# 1 mile ~ 1609.34 m\n", + "speed_gdf['limit_m/s'] = speed_gdf['limit_mph'] * 1609.34 / 3600\n", + "speed_gdf" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 1: Merge `speed_gdf` into `road_edges` ###" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "cuDF provides merging functionality just like Pandas. Since we will be using values in `road_edges` to construct our graph, we need to merge `speed_gdf` into `road_edges` (similar to a database join). You can merge on the `type` column, which both of these dataframes share." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 54.4 ms, sys: 4.66 ms, total: 59 ms\n", + "Wall time: 58.1 ms\n" + ] + } + ], + "source": [ + "%time road_edges = road_edges.merge(speed_gdf, on='type')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exercse #2 - Step 2: Add Length in Seconds Column ###" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You now need to calculate the number of seconds it will take to traverse a given road at the speed limit. This can be done by dividing a road's length in m by its speed limit in m/s. Perform this calculation on `road_edges` and store the results in a new column `length_s`." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "road_edges['length_s']=road_edges['length']/road_edges['limit_m/s']" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>src_id</th>\n", + " <th>dst_id</th>\n", + " <th>length</th>\n", + " <th>type</th>\n", + " <th>form</th>\n", + " <th>limit_mph</th>\n", + " <th>limit_m/s</th>\n", + " <th>length_s</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>idC70BECA6-F99E-47BB-9E09-815D611F5D12</td>\n", + " <td>idDB390BD6-EACC-4FD7-AA3E-933DF4B38529</td>\n", + " <td>200</td>\n", + " <td>Minor Road</td>\n", + " <td>Single Carriageway</td>\n", + " <td>30</td>\n", + " <td>13.411167</td>\n", + " <td>14.912946</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>id6584F688-B7D8-421D-AF57-819DD952B691</td>\n", + " <td>id7CE6BA3A-A498-4790-B881-4A64B2280AA0</td>\n", + " <td>115</td>\n", + " <td>Local Road</td>\n", + " <td>Single Carriageway</td>\n", + " <td>30</td>\n", + " <td>13.411167</td>\n", + " <td>8.574944</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>idB5B2189B-EB23-4CAA-A5E7-FB67AD622B0A</td>\n", + " <td>id2EEE7A72-9DF6-4013-98CE-458037738C04</td>\n", + " <td>14</td>\n", + " <td>Local Road</td>\n", + " <td>Single Carriageway</td>\n", + " <td>30</td>\n", + " <td>13.411167</td>\n", + " <td>1.043906</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>idC4093DE6-6D9A-49DE-9F9B-1FC25704D669</td>\n", + " <td>id95067828-6B99-46BD-AF69-4BE4A9187DA6</td>\n", + " <td>67</td>\n", + " <td>Local Road</td>\n", + " <td>Single Carriageway</td>\n", + " <td>30</td>\n", + " <td>13.411167</td>\n", + " <td>4.995837</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>idCCF6E708-B31E-48EC-8412-F0669885BB40</td>\n", + " <td>idB5B2189B-EB23-4CAA-A5E7-FB67AD622B0A</td>\n", + " <td>51</td>\n", + " <td>Local Road</td>\n", + " <td>Single Carriageway</td>\n", + " <td>30</td>\n", + " <td>13.411167</td>\n", + " <td>3.802801</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " src_id \\\n", + "0 idC70BECA6-F99E-47BB-9E09-815D611F5D12 \n", + "1 id6584F688-B7D8-421D-AF57-819DD952B691 \n", + "2 idB5B2189B-EB23-4CAA-A5E7-FB67AD622B0A \n", + "3 idC4093DE6-6D9A-49DE-9F9B-1FC25704D669 \n", + "4 idCCF6E708-B31E-48EC-8412-F0669885BB40 \n", + "\n", + " dst_id length type \\\n", + "0 idDB390BD6-EACC-4FD7-AA3E-933DF4B38529 200 Minor Road \n", + "1 id7CE6BA3A-A498-4790-B881-4A64B2280AA0 115 Local Road \n", + "2 id2EEE7A72-9DF6-4013-98CE-458037738C04 14 Local Road \n", + "3 id95067828-6B99-46BD-AF69-4BE4A9187DA6 67 Local Road \n", + "4 idB5B2189B-EB23-4CAA-A5E7-FB67AD622B0A 51 Local Road \n", + "\n", + " form limit_mph limit_m/s length_s \n", + "0 Single Carriageway 30 13.411167 14.912946 \n", + "1 Single Carriageway 30 13.411167 8.574944 \n", + "2 Single Carriageway 30 13.411167 1.043906 \n", + "3 Single Carriageway 30 13.411167 4.995837 \n", + "4 Single Carriageway 30 13.411167 3.802801 " + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "road_edges.head()" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "\n", + "road_edges['length_s'] = road_edges['length'] / road_edges['limit_m/s']\n", + "road_edges['length_s'].head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Click ... for solution. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exercise #3 - Step 3: Construct the Graph ###" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Construct a cuGraph `Graph` called `G_ex` using the sources and destinations found in `road_edges`, along with length-in-seconds values for the edges' weights." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "G_ex=cg.Graph()\n", + "G_ex.from_cudf_edgelist(road_edges, source='src_id', destination='dst_id', edge_attr='length')" + ] + }, + { + "cell_type": "raw", + "metadata": { + "jupyter": { + "source_hidden": true + } + }, + "source": [ + "\n", + "G_ex = cg.Graph()\n", + "G_ex.from_cudf_edgelist(road_edges, source='src_id', destination='dst_id', edge_attr='length_s')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Click ... for solution. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import IPython\n", + "app = IPython.Application.instance()\n", + "app.kernel.do_shutdown(True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Well Done!** Let's move to the [next notebook](2-03_cugraph.ipynb). " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "<img src=\"./images/DLI_Header.png\" width=400/>" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.15" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Fundamentals_of_Accelerated_Data_Science/2-03_cugraph.ipynb b/Fundamentals_of_Accelerated_Data_Science/2-03_cugraph.ipynb new file mode 100644 index 0000000..2f6f450 --- /dev/null +++ b/Fundamentals_of_Accelerated_Data_Science/2-03_cugraph.ipynb @@ -0,0 +1,2994 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "<img src=\"./images/DLI_Header.png\" width=400/>" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Fundamentals of Accelerated Data Science # " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 03 - cuGraph Single Source Shortest Path ##\n", + "\n", + "**Table of Contents**\n", + "<br>\n", + "This notebook GPU-accelerated graph analytics with cuGraph to identify the shortest path from node on the road network to every other node, both by distance, which we will demo, and by time, which you will implement. You will also visualize the results of your findings. This notebook covers the below sections:\n", + "1. [Environment](#Environment)\n", + "2. [Loading Data](#Loading-Data)\n", + "3. [Construct Graph with cuGraph](#Construct-Graph-with-cuGraph)\n", + "4. [Analyzing the Graph](#Analyzing-the-Graph)\n", + "5. [Single Source Shortest Path](#Single-Source-Shortest-Path)\n", + "6. [Analyze a Graph with Time Weights](#Analyze-a-Graph-with-Time-Weights)\n", + " * [Exercise #1 - Step 1: Construct the Graph](#Exercise-#1---Step-1:-Construct-the-Graph)\n", + " * [Exercise #2 - Step 2: Get Travel Times From a Node to All Others](#Exercise-#2---Step-2:-Get-Travel-Times-From-a-Node-to-All-Others)\n", + " * [Visualize the Node Travel Times](#Visualize-the-Node-Travel-Times)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Environment ##" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "\n", + "import cudf\n", + "import cugraph as cg\n", + "\n", + "import cuxfilter as cxf\n", + "from bokeh.palettes import Magma, Turbo256, Plasma256, Viridis256" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Loading Data ##" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We start by loading the road graph data you prepared for constructing a graph with cuGraph, with the long unique `nodeid` replaced with simple (and memory-efficient) integers we call the `graph_id`." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>src</th>\n", + " <th>dst</th>\n", + " <th>length</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>0</td>\n", + " <td>129165</td>\n", + " <td>44.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " 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weight." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>src</th>\n", + " <th>dst</th>\n", + " <th>length_s</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>0</td>\n", + " <td>129165</td>\n", + " <td>3.280848</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>1</td>\n", + " <td>1678323</td>\n", + " <td>5.219531</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>1</td>\n", + " <td>2372610</td>\n", + " <td>1.342165</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>1</td>\n", + " <td>2483057</td>\n", + " <td>2.982589</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>2</td>\n", + " <td>2</td>\n", + " <td>4.101060</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " src dst length_s\n", + "0 0 129165 3.280848\n", + "1 1 1678323 5.219531\n", + "2 1 2372610 1.342165\n", + "3 1 2483057 2.982589\n", + "4 2 2 4.101060" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "speed_graph = cudf.read_csv('./data/road_graph_speed.csv', dtype=['int32', 'int32', 'float32'])\n", + "speed_graph.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally we import the full `road_nodes` data set, which we will use below for visualizations." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>node_id</th>\n", + " <th>east</th>\n", + " <th>north</th>\n", + " <th>type</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>id02FE73D4-E88D-4119-8DC2-6E80DE6F6594</td>\n", + " <td>320608.09375</td>\n", + " <td>870994.0000</td>\n", + " <td>junction</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>id634D65C1-C38B-4868-9080-2E1E47F0935C</td>\n", + " <td>320628.50000</td>\n", + " <td>871103.8125</td>\n", + " <td>road end</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>idDC14D4D1-774E-487D-8EDE-60B129E5482C</td>\n", + " <td>320635.46875</td>\n", + " <td>870983.8750</td>\n", + " <td>junction</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>id51555819-1A39-4B41-B0C9-C6D2086D9921</td>\n", + " <td>320648.68750</td>\n", + " <td>871083.5625</td>\n", + " <td>junction</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>id9E362428-79D7-4EE3-B015-0CE3F6A78A69</td>\n", + " <td>320658.18750</td>\n", + " <td>871162.3750</td>\n", + " <td>junction</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " node_id east north type\n", + "0 id02FE73D4-E88D-4119-8DC2-6E80DE6F6594 320608.09375 870994.0000 junction\n", + "1 id634D65C1-C38B-4868-9080-2E1E47F0935C 320628.50000 871103.8125 road end\n", + "2 idDC14D4D1-774E-487D-8EDE-60B129E5482C 320635.46875 870983.8750 junction\n", + "3 id51555819-1A39-4B41-B0C9-C6D2086D9921 320648.68750 871083.5625 junction\n", + "4 id9E362428-79D7-4EE3-B015-0CE3F6A78A69 320658.18750 871162.3750 junction" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "road_nodes = cudf.read_csv('./data/road_nodes.csv', dtype=['str', 'float32', 'float32', 'str'])\n", + "road_nodes = road_nodes.drop_duplicates() # again, some road nodes appeared on multiple map tiles in the original source\n", + "road_nodes.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(3078117, 4)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "road_nodes.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3078116" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "speed_graph.src.max()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Construct Graph with cuGraph ##" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have the well-prepped `road_graph` data, we pass it to cuGraph to create our graph data structure, which we can then use for accelerated analysis. In order to do so, we first use cuGraph to instantiate a `Graph` instance, and then pass the instance edge sources, edge destinations, and edge weights, currently the length of the roads." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 154 ms, sys: 36.8 ms, total: 191 ms\n", + "Wall time: 189 ms\n" + ] + } + ], + "source": [ + "G = cg.Graph()\n", + "%time G.from_cudf_edgelist(road_graph, source='src', destination='dst', edge_attr='length')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analyzing the Graph ##" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, we check the number of nodes and edges in our graph:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3078117" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "G.number_of_nodes()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3620793" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "G.number_of_edges()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also analyze the degrees of our graph nodes. We would expect, as before, that every node would have a degree of 2 or higher, since undirected edges count as two edges (one in, one out) for each of their nodes." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "mean 4.689990\n", + "std 1.913452\n", + "min 2.000000\n", + "25% 2.000000\n", + "50% 6.000000\n", + "75% 6.000000\n", + "max 16.000000\n", + "Name: degree, dtype: float64" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "deg_df = G.degree()\n", + "deg_df['degree'].describe()[1:]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We would also expect that every degree would be a multiple of 2, for the same reason. We check that there are no nodes with odd degrees (that is, degrees with a value of 1 modulo 2):" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>degree</th>\n", + " <th>vertex</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + "Empty DataFrame\n", + "Columns: [degree, vertex]\n", + "Index: []" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "deg_df[deg_df['degree'].mod(2) == 1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Observe for reference that some roads loop from a node back to itself:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>src</th>\n", + " <th>dst</th>\n", + " <th>length</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>2</td>\n", + " <td>2</td>\n", + " <td>55.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>145</th>\n", + " <td>62</td>\n", + " <td>62</td>\n", + " <td>108.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>293</th>\n", + " <td>124</td>\n", + " <td>124</td>\n", + " <td>67.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>471</th>\n", + " <td>196</td>\n", + " <td>196</td>\n", + " <td>26.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>571</th>\n", + " <td>240</td>\n", + " <td>240</td>\n", + " <td>44.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>...</th>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " </tr>\n", + " <tr>\n", + " <th>7216602</th>\n", + " <td>3077469</td>\n", + " <td>3077469</td>\n", + " <td>78.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>7216735</th>\n", + " <td>3077519</td>\n", + " <td>3077519</td>\n", + " <td>111.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>7216849</th>\n", + " <td>3077567</td>\n", + " <td>3077567</td>\n", + " <td>69.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>7217091</th>\n", + " <td>3077670</td>\n", + " <td>3077670</td>\n", + " <td>30.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>7217294</th>\n", + " <td>3077756</td>\n", + " <td>3077756</td>\n", + " <td>45.0</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "<p>23417 rows × 3 columns</p>\n", + "</div>" + ], + "text/plain": [ + " src dst length\n", + "4 2 2 55.0\n", + "145 62 62 108.0\n", + "293 124 124 67.0\n", + "471 196 196 26.0\n", + "571 240 240 44.0\n", + "... ... ... ...\n", + "7216602 3077469 3077469 78.0\n", + "7216735 3077519 3077519 111.0\n", + "7216849 3077567 3077567 69.0\n", + "7217091 3077670 3077670 30.0\n", + "7217294 3077756 3077756 45.0\n", + "\n", + "[23417 rows x 3 columns]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "road_graph.loc[road_graph.src == road_graph.dst]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Single Source Shortest Path ##" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To demo the Single Source Shortest Path (SSSP) algorithm, we will start with the node with the highest degree. First we obtain its `graph_id`, reported by the `degree` method as `vertex`:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "652907" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "demo_node = deg_df.nlargest(1, 'degree')\n", + "demo_node_graph_id = demo_node['vertex'].iloc[0]\n", + "demo_node_graph_id" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now call `cg.sssp`, passing it the entire graph `G`, and the `graph_id` for our selected vertex. Doing so will calculate the shortest path, using the road length weights we have provided, to *every* other node in the graph - millions of paths, in seconds:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 7.47 s, sys: 35.7 ms, total: 7.5 s\n", + "Wall time: 7.45 s\n" + ] + }, + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>distance</th>\n", + " <th>vertex</th>\n", + " <th>predecessor</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>0.0</td>\n", + " <td>652907</td>\n", + " <td>-1</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>110322.0</td>\n", + " <td>1252792</td>\n", + " <td>1378560</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>213691.0</td>\n", + " <td>1606375</td>\n", + " <td>1375709</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>106434.0</td>\n", + " <td>1826781</td>\n", + " <td>2377706</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>253196.0</td>\n", + " <td>1990110</td>\n", + " <td>1652530</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " distance vertex predecessor\n", + "0 0.0 652907 -1\n", + "1 110322.0 1252792 1378560\n", + "2 213691.0 1606375 1375709\n", + "3 106434.0 1826781 2377706\n", + "4 253196.0 1990110 1652530" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%time shortest_distances_from_demo_node = cg.sssp(G, demo_node_graph_id)\n", + "shortest_distances_from_demo_node.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "mean 210086.107365\n", + "std 137145.769283\n", + "min 0.000000\n", + "25% 125054.500000\n", + "50% 181815.500000\n", + "75% 252472.250000\n", + "max 868870.500000\n", + "Name: distance, dtype: float64" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Limiting to those nodes that were connected (within ~4.3 billion meters because\n", + "# cg.sssp uses the max int value for unreachable nodes, such as those on different islands)\n", + "shortest_distances_from_demo_node['distance'].loc[shortest_distances_from_demo_node['distance'] < 2**32].describe()[1:]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analyze a Graph with Time Weights ##" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For this exercise, you are going to analyze the graph of GB's roads, but this time, instead of using raw distance for a road's weights, you will be using how long it will take to travel along the road." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exercise #1 - Step 1: Construct the Graph ###" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Construct a cuGraph graph called `G_ex` using the sources and destinations found in `speed_graph`, along with length in seconds values for the edges' weights." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "G_ex=cg.Graph()\n", + "G_ex.from_cudf_edgelist(speed_graph, source='src', destination='dst', edge_attr='length_s')" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "\n", + "G_ex = cg.Graph()\n", + "G_ex.from_cudf_edgelist(speed_graph, source='src', destination='dst', edge_attr='length_s')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Click ... for solution. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exercise #2 - Step 2: Get Travel Times From a Node to All Others ###" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Choose one of the nodes and calculate the time it would take to travel from it to all other nodes via SSSP, calling the results `ex_dist`." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " degree vertex\n", + "0 16 652907\n" + ] + }, + { + "data": { + "text/plain": [ + "mean 7424.900285\n", + "std 4666.250239\n", + "min 0.000000\n", + "25% 4484.137695\n", + "50% 6451.895508\n", + "75% 9064.045410\n", + "max 31424.103516\n", + "Name: distance, dtype: float64" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "deg = G_ex.degree()\n", + "node = deg.nlargest(1, 'degree')\n", + "\n", + "print(node)\n", + "\n", + "ex_dist=cg.sssp(G_ex, node['vertex'].iloc[0])\n", + "\n", + "ex_dist['distance'].loc[ex_dist['distance'] < 2**32].describe()[1:]" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>distance</th>\n", + " <th>vertex</th>\n", + " <th>predecessor</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>0.000000</td>\n", + " <td>652907</td>\n", + " <td>-1</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>3753.216553</td>\n", + " <td>1252792</td>\n", + " <td>423201</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>7388.645020</td>\n", + " <td>1606375</td>\n", + " <td>1311665</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>4061.988281</td>\n", + " <td>1826781</td>\n", + " <td>1963186</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>9050.323242</td>\n", + " <td>1990110</td>\n", + " <td>1652530</td>\n", + " </tr>\n", + " <tr>\n", + " <th>...</th>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3078112</th>\n", + " <td>7909.516113</td>\n", + " <td>3078094</td>\n", + " <td>710744</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3078113</th>\n", + " <td>14547.452148</td>\n", + " <td>3078095</td>\n", + " <td>1858716</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3078114</th>\n", + " <td>12944.942383</td>\n", + " <td>3078099</td>\n", + " <td>1761154</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3078115</th>\n", + " <td>8544.315430</td>\n", + " <td>3078103</td>\n", + " <td>563256</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3078116</th>\n", + " <td>5369.405273</td>\n", + " <td>3078115</td>\n", + " <td>2721612</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "<p>3078117 rows × 3 columns</p>\n", + "</div>" + ], + "text/plain": [ + " distance vertex predecessor\n", + "0 0.000000 652907 -1\n", + "1 3753.216553 1252792 423201\n", + "2 7388.645020 1606375 1311665\n", + "3 4061.988281 1826781 1963186\n", + "4 9050.323242 1990110 1652530\n", + "... ... ... ...\n", + "3078112 7909.516113 3078094 710744\n", + "3078113 14547.452148 3078095 1858716\n", + "3078114 12944.942383 3078099 1761154\n", + "3078115 8544.315430 3078103 563256\n", + "3078116 5369.405273 3078115 2721612\n", + "\n", + "[3078117 rows x 3 columns]" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ex_dist" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "\n", + "# If you have time, see what the SSSP visualization looks like starting from nodes at different extreme coordinates,\n", + "# or one of the end nodes of an especially long edge, or even one of the nodes unreachable from the main road network.\n", + "ex_deg = G_ex.degree()\n", + "ex_node = ex_deg.nlargest(1, 'degree')\n", + "\n", + "%time ex_dist = cg.sssp(G_ex, ex_node['vertex'].iloc[0])\n", + "\n", + "# limiting to those nodes that were connected (within ~4.3 billion seconds; .sssp uses the max int value for unconnected nodes)\n", + "ex_dist['distance'].loc[ex_dist['distance'] < 2**32].describe()[1:]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Click ... for solution. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Visualize the Node Travel Times ###" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In order to create a graphic showing the road network by travel time from the selected node, we first need to align the just-calculated distances with their original nodes. For that, we use the mapping from `node_id` strings to their `graph_id` integers." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>node_id</th>\n", + " <th>graph_id</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>id000000F5-5180-4C03-B05D-B01352C54F89</td>\n", + " <td>0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>id000003F8-9E09-4829-AD87-6DA4438D22D8</td>\n", + " <td>1</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>id000010DA-C89A-4198-847A-6E62815E038A</td>\n", + " <td>2</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>id000017A0-1843-4BC7-BCF7-C943B6780839</td>\n", + " <td>3</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>id00001B2A-155F-4CD3-8E06-7677ADC6AF74</td>\n", + " <td>4</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " node_id graph_id\n", + "0 id000000F5-5180-4C03-B05D-B01352C54F89 0\n", + "1 id000003F8-9E09-4829-AD87-6DA4438D22D8 1\n", + "2 id000010DA-C89A-4198-847A-6E62815E038A 2\n", + "3 id000017A0-1843-4BC7-BCF7-C943B6780839 3\n", + "4 id00001B2A-155F-4CD3-8E06-7677ADC6AF74 4" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mapping = cudf.read_csv('./data/node_graph_map.csv')\n", + "mapping.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see that the `sssp` algorithm has put the `graph_id`s in the `vertex` column, so we will merge on that." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>distance</th>\n", + " <th>vertex</th>\n", + " <th>predecessor</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>0.000000</td>\n", + " <td>652907</td>\n", + " <td>-1</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>3753.216553</td>\n", + " <td>1252792</td>\n", + " <td>423201</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>7388.645020</td>\n", + " <td>1606375</td>\n", + " <td>1311665</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>4061.988281</td>\n", + " <td>1826781</td>\n", + " <td>1963186</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>9050.323242</td>\n", + " <td>1990110</td>\n", + " <td>1652530</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " distance vertex predecessor\n", + "0 0.000000 652907 -1\n", + "1 3753.216553 1252792 423201\n", + "2 7388.645020 1606375 1311665\n", + "3 4061.988281 1826781 1963186\n", + "4 9050.323242 1990110 1652530" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ex_dist.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", 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"</table>\n", + "</div>" + ], + "text/plain": [ + " node_id east north type \\\n", + "0 id0F5B22B0-AB17-4653-A9C2-9E7DE9B37373 371540.00000 859847.000 junction \n", + "1 idBC5AA063-AA06-488F-851C-DA7C91F65321 371107.43750 859912.875 junction \n", + "2 id07664552-7DBD-488F-8F88-29485A3BE149 372712.06250 859948.125 road end \n", + "3 id0B167A97-56F4-4455-87AE-199F7B1C85AF 373182.78125 859969.125 junction \n", + "4 id7CF7B336-0AB4-45FD-9181-85DB87D3CF81 373800.78125 859987.375 junction \n", + "\n", + " graph_id distance vertex predecessor \n", + "0 184148 23863.841797 184148 46693 \n", + "1 2263810 23894.375000 2263810 46693 \n", + "2 89131 24078.251953 89131 1841117 \n", + "3 133196 24051.931641 133196 532422 \n", + "4 1502711 24115.462891 1502711 2427593 " + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "road_nodes = road_nodes.merge(mapping, on='node_id')\n", + "road_nodes = road_nodes.merge(ex_dist, left_on='graph_id', right_on='vertex')\n", + "road_nodes.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we select those columns we are going to use for the visualization.\n", + "\n", + "For color-scaling purposes, we get rid of the unreachable nodes with their extreme distances, and we invert the distance numbers so that brighter pixels indicate closer locations." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "gdf = road_nodes[['east', 'north', 'distance']]\n", + "gdf = gdf[gdf['distance'] < 2**32]\n", + "gdf['distance'] = gdf['distance'].pow(1/2).mul(-1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Otherwise, this visualization will be largely similar to the scatter plots we made to visualize the population, but instead of coloring by point density as in those cases, we will color by mean travel time to the nodes within a pixel." + ] + }, + { + "cell_type": "code", + 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inject_raw_css(\"\\n.dataframe table{\\n border: none;\\n}\\n\\n.panel-df table{\\n width: 100%;\\n border-collapse: collapse;\\n border: none;\\n}\\n.panel-df td{\\n white-space: nowrap;\\n overflow: auto;\\n text-overflow: ellipsis;\\n}\\n\");\n", + " }, function(Bokeh) {\n", + " inject_raw_css(\"\\n.multi-select{\\n color: white;\\n z-index: 100;\\n background: rgba(44,43,43,0.5);\\n border-radius: 1px;\\n width: 120px !important;\\n height: 30px !important;\\n}\\n.multi-select > .bk {\\n padding: 5px;\\n width: 120px !important;\\n height: 30px !important;\\n}\\n\\n.deck-chart {\\n z-index: 10;\\n position: initial !important;\\n}\\n\");\n", + " }, function(Bokeh) {\n", + " inject_raw_css(\"\\n.center-header {\\n text-align: center\\n}\\n.bk-input-group {\\n padding: 10px;\\n}\\n#sidebar {\\n padding-top: 10px;\\n}\\n.custom-widget-box {\\n margin-top: 20px;\\n padding: 5px;\\n border: None !important;\\n}\\n.custom-widget-box > p {\\n margin: 0px;\\n}\\n.bk-input-group {\\n color: 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You have to load it prior to loading plugins.\");\n", + " }\n", + " })\n", + " ({\n", + " \"custom/main\": function(require, module, exports) {\n", + " const models = {\n", + " \"CustomInspectTool\": require(\"custom/cuxfilter.charts.datashader.custom_extensions.graph_inspect_widget.custom_inspect_tool\").CustomInspectTool\n", + " };\n", + " require(\"base\").register_models(models);\n", + " module.exports = models;\n", + " },\n", + " \"custom/cuxfilter.charts.datashader.custom_extensions.graph_inspect_widget.custom_inspect_tool\": function(require, module, exports) {\n", + " \"use strict\";\n", + " var _a;\n", + " Object.defineProperty(exports, \"__esModule\", { value: true });\n", + " exports.CustomInspectTool = exports.CustomInspectToolView = void 0;\n", + " const inspect_tool_1 = require(\"models/tools/inspectors/inspect_tool\");\n", + " class CustomInspectToolView extends inspect_tool_1.InspectToolView {\n", + " connect_signals() {\n", + " super.connect_signals();\n", + " this.on_change([this.model.properties.active], () => {\n", + " this.model._active = this.model.active;\n", + " });\n", + " }\n", + " }\n", + " exports.CustomInspectToolView = CustomInspectToolView;\n", + " CustomInspectToolView.__name__ = \"CustomInspectToolView\";\n", + " class CustomInspectTool extends inspect_tool_1.InspectTool {\n", + " constructor(attrs) {\n", + " super(attrs);\n", + " }\n", + " }\n", + " exports.CustomInspectTool = CustomInspectTool;\n", + " _a = CustomInspectTool;\n", + " CustomInspectTool.__name__ = \"CustomInspectTool\";\n", + " (() => {\n", + " _a.prototype.default_view = CustomInspectToolView;\n", + " _a.define(({ Boolean }) => ({\n", + " _active: [Boolean, true]\n", + " }));\n", + " _a.register_alias(\"customInspect\", () => new _a());\n", + " })();\n", + " //# sourceMappingURL=graph_inspect_widget.py:CustomInspectTool.js.map\n", + " }\n", + " }, \"custom/main\");\n", + " ;\n", + " });\n", + "\n", + " },\n", + "function(Bokeh) {} // ensure no trailing comma for IE\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " if ((root.Bokeh !== undefined) || (force === true)) {\n", + " for (let i = 0; i < inline_js.length; i++) {\n", + " try {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " } catch(e) {\n", + " if (!reloading) {\n", + " throw e;\n", + " }\n", + " }\n", + " }\n", + " // Cache old bokeh versions\n", + " if (Bokeh != undefined && !reloading) {\n", + " var NewBokeh = root.Bokeh;\n", + " if (Bokeh.versions === undefined) {\n", + " Bokeh.versions = new Map();\n", + " }\n", + " if (NewBokeh.version !== Bokeh.version) {\n", + " Bokeh.versions.set(NewBokeh.version, NewBokeh)\n", + " }\n", + " root.Bokeh = Bokeh;\n", + " }\n", + " } else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(run_inline_js, 100);\n", + " } else if (!root._bokeh_failed_load) {\n", + " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", + " root._bokeh_failed_load = true;\n", + " }\n", + " root._bokeh_is_initializing = false\n", + " }\n", + "\n", + " function load_or_wait() {\n", + " // Implement a backoff loop that tries to ensure we do not load multiple\n", + " // versions of Bokeh and its dependencies at the same time.\n", + " // In recent versions we use the root._bokeh_is_initializing flag\n", + " // to determine whether there is an ongoing attempt to initialize\n", + " // bokeh, however for backward compatibility we also try to ensure\n", + " // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n", + " // before older versions are fully initialized.\n", + " if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n", + " // If the timeout and bokeh was not successfully loaded we reset\n", + " // everything and try loading again\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_is_initializing = false;\n", + " root._bokeh_onload_callbacks = undefined;\n", + " root._bokeh_is_loading = 0\n", + " console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n", + " load_or_wait();\n", + " } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n", + " setTimeout(load_or_wait, 100);\n", + " } else {\n", + " root._bokeh_is_initializing = true\n", + " root._bokeh_onload_callbacks = []\n", + " const bokeh_loaded = root.Bokeh != null && (root.Bokeh.version === py_version || (root.Bokeh.versions !== undefined && root.Bokeh.versions.has(py_version)));\n", + " if (!reloading && !bokeh_loaded) {\n", + " if (root.Bokeh) {\n", + " root.Bokeh = undefined;\n", + " }\n", + " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " }\n", + " load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n", + " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", + " run_inline_js();\n", + " });\n", + " }\n", + " }\n", + " // Give older versions of the autoload script a head-start to ensure\n", + " // they initialize before we start loading newer version.\n", + " setTimeout(load_or_wait, 100)\n", + "}(window));" + ], + "application/vnd.holoviews_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n const py_version = '3.5.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n const reloading = false;\n const Bokeh = root.Bokeh;\n\n // Set a timeout for this load but only if we are not already initializing\n if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks;\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n if (js_modules == null) js_modules = [];\n if (js_exports == null) js_exports = {};\n\n root._bokeh_onload_callbacks.push(callback);\n\n if (root._bokeh_is_loading > 0) {\n // Don't load bokeh if it is still initializing\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n } else if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n // There is nothing to load\n run_callbacks();\n return null;\n }\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n window._bokeh_on_load = on_load\n\n function on_error(e) {\n const src_el = e.srcElement\n console.error(\"failed to load \" + (src_el.href || src_el.src));\n }\n\n const skip = [];\n if (window.requirejs) {\n window.requirejs.config({'packages': {}, 'paths': {'h3': 'https://cdn.jsdelivr.net/npm/h3-js@4.1.0/dist/h3-js.umd', 'deck-gl': 'https://cdn.jsdelivr.net/npm/deck.gl@9.0.20/dist.min', 'deck-json': 'https://cdn.jsdelivr.net/npm/@deck.gl/json@9.0.20/dist.min', 'loader-csv': 'https://cdn.jsdelivr.net/npm/@loaders.gl/csv@4.2.2/dist/dist.min', 'loader-json': 'https://cdn.jsdelivr.net/npm/@loaders.gl/json@4.2.2/dist/dist.min', 'loader-tiles': 'https://cdn.jsdelivr.net/npm/@loaders.gl/3d-tiles@4.2.2/dist/dist.min', 'mapbox-gl': 'https://api.mapbox.com/mapbox-gl-js/v3.0.1/mapbox-gl', 'carto': 'https://cdn.jsdelivr.net/npm/@deck.gl/carto@^9.0.20/dist.min'}, 'shim': {'deck-json': {'deps': ['deck-gl']}, 'deck-gl': {'deps': ['h3']}}});\n require([\"h3\"], function(h3) {\n window.h3 = h3\n on_load()\n })\n require([\"deck-gl\"], function(deck) {\n window.deck = deck\n on_load()\n })\n require([\"deck-json\"], function() {\n on_load()\n })\n require([\"loader-csv\"], function() {\n on_load()\n })\n require([\"loader-json\"], function() {\n on_load()\n })\n require([\"loader-tiles\"], function() {\n on_load()\n })\n require([\"mapbox-gl\"], function(mapboxgl) {\n window.mapboxgl = mapboxgl\n on_load()\n })\n require([\"carto\"], function() {\n on_load()\n })\n root._bokeh_is_loading = css_urls.length + 8;\n } else {\n root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n }\n\n const existing_stylesheets = []\n const links = document.getElementsByTagName('link')\n for (let i = 0; i < links.length; i++) {\n const link = links[i]\n if (link.href != null) {\n existing_stylesheets.push(link.href)\n }\n }\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const escaped = encodeURI(url)\n if (existing_stylesheets.indexOf(escaped) !== -1) {\n on_load()\n continue;\n }\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n } if (((window.deck !== undefined) && (!(window.deck instanceof HTMLElement))) || window.requirejs) {\n var urls = ['https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/h3-js@4.1.0/dist/h3-js.umd.js', 'https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/deck.gl@9.0.20/dist.min.js', 'https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/@deck.gl/json@9.0.20/dist.min.js', 'https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/@loaders.gl/csv@4.2.2/dist/dist.min.js', 'https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/@loaders.gl/json@4.2.2/dist/dist.min.js', 'https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/@loaders.gl/3d-tiles@4.2.2/dist/dist.min.js', 'https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/mapbox-gl-js/v3.0.1/mapbox-gl.js', 'https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/maplibre-gl/dist/maplibre-gl.js'];\n for (var i = 0; i < urls.length; i++) {\n skip.push(encodeURI(urls[i]))\n }\n } if (((window.mapboxgl !== undefined) && (!(window.mapboxgl instanceof HTMLElement))) || window.requirejs) {\n var urls = ['https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/@deck.gl/carto@^9.0.20/dist.min.js'];\n for (var i = 0; i < urls.length; i++) {\n skip.push(encodeURI(urls[i]))\n }\n } var existing_scripts = []\n const scripts = document.getElementsByTagName('script')\n for (let i = 0; i < scripts.length; i++) {\n var script = scripts[i]\n if (script.src != null) {\n existing_scripts.push(script.src)\n }\n }\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\n }\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (let i = 0; i < js_modules.length; i++) {\n const url = js_modules[i];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\n }\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (const name in js_exports) {\n const url = js_exports[name];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) >= 0 || root[name] != null) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\n }\n var element = document.createElement('script');\n element.onerror = on_error;\n element.async = false;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n element.textContent = `\n import ${name} from \"${url}\"\n window.${name} = ${name}\n window._bokeh_on_load()\n `\n document.head.appendChild(element);\n }\n if (!js_urls.length && !js_modules.length) {\n on_load()\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/h3-js@4.1.0/dist/h3-js.umd.js\", \"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/deck.gl@9.0.20/dist.min.js\", \"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/@deck.gl/json@9.0.20/dist.min.js\", \"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/@loaders.gl/csv@4.2.2/dist/dist.min.js\", \"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/@loaders.gl/json@4.2.2/dist/dist.min.js\", \"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/@loaders.gl/3d-tiles@4.2.2/dist/dist.min.js\", \"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/mapbox-gl-js/v3.0.1/mapbox-gl.js\", \"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/maplibre-gl/dist/maplibre-gl.js\", \"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/@deck.gl/carto@^9.0.20/dist.min.js\", \"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-3.5.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.5.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.5.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.5.2.min.js\", \"https://cdn.holoviz.org/panel/1.5.2/dist/panel.min.js\"];\n const js_modules = [];\n const js_exports = {};\n const css_urls = [\"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/mapbox-gl-js/v3.0.1/mapbox-gl.css?v=1.5.2\", \"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/maplibre-gl@4.4.1/dist/maplibre-gl.css?v=1.5.2\"];\n const inline_js = [ function(Bokeh) {\n inject_raw_css(\"\\n.dataframe table{\\n border: none;\\n}\\n\\n.panel-df table{\\n width: 100%;\\n border-collapse: collapse;\\n border: none;\\n}\\n.panel-df td{\\n white-space: nowrap;\\n overflow: auto;\\n text-overflow: ellipsis;\\n}\\n\");\n }, function(Bokeh) {\n inject_raw_css(\"\\n.multi-select{\\n color: white;\\n z-index: 100;\\n background: rgba(44,43,43,0.5);\\n border-radius: 1px;\\n width: 120px !important;\\n height: 30px !important;\\n}\\n.multi-select > .bk {\\n padding: 5px;\\n width: 120px !important;\\n height: 30px !important;\\n}\\n\\n.deck-chart {\\n z-index: 10;\\n position: initial !important;\\n}\\n\");\n }, function(Bokeh) {\n inject_raw_css(\"\\n.center-header {\\n text-align: center\\n}\\n.bk-input-group {\\n padding: 10px;\\n}\\n#sidebar {\\n padding-top: 10px;\\n}\\n.custom-widget-box {\\n margin-top: 20px;\\n padding: 5px;\\n border: None !important;\\n}\\n.custom-widget-box > p {\\n margin: 0px;\\n}\\n.bk-input-group {\\n color: None !important;\\n}\\n.indicator {\\n text-align: center;\\n}\\n.widget-card {\\n margin: 5px 10px;\\n}\\n.number-card {\\n margin: 5px 10px;\\n text-align: center;\\n}\\n.number-card-value {\\n width: 100%;\\n margin: 0px;\\n}\\n\");\n }, function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n function(Bokeh) {\n (function(root, factory) {\n factory(root[\"Bokeh\"]);\n })(this, function(Bokeh) {\n let define;\n return (function outer(modules, entry) {\n if (Bokeh != null) {\n return Bokeh.register_plugin(modules, entry);\n } else {\n throw new Error(\"Cannot find Bokeh. You have to load it prior to loading plugins.\");\n }\n })\n ({\n \"custom/main\": function(require, module, exports) {\n const models = {\n \"CustomInspectTool\": require(\"custom/cuxfilter.charts.datashader.custom_extensions.graph_inspect_widget.custom_inspect_tool\").CustomInspectTool\n };\n require(\"base\").register_models(models);\n module.exports = models;\n },\n \"custom/cuxfilter.charts.datashader.custom_extensions.graph_inspect_widget.custom_inspect_tool\": function(require, module, exports) {\n \"use strict\";\n var _a;\n Object.defineProperty(exports, \"__esModule\", { value: true });\n exports.CustomInspectTool = exports.CustomInspectToolView = void 0;\n const inspect_tool_1 = require(\"models/tools/inspectors/inspect_tool\");\n class CustomInspectToolView extends inspect_tool_1.InspectToolView {\n connect_signals() {\n super.connect_signals();\n this.on_change([this.model.properties.active], () => {\n this.model._active = this.model.active;\n });\n }\n }\n exports.CustomInspectToolView = CustomInspectToolView;\n CustomInspectToolView.__name__ = \"CustomInspectToolView\";\n class CustomInspectTool extends inspect_tool_1.InspectTool {\n constructor(attrs) {\n super(attrs);\n }\n }\n exports.CustomInspectTool = CustomInspectTool;\n _a = CustomInspectTool;\n CustomInspectTool.__name__ = \"CustomInspectTool\";\n (() => {\n _a.prototype.default_view = CustomInspectToolView;\n _a.define(({ Boolean }) => ({\n _active: [Boolean, true]\n }));\n _a.register_alias(\"customInspect\", () => new _a());\n })();\n //# sourceMappingURL=graph_inspect_widget.py:CustomInspectTool.js.map\n }\n }, \"custom/main\");\n ;\n });\n\n },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n ];\n\n function run_inline_js() {\n if ((root.Bokeh !== undefined) || (force === true)) {\n for (let i = 0; i < inline_js.length; i++) {\n try {\n inline_js[i].call(root, root.Bokeh);\n } catch(e) {\n if (!reloading) {\n throw e;\n }\n }\n }\n // Cache old bokeh versions\n if (Bokeh != undefined && !reloading) {\n var NewBokeh = root.Bokeh;\n if (Bokeh.versions === undefined) {\n Bokeh.versions = new Map();\n }\n if (NewBokeh.version !== Bokeh.version) {\n Bokeh.versions.set(NewBokeh.version, NewBokeh)\n }\n root.Bokeh = Bokeh;\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n }\n root._bokeh_is_initializing = false\n }\n\n function load_or_wait() {\n // Implement a backoff loop that tries to ensure we do not load multiple\n // versions of Bokeh and its dependencies at the same time.\n // In recent versions we use the root._bokeh_is_initializing flag\n // to determine whether there is an ongoing attempt to initialize\n // bokeh, however for backward compatibility we also try to ensure\n // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n // before older versions are fully initialized.\n if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n // If the timeout and bokeh was not successfully loaded we reset\n // everything and try loading again\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_is_initializing = false;\n root._bokeh_onload_callbacks = undefined;\n root._bokeh_is_loading = 0\n console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n load_or_wait();\n } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n setTimeout(load_or_wait, 100);\n } else {\n root._bokeh_is_initializing = true\n root._bokeh_onload_callbacks = []\n const bokeh_loaded = root.Bokeh != null && (root.Bokeh.version === py_version || (root.Bokeh.versions !== undefined && root.Bokeh.versions.has(py_version)));\n if (!reloading && !bokeh_loaded) {\n if (root.Bokeh) {\n root.Bokeh = undefined;\n 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(Jupyter.notebook.kernel != null))) {\n", + " var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n", + " comm_manager.register_target(comm_id, function(comm) {\n", + " comm.on_msg(msg_handler);\n", + " });\n", + " } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n", + " window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n", + " comm.onMsg = msg_handler;\n", + " });\n", + " } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n", + " google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n", + " var messages = comm.messages[Symbol.asyncIterator]();\n", + " function processIteratorResult(result) {\n", + " var message = result.value;\n", + " console.log(message)\n", + " var content = {data: message.data, comm_id};\n", + " var buffers = []\n", + " for (var buffer of message.buffers || []) {\n", + " buffers.push(new DataView(buffer))\n", + " }\n", + " var metadata = 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" return\n", + " }\n", + " var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", + " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", + " if (id !== undefined) {\n", + " var nchildren = toinsert.length;\n", + " var html_node = toinsert[nchildren-1].children[0];\n", + " html_node.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var scripts = [];\n", + " var nodelist = html_node.querySelectorAll(\"script\");\n", + " for (var i in nodelist) {\n", + " if (nodelist.hasOwnProperty(i)) {\n", + " scripts.push(nodelist[i])\n", + " }\n", + " }\n", + "\n", + " scripts.forEach( function (oldScript) {\n", + " var newScript = document.createElement(\"script\");\n", + " var attrs = [];\n", + " var nodemap = oldScript.attributes;\n", + " for (var j in nodemap) {\n", + " if (nodemap.hasOwnProperty(j)) {\n", + " attrs.push(nodemap[j])\n", + " }\n", + " }\n", + " attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n", + " newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n", + " oldScript.parentNode.replaceChild(newScript, oldScript);\n", + " });\n", + " if (JS_MIME_TYPE in output.data) {\n", + " toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n", + " }\n", + " output_area._hv_plot_id = id;\n", + " if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n", + " window.PyViz.plot_index[id] = Bokeh.index[id];\n", + " } else {\n", + " window.PyViz.plot_index[id] = null;\n", + " }\n", + " } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", + " var bk_div = document.createElement(\"div\");\n", + " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var script_attrs = bk_div.children[0].attributes;\n", + " for (var i = 0; i < script_attrs.length; i++) {\n", + " toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " }\n", + " // store reference to server id on output_area\n", + " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", + " }\n", + "}\n", + "\n", + "/**\n", + " * Handle when an output is cleared or removed\n", + " */\n", + "function handle_clear_output(event, handle) {\n", + " var id = handle.cell.output_area._hv_plot_id;\n", + " var server_id = handle.cell.output_area._bokeh_server_id;\n", + " if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n", + " var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n", + " if (server_id !== null) {\n", + " comm.send({event_type: 'server_delete', 'id': server_id});\n", + " return;\n", + " } else if (comm !== null) {\n", + " comm.send({event_type: 'delete', 'id': id});\n", + " }\n", + " delete PyViz.plot_index[id];\n", + " if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n", + " var doc = window.Bokeh.index[id].model.document\n", + " doc.clear();\n", + " const i = window.Bokeh.documents.indexOf(doc);\n", + " if (i > -1) {\n", + " window.Bokeh.documents.splice(i, 1);\n", + " }\n", + " }\n", + "}\n", + "\n", + "/**\n", + " * Handle kernel restart event\n", + " */\n", + "function handle_kernel_cleanup(event, handle) {\n", + " delete PyViz.comms[\"hv-extension-comm\"];\n", + " window.PyViz.plot_index = {}\n", + "}\n", + "\n", + "/**\n", + " * Handle update_display_data messages\n", + " */\n", + "function handle_update_output(event, handle) {\n", + " handle_clear_output(event, {cell: {output_area: handle.output_area}})\n", + " handle_add_output(event, handle)\n", + "}\n", + "\n", + "function register_renderer(events, OutputArea) {\n", + " function append_mime(data, metadata, element) {\n", + " // create a DOM node to render to\n", + " var toinsert = this.create_output_subarea(\n", + " metadata,\n", + " CLASS_NAME,\n", + " EXEC_MIME_TYPE\n", + " );\n", + " this.keyboard_manager.register_events(toinsert);\n", + " // Render to node\n", + " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", + " render(props, toinsert[0]);\n", + " element.append(toinsert);\n", + " return toinsert\n", + " }\n", + "\n", + " events.on('output_added.OutputArea', handle_add_output);\n", + " events.on('output_updated.OutputArea', handle_update_output);\n", + " events.on('clear_output.CodeCell', handle_clear_output);\n", + " events.on('delete.Cell', handle_clear_output);\n", + " events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n", + "\n", + " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", + " safe: true,\n", + " index: 0\n", + " });\n", + "}\n", + "\n", + "if (window.Jupyter !== undefined) {\n", + " try {\n", + " var events = require('base/js/events');\n", + " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", + " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", + " register_renderer(events, OutputArea);\n", + " }\n", + " } catch(err) {\n", + " }\n", + "}\n" + ], + "application/vnd.holoviews_load.v0+json": "\nif ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n}\n\n\n function JupyterCommManager() {\n }\n\n JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n comm_manager.register_target(comm_id, function(comm) {\n comm.on_msg(msg_handler);\n });\n } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n comm.onMsg = msg_handler;\n });\n } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n var messages = 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(OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n } catch(err) {\n }\n}\n" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "<script type=\"esms-options\">{\"shimMode\": true}</script><style>*[data-root-id],\n", + "*[data-root-id] > * {\n", + " box-sizing: border-box;\n", + " font-family: var(--jp-ui-font-family);\n", + " font-size: var(--jp-ui-font-size1);\n", + " color: var(--vscode-editor-foreground, var(--jp-ui-font-color1));\n", + "}\n", + "\n", + "/* Override VSCode background color */\n", + ".cell-output-ipywidget-background:has(\n", + " > .cell-output-ipywidget-background > .lm-Widget > *[data-root-id]\n", + " ),\n", + ".cell-output-ipywidget-background:has(> .lm-Widget > *[data-root-id]) {\n", + " background-color: transparent !important;\n", + "}\n", + "</style>" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", + "\n", + " const force = false;\n", + " const py_version = '3.5.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n", + " const reloading = true;\n", + " const Bokeh = root.Bokeh;\n", + "\n", + " // Set a timeout for this load but only if we are not already initializing\n", + " if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_failed_load = false;\n", + " }\n", + "\n", + " function run_callbacks() {\n", + " try {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) {\n", + " if (callback != null)\n", + " callback();\n", + " });\n", + " } finally {\n", + " delete root._bokeh_onload_callbacks;\n", + " }\n", + " console.debug(\"Bokeh: all callbacks have finished\");\n", + " }\n", + "\n", + " function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n", + " if (css_urls == null) css_urls = [];\n", + " if (js_urls == null) js_urls = [];\n", + " if (js_modules == null) js_modules = [];\n", + " if (js_exports == null) js_exports = {};\n", + "\n", + " root._bokeh_onload_callbacks.push(callback);\n", + "\n", + " if (root._bokeh_is_loading > 0) {\n", + " // Don't load bokeh if it is still initializing\n", + " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " return null;\n", + " } else if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n", + " // There is nothing to load\n", + " run_callbacks();\n", + " return null;\n", + " }\n", + "\n", + " function on_load() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", + " run_callbacks()\n", + " }\n", + " }\n", + " window._bokeh_on_load = on_load\n", + "\n", + " function on_error(e) {\n", + " const src_el = e.srcElement\n", + " console.error(\"failed to load \" + (src_el.href || src_el.src));\n", + " }\n", + "\n", + " const skip = [];\n", + " if (window.requirejs) {\n", + " window.requirejs.config({'packages': {}, 'paths': {'h3': 'https://cdn.jsdelivr.net/npm/h3-js@4.1.0/dist/h3-js.umd', 'deck-gl': 'https://cdn.jsdelivr.net/npm/deck.gl@9.0.20/dist.min', 'deck-json': 'https://cdn.jsdelivr.net/npm/@deck.gl/json@9.0.20/dist.min', 'loader-csv': 'https://cdn.jsdelivr.net/npm/@loaders.gl/csv@4.2.2/dist/dist.min', 'loader-json': 'https://cdn.jsdelivr.net/npm/@loaders.gl/json@4.2.2/dist/dist.min', 'loader-tiles': 'https://cdn.jsdelivr.net/npm/@loaders.gl/3d-tiles@4.2.2/dist/dist.min', 'mapbox-gl': 'https://api.mapbox.com/mapbox-gl-js/v3.0.1/mapbox-gl', 'carto': 'https://cdn.jsdelivr.net/npm/@deck.gl/carto@^9.0.20/dist.min'}, 'shim': {'deck-json': {'deps': ['deck-gl']}, 'deck-gl': {'deps': ['h3']}}});\n", + " require([\"h3\"], function(h3) {\n", + " window.h3 = h3\n", + " on_load()\n", + " })\n", + " require([\"deck-gl\"], function(deck) {\n", + " window.deck = deck\n", + " on_load()\n", + " })\n", + " require([\"deck-json\"], function() {\n", + " on_load()\n", + " })\n", + " require([\"loader-csv\"], function() {\n", + " on_load()\n", + " })\n", + " require([\"loader-json\"], function() {\n", + " on_load()\n", + " })\n", + " require([\"loader-tiles\"], function() {\n", + " on_load()\n", + " })\n", + " require([\"mapbox-gl\"], function(mapboxgl) {\n", + " window.mapboxgl = mapboxgl\n", + " on_load()\n", + " })\n", + " require([\"carto\"], function() {\n", + " on_load()\n", + " })\n", + " root._bokeh_is_loading = css_urls.length + 8;\n", + " } else {\n", + " root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n", + " }\n", + "\n", + " const existing_stylesheets = []\n", + " const links = document.getElementsByTagName('link')\n", + " for (let i = 0; i < links.length; i++) {\n", + " const link = links[i]\n", + " if (link.href != null) {\n", + " existing_stylesheets.push(link.href)\n", + " }\n", + " }\n", + " for (let i = 0; i < css_urls.length; i++) {\n", + " const url = css_urls[i];\n", + " const escaped = encodeURI(url)\n", + " if (existing_stylesheets.indexOf(escaped) !== -1) {\n", + " on_load()\n", + " continue;\n", + " }\n", + " const element = document.createElement(\"link\");\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.rel = \"stylesheet\";\n", + " element.type = \"text/css\";\n", + " element.href = url;\n", + " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", + " document.body.appendChild(element);\n", + " } if (((window.deck !== undefined) && (!(window.deck instanceof HTMLElement))) || window.requirejs) {\n", + " var urls = ['https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/h3-js@4.1.0/dist/h3-js.umd.js', 'https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/deck.gl@9.0.20/dist.min.js', 'https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/@deck.gl/json@9.0.20/dist.min.js', 'https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/@loaders.gl/csv@4.2.2/dist/dist.min.js', 'https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/@loaders.gl/json@4.2.2/dist/dist.min.js', 'https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/@loaders.gl/3d-tiles@4.2.2/dist/dist.min.js', 'https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/mapbox-gl-js/v3.0.1/mapbox-gl.js', 'https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/maplibre-gl/dist/maplibre-gl.js'];\n", + " for (var i = 0; i < urls.length; i++) {\n", + " skip.push(encodeURI(urls[i]))\n", + " }\n", + " } if (((window.mapboxgl !== undefined) && (!(window.mapboxgl instanceof HTMLElement))) || window.requirejs) {\n", + " var urls = ['https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/@deck.gl/carto@^9.0.20/dist.min.js'];\n", + " for (var i = 0; i < urls.length; i++) {\n", + " skip.push(encodeURI(urls[i]))\n", + " }\n", + " } var existing_scripts = []\n", + " const scripts = document.getElementsByTagName('script')\n", + " for (let i = 0; i < scripts.length; i++) {\n", + " var script = scripts[i]\n", + " if (script.src != null) {\n", + " existing_scripts.push(script.src)\n", + " }\n", + " }\n", + " for (let i = 0; i < js_urls.length; i++) {\n", + " const url = js_urls[i];\n", + " const escaped = encodeURI(url)\n", + " if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n", + " if (!window.requirejs) {\n", + " on_load();\n", + " }\n", + " continue;\n", + " }\n", + " const element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.src = url;\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " for (let i = 0; i < js_modules.length; i++) {\n", + " const url = js_modules[i];\n", + " const escaped = encodeURI(url)\n", + " if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n", + " if (!window.requirejs) {\n", + " on_load();\n", + " }\n", + " continue;\n", + " }\n", + " var element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.src = url;\n", + " element.type = \"module\";\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " for (const name in js_exports) {\n", + " const url = js_exports[name];\n", + " const escaped = encodeURI(url)\n", + " if (skip.indexOf(escaped) >= 0 || root[name] != null) {\n", + " if (!window.requirejs) {\n", + " on_load();\n", + " }\n", + " continue;\n", + " }\n", + " var element = document.createElement('script');\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.type = \"module\";\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " element.textContent = `\n", + " import ${name} from \"${url}\"\n", + " window.${name} = ${name}\n", + " window._bokeh_on_load()\n", + " `\n", + " document.head.appendChild(element);\n", + " }\n", + " if (!js_urls.length && !js_modules.length) {\n", + " on_load()\n", + " }\n", + " };\n", + "\n", + " function inject_raw_css(css) {\n", + " const element = document.createElement(\"style\");\n", + " element.appendChild(document.createTextNode(css));\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " const js_urls = [\"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/h3-js@4.1.0/dist/h3-js.umd.js\", \"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/deck.gl@9.0.20/dist.min.js\", \"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/@deck.gl/json@9.0.20/dist.min.js\", \"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/@loaders.gl/csv@4.2.2/dist/dist.min.js\", \"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/@loaders.gl/json@4.2.2/dist/dist.min.js\", \"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/@loaders.gl/3d-tiles@4.2.2/dist/dist.min.js\", \"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/mapbox-gl-js/v3.0.1/mapbox-gl.js\", \"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/maplibre-gl/dist/maplibre-gl.js\", \"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/@deck.gl/carto@^9.0.20/dist.min.js\", \"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\"];\n", + " const js_modules = [];\n", + " const js_exports = {};\n", + " const css_urls = [\"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/mapbox-gl-js/v3.0.1/mapbox-gl.css?v=1.5.2\", \"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/maplibre-gl@4.4.1/dist/maplibre-gl.css?v=1.5.2\"];\n", + " const inline_js = [ function(Bokeh) {\n", + " inject_raw_css(\"\\n.dataframe table{\\n border: none;\\n}\\n\\n.panel-df table{\\n width: 100%;\\n border-collapse: collapse;\\n border: none;\\n}\\n.panel-df td{\\n white-space: nowrap;\\n overflow: auto;\\n text-overflow: ellipsis;\\n}\\n\");\n", + " }, function(Bokeh) {\n", + " inject_raw_css(\"\\n.multi-select{\\n color: white;\\n z-index: 100;\\n background: rgba(44,43,43,0.5);\\n border-radius: 1px;\\n width: 120px !important;\\n height: 30px !important;\\n}\\n.multi-select > .bk {\\n padding: 5px;\\n width: 120px !important;\\n height: 30px !important;\\n}\\n\\n.deck-chart {\\n z-index: 10;\\n position: initial !important;\\n}\\n\");\n", + " }, function(Bokeh) {\n", + " inject_raw_css(\"\\n.center-header {\\n text-align: center\\n}\\n.bk-input-group {\\n padding: 10px;\\n}\\n#sidebar {\\n padding-top: 10px;\\n}\\n.custom-widget-box {\\n margin-top: 20px;\\n padding: 5px;\\n border: None !important;\\n}\\n.custom-widget-box > p {\\n margin: 0px;\\n}\\n.bk-input-group {\\n color: None !important;\\n}\\n.indicator {\\n text-align: center;\\n}\\n.widget-card {\\n margin: 5px 10px;\\n}\\n.number-card {\\n margin: 5px 10px;\\n text-align: center;\\n}\\n.number-card-value {\\n width: 100%;\\n margin: 0px;\\n}\\n\");\n", + " }, function(Bokeh) {\n", + " Bokeh.set_log_level(\"info\");\n", + " },\n", + " function(Bokeh) {\n", + " (function(root, factory) {\n", + " factory(root[\"Bokeh\"]);\n", + " })(this, function(Bokeh) {\n", + " let define;\n", + " return (function outer(modules, entry) {\n", + " if (Bokeh != null) {\n", + " return Bokeh.register_plugin(modules, entry);\n", + " } else {\n", + " throw new Error(\"Cannot find Bokeh. You have to load it prior to loading plugins.\");\n", + " }\n", + " })\n", + " ({\n", + " \"custom/main\": function(require, module, exports) {\n", + " const models = {\n", + " \"CustomInspectTool\": require(\"custom/cuxfilter.charts.datashader.custom_extensions.graph_inspect_widget.custom_inspect_tool\").CustomInspectTool\n", + " };\n", + " require(\"base\").register_models(models);\n", + " module.exports = models;\n", + " },\n", + " \"custom/cuxfilter.charts.datashader.custom_extensions.graph_inspect_widget.custom_inspect_tool\": function(require, module, exports) {\n", + " \"use strict\";\n", + " var _a;\n", + " Object.defineProperty(exports, \"__esModule\", { value: true });\n", + " exports.CustomInspectTool = exports.CustomInspectToolView = void 0;\n", + " const inspect_tool_1 = require(\"models/tools/inspectors/inspect_tool\");\n", + " class CustomInspectToolView extends inspect_tool_1.InspectToolView {\n", + " connect_signals() {\n", + " super.connect_signals();\n", + " this.on_change([this.model.properties.active], () => {\n", + " this.model._active = this.model.active;\n", + " });\n", + " }\n", + " }\n", + " exports.CustomInspectToolView = CustomInspectToolView;\n", + " CustomInspectToolView.__name__ = \"CustomInspectToolView\";\n", + " class CustomInspectTool extends inspect_tool_1.InspectTool {\n", + " constructor(attrs) {\n", + " super(attrs);\n", + " }\n", + " }\n", + " exports.CustomInspectTool = CustomInspectTool;\n", + " _a = CustomInspectTool;\n", + " CustomInspectTool.__name__ = \"CustomInspectTool\";\n", + " (() => {\n", + " _a.prototype.default_view = CustomInspectToolView;\n", + " _a.define(({ Boolean }) => ({\n", + " _active: [Boolean, true]\n", + " }));\n", + " _a.register_alias(\"customInspect\", () => new _a());\n", + " })();\n", + " //# sourceMappingURL=graph_inspect_widget.py:CustomInspectTool.js.map\n", + " }\n", + " }, \"custom/main\");\n", + " ;\n", + " });\n", + "\n", + " },\n", + "function(Bokeh) {} // ensure no trailing comma for IE\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " if ((root.Bokeh !== undefined) || (force === true)) {\n", + " for (let i = 0; i < inline_js.length; i++) {\n", + " try {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " } catch(e) {\n", + " if (!reloading) {\n", + " throw e;\n", + " }\n", + " }\n", + " }\n", + " // Cache old bokeh versions\n", + " if (Bokeh != undefined && !reloading) {\n", + " var NewBokeh = root.Bokeh;\n", + " if (Bokeh.versions === undefined) {\n", + " Bokeh.versions = new Map();\n", + " }\n", + " if (NewBokeh.version !== Bokeh.version) {\n", + " Bokeh.versions.set(NewBokeh.version, NewBokeh)\n", + " }\n", + " root.Bokeh = Bokeh;\n", + " }\n", + " } else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(run_inline_js, 100);\n", + " } else if (!root._bokeh_failed_load) {\n", + " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", + " root._bokeh_failed_load = true;\n", + " }\n", + " root._bokeh_is_initializing = false\n", + " }\n", + "\n", + " function load_or_wait() {\n", + " // Implement a backoff loop that tries to ensure we do not load multiple\n", + " // versions of Bokeh and its dependencies at the same time.\n", + " // In recent versions we use the root._bokeh_is_initializing flag\n", + " // to determine whether there is an ongoing attempt to initialize\n", + " // bokeh, however for backward compatibility we also try to ensure\n", + " // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n", + " // before older versions are fully initialized.\n", + " if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n", + " // If the timeout and bokeh was not successfully loaded we reset\n", + " // everything and try loading again\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_is_initializing = false;\n", + " root._bokeh_onload_callbacks = undefined;\n", + " root._bokeh_is_loading = 0\n", + " console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n", + " load_or_wait();\n", + " } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n", + " setTimeout(load_or_wait, 100);\n", + " } else {\n", + " root._bokeh_is_initializing = true\n", + " root._bokeh_onload_callbacks = []\n", + " const bokeh_loaded = root.Bokeh != null && (root.Bokeh.version === py_version || (root.Bokeh.versions !== undefined && root.Bokeh.versions.has(py_version)));\n", + " if (!reloading && !bokeh_loaded) {\n", + " if (root.Bokeh) {\n", + " root.Bokeh = undefined;\n", + " }\n", + " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " }\n", + " load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n", + " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", + " run_inline_js();\n", + " });\n", + " }\n", + " }\n", + " // Give older versions of the autoload script a head-start to ensure\n", + " // they initialize before we start loading newer version.\n", + " setTimeout(load_or_wait, 100)\n", + "}(window));" + ], + "application/vnd.holoviews_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = false;\n const py_version = '3.5.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n const reloading = true;\n const Bokeh = root.Bokeh;\n\n // Set a timeout for this load but only if we are not already initializing\n if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks;\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n if (js_modules == null) js_modules = [];\n if (js_exports == null) js_exports = {};\n\n root._bokeh_onload_callbacks.push(callback);\n\n if (root._bokeh_is_loading > 0) {\n // Don't load bokeh if it is still initializing\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n } else if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n // There is nothing to load\n run_callbacks();\n return null;\n }\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n window._bokeh_on_load = on_load\n\n function on_error(e) {\n const src_el = e.srcElement\n console.error(\"failed to load \" + (src_el.href || src_el.src));\n }\n\n const skip = [];\n if (window.requirejs) {\n window.requirejs.config({'packages': {}, 'paths': {'h3': 'https://cdn.jsdelivr.net/npm/h3-js@4.1.0/dist/h3-js.umd', 'deck-gl': 'https://cdn.jsdelivr.net/npm/deck.gl@9.0.20/dist.min', 'deck-json': 'https://cdn.jsdelivr.net/npm/@deck.gl/json@9.0.20/dist.min', 'loader-csv': 'https://cdn.jsdelivr.net/npm/@loaders.gl/csv@4.2.2/dist/dist.min', 'loader-json': 'https://cdn.jsdelivr.net/npm/@loaders.gl/json@4.2.2/dist/dist.min', 'loader-tiles': 'https://cdn.jsdelivr.net/npm/@loaders.gl/3d-tiles@4.2.2/dist/dist.min', 'mapbox-gl': 'https://api.mapbox.com/mapbox-gl-js/v3.0.1/mapbox-gl', 'carto': 'https://cdn.jsdelivr.net/npm/@deck.gl/carto@^9.0.20/dist.min'}, 'shim': {'deck-json': {'deps': ['deck-gl']}, 'deck-gl': {'deps': ['h3']}}});\n require([\"h3\"], function(h3) {\n window.h3 = h3\n on_load()\n })\n require([\"deck-gl\"], function(deck) {\n window.deck = deck\n on_load()\n })\n require([\"deck-json\"], function() {\n on_load()\n })\n require([\"loader-csv\"], function() {\n on_load()\n })\n require([\"loader-json\"], function() {\n on_load()\n })\n require([\"loader-tiles\"], function() {\n on_load()\n })\n require([\"mapbox-gl\"], function(mapboxgl) {\n window.mapboxgl = mapboxgl\n on_load()\n })\n require([\"carto\"], function() {\n on_load()\n })\n root._bokeh_is_loading = css_urls.length + 8;\n } else {\n root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n }\n\n const existing_stylesheets = []\n const links = document.getElementsByTagName('link')\n for (let i = 0; i < links.length; i++) {\n const link = links[i]\n if (link.href != null) {\n existing_stylesheets.push(link.href)\n }\n }\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const escaped = encodeURI(url)\n if (existing_stylesheets.indexOf(escaped) !== -1) {\n on_load()\n continue;\n }\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n } if (((window.deck !== undefined) && (!(window.deck instanceof HTMLElement))) || window.requirejs) {\n var urls = ['https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/h3-js@4.1.0/dist/h3-js.umd.js', 'https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/deck.gl@9.0.20/dist.min.js', 'https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/@deck.gl/json@9.0.20/dist.min.js', 'https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/@loaders.gl/csv@4.2.2/dist/dist.min.js', 'https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/@loaders.gl/json@4.2.2/dist/dist.min.js', 'https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/@loaders.gl/3d-tiles@4.2.2/dist/dist.min.js', 'https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/mapbox-gl-js/v3.0.1/mapbox-gl.js', 'https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/maplibre-gl/dist/maplibre-gl.js'];\n for (var i = 0; i < urls.length; i++) {\n skip.push(encodeURI(urls[i]))\n }\n } if (((window.mapboxgl !== undefined) && (!(window.mapboxgl instanceof HTMLElement))) || window.requirejs) {\n var urls = ['https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/@deck.gl/carto@^9.0.20/dist.min.js'];\n for (var i = 0; i < urls.length; i++) {\n skip.push(encodeURI(urls[i]))\n }\n } var existing_scripts = []\n const scripts = document.getElementsByTagName('script')\n for (let i = 0; i < scripts.length; i++) {\n var script = scripts[i]\n if (script.src != null) {\n existing_scripts.push(script.src)\n }\n }\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\n }\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (let i = 0; i < js_modules.length; i++) {\n const url = js_modules[i];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\n }\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (const name in js_exports) {\n const url = js_exports[name];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) >= 0 || root[name] != null) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\n }\n var element = document.createElement('script');\n element.onerror = on_error;\n element.async = false;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n element.textContent = `\n import ${name} from \"${url}\"\n window.${name} = ${name}\n window._bokeh_on_load()\n `\n document.head.appendChild(element);\n }\n if (!js_urls.length && !js_modules.length) {\n on_load()\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/h3-js@4.1.0/dist/h3-js.umd.js\", \"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/deck.gl@9.0.20/dist.min.js\", \"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/@deck.gl/json@9.0.20/dist.min.js\", \"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/@loaders.gl/csv@4.2.2/dist/dist.min.js\", \"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/@loaders.gl/json@4.2.2/dist/dist.min.js\", \"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/@loaders.gl/3d-tiles@4.2.2/dist/dist.min.js\", \"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/mapbox-gl-js/v3.0.1/mapbox-gl.js\", \"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/maplibre-gl/dist/maplibre-gl.js\", \"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/@deck.gl/carto@^9.0.20/dist.min.js\", \"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\"];\n const js_modules = [];\n const js_exports = {};\n const css_urls = [\"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/mapbox-gl-js/v3.0.1/mapbox-gl.css?v=1.5.2\", \"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/maplibre-gl@4.4.1/dist/maplibre-gl.css?v=1.5.2\"];\n const inline_js = [ function(Bokeh) {\n inject_raw_css(\"\\n.dataframe table{\\n border: none;\\n}\\n\\n.panel-df table{\\n width: 100%;\\n border-collapse: collapse;\\n border: none;\\n}\\n.panel-df td{\\n white-space: nowrap;\\n overflow: auto;\\n text-overflow: ellipsis;\\n}\\n\");\n }, function(Bokeh) {\n inject_raw_css(\"\\n.multi-select{\\n color: white;\\n z-index: 100;\\n background: rgba(44,43,43,0.5);\\n border-radius: 1px;\\n width: 120px !important;\\n height: 30px !important;\\n}\\n.multi-select > .bk {\\n padding: 5px;\\n width: 120px !important;\\n height: 30px !important;\\n}\\n\\n.deck-chart {\\n z-index: 10;\\n position: initial !important;\\n}\\n\");\n }, function(Bokeh) {\n inject_raw_css(\"\\n.center-header {\\n text-align: center\\n}\\n.bk-input-group {\\n padding: 10px;\\n}\\n#sidebar {\\n padding-top: 10px;\\n}\\n.custom-widget-box {\\n margin-top: 20px;\\n padding: 5px;\\n border: None !important;\\n}\\n.custom-widget-box > p {\\n margin: 0px;\\n}\\n.bk-input-group {\\n color: None !important;\\n}\\n.indicator {\\n text-align: center;\\n}\\n.widget-card {\\n margin: 5px 10px;\\n}\\n.number-card {\\n margin: 5px 10px;\\n text-align: center;\\n}\\n.number-card-value {\\n width: 100%;\\n margin: 0px;\\n}\\n\");\n }, function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n function(Bokeh) {\n (function(root, factory) {\n factory(root[\"Bokeh\"]);\n })(this, function(Bokeh) {\n let define;\n return (function outer(modules, entry) {\n if (Bokeh != null) {\n return Bokeh.register_plugin(modules, entry);\n } else {\n throw new Error(\"Cannot find Bokeh. You have to load it prior to loading plugins.\");\n }\n })\n ({\n \"custom/main\": function(require, module, exports) {\n const models = {\n \"CustomInspectTool\": require(\"custom/cuxfilter.charts.datashader.custom_extensions.graph_inspect_widget.custom_inspect_tool\").CustomInspectTool\n };\n require(\"base\").register_models(models);\n module.exports = models;\n },\n \"custom/cuxfilter.charts.datashader.custom_extensions.graph_inspect_widget.custom_inspect_tool\": function(require, module, exports) {\n \"use strict\";\n var _a;\n Object.defineProperty(exports, \"__esModule\", { value: true });\n exports.CustomInspectTool = exports.CustomInspectToolView = void 0;\n const inspect_tool_1 = require(\"models/tools/inspectors/inspect_tool\");\n class CustomInspectToolView extends inspect_tool_1.InspectToolView {\n connect_signals() {\n super.connect_signals();\n this.on_change([this.model.properties.active], () => {\n this.model._active = this.model.active;\n });\n }\n }\n exports.CustomInspectToolView = CustomInspectToolView;\n CustomInspectToolView.__name__ = \"CustomInspectToolView\";\n class CustomInspectTool extends inspect_tool_1.InspectTool {\n constructor(attrs) {\n super(attrs);\n }\n }\n exports.CustomInspectTool = CustomInspectTool;\n _a = CustomInspectTool;\n CustomInspectTool.__name__ = \"CustomInspectTool\";\n (() => {\n _a.prototype.default_view = CustomInspectToolView;\n _a.define(({ Boolean }) => ({\n _active: [Boolean, true]\n }));\n _a.register_alias(\"customInspect\", () => new _a());\n })();\n //# sourceMappingURL=graph_inspect_widget.py:CustomInspectTool.js.map\n }\n }, \"custom/main\");\n ;\n });\n\n },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n ];\n\n function run_inline_js() {\n if ((root.Bokeh !== undefined) || (force === true)) {\n for (let i = 0; i < inline_js.length; i++) {\n try {\n inline_js[i].call(root, root.Bokeh);\n } catch(e) {\n if (!reloading) {\n throw e;\n }\n }\n }\n // Cache old bokeh versions\n if 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older versions are fully initialized.\n if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n // If the timeout and bokeh was not successfully loaded we reset\n // everything and try loading again\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_is_initializing = false;\n root._bokeh_onload_callbacks = undefined;\n root._bokeh_is_loading = 0\n console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n load_or_wait();\n } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n setTimeout(load_or_wait, 100);\n } else {\n root._bokeh_is_initializing = true\n root._bokeh_onload_callbacks = []\n const bokeh_loaded = root.Bokeh != null && (root.Bokeh.version === py_version || (root.Bokeh.versions !== undefined && root.Bokeh.versions.has(py_version)));\n if (!reloading && !bokeh_loaded) {\n if (root.Bokeh) {\n root.Bokeh = undefined;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n }\n load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n }\n // Give older versions of the autoload script a head-start to ensure\n // they initialize before we start loading newer version.\n setTimeout(load_or_wait, 100)\n}(window));" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "\n", + "if ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n", + " window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n", + "}\n", + "\n", + "\n", + " function JupyterCommManager() {\n", + " }\n", + "\n", + " JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n", + " if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n", + " var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n", + " comm_manager.register_target(comm_id, function(comm) {\n", + " comm.on_msg(msg_handler);\n", + " });\n", + " } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n", + " window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n", + " comm.onMsg = msg_handler;\n", + " });\n", + " } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n", + " google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n", + " var messages = comm.messages[Symbol.asyncIterator]();\n", + " function processIteratorResult(result) {\n", + " var message = result.value;\n", + " console.log(message)\n", + " var content = {data: message.data, comm_id};\n", + " var buffers = []\n", + " for (var buffer of message.buffers || []) {\n", + " buffers.push(new DataView(buffer))\n", + " }\n", + " var metadata = 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(msg_handler) {\n", + " comm.onMsg = msg_handler;\n", + " }\n", + " } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n", + " var comm_promise = google.colab.kernel.comms.open(comm_id)\n", + " comm_promise.then((comm) => {\n", + " window.PyViz.comms[comm_id] = comm;\n", + " if (msg_handler) {\n", + " var messages = comm.messages[Symbol.asyncIterator]();\n", + " function processIteratorResult(result) {\n", + " var message = result.value;\n", + " var content = {data: message.data};\n", + " var metadata = message.metadata || {comm_id};\n", + " var msg = {content, metadata}\n", + " msg_handler(msg);\n", + " return messages.next().then(processIteratorResult);\n", + " }\n", + " return messages.next().then(processIteratorResult);\n", + " }\n", + " }) \n", + " var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n", + " return comm_promise.then((comm) => {\n", + " comm.send(data, metadata, buffers, disposeOnDone);\n", + " });\n", + " };\n", + " var comm = 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" return\n", + " }\n", + " var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", + " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", + " if (id !== undefined) {\n", + " var nchildren = toinsert.length;\n", + " var html_node = toinsert[nchildren-1].children[0];\n", + " html_node.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var scripts = [];\n", + " var nodelist = html_node.querySelectorAll(\"script\");\n", + " for (var i in nodelist) {\n", + " if (nodelist.hasOwnProperty(i)) {\n", + " scripts.push(nodelist[i])\n", + " }\n", + " }\n", + "\n", + " scripts.forEach( function (oldScript) {\n", + " var newScript = document.createElement(\"script\");\n", + " var attrs = [];\n", + " var nodemap = oldScript.attributes;\n", + " for (var j in nodemap) {\n", + " if (nodemap.hasOwnProperty(j)) {\n", + " attrs.push(nodemap[j])\n", + " }\n", + " }\n", + " attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n", + " newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n", + " oldScript.parentNode.replaceChild(newScript, oldScript);\n", + " });\n", + " if (JS_MIME_TYPE in output.data) {\n", + " toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n", + " }\n", + " output_area._hv_plot_id = id;\n", + " if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n", + " window.PyViz.plot_index[id] = Bokeh.index[id];\n", + " } else {\n", + " window.PyViz.plot_index[id] = null;\n", + " }\n", + " } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", + " var bk_div = document.createElement(\"div\");\n", + " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var script_attrs = bk_div.children[0].attributes;\n", + " for (var i = 0; i < script_attrs.length; i++) {\n", + " toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " }\n", + " // store reference to server id on output_area\n", 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+ " var render_items = [{\"docid\":\"99850f82-9573-43b8-b5f3-b110aca07af7\",\"roots\":{\"c579838c-ec72-4a6d-aafd-3647931753b4\":\"b60bb379-3c61-4d36-b562-b64bf67c3135\"},\"root_ids\":[\"c579838c-ec72-4a6d-aafd-3647931753b4\"]}];\n", + " var docs = Object.values(docs_json)\n", + " if (!docs) {\n", + " return\n", + " }\n", + " const py_version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n", + " async function embed_document(root) {\n", + " var Bokeh = get_bokeh(root)\n", + " await Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", + " for (const render_item of render_items) {\n", + " for (const root_id of render_item.root_ids) {\n", + "\tconst id_el = document.getElementById(root_id)\n", + "\tif (id_el.children.length && id_el.children[0].hasAttribute('data-root-id')) {\n", + "\t const root_el = id_el.children[0]\n", + "\t root_el.id = root_el.id + '-rendered'\n", + "\t for (const child of root_el.children) {\n", + " // Ensure JupyterLab does not capture keyboard shortcuts\n", + " // see: https://jupyterlab.readthedocs.io/en/4.1.x/extension/notebook.html#keyboard-interaction-model\n", + "\t child.setAttribute('data-lm-suppress-shortcuts', 'true')\n", + "\t }\n", + "\t}\n", + " }\n", + " }\n", + " }\n", + " function get_bokeh(root) {\n", + " if (root.Bokeh === undefined) {\n", + " return null\n", + " } else if (root.Bokeh.version !== py_version) {\n", + " if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(py_version)) {\n", + "\treturn null\n", + " }\n", + " return root.Bokeh.versions.get(py_version);\n", + " } else if (root.Bokeh.version === py_version) {\n", + " return root.Bokeh\n", + " }\n", + " return null\n", + " }\n", + " function is_loaded(root) {\n", + " var Bokeh = get_bokeh(root)\n", + " return (Bokeh != null && Bokeh.Panel !== undefined && ( root.deck !== undefined) && ( root.deck !== undefined))\n", + " }\n", + " if (is_loaded(root)) {\n", + " embed_document(root);\n", + " } else {\n", + " var attempts = 0;\n", + " var timer = setInterval(function(root) {\n", + " if (is_loaded(root)) {\n", + " clearInterval(timer);\n", + " embed_document(root);\n", + " } else if (document.readyState == \"complete\") {\n", + " attempts++;\n", + " if (attempts > 200) {\n", + " clearInterval(timer);\n", + "\t var Bokeh = get_bokeh(root)\n", + "\t if (Bokeh == null || Bokeh.Panel == null) {\n", + " console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n", + "\t } else {\n", + "\t console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n", + "\t embed_document(root)\n", + "\t }\n", + " }\n", + " }\n", + " }, 25, root)\n", + " }\n", + "})(window);</script>" + ], + "text/plain": [ + "GridSpec(ncols=12, nrows=1)\n", + " [0] GridSpec(height=800, ncols=12, nrows=5, sizing_mode='fixed', width=1200)\n", + " [0] HoloViews(DynamicMap, height=800, sizing_mode='stretch_both', width=1200)\n", + " [1] WidgetBox(styles={'border-color': '...})\n", + " [0] Number(css_classes=['indicator'], default_color='#2B2B2B', font_size='18pt', format='{value:,}', name='Datapoints Selected', sizing_mode='stretch_width', title_size='14pt', value=3048671)\n", + " [1] Progress(sizing_mode='stretch_width', styles={'--success-bg-color': '...}, value=100)" + ] + }, + "execution_count": 31, + "metadata": { + "application/vnd.holoviews_exec.v0+json": { + "id": "c579838c-ec72-4a6d-aafd-3647931753b4" + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "dash = cxf_data.dashboard([heatmap_chart], theme=cxf.themes.dark, data_size_widget=True)\n", + "\n", + "dash.app()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import IPython\n", + "app = IPython.Application.instance()\n", + "app.kernel.do_shutdown(True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Well Done!** Let's move to the [next notebook](2-04_networkx_cugraph.ipynb). " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "<img src=\"./images/DLI_Header.png\" width=400/>" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.15" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Fundamentals_of_Accelerated_Data_Science/2-04_networkx_cugraph.ipynb b/Fundamentals_of_Accelerated_Data_Science/2-04_networkx_cugraph.ipynb new file mode 100644 index 0000000..dee3520 --- /dev/null +++ b/Fundamentals_of_Accelerated_Data_Science/2-04_networkx_cugraph.ipynb @@ -0,0 +1,692 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "2d190a78-7253-4fad-9d9c-6b4fb33c8bf2", + "metadata": { + "tags": [] + }, + "source": [ + "<img src=\"./images/DLI_Header.png\" width=400/>" + ] + }, + { + "cell_type": "markdown", + "id": "8a2c4abf-6278-4edd-83f8-f0afac4c834f", + "metadata": {}, + "source": [ + "# Fundamentals of Accelerated Data Science #" + ] + }, + { + "cell_type": "markdown", + "id": "e1e78ef4-c0de-433e-8616-bd946f69d30e", + "metadata": {}, + "source": [ + "## 04 - cuGraph as a NetworkX backend ##" + ] + }, + { + "cell_type": "markdown", + "id": "0828e0b4-7935-4b77-95ef-e06b72f0319e", + "metadata": {}, + "source": [ + "**Table of Contents**\n", + "<br>\n", + "This notebook introduces the various methods of utilizing the cuGraph backend for NetworkX and runs centrality algorithms on the dataset. This notebook covers the below sections:\n", + "1. [Background](#Background)\n", + "2. [Installation](#Installation)\n", + "3. [Utilizing nx-cugraph](#Utilizing-nx-cugraph)\n", + " * [Runtime Environment Variable](#Runtime-Environment-Variable)\n", + " * [Backend Keyword Argument](#Backend-Keyword-Argument)\n", + " * [Type-Based Dispatching](#Type-Based-Dispatching)\n", + "4. [Computing Centrality](#Computing-Centrality)\n", + " * [Creating Graph](#Creating-Graph)\n", + " * [Running Centrality Algorithms](#Running-Centrality-Algorithms)\n", + " * [Betweenness Centrality](#Betweenness-Centrality)\n", + " * [Degree Centrality](#Degree-Centrality)\n", + " * [Katz Centrality](#Katz-Centrality)\n", + " * [Pagerank Centrality](#Pagerank-Centrality)\n", + " * [Eigenvector Centrality](#Eigenvector-Centrality)\n", + " * [Visualize Results](#Visualize-Results)\n", + " * [Exercise #1 - Type Dispatch](#Exercise-#1---Type-Dispatch)" + ] + }, + { + "cell_type": "markdown", + "id": "c57b79ba-c7c7-49d2-9e21-c388bbe6ca98", + "metadata": {}, + "source": [ + "## Background ##\n", + "RAPIDS recently introduced a new backend to NetworkX called nx-cugraph. With this backend, you can automatically accelerate supported algorithms. In this notebook, we will cover the various methods of enabling the cugraph backend, and use the backend to run different centrality algorithms." + ] + }, + { + "cell_type": "markdown", + "id": "697ea4c9-b416-43d5-9d2c-28aa41ef2561", + "metadata": {}, + "source": [ + "## Installation ##\n", + "We have already prepared the environment with nx-cugraph installed. When you are using your own environment, below is the command for installation. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "8bce324e-ec19-4d87-96e5-bc134ab53baa", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Looking in indexes: https://pypi.org/simple, https://pypi.anaconda.org/rapidsai-wheels-nightly/simple\n", + "Requirement already satisfied: nx-cugraph-cu12 in /opt/conda/lib/python3.10/site-packages (25.4.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager, possibly rendering your system unusable.It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv. Use the --root-user-action option if you know what you are doing and want to suppress this warning.\u001b[0m\u001b[33m\n", + "\u001b[0m" + ] + } + ], + "source": [ + "!pip install nx-cugraph-cu12 --no-deps --extra-index-url https://pypi.anaconda.org/rapidsai-wheels-nightly/simple" + ] + }, + { + "cell_type": "markdown", + "id": "a9ea09f4-6c93-4785-bcc3-44c6f040dfc6", + "metadata": {}, + "source": [ + "## Utilizing nx-cugraph ##\n", + "There are 3 ways to utilize nx-cugraph\n", + "\n", + "1. **Environment Variable at Runtime**\n", + "2. **Backend keyword argument**\n", + "3. **Type-Based dispatching**\n", + "\n", + "Let's dig a little deeper in to each of these methods." + ] + }, + { + "cell_type": "markdown", + "id": "8b4322fd-9f56-4cbc-a00c-8fac4b2b2fe1", + "metadata": {}, + "source": [ + "### Runtime Environment Variable ###\n", + "The NETWORKX_AUTOMATIC_BACKENDS environment variable can be used to have NetworkX automatically dispatch to specified backends. Set NETWORKX_AUTOMATIC_BACKENDS=cugraph to use nx-cugraph to GPU accelerate supported APIs with no code changes. We will also be loading the cuDF pandas module to accelerate csv loading." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "b41fef7f-5d43-4481-98a7-d9f3cb54066c", + "metadata": {}, + "outputs": [], + "source": [ + "!NETWORKX_AUTOMATIC_BACKENDS=cugraph python -m cudf.pandas scripts/networkx.py" + ] + }, + { + "cell_type": "markdown", + "id": "5ffb6c4b-a03a-4bfb-9b92-14c59e6dcd75", + "metadata": {}, + "source": [ + "### Backend Keyword Argument ###\n", + "NetworkX also supports explicitly specifying a particular backend for supported APIs with the backend= keyword argument. This argument takes precedence over the NETWORKX_AUTOMATIC_BACKENDS environment variable. This method also requires that the specified backend already be installed." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "8183ecc7-8544-4914-8c07-c904ba12225a", + "metadata": {}, + "outputs": [], + "source": [ + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "\n", + "%load_ext cudf.pandas\n", + "import networkx as nx\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Load the CSV file\n", + "road_graph = pd.read_csv('./data/road_graph.csv', dtype=['int32', 'int32', 'float32'], nrows=1000)\n", + "\n", + "# Create an empty graph\n", + "G = nx.from_pandas_edgelist(road_graph, source='src', target='dst', edge_attr='length')\n", + "b = nx.betweenness_centrality(G, k=1000, backend=\"cugraph\")" + ] + }, + { + "cell_type": "markdown", + "id": "e588aa65-6281-4c19-a51c-42f044636ac0", + "metadata": {}, + "source": [ + "### Type-Based Dispatching ###\n", + "For users wanting to ensure a particular behavior, without the potential for runtime conversions, NetworkX offers type-based dispatching. To utilize this method, users must import the desired backend and create a Graph instance for it." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "5fea9300-8d75-443a-9ec0-ee65c8ccaf0f", + "metadata": {}, + "outputs": [], + "source": [ + "import networkx as nx\n", + "import nx_cugraph as nxcg\n", + "\n", + "# Loading data from previous cell\n", + "G = nx.from_pandas_edgelist(road_graph, source='src', target='dst', edge_attr='length') \n", + "\n", + "nxcg_G = nxcg.from_networkx(G) # conversion happens once here\n", + "b = nx.betweenness_centrality(nxcg_G, k=1000) # nxcg Graph type causes cugraph backend to be used, no conversion necessary" + ] + }, + { + "cell_type": "markdown", + "id": "cb5a17e1-d886-4d20-8d4b-ce900280279c", + "metadata": {}, + "source": [ + "## Computing Centrality ##\n", + "Now that we learned how to enable nx-cugraph, let's try to use it in a workflow! We will be using the backend argument for this example. First let's create a graph." + ] + }, + { + "cell_type": "markdown", + "id": "19bea37c-bccf-4815-81bd-aa1de553812d", + "metadata": {}, + "source": [ + "### Creating Graph ###" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "2b4420d7-7c89-4914-809f-4e323a12f47f", + "metadata": {}, + "outputs": [], + "source": [ + "# Create a graph from already loaded dataframe\n", + "G = nx.from_pandas_edgelist(road_graph, source='src', target='dst', edge_attr='length')" + ] + }, + { + "cell_type": "markdown", + "id": "7dc1ad5b-8454-4277-9568-0cdacbebd9f1", + "metadata": {}, + "source": [ + "### Running Centrality Algorithms ###\n", + "Now, let's run the various centrality algorithms!" + ] + }, + { + "cell_type": "markdown", + "id": "1c52b7b3-6c23-45be-9ace-34a667f132aa", + "metadata": {}, + "source": [ + "### Betweenness Centrality ###\n", + "Quantifies the number of times a node acts as a bridge along the shortest path between two other nodes, highlighting its importance in information flow" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "281374af-c7cf-4592-a34d-796c1158dab6", + "metadata": {}, + "outputs": [], + "source": [ + "b = nx.betweenness_centrality(G, backend=\"cugraph\")" + ] + }, + { + "cell_type": "markdown", + "id": "f98b2975-1f72-4bff-83c7-ace7aab65d98", + "metadata": {}, + "source": [ + "### Degree Centrality ###\n", + "Measures the number of direct connections a node has, indicating how well-connected it is within the network" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "3e0c4460-6d25-4a2b-8b8f-8f8c6ef617b0", + "metadata": {}, + "outputs": [], + "source": [ + "d = nx.degree_centrality(G, backend=\"cugraph\")" + ] + }, + { + "cell_type": "markdown", + "id": "0665a659-16b1-48b4-b3bb-9aa5659ef91c", + "metadata": {}, + "source": [ + "### Katz Centrality ###\n", + "Measures a node's centrality based on its global influence in the network, considering both direct and indirect connections" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "8ce418d2-9eda-40bc-9733-b82d8d7556b1", + "metadata": {}, + "outputs": [], + "source": [ + "k = nx.katz_centrality(G, backend=\"cugraph\")" + ] + }, + { + "cell_type": "markdown", + "id": "0712cedb-87ba-4a08-a74d-24997d02a636", + "metadata": {}, + "source": [ + "### Pagerank Centrality ###\n", + "Determines a node's importance based on the quantity and quality of links to it, similar to Google's original PageRank algorithm" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "a17ee15b-8758-484b-82b9-a158187231c5", + "metadata": {}, + "outputs": [], + "source": [ + "p = nx.pagerank(G, max_iter=10, tol=1.0e-3, backend=\"cugraph\")" + ] + }, + { + "cell_type": "markdown", + "id": "c5f57a5e-95e4-47f7-a9ec-04a99fa2c1dc", + "metadata": {}, + "source": [ + "### Eigenvector Centrality ###\n", + "Assigns scores to nodes based on the principle that connections to high-scoring nodes contribute more to the node's own score than connections to low-scoring nodes" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "3eb1e358-ae8e-4399-bf45-90616b663e9d", + "metadata": {}, + "outputs": [], + "source": [ + "e = nx.eigenvector_centrality(G, max_iter=1000, tol=1.0e-3, backend=\"cugraph\")" + ] + }, + { + "cell_type": "markdown", + "id": "0bc9178c-e66a-4c75-bf91-0c5d668b5634", + "metadata": {}, + "source": [ + "### Visualize Results ###\n", + "Now let's visualize results! We will only display the top 5 rows for readibility. " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "69b6c23d-78a0-4dbb-be19-913ad180fe94", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<style type=\"text/css\">\n", + "</style>\n", + "<table id=\"T_89e5e\" style='display:inline'>\n", + " <caption>Degree</caption>\n", + " <thead>\n", + " <tr>\n", + " <th id=\"T_89e5e_level0_col0\" class=\"col_heading level0 col0\" >vertex</th>\n", + " <th id=\"T_89e5e_level0_col1\" class=\"col_heading level0 col1\" >degree_centrality</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <td id=\"T_89e5e_row0_col0\" class=\"data row0 col0\" >24</td>\n", + " <td id=\"T_89e5e_row0_col1\" class=\"data row0 col1\" >0.002847</td>\n", + " </tr>\n", + " <tr>\n", + " <td id=\"T_89e5e_row1_col0\" class=\"data row1 col0\" >72</td>\n", + " <td id=\"T_89e5e_row1_col1\" class=\"data row1 col1\" >0.002847</td>\n", + " </tr>\n", + " <tr>\n", + " <td id=\"T_89e5e_row2_col0\" class=\"data row2 col0\" >86</td>\n", + " <td id=\"T_89e5e_row2_col1\" class=\"data row2 col1\" >0.002847</td>\n", + " </tr>\n", + " <tr>\n", + " <td id=\"T_89e5e_row3_col0\" class=\"data row3 col0\" >127</td>\n", + " <td id=\"T_89e5e_row3_col1\" class=\"data row3 col1\" >0.002847</td>\n", + " </tr>\n", + " <tr>\n", + " <td id=\"T_89e5e_row4_col0\" class=\"data row4 col0\" >133</td>\n", + " <td id=\"T_89e5e_row4_col1\" class=\"data row4 col1\" >0.002847</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "<style type=\"text/css\">\n", + "</style>\n", + "<table id=\"T_b0826\" style='display:inline'>\n", + " <caption>Betweenness</caption>\n", + " <thead>\n", + " <tr>\n", + " <th id=\"T_b0826_level0_col0\" class=\"col_heading level0 col0\" >vertex</th>\n", + " <th id=\"T_b0826_level0_col1\" class=\"col_heading level0 col1\" >betweenness_centrality</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <td id=\"T_b0826_row0_col0\" class=\"data row0 col0\" >222</td>\n", + " <td id=\"T_b0826_row0_col1\" class=\"data row0 col1\" >0.000007</td>\n", + " </tr>\n", + " <tr>\n", + " <td id=\"T_b0826_row1_col0\" class=\"data row1 col0\" >381</td>\n", + " <td id=\"T_b0826_row1_col1\" class=\"data row1 col1\" >0.000007</td>\n", + " </tr>\n", + " <tr>\n", + " <td id=\"T_b0826_row2_col0\" class=\"data row2 col0\" >24</td>\n", + " <td id=\"T_b0826_row2_col1\" class=\"data row2 col1\" >0.000006</td>\n", + " </tr>\n", + " <tr>\n", + " <td id=\"T_b0826_row3_col0\" class=\"data row3 col0\" >72</td>\n", + " <td id=\"T_b0826_row3_col1\" class=\"data row3 col1\" >0.000006</td>\n", + " </tr>\n", + " <tr>\n", + " <td id=\"T_b0826_row4_col0\" class=\"data row4 col0\" >86</td>\n", + " <td id=\"T_b0826_row4_col1\" class=\"data row4 col1\" >0.000006</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "<style type=\"text/css\">\n", + "</style>\n", + "<table id=\"T_dcc2a\" style='display:inline'>\n", + " <caption>Katz</caption>\n", + " <thead>\n", + " <tr>\n", + " <th id=\"T_dcc2a_level0_col0\" class=\"col_heading level0 col0\" >vertex</th>\n", + " <th id=\"T_dcc2a_level0_col1\" class=\"col_heading level0 col1\" >katz_centrality</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <td id=\"T_dcc2a_row0_col0\" class=\"data row0 col0\" >24</td>\n", + " <td id=\"T_dcc2a_row0_col1\" class=\"data row0 col1\" >0.033058</td>\n", + " </tr>\n", + " <tr>\n", + " <td id=\"T_dcc2a_row1_col0\" class=\"data row1 col0\" >72</td>\n", + " <td id=\"T_dcc2a_row1_col1\" class=\"data row1 col1\" >0.033058</td>\n", + " </tr>\n", + " <tr>\n", + " <td id=\"T_dcc2a_row2_col0\" class=\"data row2 col0\" >86</td>\n", + " <td id=\"T_dcc2a_row2_col1\" class=\"data row2 col1\" >0.033058</td>\n", + " </tr>\n", + " <tr>\n", + " <td id=\"T_dcc2a_row3_col0\" class=\"data row3 col0\" >127</td>\n", + " <td id=\"T_dcc2a_row3_col1\" class=\"data row3 col1\" >0.033058</td>\n", + " </tr>\n", + " <tr>\n", + " <td id=\"T_dcc2a_row4_col0\" class=\"data row4 col0\" >133</td>\n", + " <td id=\"T_dcc2a_row4_col1\" class=\"data row4 col1\" >0.033058</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "<style type=\"text/css\">\n", + "</style>\n", + "<table id=\"T_a76f4\" style='display:inline'>\n", + " <caption>PageRank</caption>\n", + " <thead>\n", + " <tr>\n", + " <th id=\"T_a76f4_level0_col0\" class=\"col_heading level0 col0\" >vertex</th>\n", + " <th id=\"T_a76f4_level0_col1\" class=\"col_heading level0 col1\" >pagerank</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <td id=\"T_a76f4_row0_col0\" class=\"data row0 col0\" >24</td>\n", + " <td id=\"T_a76f4_row0_col1\" class=\"data row0 col1\" >0.002525</td>\n", + " </tr>\n", + " <tr>\n", + " <td id=\"T_a76f4_row1_col0\" class=\"data row1 col0\" >72</td>\n", + " <td id=\"T_a76f4_row1_col1\" class=\"data row1 col1\" >0.002525</td>\n", + " </tr>\n", + " <tr>\n", + " <td id=\"T_a76f4_row2_col0\" class=\"data row2 col0\" >86</td>\n", + " <td id=\"T_a76f4_row2_col1\" class=\"data row2 col1\" >0.002525</td>\n", + " </tr>\n", + " <tr>\n", + " <td id=\"T_a76f4_row3_col0\" class=\"data row3 col0\" >127</td>\n", + " <td id=\"T_a76f4_row3_col1\" class=\"data row3 col1\" >0.002525</td>\n", + " </tr>\n", + " <tr>\n", + " <td id=\"T_a76f4_row4_col0\" class=\"data row4 col0\" >133</td>\n", + " <td id=\"T_a76f4_row4_col1\" class=\"data row4 col1\" >0.002525</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "<style type=\"text/css\">\n", + "</style>\n", + "<table id=\"T_6a3c1\" style='display:inline'>\n", + " <caption>EigenVector</caption>\n", + " <thead>\n", + " <tr>\n", + " <th id=\"T_6a3c1_level0_col0\" class=\"col_heading level0 col0\" >vertex</th>\n", + " <th id=\"T_6a3c1_level0_col1\" class=\"col_heading level0 col1\" >eigenvector_centrality</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <td id=\"T_6a3c1_row0_col0\" class=\"data row0 col0\" >24</td>\n", + " <td id=\"T_6a3c1_row0_col1\" class=\"data row0 col1\" >0.064086</td>\n", + " </tr>\n", + " <tr>\n", + " <td id=\"T_6a3c1_row1_col0\" class=\"data row1 col0\" >72</td>\n", + " <td id=\"T_6a3c1_row1_col1\" class=\"data row1 col1\" >0.064086</td>\n", + " </tr>\n", + " <tr>\n", + " <td id=\"T_6a3c1_row2_col0\" class=\"data row2 col0\" >86</td>\n", + " <td id=\"T_6a3c1_row2_col1\" class=\"data row2 col1\" >0.064086</td>\n", + " </tr>\n", + " <tr>\n", + " <td id=\"T_6a3c1_row3_col0\" class=\"data row3 col0\" >127</td>\n", + " <td id=\"T_6a3c1_row3_col1\" class=\"data row3 col1\" >0.064086</td>\n", + " </tr>\n", + " <tr>\n", + " <td id=\"T_6a3c1_row4_col0\" class=\"data row4 col0\" >133</td>\n", + " <td id=\"T_6a3c1_row4_col1\" class=\"data row4 col1\" >0.064086</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from IPython.display import display_html\n", + "dc_top = pd.DataFrame(sorted(d.items(), key=lambda x:x[1], reverse=True)[:5], columns=[\"vertex\", \"degree_centrality\"])\n", + "bc_top = pd.DataFrame(sorted(b.items(), key=lambda x:x[1], reverse=True)[:5], columns=[\"vertex\", \"betweenness_centrality\"])\n", + "katz_top = pd.DataFrame(sorted(k.items(), key=lambda x:x[1], reverse=True)[:5], columns=[\"vertex\", \"katz_centrality\"])\n", + "pr_top = pd.DataFrame(sorted(p.items(), key=lambda x:x[1], reverse=True)[:5], columns=[\"vertex\", \"pagerank\"])\n", + "ev_top = pd.DataFrame(sorted(e.items(), key=lambda x:x[1], reverse=True)[:5], columns=[\"vertex\", \"eigenvector_centrality\"])\n", + "\n", + "df1_styler = dc_top.style.set_table_attributes(\"style='display:inline'\").set_caption('Degree').hide(axis='index')\n", + "df2_styler = bc_top.style.set_table_attributes(\"style='display:inline'\").set_caption('Betweenness').hide(axis='index')\n", + "df3_styler = katz_top.style.set_table_attributes(\"style='display:inline'\").set_caption('Katz').hide(axis='index')\n", + "df4_styler = pr_top.style.set_table_attributes(\"style='display:inline'\").set_caption('PageRank').hide(axis='index')\n", + "df5_styler = ev_top.style.set_table_attributes(\"style='display:inline'\").set_caption('EigenVector').hide(axis='index')\n", + "\n", + "display_html(df1_styler._repr_html_()+df2_styler._repr_html_()+df3_styler._repr_html_()+df4_styler._repr_html_()+df5_styler._repr_html_(), raw=True)" + ] + }, + { + "cell_type": "markdown", + "id": "1a653ca9-9448-4ba5-85b2-f6c885c273a9", + "metadata": {}, + "source": [ + "### Exercise #1 - Type Dispatch ###\n", + "Use the type dispatching method to obtain pagerank centrality results with the cugraph backend." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "6eb90078-1479-4847-97b7-eb119e9d5478", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>vertex</th>\n", + " <th>pagerank</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>24</td>\n", + " <td>0.002525</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>72</td>\n", + " <td>0.002525</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>86</td>\n", + " <td>0.002525</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>127</td>\n", + " <td>0.002525</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>133</td>\n", + " <td>0.002525</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " vertex pagerank\n", + "0 24 0.002525\n", + "1 72 0.002525\n", + "2 86 0.002525\n", + "3 127 0.002525\n", + "4 133 0.002525" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p = nx.pagerank(G, max_iter=10, tol=1.0e-3, backend=\"cugraph\")\n", + "\n", + "pd.DataFrame(sorted(p.items(), key=lambda x:x[1], reverse=True)[:5], columns=[\"vertex\", \"pagerank\"])" + ] + }, + { + "cell_type": "raw", + "id": "1771c759-fbae-4831-b9b9-663514e460d7", + "metadata": {}, + "source": [ + "\n", + "import networkx as nx\n", + "import nx_cugraph as nxcg\n", + "\n", + "# Loading data from previous cell\n", + "G = nx.from_pandas_edgelist(road_graph, source='src', target='dst', edge_attr='length') \n", + "\n", + "nxcg_G = nxcg.from_networkx(G) # conversion happens once here\n", + "p = nx.pagerank(nxcg_G, max_iter=10, tol=1.0e-3) # nxcg Graph type causes cugraph backend to be used, no conversion necessary\n", + "\n", + "pd.DataFrame(sorted(p.items(), key=lambda x:x[1], reverse=True)[:5], columns=[\"vertex\", \"pagerank\"])" + ] + }, + { + "cell_type": "markdown", + "id": "bcdee8d8-c8e0-4521-b7ec-1f85f014e3ca", + "metadata": {}, + "source": [ + "Click ... for solution. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d70c78b7-551d-4d9e-b428-32b26adcd3c4", + "metadata": {}, + "outputs": [], + "source": [ + "import IPython\n", + "app = IPython.Application.instance()\n", + "app.kernel.do_shutdown(True)" + ] + }, + { + "cell_type": "markdown", + "id": "2279fdf1-82c0-4c6e-ac8e-b952f4777562", + "metadata": {}, + "source": [ + "**Well Done!** " + ] + }, + { + "cell_type": "markdown", + "id": "3fbc12b2-585c-48a9-a176-b2572040d378", + "metadata": { + "tags": [] + }, + "source": [ + "<img src=\"./images/DLI_Header.png\" width=400/>" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.15" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Fundamentals_of_Accelerated_Data_Science/3-02_k-means.ipynb b/Fundamentals_of_Accelerated_Data_Science/3-02_k-means.ipynb new file mode 100644 index 0000000..4b2c028 --- /dev/null +++ b/Fundamentals_of_Accelerated_Data_Science/3-02_k-means.ipynb @@ -0,0 +1,1992 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<img src=\"./images/DLI_Header.png\" width=400/>" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Fundamentals of Accelerated Data Science # " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 02 - K-Means ##\n", + "\n", + "**Table of Contents**\n", + "<br>\n", + "This notebook uses GPU-accelerated K-means to find the best locations for a fixed number of humanitarian supply airdrop depots. This notebook covers the below sections: \n", + "1. [Environment](#Environment)\n", + "2. [Load Data](#Load-Data)\n", + "3. [K-Means Clustering](#K-Means-Clustering)\n", + " * [Exercise #1 - Make Another `KMeans` Instance](#Exercise-#1---Make-Another-KMeans-Instance)\n", + "4. [Visualize the Clusters](#Visualize-the-Clusters)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Environment ##\n", + "For the first time we import `cuml`, the RAPIDS GPU-accelerated library containing many common machine learning algorithms. We will be visualizing the results of your work in this notebook, so we also import `cuxfilter`." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "import cudf\n", + "import cuml\n", + "\n", + "import cuxfilter as cxf" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load Data ##\n", + "For this notebook we load again the cleaned UK population data--in this case, we are not specifically looking at counties, so we omit that column and just keep the grid coordinate columns." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "northing float64\n", + "easting float64\n", + "dtype: object\n" + ] + }, + { + "data": { + "text/plain": [ + "(58479894, 2)" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "gdf = cudf.read_csv('./data/clean_uk_pop.csv', usecols=['easting', 'northing'])\n", + "print(gdf.dtypes)\n", + "gdf.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>northing</th>\n", + " <th>easting</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>515491.5313</td>\n", + " <td>430772.1875</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>503572.4688</td>\n", + " <td>434685.8750</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>517903.6563</td>\n", + " <td>432565.5313</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>517059.9063</td>\n", + " <td>427660.6250</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>509228.6875</td>\n", + " <td>425527.7813</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " northing easting\n", + "0 515491.5313 430772.1875\n", + "1 503572.4688 434685.8750\n", + "2 517903.6563 432565.5313\n", + "3 517059.9063 427660.6250\n", + "4 509228.6875 425527.7813" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gdf.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<a name='#s2-3'></a>\n", + "## K-Means Clustering ##\n", + "The unsupervised K-means clustering algorithm will look for a fixed number *k* of centroids in the data and clusters each point with its closest centroid. K-means can be effective when the number of clusters *k* is known or has a good estimate (such as from a model of the underlying mechanics of a problem).\n", + "\n", + "Assume that in addition to knowing the distribution of the population, which we do, we would like to estimate the best locations to build a fixed number of humanitarian supply depots from which we can perform airdrops and reach the population most efficiently. We can use K-means, setting *k* to the number of supply depots available and fitting on the locations of the population, to identify candidate locations.\n", + "\n", + "GPU-accelerated K-means is just as easy as its CPU-only scikit-learn counterpart. In this series of exercises, you will use it to optimize the locations for 5 supply depots.\n", + "\n", + "`cuml.KMeans()` will initialize a K-means instance. Use it now to initialize a K-means instance called `km`, passing the named argument `n_clusters` set equal to our desired number `5`. Use the `km.fit` method to fit `km` to the population's locations by passing it the population data. After fitting, add the cluster labels back to the `gdf` in a new column named `cluster`. Finally, you can use `km.cluster_centers_` to see where the algorithm created the 5 centroids.\n", + "\n", + "Below we train a K-means clustering algorithm to find 5 clusters. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>0</th>\n", + " <th>1</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>306647.898235</td>\n", + " <td>408370.452191</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>442109.465392</td>\n", + " <td>402673.747673</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>288997.149971</td>\n", + " <td>553805.430444</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>148770.463641</td>\n", + " <td>311786.805381</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>170553.110214</td>\n", + " <td>521605.459724</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " 0 1\n", + "0 306647.898235 408370.452191\n", + "1 442109.465392 402673.747673\n", + "2 288997.149971 553805.430444\n", + "3 148770.463641 311786.805381\n", + "4 170553.110214 521605.459724" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "# instantaite\n", + "km = cuml.KMeans(n_clusters=5)\n", + "\n", + "# fit\n", + "km.fit(gdf)\n", + "\n", + "# assign cluster as new column\n", + "gdf['cluster'] = km.labels_\n", + "km.cluster_centers_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<a name='#s2-e1'></a>\n", + "## Exercise #1 - Make Another `KMeans` Instance ##\n", + "\n", + "**Instructions**: <br>\n", + "* Modify the `<FIXME>` only and execute the below cell to instantiate a K-means instance with 6 clusters.\n", + "* Modify the `<FIXME>` only and execute the cell below to fit the data. " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "km = cuml.KMeans(n_clusters=6)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>0</th>\n", + " <th>1</th>\n", + " <th>2</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>156942.821777</td>\n", + " <td>425534.234660</td>\n", + " <td>3.384935</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>541098.296590</td>\n", + " <td>415800.758548</td>\n", + " <td>1.000003</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>188442.901682</td>\n", + " <td>542164.754638</td>\n", + " <td>3.716473</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>404166.790803</td>\n", + " <td>395320.596086</td>\n", + " <td>0.869631</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>301678.636519</td>\n", + " <td>435787.150921</td>\n", + " <td>0.382444</td>\n", + " </tr>\n", + " <tr>\n", + " <th>5</th>\n", + " <td>143993.814440</td>\n", + " <td>284471.494937</td>\n", + " <td>2.970024</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " 0 1 2\n", + "0 156942.821777 425534.234660 3.384935\n", + "1 541098.296590 415800.758548 1.000003\n", + "2 188442.901682 542164.754638 3.716473\n", + "3 404166.790803 395320.596086 0.869631\n", + "4 301678.636519 435787.150921 0.382444\n", + "5 143993.814440 284471.494937 2.970024" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "km.fit(gdf)\n", + "gdf['cluster'] = km.labels_\n", + "km.cluster_centers_" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "\n", + "km = cuml.KMeans(n_clusters=6)\n", + "\n", + "km.fit(gdf)\n", + "gdf['cluster'] = km.labels_\n", + "km.cluster_centers_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Click ... for solution. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<a id='#s2-4'></a>\n", + "## Visualize the Clusters ##\n", + "To help us understand where clusters are located, we make a visualization that separates them, using the same three steps as before.\n", + "\n", + "Below we plot the clusters with cuxfilter. " + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "ename": "TypeError", + "evalue": "scatter() got an unexpected keyword argument 'data_points'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[42], line 24\u001b[0m\n\u001b[1;32m 14\u001b[0m scatter_chart \u001b[38;5;241m=\u001b[39m cxf\u001b[38;5;241m.\u001b[39mcharts\u001b[38;5;241m.\u001b[39mdatashader\u001b[38;5;241m.\u001b[39mscatter(\n\u001b[1;32m 15\u001b[0m x\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124measting\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 16\u001b[0m y\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnorthing\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 20\u001b[0m pixel_shade_type\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlinear\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 21\u001b[0m )\n\u001b[1;32m 23\u001b[0m \u001b[38;5;66;03m# centroid layer as big, static points (uses a separate in-memory dataframe)\u001b[39;00m\n\u001b[0;32m---> 24\u001b[0m centroid_chart \u001b[38;5;241m=\u001b[39m \u001b[43mcxf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcharts\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdatashader\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mscatter\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 25\u001b[0m \u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43measting\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 26\u001b[0m \u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mnorthing\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 27\u001b[0m \u001b[43m \u001b[49m\u001b[43mdata_points\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcenters\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# <-- overlay from a separate DataFrame\u001b[39;49;00m\n\u001b[1;32m 28\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# point_size=18,\u001b[39;49;00m\n\u001b[1;32m 29\u001b[0m \u001b[43m \u001b[49m\u001b[43mcolor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43myellow\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# a bright color to stand out\u001b[39;49;00m\n\u001b[1;32m 30\u001b[0m \u001b[43m \u001b[49m\u001b[43mpixel_shade_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43meq_hist\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# just to make them pop; optional\u001b[39;49;00m\n\u001b[1;32m 31\u001b[0m \u001b[43m)\u001b[49m\n\u001b[1;32m 33\u001b[0m \u001b[38;5;66;03m# widget to filter shown clusters\u001b[39;00m\n\u001b[1;32m 34\u001b[0m cluster_widget \u001b[38;5;241m=\u001b[39m cxf\u001b[38;5;241m.\u001b[39mcharts\u001b[38;5;241m.\u001b[39mpanel_widgets\u001b[38;5;241m.\u001b[39mmulti_select(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcluster\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", + "\u001b[0;31mTypeError\u001b[0m: scatter() got an unexpected keyword argument 'data_points'" + ] + } + ], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "# associate a data source with cuXfilter\n", + "gdf['cluster'] = km.labels_.astype('int32')\n", + "\n", + "# collect centroids into a tiny cudf.DataFrame (no host copies)\n", + "centers_cp = km.cluster_centers_ # CuPy array on device\n", + "centers = cudf.DataFrame(centers_cp, columns=['easting', 'northing'])\n", + "centers['cluster'] = cudf.Series(cp.arange(5, dtype=cp.int32))\n", + "\n", + "\n", + "cxf_data = cxf.DataFrame.from_dataframe(gdf)\n", + "\n", + "# population points (datashaded)\n", + "scatter_chart = cxf.charts.datashader.scatter(\n", + " x='easting',\n", + " y='northing',\n", + " # optional: color by cluster if your cuxfilter version supports it\n", + " # color_column='cluster', # comment out if unsupported\n", + " point_size=1,\n", + " pixel_shade_type='linear'\n", + ")\n", + "\n", + "# centroid layer as big, static points (uses a separate in-memory dataframe)\n", + "centroid_chart = cxf.charts.datashader.scatter(\n", + " x='easting',\n", + " y='northing',\n", + " data_points=centers, # <-- overlay from a separate DataFrame\n", + " point_size=18,\n", + " color='yellow', # a bright color to stand out\n", + " pixel_shade_type='eq_hist' # just to make them pop; optional\n", + ")\n", + "\n", + "# widget to filter shown clusters\n", + "cluster_widget = cxf.charts.panel_widgets.multi_select('cluster')" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<script type=\"esms-options\">{\"shimMode\": true}</script><style>*[data-root-id],\n", + "*[data-root-id] > * {\n", + " box-sizing: border-box;\n", + " font-family: var(--jp-ui-font-family);\n", + " font-size: var(--jp-ui-font-size1);\n", + " color: var(--vscode-editor-foreground, var(--jp-ui-font-color1));\n", + "}\n", + "\n", + "/* Override VSCode background color */\n", + ".cell-output-ipywidget-background:has(\n", + " > .cell-output-ipywidget-background > .lm-Widget > *[data-root-id]\n", + " ),\n", + ".cell-output-ipywidget-background:has(> .lm-Widget > *[data-root-id]) {\n", + " background-color: transparent !important;\n", + "}\n", + "</style>" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", + "\n", + " const force = true;\n", + " const py_version = '3.5.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n", + " const reloading = false;\n", + " const Bokeh = root.Bokeh;\n", + "\n", + " // Set a timeout for this load but only if we are not already initializing\n", + " if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_failed_load = false;\n", + " }\n", + "\n", + " function run_callbacks() {\n", + " try {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) {\n", + " if (callback != null)\n", + " callback();\n", + " });\n", + " } finally {\n", + " delete root._bokeh_onload_callbacks;\n", + " }\n", + " console.debug(\"Bokeh: all callbacks have finished\");\n", + " }\n", + "\n", + " function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n", + " if (css_urls == null) css_urls = [];\n", + " if (js_urls == null) js_urls = [];\n", + " if (js_modules == null) js_modules = [];\n", + " if (js_exports == null) js_exports = {};\n", + "\n", + " root._bokeh_onload_callbacks.push(callback);\n", + "\n", + " if (root._bokeh_is_loading > 0) {\n", + " // Don't load bokeh if it is still initializing\n", + " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " return null;\n", + " } else if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n", + " // There is nothing to load\n", + " run_callbacks();\n", + " return null;\n", + " }\n", + "\n", + " function on_load() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", + " run_callbacks()\n", + " }\n", + " }\n", + " window._bokeh_on_load = on_load\n", + "\n", + " function on_error(e) {\n", + " const src_el = e.srcElement\n", + " console.error(\"failed to load \" + (src_el.href || src_el.src));\n", + " }\n", + "\n", + " const skip = [];\n", + " if (window.requirejs) {\n", + " window.requirejs.config({'packages': {}, 'paths': {'h3': 'https://cdn.jsdelivr.net/npm/h3-js@4.1.0/dist/h3-js.umd', 'deck-gl': 'https://cdn.jsdelivr.net/npm/deck.gl@9.0.20/dist.min', 'deck-json': 'https://cdn.jsdelivr.net/npm/@deck.gl/json@9.0.20/dist.min', 'loader-csv': 'https://cdn.jsdelivr.net/npm/@loaders.gl/csv@4.2.2/dist/dist.min', 'loader-json': 'https://cdn.jsdelivr.net/npm/@loaders.gl/json@4.2.2/dist/dist.min', 'loader-tiles': 'https://cdn.jsdelivr.net/npm/@loaders.gl/3d-tiles@4.2.2/dist/dist.min', 'mapbox-gl': 'https://api.mapbox.com/mapbox-gl-js/v3.0.1/mapbox-gl', 'carto': 'https://cdn.jsdelivr.net/npm/@deck.gl/carto@^9.0.20/dist.min'}, 'shim': {'deck-json': {'deps': ['deck-gl']}, 'deck-gl': {'deps': ['h3']}}});\n", + " require([\"h3\"], function(h3) {\n", + " window.h3 = h3\n", + " on_load()\n", + " })\n", + " require([\"deck-gl\"], function(deck) {\n", + " window.deck = deck\n", + " on_load()\n", + " })\n", + " require([\"deck-json\"], function() {\n", + " on_load()\n", + " })\n", + " require([\"loader-csv\"], function() {\n", + " on_load()\n", + " })\n", + " require([\"loader-json\"], function() {\n", + " on_load()\n", + " })\n", + " require([\"loader-tiles\"], function() {\n", + " on_load()\n", + " })\n", + " require([\"mapbox-gl\"], function(mapboxgl) {\n", + " window.mapboxgl = mapboxgl\n", + " on_load()\n", + " })\n", + " require([\"carto\"], function() {\n", + " on_load()\n", + " })\n", + " root._bokeh_is_loading = css_urls.length + 8;\n", + " } else {\n", + " root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n", + " }\n", + "\n", + " const existing_stylesheets = []\n", + " const links = document.getElementsByTagName('link')\n", + " for (let i = 0; i < links.length; i++) {\n", + " const link = links[i]\n", + " if (link.href != null) {\n", + " existing_stylesheets.push(link.href)\n", + " }\n", + " }\n", + " for (let i = 0; i < css_urls.length; i++) {\n", + " const url = css_urls[i];\n", + " const escaped = encodeURI(url)\n", + " if (existing_stylesheets.indexOf(escaped) !== -1) {\n", + " on_load()\n", + " continue;\n", + " }\n", + " const element = document.createElement(\"link\");\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.rel = \"stylesheet\";\n", + " element.type = \"text/css\";\n", + " element.href = url;\n", + " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", + " document.body.appendChild(element);\n", + " } if (((window.deck !== undefined) && (!(window.deck instanceof HTMLElement))) || window.requirejs) {\n", + " var urls = ['https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/h3-js@4.1.0/dist/h3-js.umd.js', 'https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/deck.gl@9.0.20/dist.min.js', 'https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/@deck.gl/json@9.0.20/dist.min.js', 'https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/@loaders.gl/csv@4.2.2/dist/dist.min.js', 'https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/@loaders.gl/json@4.2.2/dist/dist.min.js', 'https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/@loaders.gl/3d-tiles@4.2.2/dist/dist.min.js', 'https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/mapbox-gl-js/v3.0.1/mapbox-gl.js', 'https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/maplibre-gl/dist/maplibre-gl.js'];\n", + " for (var i = 0; i < urls.length; i++) {\n", + " skip.push(encodeURI(urls[i]))\n", + " }\n", + " } if (((window.mapboxgl !== undefined) && (!(window.mapboxgl instanceof HTMLElement))) || window.requirejs) {\n", + " var urls = ['https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/@deck.gl/carto@^9.0.20/dist.min.js'];\n", + " for (var i = 0; i < urls.length; i++) {\n", + " skip.push(encodeURI(urls[i]))\n", + " }\n", + " } var existing_scripts = []\n", + " const scripts = document.getElementsByTagName('script')\n", + " for (let i = 0; i < scripts.length; i++) {\n", + " var script = scripts[i]\n", + " if (script.src != null) {\n", + " existing_scripts.push(script.src)\n", + " }\n", + " }\n", + " for (let i = 0; i < js_urls.length; i++) {\n", + " const url = js_urls[i];\n", + " const escaped = encodeURI(url)\n", + " if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n", + " if (!window.requirejs) {\n", + " on_load();\n", + " }\n", + " continue;\n", + " }\n", + " const element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.src = url;\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " for (let i = 0; i < js_modules.length; i++) {\n", + " const url = js_modules[i];\n", + " const escaped = encodeURI(url)\n", + " if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n", + " if (!window.requirejs) {\n", + " on_load();\n", + " }\n", + " continue;\n", + " }\n", + " var element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.src = url;\n", + " element.type = \"module\";\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " for (const name in js_exports) {\n", + " const url = js_exports[name];\n", + " const escaped = encodeURI(url)\n", + " if (skip.indexOf(escaped) >= 0 || root[name] != null) {\n", + " if (!window.requirejs) {\n", + " on_load();\n", + " }\n", + " continue;\n", + " }\n", + " var element = document.createElement('script');\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.type = \"module\";\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " element.textContent = `\n", + " import ${name} from \"${url}\"\n", + " window.${name} = ${name}\n", + " window._bokeh_on_load()\n", + " `\n", + " document.head.appendChild(element);\n", + " }\n", + " if (!js_urls.length && !js_modules.length) {\n", + " on_load()\n", + " }\n", + " };\n", + "\n", + " function inject_raw_css(css) {\n", + " const element = document.createElement(\"style\");\n", + " element.appendChild(document.createTextNode(css));\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " const js_urls = [\"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/h3-js@4.1.0/dist/h3-js.umd.js\", \"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/deck.gl@9.0.20/dist.min.js\", \"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/@deck.gl/json@9.0.20/dist.min.js\", \"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/@loaders.gl/csv@4.2.2/dist/dist.min.js\", \"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/@loaders.gl/json@4.2.2/dist/dist.min.js\", \"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/@loaders.gl/3d-tiles@4.2.2/dist/dist.min.js\", \"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/mapbox-gl-js/v3.0.1/mapbox-gl.js\", \"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/maplibre-gl/dist/maplibre-gl.js\", \"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/@deck.gl/carto@^9.0.20/dist.min.js\", \"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-3.5.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.5.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.5.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.5.2.min.js\", \"https://cdn.holoviz.org/panel/1.5.2/dist/panel.min.js\"];\n", + " const js_modules = [];\n", + " const js_exports = {};\n", + " const css_urls = [\"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/mapbox-gl-js/v3.0.1/mapbox-gl.css?v=1.5.2\", \"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/maplibre-gl@4.4.1/dist/maplibre-gl.css?v=1.5.2\"];\n", + " const inline_js = [ function(Bokeh) {\n", + " inject_raw_css(\"\\n.dataframe table{\\n border: none;\\n}\\n\\n.panel-df table{\\n width: 100%;\\n border-collapse: collapse;\\n border: none;\\n}\\n.panel-df td{\\n white-space: nowrap;\\n overflow: auto;\\n text-overflow: ellipsis;\\n}\\n\");\n", + " }, function(Bokeh) {\n", + " inject_raw_css(\"\\n.multi-select{\\n color: white;\\n z-index: 100;\\n background: rgba(44,43,43,0.5);\\n border-radius: 1px;\\n width: 120px !important;\\n height: 30px !important;\\n}\\n.multi-select > .bk {\\n padding: 5px;\\n width: 120px !important;\\n height: 30px !important;\\n}\\n\\n.deck-chart {\\n z-index: 10;\\n position: initial !important;\\n}\\n\");\n", + " }, function(Bokeh) {\n", + " inject_raw_css(\"\\n.center-header {\\n text-align: center\\n}\\n.bk-input-group {\\n padding: 10px;\\n}\\n#sidebar {\\n padding-top: 10px;\\n}\\n.custom-widget-box {\\n margin-top: 20px;\\n padding: 5px;\\n border: None !important;\\n}\\n.custom-widget-box > p {\\n margin: 0px;\\n}\\n.bk-input-group {\\n color: None !important;\\n}\\n.indicator {\\n text-align: center;\\n}\\n.widget-card {\\n margin: 5px 10px;\\n}\\n.number-card {\\n margin: 5px 10px;\\n text-align: center;\\n}\\n.number-card-value {\\n width: 100%;\\n margin: 0px;\\n}\\n\");\n", + " }, function(Bokeh) {\n", + " Bokeh.set_log_level(\"info\");\n", + " },\n", + " function(Bokeh) {\n", + " (function(root, factory) {\n", + " factory(root[\"Bokeh\"]);\n", + " })(this, function(Bokeh) {\n", + " let define;\n", + " return (function outer(modules, entry) {\n", + " if (Bokeh != null) {\n", + " return Bokeh.register_plugin(modules, entry);\n", + " } else {\n", + " throw new Error(\"Cannot find Bokeh. You have to load it prior to loading plugins.\");\n", + " }\n", + " })\n", + " ({\n", + " \"custom/main\": function(require, module, exports) {\n", + " const models = {\n", + " \"CustomInspectTool\": require(\"custom/cuxfilter.charts.datashader.custom_extensions.graph_inspect_widget.custom_inspect_tool\").CustomInspectTool\n", + " };\n", + " require(\"base\").register_models(models);\n", + " module.exports = models;\n", + " },\n", + " \"custom/cuxfilter.charts.datashader.custom_extensions.graph_inspect_widget.custom_inspect_tool\": function(require, module, exports) {\n", + " \"use strict\";\n", + " var _a;\n", + " Object.defineProperty(exports, \"__esModule\", { value: true });\n", + " exports.CustomInspectTool = exports.CustomInspectToolView = void 0;\n", + " const inspect_tool_1 = require(\"models/tools/inspectors/inspect_tool\");\n", + " class CustomInspectToolView extends inspect_tool_1.InspectToolView {\n", + " connect_signals() {\n", + " super.connect_signals();\n", + " this.on_change([this.model.properties.active], () => {\n", + " this.model._active = this.model.active;\n", + " });\n", + " }\n", + " }\n", + " exports.CustomInspectToolView = CustomInspectToolView;\n", + " CustomInspectToolView.__name__ = \"CustomInspectToolView\";\n", + " class CustomInspectTool extends inspect_tool_1.InspectTool {\n", + " constructor(attrs) {\n", + " super(attrs);\n", + " }\n", + " }\n", + " exports.CustomInspectTool = CustomInspectTool;\n", + " _a = CustomInspectTool;\n", + " CustomInspectTool.__name__ = \"CustomInspectTool\";\n", + " (() => {\n", + " _a.prototype.default_view = CustomInspectToolView;\n", + " _a.define(({ Boolean }) => ({\n", + " _active: [Boolean, true]\n", + " }));\n", + " _a.register_alias(\"customInspect\", () => new _a());\n", + " })();\n", + " //# sourceMappingURL=graph_inspect_widget.py:CustomInspectTool.js.map\n", + " }\n", + " }, \"custom/main\");\n", + " ;\n", + " });\n", + "\n", + " },\n", + "function(Bokeh) {} // ensure no trailing comma for IE\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " if ((root.Bokeh !== undefined) || (force === true)) {\n", + " for (let i = 0; i < inline_js.length; i++) {\n", + " try {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " } catch(e) {\n", + " if (!reloading) {\n", + " throw e;\n", + " }\n", + " }\n", + " }\n", + " // Cache old bokeh versions\n", + " if (Bokeh != undefined && !reloading) {\n", + " var NewBokeh = root.Bokeh;\n", + " if (Bokeh.versions === undefined) {\n", + " Bokeh.versions = new Map();\n", + " }\n", + " if (NewBokeh.version !== Bokeh.version) {\n", + " Bokeh.versions.set(NewBokeh.version, NewBokeh)\n", + " }\n", + " root.Bokeh = Bokeh;\n", + " }\n", + " } else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(run_inline_js, 100);\n", + " } else if (!root._bokeh_failed_load) {\n", + " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", + " root._bokeh_failed_load = true;\n", + " }\n", + " root._bokeh_is_initializing = false\n", + " }\n", + "\n", + " function load_or_wait() {\n", + " // Implement a backoff loop that tries to ensure we do not load multiple\n", + " // versions of Bokeh and its dependencies at the same time.\n", + " // In recent versions we use the root._bokeh_is_initializing flag\n", + " // to determine whether there is an ongoing attempt to initialize\n", + " // bokeh, however for backward compatibility we also try to ensure\n", + " // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n", + " // before older versions are fully initialized.\n", + " if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n", + " // If the timeout and bokeh was not successfully loaded we reset\n", + " // everything and try loading again\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_is_initializing = false;\n", + " root._bokeh_onload_callbacks = undefined;\n", + " root._bokeh_is_loading = 0\n", + " console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n", + " load_or_wait();\n", + " } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n", + " setTimeout(load_or_wait, 100);\n", + " } else {\n", + " root._bokeh_is_initializing = true\n", + " root._bokeh_onload_callbacks = []\n", + " const bokeh_loaded = root.Bokeh != null && (root.Bokeh.version === py_version || (root.Bokeh.versions !== undefined && root.Bokeh.versions.has(py_version)));\n", + " if (!reloading && !bokeh_loaded) {\n", + " if (root.Bokeh) {\n", + " root.Bokeh = undefined;\n", + " }\n", + " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " }\n", + " load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n", + " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", + " run_inline_js();\n", + " });\n", + " }\n", + " }\n", + " // Give older versions of the autoload script a head-start to ensure\n", + " // they initialize before we start loading newer version.\n", + " setTimeout(load_or_wait, 100)\n", + "}(window));" + ], + "application/vnd.holoviews_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n const py_version = '3.5.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n const reloading = false;\n const Bokeh = root.Bokeh;\n\n // Set a timeout for this load but only if we are not already initializing\n if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks;\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n if (js_modules == null) js_modules = [];\n if (js_exports == null) js_exports = {};\n\n root._bokeh_onload_callbacks.push(callback);\n\n if (root._bokeh_is_loading > 0) {\n // Don't load bokeh if it is still initializing\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n } else if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n // There is nothing to load\n run_callbacks();\n return null;\n }\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n window._bokeh_on_load = on_load\n\n function on_error(e) {\n const src_el = e.srcElement\n console.error(\"failed to load \" + (src_el.href || src_el.src));\n }\n\n const skip = [];\n if (window.requirejs) {\n window.requirejs.config({'packages': {}, 'paths': {'h3': 'https://cdn.jsdelivr.net/npm/h3-js@4.1.0/dist/h3-js.umd', 'deck-gl': 'https://cdn.jsdelivr.net/npm/deck.gl@9.0.20/dist.min', 'deck-json': 'https://cdn.jsdelivr.net/npm/@deck.gl/json@9.0.20/dist.min', 'loader-csv': 'https://cdn.jsdelivr.net/npm/@loaders.gl/csv@4.2.2/dist/dist.min', 'loader-json': 'https://cdn.jsdelivr.net/npm/@loaders.gl/json@4.2.2/dist/dist.min', 'loader-tiles': 'https://cdn.jsdelivr.net/npm/@loaders.gl/3d-tiles@4.2.2/dist/dist.min', 'mapbox-gl': 'https://api.mapbox.com/mapbox-gl-js/v3.0.1/mapbox-gl', 'carto': 'https://cdn.jsdelivr.net/npm/@deck.gl/carto@^9.0.20/dist.min'}, 'shim': {'deck-json': {'deps': ['deck-gl']}, 'deck-gl': {'deps': ['h3']}}});\n require([\"h3\"], function(h3) {\n window.h3 = h3\n on_load()\n })\n require([\"deck-gl\"], function(deck) {\n window.deck = deck\n on_load()\n })\n require([\"deck-json\"], function() {\n on_load()\n })\n require([\"loader-csv\"], function() {\n on_load()\n })\n require([\"loader-json\"], function() {\n on_load()\n })\n require([\"loader-tiles\"], function() {\n on_load()\n })\n require([\"mapbox-gl\"], function(mapboxgl) {\n window.mapboxgl = mapboxgl\n on_load()\n })\n require([\"carto\"], function() {\n on_load()\n })\n root._bokeh_is_loading = css_urls.length + 8;\n } else {\n root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n }\n\n const existing_stylesheets = []\n const links = document.getElementsByTagName('link')\n for (let i = 0; i < links.length; i++) {\n const link = links[i]\n if (link.href != null) {\n existing_stylesheets.push(link.href)\n }\n }\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const escaped = encodeURI(url)\n if (existing_stylesheets.indexOf(escaped) !== -1) {\n on_load()\n continue;\n }\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n } if (((window.deck !== undefined) && (!(window.deck instanceof HTMLElement))) || window.requirejs) {\n var urls = ['https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/h3-js@4.1.0/dist/h3-js.umd.js', 'https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/deck.gl@9.0.20/dist.min.js', 'https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/@deck.gl/json@9.0.20/dist.min.js', 'https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/@loaders.gl/csv@4.2.2/dist/dist.min.js', 'https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/@loaders.gl/json@4.2.2/dist/dist.min.js', 'https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/@loaders.gl/3d-tiles@4.2.2/dist/dist.min.js', 'https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/mapbox-gl-js/v3.0.1/mapbox-gl.js', 'https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/maplibre-gl/dist/maplibre-gl.js'];\n for (var i = 0; i < urls.length; i++) {\n skip.push(encodeURI(urls[i]))\n }\n } if (((window.mapboxgl !== undefined) && (!(window.mapboxgl instanceof HTMLElement))) || window.requirejs) {\n var urls = ['https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/@deck.gl/carto@^9.0.20/dist.min.js'];\n for (var i = 0; i < urls.length; i++) {\n skip.push(encodeURI(urls[i]))\n }\n } var existing_scripts = []\n const scripts = document.getElementsByTagName('script')\n for (let i = 0; i < scripts.length; i++) {\n var script = scripts[i]\n if (script.src != null) {\n existing_scripts.push(script.src)\n }\n }\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\n }\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (let i = 0; i < js_modules.length; i++) {\n const url = js_modules[i];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\n }\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (const name in js_exports) {\n const url = js_exports[name];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) >= 0 || root[name] != null) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\n }\n var element = document.createElement('script');\n element.onerror = on_error;\n element.async = false;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n element.textContent = `\n import ${name} from \"${url}\"\n window.${name} = ${name}\n window._bokeh_on_load()\n `\n document.head.appendChild(element);\n }\n if (!js_urls.length && !js_modules.length) {\n on_load()\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/h3-js@4.1.0/dist/h3-js.umd.js\", \"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/deck.gl@9.0.20/dist.min.js\", \"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/@deck.gl/json@9.0.20/dist.min.js\", \"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/@loaders.gl/csv@4.2.2/dist/dist.min.js\", \"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/@loaders.gl/json@4.2.2/dist/dist.min.js\", \"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/@loaders.gl/3d-tiles@4.2.2/dist/dist.min.js\", \"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/mapbox-gl-js/v3.0.1/mapbox-gl.js\", \"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/maplibre-gl/dist/maplibre-gl.js\", \"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/@deck.gl/carto@^9.0.20/dist.min.js\", \"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-3.5.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.5.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.5.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.5.2.min.js\", \"https://cdn.holoviz.org/panel/1.5.2/dist/panel.min.js\"];\n const js_modules = [];\n const js_exports = {};\n const css_urls = [\"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/mapbox-gl-js/v3.0.1/mapbox-gl.css?v=1.5.2\", \"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/maplibre-gl@4.4.1/dist/maplibre-gl.css?v=1.5.2\"];\n const inline_js = [ function(Bokeh) {\n inject_raw_css(\"\\n.dataframe table{\\n border: none;\\n}\\n\\n.panel-df table{\\n width: 100%;\\n border-collapse: collapse;\\n border: none;\\n}\\n.panel-df td{\\n white-space: nowrap;\\n overflow: auto;\\n text-overflow: ellipsis;\\n}\\n\");\n }, function(Bokeh) {\n inject_raw_css(\"\\n.multi-select{\\n color: white;\\n z-index: 100;\\n background: rgba(44,43,43,0.5);\\n border-radius: 1px;\\n width: 120px !important;\\n height: 30px !important;\\n}\\n.multi-select > .bk {\\n padding: 5px;\\n width: 120px !important;\\n height: 30px !important;\\n}\\n\\n.deck-chart {\\n z-index: 10;\\n position: initial !important;\\n}\\n\");\n }, function(Bokeh) {\n inject_raw_css(\"\\n.center-header {\\n text-align: center\\n}\\n.bk-input-group {\\n padding: 10px;\\n}\\n#sidebar {\\n padding-top: 10px;\\n}\\n.custom-widget-box {\\n margin-top: 20px;\\n padding: 5px;\\n border: None !important;\\n}\\n.custom-widget-box > p {\\n margin: 0px;\\n}\\n.bk-input-group {\\n color: None !important;\\n}\\n.indicator {\\n text-align: center;\\n}\\n.widget-card {\\n margin: 5px 10px;\\n}\\n.number-card {\\n margin: 5px 10px;\\n text-align: center;\\n}\\n.number-card-value {\\n width: 100%;\\n margin: 0px;\\n}\\n\");\n }, function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n function(Bokeh) {\n (function(root, factory) {\n factory(root[\"Bokeh\"]);\n })(this, function(Bokeh) {\n let define;\n return (function outer(modules, entry) {\n if (Bokeh != null) {\n return Bokeh.register_plugin(modules, entry);\n } else {\n throw new Error(\"Cannot find Bokeh. You have to load it prior to loading plugins.\");\n }\n })\n ({\n \"custom/main\": function(require, module, exports) {\n const models = {\n \"CustomInspectTool\": require(\"custom/cuxfilter.charts.datashader.custom_extensions.graph_inspect_widget.custom_inspect_tool\").CustomInspectTool\n };\n require(\"base\").register_models(models);\n module.exports = models;\n },\n \"custom/cuxfilter.charts.datashader.custom_extensions.graph_inspect_widget.custom_inspect_tool\": function(require, module, exports) {\n \"use strict\";\n var _a;\n Object.defineProperty(exports, \"__esModule\", { value: true });\n exports.CustomInspectTool = exports.CustomInspectToolView = void 0;\n const inspect_tool_1 = require(\"models/tools/inspectors/inspect_tool\");\n class CustomInspectToolView extends inspect_tool_1.InspectToolView {\n connect_signals() {\n super.connect_signals();\n this.on_change([this.model.properties.active], () => {\n this.model._active = this.model.active;\n });\n }\n }\n exports.CustomInspectToolView = CustomInspectToolView;\n CustomInspectToolView.__name__ = \"CustomInspectToolView\";\n class CustomInspectTool extends inspect_tool_1.InspectTool {\n constructor(attrs) {\n super(attrs);\n }\n }\n exports.CustomInspectTool = CustomInspectTool;\n _a = CustomInspectTool;\n CustomInspectTool.__name__ = \"CustomInspectTool\";\n (() => {\n _a.prototype.default_view = CustomInspectToolView;\n _a.define(({ Boolean }) => ({\n _active: [Boolean, true]\n }));\n _a.register_alias(\"customInspect\", () => new _a());\n })();\n //# sourceMappingURL=graph_inspect_widget.py:CustomInspectTool.js.map\n }\n }, \"custom/main\");\n ;\n });\n\n },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n ];\n\n function run_inline_js() {\n if ((root.Bokeh !== undefined) || (force === true)) {\n for (let i = 0; i < inline_js.length; i++) {\n try {\n inline_js[i].call(root, root.Bokeh);\n } catch(e) {\n if (!reloading) {\n throw e;\n }\n }\n }\n // Cache old bokeh versions\n if 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older versions are fully initialized.\n if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n // If the timeout and bokeh was not successfully loaded we reset\n // everything and try loading again\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_is_initializing = false;\n root._bokeh_onload_callbacks = undefined;\n root._bokeh_is_loading = 0\n console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n load_or_wait();\n } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n setTimeout(load_or_wait, 100);\n } else {\n root._bokeh_is_initializing = true\n root._bokeh_onload_callbacks = []\n const bokeh_loaded = root.Bokeh != null && (root.Bokeh.version === py_version || (root.Bokeh.versions !== undefined && root.Bokeh.versions.has(py_version)));\n if (!reloading && !bokeh_loaded) {\n if (root.Bokeh) {\n root.Bokeh = undefined;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n }\n load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n }\n // Give older versions of the autoload script a head-start to ensure\n // they initialize before we start loading newer version.\n setTimeout(load_or_wait, 100)\n}(window));" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "\n", + "if ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n", + " window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n", + "}\n", + "\n", + "\n", + " function JupyterCommManager() {\n", + " }\n", + "\n", + " JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n", + " if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n", + " var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n", + " comm_manager.register_target(comm_id, function(comm) {\n", + " comm.on_msg(msg_handler);\n", + " });\n", + " } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n", + " window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n", + " comm.onMsg = msg_handler;\n", + " });\n", + " } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n", + " google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n", + " var messages = comm.messages[Symbol.asyncIterator]();\n", + " function processIteratorResult(result) {\n", + " var message = result.value;\n", + " console.log(message)\n", + " var content = {data: message.data, comm_id};\n", + " var buffers = []\n", + " for (var buffer of message.buffers || []) {\n", + " buffers.push(new DataView(buffer))\n", + " }\n", + " var metadata = 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" return\n", + " }\n", + " var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", + " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", + " if (id !== undefined) {\n", + " var nchildren = toinsert.length;\n", + " var html_node = toinsert[nchildren-1].children[0];\n", + " html_node.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var scripts = [];\n", + " var nodelist = html_node.querySelectorAll(\"script\");\n", + " for (var i in nodelist) {\n", + " if (nodelist.hasOwnProperty(i)) {\n", + " scripts.push(nodelist[i])\n", + " }\n", + " }\n", + "\n", + " scripts.forEach( function (oldScript) {\n", + " var newScript = document.createElement(\"script\");\n", + " var attrs = [];\n", + " var nodemap = oldScript.attributes;\n", + " for (var j in nodemap) {\n", + " if (nodemap.hasOwnProperty(j)) {\n", + " attrs.push(nodemap[j])\n", + " }\n", + " }\n", + " attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n", + " newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n", + " oldScript.parentNode.replaceChild(newScript, oldScript);\n", + " });\n", + " if (JS_MIME_TYPE in output.data) {\n", + " toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n", + " }\n", + " output_area._hv_plot_id = id;\n", + " if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n", + " window.PyViz.plot_index[id] = Bokeh.index[id];\n", + " } else {\n", + " window.PyViz.plot_index[id] = null;\n", + " }\n", + " } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", + " var bk_div = document.createElement(\"div\");\n", + " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var script_attrs = bk_div.children[0].attributes;\n", + " for (var i = 0; i < script_attrs.length; i++) {\n", + " toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " }\n", + " // store reference to server id on output_area\n", + " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", + " }\n", + "}\n", + "\n", + "/**\n", + " * Handle when an output is cleared or removed\n", + " */\n", + "function handle_clear_output(event, handle) {\n", + " var id = handle.cell.output_area._hv_plot_id;\n", + " var server_id = handle.cell.output_area._bokeh_server_id;\n", + " if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n", + " var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n", + " if (server_id !== null) {\n", + " comm.send({event_type: 'server_delete', 'id': server_id});\n", + " return;\n", + " } else if (comm !== null) {\n", + " comm.send({event_type: 'delete', 'id': id});\n", + " }\n", + " delete PyViz.plot_index[id];\n", + " if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n", + " var doc = window.Bokeh.index[id].model.document\n", + " doc.clear();\n", + " const i = window.Bokeh.documents.indexOf(doc);\n", + " if (i > -1) {\n", + " window.Bokeh.documents.splice(i, 1);\n", + " }\n", + " }\n", + "}\n", + "\n", + "/**\n", + " * Handle kernel restart event\n", + " */\n", + "function handle_kernel_cleanup(event, handle) {\n", + " delete PyViz.comms[\"hv-extension-comm\"];\n", + " window.PyViz.plot_index = {}\n", + "}\n", + "\n", + "/**\n", + " * Handle update_display_data messages\n", + " */\n", + "function handle_update_output(event, handle) {\n", + " handle_clear_output(event, {cell: {output_area: handle.output_area}})\n", + " handle_add_output(event, handle)\n", + "}\n", + "\n", + "function register_renderer(events, OutputArea) {\n", + " function append_mime(data, metadata, element) {\n", + " // create a DOM node to render to\n", + " var toinsert = this.create_output_subarea(\n", + " metadata,\n", + " CLASS_NAME,\n", + " EXEC_MIME_TYPE\n", + " );\n", + " this.keyboard_manager.register_events(toinsert);\n", + " // Render to node\n", + " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", + " render(props, toinsert[0]);\n", + " element.append(toinsert);\n", + " return toinsert\n", + " }\n", + "\n", + " events.on('output_added.OutputArea', handle_add_output);\n", + " events.on('output_updated.OutputArea', handle_update_output);\n", + " events.on('clear_output.CodeCell', handle_clear_output);\n", + " events.on('delete.Cell', handle_clear_output);\n", + " events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n", + "\n", + " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", + " safe: true,\n", + " index: 0\n", + " });\n", + "}\n", + "\n", + "if (window.Jupyter !== undefined) {\n", + " try {\n", + " var events = require('base/js/events');\n", + " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", + " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", + " register_renderer(events, OutputArea);\n", + " }\n", + " } catch(err) {\n", + " }\n", + "}\n" + ], + "application/vnd.holoviews_load.v0+json": "\nif ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n}\n\n\n function JupyterCommManager() {\n }\n\n JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n comm_manager.register_target(comm_id, function(comm) {\n comm.on_msg(msg_handler);\n });\n } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n comm.onMsg = msg_handler;\n });\n } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n var messages = 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window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n if (server_id !== null) {\n comm.send({event_type: 'server_delete', 'id': server_id});\n return;\n } else if (comm !== null) {\n comm.send({event_type: 'delete', 'id': id});\n }\n delete PyViz.plot_index[id];\n if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n var doc = window.Bokeh.index[id].model.document\n doc.clear();\n const i = window.Bokeh.documents.indexOf(doc);\n if (i > -1) {\n window.Bokeh.documents.splice(i, 1);\n }\n }\n}\n\n/**\n * Handle kernel restart event\n */\nfunction handle_kernel_cleanup(event, handle) {\n delete PyViz.comms[\"hv-extension-comm\"];\n window.PyViz.plot_index = {}\n}\n\n/**\n * Handle update_display_data messages\n */\nfunction handle_update_output(event, handle) {\n handle_clear_output(event, {cell: {output_area: handle.output_area}})\n handle_add_output(event, handle)\n}\n\nfunction register_renderer(events, OutputArea) {\n function append_mime(data, metadata, element) {\n // create a DOM node to render to\n var toinsert = this.create_output_subarea(\n metadata,\n CLASS_NAME,\n EXEC_MIME_TYPE\n );\n this.keyboard_manager.register_events(toinsert);\n // Render to node\n var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n render(props, toinsert[0]);\n element.append(toinsert);\n return toinsert\n }\n\n events.on('output_added.OutputArea', handle_add_output);\n events.on('output_updated.OutputArea', handle_update_output);\n events.on('clear_output.CodeCell', handle_clear_output);\n events.on('delete.Cell', handle_clear_output);\n events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n safe: true,\n index: 0\n });\n}\n\nif (window.Jupyter !== undefined) {\n try {\n var events = require('base/js/events');\n var OutputArea = require('notebook/js/outputarea').OutputArea;\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n } catch(err) {\n }\n}\n" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "<script type=\"esms-options\">{\"shimMode\": true}</script><style>*[data-root-id],\n", + "*[data-root-id] > * {\n", + " box-sizing: border-box;\n", + " font-family: var(--jp-ui-font-family);\n", + " font-size: var(--jp-ui-font-size1);\n", + " color: var(--vscode-editor-foreground, var(--jp-ui-font-color1));\n", + "}\n", + "\n", + "/* Override VSCode background color */\n", + ".cell-output-ipywidget-background:has(\n", + " > .cell-output-ipywidget-background > .lm-Widget > *[data-root-id]\n", + " ),\n", + ".cell-output-ipywidget-background:has(> .lm-Widget > *[data-root-id]) {\n", + " background-color: transparent !important;\n", + "}\n", + "</style>" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", + "\n", + " const force = false;\n", + " const py_version = '3.5.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n", + " const reloading = true;\n", + " const Bokeh = root.Bokeh;\n", + "\n", + " // Set a timeout for this load but only if we are not already initializing\n", + " if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_failed_load = false;\n", + " }\n", + "\n", + " function run_callbacks() {\n", + " try {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) {\n", + " if (callback != null)\n", + " callback();\n", + " });\n", + " } finally {\n", + " delete root._bokeh_onload_callbacks;\n", + " }\n", + " console.debug(\"Bokeh: all callbacks have finished\");\n", + " }\n", + "\n", + " function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n", + " if (css_urls == null) css_urls = [];\n", + " if (js_urls == null) js_urls = [];\n", + " if (js_modules == null) js_modules = [];\n", + " if (js_exports == null) js_exports = {};\n", + "\n", + " root._bokeh_onload_callbacks.push(callback);\n", + "\n", + " if (root._bokeh_is_loading > 0) {\n", + " // Don't load bokeh if it is still initializing\n", + " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " return null;\n", + " } else if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n", + " // There is nothing to load\n", + " run_callbacks();\n", + " return null;\n", + " }\n", + "\n", + " function on_load() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", + " run_callbacks()\n", + " }\n", + " }\n", + " window._bokeh_on_load = on_load\n", + "\n", + " function on_error(e) {\n", + " const src_el = e.srcElement\n", + " console.error(\"failed to load \" + (src_el.href || src_el.src));\n", + " }\n", + "\n", + " const skip = [];\n", + " if (window.requirejs) {\n", + " window.requirejs.config({'packages': {}, 'paths': {'h3': 'https://cdn.jsdelivr.net/npm/h3-js@4.1.0/dist/h3-js.umd', 'deck-gl': 'https://cdn.jsdelivr.net/npm/deck.gl@9.0.20/dist.min', 'deck-json': 'https://cdn.jsdelivr.net/npm/@deck.gl/json@9.0.20/dist.min', 'loader-csv': 'https://cdn.jsdelivr.net/npm/@loaders.gl/csv@4.2.2/dist/dist.min', 'loader-json': 'https://cdn.jsdelivr.net/npm/@loaders.gl/json@4.2.2/dist/dist.min', 'loader-tiles': 'https://cdn.jsdelivr.net/npm/@loaders.gl/3d-tiles@4.2.2/dist/dist.min', 'mapbox-gl': 'https://api.mapbox.com/mapbox-gl-js/v3.0.1/mapbox-gl', 'carto': 'https://cdn.jsdelivr.net/npm/@deck.gl/carto@^9.0.20/dist.min'}, 'shim': {'deck-json': {'deps': ['deck-gl']}, 'deck-gl': {'deps': ['h3']}}});\n", + " require([\"h3\"], function(h3) {\n", + " window.h3 = h3\n", + " on_load()\n", + " })\n", + " require([\"deck-gl\"], function(deck) {\n", + " window.deck = deck\n", + " on_load()\n", + " })\n", + " require([\"deck-json\"], function() {\n", + " on_load()\n", + " })\n", + " require([\"loader-csv\"], function() {\n", + " on_load()\n", + " })\n", + " require([\"loader-json\"], function() {\n", + " on_load()\n", + " })\n", + " require([\"loader-tiles\"], function() {\n", + " on_load()\n", + " })\n", + " require([\"mapbox-gl\"], function(mapboxgl) {\n", + " window.mapboxgl = mapboxgl\n", + " on_load()\n", + " })\n", + " require([\"carto\"], function() {\n", + " on_load()\n", + " })\n", + " root._bokeh_is_loading = css_urls.length + 8;\n", + " } else {\n", + " root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n", + " }\n", + "\n", + " const existing_stylesheets = []\n", + " const links = document.getElementsByTagName('link')\n", + " for (let i = 0; i < links.length; i++) {\n", + " const link = links[i]\n", + " if (link.href != null) {\n", + " existing_stylesheets.push(link.href)\n", + " }\n", + " }\n", + " for (let i = 0; i < css_urls.length; i++) {\n", + " const url = css_urls[i];\n", + " const escaped = encodeURI(url)\n", + " if (existing_stylesheets.indexOf(escaped) !== -1) {\n", + " on_load()\n", + " continue;\n", + " }\n", + " const element = document.createElement(\"link\");\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.rel = \"stylesheet\";\n", + " element.type = \"text/css\";\n", + " element.href = url;\n", + " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", + " document.body.appendChild(element);\n", + " } if (((window.deck !== undefined) && (!(window.deck instanceof HTMLElement))) || window.requirejs) {\n", + " var urls = ['https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/h3-js@4.1.0/dist/h3-js.umd.js', 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['https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/@deck.gl/carto@^9.0.20/dist.min.js'];\n", + " for (var i = 0; i < urls.length; i++) {\n", + " skip.push(encodeURI(urls[i]))\n", + " }\n", + " } var existing_scripts = []\n", + " const scripts = document.getElementsByTagName('script')\n", + " for (let i = 0; i < scripts.length; i++) {\n", + " var script = scripts[i]\n", + " if (script.src != null) {\n", + " existing_scripts.push(script.src)\n", + " }\n", + " }\n", + " for (let i = 0; i < js_urls.length; i++) {\n", + " const url = js_urls[i];\n", + " const escaped = encodeURI(url)\n", + " if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n", + " if (!window.requirejs) {\n", + " on_load();\n", + " }\n", + " continue;\n", + " }\n", + " const element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.src = url;\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " for (let i = 0; i < js_modules.length; i++) {\n", + " const url = js_modules[i];\n", + " const escaped = encodeURI(url)\n", + " if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n", + " if (!window.requirejs) {\n", + " on_load();\n", + " }\n", + " continue;\n", + " }\n", + " var element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.src = url;\n", + " element.type = \"module\";\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " for (const name in js_exports) {\n", + " const url = js_exports[name];\n", + " const escaped = encodeURI(url)\n", + " if (skip.indexOf(escaped) >= 0 || root[name] != null) {\n", + " if (!window.requirejs) {\n", + " on_load();\n", + " }\n", + " continue;\n", + " }\n", + " var element = document.createElement('script');\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.type = \"module\";\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " element.textContent = `\n", + " import ${name} from \"${url}\"\n", + " window.${name} = ${name}\n", + " window._bokeh_on_load()\n", + " `\n", + " document.head.appendChild(element);\n", + " }\n", + " if (!js_urls.length && !js_modules.length) {\n", + " on_load()\n", + " }\n", + " };\n", + "\n", + " function inject_raw_css(css) {\n", + " const element = document.createElement(\"style\");\n", + " element.appendChild(document.createTextNode(css));\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " const js_urls = [\"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/h3-js@4.1.0/dist/h3-js.umd.js\", \"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/deck.gl@9.0.20/dist.min.js\", \"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/@deck.gl/json@9.0.20/dist.min.js\", \"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/@loaders.gl/csv@4.2.2/dist/dist.min.js\", \"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/@loaders.gl/json@4.2.2/dist/dist.min.js\", \"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/@loaders.gl/3d-tiles@4.2.2/dist/dist.min.js\", \"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/mapbox-gl-js/v3.0.1/mapbox-gl.js\", \"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/maplibre-gl/dist/maplibre-gl.js\", \"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/@deck.gl/carto@^9.0.20/dist.min.js\", \"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\"];\n", + " const js_modules = [];\n", + " const js_exports = {};\n", + " const css_urls = [\"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/mapbox-gl-js/v3.0.1/mapbox-gl.css?v=1.5.2\", \"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/maplibre-gl@4.4.1/dist/maplibre-gl.css?v=1.5.2\"];\n", + " const inline_js = [ function(Bokeh) {\n", + " inject_raw_css(\"\\n.dataframe table{\\n border: none;\\n}\\n\\n.panel-df table{\\n width: 100%;\\n border-collapse: collapse;\\n border: none;\\n}\\n.panel-df td{\\n white-space: nowrap;\\n overflow: auto;\\n text-overflow: ellipsis;\\n}\\n\");\n", + " }, function(Bokeh) {\n", + " inject_raw_css(\"\\n.multi-select{\\n color: white;\\n z-index: 100;\\n background: rgba(44,43,43,0.5);\\n border-radius: 1px;\\n width: 120px !important;\\n height: 30px !important;\\n}\\n.multi-select > .bk {\\n padding: 5px;\\n width: 120px !important;\\n height: 30px !important;\\n}\\n\\n.deck-chart {\\n z-index: 10;\\n position: initial !important;\\n}\\n\");\n", + " }, function(Bokeh) {\n", + " inject_raw_css(\"\\n.center-header {\\n text-align: center\\n}\\n.bk-input-group {\\n padding: 10px;\\n}\\n#sidebar {\\n padding-top: 10px;\\n}\\n.custom-widget-box {\\n margin-top: 20px;\\n padding: 5px;\\n border: None !important;\\n}\\n.custom-widget-box > p {\\n margin: 0px;\\n}\\n.bk-input-group {\\n color: None !important;\\n}\\n.indicator {\\n text-align: center;\\n}\\n.widget-card {\\n margin: 5px 10px;\\n}\\n.number-card {\\n margin: 5px 10px;\\n text-align: center;\\n}\\n.number-card-value {\\n width: 100%;\\n margin: 0px;\\n}\\n\");\n", + " }, function(Bokeh) {\n", + " Bokeh.set_log_level(\"info\");\n", + " },\n", + " function(Bokeh) {\n", + " (function(root, factory) {\n", + " factory(root[\"Bokeh\"]);\n", + " })(this, function(Bokeh) {\n", + " let define;\n", + " return (function outer(modules, entry) {\n", + " if (Bokeh != null) {\n", + " return Bokeh.register_plugin(modules, entry);\n", + " } else {\n", + " throw new Error(\"Cannot find Bokeh. You have to load it prior to loading plugins.\");\n", + " }\n", + " })\n", + " ({\n", + " \"custom/main\": function(require, module, exports) {\n", + " const models = {\n", + " \"CustomInspectTool\": require(\"custom/cuxfilter.charts.datashader.custom_extensions.graph_inspect_widget.custom_inspect_tool\").CustomInspectTool\n", + " };\n", + " require(\"base\").register_models(models);\n", + " module.exports = models;\n", + " },\n", + " \"custom/cuxfilter.charts.datashader.custom_extensions.graph_inspect_widget.custom_inspect_tool\": function(require, module, exports) {\n", + " \"use strict\";\n", + " var _a;\n", + " Object.defineProperty(exports, \"__esModule\", { value: true });\n", + " exports.CustomInspectTool = exports.CustomInspectToolView = void 0;\n", + " const inspect_tool_1 = require(\"models/tools/inspectors/inspect_tool\");\n", + " class CustomInspectToolView extends inspect_tool_1.InspectToolView {\n", + " connect_signals() {\n", + " super.connect_signals();\n", + " this.on_change([this.model.properties.active], () => {\n", + " this.model._active = this.model.active;\n", + " });\n", + " }\n", + " }\n", + " exports.CustomInspectToolView = CustomInspectToolView;\n", + " CustomInspectToolView.__name__ = \"CustomInspectToolView\";\n", + " class CustomInspectTool extends inspect_tool_1.InspectTool {\n", + " constructor(attrs) {\n", + " super(attrs);\n", + " }\n", + " }\n", + " exports.CustomInspectTool = CustomInspectTool;\n", + " _a = CustomInspectTool;\n", + " CustomInspectTool.__name__ = \"CustomInspectTool\";\n", + " (() => {\n", + " _a.prototype.default_view = CustomInspectToolView;\n", + " _a.define(({ Boolean }) => ({\n", + " _active: [Boolean, true]\n", + " }));\n", + " _a.register_alias(\"customInspect\", () => new _a());\n", + " })();\n", + " //# sourceMappingURL=graph_inspect_widget.py:CustomInspectTool.js.map\n", + " }\n", + " }, \"custom/main\");\n", + " ;\n", + " });\n", + "\n", + " },\n", + "function(Bokeh) {} // ensure no trailing comma for IE\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " if ((root.Bokeh !== undefined) || (force === true)) {\n", + " for (let i = 0; i < inline_js.length; i++) {\n", + " try {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " } catch(e) {\n", + " if (!reloading) {\n", + " throw e;\n", + " }\n", + " }\n", + " }\n", + " // Cache old bokeh versions\n", + " if (Bokeh != undefined && !reloading) {\n", + " var NewBokeh = root.Bokeh;\n", + " if (Bokeh.versions === undefined) {\n", + " Bokeh.versions = new Map();\n", + " }\n", + " if (NewBokeh.version !== Bokeh.version) {\n", + " Bokeh.versions.set(NewBokeh.version, NewBokeh)\n", + " }\n", + " root.Bokeh = Bokeh;\n", + " }\n", + " } else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(run_inline_js, 100);\n", + " } else if (!root._bokeh_failed_load) {\n", + " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", + " root._bokeh_failed_load = true;\n", + " }\n", + " root._bokeh_is_initializing = false\n", + " }\n", + "\n", + " function load_or_wait() {\n", + " // Implement a backoff loop that tries to ensure we do not load multiple\n", + " // versions of Bokeh and its dependencies at the same time.\n", + " // In recent versions we use the root._bokeh_is_initializing flag\n", + " // to determine whether there is an ongoing attempt to initialize\n", + " // bokeh, however for backward compatibility we also try to ensure\n", + " // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n", + " // before older versions are fully initialized.\n", + " if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n", + " // If the timeout and bokeh was not successfully loaded we reset\n", + " // everything and try loading again\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_is_initializing = false;\n", + " root._bokeh_onload_callbacks = undefined;\n", + " root._bokeh_is_loading = 0\n", + " console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n", + " load_or_wait();\n", + " } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n", + " setTimeout(load_or_wait, 100);\n", + " } else {\n", + " root._bokeh_is_initializing = true\n", + " root._bokeh_onload_callbacks = []\n", + " const bokeh_loaded = root.Bokeh != null && (root.Bokeh.version === py_version || (root.Bokeh.versions !== undefined && root.Bokeh.versions.has(py_version)));\n", + " if (!reloading && !bokeh_loaded) {\n", + " if (root.Bokeh) {\n", + " root.Bokeh = undefined;\n", + " }\n", + " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " }\n", + " load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n", + " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", + " run_inline_js();\n", + " });\n", + " }\n", + " }\n", + " // Give older versions of the autoload script a head-start to ensure\n", + " // they initialize before we start loading newer version.\n", + " setTimeout(load_or_wait, 100)\n", + "}(window));" + ], + "application/vnd.holoviews_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = false;\n const py_version = '3.5.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n const reloading = true;\n const Bokeh = root.Bokeh;\n\n // Set a timeout for this load but only if we are not already initializing\n if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks;\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n if (js_modules == null) js_modules = [];\n if (js_exports == null) js_exports = {};\n\n root._bokeh_onload_callbacks.push(callback);\n\n if (root._bokeh_is_loading > 0) {\n // Don't load bokeh if it is still initializing\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n } else if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n // There is nothing to load\n run_callbacks();\n return null;\n }\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n window._bokeh_on_load = on_load\n\n function on_error(e) {\n const src_el = e.srcElement\n console.error(\"failed to load \" + (src_el.href || src_el.src));\n }\n\n const skip = [];\n if (window.requirejs) {\n window.requirejs.config({'packages': {}, 'paths': {'h3': 'https://cdn.jsdelivr.net/npm/h3-js@4.1.0/dist/h3-js.umd', 'deck-gl': 'https://cdn.jsdelivr.net/npm/deck.gl@9.0.20/dist.min', 'deck-json': 'https://cdn.jsdelivr.net/npm/@deck.gl/json@9.0.20/dist.min', 'loader-csv': 'https://cdn.jsdelivr.net/npm/@loaders.gl/csv@4.2.2/dist/dist.min', 'loader-json': 'https://cdn.jsdelivr.net/npm/@loaders.gl/json@4.2.2/dist/dist.min', 'loader-tiles': 'https://cdn.jsdelivr.net/npm/@loaders.gl/3d-tiles@4.2.2/dist/dist.min', 'mapbox-gl': 'https://api.mapbox.com/mapbox-gl-js/v3.0.1/mapbox-gl', 'carto': 'https://cdn.jsdelivr.net/npm/@deck.gl/carto@^9.0.20/dist.min'}, 'shim': {'deck-json': {'deps': ['deck-gl']}, 'deck-gl': {'deps': ['h3']}}});\n require([\"h3\"], function(h3) {\n window.h3 = h3\n on_load()\n })\n require([\"deck-gl\"], function(deck) {\n window.deck = deck\n on_load()\n })\n require([\"deck-json\"], function() {\n on_load()\n })\n require([\"loader-csv\"], function() {\n on_load()\n })\n require([\"loader-json\"], function() {\n on_load()\n })\n require([\"loader-tiles\"], function() {\n on_load()\n })\n require([\"mapbox-gl\"], function(mapboxgl) {\n window.mapboxgl = mapboxgl\n on_load()\n })\n require([\"carto\"], function() {\n on_load()\n })\n root._bokeh_is_loading = css_urls.length + 8;\n } else {\n root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n }\n\n const existing_stylesheets = []\n const links = document.getElementsByTagName('link')\n for (let i = 0; i < links.length; i++) {\n const link = links[i]\n if (link.href != null) {\n existing_stylesheets.push(link.href)\n }\n }\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const escaped = encodeURI(url)\n if (existing_stylesheets.indexOf(escaped) !== -1) {\n on_load()\n continue;\n }\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n } if (((window.deck !== undefined) && (!(window.deck instanceof HTMLElement))) || window.requirejs) {\n var urls = ['https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/h3-js@4.1.0/dist/h3-js.umd.js', 'https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/deck.gl@9.0.20/dist.min.js', 'https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/@deck.gl/json@9.0.20/dist.min.js', 'https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/@loaders.gl/csv@4.2.2/dist/dist.min.js', 'https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/@loaders.gl/json@4.2.2/dist/dist.min.js', 'https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/@loaders.gl/3d-tiles@4.2.2/dist/dist.min.js', 'https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/mapbox-gl-js/v3.0.1/mapbox-gl.js', 'https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/maplibre-gl/dist/maplibre-gl.js'];\n for (var i = 0; i < urls.length; i++) {\n skip.push(encodeURI(urls[i]))\n }\n } if (((window.mapboxgl !== undefined) && (!(window.mapboxgl instanceof HTMLElement))) || window.requirejs) {\n var urls = ['https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/@deck.gl/carto@^9.0.20/dist.min.js'];\n for (var i = 0; i < urls.length; i++) {\n skip.push(encodeURI(urls[i]))\n }\n } var existing_scripts = []\n const scripts = document.getElementsByTagName('script')\n for (let i = 0; i < scripts.length; i++) {\n var script = scripts[i]\n if (script.src != null) {\n existing_scripts.push(script.src)\n }\n }\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\n }\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (let i = 0; i < js_modules.length; i++) {\n const url = js_modules[i];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\n }\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (const name in js_exports) {\n const url = js_exports[name];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) >= 0 || root[name] != null) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\n }\n var element = document.createElement('script');\n element.onerror = on_error;\n element.async = false;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n element.textContent = `\n import ${name} from \"${url}\"\n window.${name} = ${name}\n window._bokeh_on_load()\n `\n document.head.appendChild(element);\n }\n if (!js_urls.length && !js_modules.length) {\n on_load()\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/h3-js@4.1.0/dist/h3-js.umd.js\", \"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/deck.gl@9.0.20/dist.min.js\", \"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/@deck.gl/json@9.0.20/dist.min.js\", \"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/@loaders.gl/csv@4.2.2/dist/dist.min.js\", \"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/@loaders.gl/json@4.2.2/dist/dist.min.js\", \"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/@loaders.gl/3d-tiles@4.2.2/dist/dist.min.js\", \"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/mapbox-gl-js/v3.0.1/mapbox-gl.js\", \"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/maplibre-gl/dist/maplibre-gl.js\", \"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/@deck.gl/carto@^9.0.20/dist.min.js\", \"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\"];\n const js_modules = [];\n const js_exports = {};\n const css_urls = [\"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/mapbox-gl-js/v3.0.1/mapbox-gl.css?v=1.5.2\", \"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/deckglplot/maplibre-gl@4.4.1/dist/maplibre-gl.css?v=1.5.2\"];\n const inline_js = [ function(Bokeh) {\n inject_raw_css(\"\\n.dataframe table{\\n border: none;\\n}\\n\\n.panel-df table{\\n width: 100%;\\n border-collapse: collapse;\\n border: none;\\n}\\n.panel-df td{\\n white-space: nowrap;\\n overflow: auto;\\n text-overflow: ellipsis;\\n}\\n\");\n }, function(Bokeh) {\n inject_raw_css(\"\\n.multi-select{\\n color: white;\\n z-index: 100;\\n background: rgba(44,43,43,0.5);\\n border-radius: 1px;\\n width: 120px !important;\\n height: 30px !important;\\n}\\n.multi-select > .bk {\\n padding: 5px;\\n width: 120px !important;\\n height: 30px !important;\\n}\\n\\n.deck-chart {\\n z-index: 10;\\n position: initial !important;\\n}\\n\");\n }, function(Bokeh) {\n inject_raw_css(\"\\n.center-header {\\n text-align: center\\n}\\n.bk-input-group {\\n padding: 10px;\\n}\\n#sidebar {\\n padding-top: 10px;\\n}\\n.custom-widget-box {\\n margin-top: 20px;\\n padding: 5px;\\n border: None !important;\\n}\\n.custom-widget-box > p {\\n margin: 0px;\\n}\\n.bk-input-group {\\n color: None !important;\\n}\\n.indicator {\\n text-align: center;\\n}\\n.widget-card {\\n margin: 5px 10px;\\n}\\n.number-card {\\n margin: 5px 10px;\\n text-align: center;\\n}\\n.number-card-value {\\n width: 100%;\\n margin: 0px;\\n}\\n\");\n }, function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n function(Bokeh) {\n (function(root, factory) {\n factory(root[\"Bokeh\"]);\n })(this, function(Bokeh) {\n let define;\n return (function outer(modules, entry) {\n if (Bokeh != null) {\n return Bokeh.register_plugin(modules, entry);\n } else {\n throw new Error(\"Cannot find Bokeh. You have to load it prior to loading plugins.\");\n }\n })\n ({\n \"custom/main\": function(require, module, exports) {\n const models = {\n \"CustomInspectTool\": require(\"custom/cuxfilter.charts.datashader.custom_extensions.graph_inspect_widget.custom_inspect_tool\").CustomInspectTool\n };\n require(\"base\").register_models(models);\n module.exports = models;\n },\n \"custom/cuxfilter.charts.datashader.custom_extensions.graph_inspect_widget.custom_inspect_tool\": function(require, module, exports) {\n \"use strict\";\n var _a;\n Object.defineProperty(exports, \"__esModule\", { value: true });\n exports.CustomInspectTool = exports.CustomInspectToolView = void 0;\n const inspect_tool_1 = require(\"models/tools/inspectors/inspect_tool\");\n class CustomInspectToolView extends inspect_tool_1.InspectToolView {\n connect_signals() {\n super.connect_signals();\n this.on_change([this.model.properties.active], () => {\n this.model._active = this.model.active;\n });\n }\n }\n exports.CustomInspectToolView = CustomInspectToolView;\n CustomInspectToolView.__name__ = \"CustomInspectToolView\";\n class CustomInspectTool extends inspect_tool_1.InspectTool {\n constructor(attrs) {\n super(attrs);\n }\n }\n exports.CustomInspectTool = CustomInspectTool;\n _a = CustomInspectTool;\n CustomInspectTool.__name__ = \"CustomInspectTool\";\n (() => {\n _a.prototype.default_view = CustomInspectToolView;\n _a.define(({ Boolean }) => ({\n _active: [Boolean, true]\n }));\n _a.register_alias(\"customInspect\", () => new _a());\n })();\n //# sourceMappingURL=graph_inspect_widget.py:CustomInspectTool.js.map\n }\n }, \"custom/main\");\n ;\n });\n\n },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n ];\n\n function run_inline_js() {\n if ((root.Bokeh !== undefined) || (force === true)) {\n for (let i = 0; i < inline_js.length; i++) {\n try {\n inline_js[i].call(root, root.Bokeh);\n } catch(e) {\n if (!reloading) {\n throw e;\n }\n }\n }\n // Cache old bokeh versions\n if (Bokeh != undefined && !reloading) {\n var NewBokeh = root.Bokeh;\n if (Bokeh.versions === undefined) {\n Bokeh.versions = new Map();\n }\n if (NewBokeh.version !== Bokeh.version) {\n Bokeh.versions.set(NewBokeh.version, NewBokeh)\n }\n root.Bokeh = Bokeh;\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n }\n root._bokeh_is_initializing = false\n }\n\n function load_or_wait() {\n // Implement a backoff loop that tries to ensure we do not load multiple\n // versions of Bokeh and its dependencies at the same time.\n // In recent versions we use the root._bokeh_is_initializing flag\n // to determine whether there is an ongoing attempt to initialize\n // bokeh, however for backward compatibility we also try to ensure\n // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n // before older versions are fully initialized.\n if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n // If the timeout and bokeh was not successfully loaded we reset\n // everything and try loading again\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_is_initializing = false;\n root._bokeh_onload_callbacks = undefined;\n root._bokeh_is_loading = 0\n console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n load_or_wait();\n } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n setTimeout(load_or_wait, 100);\n } else {\n root._bokeh_is_initializing = true\n root._bokeh_onload_callbacks = []\n const bokeh_loaded = root.Bokeh != null && (root.Bokeh.version === py_version || (root.Bokeh.versions !== undefined && root.Bokeh.versions.has(py_version)));\n if (!reloading && !bokeh_loaded) {\n if (root.Bokeh) {\n root.Bokeh = undefined;\n 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" return\n", + " }\n", + " var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", + " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", + " if (id !== undefined) {\n", + " var nchildren = toinsert.length;\n", + " var html_node = toinsert[nchildren-1].children[0];\n", + " html_node.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var scripts = [];\n", + " var nodelist = html_node.querySelectorAll(\"script\");\n", + " for (var i in nodelist) {\n", + " if (nodelist.hasOwnProperty(i)) {\n", + " scripts.push(nodelist[i])\n", + " }\n", + " }\n", + "\n", + " scripts.forEach( function (oldScript) {\n", + " var newScript = document.createElement(\"script\");\n", + " var attrs = [];\n", + " var nodemap = oldScript.attributes;\n", + " for (var j in nodemap) {\n", + " if (nodemap.hasOwnProperty(j)) {\n", + " attrs.push(nodemap[j])\n", + " }\n", + " }\n", + " attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n", + " newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n", + " oldScript.parentNode.replaceChild(newScript, oldScript);\n", + " });\n", + " if (JS_MIME_TYPE in output.data) {\n", + " toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n", + " }\n", + " output_area._hv_plot_id = id;\n", + " if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n", + " window.PyViz.plot_index[id] = Bokeh.index[id];\n", + " } else {\n", + " window.PyViz.plot_index[id] = null;\n", + " }\n", + " } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", + " var bk_div = document.createElement(\"div\");\n", + " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var script_attrs = bk_div.children[0].attributes;\n", + " for (var i = 0; i < script_attrs.length; i++) {\n", + " toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " }\n", + " // store reference to server id on output_area\n", + " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", + " }\n", + "}\n", + "\n", + "/**\n", + " * Handle when an output is cleared or removed\n", + " */\n", + "function handle_clear_output(event, handle) {\n", + " var id = handle.cell.output_area._hv_plot_id;\n", + " var server_id = handle.cell.output_area._bokeh_server_id;\n", + " if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n", + " var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n", + " if (server_id !== null) {\n", + " comm.send({event_type: 'server_delete', 'id': server_id});\n", + " return;\n", + " } else if (comm !== null) {\n", + " comm.send({event_type: 'delete', 'id': id});\n", + " }\n", + " delete PyViz.plot_index[id];\n", + " if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n", + " var doc = window.Bokeh.index[id].model.document\n", + " doc.clear();\n", + " const i = window.Bokeh.documents.indexOf(doc);\n", + " if (i > -1) {\n", + " window.Bokeh.documents.splice(i, 1);\n", + " }\n", + " }\n", + "}\n", + "\n", + "/**\n", + " * Handle kernel restart event\n", + " */\n", + "function handle_kernel_cleanup(event, handle) {\n", + " delete PyViz.comms[\"hv-extension-comm\"];\n", + " window.PyViz.plot_index = {}\n", + "}\n", + "\n", + "/**\n", + " * Handle update_display_data messages\n", + " */\n", + "function handle_update_output(event, handle) {\n", + " handle_clear_output(event, {cell: {output_area: handle.output_area}})\n", + " handle_add_output(event, handle)\n", + "}\n", + "\n", + "function register_renderer(events, OutputArea) {\n", + " function append_mime(data, metadata, element) {\n", + " // create a DOM node to render to\n", + " var toinsert = this.create_output_subarea(\n", + " metadata,\n", + " CLASS_NAME,\n", + " EXEC_MIME_TYPE\n", + " );\n", + " this.keyboard_manager.register_events(toinsert);\n", + " // Render to node\n", + " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", + " render(props, toinsert[0]);\n", + " element.append(toinsert);\n", + " return toinsert\n", + " }\n", + "\n", + " events.on('output_added.OutputArea', handle_add_output);\n", + " events.on('output_updated.OutputArea', handle_update_output);\n", + " events.on('clear_output.CodeCell', handle_clear_output);\n", + " events.on('delete.Cell', handle_clear_output);\n", + " events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n", + "\n", + " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", + " safe: true,\n", + " index: 0\n", + " });\n", + "}\n", + "\n", + "if (window.Jupyter !== undefined) {\n", + " try {\n", + " var events = require('base/js/events');\n", + " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", + " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", + " register_renderer(events, OutputArea);\n", + " }\n", + " } catch(err) {\n", + " }\n", + "}\n" 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(OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n } catch(err) {\n }\n}\n" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.holoviews_exec.v0+json": "", + "text/html": [ + "<div id='a22b9e4a-1604-4a25-a581-66cc655df285'>\n", + " <div id=\"dd915626-9b7c-4773-9b36-794c21e23736\" data-root-id=\"a22b9e4a-1604-4a25-a581-66cc655df285\" style=\"display: contents;\"></div>\n", + "</div>\n", + "<script type=\"application/javascript\">(function(root) {\n", + " var docs_json = {\"64cf7f66-129e-45e0-976c-53b580541bfe\":{\"version\":\"3.5.2\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"panel.models.browser.BrowserInfo\",\"id\":\"a22b9e4a-1604-4a25-a581-66cc655df285\"},{\"type\":\"object\",\"name\":\"panel.models.comm_manager.CommManager\",\"id\":\"ab46d912-dc0e-4ab8-9f7e-eccc8fb53679\",\"attributes\":{\"plot_id\":\"a22b9e4a-1604-4a25-a581-66cc655df285\",\"comm_id\":\"7dd20794d3ff43c7beed9642e5c8583c\",\"client_comm_id\":\"05e5397e18dc4b7e92bfdee8b1d2b576\"}}],\"defs\":[{\"type\":\"model\",\"name\":\"ReactiveHTML1\"},{\"type\":\"model\",\"name\":\"FlexBox1\",\"properties\":[{\"name\":\"align_content\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"align_items\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"flex_direction\",\"kind\":\"Any\",\"default\":\"row\"},{\"name\":\"flex_wrap\",\"kind\":\"Any\",\"default\":\"wrap\"},{\"name\":\"gap\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"justify_content\",\"kind\":\"Any\",\"default\":\"flex-start\"}]},{\"type\":\"model\",\"name\":\"FloatPanel1\",\"properties\":[{\"name\":\"config\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"contained\",\"kind\":\"Any\",\"default\":true},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"right-top\"},{\"name\":\"offsetx\",\"kind\":\"Any\",\"default\":null},{\"name\":\"offsety\",\"kind\":\"Any\",\"default\":null},{\"name\":\"theme\",\"kind\":\"Any\",\"default\":\"primary\"},{\"name\":\"status\",\"kind\":\"Any\",\"default\":\"normalized\"}]},{\"type\":\"model\",\"name\":\"GridStack1\",\"properties\":[{\"name\":\"mode\",\"kind\":\"Any\",\"default\":\"warn\"},{\"name\":\"ncols\",\"kind\":\"Any\",\"default\":null},{\"name\":\"nrows\",\"kind\":\"Any\",\"default\":null},{\"name\":\"allow_resize\",\"kind\":\"Any\",\"default\":true},{\"name\":\"allow_drag\",\"kind\":\"Any\",\"default\":true},{\"name\":\"state\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"drag1\",\"properties\":[{\"name\":\"slider_width\",\"kind\":\"Any\",\"default\":5},{\"name\":\"slider_color\",\"kind\":\"Any\",\"default\":\"black\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":50}]},{\"type\":\"model\",\"name\":\"click1\",\"properties\":[{\"name\":\"terminal_output\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"debug_name\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"clears\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"FastWrapper1\",\"properties\":[{\"name\":\"object\",\"kind\":\"Any\",\"default\":null},{\"name\":\"style\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"NotificationAreaBase1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"NotificationArea1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"notifications\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0},{\"name\":\"types\",\"kind\":\"Any\",\"default\":[{\"type\":\"map\",\"entries\":[[\"type\",\"warning\"],[\"background\",\"#ffc107\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas 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+ " var render_items = [{\"docid\":\"64cf7f66-129e-45e0-976c-53b580541bfe\",\"roots\":{\"a22b9e4a-1604-4a25-a581-66cc655df285\":\"dd915626-9b7c-4773-9b36-794c21e23736\"},\"root_ids\":[\"a22b9e4a-1604-4a25-a581-66cc655df285\"]}];\n", + " var docs = Object.values(docs_json)\n", + " if (!docs) {\n", + " return\n", + " }\n", + " const py_version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n", + " async function embed_document(root) {\n", + " var Bokeh = get_bokeh(root)\n", + " await Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", + " for (const render_item of render_items) {\n", + " for (const root_id of render_item.root_ids) {\n", + "\tconst id_el = document.getElementById(root_id)\n", + "\tif (id_el.children.length && id_el.children[0].hasAttribute('data-root-id')) {\n", + "\t const root_el = id_el.children[0]\n", + "\t root_el.id = root_el.id + '-rendered'\n", + "\t for (const child of root_el.children) {\n", + " // Ensure JupyterLab does not capture keyboard shortcuts\n", + " // see: https://jupyterlab.readthedocs.io/en/4.1.x/extension/notebook.html#keyboard-interaction-model\n", + "\t child.setAttribute('data-lm-suppress-shortcuts', 'true')\n", + "\t }\n", + "\t}\n", + " }\n", + " }\n", + " }\n", + " function get_bokeh(root) {\n", + " if (root.Bokeh === undefined) {\n", + " return null\n", + " } else if (root.Bokeh.version !== py_version) {\n", + " if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(py_version)) {\n", + "\treturn null\n", + " }\n", + " return root.Bokeh.versions.get(py_version);\n", + " } else if (root.Bokeh.version === py_version) {\n", + " return root.Bokeh\n", + " }\n", + " return null\n", + " }\n", + " function is_loaded(root) {\n", + " var Bokeh = get_bokeh(root)\n", + " return (Bokeh != null && Bokeh.Panel !== undefined && ( root.deck !== undefined) && ( root.deck !== undefined) && ( root.deck !== undefined) && ( root.deck !== undefined) && ( root.deck !== undefined) && ( root.deck !== undefined) && ( root.deck !== undefined))\n", + " }\n", + " if (is_loaded(root)) {\n", + " embed_document(root);\n", + " } else {\n", + " var attempts = 0;\n", + " var timer = setInterval(function(root) {\n", + " if (is_loaded(root)) {\n", + " clearInterval(timer);\n", + " embed_document(root);\n", + " } else if (document.readyState == \"complete\") {\n", + " attempts++;\n", + " if (attempts > 200) {\n", + " clearInterval(timer);\n", + "\t var Bokeh = get_bokeh(root)\n", + "\t if (Bokeh == null || Bokeh.Panel == null) {\n", + " console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n", + "\t } else {\n", + "\t console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n", + "\t embed_document(root)\n", + "\t }\n", + " }\n", + " }\n", + " }, 25, root)\n", + " }\n", + "})(window);</script>" + ] + }, + "metadata": { + "application/vnd.holoviews_exec.v0+json": { + "id": "a22b9e4a-1604-4a25-a581-66cc655df285" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# create dashboard\n", + "dash = cxf_data.dashboard(\n", + " charts=[scatter_chart, centroid_chart],\n", + " sidebar=[cluster_widget],\n", + " theme=cxf.themes.dark,\n", + " data_size_widget=True\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.holoviews_exec.v0+json": "", + "text/html": [ + "<div id='36d7794d-0a77-45dc-990d-c348e8b96d42'>\n", + " <div id=\"b9b379d8-d789-4d80-8e55-f91dd4cc5a38\" data-root-id=\"36d7794d-0a77-45dc-990d-c348e8b96d42\" style=\"display: contents;\"></div>\n", + "</div>\n", + "<script type=\"application/javascript\">(function(root) {\n", + " var docs_json = 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"outputs": [], + "source": [ + "import IPython\n", + "app = IPython.Application.instance()\n", + "app.kernel.do_shutdown(True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Well Done!** Let's move to the [next notebook](3-03_dbscan.ipynb). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<img src=\"./images/DLI_Header.png\" width=400/>" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.15" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Fundamentals_of_Accelerated_Data_Science/3-03_dbscan.ipynb b/Fundamentals_of_Accelerated_Data_Science/3-03_dbscan.ipynb new file mode 100644 index 0000000..e27f31e --- /dev/null +++ b/Fundamentals_of_Accelerated_Data_Science/3-03_dbscan.ipynb @@ -0,0 +1,241 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<img src=\"./images/DLI_Header.png\" width=400/>" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Fundamentals of Accelerated Data Science # " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 03 - DBSCAN ##\n", + "\n", + "**Table of Contents**\n", + "<br>\n", + "This notebook uses GPU-accelerated DBSCAN to identify clusters of infected people. This notebook covers the below sections: \n", + "1. [Environment](#Environment)\n", + "2. [Load Data](#Load-Data)\n", + "3. [DBSCAN Clustering](#DBSCAN-Clustering)\n", + " * [Exercise #1 - Make Another DBSCAN Instance](#Exercise-#1---Make-Another-DBSCAN-Instance)\n", + "4. [Visualize the Clusters](#Visualize-the-Clusters)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Environment ##" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import cudf\n", + "import cuml\n", + "\n", + "import cuxfilter as cxf" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load Data ##\n", + "For this notebook, we again load a subset of our population data with only the columns we need. An `infected` column has been added to the data to indicate whether or not a person is known to be infected with our simulated virus." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gdf = cudf.read_csv('./data/pop_sample.csv', dtype=['float32', 'float32', 'float32'])\n", + "print(gdf.dtypes)\n", + "gdf.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gdf.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gdf['infected'].value_counts()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## DBSCAN Clustering ##\n", + "DBSCAN is another unsupervised clustering algorithm that is particularly effective when the number of clusters is not known up front and the clusters may have concave or other unusual shapes--a situation that often applies in geospatial analytics.\n", + "\n", + "In this series of exercises you will use DBSCAN to identify clusters of infected people by location, which may help us identify groups becoming infected from common patient zeroes and assist in response planning.\n", + "\n", + "Create a DBSCAN instance by using `cuml.DBSCAN`. Pass in the named argument `eps` (the maximum distance a point can be from the nearest point in a cluster to be considered possibly in that cluster) to be `5000`. Since the `northing` and `easting` values we created are measured in meters, this will allow us to identify clusters of infected people where individuals may be separated from the rest of the cluster by up to 5 kilometers.\n", + "\n", + "Below we train a DBSCAN algorithm. We start by creating a new dataframe from rows of the original dataframe where `infected` is `1` (true), and call it `infected_df`--be sure to reset the dataframe's index afterward. Use `dbscan.fit_predict` to perform clustering on the `northing` and `easting` columns of `infected_df`, and turn the resulting series into a new column in `infected_gdf` called \"cluster\". Finally, compute the number of clusters identified by DBSCAN." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dbscan = cuml.DBSCAN(eps=5000)\n", + "# dbscan = cuml.DBSCAN(eps=10000)\n", + "\n", + "infected_df = gdf[gdf['infected'] == 1].reset_index()\n", + "infected_df['cluster'] = dbscan.fit_predict(infected_df[['northing', 'easting']])\n", + "infected_df['cluster'].nunique()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exercise #1 - Make Another DBSCAN Instance ###\n", + "\n", + "**Instructions**: <br>\n", + "* Modify the `<FIXME>` only and execute the below cell to instantiate a DBSCAN instance with `10000` for `eps`.\n", + "* Modify the `<FIXME>` only and execute the cell below to fit the data and identify infected clusters. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dbscan = cuml.DBSCAN(<<<<FIXME>>>>)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "infected_df = gdf[gdf['infected'] == 1].reset_index()\n", + "infected_df['cluster'] = dbscan.<<<<FIXME>>>>(infected_df[['northing', 'easting']])\n", + "infected_df['cluster'].nunique()" + ] + }, + { + "cell_type": "raw", + "metadata": { + "jupyter": { + "source_hidden": true + } + }, + "source": [ + "\n", + "dbscan = cuml.DBSCAN(eps=10000)\n", + "\n", + "infected_df = gdf[gdf['infected'] == 1].reset_index()\n", + "infected_df['cluster'] = dbscan.fit_predict(infected_df[['northing', 'easting']])\n", + "infected_df['cluster'].nunique()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Click ... for solution. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualize the Clusters" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Because we have the same column names as in the K-means example--`easting`, `northing`, and `cluster`--we can use the same code to visualize the clusters." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "infected_df.to_pandas().plot(kind='scatter', x='easting', y='northing', c='cluster')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import IPython\n", + "app = IPython.Application.instance()\n", + "app.kernel.do_shutdown(True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Well Done!** Let's move to the [next notebook](3-04_logistic_regression.ipynb). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<img src=\"./images/DLI_Header.png\" width=400/>" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.15" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Fundamentals_of_Accelerated_Data_Science/3-04_logistic_regression.ipynb b/Fundamentals_of_Accelerated_Data_Science/3-04_logistic_regression.ipynb new file mode 100644 index 0000000..3890ea7 --- /dev/null +++ b/Fundamentals_of_Accelerated_Data_Science/3-04_logistic_regression.ipynb @@ -0,0 +1,1904 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<img src=\"./images/DLI_Header.png\" width=400/>" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Fundamentals of Accelerated Data Science # " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 04 - Logistic Regression ##\n", + "\n", + "**Table of Contents**\n", + "<br>\n", + "This notebook uses GPU-accelerated logistic regression to predict infection risk based on features of our population members. This notebook covers the below sections: \n", + "1. [Environment](#Environment)\n", + "2. [Load Data](#Load-Data)\n", + "3. [Logistic Regression](#Logistic-Regression)\n", + " * [Viewing the Regression](#Viewing-the-Regression)\n", + " * [Estimate Probability of Infection](#Estimate-Probability-of-Infection)\n", + "4. [Model Explainability](#Model-Explainability)\n", + " * [Show Infection Prevalence is Related to Age](#Show-Infection-Prevalence-is-Related-to-Age)\n", + " * [Exercise #1 - Show Infection Prevalence is Related to Sex](#Exercise-#1---Show-Infection-Prevalence-is-Related-to-Sex)\n", + "5. [Making Predictions with Separate Training and Testing Data](#Making-Predictions-with-Separate-Training-and-Test-Data)\n", + " * [Exercise #2 - Fit Logistic Regression Model Using Training Data](#Exercise-#2---Fit-Logistic-Regression-Model-Using-Training-Data)\n", + " * [Use Test Data to Validate Model](#Use-Test-Data-to-Validate-Model)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Environment ##" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import cudf\n", + "import cuml\n", + "\n", + "import cupy as cp" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load Data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "gdf = cudf.read_csv('./data/clean_uk_pop_full.csv', usecols=['age', 'sex', 'infected'])" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "age float64\n", + "sex float64\n", + "infected float64\n", + "dtype: object" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gdf.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(58479894, 3)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gdf.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>age</th>\n", + " <th>sex</th>\n", + " <th>infected</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>0.0</td>\n", + " <td>0.0</td>\n", + " <td>0.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>0.0</td>\n", + " <td>0.0</td>\n", + " <td>0.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>0.0</td>\n", + " <td>0.0</td>\n", + " <td>0.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>0.0</td>\n", + " <td>0.0</td>\n", + " <td>0.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>0.0</td>\n", + " <td>0.0</td>\n", + " <td>0.0</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " age sex infected\n", + "0 0.0 0.0 0.0\n", + "1 0.0 0.0 0.0\n", + "2 0.0 0.0 0.0\n", + "3 0.0 0.0 0.0\n", + "4 0.0 0.0 0.0" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gdf.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Logistic Regression ##\n", + "Logistic regression can be used to estimate the probability of an outcome as a function of some (assumed independent) inputs. In our case, we would like to estimate infection risk based on population members' age and sex.\n", + "\n", + "Below we train a logistic regresion model. We first create a cuML logistic regression instance `logreg`. The `logreg.fit` method takes 2 arguments: the model's independent variables *X*, and the dependent variable *y*. Fit the `logreg` model using the `gdf` columns `age` and `sex` as *X* and the `infected` column as *y*." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<style>#sk-container-id-1 {\n", + " /* Definition of color scheme common for light and dark mode */\n", + " --sklearn-color-text: black;\n", + " --sklearn-color-line: gray;\n", + " /* Definition of color scheme for unfitted estimators */\n", + " --sklearn-color-unfitted-level-0: #fff5e6;\n", + " --sklearn-color-unfitted-level-1: #f6e4d2;\n", + " --sklearn-color-unfitted-level-2: #ffe0b3;\n", + " --sklearn-color-unfitted-level-3: chocolate;\n", + " /* Definition of color scheme for fitted estimators */\n", + " --sklearn-color-fitted-level-0: #f0f8ff;\n", + " --sklearn-color-fitted-level-1: #d4ebff;\n", + " --sklearn-color-fitted-level-2: #b3dbfd;\n", + " --sklearn-color-fitted-level-3: cornflowerblue;\n", + "\n", + " /* Specific color for light theme */\n", + " --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n", + " --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n", + " --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n", + " --sklearn-color-icon: #696969;\n", + "\n", + " @media (prefers-color-scheme: dark) {\n", + " /* Redefinition of color scheme for dark theme */\n", + " --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n", + " --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n", + " --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n", + " --sklearn-color-icon: #878787;\n", + " }\n", + "}\n", + "\n", + "#sk-container-id-1 {\n", + " color: var(--sklearn-color-text);\n", + "}\n", + "\n", + "#sk-container-id-1 pre {\n", + " padding: 0;\n", + "}\n", + "\n", + "#sk-container-id-1 input.sk-hidden--visually {\n", + " border: 0;\n", + " clip: rect(1px 1px 1px 1px);\n", + " clip: rect(1px, 1px, 1px, 1px);\n", + " height: 1px;\n", + " margin: -1px;\n", + " overflow: hidden;\n", + " padding: 0;\n", + " position: absolute;\n", + " width: 1px;\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-dashed-wrapped {\n", + " border: 1px dashed var(--sklearn-color-line);\n", + " margin: 0 0.4em 0.5em 0.4em;\n", + " box-sizing: border-box;\n", + " padding-bottom: 0.4em;\n", + " background-color: var(--sklearn-color-background);\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-container {\n", + " /* jupyter's `normalize.less` sets `[hidden] { display: none; 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input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n", + " content: \"▾\";\n", + "}\n", + "\n", + "/* Pipeline/ColumnTransformer-specific style */\n", + "\n", + "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n", + " color: var(--sklearn-color-text);\n", + " background-color: var(--sklearn-color-unfitted-level-2);\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n", + " background-color: var(--sklearn-color-fitted-level-2);\n", + "}\n", + "\n", + "/* Estimator-specific style */\n", + "\n", + "/* Colorize estimator box */\n", + "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n", + " /* unfitted */\n", + " background-color: var(--sklearn-color-unfitted-level-2);\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n", + " /* fitted */\n", + " background-color: var(--sklearn-color-fitted-level-2);\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n", + "#sk-container-id-1 div.sk-label label {\n", + " /* The background is the default theme color */\n", + " color: var(--sklearn-color-text-on-default-background);\n", + "}\n", + "\n", + "/* On hover, darken the color of the background */\n", + "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n", + " color: var(--sklearn-color-text);\n", + " background-color: var(--sklearn-color-unfitted-level-2);\n", + "}\n", + "\n", + "/* Label box, darken color on hover, fitted */\n", + "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n", + " color: var(--sklearn-color-text);\n", + " background-color: var(--sklearn-color-fitted-level-2);\n", + "}\n", + "\n", + "/* Estimator label */\n", + "\n", + "#sk-container-id-1 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div.sk-estimator.fitted:hover {\n", + " /* fitted */\n", + " background-color: var(--sklearn-color-fitted-level-2);\n", + "}\n", + "\n", + "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n", + "\n", + "/* Common style for \"i\" and \"?\" */\n", + "\n", + ".sk-estimator-doc-link,\n", + "a:link.sk-estimator-doc-link,\n", + "a:visited.sk-estimator-doc-link {\n", + " float: right;\n", + " font-size: smaller;\n", + " line-height: 1em;\n", + " font-family: monospace;\n", + " background-color: var(--sklearn-color-background);\n", + " border-radius: 1em;\n", + " height: 1em;\n", + " width: 1em;\n", + " text-decoration: none !important;\n", + " margin-left: 1ex;\n", + " /* unfitted */\n", + " border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n", + " color: var(--sklearn-color-unfitted-level-1);\n", + "}\n", + "\n", + ".sk-estimator-doc-link.fitted,\n", + "a:link.sk-estimator-doc-link.fitted,\n", + "a:visited.sk-estimator-doc-link.fitted {\n", + " /* fitted */\n", + " border: var(--sklearn-color-fitted-level-1) 1pt solid;\n", + " color: var(--sklearn-color-fitted-level-1);\n", + "}\n", + "\n", + "/* On hover */\n", + "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n", + ".sk-estimator-doc-link:hover,\n", + "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n", + ".sk-estimator-doc-link:hover {\n", + " /* unfitted */\n", + " background-color: var(--sklearn-color-unfitted-level-3);\n", + " color: var(--sklearn-color-background);\n", + " text-decoration: none;\n", + "}\n", + "\n", + "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n", + ".sk-estimator-doc-link.fitted:hover,\n", + "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n", + ".sk-estimator-doc-link.fitted:hover {\n", + " /* fitted */\n", + " background-color: var(--sklearn-color-fitted-level-3);\n", + " color: var(--sklearn-color-background);\n", + " text-decoration: none;\n", + "}\n", + "\n", + "/* Span, style for the box shown on hovering the info icon */\n", + ".sk-estimator-doc-link span {\n", + " display: none;\n", + " z-index: 9999;\n", + " position: relative;\n", + " font-weight: normal;\n", + " right: .2ex;\n", + " padding: .5ex;\n", + " margin: .5ex;\n", + " width: min-content;\n", + " min-width: 20ex;\n", + " max-width: 50ex;\n", + " color: var(--sklearn-color-text);\n", + " box-shadow: 2pt 2pt 4pt #999;\n", + " /* unfitted */\n", + " background: var(--sklearn-color-unfitted-level-0);\n", + " border: .5pt solid var(--sklearn-color-unfitted-level-3);\n", + "}\n", + "\n", + ".sk-estimator-doc-link.fitted span {\n", + " /* fitted */\n", + " background: var(--sklearn-color-fitted-level-0);\n", + " border: var(--sklearn-color-fitted-level-3);\n", + "}\n", + "\n", + ".sk-estimator-doc-link:hover span {\n", + " display: block;\n", + "}\n", + "\n", + "/* \"?\"-specific style due to the `<a>` HTML tag */\n", + "\n", + "#sk-container-id-1 a.estimator_doc_link {\n", + " float: right;\n", + " font-size: 1rem;\n", + " line-height: 1em;\n", + " font-family: monospace;\n", + " background-color: var(--sklearn-color-background);\n", + " border-radius: 1rem;\n", + " height: 1rem;\n", + " width: 1rem;\n", + " text-decoration: none;\n", + " /* unfitted */\n", + " color: var(--sklearn-color-unfitted-level-1);\n", + " border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n", + "}\n", + "\n", + "#sk-container-id-1 a.estimator_doc_link.fitted {\n", + " /* fitted */\n", + " border: var(--sklearn-color-fitted-level-1) 1pt solid;\n", + " color: var(--sklearn-color-fitted-level-1);\n", + "}\n", + "\n", + "/* On hover */\n", + "#sk-container-id-1 a.estimator_doc_link:hover {\n", + " /* unfitted */\n", + " background-color: var(--sklearn-color-unfitted-level-3);\n", + " color: var(--sklearn-color-background);\n", + " text-decoration: none;\n", + "}\n", + "\n", + "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n", + " /* fitted */\n", + " background-color: var(--sklearn-color-fitted-level-3);\n", + "}\n", + "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\"> LogisticRegression<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression()</pre></div> </div></div></div></div>" + ], + "text/plain": [ + "LogisticRegression()" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "logreg = cuml.LogisticRegression()\n", + "logreg.fit(gdf[['age', 'sex']], gdf['infected'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Viewing the Regression ###\n", + "After fitting the model, we could use `logreg.predict` to estimate whether someone has more than a 50% chance to be infected, but since the virus has low prevalence in the population (around 1-2%, in this data set), individual probabilities of infection are well below 50% and the model should correctly predict that no one is individually likely to have the infection.\n", + "\n", + "However, we also have access to the model coefficients at `logreg.coef_` as well as the intercept at `logreg.intercept_`. Both of these values are cuDF Series. \n", + "\n", + "Below we view these values. Notice that changing sex from 0 to 1 has the same effect via the coefficients as changing the age by ~48 years." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "cudf.core.dataframe.DataFrame" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(logreg.coef_)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "cudf.core.series.Series" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(logreg.intercept_)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Coefficients: [age, sex]\n", + "[0 0.014861\n", + "Name: 0, dtype: float64, 0 0.695666\n", + "Name: 1, dtype: float64]\n", + "Intercept:\n", + "-5.222369426308725\n" + ] + } + ], + "source": [ + "logreg_coef = logreg.coef_\n", + "logreg_int = logreg.intercept_\n", + "\n", + "print(\"Coefficients: [age, sex]\")\n", + "print([logreg_coef[0], logreg_coef[1]])\n", + "\n", + "print(\"Intercept:\")\n", + "print(logreg_int[0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Estimate Probability of Infection ###\n", + "As with all logistic regressions, the coefficients allow us to calculate the logit for each; from that, we can calculate the estimated percentage risk of infection. \n", + "\n", + "**Note**: Remembering that a 1 indicates 'infected', we assign that class' probability to a new column in the original dataframe. " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>0</th>\n", + " <th>1</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>0.994634</td>\n", + " <td>0.005366</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>0.994634</td>\n", + " <td>0.005366</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>0.994634</td>\n", + " <td>0.005366</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>0.994634</td>\n", + " <td>0.005366</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>0.994634</td>\n", + " <td>0.005366</td>\n", + " </tr>\n", + " <tr>\n", + " <th>...</th>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " </tr>\n", + " <tr>\n", + " <th>58479889</th>\n", + " <td>0.960428</td>\n", + " <td>0.039572</td>\n", + " </tr>\n", + " <tr>\n", + " <th>58479890</th>\n", + " <td>0.960428</td>\n", + " <td>0.039572</td>\n", + " </tr>\n", + " <tr>\n", + " <th>58479891</th>\n", + " <td>0.960428</td>\n", + " <td>0.039572</td>\n", + " </tr>\n", + " <tr>\n", + " <th>58479892</th>\n", + " <td>0.960428</td>\n", + " <td>0.039572</td>\n", + " </tr>\n", + " <tr>\n", + " <th>58479893</th>\n", + " <td>0.960428</td>\n", + " <td>0.039572</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "<p>58479894 rows × 2 columns</p>\n", + "</div>" + ], + "text/plain": [ + " 0 1\n", + "0 0.994634 0.005366\n", + "1 0.994634 0.005366\n", + "2 0.994634 0.005366\n", + "3 0.994634 0.005366\n", + "4 0.994634 0.005366\n", + "... ... ...\n", + "58479889 0.960428 0.039572\n", + "58479890 0.960428 0.039572\n", + "58479891 0.960428 0.039572\n", + "58479892 0.960428 0.039572\n", + "58479893 0.960428 0.039572\n", + "\n", + "[58479894 rows x 2 columns]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "class_probs = logreg.predict_proba(gdf[['age', 'sex']])\n", + "class_probs" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "gdf['risk'] = class_probs[1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Looking at the original records with their new estimated risks, we can see how estimated risk varies across individuals." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>age</th>\n", + " <th>sex</th>\n", + " <th>infected</th>\n", + " <th>risk</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>57475949</th>\n", + " <td>84.0</td>\n", + " <td>1.0</td>\n", + " <td>0.0</td>\n", + " <td>0.036319</td>\n", + " </tr>\n", + " <tr>\n", + " <th>43073547</th>\n", + " <td>39.0</td>\n", + " <td>1.0</td>\n", + " <td>0.0</td>\n", + " <td>0.018944</td>\n", + " </tr>\n", + " <tr>\n", + " <th>46406333</th>\n", + " <td>48.0</td>\n", + " <td>1.0</td>\n", + " <td>0.0</td>\n", + " <td>0.021596</td>\n", + " </tr>\n", + " <tr>\n", + " <th>17837831</th>\n", + " <td>48.0</td>\n", + " <td>0.0</td>\n", + " <td>0.0</td>\n", + " <td>0.010889</td>\n", + " </tr>\n", + " <tr>\n", + " <th>16785013</th>\n", + " <td>45.0</td>\n", + " <td>0.0</td>\n", + " <td>0.0</td>\n", + " <td>0.010419</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " age sex infected risk\n", + "57475949 84.0 1.0 0.0 0.036319\n", + "43073547 39.0 1.0 0.0 0.018944\n", + "46406333 48.0 1.0 0.0 0.021596\n", + "17837831 48.0 0.0 0.0 0.010889\n", + "16785013 45.0 0.0 0.0 0.010419" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gdf.take(cp.random.choice(gdf.shape[0], size=5, replace=False))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Model Explainability ##\n", + "Model explainability refers to the ability to understand and explain the decisions and reasoning underlying the predictions from machine learning models. It can be achieved by investigating how the feature variables are related to the target variable. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Show Infection Prevalence is Related to Age ###\n", + "The positive coefficient on age suggests that the virus is more prevalent in older people, even when controlling for sex.\n", + "\n", + "For this exercise, show that infection prevalence has some relationship to age by printing the mean `infected` values for the oldest and youngest members of the population when grouped by age:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " infected\n", + "age \n", + "66.0 0.020700\n", + "71.0 0.021292\n", + "64.0 0.020675\n", + "77.0 0.022102\n", + "82.0 0.022929\n", + " infected\n", + "age \n", + "33.0 0.015707\n", + "76.0 0.021928\n", + "74.0 0.021807\n", + "79.0 0.022518\n", + "86.0 0.023417\n" + ] + } + ], + "source": [ + "# %load solutions/risk_by_age\n", + "age_groups = gdf[['age', 'infected']].groupby(['age'])\n", + "print(age_groups.mean().head())\n", + "print(age_groups.mean().tail())\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exercise #1 - Show Infection Prevalence is Related to Sex ###\n", + "Similarly, the positive coefficient on sex suggests that the virus is more prevalent in people with sex = `1` (females), even when controlling for age.\n", + "\n", + "**Instructions**: <br>\n", + "* Modify the `<FIXME>` only and execute the below cell to show that infection prevalence has some relationship to sex by printing the mean `infected` values for the population when grouped by sex. ." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>infected</th>\n", + " </tr>\n", + " <tr>\n", + " <th>sex</th>\n", + " <th></th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0.0</th>\n", + " <td>0.010140</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1.0</th>\n", + " <td>0.020713</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " infected\n", + "sex \n", + "0.0 0.010140\n", + "1.0 0.020713" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sex_groups = gdf[['sex', 'infected']].groupby(['sex'])\n", + "sex_groups.mean()" + ] + }, + { + "cell_type": "raw", + "metadata": { + "scrolled": true + }, + "source": [ + "\n", + "sex_groups = gdf[['sex', 'infected']].groupby(['sex'])\n", + "sex_groups.mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Click ... for solution. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Making Predictions with Separate Training and Test Data ##\n", + "The typical process involves training the model on the training set, then using the test set to evaluate its performance. This provides a more realistic assessment of how well the model will perform on new, unseen data in real-world applications. By testing on a separate dataset, you can detect if your model is **overfitting** to the training data. Overfitting occurs when a model performs well on training data but poorly on new data. In many cases, you don't have access to truly new data, so splitting your existing data simulates this scenario. \n", + "\n", + "cuML gives us a simple method for producing paired training/testing data:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "X_train, X_test, y_train, y_test = cuml.train_test_split(gdf[['age', 'sex']], gdf['infected'], train_size=0.9)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exercise #2 - Fit Logistic Regression Model Using Training Data ###\n", + "\n", + "**Instructions**: <br>\n", + "* Execute the below cell to create a new logistic regression model `logreg`\n", + "* Modify the `<FIXME>` only and execute the cell below to fit the new model with the *X* and *y* training data just created." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "logreg = cuml.LogisticRegression()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "<style>#sk-container-id-3 {\n", + " /* Definition of color scheme common for light and dark mode */\n", + " --sklearn-color-text: black;\n", + " --sklearn-color-line: gray;\n", + " /* Definition of color scheme for unfitted estimators */\n", + " --sklearn-color-unfitted-level-0: #fff5e6;\n", + " --sklearn-color-unfitted-level-1: #f6e4d2;\n", + " --sklearn-color-unfitted-level-2: #ffe0b3;\n", + " --sklearn-color-unfitted-level-3: chocolate;\n", + " /* Definition of color scheme for fitted estimators */\n", + " --sklearn-color-fitted-level-0: #f0f8ff;\n", + " --sklearn-color-fitted-level-1: #d4ebff;\n", + " --sklearn-color-fitted-level-2: #b3dbfd;\n", + " --sklearn-color-fitted-level-3: cornflowerblue;\n", + "\n", + " /* Specific color for light theme */\n", + " --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n", + " --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n", + " --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n", + " --sklearn-color-icon: #696969;\n", + "\n", + " @media (prefers-color-scheme: dark) {\n", + " /* Redefinition of color scheme for dark theme */\n", + " --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n", + " --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n", + " --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n", + " --sklearn-color-icon: #878787;\n", + " }\n", + "}\n", + "\n", + "#sk-container-id-3 {\n", + " color: var(--sklearn-color-text);\n", + "}\n", + "\n", + "#sk-container-id-3 pre {\n", + " padding: 0;\n", + "}\n", + "\n", + "#sk-container-id-3 input.sk-hidden--visually {\n", + " border: 0;\n", + " clip: rect(1px 1px 1px 1px);\n", + " clip: rect(1px, 1px, 1px, 1px);\n", + " height: 1px;\n", + " margin: -1px;\n", + " overflow: hidden;\n", + " padding: 0;\n", + " position: absolute;\n", + " width: 1px;\n", + "}\n", + "\n", + "#sk-container-id-3 div.sk-dashed-wrapped {\n", + " border: 1px dashed var(--sklearn-color-line);\n", + " margin: 0 0.4em 0.5em 0.4em;\n", + " box-sizing: border-box;\n", + " padding-bottom: 0.4em;\n", + " background-color: var(--sklearn-color-background);\n", + "}\n", + "\n", + "#sk-container-id-3 div.sk-container {\n", + " /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n", + " but bootstrap.min.css set `[hidden] { display: none !important; }`\n", + " so we also need the `!important` here to be able to override the\n", + " default hidden behavior on the sphinx rendered scikit-learn.org.\n", + " See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n", + " display: inline-block !important;\n", + " position: relative;\n", + "}\n", + "\n", + "#sk-container-id-3 div.sk-text-repr-fallback {\n", + " display: none;\n", + "}\n", + "\n", + "div.sk-parallel-item,\n", + "div.sk-serial,\n", + "div.sk-item {\n", + " /* draw centered vertical line to link estimators */\n", + " background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n", + " background-size: 2px 100%;\n", + " background-repeat: no-repeat;\n", + " background-position: center center;\n", + "}\n", + "\n", + "/* Parallel-specific style estimator block */\n", + "\n", + "#sk-container-id-3 div.sk-parallel-item::after {\n", + " content: \"\";\n", + " width: 100%;\n", + " border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n", + " flex-grow: 1;\n", + "}\n", + "\n", + "#sk-container-id-3 div.sk-parallel {\n", + " display: flex;\n", + " align-items: stretch;\n", + " justify-content: center;\n", + " background-color: var(--sklearn-color-background);\n", + " position: relative;\n", + "}\n", + "\n", + "#sk-container-id-3 div.sk-parallel-item {\n", + " display: flex;\n", + " flex-direction: column;\n", + "}\n", + "\n", + "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n", + " align-self: flex-end;\n", + " width: 50%;\n", + "}\n", + "\n", + "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n", + " align-self: flex-start;\n", + " width: 50%;\n", + "}\n", + "\n", + "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n", + " width: 0;\n", + "}\n", + "\n", + "/* Serial-specific style estimator block */\n", + "\n", + "#sk-container-id-3 div.sk-serial {\n", + " display: flex;\n", + " flex-direction: column;\n", + " align-items: center;\n", + " background-color: var(--sklearn-color-background);\n", + " padding-right: 1em;\n", + " padding-left: 1em;\n", + "}\n", + "\n", + "\n", + "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n", + "clickable and can be expanded/collapsed.\n", + "- Pipeline and ColumnTransformer use this feature and define the default style\n", + "- Estimators will overwrite some part of the style using the `sk-estimator` class\n", + "*/\n", + "\n", + "/* Pipeline and ColumnTransformer style (default) */\n", + "\n", + "#sk-container-id-3 div.sk-toggleable {\n", + " /* Default theme specific background. It is overwritten whether we have a\n", + " specific estimator or a Pipeline/ColumnTransformer */\n", + " background-color: var(--sklearn-color-background);\n", + "}\n", + "\n", + "/* Toggleable label */\n", + "#sk-container-id-3 label.sk-toggleable__label {\n", + " cursor: pointer;\n", + " display: block;\n", + " width: 100%;\n", + " margin-bottom: 0;\n", + " padding: 0.5em;\n", + " box-sizing: border-box;\n", + " text-align: center;\n", + "}\n", + "\n", + "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n", + " /* Arrow on the left of the label */\n", + " content: \"▸\";\n", + " float: left;\n", + " margin-right: 0.25em;\n", + " color: var(--sklearn-color-icon);\n", + "}\n", + "\n", + "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n", + " color: var(--sklearn-color-text);\n", + "}\n", + "\n", + "/* Toggleable content - dropdown */\n", + "\n", + "#sk-container-id-3 div.sk-toggleable__content {\n", + " max-height: 0;\n", + " max-width: 0;\n", + " overflow: hidden;\n", + " text-align: left;\n", + " /* unfitted */\n", + " background-color: var(--sklearn-color-unfitted-level-0);\n", + "}\n", + "\n", + "#sk-container-id-3 div.sk-toggleable__content.fitted {\n", + " /* fitted */\n", + " background-color: var(--sklearn-color-fitted-level-0);\n", + "}\n", + "\n", + "#sk-container-id-3 div.sk-toggleable__content pre {\n", + " margin: 0.2em;\n", + " border-radius: 0.25em;\n", + " color: var(--sklearn-color-text);\n", + " /* unfitted */\n", + " background-color: var(--sklearn-color-unfitted-level-0);\n", + "}\n", + "\n", + "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n", + " /* unfitted */\n", + " background-color: var(--sklearn-color-fitted-level-0);\n", + "}\n", + "\n", + "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n", + " /* Expand drop-down */\n", + " max-height: 200px;\n", + " max-width: 100%;\n", + " overflow: auto;\n", + "}\n", + "\n", + "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n", + " content: \"▾\";\n", + "}\n", + "\n", + "/* Pipeline/ColumnTransformer-specific style */\n", + "\n", + "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n", + " color: var(--sklearn-color-text);\n", + " background-color: var(--sklearn-color-unfitted-level-2);\n", + "}\n", + "\n", + "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n", + " background-color: var(--sklearn-color-fitted-level-2);\n", + "}\n", + "\n", + "/* Estimator-specific style */\n", + "\n", + "/* Colorize estimator box */\n", + "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n", + " /* unfitted */\n", + " background-color: var(--sklearn-color-unfitted-level-2);\n", + "}\n", + "\n", + "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n", + " /* fitted */\n", + " background-color: var(--sklearn-color-fitted-level-2);\n", + "}\n", + "\n", + "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n", + "#sk-container-id-3 div.sk-label label {\n", + " /* The background is the default theme color */\n", + " color: var(--sklearn-color-text-on-default-background);\n", + "}\n", + "\n", + "/* On hover, darken the color of the background */\n", + "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n", + " color: var(--sklearn-color-text);\n", + " background-color: var(--sklearn-color-unfitted-level-2);\n", + "}\n", + "\n", + "/* Label box, darken color on hover, fitted */\n", + "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n", + " color: var(--sklearn-color-text);\n", + " background-color: var(--sklearn-color-fitted-level-2);\n", + "}\n", + "\n", + "/* Estimator label */\n", + "\n", + "#sk-container-id-3 div.sk-label label {\n", + " font-family: monospace;\n", + " font-weight: bold;\n", + " display: inline-block;\n", + " line-height: 1.2em;\n", + "}\n", + "\n", + "#sk-container-id-3 div.sk-label-container {\n", + " text-align: center;\n", + "}\n", + "\n", + "/* Estimator-specific */\n", + "#sk-container-id-3 div.sk-estimator {\n", + " font-family: monospace;\n", + " border: 1px dotted var(--sklearn-color-border-box);\n", + " border-radius: 0.25em;\n", + " box-sizing: border-box;\n", + " margin-bottom: 0.5em;\n", + " /* unfitted */\n", + " background-color: var(--sklearn-color-unfitted-level-0);\n", + "}\n", + "\n", + "#sk-container-id-3 div.sk-estimator.fitted {\n", + " /* fitted */\n", + " background-color: var(--sklearn-color-fitted-level-0);\n", + "}\n", + "\n", + "/* on hover */\n", + "#sk-container-id-3 div.sk-estimator:hover {\n", + " /* unfitted */\n", + " background-color: var(--sklearn-color-unfitted-level-2);\n", + "}\n", + "\n", + "#sk-container-id-3 div.sk-estimator.fitted:hover {\n", + " /* fitted */\n", + " background-color: var(--sklearn-color-fitted-level-2);\n", + "}\n", + "\n", + "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n", + "\n", + "/* Common style for \"i\" and \"?\" */\n", + "\n", + ".sk-estimator-doc-link,\n", + "a:link.sk-estimator-doc-link,\n", + "a:visited.sk-estimator-doc-link {\n", + " float: right;\n", + " font-size: smaller;\n", + " line-height: 1em;\n", + " font-family: monospace;\n", + " background-color: var(--sklearn-color-background);\n", + " border-radius: 1em;\n", + " height: 1em;\n", + " width: 1em;\n", + " text-decoration: none !important;\n", + " margin-left: 1ex;\n", + " /* unfitted */\n", + " border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n", + " color: var(--sklearn-color-unfitted-level-1);\n", + "}\n", + "\n", + ".sk-estimator-doc-link.fitted,\n", + "a:link.sk-estimator-doc-link.fitted,\n", + "a:visited.sk-estimator-doc-link.fitted {\n", + " /* fitted */\n", + " border: var(--sklearn-color-fitted-level-1) 1pt solid;\n", + " color: var(--sklearn-color-fitted-level-1);\n", + "}\n", + "\n", + "/* On hover */\n", + "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n", + ".sk-estimator-doc-link:hover,\n", + "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n", + ".sk-estimator-doc-link:hover {\n", + " /* unfitted */\n", + " background-color: var(--sklearn-color-unfitted-level-3);\n", + " color: var(--sklearn-color-background);\n", + " text-decoration: none;\n", + "}\n", + "\n", + "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n", + ".sk-estimator-doc-link.fitted:hover,\n", + "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n", + ".sk-estimator-doc-link.fitted:hover {\n", + " /* fitted */\n", + " background-color: var(--sklearn-color-fitted-level-3);\n", + " color: var(--sklearn-color-background);\n", + " text-decoration: none;\n", + "}\n", + "\n", + "/* Span, style for the box shown on hovering the info icon */\n", + ".sk-estimator-doc-link span {\n", + " display: none;\n", + " z-index: 9999;\n", + " position: relative;\n", + " font-weight: normal;\n", + " right: .2ex;\n", + " padding: .5ex;\n", + " margin: .5ex;\n", + " width: min-content;\n", + " min-width: 20ex;\n", + " max-width: 50ex;\n", + " color: var(--sklearn-color-text);\n", + " box-shadow: 2pt 2pt 4pt #999;\n", + " /* unfitted */\n", + " background: var(--sklearn-color-unfitted-level-0);\n", + " border: .5pt solid var(--sklearn-color-unfitted-level-3);\n", + "}\n", + "\n", + ".sk-estimator-doc-link.fitted span {\n", + " /* fitted */\n", + " background: var(--sklearn-color-fitted-level-0);\n", + " border: var(--sklearn-color-fitted-level-3);\n", + "}\n", + "\n", + ".sk-estimator-doc-link:hover span {\n", + " display: block;\n", + "}\n", + "\n", + "/* \"?\"-specific style due to the `<a>` HTML tag */\n", + "\n", + "#sk-container-id-3 a.estimator_doc_link {\n", + " float: right;\n", + " font-size: 1rem;\n", + " line-height: 1em;\n", + " font-family: monospace;\n", + " background-color: var(--sklearn-color-background);\n", + " border-radius: 1rem;\n", + " height: 1rem;\n", + " width: 1rem;\n", + " text-decoration: none;\n", + " /* unfitted */\n", + " color: var(--sklearn-color-unfitted-level-1);\n", + " border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n", + "}\n", + "\n", + "#sk-container-id-3 a.estimator_doc_link.fitted {\n", + " /* fitted */\n", + " border: var(--sklearn-color-fitted-level-1) 1pt solid;\n", + " color: var(--sklearn-color-fitted-level-1);\n", + "}\n", + "\n", + "/* On hover */\n", + "#sk-container-id-3 a.estimator_doc_link:hover {\n", + " /* unfitted */\n", + " background-color: var(--sklearn-color-unfitted-level-3);\n", + " color: var(--sklearn-color-background);\n", + " text-decoration: none;\n", + "}\n", + "\n", + "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n", + " /* fitted */\n", + " background-color: var(--sklearn-color-fitted-level-3);\n", + "}\n", + "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\"> LogisticRegression<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression()</pre></div> </div></div></div></div>" + ], + "text/plain": [ + "LogisticRegression()" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "logreg.fit(X_train, y_train)" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "\n", + "logreg.fit(X_train, y_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Click ... for solution. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Use Test Data to Validate Model ###\n", + "We can now use the same procedure as above to predict infection risk using the test data:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "16557172 0.010267\n", + "21666454 0.012426\n", + "25019343 0.014598\n", + "32613710 0.012409\n", + "5458911 0.006700\n", + " ... \n", + "29193786 0.010716\n", + "10832641 0.008235\n", + "50674662 0.024982\n", + "15628357 0.009970\n", + "44635132 0.020095\n", + "Name: 1, Length: 5847990, dtype: float64" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y_test_pred = logreg.predict_proba(X_test, convert_dtype=True)[1]\n", + "y_test_pred.index = X_test.index\n", + "y_test_pred" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we saw before, very few people are actually infected in the population, even among the highest-risk groups. As a simple way to check our model, we split the test set into above-average predicted risk and below-average predicted risk, then observe that the prevalence of infections correlates closely to those predicted risks." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>age</th>\n", + " <th>sex</th>\n", + " <th>infected</th>\n", + " <th>predicted_risk</th>\n", + " </tr>\n", + " <tr>\n", + " <th>high_risk</th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>False</th>\n", + " <td>29.536875</td>\n", + " <td>0.252218</td>\n", + " <td>0.009992</td>\n", + " <td>0.010328</td>\n", + " </tr>\n", + " <tr>\n", + " <th>True</th>\n", + " <td>56.187385</td>\n", + " <td>0.889923</td>\n", + " <td>0.023676</td>\n", + " <td>0.023323</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " age sex infected predicted_risk\n", + "high_risk \n", + "False 29.536875 0.252218 0.009992 0.010328\n", + "True 56.187385 0.889923 0.023676 0.023323" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test_results = cudf.DataFrame()\n", + "test_results['age'] = X_test['age']\n", + "test_results['sex'] = X_test['sex']\n", + "test_results['infected'] = y_test\n", + "test_results['predicted_risk'] = y_test_pred\n", + "\n", + "test_results['high_risk'] = test_results['predicted_risk'] > test_results['predicted_risk'].mean()\n", + "\n", + "risk_groups = test_results.groupby('high_risk')\n", + "risk_groups.mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, in a few milliseconds, we can do a two-tier analysis by sex and age:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 310 ms, sys: 56.2 ms, total: 367 ms\n", + "Wall time: 366 ms\n" + ] + }, + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th></th>\n", + " <th>infected</th>\n", + " <th>predicted_risk</th>\n", + " </tr>\n", + " <tr>\n", + " <th>sex</th>\n", + " <th>age</th>\n", + " <th></th>\n", + " <th></th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th rowspan=\"3\" valign=\"top\">0.0</th>\n", + " <th>6.0</th>\n", + " <td>0.003474</td>\n", + " <td>0.005867</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1.0</th>\n", + " <td>0.000764</td>\n", + " <td>0.005449</td>\n", + " </tr>\n", + " <tr>\n", + " <th>14.0</th>\n", + " <td>0.006123</td>\n", + " <td>0.006602</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1.0</th>\n", + " <th>5.0</th>\n", + " <td>0.006264</td>\n", + " <td>0.011532</td>\n", + " </tr>\n", + " <tr>\n", + " <th rowspan=\"6\" valign=\"top\">0.0</th>\n", + " <th>60.0</th>\n", + " <td>0.013377</td>\n", + " <td>0.012984</td>\n", + " </tr>\n", + " <tr>\n", + " <th>...</th>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " </tr>\n", + " <tr>\n", + " <th>35.0</th>\n", + " <td>0.010341</td>\n", + " <td>0.008995</td>\n", + " </tr>\n", + " <tr>\n", + " <th>13.0</th>\n", + " <td>0.005650</td>\n", + " <td>0.006505</td>\n", + " </tr>\n", + " <tr>\n", + " <th>32.0</th>\n", + " <td>0.010420</td>\n", + " <td>0.008606</td>\n", + " </tr>\n", + " <tr>\n", + " <th>19.0</th>\n", + " <td>0.007208</td>\n", + " <td>0.007107</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1.0</th>\n", + " <th>12.0</th>\n", + " <td>0.010666</td>\n", + " <td>0.012778</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "<p>182 rows × 2 columns</p>\n", + "</div>" + ], + "text/plain": [ + " infected predicted_risk\n", + "sex age \n", + "0.0 6.0 0.003474 0.005867\n", + " 1.0 0.000764 0.005449\n", + " 14.0 0.006123 0.006602\n", + "1.0 5.0 0.006264 0.011532\n", + "0.0 60.0 0.013377 0.012984\n", + "... ... ...\n", + " 35.0 0.010341 0.008995\n", + " 13.0 0.005650 0.006505\n", + " 32.0 0.010420 0.008606\n", + " 19.0 0.007208 0.007107\n", + "1.0 12.0 0.010666 0.012778\n", + "\n", + "[182 rows x 2 columns]" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%time\n", + "s_groups = test_results[['sex', 'age', 'infected', 'predicted_risk']].groupby(['sex', 'age'])\n", + "s_groups.mean()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import IPython\n", + "app = IPython.Application.instance()\n", + "app.kernel.do_shutdown(True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Well Done!** Let's move to the [next notebook](3-05_knn.ipynb). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<img src=\"./images/DLI_Header.png\" width=400/>" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.15" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Fundamentals_of_Accelerated_Data_Science/3-05_knn.ipynb b/Fundamentals_of_Accelerated_Data_Science/3-05_knn.ipynb new file mode 100644 index 0000000..e6d543f --- /dev/null +++ b/Fundamentals_of_Accelerated_Data_Science/3-05_knn.ipynb @@ -0,0 +1,300 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<img src=\"./images/DLI_Header.png\" width=400/>" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Fundamentals of Accelerated Data Science # " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 05 - KNN ##\n", + "\n", + "**Table of Contents**\n", + "<br>\n", + "This notebook uses GPU-accelerated k-nearest neighbors to identify the nearest road nodes to hospitals. This notebook covers the below sections: \n", + "1. [Environment](#Environment)\n", + "2. [Load Data](#Load-Data)\n", + " * [Road Nodes](#Road-Nodes)\n", + " * [Hospitals](#Hospitals)\n", + "3. [K-Nearest Neighbors](#K-Nearest-Neighbors)\n", + " * [Road Nodes Closest to Each Hospital](#Road-Nodes-Closest-to-Each-Hospital)\n", + " * [Viewing a Specific Hospital](#Viewing-a-Specific-Hospital)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Environment ##" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import cudf\n", + "import cuml" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Road Nodes ###\n", + "We begin by reading our road nodes data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# road_nodes = cudf.read_csv('./data/road_nodes_2-06.csv', dtype=['str', 'float32', 'float32', 'str'])\n", + "road_nodes = cudf.read_csv('./data/road_nodes.csv', dtype=['str', 'float32', 'float32', 'str'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "road_nodes.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "road_nodes.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "road_nodes.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Hospitals ###\n", + "Next we load the hospital data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "hospitals = cudf.read_csv('./data/clean_hospitals_full.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "hospitals.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "hospitals.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "hospitals.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## K-Nearest Neighbors ##\n", + "We are going to use the [k-nearest neighbors](https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm) algorithm to find the nearest *k* road nodes for every hospital. We will need to fit a KNN model with road data, and then give our trained model hospital locations so that it can return the nearest roads.\n", + "\n", + "Create a k-nearest neighbors model `knn` by using the `cuml.NearestNeighbors` constructor, passing it the named argument `n_neighbors` set to 3.\n", + "\n", + "Create a new dataframe `road_locs` using the `road_nodes` columns `east` and `north`. The order of the columns doesn't matter, except that we will need them to remain consistent over multiple operations, so please use the ordering `['east', 'north']`.\n", + "\n", + "Fit the `knn` model with `road_locs` using the `knn.fit` method." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "knn = cuml.NearestNeighbors(n_neighbors=3)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "road_locs = road_nodes[['east', 'north']]\n", + "knn.fit(road_locs)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Road Nodes Closest to Each Hospital ###\n", + "Use the `knn.kneighbors` method to find the 3 closest road nodes to each hospital. `knn.kneighbors` expects 2 arguments: `X`, for which you should use the `easting` and `northing` columns of `hospitals` (remember to retain the same column order as when you fit the `knn` model above), and `n_neighbors`, the number of neighbors to search for--in this case, 3. \n", + "\n", + "`knn.kneighbors` will return 2 cudf dataframes, which you should name `distances` and `indices` respectively." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "distances, indices = knn.kneighbors(hospitals[['easting', 'northing']], 3) # order has to match the knn fit order (east, north)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Viewing a Specific Hospital ###\n", + "We can now use `indices`, `hospitals`, and `road_nodes` to derive information specific to a given hospital. Here we will examine the hospital at index `10`. First we view the hospital's grid coordinates:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "SELECTED_RESULT = 10\n", + "print('hospital coordinates:\\n', hospitals.loc[SELECTED_RESULT, ['easting', 'northing']], sep='')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we view the road node IDs for the 3 closest road nodes:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "nearest_road_nodes = indices.iloc[SELECTED_RESULT, 0:3]\n", + "print('node_id:\\n', nearest_road_nodes, sep='')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And finally the grid coordinates for the 3 nearest road nodes, which we can confirm are located in order of increasing distance from the hospital:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print('road_node coordinates:\\n', road_nodes.loc[nearest_road_nodes, ['east', 'north']], sep='')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import IPython\n", + "app = IPython.Application.instance()\n", + "app.kernel.do_shutdown(True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Well Done!** Let's move to the [next notebook](3-06_xgboost.ipynb). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<img src=\"./images/DLI_Header.png\" width=400/>" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.15" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Fundamentals_of_Accelerated_Data_Science/3-06_xgboost.ipynb b/Fundamentals_of_Accelerated_Data_Science/3-06_xgboost.ipynb new file mode 100644 index 0000000..c0ec348 --- /dev/null +++ b/Fundamentals_of_Accelerated_Data_Science/3-06_xgboost.ipynb @@ -0,0 +1,850 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<img src=\"./images/DLI_Header.png\" width=400/>" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Fundamentals of Accelerated Data Science # " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 06 - XGBoost ##\n", + "\n", + "**Table of Contents**\n", + "<br>\n", + "This notebook uses use GPU-accelerated gradient boosting to predict the probability that a given individual is infected with the simulated virus. This notebook covers the below sections: \n", + "1. [Environment](#Environment)\n", + "2. [Load Data](#Load-Data)\n", + "3. [Train-Test Split](#Train-Test-Split)\n", + "4. [XGBoost](#XGBoost)\n", + " * [Setting XGBoost Parameters](#Setting-XGBoost-Parameters)\n", + " * [Training the Model](#Training-the-Model)\n", + "5. [Inspecting the Model](#Inspecting-the-Model)\n", + "6. [Making Predictions](#Making-Predictions)\n", + "7. [(Optional) Comparison: CPU-Only XGBoost](#(Optional)-Comparison:-CPU-Only-XGBoost)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Environment ##\n", + "We will be using the [XGBoost](https://xgboost.readthedocs.io/en/latest/) library to create the gradient boosted model for this exercise.\n", + "\n", + "In addition to the usual RAPIDS components, we import several libraries that will help us understand and evaluate the XGBoost model once we have trained it. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "XGBoost version: 2.1.1\n" + ] + } + ], + "source": [ + "import cudf\n", + "import cuml\n", + "import cupy as cp\n", + "\n", + "from cuml.model_selection import train_test_split\n", + "\n", + "# visualization\n", + "%matplotlib inline\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "import graphviz\n", + "\n", + "# model analysis\n", + "import numpy as np\n", + "from sklearn.metrics import roc_curve\n", + "from sklearn.metrics import auc\n", + "\n", + "import xgboost as xgb\n", + "\n", + "# xgboost version 1.0 or later is required to directly convert from cudf Dataframes to xgboost DMatrix format\n", + "print('XGBoost version: ', xgb.__version__)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load Data ##\n", + "For this notebook we load a subset of the population data, including both the columns we used before for logistic regression and the coordinate columns. XGBoost allows us to use data that have nonlinear relationships with the outcome of interest, and geospatial data often fall into that category." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "gdf = cudf.read_csv('./data/clean_uk_pop_full.csv', usecols=['age', 'sex', 'northing', 'easting', 'infected'])" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "age float64\n", + "sex float64\n", + "northing float64\n", + "easting float64\n", + "infected float64\n", + "dtype: object" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gdf.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(58479894, 5)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gdf.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>age</th>\n", + " <th>sex</th>\n", + " <th>northing</th>\n", + " <th>easting</th>\n", + " <th>infected</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>0.0</td>\n", + " <td>0.0</td>\n", + " <td>515491.5313</td>\n", + " <td>430772.1875</td>\n", + " <td>0.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>0.0</td>\n", + " <td>0.0</td>\n", + " <td>503572.4688</td>\n", + " <td>434685.8750</td>\n", + " <td>0.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>0.0</td>\n", + " <td>0.0</td>\n", + " <td>517903.6563</td>\n", + " <td>432565.5313</td>\n", + " <td>0.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>0.0</td>\n", + " <td>0.0</td>\n", + " <td>517059.9063</td>\n", + " <td>427660.6250</td>\n", + " <td>0.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>0.0</td>\n", + " <td>0.0</td>\n", + " <td>509228.6875</td>\n", + " <td>425527.7813</td>\n", + " <td>0.0</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " age sex northing easting infected\n", + "0 0.0 0.0 515491.5313 430772.1875 0.0\n", + "1 0.0 0.0 503572.4688 434685.8750 0.0\n", + "2 0.0 0.0 517903.6563 432565.5313 0.0\n", + "3 0.0 0.0 517059.9063 427660.6250 0.0\n", + "4 0.0 0.0 509228.6875 425527.7813 0.0" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gdf.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Before splitting the data for training and testing, we check our memory status. We want to be below half the memory usage on the active GPU so that the temporary increase from the split still fits in memory." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sat Oct 18 07:07:46 2025 \n", + "+-----------------------------------------------------------------------------+\n", + "| NVIDIA-SMI 525.85.12 Driver Version: 525.85.12 CUDA Version: 12.0 |\n", + "|-------------------------------+----------------------+----------------------+\n", + "| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n", + "| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n", + "| | | MIG M. |\n", + "|===============================+======================+======================|\n", + "| 0 Tesla T4 On | 00000000:00:1B.0 Off | 0 |\n", + "| N/A 30C P0 32W / 70W | 3618MiB / 15360MiB | 0% Default |\n", + "| | | N/A |\n", + "+-------------------------------+----------------------+----------------------+\n", + "| 1 Tesla T4 On | 00000000:00:1C.0 Off | 0 |\n", + "| N/A 29C P0 25W / 70W | 168MiB / 15360MiB | 0% Default |\n", + "| | | N/A |\n", + "+-------------------------------+----------------------+----------------------+\n", + "| 2 Tesla T4 On | 00000000:00:1D.0 Off | 0 |\n", + "| N/A 29C P0 25W / 70W | 168MiB / 15360MiB | 0% Default |\n", + "| | | N/A |\n", + "+-------------------------------+----------------------+----------------------+\n", + "| 3 Tesla T4 On | 00000000:00:1E.0 Off | 0 |\n", + "| N/A 29C P0 26W / 70W | 168MiB / 15360MiB | 0% Default |\n", + "| | | N/A |\n", + "+-------------------------------+----------------------+----------------------+\n", + " \n", + "+-----------------------------------------------------------------------------+\n", + "| Processes: |\n", + "| GPU GI CI PID Type Process name GPU Memory |\n", + "| ID ID Usage |\n", + "|=============================================================================|\n", + "+-----------------------------------------------------------------------------+\n" + ] + } + ], + "source": [ + "!nvidia-smi" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Train-Test Split ##\n", + "We again use the splitting method to create training and testing data subsets, keeping in mind that we will be using additional memory by doing so." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "x_train, x_test, y_train, y_test = train_test_split(gdf[['age', 'sex', 'northing', 'easting']], gdf['infected'])" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sat Oct 18 07:08:03 2025 \n", + "+-----------------------------------------------------------------------------+\n", + "| NVIDIA-SMI 525.85.12 Driver Version: 525.85.12 CUDA Version: 12.0 |\n", + "|-------------------------------+----------------------+----------------------+\n", + "| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n", + "| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n", + "| | | MIG M. |\n", + "|===============================+======================+======================|\n", + "| 0 Tesla T4 On | 00000000:00:1B.0 Off | 0 |\n", + "| N/A 30C P0 25W / 70W | 7218MiB / 15360MiB | 0% Default |\n", + "| | | N/A |\n", + "+-------------------------------+----------------------+----------------------+\n", + "| 1 Tesla T4 On | 00000000:00:1C.0 Off | 0 |\n", + "| N/A 29C P0 25W / 70W | 168MiB / 15360MiB | 0% Default |\n", + "| | | N/A |\n", + "+-------------------------------+----------------------+----------------------+\n", + "| 2 Tesla T4 On | 00000000:00:1D.0 Off | 0 |\n", + "| N/A 29C P0 25W / 70W | 168MiB / 15360MiB | 0% Default |\n", + "| | | N/A |\n", + "+-------------------------------+----------------------+----------------------+\n", + "| 3 Tesla T4 On | 00000000:00:1E.0 Off | 0 |\n", + "| N/A 29C P0 26W / 70W | 168MiB / 15360MiB | 0% Default |\n", + "| | | N/A |\n", + "+-------------------------------+----------------------+----------------------+\n", + " \n", + "+-----------------------------------------------------------------------------+\n", + "| Processes: |\n", + "| GPU GI CI PID Type Process name GPU Memory |\n", + "| ID ID Usage |\n", + "|=============================================================================|\n", + "+-----------------------------------------------------------------------------+\n" + ] + } + ], + "source": [ + "!nvidia-smi" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Note**: Now that we have training and testing subsets, we can delete the original data so we have room for the algorithm's memory overhead--not critical in this case, but a useful practice when trying to train on as much data as possible." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "del(gdf)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sat Oct 18 07:08:25 2025 \n", + "+-----------------------------------------------------------------------------+\n", + "| NVIDIA-SMI 525.85.12 Driver Version: 525.85.12 CUDA Version: 12.0 |\n", + "|-------------------------------+----------------------+----------------------+\n", + "| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n", + "| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n", + "| | | MIG M. |\n", + "|===============================+======================+======================|\n", + "| 0 Tesla T4 On | 00000000:00:1B.0 Off | 0 |\n", + "| N/A 29C P0 25W / 70W | 6770MiB / 15360MiB | 0% Default |\n", + "| | | N/A |\n", + "+-------------------------------+----------------------+----------------------+\n", + "| 1 Tesla T4 On | 00000000:00:1C.0 Off | 0 |\n", + "| N/A 29C P0 25W / 70W | 168MiB / 15360MiB | 0% Default |\n", + "| | | N/A |\n", + "+-------------------------------+----------------------+----------------------+\n", + "| 2 Tesla T4 On | 00000000:00:1D.0 Off | 0 |\n", + "| N/A 28C P0 25W / 70W | 168MiB / 15360MiB | 0% Default |\n", + "| | | N/A |\n", + "+-------------------------------+----------------------+----------------------+\n", + "| 3 Tesla T4 On | 00000000:00:1E.0 Off | 0 |\n", + "| N/A 29C P0 26W / 70W | 168MiB / 15360MiB | 0% Default |\n", + "| | | N/A |\n", + "+-------------------------------+----------------------+----------------------+\n", + " \n", + "+-----------------------------------------------------------------------------+\n", + "| Processes: |\n", + "| GPU GI CI PID Type Process name GPU Memory |\n", + "| ID ID Usage |\n", + "|=============================================================================|\n", + "+-----------------------------------------------------------------------------+\n" + ] + } + ], + "source": [ + "!nvidia-smi" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## XGBoost ##" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Setting XGBoost Parameters ###\n", + "We can now set the parameters for this XGBoost training run. These will determine the type and size of trees being created, as well as the way we measure success. \n", + "\n", + "The `cuda` device is a critical parameter: it tells XGBoost that we want the training to run on GPU.\n", + "\n", + "For our use case, we again want to predict the probability that an individual is infected with the virus, so we set the `binary:logistic` objective (binary outcome, using the logistic method to get probabilities).\n", + "\n", + "Other parameter options and meanings can be found at [XGBoost parameters](https://xgboost.readthedocs.io/en/latest/parameter.html)." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "params = {\n", + " 'max_depth': 8,\n", + " 'max_leaves': 2**8,\n", + " 'device': 'cuda',\n", + " 'tree_method': 'hist',\n", + " 'objective': 'binary:logistic',\n", + " 'grow_policy': 'lossguide',\n", + " 'eval_metric': 'logloss',\n", + " 'subsample': '0.8'\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Training the Model ###\n", + "XGBoost uses a special, highly efficient data structure called a `DMatrix`, so we pass the training dataframes into it to create one.\n", + "\n", + "Note that the data is remaining on GPU, passing directly from cuDF to XGBoost." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "dtrain = xgb.DMatrix(x_train, y_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we are ready to train the model." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 17.3 s, sys: 258 ms, total: 17.6 s\n", + "Wall time: 17 s\n" + ] + } + ], + "source": [ + "%time model = xgb.train(params, dtrain, num_boost_round=100)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "model.save_model('xgboost_model.json')" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " age sex northing easting\n", + "9817882 27.0 0.0 408680.4375 363450.7813\n", + "28115611 82.0 0.0 104639.3516 535646.8750\n", + "35995212 21.0 1.0 528217.6875 326854.0313\n", + "52342505 63.0 1.0 279781.4063 249322.5156\n", + "54319909 70.0 1.0 367499.1250 445179.1563\n", + "... ... ... ... ...\n", + "27727793 79.0 0.0 186742.3594 280780.4063\n", + "38375961 27.0 1.0 220378.7656 557870.0625\n", + "10802298 29.0 0.0 183926.6406 527426.3125\n", + "23805603 64.0 0.0 343521.1563 432138.8125\n", + "10095092 27.0 0.0 114831.9844 448982.6875\n", + "\n", + "[43859920 rows x 4 columns]\n", + "9817882 0.0\n", + "28115611 0.0\n", + "35995212 0.0\n", + "52342505 0.0\n", + "54319909 0.0\n", + " ... \n", + "27727793 0.0\n", + "38375961 0.0\n", + "10802298 0.0\n", + "23805603 1.0\n", + "10095092 0.0\n", + "Name: infected, Length: 43859920, dtype: float64\n" + ] + } + ], + "source": [ + "print(x_train)\n", + "print(y_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Note**: As a point of comparison, code to run the CPU-only version of XGBoost is provided at the bottom of this exercise." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Inspecting the Model ##\n", + "We can examine the model in several ways. First, we can see which features the model believes to be most important in its assessment. Higher F scores indicate higher estimated importance.\n", + "\n", + "There appears to be a strong geospatial component to the infection distribution, since the easting and northing features have the highest F scores. In addition, age appears to have a stronger impact than sex in determining infection rates (consistent with the results we received from the logistic regression analysis)." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 640x480 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax = xgb.plot_importance(model, height=.8)\n", + "ax.grid(False)\n", + "ax.set_title('F score by feature')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also pull out individual trees within the model and see exactly which decisions it uses for its contributions to the ensemble. Notice that as with all ensemble methods, individual trees may not appear to produce major differences in outcomes (values on the leaf nodes); the aggregation of often individually-weak trees into a strong model gives XGBoost its power.\n", + "\n", + "Try changing `num_trees` to examine different trees in the model. Changing `rankdir` to `'TB'` will reorient the trees to top-down." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "<Figure size 10000x10000 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "xgb.plot_tree(model, num_trees=0, rankdir='LR')\n", + "\n", + "# get current figure to set the size\n", + "fig = matplotlib.pyplot.gcf()\n", + "fig.set_size_inches(100, 100)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Making Predictions ##\n", + "Once we are comfortable with the model, we move to making predictions with it. We confirm that we will be making predictions on over 11 million rows of data." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(14619974, 4)" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x_test.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We convert the *X* matrix to a `DMatrix` as before, then make a prediction for each row. Notice the amount of time it takes to make over 11 million predictions." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 393 ms, sys: 16 ms, total: 409 ms\n", + "Wall time: 409 ms\n" + ] + } + ], + "source": [ + "dtest = xgb.DMatrix(x_test)\n", + "%time y_pred = model.predict(dtest)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we would like to see how good those predictions were. One common evaluation method is to calculate the area under the curve (AUC) of the receiver operating characteristic (ROC) curve.\n", + "\n", + "The predictions are a `numpy` array, so we convert the test labels to match, then run the ROC curve calculation." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "y_test_cpu = cp.asnumpy(cp.array(y_test))\n", + "false_pos_rate, true_pos_rate, thresholds = roc_curve(y_test_cpu, y_pred)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Last, we can plot the curve and calculate the AUC score to help us assess the trade offs between true and false positive rates." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 500x500 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "auc_result = auc(false_pos_rate, true_pos_rate)\n", + "\n", + "fig, ax = plt.subplots(figsize=(5, 5))\n", + "ax.plot(false_pos_rate, true_pos_rate, lw=3,\n", + " label='AUC = {:.2f}'.format(auc_result))\n", + "ax.plot([0, 1], [0, 1], 'k--', lw=2)\n", + "ax.set(\n", + " xlim=(0, 1),\n", + " ylim=(0, 1),\n", + " title=\"ROC Curve\",\n", + " xlabel=\"False Positive Rate\",\n", + " ylabel=\"True Positive Rate\",\n", + ")\n", + "ax.legend(loc='lower right');\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## (Optional) Comparison: CPU-Only XGBoost ##\n", + "Below we provide code for training and inferring from a CPU-only XGBoost using the same model parameters other than switching the histogram tree method from GPU to CPU." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 30min 4s, sys: 18 s, total: 30min 22s\n", + "Wall time: 1min 5s\n" + ] + } + ], + "source": [ + "params['device'] = 'cpu'\n", + "dtrain_cpu = xgb.DMatrix(x_train.to_pandas(), y_train.to_pandas())\n", + "%time model_cpu = xgb.train(params, dtrain_cpu, num_boost_round=100)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 1min 5s, sys: 53.8 ms, total: 1min 5s\n", + "Wall time: 1.49 s\n" + ] + } + ], + "source": [ + "dtest_cpu = xgb.DMatrix(x_test.to_pandas())\n", + "%time y_pred_cpu = model_cpu.predict(dtest_cpu)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'status': 'ok', 'restart': True}" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import IPython\n", + "app = IPython.Application.instance()\n", + "app.kernel.do_shutdown(True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Well Done!** Let's move to the [next notebook](3-07_triton.ipynb). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<img src=\"./images/DLI_Header.png\" width=400/>" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.15" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Fundamentals_of_Accelerated_Data_Science/3-07_triton.ipynb b/Fundamentals_of_Accelerated_Data_Science/3-07_triton.ipynb new file mode 100644 index 0000000..757b3fa --- /dev/null +++ b/Fundamentals_of_Accelerated_Data_Science/3-07_triton.ipynb @@ -0,0 +1,732 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "453524a2-0d79-4512-8d97-0240ec1ea08e", + "metadata": {}, + "source": [ + "<img src=\"./images/DLI_Header.png\" width=400/>" + ] + }, + { + "cell_type": "markdown", + "id": "c57b540f-18e8-42c3-a688-c30103c5f462", + "metadata": {}, + "source": [ + "# Fundamentals of Accelerated Data Science # " + ] + }, + { + "cell_type": "markdown", + "id": "61aa05f5-febf-487c-bd59-d489efd98709", + "metadata": {}, + "source": [ + "## 07 - Triton ##\n", + "\n", + "**Table of Contents**\n", + "<br>\n", + "This notebook covers the process of deploying a model with Triton Inference Server and walks through the various tools available within Triton. This notebook covers the below sections: \n", + "1. [Background](#s1)\n", + "2. [Preparing the Model](#s1)\n", + " * [Load the Model](#s1)\n", + " * [Creating the Folder Structure](#s1)\n", + " * [Creating the Configuration File](#s1)\n", + "3. [Loading the Model in Triton](#s1)\n", + " * [Starting Triton](#s1)\n", + " * [Check Status of Triton Server](#s1)\n", + "4. [Testing Inference](#s1)\n", + " * [Triton Client](#s1)\n", + " * [Verifying Results for Local Model and Triton](#s1)\n", + "5. [Analyzing Performance](#s1)\n", + " * [Customizing Perf Analyzer](#s1)\n", + " * [Exercise #1 - Testing Perf Analyzer](#s1)\n", + "6. [Model Analyzer](#s1)" + ] + }, + { + "cell_type": "markdown", + "id": "d4d08487-8161-4d8c-b402-cdeab3fcbe2a", + "metadata": {}, + "source": [ + "# Background\n", + "NVIDIA offers a framework for deploying ML models called Triton. Triton will automatically handle all inference requests that comes to the server. Triton supports multiple backends including PyTorch, TensorFlow, Forest Inference Library (FIL), etc. In this notebook, we will focus on the FIL backend using the xgboost model that was trained previously. " + ] + }, + { + "cell_type": "markdown", + "id": "aa0a7d94-c0a9-4b86-92bb-fafffa1ed021", + "metadata": {}, + "source": [ + "## Preparing the Model\n", + "### Load the Model\n", + "Let's start with loading the previous XGBoost model." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "b6b2149c-3f83-46cd-a1eb-70db7067cf5b", + "metadata": {}, + "outputs": [], + "source": [ + "import xgboost as xgb\n", + "model = xgb.Booster({'nthread': 4}) # init model\n", + "model.load_model('xgboost_model.json') # load model data" + ] + }, + { + "cell_type": "markdown", + "id": "a7896106-8410-40c3-8b6d-e0bdb94f69c3", + "metadata": {}, + "source": [ + "### Creating the Folder Structure\n", + "In the previous notebook, we just saved the XGBoost model to the working directory. Triton expects the model to be in a particular structure: Model name should be the top level directory, and the version number should be next. This allows for multiple models and versions of those models to be hosted simulatenously. Let's create that folder structure and save the model!" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "28c42b97-2691-46d1-9b3a-7d19685d1d7f", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "# Create the model repository directory. The name of this directory is arbitrary.\n", + "REPO_PATH = os.path.abspath('models')\n", + "os.makedirs(REPO_PATH, exist_ok=True)\n", + "\n", + "# The name of the model directory determines the name of the model as reported by Triton\n", + "model_dir = os.path.join(REPO_PATH, \"virus_prediction\")\n", + "\n", + "# We can store multiple versions of the model in the same directory. In our case, we have just one version, so we will add a single directory, named '1'.\n", + "version_dir = os.path.join(model_dir, '1')\n", + "os.makedirs(version_dir, exist_ok=True)\n", + "\n", + "# The default filename for XGBoost models saved in json format is 'xgboost.json'.\n", + "# It is recommended that you use this filename to avoid having to specify a name in the configuration file.\n", + "model_file = os.path.join(version_dir, 'xgboost.json')\n", + "model.save_model(model_file)" + ] + }, + { + "cell_type": "markdown", + "id": "900e5837-4807-4fd4-af21-0973a4942f1b", + "metadata": {}, + "source": [ + "### Creating the Configuration File\n", + "Triton also requires a configuration file that provides details about the model and deployment. For this notebook, we will assume default parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "61d898fb-a8d2-4d1c-a13f-2c4be6c18969", + "metadata": {}, + "outputs": [], + "source": [ + "config_text = f\"\"\"backend: \"fil\"\n", + "max_batch_size: 32768\n", + "input [ \n", + " {{ \n", + " name: \"input__0\"\n", + " data_type: TYPE_FP32\n", + " dims: [ 4 ] \n", + " }} \n", + "]\n", + "output [\n", + " {{\n", + " name: \"output__0\"\n", + " data_type: TYPE_FP32\n", + " dims: [ 1 ]\n", + " }}\n", + "]\n", + "instance_group [{{ kind: KIND_GPU }}]\n", + "parameters [\n", + " {{\n", + " key: \"model_type\"\n", + " value: {{ string_value: \"xgboost_json\" }}\n", + " }},\n", + " {{\n", + " key: \"output_class\"\n", + " value: {{ string_value: \"false\" }}\n", + " }},\n", + " {{\n", + " key: \"storage_type\"\n", + " value: {{ string_value: \"AUTO\" }}\n", + " }}\n", + "]\n", + "\n", + "dynamic_batching {{\n", + " max_queue_delay_microseconds: 100\n", + "}}\"\"\"\n", + "config_path = os.path.join(model_dir, 'config.pbtxt')\n", + "with open(config_path, 'w') as file_:\n", + " file_.write(config_text)" + ] + }, + { + "cell_type": "markdown", + "id": "f262d4d9-819a-4d28-bb26-7eb737c3387a", + "metadata": {}, + "source": [ + "Now the model is ready to be loaded in Triton! The model repository should look like this.\n", + "\n", + "```\n", + "model/\n", + "`-- virus_prediction\n", + " |-- 1\n", + " | `-- xgboost.json\n", + " `-- config.pbtxt\n", + "```\n" + ] + }, + { + "cell_type": "markdown", + "id": "b5876d7a-3416-4ac9-827a-53796a1f7f93", + "metadata": {}, + "source": [ + "## Loading the Model in Triton\n", + "Next, we will need to start the Triton server. For this course, the server has already been started, but we will briefly go the steps required." + ] + }, + { + "cell_type": "markdown", + "id": "709cf499-9faa-4746-9252-16f41fa548e6", + "metadata": {}, + "source": [ + "### Starting Triton\n", + "Triton is available as buildable source code or a pre-built docker image. For simplicity, we recommend that most users start with the docker images. This is how you would start the docker container in the console." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1283515c-c812-41c5-a100-e4dc5788781e", + "metadata": {}, + "outputs": [], + "source": [ + "#!docker run --gpus=1 --rm -p 8000:8000 -p 8001:8001 -p 8002:8002 -v /full/path/to/docs/examples/model_repository:/models nvcr.io/nvidia/tritonserver:<xx.yy>-py3 tritonserver --model-repository=/models" + ] + }, + { + "cell_type": "markdown", + "id": "2ded6e36-0a1d-488e-8b10-f482ced8ea07", + "metadata": {}, + "source": [ + "Wow! That's a lot of inputs. Let's break it down a little bit more.\n", + "\n", + "- **gpus=1** : Passes through the first GPU to the Triton Inference Server\n", + "- **rm** : Removes the container after completing execution\n", + "- **p 8000:8000** : Forwards the GTPCInferenceService Port\n", + "- **p 8001:8001** : Forwards the HTTPService Port\n", + "- **p 8002:8002** : Forwards the Metrics Port\n", + "- **v /full/path/to/docs/examples/model_repository:/models** : Mounts the path to the models folder on the host machine to the Triton Inference Server container\n", + "- **nvcr.io/nvidia/tritonserver:<xx.yy>-py3** : Name of the Triton Inference Server image. The version number will change depending on the latest release\n", + "- **tritonserver --model-repository=/models**: The command to run in the container. In this case, we start the Triton Inference Server and point to the models folder\n" + ] + }, + { + "cell_type": "markdown", + "id": "42967a82-fdfd-413e-8006-eb78a3ca805b", + "metadata": {}, + "source": [ + "As we mentioned before, the server has already been started for this lab. Let's check our connection to the server! We are using \"triton\" as the hostname because the default docker network will resolve \"triton\" to the ip address of the Triton Inference Server." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "ac240cdc-f0dc-45ad-9838-58066a85696a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "* Trying 172.18.0.3:8000...\n", + "* Connected to triton (172.18.0.3) port 8000 (#0)\n", + "> GET /v2/health/ready HTTP/1.1\n", + "> Host: triton:8000\n", + "> User-Agent: curl/7.81.0\n", + "> Accept: */*\n", + "> \n", + "* Mark bundle as not supporting multiuse\n", + "< HTTP/1.1 200 OK\n", + "< Content-Length: 0\n", + "< Content-Type: text/plain\n", + "< \n", + "* Connection #0 to host triton left intact\n" + ] + } + ], + "source": [ + "!curl -v triton:8000/v2/health/ready" + ] + }, + { + "cell_type": "markdown", + "id": "d92b9b35-51c6-4eba-a9c0-9a92d19e0a0b", + "metadata": {}, + "source": [ + "Now let's see if the model has been loaded!" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "d80b37c5-1242-489f-8463-387e657264a1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[{\"name\":\"virus_prediction\",\"version\":\"1\",\"state\":\"READY\"}]" + ] + } + ], + "source": [ + "!curl -X POST http://triton:8000/v2/repository/index" + ] + }, + { + "cell_type": "markdown", + "id": "06902d34-7763-4693-b919-f70ee4d60dfa", + "metadata": {}, + "source": [ + "If all went well, we should be able to see the model \"virus prediction\" show up as ready." + ] + }, + { + "cell_type": "markdown", + "id": "2429b1bf-9372-42a5-86ea-be07cabf30ed", + "metadata": {}, + "source": [ + "## Testing Inference" + ] + }, + { + "cell_type": "markdown", + "id": "0c2616f6-2de8-4e84-96d1-ad92b5f4b732", + "metadata": {}, + "source": [ + "### Triton Client\n", + "To test the deployment we will use the Triton Client library. Let's see how we can create an instance of the client. " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "ec652ce4-a2d6-4b3a-8e2f-faac51da5487", + "metadata": {}, + "outputs": [], + "source": [ + "import time\n", + "import tritonclient.grpc as triton_grpc\n", + "from tritonclient import utils as triton_utils\n", + "HOST = \"triton\"\n", + "PORT = 8001\n", + "TIMEOUT = 60" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "22af3af5-1d15-406f-91cb-2208c938d7ca", + "metadata": {}, + "outputs": [], + "source": [ + "client = triton_grpc.InferenceServerClient(url=f'{HOST}:{PORT}')" + ] + }, + { + "cell_type": "markdown", + "id": "84c3a594-e377-47c9-b3e7-0da321138bf5", + "metadata": {}, + "source": [ + "Now let's make sure the Triton server is ready by sending a sample inference request. First let's load the training data. We will only load 32768 rows, as that's the max batch size we specified." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "b57396e8-bc06-43dd-bf4c-2e34b24f5962", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " infected\n", + "2463608 0.0\n", + "1172801 0.0\n", + "1384471 0.0\n", + "3971592 0.0\n", + "3828297 0.0\n", + "... ...\n", + "22018 0.0\n", + "503309 0.0\n", + "4751695 0.0\n", + "3003154 0.0\n", + "4605113 0.0\n", + "\n", + "[32768 rows x 1 columns]\n" + ] + } + ], + "source": [ + "import cudf \n", + "import numpy as np\n", + "df = cudf.read_csv('./data/clean_uk_pop_full.csv', usecols=['age', 'sex', 'northing', 'easting', 'infected'], nrows=5000000)\n", + "df = df.sample(32768)\n", + "input_data = df.drop('infected', axis=1)\n", + "target = df[['infected']]\n", + "print(target)" + ] + }, + { + "cell_type": "markdown", + "id": "d24f3792-a541-4b9f-acdc-e72c64897804", + "metadata": {}, + "source": [ + "Now we convert it to a numpy array and force the type to be float32 (the same type we specified in the config file)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "1ab43811-f273-48cf-b473-ea3d4786cab4", + "metadata": {}, + "outputs": [], + "source": [ + "converted_df = input_data.to_numpy(dtype='float32')" + ] + }, + { + "cell_type": "markdown", + "id": "ed49f912-5e35-4fe8-b6fa-b91f025cc07e", + "metadata": {}, + "source": [ + "Since we limited our batch size to 32768, let's splice the array and attempt the inference." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "77e90afc-9ea6-45ad-83bb-2ac7f154b3f8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 5.03 ms, sys: 347 μs, total: 5.38 ms\n", + "Wall time: 6.64 ms\n" + ] + } + ], + "source": [ + "%%time\n", + "batched_data = converted_df[:32768]\n", + "# Prepare the input tensor\n", + "input_tensor = triton_grpc.InferInput(\"input__0\", batched_data.shape, 'FP32')\n", + "input_tensor.set_data_from_numpy(batched_data)\n", + "\n", + "# Prepare the output\n", + "output = triton_grpc.InferRequestedOutput(\"output__0\")\n", + "\n", + "# Send inference request\n", + "response = client.infer(\"virus_prediction\", [input_tensor], outputs=[output])\n", + "\n", + "# Get the output data\n", + "output_data = response.as_numpy(\"output__0\")" + ] + }, + { + "cell_type": "markdown", + "id": "923c2923-acc1-48cb-bc41-3adafbf7eca2", + "metadata": {}, + "source": [ + "Let's make sure the results we got were the same as with the local model." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "f809c2db-11f9-48de-9913-3e032e14a553", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.metrics import mean_squared_error, r2_score\n", + "from sklearn.metrics import roc_curve\n", + "from sklearn.metrics import auc\n", + "\n", + "xgb_data = xgb.DMatrix(input_data)\n", + "y_test = model.predict(xgb_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "52ada8b8-0f47-435b-90df-57c5056bfc26", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 500x500 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Check that we got the same accuracy as previously\n", + "#target = target.to_numpy()\n", + "import matplotlib.pyplot as plt\n", + "\n", + "false_pos_rate, true_pos_rate, thresholds = roc_curve(target.to_numpy(), y_test)\n", + "auc_result = auc(false_pos_rate, true_pos_rate)\n", + "\n", + "fig, ax = plt.subplots(figsize=(5, 5))\n", + "ax.plot(false_pos_rate, true_pos_rate, lw=3,\n", + " label='AUC = {:.2f}'.format(auc_result))\n", + "ax.plot([0, 1], [0, 1], 'k--', lw=2)\n", + "ax.set(\n", + " xlim=(0, 1),\n", + " ylim=(0, 1),\n", + " title=\"ROC Curve\",\n", + " xlabel=\"False Positive Rate\",\n", + " ylabel=\"True Positive Rate\",\n", + ")\n", + "ax.legend(loc='lower right');\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "30562baa-f7c9-40d8-ab5d-40b3c642b666", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 500x500 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Check that we got the same accuracy as previously\n", + "#target = target.to_numpy()\n", + "import matplotlib.pyplot as plt\n", + "\n", + "false_pos_rate, true_pos_rate, thresholds = roc_curve(target.to_numpy(), output_data)\n", + "auc_result = auc(false_pos_rate, true_pos_rate)\n", + "\n", + "fig, ax = plt.subplots(figsize=(5, 5))\n", + "ax.plot(false_pos_rate, true_pos_rate, lw=3,\n", + " label='AUC = {:.2f}'.format(auc_result))\n", + "ax.plot([0, 1], [0, 1], 'k--', lw=2)\n", + "ax.set(\n", + " xlim=(0, 1),\n", + " ylim=(0, 1),\n", + " title=\"ROC Curve\",\n", + " xlabel=\"False Positive Rate\",\n", + " ylabel=\"True Positive Rate\",\n", + ")\n", + "ax.legend(loc='lower right');\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "b00984cb-6e04-4951-a864-a67cc4a22b6f", + "metadata": {}, + "source": [ + "As we can see, our AUC score is the same between both inference options!" + ] + }, + { + "cell_type": "markdown", + "id": "a459c429-b902-4056-ad2e-05d53f82537c", + "metadata": {}, + "source": [ + "## Analyzing Performance" + ] + }, + { + "cell_type": "markdown", + "id": "e724bd09-057b-408a-896a-b48a7a4362d1", + "metadata": {}, + "source": [ + "Earlier, we tested a *relatively* small inference request. What if we want to see the max throughput of the model? Luckily, Triton offers a performance analysis tool that generates synthetic data to collect latency and throughput numbers. Let's try it out." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "15129aef-3d3f-4c65-b2ff-1d0b2599ffd7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "*** Measurement Settings ***\n", + " Batch size: 1\n", + " Service Kind: TRITON\n", + " Using \"time_windows\" mode for stabilization\n", + " Stabilizing using average latency and throughput\n", + " Measurement window: 5000 msec\n", + " Using synchronous calls for inference\n", + "\n", + "Request concurrency: 1\n" + ] + } + ], + "source": [ + "!perf_analyzer -m virus_prediction -u \"triton:8000\"" + ] + }, + { + "cell_type": "markdown", + "id": "12fe9a4c-e5db-4474-bc9f-76e0f3485506", + "metadata": {}, + "source": [ + "That's a lot of information to take in. Let's break it down.\n", + "\n", + "**Measurement window**: Timeframe that measurements are taken in \n", + "\n", + "**Batch Size**: Number of inputs in each request\n", + "\n", + "\n", + "**Concurrency**: Number of simulatenous connections\n", + "**Latency**: Time taken to recieve results\n", + "**p50/90/95/99**: Different percentiles for latency\n", + "\n", + "Based on these results, we can see that our throughput is roughly ~2300 inferences per second with a single concurrent connection, and the average latency for each requst is 434 usec." + ] + }, + { + "cell_type": "markdown", + "id": "27e8c5c0-7f3e-41e3-881d-38f9d0aa5d3e", + "metadata": {}, + "source": [ + "# Customizing Perf Analyzer\n", + "The Performance Analyzer tool for Triton has many knobs that can be turned to analyze results. Let's enable GPU metric collection, and increase the batch size and concurrency range values!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6353c5bb-ebd5-492e-ad62-90e8929849d4", + "metadata": {}, + "outputs": [], + "source": [ + "!perf_analyzer --collect-metrics -m virus_prediction -u \"triton:8000\" -b 8 --concurrency-range 2:8:2" + ] + }, + { + "cell_type": "markdown", + "id": "6d0340a9-da5a-400f-9127-b3f4fe1facad", + "metadata": {}, + "source": [ + "The results show that our model configuration gives a throughput of about ~156171 inferences per second. Notice how there are significant throughput gains as we increase the number of concurrent connections. With low concurrency values, Triton is idle during the time when the response is returned to the client and the next request is received at the server. Throughput increases when we increase concurrency values because Triton overlaps the processing of one request with the communication of the other. " + ] + }, + { + "cell_type": "markdown", + "id": "ea13ffda-b03d-435e-a01e-5f8ceaa076de", + "metadata": {}, + "source": [ + "# Exercise \n", + "Please take this time to experiment with the perf analyzer tool. A full list of parameters can be seen with the --help argument. Some parameters that we recommend trying out are listed below: \n", + "* -b <value> : batch size\n", + "* --concurrency-range <start:end:step> : range of concurrency values to test\n", + "* --collect-metrics : enable collection of GPU metrics" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e8bb02a7-92c1-4373-afb6-c4a507ddf20d", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "e6ccd1e7-989b-49b5-8ece-0583e9890c66", + "metadata": {}, + "source": [ + "## Model Analyzer\n", + "Although it is out of scope for this course, we would like to introduce the Model Analyzer tool that is available as part of Triton. This tool searches through different parameter configurations to find optimal parameters that maximize inference throughput. With some minor processing, results can be viewed in a PDF format as well. An example of the output is shown below. More information about the Model Analyzer can be found [here](https://docs.nvidia.com/deeplearning/triton-inference-server/user-guide/docs/user_guide/model_analyzer.html). " + ] + }, + { + "cell_type": "markdown", + "id": "a0c0c90f-d8e6-4ce5-a9c9-197a46e1d8a2", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "91558f00-41d2-493d-8517-9468efcae64e", + "metadata": {}, + "outputs": [], + "source": [ + "import IPython\n", + "app = IPython.Application.instance()\n", + "app.kernel.do_shutdown(True)" + ] + }, + { + "cell_type": "markdown", + "id": "c7e473f9-9ccf-43c6-aa86-eda25150a5ca", + "metadata": {}, + "source": [ + "**Well Done!** Let's move to the [next notebook](3-08_k-means_dask.ipynb). " + ] + }, + { + "cell_type": "markdown", + "id": "832c59bf-517c-4703-9161-1f9997214e19", + "metadata": {}, + "source": [ + "<img src=\"./images/DLI_Header.png\" width=400/>" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.15" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Fundamentals_of_Accelerated_Data_Science/3-08_k-means_dask.ipynb b/Fundamentals_of_Accelerated_Data_Science/3-08_k-means_dask.ipynb new file mode 100644 index 0000000..00782f4 --- /dev/null +++ b/Fundamentals_of_Accelerated_Data_Science/3-08_k-means_dask.ipynb @@ -0,0 +1,669 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<img src=\"./images/DLI_Header.png\" width=400/>" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Fundamentals of Accelerated Data Science # " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 08 - Multi-GPU K-Means with Dask ##\n", + "\n", + "**Table of Contents**\n", + "<br>\n", + "This notebook uses GPU-accelerated K-means to identify population clusters in a multi-node, multi-GPU scalable way with Dask. This notebook covers the below sections: \n", + "1. [Environment](#Environment)\n", + "2. [Load and Persist Data](#Load-and-Persist-Data)\n", + "3. [Training the Model](#Training-the-Model)\n", + " * [Exercise #1 - Count Members of the Southernmost Cluster](#Exercise-#1---Count-Members-of-the-Southernmost-Cluster)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Environment ##\n", + "First we import the needed modules to create a Dask cuDF cluster. As we did before, we need to import CUDA context creators after setting up the cluster so they don't lock to a single device. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import subprocess\n", + "import logging\n", + "\n", + "from dask.distributed import Client, wait, progress\n", + "from dask_cuda import LocalCUDACluster" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import cudf\n", + "import dask_cudf\n", + "\n", + "import cuml\n", + "from cuml.dask.cluster import KMeans" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# create cluster\n", + "cmd = \"hostname --all-ip-addresses\"\n", + "process = subprocess.Popen(cmd.split(), stdout=subprocess.PIPE)\n", + "output, error = process.communicate()\n", + "IPADDR = str(output.decode()).split()[0]\n", + "\n", + "cluster = LocalCUDACluster(ip=IPADDR, silence_logs=logging.ERROR)\n", + "client = Client(cluster)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load and Persist Data ##\n", + "We will begin by loading the data, The data set has the two grid coordinate columns, `easting` and `northing`, derived from the main population data set we have prepared." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "ddf = dask_cudf.read_csv('./data/uk_pop5x_coords.csv', dtype=['float32', 'float32'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Training the Model ##\n", + "Training the K-means model is very similar to both the scikit-learn version and the cuML single-GPU version--by setting up the client and importing from the `cuml.dask.cluster` module, the algorithm will automatically use the local Dask cluster we have set up.\n", + "\n", + "Note that calling `.fit` triggers Dask computation.\n", + "\n", + "Once we have the fit model, we extract the cluster centers and rename the columns from their generic `0` and `1` to reflect the data on which they were trained." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 5.17 s, sys: 2.95 s, total: 8.12 s\n", + "Wall time: 1min 57s\n" + ] + }, + { + "data": { + "text/html": [ + "<style>#sk-container-id-1 {\n", + " /* Definition of color scheme common for light and dark mode */\n", + " --sklearn-color-text: black;\n", + " --sklearn-color-line: gray;\n", + " /* Definition of color scheme for unfitted estimators */\n", + " --sklearn-color-unfitted-level-0: #fff5e6;\n", + " --sklearn-color-unfitted-level-1: #f6e4d2;\n", + " --sklearn-color-unfitted-level-2: #ffe0b3;\n", + " --sklearn-color-unfitted-level-3: chocolate;\n", + " /* Definition of color scheme for fitted estimators */\n", + " --sklearn-color-fitted-level-0: #f0f8ff;\n", + " --sklearn-color-fitted-level-1: #d4ebff;\n", + " --sklearn-color-fitted-level-2: #b3dbfd;\n", + " --sklearn-color-fitted-level-3: cornflowerblue;\n", + "\n", + " /* Specific color for light theme */\n", + " --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n", + " --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n", + " --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n", + " --sklearn-color-icon: #696969;\n", + "\n", + " @media (prefers-color-scheme: dark) {\n", + " /* Redefinition of color scheme for dark theme */\n", + " --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n", + " --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n", + " --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n", + " --sklearn-color-icon: #878787;\n", + " }\n", + "}\n", + "\n", + "#sk-container-id-1 {\n", + " color: var(--sklearn-color-text);\n", + "}\n", + "\n", + "#sk-container-id-1 pre {\n", + " padding: 0;\n", + "}\n", + "\n", + "#sk-container-id-1 input.sk-hidden--visually {\n", + " border: 0;\n", + " clip: rect(1px 1px 1px 1px);\n", + " clip: rect(1px, 1px, 1px, 1px);\n", + " height: 1px;\n", + " margin: -1px;\n", + " overflow: hidden;\n", + " padding: 0;\n", + " position: absolute;\n", + " width: 1px;\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-dashed-wrapped {\n", + " border: 1px dashed var(--sklearn-color-line);\n", + " margin: 0 0.4em 0.5em 0.4em;\n", + " box-sizing: border-box;\n", + " padding-bottom: 0.4em;\n", + " background-color: var(--sklearn-color-background);\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-container {\n", + " /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n", + " but bootstrap.min.css set `[hidden] { display: none !important; }`\n", + " so we also need the `!important` here to be able to override the\n", + " default hidden behavior on the sphinx rendered scikit-learn.org.\n", + " See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n", + " display: inline-block !important;\n", + " position: relative;\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-text-repr-fallback {\n", + " display: none;\n", + "}\n", + "\n", + "div.sk-parallel-item,\n", + "div.sk-serial,\n", + "div.sk-item {\n", + " /* draw centered vertical line to link estimators */\n", + " background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n", + " background-size: 2px 100%;\n", + " background-repeat: no-repeat;\n", + " background-position: center center;\n", + "}\n", + "\n", + "/* Parallel-specific style estimator block */\n", + "\n", + "#sk-container-id-1 div.sk-parallel-item::after {\n", + " content: \"\";\n", + " width: 100%;\n", + " border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n", + " flex-grow: 1;\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-parallel {\n", + " display: flex;\n", + " align-items: stretch;\n", + " justify-content: center;\n", + " background-color: var(--sklearn-color-background);\n", + " position: relative;\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-parallel-item {\n", + " display: flex;\n", + " flex-direction: column;\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n", + " align-self: flex-end;\n", + " width: 50%;\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n", + " align-self: flex-start;\n", + " width: 50%;\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n", + " width: 0;\n", + "}\n", + "\n", + "/* Serial-specific style estimator block */\n", + "\n", + "#sk-container-id-1 div.sk-serial {\n", + " display: flex;\n", + " flex-direction: column;\n", + " align-items: center;\n", + " background-color: var(--sklearn-color-background);\n", + " padding-right: 1em;\n", + " padding-left: 1em;\n", + "}\n", + "\n", + "\n", + "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n", + "clickable and can be expanded/collapsed.\n", + "- Pipeline and ColumnTransformer use this feature and define the default style\n", + "- Estimators will overwrite some part of the style using the `sk-estimator` class\n", + "*/\n", + "\n", + "/* Pipeline and ColumnTransformer style (default) */\n", + "\n", + "#sk-container-id-1 div.sk-toggleable {\n", + " /* Default theme specific background. It is overwritten whether we have a\n", + " specific estimator or a Pipeline/ColumnTransformer */\n", + " background-color: var(--sklearn-color-background);\n", + "}\n", + "\n", + "/* Toggleable label */\n", + "#sk-container-id-1 label.sk-toggleable__label {\n", + " cursor: pointer;\n", + " display: block;\n", + " width: 100%;\n", + " margin-bottom: 0;\n", + " padding: 0.5em;\n", + " box-sizing: border-box;\n", + " text-align: center;\n", + "}\n", + "\n", + "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n", + " /* Arrow on the left of the label */\n", + " content: \"▸\";\n", + " float: left;\n", + " margin-right: 0.25em;\n", + " color: var(--sklearn-color-icon);\n", + "}\n", + "\n", + "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n", + " color: var(--sklearn-color-text);\n", + "}\n", + "\n", + "/* Toggleable content - dropdown */\n", + "\n", + "#sk-container-id-1 div.sk-toggleable__content {\n", + " max-height: 0;\n", + " max-width: 0;\n", + " overflow: hidden;\n", + " text-align: left;\n", + " /* unfitted */\n", + " background-color: var(--sklearn-color-unfitted-level-0);\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-toggleable__content.fitted {\n", + " /* fitted */\n", + " background-color: var(--sklearn-color-fitted-level-0);\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-toggleable__content pre {\n", + " margin: 0.2em;\n", + " border-radius: 0.25em;\n", + " color: var(--sklearn-color-text);\n", + " /* unfitted */\n", + " background-color: var(--sklearn-color-unfitted-level-0);\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n", + " /* unfitted */\n", + " background-color: var(--sklearn-color-fitted-level-0);\n", + "}\n", + "\n", + "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n", + " /* Expand drop-down */\n", + " max-height: 200px;\n", + " max-width: 100%;\n", + " overflow: auto;\n", + "}\n", + "\n", + "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n", + " content: \"▾\";\n", + "}\n", + "\n", + "/* Pipeline/ColumnTransformer-specific style */\n", + "\n", + "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n", + " color: var(--sklearn-color-text);\n", + " background-color: var(--sklearn-color-unfitted-level-2);\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n", + " background-color: var(--sklearn-color-fitted-level-2);\n", + "}\n", + "\n", + "/* Estimator-specific style */\n", + "\n", + "/* Colorize estimator box */\n", + "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n", + " /* unfitted */\n", + " background-color: var(--sklearn-color-unfitted-level-2);\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n", + " /* fitted */\n", + " background-color: var(--sklearn-color-fitted-level-2);\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n", + "#sk-container-id-1 div.sk-label label {\n", + " /* The background is the default theme color */\n", + " color: var(--sklearn-color-text-on-default-background);\n", + "}\n", + "\n", + "/* On hover, darken the color of the background */\n", + "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n", + " color: var(--sklearn-color-text);\n", + " background-color: var(--sklearn-color-unfitted-level-2);\n", + "}\n", + "\n", + "/* Label box, darken color on hover, fitted */\n", + "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n", + " color: var(--sklearn-color-text);\n", + " background-color: var(--sklearn-color-fitted-level-2);\n", + "}\n", + "\n", + "/* Estimator label */\n", + "\n", + "#sk-container-id-1 div.sk-label label {\n", + " font-family: monospace;\n", + " font-weight: bold;\n", + " display: inline-block;\n", + " line-height: 1.2em;\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-label-container {\n", + " text-align: center;\n", + "}\n", + "\n", + "/* Estimator-specific */\n", + "#sk-container-id-1 div.sk-estimator {\n", + " font-family: monospace;\n", + " border: 1px dotted var(--sklearn-color-border-box);\n", + " border-radius: 0.25em;\n", + " box-sizing: border-box;\n", + " margin-bottom: 0.5em;\n", + " /* unfitted */\n", + " background-color: var(--sklearn-color-unfitted-level-0);\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-estimator.fitted {\n", + " /* fitted */\n", + " background-color: var(--sklearn-color-fitted-level-0);\n", + "}\n", + "\n", + "/* on hover */\n", + "#sk-container-id-1 div.sk-estimator:hover {\n", + " /* unfitted */\n", + " background-color: var(--sklearn-color-unfitted-level-2);\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-estimator.fitted:hover {\n", + " /* fitted */\n", + " background-color: var(--sklearn-color-fitted-level-2);\n", + "}\n", + "\n", + "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n", + "\n", + "/* Common style for \"i\" and \"?\" */\n", + "\n", + ".sk-estimator-doc-link,\n", + "a:link.sk-estimator-doc-link,\n", + "a:visited.sk-estimator-doc-link {\n", + " float: right;\n", + " font-size: smaller;\n", + " line-height: 1em;\n", + " font-family: monospace;\n", + " background-color: var(--sklearn-color-background);\n", + " border-radius: 1em;\n", + " height: 1em;\n", + " width: 1em;\n", + " text-decoration: none !important;\n", + " margin-left: 1ex;\n", + " /* unfitted */\n", + " border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n", + " color: var(--sklearn-color-unfitted-level-1);\n", + "}\n", + "\n", + ".sk-estimator-doc-link.fitted,\n", + "a:link.sk-estimator-doc-link.fitted,\n", + "a:visited.sk-estimator-doc-link.fitted {\n", + " /* fitted */\n", + " border: var(--sklearn-color-fitted-level-1) 1pt solid;\n", + " color: var(--sklearn-color-fitted-level-1);\n", + "}\n", + "\n", + "/* On hover */\n", + "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n", + ".sk-estimator-doc-link:hover,\n", + "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n", + ".sk-estimator-doc-link:hover {\n", + " /* unfitted */\n", + " background-color: var(--sklearn-color-unfitted-level-3);\n", + " color: var(--sklearn-color-background);\n", + " text-decoration: none;\n", + "}\n", + "\n", + "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n", + ".sk-estimator-doc-link.fitted:hover,\n", + "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n", + ".sk-estimator-doc-link.fitted:hover {\n", + " /* fitted */\n", + " background-color: var(--sklearn-color-fitted-level-3);\n", + " color: var(--sklearn-color-background);\n", + " text-decoration: none;\n", + "}\n", + "\n", + "/* Span, style for the box shown on hovering the info icon */\n", + ".sk-estimator-doc-link span {\n", + " display: none;\n", + " z-index: 9999;\n", + " position: relative;\n", + " font-weight: normal;\n", + " right: .2ex;\n", + " padding: .5ex;\n", + " margin: .5ex;\n", + " width: min-content;\n", + " min-width: 20ex;\n", + " max-width: 50ex;\n", + " color: var(--sklearn-color-text);\n", + " box-shadow: 2pt 2pt 4pt #999;\n", + " /* unfitted */\n", + " background: var(--sklearn-color-unfitted-level-0);\n", + " border: .5pt solid var(--sklearn-color-unfitted-level-3);\n", + "}\n", + "\n", + ".sk-estimator-doc-link.fitted span {\n", + " /* fitted */\n", + " background: var(--sklearn-color-fitted-level-0);\n", + " border: var(--sklearn-color-fitted-level-3);\n", + "}\n", + "\n", + ".sk-estimator-doc-link:hover span {\n", + " display: block;\n", + "}\n", + "\n", + "/* \"?\"-specific style due to the `<a>` HTML tag */\n", + "\n", + "#sk-container-id-1 a.estimator_doc_link {\n", + " float: right;\n", + " font-size: 1rem;\n", + " line-height: 1em;\n", + " font-family: monospace;\n", + " background-color: var(--sklearn-color-background);\n", + " border-radius: 1rem;\n", + " height: 1rem;\n", + " width: 1rem;\n", + " text-decoration: none;\n", + " /* unfitted */\n", + " color: var(--sklearn-color-unfitted-level-1);\n", + " border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n", + "}\n", + "\n", + "#sk-container-id-1 a.estimator_doc_link.fitted {\n", + " /* fitted */\n", + " border: var(--sklearn-color-fitted-level-1) 1pt solid;\n", + " color: var(--sklearn-color-fitted-level-1);\n", + "}\n", + "\n", + "/* On hover */\n", + "#sk-container-id-1 a.estimator_doc_link:hover {\n", + " /* unfitted */\n", + " background-color: var(--sklearn-color-unfitted-level-3);\n", + " color: var(--sklearn-color-background);\n", + " text-decoration: none;\n", + "}\n", + "\n", + "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n", + " /* fitted */\n", + " background-color: var(--sklearn-color-fitted-level-3);\n", + "}\n", + "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeansMG()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\"> KMeansMG<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KMeansMG()</pre></div> </div></div></div></div>" + ], + "text/plain": [ + "KMeansMG()" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%time\n", + "dkm = KMeans(n_clusters=20)\n", + "dkm.fit(ddf)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "northing float32\n", + "easting float32\n", + "dtype: object" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cluster_centers = dkm.cluster_centers_\n", + "cluster_centers.columns = ddf.columns\n", + "cluster_centers.dtypes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exercise #1 - Count Members of the Southernmost Cluster ###\n", + "Using the `cluster_centers`, identify which cluster is the southernmost (has the lowest `northing` value) with the `nsmallest` method, then use `dkm.predict` to get labels for the data, and finally filter the labels to determine how many individuals the model estimated were in that cluster. \n", + "\n", + "**Instructions**: <br>\n", + "* Modify the `<FIXME>` only and execute the below cell to estimate the number of individuals in the southernmost cluster. " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "31435157" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "south_idx = cluster_centers.nsmallest(1, 'northing').index[0]\n", + "labels_predicted = dkm.predict(ddf)\n", + "labels_predicted[labels_predicted==south_idx].compute().shape[0]" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "\n", + "south_idx = cluster_centers.nsmallest(1, 'northing').index[0]\n", + "labels_predicted = dkm.predict(ddf)\n", + "labels_predicted[labels_predicted==south_idx].compute().shape[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Click ... for solution. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import IPython\n", + "app = IPython.Application.instance()\n", + "app.kernel.do_shutdown(True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Well Done!**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<img src=\"./images/DLI_Header.png\" width=400/>" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.15" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Fundamentals_of_Accelerated_Data_Science/4-02_find_infected.ipynb b/Fundamentals_of_Accelerated_Data_Science/4-02_find_infected.ipynb new file mode 100644 index 0000000..9b857e2 --- /dev/null +++ b/Fundamentals_of_Accelerated_Data_Science/4-02_find_infected.ipynb @@ -0,0 +1,1007 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<a href=\"https://www.nvidia.com/dli\"><img src=\"images/DLI_Header.png\" alt=\"Header\" style=\"width: 400px;\"/></a>" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Week 1: Find Clusters of Infected People" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<span style=\"color:red\">\n", + "**URGENT WARNING**\n", + "\n", + "We have been receiving reports from health facilities that a new, fast-spreading virus has been discovered in the population. To prepare our response, we need to understand the geospatial distribution of those who have been infected. Find out whether there are identifiable clusters of infected individuals and where they are. \n", + "</span>\n", + "\n", + "Your goal for this notebook will be to estimate the location of dense geographic clusters of infected people using incoming data from week 1 of the simulated epidemic." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext cudf.pandas\n", + "import pandas as pd\n", + "import cuml\n", + "\n", + "import cupy as cp" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Begin by loading the data you've received about week 1 of the outbreak into a cuDF-accelerated pandas DataFrame. The data is located at `'./data/week1.csv'`. For this notebook you will only need the `'lat'`, `'long'`, and `'infected'` columns. Either drop the columns after loading, or use the `pd.read_csv` named argument `usecols` to provide a list of only the columns you need." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>lat</th>\n", + " <th>long</th>\n", + " <th>infected</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>54.522510</td>\n", + " <td>-1.571896</td>\n", + " <td>False</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>54.554030</td>\n", + " <td>-1.524968</td>\n", + " <td>False</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>54.552486</td>\n", + " <td>-1.435203</td>\n", + " <td>False</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>54.537189</td>\n", + " <td>-1.566215</td>\n", + " <td>False</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>54.528212</td>\n", + " <td>-1.588462</td>\n", + " <td>False</td>\n", + " </tr>\n", + " <tr>\n", + " <th>...</th>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " </tr>\n", + " <tr>\n", + " <th>58479889</th>\n", + " <td>51.634416</td>\n", + " <td>-2.925863</td>\n", + " <td>False</td>\n", + " </tr>\n", + " <tr>\n", + " <th>58479890</th>\n", + " <td>51.556972</td>\n", + " <td>-3.036290</td>\n", + " <td>False</td>\n", + " </tr>\n", + " <tr>\n", + " <th>58479891</th>\n", + " <td>51.588992</td>\n", + " <td>-2.921915</td>\n", + " <td>False</td>\n", + " </tr>\n", + " <tr>\n", + " <th>58479892</th>\n", + " <td>51.590974</td>\n", + " <td>-2.954539</td>\n", + " <td>False</td>\n", + " </tr>\n", + " <tr>\n", + " <th>58479893</th>\n", + " <td>51.576716</td>\n", + " <td>-2.952142</td>\n", + " <td>False</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "<p>58479894 rows × 3 columns</p>\n", + "</div>" + ], + "text/plain": [ + " lat long infected\n", + "0 54.522510 -1.571896 False\n", + "1 54.554030 -1.524968 False\n", + "2 54.552486 -1.435203 False\n", + "3 54.537189 -1.566215 False\n", + "4 54.528212 -1.588462 False\n", + "... ... ... ...\n", + "58479889 51.634416 -2.925863 False\n", + "58479890 51.556972 -3.036290 False\n", + "58479891 51.588992 -2.921915 False\n", + "58479892 51.590974 -2.954539 False\n", + "58479893 51.576716 -2.952142 False\n", + "\n", + "[58479894 rows x 3 columns]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.read_csv('./data/week1.csv', usecols = [\"lat\", \"long\", \"infected\"])\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Make Data Frame of the Infected" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Make a new DataFrame `infected_df` that contains only the infected members of the population.\n", + "\n", + "**Tip**: Reset the index of `infected_df` with `.reset_index(drop=True)`. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "infected_df = df[df[\"infected\"] == True]\n", + "infected_df = infected_df.reset_index(drop=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Make Grid Coordinates for Infected Locations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Provided for you in the next cell (which you can expand by clicking on the \"...\" and contract again after executing by clicking on the blue left border of the cell) is the lat/long to OSGB36 grid coordinates converter you used earlier in the workshop. Use this converter to create grid coordinate values stored in `northing` and `easting` columns of the `infected_df` you created in the last step." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# https://www.ordnancesurvey.co.uk/docs/support/guide-coordinate-systems-great-britain.pdf\n", + "\n", + "def latlong2osgbgrid_cupy(lat, long, input_degrees=True):\n", + " '''\n", + " Converts latitude and longitude (ellipsoidal) coordinates into northing and easting (grid) coordinates, using a Transverse Mercator projection.\n", + " \n", + " Inputs:\n", + " lat: latitude coordinate (N)\n", + " long: longitude coordinate (E)\n", + " input_degrees: if True (default), interprets the coordinates as degrees; otherwise, interprets coordinates as radians\n", + " \n", + " Output:\n", + " (northing, easting)\n", + " '''\n", + " \n", + " if input_degrees:\n", + " lat = lat * cp.pi/180\n", + " long = long * cp.pi/180\n", + "\n", + " a = 6377563.396\n", + " b = 6356256.909\n", + " e2 = (a**2 - b**2) / a**2\n", + "\n", + " N0 = -100000 # northing of true origin\n", + " E0 = 400000 # easting of true origin\n", + " F0 = .9996012717 # scale factor on central meridian\n", + " phi0 = 49 * cp.pi / 180 # latitude of true origin\n", + " lambda0 = -2 * cp.pi / 180 # longitude of true origin and central meridian\n", + " \n", + " sinlat = cp.sin(lat)\n", + " coslat = cp.cos(lat)\n", + " tanlat = cp.tan(lat)\n", + " \n", + " latdiff = lat-phi0\n", + " longdiff = long-lambda0\n", + "\n", + " n = (a-b) / (a+b)\n", + " nu = a * F0 * (1 - e2 * sinlat ** 2) ** -.5\n", + " rho = a * F0 * (1 - e2) * (1 - e2 * sinlat ** 2) ** -1.5\n", + " eta2 = nu / rho - 1\n", + " M = b * F0 * ((1 + n + 5/4 * (n**2 + n**3)) * latdiff - \n", + " (3*(n+n**2) + 21/8 * n**3) * cp.sin(latdiff) * cp.cos(lat+phi0) +\n", + " 15/8 * (n**2 + n**3) * cp.sin(2*(latdiff)) * cp.cos(2*(lat+phi0)) - \n", + " 35/24 * n**3 * cp.sin(3*(latdiff)) * cp.cos(3*(lat+phi0)))\n", + " I = M + N0\n", + " II = nu/2 * sinlat * coslat\n", + " III = nu/24 * sinlat * coslat ** 3 * (5 - tanlat ** 2 + 9 * eta2)\n", + " IIIA = nu/720 * sinlat * coslat ** 5 * (61-58 * tanlat**2 + tanlat**4)\n", + " IV = nu * coslat\n", + " V = nu / 6 * coslat**3 * (nu/rho - cp.tan(lat)**2)\n", + " VI = nu / 120 * coslat ** 5 * (5 - 18 * tanlat**2 + tanlat**4 + 14 * eta2 - 58 * tanlat**2 * eta2)\n", + "\n", + " northing = I + II * longdiff**2 + III * longdiff**4 + IIIA * longdiff**6\n", + " easting = E0 + IV * longdiff + V * longdiff**3 + VI * longdiff**5\n", + "\n", + " return(northing, easting)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>lat</th>\n", + " <th>long</th>\n", + " <th>infected</th>\n", + " <th>northing</th>\n", + " <th>easting</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>54.472766</td>\n", + " <td>-1.654932</td>\n", + " <td>True</td>\n", + " <td>508670.060234</td>\n", + " <td>422359.759523</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>54.529717</td>\n", + " <td>-1.667143</td>\n", + " <td>True</td>\n", + " <td>515002.666798</td>\n", + " <td>421538.547038</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>54.512986</td>\n", + " <td>-1.589866</td>\n", + " <td>True</td>\n", + " <td>513167.535850</td>\n", + " <td>426549.874086</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>54.522322</td>\n", + " <td>-1.380694</td>\n", + " <td>True</td>\n", + " <td>514305.280055</td>\n", + " <td>440081.234798</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>54.541660</td>\n", + " <td>-1.613490</td>\n", + " <td>True</td>\n", + " <td>516349.132042</td>\n", + " <td>425003.005560</td>\n", + " </tr>\n", + " <tr>\n", + " <th>...</th>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " </tr>\n", + " <tr>\n", + " <th>18143</th>\n", + " <td>52.428347</td>\n", + " <td>-3.322932</td>\n", + " <td>True</td>\n", + " <td>282016.338253</td>\n", + " <td>310060.098268</td>\n", + " </tr>\n", + " <tr>\n", + " <th>18144</th>\n", + " <td>52.415895</td>\n", + " <td>-3.263942</td>\n", + " <td>True</td>\n", + " <td>280559.681381</td>\n", + " <td>314046.146547</td>\n", + " </tr>\n", + " <tr>\n", + " <th>18145</th>\n", + " <td>52.539934</td>\n", + " <td>-3.617128</td>\n", + " <td>True</td>\n", + " <td>294832.815870</td>\n", + " <td>290338.202721</td>\n", + " </tr>\n", + " <tr>\n", + " <th>18146</th>\n", + " <td>52.435490</td>\n", + " <td>-3.597263</td>\n", + " <td>True</td>\n", + " <td>283187.465568</td>\n", + " <td>291428.293249</td>\n", + " </tr>\n", + " <tr>\n", + " <th>18147</th>\n", + " <td>52.700105</td>\n", + " <td>-3.375221</td>\n", + " <td>True</td>\n", + " <td>312306.356272</td>\n", + " <td>307081.485707</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "<p>18148 rows × 5 columns</p>\n", + "</div>" + ], + "text/plain": [ + " lat long infected northing easting\n", + "0 54.472766 -1.654932 True 508670.060234 422359.759523\n", + "1 54.529717 -1.667143 True 515002.666798 421538.547038\n", + "2 54.512986 -1.589866 True 513167.535850 426549.874086\n", + "3 54.522322 -1.380694 True 514305.280055 440081.234798\n", + "4 54.541660 -1.613490 True 516349.132042 425003.005560\n", + "... ... ... ... ... ...\n", + "18143 52.428347 -3.322932 True 282016.338253 310060.098268\n", + "18144 52.415895 -3.263942 True 280559.681381 314046.146547\n", + "18145 52.539934 -3.617128 True 294832.815870 290338.202721\n", + "18146 52.435490 -3.597263 True 283187.465568 291428.293249\n", + "18147 52.700105 -3.375221 True 312306.356272 307081.485707\n", + "\n", + "[18148 rows x 5 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cupy_lat = cp.asarray(infected_df['lat'])\n", + "cupy_long = cp.asarray(infected_df['long'])\n", + "\n", + "infected_df['northing'], infected_df['easting'] = latlong2osgbgrid_cupy(cupy_lat, cupy_long)\n", + "infected_df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Find Clusters of Infected People" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Use DBSCAN to find clusters of at least 25 infected people where no member is more than 2000m from at least one other cluster member. Create a new column in `infected_df` which contains the cluster to which each infected person belongs." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>lat</th>\n", + " <th>long</th>\n", + " <th>infected</th>\n", + " <th>northing</th>\n", + " <th>easting</th>\n", + " <th>cluster</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>54.472766</td>\n", + " <td>-1.654932</td>\n", + " <td>True</td>\n", + " <td>508670.060234</td>\n", + " <td>422359.759523</td>\n", + " <td>-1</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>54.529717</td>\n", + " <td>-1.667143</td>\n", + " <td>True</td>\n", + " <td>515002.666798</td>\n", + " <td>421538.547038</td>\n", + " <td>-1</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>54.512986</td>\n", + " <td>-1.589866</td>\n", + " <td>True</td>\n", + " <td>513167.535850</td>\n", + " <td>426549.874086</td>\n", + " <td>-1</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>54.522322</td>\n", + " <td>-1.380694</td>\n", + " <td>True</td>\n", + " <td>514305.280055</td>\n", + " <td>440081.234798</td>\n", + " <td>-1</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>54.541660</td>\n", + " <td>-1.613490</td>\n", + " <td>True</td>\n", + " <td>516349.132042</td>\n", + " <td>425003.005560</td>\n", + " <td>-1</td>\n", + " </tr>\n", + " <tr>\n", + " <th>...</th>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " </tr>\n", + " <tr>\n", + " <th>18143</th>\n", + " <td>52.428347</td>\n", + " <td>-3.322932</td>\n", + " <td>True</td>\n", + " <td>282016.338253</td>\n", + " <td>310060.098268</td>\n", + " <td>-1</td>\n", + " </tr>\n", + " <tr>\n", + " <th>18144</th>\n", + " <td>52.415895</td>\n", + " <td>-3.263942</td>\n", + " <td>True</td>\n", + " <td>280559.681381</td>\n", + " <td>314046.146547</td>\n", + " <td>-1</td>\n", + " </tr>\n", + " <tr>\n", + " <th>18145</th>\n", + " <td>52.539934</td>\n", + " <td>-3.617128</td>\n", + " <td>True</td>\n", + " <td>294832.815870</td>\n", + " <td>290338.202721</td>\n", + " <td>-1</td>\n", + " </tr>\n", + " <tr>\n", + " <th>18146</th>\n", + " <td>52.435490</td>\n", + " <td>-3.597263</td>\n", + " <td>True</td>\n", + " <td>283187.465568</td>\n", + " <td>291428.293249</td>\n", + " <td>-1</td>\n", + " </tr>\n", + " <tr>\n", + " <th>18147</th>\n", + " <td>52.700105</td>\n", + " <td>-3.375221</td>\n", + " <td>True</td>\n", + " <td>312306.356272</td>\n", + " <td>307081.485707</td>\n", + " <td>-1</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "<p>18148 rows × 6 columns</p>\n", + "</div>" + ], + "text/plain": [ + " lat long infected northing easting cluster\n", + "0 54.472766 -1.654932 True 508670.060234 422359.759523 -1\n", + "1 54.529717 -1.667143 True 515002.666798 421538.547038 -1\n", + "2 54.512986 -1.589866 True 513167.535850 426549.874086 -1\n", + "3 54.522322 -1.380694 True 514305.280055 440081.234798 -1\n", + "4 54.541660 -1.613490 True 516349.132042 425003.005560 -1\n", + "... ... ... ... ... ... ...\n", + "18143 52.428347 -3.322932 True 282016.338253 310060.098268 -1\n", + "18144 52.415895 -3.263942 True 280559.681381 314046.146547 -1\n", + "18145 52.539934 -3.617128 True 294832.815870 290338.202721 -1\n", + "18146 52.435490 -3.597263 True 283187.465568 291428.293249 -1\n", + "18147 52.700105 -3.375221 True 312306.356272 307081.485707 -1\n", + "\n", + "[18148 rows x 6 columns]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dbscan = cuml.DBSCAN(eps = 2000, min_samples=25, metric='euclidean')\n", + "infected_df['cluster'] = dbscan.fit_predict(infected_df[['northing','easting']])\n", + "infected_df" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "15" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "infected_df['cluster'].nunique()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Find the Centroid of Each Cluster" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Use grouping to find the mean `northing` and `easting` values for each cluster identified above." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>northing</th>\n", + " <th>easting</th>\n", + " </tr>\n", + " <tr>\n", + " <th>cluster</th>\n", + " <th></th>\n", + " <th></th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>-1</th>\n", + " <td>378085.504251</td>\n", + " <td>401877.070477</td>\n", + " </tr>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>397661.052147</td>\n", + " <td>371410.022807</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>436475.467158</td>\n", + " <td>332980.455514</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>347062.237166</td>\n", + " <td>389386.821165</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>359668.638420</td>\n", + " <td>379638.020073</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>391630.079963</td>\n", + " <td>431158.142881</td>\n", + " </tr>\n", + " <tr>\n", + " <th>5</th>\n", + " <td>386471.292123</td>\n", + " <td>426559.091880</td>\n", + " </tr>\n", + " <tr>\n", + " <th>6</th>\n", + " <td>434970.334950</td>\n", + " <td>406985.282976</td>\n", + " </tr>\n", + " <tr>\n", + " <th>7</th>\n", + " <td>412772.647531</td>\n", + " <td>410069.665645</td>\n", + " </tr>\n", + " <tr>\n", + " <th>8</th>\n", + " <td>415807.314112</td>\n", + " <td>414765.634582</td>\n", + " </tr>\n", + " <tr>\n", + " <th>9</th>\n", + " <td>417322.517251</td>\n", + " <td>409583.740733</td>\n", + " </tr>\n", + " <tr>\n", + " <th>10</th>\n", + " <td>334208.230907</td>\n", + " <td>435937.780795</td>\n", + " </tr>\n", + " <tr>\n", + " <th>11</th>\n", + " <td>300567.933051</td>\n", + " <td>391901.512758</td>\n", + " </tr>\n", + " <tr>\n", + " <th>12</th>\n", + " <td>291539.411185</td>\n", + " <td>401640.667572</td>\n", + " </tr>\n", + " <tr>\n", + " <th>13</th>\n", + " <td>289854.874937</td>\n", + " <td>394518.294994</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " northing easting\n", + "cluster \n", + "-1 378085.504251 401877.070477\n", + " 0 397661.052147 371410.022807\n", + " 1 436475.467158 332980.455514\n", + " 2 347062.237166 389386.821165\n", + " 3 359668.638420 379638.020073\n", + " 4 391630.079963 431158.142881\n", + " 5 386471.292123 426559.091880\n", + " 6 434970.334950 406985.282976\n", + " 7 412772.647531 410069.665645\n", + " 8 415807.314112 414765.634582\n", + " 9 417322.517251 409583.740733\n", + " 10 334208.230907 435937.780795\n", + " 11 300567.933051 391901.512758\n", + " 12 291539.411185 401640.667572\n", + " 13 289854.874937 394518.294994" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "centroids_df = infected_df[['northing', 'easting', 'cluster']].groupby('cluster').mean()\n", + "centroids_df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Find the number of people in each cluster by counting the number of appearances of each cluster's label in the column produced by DBSCAN." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "cluster\n", + " 0 8638\n", + "-1 8449\n", + " 2 403\n", + " 8 94\n", + " 12 72\n", + " 13 71\n", + " 1 68\n", + " 11 68\n", + " 4 66\n", + " 10 64\n", + " 5 43\n", + " 7 39\n", + " 6 27\n", + " 3 25\n", + " 9 21\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "infected_df['cluster'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "northing 397661.052147\n", + "easting 371410.022807\n", + "Name: 0, dtype: float64" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "idx = infected_df['cluster'].value_counts().idxmax()\n", + "maxxx = centroids_df.loc[idx]\n", + "maxxx.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Find the Centroid of the Cluster with the Most Members ##" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Use the cluster label for with the most people to filter `centroid_df` and write the answer to `my_assessment/question_1.json`. " + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.10/site-packages/cudf/io/json.py:194: UserWarning: Using CPU via Pandas to write JSON dataset\n", + " warnings.warn(\"Using CPU via Pandas to write JSON dataset\")\n" + ] + } + ], + "source": [ + "centroids_df.loc[infected_df['cluster'].value_counts().idxmax()].to_json('my_assessment/question_1.json')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Check Submission ##" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\"northing\":397661.0521473523,\"easting\":371410.0228066591}" + ] + } + ], + "source": [ + "!cat my_assessment/question_1.json" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Tip**: Your submission file should contain one line of text, similar to: \n", + "\n", + "```\n", + "{'northing':XXX.XX,'easting':XXX.XX}\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div align=\"center\"><h2>Please Restart the Kernel</h2></div>" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'status': 'ok', 'restart': True}" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import IPython\n", + "app = IPython.Application.instance()\n", + "app.kernel.do_shutdown(True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<a href=\"https://www.nvidia.com/dli\"><img src=\"images/DLI_Header.png\" alt=\"Header\" style=\"width: 400px;\"/></a>" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.15" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Fundamentals_of_Accelerated_Data_Science/4-03_nearest_facilities.ipynb b/Fundamentals_of_Accelerated_Data_Science/4-03_nearest_facilities.ipynb new file mode 100644 index 0000000..cc49bf1 --- /dev/null +++ b/Fundamentals_of_Accelerated_Data_Science/4-03_nearest_facilities.ipynb @@ -0,0 +1,4102 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<a href=\"https://www.nvidia.com/dli\"><img src=\"images/DLI_Header.png\" alt=\"Header\" style=\"width: 400px;\"/></a>" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Week 2: Identify Nearest Health Facilities" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<span style=\"color:red\">\n", + "**UPDATE**\n", + "\n", + "Thank you for your analysis. Despite our warning efforts so far, the virus continues to spread rapidly. We want to get infected individuals treatment as quickly as possible, so we need your help to calculate which hospital or clinic is closest to each known infected individual in the population.\n", + "</span>\n", + "\n", + "Your goal for this notebook will be to identify the nearest hospital or clinic for each infected person." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext cudf.pandas\n", + "import pandas as pd\n", + "import cuml\n", + "import cupy as cp" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load Population Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Begin by loading the `lat`, `long` and `infected` columns from `'./data/week2.csv'` into a cuDF-accelerated pandas DataFrame called `df`." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>lat</th>\n", + " <th>long</th>\n", + " <th>infected</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>54.522510</td>\n", + " <td>-1.571896</td>\n", + " <td>0.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>54.554030</td>\n", + " <td>-1.524968</td>\n", + " <td>0.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>54.552486</td>\n", + " <td>-1.435203</td>\n", + " <td>0.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>54.537189</td>\n", + " <td>-1.566215</td>\n", + " <td>0.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>54.528212</td>\n", + " <td>-1.588462</td>\n", + " <td>0.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>...</th>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " </tr>\n", + " <tr>\n", + " <th>58479889</th>\n", + " <td>51.634416</td>\n", + " <td>-2.925863</td>\n", + " <td>0.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>58479890</th>\n", + " <td>51.556972</td>\n", + " <td>-3.036290</td>\n", + " <td>0.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>58479891</th>\n", + " <td>51.588992</td>\n", + " <td>-2.921915</td>\n", + " <td>0.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>58479892</th>\n", + " <td>51.590974</td>\n", + " <td>-2.954539</td>\n", + " <td>0.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>58479893</th>\n", + " <td>51.576716</td>\n", + " <td>-2.952142</td>\n", + " <td>0.0</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "<p>58479894 rows × 3 columns</p>\n", + "</div>" + ], + "text/plain": [ + " lat long infected\n", + "0 54.522510 -1.571896 0.0\n", + "1 54.554030 -1.524968 0.0\n", + "2 54.552486 -1.435203 0.0\n", + "3 54.537189 -1.566215 0.0\n", + "4 54.528212 -1.588462 0.0\n", + "... ... ... ...\n", + "58479889 51.634416 -2.925863 0.0\n", + "58479890 51.556972 -3.036290 0.0\n", + "58479891 51.588992 -2.921915 0.0\n", + "58479892 51.590974 -2.954539 0.0\n", + "58479893 51.576716 -2.952142 0.0\n", + "\n", + "[58479894 rows x 3 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.read_csv('./data/week2.csv', usecols=['lat', 'long', 'infected'])\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load Hospital and Clinics Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For this step, your goal is to create an `all_med` cuDF-accelearted pandas DataFrame that contains the latitudes and longitudes of all the hospitals (data found at `'./data/hospitals.csv'`) and clinics (data found at `'./data/clinics.csv'`)." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>OrganisationID</th>\n", + " <th>OrganisationCode</th>\n", + " <th>OrganisationType</th>\n", + " <th>SubType</th>\n", + " <th>Sector</th>\n", + " <th>OrganisationStatus</th>\n", + " <th>IsPimsManaged</th>\n", + " <th>OrganisationName</th>\n", + " <th>Address1</th>\n", + " <th>Address2</th>\n", + " <th>...</th>\n", + " <th>County</th>\n", + " <th>Postcode</th>\n", + " <th>Latitude</th>\n", + " <th>Longitude</th>\n", + " <th>ParentODSCode</th>\n", + " <th>ParentName</th>\n", + " <th>Phone</th>\n", + " <th>Email</th>\n", + " <th>Website</th>\n", + " <th>Fax</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>17970</td>\n", + " <td>NDA07</td>\n", + " <td>Hospital</td>\n", + " <td>Hospital</td>\n", + " <td>Independent Sector</td>\n", + " <td>Visible</td>\n", + " <td>True</td>\n", + " <td>Walton Community Hospital - Virgin Care Servic...</td>\n", + " <td><NA></td>\n", + " <td>Rodney Road</td>\n", + " <td>...</td>\n", + " <td>Surrey</td>\n", + " <td>KT12 3LD</td>\n", + " <td>51.37999725</td>\n", + " <td>-0.406042069</td>\n", + " <td>NDA</td>\n", + " <td>Virgin Care Services Ltd</td>\n", + " <td>01932 414205</td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td>01932 253674</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>17981</td>\n", + " <td>NDA18</td>\n", + " <td>Hospital</td>\n", + " <td>Hospital</td>\n", + " <td>Independent Sector</td>\n", + " <td>Visible</td>\n", + " <td>True</td>\n", + " <td>Woking Community Hospital (Virgin Care)</td>\n", + " <td><NA></td>\n", + " <td>Heathside Road</td>\n", + " <td>...</td>\n", + " <td>Surrey</td>\n", + " <td>GU22 7HS</td>\n", + " <td>51.31513214</td>\n", + " <td>-0.556289494</td>\n", + " <td>NDA</td>\n", + " <td>Virgin Care Services Ltd</td>\n", + " <td>01483 715911</td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>18102</td>\n", + " <td>NLT02</td>\n", + " <td>Hospital</td>\n", + " <td>Hospital</td>\n", + " <td>NHS Sector</td>\n", + " <td>Visible</td>\n", + " <td>True</td>\n", + " <td>North Somerset Community Hospital</td>\n", + " <td>North Somerset Community Hospital</td>\n", + " <td>Old Street</td>\n", + " <td>...</td>\n", + " <td>Avon</td>\n", + " <td>BS21 6BS</td>\n", + " <td>51.43719482</td>\n", + " <td>-2.847192764</td>\n", + " <td>NLT</td>\n", + " <td>North Somerset Community Partnership Community...</td>\n", + " <td>01275 872212</td>\n", + " <td><NA></td>\n", + " <td>http://www.nscphealth.co.uk</td>\n", + " <td><NA></td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>18138</td>\n", + " <td>NMP01</td>\n", + " <td>Hospital</td>\n", + " <td>Hospital</td>\n", + " <td>Independent Sector</td>\n", + " <td>Visible</td>\n", + " <td>False</td>\n", + " <td>Bridgewater Hospital</td>\n", + " <td>120 Princess Road</td>\n", + " <td><NA></td>\n", + " <td>...</td>\n", + " <td>Greater Manchester</td>\n", + " <td>M15 5AT</td>\n", + " <td>53.4597435</td>\n", + " <td>-2.245468855</td>\n", + " <td>NMP</td>\n", + " <td>Bridgewater Hospital (Manchester) Ltd</td>\n", + " <td>0161 2270000</td>\n", + " <td><NA></td>\n", + " <td>www.bridgewaterhospital.com</td>\n", + " <td><NA></td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>18142</td>\n", + " <td>NMV01</td>\n", + " <td>Hospital</td>\n", + " <td>Hospital</td>\n", + " <td>Independent Sector</td>\n", + " <td>Visible</td>\n", + " <td>True</td>\n", + " <td>Kneesworth House</td>\n", + " <td>Old North Road</td>\n", + " <td>Bassingbourn</td>\n", + " <td>...</td>\n", + " <td><NA></td>\n", + " <td>SG8 5JP</td>\n", + " <td>52.07812119</td>\n", + " <td>-0.030604055</td>\n", + " <td>NMV</td>\n", + " <td>Partnerships In Care Ltd</td>\n", + " <td>01763 255 700</td>\n", + " <td>reception_kneesworthhouse@partnershipsincare.c...</td>\n", + " <td>www.partnershipsincare.co.uk</td>\n", + " <td><NA></td>\n", + " </tr>\n", + " <tr>\n", + " <th>...</th>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1224</th>\n", + " <td>10617466</td>\n", + " <td>RTH18</td>\n", + " <td>Hospital</td>\n", + " <td>UNKNOWN</td>\n", + " <td>Independent Sector</td>\n", + " <td>Visible</td>\n", + " <td>False</td>\n", + " <td>University Hospital Of Wales</td>\n", + " <td>C/O John Radcliffe Hospital</td>\n", + " <td>Headley Way</td>\n", + " <td>...</td>\n", + " <td><NA></td>\n", + " <td>OX3 9DU</td>\n", + " <td>51.76387405</td>\n", + " <td>-1.219792008</td>\n", + " <td>RTH</td>\n", + " <td>Oxford University Hospitals NHS Foundation Trust</td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " </tr>\n", + " <tr>\n", + " <th>1225</th>\n", + " <td>10617482</td>\n", + " <td>RX3KI</td>\n", + " <td>Hospital</td>\n", + " <td>UNKNOWN</td>\n", + " <td>Independent Sector</td>\n", + " <td>Visible</td>\n", + " <td>False</td>\n", + " <td>Foss Park Hospital</td>\n", + " <td>Huntington House</td>\n", + " <td>Jockey Lane</td>\n", + " <td>...</td>\n", + " <td><NA></td>\n", + " <td>YO32 9XW</td>\n", + " <td>53.98666382</td>\n", + " <td>-1.051122308</td>\n", + " <td>RX3</td>\n", + " <td>Tees, Esk and Wear Valleys NHS Foundation Trust</td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " </tr>\n", + " <tr>\n", + " <th>1226</th>\n", + " <td>10617567</td>\n", + " <td>RGT1W</td>\n", + " <td>Hospital</td>\n", + " <td>UNKNOWN</td>\n", + " <td>Independent Sector</td>\n", + " <td>Visible</td>\n", + " <td>False</td>\n", + " <td>Jersey General Hospital</td>\n", + " <td>The Parade</td>\n", + " <td><NA></td>\n", + " <td>...</td>\n", + " <td><NA></td>\n", + " <td>JE1 3UH</td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td>RGT</td>\n", + " <td>Cambridge University Hospitals NHS Foundation ...</td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " </tr>\n", + " <tr>\n", + " <th>1227</th>\n", + " <td>10617714</td>\n", + " <td>RAX0A</td>\n", + " <td>Hospital</td>\n", + " <td>UNKNOWN</td>\n", + " <td>Independent Sector</td>\n", + " <td>Visible</td>\n", + " <td>False</td>\n", + " <td>London North West University Hospitals</td>\n", + " <td>Watford Road</td>\n", + " <td><NA></td>\n", + " <td>...</td>\n", + " <td><NA></td>\n", + " <td>HA1 3UJ</td>\n", + " <td>51.57540894</td>\n", + " <td>-0.322022736</td>\n", + " <td>RAX</td>\n", + " <td>Kingston Hospital NHS Foundation Trust</td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " </tr>\n", + " <tr>\n", + " <th>1228</th>\n", + " <td>10617726</td>\n", + " <td>RDU5A</td>\n", + " <td>Hospital</td>\n", + " <td>UNKNOWN</td>\n", + " <td>Independent Sector</td>\n", + " <td>Visible</td>\n", + " <td>False</td>\n", + " <td>John Radcliffe Hospital</td>\n", + " <td>Headley Way</td>\n", + " <td>Headington</td>\n", + " <td>...</td>\n", + " <td><NA></td>\n", + " <td>OX3 9DU</td>\n", + " <td>51.76387405</td>\n", + " <td>-1.219792008</td>\n", + " <td>RDU</td>\n", + " <td>Frimley Health NHS Foundation Trust</td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "<p>1229 rows × 22 columns</p>\n", + "</div>" + ], + "text/plain": [ + " OrganisationID OrganisationCode OrganisationType SubType \\\n", + "0 17970 NDA07 Hospital Hospital \n", + "1 17981 NDA18 Hospital Hospital \n", + "2 18102 NLT02 Hospital Hospital \n", + "3 18138 NMP01 Hospital Hospital \n", + "4 18142 NMV01 Hospital Hospital \n", + "... ... ... ... ... \n", + "1224 10617466 RTH18 Hospital UNKNOWN \n", + "1225 10617482 RX3KI Hospital UNKNOWN \n", + "1226 10617567 RGT1W Hospital UNKNOWN \n", + "1227 10617714 RAX0A Hospital UNKNOWN \n", + "1228 10617726 RDU5A Hospital UNKNOWN \n", + "\n", + " Sector OrganisationStatus IsPimsManaged \\\n", + "0 Independent Sector Visible True \n", + "1 Independent Sector Visible True \n", + "2 NHS Sector Visible True \n", + "3 Independent Sector Visible False \n", + "4 Independent Sector Visible True \n", + "... ... ... ... \n", + "1224 Independent Sector Visible False \n", + "1225 Independent Sector Visible False \n", + "1226 Independent Sector Visible False \n", + "1227 Independent Sector Visible False \n", + "1228 Independent Sector Visible False \n", + "\n", + " OrganisationName \\\n", + "0 Walton Community Hospital - Virgin Care Servic... \n", + "1 Woking Community Hospital (Virgin Care) \n", + "2 North Somerset Community Hospital \n", + "3 Bridgewater Hospital \n", + "4 Kneesworth House \n", + "... ... \n", + "1224 University Hospital Of Wales \n", + "1225 Foss Park Hospital \n", + "1226 Jersey General Hospital \n", + "1227 London North West University Hospitals \n", + "1228 John Radcliffe Hospital \n", + "\n", + " Address1 Address2 ... \\\n", + "0 <NA> Rodney Road ... \n", + "1 <NA> Heathside Road ... \n", + "2 North Somerset Community Hospital Old Street ... \n", + "3 120 Princess Road <NA> ... \n", + "4 Old North Road Bassingbourn ... \n", + "... ... ... ... \n", + "1224 C/O John Radcliffe Hospital Headley Way ... \n", + "1225 Huntington House Jockey Lane ... \n", + "1226 The Parade <NA> ... \n", + "1227 Watford Road <NA> ... \n", + "1228 Headley Way Headington ... \n", + "\n", + " County Postcode Latitude Longitude ParentODSCode \\\n", + "0 Surrey KT12 3LD 51.37999725 -0.406042069 NDA \n", + "1 Surrey GU22 7HS 51.31513214 -0.556289494 NDA \n", + "2 Avon BS21 6BS 51.43719482 -2.847192764 NLT \n", + "3 Greater Manchester M15 5AT 53.4597435 -2.245468855 NMP \n", + "4 <NA> SG8 5JP 52.07812119 -0.030604055 NMV \n", + "... ... ... ... ... ... \n", + "1224 <NA> OX3 9DU 51.76387405 -1.219792008 RTH \n", + "1225 <NA> YO32 9XW 53.98666382 -1.051122308 RX3 \n", + "1226 <NA> JE1 3UH <NA> <NA> RGT \n", + "1227 <NA> HA1 3UJ 51.57540894 -0.322022736 RAX \n", + "1228 <NA> OX3 9DU 51.76387405 -1.219792008 RDU \n", + "\n", + " ParentName Phone \\\n", + "0 Virgin Care Services Ltd 01932 414205 \n", + "1 Virgin Care Services Ltd 01483 715911 \n", + "2 North Somerset Community Partnership Community... 01275 872212 \n", + "3 Bridgewater Hospital (Manchester) Ltd 0161 2270000 \n", + "4 Partnerships In Care Ltd 01763 255 700 \n", + "... ... ... \n", + "1224 Oxford University Hospitals NHS Foundation Trust <NA> \n", + "1225 Tees, Esk and Wear Valleys NHS Foundation Trust <NA> \n", + "1226 Cambridge University Hospitals NHS Foundation ... <NA> \n", + "1227 Kingston Hospital NHS Foundation Trust <NA> \n", + "1228 Frimley Health NHS Foundation Trust <NA> \n", + "\n", + " Email \\\n", + "0 <NA> \n", + "1 <NA> \n", + "2 <NA> \n", + "3 <NA> \n", + "4 reception_kneesworthhouse@partnershipsincare.c... \n", + "... ... \n", + "1224 <NA> \n", + "1225 <NA> \n", + "1226 <NA> \n", + "1227 <NA> \n", + "1228 <NA> \n", + "\n", + " Website Fax \n", + "0 <NA> 01932 253674 \n", + "1 <NA> <NA> \n", + "2 http://www.nscphealth.co.uk <NA> \n", + "3 www.bridgewaterhospital.com <NA> \n", + "4 www.partnershipsincare.co.uk <NA> \n", + "... ... ... \n", + "1224 <NA> <NA> \n", + "1225 <NA> <NA> \n", + "1226 <NA> <NA> \n", + "1227 <NA> <NA> \n", + "1228 <NA> <NA> \n", + "\n", + "[1229 rows x 22 columns]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hospitals_df = pd.read_csv('./data/hospitals.csv')\n", + "hospitals_df" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>OrganisationID</th>\n", + " <th>OrganisationCode</th>\n", + " <th>OrganisationType</th>\n", + " <th>SubType</th>\n", + " <th>OrganisationStatus</th>\n", + " <th>IsPimsManaged</th>\n", + " <th>OrganisationName</th>\n", + " <th>Address1</th>\n", + " <th>Address2</th>\n", + " <th>Address3</th>\n", + " <th>City</th>\n", + " <th>County</th>\n", + " <th>Postcode</th>\n", + " <th>Latitude</th>\n", + " <th>Longitude</th>\n", + " <th>Phone</th>\n", + " <th>Email</th>\n", + " <th>Website</th>\n", + " <th>Fax</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>12366</td>\n", + " <td>F81157</td>\n", + " <td>Clinic</td>\n", + " <td>UNKNOWN</td>\n", + " <td>Visible</td>\n", + " <td>True</td>\n", + " <td>Frinton Road Medical Centre</td>\n", + " <td><NA></td>\n", + " <td>68 Frinton Road, Hollond-on-Sea</td>\n", + " <td><NA></td>\n", + " <td>Clacton-on-Sea</td>\n", + " <td><NA></td>\n", + " <td>CO15 5UW</td>\n", + " 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Sea</td>\n", + " <td><NA></td>\n", + " <td>CO15 2NB</td>\n", + " <td>51.780621</td>\n", + " <td>1.117907</td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>17937</td>\n", + " <td>NAA01</td>\n", + " <td>Clinic</td>\n", + " <td>UNKNOWN</td>\n", + " <td>Visible</td>\n", + " <td>False</td>\n", + " <td>St Chads Clinic</td>\n", + " <td>St. Chads Drive</td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td>Liverpool</td>\n", + " <td>Merseyside</td>\n", + " <td>L32 8RE</td>\n", + " <td>53.482368</td>\n", + " <td>-2.885404</td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>17938</td>\n", + " <td>NAA02</td>\n", + " <td>Clinic</td>\n", + " <td>UNKNOWN</td>\n", + " <td>Visible</td>\n", + " <td>False</td>\n", + " <td>Whiston Health Centre</td>\n", + " <td>Old Colliery Road</td>\n", + " <td>Whiston</td>\n", + " <td><NA></td>\n", + " <td>Prescot</td>\n", + " <td>Merseyside</td>\n", + " <td>L35 3SX</td>\n", + " <td>53.415630</td>\n", + " <td>-2.800874</td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " </tr>\n", + " <tr>\n", + " <th>...</th>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " </tr>\n", + " <tr>\n", + " <th>19077</th>\n", + " <td>10617742</td>\n", + " <td>NBG7L</td>\n", + " <td>Clinic</td>\n", + " <td>UNKNOWN</td>\n", + " <td>Visible</td>\n", + " <td>False</td>\n", + " <td>Sunnybank Medical Centre</td>\n", + " <td>Town Gate</td>\n", + " <td>Wyke</td>\n", + " <td><NA></td>\n", + " <td>Bradford</td>\n", + " <td><NA></td>\n", + " <td>BD12 9NG</td>\n", + " <td>53.737923</td>\n", + " <td>-1.767982</td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " </tr>\n", + " <tr>\n", + " <th>19078</th>\n", + " <td>10617743</td>\n", + " <td>NFH02</td>\n", + " <td>Clinic</td>\n", + " <td>UNKNOWN</td>\n", + " <td>Visible</td>\n", + " <td>False</td>\n", + " <td>Somerset Surgical Services At Mendip Vale Medi...</td>\n", + " <td>Pudding Pie Lane</td>\n", + " <td>Langford</td>\n", + " <td><NA></td>\n", + " <td>Bristol</td>\n", + " <td><NA></td>\n", + " <td>BS40 5EL</td>\n", + " <td>51.341579</td>\n", + " <td>-2.784808</td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " </tr>\n", + " <tr>\n", + " <th>19079</th>\n", + " <td>10617744</td>\n", + " <td>NFH03</td>\n", + " <td>Clinic</td>\n", + " <td>UNKNOWN</td>\n", + " <td>Visible</td>\n", + " <td>False</td>\n", + " <td>Somerset Surgical Services At Axbridge Medical...</td>\n", + " <td>Houlgate Way</td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td>Axbridge</td>\n", + " <td><NA></td>\n", + " <td>BS26 2BJ</td>\n", + " <td>51.286285</td>\n", + " <td>-2.820388</td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " </tr>\n", + " <tr>\n", + " <th>19080</th>\n", + " <td>10617746</td>\n", + " <td>NEMAM</td>\n", + " <td>Clinic</td>\n", + " <td>UNKNOWN</td>\n", + " <td>Visible</td>\n", + " <td>False</td>\n", + " <td>Lawton House Surgery</td>\n", + " <td>Bromley Road</td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td>Congleton</td>\n", + " <td><NA></td>\n", + " <td>CW12 1QG</td>\n", + " <td>53.163048</td>\n", + " <td>-2.205781</td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " </tr>\n", + " <tr>\n", + " <th>19081</th>\n", + " <td>10617747</td>\n", + " <td>NFH04</td>\n", + " <td>Clinic</td>\n", + " <td>UNKNOWN</td>\n", + " <td>Visible</td>\n", + " <td>False</td>\n", + " <td>Somerset Surgical Services At Wedmore Medical ...</td>\n", + " <td>St. Medard Road</td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td>Wedmore</td>\n", + " <td><NA></td>\n", + " <td>BS28 4AY</td>\n", + " <td>51.227219</td>\n", + " <td>-2.816258</td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "<p>19082 rows × 19 columns</p>\n", + "</div>" + ], + "text/plain": [ + " OrganisationID OrganisationCode OrganisationType SubType \\\n", + "0 12366 F81157 Clinic UNKNOWN \n", + "1 12450 F81670 Clinic UNKNOWN \n", + "2 12461 F81681 Clinic UNKNOWN \n", + "3 17937 NAA01 Clinic UNKNOWN \n", + "4 17938 NAA02 Clinic UNKNOWN \n", + "... ... ... ... ... \n", + "19077 10617742 NBG7L Clinic UNKNOWN \n", + "19078 10617743 NFH02 Clinic UNKNOWN \n", + "19079 10617744 NFH03 Clinic UNKNOWN \n", + "19080 10617746 NEMAM Clinic UNKNOWN \n", + "19081 10617747 NFH04 Clinic UNKNOWN \n", + "\n", + " OrganisationStatus IsPimsManaged \\\n", + "0 Visible True \n", + "1 Visible True \n", + "2 Visible True \n", + "3 Visible False \n", + "4 Visible False \n", + "... ... ... \n", + "19077 Visible False \n", + "19078 Visible False \n", + "19079 Visible False \n", + "19080 Visible False \n", + "19081 Visible False \n", + "\n", + " OrganisationName Address1 \\\n", + "0 Frinton Road Medical Centre <NA> \n", + "1 Epping Close Surgery <NA> \n", + "2 Green Elms Medical Centre <NA> \n", + "3 St Chads Clinic St. Chads Drive \n", + "4 Whiston Health Centre Old Colliery Road \n", + "... ... ... \n", + "19077 Sunnybank Medical Centre Town Gate \n", + "19078 Somerset Surgical Services At Mendip Vale Medi... Pudding Pie Lane \n", + "19079 Somerset Surgical Services At Axbridge Medical... Houlgate Way \n", + "19080 Lawton House Surgery Bromley Road \n", + "19081 Somerset Surgical Services At Wedmore Medical ... St. Medard Road \n", + "\n", + " Address2 Address3 City \\\n", + "0 68 Frinton Road, Hollond-on-Sea <NA> Clacton-on-Sea \n", + "1 Epping Close <NA> Clacton on Sea \n", + "2 32 Crossways <NA> Jaywick, Clacton on Sea \n", + "3 <NA> <NA> Liverpool \n", + "4 Whiston <NA> Prescot \n", + "... ... ... ... \n", + "19077 Wyke <NA> Bradford \n", + "19078 Langford <NA> Bristol \n", + "19079 <NA> <NA> Axbridge \n", + "19080 <NA> <NA> Congleton \n", + "19081 <NA> <NA> Wedmore \n", + "\n", + " County Postcode Latitude Longitude Phone Email Website \\\n", + "0 <NA> CO15 5UW 51.804237 1.186376 <NA> <NA> <NA> \n", + "1 <NA> CO15 4UZ 51.815262 1.154707 01255 222668 <NA> <NA> \n", + "2 <NA> CO15 2NB 51.780621 1.117907 <NA> <NA> <NA> \n", + "3 Merseyside L32 8RE 53.482368 -2.885404 <NA> <NA> <NA> \n", + "4 Merseyside L35 3SX 53.415630 -2.800874 <NA> <NA> <NA> \n", + "... ... ... ... ... ... ... ... \n", + "19077 <NA> BD12 9NG 53.737923 -1.767982 <NA> <NA> <NA> \n", + "19078 <NA> BS40 5EL 51.341579 -2.784808 <NA> <NA> <NA> \n", + "19079 <NA> BS26 2BJ 51.286285 -2.820388 <NA> <NA> <NA> \n", + "19080 <NA> CW12 1QG 53.163048 -2.205781 <NA> <NA> <NA> \n", + "19081 <NA> BS28 4AY 51.227219 -2.816258 <NA> <NA> <NA> \n", + "\n", + " Fax \n", + "0 <NA> \n", + "1 <NA> \n", + "2 <NA> \n", + "3 <NA> \n", + "4 <NA> \n", + "... ... \n", + "19077 <NA> \n", + "19078 <NA> \n", + "19079 <NA> \n", + "19080 <NA> \n", + "19081 <NA> \n", + "\n", + "[19082 rows x 19 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clinics_df = pd.read_csv('./data/clinics.csv')\n", + "clinics_df" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>OrganisationID</th>\n", + " <th>OrganisationCode</th>\n", + " <th>OrganisationType</th>\n", + " <th>SubType</th>\n", + " <th>Sector</th>\n", + " <th>OrganisationStatus</th>\n", + " <th>IsPimsManaged</th>\n", + " <th>OrganisationName</th>\n", + " <th>Address1</th>\n", + " <th>Address2</th>\n", + " <th>...</th>\n", + " <th>County</th>\n", + " <th>Postcode</th>\n", + " <th>Latitude</th>\n", + " <th>Longitude</th>\n", + " <th>ParentODSCode</th>\n", + " <th>ParentName</th>\n", + " <th>Phone</th>\n", + " <th>Email</th>\n", + " <th>Website</th>\n", + " <th>Fax</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>17970</td>\n", + " <td>NDA07</td>\n", + " <td>Hospital</td>\n", + " <td>Hospital</td>\n", + " <td>Independent Sector</td>\n", + " <td>Visible</td>\n", 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<td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>18102</td>\n", + " <td>NLT02</td>\n", + " <td>Hospital</td>\n", + " <td>Hospital</td>\n", + " <td>NHS Sector</td>\n", + " <td>Visible</td>\n", + " <td>True</td>\n", + " <td>North Somerset Community Hospital</td>\n", + " <td>North Somerset Community Hospital</td>\n", + " <td>Old Street</td>\n", + " <td>...</td>\n", + " <td>Avon</td>\n", + " <td>BS21 6BS</td>\n", + " <td>51.437195</td>\n", + " <td>-2.847193</td>\n", + " <td>NLT</td>\n", + " <td>North Somerset Community Partnership Community...</td>\n", + " <td>01275 872212</td>\n", + " <td><NA></td>\n", + " <td>http://www.nscphealth.co.uk</td>\n", + " <td><NA></td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>18138</td>\n", + " <td>NMP01</td>\n", + " <td>Hospital</td>\n", + " <td>Hospital</td>\n", + " <td>Independent Sector</td>\n", + " <td>Visible</td>\n", + " <td>False</td>\n", + " <td>Bridgewater 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<td>reception_kneesworthhouse@partnershipsincare.c...</td>\n", + " <td>www.partnershipsincare.co.uk</td>\n", + " <td><NA></td>\n", + " </tr>\n", + " <tr>\n", + " <th>...</th>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " </tr>\n", + " <tr>\n", + " <th>19077</th>\n", + " <td>10617742</td>\n", + " <td>NBG7L</td>\n", + " <td>Clinic</td>\n", + " <td>UNKNOWN</td>\n", + " <td><NA></td>\n", + " <td>Visible</td>\n", + " <td>False</td>\n", + " <td>Sunnybank Medical Centre</td>\n", + " <td>Town Gate</td>\n", + " <td>Wyke</td>\n", + " <td>...</td>\n", + " <td><NA></td>\n", + " <td>BD12 9NG</td>\n", + " <td>53.737923</td>\n", + " <td>-1.767982</td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " </tr>\n", + " <tr>\n", + " <th>19078</th>\n", + " <td>10617743</td>\n", + " <td>NFH02</td>\n", + " <td>Clinic</td>\n", + " <td>UNKNOWN</td>\n", + " <td><NA></td>\n", + " <td>Visible</td>\n", + " <td>False</td>\n", + " <td>Somerset Surgical Services At Mendip Vale Medi...</td>\n", + " <td>Pudding Pie Lane</td>\n", + " <td>Langford</td>\n", + " <td>...</td>\n", + " <td><NA></td>\n", + " <td>BS40 5EL</td>\n", + " <td>51.341579</td>\n", + " <td>-2.784808</td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " </tr>\n", + " <tr>\n", + " <th>19079</th>\n", + " <td>10617744</td>\n", + " <td>NFH03</td>\n", + " <td>Clinic</td>\n", + " <td>UNKNOWN</td>\n", + " <td><NA></td>\n", + " <td>Visible</td>\n", + " <td>False</td>\n", + " <td>Somerset Surgical Services At Axbridge Medical...</td>\n", + " <td>Houlgate Way</td>\n", + " <td><NA></td>\n", + " <td>...</td>\n", + " <td><NA></td>\n", + " <td>BS26 2BJ</td>\n", + " <td>51.286285</td>\n", + " <td>-2.820388</td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " </tr>\n", + " <tr>\n", + " <th>19080</th>\n", + " <td>10617746</td>\n", + " <td>NEMAM</td>\n", + " <td>Clinic</td>\n", + " <td>UNKNOWN</td>\n", + " <td><NA></td>\n", + " <td>Visible</td>\n", + " <td>False</td>\n", + " <td>Lawton House Surgery</td>\n", + " <td>Bromley Road</td>\n", + " <td><NA></td>\n", + " <td>...</td>\n", + " <td><NA></td>\n", + " <td>CW12 1QG</td>\n", + " <td>53.163048</td>\n", + " <td>-2.205781</td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " </tr>\n", + " <tr>\n", + " <th>19081</th>\n", + " <td>10617747</td>\n", + " <td>NFH04</td>\n", + " <td>Clinic</td>\n", + " <td>UNKNOWN</td>\n", + " <td><NA></td>\n", + " <td>Visible</td>\n", + " <td>False</td>\n", + " <td>Somerset Surgical Services At Wedmore Medical ...</td>\n", + " <td>St. Medard Road</td>\n", + " <td><NA></td>\n", + " <td>...</td>\n", + " <td><NA></td>\n", + " <td>BS28 4AY</td>\n", + " <td>51.227219</td>\n", + " <td>-2.816258</td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "<p>20311 rows × 22 columns</p>\n", + "</div>" + ], + "text/plain": [ + " OrganisationID OrganisationCode OrganisationType SubType \\\n", + "0 17970 NDA07 Hospital Hospital \n", + "1 17981 NDA18 Hospital Hospital \n", + "2 18102 NLT02 Hospital Hospital \n", + "3 18138 NMP01 Hospital Hospital \n", + "4 18142 NMV01 Hospital Hospital \n", + "... ... ... ... ... \n", + "19077 10617742 NBG7L Clinic UNKNOWN \n", + "19078 10617743 NFH02 Clinic UNKNOWN \n", + "19079 10617744 NFH03 Clinic UNKNOWN \n", + "19080 10617746 NEMAM Clinic UNKNOWN \n", + "19081 10617747 NFH04 Clinic UNKNOWN \n", + "\n", + " Sector OrganisationStatus IsPimsManaged \\\n", + "0 Independent Sector Visible True \n", + "1 Independent Sector Visible True \n", + "2 NHS Sector Visible True \n", + "3 Independent Sector Visible False \n", + "4 Independent Sector Visible True \n", + "... ... ... ... \n", + "19077 <NA> Visible False \n", + "19078 <NA> Visible False \n", + "19079 <NA> Visible False \n", + "19080 <NA> Visible False \n", + "19081 <NA> Visible False \n", + "\n", + " OrganisationName \\\n", + "0 Walton Community Hospital - Virgin Care Servic... \n", + "1 Woking Community Hospital (Virgin Care) \n", + "2 North Somerset Community Hospital \n", + "3 Bridgewater Hospital \n", + "4 Kneesworth House \n", + "... ... \n", + "19077 Sunnybank Medical Centre \n", + "19078 Somerset Surgical Services At Mendip Vale Medi... \n", + "19079 Somerset Surgical Services At Axbridge Medical... \n", + "19080 Lawton House Surgery \n", + "19081 Somerset Surgical Services At Wedmore Medical ... \n", + "\n", + " Address1 Address2 ... \\\n", + "0 <NA> Rodney Road ... \n", + "1 <NA> Heathside Road ... \n", + "2 North Somerset Community Hospital Old Street ... \n", + "3 120 Princess Road <NA> ... \n", + "4 Old North Road Bassingbourn ... \n", + "... ... ... ... \n", + "19077 Town Gate Wyke ... \n", + "19078 Pudding Pie Lane Langford ... \n", + "19079 Houlgate Way <NA> ... \n", + "19080 Bromley Road <NA> ... \n", + "19081 St. Medard Road <NA> ... \n", + "\n", + " County Postcode Latitude Longitude ParentODSCode \\\n", + "0 Surrey KT12 3LD 51.379997 -0.406042 NDA \n", + "1 Surrey GU22 7HS 51.315132 -0.556289 NDA \n", + "2 Avon BS21 6BS 51.437195 -2.847193 NLT \n", + "3 Greater Manchester M15 5AT 53.459743 -2.245469 NMP \n", + "4 <NA> SG8 5JP 52.078121 -0.030604 NMV \n", + "... ... ... ... ... ... \n", + "19077 <NA> BD12 9NG 53.737923 -1.767982 <NA> \n", + "19078 <NA> BS40 5EL 51.341579 -2.784808 <NA> \n", + "19079 <NA> BS26 2BJ 51.286285 -2.820388 <NA> \n", + "19080 <NA> CW12 1QG 53.163048 -2.205781 <NA> \n", + "19081 <NA> BS28 4AY 51.227219 -2.816258 <NA> \n", + "\n", + " ParentName Phone \\\n", + "0 Virgin Care Services Ltd 01932 414205 \n", + "1 Virgin Care Services Ltd 01483 715911 \n", + "2 North Somerset Community Partnership Community... 01275 872212 \n", + "3 Bridgewater Hospital (Manchester) Ltd 0161 2270000 \n", + "4 Partnerships In Care Ltd 01763 255 700 \n", + "... ... ... \n", + "19077 <NA> <NA> \n", + "19078 <NA> <NA> \n", + "19079 <NA> <NA> \n", + "19080 <NA> <NA> \n", + "19081 <NA> <NA> \n", + "\n", + " Email \\\n", + "0 <NA> \n", + "1 <NA> \n", + "2 <NA> \n", + "3 <NA> \n", + "4 reception_kneesworthhouse@partnershipsincare.c... \n", + "... ... \n", + "19077 <NA> \n", + "19078 <NA> \n", + "19079 <NA> \n", + "19080 <NA> \n", + "19081 <NA> \n", + "\n", + " Website Fax \n", + "0 <NA> 01932 253674 \n", + "1 <NA> <NA> \n", + "2 http://www.nscphealth.co.uk <NA> \n", + "3 www.bridgewaterhospital.com <NA> \n", + "4 www.partnershipsincare.co.uk <NA> \n", + "... ... ... \n", + "19077 <NA> <NA> \n", + "19078 <NA> <NA> \n", + "19079 <NA> <NA> \n", + "19080 <NA> <NA> \n", + "19081 <NA> <NA> \n", + "\n", + "[20311 rows x 22 columns]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "all_med = pd.concat([hospitals_df, clinics_df])\n", + "all_med" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since we will be using the coordinates of those facilities, keep only those rows that are non-null in both `Latitude` and `Longitude`.\n", + "\n", + "**Tip**: Reset the index of `all_med` with `.reset_index(drop=True)`. " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>OrganisationID</th>\n", + " <th>OrganisationCode</th>\n", + " <th>OrganisationType</th>\n", + " <th>SubType</th>\n", + " <th>Sector</th>\n", + " <th>OrganisationStatus</th>\n", + " <th>IsPimsManaged</th>\n", + " <th>OrganisationName</th>\n", + " <th>Address1</th>\n", + " <th>Address2</th>\n", + " <th>...</th>\n", + " <th>County</th>\n", + " <th>Postcode</th>\n", + " <th>Latitude</th>\n", + " <th>Longitude</th>\n", + " <th>ParentODSCode</th>\n", + " <th>ParentName</th>\n", + " <th>Phone</th>\n", + " <th>Email</th>\n", + " <th>Website</th>\n", + " <th>Fax</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>17970</td>\n", + " <td>NDA07</td>\n", + " <td>Hospital</td>\n", + " <td>Hospital</td>\n", + " <td>Independent Sector</td>\n", + " <td>Visible</td>\n", + " <td>True</td>\n", + " <td>Walton Community Hospital - Virgin Care Servic...</td>\n", + " <td><NA></td>\n", + " <td>Rodney Road</td>\n", + " <td>...</td>\n", + " <td>Surrey</td>\n", + " <td>KT12 3LD</td>\n", + " <td>51.379997</td>\n", + " <td>-0.406042</td>\n", + " <td>NDA</td>\n", + " <td>Virgin Care Services Ltd</td>\n", + " <td>01932 414205</td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td>01932 253674</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>17981</td>\n", + " <td>NDA18</td>\n", + " <td>Hospital</td>\n", + " <td>Hospital</td>\n", + " <td>Independent Sector</td>\n", + " <td>Visible</td>\n", + " <td>True</td>\n", + " <td>Woking Community Hospital (Virgin Care)</td>\n", + " <td><NA></td>\n", + " <td>Heathside Road</td>\n", + " <td>...</td>\n", + " <td>Surrey</td>\n", + " <td>GU22 7HS</td>\n", + " <td>51.315132</td>\n", + " <td>-0.556289</td>\n", + " <td>NDA</td>\n", + " <td>Virgin Care Services Ltd</td>\n", + " <td>01483 715911</td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>18102</td>\n", + " <td>NLT02</td>\n", + " <td>Hospital</td>\n", + " <td>Hospital</td>\n", + " <td>NHS Sector</td>\n", + " <td>Visible</td>\n", + " <td>True</td>\n", + " <td>North Somerset Community Hospital</td>\n", + " <td>North Somerset Community Hospital</td>\n", + " <td>Old Street</td>\n", + " <td>...</td>\n", + " <td>Avon</td>\n", + " <td>BS21 6BS</td>\n", + " <td>51.437195</td>\n", + " <td>-2.847193</td>\n", + " <td>NLT</td>\n", + " <td>North Somerset Community Partnership Community...</td>\n", + " <td>01275 872212</td>\n", + " <td><NA></td>\n", + " <td>http://www.nscphealth.co.uk</td>\n", + " <td><NA></td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>18138</td>\n", + " <td>NMP01</td>\n", + " <td>Hospital</td>\n", + " <td>Hospital</td>\n", + " <td>Independent Sector</td>\n", + " <td>Visible</td>\n", + " <td>False</td>\n", + " <td>Bridgewater Hospital</td>\n", + " <td>120 Princess Road</td>\n", + " <td><NA></td>\n", + " <td>...</td>\n", + " <td>Greater Manchester</td>\n", + " <td>M15 5AT</td>\n", + " <td>53.459743</td>\n", + " <td>-2.245469</td>\n", + " <td>NMP</td>\n", + " <td>Bridgewater Hospital (Manchester) Ltd</td>\n", + " <td>0161 2270000</td>\n", + " <td><NA></td>\n", + " <td>www.bridgewaterhospital.com</td>\n", + " <td><NA></td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>18142</td>\n", + " <td>NMV01</td>\n", + " <td>Hospital</td>\n", + " <td>Hospital</td>\n", + " <td>Independent Sector</td>\n", + " <td>Visible</td>\n", + " <td>True</td>\n", + " <td>Kneesworth House</td>\n", + " <td>Old North Road</td>\n", + " <td>Bassingbourn</td>\n", + " <td>...</td>\n", + " <td><NA></td>\n", + " <td>SG8 5JP</td>\n", + " <td>52.078121</td>\n", + " <td>-0.030604</td>\n", + " <td>NMV</td>\n", + " <td>Partnerships In Care Ltd</td>\n", + " <td>01763 255 700</td>\n", + " <td>reception_kneesworthhouse@partnershipsincare.c...</td>\n", + " <td>www.partnershipsincare.co.uk</td>\n", + " <td><NA></td>\n", + " </tr>\n", + " <tr>\n", + " <th>...</th>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " </tr>\n", + " <tr>\n", + " <th>20296</th>\n", + " <td>10617742</td>\n", + " <td>NBG7L</td>\n", + " <td>Clinic</td>\n", + " <td>UNKNOWN</td>\n", + " <td><NA></td>\n", + " <td>Visible</td>\n", + " <td>False</td>\n", + " <td>Sunnybank Medical Centre</td>\n", + " <td>Town Gate</td>\n", + " <td>Wyke</td>\n", + " <td>...</td>\n", + " <td><NA></td>\n", + " <td>BD12 9NG</td>\n", + " <td>53.737923</td>\n", + " <td>-1.767982</td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " </tr>\n", + " <tr>\n", + " <th>20297</th>\n", + " <td>10617743</td>\n", + " <td>NFH02</td>\n", + " <td>Clinic</td>\n", + " <td>UNKNOWN</td>\n", + " <td><NA></td>\n", + " <td>Visible</td>\n", + " <td>False</td>\n", + " <td>Somerset Surgical Services At Mendip Vale Medi...</td>\n", + " <td>Pudding Pie Lane</td>\n", + " <td>Langford</td>\n", + " <td>...</td>\n", + " <td><NA></td>\n", + " <td>BS40 5EL</td>\n", + " <td>51.341579</td>\n", + " <td>-2.784808</td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " </tr>\n", + " <tr>\n", + " <th>20298</th>\n", + " <td>10617744</td>\n", + " <td>NFH03</td>\n", + " <td>Clinic</td>\n", + " <td>UNKNOWN</td>\n", + " <td><NA></td>\n", + " <td>Visible</td>\n", + " <td>False</td>\n", + " <td>Somerset Surgical Services At Axbridge Medical...</td>\n", + " <td>Houlgate Way</td>\n", + " <td><NA></td>\n", + " <td>...</td>\n", + " <td><NA></td>\n", + " <td>BS26 2BJ</td>\n", + " <td>51.286285</td>\n", + " <td>-2.820388</td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " </tr>\n", + " <tr>\n", + " <th>20299</th>\n", + " <td>10617746</td>\n", + " <td>NEMAM</td>\n", + " <td>Clinic</td>\n", + " <td>UNKNOWN</td>\n", + " <td><NA></td>\n", + " <td>Visible</td>\n", + " <td>False</td>\n", + " <td>Lawton House Surgery</td>\n", + " <td>Bromley Road</td>\n", + " <td><NA></td>\n", + " <td>...</td>\n", + " <td><NA></td>\n", + " <td>CW12 1QG</td>\n", + " <td>53.163048</td>\n", + " <td>-2.205781</td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " </tr>\n", + " <tr>\n", + " <th>20300</th>\n", + " <td>10617747</td>\n", + " <td>NFH04</td>\n", + " <td>Clinic</td>\n", + " <td>UNKNOWN</td>\n", + " <td><NA></td>\n", + " <td>Visible</td>\n", + " <td>False</td>\n", + " <td>Somerset Surgical Services At Wedmore Medical ...</td>\n", + " <td>St. Medard Road</td>\n", + " <td><NA></td>\n", + " <td>...</td>\n", + " <td><NA></td>\n", + " <td>BS28 4AY</td>\n", + " <td>51.227219</td>\n", + " <td>-2.816258</td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "<p>20301 rows × 22 columns</p>\n", + "</div>" + ], + "text/plain": [ + " OrganisationID OrganisationCode OrganisationType SubType \\\n", + "0 17970 NDA07 Hospital Hospital \n", + "1 17981 NDA18 Hospital Hospital \n", + "2 18102 NLT02 Hospital Hospital \n", + "3 18138 NMP01 Hospital Hospital \n", + "4 18142 NMV01 Hospital Hospital \n", + "... ... ... ... ... \n", + "20296 10617742 NBG7L Clinic UNKNOWN \n", + "20297 10617743 NFH02 Clinic UNKNOWN \n", + "20298 10617744 NFH03 Clinic UNKNOWN \n", + "20299 10617746 NEMAM Clinic UNKNOWN \n", + "20300 10617747 NFH04 Clinic UNKNOWN \n", + "\n", + " Sector OrganisationStatus IsPimsManaged \\\n", + "0 Independent Sector Visible True \n", + "1 Independent Sector Visible True \n", + "2 NHS Sector Visible True \n", + "3 Independent Sector Visible False \n", + "4 Independent Sector Visible True \n", + "... ... ... ... \n", + "20296 <NA> Visible False \n", + "20297 <NA> Visible False \n", + "20298 <NA> Visible False \n", + "20299 <NA> Visible False \n", + "20300 <NA> Visible False \n", + "\n", + " OrganisationName \\\n", + "0 Walton Community Hospital - Virgin Care Servic... \n", + "1 Woking Community Hospital (Virgin Care) \n", + "2 North Somerset Community Hospital \n", + "3 Bridgewater Hospital \n", + "4 Kneesworth House \n", + "... ... \n", + "20296 Sunnybank Medical Centre \n", + "20297 Somerset Surgical Services At Mendip Vale Medi... \n", + "20298 Somerset Surgical Services At Axbridge Medical... \n", + "20299 Lawton House Surgery \n", + "20300 Somerset Surgical Services At Wedmore Medical ... \n", + "\n", + " Address1 Address2 ... \\\n", + "0 <NA> Rodney Road ... \n", + "1 <NA> Heathside Road ... \n", + "2 North Somerset Community Hospital Old Street ... \n", + "3 120 Princess Road <NA> ... \n", + "4 Old North Road Bassingbourn ... \n", + "... ... ... ... \n", + "20296 Town Gate Wyke ... \n", + "20297 Pudding Pie Lane Langford ... \n", + "20298 Houlgate Way <NA> ... \n", + "20299 Bromley Road <NA> ... \n", + "20300 St. Medard Road <NA> ... \n", + "\n", + " County Postcode Latitude Longitude ParentODSCode \\\n", + "0 Surrey KT12 3LD 51.379997 -0.406042 NDA \n", + "1 Surrey GU22 7HS 51.315132 -0.556289 NDA \n", + "2 Avon BS21 6BS 51.437195 -2.847193 NLT \n", + "3 Greater Manchester M15 5AT 53.459743 -2.245469 NMP \n", + "4 <NA> SG8 5JP 52.078121 -0.030604 NMV \n", + "... ... ... ... ... ... \n", + "20296 <NA> BD12 9NG 53.737923 -1.767982 <NA> \n", + "20297 <NA> BS40 5EL 51.341579 -2.784808 <NA> \n", + "20298 <NA> BS26 2BJ 51.286285 -2.820388 <NA> \n", + "20299 <NA> CW12 1QG 53.163048 -2.205781 <NA> \n", + "20300 <NA> BS28 4AY 51.227219 -2.816258 <NA> \n", + "\n", + " ParentName Phone \\\n", + "0 Virgin Care Services Ltd 01932 414205 \n", + "1 Virgin Care Services Ltd 01483 715911 \n", + "2 North Somerset Community Partnership Community... 01275 872212 \n", + "3 Bridgewater Hospital (Manchester) Ltd 0161 2270000 \n", + "4 Partnerships In Care Ltd 01763 255 700 \n", + "... ... ... \n", + "20296 <NA> <NA> \n", + "20297 <NA> <NA> \n", + "20298 <NA> <NA> \n", + "20299 <NA> <NA> \n", + "20300 <NA> <NA> \n", + "\n", + " Email \\\n", + "0 <NA> \n", + "1 <NA> \n", + "2 <NA> \n", + "3 <NA> \n", + "4 reception_kneesworthhouse@partnershipsincare.c... \n", + "... ... \n", + "20296 <NA> \n", + "20297 <NA> \n", + "20298 <NA> \n", + "20299 <NA> \n", + "20300 <NA> \n", + "\n", + " Website Fax \n", + "0 <NA> 01932 253674 \n", + "1 <NA> <NA> \n", + "2 http://www.nscphealth.co.uk <NA> \n", + "3 www.bridgewaterhospital.com <NA> \n", + "4 www.partnershipsincare.co.uk <NA> \n", + "... ... ... \n", + "20296 <NA> <NA> \n", + "20297 <NA> <NA> \n", + "20298 <NA> <NA> \n", + "20299 <NA> <NA> \n", + "20300 <NA> <NA> \n", + "\n", + "[20301 rows x 22 columns]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "all_med = all_med.dropna(subset=['Latitude', 'Longitude'])\n", + "\n", + "all_med = all_med.reset_index(drop=True)\n", + "\n", + "all_med " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Make Grid Coordinates for Medical Facilities" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Provided for you in the next cell (which you can expand by clicking on the \"...\", and contract again after executing by clicking on the blue left border of the cell) is the lat/long to grid coordinates converter you have used earlier in the workshop. Use this converter to create grid coordinate values stored in `northing` and `easting` columns of the `all_med` DataFrame you created in the last step." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# https://www.ordnancesurvey.co.uk/docs/support/guide-coordinate-systems-great-britain.pdf\n", + "\n", + "def latlong2osgbgrid_cupy(lat, long, input_degrees=True):\n", + " '''\n", + " Converts latitude and longitude (ellipsoidal) coordinates into northing and easting (grid) coordinates, using a Transverse Mercator projection.\n", + " \n", + " Inputs:\n", + " lat: latitude coordinate (N)\n", + " long: longitude coordinate (E)\n", + " input_degrees: if True (default), interprets the coordinates as degrees; otherwise, interprets coordinates as radians\n", + " \n", + " Output:\n", + " (northing, easting)\n", + " '''\n", + " \n", + " if input_degrees:\n", + " lat = lat * cp.pi/180\n", + " long = long * cp.pi/180\n", + "\n", + " a = 6377563.396\n", + " b = 6356256.909\n", + " e2 = (a**2 - b**2) / a**2\n", + "\n", + " N0 = -100000 # northing of true origin\n", + " E0 = 400000 # easting of true origin\n", + " F0 = .9996012717 # scale factor on central meridian\n", + " phi0 = 49 * cp.pi / 180 # latitude of true origin\n", + " lambda0 = -2 * cp.pi / 180 # longitude of true origin and central meridian\n", + " \n", + " sinlat = cp.sin(lat)\n", + " coslat = cp.cos(lat)\n", + " tanlat = cp.tan(lat)\n", + " \n", + " latdiff = lat-phi0\n", + " longdiff = long-lambda0\n", + "\n", + " n = (a-b) / (a+b)\n", + " nu = a * F0 * (1 - e2 * sinlat ** 2) ** -.5\n", + " rho = a * F0 * (1 - e2) * (1 - e2 * sinlat ** 2) ** -1.5\n", + " eta2 = nu / rho - 1\n", + " M = b * F0 * ((1 + n + 5/4 * (n**2 + n**3)) * latdiff - \n", + " (3*(n+n**2) + 21/8 * n**3) * cp.sin(latdiff) * cp.cos(lat+phi0) +\n", + " 15/8 * (n**2 + n**3) * cp.sin(2*(latdiff)) * cp.cos(2*(lat+phi0)) - \n", + " 35/24 * n**3 * cp.sin(3*(latdiff)) * cp.cos(3*(lat+phi0)))\n", + " I = M + N0\n", + " II = nu/2 * sinlat * coslat\n", + " III = nu/24 * sinlat * coslat ** 3 * (5 - tanlat ** 2 + 9 * eta2)\n", + " IIIA = nu/720 * sinlat * coslat ** 5 * (61-58 * tanlat**2 + tanlat**4)\n", + " IV = nu * coslat\n", + " V = nu / 6 * coslat**3 * (nu/rho - cp.tan(lat)**2)\n", + " VI = nu / 120 * coslat ** 5 * (5 - 18 * tanlat**2 + tanlat**4 + 14 * eta2 - 58 * tanlat**2 * eta2)\n", + "\n", + " northing = I + II * longdiff**2 + III * longdiff**4 + IIIA * longdiff**6\n", + " easting = E0 + IV * longdiff + V * longdiff**3 + VI * longdiff**5\n", + "\n", + " return(northing, easting)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>OrganisationID</th>\n", + " <th>OrganisationCode</th>\n", + " <th>OrganisationType</th>\n", + " <th>SubType</th>\n", + " <th>Sector</th>\n", + " <th>OrganisationStatus</th>\n", + " <th>IsPimsManaged</th>\n", + " <th>OrganisationName</th>\n", + " <th>Address1</th>\n", + " <th>Address2</th>\n", + " <th>...</th>\n", + " <th>Latitude</th>\n", + " <th>Longitude</th>\n", + " <th>ParentODSCode</th>\n", + " <th>ParentName</th>\n", + " <th>Phone</th>\n", + " <th>Email</th>\n", + " <th>Website</th>\n", + " <th>Fax</th>\n", + " <th>northing</th>\n", + " <th>easting</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>17970</td>\n", + " <td>NDA07</td>\n", + " <td>Hospital</td>\n", + " <td>Hospital</td>\n", + " <td>Independent Sector</td>\n", + " <td>Visible</td>\n", + " <td>True</td>\n", + " <td>Walton Community Hospital - Virgin Care Servic...</td>\n", + " <td><NA></td>\n", + " <td>Rodney Road</td>\n", + " <td>...</td>\n", + " <td>51.379997</td>\n", + " <td>-0.406042</td>\n", + " <td>NDA</td>\n", + " <td>Virgin Care Services Ltd</td>\n", + " <td>01932 414205</td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td>01932 253674</td>\n", + " <td>165810.473974</td>\n", + " <td>510917.517174</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>17981</td>\n", + " <td>NDA18</td>\n", + " <td>Hospital</td>\n", + " <td>Hospital</td>\n", + " <td>Independent Sector</td>\n", + " <td>Visible</td>\n", + " <td>True</td>\n", + " <td>Woking Community Hospital (Virgin Care)</td>\n", + " <td><NA></td>\n", + " <td>Heathside Road</td>\n", + " <td>...</td>\n", + " <td>51.315132</td>\n", + " <td>-0.556289</td>\n", + " <td>NDA</td>\n", + " <td>Virgin Care Services Ltd</td>\n", + " <td>01483 715911</td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td>158381.343420</td>\n", + " <td>500604.836652</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>18102</td>\n", + " <td>NLT02</td>\n", + " <td>Hospital</td>\n", + " <td>Hospital</td>\n", + " <td>NHS Sector</td>\n", + " <td>Visible</td>\n", + " <td>True</td>\n", + " <td>North Somerset Community Hospital</td>\n", + " <td>North Somerset Community Hospital</td>\n", + " <td>Old Street</td>\n", + " <td>...</td>\n", + " <td>51.437195</td>\n", + " <td>-2.847193</td>\n", + " <td>NLT</td>\n", + " <td>North Somerset Community Partnership Community...</td>\n", + " <td>01275 872212</td>\n", + " <td><NA></td>\n", + " <td>http://www.nscphealth.co.uk</td>\n", + " <td><NA></td>\n", + " <td>171305.775859</td>\n", + " <td>341119.365090</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>18138</td>\n", + " <td>NMP01</td>\n", + " <td>Hospital</td>\n", + " <td>Hospital</td>\n", + " <td>Independent Sector</td>\n", + " <td>Visible</td>\n", + " <td>False</td>\n", + " <td>Bridgewater Hospital</td>\n", + " <td>120 Princess Road</td>\n", + " <td><NA></td>\n", + " <td>...</td>\n", + " <td>53.459743</td>\n", + " <td>-2.245469</td>\n", + " <td>NMP</td>\n", + " <td>Bridgewater Hospital (Manchester) Ltd</td>\n", + " <td>0161 2270000</td>\n", + " <td><NA></td>\n", + " <td>www.bridgewaterhospital.com</td>\n", + " <td><NA></td>\n", + " <td>395944.561405</td>\n", + " <td>383703.600293</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>18142</td>\n", + " <td>NMV01</td>\n", + " <td>Hospital</td>\n", + " <td>Hospital</td>\n", + " <td>Independent Sector</td>\n", + " <td>Visible</td>\n", + " <td>True</td>\n", + " <td>Kneesworth House</td>\n", + " <td>Old North Road</td>\n", + " <td>Bassingbourn</td>\n", + " <td>...</td>\n", + " <td>52.078121</td>\n", + " <td>-0.030604</td>\n", + " <td>NMV</td>\n", + " <td>Partnerships In Care Ltd</td>\n", + " <td>01763 255 700</td>\n", + " <td>reception_kneesworthhouse@partnershipsincare.c...</td>\n", + " <td>www.partnershipsincare.co.uk</td>\n", + " <td><NA></td>\n", + " <td>244071.710013</td>\n", + " <td>534945.182860</td>\n", + " </tr>\n", + " <tr>\n", + " <th>...</th>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " </tr>\n", + " <tr>\n", + " <th>20296</th>\n", + " <td>10617742</td>\n", + " <td>NBG7L</td>\n", + " <td>Clinic</td>\n", + " <td>UNKNOWN</td>\n", + " <td><NA></td>\n", + " <td>Visible</td>\n", + " <td>False</td>\n", + " <td>Sunnybank Medical Centre</td>\n", + " <td>Town Gate</td>\n", + " <td>Wyke</td>\n", + " <td>...</td>\n", + " <td>53.737923</td>\n", + " <td>-1.767982</td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td>426887.040596</td>\n", + " <td>415302.519662</td>\n", + " </tr>\n", + " <tr>\n", + " <th>20297</th>\n", + " <td>10617743</td>\n", + " <td>NFH02</td>\n", + " <td>Clinic</td>\n", + " <td>UNKNOWN</td>\n", + " <td><NA></td>\n", + " <td>Visible</td>\n", + " <td>False</td>\n", + " <td>Somerset Surgical Services At Mendip Vale Medi...</td>\n", + " <td>Pudding Pie Lane</td>\n", + " <td>Langford</td>\n", + " <td>...</td>\n", + " <td>51.341579</td>\n", + " <td>-2.784808</td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td>160625.127012</td>\n", + " <td>345341.262026</td>\n", + " </tr>\n", + " <tr>\n", + " <th>20298</th>\n", + " <td>10617744</td>\n", + " <td>NFH03</td>\n", + " <td>Clinic</td>\n", + " <td>UNKNOWN</td>\n", + " <td><NA></td>\n", + " <td>Visible</td>\n", + " <td>False</td>\n", + " <td>Somerset Surgical Services At Axbridge Medical...</td>\n", + " <td>Houlgate Way</td>\n", + " <td><NA></td>\n", + " <td>...</td>\n", + " <td>51.286285</td>\n", + " <td>-2.820388</td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td>154503.665383</td>\n", + " <td>342794.591249</td>\n", + " </tr>\n", + " <tr>\n", + " <th>20299</th>\n", + " <td>10617746</td>\n", + " <td>NEMAM</td>\n", + " <td>Clinic</td>\n", + " <td>UNKNOWN</td>\n", + " <td><NA></td>\n", + " <td>Visible</td>\n", + " <td>False</td>\n", + " <td>Lawton House Surgery</td>\n", + " <td>Bromley Road</td>\n", + " <td><NA></td>\n", + " <td>...</td>\n", + " <td>53.163048</td>\n", + " <td>-2.205781</td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td>362932.494686</td>\n", + " <td>386243.408863</td>\n", + " </tr>\n", + " <tr>\n", + " <th>20300</th>\n", + " <td>10617747</td>\n", + " <td>NFH04</td>\n", + " <td>Clinic</td>\n", + " <td>UNKNOWN</td>\n", + " <td><NA></td>\n", + " <td>Visible</td>\n", + " <td>False</td>\n", + " <td>Somerset Surgical Services At Wedmore Medical ...</td>\n", + " <td>St. Medard Road</td>\n", + " <td><NA></td>\n", + " <td>...</td>\n", + " <td>51.227219</td>\n", + " <td>-2.816258</td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td>147932.429756</td>\n", + " <td>343009.593212</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "<p>20301 rows × 24 columns</p>\n", + "</div>" + ], + "text/plain": [ + " OrganisationID OrganisationCode OrganisationType SubType \\\n", + "0 17970 NDA07 Hospital Hospital \n", + "1 17981 NDA18 Hospital Hospital \n", + "2 18102 NLT02 Hospital Hospital \n", + "3 18138 NMP01 Hospital Hospital \n", + "4 18142 NMV01 Hospital Hospital \n", + "... ... ... ... ... \n", + "20296 10617742 NBG7L Clinic UNKNOWN \n", + "20297 10617743 NFH02 Clinic UNKNOWN \n", + "20298 10617744 NFH03 Clinic UNKNOWN \n", + "20299 10617746 NEMAM Clinic UNKNOWN \n", + "20300 10617747 NFH04 Clinic UNKNOWN \n", + "\n", + " Sector OrganisationStatus IsPimsManaged \\\n", + "0 Independent Sector Visible True \n", + "1 Independent Sector Visible True \n", + "2 NHS Sector Visible True \n", + "3 Independent Sector Visible False \n", + "4 Independent Sector Visible True \n", + "... ... ... ... \n", + "20296 <NA> Visible False \n", + "20297 <NA> Visible False \n", + "20298 <NA> Visible False \n", + "20299 <NA> Visible False \n", + "20300 <NA> Visible False \n", + "\n", + " OrganisationName \\\n", + "0 Walton Community Hospital - Virgin Care Servic... \n", + "1 Woking Community Hospital (Virgin Care) \n", + "2 North Somerset Community Hospital \n", + "3 Bridgewater Hospital \n", + "4 Kneesworth House \n", + "... ... \n", + "20296 Sunnybank Medical Centre \n", + "20297 Somerset Surgical Services At Mendip Vale Medi... \n", + "20298 Somerset Surgical Services At Axbridge Medical... \n", + "20299 Lawton House Surgery \n", + "20300 Somerset Surgical Services At Wedmore Medical ... \n", + "\n", + " Address1 Address2 ... Latitude \\\n", + "0 <NA> Rodney Road ... 51.379997 \n", + "1 <NA> Heathside Road ... 51.315132 \n", + "2 North Somerset Community Hospital Old Street ... 51.437195 \n", + "3 120 Princess Road <NA> ... 53.459743 \n", + "4 Old North Road Bassingbourn ... 52.078121 \n", + "... ... ... ... ... \n", + "20296 Town Gate Wyke ... 53.737923 \n", + "20297 Pudding Pie Lane Langford ... 51.341579 \n", + "20298 Houlgate Way <NA> ... 51.286285 \n", + "20299 Bromley Road <NA> ... 53.163048 \n", + "20300 St. Medard Road <NA> ... 51.227219 \n", + "\n", + " Longitude ParentODSCode \\\n", + "0 -0.406042 NDA \n", + "1 -0.556289 NDA \n", + "2 -2.847193 NLT \n", + "3 -2.245469 NMP \n", + "4 -0.030604 NMV \n", + "... ... ... \n", + "20296 -1.767982 <NA> \n", + "20297 -2.784808 <NA> \n", + "20298 -2.820388 <NA> \n", + "20299 -2.205781 <NA> \n", + "20300 -2.816258 <NA> \n", + "\n", + " ParentName Phone \\\n", + "0 Virgin Care Services Ltd 01932 414205 \n", + "1 Virgin Care Services Ltd 01483 715911 \n", + "2 North Somerset Community Partnership Community... 01275 872212 \n", + "3 Bridgewater Hospital (Manchester) Ltd 0161 2270000 \n", + "4 Partnerships In Care Ltd 01763 255 700 \n", + "... ... ... \n", + "20296 <NA> <NA> \n", + "20297 <NA> <NA> \n", + "20298 <NA> <NA> \n", + "20299 <NA> <NA> \n", + "20300 <NA> <NA> \n", + "\n", + " Email \\\n", + "0 <NA> \n", + "1 <NA> \n", + "2 <NA> \n", + "3 <NA> \n", + "4 reception_kneesworthhouse@partnershipsincare.c... \n", + "... ... \n", + "20296 <NA> \n", + "20297 <NA> \n", + "20298 <NA> \n", + "20299 <NA> \n", + "20300 <NA> \n", + "\n", + " Website Fax northing \\\n", + "0 <NA> 01932 253674 165810.473974 \n", + "1 <NA> <NA> 158381.343420 \n", + "2 http://www.nscphealth.co.uk <NA> 171305.775859 \n", + "3 www.bridgewaterhospital.com <NA> 395944.561405 \n", + "4 www.partnershipsincare.co.uk <NA> 244071.710013 \n", + "... ... ... ... \n", + "20296 <NA> <NA> 426887.040596 \n", + "20297 <NA> <NA> 160625.127012 \n", + "20298 <NA> <NA> 154503.665383 \n", + "20299 <NA> <NA> 362932.494686 \n", + "20300 <NA> <NA> 147932.429756 \n", + "\n", + " easting \n", + "0 510917.517174 \n", + "1 500604.836652 \n", + "2 341119.365090 \n", + "3 383703.600293 \n", + "4 534945.182860 \n", + "... ... \n", + "20296 415302.519662 \n", + "20297 345341.262026 \n", + "20298 342794.591249 \n", + "20299 386243.408863 \n", + "20300 343009.593212 \n", + "\n", + "[20301 rows x 24 columns]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cupy_lat = cp.asarray(all_med['Latitude'])\n", + "cupy_long = cp.asarray(all_med['Longitude'])\n", + "\n", + "all_med['northing'], all_med['easting'] = latlong2osgbgrid_cupy(cupy_lat, cupy_long)\n", + "all_med" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Find Closest Hospital or Clinic for Infected" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fit `cuml.NearestNeighbors` with `all_med`'s `northing` and `easting` values, using the named argument `n_neighbors` set to `1`, and save the model as `knn`." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<style>#sk-container-id-1 {\n", + " /* Definition of color scheme common for light and dark mode */\n", + " --sklearn-color-text: black;\n", + " --sklearn-color-line: gray;\n", + " /* Definition of color scheme for unfitted estimators */\n", + " --sklearn-color-unfitted-level-0: #fff5e6;\n", + " --sklearn-color-unfitted-level-1: #f6e4d2;\n", + " --sklearn-color-unfitted-level-2: #ffe0b3;\n", + " --sklearn-color-unfitted-level-3: chocolate;\n", + " /* Definition of color scheme for fitted estimators */\n", + " --sklearn-color-fitted-level-0: #f0f8ff;\n", + " --sklearn-color-fitted-level-1: #d4ebff;\n", + " --sklearn-color-fitted-level-2: #b3dbfd;\n", + " --sklearn-color-fitted-level-3: cornflowerblue;\n", + "\n", + " /* Specific color for light theme */\n", + " 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flex-grow: 1;\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-parallel {\n", + " display: flex;\n", + " align-items: stretch;\n", + " justify-content: center;\n", + " background-color: var(--sklearn-color-background);\n", + " position: relative;\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-parallel-item {\n", + " display: flex;\n", + " flex-direction: column;\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n", + " align-self: flex-end;\n", + " width: 50%;\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n", + " align-self: flex-start;\n", + " width: 50%;\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n", + " width: 0;\n", + "}\n", + "\n", + "/* Serial-specific style estimator block */\n", + "\n", + "#sk-container-id-1 div.sk-serial {\n", + " display: flex;\n", + " flex-direction: column;\n", + " align-items: center;\n", + " background-color: var(--sklearn-color-background);\n", + " padding-right: 1em;\n", + " padding-left: 1em;\n", + "}\n", + "\n", + "\n", + "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n", + "clickable and can be expanded/collapsed.\n", + "- Pipeline and ColumnTransformer use this feature and define the default style\n", + "- Estimators will overwrite some part of the style using the `sk-estimator` class\n", + "*/\n", + "\n", + "/* Pipeline and ColumnTransformer style (default) */\n", + "\n", + "#sk-container-id-1 div.sk-toggleable {\n", + " /* Default theme specific background. It is overwritten whether we have a\n", + " specific estimator or a Pipeline/ColumnTransformer */\n", + " background-color: var(--sklearn-color-background);\n", + "}\n", + "\n", + "/* Toggleable label */\n", + "#sk-container-id-1 label.sk-toggleable__label {\n", + " cursor: pointer;\n", + " display: block;\n", + " width: 100%;\n", + " margin-bottom: 0;\n", + " padding: 0.5em;\n", + " box-sizing: border-box;\n", + " text-align: center;\n", + "}\n", + "\n", + "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n", + " /* Arrow on the left of the label */\n", + " content: \"▸\";\n", + " float: left;\n", + " margin-right: 0.25em;\n", + " color: var(--sklearn-color-icon);\n", + "}\n", + "\n", + "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n", + " color: var(--sklearn-color-text);\n", + "}\n", + "\n", + "/* Toggleable content - dropdown */\n", + "\n", + "#sk-container-id-1 div.sk-toggleable__content {\n", + " max-height: 0;\n", + " max-width: 0;\n", + " overflow: hidden;\n", + " text-align: left;\n", + " /* unfitted */\n", + " background-color: var(--sklearn-color-unfitted-level-0);\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-toggleable__content.fitted {\n", + " /* fitted */\n", + " background-color: var(--sklearn-color-fitted-level-0);\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-toggleable__content pre {\n", + " margin: 0.2em;\n", + " border-radius: 0.25em;\n", + " color: var(--sklearn-color-text);\n", + " /* unfitted */\n", + " background-color: var(--sklearn-color-unfitted-level-0);\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n", + " /* unfitted */\n", + " background-color: var(--sklearn-color-fitted-level-0);\n", + "}\n", + "\n", + "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n", + " /* Expand drop-down */\n", + " max-height: 200px;\n", + " max-width: 100%;\n", + " overflow: auto;\n", + "}\n", + "\n", + "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n", + " content: \"▾\";\n", + "}\n", + "\n", + "/* Pipeline/ColumnTransformer-specific style */\n", + "\n", + "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n", + " color: var(--sklearn-color-text);\n", + " background-color: var(--sklearn-color-unfitted-level-2);\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n", + " background-color: var(--sklearn-color-fitted-level-2);\n", + "}\n", + "\n", + "/* Estimator-specific style */\n", + "\n", + "/* Colorize estimator box */\n", + "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n", + " /* unfitted */\n", + " background-color: var(--sklearn-color-unfitted-level-2);\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n", + " /* fitted */\n", + " background-color: var(--sklearn-color-fitted-level-2);\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n", + "#sk-container-id-1 div.sk-label label {\n", + " /* The background is the default theme color */\n", + " color: var(--sklearn-color-text-on-default-background);\n", + "}\n", + "\n", + "/* On hover, darken the color of the background */\n", + "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n", + " color: var(--sklearn-color-text);\n", + " background-color: var(--sklearn-color-unfitted-level-2);\n", + "}\n", + "\n", + "/* Label box, darken color on hover, fitted */\n", + "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n", + " color: var(--sklearn-color-text);\n", + " background-color: var(--sklearn-color-fitted-level-2);\n", + "}\n", + "\n", + "/* Estimator label */\n", + "\n", + "#sk-container-id-1 div.sk-label label {\n", + " font-family: monospace;\n", + " font-weight: bold;\n", + " display: inline-block;\n", + " line-height: 1.2em;\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-label-container {\n", + " text-align: center;\n", + "}\n", + "\n", + "/* Estimator-specific */\n", + "#sk-container-id-1 div.sk-estimator {\n", + " font-family: monospace;\n", + " border: 1px dotted var(--sklearn-color-border-box);\n", + " border-radius: 0.25em;\n", + " box-sizing: border-box;\n", + " margin-bottom: 0.5em;\n", + " /* unfitted */\n", + " background-color: var(--sklearn-color-unfitted-level-0);\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-estimator.fitted {\n", + " /* fitted */\n", + " background-color: var(--sklearn-color-fitted-level-0);\n", + "}\n", + "\n", + "/* on hover */\n", + "#sk-container-id-1 div.sk-estimator:hover {\n", + " /* unfitted */\n", + " background-color: var(--sklearn-color-unfitted-level-2);\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-estimator.fitted:hover {\n", + " /* fitted */\n", + " background-color: var(--sklearn-color-fitted-level-2);\n", + "}\n", + "\n", + "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n", + "\n", + "/* Common style for \"i\" and \"?\" */\n", + "\n", + ".sk-estimator-doc-link,\n", + "a:link.sk-estimator-doc-link,\n", + "a:visited.sk-estimator-doc-link {\n", + " float: right;\n", + " font-size: smaller;\n", + " line-height: 1em;\n", + " font-family: monospace;\n", + " background-color: var(--sklearn-color-background);\n", + " border-radius: 1em;\n", + " height: 1em;\n", + " width: 1em;\n", + " text-decoration: none !important;\n", + " margin-left: 1ex;\n", + " /* unfitted */\n", + " border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n", + " color: var(--sklearn-color-unfitted-level-1);\n", + "}\n", + "\n", + ".sk-estimator-doc-link.fitted,\n", + "a:link.sk-estimator-doc-link.fitted,\n", + "a:visited.sk-estimator-doc-link.fitted {\n", + " /* fitted */\n", + " border: var(--sklearn-color-fitted-level-1) 1pt solid;\n", + " color: var(--sklearn-color-fitted-level-1);\n", + "}\n", + "\n", + "/* On hover */\n", + "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n", + ".sk-estimator-doc-link:hover,\n", + "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n", + ".sk-estimator-doc-link:hover {\n", + " /* unfitted */\n", + " background-color: var(--sklearn-color-unfitted-level-3);\n", + " color: var(--sklearn-color-background);\n", + " text-decoration: none;\n", + "}\n", + "\n", + "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n", + ".sk-estimator-doc-link.fitted:hover,\n", + "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n", + ".sk-estimator-doc-link.fitted:hover {\n", + " /* fitted */\n", + " background-color: var(--sklearn-color-fitted-level-3);\n", + " color: var(--sklearn-color-background);\n", + " text-decoration: none;\n", + "}\n", + "\n", + "/* Span, style for the box shown on hovering the info icon */\n", + ".sk-estimator-doc-link span {\n", + " display: none;\n", + " z-index: 9999;\n", + " position: relative;\n", + " font-weight: normal;\n", + " right: .2ex;\n", + " padding: .5ex;\n", + " margin: .5ex;\n", + " width: min-content;\n", + " min-width: 20ex;\n", + " max-width: 50ex;\n", + " color: var(--sklearn-color-text);\n", + " box-shadow: 2pt 2pt 4pt #999;\n", + " /* unfitted */\n", + " background: var(--sklearn-color-unfitted-level-0);\n", + " border: .5pt solid var(--sklearn-color-unfitted-level-3);\n", + "}\n", + "\n", + ".sk-estimator-doc-link.fitted span {\n", + " /* fitted */\n", + " background: var(--sklearn-color-fitted-level-0);\n", + " border: var(--sklearn-color-fitted-level-3);\n", + "}\n", + "\n", + ".sk-estimator-doc-link:hover span {\n", + " display: block;\n", + "}\n", + "\n", + "/* \"?\"-specific style due to the `<a>` HTML tag */\n", + "\n", + "#sk-container-id-1 a.estimator_doc_link {\n", + " float: right;\n", + " font-size: 1rem;\n", + " line-height: 1em;\n", + " font-family: monospace;\n", + " background-color: var(--sklearn-color-background);\n", + " border-radius: 1rem;\n", + " height: 1rem;\n", + " width: 1rem;\n", + " text-decoration: none;\n", + " /* unfitted */\n", + " color: var(--sklearn-color-unfitted-level-1);\n", + " border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n", + "}\n", + "\n", + "#sk-container-id-1 a.estimator_doc_link.fitted {\n", + " /* fitted */\n", + " border: var(--sklearn-color-fitted-level-1) 1pt solid;\n", + " color: var(--sklearn-color-fitted-level-1);\n", + "}\n", + "\n", + "/* On hover */\n", + "#sk-container-id-1 a.estimator_doc_link:hover {\n", + " /* unfitted */\n", + " background-color: var(--sklearn-color-unfitted-level-3);\n", + " color: var(--sklearn-color-background);\n", + " text-decoration: none;\n", + "}\n", + "\n", + "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n", + " /* fitted */\n", + " background-color: var(--sklearn-color-fitted-level-3);\n", + "}\n", + "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>NearestNeighbors()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\"> NearestNeighbors<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>NearestNeighbors()</pre></div> </div></div></div></div>" + ], + "text/plain": [ + "NearestNeighbors()" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "knn = cuml.NearestNeighbors(n_neighbors=1)\n", + "knn.fit(all_med[['northing', 'easting']])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Save every infected member in `df` into a new dataframe called `infected_df`.\n", + "\n", + "**Tip**: Reset the index of `infected_df` with `.reset_index(drop=True)`. " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>lat</th>\n", + " <th>long</th>\n", + " <th>infected</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>53.715826</td>\n", + " <td>-2.430079</td>\n", + " <td>1.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>53.664881</td>\n", + " <td>-2.425673</td>\n", + " <td>1.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>53.696765</td>\n", + " <td>-2.488940</td>\n", + " <td>1.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>53.696966</td>\n", + " <td>-2.488897</td>\n", + " <td>1.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>53.727804</td>\n", + " <td>-2.392959</td>\n", + " <td>1.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>...</th>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " </tr>\n", + " <tr>\n", + " <th>70875</th>\n", + " <td>51.662717</td>\n", + " <td>-2.926850</td>\n", + " <td>1.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>70876</th>\n", + " <td>51.599350</td>\n", + " <td>-2.959175</td>\n", + " <td>1.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>70877</th>\n", + " <td>51.543825</td>\n", + " <td>-2.822984</td>\n", + " <td>1.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>70878</th>\n", + " <td>51.562536</td>\n", + " <td>-2.879492</td>\n", + " <td>1.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>70879</th>\n", + " <td>51.628748</td>\n", + " <td>-2.837151</td>\n", + " <td>1.0</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "<p>70880 rows × 3 columns</p>\n", + "</div>" + ], + "text/plain": [ + " lat long infected\n", + "0 53.715826 -2.430079 1.0\n", + "1 53.664881 -2.425673 1.0\n", + "2 53.696765 -2.488940 1.0\n", + "3 53.696966 -2.488897 1.0\n", + "4 53.727804 -2.392959 1.0\n", + "... ... ... ...\n", + "70875 51.662717 -2.926850 1.0\n", + "70876 51.599350 -2.959175 1.0\n", + "70877 51.543825 -2.822984 1.0\n", + "70878 51.562536 -2.879492 1.0\n", + "70879 51.628748 -2.837151 1.0\n", + "\n", + "[70880 rows x 3 columns]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "infected_filter = df['infected']==1\n", + "infected_df = df[infected_filter]\n", + "infected_df.reset_index(drop=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create `northing` and `easting` values for `infected_df`." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>lat</th>\n", + " <th>long</th>\n", + " <th>infected</th>\n", + " <th>northing</th>\n", + " <th>easting</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>1346586</th>\n", + " <td>53.715826</td>\n", + " <td>-2.430079</td>\n", + " <td>1.0</td>\n", + " <td>424489.783814</td>\n", + " <td>371619.678741</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1350932</th>\n", + " <td>53.664881</td>\n", + " <td>-2.425673</td>\n", + " <td>1.0</td>\n", + " <td>418820.687944</td>\n", + " <td>371876.492369</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1352085</th>\n", + " <td>53.696765</td>\n", + " <td>-2.488940</td>\n", + " <td>1.0</td>\n", + " <td>422394.398940</td>\n", + " <td>367721.000265</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1352799</th>\n", + " <td>53.696966</td>\n", + " <td>-2.488897</td>\n", + " <td>1.0</td>\n", + " <td>422416.821887</td>\n", + " <td>367723.973098</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1357529</th>\n", + " <td>53.727804</td>\n", + " <td>-2.392959</td>\n", + " <td>1.0</td>\n", + " <td>425808.109929</td>\n", + " <td>374076.557677</td>\n", + " </tr>\n", + " <tr>\n", + " <th>...</th>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " </tr>\n", + " <tr>\n", + " <th>58459189</th>\n", + " <td>51.662717</td>\n", + " <td>-2.926850</td>\n", + " <td>1.0</td>\n", + " <td>196451.131712</td>\n", + " <td>335900.971171</td>\n", + " </tr>\n", + " <tr>\n", + " <th>58459551</th>\n", + " <td>51.599350</td>\n", + " <td>-2.959175</td>\n", + " <td>1.0</td>\n", + " <td>189433.449408</td>\n", + " <td>333573.002864</td>\n", + " </tr>\n", + " <tr>\n", + " <th>58461935</th>\n", + " <td>51.543825</td>\n", + " <td>-2.822984</td>\n", + " <td>1.0</td>\n", + " <td>183143.984735</td>\n", + " <td>342935.130365</td>\n", + " </tr>\n", + " <tr>\n", + " <th>58464082</th>\n", + " <td>51.562536</td>\n", + " <td>-2.879492</td>\n", + " <td>1.0</td>\n", + " <td>185270.296666</td>\n", + " <td>339042.062616</td>\n", + " </tr>\n", + " <tr>\n", + " <th>58464209</th>\n", + " <td>51.628748</td>\n", + " <td>-2.837151</td>\n", + " <td>1.0</td>\n", + " <td>192598.790941</td>\n", + " <td>342060.980289</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "<p>70880 rows × 5 columns</p>\n", + "</div>" + ], + "text/plain": [ + " lat long infected northing easting\n", + "1346586 53.715826 -2.430079 1.0 424489.783814 371619.678741\n", + "1350932 53.664881 -2.425673 1.0 418820.687944 371876.492369\n", + "1352085 53.696765 -2.488940 1.0 422394.398940 367721.000265\n", + "1352799 53.696966 -2.488897 1.0 422416.821887 367723.973098\n", + "1357529 53.727804 -2.392959 1.0 425808.109929 374076.557677\n", + "... ... ... ... ... ...\n", + "58459189 51.662717 -2.926850 1.0 196451.131712 335900.971171\n", + "58459551 51.599350 -2.959175 1.0 189433.449408 333573.002864\n", + "58461935 51.543825 -2.822984 1.0 183143.984735 342935.130365\n", + "58464082 51.562536 -2.879492 1.0 185270.296666 339042.062616\n", + "58464209 51.628748 -2.837151 1.0 192598.790941 342060.980289\n", + "\n", + "[70880 rows x 5 columns]" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cupy_infected_lat = cp.asarray(infected_df['lat'])\n", + "cupy_infected_long = cp.asarray(infected_df['long'])\n", + "\n", + "infected_df['northing'], infected_df['easting'] = latlong2osgbgrid_cupy(cupy_infected_lat, cupy_infected_long)\n", + "infected_df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Use `knn.kneighbors` with `n_neighbors=1` on `infected_df`'s `northing` and `easting` values. Save the return values in `distances` and `indices`." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>lat</th>\n", + " <th>long</th>\n", + " <th>infected</th>\n", + " <th>northing</th>\n", + " <th>easting</th>\n", + " <th>distance</th>\n", + " <th>closest_clinic_hospital</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>1346586</th>\n", + " <td>53.715826</td>\n", + " <td>-2.430079</td>\n", + " <td>1.0</td>\n", + " <td>424489.783814</td>\n", + " <td>371619.678741</td>\n", + " <td>2562.830078</td>\n", + " <td>18316</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1350932</th>\n", + " <td>53.664881</td>\n", + " <td>-2.425673</td>\n", + " <td>1.0</td>\n", + " <td>418820.687944</td>\n", + " <td>371876.492369</td>\n", + " <td>2988.618164</td>\n", + " <td>12816</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1352085</th>\n", + " <td>53.696765</td>\n", + " <td>-2.488940</td>\n", + " <td>1.0</td>\n", + " <td>422394.398940</td>\n", + " <td>367721.000265</td>\n", + " <td>1162.685059</td>\n", + " <td>7785</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1352799</th>\n", + " <td>53.696966</td>\n", + " <td>-2.488897</td>\n", + " <td>1.0</td>\n", + " <td>422416.821887</td>\n", + " <td>367723.973098</td>\n", + " <td>1162.283203</td>\n", + " <td>7785</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1357529</th>\n", + " <td>53.727804</td>\n", + " <td>-2.392959</td>\n", + " <td>1.0</td>\n", + " <td>425808.109929</td>\n", + " <td>374076.557677</td>\n", + " <td>1455.115601</td>\n", + " <td>4962</td>\n", + " </tr>\n", + " <tr>\n", + " <th>...</th>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " </tr>\n", + " <tr>\n", + " <th>58459189</th>\n", + " <td>51.662717</td>\n", + " <td>-2.926850</td>\n", + " <td>1.0</td>\n", + " <td>196451.131712</td>\n", + " <td>335900.971171</td>\n", + " <td>8617.324219</td>\n", + " <td>10015</td>\n", + " </tr>\n", + " <tr>\n", + " <th>58459551</th>\n", + " <td>51.599350</td>\n", + " <td>-2.959175</td>\n", + " <td>1.0</td>\n", + " <td>189433.449408</td>\n", + " <td>333573.002864</td>\n", + " <td>1306.979248</td>\n", + " <td>10015</td>\n", + " </tr>\n", + " <tr>\n", + " <th>58461935</th>\n", + " <td>51.543825</td>\n", + " <td>-2.822984</td>\n", + " <td>1.0</td>\n", + " <td>183143.984735</td>\n", + " <td>342935.130365</td>\n", + " <td>7633.295410</td>\n", + " <td>3814</td>\n", + " </tr>\n", + " <tr>\n", + " <th>58464082</th>\n", + " <td>51.562536</td>\n", + " <td>-2.879492</td>\n", + " <td>1.0</td>\n", + " <td>185270.296666</td>\n", + " <td>339042.062616</td>\n", + " <td>7071.362305</td>\n", + " <td>10015</td>\n", + " </tr>\n", + " <tr>\n", + " <th>58464209</th>\n", + " <td>51.628748</td>\n", + " <td>-2.837151</td>\n", + " <td>1.0</td>\n", + " <td>192598.790941</td>\n", + " <td>342060.980289</td>\n", + " <td>10222.643555</td>\n", + " <td>10015</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "<p>70880 rows × 7 columns</p>\n", + "</div>" + ], + "text/plain": [ + " lat long infected northing easting \\\n", + "1346586 53.715826 -2.430079 1.0 424489.783814 371619.678741 \n", + "1350932 53.664881 -2.425673 1.0 418820.687944 371876.492369 \n", + "1352085 53.696765 -2.488940 1.0 422394.398940 367721.000265 \n", + "1352799 53.696966 -2.488897 1.0 422416.821887 367723.973098 \n", + "1357529 53.727804 -2.392959 1.0 425808.109929 374076.557677 \n", + "... ... ... ... ... ... \n", + "58459189 51.662717 -2.926850 1.0 196451.131712 335900.971171 \n", + "58459551 51.599350 -2.959175 1.0 189433.449408 333573.002864 \n", + "58461935 51.543825 -2.822984 1.0 183143.984735 342935.130365 \n", + "58464082 51.562536 -2.879492 1.0 185270.296666 339042.062616 \n", + "58464209 51.628748 -2.837151 1.0 192598.790941 342060.980289 \n", + "\n", + " distance closest_clinic_hospital \n", + "1346586 2562.830078 18316 \n", + "1350932 2988.618164 12816 \n", + "1352085 1162.685059 7785 \n", + "1352799 1162.283203 7785 \n", + "1357529 1455.115601 4962 \n", + "... ... ... \n", + "58459189 8617.324219 10015 \n", + "58459551 1306.979248 10015 \n", + "58461935 7633.295410 3814 \n", + "58464082 7071.362305 10015 \n", + "58464209 10222.643555 10015 \n", + "\n", + "[70880 rows x 7 columns]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "neigh = infected_df[['northing', 'easting']]\n", + "\n", + "distances, indices = knn.kneighbors(neigh, n_neighbors=1)\n", + "infected_df['distance']=distances\n", + "infected_df['closest_clinic_hospital']=indices\n", + "infected_df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### View Closest Clinic/Hospital ###" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`indices`, returned from your use of `knn.kneighbors` immediately above, should map person indices to their closest clinic/hospital indices:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here you can print an infected individual's coordinates from `infected_df`:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "lat 53.715826\n", + "long -2.430079\n", + "infected 1.000000\n", + "northing 424489.783814\n", + "easting 371619.678741\n", + "distance 2562.830078\n", + "closest_clinic_hospital 18316.000000\n", + "Name: 1346586, dtype: float64" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "infected_df.iloc[0] # get the coords of an infected individual (in this case, individual 0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You should be able to used the mapped index for the nearest facility to see that indeed the nearest facility is at a nearby coordinate:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>OrganisationID</th>\n", + " <th>OrganisationCode</th>\n", + " <th>OrganisationType</th>\n", + " <th>SubType</th>\n", + " <th>Sector</th>\n", + " <th>OrganisationStatus</th>\n", + " <th>IsPimsManaged</th>\n", + " <th>OrganisationName</th>\n", + " <th>Address1</th>\n", + " <th>Address2</th>\n", + " <th>...</th>\n", + " <th>Latitude</th>\n", + " <th>Longitude</th>\n", + " <th>ParentODSCode</th>\n", + " <th>ParentName</th>\n", + " <th>Phone</th>\n", + " <th>Email</th>\n", + " <th>Website</th>\n", + " <th>Fax</th>\n", + " <th>northing</th>\n", + " <th>easting</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>18316</th>\n", + " <td>9445963</td>\n", + " <td>NPR0F</td>\n", + " <td>Clinic</td>\n", + " <td>UNKNOWN</td>\n", + " <td><NA></td>\n", + " <td>Visible</td>\n", + " <td>False</td>\n", + " <td>Community Dermatology Service - About Health</td>\n", + " <td>The Innovation Centre</td>\n", + " <td>1 Evolution Park</td>\n", + " <td>...</td>\n", + " <td>53.733227</td>\n", + " <td>-2.455536</td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td>426435.969251</td>\n", + " <td>369952.220236</td>\n", + " </tr>\n", + " <tr>\n", + " <th>12816</th>\n", + " <td>4209876</td>\n", + " <td>NBG84</td>\n", + " <td>Clinic</td>\n", + " <td>UNKNOWN</td>\n", + " <td><NA></td>\n", + " <td>Visible</td>\n", + " <td>False</td>\n", + " <td>Mediscan - Edgworth Medical Centre</td>\n", + " <td>354 Bolton Road</td>\n", + " <td>Edgworth</td>\n", + " <td>...</td>\n", + " <td>53.646824</td>\n", + " <td>-2.392187</td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td>416799.229665</td>\n", + " <td>374077.751691</td>\n", + " </tr>\n", + " <tr>\n", + " <th>7785</th>\n", + " <td>76801</td>\n", + " <td>NYNFD</td>\n", + " <td>Clinic</td>\n", + " <td>UNKNOWN</td>\n", + " <td><NA></td>\n", + " <td>Visible</td>\n", + " <td>False</td>\n", + " <td>Specsavers Hearcare - Lancashire - Darwen</td>\n", + " <td>Darwen Health Centre</td>\n", + " <td>James Street West</td>\n", + " <td>...</td>\n", + " <td>53.695747</td>\n", + " <td>-2.471412</td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td>422273.431827</td>\n", + " <td>368877.384334</td>\n", + " </tr>\n", + " <tr>\n", + " <th>7785</th>\n", + " <td>76801</td>\n", + " <td>NYNFD</td>\n", + " <td>Clinic</td>\n", + " <td>UNKNOWN</td>\n", + " <td><NA></td>\n", + " <td>Visible</td>\n", + " <td>False</td>\n", + " <td>Specsavers Hearcare - Lancashire - Darwen</td>\n", + " <td>Darwen Health Centre</td>\n", + " <td>James Street West</td>\n", + " <td>...</td>\n", + " <td>53.695747</td>\n", + " <td>-2.471412</td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td>422273.431827</td>\n", + " <td>368877.384334</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4962</th>\n", + " <td>65435</td>\n", + " <td>RXR57</td>\n", + " <td>Clinic</td>\n", + " <td>UNKNOWN</td>\n", + " <td><NA></td>\n", + " <td>Visible</td>\n", + " <td>False</td>\n", + " <td>Stonebridge House Surgery</td>\n", + " <td>Heys Lane</td>\n", + " <td>Oswaldtwistle</td>\n", + " <td>...</td>\n", + " <td>53.740868</td>\n", + " <td>-2.394079</td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td>427261.739714</td>\n", + " <td>374010.710918</td>\n", + " </tr>\n", + " <tr>\n", + " <th>...</th>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " </tr>\n", + " <tr>\n", + " <th>10015</th>\n", + " <td>1095623</td>\n", + " <td>NTN57</td>\n", + " <td>Clinic</td>\n", + " <td>UNKNOWN</td>\n", + " <td><NA></td>\n", + " <td>Visible</td>\n", + " <td>True</td>\n", + " <td>Priory Mount Eveswell</td>\n", + " <td><NA></td>\n", + " <td>22 Eveswell Park Road</td>\n", + " <td>...</td>\n", + " <td>51.590332</td>\n", + " <td>-2.971274</td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td>01633 271 784</td>\n", + " <td>mounteveswell@priorygroup.com</td>\n", + " <td>http://www.priorygroup.com/</td>\n", + " <td>01633 277 509</td>\n", + " <td>188441.704765</td>\n", + " <td>332721.739274</td>\n", + " </tr>\n", + " <tr>\n", + " <th>10015</th>\n", + " <td>1095623</td>\n", + " <td>NTN57</td>\n", + " <td>Clinic</td>\n", + " <td>UNKNOWN</td>\n", + " <td><NA></td>\n", + " <td>Visible</td>\n", + " <td>True</td>\n", + " <td>Priory Mount Eveswell</td>\n", + " <td><NA></td>\n", + " <td>22 Eveswell Park Road</td>\n", + " <td>...</td>\n", + " <td>51.590332</td>\n", + " <td>-2.971274</td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td>01633 271 784</td>\n", + " <td>mounteveswell@priorygroup.com</td>\n", + " <td>http://www.priorygroup.com/</td>\n", + " <td>01633 277 509</td>\n", + " <td>188441.704765</td>\n", + " <td>332721.739274</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3814</th>\n", + " <td>62305</td>\n", + " <td>NYNE8</td>\n", + " <td>Clinic</td>\n", + " <td>UNKNOWN</td>\n", + " <td><NA></td>\n", + " <td>Visible</td>\n", + " <td>False</td>\n", + " <td>Specsavers Hearcare - North Somerset - Portishead</td>\n", + " <td>7 The Precinct</td>\n", + " <td>Portishead</td>\n", + " <td>...</td>\n", + " <td>51.484352</td>\n", + " <td>-2.768061</td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td>176489.080200</td>\n", + " <td>346674.024103</td>\n", + " </tr>\n", + " <tr>\n", + " <th>10015</th>\n", + " <td>1095623</td>\n", + " <td>NTN57</td>\n", + " <td>Clinic</td>\n", + " <td>UNKNOWN</td>\n", + " <td><NA></td>\n", + " <td>Visible</td>\n", + " <td>True</td>\n", + " <td>Priory Mount Eveswell</td>\n", + " <td><NA></td>\n", + " <td>22 Eveswell Park Road</td>\n", + " <td>...</td>\n", + " <td>51.590332</td>\n", + " <td>-2.971274</td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td>01633 271 784</td>\n", + " <td>mounteveswell@priorygroup.com</td>\n", + " <td>http://www.priorygroup.com/</td>\n", + " <td>01633 277 509</td>\n", + " <td>188441.704765</td>\n", + " <td>332721.739274</td>\n", + " </tr>\n", + " <tr>\n", + " <th>10015</th>\n", + " <td>1095623</td>\n", + " <td>NTN57</td>\n", + " <td>Clinic</td>\n", + " <td>UNKNOWN</td>\n", + " <td><NA></td>\n", + " <td>Visible</td>\n", + " <td>True</td>\n", + " <td>Priory Mount Eveswell</td>\n", + " <td><NA></td>\n", + " <td>22 Eveswell Park Road</td>\n", + " <td>...</td>\n", + " <td>51.590332</td>\n", + " <td>-2.971274</td>\n", + " <td><NA></td>\n", + " <td><NA></td>\n", + " <td>01633 271 784</td>\n", + " <td>mounteveswell@priorygroup.com</td>\n", + " <td>http://www.priorygroup.com/</td>\n", + " <td>01633 277 509</td>\n", + " <td>188441.704765</td>\n", + " <td>332721.739274</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "<p>70880 rows × 24 columns</p>\n", + "</div>" + ], + "text/plain": [ + " OrganisationID OrganisationCode OrganisationType SubType Sector \\\n", + "18316 9445963 NPR0F Clinic UNKNOWN <NA> \n", + "12816 4209876 NBG84 Clinic UNKNOWN <NA> \n", + "7785 76801 NYNFD Clinic UNKNOWN <NA> \n", + "7785 76801 NYNFD Clinic UNKNOWN <NA> \n", + "4962 65435 RXR57 Clinic UNKNOWN <NA> \n", + "... ... ... ... ... ... \n", + "10015 1095623 NTN57 Clinic UNKNOWN <NA> \n", + "10015 1095623 NTN57 Clinic UNKNOWN <NA> \n", + "3814 62305 NYNE8 Clinic UNKNOWN <NA> \n", + "10015 1095623 NTN57 Clinic UNKNOWN <NA> \n", + "10015 1095623 NTN57 Clinic UNKNOWN <NA> \n", + "\n", + " OrganisationStatus IsPimsManaged \\\n", + "18316 Visible False \n", + "12816 Visible False \n", + "7785 Visible False \n", + "7785 Visible False \n", + "4962 Visible False \n", + "... ... ... \n", + "10015 Visible True \n", + "10015 Visible True \n", + "3814 Visible False \n", + "10015 Visible True \n", + "10015 Visible True \n", + "\n", + " OrganisationName \\\n", + "18316 Community Dermatology Service - About Health \n", + "12816 Mediscan - Edgworth Medical Centre \n", + "7785 Specsavers Hearcare - Lancashire - Darwen \n", + "7785 Specsavers Hearcare - Lancashire - Darwen \n", + "4962 Stonebridge House Surgery \n", + "... ... \n", + "10015 Priory Mount Eveswell \n", + "10015 Priory Mount Eveswell \n", + "3814 Specsavers Hearcare - North Somerset - Portishead \n", + "10015 Priory Mount Eveswell \n", + "10015 Priory Mount Eveswell \n", + "\n", + " Address1 Address2 ... Latitude \\\n", + "18316 The Innovation Centre 1 Evolution Park ... 53.733227 \n", + "12816 354 Bolton Road Edgworth ... 53.646824 \n", + "7785 Darwen Health Centre James Street West ... 53.695747 \n", + "7785 Darwen Health Centre James Street West ... 53.695747 \n", + "4962 Heys Lane Oswaldtwistle ... 53.740868 \n", + "... ... ... ... ... \n", + "10015 <NA> 22 Eveswell Park Road ... 51.590332 \n", + "10015 <NA> 22 Eveswell Park Road ... 51.590332 \n", + "3814 7 The Precinct Portishead ... 51.484352 \n", + "10015 <NA> 22 Eveswell Park Road ... 51.590332 \n", + "10015 <NA> 22 Eveswell Park Road ... 51.590332 \n", + "\n", + " Longitude ParentODSCode ParentName Phone \\\n", + "18316 -2.455536 <NA> <NA> <NA> \n", + "12816 -2.392187 <NA> <NA> <NA> \n", + "7785 -2.471412 <NA> <NA> <NA> \n", + "7785 -2.471412 <NA> <NA> <NA> \n", + "4962 -2.394079 <NA> <NA> <NA> \n", + "... ... ... ... ... \n", + "10015 -2.971274 <NA> <NA> 01633 271 784 \n", + "10015 -2.971274 <NA> <NA> 01633 271 784 \n", + "3814 -2.768061 <NA> <NA> <NA> \n", + "10015 -2.971274 <NA> <NA> 01633 271 784 \n", + "10015 -2.971274 <NA> <NA> 01633 271 784 \n", + "\n", + " Email Website \\\n", + "18316 <NA> <NA> \n", + "12816 <NA> <NA> \n", + "7785 <NA> <NA> \n", + "7785 <NA> <NA> \n", + "4962 <NA> <NA> \n", + "... ... ... \n", + "10015 mounteveswell@priorygroup.com http://www.priorygroup.com/ \n", + "10015 mounteveswell@priorygroup.com http://www.priorygroup.com/ \n", + "3814 <NA> <NA> \n", + "10015 mounteveswell@priorygroup.com http://www.priorygroup.com/ \n", + "10015 mounteveswell@priorygroup.com http://www.priorygroup.com/ \n", + "\n", + " Fax northing easting \n", + "18316 <NA> 426435.969251 369952.220236 \n", + "12816 <NA> 416799.229665 374077.751691 \n", + "7785 <NA> 422273.431827 368877.384334 \n", + "7785 <NA> 422273.431827 368877.384334 \n", + "4962 <NA> 427261.739714 374010.710918 \n", + "... ... ... ... \n", + "10015 01633 277 509 188441.704765 332721.739274 \n", + "10015 01633 277 509 188441.704765 332721.739274 \n", + "3814 <NA> 176489.080200 346674.024103 \n", + "10015 01633 277 509 188441.704765 332721.739274 \n", + "10015 01633 277 509 188441.704765 332721.739274 \n", + "\n", + "[70880 rows x 24 columns]" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "all_med.iloc[infected_df['closest_clinic_hospital']] # printing the entry for facility 1234 (replace with the index identified as closest to the individual)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## New Patients ##" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Use the `knn` model to find the closest clinic/hospital for the below new patients. " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>name</th>\n", + " <th>northing</th>\n", + " <th>easting</th>\n", + " <th>distance</th>\n", + " <th>closest_clinic_hospital</th>\n", + " <th>OrganisationID</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>John Smith</td>\n", + " <td>462643.228292</td>\n", + " <td>363859.758057</td>\n", + " <td>7286.685059</td>\n", + " <td>4637</td>\n", + " <td>64351</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>Greg Brown</td>\n", + " <td>409324.103047</td>\n", + " <td>464084.508506</td>\n", + " <td>1561.915405</td>\n", + " <td>3886</td>\n", + " <td>62389</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " name northing easting distance \\\n", + "0 John Smith 462643.228292 363859.758057 7286.685059 \n", + "1 Greg Brown 409324.103047 464084.508506 1561.915405 \n", + "\n", + " closest_clinic_hospital OrganisationID \n", + "0 4637 64351 \n", + "1 3886 62389 " + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# DO NOT CHANGE THIS CELL\n", + "patients=[\n", + " {\n", + " \"name\":\"John Smith\", \n", + " \"northing\":462643.2282915547,\n", + " \"easting\":363859.7580565622\n", + " }, \n", + " {\n", + " \"name\":\"Greg Brown\", \n", + " \"northing\":409324.1030472915,\n", + " \"easting\":464084.5085059871\n", + " }\n", + "]\n", + "\n", + "patients_df=pd.DataFrame(patients)\n", + "\n", + "patients_df['distance'], patients_df['closest_clinic_hospital'] = knn.kneighbors(patients_df[['northing', 'easting']])\n", + "\n", + "patients_df['OrganisationID'] = patients_df.apply(lambda x: all_med.loc[x['closest_clinic_hospital'], \"OrganisationID\"], axis=1)\n", + "\n", + "patients_df" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.10/site-packages/cudf/io/json.py:194: UserWarning: Using CPU via Pandas to write JSON dataset\n", + " warnings.warn(\"Using CPU via Pandas to write JSON dataset\")\n" + ] + } + ], + "source": [ + "patients_df.to_json('my_assessment/question_2.json', orient='records')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Check Submission ##" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[{\"name\":\"John Smith\",\"northing\":462643.2282915547,\"easting\":363859.7580565622,\"distance\":7286.6850585938,\"closest_clinic_hospital\":4637,\"OrganisationID\":64351},{\"name\":\"Greg Brown\",\"northing\":409324.1030472915,\"easting\":464084.5085059871,\"distance\":1561.9154052734,\"closest_clinic_hospital\":3886,\"OrganisationID\":62389}]" + ] + } + ], + "source": [ + "!cat my_assessment/question_2.json" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Tip**: Your submission file should contain one line of text, similar to: \n", + "\n", + "```\n", + "[{'name':'John Smith','northing':XXX.XX,'easting':XXX.XX,'closest_clinic_hospital':XX,'OrganisationID':XX},{'name':'Greg Brown','northing':XXX.XX,'easting':XXX.XX,'closest_clinic_hospital':XX,'OrganisationID':XX}]\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div align=\"center\"><h2>Please Restart the Kernel</h2></div>" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "...before moving to the next notebook." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'status': 'ok', 'restart': True}" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import IPython\n", + "app = IPython.Application.instance()\n", + "app.kernel.do_shutdown(True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<a href=\"https://www.nvidia.com/dli\"><img src=\"images/DLI_Header.png\" alt=\"Header\" style=\"width: 400px;\"/></a>" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.15" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Fundamentals_of_Accelerated_Data_Science/4-04_identify_risk_factors.ipynb b/Fundamentals_of_Accelerated_Data_Science/4-04_identify_risk_factors.ipynb new file mode 100644 index 0000000..1e632df --- /dev/null +++ b/Fundamentals_of_Accelerated_Data_Science/4-04_identify_risk_factors.ipynb @@ -0,0 +1,1412 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<a href=\"https://www.nvidia.com/dli\"><img src=\"images/DLI_Header.png\" alt=\"Header\" style=\"width: 400px;\"/></a>" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Week 3: Identify Risk Factors for Infection" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<span style=\"color:red\">\n", + "**UPDATE**\n", + "\n", + "Thank you again for the previous analysis. We will next be publishing a public health advisory that warns of specific infection risk factors of which individuals should be aware. Please advise as to which population characteristics are associated with higher infection rates. \n", + "</span>\n", + "\n", + "Your goal for this notebook will be to identify key potential demographic and economic risk factors for infection by comparing the infected and uninfected populations." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext cudf.pandas\n", + "import pandas as pd\n", + "import cuml" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Begin by loading the data you've received about week 3 of the outbreak into a cuDF-accelerated pandas DataFrame. The data is located at `./data/week3.csv`. For this notebook you will need all columns of the data." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>age</th>\n", + " <th>sex</th>\n", + " <th>employment</th>\n", + " <th>infected</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>U</td>\n", + " <td>0.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>U</td>\n", + " <td>0.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>U</td>\n", + " <td>0.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>U</td>\n", + " <td>0.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>U</td>\n", + " <td>0.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>...</th>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " </tr>\n", + " <tr>\n", + " <th>58479889</th>\n", + " <td>90</td>\n", + " <td>f</td>\n", + " <td>V</td>\n", + " <td>0.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>58479890</th>\n", + " <td>90</td>\n", + " <td>f</td>\n", + " <td>V</td>\n", + " <td>0.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>58479891</th>\n", + " <td>90</td>\n", + " <td>f</td>\n", + " <td>V</td>\n", + " <td>0.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>58479892</th>\n", + " <td>90</td>\n", + " <td>f</td>\n", + " <td>V</td>\n", + " <td>0.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>58479893</th>\n", + " <td>90</td>\n", + " <td>f</td>\n", + " <td>V</td>\n", + " <td>0.0</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "<p>58479894 rows × 4 columns</p>\n", + "</div>" + ], + "text/plain": [ + " age sex employment infected\n", + "0 0 m U 0.0\n", + "1 0 m U 0.0\n", + "2 0 m U 0.0\n", + "3 0 m U 0.0\n", + "4 0 m U 0.0\n", + "... ... .. ... ...\n", + "58479889 90 f V 0.0\n", + "58479890 90 f V 0.0\n", + "58479891 90 f V 0.0\n", + "58479892 90 f V 0.0\n", + "58479893 90 f V 0.0\n", + "\n", + "[58479894 rows x 4 columns]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.read_csv('./data/week3.csv')\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Calculate Infection Rates by Employment Code" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Convert the `infected` column to type `float32`. For people who are not infected, the float32 `infected` value should be `0.0`, and for infected people it should be `1.0`." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>age</th>\n", + " <th>sex</th>\n", + " <th>employment</th>\n", + " <th>infected</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>U</td>\n", + " <td>0.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>U</td>\n", + " <td>0.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>U</td>\n", + " <td>0.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>U</td>\n", + " <td>0.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>0</td>\n", + " <td>m</td>\n", + " <td>U</td>\n", + " <td>0.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>...</th>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " </tr>\n", + " <tr>\n", + " <th>58479889</th>\n", + " <td>90</td>\n", + " <td>f</td>\n", + " <td>V</td>\n", + " <td>0.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>58479890</th>\n", + " <td>90</td>\n", + " <td>f</td>\n", + " <td>V</td>\n", + " <td>0.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>58479891</th>\n", + " <td>90</td>\n", + " <td>f</td>\n", + " <td>V</td>\n", + " <td>0.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>58479892</th>\n", + " <td>90</td>\n", + " <td>f</td>\n", + " <td>V</td>\n", + " <td>0.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>58479893</th>\n", + " <td>90</td>\n", + " <td>f</td>\n", + " <td>V</td>\n", + " <td>0.0</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "<p>58479894 rows × 4 columns</p>\n", + "</div>" + ], + "text/plain": [ + " age sex employment infected\n", + "0 0 m U 0.0\n", + "1 0 m U 0.0\n", + "2 0 m U 0.0\n", + "3 0 m U 0.0\n", + "4 0 m U 0.0\n", + "... ... .. ... ...\n", + "58479889 90 f V 0.0\n", + "58479890 90 f V 0.0\n", + "58479891 90 f V 0.0\n", + "58479892 90 f V 0.0\n", + "58479893 90 f V 0.0\n", + "\n", + "[58479894 rows x 4 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['infected'] = df['infected'].astype('float32')\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, produce a list of employment types and their associated **rates** of infection, sorted from highest to lowest rate of infection.\n", + "\n", + "**NOTE**: The infection **rate** for each employment type should be the percentage of total individuals within an employment type who are infected. Therefore, if employment type \"X\" has 1000 people, and 10 of them are infected, the infection **rate** would be .01. If employment type \"Z\" has 10,000 people, and 50 of them are infected, the infection rate would be .005, and would be **lower** than for type \"X\", even though more people within that employment type were infected." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>total_people</th>\n", + " <th>infected_people</th>\n", + " </tr>\n", + " <tr>\n", + " <th>employment</th>\n", + " <th></th>\n", + " <th></th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>A</th>\n", + " <td>305755</td>\n", + " <td>1178.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>B, D, E</th>\n", + " <td>486785</td>\n", + " <td>1837.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>C</th>\n", + " <td>2653753</td>\n", + " <td>10301.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>F</th>\n", + " <td>2075628</td>\n", + " <td>6604.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>G</th>\n", + " <td>3549465</td>\n", + " <td>17561.0</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " total_people infected_people\n", + "employment \n", + "A 305755 1178.0\n", + "B, D, E 486785 1837.0\n", + "C 2653753 10301.0\n", + "F 2075628 6604.0\n", + "G 3549465 17561.0" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "emp_groups = df.groupby('employment').agg(\n", + " total_people=('employment', 'size'),\n", + " infected_people=('infected','sum')\n", + ")\n", + "emp_groups.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>total_people</th>\n", + " <th>infected_people</th>\n", + " <th>infection_rate</th>\n", + " </tr>\n", + " <tr>\n", + " <th>employment</th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>A</th>\n", + " <td>305755</td>\n", + " <td>1178.0</td>\n", + " <td>0.003853</td>\n", + " </tr>\n", + " <tr>\n", + " <th>B, D, E</th>\n", + " <td>486785</td>\n", + " <td>1837.0</td>\n", + " <td>0.003774</td>\n", + " </tr>\n", + " <tr>\n", + " <th>C</th>\n", + " <td>2653753</td>\n", + " <td>10301.0</td>\n", + " <td>0.003882</td>\n", + " </tr>\n", + " <tr>\n", + " <th>F</th>\n", + " <td>2075628</td>\n", + " <td>6604.0</td>\n", + " <td>0.003182</td>\n", + " </tr>\n", + " <tr>\n", + " <th>G</th>\n", + " <td>3549465</td>\n", + " <td>17561.0</td>\n", + " <td>0.004948</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " total_people infected_people infection_rate\n", + "employment \n", + "A 305755 1178.0 0.003853\n", + "B, D, E 486785 1837.0 0.003774\n", + "C 2653753 10301.0 0.003882\n", + "F 2075628 6604.0 0.003182\n", + "G 3549465 17561.0 0.004948" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "emp_groups['infection_rate'] = emp_groups['infected_people'] / emp_groups['total_people']\n", + "emp_groups.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>total_people</th>\n", + " <th>infected_people</th>\n", + " <th>infection_rate</th>\n", + " </tr>\n", + " <tr>\n", + " <th>employment</th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>Q</th>\n", + " <td>3802602</td>\n", + " <td>48505.0</td>\n", + " <td>0.012756</td>\n", + " </tr>\n", + " <tr>\n", + " <th>I</th>\n", + " <td>1556575</td>\n", + " <td>16116.0</td>\n", + " <td>0.010354</td>\n", + " </tr>\n", + " <tr>\n", + " <th>V</th>\n", + " <td>10098466</td>\n", + " <td>76648.0</td>\n", + " <td>0.007590</td>\n", + " </tr>\n", + " <tr>\n", + " <th>P</th>\n", + " <td>3006149</td>\n", + " <td>18609.0</td>\n", + " <td>0.006190</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Z</th>\n", + " <td>7161907</td>\n", + " <td>40498.0</td>\n", + " <td>0.005655</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " total_people infected_people infection_rate\n", + "employment \n", + "Q 3802602 48505.0 0.012756\n", + "I 1556575 16116.0 0.010354\n", + "V 10098466 76648.0 0.007590\n", + "P 3006149 18609.0 0.006190\n", + "Z 7161907 40498.0 0.005655" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "emp_rate_df = emp_groups.sort_values(by='infection_rate', ascending=False)\n", + "emp_rate_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# emp_groups = df[['employment', 'infected']].groupby('employment')['infected']\n", + "# emp_rate_df = emp_groups.mean().rename('infection_rate').sort_values(by='infection_rate', ascending=False)\n", + "# emp_rate_df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, read in the employment codes guide from `./data/code_guide.csv` to interpret which employment types are seeing the highest rates of infection." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>Code</th>\n", + " <th>Field</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>A</td>\n", + " <td>Agriculture, forestry & fishing</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>B, D, E</td>\n", + " <td>Mining, energy and water supply</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>C</td>\n", + " <td>Manufacturing</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>F</td>\n", + " <td>Construction</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>G</td>\n", + " <td>Wholesale, retail & repair of motor vehicles</td>\n", + " </tr>\n", + " <tr>\n", + " <th>5</th>\n", + " <td>H</td>\n", + " <td>Transport & storage</td>\n", + " </tr>\n", + " <tr>\n", + " <th>6</th>\n", + " <td>I</td>\n", + " <td>Accommodation & food services</td>\n", + " </tr>\n", + " <tr>\n", + " <th>7</th>\n", + " <td>J</td>\n", + " <td>Information & communication</td>\n", + " </tr>\n", + " <tr>\n", + " <th>8</th>\n", + " <td>K</td>\n", + " <td>Financial & insurance activities</td>\n", + " </tr>\n", + " <tr>\n", + " <th>9</th>\n", + " <td>L</td>\n", + " <td>Real estate activities</td>\n", + " </tr>\n", + " <tr>\n", + " <th>10</th>\n", + " <td>M</td>\n", + " <td>Professional, scientific & technical activities</td>\n", + " </tr>\n", + " <tr>\n", + " <th>11</th>\n", + " <td>N</td>\n", + " <td>Administrative & support services</td>\n", + " </tr>\n", + " <tr>\n", + " <th>12</th>\n", + " <td>O</td>\n", + " <td>Public admin & defence; social security</td>\n", + " </tr>\n", + " <tr>\n", + " <th>13</th>\n", + " <td>P</td>\n", + " <td>Education</td>\n", + " </tr>\n", + " <tr>\n", + " <th>14</th>\n", + " <td>Q</td>\n", + " <td>Human health & social work activities</td>\n", + " </tr>\n", + " <tr>\n", + " <th>15</th>\n", + " <td>R, S, T</td>\n", + " <td>Other services</td>\n", + " </tr>\n", + " <tr>\n", + " <th>16</th>\n", + " <td>U</td>\n", + " <td>Student</td>\n", + " </tr>\n", + " <tr>\n", + " <th>17</th>\n", + " <td>V</td>\n", + " <td>Retired</td>\n", + " </tr>\n", + " <tr>\n", + " <th>18</th>\n", + " <td>X</td>\n", + " <td>Outside the UK or not specified</td>\n", + " </tr>\n", + " <tr>\n", + " <th>19</th>\n", + " <td>Y</td>\n", + " <td>Pre-school child</td>\n", + " </tr>\n", + " <tr>\n", + " <th>20</th>\n", + " <td>Z</td>\n", + " <td>Not formally employed</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " Code Field\n", + "0 A Agriculture, forestry & fishing\n", + "1 B, D, E Mining, energy and water supply\n", + "2 C Manufacturing\n", + "3 F Construction\n", + "4 G Wholesale, retail & repair of motor vehicles\n", + "5 H Transport & storage\n", + "6 I Accommodation & food services\n", + "7 J Information & communication\n", + "8 K Financial & insurance activities\n", + "9 L Real estate activities\n", + "10 M Professional, scientific & technical activities\n", + "11 N Administrative & support services\n", + "12 O Public admin & defence; social security\n", + "13 P Education\n", + "14 Q Human health & social work activities\n", + "15 R, S, T Other services\n", + "16 U Student\n", + "17 V Retired\n", + "18 X Outside the UK or not specified\n", + "19 Y Pre-school child\n", + "20 Z Not formally employed" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "emp_codes = pd.read_csv('./data/code_guide.csv')\n", + "emp_codes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Get Top 2 Employment Type with Highest Rate of Infection ###" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we ask you to get the top two employment types that have the highest rate of infection. We start by using `.sort_values()` to sort `emp_rate_df` by the rate of infection. We then take the first 2 results. \n", + "\n", + "We will also need to index `emp_codes` to get the respeictve field name. " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>total_people</th>\n", + " <th>infected_people</th>\n", + " <th>infection_rate</th>\n", + " </tr>\n", + " <tr>\n", + " <th>employment</th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>Q</th>\n", + " <td>3802602</td>\n", + " <td>48505.0</td>\n", + " <td>0.012756</td>\n", + " </tr>\n", + " <tr>\n", + " <th>I</th>\n", + " <td>1556575</td>\n", + " <td>16116.0</td>\n", + " <td>0.010354</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " total_people infected_people infection_rate\n", + "employment \n", + "Q 3802602 48505.0 0.012756\n", + "I 1556575 16116.0 0.010354" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "top_inf_emp = emp_rate_df.sort_values(by='infection_rate', ascending=False).iloc[:2]\n", + "top_inf_emp.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "6 Accommodation & food services\n", + "14 Human health & social work activities\n", + "Name: Field, dtype: object" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "top_inf_emp = emp_rate_df.sort_values(by='infection_rate', ascending=False).iloc[:2].index\n", + "top_inf_emp_df = emp_codes.loc[emp_codes['Code'].isin(top_inf_emp), 'Field']\n", + "top_inf_emp_df" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.10/site-packages/cudf/io/json.py:194: UserWarning: Using CPU via Pandas to write JSON dataset\n", + " warnings.warn(\"Using CPU via Pandas to write JSON dataset\")\n" + ] + } + ], + "source": [ + "top_inf_emp_df.to_json('my_assessment/question_3.json', orient='records')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Calculate Infection Rates by Employment Code and Sex" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We want to see if there is an effect of `sex` on infection rate, either in addition to `employment` or confounding it. Group by both `employment` and `sex` simultaneously to get the infection rate for the intersection of those categories." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th></th>\n", + " <th>age</th>\n", + " <th>infected</th>\n", + " </tr>\n", + " <tr>\n", + " <th>employment</th>\n", + " <th>sex</th>\n", + " <th></th>\n", + " <th></th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>I</th>\n", + " <th>f</th>\n", + " <td>41.377627</td>\n", + " <td>0.015064</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Q</th>\n", + " <th>f</th>\n", + " <td>41.385400</td>\n", + " <td>0.014947</td>\n", + " </tr>\n", + " <tr>\n", + " <th>V</th>\n", + " <th>f</th>\n", + " <td>76.022214</td>\n", + " <td>0.010852</td>\n", + " </tr>\n", + " <tr>\n", + " <th>B, D, E</th>\n", + " <th>f</th>\n", + " <td>41.425618</td>\n", + " <td>0.007973</td>\n", + " </tr>\n", + " <tr>\n", + " <th>R, S, T</th>\n", + " <th>f</th>\n", + " <td>41.371672</td>\n", + " <td>0.007748</td>\n", + " </tr>\n", + " <tr>\n", + " <th>O</th>\n", + " <th>f</th>\n", + " <td>41.396246</td>\n", + " <td>0.007719</td>\n", + " </tr>\n", + " <tr>\n", + " <th>K</th>\n", + " <th>f</th>\n", + " <td>41.377495</td>\n", + " <td>0.007672</td>\n", + " </tr>\n", + " <tr>\n", + " <th>M</th>\n", + " <th>f</th>\n", + " <td>41.401898</td>\n", + " <td>0.007645</td>\n", + " </tr>\n", + " <tr>\n", + " <th>J</th>\n", + " <th>f</th>\n", + " <td>41.385772</td>\n", + " <td>0.007645</td>\n", + " </tr>\n", + " <tr>\n", + " <th>C</th>\n", + " <th>f</th>\n", + " <td>41.391365</td>\n", + " <td>0.007630</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Z</th>\n", + " <th>f</th>\n", + " <td>41.384618</td>\n", + " <td>0.007629</td>\n", + " </tr>\n", + " <tr>\n", + " <th>P</th>\n", + " <th>f</th>\n", + " <td>41.399302</td>\n", + " <td>0.007584</td>\n", + " </tr>\n", + " <tr>\n", + " <th>F</th>\n", + " <th>f</th>\n", + " <td>41.415191</td>\n", + " <td>0.007577</td>\n", + " </tr>\n", + " <tr>\n", + " <th>G</th>\n", + " <th>f</th>\n", + " <td>41.394893</td>\n", + " <td>0.007556</td>\n", + " </tr>\n", + " <tr>\n", + " <th>A</th>\n", + " <th>f</th>\n", + " <td>41.385010</td>\n", + " <td>0.007491</td>\n", + " </tr>\n", + " <tr>\n", + " <th>X</th>\n", + " <th>f</th>\n", + " <td>41.401447</td>\n", + " <td>0.007391</td>\n", + " </tr>\n", + " <tr>\n", + " <th>N</th>\n", + " <th>f</th>\n", + " <td>41.386565</td>\n", + " <td>0.007389</td>\n", + " </tr>\n", + " <tr>\n", + " <th>H</th>\n", + " <th>f</th>\n", + " <td>41.416857</td>\n", + " <td>0.007385</td>\n", + " </tr>\n", + " <tr>\n", + " <th>L</th>\n", + " <th>f</th>\n", + " <td>41.412315</td>\n", + " <td>0.007221</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Q</th>\n", + " <th>m</th>\n", + " <td>41.015871</td>\n", + " <td>0.005120</td>\n", + " </tr>\n", + " <tr>\n", + " <th>I</th>\n", + " <th>m</th>\n", + " <td>40.984340</td>\n", + " <td>0.005117</td>\n", + " </tr>\n", + " <tr>\n", + " <th>V</th>\n", + " <th>m</th>\n", + " <td>74.962331</td>\n", + " <td>0.003685</td>\n", + " </tr>\n", + " <tr>\n", + " <th>G</th>\n", + " <th>m</th>\n", + " <td>41.001163</td>\n", + " <td>0.002596</td>\n", + " </tr>\n", + " <tr>\n", + " <th>P</th>\n", + " <th>m</th>\n", + " <td>40.973641</td>\n", + " <td>0.002577</td>\n", + " </tr>\n", + " <tr>\n", + " <th>C</th>\n", + " <th>m</th>\n", + " <td>40.997851</td>\n", + " <td>0.002569</td>\n", + " </tr>\n", + " <tr>\n", + " <th>J</th>\n", + " <th>m</th>\n", + " <td>41.007800</td>\n", + " <td>0.002546</td>\n", + " </tr>\n", + " <tr>\n", + " <th>O</th>\n", + " <th>m</th>\n", + " <td>41.017959</td>\n", + " <td>0.002543</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Z</th>\n", + " <th>m</th>\n", + " <td>41.010787</td>\n", + " <td>0.002543</td>\n", + " </tr>\n", + " <tr>\n", + " <th>R, S, T</th>\n", + " <th>m</th>\n", + " <td>41.021642</td>\n", + " <td>0.002542</td>\n", + " </tr>\n", + " <tr>\n", + " <th>N</th>\n", + " <th>m</th>\n", + " <td>40.975332</td>\n", + " <td>0.002538</td>\n", + " </tr>\n", + " <tr>\n", + " <th>F</th>\n", + " <th>m</th>\n", + " <td>41.007458</td>\n", + " <td>0.002535</td>\n", + " </tr>\n", + " <tr>\n", + " <th>M</th>\n", + " <th>m</th>\n", + " <td>40.989591</td>\n", + " <td>0.002520</td>\n", + " </tr>\n", + " <tr>\n", + " <th>A</th>\n", + " <th>m</th>\n", + " <td>40.992202</td>\n", + " <td>0.002514</td>\n", + " </tr>\n", + " <tr>\n", + " <th>K</th>\n", + " <th>m</th>\n", + " <td>40.988683</td>\n", + " <td>0.002490</td>\n", + " </tr>\n", + " <tr>\n", + " <th>H</th>\n", + " <th>m</th>\n", + " <td>40.978731</td>\n", + " <td>0.002482</td>\n", + " </tr>\n", + " <tr>\n", + " <th>B, D, E</th>\n", + " <th>m</th>\n", + " <td>41.014025</td>\n", + " <td>0.002462</td>\n", + " </tr>\n", + " <tr>\n", + " <th>X</th>\n", + " <th>m</th>\n", + " <td>41.024242</td>\n", + " <td>0.002435</td>\n", + " </tr>\n", + " <tr>\n", + " <th>L</th>\n", + " <th>m</th>\n", + " <td>40.953436</td>\n", + " <td>0.002197</td>\n", + " </tr>\n", + " <tr>\n", + " <th rowspan=\"2\" valign=\"top\">U</th>\n", + " <th>f</th>\n", + " <td>8.330251</td>\n", + " <td>0.000329</td>\n", + " </tr>\n", + " <tr>\n", + " <th>m</th>\n", + " <td>8.331235</td>\n", + " <td>0.000110</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " age infected\n", + "employment sex \n", + "I f 41.377627 0.015064\n", + "Q f 41.385400 0.014947\n", + "V f 76.022214 0.010852\n", + "B, D, E f 41.425618 0.007973\n", + "R, S, T f 41.371672 0.007748\n", + "O f 41.396246 0.007719\n", + "K f 41.377495 0.007672\n", + "M f 41.401898 0.007645\n", + "J f 41.385772 0.007645\n", + "C f 41.391365 0.007630\n", + "Z f 41.384618 0.007629\n", + "P f 41.399302 0.007584\n", + "F f 41.415191 0.007577\n", + "G f 41.394893 0.007556\n", + "A f 41.385010 0.007491\n", + "X f 41.401447 0.007391\n", + "N f 41.386565 0.007389\n", + "H f 41.416857 0.007385\n", + "L f 41.412315 0.007221\n", + "Q m 41.015871 0.005120\n", + "I m 40.984340 0.005117\n", + "V m 74.962331 0.003685\n", + "G m 41.001163 0.002596\n", + "P m 40.973641 0.002577\n", + "C m 40.997851 0.002569\n", + "J m 41.007800 0.002546\n", + "O m 41.017959 0.002543\n", + "Z m 41.010787 0.002543\n", + "R, S, T m 41.021642 0.002542\n", + "N m 40.975332 0.002538\n", + "F m 41.007458 0.002535\n", + "M m 40.989591 0.002520\n", + "A m 40.992202 0.002514\n", + "K m 40.988683 0.002490\n", + "H m 40.978731 0.002482\n", + "B, D, E m 41.014025 0.002462\n", + "X m 41.024242 0.002435\n", + "L m 40.953436 0.002197\n", + "U f 8.330251 0.000329\n", + " m 8.331235 0.000110" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "simul_groups = df.groupby(['employment', 'sex'])\n", + "simul_groups.mean().sort_values('infected', ascending=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Check Submission ##" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[\"Accommodation & food services\",\"Human health & social work activities\"]" + ] + } + ], + "source": [ + "!cat my_assessment/question_3.json" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Tip**: Your submission file should contain one line of text, similar to: \n", + "\n", + "```\n", + "[\"Agriculture, forestry & fishing\",\"Mining, energy and water supply\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div align=\"center\"><h2>Please Restart the Kernel</h2></div>" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you plan to continue work in other notebooks, please shutdown the kernel." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'status': 'ok', 'restart': True}" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import IPython\n", + "app = IPython.Application.instance()\n", + "app.kernel.do_shutdown(True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<a href=\"https://www.nvidia.com/dli\"><img src=\"images/DLI_Header.png\" alt=\"Header\" style=\"width: 400px;\"/></a>" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.15" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Fundamentals_of_Accelerated_Data_Science/bumashka.png b/Fundamentals_of_Accelerated_Data_Science/bumashka.png Binary files differnew file mode 100644 index 0000000..b0d9474 --- /dev/null +++ b/Fundamentals_of_Accelerated_Data_Science/bumashka.png |
