{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Fundamentals of Accelerated Data Science # " ] }, { "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": "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": [ "
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northingeasting
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" ], "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": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "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": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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northingeastingcluster
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" ], "text/plain": [ " northing easting cluster\n", "0 515491.5313 430772.1875 1\n", "1 503572.4688 434685.8750 1\n", "2 517903.6563 432565.5313 1\n", "3 517059.9063 427660.6250 1\n", "4 509228.6875 425527.7813 1\n", "... ... ... ...\n", "58479889 192393.7813 340519.8438 3\n", "58479890 183451.8906 337749.6250 3\n", "58479891 189997.5156 341165.1250 3\n", "58479892 184439.1250 335227.6563 3\n", "58479893 187036.3281 342634.5313 3\n", "\n", "[58479894 rows x 3 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gdf" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from pyproj import CRS\n", "import numpy as np\n", "from pyproj import Transformer\n", "\n", "crs_UK = CRS.from_epsg(27700) # GB coord system\n", "crs_world = CRS.from_epsg(3857) # used by maps WGS 84\n", "\n", "transformed = Transformer.from_crs(crs_UK, crs_world).transform(gdf[\"easting\"].to_numpy(), gdf[\"northing\"].to_numpy())" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "tdf = cudf.DataFrame(np.column_stack(transformed), columns=[\"easting\", \"northing\"])\n", "tdf[\"cluster\"] = gdf[\"cluster\"]" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "5" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ncolors = gdf[\"cluster\"].nunique()\n", "ncolors" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "from bokeh.palettes import Category10 # 10 will be enough" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "cxf_data = cxf.DataFrame.from_dataframe(tdf)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "scatter_chart = cxf.charts.datashader.scatter(\n", " x='easting', y='northing', \n", " aggregate_col=\"cluster\",\n", " aggregate_fn=\"mean\",\n", " tile_provider=\"CartoDark\",\n", " legend=False,\n", " color_palette=Category10[ncolors],\n", " unselected_alpha=0,\n", ")\n", "cluster_widget = cxf.charts.panel_widgets.multi_select('cluster')\n" ] }, { "cell_type": "code", "execution_count": 87, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ] }, "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 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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. 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