From 3c47160257a2c61ed7ed98501260bdf8ab0c1ebb Mon Sep 17 00:00:00 2001 From: leshe4ka46 Date: Sat, 27 Dec 2025 23:10:41 +0300 Subject: upd --- 3-02_k-means-map.ipynb | 1036 ++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 1036 insertions(+) create mode 100644 3-02_k-means-map.ipynb (limited to '3-02_k-means-map.ipynb') diff --git a/3-02_k-means-map.ipynb b/3-02_k-means-map.ipynb new file mode 100644 index 0000000..2c85738 --- /dev/null +++ b/3-02_k-means-map.ipynb @@ -0,0 +1,1036 @@ +{ + "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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" + ], + "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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" + ], + "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 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