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Diffstat (limited to 'ZestySalesmanNoMinimize.ipynb')
| -rw-r--r-- | ZestySalesmanNoMinimize.ipynb | 706 |
1 files changed, 0 insertions, 706 deletions
diff --git a/ZestySalesmanNoMinimize.ipynb b/ZestySalesmanNoMinimize.ipynb deleted file mode 100644 index 9632a4a..0000000 --- a/ZestySalesmanNoMinimize.ipynb +++ /dev/null @@ -1,706 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "initial_id", - "metadata": { - "collapsed": true, - "ExecuteTime": { - "end_time": "2023-11-07T15:38:40.404048Z", - "start_time": "2023-11-07T15:38:38.873539Z" - } - }, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import folium\n", - "import utils" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "outputs": [], - "source": [ - "# Load the data\n", - "ListA = pd.read_csv('List A.csv')\n", - "ListB = pd.read_csv('List B.csv')\n", - "ListC = pd.read_csv('List C.csv')\n", - "ListD = pd.read_csv('List D.csv')" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-07T15:38:40.423960Z", - "start_time": "2023-11-07T15:38:40.404555Z" - } - }, - "id": "73b780e762c9de37" - }, - { - "cell_type": "code", - "execution_count": 3, - "outputs": [], - "source": [ - "# Create three centroids, one in the North End, one in the Financial District, and one in the Back Bay\n", - "centroids = [[42.364506, -71.054733], [42.358894, -71.056742], [42.3505, -71.0760]]\n", - "\n", - "northeastern_coordinate = \"-71.09033,42.33976\"" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-07T15:38:40.424218Z", - "start_time": "2023-11-07T15:38:40.420196Z" - } - }, - "id": "be4c8c1d77842ef7" - }, - { - "cell_type": "code", - "execution_count": 4, - "outputs": [], - "source": [ - "# Combine the two lists and add a column to indicate the list\n", - "ListA['list'] = 'A'\n", - "ListB['list'] = 'B'\n", - "ListC['list'] = 'C'\n", - "ListD['list'] = 'D'\n", - "\n", - "TotalList = pd.concat([ListA, ListB, ListC, ListD])" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-07T15:38:40.442314Z", - "start_time": "2023-11-07T15:38:40.425419Z" - } - }, - "id": "ffe4025e97a6c6b9" - }, - { - "cell_type": "code", - "execution_count": 5, - "outputs": [], - "source": [ - "# Remove all columns but name and gps\n", - "TotalList = TotalList[['name', 'gps', 'list']]" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-07T15:38:40.442798Z", - "start_time": "2023-11-07T15:38:40.430053Z" - } - }, - "id": "72657779b4484aae" - }, - { - "cell_type": "code", - "execution_count": 6, - "outputs": [], - "source": [ - "# Convert the gps column to a list of lists for k-means\n", - "TotalList['gps'] = TotalList['gps'].apply(lambda x: x.strip('[]').split(','))\n", - "TotalList['gps'] = TotalList['gps'].apply(lambda x: [float(i) for i in x])" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-07T15:38:40.442952Z", - "start_time": "2023-11-07T15:38:40.432847Z" - } - }, - "id": "a157ffaec020a29a" - }, - { - "cell_type": "code", - "execution_count": 7, - "outputs": [ - { - "data": { - "text/plain": " name gps list \\\n0 521 Commercial Street #525 [42.3688272, -71.0553792] A \n1 Acorn St [42.3576234, -71.0688746] A \n2 Arlington's Great Meadows [42.4299758, -71.2038948] A \n3 Arthur Fiedler Statue [42.3565057, -71.0754527] A \n4 BU Beach [42.3511927, -71.1060828] A \n.. ... ... ... \n33 The Quiet Few [42.3670906, -71.0359889] D \n34 The Tall Ship Boston [42.3649544, -71.0414523] D \n35 Toasted Flats [42.3711266, -71.0371343] D \n36 Vega Market [42.3891835, -71.033703] D \n37 Winthrop High School [42.3803348, -70.9799864] D \n\n normalized_gps \n0 [0.7251058917247415, 0.7797482353989729] \n1 [0.6747391031099019, 0.7451825969538083] \n2 [1.0, 0.3993566550776867] \n3 [0.6697144722136962, 0.7283341725828262] \n4 [0.6458298305822171, 0.6498815915448888] \n.. ... \n33 [0.717298990038831, 0.8294124246148072] \n34 [0.7076956827824702, 0.8154190706511427] \n35 [0.7354428661210094, 0.8264787225922622] \n36 [0.8166178304491644, 0.8352672783369615] \n37 [0.7768384161061446, 0.972851090162032] \n\n[169 rows x 4 columns]", - "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>gps</th>\n <th>list</th>\n <th>normalized_gps</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>521 Commercial Street #525</td>\n <td>[42.3688272, -71.0553792]</td>\n <td>A</td>\n <td>[0.7251058917247415, 0.7797482353989729]</td>\n </tr>\n <tr>\n <th>1</th>\n <td>Acorn St</td>\n <td>[42.3576234, -71.0688746]</td>\n <td>A</td>\n <td>[0.6747391031099019, 0.7451825969538083]</td>\n </tr>\n <tr>\n <th>2</th>\n <td>Arlington's Great Meadows</td>\n <td>[42.4299758, -71.2038948]</td>\n <td>A</td>\n <td>[1.0, 0.3993566550776867]</td>\n </tr>\n <tr>\n <th>3</th>\n <td>Arthur Fiedler Statue</td>\n <td>[42.3565057, -71.0754527]</td>\n <td>A</td>\n <td>[0.6697144722136962, 0.7283341725828262]</td>\n </tr>\n <tr>\n <th>4</th>\n <td>BU Beach</td>\n <td>[42.3511927, -71.1060828]</td>\n <td>A</td>\n <td>[0.6458298305822171, 0.6498815915448888]</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>33</th>\n <td>The Quiet Few</td>\n <td>[42.3670906, -71.0359889]</td>\n <td>D</td>\n <td>[0.717298990038831, 0.8294124246148072]</td>\n </tr>\n <tr>\n <th>34</th>\n <td>The Tall Ship Boston</td>\n <td>[42.3649544, -71.0414523]</td>\n <td>D</td>\n <td>[0.7076956827824702, 0.8154190706511427]</td>\n </tr>\n <tr>\n <th>35</th>\n <td>Toasted Flats</td>\n <td>[42.3711266, -71.0371343]</td>\n <td>D</td>\n <td>[0.7354428661210094, 0.8264787225922622]</td>\n </tr>\n <tr>\n <th>36</th>\n <td>Vega Market</td>\n <td>[42.3891835, -71.033703]</td>\n <td>D</td>\n <td>[0.8166178304491644, 0.8352672783369615]</td>\n </tr>\n <tr>\n <th>37</th>\n <td>Winthrop High School</td>\n <td>[42.3803348, -70.9799864]</td>\n <td>D</td>\n <td>[0.7768384161061446, 0.972851090162032]</td>\n </tr>\n </tbody>\n</table>\n<p>169 rows × 4 columns</p>\n</div>" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Create a new column with normalized gps coordinates and centroids\n", - "TotalList['normalized_gps'], norm_centroids = utils.normalize_gps(TotalList['gps'].values.tolist(), centroids)\n", - "display(TotalList)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-07T15:38:40.443962Z", - "start_time": "2023-11-07T15:38:40.436165Z" - } - }, - "id": "a03ebde91b87fa3b" - }, - { - "cell_type": "markdown", - "source": [ - "# 2 Routes" - ], - "metadata": { - "collapsed": false - }, - "id": "4bd41be9aca5094b" - }, - { - "cell_type": "markdown", - "source": [ - "## Cluster and Minimize" - ], - "metadata": { - "collapsed": false - }, - "id": "90d1d2f1a931597f" - }, - { - "cell_type": "code", - "execution_count": 8, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/garrinshieh/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", - " super()._check_params_vs_input(X, default_n_init=10)\n", - "/Users/garrinshieh/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: RuntimeWarning: Explicit initial center position passed: performing only one init in KMeans instead of n_init=10.\n", - " super()._check_params_vs_input(X, default_n_init=10)\n" - ] - } - ], - "source": [ - "# Cluster and minimize the data\n", - "norm_centroids_2 = norm_centroids[:2]\n", - "_, route_1_coordinates, route_2_coordinates = utils.cluster_and_minimize_2(TotalList, centroids, norm_centroids_2,\n", - " northeastern_coordinate, 0.5, minimize=False)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-07T15:38:43.183771Z", - "start_time": "2023-11-07T15:38:40.443836Z" - } - }, - "id": "ee9b3c1ecb360976" - }, - { - "cell_type": "markdown", - "source": [ - "## Create JSON" - ], - "metadata": { - "collapsed": false - }, - "id": "c85b8ef869e35006" - }, - { - "cell_type": "code", - "execution_count": 9, - "outputs": [], - "source": [ - "# Create a JSON request for the API\n", - "# This is the data we want to get from the API\n", - "route_1 = utils.list_to_string(route_1_coordinates)\n", - "route_2 = utils.list_to_string(route_2_coordinates)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-07T15:38:43.192691Z", - "start_time": "2023-11-07T15:38:43.184382Z" - } - }, - "id": "aa618161182b5b07" - }, - { - "cell_type": "code", - "execution_count": 10, - "outputs": [], - "source": [ - "# Create a dataframe from the JSON\n", - "df1 = utils.create_json_df(route_1, utils.list_to_string([centroids[0]]), northeastern_coordinate)\n", - "df2 = utils.create_json_df(route_2, utils.list_to_string([centroids[1]]), northeastern_coordinate)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-07T15:38:45.665442Z", - "start_time": "2023-11-07T15:38:43.186783Z" - } - }, - "id": "32c485788eedd94" - }, - { - "cell_type": "code", - "execution_count": 11, - "outputs": [], - "source": [ - "# Add columns for the route number\n", - "df1['route'] = 1\n", - "df2['route'] = 2\n", - "\n", - "# Concatenate the two dataframes\n", - "df = pd.concat([df1, df2], ignore_index=True)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-07T15:38:45.678738Z", - "start_time": "2023-11-07T15:38:45.666570Z" - } - }, - "id": "49dba1f17ca8337e" - }, - { - "cell_type": "code", - "execution_count": 12, - "outputs": [ - { - "data": { - "text/plain": " waypoint_index trips_index \\\n0 0 0 \n1 1 0 \n2 2 0 \n3 3 0 \n4 4 0 \n.. ... ... \n168 84 0 \n169 85 0 \n170 86 0 \n171 87 0 \n172 88 0 \n\n hint distance \\\n0 t4YsgAGHLIAAAAAAVQEAAAAAAAAwAAAAAAAAAHV0F0IAAA... 19.432511 \n1 IzYEgGw1BIASAAAArwAAADMAAACUAwAAynkIQGUkmkEXlL... 6.024489 \n2 5IgsgAqJLID7AAAAHgAAAAwAAAAIAAAAz5ffQcMBVEDFYK... 5.871835 \n3 G4gsgDiILICSAwAA5gAAAOkAAAAAAAAAQljLQnyXy0Fhy8... 2.602121 \n4 gIosgLaKLIDOAAAArgAAAFwBAAAAAAAAp3O3QafxmUEQiR... 15.458439 \n.. ... ... \n168 7hAigPYQIoA2AgAAYwEAAAAAAAAAAAAAnsd7Qq9XHUIAAA... 7.478611 \n169 bwwigH0MIoAFAAAAEAAAAFUAAAArAAAAag0xP3921D-BFx... 8.340476 \n170 MQwigFwMIoAoAAAANQAAABwAAAB-AAAAoidqQSAYl0GvUh... 11.504463 \n171 k4chgBiIIYAKAAAAFwAAAPQDAAB_AgAAHn2aP-biHUBi6e... 36.240351 \n172 DoUhgBeFIYCcAAAAJgAAAAAAAAARAAAAm0CKQdkZiEAAAA... 0.236958 \n\n name location lat lon \\\n0 [-71.054865, 42.364361] -71.054865 42.364361 \n1 [-71.055569, 42.364032] -71.055569 42.364032 \n2 [-71.055582, 42.365251] -71.055582 42.365251 \n3 [-71.056164, 42.366918] -71.056164 42.366918 \n4 [-71.055561, 42.368861] -71.055561 42.368861 \n.. ... ... ... ... \n168 [-71.096959, 42.344689] -71.096959 42.344689 \n169 [-71.095003, 42.342001] -71.095003 42.342001 \n170 [-71.094327, 42.341231] -71.094327 42.341231 \n171 [-71.093834, 42.339096] -71.093834 42.339096 \n172 Northeastern (Inbound) [-71.090331, 42.339762] -71.090331 42.339762 \n\n route \n0 1 \n1 1 \n2 1 \n3 1 \n4 1 \n.. ... \n168 2 \n169 2 \n170 2 \n171 2 \n172 2 \n\n[173 rows x 9 columns]", - "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>waypoint_index</th>\n <th>trips_index</th>\n <th>hint</th>\n <th>distance</th>\n <th>name</th>\n <th>location</th>\n <th>lat</th>\n <th>lon</th>\n <th>route</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>0</td>\n <td>0</td>\n <td>t4YsgAGHLIAAAAAAVQEAAAAAAAAwAAAAAAAAAHV0F0IAAA...</td>\n <td>19.432511</td>\n <td></td>\n <td>[-71.054865, 42.364361]</td>\n <td>-71.054865</td>\n <td>42.364361</td>\n <td>1</td>\n </tr>\n <tr>\n <th>1</th>\n <td>1</td>\n <td>0</td>\n <td>IzYEgGw1BIASAAAArwAAADMAAACUAwAAynkIQGUkmkEXlL...</td>\n <td>6.024489</td>\n <td></td>\n <td>[-71.055569, 42.364032]</td>\n <td>-71.055569</td>\n <td>42.364032</td>\n <td>1</td>\n </tr>\n <tr>\n <th>2</th>\n <td>2</td>\n <td>0</td>\n <td>5IgsgAqJLID7AAAAHgAAAAwAAAAIAAAAz5ffQcMBVEDFYK...</td>\n <td>5.871835</td>\n <td></td>\n <td>[-71.055582, 42.365251]</td>\n <td>-71.055582</td>\n <td>42.365251</td>\n <td>1</td>\n </tr>\n <tr>\n <th>3</th>\n <td>3</td>\n <td>0</td>\n <td>G4gsgDiILICSAwAA5gAAAOkAAAAAAAAAQljLQnyXy0Fhy8...</td>\n <td>2.602121</td>\n <td></td>\n <td>[-71.056164, 42.366918]</td>\n <td>-71.056164</td>\n <td>42.366918</td>\n <td>1</td>\n </tr>\n <tr>\n <th>4</th>\n <td>4</td>\n <td>0</td>\n <td>gIosgLaKLIDOAAAArgAAAFwBAAAAAAAAp3O3QafxmUEQiR...</td>\n <td>15.458439</td>\n <td></td>\n <td>[-71.055561, 42.368861]</td>\n <td>-71.055561</td>\n <td>42.368861</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 <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>168</th>\n <td>84</td>\n <td>0</td>\n <td>7hAigPYQIoA2AgAAYwEAAAAAAAAAAAAAnsd7Qq9XHUIAAA...</td>\n <td>7.478611</td>\n <td></td>\n <td>[-71.096959, 42.344689]</td>\n <td>-71.096959</td>\n <td>42.344689</td>\n <td>2</td>\n </tr>\n <tr>\n <th>169</th>\n <td>85</td>\n <td>0</td>\n <td>bwwigH0MIoAFAAAAEAAAAFUAAAArAAAAag0xP3921D-BFx...</td>\n <td>8.340476</td>\n <td></td>\n <td>[-71.095003, 42.342001]</td>\n <td>-71.095003</td>\n <td>42.342001</td>\n <td>2</td>\n </tr>\n <tr>\n <th>170</th>\n <td>86</td>\n <td>0</td>\n <td>MQwigFwMIoAoAAAANQAAABwAAAB-AAAAoidqQSAYl0GvUh...</td>\n <td>11.504463</td>\n <td></td>\n <td>[-71.094327, 42.341231]</td>\n <td>-71.094327</td>\n <td>42.341231</td>\n <td>2</td>\n </tr>\n <tr>\n <th>171</th>\n <td>87</td>\n <td>0</td>\n <td>k4chgBiIIYAKAAAAFwAAAPQDAAB_AgAAHn2aP-biHUBi6e...</td>\n <td>36.240351</td>\n <td></td>\n <td>[-71.093834, 42.339096]</td>\n <td>-71.093834</td>\n <td>42.339096</td>\n <td>2</td>\n </tr>\n <tr>\n <th>172</th>\n <td>88</td>\n <td>0</td>\n <td>DoUhgBeFIYCcAAAAJgAAAAAAAAARAAAAm0CKQdkZiEAAAA...</td>\n <td>0.236958</td>\n <td>Northeastern (Inbound)</td>\n <td>[-71.090331, 42.339762]</td>\n <td>-71.090331</td>\n <td>42.339762</td>\n <td>2</td>\n </tr>\n </tbody>\n</table>\n<p>173 rows × 9 columns</p>\n</div>" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "display(df)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-07T15:38:45.679592Z", - "start_time": "2023-11-07T15:38:45.669550Z" - } - }, - "id": "f231d9a35358988c" - }, - { - "cell_type": "markdown", - "source": [ - "## Map" - ], - "metadata": { - "collapsed": false - }, - "id": "75be92e34a36147f" - }, - { - "cell_type": "code", - "execution_count": 13, - "outputs": [ - { - "data": { - "text/plain": "<folium.folium.Map at 0x146a39e50>", - "text/html": "<div style=\"width:100%;\"><div 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allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>" - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Create a map\n", - "m = folium.Map(location=[df['lon'].mean(), df['lat'].mean()], zoom_start=11)\n", - "\n", - "# Add the points and lines for the two routes with different colors\n", - "colors = ['red', 'blue']\n", - "\n", - "for route in df['route'].unique():\n", - " df_route = df[df['route'] == route]\n", - " folium.PolyLine(df_route[['lon', 'lat']].values.tolist(), color=colors[route - 1]).add_to(m)\n", - " for i in range(len(df_route)):\n", - " folium.CircleMarker(df_route[['lon', 'lat']].iloc[i].values.tolist(), radius=3, color=colors[route - 1]).add_to(\n", - " m)\n", - "\n", - "# Display the map\n", - "m" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-07T15:38:45.781790Z", - "start_time": "2023-11-07T15:38:45.677705Z" - } - }, - "id": "80fd847da2833913" - }, - { - "cell_type": "markdown", - "source": [ - "## Results" - ], - "metadata": { - "collapsed": false - }, - "id": "a7b562f75f7e0813" - }, - { - "cell_type": "code", - "execution_count": 14, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Route 1 has 82 waypoints\n", - "Route 2 has 87 waypoints\n" - ] - } - ], - "source": [ - "# Get the number of waypoints for each route\n", - "route_1_waypoints = len(route_1_coordinates)\n", - "route_2_waypoints = len(route_2_coordinates)\n", - "print(\"Route 1 has {} waypoints\".format(route_1_waypoints))\n", - "print(\"Route 2 has {} waypoints\".format(route_2_waypoints))" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-07T15:38:45.782104Z", - "start_time": "2023-11-07T15:38:45.736109Z" - } - }, - "id": "f53c97acec1c2fc4" - }, - { - "cell_type": "code", - "execution_count": 15, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The trip will take 11.154166666666667 hours\n", - "The trip will take 14.8425 hours\n" - ] - } - ], - "source": [ - "trip_hrs_1 = utils.get_trip_time(route_1, route_1_waypoints, utils.list_to_string([centroids[0]]),\n", - " northeastern_coordinate)\n", - "print(\"The trip will take {} hours\".format(trip_hrs_1))\n", - "trip_hrs_2 = utils.get_trip_time(route_2, route_2_waypoints, utils.list_to_string([centroids[1]]),\n", - " northeastern_coordinate)\n", - "print(\"The trip will take {} hours\".format(trip_hrs_2))" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-07T15:38:48.049877Z", - "start_time": "2023-11-07T15:38:45.738475Z" - } - }, - "id": "a3ec09dfb5cbb5b3" - }, - { - "cell_type": "markdown", - "source": [ - "# 3 Routes" - ], - "metadata": { - "collapsed": false - }, - "id": "de7b5856172d213c" - }, - { - "cell_type": "code", - "execution_count": 16, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/garrinshieh/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", - " super()._check_params_vs_input(X, default_n_init=10)\n", - "/Users/garrinshieh/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: RuntimeWarning: Explicit initial center position passed: performing only one init in KMeans instead of n_init=10.\n", - " super()._check_params_vs_input(X, default_n_init=10)\n" - ] - } - ], - "source": [ - "# Cluster and minimize the data\n", - "_, route_1_coordinates, route_2_coordinates, route_3_coordinates = utils.cluster_and_minimize_3(TotalList, centroids,\n", - " norm_centroids,\n", - " northeastern_coordinate,\n", - " 0.2, minimize=False)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-07T15:38:50.390169Z", - "start_time": "2023-11-07T15:38:48.048739Z" - } - }, - "id": "bb6e00857e8175c0" - }, - { - "cell_type": "markdown", - "source": [ - "## Create JSON" - ], - "metadata": { - "collapsed": false - }, - "id": "19afb4f687b37383" - }, - { - "cell_type": "code", - "execution_count": 17, - "outputs": [], - "source": [ - "# Create a JSON request for the API\n", - "# This is the data we want to get from the API\n", - "route_1 = utils.list_to_string(route_1_coordinates)\n", - "route_2 = utils.list_to_string(route_2_coordinates)\n", - "route_3 = utils.list_to_string(route_3_coordinates)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-07T15:38:50.404500Z", - "start_time": "2023-11-07T15:38:50.392611Z" - } - }, - "id": "e886e061f86a2118" - }, - { - "cell_type": "code", - "execution_count": 18, - "outputs": [], - "source": [ - "# Create a dataframe from the JSON\n", - "df1 = utils.create_json_df(route_1, utils.list_to_string([centroids[0]]), northeastern_coordinate)\n", - "df2 = utils.create_json_df(route_2, utils.list_to_string([centroids[1]]), northeastern_coordinate)\n", - "df3 = utils.create_json_df(route_3, utils.list_to_string([centroids[2]]), northeastern_coordinate)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-07T15:38:52.713204Z", - "start_time": "2023-11-07T15:38:50.400202Z" - } - }, - "id": "23e4682fe9e30631" - }, - { - "cell_type": "code", - "execution_count": 19, - "outputs": [], - "source": [ - "# Add columns for the route number\n", - "df1['route'] = 1\n", - "df2['route'] = 2\n", - "df3['route'] = 3\n", - "\n", - "# Concatenate the three dataframes\n", - "df = pd.concat([df1, df2, df3], ignore_index=True)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-07T15:38:52.724854Z", - "start_time": "2023-11-07T15:38:52.718223Z" - } - }, - "id": "c3a5c5d6f3ac46c0" - }, - { - "cell_type": "code", - "execution_count": 20, - "outputs": [ - { - "data": { - "text/plain": " waypoint_index trips_index \\\n0 0 0 \n1 1 0 \n2 2 0 \n3 3 0 \n4 4 0 \n.. ... ... \n170 39 0 \n171 40 0 \n172 41 0 \n173 42 0 \n174 43 0 \n\n hint distance \\\n0 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"execute_result" - } - ], - "source": [ - "# Create a map\n", - "m = folium.Map(location=[df['lon'].mean(), df['lat'].mean()], zoom_start=11)\n", - "\n", - "# Add the points and lines for the three routes with different colors\n", - "colors = ['red', 'blue', 'green']\n", - "\n", - "for route in df['route'].unique():\n", - " df_route = df[df['route'] == route]\n", - " folium.PolyLine(df_route[['lon', 'lat']].values.tolist(), color=colors[route - 1]).add_to(m)\n", - " for i in range(len(df_route)):\n", - " folium.CircleMarker(df_route[['lon', 'lat']].iloc[i].values.tolist(), radius=3, color=colors[route - 1]).add_to(\n", - " m)\n", - " \n", - "# Display the map\n", - "m" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-07T15:38:52.827726Z", - "start_time": "2023-11-07T15:38:52.736404Z" - } - }, - "id": "702adaec008a6ec8" - }, - { - "cell_type": "markdown", - "source": [ - "## Results" - ], - "metadata": { - "collapsed": false - }, - "id": "a947e49e27c734e9" - }, - { - "cell_type": "code", - "execution_count": 22, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Route 1 has 55 waypoints\n", - "Route 2 has 72 waypoints\n", - "Route 3 has 42 waypoints\n" - ] - } - ], - "source": [ - "# Get the number of waypoints for each route\n", - "route_1_waypoints = len(route_1_coordinates)\n", - "route_2_waypoints = len(route_2_coordinates)\n", - "route_3_waypoints = len(route_3_coordinates)\n", - "print(\"Route 1 has {} waypoints\".format(route_1_waypoints))\n", - "print(\"Route 2 has {} waypoints\".format(route_2_waypoints))\n", - "print(\"Route 3 has {} waypoints\".format(route_3_waypoints))" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-07T15:38:52.840867Z", - "start_time": "2023-11-07T15:38:52.802540Z" - } - }, - "id": "4106acf2adad01d7" - }, - { - "cell_type": "code", - "execution_count": 23, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The trip will take 8.883333333333333 hours\n", - "The trip will take 8.458055555555555 hours\n", - "The trip will take 10.230555555555556 hours\n" - ] - } - ], - "source": [ - "# Get the trip time for each route\n", - "trip_hrs_1 = utils.get_trip_time(route_1, route_1_waypoints, utils.list_to_string([centroids[0]]),\n", - " northeastern_coordinate)\n", - "print(\"The trip will take {} hours\".format(trip_hrs_1))\n", - "trip_hrs_2 = utils.get_trip_time(route_2, route_2_waypoints, utils.list_to_string([centroids[1]]),\n", - " northeastern_coordinate)\n", - "print(\"The trip will take {} hours\".format(trip_hrs_2))\n", - "trip_hrs_3 = utils.get_trip_time(route_3, route_3_waypoints, utils.list_to_string([centroids[2]]),\n", - " northeastern_coordinate)\n", - "print(\"The trip will take {} hours\".format(trip_hrs_3))" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-07T15:38:54.879411Z", - "start_time": "2023-11-07T15:38:52.805867Z" - } - }, - "id": "c58106faf0fc7f4e" - }, - { - "cell_type": "code", - "execution_count": 23, - "outputs": [], - "source": [], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-07T15:38:54.879657Z", - "start_time": "2023-11-07T15:38:54.867101Z" - } - }, - "id": "a2f10e3152b95a69" - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} |