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Cyber/EDA.ipynb

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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np \n",
"\n",
"spatio_data_file = \"./data/SpatialFeatures.csv\"\n",
"raw_spatio_feature = pd.read_csv(spatio_data_file)\n",
"raw_spatio_feature\n",
"bikeLane_matrix = raw_spatio_feature[\"BikeLane_miles\"]\n",
"bikeLane_matrix = bikeLane_matrix.fillna(0).to_numpy()\n",
"bikeLane_matrix = bikeLane_matrix/bikeLane_matrix.max()\n",
"bikeLane_matrix_board = bikeLane_matrix[np.newaxis,:] * np.ones((1248,1))"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"pd.to_pickle(raw_spatio_feature,\"./data/raw_feature.pkl\")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"raw_data = pd.read_pickle(\"./data/raw_feature.pkl\")\n",
"X_train = pd.read_pickle(\"./data/X_train.pkl\")\n",
"y_train = pd.read_pickle(\"./data/y_train.pkl\")\n",
"X_test = pd.read_pickle(\"./data/X_test.pkl\")\n",
"y_test = pd.read_pickle(\"./data/y_test.pkl\")\n",
"\n",
"\n",
"trip_avg_in = y_train.mean(axis = 0)[0]\n",
"trip_avg_out = y_train.mean(axis=0)[1]"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x17de86c4190>"
]
},
"execution_count": 6,
"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": [
"import matplotlib.pyplot as plt \n",
"plt.imshow(trip_avg_in)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x17de87647d0>"
]
},
"execution_count": 7,
"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": [
"plt.imshow(trip_avg_out)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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"\n",
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"\n",
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" text-align: right;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Join_Count</th>\n",
" <th>TARGET_FID</th>\n",
" <th>BlockID</th>\n",
" <th>BikeLane_miles</th>\n",
" <th>AADT</th>\n",
" <th>SpeedLimit</th>\n",
" <th>AvgSpeed</th>\n",
" <th>Pop2019</th>\n",
" <th>Pop_Density</th>\n",
" <th>Shape_Length</th>\n",
" <th>Shape_Area</th>\n",
" <th>StationNum</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>32638.666667</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>7840.490288</td>\n",
" <td>3.787844e+06</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>7840.686448</td>\n",
" <td>3.787843e+06</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>0.807055</td>\n",
" <td>21234.741935</td>\n",
" <td>25.0</td>\n",
" <td>15.0</td>\n",
" <td>1049.0</td>\n",
" <td>0.029594</td>\n",
" <td>7840.882970</td>\n",
" <td>3.787843e+06</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>2.096221</td>\n",
" <td>5915.125000</td>\n",
" <td>25.0</td>\n",
" <td>19.0</td>\n",
" <td>1816.0</td>\n",
" <td>0.030252</td>\n",
" <td>7841.080477</td>\n",
" <td>3.787844e+06</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>3</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>0.984320</td>\n",
" <td>23940.078947</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1892.0</td>\n",
" <td>0.022647</td>\n",
" <td>7841.277327</td>\n",
" <td>3.787843e+06</td>\n",
" <td>3</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",
" </tr>\n",
" <tr>\n",
" <th>123</th>\n",
" <td>3</td>\n",
" <td>124</td>\n",
" <td>124</td>\n",
" <td>1.955707</td>\n",
" <td>9050.487805</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1986.0</td>\n",
" <td>0.046045</td>\n",
" <td>7840.280937</td>\n",
" <td>3.787843e+06</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>124</th>\n",
" <td>3</td>\n",
" <td>125</td>\n",
" <td>125</td>\n",
" <td>1.440449</td>\n",
" <td>9121.500000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1198.0</td>\n",
" <td>0.039726</td>\n",
" <td>7840.085072</td>\n",
" <td>3.787843e+06</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>125</th>\n",
" <td>2</td>\n",
" <td>126</td>\n",
" <td>126</td>\n",
" <td>0.985704</td>\n",
" <td>8770.901961</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>555.0</td>\n",
" <td>0.015125</td>\n",
" <td>7839.890518</td>\n",
" <td>3.787843e+06</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>126</th>\n",
" <td>4</td>\n",
" <td>127</td>\n",
" <td>127</td>\n",
" <td>0.228593</td>\n",
" <td>12266.085714</td>\n",
" <td>25.0</td>\n",
" <td>16.0</td>\n",
" <td>814.0</td>\n",
" <td>0.031125</td>\n",
" <td>7839.696621</td>\n",
" <td>3.787844e+06</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>127</th>\n",
" <td>4</td>\n",
" <td>128</td>\n",
" <td>128</td>\n",
" <td>1.619940</td>\n",
" <td>11077.037037</td>\n",
" <td>25.0</td>\n",
" <td>16.0</td>\n",
" <td>1385.0</td>\n",
" <td>0.059671</td>\n",
" <td>7839.501980</td>\n",
" <td>3.787843e+06</td>\n",
" <td>4</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>128 rows × 12 columns</p>\n",
"</div>"
],
"text/plain": [
" Join_Count TARGET_FID BlockID BikeLane_miles AADT \\\n",
"0 0 1 1 NaN 32638.666667 \n",
"1 0 2 2 NaN NaN \n",
"2 3 3 3 0.807055 21234.741935 \n",
"3 3 4 4 2.096221 5915.125000 \n",
"4 3 5 5 0.984320 23940.078947 \n",
".. ... ... ... ... ... \n",
"123 3 124 124 1.955707 9050.487805 \n",
"124 3 125 125 1.440449 9121.500000 \n",
"125 2 126 126 0.985704 8770.901961 \n",
"126 4 127 127 0.228593 12266.085714 \n",
"127 4 128 128 1.619940 11077.037037 \n",
"\n",
" SpeedLimit AvgSpeed Pop2019 Pop_Density Shape_Length Shape_Area \\\n",
"0 NaN NaN 0.0 0.000000 7840.490288 3.787844e+06 \n",
"1 NaN NaN 0.0 0.000000 7840.686448 3.787843e+06 \n",
"2 25.0 15.0 1049.0 0.029594 7840.882970 3.787843e+06 \n",
"3 25.0 19.0 1816.0 0.030252 7841.080477 3.787844e+06 \n",
"4 NaN NaN 1892.0 0.022647 7841.277327 3.787843e+06 \n",
".. ... ... ... ... ... ... \n",
"123 NaN NaN 1986.0 0.046045 7840.280937 3.787843e+06 \n",
"124 NaN NaN 1198.0 0.039726 7840.085072 3.787843e+06 \n",
"125 NaN NaN 555.0 0.015125 7839.890518 3.787843e+06 \n",
"126 25.0 16.0 814.0 0.031125 7839.696621 3.787844e+06 \n",
"127 25.0 16.0 1385.0 0.059671 7839.501980 3.787843e+06 \n",
"\n",
" StationNum \n",
"0 0 \n",
"1 0 \n",
"2 3 \n",
"3 3 \n",
"4 3 \n",
".. ... \n",
"123 3 \n",
"124 3 \n",
"125 2 \n",
"126 4 \n",
"127 4 \n",
"\n",
"[128 rows x 12 columns]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"raw_data"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"from scipy.stats import pearsonr\n",
"def get_feature_in_2d(df,feature_name):\n",
"\n",
" feature = df[feature_name].fillna(0)\n",
" feature_arranged = np.reshape(feature,(16,8))[::-1,:]\n",
" return feature_arranged\n",
"\n",
"def cal_pearson(feature_matrix,trip_matrix,filter_zero = False,log_text = None):\n",
" f_array = feature_matrix.flatten()\n",
" t_array = trip_matrix.flatten()\n",
" f_array_filter0 = f_array[f_array!=0]\n",
" t_array_filter0 = t_array[f_array!=0]\n",
" if filter_zero:\n",
" correlation, p_value = pearsonr(f_array_filter0, t_array_filter0)\n",
" else:\n",
" correlation, p_value = pearsonr(f_array, t_array) \n",
" if log_text!=None:\n",
" print(log_text)\n",
" print(\"Pearson correlation coefficient:\", correlation)\n",
" print(\"P-value:\", p_value)\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"file_path = \"pearson_output_filter0.txt\"\n",
"\n",
"with open(file_path,\"w\") as file:\n",
" original_stdout = sys.stdout\n",
" sys.stdout = file\n",
"\n",
" feature_name_list = raw_data.columns\n",
" feature_name_list\n",
" for feature_name in feature_name_list:\n",
" temp_feature = get_feature_in_2d(raw_data,feature_name)\n",
" cal_pearson(temp_feature,trip_avg_in,log_text=feature_name,filter_zero=True)\n",
" print(\"\")\n",
" sys.stdout = original_stdout"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"# feature_bikeLane = raw_data[\"BikeLane_miles\"]\n",
"# feature_bikeLane\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x17dff3d47d0>"
]
},
"execution_count": 12,
"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": [
"feature_bikeLane = get_feature_in_2d(raw_data,\"BikeLane_miles\")\n",
"plot_data = get_feature_in_2d(raw_data,\"BikeLane_miles\")\n",
"\n",
"plt.imshow(plot_data)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Pearson correlation coefficient: 0.6651100883626\n",
"P-value: 1.1054968365962953e-17\n"
]
}
],
"source": [
"# calculate pearson corralation\n",
"\n",
"correlation, p_value = pearsonr(feature_bikeLane.flatten(), trip_avg_out.flatten())\n",
"\n",
"print(\"Pearson correlation coefficient:\", correlation)\n",
"print(\"P-value:\", p_value)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Pearson correlation coefficient: 0.9981870630298622\n",
"P-value: 1.174508784681668e-155\n"
]
}
],
"source": [
"cal_pearson(trip_avg_out,trip_avg_in,filter_zero=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.11.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}