diff --git a/Homeworks/Homework-II/Kaznowska_Julia/Homework2_Kaznowska_Julia.ipynb b/Homeworks/Homework-II/Kaznowska_Julia/Homework2_Kaznowska_Julia.ipynb new file mode 100644 index 0000000..49025ad --- /dev/null +++ b/Homeworks/Homework-II/Kaznowska_Julia/Homework2_Kaznowska_Julia.ipynb @@ -0,0 +1,149135 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Homework 2\n", + "Julia Kaznowska
\n", + "Warsaw University of Technology
\n", + "Faculty of Mathematics and Information Science
\n", + "WB XAI tabular" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Importing libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")\n", + "pd.options.mode.chained_assignment = None" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creating own model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Handling missing values" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "data = pd.read_csv('housing.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "longitude 0\n", + "latitude 0\n", + "housing_median_age 0\n", + "total_rooms 0\n", + "total_bedrooms 207\n", + "population 0\n", + "households 0\n", + "median_income 0\n", + "median_house_value 0\n", + "ocean_proximity 0\n", + "dtype: int64" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.isnull().sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Filling na" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "bedroom_mean = data['total_bedrooms'].mean()\n", + "data['total_bedrooms'].fillna(bedroom_mean, inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Adding variables" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "data[\"rooms_per_household\"] = data[\"total_rooms\"]/data[\"households\"]\n", + "data[\"bedrooms_per_room\"] = data[\"total_bedrooms\"]/data[\"total_rooms\"]\n", + "data[\"population_per_household\"]=data[\"population\"]/data[\"households\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Encoding categorical variables" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "X = data.drop(['median_house_value'], axis=1)\n", + "y = data.iloc[:,-5]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomeocean_proximityrooms_per_householdbedrooms_per_roompopulation_per_household
0-122.2337.8841.0880.0129.0322.0126.08.325236.9841270.1465912.555556
1-122.2237.8621.07099.01106.02401.01138.08.301436.2381370.1557972.109842
2-122.2437.8552.01467.0190.0496.0177.07.257438.2881360.1295162.802260
3-122.2537.8552.01274.0235.0558.0219.05.643135.8173520.1844582.547945
4-122.2537.8552.01627.0280.0565.0259.03.846236.2818530.1720962.181467
\n", + "
" + ], + "text/plain": [ + " longitude latitude housing_median_age total_rooms total_bedrooms \\\n", + "0 -122.23 37.88 41.0 880.0 129.0 \n", + "1 -122.22 37.86 21.0 7099.0 1106.0 \n", + "2 -122.24 37.85 52.0 1467.0 190.0 \n", + "3 -122.25 37.85 52.0 1274.0 235.0 \n", + "4 -122.25 37.85 52.0 1627.0 280.0 \n", + "\n", + " population households median_income ocean_proximity \\\n", + "0 322.0 126.0 8.3252 3 \n", + "1 2401.0 1138.0 8.3014 3 \n", + "2 496.0 177.0 7.2574 3 \n", + "3 558.0 219.0 5.6431 3 \n", + "4 565.0 259.0 3.8462 3 \n", + "\n", + " rooms_per_household bedrooms_per_room population_per_household \n", + "0 6.984127 0.146591 2.555556 \n", + "1 6.238137 0.155797 2.109842 \n", + "2 8.288136 0.129516 2.802260 \n", + "3 5.817352 0.184458 2.547945 \n", + "4 6.281853 0.172096 2.181467 " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.preprocessing import LabelEncoder\n", + "\n", + "le = LabelEncoder()\n", + "X['ocean_proximity'] = le.fit_transform(X['ocean_proximity'])\n", + "X.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Splitting data" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Standarising data" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.preprocessing import StandardScaler\n", + "ss = StandardScaler()\n", + "column_names = X_train.columns\n", + "X_train = pd.DataFrame(ss.fit_transform(X_train), columns =column_names)\n", + "X_test = pd.DataFrame(ss.fit_transform(X_test), columns =column_names)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Model" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.ensemble import RandomForestRegressor\n", + "model = RandomForestRegressor(max_depth=10, n_estimators = 30)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "RandomForestRegressor(max_depth=10, n_estimators=30)" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.fit(X_train, y_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Checking performance" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.78825043 0.78866123 0.77700024 0.77299018 0.76414049]\n", + "0.7081731503326287\n" + ] + } + ], + "source": [ + "from sklearn.model_selection import cross_val_score\n", + "from sklearn.metrics import r2_score\n", + "print(cross_val_score(model, X_train, y_train, scoring = 'r2'))\n", + "print(r2_score(y_test, model.predict(X_test)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "R2 score is good for both train and test datasets." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Model predictions" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([154287.13923419])" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.predict(X_test.iloc[[2]])" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "14783 151500.0\n", + "Name: median_house_value, dtype: float64" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y_test.iloc[[2]]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Lime predictions " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "I decided to check the most important variables first." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from matplotlib import pyplot as plt\n", + "plt.barh(X_train.columns.values.tolist(), model.feature_importances_)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that 6 of them can be called the most important (with `median_income` being on the lead). " + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "from lime import lime_tabular\n", + "explainer = lime_tabular.LimeTabularExplainer(X_train.values, feature_names=X_train.columns.values.tolist(),\n", + " class_names=['median_house_value'], mode='regression', \n", + " categorical_names = ['ocean_proximity'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### First observation" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + "
\n", + " \n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "exp = explainer.explain_instance(X_test.values[2], model.predict, num_features=6)\n", + "exp.show_in_notebook(show_table=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + " \n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "exp1 = explainer.explain_instance(X_test.values[313], model.predict, num_features=6)\n", + "exp2 = explainer.explain_instance(X_test.values[123], model.predict, num_features=6)\n", + "exp3 = explainer.explain_instance(X_test.values[69], model.predict, num_features=6)\n", + "\n", + "exp1.show_in_notebook(show_table=True)\n", + "exp2.show_in_notebook(show_table=True)\n", + "exp3.show_in_notebook(show_table=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can see, the explainer is working. It is also visible that although many variables are the same for 6 most influencing ones, there are some crucial differences. There are observations in which `median_income` is the most important (as we could predict by looking at `feature_importance` of the model), but in others this variable doesn't even appear. This means that explanations probably aren't very stable. " + ] + } + ], + "metadata": { + "interpreter": { + "hash": "7a6c7221b3b3971f6f9ce379d328f92d54aac19d86d8c895482784a858ac35df" + }, + "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": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Homeworks/Homework-II/Kaznowska_Julia/housing.csv b/Homeworks/Homework-II/Kaznowska_Julia/housing.csv new file mode 100644 index 0000000..d5e3fa2 --- /dev/null +++ b/Homeworks/Homework-II/Kaznowska_Julia/housing.csv @@ -0,0 +1,20641 @@ +longitude,latitude,housing_median_age,total_rooms,total_bedrooms,population,households,median_income,median_house_value,ocean_proximity +-122.23,37.88,41.0,880.0,129.0,322.0,126.0,8.3252,452600.0,NEAR BAY 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+-122.22,37.77,52.0,1137.0,301.0,866.0,259.0,2.59,96400.0,NEAR BAY +-122.23,37.77,52.0,769.0,206.0,612.0,183.0,2.57,72000.0,NEAR BAY +-122.23,37.78,52.0,472.0,146.0,415.0,126.0,2.6429,71300.0,NEAR BAY +-122.23,37.78,52.0,862.0,215.0,994.0,213.0,3.0257,80800.0,NEAR BAY +-122.22,37.78,50.0,1920.0,530.0,1525.0,477.0,1.4886,128800.0,NEAR BAY +-122.23,37.78,43.0,1420.0,472.0,1506.0,438.0,1.9338,112500.0,NEAR BAY +-122.23,37.78,52.0,986.0,258.0,1008.0,255.0,1.4844,119400.0,NEAR BAY +-122.23,37.78,44.0,2340.0,825.0,2813.0,751.0,1.6009,118100.0,NEAR BAY +-122.23,37.79,48.0,1696.0,396.0,1481.0,343.0,2.0375,122500.0,NEAR BAY +-122.23,37.79,49.0,1175.0,217.0,859.0,219.0,2.293,106300.0,NEAR BAY +-122.22,37.79,37.0,2343.0,574.0,1608.0,523.0,2.1494,132500.0,NEAR BAY +-122.23,37.79,30.0,610.0,145.0,425.0,140.0,1.6198,122700.0,NEAR BAY +-122.23,37.79,40.0,930.0,199.0,564.0,184.0,1.3281,113300.0,NEAR BAY +-122.22,37.79,44.0,1487.0,314.0,961.0,272.0,3.5156,109500.0,NEAR BAY 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+-122.19,37.77,52.0,2329.0,445.0,1144.0,417.0,3.5114,151200.0,NEAR BAY +-122.19,37.78,52.0,2492.0,415.0,1109.0,375.0,4.3125,164400.0,NEAR BAY +-122.19,37.78,52.0,2198.0,397.0,984.0,369.0,3.22,156500.0,NEAR BAY +-122.18,37.78,33.0,142.0,31.0,575.0,47.0,3.875,225000.0,NEAR BAY +-122.19,37.78,49.0,1183.0,205.0,496.0,209.0,5.2328,174200.0,NEAR BAY +-122.19,37.78,52.0,1070.0,193.0,555.0,190.0,3.7262,166900.0,NEAR BAY +-122.2,37.78,45.0,1766.0,332.0,869.0,327.0,4.5893,163500.0,NEAR BAY +-122.18,37.79,39.0,617.0,95.0,236.0,106.0,5.2578,253000.0,NEAR BAY +-122.18,37.79,41.0,1411.0,233.0,626.0,214.0,7.0875,240700.0,NEAR BAY +-122.18,37.79,46.0,2109.0,387.0,922.0,329.0,3.9712,208100.0,NEAR BAY +-122.19,37.79,47.0,1229.0,243.0,582.0,256.0,2.9514,198100.0,NEAR BAY +-122.19,37.79,50.0,954.0,217.0,546.0,201.0,2.6667,172800.0,NEAR BAY +-122.18,37.81,37.0,1643.0,262.0,620.0,266.0,5.4446,336700.0,NEAR BAY +-122.18,37.8,34.0,1355.0,195.0,442.0,195.0,6.2838,318200.0,NEAR BAY 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+-122.17,37.77,31.0,2424.0,533.0,1360.0,452.0,1.871,90700.0,NEAR BAY +-122.17,37.76,41.0,1594.0,367.0,1074.0,355.0,1.9356,90600.0,NEAR BAY +-122.17,37.76,47.0,2118.0,413.0,965.0,382.0,2.1842,107900.0,NEAR BAY +-122.18,37.76,37.0,1575.0,358.0,933.0,320.0,2.2917,107000.0,NEAR BAY +-122.17,37.76,38.0,1764.0,397.0,987.0,354.0,2.4333,98200.0,NEAR BAY +-122.18,37.76,50.0,1187.0,261.0,907.0,246.0,1.9479,89500.0,NEAR BAY +-122.18,37.76,52.0,754.0,175.0,447.0,165.0,3.9063,93800.0,NEAR BAY +-122.18,37.76,49.0,2308.0,452.0,1299.0,451.0,1.8407,96700.0,NEAR BAY +-122.18,37.77,27.0,909.0,236.0,396.0,157.0,2.0786,97500.0,NEAR BAY +-122.18,37.77,42.0,1180.0,257.0,877.0,268.0,2.8125,97300.0,NEAR BAY +-122.18,37.76,43.0,2018.0,408.0,1111.0,367.0,1.8913,91200.0,NEAR BAY +-122.19,37.76,49.0,1368.0,282.0,790.0,269.0,1.7056,91400.0,NEAR BAY +-122.18,37.77,52.0,2744.0,547.0,1479.0,554.0,2.2768,96200.0,NEAR BAY +-122.18,37.77,51.0,2107.0,471.0,1173.0,438.0,3.2552,120100.0,NEAR BAY +-122.18,37.77,52.0,1748.0,362.0,1029.0,366.0,2.0556,100000.0,NEAR BAY +-122.19,37.76,52.0,2024.0,391.0,1030.0,350.0,2.4659,94700.0,NEAR BAY +-122.2,37.76,47.0,1116.0,259.0,826.0,279.0,1.75,85700.0,NEAR BAY +-122.19,37.77,41.0,2036.0,510.0,1412.0,454.0,2.0469,89300.0,NEAR BAY +-122.19,37.77,45.0,1852.0,393.0,1132.0,349.0,2.7159,101400.0,NEAR BAY +-122.19,37.76,41.0,921.0,207.0,522.0,159.0,1.2083,72500.0,NEAR BAY +-122.19,37.76,45.0,995.0,238.0,630.0,237.0,1.925,74100.0,NEAR BAY +-122.2,37.75,36.0,606.0,132.0,531.0,133.0,1.5809,70000.0,NEAR BAY +-122.2,37.76,37.0,2680.0,736.0,1925.0,667.0,1.4097,84600.0,NEAR BAY +-122.19,37.76,38.0,1493.0,370.0,1144.0,351.0,0.7683,81800.0,NEAR BAY +-122.19,37.75,19.0,2207.0,565.0,1481.0,520.0,1.3194,81400.0,NEAR BAY +-122.19,37.75,28.0,856.0,189.0,435.0,162.0,0.8012,81800.0,NEAR BAY +-122.19,37.76,26.0,1293.0,297.0,984.0,303.0,1.9479,85800.0,NEAR BAY +-122.19,37.74,36.0,847.0,212.0,567.0,159.0,1.1765,87100.0,NEAR BAY +-122.18,37.74,42.0,541.0,154.0,380.0,123.0,2.3456,83500.0,NEAR BAY +-122.19,37.73,44.0,1066.0,253.0,825.0,244.0,2.1538,79700.0,NEAR BAY +-122.19,37.74,43.0,707.0,147.0,417.0,155.0,2.5139,83400.0,NEAR BAY +-122.19,37.73,45.0,1528.0,291.0,801.0,287.0,1.2625,84700.0,NEAR BAY +-122.18,37.73,42.0,909.0,215.0,646.0,198.0,2.9063,80000.0,NEAR BAY +-122.18,37.73,43.0,1391.0,293.0,855.0,285.0,2.5192,76400.0,NEAR BAY +-122.18,37.73,44.0,548.0,119.0,435.0,136.0,2.1111,79700.0,NEAR BAY +-122.18,37.73,42.0,4074.0,874.0,2736.0,780.0,2.455,82400.0,NEAR BAY +-122.17,37.74,41.0,1613.0,445.0,1481.0,414.0,2.4028,97700.0,NEAR BAY +-122.17,37.74,47.0,463.0,134.0,327.0,137.0,2.15,97200.0,NEAR BAY +-122.17,37.74,43.0,818.0,193.0,494.0,179.0,2.4776,101600.0,NEAR BAY +-122.17,37.73,43.0,1473.0,371.0,1231.0,341.0,2.1587,86500.0,NEAR BAY +-122.18,37.74,35.0,504.0,126.0,323.0,109.0,1.8438,90500.0,NEAR BAY +-122.17,37.74,46.0,1026.0,226.0,749.0,225.0,3.0298,107600.0,NEAR BAY +-122.17,37.74,46.0,769.0,183.0,693.0,178.0,2.25,84200.0,NEAR BAY +-122.18,37.74,46.0,2103.0,391.0,1339.0,354.0,2.2467,88900.0,NEAR BAY +-122.18,37.75,45.0,330.0,76.0,282.0,80.0,4.0469,80700.0,NEAR BAY +-122.18,37.75,46.0,941.0,218.0,621.0,195.0,1.325,87100.0,NEAR BAY +-122.17,37.75,38.0,992.0,,732.0,259.0,1.6196,85100.0,NEAR BAY +-122.18,37.75,45.0,990.0,261.0,901.0,260.0,2.1731,82000.0,NEAR BAY +-122.19,37.75,36.0,1126.0,263.0,482.0,150.0,1.9167,82800.0,NEAR BAY +-122.18,37.75,43.0,1036.0,233.0,652.0,213.0,2.069,84600.0,NEAR BAY +-122.18,37.75,36.0,1047.0,214.0,651.0,166.0,1.712,82100.0,NEAR BAY +-122.17,37.76,33.0,1280.0,307.0,999.0,286.0,2.5625,89300.0,NEAR BAY +-122.17,37.75,43.0,1587.0,320.0,907.0,306.0,1.9821,98300.0,NEAR BAY +-122.17,37.75,41.0,1257.0,271.0,828.0,230.0,2.5043,92300.0,NEAR BAY +-122.17,37.75,44.0,1218.0,248.0,763.0,254.0,2.3281,88800.0,NEAR BAY +-122.17,37.75,48.0,1751.0,390.0,935.0,349.0,1.4375,90000.0,NEAR BAY +-122.16,37.76,45.0,2299.0,514.0,1437.0,484.0,2.5122,95500.0,NEAR BAY +-122.16,37.75,38.0,2457.0,624.0,1516.0,482.0,1.5625,91700.0,NEAR BAY +-122.17,37.75,47.0,998.0,211.0,597.0,185.0,3.1587,100400.0,NEAR BAY +-122.17,37.76,40.0,1685.0,343.0,949.0,342.0,1.8426,94800.0,NEAR BAY +-122.16,37.76,46.0,1827.0,307.0,881.0,302.0,4.6696,164300.0,NEAR BAY +-122.16,37.76,36.0,2781.0,574.0,1438.0,519.0,2.4598,155500.0,NEAR BAY +-122.15,37.76,39.0,1823.0,286.0,763.0,270.0,6.0749,196900.0,NEAR BAY +-122.14,37.77,27.0,2229.0,365.0,1297.0,355.0,4.8304,279100.0,NEAR BAY +-122.14,37.76,34.0,1513.0,231.0,545.0,211.0,5.5701,252800.0,NEAR BAY +-122.13,37.76,26.0,3266.0,491.0,1222.0,533.0,5.37,275400.0,NEAR BAY +-122.12,37.75,33.0,1809.0,261.0,808.0,219.0,6.86,250000.0,NEAR BAY +-122.12,37.75,28.0,794.0,111.0,329.0,109.0,7.6923,329800.0,NEAR BAY +-122.14,37.75,36.0,690.0,105.0,299.0,109.0,4.0313,195500.0,NEAR BAY +-122.14,37.75,33.0,1334.0,200.0,579.0,202.0,6.8323,255900.0,NEAR BAY +-122.13,37.75,30.0,414.0,54.0,137.0,50.0,4.975,311100.0,NEAR BAY +-122.13,37.75,36.0,768.0,93.0,229.0,93.0,5.3602,330000.0,NEAR BAY +-122.13,37.74,41.0,4400.0,666.0,1476.0,648.0,5.0,248900.0,NEAR BAY +-122.16,37.75,46.0,954.0,161.0,429.0,154.0,2.925,142900.0,NEAR BAY +-122.15,37.75,40.0,1445.0,256.0,849.0,255.0,3.8913,126300.0,NEAR BAY +-122.15,37.75,44.0,1938.0,399.0,946.0,331.0,3.225,135800.0,NEAR BAY +-122.15,37.74,41.0,856.0,178.0,571.0,191.0,3.1458,130600.0,NEAR BAY +-122.16,37.75,24.0,1790.0,454.0,1137.0,386.0,2.537,107900.0,NEAR BAY +-122.16,37.75,44.0,617.0,131.0,378.0,135.0,2.5568,111100.0,NEAR BAY +-122.15,37.74,43.0,1383.0,275.0,853.0,272.0,3.5083,122000.0,NEAR BAY +-122.15,37.74,49.0,1325.0,277.0,764.0,282.0,3.3125,118000.0,NEAR BAY +-122.16,37.75,40.0,1227.0,294.0,928.0,261.0,1.8235,95200.0,NEAR BAY +-122.16,37.75,35.0,667.0,140.0,406.0,133.0,3.8047,94300.0,NEAR BAY +-122.17,37.74,34.0,1223.0,281.0,824.0,280.0,2.2917,92500.0,NEAR BAY +-122.17,37.75,37.0,1379.0,287.0,835.0,259.0,2.4962,91800.0,NEAR BAY +-122.16,37.74,43.0,1534.0,300.0,826.0,295.0,4.0417,109400.0,NEAR BAY +-122.16,37.74,46.0,1029.0,181.0,567.0,211.0,3.4844,129500.0,NEAR BAY +-122.16,37.74,52.0,771.0,147.0,355.0,144.0,4.1458,143400.0,NEAR BAY +-122.16,37.74,47.0,824.0,223.0,533.0,166.0,2.625,98200.0,NEAR BAY +-122.16,37.74,44.0,1097.0,239.0,609.0,215.0,2.0227,103100.0,NEAR BAY +-122.29,37.9,49.0,1283.0,238.0,576.0,236.0,3.3333,276800.0,NEAR BAY +-122.29,37.9,52.0,1604.0,263.0,594.0,286.0,5.338,270900.0,NEAR BAY +-122.29,37.89,52.0,2248.0,422.0,870.0,377.0,3.4732,246200.0,NEAR BAY +-122.3,37.9,38.0,2263.0,522.0,1027.0,509.0,3.5125,224200.0,NEAR BAY +-122.29,37.89,52.0,1571.0,349.0,693.0,326.0,3.1375,229100.0,NEAR BAY +-122.3,37.89,46.0,1520.0,402.0,815.0,375.0,2.8036,211600.0,NEAR BAY +-122.3,37.9,15.0,5083.0,1212.0,2420.0,1146.0,4.5824,256100.0,NEAR BAY +-122.3,37.89,36.0,1077.0,293.0,518.0,276.0,3.0208,206300.0,NEAR BAY +-122.3,37.89,52.0,1248.0,283.0,620.0,275.0,4.0875,221300.0,NEAR BAY +-122.33,37.89,42.0,1342.0,291.0,551.0,266.0,4.5268,207400.0,NEAR BAY +-122.34,37.88,37.0,3061.0,930.0,2556.0,924.0,1.7375,350000.0,NEAR BAY +-122.29,37.89,52.0,3171.0,698.0,1498.0,696.0,3.1795,218200.0,NEAR BAY +-122.29,37.88,50.0,1211.0,261.0,523.0,227.0,3.8672,216700.0,NEAR BAY +-122.29,37.89,52.0,979.0,175.0,374.0,153.0,5.1675,270600.0,NEAR BAY +-122.29,37.89,52.0,2269.0,380.0,1004.0,371.0,5.1696,261400.0,NEAR BAY +-122.28,37.88,52.0,1844.0,332.0,769.0,334.0,4.2614,261300.0,NEAR BAY +-122.29,37.89,52.0,2178.0,421.0,940.0,423.0,5.0551,232200.0,NEAR BAY +-122.27,37.9,42.0,1650.0,274.0,645.0,256.0,5.6228,375400.0,NEAR BAY +-122.26,37.9,52.0,1927.0,279.0,705.0,288.0,7.8864,357300.0,NEAR BAY +-122.27,37.9,52.0,2079.0,273.0,684.0,275.0,7.9556,374400.0,NEAR BAY +-122.27,37.9,52.0,1803.0,240.0,572.0,236.0,6.174,358800.0,NEAR BAY +-122.27,37.9,52.0,2041.0,270.0,671.0,253.0,6.9414,417500.0,NEAR BAY +-122.27,37.89,52.0,3046.0,373.0,975.0,365.0,8.8342,430500.0,NEAR BAY +-122.28,37.9,52.0,2318.0,328.0,779.0,312.0,7.1754,362900.0,NEAR BAY +-122.28,37.9,52.0,2003.0,250.0,658.0,244.0,10.0825,397000.0,NEAR BAY +-122.28,37.9,52.0,2261.0,328.0,819.0,335.0,4.9083,346800.0,NEAR BAY +-122.28,37.89,52.0,1225.0,169.0,412.0,168.0,5.7912,327100.0,NEAR BAY +-122.28,37.89,52.0,2315.0,408.0,835.0,369.0,4.5893,290100.0,NEAR BAY +-122.28,37.89,52.0,2070.0,329.0,722.0,306.0,5.4171,292000.0,NEAR BAY +-122.28,37.89,52.0,2616.0,473.0,1085.0,487.0,4.125,270900.0,NEAR BAY +-122.27,37.89,52.0,2640.0,366.0,973.0,355.0,7.266,371100.0,NEAR BAY +-122.27,37.89,52.0,1978.0,293.0,723.0,272.0,5.3989,335600.0,NEAR BAY +-122.26,37.9,37.0,2220.0,335.0,903.0,362.0,7.8336,371300.0,NEAR BAY +-122.25,37.89,41.0,1125.0,195.0,356.0,181.0,6.1593,344000.0,NEAR BAY +-122.26,37.89,52.0,3078.0,494.0,1005.0,462.0,6.381,342200.0,NEAR BAY +-122.26,37.89,52.0,3706.0,531.0,1205.0,504.0,6.6828,370900.0,NEAR BAY +-122.25,37.89,37.0,3000.0,457.0,987.0,450.0,7.5385,350000.0,NEAR BAY +-122.25,37.89,42.0,2863.0,460.0,1031.0,448.0,6.7138,368600.0,NEAR BAY +-122.26,37.88,52.0,2551.0,417.0,894.0,404.0,6.2425,391800.0,NEAR BAY +-122.26,37.88,52.0,2255.0,410.0,823.0,377.0,5.7979,415300.0,NEAR BAY +-122.27,37.88,52.0,3360.0,648.0,1232.0,621.0,4.2813,284900.0,NEAR BAY +-122.27,37.88,52.0,1693.0,391.0,669.0,367.0,3.5417,287500.0,NEAR BAY +-122.27,37.88,44.0,2252.0,592.0,989.0,550.0,3.0132,272900.0,NEAR BAY +-122.28,37.88,52.0,2495.0,491.0,1058.0,464.0,4.1429,259600.0,NEAR BAY +-122.28,37.88,52.0,1193.0,200.0,506.0,207.0,4.1912,254500.0,NEAR BAY +-122.28,37.88,52.0,1172.0,215.0,489.0,218.0,3.9167,235600.0,NEAR BAY +-122.29,37.88,52.0,2159.0,424.0,824.0,388.0,3.8897,218400.0,NEAR BAY +-122.29,37.88,48.0,2365.0,490.0,1034.0,475.0,3.1065,229200.0,NEAR BAY +-122.29,37.88,46.0,1895.0,442.0,920.0,425.0,2.9926,192100.0,NEAR BAY +-122.29,37.88,52.0,1650.0,395.0,841.0,380.0,3.556,179300.0,NEAR BAY +-122.3,37.88,45.0,453.0,146.0,749.0,137.0,1.475,187500.0,NEAR BAY +-122.3,37.88,52.0,409.0,97.0,208.0,98.0,1.6971,138800.0,NEAR BAY +-122.3,37.87,10.0,503.0,118.0,228.0,100.0,2.1705,150000.0,NEAR BAY +-122.3,37.86,50.0,499.0,127.0,287.0,128.0,2.75,140600.0,NEAR BAY +-122.29,37.85,52.0,477.0,119.0,218.0,106.0,2.5682,120000.0,NEAR BAY +-122.3,37.88,46.0,1647.0,376.0,854.0,355.0,2.9,144800.0,NEAR BAY +-122.3,37.87,52.0,3123.0,749.0,1695.0,684.0,2.2208,144800.0,NEAR BAY +-122.28,37.88,52.0,957.0,188.0,403.0,172.0,3.2344,245500.0,NEAR BAY +-122.28,37.87,52.0,589.0,132.0,288.0,131.0,3.5156,200000.0,NEAR BAY +-122.28,37.87,49.0,2026.0,548.0,963.0,521.0,1.9805,173700.0,NEAR BAY +-122.29,37.87,50.0,1829.0,536.0,1129.0,516.0,2.6684,185600.0,NEAR BAY +-122.29,37.87,52.0,895.0,198.0,386.0,204.0,3.875,182600.0,NEAR BAY +-122.28,37.88,52.0,1909.0,416.0,811.0,406.0,3.006,227900.0,NEAR BAY +-122.28,37.87,46.0,3022.0,696.0,1293.0,675.0,2.543,220700.0,NEAR BAY +-122.28,37.87,46.0,1777.0,446.0,805.0,431.0,2.8676,212000.0,NEAR BAY +-122.27,37.88,52.0,2803.0,930.0,1372.0,876.0,2.1907,271400.0,NEAR BAY +-122.27,37.87,30.0,1465.0,439.0,862.0,425.0,1.7778,268800.0,NEAR BAY +-122.27,37.88,37.0,2619.0,682.0,1152.0,616.0,2.52,277800.0,NEAR BAY +-122.26,37.88,52.0,1149.0,255.0,483.0,249.0,4.2788,332500.0,NEAR BAY +-122.26,37.88,52.0,2363.0,604.0,1558.0,573.0,2.944,338900.0,NEAR BAY +-122.26,37.88,52.0,2604.0,837.0,1798.0,769.0,1.725,287500.0,NEAR BAY +-122.25,37.87,41.0,685.0,141.0,266.0,123.0,5.2289,384600.0,NEAR BAY +-122.25,37.87,42.0,1756.0,465.0,2184.0,422.0,2.5562,371400.0,NEAR BAY +-122.25,37.87,52.0,1204.0,460.0,2016.0,477.0,0.949,350000.0,NEAR BAY +-122.25,37.87,52.0,609.0,236.0,1349.0,250.0,1.1696,500001.0,NEAR BAY +-122.26,37.87,52.0,1087.0,371.0,3337.0,350.0,1.4012,175000.0,NEAR BAY +-122.26,37.87,52.0,2773.0,998.0,1721.0,949.0,1.1859,241700.0,NEAR BAY +-122.27,37.87,35.0,3218.0,1108.0,1675.0,1000.0,1.7464,216700.0,NEAR BAY +-122.27,37.87,49.0,1350.0,368.0,707.0,350.0,2.8846,211300.0,NEAR BAY +-122.27,37.87,52.0,3084.0,698.0,1424.0,694.0,2.7372,210200.0,NEAR BAY +-122.28,37.86,52.0,938.0,195.0,393.0,189.0,3.8594,196400.0,NEAR BAY +-122.28,37.87,52.0,1813.0,353.0,828.0,339.0,3.5625,191700.0,NEAR BAY +-122.28,37.87,52.0,1233.0,300.0,571.0,292.0,2.2788,182300.0,NEAR BAY +-122.29,37.87,44.0,2539.0,755.0,1382.0,713.0,2.537,175000.0,NEAR BAY +-122.29,37.87,46.0,1267.0,324.0,792.0,321.0,2.525,165900.0,NEAR BAY +-122.29,37.86,52.0,1665.0,404.0,815.0,372.0,1.9946,156900.0,NEAR BAY +-122.28,37.86,52.0,1659.0,367.0,788.0,346.0,2.8214,164300.0,NEAR BAY +-122.29,37.87,52.0,2225.0,460.0,1145.0,430.0,2.6165,150000.0,NEAR BAY +-122.29,37.86,50.0,2485.0,607.0,1354.0,563.0,1.9483,150500.0,NEAR BAY +-122.28,37.86,52.0,2031.0,450.0,958.0,445.0,1.9327,169900.0,NEAR BAY +-122.28,37.86,49.0,2932.0,668.0,1361.0,608.0,1.9798,147400.0,NEAR BAY +-122.28,37.85,52.0,2246.0,472.0,1005.0,449.0,2.4167,152700.0,NEAR BAY +-122.27,37.86,49.0,2052.0,435.0,924.0,414.0,2.5417,182700.0,NEAR BAY +-122.28,37.86,52.0,1999.0,417.0,780.0,358.0,3.3906,179300.0,NEAR BAY +-122.28,37.86,52.0,3007.0,691.0,1582.0,636.0,2.5652,157700.0,NEAR BAY +-122.28,37.86,41.0,2214.0,550.0,1213.0,568.0,2.2845,153100.0,NEAR BAY +-122.27,37.86,52.0,1088.0,305.0,486.0,267.0,2.6071,250000.0,NEAR BAY +-122.27,37.86,52.0,834.0,186.0,494.0,175.0,3.15,206300.0,NEAR BAY +-122.27,37.86,52.0,1769.0,372.0,849.0,365.0,2.6914,218800.0,NEAR BAY +-122.27,37.86,52.0,2307.0,583.0,1127.0,548.0,1.8447,198200.0,NEAR BAY +-122.26,37.86,35.0,5161.0,1744.0,3276.0,1742.0,1.6307,253600.0,NEAR BAY +-122.26,37.86,52.0,3497.0,832.0,1493.0,794.0,2.9044,257400.0,NEAR BAY +-122.26,37.86,52.0,3774.0,744.0,1461.0,679.0,2.9405,289500.0,NEAR BAY +-122.26,37.86,52.0,2888.0,604.0,1253.0,538.0,3.3893,241700.0,NEAR BAY +-122.25,37.86,48.0,2153.0,517.0,1656.0,459.0,3.0417,489600.0,NEAR BAY +-122.25,37.86,52.0,1389.0,191.0,514.0,202.0,7.0897,446200.0,NEAR BAY +-122.25,37.86,52.0,1709.0,318.0,719.0,295.0,5.0463,456300.0,NEAR BAY +-122.25,37.86,52.0,1587.0,444.0,878.0,449.0,1.7652,336800.0,NEAR BAY +-122.24,37.86,52.0,1668.0,225.0,517.0,214.0,7.8521,500001.0,NEAR BAY +-122.24,37.85,52.0,3726.0,474.0,1366.0,496.0,9.3959,500001.0,NEAR BAY +-122.25,37.86,52.0,4048.0,663.0,1316.0,590.0,5.3794,376900.0,NEAR BAY +-122.26,37.85,52.0,3618.0,768.0,1508.0,755.0,3.2619,309600.0,NEAR BAY +-122.27,37.85,52.0,4076.0,920.0,1800.0,815.0,2.7054,182300.0,NEAR BAY +-122.27,37.85,47.0,2077.0,400.0,719.0,326.0,2.2431,172700.0,NEAR BAY +-122.27,37.85,50.0,1279.0,300.0,675.0,255.0,1.9028,150800.0,NEAR BAY +-122.27,37.85,52.0,1974.0,426.0,875.0,363.0,1.5817,153600.0,NEAR BAY +-122.27,37.85,52.0,335.0,83.0,152.0,77.0,2.2841,106300.0,NEAR BAY +-122.27,37.85,47.0,1375.0,307.0,843.0,319.0,1.3785,142300.0,NEAR BAY +-122.28,37.85,52.0,610.0,145.0,281.0,132.0,2.9018,119400.0,NEAR BAY +-122.28,37.85,44.0,1025.0,198.0,506.0,204.0,1.73,147900.0,NEAR BAY +-122.28,37.85,48.0,2063.0,484.0,1054.0,466.0,2.2625,132900.0,NEAR BAY +-122.29,37.84,35.0,1872.0,419.0,1017.0,414.0,2.2106,132500.0,NEAR BAY +-122.28,37.83,52.0,3108.0,813.0,1623.0,765.0,2.6997,126900.0,NEAR BAY +-122.3,37.84,14.0,7355.0,2408.0,3100.0,2051.0,4.0018,143800.0,NEAR BAY +-122.23,37.83,52.0,2990.0,379.0,947.0,361.0,7.8772,500001.0,NEAR BAY +-122.22,37.82,39.0,2492.0,310.0,808.0,315.0,11.8603,500001.0,NEAR BAY +-122.22,37.82,42.0,2991.0,335.0,1018.0,335.0,13.499,500001.0,NEAR BAY +-122.23,37.82,52.0,3242.0,366.0,1001.0,352.0,12.2138,500001.0,NEAR BAY +-122.23,37.82,52.0,3051.0,381.0,1005.0,369.0,8.1872,466100.0,NEAR BAY +-122.23,37.82,52.0,3494.0,396.0,1192.0,383.0,12.3804,500001.0,NEAR BAY +-122.24,37.83,52.0,1757.0,246.0,585.0,227.0,5.8948,457800.0,NEAR BAY +-122.24,37.83,52.0,2449.0,312.0,916.0,316.0,8.1194,471600.0,NEAR BAY +-122.23,37.82,52.0,1611.0,203.0,556.0,179.0,8.7477,500001.0,NEAR BAY +-122.24,37.82,52.0,3665.0,517.0,1470.0,520.0,6.155,398600.0,NEAR BAY +-122.25,37.82,52.0,2474.0,403.0,1104.0,398.0,5.883,340700.0,NEAR BAY +-122.23,37.76,52.0,3037.0,516.0,1242.0,518.0,5.2128,289900.0,NEAR BAY +-122.23,37.76,52.0,2269.0,323.0,805.0,321.0,4.7188,335300.0,NEAR BAY +-122.23,37.76,52.0,1316.0,177.0,378.0,162.0,5.2915,333000.0,NEAR BAY +-122.24,37.77,52.0,1153.0,235.0,481.0,223.0,2.6411,241000.0,NEAR BAY +-122.23,37.77,52.0,772.0,179.0,409.0,160.0,3.3214,189600.0,NEAR BAY +-122.24,37.77,52.0,1711.0,386.0,885.0,373.0,3.6417,206300.0,NEAR BAY +-122.25,37.77,52.0,2650.0,566.0,1468.0,567.0,3.0161,215700.0,NEAR BAY +-122.25,37.77,52.0,1038.0,220.0,482.0,215.0,3.1771,210200.0,NEAR BAY +-122.25,37.77,52.0,1527.0,320.0,825.0,264.0,3.4531,208800.0,NEAR BAY +-122.25,37.77,52.0,859.0,157.0,429.0,158.0,4.3098,197900.0,NEAR BAY +-122.26,37.78,52.0,970.0,217.0,528.0,208.0,3.3438,201300.0,NEAR BAY +-122.26,37.78,52.0,1045.0,239.0,496.0,216.0,2.9213,190800.0,NEAR BAY +-122.27,37.78,52.0,1408.0,280.0,718.0,265.0,2.6806,207900.0,NEAR BAY +-122.27,37.78,52.0,1222.0,264.0,630.0,265.0,3.7708,215300.0,NEAR BAY +-122.27,37.78,45.0,1169.0,263.0,723.0,286.0,3.9444,212900.0,NEAR BAY +-122.27,37.78,13.0,2020.0,535.0,959.0,486.0,5.2601,292700.0,NEAR BAY +-122.28,37.79,30.0,4145.0,869.0,3668.0,855.0,2.5444,275000.0,NEAR BAY +-122.3,37.77,42.0,2038.0,368.0,2037.0,355.0,2.6447,200000.0,NEAR BAY +-122.28,37.78,29.0,5154.0,,3741.0,1273.0,2.5762,173400.0,NEAR BAY +-122.29,37.78,42.0,1241.0,309.0,821.0,300.0,1.9427,102200.0,NEAR BAY +-122.28,37.77,52.0,1468.0,363.0,870.0,347.0,2.9688,220800.0,NEAR BAY +-122.28,37.77,27.0,3997.0,1073.0,1901.0,966.0,3.75,242800.0,NEAR BAY +-122.29,37.76,18.0,2873.0,763.0,1243.0,663.0,5.1702,265400.0,NEAR BAY +-122.28,37.78,50.0,1487.0,306.0,730.0,327.0,2.5139,219000.0,NEAR BAY +-122.26,37.77,52.0,1670.0,350.0,793.0,299.0,2.9732,282100.0,NEAR BAY +-122.27,37.77,52.0,2252.0,388.0,1033.0,434.0,5.5337,372000.0,NEAR BAY +-122.27,37.77,52.0,1731.0,377.0,872.0,363.0,4.1667,225800.0,NEAR BAY +-122.27,37.77,52.0,1710.0,481.0,849.0,457.0,2.7115,220800.0,NEAR BAY +-122.27,37.77,52.0,2388.0,559.0,1121.0,518.0,3.3269,234500.0,NEAR BAY +-122.25,37.77,52.0,2156.0,458.0,872.0,445.0,3.2685,254200.0,NEAR BAY +-122.26,37.77,52.0,1565.0,315.0,637.0,297.0,4.7778,351800.0,NEAR BAY +-122.26,37.77,52.0,1704.0,371.0,663.0,340.0,4.226,275000.0,NEAR BAY +-122.26,37.77,52.0,1210.0,168.0,411.0,172.0,3.3571,405400.0,NEAR BAY +-122.26,37.77,52.0,2097.0,444.0,915.0,413.0,2.9899,228100.0,NEAR BAY +-122.26,37.77,52.0,1848.0,479.0,921.0,477.0,2.875,234000.0,NEAR BAY +-122.24,37.77,43.0,955.0,284.0,585.0,266.0,2.3882,162500.0,NEAR BAY +-122.25,37.77,43.0,4329.0,1110.0,2086.0,1053.0,2.975,243400.0,NEAR BAY +-122.23,37.76,52.0,3011.0,542.0,1303.0,535.0,5.1039,273800.0,NEAR BAY +-122.23,37.76,52.0,1049.0,185.0,374.0,176.0,4.1458,248500.0,NEAR BAY +-122.24,37.76,52.0,2504.0,516.0,979.0,472.0,3.4762,244000.0,NEAR BAY +-122.24,37.76,52.0,1846.0,471.0,827.0,446.0,2.6833,240900.0,NEAR BAY +-122.23,37.76,52.0,1705.0,246.0,658.0,253.0,5.75,306300.0,NEAR BAY +-122.23,37.75,50.0,1542.0,289.0,654.0,268.0,3.9632,240000.0,NEAR BAY +-122.24,37.75,45.0,891.0,,384.0,146.0,4.9489,247100.0,NEAR BAY +-122.24,37.75,27.0,4051.0,753.0,1499.0,797.0,4.8711,286600.0,NEAR BAY +-122.24,37.76,52.0,2646.0,581.0,1128.0,522.0,3.0718,266700.0,NEAR BAY +-122.24,37.76,49.0,2428.0,525.0,1110.0,492.0,3.6719,229800.0,NEAR BAY +-122.24,37.76,52.0,2567.0,436.0,1119.0,415.0,4.6094,229300.0,NEAR BAY +-122.24,37.73,21.0,7031.0,1249.0,2930.0,1235.0,4.5213,228400.0,NEAR BAY +-122.25,37.74,25.0,1914.0,365.0,897.0,390.0,4.4562,206200.0,NEAR BAY +-122.24,37.72,5.0,18634.0,2885.0,7427.0,2718.0,7.611,350700.0,NEAR BAY +-122.27,37.73,31.0,5785.0,1379.0,2973.0,1312.0,3.2689,231000.0,NEAR BAY +-122.25,37.76,52.0,2876.0,648.0,1340.0,632.0,3.567,252900.0,NEAR BAY +-122.27,37.74,28.0,6909.0,1554.0,2974.0,1484.0,3.6875,353900.0,NEAR BAY +-122.27,37.77,23.0,5679.0,1270.0,2690.0,1151.0,4.7695,291700.0,NEAR BAY 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+-122.15,37.73,52.0,1028.0,129.0,317.0,143.0,4.9135,275000.0,NEAR BAY +-122.15,37.73,45.0,3758.0,819.0,1573.0,736.0,2.8355,245400.0,NEAR BAY +-122.17,37.73,46.0,2163.0,470.0,925.0,435.0,3.25,177500.0,NEAR BAY +-122.16,37.73,49.0,1699.0,408.0,768.0,385.0,2.8301,171600.0,NEAR BAY +-122.17,37.73,52.0,1555.0,289.0,620.0,292.0,3.7159,183300.0,NEAR BAY +-122.16,37.73,52.0,1114.0,206.0,425.0,207.0,2.5625,175000.0,NEAR BAY +-122.18,37.72,45.0,1498.0,313.0,1003.0,305.0,3.8047,156700.0,NEAR BAY +-122.18,37.71,45.0,726.0,147.0,519.0,135.0,3.375,157500.0,NEAR BAY +-122.17,37.71,38.0,890.0,200.0,481.0,198.0,3.244,179800.0,NEAR BAY +-122.18,37.7,36.0,2639.0,533.0,1209.0,519.0,4.0268,205500.0,NEAR BAY +-122.18,37.7,35.0,2562.0,554.0,1398.0,525.0,3.3906,178900.0,NEAR BAY +-122.19,37.71,36.0,361.0,69.0,158.0,58.0,5.5461,262500.0,NEAR BAY +-122.17,37.72,42.0,3008.0,659.0,1817.0,664.0,3.371,165000.0,NEAR BAY +-122.17,37.72,46.0,1369.0,284.0,766.0,289.0,3.5313,159700.0,NEAR BAY 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+-122.13,37.69,34.0,1131.0,278.0,560.0,237.0,2.875,161700.0,NEAR BAY +-122.12,37.71,38.0,1164.0,284.0,632.0,289.0,3.0345,152100.0,NEAR BAY +-122.12,37.7,41.0,3495.0,787.0,1849.0,750.0,2.679,144900.0,NEAR BAY +-122.12,37.7,17.0,2488.0,617.0,1287.0,538.0,2.9922,179900.0,NEAR BAY +-122.12,37.69,35.0,2681.0,508.0,1580.0,536.0,4.1042,179100.0,NEAR BAY +-122.12,37.7,19.0,2495.0,635.0,1571.0,579.0,2.5833,159900.0,NEAR BAY +-122.11,37.7,23.0,1689.0,461.0,828.0,443.0,2.1552,161400.0,NEAR BAY +-122.11,37.7,29.0,1298.0,306.0,835.0,338.0,2.3274,170400.0,NEAR BAY +-122.11,37.7,19.0,2693.0,789.0,1765.0,724.0,2.4206,137500.0,NEAR BAY +-122.1,37.69,41.0,746.0,,387.0,161.0,3.9063,178400.0,NEAR BAY +-122.11,37.69,37.0,2444.0,651.0,1562.0,618.0,2.6464,155200.0,NEAR BAY +-122.11,37.69,42.0,1472.0,310.0,768.0,309.0,3.4643,160900.0,NEAR BAY +-122.12,37.69,10.0,2227.0,560.0,1140.0,472.0,2.3973,167300.0,NEAR BAY +-122.03,37.69,20.0,200.0,25.0,83.0,31.0,6.5,340000.0,NEAR BAY 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+-122.11,37.66,29.0,2544.0,643.0,2332.0,603.0,3.2091,150000.0,NEAR BAY +-122.1,37.66,36.0,1305.0,225.0,768.0,234.0,4.275,185300.0,NEAR BAY +-122.11,37.66,36.0,1755.0,316.0,913.0,299.0,4.1302,172700.0,NEAR BAY +-122.11,37.66,35.0,2843.0,652.0,1726.0,643.0,3.09,174100.0,NEAR BAY +-122.1,37.65,25.0,2538.0,494.0,1185.0,501.0,4.5417,194400.0,NEAR BAY +-122.1,37.64,28.0,1784.0,311.0,735.0,278.0,4.6635,206700.0,NEAR BAY +-122.1,37.65,31.0,1797.0,327.0,796.0,319.0,4.4427,204500.0,NEAR BAY +-122.1,37.65,36.0,410.0,76.0,252.0,82.0,4.5303,175000.0,NEAR BAY +-122.12,37.65,26.0,162.0,27.0,86.0,25.0,2.375,137500.0,NEAR BAY +-122.1,37.63,18.0,9963.0,2031.0,5613.0,1946.0,3.8171,187200.0,NEAR BAY +-122.1,37.61,35.0,2361.0,458.0,1727.0,467.0,4.5281,173600.0,NEAR BAY +-122.13,37.66,19.0,862.0,167.0,407.0,183.0,4.3456,163000.0,NEAR BAY +-122.11,37.65,18.0,4335.0,808.0,2041.0,734.0,3.4861,331600.0,NEAR BAY +-122.11,37.64,31.0,1487.0,280.0,854.0,301.0,5.2312,197600.0,NEAR BAY +-122.11,37.64,8.0,3592.0,849.0,1907.0,746.0,3.6708,197900.0,NEAR BAY +-122.12,37.64,40.0,432.0,102.0,264.0,77.0,3.8875,228100.0,NEAR BAY +-122.09,37.63,34.0,1457.0,242.0,735.0,249.0,3.9167,189500.0,NEAR BAY +-122.09,37.64,32.0,1578.0,284.0,836.0,292.0,3.9063,184200.0,NEAR BAY +-122.1,37.63,29.0,2172.0,435.0,1377.0,408.0,3.7895,180900.0,NEAR BAY +-122.08,37.64,36.0,1340.0,245.0,789.0,248.0,3.8,172000.0,NEAR BAY +-122.08,37.64,36.0,1116.0,199.0,662.0,226.0,5.7309,177900.0,NEAR BAY +-122.09,37.64,36.0,1885.0,307.0,853.0,271.0,4.1141,173100.0,NEAR BAY +-122.09,37.64,36.0,1180.0,212.0,664.0,200.0,5.2838,172600.0,NEAR BAY +-122.08,37.64,30.0,5267.0,1253.0,4065.0,1113.0,3.3479,182100.0,NEAR BAY +-122.08,37.64,23.0,1897.0,440.0,1109.0,418.0,3.142,179500.0,NEAR BAY +-122.08,37.63,31.0,767.0,171.0,548.0,185.0,3.7614,176000.0,NEAR BAY +-122.08,37.63,33.0,691.0,127.0,431.0,149.0,4.25,192600.0,NEAR BAY +-122.08,37.64,36.0,786.0,133.0,463.0,160.0,3.9338,182700.0,NEAR BAY +-122.07,37.64,22.0,5861.0,1516.0,5436.0,1463.0,2.5158,134900.0,NEAR BAY +-122.07,37.63,27.0,2784.0,723.0,2028.0,693.0,2.4808,157600.0,NEAR BAY +-122.07,37.64,25.0,4524.0,860.0,2426.0,862.0,4.7083,190900.0,NEAR BAY +-122.06,37.64,37.0,1468.0,304.0,1038.0,282.0,4.1652,158200.0,NEAR BAY +-122.06,37.64,20.0,1655.0,450.0,857.0,430.0,3.5541,350000.0,NEAR BAY +-122.06,37.65,33.0,1227.0,286.0,848.0,291.0,3.8036,158200.0,NEAR BAY +-122.06,37.64,33.0,1160.0,252.0,729.0,220.0,3.8259,146100.0,NEAR BAY +-122.04,37.63,33.0,952.0,172.0,369.0,159.0,3.2331,226700.0,NEAR BAY +-122.03,37.62,35.0,2072.0,352.0,1001.0,350.0,4.7109,198700.0,NEAR BAY +-122.03,37.62,32.0,2964.0,547.0,1472.0,527.0,4.2468,221200.0,NEAR BAY +-122.04,37.62,35.0,1032.0,173.0,453.0,176.0,6.396,208500.0,NEAR BAY +-122.04,37.62,35.0,657.0,118.0,328.0,134.0,3.8125,204200.0,NEAR BAY +-122.04,37.62,32.0,1540.0,324.0,793.0,302.0,3.2857,193200.0,NEAR BAY +-122.03,37.62,35.0,1298.0,236.0,632.0,204.0,3.8929,209500.0,NEAR BAY +-122.03,37.61,36.0,1409.0,271.0,1002.0,281.0,3.7262,164900.0,NEAR BAY +-122.03,37.61,37.0,1383.0,259.0,808.0,241.0,4.0125,161400.0,NEAR BAY +-122.04,37.61,36.0,1151.0,216.0,727.0,215.0,4.1719,187000.0,NEAR BAY +-122.04,37.62,35.0,899.0,179.0,455.0,185.0,4.2857,190400.0,NEAR BAY +-122.08,37.63,37.0,1793.0,364.0,1534.0,346.0,3.6458,156600.0,NEAR BAY +-122.08,37.62,17.0,2485.0,518.0,1139.0,550.0,2.6875,157300.0,NEAR BAY +-122.07,37.63,35.0,1931.0,376.0,1175.0,337.0,3.7292,168100.0,NEAR BAY +-122.07,37.63,24.0,2329.0,465.0,1401.0,453.0,4.5913,177600.0,NEAR BAY +-122.06,37.63,12.0,6711.0,1374.0,3388.0,1289.0,3.8625,208900.0,NEAR BAY +-122.06,37.63,23.0,1939.0,356.0,841.0,364.0,3.3611,169200.0,NEAR BAY +-122.05,37.61,16.0,1642.0,346.0,705.0,351.0,2.8971,163900.0,NEAR BAY +-122.08,37.63,34.0,1619.0,293.0,1148.0,310.0,4.0326,164700.0,NEAR BAY +-122.08,37.63,35.0,517.0,108.0,391.0,107.0,4.0682,156900.0,NEAR BAY +-122.09,37.63,36.0,1570.0,274.0,992.0,249.0,5.3644,168800.0,NEAR BAY +-122.09,37.63,35.0,1213.0,221.0,790.0,243.0,4.7019,174100.0,NEAR BAY +-122.08,37.62,27.0,1826.0,309.0,1016.0,313.0,5.64,206500.0,NEAR BAY +-122.08,37.61,26.0,2261.0,443.0,1039.0,395.0,3.7931,203900.0,NEAR BAY +-121.99,37.61,9.0,3666.0,711.0,2341.0,703.0,4.6458,217000.0,<1H OCEAN +-122.02,37.6,36.0,1633.0,345.0,1382.0,338.0,3.694,159600.0,NEAR BAY +-122.02,37.6,31.0,2155.0,522.0,1858.0,437.0,2.652,159800.0,NEAR BAY +-122.02,37.6,32.0,1295.0,280.0,1156.0,300.0,3.5,154300.0,NEAR BAY +-122.02,37.6,32.0,1295.0,295.0,1097.0,328.0,3.2386,149600.0,NEAR BAY +-122.03,37.61,33.0,1518.0,302.0,831.0,268.0,5.097,188600.0,NEAR BAY +-122.04,37.6,17.0,3314.0,638.0,1873.0,602.0,4.3875,238500.0,NEAR BAY +-122.06,37.6,22.0,3009.0,497.0,1640.0,514.0,4.625,235300.0,NEAR BAY +-122.08,37.61,6.0,2605.0,474.0,1568.0,433.0,5.0406,261400.0,NEAR BAY +-122.06,37.6,17.0,5159.0,832.0,3174.0,817.0,5.8704,234400.0,NEAR BAY +-122.06,37.6,18.0,1726.0,276.0,1186.0,310.0,5.3226,231700.0,NEAR BAY 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BAY +-122.05,37.57,7.0,10648.0,1818.0,6075.0,1797.0,6.1047,278200.0,NEAR BAY +-121.93,37.49,5.0,1150.0,311.0,648.0,245.0,3.5714,300000.0,<1H OCEAN +-122.07,37.52,3.0,14014.0,2861.0,7205.0,2753.0,6.0824,273500.0,NEAR BAY +-122.02,37.56,23.0,4332.0,857.0,2461.0,829.0,4.3594,223400.0,NEAR BAY +-122.02,37.56,35.0,1716.0,312.0,914.0,316.0,5.5737,214500.0,NEAR BAY +-122.03,37.56,31.0,4981.0,964.0,2841.0,924.0,4.8962,220200.0,NEAR BAY +-122.03,37.56,24.0,8444.0,1492.0,4446.0,1491.0,4.6978,240300.0,NEAR BAY +-122.01,37.56,6.0,3028.0,778.0,1531.0,736.0,4.4259,158000.0,NEAR BAY +-122.01,37.55,26.0,2068.0,532.0,1434.0,495.0,3.3008,224200.0,NEAR BAY +-122.02,37.55,33.0,1325.0,274.0,909.0,267.0,4.5687,177200.0,NEAR BAY +-122.01,37.56,24.0,2563.0,485.0,1174.0,501.0,3.8179,216100.0,NEAR BAY +-121.99,37.56,18.0,5505.0,1005.0,2641.0,971.0,5.0,269700.0,<1H OCEAN +-121.99,37.56,20.0,6462.0,1294.0,3288.0,1235.0,4.3393,231200.0,<1H OCEAN +-121.96,37.55,4.0,3746.0,993.0,1606.0,838.0,4.1387,162500.0,<1H OCEAN +-121.97,37.56,13.0,8918.0,1823.0,4518.0,1772.0,4.8052,254000.0,<1H OCEAN +-121.97,37.54,31.0,1949.0,344.0,986.0,322.0,4.6349,196200.0,<1H OCEAN +-121.97,37.55,17.0,4924.0,1247.0,3080.0,1182.0,3.168,189400.0,<1H OCEAN +-121.98,37.54,17.0,5133.0,1375.0,3386.0,1339.0,3.1326,220800.0,<1H OCEAN +-121.99,37.55,16.0,6647.0,2098.0,4649.0,1903.0,2.9074,213800.0,<1H OCEAN +-121.94,37.56,15.0,5674.0,748.0,2412.0,714.0,8.3996,442900.0,<1H OCEAN +-121.95,37.55,21.0,10687.0,1540.0,4552.0,1520.0,6.6478,333400.0,<1H OCEAN +-121.93,37.54,25.0,1354.0,192.0,596.0,220.0,6.629,352400.0,<1H OCEAN +-121.94,37.54,27.0,3715.0,526.0,1631.0,538.0,6.2179,305300.0,<1H OCEAN +-121.94,37.53,33.0,2095.0,342.0,941.0,304.0,5.761,259600.0,<1H OCEAN +-121.95,37.54,29.0,3517.0,645.0,1724.0,585.0,4.6641,248900.0,<1H OCEAN +-121.94,37.54,31.0,2537.0,382.0,1067.0,410.0,6.7599,356000.0,<1H OCEAN +-121.96,37.54,14.0,5106.0,1207.0,2738.0,1108.0,3.9909,236000.0,<1H OCEAN 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OCEAN +-121.92,37.49,10.0,7441.0,1588.0,3571.0,1466.0,5.1643,193100.0,<1H OCEAN +-121.92,37.48,23.0,4314.0,676.0,1972.0,623.0,5.3813,264400.0,<1H OCEAN +-121.92,37.47,26.0,2016.0,322.0,1105.0,357.0,6.0878,246900.0,<1H OCEAN +-121.91,37.47,13.0,5377.0,744.0,2759.0,760.0,6.868,337300.0,<1H OCEAN +-122.03,37.55,22.0,9167.0,1373.0,4319.0,1404.0,6.992,284800.0,NEAR BAY +-122.03,37.55,26.0,3087.0,532.0,1597.0,483.0,4.9118,217300.0,NEAR BAY +-122.04,37.55,23.0,3170.0,532.0,1446.0,515.0,4.4357,291700.0,NEAR BAY +-122.04,37.54,26.0,2145.0,369.0,1285.0,377.0,4.9464,223800.0,NEAR BAY +-122.05,37.54,25.0,4209.0,731.0,2568.0,703.0,5.2882,223100.0,NEAR BAY +-122.05,37.55,23.0,4247.0,835.0,2357.0,823.0,5.1321,211300.0,NEAR BAY +-122.04,37.53,25.0,4458.0,922.0,2998.0,890.0,3.9667,218500.0,NEAR BAY +-122.04,37.5,17.0,407.0,97.0,307.0,100.0,3.1696,156300.0,NEAR BAY +-122.06,37.54,20.0,6483.0,1068.0,3526.0,1060.0,5.0838,248200.0,NEAR BAY +-122.03,37.54,35.0,1867.0,343.0,1213.0,338.0,4.8214,186000.0,NEAR BAY +-122.03,37.54,6.0,2918.0,672.0,1911.0,639.0,4.1406,178200.0,NEAR BAY +-122.04,37.53,34.0,2316.0,478.0,1524.0,467.0,3.7364,190400.0,NEAR BAY +-122.02,37.54,31.0,1240.0,264.0,719.0,236.0,3.535,210300.0,NEAR BAY +-122.03,37.54,16.0,4458.0,856.0,3038.0,870.0,5.0739,208000.0,NEAR BAY +-122.03,37.53,18.0,1746.0,437.0,1268.0,404.0,3.256,183300.0,NEAR BAY +-122.01,37.53,19.0,4572.0,712.0,2346.0,709.0,6.0667,245700.0,NEAR BAY +-122.02,37.53,21.0,4280.0,673.0,2216.0,681.0,5.7072,242200.0,NEAR BAY +-122.0,37.51,7.0,6352.0,1390.0,3223.0,1316.0,4.9867,181700.0,<1H OCEAN +-121.92,37.72,4.0,7477.0,1576.0,2937.0,1506.0,5.1437,299400.0,<1H OCEAN +-121.92,37.72,22.0,4638.0,716.0,2302.0,687.0,5.347,219500.0,<1H OCEAN +-121.91,37.71,25.0,4377.0,668.0,2038.0,671.0,5.7233,231800.0,<1H OCEAN +-121.93,37.72,26.0,3816.0,637.0,1935.0,642.0,4.4697,221300.0,<1H OCEAN +-121.93,37.72,26.0,2806.0,459.0,1453.0,444.0,4.9107,213800.0,<1H OCEAN +-121.93,37.71,26.0,4822.0,845.0,2288.0,805.0,4.2281,206000.0,<1H OCEAN 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+-121.99,37.92,14.0,1780.0,224.0,764.0,226.0,9.0243,427700.0,INLAND +-121.93,37.89,13.0,2085.0,292.0,852.0,264.0,7.3445,366700.0,INLAND +-121.92,37.96,14.0,5332.0,884.0,2093.0,839.0,5.2798,237400.0,INLAND +-121.94,37.95,18.0,2541.0,355.0,986.0,346.0,7.1978,288000.0,INLAND +-121.91,37.93,13.0,1610.0,198.0,703.0,217.0,8.7059,329400.0,INLAND +-121.94,37.94,26.0,1299.0,174.0,533.0,180.0,6.2296,291700.0,INLAND +-121.94,37.93,16.0,3421.0,427.0,1341.0,428.0,7.5695,320400.0,INLAND +-121.93,37.93,16.0,2876.0,391.0,1089.0,377.0,6.299,286900.0,INLAND +-121.93,37.93,16.0,2169.0,262.0,877.0,245.0,6.6049,312600.0,INLAND +-121.95,37.94,27.0,1469.0,216.0,578.0,219.0,5.9346,253600.0,INLAND +-122.25,38.02,16.0,1803.0,267.0,946.0,266.0,5.7001,205100.0,NEAR BAY +-122.25,38.03,15.0,3338.0,532.0,1834.0,520.0,5.6293,197600.0,NEAR BAY +-122.21,38.02,15.0,2150.0,327.0,1094.0,324.0,6.0224,198500.0,NEAR BAY +-122.2,37.96,9.0,6306.0,962.0,2581.0,911.0,6.7741,310700.0,NEAR BAY +-122.23,38.06,52.0,1350.0,266.0,490.0,257.0,3.125,171100.0,NEAR BAY +-122.23,38.05,52.0,1736.0,358.0,638.0,297.0,2.5517,147100.0,NEAR BAY +-122.21,38.06,52.0,2735.0,559.0,1076.0,487.0,3.6154,155700.0,NEAR BAY +-122.2,38.04,31.0,3029.0,500.0,1236.0,487.0,5.6022,197000.0,NEAR BAY +-122.26,38.03,25.0,2239.0,361.0,928.0,353.0,4.4474,203700.0,NEAR BAY +-122.27,38.04,47.0,1685.0,405.0,835.0,372.0,2.3103,134500.0,NEAR BAY +-122.26,38.03,41.0,1631.0,282.0,752.0,288.0,3.9345,150200.0,NEAR BAY +-122.26,38.04,41.0,2512.0,539.0,1179.0,480.0,2.694,123000.0,NEAR BAY +-122.25,38.05,30.0,1255.0,297.0,779.0,307.0,1.6767,147700.0,NEAR BAY +-122.32,38.06,4.0,7999.0,1611.0,3596.0,1396.0,5.0969,174200.0,NEAR BAY +-122.28,38.0,26.0,2335.0,413.0,980.0,417.0,3.4471,178900.0,NEAR BAY +-122.29,38.0,16.0,4986.0,1081.0,2805.0,1016.0,4.025,173200.0,NEAR BAY +-122.36,38.03,32.0,2159.0,393.0,981.0,369.0,4.3173,175400.0,NEAR BAY +-122.3,38.0,34.0,1712.0,317.0,956.0,341.0,4.4394,162000.0,NEAR BAY +-122.31,38.0,26.0,3735.0,641.0,1708.0,633.0,4.621,191100.0,NEAR BAY +-122.31,38.01,18.0,4123.0,874.0,1895.0,772.0,3.2759,195000.0,NEAR BAY +-122.28,37.99,28.0,3801.0,622.0,1654.0,571.0,4.375,193300.0,NEAR BAY +-122.26,37.98,28.0,2038.0,329.0,947.0,349.0,5.1178,198000.0,NEAR BAY +-122.27,37.99,16.0,4921.0,737.0,2312.0,725.0,5.8899,243200.0,NEAR BAY +-122.27,37.98,23.0,3455.0,479.0,1375.0,474.0,6.0289,218600.0,NEAR BAY +-122.26,38.02,5.0,3846.0,786.0,2053.0,716.0,5.0473,184800.0,NEAR BAY +-122.24,38.01,11.0,3751.0,565.0,1949.0,555.0,5.7862,269400.0,NEAR BAY +-122.24,38.01,16.0,2084.0,315.0,1154.0,307.0,6.0102,235600.0,NEAR BAY +-122.25,38.0,16.0,2978.0,411.0,1531.0,400.0,6.5006,237700.0,NEAR BAY +-122.26,38.0,14.0,2338.0,391.0,1003.0,398.0,4.2269,170500.0,NEAR BAY +-122.26,38.0,6.0,678.0,104.0,318.0,91.0,5.2375,246400.0,NEAR BAY +-122.27,38.0,15.0,1216.0,166.0,572.0,178.0,5.8418,240300.0,NEAR BAY +-122.27,38.0,12.0,1592.0,242.0,969.0,233.0,6.1576,248700.0,NEAR BAY 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OCEAN +-121.92,36.62,47.0,1811.0,401.0,948.0,375.0,3.0379,249300.0,NEAR OCEAN +-121.91,36.62,52.0,1431.0,300.0,657.0,293.0,3.2865,240100.0,<1H OCEAN +-121.92,36.62,52.0,867.0,199.0,391.0,187.0,2.6713,234600.0,NEAR OCEAN +-121.92,36.62,52.0,1410.0,303.0,578.0,285.0,2.5625,235400.0,NEAR OCEAN +-121.92,36.62,52.0,728.0,161.0,313.0,142.0,3.4327,254500.0,NEAR OCEAN +-121.91,36.62,40.0,1292.0,271.0,504.0,230.0,2.475,258300.0,<1H OCEAN +-121.91,36.62,30.0,724.0,167.0,325.0,155.0,3.3333,247900.0,<1H OCEAN +-121.71,36.78,19.0,2371.0,324.0,944.0,332.0,5.9175,240200.0,<1H OCEAN +-121.92,36.62,52.0,2584.0,599.0,790.0,444.0,2.5263,286400.0,NEAR OCEAN +-121.91,36.62,52.0,541.0,157.0,240.0,145.0,3.5865,290000.0,<1H OCEAN +-121.91,36.62,52.0,1220.0,267.0,488.0,265.0,3.7454,243800.0,<1H OCEAN +-121.91,36.63,42.0,817.0,194.0,391.0,193.0,2.1776,279200.0,<1H OCEAN +-121.93,36.63,28.0,3983.0,852.0,1582.0,778.0,3.5147,313900.0,NEAR OCEAN +-121.93,36.63,33.0,1740.0,342.0,638.0,329.0,3.1912,319800.0,NEAR 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OCEAN +-117.11,32.76,28.0,1457.0,397.0,672.0,342.0,1.9799,122700.0,NEAR OCEAN +-117.11,32.76,21.0,2226.0,600.0,1085.0,533.0,2.2604,126300.0,NEAR OCEAN +-117.11,32.76,19.0,2188.0,616.0,1304.0,607.0,2.0852,114400.0,NEAR OCEAN +-117.1,32.75,17.0,871.0,379.0,955.0,351.0,1.4375,96400.0,NEAR OCEAN +-117.1,32.75,11.0,2393.0,726.0,1905.0,711.0,1.3448,91300.0,NEAR OCEAN +-117.11,32.75,11.0,1607.0,478.0,1384.0,450.0,2.05,100000.0,NEAR OCEAN +-117.11,32.75,21.0,2127.0,658.0,1812.0,603.0,1.6896,100000.0,NEAR OCEAN +-117.11,32.75,18.0,1943.0,587.0,1329.0,522.0,1.7696,103100.0,NEAR OCEAN +-117.09,32.76,29.0,1650.0,496.0,882.0,445.0,2.2287,140000.0,NEAR OCEAN +-117.09,32.76,31.0,1235.0,387.0,816.0,397.0,1.5517,122500.0,NEAR OCEAN +-117.1,32.76,31.0,987.0,267.0,619.0,250.0,2.9286,151800.0,NEAR OCEAN +-117.1,32.75,16.0,2426.0,799.0,1505.0,754.0,1.6444,103400.0,NEAR OCEAN +-117.1,32.75,21.0,2063.0,609.0,1686.0,558.0,1.4828,94800.0,NEAR OCEAN 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OCEAN +-117.23,32.74,16.0,735.0,139.0,299.0,134.0,4.6354,179200.0,NEAR OCEAN +-117.23,32.74,16.0,1953.0,404.0,798.0,385.0,4.8167,169800.0,NEAR OCEAN +-117.23,32.74,35.0,2615.0,525.0,1312.0,547.0,4.1339,238200.0,NEAR OCEAN +-117.24,32.74,45.0,1718.0,293.0,757.0,329.0,4.05,284900.0,NEAR OCEAN +-117.24,32.75,36.0,2831.0,669.0,1279.0,660.0,2.9896,252700.0,NEAR OCEAN +-117.24,32.75,36.0,1856.0,475.0,822.0,416.0,2.3042,220600.0,NEAR OCEAN +-117.24,32.75,41.0,1989.0,514.0,1015.0,489.0,2.79,226000.0,NEAR OCEAN +-117.25,32.74,36.0,1240.0,310.0,577.0,319.0,2.6625,248200.0,NEAR OCEAN +-117.25,32.75,32.0,3551.0,1037.0,1731.0,935.0,2.2017,208300.0,NEAR OCEAN +-117.25,32.75,36.0,1929.0,526.0,974.0,491.0,1.7622,205800.0,NEAR OCEAN +-117.24,32.75,33.0,1980.0,614.0,1057.0,567.0,2.2042,231300.0,NEAR OCEAN +-117.25,32.75,37.0,1189.0,377.0,645.0,377.0,2.4672,216700.0,NEAR OCEAN +-117.28,32.75,34.0,981.0,313.0,508.0,304.0,2.2328,266700.0,NEAR OCEAN 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OCEAN +-117.25,32.79,27.0,848.0,300.0,455.0,298.0,3.0774,275000.0,NEAR OCEAN +-117.25,32.79,25.0,1627.0,375.0,735.0,378.0,3.6429,317100.0,NEAR OCEAN +-117.28,32.8,20.0,1838.0,540.0,615.0,325.0,3.5486,193800.0,NEAR OCEAN +-117.23,32.8,21.0,2429.0,579.0,1011.0,538.0,3.225,229400.0,NEAR OCEAN +-117.23,32.8,31.0,1403.0,388.0,724.0,371.0,2.6403,216100.0,NEAR OCEAN +-117.23,32.8,28.0,3379.0,918.0,1849.0,849.0,3.0293,241700.0,NEAR OCEAN +-117.24,32.8,26.0,3433.0,873.0,1492.0,798.0,2.9258,234800.0,NEAR OCEAN +-117.24,32.8,28.0,1072.0,331.0,692.0,321.0,2.1357,187500.0,NEAR OCEAN +-117.24,32.8,18.0,2205.0,661.0,874.0,580.0,3.8018,112500.0,NEAR OCEAN +-117.24,32.8,19.0,1863.0,497.0,868.0,503.0,2.288,210000.0,NEAR OCEAN +-117.24,32.8,30.0,1917.0,462.0,828.0,437.0,2.4671,276300.0,NEAR OCEAN +-117.24,32.8,29.0,3376.0,882.0,1513.0,843.0,3.101,238200.0,NEAR OCEAN +-117.25,32.81,32.0,2402.0,551.0,1020.0,532.0,3.3942,307400.0,NEAR OCEAN +-117.25,32.8,35.0,2281.0,506.0,1005.0,496.0,4.2296,275000.0,NEAR OCEAN +-117.25,32.8,26.0,2442.0,659.0,1134.0,624.0,3.3274,295500.0,NEAR OCEAN +-117.26,32.81,25.0,2076.0,586.0,1060.0,554.0,2.8421,227800.0,NEAR OCEAN +-117.26,32.81,30.0,1328.0,346.0,577.0,328.0,2.3284,290600.0,NEAR OCEAN +-117.26,32.81,37.0,1616.0,421.0,650.0,395.0,2.92,326500.0,NEAR OCEAN +-117.29,32.81,35.0,1878.0,308.0,598.0,257.0,6.9553,500001.0,NEAR OCEAN +-117.26,32.8,30.0,1446.0,385.0,650.0,344.0,3.744,450000.0,NEAR OCEAN +-117.24,32.81,33.0,1588.0,289.0,683.0,301.0,5.4103,332400.0,NEAR OCEAN +-117.24,32.81,34.0,2420.0,391.0,917.0,392.0,6.5881,394400.0,NEAR OCEAN +-117.25,32.81,39.0,1846.0,350.0,765.0,329.0,3.9187,311900.0,NEAR OCEAN +-117.27,32.84,34.0,1655.0,450.0,870.0,411.0,3.2109,376000.0,NEAR OCEAN +-117.28,32.84,41.0,1420.0,338.0,640.0,314.0,2.9306,360300.0,NEAR OCEAN +-117.27,32.83,39.0,1877.0,426.0,805.0,409.0,3.875,410000.0,NEAR OCEAN +-117.28,32.83,34.0,2392.0,653.0,933.0,619.0,3.7306,500000.0,NEAR OCEAN 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+-122.31,37.54,38.0,1946.0,407.0,975.0,417.0,4.0726,385400.0,NEAR OCEAN +-122.31,37.54,42.0,1159.0,261.0,465.0,247.0,3.1842,352800.0,NEAR OCEAN +-122.3,37.55,35.0,3675.0,735.0,1930.0,715.0,3.9833,342800.0,NEAR OCEAN +-122.31,37.55,45.0,507.0,140.0,305.0,139.0,2.6159,272900.0,NEAR OCEAN +-122.31,37.56,45.0,1685.0,321.0,815.0,314.0,4.2955,309700.0,NEAR OCEAN +-122.31,37.56,36.0,1727.0,340.0,952.0,337.0,4.7917,316000.0,NEAR OCEAN +-122.31,37.56,45.0,1792.0,301.0,829.0,318.0,4.9013,330100.0,NEAR OCEAN +-122.31,37.56,40.0,1351.0,330.0,701.0,297.0,3.32,292900.0,NEAR OCEAN +-122.31,37.57,37.0,1437.0,305.0,979.0,331.0,4.0,273700.0,NEAR OCEAN +-122.31,37.57,31.0,2197.0,477.0,1193.0,394.0,4.6371,271100.0,NEAR OCEAN +-122.3,37.57,36.0,1973.0,352.0,1169.0,370.0,5.033,270900.0,NEAR BAY +-122.3,37.56,37.0,1962.0,367.0,1267.0,382.0,4.7344,271800.0,NEAR OCEAN +-122.3,37.57,36.0,2406.0,436.0,1189.0,403.0,4.7917,276100.0,NEAR BAY +-122.29,37.56,34.0,1693.0,281.0,846.0,291.0,5.3683,339400.0,NEAR BAY 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+-122.26,37.54,5.0,1649.0,388.0,779.0,376.0,6.9635,417300.0,NEAR BAY +-122.27,37.54,5.0,2140.0,420.0,990.0,394.0,6.035,438800.0,NEAR BAY +-122.27,37.54,16.0,3913.0,565.0,1752.0,557.0,7.3644,419700.0,NEAR BAY +-122.27,37.54,15.0,2126.0,310.0,905.0,306.0,8.9083,500001.0,NEAR BAY +-122.26,37.56,23.0,7283.0,1342.0,3399.0,1298.0,5.6683,391000.0,NEAR BAY +-122.26,37.57,23.0,7995.0,1254.0,3484.0,1198.0,6.5948,404000.0,NEAR BAY +-122.25,37.56,19.0,7976.0,1406.0,3437.0,1338.0,5.6396,430300.0,NEAR BAY +-122.29,37.55,27.0,3789.0,874.0,2243.0,866.0,4.39,270100.0,NEAR OCEAN +-122.28,37.54,24.0,5114.0,1357.0,3169.0,1268.0,3.9699,293200.0,NEAR OCEAN +-122.29,37.54,43.0,2268.0,438.0,1151.0,449.0,4.9091,293200.0,NEAR OCEAN +-122.28,37.54,37.0,991.0,180.0,463.0,177.0,5.1701,294200.0,NEAR OCEAN +-122.29,37.54,39.0,1459.0,285.0,761.0,291.0,5.0081,298100.0,NEAR OCEAN +-122.29,37.54,41.0,1743.0,349.0,811.0,349.0,4.9464,282400.0,NEAR OCEAN +-122.29,37.53,35.0,2043.0,511.0,1089.0,504.0,3.0278,310600.0,NEAR OCEAN +-122.28,37.53,15.0,5417.0,1199.0,2593.0,1098.0,4.8047,438000.0,NEAR OCEAN +-122.28,37.53,34.0,1980.0,385.0,970.0,391.0,5.1207,310900.0,NEAR OCEAN +-122.27,37.53,43.0,1145.0,230.0,586.0,254.0,3.5,267400.0,NEAR OCEAN +-122.28,37.53,25.0,3710.0,1015.0,2068.0,958.0,3.5445,286700.0,NEAR OCEAN +-122.29,37.52,33.0,4104.0,751.0,1837.0,771.0,5.3506,388100.0,NEAR OCEAN +-122.28,37.52,27.0,2958.0,655.0,1285.0,577.0,4.0801,397800.0,NEAR OCEAN +-122.28,37.52,29.0,1526.0,355.0,724.0,315.0,4.0313,435200.0,NEAR OCEAN +-122.28,37.52,38.0,2197.0,357.0,1228.0,373.0,5.4719,397900.0,NEAR OCEAN +-122.29,37.52,38.0,3767.0,603.0,1455.0,615.0,6.8787,386800.0,NEAR OCEAN +-122.3,37.52,38.0,2769.0,387.0,994.0,395.0,5.5902,417000.0,NEAR OCEAN +-122.3,37.51,35.0,2789.0,445.0,1156.0,404.0,5.4322,391000.0,NEAR OCEAN +-122.3,37.52,32.0,2297.0,347.0,871.0,342.0,8.1039,382200.0,NEAR OCEAN +-122.31,37.52,24.0,2328.0,335.0,969.0,354.0,7.7364,435800.0,NEAR OCEAN +-122.32,37.52,26.0,4042.0,591.0,1611.0,578.0,8.4693,419200.0,NEAR OCEAN +-122.31,37.5,22.0,14034.0,3020.0,6266.0,2952.0,4.3939,491200.0,NEAR OCEAN +-122.27,37.52,35.0,1051.0,259.0,517.0,234.0,3.7,339700.0,NEAR OCEAN +-122.27,37.51,36.0,1406.0,224.0,598.0,237.0,5.8964,414800.0,NEAR OCEAN +-122.29,37.51,35.0,3040.0,520.0,1374.0,518.0,6.1004,426400.0,NEAR OCEAN +-122.26,37.52,34.0,483.0,131.0,291.0,157.0,3.0833,256300.0,NEAR OCEAN +-122.26,37.51,46.0,672.0,149.0,351.0,136.0,5.3264,258100.0,NEAR OCEAN +-122.25,37.51,45.0,989.0,174.0,504.0,180.0,4.8382,289400.0,NEAR OCEAN +-122.25,37.5,44.0,348.0,79.0,154.0,73.0,4.7708,253800.0,NEAR OCEAN +-122.27,37.51,39.0,3996.0,793.0,1744.0,761.0,4.5075,364900.0,NEAR OCEAN +-122.26,37.51,29.0,3703.0,1075.0,1611.0,1025.0,2.7075,323800.0,NEAR OCEAN +-122.26,37.5,24.0,2307.0,510.0,842.0,507.0,3.6111,341500.0,NEAR OCEAN +-122.26,37.5,52.0,878.0,186.0,393.0,186.0,3.7045,360500.0,NEAR OCEAN +-122.25,37.5,45.0,1812.0,336.0,752.0,329.0,4.95,345000.0,NEAR OCEAN +-122.25,37.49,43.0,2607.0,477.0,1225.0,461.0,4.224,349600.0,NEAR OCEAN +-122.25,37.49,40.0,2709.0,521.0,1156.0,510.0,4.6366,395500.0,NEAR OCEAN +-122.25,37.49,44.0,4420.0,743.0,1790.0,735.0,6.142,394700.0,NEAR OCEAN +-122.26,37.5,44.0,6983.0,1131.0,2818.0,1115.0,5.6271,374800.0,NEAR OCEAN +-122.28,37.51,33.0,4719.0,,1980.0,757.0,6.1064,405000.0,NEAR OCEAN +-122.28,37.5,33.0,6499.0,998.0,2694.0,957.0,7.4787,431300.0,NEAR OCEAN +-122.28,37.49,25.0,7335.0,1157.0,2626.0,1049.0,6.5475,500001.0,NEAR OCEAN +-122.28,37.49,29.0,4148.0,635.0,1638.0,627.0,6.912,457200.0,NEAR OCEAN +-122.27,37.48,26.0,3542.0,507.0,1392.0,524.0,8.5184,500001.0,NEAR OCEAN +-122.26,37.48,34.0,4453.0,682.0,1805.0,672.0,5.6038,451300.0,NEAR OCEAN +-122.29,37.48,15.0,5480.0,892.0,2009.0,831.0,7.4678,500001.0,NEAR OCEAN +-122.27,37.47,44.0,3022.0,473.0,1235.0,477.0,6.7058,495900.0,NEAR OCEAN +-122.26,37.46,26.0,5067.0,750.0,1996.0,728.0,7.0001,500001.0,NEAR OCEAN +-122.28,37.47,44.0,863.0,114.0,281.0,99.0,6.8879,500001.0,NEAR OCEAN +-122.24,37.47,41.0,1183.0,203.0,455.0,171.0,5.1071,314100.0,NEAR OCEAN +-122.25,37.48,37.0,3507.0,569.0,1663.0,608.0,5.0863,440300.0,NEAR OCEAN +-122.24,37.47,40.0,1504.0,270.0,689.0,287.0,6.1244,308800.0,NEAR OCEAN +-122.25,37.47,38.0,645.0,124.0,265.0,103.0,5.4688,305000.0,NEAR OCEAN +-122.25,37.47,35.0,3183.0,515.0,1313.0,487.0,5.9062,383200.0,NEAR OCEAN +-122.25,37.48,45.0,2743.0,390.0,974.0,400.0,7.1621,500001.0,NEAR OCEAN +-122.24,37.48,45.0,4126.0,696.0,1722.0,668.0,4.8966,362100.0,NEAR OCEAN +-122.24,37.48,40.0,4459.0,1027.0,2080.0,982.0,3.5322,361900.0,NEAR OCEAN +-122.24,37.48,47.0,2423.0,407.0,1010.0,407.0,6.2154,362700.0,NEAR OCEAN +-122.24,37.49,30.0,2956.0,590.0,1191.0,594.0,3.7463,427600.0,NEAR OCEAN +-122.24,37.49,38.0,4105.0,950.0,2561.0,909.0,3.8684,265600.0,NEAR OCEAN +-122.21,37.48,39.0,1535.0,340.0,1204.0,370.0,2.8482,247200.0,NEAR BAY +-122.23,37.49,11.0,840.0,329.0,1338.0,345.0,2.3333,241700.0,NEAR OCEAN +-122.21,37.49,24.0,2528.0,947.0,2437.0,861.0,2.2746,225000.0,NEAR BAY +-122.22,37.48,47.0,2570.0,783.0,3107.0,724.0,2.8058,229500.0,NEAR OCEAN +-122.22,37.48,34.0,1541.0,584.0,1564.0,558.0,2.56,250000.0,NEAR OCEAN +-122.24,37.49,19.0,322.0,112.0,191.0,102.0,2.5833,500001.0,NEAR OCEAN +-122.24,37.55,3.0,6164.0,1175.0,2198.0,975.0,6.7413,435900.0,NEAR BAY +-122.25,37.53,16.0,4428.0,664.0,1677.0,623.0,7.6864,422500.0,NEAR BAY +-122.26,37.53,4.0,5233.0,1109.0,1690.0,907.0,6.2007,311800.0,NEAR BAY +-122.25,37.52,14.0,1472.0,291.0,876.0,292.0,4.3594,366000.0,NEAR BAY +-122.21,37.52,18.0,2962.0,945.0,1639.0,851.0,2.7399,87500.0,NEAR BAY +-122.19,37.48,35.0,7067.0,1646.0,5380.0,1597.0,4.1776,265300.0,NEAR BAY +-122.21,37.48,20.0,505.0,216.0,326.0,216.0,2.9286,237500.0,NEAR BAY +-122.21,37.48,37.0,1326.0,335.0,1771.0,335.0,3.0147,218100.0,NEAR BAY +-122.2,37.48,32.0,640.0,166.0,991.0,160.0,1.9844,270000.0,NEAR BAY 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OCEAN +-122.2,37.4,30.0,2612.0,338.0,980.0,324.0,10.0481,500001.0,NEAR OCEAN +-122.21,37.38,28.0,4518.0,578.0,1489.0,559.0,11.3176,500001.0,NEAR OCEAN +-122.2,37.35,17.0,3095.0,442.0,1173.0,424.0,13.2986,500001.0,NEAR BAY +-122.21,37.37,34.0,1476.0,217.0,613.0,223.0,8.2883,500001.0,NEAR OCEAN +-122.22,37.37,26.0,440.0,202.0,322.0,218.0,5.1831,350000.0,NEAR OCEAN +-122.22,37.36,34.0,1559.0,243.0,600.0,242.0,8.7382,500001.0,NEAR OCEAN +-122.22,37.4,32.0,2297.0,287.0,814.0,283.0,15.0001,500001.0,NEAR OCEAN +-122.25,37.45,34.0,2999.0,365.0,927.0,369.0,10.2811,500001.0,NEAR OCEAN +-122.27,37.45,41.0,830.0,136.0,353.0,153.0,6.3824,500001.0,NEAR OCEAN +-122.24,37.43,36.0,2410.0,361.0,934.0,377.0,7.652,500001.0,NEAR OCEAN +-122.27,37.43,33.0,1601.0,223.0,629.0,215.0,15.0001,500001.0,NEAR OCEAN +-122.25,37.39,33.0,370.0,42.0,153.0,53.0,10.6514,500001.0,NEAR OCEAN +-122.26,37.38,28.0,1103.0,164.0,415.0,154.0,7.8633,500001.0,NEAR OCEAN 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OCEAN +-122.06,37.4,21.0,12855.0,3226.0,7273.0,3052.0,4.3351,267400.0,NEAR BAY +-122.06,37.39,26.0,18.0,4.0,8.0,4.0,3.75,375000.0,NEAR BAY +-122.06,37.39,22.0,1236.0,290.0,413.0,274.0,3.6875,40000.0,NEAR BAY +-122.06,37.38,20.0,3401.0,768.0,1497.0,747.0,4.2188,500001.0,NEAR BAY +-122.06,37.38,21.0,1798.0,399.0,837.0,410.0,5.6999,470000.0,NEAR BAY +-122.06,37.37,18.0,3058.0,661.0,1377.0,675.0,6.1299,500001.0,<1H OCEAN +-122.06,37.38,20.0,2976.0,766.0,1227.0,634.0,4.8625,262500.0,NEAR BAY +-122.07,37.39,19.0,1465.0,342.0,646.0,345.0,4.712,289300.0,NEAR BAY +-122.08,37.4,23.0,806.0,158.0,373.0,156.0,5.9291,284600.0,NEAR BAY +-122.07,37.4,16.0,3352.0,813.0,1440.0,729.0,3.7359,262500.0,NEAR BAY +-122.07,37.41,26.0,1184.0,225.0,815.0,218.0,5.7657,322300.0,NEAR BAY +-122.07,37.4,15.0,2940.0,910.0,943.0,711.0,4.359,192200.0,NEAR BAY +-122.08,37.4,19.0,3565.0,858.0,1639.0,744.0,4.1544,277000.0,NEAR BAY +-122.08,37.4,25.0,1750.0,341.0,999.0,319.0,5.806,308700.0,NEAR BAY +-122.08,37.41,20.0,1896.0,456.0,1069.0,436.0,4.6875,288900.0,NEAR BAY +-122.09,37.4,36.0,1575.0,379.0,1036.0,382.0,5.1408,264700.0,NEAR BAY +-122.1,37.41,33.0,6277.0,1274.0,3025.0,1211.0,5.4721,343300.0,NEAR BAY +-122.09,37.41,8.0,1480.0,414.0,856.0,445.0,2.8203,284100.0,NEAR BAY +-122.09,37.41,14.0,753.0,193.0,421.0,153.0,4.2463,266700.0,NEAR BAY +-122.09,37.41,18.0,1476.0,473.0,838.0,415.0,3.575,274000.0,NEAR BAY +-122.09,37.4,22.0,1489.0,436.0,662.0,470.0,3.5179,197200.0,NEAR BAY +-122.09,37.4,17.0,748.0,184.0,412.0,180.0,3.4375,290600.0,NEAR BAY +-122.09,37.42,23.0,4874.0,1251.0,2699.0,1163.0,3.8003,229800.0,NEAR BAY +-122.11,37.41,27.0,5110.0,1599.0,2764.0,1482.0,3.4198,351900.0,NEAR BAY +-122.09,37.4,26.0,3218.0,1021.0,2087.0,964.0,3.2875,182700.0,NEAR BAY +-122.1,37.4,27.0,3410.0,1156.0,2314.0,1086.0,3.4868,165600.0,NEAR BAY +-122.1,37.4,23.0,1755.0,508.0,1374.0,506.0,4.3077,293500.0,NEAR BAY +-122.1,37.4,19.0,1085.0,288.0,1009.0,305.0,3.9091,276000.0,NEAR BAY +-122.11,37.4,16.0,1994.0,489.0,1173.0,472.0,4.1875,266400.0,NEAR BAY +-122.11,37.4,15.0,255.0,63.0,138.0,74.0,4.6591,175000.0,NEAR BAY +-122.1,37.4,23.0,514.0,210.0,367.0,206.0,3.1736,181300.0,NEAR BAY +-122.08,37.4,52.0,766.0,203.0,448.0,196.0,2.5208,316700.0,NEAR BAY +-122.09,37.39,43.0,2065.0,535.0,1029.0,500.0,3.7318,327700.0,NEAR BAY +-122.09,37.39,30.0,1722.0,490.0,1057.0,517.0,3.725,261300.0,NEAR BAY +-122.09,37.4,24.0,3983.0,1126.0,2645.0,1072.0,3.6742,275000.0,NEAR BAY +-122.08,37.39,44.0,1498.0,430.0,848.0,400.0,2.8438,307100.0,NEAR BAY +-122.08,37.39,46.0,1115.0,248.0,543.0,248.0,3.2083,334300.0,NEAR BAY +-122.08,37.39,4.0,2292.0,,1050.0,584.0,4.8036,340000.0,NEAR BAY +-122.07,37.39,30.0,1695.0,480.0,932.0,447.0,3.5045,352500.0,NEAR BAY +-122.07,37.39,37.0,1169.0,239.0,589.0,249.0,5.0131,330300.0,NEAR BAY +-122.07,37.38,26.0,1272.0,306.0,562.0,284.0,4.5644,280200.0,NEAR BAY +-122.08,37.39,39.0,2210.0,483.0,1023.0,450.0,4.5833,342400.0,NEAR BAY +-122.1,37.39,31.0,1117.0,304.0,591.0,302.0,3.5909,353100.0,NEAR BAY +-122.1,37.39,36.0,1860.0,367.0,794.0,366.0,5.0871,354500.0,NEAR BAY +-122.09,37.39,34.0,1508.0,483.0,774.0,443.0,2.7279,365600.0,NEAR BAY +-122.09,37.39,36.0,1035.0,196.0,475.0,205.0,5.5385,359000.0,NEAR BAY +-122.09,37.38,34.0,1959.0,342.0,849.0,357.0,6.2884,414700.0,NEAR BAY +-122.09,37.38,36.0,2587.0,416.0,1055.0,410.0,6.1995,407200.0,NEAR BAY +-122.08,37.38,33.0,2771.0,659.0,1496.0,581.0,3.4042,353600.0,NEAR BAY +-122.08,37.38,36.0,857.0,156.0,448.0,168.0,5.0086,366700.0,NEAR BAY +-122.08,37.38,36.0,1199.0,198.0,485.0,199.0,5.0796,373400.0,NEAR BAY +-122.08,37.38,36.0,782.0,130.0,348.0,128.0,6.828,383900.0,NEAR BAY +-122.08,37.37,29.0,1229.0,,707.0,194.0,7.1108,465000.0,NEAR BAY +-122.09,37.37,27.0,1269.0,186.0,464.0,182.0,6.8374,500001.0,NEAR BAY +-122.09,37.37,34.0,2165.0,355.0,776.0,339.0,5.2971,442100.0,NEAR BAY +-122.08,37.38,25.0,830.0,228.0,368.0,174.0,3.3917,342900.0,NEAR BAY +-122.07,37.37,22.0,3770.0,727.0,1657.0,762.0,4.8021,457500.0,NEAR BAY +-122.07,37.37,30.0,2937.0,407.0,1097.0,407.0,7.9813,473500.0,NEAR BAY +-122.07,37.36,21.0,3244.0,426.0,1158.0,415.0,7.5,500001.0,<1H OCEAN +-122.06,37.36,34.0,1747.0,250.0,662.0,257.0,6.8268,500001.0,<1H OCEAN +-122.07,37.36,28.0,4612.0,608.0,1686.0,567.0,10.0346,500001.0,<1H OCEAN +-122.08,37.36,31.0,2717.0,376.0,1001.0,381.0,9.281,500001.0,NEAR BAY +-122.09,37.36,37.0,1550.0,238.0,805.0,250.0,5.0222,500001.0,NEAR BAY +-122.09,37.36,37.0,2269.0,325.0,930.0,321.0,7.5274,500001.0,NEAR BAY +-122.08,37.36,28.0,2181.0,284.0,728.0,238.0,8.2266,500001.0,NEAR BAY +-122.06,37.35,30.0,2040.0,294.0,787.0,278.0,8.758,500001.0,<1H OCEAN +-122.07,37.34,33.0,1208.0,198.0,495.0,216.0,5.4659,500001.0,<1H OCEAN +-122.07,37.34,35.0,1172.0,184.0,512.0,175.0,7.3561,500001.0,<1H OCEAN +-122.07,37.35,35.0,1447.0,205.0,619.0,206.0,9.8144,500001.0,<1H OCEAN +-122.07,37.35,35.0,1579.0,210.0,570.0,196.0,8.5888,500001.0,<1H OCEAN +-122.08,37.35,35.0,1347.0,207.0,548.0,189.0,7.7068,500001.0,NEAR BAY +-122.07,37.34,30.0,1851.0,238.0,631.0,236.0,10.1007,500001.0,<1H OCEAN +-122.08,37.34,23.0,2597.0,335.0,922.0,338.0,10.5142,500001.0,<1H OCEAN +-122.08,37.34,28.0,1643.0,216.0,594.0,205.0,12.367,500001.0,<1H OCEAN +-122.08,37.35,33.0,2398.0,317.0,832.0,314.0,10.3591,500001.0,NEAR BAY +-122.1,37.38,37.0,4167.0,612.0,1577.0,597.0,7.5655,500001.0,NEAR BAY +-122.1,37.37,37.0,2511.0,354.0,945.0,348.0,8.3924,500001.0,NEAR BAY +-122.1,37.37,40.0,2224.0,354.0,929.0,345.0,8.1064,500001.0,NEAR BAY +-122.11,37.37,22.0,1477.0,195.0,520.0,187.0,10.3329,500001.0,NEAR BAY +-122.11,37.38,36.0,3598.0,500.0,1296.0,533.0,7.8177,500001.0,NEAR BAY +-122.11,37.38,22.0,3638.0,719.0,1329.0,650.0,5.0804,500001.0,NEAR BAY +-122.11,37.37,49.0,1068.0,190.0,410.0,171.0,7.2045,500001.0,NEAR BAY +-122.12,37.37,18.0,1617.0,231.0,555.0,222.0,8.9021,500001.0,NEAR BAY +-122.11,37.4,31.0,2836.0,490.0,1138.0,481.0,4.9519,500001.0,NEAR BAY +-122.11,37.39,36.0,1660.0,261.0,655.0,249.0,6.3967,500001.0,NEAR BAY +-122.1,37.39,35.0,2471.0,349.0,881.0,342.0,7.6229,500001.0,NEAR BAY +-122.12,37.4,31.0,2356.0,405.0,921.0,358.0,7.0245,500001.0,NEAR BAY +-122.12,37.39,34.0,3561.0,497.0,1336.0,501.0,8.9172,500001.0,NEAR BAY +-122.12,37.38,34.0,1443.0,218.0,504.0,200.0,8.4709,500001.0,NEAR BAY +-122.12,37.4,32.0,3514.0,473.0,1583.0,480.0,10.3894,500001.0,NEAR BAY +-122.13,37.4,29.0,6027.0,1195.0,2687.0,1171.0,5.1335,461200.0,NEAR BAY +-122.13,37.41,36.0,4787.0,900.0,2039.0,890.0,5.4063,440900.0,NEAR BAY +-122.14,37.41,35.0,2419.0,426.0,949.0,433.0,6.4588,437100.0,NEAR BAY +-122.14,37.42,46.0,206.0,44.0,134.0,51.0,4.15,265000.0,NEAR BAY +-122.13,37.42,36.0,3982.0,1045.0,2251.0,995.0,3.5364,314100.0,NEAR BAY +-122.12,37.41,33.0,2892.0,617.0,1250.0,581.0,5.3727,360900.0,NEAR BAY +-122.11,37.43,35.0,3584.0,535.0,1405.0,538.0,7.3023,451300.0,NEAR BAY +-122.11,37.43,35.0,3905.0,565.0,1562.0,553.0,7.313,463700.0,NEAR BAY +-122.11,37.42,32.0,3058.0,595.0,1267.0,540.0,6.4949,417800.0,NEAR BAY +-122.11,37.41,35.0,2712.0,428.0,1084.0,425.0,7.1382,443800.0,NEAR BAY +-122.11,37.41,33.0,1641.0,284.0,659.0,282.0,6.0884,432900.0,NEAR BAY +-122.12,37.42,35.0,2445.0,533.0,1187.0,519.0,5.2803,362100.0,NEAR BAY +-122.12,37.42,36.0,2607.0,551.0,1165.0,523.0,5.1524,373100.0,NEAR BAY +-122.13,37.43,40.0,3454.0,648.0,1498.0,647.0,5.2114,438400.0,NEAR BAY +-122.13,37.43,32.0,4398.0,878.0,1799.0,792.0,4.7375,431900.0,NEAR BAY +-122.12,37.43,36.0,3212.0,553.0,1455.0,574.0,6.46,425500.0,NEAR BAY +-122.12,37.44,33.0,2974.0,623.0,1435.0,588.0,5.485,406300.0,NEAR BAY +-122.13,37.44,42.0,2390.0,462.0,1146.0,468.0,6.3111,397400.0,NEAR BAY +-122.12,37.44,33.0,1509.0,303.0,748.0,268.0,4.875,373400.0,NEAR BAY +-122.11,37.44,35.0,2016.0,349.0,1023.0,376.0,5.6413,376600.0,NEAR BAY +-122.12,37.43,33.0,3262.0,668.0,1411.0,626.0,5.316,398100.0,NEAR BAY +-122.13,37.45,37.0,2295.0,332.0,933.0,332.0,6.7257,500001.0,NEAR BAY +-122.13,37.45,37.0,1287.0,197.0,510.0,206.0,7.9029,500001.0,NEAR BAY +-122.13,37.45,41.0,3233.0,540.0,1251.0,506.0,6.6354,500001.0,NEAR BAY +-122.13,37.44,43.0,3004.0,440.0,1088.0,427.0,9.1508,500001.0,NEAR BAY +-122.13,37.44,38.0,2835.0,447.0,1148.0,446.0,5.9277,446600.0,NEAR BAY +-122.15,37.46,39.0,906.0,109.0,353.0,112.0,10.3942,500001.0,NEAR BAY +-122.16,37.45,52.0,1135.0,219.0,441.0,200.0,7.5418,492000.0,NEAR BAY +-122.15,37.46,52.0,1803.0,257.0,683.0,259.0,10.9508,500001.0,NEAR BAY +-122.14,37.45,52.0,3841.0,537.0,1391.0,540.0,7.8647,500001.0,NEAR BAY +-122.15,37.45,52.0,2117.0,353.0,734.0,328.0,6.767,500001.0,NEAR BAY +-122.14,37.45,48.0,2074.0,297.0,700.0,279.0,8.7051,500001.0,NEAR BAY +-122.15,37.45,52.0,568.0,91.0,219.0,75.0,6.1575,500001.0,NEAR BAY +-122.16,37.45,47.0,4234.0,,1808.0,1093.0,4.2297,425000.0,NEAR BAY +-122.16,37.45,19.0,2207.0,810.0,1304.0,775.0,2.1406,402500.0,NEAR BAY +-122.16,37.45,37.0,2926.0,874.0,1363.0,815.0,4.5987,356000.0,NEAR BAY 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b/Homeworks/Homework-III/Kaznowska_Julia/HW3.ipynb @@ -0,0 +1,28711 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "61074437", + "metadata": {}, + "source": [ + "# Homework 3\n", + "Julia Kaznowska
\n", + "Warsaw University of Technology
\n", + "Faculty of Mathematics and Information Science
\n", + "WB XAI tabular" + ] + }, + { + "cell_type": "markdown", + "id": "823a28cc", + "metadata": {}, + "source": [ + "## Importing libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "6b2d3c5e", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import random\n", + "\n", + "random.seed(2137)\n", + "\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")\n", + "pd.options.mode.chained_assignment = None" + ] + }, + { + "cell_type": "markdown", + "id": "a2e5d33c", + "metadata": {}, + "source": [ + "## Preparing variables (copied from previous hw)" + ] + }, + { + "cell_type": "markdown", + "id": "5859fed4", + "metadata": {}, + "source": [ + "### Handling missing values" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "2d4c83a4", + "metadata": {}, + "outputs": [], + "source": [ + "data = pd.read_csv('housing.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "17da6548", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "longitude 0\n", + "latitude 0\n", + "housing_median_age 0\n", + "total_rooms 0\n", + "total_bedrooms 207\n", + "population 0\n", + "households 0\n", + "median_income 0\n", + "median_house_value 0\n", + "ocean_proximity 0\n", + "dtype: int64" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.isnull().sum()" + ] + }, + { + "cell_type": "markdown", + "id": "ef37379a", + "metadata": {}, + "source": [ + "### Filling na" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "id": "c3c8e96c", + "metadata": {}, + "outputs": [], + "source": [ + "bedroom_mean = data['total_bedrooms'].mean()\n", + "data['total_bedrooms'].fillna(bedroom_mean, inplace=True)" + ] + }, + { + "cell_type": "markdown", + "id": "2b31ea7d", + "metadata": {}, + "source": [ + "### Adding variables" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "id": "ac1832a4", + "metadata": {}, + "outputs": [], + "source": [ + "data[\"rooms_per_household\"] = data[\"total_rooms\"]/data[\"households\"]\n", + "data[\"bedrooms_per_room\"] = data[\"total_bedrooms\"]/data[\"total_rooms\"]\n", + "data[\"population_per_household\"]=data[\"population\"]/data[\"households\"]" + ] + }, + { + "cell_type": "markdown", + "id": "0c9c1d40", + "metadata": {}, + "source": [ + "### Encoding categorical variables" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "id": "6b059f44", + "metadata": {}, + "outputs": [], + "source": [ + "X = data.drop(['median_house_value'], axis=1)\n", + "y = data.iloc[:,-5]" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "id": "a2a76699", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomeocean_proximityrooms_per_householdbedrooms_per_roompopulation_per_household
0-122.2337.8841.0880.0129.0322.0126.08.325236.9841270.1465912.555556
1-122.2237.8621.07099.01106.02401.01138.08.301436.2381370.1557972.109842
2-122.2437.8552.01467.0190.0496.0177.07.257438.2881360.1295162.802260
3-122.2537.8552.01274.0235.0558.0219.05.643135.8173520.1844582.547945
4-122.2537.8552.01627.0280.0565.0259.03.846236.2818530.1720962.181467
\n", + "
" + ], + "text/plain": [ + " longitude latitude housing_median_age total_rooms total_bedrooms \\\n", + "0 -122.23 37.88 41.0 880.0 129.0 \n", + "1 -122.22 37.86 21.0 7099.0 1106.0 \n", + "2 -122.24 37.85 52.0 1467.0 190.0 \n", + "3 -122.25 37.85 52.0 1274.0 235.0 \n", + "4 -122.25 37.85 52.0 1627.0 280.0 \n", + "\n", + " population households median_income ocean_proximity \\\n", + "0 322.0 126.0 8.3252 3 \n", + "1 2401.0 1138.0 8.3014 3 \n", + "2 496.0 177.0 7.2574 3 \n", + "3 558.0 219.0 5.6431 3 \n", + "4 565.0 259.0 3.8462 3 \n", + "\n", + " rooms_per_household bedrooms_per_room population_per_household \n", + "0 6.984127 0.146591 2.555556 \n", + "1 6.238137 0.155797 2.109842 \n", + "2 8.288136 0.129516 2.802260 \n", + "3 5.817352 0.184458 2.547945 \n", + "4 6.281853 0.172096 2.181467 " + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.preprocessing import LabelEncoder\n", + "\n", + "le = LabelEncoder()\n", + "X['ocean_proximity'] = le.fit_transform(X['ocean_proximity'])\n", + "X.head()" + ] + }, + { + "cell_type": "markdown", + "id": "f7035922", + "metadata": {}, + "source": [ + "### Splitting data" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "id": "452a7fb9", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2)" + ] + }, + { + "cell_type": "markdown", + "id": "8ec7b008", + "metadata": {}, + "source": [ + "## Creating models" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "id": "82d07eff", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.ensemble import RandomForestRegressor\n", + "from sklearn.ensemble import GradientBoostingRegressor\n", + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.tree import DecisionTreeRegressor\n", + "model1 = RandomForestRegressor(max_depth=7, n_estimators = 25, max_features=\"sqrt\")\n", + "model2 = GradientBoostingRegressor(learning_rate = 0.2, max_features=\"sqrt\")\n", + "model3 = DecisionTreeRegressor()" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "id": "2ad73807", + "metadata": {}, + "outputs": [], + "source": [ + "model1.fit(X_train, y_train)\n", + "model2.fit(X_train, y_train)\n", + "model3.fit(X_train, y_train);" + ] + }, + { + "cell_type": "markdown", + "id": "fd218595", + "metadata": {}, + "source": [ + "### Checking performance" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "id": "454c39fa", + "metadata": {}, + "outputs": [], + "source": [ + "def print_scoring(model):\n", + " train = cross_val_score(model, X_train, y_train, scoring = 'r2')\n", + " test = r2_score(y_test, model.predict(X_test))\n", + " print(\"Training data:\", np.round(np.mean(train), 2))\n", + " print(\"Test data:\", np.round(test, 2))" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "id": "f652835a", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Random Forest:\n", + "Training data: 0.73\n", + "Test data: 0.73\n", + "Gradient Boosting:\n", + "Training data: 0.79\n", + "Test data: 0.78\n", + "Decision Tree:\n", + "Training data: 0.62\n", + "Test data: 0.63\n" + ] + } + ], + "source": [ + "from sklearn.model_selection import cross_val_score\n", + "from sklearn.metrics import r2_score\n", + "print(\"Random Forest:\")\n", + "print_scoring(model1)\n", + "print(\"Gradient Boosting:\")\n", + "print_scoring(model2)\n", + "print(\"Decision Tree:\")\n", + "print_scoring(model3)" + ] + }, + { + "cell_type": "markdown", + "id": "649077a8", + "metadata": {}, + "source": [ + "After tuning hiperparameters the models have satisfying scorings. I chose `Gradient Boosting` as the best performing one, as it has the highest scorings. " + ] + }, + { + "cell_type": "markdown", + "id": "ca542b30", + "metadata": {}, + "source": [ + "## Points from homework" + ] + }, + { + "cell_type": "markdown", + "id": "80d19085", + "metadata": {}, + "source": [ + "### Model prediction" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "id": "178311ba", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([98696.81030574])" + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model2.predict(X_test.iloc[[312]])" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "id": "efe71a0b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "13550 67000.0\n", + "Name: median_house_value, dtype: float64" + ] + }, + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y_test.iloc[[312]]" + ] + }, + { + "cell_type": "markdown", + "id": "82b8cb8f", + "metadata": {}, + "source": [ + "### Ceteris Paribus profiles for best model" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "id": "b3e24662", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Preparation of a new explainer is initiated\n", + "\n", + " -> data : 16512 rows 12 cols\n", + " -> target variable : Parameter 'y' was a pandas.Series. Converted to a numpy.ndarray.\n", + " -> target variable : 16512 values\n", + " -> model_class : sklearn.ensemble._gb.GradientBoostingRegressor (default)\n", + " -> label : Gradient Boosting\n", + " -> predict function : will be used (default)\n", + " -> predict function : Accepts pandas.DataFrame and numpy.ndarray.\n", + " -> predicted values : min = 1.37e+04, mean = 2.06e+05, max = 6.11e+05\n", + " -> model type : regression will be used (default)\n", + " -> residual function : difference between y and yhat (default)\n", + " -> residuals : min = -2.66e+05, mean = 4.35e-12, max = 3.41e+05\n", + " -> model_info : package sklearn\n", + "\n", + "A new explainer has been created!\n" + ] + } + ], + "source": [ + "import dalex as dx\n", + "gb_exp = dx.Explainer(model2, X_train, y_train, label = \"Gradient Boosting\")" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "id": "8d13b7e6", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Calculating ceteris paribus: 100%|████████████████████████████████████████████████████| 12/12 [00:00<00:00, 299.94it/s]\n" + ] + }, + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "displaylogo": false, + "modeBarButtonsToRemove": [ + "sendDataToCloud", + "lasso2d", + "autoScale2d", + "select2d", + "zoom2d", + "pan2d", + "zoomIn2d", + "zoomOut2d", + "resetScale2d", + "toggleSpikelines", + "hoverCompareCartesian", + "hoverClosestCartesian" + ], + "plotlyServerURL": "https://plot.ly", + "staticPlot": false, + "toImageButtonOptions": { + "height": null, + "width": null + } + }, + "data": [ + { + "customdata": [ + [ + "
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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cp_312_gb = gb_exp.predict_profile(X_test.iloc[[312]])\n", + "cp_312_gb.plot()" + ] + }, + { + "cell_type": "markdown", + "id": "f0a3e993", + "metadata": {}, + "source": [ + "As we can see, 6 of the variables used to explain the predicted value of the 312th observation have almost constant distribuation (changes happen only at left limits). General trends for non-constant observations are downward for `latitude` and `longitude` and upward for `housing_median_age`, `median_income`, `ocean_proximity` and `rooms_per_household`. The lowest prediction is a little bit less than 200k for the smallest `median_income`, whereas the highest price is about 550k for `longitude` around -122.5. " + ] + }, + { + "cell_type": "markdown", + "id": "fd946598", + "metadata": {}, + "source": [ + "### Ceteris Paribus profiles for other models" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "id": "5b12eeaf", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Preparation of a new explainer is initiated\n", + "\n", + " -> data : 16512 rows 12 cols\n", + " -> target variable : Parameter 'y' was a pandas.Series. Converted to a numpy.ndarray.\n", + " -> target variable : 16512 values\n", + " -> model_class : sklearn.ensemble._forest.RandomForestRegressor (default)\n", + " -> label : Random Forest\n", + " -> predict function : will be used (default)\n", + " -> predict function : Accepts pandas.DataFrame and numpy.ndarray.\n", + " -> predicted values : min = 8.4e+04, mean = 2.06e+05, max = 4.94e+05\n", + " -> model type : regression will be used (default)\n", + " -> residual function : difference between y and yhat (default)\n", + " -> residuals : min = -2.36e+05, mean = -79.1, max = 3.64e+05\n", + " -> model_info : package sklearn\n", + "\n", + "A new explainer has been created!\n", + "Preparation of a new explainer is initiated\n", + "\n", + " -> data : 16512 rows 12 cols\n", + " -> target variable : Parameter 'y' was a pandas.Series. Converted to a numpy.ndarray.\n", + " -> target variable : 16512 values\n", + " -> model_class : sklearn.tree._classes.DecisionTreeRegressor (default)\n", + " -> label : Decision Tree\n", + " -> predict function : will be used (default)\n", + " -> predict function : Accepts pandas.DataFrame and numpy.ndarray.\n", + " -> predicted values : min = 1.5e+04, mean = 2.06e+05, max = 5e+05\n", + " -> model type : regression will be used (default)\n", + " -> residual function : difference between y and yhat (default)\n", + " -> residuals : min = 0.0, mean = 0.0, max = 0.0\n", + " -> model_info : package sklearn\n", + "\n", + "A new explainer has been created!\n" + ] + } + ], + "source": [ + "rf_exp = dx.Explainer(model1, X_train, y_train, label = \"Random Forest\")\n", + "dt_exp = dx.Explainer(model3, X_train, y_train, label = \"Decision Tree\")" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "id": "7527003a", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Calculating ceteris paribus: 100%|████████████████████████████████████████████████████| 12/12 [00:00<00:00, 249.94it/s]\n", + "Calculating ceteris paribus: 100%|████████████████████████████████████████████████████| 12/12 [00:00<00:00, 279.02it/s]\n" + ] + }, + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "displaylogo": false, + "modeBarButtonsToRemove": [ + "sendDataToCloud", + "lasso2d", + "autoScale2d", + "select2d", + "zoom2d", + "pan2d", + "zoomIn2d", + "zoomOut2d", + "resetScale2d", + "toggleSpikelines", + "hoverCompareCartesian", + "hoverClosestCartesian" + ], + "plotlyServerURL": "https://plot.ly", + "staticPlot": false, + "toImageButtonOptions": { + "height": null, + "width": null + } + }, + "data": [ + { + "customdata": [ + [ + "
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"title": { + "text": "" + }, + "type": "linear", + "zeroline": false + }, + "yaxis9": { + "anchor": "x9", + "automargin": true, + "domain": [ + 0.7, + 0.825 + ], + "fixedrange": true, + "gridwidth": 2, + "matches": "y", + "range": [ + -18024.58002153668, + 547094.2345474125 + ], + "tickcolor": "white", + "ticklen": 3, + "ticks": "outside", + "title": { + "text": "" + }, + "type": "linear", + "zeroline": false + } + } + }, + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cp_312_rf = rf_exp.predict_profile(X_test.iloc[[312]])\n", + "cp_312_dt = dt_exp.predict_profile(X_test.iloc[[312]])\n", + "cp_312_gb.plot([cp_312_rf, cp_312_dt])" + ] + }, + { + "cell_type": "markdown", + "id": "3663dd3a", + "metadata": {}, + "source": [ + "After adding CP-profiles for the other two models, we can see that both of them are mostly similar to `Gradient Boosting`, with `Random Forest` being a little bit closer than `Decision Tree`.
\n", + "All the models have different distributions when it comes to `longitude`. New models differ from `GB` for the `rooms_per_household` variable. `Decision Tree` varies significantly when it comes to `latitude` and `median_income` variables.
\n", + "Those results clearly show us that `Random Forest` model, having second best scoring has more similar CP profiles to the best model, than `Decision Tree`, which was third. It makes us happy, because it makes sense :) " + ] + } + ], + "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": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Homeworks/Homework-IV/Kaznowska_Julia/hw4.html b/Homeworks/Homework-IV/Kaznowska_Julia/hw4.html new file mode 100644 index 0000000..1194a2d --- /dev/null +++ b/Homeworks/Homework-IV/Kaznowska_Julia/hw4.html @@ -0,0 +1,15486 @@ + + + + + +hw4 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/Homeworks/Homework-IV/Kaznowska_Julia/hw4.ipynb b/Homeworks/Homework-IV/Kaznowska_Julia/hw4.ipynb new file mode 100644 index 0000000..53127af --- /dev/null +++ b/Homeworks/Homework-IV/Kaznowska_Julia/hw4.ipynb @@ -0,0 +1,848 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "fdb7fcc9", + "metadata": {}, + "source": [ + "# Homework 4\n", + "Julia Kaznowska
\n", + "Warsaw University of Technology
\n", + "Faculty of Mathematics and Information Science
\n", + "WB XAI tabular" + ] + }, + { + "cell_type": "markdown", + "id": "7b75e3e4", + "metadata": {}, + "source": [ + "## Importing libraries, models and data" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "e228c0cb", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import random\n", + "\n", + "random.seed(2137)\n", + "\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")\n", + "pd.options.mode.chained_assignment = None\n", + "\n", + "import pickle\n", + "rf = pickle.load(open('RandomForest', 'rb'))\n", + "gb = pickle.load(open('GradientBoosting', 'rb'))\n", + "dt = pickle.load(open('DecisionTree', 'rb'))\n", + "\n", + "X = pickle.load(open('X', 'rb'))\n", + "y = pickle.load(open('y', 'rb'))\n", + "\n", + "from sklearn.model_selection import train_test_split\n", + "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2)" + ] + }, + { + "cell_type": "markdown", + "id": "74d96e51", + "metadata": {}, + "source": [ + "## Permutational importance " + ] + }, + { + "cell_type": "markdown", + "id": "718a8e8a", + "metadata": {}, + "source": [ + "First things first I am going to check permutation importance of the best performing model - `Gradient Boosting`." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "c55ed4c3", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + "\n", + "\n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
WeightFeature
\n", + " 0.4497\n", + " \n", + " ± 0.0073\n", + " \n", + " \n", + " median_income\n", + "
\n", + " 0.3886\n", + " \n", + " ± 0.0079\n", + " \n", + " \n", + " longitude\n", + "
\n", + " 0.3557\n", + " \n", + " ± 0.0095\n", + " \n", + " \n", + " latitude\n", + "
\n", + " 0.1251\n", + " \n", + " ± 0.0028\n", + " \n", + " \n", + " population_per_household\n", + "
\n", + " 0.0752\n", + " \n", + " ± 0.0026\n", + " \n", + " \n", + " ocean_proximity\n", + "
\n", + " 0.0333\n", + " \n", + " ± 0.0010\n", + " \n", + " \n", + " bedrooms_per_room\n", + "
\n", + " 0.0287\n", + " \n", + " ± 0.0003\n", + " \n", + " \n", + " housing_median_age\n", + "
\n", + " 0.0150\n", + " \n", + " ± 0.0008\n", + " \n", + " \n", + " rooms_per_household\n", + "
\n", + " 0.0123\n", + " \n", + " ± 0.0010\n", + " \n", + " \n", + " total_bedrooms\n", + "
\n", + " 0.0083\n", + " \n", + " ± 0.0008\n", + " \n", + " \n", + " total_rooms\n", + "
\n", + " 0.0043\n", + " \n", + " ± 0.0004\n", + " \n", + " \n", + " population\n", + "
\n", + " 0.0020\n", + " \n", + " ± 0.0003\n", + " \n", + " \n", + " households\n", + "
\n", + " \n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import eli5\n", + "from eli5.sklearn import PermutationImportance\n", + "\n", + "perm_gb = PermutationImportance(gb, random_state=1).fit(X_train, y_train)\n", + "eli5.show_weights(perm_gb, feature_names = X_train.columns.tolist())" + ] + }, + { + "cell_type": "markdown", + "id": "1f92cff6", + "metadata": {}, + "source": [ + "We can see that the most important feature is `median income`, followed right by `longitude` and `latitude` - our coordinates. In the top 5 variables there also is `ocean proximity` - another feature connected with localisation. This means that `Gradient Boosting` model puts big emphasis on localisation of households. We can also observe that the variables we created are far more important than base variables. It is worth checking whether the model would give better scorings when trained without those base features." + ] + }, + { + "cell_type": "markdown", + "id": "9db868de", + "metadata": {}, + "source": [ + "## Permutational importance for other models" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "3006e76e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + "\n", + "\n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
WeightFeature
\n", + " 0.3867\n", + " \n", + " ± 0.0078\n", + " \n", + " \n", + " median_income\n", + "
\n", + " 0.1145\n", + " \n", + " ± 0.0026\n", + " \n", + " \n", + " ocean_proximity\n", + "
\n", + " 0.1135\n", + " \n", + " ± 0.0037\n", + " \n", + " \n", + " population_per_household\n", + "
\n", + " 0.0890\n", + " \n", + " ± 0.0016\n", + " \n", + " \n", + " longitude\n", + "
\n", + " 0.0884\n", + " \n", + " ± 0.0016\n", + " \n", + " \n", + " latitude\n", + "
\n", + " 0.0452\n", + " \n", + " ± 0.0020\n", + " \n", + " \n", + " bedrooms_per_room\n", + "
\n", + " 0.0320\n", + " \n", + " ± 0.0016\n", + " \n", + " \n", + " rooms_per_household\n", + "
\n", + " 0.0267\n", + " \n", + " ± 0.0006\n", + " \n", + " \n", + " housing_median_age\n", + "
\n", + " 0.0058\n", + " \n", + " ± 0.0008\n", + " \n", + " \n", + " total_rooms\n", + "
\n", + " 0.0055\n", + " \n", + " ± 0.0003\n", + " \n", + " \n", + " population\n", + "
\n", + " 0.0044\n", + " \n", + " ± 0.0006\n", + " \n", + " \n", + " households\n", + "
\n", + " 0.0028\n", + " \n", + " ± 0.0005\n", + " \n", + " \n", + " total_bedrooms\n", + "
\n", + " \n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "perm_rf = PermutationImportance(rf, random_state=1).fit(X_train, y_train)\n", + "eli5.show_weights(perm_rf, feature_names = X_train.columns.tolist())" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "d466331f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + "\n", + "\n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
WeightFeature
\n", + " 0.9741\n", + " \n", + " ± 0.0250\n", + " \n", + " \n", + " median_income\n", + "
\n", + " 0.5442\n", + " \n", + " ± 0.0176\n", + " \n", + " \n", + " latitude\n", + "
\n", + " 0.4874\n", + " \n", + " ± 0.0198\n", + " \n", + " \n", + " longitude\n", + "
\n", + " 0.4532\n", + " \n", + " ± 0.0214\n", + " \n", + " \n", + " population_per_household\n", + "
\n", + " 0.3769\n", + " \n", + " ± 0.0072\n", + " \n", + " \n", + " ocean_proximity\n", + "
\n", + " 0.2229\n", + " \n", + " ± 0.0068\n", + " \n", + " \n", + " housing_median_age\n", + "
\n", + " 0.1355\n", + " \n", + " ± 0.0068\n", + " \n", + " \n", + " rooms_per_household\n", + "
\n", + " 0.1008\n", + " \n", + " ± 0.0043\n", + " \n", + " \n", + " bedrooms_per_room\n", + "
\n", + " 0.0498\n", + " \n", + " ± 0.0065\n", + " \n", + " \n", + " total_bedrooms\n", + "
\n", + " 0.0454\n", + " \n", + " ± 0.0058\n", + " \n", + " \n", + " population\n", + "
\n", + " 0.0441\n", + " \n", + " ± 0.0052\n", + " \n", + " \n", + " total_rooms\n", + "
\n", + " 0.0417\n", + " \n", + " ± 0.0045\n", + " \n", + " \n", + " households\n", + "
\n", + " \n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "perm_dt = PermutationImportance(dt, random_state=1).fit(X_train, y_train)\n", + "eli5.show_weights(perm_dt, feature_names = X_train.columns.tolist())" + ] + }, + { + "cell_type": "markdown", + "id": "fa6cd0c7", + "metadata": {}, + "source": [ + "Permutation importance for those two models provide us with useful information. In both of them `median income` plays a big role, with other features being somehow far behind it, which is different from previous model. All the other observations made are very similar to the `gradient boosting` model. In the top 5 we can find all the variables connected with localisation (`longitude`, `latitude` and `ocean proximity`). New variables are also more important than the variables they were based on. It leads us to similar conclusion - maybe it is worth it to get rid of old variables?" + ] + } + ], + "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": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Homeworks/Homework-V/Kaznowska_Julia/hw5.html b/Homeworks/Homework-V/Kaznowska_Julia/hw5.html new file mode 100644 index 0000000..c81f9fb --- /dev/null +++ b/Homeworks/Homework-V/Kaznowska_Julia/hw5.html @@ -0,0 +1,15019 @@ + + + + + +hw5 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/Homeworks/Homework-V/Kaznowska_Julia/hw5.ipynb b/Homeworks/Homework-V/Kaznowska_Julia/hw5.ipynb new file mode 100644 index 0000000..2b8a30c --- /dev/null +++ b/Homeworks/Homework-V/Kaznowska_Julia/hw5.ipynb @@ -0,0 +1,318 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "49f08339", + "metadata": {}, + "source": [ + "# Homework 5\n", + "Julia Kaznowska
\n", + "Warsaw University of Technology
\n", + "Faculty of Mathematics and Information Science
\n", + "WB XAI tabular" + ] + }, + { + "cell_type": "markdown", + "id": "7b75e3e4", + "metadata": {}, + "source": [ + "## Importing libraries, model and data" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "e228c0cb", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import random\n", + "import matplotlib.pyplot as plt\n", + "import plotly.graph_objects as go\n", + "\n", + "random.seed(2137)\n", + "\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")\n", + "pd.options.mode.chained_assignment = None\n", + "\n", + "import pickle\n", + "gb = pickle.load(open('GradientBoosting', 'rb'))\n", + "\n", + "X = pickle.load(open('X', 'rb'))\n", + "y = pickle.load(open('y', 'rb'))\n", + "\n", + "from sklearn.model_selection import train_test_split\n", + "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "fbf506a6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Preparation of a new explainer is initiated\n", + "\n", + " -> data : 16512 rows 12 cols\n", + " -> target variable : Parameter 'y' was a pandas.Series. Converted to a numpy.ndarray.\n", + " -> target variable : 16512 values\n", + " -> model_class : sklearn.ensemble._gb.GradientBoostingRegressor (default)\n", + " -> label : Gradient Boosting\n", + " -> predict function : will be used (default)\n", + " -> predict function : Accepts pandas.DataFrame and numpy.ndarray.\n", + " -> predicted values : min = 1.47e+04, mean = 2.07e+05, max = 5.8e+05\n", + " -> model type : regression will be used (default)\n", + " -> residual function : difference between y and yhat (default)\n", + " -> residuals : min = -2.77e+05, mean = -4.09e-12, max = 3.39e+05\n", + " -> model_info : package sklearn\n", + "\n", + "A new explainer has been created!\n" + ] + } + ], + "source": [ + "gb.fit(X_train, y_train)\n", + "\n", + "import dalex as dx\n", + "gb_exp = dx.Explainer(gb, X_train, y_train, label = \"Gradient Boosting\")" + ] + }, + { + "cell_type": "markdown", + "id": "d3b224d4", + "metadata": {}, + "source": [ + "## Partial Dependence Profiles " + ] + }, + { + "cell_type": "markdown", + "id": "815ccf4b", + "metadata": {}, + "source": [ + "In order to make the file smaller I had to reduce the number of observations that was taken for plotting PDP, it doesn't affect the results in any way though." + ] + }, + { + "cell_type": "markdown", + "id": "f67c77fe", + "metadata": {}, + "source": [ + "Exporting notebook with plots made them disappear, so I had to change them to images. Code is still included for reproductivity." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "741f7c0c", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "#pd_gb = gb_exp.model_profile(N = 25)\n", + "#pd_gb.plot(geom = 'profiles', variables = X_train.columns.tolist())\n", + "#pd_gb.result['_label_'] = 'PD profiles'" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "72f6b6ad", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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YcMxnHjJdAAAAHDI8zBQjAADSBUEXAAAAAACAJCDoAgAA4BBNMQIAAOmBoAsAAAAAAEASEHQBAAAAAABIAoIuAAAADhkcpJAuAADpgqALAACAQw4fPuw/AgAAqUbQBQAAAAAAIAkIugAAADiE1YsAAEgfBF0AAAAAAACSgKALAAAAAABAEhB0AQAAcERWVpa9p5guAADpgaALAAAAAABAEhB0AQAAAAAASAKCLgAAAI5YtMhr2o2OMr0IAIB0QNAFAAAAAAAgCQi6AAAAAAAAJAFBFwAAAEcsWsTqRQAApBOCLgAAAAAAAElA0AUAAAAAACAJCLoAAAA4IiuL6UUAAKQTgi4AAAAAAABJQNAFAAAAAAAgCQi6AAAAOCQvL9cMDQ37WwAAIJUIugAAAAAAACQBQRcAAACHLFqURSFdAADSBEEXAAAAh+Tm5pjh4RF/CwAApBJBFwAAAAAAgCQg6AIAAAAAAJAEBF0AAAAcwupFAACkD4IuAAAAAAAASUDQBQAAAAAAIAkIugAAADiE6UUAAKQPgi5p4tu3v2xvAAAAAADADQRd5kgBkvc13u9veba+0mquvvjndv95Jzx4RBDl8UeazIbTf2qf1722AQAAAACAmwi6zIGCK1s2N/tb4z5/zS/N2nU15oWmK8x9j20wjz30zlhgZXdTl/nydc+YjXecZZ/X/e1feN7uBwAASKSsrCwzMjLqbwEAgFQh6DJLne2DNrhy461n+Hs8m5/eZ++vv/kUe9/QWGquvuEk8+A9W+32Qw+8ac7f0GjWnVtnt3V/1nn1dn8sypK57IM/sz8PAAAAAABkHoIus/Tdr//GrL9kpVlzcrW/x/Pis/tM7bIif8tTv6LUvLal1T5WZkxtQ7F9HNB2rIyZTfdvt1kyP9i03pRV5Pl7AQAA4qNMl8OHD/tbAAAgVbIiF2SuyHFSNsvffvUl88///kd2W7VZNFVIgvotQaaLKEvlwyf/2L5GtV4+dePasUwX0dQjZcLc+/CFY19/xtl15qZrnzJ3P3jBEYGdWNra2vxHAABgviorK/1HmUttg46OXlNcnG9ycrL9vQAAYC7m2zYg0yVOCqDceuNz5it3vt/fk3j7d/fYgMtV150YV8AFAAAAAACkLzJd4vS1L202xaW5EzJZJme6aKqQslYCyoy54conxjJdVGQ3+uujv0aPN9233Vx65Wp7//DzlzK1CHEJsp1cGJ1F4nF8YDrR2ZIcI25paTkUeU9LTW5urr8HcBPXOYQNx3zmIdMlTv/6T2+aB76/1QZagpvoXgETTQvav7fX7gvs2dll3rPWy1hRwEWZLNG0rf0BBVwUlFGB3c998gl/LwAAQHxGRkbsvWq6DA+zehEAAKlG0CVOylaZfAv2K1CiWi0lpbljtVm0FPS9d71qNvzJMXb7kstXmeee3DO2ypHun3i0ye6f7Iu3r7MBnB/e/Tt/DwAAwMxYJhoAgPRC0CWBVO9F04WU/XLl+kftKkeXXrHaPqclpG/+xpm2Loye1/1ffeccu38yTSvaeMdZ5nvf2GK2vuKtfgQAABCPwcEh/xEAAEg1aroAGY55nZgOxwemQ00X9wQBl87OHlNYmG+KiwvtNuAqrnMIG475zEOmCwAAgEOCui4AACD1CLoAAAA4RHVdFi1aZEhmBgAg9Qi6AAAAOCY7exFFdQEASAPUdEFa6u3tM/39Q2Z4mBTpmfT1eUuVFxYW2XsgGscHphMcH8IxYkx5eYnJz8/1tzKTarp0d/fZoIvobwJcRn0LhA3HfOYh6IKk6u8fjDTq+008R9no6KgdldN9Xl6uyc3Njtwyu/G7EDo7O+19WVmZvQeicXxgOsHxIRwjxl57gmBFphoYUNCl1xQU5NkATGUl7yvcRgcUYcMxn3kIujhODa/I22xKShZm9QJlpgwNDdtAixp+ubk5Jj8/zyxalOW/YmpZWVm2savXI36ceDEdjg9MJzg+hGPEDX19A/bar+u+HldVlfvPAG7iOoew4ZjPPNR0yWAzzdVWAKSrqzfS+Oqx2SNTUdyttbXDBkumC8GNjh6232fyTT+ns7Pb7Nt3MPJ92v0RtnxTV7fYLF5cYUpLi+ySlTPdiooKCLgAADBPGvQAAADpgaBLBhscHDS9vf3+1pE0wqVMEwVSpmuAKTCjFORDhzpNS0ur6ejo9p/xlp3s6ekzzc2tkecORW5tR9wOHGizWSo1NVVm6dIqs2RJpQ2gAAAAAAAQZgRdMlh2drYNmEw1Q0zFaJVlomyTrq4ef+9ECqoocKNgydKllaasrMQGUJqbxwMsyoApKfG+T6xbbW115OcU2+Up9bUAACB1lIWqNsLwMKsXAQCQagRdMlhQ8M+r2zKRslxycrx6Ksp2ERW1nayzs8c+r++jgElhYb4pKys2ixeXm4qKEjtFqKKi1E7/ycnJjnkj0AIAQHrgkgwAQHoh6JLhKitLjZaGVDZKNAViFCgJKFOlp2dicEZTigYGBmMuJ6kRMgV1AABAJsmy04o18KJsVgAAkFoEXTKcgiOa1qO6KwEFU9To0nKRAdVYGRwcNv39A/4eLzCjTBhlqwAAgMynTBcFW1icEgCA9EDQxQGqq6KpQ97qQ4dNW1tXzCWiNXWop8crvKsVh1RcVxkwAADAHVptEAAApAeCLg5QPRZlsmiakbJcVEAvP//IqUEqqqtgi7S3d9llnYN6LwAAwA1elsthW3NNbQIAAJA6BF0coalECrh0dPTYAIymHE2mqUjKdlHARVkxKpgLAADcEVz/le2yaJGCLmS9AACQSgRdHKGit5pSpBGtWIVxAwrIaGUjvVYZMgAAAAAAIDnodTtEAZUlSyr8rdgUaNGKR9RyAQDAPRMzXRaNTSsGAACpQdDFIZq7rSlE09FrVMtF9wAAwC2aPhzwLvVMLwIAIJUIuiDpWLUSAICFERTOpYAuAADpgaALkkorKLS2tpvOzh5/DwAASBZlsuraOzIyYrNfh4aYXgQAQCoRdEFSHTzYbpel7unpM729/f5eAACQLMpyIcsUAID0QNDFcVoeuq2t099aOGrwHTjQZldV0tLUVVVlpqOjm8ALAAALYHh4vLYLAABIHYIuGSy6WF4sg4NDpr9/0N5mem0iaaWEAwe8DBcFXJTqnJ+fZ1dN0jSjhfxdAAAIE61aNDIyGrkWj/qrF3HNBQAglbIOa+IvMlJfX78NcJSWFvt7JlKAQ42tgoI8MzAwZCoqSue9alGQsqx7fe9YR093d6/NcNHPm6yrq9f09w+YxYsrpvxdvJ8RNBqZiz6Tri4vk6m0tMzeA9E4PjCd4PgQjhFj8vNzZ1wFMN0po/Ttt/fY62xxcYGt7VJZyXsLd7W1tdn7yspKew+4jmM+8xB0yWDKGDl0qMMsXVoVM4Cxb99B2+jKyckxLS2HIg3qIlNUVOA/G78gU0Z1WbT0ZPQRo+DKZPo5sfYH9DtrBK6oKN/f49Gh2NOj6Ufez9AIXU5OZjd+F0JPj1ekuLg4dvAN4cbxgekEx4dwjMx8/coEfX0D5t1395ns7EWmuro8ci3NIugCp9EBRdhwzGcegi4ZTvVaFJiYnO2iAImmFwUNLT1ua+uyQRg1xKajTBPVglGmiR4H04OUMaMRQG2rETfXrBl9T/1++v7R9D3z8/Pt76fHCrpgZpx4MR2OD0wnOD6EY8QNynRpatpvCgu966mu3UuW8N7CXVznEDYc85mHXm2GKysrGQuwRFPGSHFxoenq6rGZJVoyMjc32742FgVA9Fxra4ctgCuqx7J4caXNpCkvL7GBFwV41Iiba8BFFExRkEjTj6Jv+luU2q2fQcAFAIDZ0/VZl+jDh5VRWmDbA5PbCAAAYOGQ6ZLBNE9bmSdallmNLKURi1KLdVOatJ5T8EUNLu82bJYtW2xfr5osqvWiqUOqnaKUagVWSkoK7fdBZiDajelwfGA6ZLq4R9f/pqZ9tpj9smVL7GDKTBmuQCbr7/cGFAsKaL8iHDjmF5YSEdSfng+CLhlMhXS1SoEOAmWn6IBQ4GT//lYbgFFAZWBg0E4pEgVpmpsPmd7egcjXFNgGmdKOFWjRY2QmOtWYDscHpkPQxT2qw/buu3ttxmhjY60tqs/0IriM6xzChmM+8zD0kcGU5dLd3WcL0HopxH22sSUKomhbgZiAXl9Xt9gGXJQFo2BMSUkRARcAAByhwZiAaqgBAIDUIuiSwZTVovonWqJZ2Sqqy6IRLQVUlF6cl5dzxCoMmlZUVVU2FpwBAADu0EBMEHghmRkAgNRjelGGU6BFy0Era0V1WVQ4V4Vvg+lGCsZMpre8paXNVFaWHhGUSSf6PTk8Z9be3m7vKyq8aWRANI4PTCc4PoRjJChCO/dC8elAhXM1vUhZrPX1S+3ATE1Nlf8s4B6mWiBsOOYzD0EXByiAopWJguWhVcelq6t3rJZLLMGKR8HXLDQvoOJvjDlsfycV9lXdmWC5akyvr88rplVYSDEtHInjA9MJjg/hGFHgqdTWOstkCrrs2LHHrgaowvnaJugCl9EBRdhwzGcegi5p4tu3v2zvr7/5FHs/G3oLle2iAIpGtlQsd6aGo75GKxtpKeiFznY5dKjTBldi0e+lwsD63b3VFgi6zIRMBkyH4wPTIdNlokWLMj/TpbfXC7po+nFDw1KCLnAeHVCEDcd85iHoEqfO9kHz9Zs3my0vtZjWlj5TWJRj/uCPV5qb/mad/wpjtr7Sau7Y+KJ5bUurff7SK1dPCKI8/kiT+datv7JfX7200Hx242nmgosa7XPzCbqIMlu0BHRhYUHkg9hpG1hauWA6Cn6Iarwkm6ZBqSHY0+PVnyktLbaN24myYuzDTDjxYjocH5hOcHwIx4gbvKDLXjtwsXz5Uts+UBF9wFVc5xA2HPOZh0K6cXr9t62mtqHYfP8nHzYvNF1h7ntsg/nN5mbzw7t/57/CmM9f80uzdl3N2POPPfSODbTI7qYu8+XrnjEb7zjLPq/727/wvN2fCMoM0bSczs5umykyU8BFVHBXgRrVgkkmNQAVCBocHLQZOMrI0QicfseJNwIuAADMF+NpAACkDzJd5uFrX9psiktzbXbK5qf3mVtvfM48+tJH/GeN2XT/dvPov7xt7n34QpvJsn93j7ntu+f4zxpzy6efsYEcff3kTBdtP/tvu80PNq03ZRWxpwmp5kl0cEXBDRXSramp9vfMrL29ywZAtHR0MqiAn5a1VqBF88vjpaPy8GGWuowH00cwHY4PTIfpRRO5UEhXbYG33tptM12WLVti67wp+5UaaXAVo/4IG475zEPQZY6CIMs37/l9s+bkahsk2bK52QZYAnrNDVc+YTNbrr745zYLJnr6UPTXRAddFKy5965XzY8ev2jKgIv09fXb4ERRUYG/xxvdmk3DStkxmma0dGllwhtkCrgorbm6ujyuujH63dVY1O+kRiJHZnwolIrpcHxgOhTSnciFQrp9fQPm7bd3Rx5lmfr6JTajNRX124CFQgcUYcMxn3kIuszS+xrvt/eq2XL9LaeYS69YbbcnZ6qI6sB8+OQfjwVdPnXjWrPu3Dr/Wa/Gy4P3bJ0QdDnj7Dpz07VPmbsfvMAGc6ajYrR797ZEGlPzy1JRkT0V4M3Ly/H3jFMAZGhoxNZkCaYh6bGOmiDAo1iNGnOaHpSdnWWf09f09w9F9mf7wZx4DrMs+zt43yfbfi8AABaKrldVVZlddFZtgzfeaDKjo4cjf0uJvS8qyrNZrQAAYPbmG+Ai6DJHymL526++ZM7+UEPM6UEy26CLph899+Qec9V1J5pPfuZE/1XT27lzrw1UTDV1R40tTUOarrE1OOgt0Ty5pooXXBlf2lmpyrrpdVlZiyLfc5F9Xl+vIIt4s52y7NcWFeXbpaz1NYnOogEAIBkyfeRQgyXbtr1rr93l5arxlmXbCBpcAQAAs0fQJYWC1YhUx0VBk/lOL9p033a74pHuH37+0mmnFgVGRkZMS0ubWbKk8ojAit7aAwfabdBFgY+ysmL7GmWRxKLRMQVLenr6zMDAkP/aRaa4uMA21nJypm6w6WdpKpHSmhVf0ZSiqX4OEosUQ0yH4wPTCY4P4Rhxg7JSt2/faa/n1dVldgqyruEqsg+4iOscwoZjPvOwelGCaFrQ/r29/pZnz84u85613hQhBVyUyRJN29ofCJaYPuu8evO5Tz7h752aaroosKFiuqqfMpkCICpGW1tbbeepd3R02wCNarhMjrUp0KIVhlRYV4EWzQNX4b3FiyvsMtTTBVxEmSwK6uhrli6tIuACAEAK6HocZK4q8AIAAFKLoEuctDT01lda/S1jH99z5ytm/SUr7bamDZWU5o5NM9JS0CqGu+FPjrHbl1y+yk4dUvaL6P6JR5vs/sm+ePs6G8CJXo46FsVNWls7bGBEARalFEdTUEb1WvbsabFZKFqhSK9VY0wFa0WrHbW0HLJfq0J7CppopSEAAJB5lAEbIOgCAEDqMb0oTgqA/N+f7jBNb3fa7f+/vTsBj+Ss7zz+n9HZLbXUkkbSHPLINrbB5rDDYYfDbDbGAccBgiEJG+wNuxBgMVdCSIh5cBIneAPED7AcCRvIE9ZOwpPDBFgHguMc2AvYXDaHDcae8Rwa3VIfUrfOme3fW1WalkYqSaPpHlX19/M8nVJVt1oi81pV/av/+3+7ehIucCmfLqQg5rabH7AfPjjuGu0GlSuBYDrS+EjRff87bn62Xf3SfvdcENYErw+mJv3FF65Zs6GulmLO5abcVB71ZJGOjpTbKmRRmKIKldbWZvda8e6A7XQXZU1Nja4fjMKY7biqgaY7qScNwmWzWbdtb293W6Ac4wNhgvEhjBFz03BU7RllOnceODDgeq21tiasuzvtbqxwQwVxxVQL1BrGfPQQukSYpgJlMlOlC+UWN2dbX+uiqlAouqlEmuKjpS9V3SKqfFH4oucUvqiyJXiuknSnrXyY6Wv1mVkZqOi4fketeqQlo/V81C9+q2Fubs5tGxujvcwpKoPxgTDB+BDGiLnpsepjFmUKXY4cGXY3Y9RAd+/eXYQuiDU+gKLWMOajh9AlwhS6qJollWpxPVtmZmZLjzlXwaLQQlT50tzc5L4OaEpRUPmyZ88utz3TNKzUZ0YXfeorUxpqS8c1FUqBihrurqTfXQGSVj5a+XtjdfzhRRjGB8IE40MYI/Gg0GVoaNzdYFEV6759Pe5YZyehC+KJ8xxqDWM+eigjiDCVQS8uaknnE+7iSsGLyojVsyWoEtE26N8SUJihShetTqQA5kzRnbRcTj1iJl0YpBWQUqlk6XfqdCso6Gfqd1XQc955e1d57LO+vh53YUjgAgDA6fEqSb1rAd3gWNnzDQAAVA+hS4QpNFFPlmD6jsKVHTt2ukoSBS+JRJMLQfTQ8wGFMdpXVUnQC2aztCSlqmn0M7Xq0ejopI2NZVyvGE130rQlPVS1Mj1dsPHxnCtz7u3tcr+Xfgf1lln+WKX0BQAAbIquD0Tn+vLzPwAAqD6mF0WYwpVjx0bd1+l0q7ujpSBF03oUYijwmJjwmiQGYYfon1yVKJ2d7S4wUVWJlntejV6rIEV3yVS5oq/n5rw7Zrqo089RmKIS5tWa8arRr6Y8aS65KnPCaCTq5+hnLCwsuJWXvLt1CDMz41UyNTdHuw8BKoPxgTDB+BDGiPmVltHubaPzqCpOJyZy7mZGX1+3Ow/rOgCII6ZaoNYw5qOH0CXCtEKRQhVVnShQUXgS9HJRiBJUuCi40Gv6+3e7KT4S9ILR83qfnp6Opef0evVh0ZQlBS26aFMFjfqsKDhR2KImvetR4KLgRJU4mk++Hr3O+1neCktqZrheUAOzqSlvilhr6+rBGWob4wNhgvEhjBErnXcSkT/v6Fyqc/zYWNadU/fu7XbXBmoS7J/mgViZnMy4bUdH5ReHALYDxnx1BZ+Rt4LQJcI0tUdTelTxot4pCiqCf85gNSMFJTo+OZl3/VKCO3iqcNEAUh+YwcExa2tLWl1dvQXNePU+WmpSF6B63WYHWxC46HdTBYyWtV6PfoYudrVlqtHGkXYjDOMDYYLxIYyReAhCF1W66CbK/v297nrBO69ybkX8FIsFt00kkm4LxB1jvrr0OVufrbeC0CXCVL2iFQo0nUirFOliSr1SVPWi6hVVlyjEaG1Nup4rmrajVQy8PitFF4io74pCFlW1aBlnvVbfH8wHX49+h5VUYaP313Oa7tTb2+l+L1QGH6oRhvGBMIQu8aNz+fDwROnfNl86Dx+3/ft73A0ULvcQV9msN5W+vX39G3xAHDDmq0szPLb6WZbQJcKKxRlXTaKlozX9RysEKUDRdmBgzDXZVZWJghXd+Tp0aNBvvttQ+o+01X1fsGS0phE1NHg9WlbSnbKgr4uCHG+6knqvHF91ACrMUeCin6tpTputksHm8KEaYRgfCEPoEj86T4+PZ121q3qw7d27y61sCMQV5znUGsZ89FB+EGEKMxSEJBLNblpQQ0ODq1TRPO4gDNEdL4Uz2tcUH+2r6a4qYTTHW8+JmuEGgYtiOIUmqljR9CWVKWs6ksqTFdpoipKa8Cqw0RxxhStqlCsKgfS9u3d3uWBHmZ6qbAqFonseAABU3skbHtxbAwDgbCJ0iTCFLKJ52gpVMhmtRNToAhhdZOmCS9UowdQhPafyKO0rKKmvr7fR0YwLUwKaqjQ8PO6CEjXTVTNbTQ9SfxdVuyiIUYWMghitnHTw4DE7cOCYPfHEoAtb9Nrzz9/nSplFoY1CnGx2muAFAIAKW1m/rJszAADg7GF6UYSpSkXTgjSlSGGJ/iU1nUfH6ut3utBFU4xUXqwGQKqCUcWKXqtKFV2IKSjRVsNgfn7RhTcKZ7Sv54JlovVe6gWjiphyqo5R3xZVwJT3gVHIo4obTVlSAKNgZ2qqaOo9s9oUpkAwhUk/X4ERw3N9MzNemNXc7AVdQDnGB8IE40MYIyrVjv6S0bnpog1OZG1HUdWs3pRjNdIH4oqpFqg1jPnoIXSJME0P0uoE6tGiAEbVJApXype71OpEqoTRdCJdTCpA0Vxv9WVRiKJ9bYP38Xq2qFdLvTU11bumuqqoUXizUXpPNfBTgKL3VP8XVdXoeKEw66pmTpY9L6fQR98ThDhhAQ08/OFFGMYHwgTjQxgj8fC9yay961vfs59pTdnPJ1vcdUB//27/WSB+OM+h1jDmo4fQJeI0zUcVIQoxNPVHQUWw+pCOaRpR8Lr29pSrVNH0Im9Vg0V3R0/5h0IR0fcpFFH1iiiAUUCjgEchiqphVtJ7KizR1CVNawoCF/0eXnWMVwkjqrTR+61X8aKAZnHxBJUuG5DLeR3M29roYI5TMT4QJhgfwhjRtN3oh/1B6CIf7N1jqVTCzjtvn9sH4ogPoKg1jPnoIXSJOAUuo6MTLvBQNYvCCk0jUuCiRrYKPkSVMCoz7uz0Gt7K4OC4C2A0BFQhE7w2EEw7Uiij99M0IVWhrKSQRX1k9PrSK01Tm9RIt7l5+fsF1OdFUqkWtw3Mz8+76UyqthH9XL0fwhWL3vSARIKpATgV4wNhgvEhjBGvOjTq04vKQ5cP9Ox25+4LLuhz+0Ac8QEUtYYxHz2ELjGgChdVpHjNamesp6fD7e/a1eFCE00vEjXHVRATBCcKYp54Ysg1yw0qYsqpOkVhjgIXvUewFLSqXvS+eqhqRa9TNYtCG71Oy1d7gcnq9D3ZbN4FRuU0LSp46E5j8HMRjj+8CMP4QJhgfAhjJB7KQ5e3dXTZhW2tdtFF+90+EEec51BrGPPRQ+gSA6owUU8WhR8KVhSA7N27y22DqT7d3WkXzig00dLRooa6hw8PuyBm375udyygYEWhSLDVewTLSisUURijKUUKRbxpRWeeF+osD2ZwqkzGqxxKp08NzgDGB8IE40MYI15zeAX+UVYeutyo0KUlaRdffK7bB+KID6CoNYz56CF0iTBVnqjBrUIPBSoKR9SoNpfL2wUXnONeox4quoBUQKJpR2qi293d4SpRFLioOkXVMeedt9dVoGh6ksIYTfNRRYzmtyu80eN0KOSRjYQnGolaplpBj1Zc0v+eqF/8VkOxWHDbRCLptkA5xgfCBONDGCMKnlpP+3y3XZSHLte3p+1Zra12ySXnuX0gjvgAilrDmI8eQpcIU+iiPi6aTiRDQ+Mu3AiOeWFLnevXogqYRKLZrSSkY/m8V/WyZ88uGxwccw1vVc2iyhWFM5oDvlkaSnoPhSZaxlrvH4QmG8tOvBdpupOqdtbqCYPl+MOLMIwPhAnGhzBG4qE8dLm2NWVXtbXZhRees2pPNiAOOM+h1jDmo4f1eCNMwYQqU4IqEq06pNLotrYW1yB3enrGBSiini2FQtF9T1DJomPa18pFmjLU09PpHpsJXPTzFeBodSQ9VEmjGE+/g4IfVdUE77v+o8N6ezvd70zgAgDA1mRK52hurQEAcHZR6RJhXzk2bLc/9oT9wUVPsvN371qqNFG1yoEDx9xKReUNcqeniy5wUaNbhSUKRBS6jIxMrrp6kVYRChrnBn1dVqOVHvS9eq+6uvoNVrXgTCHtRhjGB8JQ6RI/5ZUuFzU22uvTna6XW9RXZQLWUih40ySTSaZIojYw5qtLBQkrPydvFqFLhCl0ue2Hj9rTUq323ssusbRfHaLKl4MHj9nwieP27fk5N2vn1eeeY92l56emiq4yRdOJ1EBXcjktG21u35seNG9a2UiCxrlqottYungjUNl++FCNMIwPhCF0iZ/y0GV/Q4O9paPL9u/vded9II6y2Zzbtre3uS0Qd4z56vIKC7a2cAyhS4QFoYv8wcUX2hX7el0PlbGxrGWKM/aexw9Ypqw65YtXPd8ad+50FS+6+Ar6rajPy+hoxjXbVTNdNefV9B4u0KKBD9UIw/hAGEKX+CkPXdKli8Tf7ey2/v7e0r8vF+eIJ85zqDWM+eihp0tM/NPgsJsOpCqX+fl5+9/HBpcFLvL+7//IbVUiVb4qULBEpsqm1FOls7OdwAUAgIhTTxfRFGEAAHB2ELrExP2ZnN13dMhNDRqYnbUf5Kf8Z066b2Tc/s/jh/y9kxS67N7dZalUy7IwBgAARNvQwrxbURAAAJwdhC4xcufQiA1NZO2jh47a4hqzxv7p6KA9ms37ewAAIM6GFxaWVjkEAADVR0+XCCvv6RL4qbaUfTcXHqp0NjXaHVdebnVUtcQC8zoRhvGBMPR0iZ/yni7yqtY2u3pPj/X19VDNiljKZDJum06fXLETiDPGfHVpZeCtnj8JXSLs8fyUvfkb3/X3NucV+/fam578JH9ve1KPGqwvm826bXt7u9sC5RgfCBOMD2GMeCsUaMptlK0MXZ7a2GT/Y+9ea22lVxviaWam6LbNzQm3BeKOMV9dbW0trifqVhC6RNjM4qJ98AeP2n0jY/6RU714X6+9oLHZ3nvw1F4uH778Uru4CkuNafnpuTmvmV8YDUUtWX3ixHG3ipLoAhjhpqe9tfpbWlirH6difCBMMD6EMeJdWDU2Nvh70bQydLmkscneek6f9ffv9o8A8cJdf9Qaxnx1UekC+/rouP3R9x6xBT+kKJdqqLdPPe/ZdrwwYx965DF7YGp5c92+ZMI+/fxn+3tnlkITXcxPT8+4pag3chGrwdxQ+p31em0pg94Ypo8gDOMDYZheFD8rQ5e99fV2U1+fXXjhOf4RIF44z6HWMOajh9Al4ooLi/ZbpYurx1ZZreg15++3//qkfldBcuDAgP3RwIAdm12+gsEtlz3Vruju9PfWtlD6OXofNePTtB/tB6shqBRbQUlzc2NpzwtKpqYK7lhra8ISCUqaK4k/vAjD+EAYQpf4WRm6dNfV2Xv37fNDF25mIH4yGe/vWDrN3zDUBsZ8dakOgEoXONf8y312vOyfsqlup33hZ5/v75kdPjxkP56ZsT85dMQ/4rlm3257+8UX2GqjYGFhwWZn52xqqrg00LRpbm6yhoY6q6+vdxUsCmI0LUhVLQpj9FxHR+WnLcHDh2qEYXwgDKFL/KwMXVp27rRbevdYZ2eqtEfogvihvwVqDWO+ujT1WIUEW1H3+yX+14iwH2fzNlDw/gOUDz77GdbT3OTvedN9WheP28CJ4zZYnPGPmh2eLthVLa2uMmV6urjsUSzOumk+ra1Jf7Al3UMVLQ0NDVZX5/VbUSCj3iuJRJNrMqQtqmdmxvv3TCT4w4tTMT4QJhgfwhiJh+GZWbv72LC/Z7Z44oRdk05bU5OqUYH4WVxccNu6unq3BeKOMV9dzc0NW+73RqVLTBQXF+0zjzxuR6aL9sK93fbic/b6z3hUsTIwMGpfnZ2xvzl6zD/qedMF59orzts+c71HSxeMj+en3epMYyumQ+FUc7OzbtvYRNiFUzE+ECYYH8IYMfuFvt32pFSrvxdNP87l7W33P+jveX63p9cu3r3L3wPipVj0bjoSHKNWMOarK5VKWjK5tXYZhC7bxEdv/Y7bvvWmZ7rt6ZiZmbVcrmA9PauXiB86NGSFuh32jkce9Y94upub7DMveI7VbXGu2koKTf7x8DH7in/Hbb+/MsY5LYmlr8up6uZI6XF4+mTFDgAA1XLzpZfY83u6/L1oOpibsjfd/11/z/Oy9rS99qkX+HtAvOTzebdNpTSFDog/xnx1qcpFsz+2gtBlExSMfPlzB218pGiJZL1dd8NFy0KShx8at9tufsB++OD4qs/f/cVD9uFbvuW+v6snYe+4+dl29Uv73XNnInRZjf55g34s4+MZKxRm7Z8K03bn4QF3LHD7lZcvm450OubU26X0uH90wu4dHrOvjY77zwAAsP3FIXQZyE/bf/+Gd00ReElbu/3qufv8PSBeuOuPWsOYr65UqmXLlS47/S3WoUBl6Oi0ffLvf86+ceh6+/hnr7Y7b3/U7rzjZNXIu17/73bZFb3u+du/fK0LaBS0yNFDeXvvW+61m297nnte21t/++vueCVNTORsZGTShS8tLUnX8Pbn9/RYw87l//Rv+Nq3/a827v8eGXTLVf/qV+93j9d89QG74d4H7P0/+DGBCwAAZ0HzivO7DCzMuYb5PHjw4MGDB4/NPkr/Z4uodNmCP/7d+60l1eCqU+7/6qDd8s6v2V3ffKX/rLlA5q6/e9w+/flrXCWLQpv3feJK/1mz97z5Xtvd1+K+f2Wli/bv+5ej9ud3vsTa0qs3v9PSzWFNfbSS0NhYxpqaGtzX6XTK9XVpbmm233zkURtf0S/lL57/bNuXXDsx/dbYpD00mbHvT2btkWxlwyIAAKotDpUuaoL/i/c94O95Wuvq7AP9XmUtEDfz8/Nuq0UegFrAmK+urq720uforfV7I3TZgrddf48967m99ms3Ps2FJA/eP+wCloCCmLffcI+rbHndy7/kqmDKpw+Vf0956KKw5tMf+b79zd0vXTNwkWJxxubmFqy9ffVBkMtN2+Liolu+OZudciGN/rlnZuZspKne3vO9R/xXet540fl2Xf8+t7pRtvTaQ9MFGy59/T0XtOT8V21eb3OTPbOrw3KlPxCzs/OWO37cJuZPbZDbsGOntTc22JPbU3Zx6bGrqcme3tHuP4u1sCQwwjA+EIYlo+NHoctrv/5ty5TO/4FkXZ198rKnWeNOloxG/ExNTbtta2uL2wJxx5ivLq3Mu9UVAAldTpOmG9346rvt81+/zgUjKytVJJeZs5+79G+XQpc3vPMyu+KFe/xnvR4vn/3Uw8tCl8tfsMfe/cb/cNOXLrk0/G6bqleOHh20trbkUt+WgP5V1VQ3lWq2nX6psQKP6elZt921K2Xv/tEBG6jQ6kANpd+nP5mwn9mVthd1d/pHlcwulC4I50q/V+KU3zmgIanf//jx4+4BAEA11NfXW1fXyXNWFKmp/ice+pH9c+7kzZKm0nXAx57xZEvV1/lHAADARm31xhShy2kIApebPvDc0Ea4mw1dNP3oa/82YK99y9Nc9cxGHDs25MKL5ubl6ZuCDf3TJpPLm+MqqBkaylh3d5t97OBR+6aflJ4JT0klrT/R7O6oXef6xqweqkxNzbgwpalpeRfoxcUTrnInoEwmCIwAAKi0RKKxdH6M9tLKs7Nz9vEHHyF0AQDgDCF0qbIgcHnre55p111/kX90+VShwGanF6kxr1Y80jaooFnPxMSEZbMFa2tLLAsodCyZbFx1eavx8byrJCnuPGHvOnDYP7pxraWLtsvTbXZ5R5vtaW6ydH29NdVtPBzRz9ZF4fHjy4eeQhb9vjtdWLPD3wIAUD1Rn2alqcRffvgx+/jomH9EZ1SzPzz3HOsJ6QMHAABOpeKG3t5uf+/0ELpswlqBi5zJRro6PjQwtSzACTM5mXMVLN3d3oXi1FTB9W3ZtSvt9lcaG8taLjflGuzeMTRi/zpxck7/ap7R0W57ks3W3dTkvr60c/X3PdN04Yj1ZbNZt21vp/8NTsX4QJhgfAhjRNOL6iJfYalpvE8cGrK3HDzoH/H82XMuDW2WD0QV5znUGsZ8ddXV1ZUeW7s2IHTZoLDAJfArP/sFe8GL+lxwoqWg3/iqr9jr3v5093rt3/CSu+yPP/mf3BSjoArm77/6cuvrTy0LXTQt6b9c/UX75dc+OXSakf7lgrYoQ0Pjpf/wWl2QMjw84TosJxKrryeuQGZgYMRdXOZ37rC/HBqxqdlZWyi9YXtDg11Qep8L29tsX0vCLi5tzzQ1911cPO5+/3IaipqLruc1zUgXvmv1fcFJxWLBbROJpNsC5RgfCBOMD2GM6ALWO49Gmc6vR0vn+Lf95HGbKeuLduNTnmQvO2evvwfEBw3jUWsY89FD6LJBCkX+6pMP+3vLafqQKJi57eYH7IcPjlsiWe+mCpVPJ1IPlw/f8i0bHylaV0/C3nHzs9fsCROEMn/xhWvWbKir1Yv0z5dMJkr/8eXt+PFFS6VabXw8Y7t3d60ZWOiC7ODBY9bd3W6ahtTX12OjoxPW0pKw1tYzf9GtEaYwZXq66O7AyVq/W2trwk0x2mqH6FrCH16EYXwgDKsXxY/O8ccGx+y3fvKYZRZOrmD0snP22I1PucDfA+KD8xxqDWM+eghdIkyhy/T0jFs7XP+Kk5NZW1g4bm1tLaalrdaiJrYHDw7a7t2dNjIyae3tLdbZWZnyNFXVZDJ5a2ysd6tC6PdSeZZCF6pYzgz+8CIM4wNhCF3iR5d1g4Nj9u5HH7fh+ZPTdC8rneff/6xn+HtAfHCeQ61hzEcPoUuEKTxRaKKpRM3NTa6SRI+enpPLXebzBTddRyXT5SGHV+nSUXreW72os7Ntw9UlGjJBvxWFPFrtYeUceL2mUJhxP18/OywEWovegyWj15fJZNw2na5Orx1EC+MDYYLxIYwRb8W8qN8Q0LlTU45v/skBOzw76x8tXZw3NdifPv0Sfw+Ij+lp71q2paXFbYG4Y8xXl1p2rFwpeLMIXSJMoYYa6CpoCaYTaUWgYNUflRg/8cQxd1zBTDrdtvTckSPDlkw2u329hx6pVNJN7fFWEPJCFIUeeh89rxWH9NCI0Xvqe/U6TRlS8KIB2dDQUHr+uGm6k4aWfm7p1RsKT/S++l79DPV00c+N+sVvNdCzA2EYHwhDT5fldM6Kw/RWVbr8ycHD9gP/wjzw15f/lDWuuEkCRN3UVN5tW1tTbgvEHWO+unRdoFkbW0HoEmG53LR7KCRZbUrRxETWrVSkZE5Nc7Xt6GhzQYZCkWJx1lKphAtU1EtlaqroKli88OPEssBD4UpLS7P7GSurWhSoaArR7Oy86+6s95ubm3MDNHiPjWcnO9zP8X7fzVfH1CJKDBGG8YEwTC+Kp/HxrH3qwGH717JKJvn4FT9lF7TpZggQH5znUGsY89HD7Y4IUzChqpBkssmy2Sn/6ElB4KKgJKgeGRvzLsD0PWpuq8oUhSTaKpDR1CRNO0qnU65XTE9Ph/X2drqtGu2uDFxE76NpRqLQRklgb2+X+x69l95zow/9LDXzJXABAOD06Dy8r5GG9AAAbAdUukSYphIdOzaytHa4QhE9REtCFwqzds45ve7i6+jRkaVqFVWkKBA5cGDAPZ/JTLn9zUzlUZWMpjWpX0xjY4MLSRQC6cGUoOoi7UYYxgfCUOkST1NTBft66frgTw4d8Y94fvOpF9ol7W3+HhAPuVzObdvaGNuoDYz56kqXPuumGhr8vdND6BJxWsFoYGDMenrSLkzZtSvtpvqowa6qTBYWFlwoov4tx46NucoXTQdSFcvg4Lh7vS7OVGGykbBEqxHp9aqOUQWN+sCsVv2C6uFDNcIwPhCG0CWeVHWqytb/9sMf+UcAAMDpeMNF59sr+/f5e6eHT8sRpsBFgYqqV7LZaVfxoossrRik4+r1oma0mv6jChQtDa3KFAUmOqbqGF2YKWvR9KOVVEmj79dzmr6k1RC0VYCjxr1alYjABQCA7UU3UXQNUEflKQAAZx2fmCNuYiLnqlbUr0UXWQpJFKzo2Pz8vKtGUS2TjnurFe10Acz09Iy7IFMljF6jFYgCeq3ed2RkwkZHJ13ljAIarz9Lh3sfAACwPXnn90W7qqvTPwIAAM4WQpcIUx+VoHJFqxBp2o+qXlShomlE8/OLrhrFa6Q771YT0ipHer0eCmkU1uh9tPy0qlgUsqhaRstB79rV7oIWNcVVc1sFL/RrAQBg+9P5+sUdaWvivA0AwFlFT5eIU0NbBSbqraKeLfrnVNCia6z6+np3XNODFMh0daVdP5cjR4ZdMKPXKWjZv7/X9YDRlCQFNNulkkVTn7C+bDbrtu3t7W4LlGN8IEwwPoQx4p03ddMhDoaHJ9yNmcGJnD22MG/zdfrfRQCD+FkojW+pr99ao0sgKhjz1fWsPd128a6t9b0jdIk4/fPpwkoVLOrTkstN2/79u210NGPd3Wk3nSh4jfbV90UBSy43ZZ2d7W67d2+3C1wqQSGP91h/mOn3VKNeVeEocNHvzg269RWLRbdNJLyVq4ByjA+ECcaHMEYUPLW6qtA40BRhnVd1XaD/TVSqIq5mZ2fdtqmpyW2BuGPMV1c6nXKFDFtB6BIDWrpZF1WaBqTKFYUc9fU7SxePKf8V5qYNpVItbtqRXqPgRdOHtFy0Vi4609UturumJatVYbMZGtCaix6Xi95qYHUahGF8IAyrF8WXzvu6eTE+nrV9+7qtpYVQDfHEeQ61hjEfPYQuMaGLK00N0iNYLloNcwPBMs9K6hSIHDx4zJLJhAtompoa3Ou3Qu+jaUxq0KtGvLqhpuBEYY56wbDKUeXwhxdhGB8IQ+gSX0Hoosb4qnTV+R+II85zqDWM+ejhk3BMqEJEvV2mpopuCpGCjnIKQIIVinbs0LSdnS4oUbmxjp9O9KYpQKqaUeNdBT3qL6Of09GRsp6eTlemraoVAhcAAKpL53f1p9G9Nd10AQAAZweVLjGipZ0VfOzalXZhx0oKRhSIqALmiScG3VYPhSfqA7PR+d4KaVQ5o+9T00GtmKQVkNay0Z4uEqzGpJ+h/y1Y38yM15OhuZnScZyK8YEwwfgQxojuGra5abhxMDGRdTc9dO5X37eeHt0Rpa8L4ieT8e76p9Pc9UdtYMxXlz4ib7UvGqFLjCioUOWJQpfVTE7mXEiiqhhvBaPjroGueq+cf/7eNQeTN0JOuK0u4hSK6D00dShsAGpoqfomn58u7W1soOrt9DvqoldVM+VTpLA6SgwRhvGBMEwviq9sVufeE+7crwpYzqeIq5mZGbdtbt4eq28ClcaYry7duNhqXzRClxqi1Y00/UihjOZ4T07mLZlschUlfX09p6xgpOPq06KKFo0ShTSqlNHrwqYMKfzRz1KYo34xGqQrpzuthalIm8eHaoRhfCAMoUt8FYsz7jyezxfdjZL29hb/GSBeMhlv6ft0mmXvURsY89Wlz6dUumDD1OB2bGzSrXKkMOXo0RFXTRKEKVrdSNN78vmCu1BTuNLYWO+mKuku2WpTlgJ6P2+55wWbm1twVTB6hH0Pzgw+VCMM4wNhCF3iR9WoqmqZnZ13labz84uu8nTv3l3+K4B44TyHWsOYjx5ClxozMjLhGtzqguzw4WEXrCgoaWhosESi0QUmWgFJx8PmtWvYKKBR5Ywu7FTJovf0gpoGypiriD+8CMP4QBhCl/hRdaoeuqmiilbdnAvO00Ac0bsMtYYxX11ML8KmZbN5W1w8YZ2dbW7ZaF2EqVxK29bWpBtUYTRcdDGn6UOqbEkk1ES30QUtODv4UI0wjA+EIXSJH1Weagrxnj27bHBwzN1IUdXpVi8Yge2K8xxqDWM+erjtUWOSyYTNz6tHy4myoOSEm++9MnCZni6e8lCljC7mNN1Iy0LrewhcAADYHnQrTb3VNHUYAACcfVS61Bj9cw8NjVtvb6eNj2dds9udO3dYa2vCtFSmloJWPxfNCdfdsZW0spCOV7JMWVU0KoXezFLTtaxQ0AoVCtRokohTMT4QJhgfwhgxdwMi6jcSNGX44MFB279/t6tu1f8enft1jgfiiLv+qDWM+eghdIk59VzRfO7y5Z2D5SPVk0VLSqr/iroye31Zdrp54JpqtBF6D71XQEGJGvYqtNHXwcpH2l+f+sQcX2rcq9+F1YzWl8/n3TaVSrktUI7xgTDB+BDGiObHNy47p0WVphXppkVzswKkHS500cqFQBzxARS1hjEfPYQuEablIBWQrBVM6G7XyIj+o9xhapKbTqdc8KIwZHh4wlWsaKWioKeLpgtttIJFF3OqitGUI32/5orrfVUlo8oZHdNDP6O0cRUyG6H/LRqShUKx9Psfd31jEI61+hGG8YEwwfgQxohZV1db6f8Pp1Z5RkmwetFPfnKkdG5ududhNclXhSsQR3wARa1hzEcPoUuEKXTJ54vW3d3hgo2VMpm8eyiYEV2E6bX6J1dvFpUcFwozLkBJp1tNS0ZvhMKcsbGMq2DRe2k5Sm8FpHoX7OhOoSpcZHp6xlWvbGSY6TVedcyC+129yhtWQVpPseh1ME8kaJKIUzE+ECYYH8IY0QWsQpe1V+6LAt380PlT52mdTzVlSj1eFLroZggQN3wARa1hzEcPoUuE6cJKfVm0EpGmD5XTtJ/BwXHXGyWZbFoKPRRidHa2u7BFKxzoQkx0IdbV1b7uXPZsdspVnygcES0/rVBHF3gKgSYm8i5wCSpmVPWiUu2NTBPS79DQUFe68G92v2dQLYNwmYz3hzed5g8vTsX4QJhgfAhjxDsPRf20o2sDPXQdkMsVSufpFneu1/kYiCNuLqDWMOarS4UJKz9rbxahS4SpX4ua5OmunO7OlQcb6tuigERVKKpA0UMXXPrn1usUsOgumKpQ1DtF5dQKU/Q6DSqFLwpOdNGmizW9j8KUoIJFQYumFJ04cdwFO95rFtz36RFUqngPqlUqibQbYRgfCBOMD2GMxIMCF00t7ulJu6nEDQ0Ntm9ftzufA3GUyWTdNp1ud1sg7hjz1aXPzlstBCB0ibB8ftrGx3MuKOnoSC0lcJouNDg46i681EtFlTCqiFEQooeCFi31rKWjvdWLdtru3V0uINH3qFeL5oSX3/HzAp0d7qJNIY+mEeVyU6XXz1lTU4M7FvV58FHFh2qEYXwgDKFL/GgFwCNHRuyCC/rs6NERd0NEU4tURQrEERWdqDWM+erS52FClxqmJrYDAyMu7FDwsmfPLndcAYsCGVWnKIzRigWa+jM6mlkaMApYFMbobpi+V0tGl/d00bCYnMy7ShbNCRd9rypk9HpVyaiPi5rvllfYnEn6HYKfjbXlcl7a3dZG2o1TMT4QJhgfwhix0vltY9NhtzOFLE88MWjnnbfXJiayS1WsQFzRMB61hjFfXSpW0AyPrSB0iTBN9RkYGC1dIKoXSr0bEKp2UTmxKlbk3HP3uudFwYsqY7TVFKDe3i4bGho3rWygYaDvVZ8XVcqoZ4uOq4pFpcl6D12I6mcqjNHPU6BzuqmfesqspN9Bv7d+tkaltlworq9QKLhtMrmxZb5RWxgfCBOMD2GMeBdW6/U22+5UkXrgwDHr79/tbs5olcJ9+3rcORaIo0wm47bpNMuiozYw5qtLn4G32heN0CXi1LdF1Sqa3qOARCmcKl0UnmgKkFYrKqf+K0eODLmwpKenw+0rANHqBhpQCjmCx0pB4KLGvBtd6Wil4D2U1axcRlrHdLGrn63fRVOhsD6mjyAM4wNhmF4UP7qsO3RoyDXQVSXs4OCYnX/+Pv9ZIH44z6HWMOajJ9o1tHB35VQOrXJi9WFRCKMLrh07drrnVlIwo9WLNG1HjXi1rx4vCmi0EpGqXRR6eEs3e4HMxETOXbTpvU83cNHvFzT31VQmTUvS9Kbyh5oBKzQKGvECAIDN0U0VndvVs01b3VrT9QEAADg7qHSJAQUjmmaUSDSW9na4fQUoapy3Gl18PfroYUunW12FiyplgmlIGg3qB6P30IWbwg9NMwqbx+aNoBOuakbNeRXmrKT3CqY/4cwi7UYYxgfCUOkST2qIr6rXvr5eO3Jk2Hbtai+dx5k+hniiqShqDWO+ujQbY6sIXbaJj976Hbd9603PdNvNOnZs1DXL05QdTS3av3+3u8O1GlWxPPbYUVd23NzcYMEKRApGNBwUjLS0NJf215+/phWM9HPVB0bfq3BG37vSRt4Lp4cP1QjD+EAYQpd40nXAsWNj1tfX7Xq5qaJVVbFAHBUK3s2+ZHJrjS6BqGDMV1dra3LLhQOELpuUy8zZr1/3ZWtNNdinP3+Nf9Tz8EPjdtvND9gPHxy3RLLerrvhomUhyt1fPGQfvuVbNj5StK6ehL3j5mfb1S/td89tNXRRo7zR0UkXqGhZyL17u9dM5fQvfvDggOub4pUen/ADGC373OiOr0ffo2lHqnBR0KKwhylBZwcfqhGG8YEwhC7xdeDAgKt4VRVqY+PqvdqAOMhmdT1q1t7e5rZA3DHmq0ufc7Xy71YQumyCQpUbX3239exJrhq6XPucf7CXvOI8F5wcPZS3N77qK0vBivZf9cLP20duv8queOEeu/+rg/buN/6H3f7la62vP7Xl0EW0fLSqTvr796wbgKgyRhdiep0qXLq62lxYsxG6Y5bN5t3X6gWzkZAGlcOHaoRhfCAMoUt86ZqgqanRnecVuIRNEwaijPMcag1jPnoIXTbhbdffY29612V2z12H7MH7h5eFLgpRbnnn1+yub77SP2J25x2P2l1/97h7nUKVoaPT9r5PXOk/a/aeN99ru/taXNCyMnTR/n3/ctT+/M6XWFtavVrWpz4sWh5y5YpFq8lk8u7hNc094fd3Wb9Brprrap64Lt5Wa9R7JgRTlbTspQIehMvlvLS7rY20G6difCBMMD6EMeKtoLfVu1nbxdDQuDuXqgF+XV2dK48G4ogPoKg1jPnoIXQ5DQpEVoYuqx1TEPP2G+6xbxy63l738i/ZZVf0LqtkKf+e8tBFYc2nP/J9+5u7X7rhwCWgf05VrqxHr1Nfl46OVOmie9o13u3qavef9ShgKbewsOC/NuWa62pFIk1n0s/Thepq9JqV77MWhS2aoxiMSP2Oa70vTpqennbblpbKhGCINsYHwgTjQxgj5prLx+W8MzaWsWJx1p2vFSRphUAgjvgAilrDmI8eQpfTsFboIuWhivq//Nylf7sUurzhnZe5qUUB9Xj57KceXha6XP6CPW7a0cc/e7VdcmmXOxYm+I/udIyMZEsXmAnL54uuya3uhqmyRENCAUgwbUgVJxolek4XbvX1O13YoucVuASVKcp6ygOf4DWb6fWiyhv9Lt77er8LAADVoIqQrq7VV/6LEl0bTE/Puoa6XkXrcWtvJ1QDAOB0bDXgInQ5DZUKXTT96Gv/NmCvfcvT7NdufJr/qnBbCV3Gx/Nu9aKFheNuBSOtbKDAQw9dpGlozM8vLh3TxagClyBsOXHiuAtcgn011dV+QMe94MSrWlmPvlev11bLWgc/FwCAalAz+e7uXf5edOnaYG5uwaamZkrnUe+GiG6ylL7yXgAAADZEH3M7O7d2Q4bQ5TSsFbpsdXrRnbc/6lY80vbzX79uQ1OLthK6ZDLTLtTQqgaFwpwLYLyQw6sy8apavEa72tdUocVFBSIL7jU6rkF4MlQ54YIZHV/5mo3Qa4OfqQBI+wAAVFMcyrV1baDzdiZTcDdLpPymCAAA2JhEotF27+7x904PoctpWCtgOVONdHV8aGBq2ftXwvi45nvP2b593aULtLzNz88vXZRpTrsCGK2GFGhpaXbHw5aHVv8WTUPSHHJURxC8Ma8Tq2F8IEx5cM8YiZ/BwTG33bMn+tU7wFo4z6HWMOajh7V+zxBNG9Iy0kF4oiWi1Qz32l96ktt/xWsudFOHFM6ItloFScdX+p1br7ChYwX7zMd/4B+pDK1ApJBEFSWdnW3W29tlPT2dtmtX2pVYa3qQvtYxXbC1tbWWjjetGbiIQhkCFwAAzj5NFZaZmVm3BQAA1Ufocgb93oee7ypgfrr/DrvhJXfZS15xnl13/UXuub7+lN30gee6ahg9r+0ffuxKd3wlTSu6+bbn2Z9+4EF7+KFx/+iZp4oVzeApr2bR9B5dpCmQ0YpG+lrHAABAtOjmiWx0FUEAAHDmMb2oxj3xxKCrcmlrY1WDqKLEEGEYHwjD9KJ4y+Wm3bLRutRrbU36R4F4yefzbptKnXojE4gjxnx1NTU1LFWOni5Clxo3MjLherAw3zu6+FCNMIwPhCF0ibdCYcaFLslks9sCcTQ9Pe22LS3cQERtYMxXl86har2xFYQuNW5yMmf5fMHOOad326wWpOlOaupbPu0JaysUCm6bTHIXE6difCBMMD6EMWKud5nuaEWZphKpv5rMzs5bLjdl3d0Eaogvbi6g1jDmo4fQpcZNTxdtaGjCzj9/7xkLXbylpRfdxZ6WrFQlzUbpe4O+Mk1NW0sUa0Uul3PbtrY2twXKMT4QJhgfwhjxSojr6tZuFh8FCl00rUiN8HWJNzw8Ybt3d/nPAvHDB1DUGsZ89BC61DiFHAMDI9bb22mJRLN/dHMWFhZKF3kLNjs754IWNe7TctNa5UgXrwpRgmZ+YZT5ELRsHn94EYbxgTDB+BDGSDzovD42lnErEupcPDw87ipdNnIeBqKI8xxqDWM+ejgD1zgFHTt27HShyWapmmVkZLJ0cZe1qamCNTbWly7s0u7R2dluqVSLJZNNdvz4CXfhpzuImg+31oPABQCArdEND1WuKnwRnVvp5wIAwNlD6FLjtGx0ff1Om56eceHIRqkPzLFjY0sXdaqXmpqacQGMmvPqMTqq7aTNzMza+Hi2tD/pqmEAAEBlKHBpaWl2597A6dxYAQAAZwbTi2CZTN6tcKC7Y1o6urm5yX/mVApZJifzpQu4OVchk063rrqEVjDFKChnXlhYtGJxxqamiu71+jkrS501FFU9owa6er32NxME1apisei2iUTCbYFyjA+ECcaHMEbMndPiUnWpaUVdXWl33laPF00jBuKIqRaoNYz56CF0gZsapJBDF5q6MFMoopUPVuurq+f1Wi2d1d7e4iplNkNNdfXzVq942WF1deoF4/189YLRA+EymYzbptNptwXKMT4QJhgfwhjxbhhsl5X8tkrVpZrm29BQ56pOo94gGFhLseitwpZIsAIbagNjvrpaWxPus+9WELrANcIdHc24oENhyPz8ojumEKb82lNVJ6qGSSSaShfnKf8ozjbSboRhfCBMMD6EMRIvqi4tFGatq6vdTQHWeR2Io2w267bt7e1uC8QdY766NrooTBhCF7hlnTVlSI1uW1pUXr7DTR/SVKCVNC1otelEOHv4UI0wjA+EIXSJt8HBMduzZ5e/B8QT5znUGsZ89DB3Ay65052w1takK6tWdYuqXHRs5YPABQCAaNCqgtnslL8HAADOBkIXAACAGEokmpdWGQQAAGcHoQsAAEAMNTc3uub3amIPAADODkIXAACAGNL0YQUvMzMz/hEAAFBtNNIFIo5mWgjD+EAYGunGn1YeHB4ed6svAHFUKHgLPySTWgwCiD/GfHWp7ylLRgM1jg/VCMP4QBhCl9qg4GVxcdHfA+KF5XNRaxjz1cWS0QD4UI1QjA+EIXSJn7m5ebdiUSrVYk1NDW5VQiDOOM+h1jDmo4daUwAAgJhoaKi3+vo6y2TyNjo66UIYAABw9lDpAkQcaTfCMD4QhkqX+Dp+/Ljl8wUrFmds5846/ygQP8ViwW0TiaTbAnHHmK+u1tYEPV2AWseHaoRhfCAMoUv8acno+fkFfw+In1wu57ZtbW1uC8QdY766ggrSrSB0ASKOD9UIw/hAGEIXAFHHeQ61hjEfPfR0AQAAAAAAqABCFwAAAAAAgAogdAEAAAAAAKgAQhcAAAAAAIAKIHQBAAAAAACoAEIXAAAAAACACiB0AQAAAAAAqABCFwAAAAAAgAogdAEAAAAAAKgAQhcAAAAAAIAKIHQBAAAAAACoAEIXAAAAAACACiB0AQAAAAAAqABCl23i7i8esmuf8w/20/13uK32AQAAAABAdBG6bANHD+XtvW+5126+7Xn2jUPXu+2tv/11dxwAAAAAAEQTocs28Lm/+olddW2/XfHCPW5f2+f9533uOAAAAAAAiCZCl23gwfuHbXdfi7/n0b6OAwAAAACAaNpxosT/GmfJ617+JXvDOy9bqnQR9XT57Kcetk9//hr/yOomJyf9rwAAwFZ1dHT4X0UX1wYAAJw5W702oNIFAAAAAACgAghdtokH7hv0v/L86Pvj/lcAAAAAACCKmF60DXz01u/Y0NFpe98nrvSPmL3nzfe6vi5vvemZ/hFgdUEZeRxK4nHmMT4QpnwaCmMEQBRxnkOtYcxHD5Uu28ArXnOhfe3fBuz+r3rVLtrec9chdxwAAAAAAEQTocs20Nefsps+8Fy75Z1fs5/uv8Nt//BjV7rjAAAAAAAgmpheBEQcJYYIw/hAGKYXAYg6znOoNYz56KHSBQAAAAAAoAIIXQAAAAAAACqA0AUAAAAAAKACCF0AAAAAAAAqgNAFAAA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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from IPython.display import Image\n", + "from IPython.display import display\n", + "pdp1 = Image(filename='pdp1.png')\n", + "pdp2 = Image(filename='pdp2.png')\n", + "pdp3 = Image(filename='pdp3.png') \n", + "display(pdp1, pdp2, pdp3)" + ] + }, + { + "cell_type": "markdown", + "id": "73066ac7", + "metadata": {}, + "source": [ + "As we can see, PD profiles for all the variables do not show any correlations between them. We can say that because every observation for every variable is plotted parallel to one another." + ] + }, + { + "cell_type": "markdown", + "id": "c3b8380b", + "metadata": {}, + "source": [ + "## Accumulated Local Dependence " + ] + }, + { + "cell_type": "markdown", + "id": "f60438f2", + "metadata": {}, + "source": [ + "At first plots for ALE and PDP were separated and then joined together, but because of their similarities I decided to just show them together." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "d580daa7", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "#ale_gb = gb_exp.model_profile(type = 'accumulated')\n", + "#ale_gb.result['_label_'] = 'AL profiles'" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "3686942f", + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "#ale_gb.plot(pd_gb)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "77ee1271", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from IPython.display import Image\n", + "from IPython.display import display\n", + "pdp1 = Image(filename='alepdp1.png')\n", + "pdp2 = Image(filename='alepdp2.png')\n", + "pdp3 = Image(filename='alepdp3.png') \n", + "display(pdp1, pdp2, pdp3)" + ] + }, + { + "cell_type": "markdown", + "id": "a3667f6b", + "metadata": {}, + "source": [ + "Those plots confirm our observations that they are very similar to each other. The changes in direction are the same throughout the plots, the only thing that differs is the value. " + ] + }, + { + "cell_type": "markdown", + "id": "af855664", + "metadata": {}, + "source": [ + "## Summary" + ] + }, + { + "cell_type": "markdown", + "id": "f7cab13e", + "metadata": {}, + "source": [ + "Parallel lines on PDP show us, that the model cannot find any interactions between variables. It is also confirmed by ALE, which are also parallel to PDP.
\n", + "When talking about changes in value chained to changes in variables, all the conclusions from Homework 4 for this model still apply. Variables with the biggest dispersion are `longitude`, `latitude`, `median income` and `rooms per household`. Other variables are mostly constant or close to constant." + ] + } + ], + "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": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}