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@thomasjpfan
Last active August 16, 2019 19:07
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Partial dependence api issues
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{
"cells": [
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The autoreload extension is already loaded. To reload it, use:\n",
" %reload_ext autoreload\n"
]
}
],
"source": [
"%load_ext autoreload\n",
"%autoreload 2\n",
"\n",
"%matplotlib inline\n",
"\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"plt.rcParams['font.size'] = 12\n",
"plt.rcParams['figure.figsize'] = [12, 8]\n",
"plt.rcParams['lines.linewidth'] = 2.5"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"MLPRegressor(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,\n",
" beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
" hidden_layer_sizes=(100, 100), learning_rate='constant',\n",
" learning_rate_init=0.001, max_fun=15000, max_iter=500,\n",
" momentum=0.9, n_iter_no_change=10, nesterovs_momentum=True,\n",
" power_t=0.5, random_state=0, shuffle=True, solver='adam', tol=0.01,\n",
" validation_fraction=0.1, verbose=False, warm_start=False)"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.datasets import load_boston\n",
"from sklearn.neural_network import MLPRegressor\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.pipeline import make_pipeline\n",
"from sklearn.tree import DecisionTreeRegressor\n",
"from sklearn.inspection import plot_partial_dependence\n",
"\n",
"\n",
"##############################################################################\n",
"# Train models on the boston housing price dataset\n",
"# ================================================\n",
"#\n",
"# First, we load the boston housing price dataset and split the the dataset\n",
"# into a training and test set. Then, we train a histogram gradient boosting\n",
"# decision tree and a multi-layer perceptron on the training set.\n",
"\n",
"from sklearn.preprocessing import scale\n",
"boston = load_boston()\n",
"X, y = boston.data, boston.target\n",
"X_train, X_test, y_train, y_test = train_test_split(scale(X), y, test_size=0.1,\n",
" random_state=0)\n",
"\n",
"hgbr = DecisionTreeRegressor()\n",
"mlp = MLPRegressor(hidden_layer_sizes=(100, 100),\n",
" tol=1e-2, max_iter=500, random_state=0)\n",
"hgbr.fit(X_train, y_train)\n",
"mlp.fit(X_train, y_train)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Bounds are weird"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 864x576 with 3 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"mlp_disp = plot_partial_dependence(mlp, X_test, [\"LSTAT\", \"RM\"],\n",
" feature_names=boston.feature_names,\n",
" n_cols=3, line_kw={\"c\": \"red\"})"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x576 with 3 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"hgbr_disp = plot_partial_dependence(hgbr, X_test, [\"LSTAT\", \"RM\"],\n",
" feature_names=boston.feature_names,\n",
" n_cols=3, line_kw={\"c\": \"blue\"})"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<sklearn.inspection._plot.partial_dependence.PartialDependenceDisplay at 0x1a1edc62d0>"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"plot_partial_dependence(hgbr, X_test, [\"LSTAT\", \"RM\"],\n",
" feature_names=boston.feature_names, ax=mlp_disp.axes_,\n",
" n_cols=3, line_kw={\"c\": \"blue\"})"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x576 with 3 Axes>"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"h = plot_partial_dependence(hgbr, X_test, [\"LSTAT\", \"RM\"],\n",
" feature_names=boston.feature_names, ax=mlp_disp.axes_,\n",
" n_cols=3, line_kw={\"c\": \"blue\"})\n",
"h.figure_"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "sk-master",
"language": "python",
"name": "conda-env-sk-master-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.4"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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