Fix get_feature_names_out for FunctionTransformer
Aurélien Geron
parent
0573deb5d3
commit
576cec95d9
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@ -2302,6 +2302,13 @@
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"outlier_pred"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"If you wanted to drop outliers, you would run the following code:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 61,
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@ -3467,20 +3474,11 @@
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"cell_type": "code",
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"execution_count": 104,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Monkey-patching SimpleImputer.get_feature_names_out()\n",
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"Monkey-patching FunctionTransformer.get_feature_names_out()\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"def monkey_patch_get_signature_names_out():\n",
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" \"\"\"Monkey patch some classes which did not handle get_feature_names_out()\n",
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" correctly in 1.0.0.\"\"\"\n",
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" correctly in Scikit-Learn 1.0.*.\"\"\"\n",
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" from inspect import Signature, signature, Parameter\n",
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" import pandas as pd\n",
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" from sklearn.impute import SimpleImputer\n",
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@ -3508,12 +3506,10 @@
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" Parameter(\"feature_names_out\", Parameter.KEYWORD_ONLY)])\n",
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"\n",
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" def get_feature_names_out(self, names=None):\n",
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" if self.feature_names_out is None:\n",
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" if callable(self.feature_names_out):\n",
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" return self.feature_names_out(self, names)\n",
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" assert self.feature_names_out == \"one-to-one\"\n",
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" return default_get_feature_names_out(self, names)\n",
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" elif callable(self.feature_names_out):\n",
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" return self.feature_names_out(names)\n",
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" else:\n",
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" return self.feature_names_out\n",
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"\n",
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" FunctionTransformer.__init__ = __init__\n",
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" FunctionTransformer.get_feature_names_out = get_feature_names_out\n",
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@ -3896,28 +3892,28 @@
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"def column_ratio(X):\n",
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" return X[:, [0]] / X[:, [1]]\n",
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"\n",
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"def ratio_pipeline(name=None):\n",
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"def ratio_name(function_transformer, feature_names_in):\n",
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" return [\"ratio\"] # feature names out\n",
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"\n",
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"def ratio_pipeline():\n",
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" return make_pipeline(\n",
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" SimpleImputer(strategy=\"median\"),\n",
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" FunctionTransformer(column_ratio,\n",
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" feature_names_out=[name]),\n",
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" FunctionTransformer(column_ratio, feature_names_out=ratio_name),\n",
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" StandardScaler())\n",
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"\n",
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"log_pipeline = make_pipeline(SimpleImputer(strategy=\"median\"),\n",
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" FunctionTransformer(np.log),\n",
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"log_pipeline = make_pipeline(\n",
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" SimpleImputer(strategy=\"median\"),\n",
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" FunctionTransformer(np.log, feature_names_out=\"one-to-one\"),\n",
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" StandardScaler())\n",
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"cluster_simil = ClusterSimilarity(n_clusters=10, gamma=1., random_state=42)\n",
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"default_num_pipeline = make_pipeline(SimpleImputer(strategy=\"median\"),\n",
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" StandardScaler())\n",
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"preprocessing = ColumnTransformer([\n",
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" (\"bedrooms_ratio\", ratio_pipeline(\"bedrooms_ratio\"),\n",
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" [\"total_bedrooms\", \"total_rooms\"]),\n",
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" (\"rooms_per_house\", ratio_pipeline(\"rooms_per_house\"),\n",
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" [\"total_rooms\", \"households\"]),\n",
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" (\"people_per_house\", ratio_pipeline(\"people_per_house\"),\n",
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" [\"population\", \"households\"]),\n",
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" (\"log\", log_pipeline, [\"total_bedrooms\", \"total_rooms\",\n",
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" \"population\", \"households\", \"median_income\"]),\n",
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" (\"bedrooms\", ratio_pipeline(), [\"total_bedrooms\", \"total_rooms\"]),\n",
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" (\"rooms_per_house\", ratio_pipeline(), [\"total_rooms\", \"households\"]),\n",
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" (\"people_per_house\", ratio_pipeline(), [\"population\", \"households\"]),\n",
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" (\"log\", log_pipeline, [\"total_bedrooms\", \"total_rooms\", \"population\",\n",
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" \"households\", \"median_income\"]),\n",
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" (\"geo\", cluster_simil, [\"latitude\", \"longitude\"]),\n",
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" (\"cat\", cat_pipeline, make_column_selector(dtype_include=object)),\n",
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" ],\n",
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@ -3953,9 +3949,8 @@
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{
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"data": {
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"text/plain": [
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"array(['bedrooms_ratio__bedrooms_ratio',\n",
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" 'rooms_per_house__rooms_per_house',\n",
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" 'people_per_house__people_per_house', 'log__total_bedrooms',\n",
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"array(['bedrooms__ratio', 'rooms_per_house__ratio',\n",
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" 'people_per_house__ratio', 'log__total_bedrooms',\n",
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" 'log__total_rooms', 'log__population', 'log__households',\n",
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" 'log__median_income', 'geo__Cluster 0 similarity',\n",
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" 'geo__Cluster 1 similarity', 'geo__Cluster 2 similarity',\n",
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@ -4004,12 +3999,12 @@
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" SimpleImputer(strategy='median')),\n",
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" ('standardscaler',\n",
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" StandardScaler())]),\n",
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" transformers=[('bedrooms_ratio',\n",
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" transformers=[('bedrooms',\n",
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" Pipeline(steps=[('simpleimputer',\n",
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" SimpleImputer(strategy='median')),\n",
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" ('functiontransformer',\n",
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" FunctionTransformer(feature_names_out=['bedrooms_ratio'],\n",
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" f...\n",
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" FunctionTransformer(feature_names_out=<function ratio_name at 0x1a5...\n",
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" 'households',\n",
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" 'median_income']),\n",
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" ('geo',\n",
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" ClusterSimilarity(random_state=42),\n",
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@ -4019,7 +4014,7 @@
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" SimpleImputer(strategy='most_frequent')),\n",
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" ('onehotencoder',\n",
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" OneHotEncoder(handle_unknown='ignore'))]),\n",
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" <sklearn.compose._column_transformer.make_column_selector object at 0x7f9b50613dc0>)])),\n",
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" <sklearn.compose._column_transformer.make_column_selector object at 0x1a57e3a00>)])),\n",
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" ('linearregression', LinearRegression())])"
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]
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},
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@ -4146,12 +4141,12 @@
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" SimpleImputer(strategy='median')),\n",
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" ('standardscaler',\n",
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" StandardScaler())]),\n",
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" transformers=[('bedrooms_ratio',\n",
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" transformers=[('bedrooms',\n",
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" Pipeline(steps=[('simpleimputer',\n",
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" SimpleImputer(strategy='median')),\n",
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" ('functiontransformer',\n",
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" FunctionTransformer(feature_names_out=['bedrooms_ratio'],\n",
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" f...\n",
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" FunctionTransformer(feature_names_out=<function ratio_name at 0x1a5...\n",
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" ('geo',\n",
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" ClusterSimilarity(random_state=42),\n",
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" ['latitude', 'longitude']),\n",
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" ('cat',\n",
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@ -4159,7 +4154,7 @@
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" SimpleImputer(strategy='most_frequent')),\n",
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" ('onehotencoder',\n",
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" OneHotEncoder(handle_unknown='ignore'))]),\n",
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" <sklearn.compose._column_transformer.make_column_selector object at 0x7f9b50613dc0>)])),\n",
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" <sklearn.compose._column_transformer.make_column_selector object at 0x1a57e3a00>)])),\n",
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" ('decisiontreeregressor',\n",
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" DecisionTreeRegressor(random_state=42))])"
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]
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@ -4399,12 +4394,12 @@
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" SimpleImputer(strategy='median')),\n",
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" ('standardscaler',\n",
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" StandardScaler())]),\n",
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" transformers=[('bedrooms_ratio',\n",
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" transformers=[('bedrooms',\n",
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" Pipeline(steps=[('simpleimputer',\n",
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" SimpleImputer(strategy='median')),\n",
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" ('functiontransformer',\n",
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" FunctionTransformer(feature_names_...\n",
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" <sklearn.compose._column_transformer.make_column_selector object at 0x7f9b50613dc0>)])),\n",
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" FunctionTransformer(feature_names_out=<f...\n",
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" <sklearn.compose._column_transformer.make_column_selector object at 0x1a57e3a00>)])),\n",
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" ('random_forest',\n",
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" RandomForestRegressor(random_state=42))]),\n",
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" param_grid=[{'preprocessing__geo__n_clusters': [5, 8, 10],\n",
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@ -4502,12 +4497,11 @@
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" SimpleImputer(strategy='median')),\n",
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" ('standardscaler',\n",
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" StandardScaler())]),\n",
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" transformers=[('bedrooms_ratio',\n",
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" transformers=[('bedrooms',\n",
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" Pipeline(steps=[('simpleimputer',\n",
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" SimpleImputer(strategy='median')),\n",
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" ('functiontransformer',\n",
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" FunctionTransformer(feature_names_out=['bedrooms_ratio'],\n",
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" func=...\n",
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" FunctionTransformer(feature_names_out=<function ratio_name at 0x1a5b6fd...\n",
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" ClusterSimilarity(n_clusters=15,\n",
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" random_state=42),\n",
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" ['latitude', 'longitude']),\n",
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@ -4516,7 +4510,7 @@
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" SimpleImputer(strategy='most_frequent')),\n",
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" ('onehotencoder',\n",
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" OneHotEncoder(handle_unknown='ignore'))]),\n",
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" <sklearn.compose._column_transformer.make_column_selector object at 0x7f9b410ec490>)])),\n",
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" <sklearn.compose._column_transformer.make_column_selector object at 0x1a5cdffd0>)])),\n",
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" ('random_forest',\n",
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" RandomForestRegressor(max_features=6, random_state=42))])"
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]
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@ -4657,13 +4651,6 @@
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"## Randomized Search"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**Warning:** the following cell may take a few minutes to run:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 137,
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@ -4681,6 +4668,13 @@
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"Try 30 (`n_iter` × `cv`) random combinations of hyperparameters:"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**Warning:** the following cell may take a few minutes to run:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 138,
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@ -4695,16 +4689,16 @@
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" SimpleImputer(strategy='median')),\n",
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" ('standardscaler',\n",
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" StandardScaler())]),\n",
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" transformers=[('bedrooms_ratio',\n",
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" transformers=[('bedrooms',\n",
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" Pipeline(steps=[('simpleimputer',\n",
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" SimpleImputer(strategy='median')),\n",
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" ('functiontransformer',\n",
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" FunctionTransformer(feature_...\n",
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" <sklearn.compose._column_transformer.make_column_selector object at 0x7f9b50613dc0>)])),\n",
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" FunctionTransformer(feature_names_...\n",
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" <sklearn.compose._column_transformer.make_column_selector object at 0x1a57e3a00>)])),\n",
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" ('random_forest',\n",
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" RandomForestRegressor(random_state=42))]),\n",
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" param_distributions={'preprocessing__geo__n_clusters': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f9b103bb760>,\n",
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" 'random_forest__max_features': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f9b410decd0>},\n",
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" param_distributions={'preprocessing__geo__n_clusters': <scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x1a4bfcb20>,\n",
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" 'random_forest__max_features': <scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x1a57c7bb0>},\n",
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" random_state=42, scoring='neg_root_mean_squared_error')"
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]
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},
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@ -5046,9 +5040,9 @@
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"text/plain": [
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"[(0.18694559869103852, 'log__median_income'),\n",
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" (0.0748194905715524, 'cat__ocean_proximity_INLAND'),\n",
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" (0.06926417748515576, 'bedrooms_ratio__bedrooms_ratio'),\n",
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" (0.05446998753775219, 'rooms_per_house__rooms_per_house'),\n",
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" (0.05262301809680712, 'people_per_house__people_per_house'),\n",
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" (0.06926417748515576, 'bedrooms__ratio'),\n",
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" (0.05446998753775219, 'rooms_per_house__ratio'),\n",
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" (0.05262301809680712, 'people_per_house__ratio'),\n",
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" (0.03819415873915732, 'geo__Cluster 0 similarity'),\n",
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" (0.02879263999929514, 'geo__Cluster 28 similarity'),\n",
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" (0.023530192521380392, 'geo__Cluster 24 similarity'),\n",
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@ -5333,7 +5327,7 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Also works with pickle, but joblib is more efficient."
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"You could use pickle instead, but joblib is more efficient."
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]
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},
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{
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@ -5371,12 +5365,12 @@
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" SimpleImputer(strategy='median')),\n",
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" ('standardscaler',\n",
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" StandardScaler())]),\n",
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" transformers=[('bedrooms_ratio',\n",
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" transformers=[('bedrooms',\n",
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" Pipeline(steps=[('simpleimputer',\n",
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" SimpleImputer(strategy='median')),\n",
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" ('functiontransformer',\n",
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" FunctionTransformer(feature_names_...\n",
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" <sklearn.compose._column_transformer.make_column_selector object at 0x7f9b50613dc0>)])),\n",
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" FunctionTransformer(feature_names_out=<f...\n",
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" <sklearn.compose._column_transformer.make_column_selector object at 0x1a57e3a00>)])),\n",
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" ('svr', SVR())]),\n",
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" param_grid=[{'svr__C': [10.0, 30.0, 100.0, 300.0, 1000.0, 3000.0,\n",
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" 10000.0, 30000.0],\n",
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@ -5508,16 +5502,16 @@
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" SimpleImputer(strategy='median')),\n",
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" ('standardscaler',\n",
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" StandardScaler())]),\n",
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" transformers=[('bedrooms_ratio',\n",
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" transformers=[('bedrooms',\n",
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" Pipeline(steps=[('simpleimputer',\n",
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" SimpleImputer(strategy='median')),\n",
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" ('functiontransformer',\n",
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" FunctionTransformer(feature_...\n",
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" <sklearn.compose._column_transformer.make_column_selector object at 0x7f9b50613dc0>)])),\n",
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" FunctionTransformer(feature_names_...\n",
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" <sklearn.compose._column_transformer.make_column_selector object at 0x1a57e3a00>)])),\n",
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" ('svr', SVR())]),\n",
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" n_iter=50,\n",
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" param_distributions={'svr__C': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f9ae254b9d0>,\n",
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" 'svr__gamma': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f9b734dbe50>,\n",
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" param_distributions={'svr__C': <scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x1a5d4c3a0>,\n",
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" 'svr__gamma': <scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x1a5d9ca00>,\n",
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" 'svr__kernel': ['linear', 'rbf']},\n",
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" random_state=42, scoring='neg_root_mean_squared_error')"
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]
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@ -5970,20 +5964,19 @@
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"text/plain": [
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"RandomizedSearchCV(cv=3,\n",
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" estimator=Pipeline(steps=[('preprocessing',\n",
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" ColumnTransformer(transformers=[('bedrooms_ratio',\n",
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" ColumnTransformer(transformers=[('bedrooms',\n",
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" Pipeline(steps=[('simpleimputer',\n",
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" SimpleImputer(strategy='median')),\n",
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" ('functiontransformer',\n",
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" FunctionTransformer(feature_names_out=['bedrooms_ratio'],\n",
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" func=<function column_ratio at 0x7f9b505e5670>)),\n",
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" FunctionTransformer(feature_names_out=<function ratio_name at 0x1a5b6fd90>,\n",
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" func=<function column_ratio at 0x1a5695bd0>)),\n",
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" ('standardscaler',\n",
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" StandardScaler())]),\n",
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" ['...\n",
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" StandardScaler()...\n",
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" param_distributions={'preprocessing__geo__estimator__n_neighbors': range(1, 30),\n",
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" 'preprocessing__geo__estimator__weights': ['distance',\n",
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" 'uniform'],\n",
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" 'svr__C': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f9acb940bb0>,\n",
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" 'svr__gamma': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f9acb940a30>},\n",
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" 'svr__C': <scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x1a63fda80>,\n",
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" 'svr__gamma': <scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x1a63fe410>},\n",
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" random_state=42, scoring='neg_root_mean_squared_error')"
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]
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},
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@ -6186,6 +6179,7 @@
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"source": [
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"scaler = StandardScalerClone()\n",
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"X_back = scaler.inverse_transform(scaler.fit_transform(X))\n",
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"\n",
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"assert np.allclose(X, X_back)"
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]
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},
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