Replace FeatureUnion + DataFrameSelector with new ColumnTransformer
Aurélien Geron
parent
060751a976
commit
e2d450708a
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@ -70,7 +70,7 @@
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"\n",
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"# Ignore useless warnings (see SciPy issue #5998)\n",
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"import warnings\n",
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"warnings.filterwarnings(action=\"ignore\", module=\"scipy\", message=\"^internal gelsd\")"
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"warnings.filterwarnings(action=\"ignore\", module=\"scipy\", message=\"internal gelsd\")"
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]
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},
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{
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@ -980,12 +980,56 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"And a transformer to just select a subset of the Pandas DataFrame columns:"
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"**Warning**: earlier versions of the book applied different transformations to different columns using a solution based on a `DataFrameSelector` transformer and a `FeatureUnion` (see below). It is now preferable to use the `ColumnTransformer` class that will be introduced in Scikit-Learn 0.20. For now we import it from `future_encoders.py`, but when Scikit-Learn 0.20 is released, you can import it from `sklearn.compose` instead:"
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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": 71,
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"execution_count": 72,
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"metadata": {},
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"outputs": [],
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"source": [
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"from future_encoders import ColumnTransformer\n",
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"\n",
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"num_attribs = list(housing_num)\n",
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"cat_attribs = [\"ocean_proximity\"]\n",
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"\n",
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"full_pipeline = ColumnTransformer([\n",
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" (\"num\", num_pipeline, num_attribs),\n",
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" (\"cat\", OneHotEncoder(), cat_attribs),\n",
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" ])\n",
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"\n",
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"housing_prepared = full_pipeline.fit_transform(housing)"
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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": 73,
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"metadata": {},
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"outputs": [],
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"source": [
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"housing_prepared"
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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": 74,
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"metadata": {},
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"outputs": [],
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"source": [
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"housing_prepared.shape"
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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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"For reference, here is the old solution based on a `DataFrameSelector` transformer (to just select a subset of the Pandas `DataFrame` columns), and a `FeatureUnion`:"
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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": 75,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -993,7 +1037,7 @@
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"\n",
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"# Create a class to select numerical or categorical columns \n",
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"# since Scikit-Learn doesn't handle DataFrames yet\n",
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"class DataFrameSelector(BaseEstimator, TransformerMixin):\n",
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"class OldDataFrameSelector(BaseEstimator, TransformerMixin):\n",
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" def __init__(self, attribute_names):\n",
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" self.attribute_names = attribute_names\n",
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" def fit(self, X, y=None):\n",
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@ -1011,57 +1055,64 @@
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},
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{
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"cell_type": "code",
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"execution_count": 72,
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"execution_count": 76,
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"metadata": {},
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"outputs": [],
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"source": [
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"num_attribs = list(housing_num)\n",
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"cat_attribs = [\"ocean_proximity\"]\n",
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"\n",
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"num_pipeline = Pipeline([\n",
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" ('selector', DataFrameSelector(num_attribs)),\n",
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"old_num_pipeline = Pipeline([\n",
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" ('selector', OldDataFrameSelector(num_attribs)),\n",
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" ('imputer', Imputer(strategy=\"median\")),\n",
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" ('attribs_adder', CombinedAttributesAdder()),\n",
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" ('std_scaler', StandardScaler()),\n",
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" ])\n",
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"\n",
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"cat_pipeline = Pipeline([\n",
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" ('selector', DataFrameSelector(cat_attribs)),\n",
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"old_cat_pipeline = Pipeline([\n",
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" ('selector', OldDataFrameSelector(cat_attribs)),\n",
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" ('cat_encoder', OneHotEncoder(sparse=False)),\n",
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" ])"
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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": 73,
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"execution_count": 77,
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.pipeline import FeatureUnion\n",
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"\n",
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"full_pipeline = FeatureUnion(transformer_list=[\n",
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" (\"num_pipeline\", num_pipeline),\n",
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" (\"cat_pipeline\", cat_pipeline),\n",
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"old_full_pipeline = FeatureUnion(transformer_list=[\n",
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" (\"num_pipeline\", old_num_pipeline),\n",
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" (\"cat_pipeline\", old_cat_pipeline),\n",
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" ])"
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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": 74,
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"execution_count": 78,
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"metadata": {},
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"outputs": [],
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"source": [
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"housing_prepared = full_pipeline.fit_transform(housing)\n",
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"housing_prepared"
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"old_housing_prepared = old_full_pipeline.fit_transform(housing)\n",
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"old_housing_prepared"
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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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"The result is the same as with the `ColumnTransformer`:"
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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": 75,
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"execution_count": 79,
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"metadata": {},
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"outputs": [],
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"source": [
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"housing_prepared.shape"
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"np.allclose(housing_prepared, old_housing_prepared)"
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]
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},
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{
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@ -1073,7 +1124,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 76,
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"execution_count": 80,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -1085,11 +1136,11 @@
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},
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{
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"cell_type": "code",
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"execution_count": 77,
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"execution_count": 81,
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"metadata": {},
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"outputs": [],
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"source": [
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"# let's try the full pipeline on a few training instances\n",
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"# let's try the full preprocessing pipeline on a few training instances\n",
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"some_data = housing.iloc[:5]\n",
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"some_labels = housing_labels.iloc[:5]\n",
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"some_data_prepared = full_pipeline.transform(some_data)\n",
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@ -1106,7 +1157,7 @@
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},
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{
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"cell_type": "code",
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"metadata": {},
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"outputs": [],
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"source": [
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"extra_attribs = [\"rooms_per_hhold\", \"pop_per_hhold\", \"bedrooms_per_room\"]\n",
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"cat_encoder = cat_pipeline.named_steps[\"cat_encoder\"]\n",
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"#cat_encoder = cat_pipeline.named_steps[\"cat_encoder\"] # old solution\n",
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"cat_encoder = full_pipeline.named_transformers_[\"cat\"]\n",
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"cat_one_hot_attribs = list(cat_encoder.categories_[0])\n",
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"attributes = num_attribs + extra_attribs + cat_one_hot_attribs\n",
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"sorted(zip(feature_importances, attributes), reverse=True)"
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|||
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{
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||||
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||||
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|||
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@ -1734,7 +1786,7 @@
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{
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@ -1757,7 +1809,7 @@
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{
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"cell_type": "code",
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"execution_count": 117,
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@ -1782,7 +1834,7 @@
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{
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||||
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||||
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||||
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||||
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|
@ -1821,7 +1873,7 @@
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|||
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|
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{
|
||||
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||||
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||||
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||||
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||||
|
|
@ -1857,7 +1909,7 @@
|
|||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
"metadata": {},
|
||||
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|
||||
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|
||||
|
|
@ -1873,7 +1925,7 @@
|
|||
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|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 121,
|
||||
"execution_count": 125,
|
||||
"metadata": {},
|
||||
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|
||||
"source": [
|
||||
|
|
@ -1883,7 +1935,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 122,
|
||||
"execution_count": 126,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1899,7 +1951,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 123,
|
||||
"execution_count": 127,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1915,7 +1967,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 124,
|
||||
"execution_count": 128,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1927,7 +1979,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 125,
|
||||
"execution_count": 129,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1943,7 +1995,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 126,
|
||||
"execution_count": 130,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1959,7 +2011,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 127,
|
||||
"execution_count": 131,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1989,7 +2041,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 128,
|
||||
"execution_count": 132,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -2002,7 +2054,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 129,
|
||||
"execution_count": 133,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -2018,7 +2070,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 130,
|
||||
"execution_count": 134,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -2052,12 +2104,12 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 131,
|
||||
"execution_count": 136,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"param_grid = [\n",
|
||||
" {'preparation__num_pipeline__imputer__strategy': ['mean', 'median', 'most_frequent'],\n",
|
||||
" {'preparation__num__imputer__strategy': ['mean', 'median', 'most_frequent'],\n",
|
||||
" 'feature_selection__k': list(range(1, len(feature_importances) + 1))}\n",
|
||||
"]\n",
|
||||
"\n",
|
||||
|
|
@ -2068,7 +2120,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 132,
|
||||
"execution_count": 137,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -2106,7 +2158,7 @@
|
|||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.5.2"
|
||||
"version": "3.6.5"
|
||||
},
|
||||
"nav_menu": {
|
||||
"height": "279px",
|
||||
|
|
|
|||
Loading…
Reference in New Issue