FEA Refactor CategoricalEncoder into OneHotEncoder and OrdinalEncoder (scikit-learn#10523)

Deprecated some OneHotEncoder behaviour

Author: jorisvandenbossche

doc/datasets/index.rst

@@ -456,9 +456,8 @@ refer to:
   for reading WAV files into a numpy array
 
 Categorical (or nominal) features stored as strings (common in pandas DataFrames) 
-will need converting to integers, and integer categorical variables may be best 
-exploited when encoded as one-hot variables 
-(:class:`sklearn.preprocessing.OneHotEncoder`) or similar. 
+will need converting to numerical features using :class:`sklearn.preprocessing.OneHotEncoder`
+or :class:`sklearn.preprocessing.OrdinalEncoder` or similar.
 See :ref:`preprocessing`.
 
 Note: if you manage your own numerical data it is recommended to use an 

doc/glossary.rst

@@ -165,8 +165,10 @@ General Concepts
         tree-based models such as random forests and gradient boosting
         models that often work better and faster with integer-coded
         categorical variables.
-        :class:`~sklearn.preprocessing.CategoricalEncoder` helps
-        encoding string-valued categorical features.
+        :class:`~sklearn.preprocessing.OrdinalEncoder` helps encoding
+        string-valued categorical features as ordinal integers, and
+        :class:`~sklearn.preprocessing.OneHotEncoder` can be used to
+        one-hot encode categorical features.
         See also :ref:`preprocessing_categorical_features` and the
         `http://contrib.scikit-learn.org/categorical-encoding
         <category_encoders>`_ package for tools related to encoding

doc/modules/classes.rst

@@ -1248,7 +1248,7 @@ Model validation
    preprocessing.MinMaxScaler
    preprocessing.Normalizer
    preprocessing.OneHotEncoder
-   preprocessing.CategoricalEncoder
+   preprocessing.OrdinalEncoder
    preprocessing.PolynomialFeatures
    preprocessing.PowerTransformer
    preprocessing.QuantileTransformer

doc/modules/preprocessing.rst

@@ -508,15 +508,13 @@ Such features can be efficiently coded as integers, for instance
 ``[1, 2, 1]``.
 
 To convert categorical features to such integer codes, we can use the
-:class:`CategoricalEncoder`. When specifying that we want to perform an
-ordinal encoding, the estimator transforms each categorical feature to one
+:class:`OrdinalEncoder`. This estimator transforms each categorical feature to one
 new feature of integers (0 to n_categories - 1)::
 
-    >>> enc = preprocessing.CategoricalEncoder(encoding='ordinal')
+    >>> enc = preprocessing.OrdinalEncoder()
     >>> X = [['male', 'from US', 'uses Safari'], ['female', 'from Europe', 'uses Firefox']]
     >>> enc.fit(X)  # doctest: +ELLIPSIS
-    CategoricalEncoder(categories='auto', dtype=<... 'numpy.float64'>,
-              encoding='ordinal', handle_unknown='error')
+    OrdinalEncoder(categories='auto', dtype=<... 'numpy.float64'>)
     >>> enc.transform([['female', 'from US', 'uses Safari']])
     array([[0., 1., 1.]])
 
@@ -528,18 +526,19 @@ browsers was ordered arbitrarily).
 Another possibility to convert categorical features to features that can be used
 with scikit-learn estimators is to use a one-of-K, also known as one-hot or
 dummy encoding.
-This type of encoding is the default behaviour of the :class:`CategoricalEncoder`.
-The :class:`CategoricalEncoder` then transforms each categorical feature with
+This type of encoding can be obtained with the :class:`OneHotEncoder`,
+which transforms each categorical feature with
 ``n_categories`` possible values into ``n_categories`` binary features, with
 one of them 1, and all others 0.
 
 Continuing the example above::
 
-  >>> enc = preprocessing.CategoricalEncoder()
+  >>> enc = preprocessing.OneHotEncoder()
   >>> X = [['male', 'from US', 'uses Safari'], ['female', 'from Europe', 'uses Firefox']]
   >>> enc.fit(X)  # doctest: +ELLIPSIS
-  CategoricalEncoder(categories='auto', dtype=<... 'numpy.float64'>,
-            encoding='onehot', handle_unknown='error')
+  OneHotEncoder(categorical_features=None, categories=None,
+         dtype=<... 'numpy.float64'>, handle_unknown='error',
+         n_values=None, sparse=True)
   >>> enc.transform([['female', 'from US', 'uses Safari'],
   ...                ['male', 'from Europe', 'uses Safari']]).toarray()
   array([[1., 0., 0., 1., 0., 1.],
@@ -558,14 +557,15 @@ dataset::
     >>> genders = ['female', 'male']
     >>> locations = ['from Africa', 'from Asia', 'from Europe', 'from US']
     >>> browsers = ['uses Chrome', 'uses Firefox', 'uses IE', 'uses Safari']
-    >>> enc = preprocessing.CategoricalEncoder(categories=[genders, locations, browsers])
+    >>> enc = preprocessing.OneHotEncoder(categories=[genders, locations, browsers])
     >>> # Note that for there are missing categorical values for the 2nd and 3rd
     >>> # feature
     >>> X = [['male', 'from US', 'uses Safari'], ['female', 'from Europe', 'uses Firefox']]
     >>> enc.fit(X) # doctest: +ELLIPSIS
-    CategoricalEncoder(categories=[...],
-              dtype=<... 'numpy.float64'>, encoding='onehot',
-              handle_unknown='error')
+    OneHotEncoder(categorical_features=None,
+           categories=[...],
+           dtype=<... 'numpy.float64'>, handle_unknown='error',
+           n_values=None, sparse=True)
     >>> enc.transform([['female', 'from Asia', 'uses Chrome']]).toarray()
     array([[1., 0., 0., 1., 0., 0., 1., 0., 0., 0.]])
 
@@ -577,11 +577,12 @@ during transform, no error will be raised but the resulting one-hot encoded
 columns for this feature will be all zeros
 (``handle_unknown='ignore'`` is only supported for one-hot encoding)::
 
-    >>> enc = preprocessing.CategoricalEncoder(handle_unknown='ignore')
+    >>> enc = preprocessing.OneHotEncoder(handle_unknown='ignore')
     >>> X = [['male', 'from US', 'uses Safari'], ['female', 'from Europe', 'uses Firefox']]
     >>> enc.fit(X) # doctest: +ELLIPSIS
-    CategoricalEncoder(categories='auto', dtype=<... 'numpy.float64'>,
-              encoding='onehot', handle_unknown='ignore')
+    OneHotEncoder(categorical_features=None, categories=None,
+           dtype=<... 'numpy.float64'>, handle_unknown='ignore',
+           n_values=None, sparse=True)
     >>> enc.transform([['female', 'from Asia', 'uses Chrome']]).toarray()
     array([[1., 0., 0., 0., 0., 0.]])
 

doc/whats_new/v0.20.rst

@@ -70,14 +70,14 @@ Classifiers and regressors
 
 Preprocessing
 
-- Added :class:`preprocessing.CategoricalEncoder`, which allows to encode
-  categorical features as a numeric array, either using a one-hot (or dummy)
-  encoding scheme or by converting to ordinal integers.  Compared to the
-  existing :class:`~preprocessing.OneHotEncoder`, this new class handles
+- Expanded :class:`preprocessing.OneHotEncoder` to allow to encode
+  categorical string features as a numeric array using a one-hot (or dummy)
+  encoding scheme, and added :class:`preprocessing.OrdinalEncoder` to
+  convert to ordinal integers.  Those two classes now handle
   encoding of all feature types (also handles string-valued features) and
   derives the categories based on the unique values in the features instead of
-  the maximum value in the features. :issue:`9151` by :user:`Vighnesh Birodkar
-  <vighneshbirodkar>` and `Joris Van den Bossche`_.
+  the maximum value in the features. :issue:`9151` and :issue:`10521` by
+  :user:`Vighnesh Birodkar <vighneshbirodkar>` and `Joris Van den Bossche`_.
 
 - Added :class:`compose.ColumnTransformer`, which allows to apply
   different transformers to different columns of arrays or pandas
@@ -584,6 +584,17 @@ Linear, kernelized and related models
   :class:`linear_model.LogisticRegression` when ``verbose`` is set to 0.
   :issue:`10881` by :user:`Alexandre Sevin <AlexandreSev>`.
 
+Preprocessing
+
+- Deprecate ``n_values`` and ``categorical_features`` parameters and
+  ``active_features_``, ``feature_indices_`` and ``n_values_`` attributes
+  of :class:`preprocessing.OneHotEncoder`. The ``n_values`` parameter can be
+  replaced with the new ``categories`` parameter, and the attributes with the
+  new ``categories_`` attribute. Selecting the categorical features with
+  the ``categorical_features`` parameter is now better supported using the
+  :class:`compose.ColumnTransformer`.
+  :issue:`10521` by `Joris Van den Bossche`_.
+
 Decomposition, manifold learning and clustering
 
 - Deprecate ``precomputed`` parameter in function

examples/ensemble/plot_feature_transformation.py

@@ -34,7 +34,7 @@
 from sklearn.linear_model import LogisticRegression
 from sklearn.ensemble import (RandomTreesEmbedding, RandomForestClassifier,
                               GradientBoostingClassifier)
-from sklearn.preprocessing import CategoricalEncoder
+from sklearn.preprocessing import OneHotEncoder
 from sklearn.model_selection import train_test_split
 from sklearn.metrics import roc_curve
 from sklearn.pipeline import make_pipeline
@@ -62,7 +62,7 @@
 
 # Supervised transformation based on random forests
 rf = RandomForestClassifier(max_depth=3, n_estimators=n_estimator)
-rf_enc = CategoricalEncoder()
+rf_enc = OneHotEncoder()
 rf_lm = LogisticRegression()
 rf.fit(X_train, y_train)
 rf_enc.fit(rf.apply(X_train))
@@ -72,7 +72,7 @@
 fpr_rf_lm, tpr_rf_lm, _ = roc_curve(y_test, y_pred_rf_lm)
 
 grd = GradientBoostingClassifier(n_estimators=n_estimator)
-grd_enc = CategoricalEncoder()
+grd_enc = OneHotEncoder()
 grd_lm = LogisticRegression()
 grd.fit(X_train, y_train)
 grd_enc.fit(grd.apply(X_train)[:, :, 0])

sklearn/compose/_column_transformer.py

@@ -636,19 +636,19 @@ def make_column_transformer(*transformers, **kwargs):
 
     Examples
     --------
-    >>> from sklearn.preprocessing import StandardScaler, CategoricalEncoder
+    >>> from sklearn.preprocessing import StandardScaler, OneHotEncoder
     >>> from sklearn.compose import make_column_transformer
     >>> make_column_transformer(
     ...     (['numerical_column'], StandardScaler()),
-    ...     (['categorical_column'], CategoricalEncoder()))
+    ...     (['categorical_column'], OneHotEncoder()))
     ...     # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS
     ColumnTransformer(n_jobs=1, remainder='passthrough',
              transformer_weights=None,
              transformers=[('standardscaler',
                             StandardScaler(...),
                             ['numerical_column']),
-                           ('categoricalencoder',
-                            CategoricalEncoder(...),
+                           ('onehotencoder',
+                            OneHotEncoder(...),
                             ['categorical_column'])])
 
     """

sklearn/ensemble/forest.py

@@ -1932,7 +1932,8 @@ def fit_transform(self, X, y=None, sample_weight=None):
         super(RandomTreesEmbedding, self).fit(X, y,
                                               sample_weight=sample_weight)
 
-        self.one_hot_encoder_ = OneHotEncoder(sparse=self.sparse_output)
+        self.one_hot_encoder_ = OneHotEncoder(sparse=self.sparse_output,
+                                              categories='auto')
         return self.one_hot_encoder_.fit_transform(self.apply(X))
 
     def transform(self, X):

sklearn/feature_extraction/dict_vectorizer.py

@@ -39,7 +39,7 @@ class DictVectorizer(BaseEstimator, TransformerMixin):
     However, note that this transformer will only do a binary one-hot encoding
     when feature values are of type string. If categorical features are
     represented as numeric values such as int, the DictVectorizer can be
-    followed by :class:`sklearn.preprocessing.CategoricalEncoder` to complete
+    followed by :class:`sklearn.preprocessing.OneHotEncoder` to complete
     binary one-hot encoding.
 
     Features that do not occur in a sample (mapping) will have a zero value
@@ -89,7 +89,7 @@ class DictVectorizer(BaseEstimator, TransformerMixin):
     See also
     --------
     FeatureHasher : performs vectorization using only a hash function.
-    sklearn.preprocessing.CategoricalEncoder : handles nominal/categorical
+    sklearn.preprocessing.OrdinalEncoder : handles nominal/categorical
       features encoded as columns of arbitrary data types.
     """
 

sklearn/feature_extraction/hashing.py

@@ -81,8 +81,7 @@ class FeatureHasher(BaseEstimator, TransformerMixin):
     See also
     --------
     DictVectorizer : vectorizes string-valued features using a hash table.
-    sklearn.preprocessing.OneHotEncoder : handles nominal/categorical features
-        encoded as columns of integers.
+    sklearn.preprocessing.OneHotEncoder : handles nominal/categorical features.
     """
 
     def __init__(self, n_features=(2 ** 20), input_type="dict",