TST create instances from exotic estimators for docstring params check (

scikit-learn#20243)

Co-authored-by: Olivier Grisel <[email protected]>

sklearn/cluster/_bicluster.py

@@ -400,6 +400,9 @@ class SpectralBiclustering(BaseSpectral):
     column_labels_ : array-like of shape (n_cols,)
         Column partition labels.
 
+    biclusters_ : tuple of two ndarrays
+        The tuple contains the `rows_` and `columns_` arrays.
+
     n_features_in_ : int
         Number of features seen during :term:`fit`.
 

sklearn/decomposition/_dict_learning.py

@@ -1033,6 +1033,14 @@ class SparseCoder(_BaseSparseCoding, BaseEstimator):
            This attribute is deprecated in 0.24 and will be removed in
            1.1 (renaming of 0.26). Use `dictionary` instead.
 
+    n_components_ : int
+        Number of atoms.
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
     Examples
     --------
     >>> import numpy as np

sklearn/ensemble/_stacking.py

@@ -351,6 +351,12 @@ class StackingClassifier(ClassifierMixin, _BaseStacking):
     named_estimators_ : :class:`~sklearn.utils.Bunch`
         Attribute to access any fitted sub-estimators by name.
 
+    n_features_in_ : int
+        Number of features seen during :term:`fit`. Only defined if the
+        underlying classifier exposes such an attribute when fit.
+
+        .. versionadded:: 0.24
+
     final_estimator_ : estimator
         The classifier which predicts given the output of `estimators_`.
 
@@ -611,10 +617,18 @@ class StackingRegressor(RegressorMixin, _BaseStacking):
     named_estimators_ : :class:`~sklearn.utils.Bunch`
         Attribute to access any fitted sub-estimators by name.
 
+    n_features_in_ : int
+        Number of features seen during :term:`fit`. Only defined if the
+        underlying regressor exposes such an attribute when fit.
+
+        .. versionadded:: 0.24
 
     final_estimator_ : estimator
         The regressor to stacked the base estimators fitted.
 
+    stack_method_ : list of str
+        The method used by each base estimator.
+
     References
     ----------
     .. [1] Wolpert, David H. "Stacked generalization." Neural networks 5.2

sklearn/ensemble/_voting.py

@@ -199,9 +199,19 @@ class VotingClassifier(ClassifierMixin, _BaseVoting):
 
         .. versionadded:: 0.20
 
-    classes_ : array-like of shape (n_predictions,)
+    le_ : :class:`~sklearn.preprocessing.LabelEncoder`
+        Transformer used to encode the labels during fit and decode during
+        prediction.
+
+    classes_ : ndarray of shape (n_classes,)
         The classes labels.
 
+    n_features_in_ : int
+        Number of features seen during :term:`fit`. Only defined if the
+        underlying classifier exposes such an attribute when fit.
+
+        .. versionadded:: 0.24
+
     See Also
     --------
     VotingRegressor : Prediction voting regressor.
@@ -431,6 +441,12 @@ class VotingRegressor(RegressorMixin, _BaseVoting):
 
         .. versionadded:: 0.20
 
+    n_features_in_ : int
+        Number of features seen during :term:`fit`. Only defined if the
+        underlying regressor exposes such an attribute when fit.
+
+        .. versionadded:: 0.24
+
     See Also
     --------
     VotingClassifier : Soft Voting/Majority Rule classifier.

sklearn/feature_extraction/text.py

@@ -949,9 +949,9 @@ class CountVectorizer(_VectorizerMixin, BaseEstimator):
     vocabulary_ : dict
         A mapping of terms to feature indices.
 
-    fixed_vocabulary_: boolean
+    fixed_vocabulary_ : bool
         True if a fixed vocabulary of term to indices mapping
-        is provided by the user
+        is provided by the user.
 
     stop_words_ : set
         Terms that were ignored because they either:
@@ -1684,9 +1684,9 @@ class TfidfVectorizer(CountVectorizer):
     vocabulary_ : dict
         A mapping of terms to feature indices.
 
-    fixed_vocabulary_: bool
+    fixed_vocabulary_ : bool
         True if a fixed vocabulary of term to indices mapping
-        is provided by the user
+        is provided by the user.
 
     idf_ : array of shape (n_features,)
         The inverse document frequency (IDF) vector; only defined

sklearn/feature_selection/_from_model.py

@@ -127,6 +127,12 @@ class SelectFromModel(MetaEstimatorMixin, SelectorMixin, BaseEstimator):
         This is stored only when a non-fitted estimator is passed to the
         ``SelectFromModel``, i.e when prefit is False.
 
+    n_features_in_ : int
+        Number of features seen during :term:`fit`. Only defined if the
+        underlying estimator exposes such an attribute when fit.
+
+        .. versionadded:: 0.24
+
     threshold_ : float
         The threshold value used for feature selection.
 

sklearn/feature_selection/_rfe.py

@@ -99,12 +99,21 @@ class RFE(SelectorMixin, MetaEstimatorMixin, BaseEstimator):
 
     Attributes
     ----------
+    classes_ : ndarray of shape (n_classes,)
+        The classes labels. Only available when `estimator` is a classifier.
+
     estimator_ : ``Estimator`` instance
         The fitted estimator used to select features.
 
     n_features_ : int
         The number of selected features.
 
+    n_features_in_ : int
+        Number of features seen during :term:`fit`. Only defined if the
+        underlying estimator exposes such an attribute when fit.
+
+        .. versionadded:: 0.24
+
     ranking_ : ndarray of shape (n_features,)
         The feature ranking, such that ``ranking_[i]`` corresponds to the
         ranking position of the i-th feature. Selected (i.e., estimated
@@ -464,6 +473,9 @@ class RFECV(RFE):
 
     Attributes
     ----------
+    classes_ : ndarray of shape (n_classes,)
+        The classes labels. Only available when `estimator` is a classifier.
+
     estimator_ : ``Estimator`` instance
         The fitted estimator used to select features.
 
@@ -475,6 +487,12 @@ class RFECV(RFE):
     n_features_ : int
         The number of selected features with cross-validation.
 
+    n_features_in_ : int
+        Number of features seen during :term:`fit`. Only defined if the
+        underlying estimator exposes such an attribute when fit.
+
+        .. versionadded:: 0.24
+
     ranking_ : narray of shape (n_features,)
         The feature ranking, such that `ranking_[i]`
         corresponds to the ranking

sklearn/feature_selection/_sequential.py

@@ -76,6 +76,12 @@ class SequentialFeatureSelector(SelectorMixin, MetaEstimatorMixin,
 
     Attributes
     ----------
+    n_features_in_ : int
+        Number of features seen during :term:`fit`. Only defined if the
+        underlying estimator exposes such an attribute when fit.
+
+        .. versionadded:: 0.24
+
     n_features_to_select_ : int
         The number of features that were selected.
 

sklearn/pipeline.py

@@ -83,6 +83,10 @@ class Pipeline(_BaseComposition):
         Read-only attribute to access any step parameter by user given name.
         Keys are step names and values are steps parameters.
 
+    classes_ : ndarray of shape (n_classes,)
+        The classes labels. Only exist if the last step of the pipeline is a
+        classifier.
+
     n_features_in_ : int
         Number of features seen during :term:`fit`. Only defined if the
         underlying first estimator in `steps` exposes such an attribute

sklearn/random_projection.py

@@ -459,6 +459,11 @@ class GaussianRandomProjection(BaseRandomProjection):
     components_ : ndarray of shape (n_components, n_features)
         Random matrix used for the projection.
 
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
     Examples
     --------
     >>> import numpy as np
@@ -586,6 +591,11 @@ class SparseRandomProjection(BaseRandomProjection):
     density_ : float in range 0.0 - 1.0
         Concrete density computed from when density = "auto".
 
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
     Examples
     --------
     >>> import numpy as np

sklearn/tests/test_docstring_parameters.py

@@ -24,6 +24,7 @@
 from sklearn.externals._pep562 import Pep562
 from sklearn.datasets import make_classification
 from sklearn.linear_model import LogisticRegression
+from sklearn.preprocessing import FunctionTransformer
 
 import pytest
 
@@ -175,6 +176,27 @@ def _construct_searchcv_instance(SearchCV):
     return SearchCV(LogisticRegression(), {"C": [0.1, 1]})
 
 
+def _construct_compose_pipeline_instance(Estimator):
+    # Minimal / degenerate instances: only useful to test the docstrings.
+    if Estimator.__name__ == "ColumnTransformer":
+        return Estimator(transformers=[("transformer", "passthrough", [0, 1])])
+    elif Estimator.__name__ == "Pipeline":
+        return Estimator(steps=[("clf", LogisticRegression())])
+    elif Estimator.__name__ == "FeatureUnion":
+        return Estimator(transformer_list=[
+            ("transformer", FunctionTransformer())
+        ])
+
+
+def _construct_sparse_coder(Estimator):
+    # XXX: hard-coded assumption that n_features=3
+    dictionary = np.array(
+        [[0, 1, 0], [-1, -1, 2], [1, 1, 1], [0, 1, 1], [0, 2, 1]],
+        dtype=np.float64,
+    )
+    return Estimator(dictionary=dictionary)
+
+
 N_FEATURES_MODULES_TO_IGNORE = {
     'model_selection',
     'multioutput',
@@ -190,56 +212,76 @@ def test_fit_docstring_attributes(name, Estimator):
     doc = docscrape.ClassDoc(Estimator)
     attributes = doc['Attributes']
 
-    IGNORED = {
-        'ClassifierChain',
-        'CountVectorizer', 'DictVectorizer',
-        'GaussianRandomProjection',
-        'MultiOutputClassifier', 'MultiOutputRegressor',
-        'NoSampleWeightWrapper', 'RFE', 'RFECV',
-        'RegressorChain', 'SelectFromModel',
-        'SparseCoder', 'SparseRandomProjection',
-        'SpectralBiclustering', 'StackingClassifier',
-        'StackingRegressor', 'TfidfVectorizer', 'VotingClassifier',
-        'VotingRegressor', 'SequentialFeatureSelector',
-    }
-
-    if Estimator.__name__ in IGNORED or Estimator.__name__.startswith('_'):
-        pytest.skip("Estimator cannot be fit easily to test fit attributes")
-
     if Estimator.__name__ in (
         "HalvingRandomSearchCV",
         "RandomizedSearchCV",
         "HalvingGridSearchCV",
         "GridSearchCV",
     ):
         est = _construct_searchcv_instance(Estimator)
+    elif Estimator.__name__ in (
+        "ColumnTransformer",
+        "Pipeline",
+        "FeatureUnion",
+    ):
+        est = _construct_compose_pipeline_instance(Estimator)
+    elif Estimator.__name__ == "SparseCoder":
+        est = _construct_sparse_coder(Estimator)
     else:
         est = _construct_instance(Estimator)
 
     if Estimator.__name__ == 'SelectKBest':
-        est.k = 2
-
-    if Estimator.__name__ == 'DummyClassifier':
-        est.strategy = "stratified"
-
-    if 'PLS' in Estimator.__name__ or 'CCA' in Estimator.__name__:
-        est.n_components = 1  # default = 2 is invalid for single target.
+        est.set_params(k=2)
+    elif Estimator.__name__ == 'DummyClassifier':
+        est.set_params(strategy="stratified")
+    elif Estimator.__name__ == 'CCA' or Estimator.__name__.startswith('PLS'):
+        # default = 2 is invalid for single target
+        est.set_params(n_components=1)
+    elif Estimator.__name__ in (
+        "GaussianRandomProjection",
+        "SparseRandomProjection",
+    ):
+        # default="auto" raises an error with the shape of `X`
+        est.set_params(n_components=2)
 
     # FIXME: TO BE REMOVED for 1.1 (avoid FutureWarning)
     if Estimator.__name__ == 'NMF':
-        est.init = 'nndsvda'
+        est.set_params(init='nndsvda')
 
     # FIXME: TO BE REMOVED for 1.2 (avoid FutureWarning)
     if Estimator.__name__ == 'TSNE':
-        est.learning_rate = 200.0
-        est.init = 'random'
-
-    X, y = make_classification(n_samples=20, n_features=3,
-                               n_redundant=0, n_classes=2,
-                               random_state=2)
+        est.set_params(learning_rate=200.0, init='random')
+
+    # For PLS, TODO remove in 1.1
+    skipped_attributes = {"x_scores_", "y_scores_"}
+
+    if Estimator.__name__.endswith("Vectorizer"):
+        # Vectorizer require some specific input data
+        if Estimator.__name__ in (
+            "CountVectorizer",
+            "HashingVectorizer",
+            "TfidfVectorizer",
+        ):
+            X = [
+                "This is the first document.",
+                "This document is the second document.",
+                "And this is the third one.",
+                "Is this the first document?",
+            ]
+        elif Estimator.__name__ == "DictVectorizer":
+            X = [{"foo": 1, "bar": 2}, {"foo": 3, "baz": 1}]
+        y = None
+    else:
+        X, y = make_classification(
+            n_samples=20,
+            n_features=3,
+            n_redundant=0,
+            n_classes=2,
+            random_state=2,
+        )
 
-    y = _enforce_estimator_tags_y(est, y)
-    X = _enforce_estimator_tags_x(est, X)
+        y = _enforce_estimator_tags_y(est, y)
+        X = _enforce_estimator_tags_x(est, X)
 
     if '1dlabels' in est._get_tags()['X_types']:
         est.fit(y)
@@ -248,9 +290,6 @@ def test_fit_docstring_attributes(name, Estimator):
     else:
         est.fit(X, y)
 
-    skipped_attributes = {'x_scores_',  # For PLS, TODO remove in 1.1
-                          'y_scores_'}  # For PLS, TODO remove in 1.1
-
     module = est.__module__.split(".")[1]
     if module in N_FEATURES_MODULES_TO_IGNORE:
         skipped_attributes.add("n_features_in_")