[MRG+1] Fix estimators to work if sample_weight parameter is pandas S…

…eries type (scikit-learn#7825)

* addressed comments in the PR about parameters in check_array

* update the test case for the evaluation of estimators with pandas series

* bug fix, need to check for *not* None explicitly

* updated with isinstance check if the documentation says there is acceptance of floats

* ran pep8 linter on modified files

* moving the test case to estimators_check

* add a predict function into the testing pandas.Series class

* avoid running anything beyond the newly added meta checks

* check if pandas is installed before running the specific test

* changed the order of the try-catch to check for sample_weight param beforehand

* pass on import error rather than printing something to std out

* improve test case naming and pd.Series check in the bad estimator class

* address a pep8 linter error with unused import

* pep8 warning disabled for potential unused import

* throw a warning when SkipTest is raised

* add a SkipTestWarning

* updated the whats_new.rst with this issue

* rebase and fix a spacing issue

doc/whats_new.rst

@@ -104,6 +104,10 @@ Bug fixes
      sparse array X and initial centroids, where X's means were unnecessarily
      being subtracted from the centroids. :issue:`7872` by `Josh Karnofsky <https://github.com/jkarno>`_.
 
+   - Fix estimators to accept a ``sample_weight`` parameter of type
+     ``pandas.Series`` in their ``fit`` function. :issue:`7825` by
+     `Kathleen Chen`_.
+
 .. _changes_0_18_1:
 
 Version 0.18.1
@@ -4824,3 +4828,5 @@ David Huard, Dave Morrill, Ed Schofield, Travis Oliphant, Pearu Peterson.
 .. _Srivatsan Ramesh: https://github.com/srivatsan-ramesh
 
 .. _Ron Weiss: http://www.ee.columbia.edu/~ronw
+
+.. _Kathleen Chen: https://github.com/kchen17

sklearn/ensemble/weight_boosting.py

@@ -116,6 +116,7 @@ def fit(self, X, y, sample_weight=None):
             sample_weight = np.empty(X.shape[0], dtype=np.float64)
             sample_weight[:] = 1. / X.shape[0]
         else:
+            sample_weight = check_array(sample_weight, ensure_2d=False)
             # Normalize existing weights
             sample_weight = sample_weight / sample_weight.sum(dtype=np.float64)
 

sklearn/exceptions.py

@@ -11,6 +11,7 @@
            'EfficiencyWarning',
            'FitFailedWarning',
            'NonBLASDotWarning',
+           'SkipTestWarning',
            'UndefinedMetricWarning']
 
 
@@ -138,6 +139,15 @@ class NonBLASDotWarning(EfficiencyWarning):
     """
 
 
+class SkipTestWarning(UserWarning):
+    """Warning class used to notify the user of a test that was skipped.
+
+    For example, one of the estimator checks requires a pandas import.
+    If the pandas package cannot be imported, the test will be skipped rather
+    than register as a failure.
+    """
+
+
 class UndefinedMetricWarning(UserWarning):
     """Warning used when the metric is invalid
 

sklearn/kernel_ridge.py

@@ -9,7 +9,7 @@
 from .base import BaseEstimator, RegressorMixin
 from .metrics.pairwise import pairwise_kernels
 from .linear_model.ridge import _solve_cholesky_kernel
-from .utils import check_X_y
+from .utils import check_array, check_X_y
 from .utils.validation import check_is_fitted
 
 
@@ -135,7 +135,7 @@ def fit(self, X, y=None, sample_weight=None):
         y : array-like, shape = [n_samples] or [n_samples, n_targets]
             Target values
 
-        sample_weight : float or numpy array of shape [n_samples]
+        sample_weight : float or array-like of shape [n_samples]
             Individual weights for each sample, ignored if None is passed.
 
         Returns
@@ -145,6 +145,8 @@ def fit(self, X, y=None, sample_weight=None):
         # Convert data
         X, y = check_X_y(X, y, accept_sparse=("csr", "csc"), multi_output=True,
                          y_numeric=True)
+        if sample_weight is not None and not isinstance(sample_weight, float):
+            sample_weight = check_array(sample_weight, ensure_2d=False)
 
         K = self._get_kernel(X)
         alpha = np.atleast_1d(self.alpha)

sklearn/linear_model/ridge.py

@@ -957,6 +957,8 @@ def fit(self, X, y, sample_weight=None):
         """
         X, y = check_X_y(X, y, ['csr', 'csc', 'coo'], dtype=np.float64,
                          multi_output=True, y_numeric=True)
+        if sample_weight is not None and not isinstance(sample_weight, float):
+            sample_weight = check_array(sample_weight, ensure_2d=False)
         n_samples, n_features = X.shape
 
         X, y, X_offset, y_offset, X_scale = LinearModel._preprocess_data(

sklearn/utils/estimator_checks.py

@@ -45,10 +45,12 @@
 from sklearn.decomposition import NMF, ProjectedGradientNMF
 from sklearn.exceptions import ConvergenceWarning
 from sklearn.exceptions import DataConversionWarning
+from sklearn.exceptions import SkipTestWarning
 from sklearn.model_selection import train_test_split
 
 from sklearn.utils import shuffle
 from sklearn.utils.fixes import signature
+from sklearn.utils.validation import has_fit_parameter
 from sklearn.preprocessing import StandardScaler
 from sklearn.datasets import load_iris, load_boston, make_blobs
 
@@ -80,6 +82,7 @@ def _yield_non_meta_checks(name, Estimator):
     yield check_estimators_dtypes
     yield check_fit_score_takes_y
     yield check_dtype_object
+    yield check_sample_weights_pandas_series
     yield check_estimators_fit_returns_self
 
     # Check that all estimator yield informative messages when
@@ -198,7 +201,6 @@ def _yield_transformer_checks(name, Transformer):
         yield check_transformer_n_iter
 
 
-
 def _yield_clustering_checks(name, Clusterer):
     yield check_clusterer_compute_labels_predict
     if name not in ('WardAgglomeration', "FeatureAgglomeration"):
@@ -252,7 +254,12 @@ def check_estimator(Estimator):
     name = Estimator.__name__
     check_parameters_default_constructible(name, Estimator)
     for check in _yield_all_checks(name, Estimator):
-        check(name, Estimator)
+        try:
+            check(name, Estimator)
+        except SkipTest as message:
+            # the only SkipTest thrown currently results from not
+            # being able to import pandas.
+            warnings.warn(message, SkipTestWarning)
 
 
 def _boston_subset(n_samples=200):
@@ -381,6 +388,28 @@ def check_estimator_sparse_data(name, Estimator):
             raise
 
 
+@ignore_warnings(category=DeprecationWarning)
+def check_sample_weights_pandas_series(name, Estimator):
+    # check that estimators will accept a 'sample_weight' parameter of
+    # type pandas.Series in the 'fit' function.
+    estimator = Estimator()
+    if has_fit_parameter(estimator, "sample_weight"):
+        try:
+            import pandas as pd
+            X = pd.DataFrame([[1, 1], [1, 2], [1, 3], [2, 1], [2, 2], [2, 3]])
+            y = pd.Series([1, 1, 1, 2, 2, 2])
+            weights = pd.Series([1] * 6)
+            try:
+                estimator.fit(X, y, sample_weight=weights)
+            except ValueError:
+                raise ValueError("Estimator {0} raises error if "
+                                 "'sample_weight' parameter is of "
+                                 "type pandas.Series".format(name))
+        except ImportError:
+            raise SkipTest("pandas is not installed: not testing for "
+                           "input of type pandas.Series to class weight.")
+
+
 @ignore_warnings(category=(DeprecationWarning, UserWarning))
 def check_dtype_object(name, Estimator):
     # check that estimators treat dtype object as numeric if possible

sklearn/utils/tests/test_estimator_checks.py

@@ -73,6 +73,25 @@ def predict(self, X):
         return np.ones(X.shape[0])
 
 
+class NoSampleWeightPandasSeriesType(BaseEstimator):
+    def fit(self, X, y, sample_weight=None):
+        # Convert data
+        X, y = check_X_y(X, y,
+                         accept_sparse=("csr", "csc"),
+                         multi_output=True,
+                         y_numeric=True)
+        # Function is only called after we verify that pandas is installed
+        from pandas import Series
+        if isinstance(sample_weight, Series):
+            raise ValueError("Estimator does not accept 'sample_weight'"
+                             "of type pandas.Series")
+        return self
+
+    def predict(self, X):
+        X = check_array(X)
+        return np.ones(X.shape[0])
+
+
 def test_check_estimator():
     # tests that the estimator actually fails on "bad" estimators.
     # not a complete test of all checks, which are very extensive.
@@ -86,6 +105,15 @@ def test_check_estimator():
     # check that fit does input validation
     msg = "TypeError not raised"
     assert_raises_regex(AssertionError, msg, check_estimator, BaseBadClassifier)
+    # check that sample_weights in fit accepts pandas.Series type
+    try:
+        from pandas import Series  # noqa
+        msg = ("Estimator NoSampleWeightPandasSeriesType raises error if "
+               "'sample_weight' parameter is of type pandas.Series")
+        assert_raises_regex(
+            ValueError, msg, check_estimator, NoSampleWeightPandasSeriesType)
+    except ImportError:
+        pass
     # check that predict does input validation (doesn't accept dicts in input)
     msg = "Estimator doesn't check for NaN and inf in predict"
     assert_raises_regex(AssertionError, msg, check_estimator, NoCheckinPredict)