scikit-learn · ogrisel · Jul 5, 2018 · Jun 18, 2018 · Jun 18, 2018 · Jun 18, 2018
diff --git a/doc/whats_new/v0.20.rst b/doc/whats_new/v0.20.rst
@@ -260,27 +260,28 @@ Preprocessing
   classes found which are ignored.
   :issue:`10913` by :user:`Rodrigo Agundez <rragundez>`.
 
-- :class:`preprocessing.QuantileTransformer` handles and ignores NaN values.
-  :issue:`10404` by :user:`Guillaume Lemaitre <glemaitre>`.
-
-- Updated :class:`preprocessing.MinMaxScaler` and
-  :func:`preprocessing.minmax_scale` to pass through NaN values.
-  :issue:`10404` and :issue:`11243` by :user:`Lucija Gregov <LucijaGregov>` and
+- NaN values are ignored and handled in the following preprocessing methods:
+  :class:`preprocessing.MaxAbsScaler`,
+  :class:`preprocessing.MinMaxScaler`,
+  :class:`preprocessing.RobustScaler`,
+  :class:`preprocessing.StandardScaler`,
+  :class:`preprocessing.PowerTransformer`,
+  :class:`preprocessing.QuantileTransformer` classes and
+  :func:`preprocessing.maxabs_scale`,
+  :func:`preprocessing.minmax_scale`,
+  :func:`preprocessing.robust_scale`,
+  :func:`preprocessing.scale`,
+  :func:`preprocessing.power_transform`,
+  :func:`preprocessing.quantile_transform` functions respectively addressed in
+  issues :issue:`11011`, :issue:`11005`, :issue:`11308`, :issue:`11206`,
+  :issue:`11306`, and :issue:`10437`.
+  By :user:`Lucija Gregov <LucijaGregov>` and
   :user:`Guillaume Lemaitre <glemaitre>`.
 
-- :class:`preprocessing.StandardScaler` and :func:`preprocessing.scale`
-  ignore and pass-through NaN values.
-  :issue:`11206` by :user:`Guillaume Lemaitre <glemaitre>`.
-
-- :class:`preprocessing.MaxAbsScaler` and :func:`preprocessing.maxabs_scale`
-  handles and ignores NaN values.
-  :issue:`11011` by `Lucija Gregov <LucihaGregov>` and
-  :user:`Guillaume Lemaitre <glemaitre>`
-
-- :class:`preprocessing.PowerTransformer` and
-  :func:`preprocessing.power_transform` ignore and pass-through NaN values.
-  :issue:`11306` by :user:`Guillaume Lemaitre <glemaitre>`.
-
+- :class:`preprocessing.RobustScaler` and :func:`preprocessing.robust_scale`
+  can be fitted using sparse matrices.
+  :issue:`11308` by :user:`Guillaume Lemaitre <glemaitre>`.
+
 Model evaluation and meta-estimators
 
 - A scorer based on :func:`metrics.brier_score_loss` is also available.

diff --git a/sklearn/preprocessing/data.py b/sklearn/preprocessing/data.py
@@ -24,7 +24,7 @@
 from ..utils import check_array
 from ..utils.extmath import row_norms
 from ..utils.extmath import _incremental_mean_and_var
-from ..utils.fixes import boxcox, nanpercentile
+from ..utils.fixes import boxcox, nanpercentile, nanmedian
 from ..utils.sparsefuncs_fast import (inplace_csr_row_normalize_l1,
                                       inplace_csr_row_normalize_l2)
 from ..utils.sparsefuncs import (inplace_column_scale,
@@ -1092,18 +1092,6 @@ def __init__(self, with_centering=True, with_scaling=True,
         self.quantile_range = quantile_range
         self.copy = copy
 
-    def _check_array(self, X, copy):
-        """Makes sure centering is not enabled for sparse matrices."""
-        X = check_array(X, accept_sparse=('csr', 'csc'), copy=self.copy,
-                        estimator=self, dtype=FLOAT_DTYPES)
-
-        if sparse.issparse(X):
-            if self.with_centering:
-                raise ValueError(
-                    "Cannot center sparse matrices: use `with_centering=False`"
-                    " instead. See docstring for motivation and alternatives.")
-        return X
-
     def fit(self, X, y=None):
         """Compute the median and quantiles to be used for scaling.
 
@@ -1113,39 +1101,60 @@ def fit(self, X, y=None):
             The data used to compute the median and quantiles
             used for later scaling along the features axis.
         """
-        if sparse.issparse(X):
-            raise TypeError("RobustScaler cannot be fitted on sparse inputs")
-        X = self._check_array(X, self.copy)
+        # at fit, convert sparse matrices to csc for optimized computation of
+        # the quantiles
+        X = check_array(X, accept_sparse='csc', copy=self.copy, estimator=self,
+                        dtype=FLOAT_DTYPES, force_all_finite='allow-nan')
+
+        q_min, q_max = self.quantile_range
+        if not 0 <= q_min <= q_max <= 100:
+            raise ValueError("Invalid quantile range: %s" %
+                             str(self.quantile_range))
+
         if self.with_centering:
-            self.center_ = np.median(X, axis=0)
+            if sparse.issparse(X):
+                raise ValueError(
+                    "Cannot center sparse matrices: use `with_centering=False`"
+                    " instead. See docstring for motivation and alternatives.")
+            self.center_ = nanmedian(X, axis=0)
+        else:
+            self.center_ = None
 
         if self.with_scaling:
-            q_min, q_max = self.quantile_range
-            if not 0 <= q_min <= q_max <= 100:
-                raise ValueError("Invalid quantile range: %s" %
-                                 str(self.quantile_range))
+            quantiles = []
+            for feature_idx in range(X.shape[1]):
+                if sparse.issparse(X):
+                    column_nnz_data = X.data[X.indptr[feature_idx]:
+                                             X.indptr[feature_idx + 1]]
+                    column_data = np.zeros(shape=X.shape[0], dtype=X.dtype)
+                    column_data[:len(column_nnz_data)] = column_nnz_data
+                else:
+                    column_data = X[:, feature_idx]
 
-            q = np.percentile(X, self.quantile_range, axis=0)
-            self.scale_ = (q[1] - q[0])
+                quantiles.append(nanpercentile(column_data,
+                                               self.quantile_range))
+
+            quantiles = np.transpose(quantiles)
+
+            self.scale_ = quantiles[1] - quantiles[0]
             self.scale_ = _handle_zeros_in_scale(self.scale_, copy=False)
+        else:
+            self.scale_ = None
+
         return self
 
     def transform(self, X):
         """Center and scale the data.
 
-        Can be called on sparse input, provided that ``RobustScaler`` has been
-        fitted to dense input and ``with_centering=False``.
-
         Parameters
         ----------
         X : {array-like, sparse matrix}
             The data used to scale along the specified axis.
         """
-        if self.with_centering:
-            check_is_fitted(self, 'center_')
-        if self.with_scaling:
-            check_is_fitted(self, 'scale_')
-        X = self._check_array(X, self.copy)
+        check_is_fitted(self, 'center_', 'scale_')
+        X = check_array(X, accept_sparse=('csr', 'csc'), copy=self.copy,
+                        estimator=self, dtype=FLOAT_DTYPES,
+                        force_all_finite='allow-nan')
 
         if sparse.issparse(X):
             if self.with_scaling:
@@ -1165,11 +1174,10 @@ def inverse_transform(self, X):
         X : array-like
             The data used to scale along the specified axis.
         """
-        if self.with_centering:
-            check_is_fitted(self, 'center_')
-        if self.with_scaling:
-            check_is_fitted(self, 'scale_')
-        X = self._check_array(X, self.copy)
+        check_is_fitted(self, 'center_', 'scale_')
+        X = check_array(X, accept_sparse=('csr', 'csc'), copy=self.copy,
+                        estimator=self, dtype=FLOAT_DTYPES,
+                        force_all_finite='allow-nan')
 
         if sparse.issparse(X):
             if self.with_scaling:
@@ -1242,7 +1250,8 @@ def robust_scale(X, axis=0, with_centering=True, with_scaling=True,
         (e.g. as part of a preprocessing :class:`sklearn.pipeline.Pipeline`).
     """
     X = check_array(X, accept_sparse=('csr', 'csc'), copy=False,
-                    ensure_2d=False, dtype=FLOAT_DTYPES)
+                    ensure_2d=False, dtype=FLOAT_DTYPES,
+                    force_all_finite='allow-nan')
     original_ndim = X.ndim
 
     if original_ndim == 1:

diff --git a/sklearn/preprocessing/tests/test_common.py b/sklearn/preprocessing/tests/test_common.py
@@ -13,12 +13,14 @@
 from sklearn.preprocessing import scale
 from sklearn.preprocessing import power_transform
 from sklearn.preprocessing import quantile_transform
+from sklearn.preprocessing import robust_scale
 
 from sklearn.preprocessing import MaxAbsScaler
 from sklearn.preprocessing import MinMaxScaler
 from sklearn.preprocessing import StandardScaler
 from sklearn.preprocessing import PowerTransformer
 from sklearn.preprocessing import QuantileTransformer
+from sklearn.preprocessing import RobustScaler
 
 from sklearn.utils.testing import assert_array_equal
 from sklearn.utils.testing import assert_allclose
@@ -38,7 +40,9 @@ def _get_valid_samples_by_column(X, col):
      (StandardScaler(), scale, False, False),
      (StandardScaler(with_mean=False), scale, True, False),
      (PowerTransformer(), power_transform, False, True),
-     (QuantileTransformer(n_quantiles=10), quantile_transform, True, False)]
+     (QuantileTransformer(n_quantiles=10), quantile_transform, True, False),
+     (RobustScaler(), robust_scale, False, False),
+     (RobustScaler(with_centering=False), robust_scale, True, False)]
 )
 def test_missing_value_handling(est, func, support_sparse, strictly_positive):
     # check that the preprocessing method let pass nan

diff --git a/sklearn/preprocessing/tests/test_data.py b/sklearn/preprocessing/tests/test_data.py
@@ -906,6 +906,52 @@ def test_scale_input_finiteness_validation():
                         scale, X)
 
 
+def test_robust_scaler_error_sparse():
+    X_sparse = sparse.rand(1000, 10)
+    scaler = RobustScaler(with_centering=True)
+    err_msg = "Cannot center sparse matrices"
+    with pytest.raises(ValueError, match=err_msg):
+        scaler.fit(X_sparse)
+
+
+@pytest.mark.parametrize("with_centering", [True, False])
+@pytest.mark.parametrize("with_scaling", [True, False])
+@pytest.mark.parametrize("X", [np.random.randn(10, 3),
+                               sparse.rand(10, 3, density=0.5)])
+def test_robust_scaler_attributes(X, with_centering, with_scaling):
+    # check consistent type of attributes
+    if with_centering and sparse.issparse(X):
+        pytest.skip("RobustScaler cannot center sparse matrix")
+
+    scaler = RobustScaler(with_centering=with_centering,
+                          with_scaling=with_scaling)
+    scaler.fit(X)
+
+    if with_centering:
+        assert isinstance(scaler.center_, np.ndarray)
+    else:
+        assert scaler.center_ is None
+    if with_scaling:
+        assert isinstance(scaler.scale_, np.ndarray)
+    else:
+        assert scaler.scale_ is None
+
+
+def test_robust_scaler_col_zero_sparse():
+    # check that the scaler is working when there is not data materialized in a
+    # column of a sparse matrix
+    X = np.random.randn(10, 5)
+    X[:, 0] = 0
+    X = sparse.csr_matrix(X)
+
+    scaler = RobustScaler(with_centering=False)
+    scaler.fit(X)
+    assert scaler.scale_[0] == pytest.approx(1)
+
+    X_trans = scaler.transform(X)
+    assert_allclose(X[:, 0].toarray(), X_trans[:, 0].toarray())
+
+
 def test_robust_scaler_2d_arrays():
     # Test robust scaling of 2d array along first axis
     rng = np.random.RandomState(0)
@@ -919,6 +965,29 @@ def test_robust_scaler_2d_arrays():
     assert_array_almost_equal(X_scaled.std(axis=0)[0], 0)
 
 
+@pytest.mark.parametrize("density", [0, 0.05, 0.1, 0.5, 1])
+@pytest.mark.parametrize("strictly_signed",
+                         ['positive', 'negative', 'zeros', None])
+def test_robust_scaler_equivalence_dense_sparse(density, strictly_signed):
+    # Check the equivalence of the fitting with dense and sparse matrices
+    X_sparse = sparse.rand(1000, 5, density=density).tocsc()
+    if strictly_signed == 'positive':
+        X_sparse.data = np.abs(X_sparse.data)
+    elif strictly_signed == 'negative':
+        X_sparse.data = - np.abs(X_sparse.data)
+    elif strictly_signed == 'zeros':
+        X_sparse.data = np.zeros(X_sparse.data.shape, dtype=np.float64)
+    X_dense = X_sparse.toarray()
+
+    scaler_sparse = RobustScaler(with_centering=False)
+    scaler_dense = RobustScaler(with_centering=False)
+
+    scaler_sparse.fit(X_sparse)
+    scaler_dense.fit(X_dense)
+
+    assert_allclose(scaler_sparse.scale_, scaler_dense.scale_)
+
+
 def test_robust_scaler_transform_one_row_csr():
     # Check RobustScaler on transforming csr matrix with one row
     rng = np.random.RandomState(0)

diff --git a/sklearn/utils/estimator_checks.py b/sklearn/utils/estimator_checks.py
@@ -79,7 +79,7 @@
                 'RandomForestRegressor', 'Ridge', 'RidgeCV']
 
 ALLOW_NAN = ['Imputer', 'SimpleImputer', 'ChainedImputer',
-             'MaxAbsScaler', 'MinMaxScaler', 'StandardScaler',
+             'MaxAbsScaler', 'MinMaxScaler', 'RobustScaler', 'StandardScaler',
              'PowerTransformer', 'QuantileTransformer']
 
 

diff --git a/sklearn/utils/fixes.py b/sklearn/utils/fixes.py
@@ -280,6 +280,19 @@ def nanpercentile(a, q):
     from numpy import nanpercentile  # noqa
 
 
+if np_version < (1, 9):
+    def nanmedian(a, axis=None):
+        if axis is None:
+            data = a.reshape(-1)
+            return np.median(np.compress(~np.isnan(data), data))
+        else:
+            data = a.T if not axis else a
+            return np.array([np.median(np.compress(~np.isnan(row), row))
+                             for row in data])
+else:
+    from numpy import nanmedian  # noqa
+
+
 # Fix for behavior inconsistency on numpy.equal for object dtypes.
 # For numpy versions < 1.13, numpy.equal tests element-wise identity of objects
 # instead of equality. This fix returns the mask of NaNs in an array of

diff --git a/sklearn/utils/tests/test_fixes.py b/sklearn/utils/tests/test_fixes.py
@@ -14,6 +14,7 @@
 
 from sklearn.utils.fixes import divide
 from sklearn.utils.fixes import MaskedArray
+from sklearn.utils.fixes import nanmedian
 from sklearn.utils.fixes import nanpercentile
 
 
@@ -31,6 +32,22 @@ def test_masked_array_obj_dtype_pickleable():
         assert_array_equal(marr.mask, marr_pickled.mask)
 
 
+@pytest.mark.parametrize(
+    "axis, expected_median",
+    [(None, 4.0),
+     (0, np.array([1., 3.5, 3.5, 4., 7., np.nan])),
+     (1, np.array([1., 6.]))]
+)
+def test_nanmedian(axis, expected_median):
+    X = np.array([[1, 1, 1, 2, np.nan, np.nan],
+                  [np.nan, 6, 6, 6, 7, np.nan]])
+    median = nanmedian(X, axis=axis)
+    if axis is None:
+        assert median == pytest.approx(expected_median)
+    else:
+        assert_allclose(median, expected_median)
+
+
 @pytest.mark.parametrize(
     "a, q, expected_percentile",
     [(np.array([1, 2, 3, np.nan]), [0, 50, 100], np.array([1., 2., 3.])),