DOC added to preprocessing docstring examples (#11752)

adrinjalali · rth · commit 76d763be80dc · 2018-08-07T15:24:05.000+03:00
diff --git a/sklearn/preprocessing/data.py b/sklearn/preprocessing/data.py
@@ -831,6 +831,20 @@ class MaxAbsScaler(BaseEstimator, TransformerMixin):
         The number of samples processed by the estimator. Will be reset on
         new calls to fit, but increments across ``partial_fit`` calls.
 
+    Examples
+    --------
+    >>> from sklearn.preprocessing import MaxAbsScaler
+    >>> X = [[ 1., -1.,  2.],
+    ...      [ 2.,  0.,  0.],
+    ...      [ 0.,  1., -1.]]
+    >>> transformer = MaxAbsScaler().fit(X)
+    >>> transformer
+    MaxAbsScaler(copy=True)
+    >>> transformer.transform(X)
+    array([[ 0.5, -1. ,  1. ],
+           [ 1. ,  0. ,  0. ],
+           [ 0. ,  1. , -0.5]])
+
     See also
     --------
     maxabs_scale: Equivalent function without the estimator API.
@@ -1068,6 +1082,21 @@ class RobustScaler(BaseEstimator, TransformerMixin):
         .. versionadded:: 0.17
            *scale_* attribute.
 
+    Examples
+    --------
+    >>> from sklearn.preprocessing import RobustScaler
+    >>> X = [[ 1., -2.,  2.],
+    ...      [ -2.,  1.,  3.],
+    ...      [ 4.,  1., -2.]]
+    >>> transformer = RobustScaler().fit(X)
+    >>> transformer
+    RobustScaler(copy=True, quantile_range=(25.0, 75.0), with_centering=True,
+           with_scaling=True)
+    >>> transformer.transform(X)
+    array([[ 0. , -2. ,  0. ],
+           [-1. ,  0. ,  0.4],
+           [ 1. ,  0. , -1.6]])
+
     See also
     --------
     robust_scale: Equivalent function without the estimator API.
@@ -1580,6 +1609,20 @@ class Normalizer(BaseEstimator, TransformerMixin):
         copy (if the input is already a numpy array or a scipy.sparse
         CSR matrix).
 
+    Examples
+    --------
+    >>> from sklearn.preprocessing import Normalizer
+    >>> X = [[4, 1, 2, 2],
+    ...      [1, 3, 9, 3],
+    ...      [5, 7, 5, 1]]
+    >>> transformer = Normalizer().fit(X) # fit does nothing.
+    >>> transformer
+    Normalizer(copy=True, norm='l2')
+    >>> transformer.transform(X)
+    array([[0.8, 0.2, 0.4, 0.4],
+           [0.1, 0.3, 0.9, 0.3],
+           [0.5, 0.7, 0.5, 0.1]])
+
     Notes
     -----
     This estimator is stateless (besides constructor parameters), the
@@ -1707,6 +1750,20 @@ class Binarizer(BaseEstimator, TransformerMixin):
         set to False to perform inplace binarization and avoid a copy (if
         the input is already a numpy array or a scipy.sparse CSR matrix).
 
+    Examples
+    --------
+    >>> from sklearn.preprocessing import Binarizer
+    >>> X = [[ 1., -1.,  2.],
+    ...      [ 2.,  0.,  0.],
+    ...      [ 0.,  1., -1.]]
+    >>> transformer = Binarizer().fit(X) # fit does nothing.
+    >>> transformer
+    Binarizer(copy=True, threshold=0.0)
+    >>> transformer.transform(X)
+    array([[1., 0., 1.],
+           [1., 0., 0.],
+           [0., 1., 0.]])
+
     Notes
     -----
     If the input is a sparse matrix, only the non-zero values are subject
@@ -1771,7 +1828,28 @@ class KernelCenterer(BaseEstimator, TransformerMixin):
     sklearn.preprocessing.StandardScaler(with_std=False).
 
     Read more in the :ref:`User Guide <kernel_centering>`.
+
+    Examples
+    --------
+    >>> from sklearn.preprocessing import KernelCenterer
+    >>> from sklearn.metrics.pairwise import pairwise_kernels
+    >>> X = [[ 1., -2.,  2.],
+    ...      [ -2.,  1.,  3.],
+    ...      [ 4.,  1., -2.]]
+    >>> K = pairwise_kernels(X, metric='linear')
+    >>> K
+    array([[  9.,   2.,  -2.],
+           [  2.,  14., -13.],
+           [ -2., -13.,  21.]])
+    >>> transformer = KernelCenterer().fit(K)
+    >>> transformer
+    KernelCenterer()
+    >>> transformer.transform(K)
+    array([[  5.,   0.,  -5.],
+           [  0.,  14., -14.],
+           [ -5., -14.,  19.]])
     """
+
     def __init__(self):
         # Needed for backported inspect.signature compatibility with PyPy
         pass
