DOC Ensures that LedoitWolf passes numpydoc validation (scikit-learn#20578)

Author: genvalen

maint_tools/test_docstrings.py

@@ -55,7 +55,6 @@
     "LabelBinarizer",
     "LabelPropagation",
     "LabelSpreading",
-    "LedoitWolf",
     "LinearSVR",
     "LocalOutlierFactor",
     "LocallyLinearEmbedding",

sklearn/covariance/_shrunk_covariance.py

@@ -338,7 +338,7 @@ def ledoit_wolf(X, *, assume_centered=False, block_size=1000):
 
 
 class LedoitWolf(EmpiricalCovariance):
-    """LedoitWolf Estimator
+    """LedoitWolf Estimator.
 
     Ledoit-Wolf is a particular form of shrinkage, where the shrinkage
     coefficient is computed using O. Ledoit and M. Wolf's formula as
@@ -385,22 +385,19 @@ class LedoitWolf(EmpiricalCovariance):
 
         .. versionadded:: 0.24
 
-    Examples
+    See Also
     --------
-    >>> import numpy as np
-    >>> from sklearn.covariance import LedoitWolf
-    >>> real_cov = np.array([[.4, .2],
-    ...                      [.2, .8]])
-    >>> np.random.seed(0)
-    >>> X = np.random.multivariate_normal(mean=[0, 0],
-    ...                                   cov=real_cov,
-    ...                                   size=50)
-    >>> cov = LedoitWolf().fit(X)
-    >>> cov.covariance_
-    array([[0.4406..., 0.1616...],
-           [0.1616..., 0.8022...]])
-    >>> cov.location_
-    array([ 0.0595... , -0.0075...])
+    EllipticEnvelope : An object for detecting outliers in
+        a Gaussian distributed dataset.
+    EmpiricalCovariance : Maximum likelihood covariance estimator.
+    GraphicalLasso : Sparse inverse covariance estimation
+        with an l1-penalized estimator.
+    GraphicalLassoCV : Sparse inverse covariance with cross-validated
+        choice of the l1 penalty.
+    MinCovDet : Minimum Covariance Determinant
+        (robust estimator of covariance).
+    OAS : Oracle Approximating Shrinkage Estimator.
+    ShrunkCovariance : Covariance estimator with shrinkage.
 
     Notes
     -----
@@ -416,6 +413,23 @@ class LedoitWolf(EmpiricalCovariance):
     "A Well-Conditioned Estimator for Large-Dimensional Covariance Matrices",
     Ledoit and Wolf, Journal of Multivariate Analysis, Volume 88, Issue 2,
     February 2004, pages 365-411.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from sklearn.covariance import LedoitWolf
+    >>> real_cov = np.array([[.4, .2],
+    ...                      [.2, .8]])
+    >>> np.random.seed(0)
+    >>> X = np.random.multivariate_normal(mean=[0, 0],
+    ...                                   cov=real_cov,
+    ...                                   size=50)
+    >>> cov = LedoitWolf().fit(X)
+    >>> cov.covariance_
+    array([[0.4406..., 0.1616...],
+           [0.1616..., 0.8022...]])
+    >>> cov.location_
+    array([ 0.0595... , -0.0075...])
     """
 
     def __init__(self, *, store_precision=True, assume_centered=False, block_size=1000):
@@ -425,8 +439,7 @@ def __init__(self, *, store_precision=True, assume_centered=False, block_size=10
         self.block_size = block_size
 
     def fit(self, X, y=None):
-        """Fit the Ledoit-Wolf shrunk covariance model according to the given
-        training data and parameters.
+        """Fit the Ledoit-Wolf shrunk covariance model to X.
 
         Parameters
         ----------
@@ -439,6 +452,7 @@ def fit(self, X, y=None):
         Returns
         -------
         self : object
+            Returns the instance itself.
         """
         # Not calling the parent object to fit, to avoid computing the
         # covariance matrix (and potentially the precision)