8000 DOC remove sphinx warnings when generating the doc · raghavrv/scikit-learn@a13a585 · GitHub
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lesteveogrisel
lesteve
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ogrisel
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DOC remove sphinx warnings when generating the doc
and fixes a few other doc problems while I was at it. The main take-home message is to remember that you need a backslash when your parameter type description exceeds one line.
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‎doc/modules/model_evaluation.rst

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@@ -863,7 +863,7 @@ Receiver operating characteristic (ROC)
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The function :func:`roc_curve` computes the
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`receiver operating characteristic curve, or ROC curve <http://en.wikipedia.org/wiki/Receiver_operating_characteristic>`_.
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Quoting Wikipedia :
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Quoting Wikipedia :
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"A receiver operating characteristic (ROC), or simply ROC curve, is a
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graphical plot which illustrates the performance of a binary classifier

‎doc/whats_new.rst

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@@ -254,7 +254,7 @@ Documentation improvements
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- Added silhouette plots for analysis of KMeans clustering using
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:func:`metrics.silhouette_samples` and :func:`metrics.silhouette_score`.
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See :ref:`examples_cluster_plot_kmeans_silhouette_analysis.py`
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See :ref:`example_cluster_plot_kmeans_silhouette_analysis.py`
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Bug fixes
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.........

‎sklearn/isotonic.py

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@@ -198,8 +198,7 @@ class IsotonicRegression(BaseEstimator, TransformerMixin, RegressorMixin):
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Maximum value of input array `X_` for right bound.
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f_ : function
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The stepwise interpolating function that covers the domain
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X_.
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The stepwise interpolating function that covers the domain `X_`.
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Notes
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-----

‎sklearn/lda.py

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@@ -151,6 +151,7 @@ class LDA(BaseEstimator, LinearClassifierMixin, TransformerMixin):
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- None: no shrinkage (default).
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- 'auto': automatic shrinkage using the Ledoit-Wolf lemma.
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- float between 0 and 1: fixed shrinkage parameter.
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Note that shrinkage works only with 'lsqr' and 'eigen' solvers.
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priors : array, optional, shape (n_classes,)

‎sklearn/linear_model/coordinate_descent.py

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@@ -185,7 +185,7 @@ def lasso_path(X, y, eps=1e-3, n_alphas=100, alphas=None,
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alphas : array, shape (n_alphas,)
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The alphas along the path where models are computed.
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coefs : array, shape (n_features, n_alphas) or
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coefs : array, shape (n_features, n_alphas) or \
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(n_outputs, n_features, n_alphas)
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Coefficients along the path.
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@@ -331,7 +331,7 @@ def enet_path(X, y, l1_ratio=0.5, eps=1e-3, n_alphas=100, alphas=None,
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alphas : array, shape (n_alphas,)
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The alphas along the path where models are computed.
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coefs : array, shape (n_features, n_alphas) or
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coefs : array, shape (n_features, n_alphas) or \
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(n_outputs, n_features, n_alphas)
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Coefficients along the path.
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@@ -1815,8 +1815,8 @@ class MultiTaskElasticNetCV(LinearModelCV, RegressorMixin):
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alpha_ : float
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The amount of penalization chosen by cross validation
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mse_path_ : array, shape (n_alphas, n_folds) or
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(n_l1_ratio, n_alphas, n_folds)
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mse_path_ : array, shape (n_alphas, n_folds) or \
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(n_l1_ratio, n_alphas, n_folds)
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mean square error for the test set on each fold, varying alpha
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alphas_ : numpy array, shape (n_alphas,) or (n_l1_ratio, n_alphas)

‎sklearn/linear_model/logistic.py

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@@ -1242,8 +1242,8 @@ class LogisticRegressionCV(LogisticRegression, BaseEstimator,
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Array of C i.e. inverse of regularization parameter values used
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for cross-validation.
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coefs_paths_ : array, shape ``(n_folds, len(Cs_), n_features)`` or
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``(n_folds, len(Cs_), n_features + 1)``
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coefs_paths_ : array, shape ``(n_folds, len(Cs_), n_features)`` or \
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``(n_folds, len(Cs_), n_features + 1)``
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dict with classes as the keys, and the path of coefficients obtained
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during cross-validating across each fold and then across each Cs
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after doing an OvR for the corresponding class as values.

‎sklearn/metrics/classification.py

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@@ -1470,17 +1470,17 @@ def hinge_loss(y_true, pred_decision, labels=None, sample_weight=None):
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References
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----------
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.. [1] `Wikipedia entry on the Hinge loss
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<http://en.wikipedia.org/wiki/Hinge_loss>`_
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<http://en.wikipedia.org/wiki/Hinge_loss>`_
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.. [2] Koby Crammer, Yoram Singer. On the Algorithmic
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Implementation of Multiclass Kernel-based Vector
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Machines. Journal of Machine Learning Research 2,
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(2001), 265-292
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.. [3] 'L1 AND L2 Regularization for Multiclass Hinge Loss Models
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.. [3] `L1 AND L2 Regularization for Multiclass Hinge Loss Models
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by Robert C. Moore, John DeNero.
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<http://www.ttic.edu/sigml/symposium2011/papers/
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Moore+DeNero_Regularization.pdf>'
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Moore+DeNero_Regularization.pdf>`_
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Examples
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--------
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0.30...
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In the multiclass case:
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>>> X = np.array([[0], [1], [2], [3]])
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>>> Y = np.array([0, 1, 2, 3])
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>>> labels = np.array([0, 1, 2, 3])

‎sklearn/neighbors/ball_tree.c

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‎sklearn/neighbors/binary_tree.pxi

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@@ -254,7 +254,7 @@ cdef NodeData_t[::1] get_memview_NodeData_1D(
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CLASS_DOC = \
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"""{BinaryTree} for fast generalized N-point problems
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{BinaryTree}(X, leaf_size=40, metric='minkowski', **kwargs)
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{BinaryTree}(X, leaf_size=40, metric='minkowski', \\**kwargs)
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Parameters
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----------

‎sklearn/neighbors/kd_tree.c

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‎sklearn/preprocessing/label.py

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@@ -282,15 +282,15 @@ def __init__(self, neg_label=0, pos_label=1, sparse_output=False):
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self.sparse_output = sparse_output
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@property
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@deprecated("Attribute indicator_matrix_ is deprecated and will be "
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"removed in 0.17. Use 'y_type_ == 'multilabel-indicator'' "
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@deprecated("Attribute ``indicator_matrix_`` is deprecated and will be "
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"removed in 0.17. Use ``y_type_ == 'multilabel-indicator'`` "
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"instead")
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def indicator_matrix_(self):
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return self.y_type_ == 'multilabel-indicator'
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@property
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@deprecated("Attribute multilabel_ is deprecated and will be removed "
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"in 0.17. Use 'y_type_.startswith('multilabel')' "
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@deprecated("Attribute ``multilabel_`` is deprecated and will be removed "
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"in 0.17. Use ``y_type_.startswith('multilabel')`` "
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"instead")
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def multilabel_(self):
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return self.y_type_.startswith('multilabel')

‎sklearn/svm/classes.py

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@@ -232,7 +232,7 @@ class LinearSVR(LinearModel, RegressorMixin):
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Penalty parameter C of the error term. The penalty is a squared
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l2 penalty. The bigger this parameter, the less regularization is used.
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loss : string, 'epsilon_insensitive' or 'squared_epsilon_insensitive'
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loss : string, 'epsilon_insensitive' or 'squared_epsilon_insensitive' \
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(default='epsilon_insensitive')
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Specifies the loss function. 'l1' is the epsilon-insensitive loss
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(standard SVR) while 'l2' is the squared epsilon-insensitive loss.

‎sklearn/tree/tree.py

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@@ -444,8 +444,7 @@ class DecisionTreeClassifier(BaseDecisionTree, ClassifierMixin):
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If None then unlimited number of leaf nodes.
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If not None then ``max_depth`` will be ignored.
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class_weight : dict, list of dicts, "auto" or None, optional
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(default=None)
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class_weight : dict, list of dicts, "auto" or None, optional (default=None)
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Weights associated with classes in the form ``{class_label: weight}``.
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If not given, all classes are supposed to have 3DFF weight one. For
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multi-output problems, a list of dicts can be provided in the same

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