More doc fixes. Latex builds again.

Author: amueller

doc/datasets/index.rst

@@ -255,6 +255,20 @@ features::
  _`Faster API-compatible implementation`: https://github.com/mblondel/svmlight-loader
 
 
+.. make sure everything is in a toc tree
+
+.. toctree::
+    :maxdepth: 2
+    :hidden:
+
+    olivetti_faces
+    twenty_newsgroups
+    mldata
+    labeled_faces
+    covtype
+    rcv1
+
+
 .. include:: olivetti_faces.rst
 
 .. include:: twenty_newsgroups.rst

doc/modules/clustering.rst

@@ -4,7 +4,7 @@
 Clustering
 ==========
 
-`Clustering <https://en.wikipedia.org/wiki/Cluster_analysis>`_ of
+`Clustering <https://en.wikipedia.org/wiki/Cluster_analysis>`__ of
 unlabeled data can be performed with the module :mod:`sklearn.cluster`.
 
 Each clustering algorithm comes in two variants: a class, that implements

doc/whats_new.rst

@@ -235,7 +235,7 @@ Enhancements
 
    - The "Wisconsin Breast Cancer" classical two-class classification dataset
      is now included in scikit-learn, available with
-     :fun:`sklearn.dataset.load_breast_cancer`.
+     :func:`sklearn.dataset.load_breast_cancer`.
 
    - Upgraded to joblib 0.9.3 to benefit from the new automatic batching of
      short tasks. This makes it possible for scikit-learn to benefit from

examples/applications/face_recognition.py

@@ -10,7 +10,7 @@
 
 .. _LFW: http://vis-www.cs.umass.edu/lfw/
 
-Expected results for the top 5 most represented people in the dataset::
+Expected results for the top 5 most represented people in the dataset:
 
 ================== ============ ======= ========== =======
                    precision    recall  f1-score   support

examples/ensemble/plot_partial_dependence.py

@@ -3,7 +3,7 @@
 Partial Dependence Plots
 ========================
 
-Partial dependence plots show the dependence between the target function [1]_
+Partial dependence plots show the dependence between the target function [2]_
 and a set of 'target' features, marginalizing over the
 values of all other features (the complement features). Due to the limits
 of human perception the size of the target feature set must be small (usually,
@@ -13,7 +13,7 @@
 
 This example shows how to obtain partial dependence plots from a
 :class:`~sklearn.ensemble.GradientBoostingRegressor` trained on the California
-housing dataset. The example is taken from [HTF2009]_.
+housing dataset. The example is taken from [1]_.
 
 The plot shows four one-way and one two-way partial dependence plots.
 The target variables for the one-way PDP are:
@@ -38,10 +38,10 @@
 of the house age, whereas for values less than two there is a strong dependence
 on age.
 
-.. [HTF2009] T. Hastie, R. Tibshirani and J. Friedman,
+.. [1] T. Hastie, R. Tibshirani and J. Friedman,
     "Elements of Statistical Learning Ed. 2", Springer, 2009.
 
-.. [1] For classification you can think of it as the regression score before
+.. [2] For classification you can think of it as the regression score before
        the link function.
 """
 print(__doc__)

sklearn/calibration.py

@@ -46,20 +46,21 @@ class CalibratedClassifierCV(BaseEstimator, ClassifierMixin):
         to offer more accurate predict_proba outputs. If cv=prefit, the
         classifier must have been fit already on data.
 
-    method : 'sigmoid' | 'isotonic'
+    method : 'sigmoid' or 'isotonic'
         The method to use for calibration. Can be 'sigmoid' which
         corresponds to Platt's method or 'isotonic' which is a
         non-parameteric approach. It is not advised to use isotonic calibration
-        with too few calibration samples (<<1000) since it tends to overfit.
+        with too few calibration samples ``(<<1000)`` since it tends to overfit.
         Use sigmoids (Platt's calibration) in this case.
 
-    cv : integer/cross-validation generator/iterable or "prefit", optional
+    cv : integer, cross-validation generator, iterable or "prefit", optional
         Determines the cross-validation splitting strategy.
         Possible inputs for cv are:
-          - None, to use the default 3-fold cross-validation,
-          - integer, to specify the number of folds.
-          - An object to be used as a cross-validation generator.
-          - An iterable yielding train/test splits.
+
+        - None, to use the default 3-fold cross-validation,
+        - integer, to specify the number of folds.
+        - An object to be used as a cross-validation generator.
+        - An iterable yielding train/test splits.
 
         For integer/None inputs, if ``y`` is binary or multiclass,
         :class:`StratifiedKFold` used. If ``y`` is neither binary nor

sklearn/covariance/graph_lasso_.py

@@ -468,10 +468,11 @@ class GraphLassoCV(GraphLasso):
     cv : int, cross-validation generator or an iterable, optional
         Determines the cross-validation splitting strategy.
         Possible inputs for cv are:
-          - None, to use the default 3-fold cross-validation,
-          - integer, to specify the number of folds.
-          - An object to be used as a cross-validation generator.
-          - An iterable yielding train/test splits.
+
+        - None, to use the default 3-fold cross-validation,
+        - integer, to specify the number of folds.
+        - An object to be used as a cross-validation generator.
+        - An iterable yielding train/test splits.
 
         For integer/None inputs :class:`KFold` is used.
 

sklearn/cross_validation.py

@@ -1089,7 +1089,7 @@ def __len__(self):
 
 
 class LabelShuffleSplit(ShuffleSplit):
-    '''Shuffle-Labels-Out cross-validation iterator
+    """Shuffle-Labels-Out cross-validation iterator
 
     Provides randomized train/test indices to split data according to a
     third-party provided label. This label information can be used to encode
@@ -1118,7 +1118,7 @@ class LabelShuffleSplit(ShuffleSplit):
         Labels of samples
 
     n_iter : int (default 5)
-        Number of re-shuffling & splitting iterations.
+        Number of re-shuffling and splitting iterations.
 
     test_size : float (default 0.2), int, or None
         If float, should be between 0.0 and 1.0 and represent the
@@ -1134,7 +1134,8 @@ class LabelShuffleSplit(ShuffleSplit):
 
     random_state : int or RandomState
         Pseudo-random number generator state used for random sampling.
-    '''
+
+    """
     def __init__(self, labels, n_iter=5, test_size=0.2, train_size=None,
                  random_state=None):
 
@@ -1208,10 +1209,11 @@ def cross_val_predict(estimator, X, y=None, cv=None, n_jobs=1,
     cv : int, cross-validation generator or an iterable, optional
         Determines the cross-validation splitting strategy.
         Possible inputs for cv are:
-          - None, to use the default 3-fold cross-validation,
-          - integer, to specify the number of folds.
-          - An object to be used as a cross-validation generator.
-          - An iterable yielding train/test splits.
+
+        - None, to use the default 3-fold cross-validation,
+        - integer, to specify the number of folds.
+        - An object to be used as a cross-validation generator.
+        - An iterable yielding train/test splits.
 
         For integer/None inputs, if ``y`` is binary or multiclass,
         :class:`StratifiedKFold` used. If the estimator is a classifier
@@ -1382,10 +1384,11 @@ def cross_val_score(estimator, X, y=None, scoring=None, cv=None, n_jobs=1,
     cv : int, cross-validation generator or an iterable, optional
         Determines the cross-validation splitting strategy.
         Possible inputs for cv are:
-          - None, to use the default 3-fold cross-validation,
-          - integer, to specify the number of folds.
-          - An object to be used as a cross-validation generator.
-          - An iterable yielding train/test splits.
+
+        - None, to use the default 3-fold cross-validation,
+        - integer, to specify the number of folds.
+        - An object to be used as a cross-validation generator.
+        - An iterable yielding train/test splits.
 
         For integer/None inputs, if ``y`` is binary or multiclass,
         :class:`StratifiedKFold` used. If the estimator is a classifier
@@ -1643,10 +1646,11 @@ def check_cv(cv, X=None, y=None, classifier=False):
     cv : int, cross-validation generator or an iterable, optional
         Determines the cross-validation splitting strategy.
         Possible inputs for cv are:
-          - None, to use the default 3-fold cross-validation,
-          - integer, to specify the number of folds.
-          - An object to be used as a cross-validation generator.
-          - An iterable yielding train/test splits.
+
+        - None, to use the default 3-fold cross-validation,
+        - integer, to specify the number of folds.
+        - An object to be used as a cross-validation generator.
+        - An iterable yielding train/test splits.
 
         For integer/None inputs, if ``y`` is binary or multiclass,
         :class:`StratifiedKFold` used. If the estimator is a classifier
@@ -1716,10 +1720,11 @@ def permutation_test_score(estimator, X, y, cv=None,
     cv : int, cross-validation generator or an iterable, optional
         Determines the cross-validation splitting strategy.
         Possible inputs for cv are:
-          - None, to use the default 3-fold cross-validation,
-          - integer, to specify the number of folds.
-          - An object to be used as a cross-validation generator.
-          - An iterable yielding train/test splits.
+
+        - None, to use the default 3-fold cross-validation,
+        - integer, to specify the number of folds.
+        - An object to be used as a cross-validation generator.
+        - An iterable yielding train/test splits.
 
         For integer/None inputs, if ``y`` is binary or multiclass,
         :class:`StratifiedKFold` used. If the estimator is a classifier