scikit-learn · thomasjpfan · Oct 4, 2019 · Oct 4, 2019 · Oct 4, 2019 · Oct 4, 2019
diff --git a/doc/contents.rst b/doc/contents.rst
@@ -2,6 +2,7 @@
 .. include:: tune_toc.rst
 
 .. Places global toc into the sidebar
+
 :globalsidebartoc: True
 
 =================

diff --git a/doc/developers/index.rst b/doc/developers/index.rst
@@ -1,4 +1,5 @@
 .. Places global toc into the sidebar
+
 :globalsidebartoc: True
 
 .. _developers_guide:

diff --git a/doc/preface.rst b/doc/preface.rst
@@ -2,6 +2,7 @@
    useful for PDF output as this section is not linked from elsewhere.
 
 .. Places global toc into the sidebar
+
 :globalsidebartoc: True
 
 .. _preface_menu:

diff --git a/doc/tutorial/index.rst b/doc/tutorial/index.rst
@@ -1,4 +1,5 @@
 .. Places global toc into the sidebar
+
 :globalsidebartoc: True
 
 .. _tutorial_menu:

diff --git a/doc/user_guide.rst b/doc/user_guide.rst
@@ -1,4 +1,5 @@
 .. Places global toc into the sidebar
+
 :globalsidebartoc: True
 
 .. title:: User guide: contents

diff --git a/examples/neighbors/approximate_nearest_neighbors.py b/examples/neighbors/approximate_nearest_neighbors.py
@@ -18,29 +18,28 @@
 compatibility reasons, one extra neighbor is computed when
 `mode == 'distance'`. Please note that we do the same in the proposed wrappers.
 
-Sample output:
-
-```
-Benchmarking on MNIST_2000:
----------------------------
-AnnoyTransformer:                    0.583 sec
-NMSlibTransformer:                   0.321 sec
-KNeighborsTransformer:               1.225 sec
-TSNE with AnnoyTransformer:          4.903 sec
-TSNE with NMSlibTransformer:         5.009 sec
-TSNE with KNeighborsTransformer:     6.210 sec
-TSNE with internal NearestNeighbors: 6.365 sec
-
-Benchmarking on MNIST_10000:
-----------------------------
-AnnoyTransformer:                    4.457 sec
-NMSlibTransformer:                   2.080 sec
-KNeighborsTransformer:               30.680 sec
-TSNE with AnnoyTransformer:          30.225 sec
-TSNE with NMSlibTransformer:         43.295 sec
-TSNE with KNeighborsTransformer:     64.845 sec
-TSNE with internal NearestNeighbors: 64.984 sec
-```
+Sample output::
+
+    Benchmarking on MNIST_2000:
+    ---------------------------
+    AnnoyTransformer:                    0.583 sec
+    NMSlibTransformer:                   0.321 sec
+    KNeighborsTransformer:               1.225 sec
+    TSNE with AnnoyTransformer:          4.903 sec
+    TSNE with NMSlibTransformer:         5.009 sec
+    TSNE with KNeighborsTransformer:     6.210 sec
+    TSNE with internal NearestNeighbors: 6.365 sec
+
+    Benchmarking on MNIST_10000:
+    ----------------------------
+    AnnoyTransformer:                    4.457 sec
+    NMSlibTransformer:                   2.080 sec
+    KNeighborsTransformer:               30.680 sec
+    TSNE with AnnoyTransformer:          30.225 sec
+    TSNE with NMSlibTransformer:         43.295 sec
+    TSNE with KNeighborsTransformer:     64.845 sec
+    TSNE with internal NearestNeighbors: 64.984 sec
+
 """
 # Author: Tom Dupre la Tour
 #

diff --git a/sklearn/ensemble/_stacking.py b/sklearn/ensemble/_stacking.py
@@ -342,8 +342,8 @@ class StackingClassifier(ClassifierMixin, _BaseStacking):
           `'predict_proba'`, `'decision_function'` or `'predict'` in that
           order.
         * otherwise, one of `'predict_proba'`, `'decision_function'` or
-         `'predict'`. If the method is not implemented by the estimator, it
-         will raise an error.
+          `'predict'`. If the method is not implemented by the estimator, it
+          will raise an error.
 
     n_jobs : int, default=None
         The number of jobs to run in parallel all `estimators` `fit`.

diff --git a/sklearn/ensemble/forest.py b/sklearn/ensemble/forest.py
@@ -989,10 +989,11 @@ class RandomForestClassifier(ForestClassifier):
     max_samples : int or float, default=None
         If bootstrap is True, the number of samples to draw from X
         to train each base estimator.
-            - If None (default), then draw `X.shape[0]` samples.
-            - If int, then draw `max_samples` samples.
-            - If float, then draw `max_samples * X.shape[0]` samples. Thus,
-              `max_samples` should be in the interval `(0, 1)`.
+
+        - If None (default), then draw `X.shape[0]` samples.
+        - If int, then draw `max_samples` samples.
+        - If float, then draw `max_samples * X.shape[0]` samples. Thus,
+          `max_samples` should be in the interval `(0, 1)`.
 
         .. versionadded:: 0.22
 
@@ -1277,10 +1278,11 @@ class RandomForestRegressor(ForestRegressor):
     max_samples : int or float, default=None
         If bootstrap is True, the number of samples to draw from X
         to train each base estimator.
-            - If None (default), then draw `X.shape[0]` samples.
-            - If int, then draw `max_samples` samples.
-            - If float, then draw `max_samples * X.shape[0]` samples. Thus,
-              `max_samples` should be in the interval `(0, 1)`.
+
+        - If None (default), then draw `X.shape[0]` samples.
+        - If int, then draw `max_samples` samples.
+        - If float, then draw `max_samples * X.shape[0]` samples. Thus,
+          `max_samples` should be in the interval `(0, 1)`.
 
         .. versionadded:: 0.22
 
@@ -1576,10 +1578,11 @@ class ExtraTreesClassifier(ForestClassifier):
     max_samples : int or float, default=None
         If bootstrap is True, the number of samples to draw from X
         to train each base estimator.
-            - If None (default), then draw `X.shape[0]` samples.
-            - If int, then draw `max_samples` samples.
-            - If float, then draw `max_samples * X.shape[0]` samples. Thus,
-              `max_samples` should be in the interval `(0, 1)`.
+
+        - If None (default), then draw `X.shape[0]` samples.
+        - If int, then draw `max_samples` samples.
+        - If float, then draw `max_samples * X.shape[0]` samples. Thus,
+          `max_samples` should be in the interval `(0, 1)`.
 
         .. versionadded:: 0.22
 
@@ -1841,10 +1844,11 @@ class ExtraTreesRegressor(ForestRegressor):
     max_samples : int or float, default=None
         If bootstrap is True, the number of samples to draw from X
         to train each base estimator.
-            - If None (default), then draw `X.shape[0]` samples.
-            - If int, then draw `max_samples` samples.
-            - If float, then draw `max_samples * X.shape[0]` samples. Thus,
-              `max_samples` should be in the interval `(0, 1)`.
+
+        - If None (default), then draw `X.shape[0]` samples.
+        - If int, then draw `max_samples` samples.
+        - If float, then draw `max_samples * X.shape[0]` samples. Thus,
+          `max_samples` should be in the interval `(0, 1)`.
 
         .. versionadded:: 0.22
 
@@ -2069,10 +2073,11 @@ class RandomTreesEmbedding(BaseForest):
     max_samples : int or float, default=None
         If bootstrap is True, the number of samples to draw from X
         to train each base estimator.
-            - If None (default), then draw `X.shape[0]` samples.
-            - If int, then draw `max_samples` samples.
-            - If float, then draw `max_samples * X.shape[0]` samples. Thus,
-              `max_samples` should be in the interval `(0, 1)`.
+
+        - If None (default), then draw `X.shape[0]` samples.
+        - If int, then draw `max_samples` samples.
+        - If float, then draw `max_samples * X.shape[0]` samples. Thus,
+          `max_samples` should be in the interval `(0, 1)`.
 
         .. versionadded:: 0.22
 

diff --git a/sklearn/ensemble/partial_dependence.py b/sklearn/ensemble/partial_dependence.py
@@ -211,6 +211,7 @@ def plot_partial_dependence(gbrt, X, features, feature_names=None,
     ----------
     gbrt : BaseGradientBoosting
         A fitted gradient boosting model.
+
     X : array-like of shape (n_samples, n_features)
         The data on which ``gbrt`` was trained.
 

diff --git a/sklearn/inspection/partial_dependence.py b/sklearn/inspection/partial_dependence.py
@@ -683,11 +683,13 @@ class PartialDependenceDisplay:
         Feature names corrsponding to the indicies in ``features``.
 
     target_idx : int
+
         - In a multiclass setting, specifies the class for which the PDPs
           should be computed. Note that for binary classification, the
           positive class (index 1) is always used.
         - In a multioutput setting, specifies the task for which the PDPs
           should be computed.
+
         Ignored in binary classification or classical regression settings.
 
     pdp_lim : dict

diff --git a/sklearn/manifold/isomap.py b/sklearn/manifold/isomap.py
@@ -168,8 +168,8 @@ def _fit_transform(self, X):
         self.embedding_ = self.kernel_pca_.fit_transform(G)
 
     @property
-    @deprecated("Attribute training_data_ was deprecated in version 0.22 and "
-                "will be removed in 0.24.")
+    @deprecated("Attribute `training_data_` was deprecated in version 0.22 and"
+                " will be removed in 0.24.")
     def training_data_(self):
         check_is_fitted(self)
         return self.nbrs_._fit_X

diff --git a/sklearn/utils/__init__.py b/sklearn/utils/__init__.py
@@ -285,10 +285,13 @@ def safe_indexing(X, indices, axis=0):
     X : array-like, sparse-matrix, list, pandas.DataFrame, pandas.Series
         Data from which to sample rows, items or columns. `list` are only
         supported when `axis=0`.
+
     indices : bool, int, str, slice, array-like
+
         - If `axis=0`, boolean and integer array-like, integer slice,
           and scalar integer are supported.
         - If `axis=1`:
+
             - to select a single column, `indices` can be of `int` type for
               all `X` types and `str` only for dataframe. The selected subset
               will be 1D, unless `X` is a sparse matrix in which case it will
@@ -298,6 +301,7 @@ def safe_indexing(X, indices, axis=0):
               these containers can be one of the following: `int`, 'bool' and
               `str`. However, `str` is only supported when `X` is a dataframe.
               The selected subset will be 2D.
+
     axis : int, default=0
         The axis along which `X` will be subsampled. `axis=0` will select
         rows while `axis=1` will select columns.