Revert "Fix sphinx doc warnings"

This reverts commit 80b34af.

python/cuml/cluster/agglomerative.pyx

@@ -106,24 +106,21 @@ class AgglomerativeClustering(Base, ClusterMixin, CMajorInputTagMixin):
         Which linkage criterion to use. The linkage criterion determines
         which distance to use between sets of observations. The algorithm
         will merge the pairs of clusters that minimize this criterion.
-
-         * 'single' uses the minimum of the distances between all
-           observations of the two sets.
-
+        - 'single' uses the minimum of the distances between all
+          observations of the two sets.
     n_neighbors : int (default = 15)
         The number of neighbors to compute when connectivity = "knn"
     connectivity : {"pairwise", "knn"}, (default = "knn")
         The type of connectivity matrix to compute.
-         * 'pairwise' will compute the entire fully-connected graph of
-           pairwise distances between each set of points. This is the
-           fastest to compute and can be very fast for smaller datasets
-           but requires O(n^2) space.
-         * 'knn' will sparsify the fully-connected connectivity matrix to
-           save memory and enable much larger inputs. "n_neighbors" will
-           control the amount of memory used and the graph will be connected
-           automatically in the event "n_neighbors" was not large enough
-           to connect it.
-
+        - 'pairwise' will compute the entire fully-connected graph of
+          pairwise distances between each set of points. This is the
+          fastest to compute and can be very fast for smaller datasets
+          but requires O(n^2) space.
+        - 'knn' will sparsify the fully-connected connectivity matrix to
+          save memory and enable much larger inputs. "n_neighbors" will
+          control the amount of memory used and the graph will be connected
+          automatically in the event "n_neighbors" was not large enough
+          to connect it.
     output_type : {'input', 'cudf', 'cupy', 'numpy', 'numba'}, default=None
         Variable to control output type of the results and attributes of
         the estimator. If None, it'll inherit the output type set at the

python/cuml/cluster/hdbscan.pyx

@@ -291,6 +291,7 @@ class HDBSCAN(Base, ClusterMixin, CMajorInputTagMixin):
 
     alpha : float, optional (default=1.0)
         A distance scaling parameter as used in robust single linkage.
+        See [2]_ for more information.
 
     verbose : int or boolean, default=False
         Sets logging level. It must be one of `cuml.common.logger.level_*`.
@@ -308,7 +309,7 @@ class HDBSCAN(Base, ClusterMixin, CMajorInputTagMixin):
 
     cluster_selection_epsilon : float, optional (default=0.0)
         A distance threshold. Clusters below this value will be merged.
-        Note that this should not be used
+        See [3]_ for more information. Note that this should not be used
         if we want to predict the cluster labels for new points in future
         (e.g. using approximate_predict), as the approximate_predict function
         is not aware of this argument.
@@ -339,7 +340,6 @@ class HDBSCAN(Base, ClusterMixin, CMajorInputTagMixin):
         to find the most persistent clusters. Alternatively you can instead
         select the clusters at the leaves of the tree -- this provides the
         most fine grained and homogeneous clusters. Options are:
-
             * ``eom``
             * ``leaf``
 
@@ -349,17 +349,17 @@ class HDBSCAN(Base, ClusterMixin, CMajorInputTagMixin):
         the case that you feel this is a valid result for your dataset.
 
     gen_min_span_tree : bool, optional (default=False)
-        Whether to populate the `minimum_spanning_tree_` member for
+        Whether to populate the minimum_spanning_tree_ member for
         utilizing plotting tools. This requires the `hdbscan` CPU Python
         package to be installed.
 
     gen_condensed_tree : bool, optional (default=False)
-        Whether to populate the `condensed_tree_` member for
+        Whether to populate the condensed_tree_ member for
         utilizing plotting tools. This requires the `hdbscan` CPU
         Python package to be installed.
 
     gen_single_linkage_tree_ : bool, optinal (default=False)
-        Whether to populate the `single_linkage_tree_` member for
+        Whether to populate the single_linkage_tree_ member for
         utilizing plotting tools. This requires the `hdbscan` CPU
         Python package t be installed.
 

python/cuml/dask/cluster/kmeans.py

@@ -141,14 +141,12 @@ def fit(self, X, sample_weight=None):
         X : Dask cuDF DataFrame or CuPy backed Dask Array
         Training data to cluster.
 
-        sample_weight : Dask cuDF DataFrame or CuPy backed Dask Array \
-                shape = (n_samples,), default=None # noqa
-
+        sample_weight : Dask cuDF DataFrame or CuPy backed Dask Array
+                        shape = (n_samples,), default=None # noqa
             The weights for each observation in X. If None, all observations
             are assigned equal weight.
             Acceptable formats: cuDF DataFrame, NumPy ndarray, Numba device
             ndarray, cuda array interface compliant array like CuPy
-
         """
 
         sample_weight = self._check_normalize_sample_weight(sample_weight)

python/cuml/dask/ensemble/randomforestclassifier.py

@@ -82,7 +82,6 @@ class RandomForestClassifier(BaseRandomForestModel, DelayedPredictionMixin,
          * ``4`` or ``'poisson'`` for poisson half deviance
          * ``5`` or ``'gamma'`` for gamma half deviance
          * ``6`` or ``'inverse_gaussian'`` for inverse gaussian deviance
-
         ``2``, ``'mse'``, ``4``, ``'poisson'``, ``5``, ``'gamma'``, ``6``,
         ``'inverse_gaussian'`` not valid for classification
     bootstrap : boolean (default = True)
@@ -106,7 +105,6 @@ class RandomForestClassifier(BaseRandomForestModel, DelayedPredictionMixin,
          * If ``'sqrt'`` then ``max_features=1/sqrt(n_features)``.
          * If ``'log2'`` then ``max_features=log2(n_features)/n_features``.
          * If ``None``, then ``max_features = 1.0``.
-
     n_bins : int (default = 128)
         Maximum number of bins used by the split algorithm per feature.
     min_samples_leaf : int or float (default = 1)
@@ -116,7 +114,6 @@ class RandomForestClassifier(BaseRandomForestModel, DelayedPredictionMixin,
          * If ``float``, then ``min_samples_leaf`` represents a fraction
            and ``ceil(min_samples_leaf * n_rows)`` is the minimum number of
            samples for each leaf node.
-
     min_samples_split : int or float (default = 2)
         The minimum number of samples required to split an internal
         node.\n
@@ -125,7 +122,6 @@ class RandomForestClassifier(BaseRandomForestModel, DelayedPredictionMixin,
          * If type ``float``, then ``min_samples_split`` represents a fraction
            and ``ceil(min_samples_split * n_rows)`` is the minimum number of
            samples for each split.
-
     n_streams : int (default = 4 )
         Number of parallel streams used for forest building
     workers : optional, list of strings

python/cuml/dask/ensemble/randomforestregressor.py

@@ -75,7 +75,6 @@ class RandomForestRegressor(BaseRandomForestModel, DelayedPredictionMixin,
          * ``4`` or ``'poisson'`` for poisson half deviance
          * ``5`` or ``'gamma'`` for gamma half deviance
          * ``6`` or ``'inverse_gaussian'`` for inverse gaussian deviance
-
         ``0``, ``'gini'``, ``1``, ``'entropy'`` not valid for regression
     bootstrap : boolean (default = True)
         Control bootstrapping.\n

python/cuml/ensemble/randomforestclassifier.pyx

@@ -159,7 +159,6 @@ class RandomForestClassifier(BaseRandomForestModel,
          * ``4`` or ``'poisson'`` for poisson half deviance
          * ``5`` or ``'gamma'`` for gamma half deviance
          * ``6`` or ``'inverse_gaussian'`` for inverse gaussian deviance
-
         only ``0``/``'gini'`` and ``1``/``'entropy'`` valid for classification
     bootstrap : boolean (default = True)
         Control bootstrapping.\n

python/cuml/ensemble/randomforestregressor.pyx

@@ -158,7 +158,6 @@ class RandomForestRegressor(BaseRandomForestModel,
          * ``4`` or ``'poisson'`` for poisson half deviance
          * ``5`` or ``'gamma'`` for gamma half deviance
          * ``6`` or ``'inverse_gaussian'`` for inverse gaussian deviance
-
         ``0``, ``'gini'``, ``1`` and ``'entropy'`` not valid for regression.
     bootstrap : boolean (default = True)
         Control bootstrapping.\n

python/cuml/feature_extraction/_tfidf_vectorizer.py

@@ -260,7 +260,6 @@ def transform(self, raw_documents):
     def get_feature_names(self):
         """
         Array mapping from feature integer indices to feature name.
-
         Returns
         -------
         feature_names : Series

python/cuml/fil/fil.pyx

@@ -578,6 +578,7 @@ class ForestInference(Base,
 
         Parameters
         ----------
+    {}
         preds : gpuarray or cudf.Series, shape = (n_samples,)
            Optional 'out' location to store inference results
 
@@ -606,6 +607,7 @@ class ForestInference(Base,
 
         Parameters
         ----------
+    {}
         preds : gpuarray or cudf.Series, shape = (n_samples,2)
            Binary probability output
            Optional 'out' location to store inference results

python/cuml/metrics/pairwise_distances.pyx

@@ -341,7 +341,6 @@ def sparse_pairwise_distances(X, Y=None, metric="euclidean", handle=None,
         See the documentation for scipy.spatial.distance for details on these
         metrics.
     - ['inner_product', 'hellinger']
-
     Parameters
     ----------
     X : array-like (device or host) of shape (n_samples_x, n_features)

python/cuml/naive_bayes/naive_bayes.py

@@ -1378,7 +1378,6 @@ def _check_X(self, X):
 
     def fit(self, X, y, sample_weight=None) -> "CategoricalNB":
         """Fit Naive Bayes classifier according to X, y
-
         Parameters
         ----------
         X : array-like of shape (n_samples, n_features)
@@ -1394,7 +1393,6 @@ def fit(self, X, y, sample_weight=None) -> "CategoricalNB":
         sample_weight : array-like of shape (n_samples), default=None
             Weights applied to individual samples (1. for unweighted).
             Currently sample weight is ignored.
-
         Returns
         -------
         self : object
@@ -1412,7 +1410,6 @@ def partial_fit(self, X, y, classes=None,
         This method has some performance overhead hence it is better to call
         partial_fit on chunks of data that are as large as possible
         (as long as fitting in the memory budget) to hide the overhead.
-
         Parameters
         ----------
         X : array-like of shape (n_samples, n_features)
@@ -1432,7 +1429,6 @@ def partial_fit(self, X, y, classes=None,
         sample_weight : array-like of shape (n_samples), default=None
             Weights applied to individual samples (1. for unweighted).
             Currently sample weight is ignored.
-
         Returns
         -------
         self : object

python/cuml/preprocessing/TargetEncoder.py

@@ -49,7 +49,7 @@ class TargetEncoder:
         'continuous': consecutive samples are grouped into one folds.
         'interleaved': samples are assign to each fold in a round robin way.
         'customize': customize splitting by providing a `fold_ids` array
-        in `fit()` or `fit_transform()` functions.
+                     in `fit()` or `fit_transform()` functions.
     output_type: {'cupy', 'numpy', 'auto'}, default = 'auto'
         The data type of output. If 'auto', it matches input data.