scikit-learn · welch · Nov 16, 2015 · Nov 19, 2015
diff --git a/sklearn/datasets/kddcup99.py b/sklearn/datasets/kddcup99.py
@@ -152,6 +152,8 @@ def fetch_kddcup99(subset=None, shuffle=False, random_state=None,
     .. [2] A Geometric Framework for Unsupervised Anomaly Detection: Detecting
            Intrusions in Unlabeled Data (2002) by Eleazar Eskin, Andrew Arnold,
            Michael Prerau, Leonid Portnoy, Sal Stolfo
+
+    .. versionadded:: 0.18
     """
     kddcup99 = _fetch_brute_kddcup99(shuffle=shuffle, percent10=percent10)
 
@@ -248,6 +250,7 @@ def _fetch_brute_kddcup99(subset=None, data_home=None,
         dataset.DESCR : string
             Description of the kddcup99 dataset.
 
+    .. versionadded:: 0.18
     """
 
     data_home = get_data_home(data_home=data_home)

diff --git a/sklearn/decomposition/kernel_pca.py b/sklearn/decomposition/kernel_pca.py
@@ -81,6 +81,8 @@ class KernelPCA(BaseEstimator, TransformerMixin):
         A pseudo random number generator used for the initialization of the
         residuals when eigen_solver == 'arpack'.
 
+        .. versionadded:: 0.18
+
     Attributes
     ----------
 

diff --git a/sklearn/decomposition/pca.py b/sklearn/decomposition/pca.py
@@ -150,6 +150,8 @@ class PCA(BaseEstimator, TransformerMixin):
     explained_variance_ : array, [n_components]
         The amount of variance explained by each of the selected components.
 
+        .. versionadded:: 0.18
+
     explained_variance_ratio_ : array, [n_components]
         Percentage of variance explained by each of the selected components.
 

diff --git a/sklearn/ensemble/forest.py b/sklearn/ensemble/forest.py
@@ -202,6 +202,8 @@ def decision_path(self, X):
         n_nodes_ptr : array of size (n_estimators + 1, )
             The columns from indicator[n_nodes_ptr[i]:n_nodes_ptr[i+1]]
             gives the indicator value for the i-th estimator.
+
+        .. versionadded:: 0.18
         """
         X = self._validate_X_predict(X)
         indicators = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
@@ -792,6 +794,9 @@ class RandomForestClassifier(ForestClassifier):
           `ceil(min_samples_split * n_samples)` are the minimum
           number of samples for each split.
 
+        .. versionchanged:: 0.18
+           Added float values for percentages.
+
     min_samples_leaf : int, float, optional (default=1)
         The minimum number of samples required to be at a leaf node:
 
@@ -800,6 +805,9 @@ class RandomForestClassifier(ForestClassifier):
           `ceil(min_samples_leaf * n_samples)` are the minimum
           number of samples for each node.
 
+        .. versionchanged:: 0.18
+           Added float values for percentages.
+
     min_weight_fraction_leaf : float, optional (default=0.)
         The minimum weighted fraction of the input samples required to be at a
         leaf node.
@@ -985,6 +993,9 @@ class RandomForestRegressor(ForestRegressor):
           `ceil(min_samples_split * n_samples)` are the minimum
           number of samples for each split.
 
+        .. versionchanged:: 0.18
+           Added float values for percentages.
+
     min_samples_leaf : int, float, optional (default=1)
         The minimum number of samples required to be at a leaf node:
 
@@ -993,6 +1004,9 @@ class RandomForestRegressor(ForestRegressor):
           `ceil(min_samples_leaf * n_samples)` are the minimum
           number of samples for each node.
 
+        .. versionchanged:: 0.18
+           Added float values for percentages.
+
     min_weight_fraction_leaf : float, optional (default=0.)
         The minimum weighted fraction of the input samples required to be at a
         leaf node.
@@ -1144,6 +1158,9 @@ class ExtraTreesClassifier(ForestClassifier):
           `ceil(min_samples_split * n_samples)` are the minimum
           number of samples for each split.
 
+        .. versionchanged:: 0.18
+           Added float values for percentages.
+
     min_samples_leaf : int, float, optional (default=1)
         The minimum number of samples required to be at a leaf node:
 
@@ -1152,6 +1169,9 @@ class ExtraTreesClassifier(ForestClassifier):
           `ceil(min_samples_leaf * n_samples)` are the minimum
           number of samples for each node.
 
+        .. versionchanged:: 0.18
+           Added float values for percentages.
+
     min_weight_fraction_leaf : float, optional (default=0.)
         The minimum weighted fraction of the input samples required to be at a
         leaf node.
@@ -1337,6 +1357,9 @@ class ExtraTreesRegressor(ForestRegressor):
           `ceil(min_samples_split * n_samples)` are the minimum
           number of samples for each split.
 
+        .. versionchanged:: 0.18
+           Added float values for percentages.
+
     min_samples_leaf : int, float, optional (default=1)
         The minimum number of samples required to be at a leaf node:
 
@@ -1345,6 +1368,9 @@ class ExtraTreesRegressor(ForestRegressor):
           `ceil(min_samples_leaf * n_samples)` are the minimum
           number of samples for each node.
 
+        .. versionchanged:: 0.18
+           Added float values for percentages.
+
     min_weight_fraction_leaf : float, optional (default=0.)
         The minimum weighted fraction of the input samples required to be at a
         leaf node.
@@ -1482,6 +1508,9 @@ class RandomTreesEmbedding(BaseForest):
           `ceil(min_samples_split * n_samples)` is the minimum
           number of samples for each split.
 
+        .. versionchanged:: 0.18
+           Added float values for percentages.
+
     min_samples_leaf : int, float, optional (default=1)
         The minimum number of samples required to be at a leaf node:
 
@@ -1490,6 +1519,9 @@ class RandomTreesEmbedding(BaseForest):
           `ceil(min_samples_leaf * n_samples)` is the minimum
           number of samples for each node.
 
+        .. versionchanged:: 0.18
+           Added float values for percentages.
+
     min_weight_fraction_leaf : float, optional (default=0.)
         The minimum weighted fraction of the input samples required to be at a
         leaf node.

diff --git a/sklearn/ensemble/gradient_boosting.py b/sklearn/ensemble/gradient_boosting.py
@@ -1304,6 +1304,9 @@ class GradientBoostingClassifier(BaseGradientBoosting, ClassifierMixin):
           `ceil(min_samples_split * n_samples)` are the minimum
           number of samples for each split.
 
+        .. versionchanged:: 0.18
+           Added float values for percentages.
+
     min_samples_leaf : int, float, optional (default=1)
         The minimum number of samples required to be at a leaf node:
 
@@ -1312,6 +1315,8 @@ class GradientBoostingClassifier(BaseGradientBoosting, ClassifierMixin):
           `ceil(min_samples_leaf * n_samples)` are the minimum
           number of samples for each node.
 
+        .. versionchanged:: 0.18
+           Added float values for percentages.
 
     min_weight_fraction_leaf : float, optional (default=0.)
         The minimum weighted fraction of the input samples required to be at a
@@ -1651,6 +1656,9 @@ class GradientBoostingRegressor(BaseGradientBoosting, RegressorMixin):
           `ceil(min_samples_split * n_samples)` are the minimum
           number of samples for each split.
 
+        .. versionchanged:: 0.18
+           Added float values for percentages.
+
     min_samples_leaf : int, float, optional (default=1)
         The minimum number of samples required to be at a leaf node:
 
@@ -1659,6 +1667,9 @@ class GradientBoostingRegressor(BaseGradientBoosting, RegressorMixin):
           `ceil(min_samples_leaf * n_samples)` are the minimum
           number of samples for each node.
 
+        .. versionchanged:: 0.18
+           Added float values for percentages.
+
     min_weight_fraction_leaf : float, optional (default=0.)
         The minimum weighted fraction of the input samples required to be at a
         leaf node.

diff --git a/sklearn/ensemble/iforest.py b/sklearn/ensemble/iforest.py
@@ -97,6 +97,8 @@ class IsolationForest(BaseBagging):
     .. [2] Liu, Fei Tony, Ting, Kai Ming and Zhou, Zhi-Hua. "Isolation-based
            anomaly detection." ACM Transactions on Knowledge Discovery from
            Data (TKDD) 6.1 (2012): 3.
+
+    .. versionadded:: 0.18
     """
 
     def __init__(self,

diff --git a/sklearn/exceptions.py b/sklearn/exceptions.py
@@ -30,15 +30,26 @@ class NotFittedError(ValueError, AttributeError):
     ...     print(repr(e))
     ...                        # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS
     NotFittedError('This LinearSVC instance is not fitted yet',)
+
+    .. versionchanged:: 0.18
+       Moved from sklearn.utils.validation.
     """
 
 
 class ChangedBehaviorWarning(UserWarning):
-    """Warning class used to notify the user of any change in the behavior."""
+    """Warning class used to notify the user of any change in the behavior.
+
+    .. versionchanged:: 0.18
+       Moved from sklearn.base.
+    """
 
 
 class ConvergenceWarning(UserWarning):
-    """Custom warning to capture convergence problems"""
+    """Custom warning to capture convergence problems
+
+    .. versionchanged:: 0.18
+       Moved from sklearn.utils.
+    """
 
 
 class DataConversionWarning(UserWarning):
@@ -53,6 +64,9 @@ class DataConversionWarning(UserWarning):
         - requests a non-copying operation, but a copy is required to meet the
           implementation's data-type expectations;
         - passes an input whose shape can be interpreted ambiguously.
+
+    .. versionchanged:: 0.18
+       Moved from sklearn.utils.validation.
     """
 
 
@@ -64,6 +78,9 @@ class DataDimensionalityWarning(UserWarning):
     projection space, is higher than the number of features, which quantifies
     the dimensionality of the original source space, to imply that the
     dimensionality of the problem will not be reduced.
+
+    .. versionchanged:: 0.18
+       Moved from sklearn.utils.
     """
 
 
@@ -73,6 +90,8 @@ class EfficiencyWarning(UserWarning):
     This warning notifies the user that the efficiency may not be optimal due
     to some reason which may be included as a part of the warning message.
     This may be subclassed into a more specific Warning class.
+
+    .. versionadded:: 0.18
     """
 
 
@@ -102,6 +121,9 @@ class FitFailedWarning(RuntimeWarning):
     FitFailedWarning("Classifier fit failed. The score on this train-test
     partition for these parameters will be set to 0.000000. Details:
     \\nValueError('Penalty term must be positive; got (C=-2)',)",)
+
+    .. versionchanged:: 0.18
+       Moved from sklearn.cross_validation.
     """
 
 
@@ -110,8 +132,15 @@ class NonBLASDotWarning(EfficiencyWarning):
 
     This warning is used to notify the user that BLAS was not used for dot
     operation and hence the efficiency may be affected.
+
+    .. versionchanged:: 0.18
+       Moved from sklearn.utils.validation, extends EfficiencyWarning.
     """
 
 
 class UndefinedMetricWarning(UserWarning):
-    """Warning used when the metric is invalid"""
+    """Warning used when the metric is invalid
+
+    .. versionchanged:: 0.18
+       Moved from sklearn.base.
+    """
diff --git a/sklearn/gaussian_process/gpc.py b/sklearn/gaussian_process/gpc.py
@@ -138,6 +138,8 @@ def optimizer(obj_func, initial_theta, bounds):
 
     log_marginal_likelihood_value_: float
         The log-marginal-likelihood of ``self.kernel_.theta``
+
+    .. versionadded:: 0.18
     """
     def __init__(self, kernel=None, optimizer="fmin_l_bfgs_b",
                  n_restarts_optimizer=0, max_iter_predict=100,
@@ -546,6 +548,8 @@ def optimizer(obj_func, initial_theta, bounds):
 
     n_classes_ : int
         The number of classes in the training data
+
+    .. versionadded:: 0.18
     """
     def __init__(self, kernel=None, optimizer="fmin_l_bfgs_b",
                  n_restarts_optimizer=0, max_iter_predict=100,

diff --git a/sklearn/gaussian_process/gpr.py b/sklearn/gaussian_process/gpr.py
@@ -122,6 +122,8 @@ def optimizer(obj_func, initial_theta, bounds):
 
     log_marginal_likelihood_value_: float
         The log-marginal-likelihood of ``self.kernel_.theta``
+
+    .. versionadded:: 0.18
     """
     def __init__(self, kernel=None, alpha=1e-10,
                  optimizer="fmin_l_bfgs_b", n_restarts_optimizer=0,