8000 insert versionadded versionchanged directives in docstrings for 0.18 · scikit-learn/scikit-learn@803d68d · GitHub
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insert versionadded versionchanged directives in docstrings for 0.18
indicate where exception classes were moved from
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‎sklearn/datasets/kddcup99.py

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@@ -156,6 +156,8 @@ def fetch_kddcup99(subset=None, shuffle=False, random_state=None,
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.. [2] A Geometric Framework for Unsupervised Anomaly Detection: Detecting
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Intrusions in Unlabeled Data (2002) by Eleazar Eskin, Andrew Arnold,
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Michael Prerau, Leonid Portnoy, Sal Stolfo
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.. versionadded:: 0.18
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"""
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kddcup99 = _fetch_brute_kddcup99(shuffle=shuffle, percent10=percent10,
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download_if_missing=download_if_missing)

‎sklearn/decomposition/kernel_pca.py

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@@ -78,15 +78,21 @@ class KernelPCA(BaseEstimator, TransformerMixin):
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A pseudo random number generator used for the initialization of the
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residuals when eigen_solver == 'arpack'.
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.. versionadded:: 0.18
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n_jobs : int, default=1
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The number of parallel jobs to run.
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If `-1`, then the number of jobs is set to the number of CPU cores.
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.. versionadded:: 0.18
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copy_X : boolean, default=True
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If True, input X is copied and stored by the model in the `X_fit_`
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attribute. If no further changes will be done to X, setting
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`copy_X=False` saves memory by storing a reference.
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.. versionadded:: 0.18
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Attributes
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----------
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lambdas_ : array, (n_components,)

‎sklearn/decomposition/pca.py

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@@ -194,6 +194,8 @@ class PCA(_BasePCA):
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explained_variance_ : array, [n_components]
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The amount of variance explained by each of the selected components.
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.. versionadded:: 0.18
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explained_variance_ratio_ : array, [n_components]
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Percentage of variance explained by each of the selected components.
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‎sklearn/ensemble/forest.py

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@@ -197,6 +197,8 @@ def decision_path(self, X):
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n_nodes_ptr : array of size (n_estimators + 1, )
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The columns from indicator[n_nodes_ptr[i]:n_nodes_ptr[i+1]]
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gives the indicator value for the i-th estimator.
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.. versionadded:: 0.18
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"""
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X = self._validate_X_predict(X)
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indicators = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
@@ -786,6 +788,9 @@ class RandomForestClassifier(ForestClassifier):
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`ceil(min_samples_split * n_samples)` are the minimum
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number of samples for each split.
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.. versionchanged:: 0.18
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Added float values for percentages.
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min_samples_leaf : int, float, optional (default=1)
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The minimum number of samples required to be at a leaf node:
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@@ -794,6 +799,9 @@ class RandomForestClassifier(ForestClassifier):
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`ceil(min_samples_leaf * n_samples)` are the minimum
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number of samples for each node.
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.. versionchanged:: 0.18
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Added float values for percentages.
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min_weight_fraction_leaf : float, optional (default=0.)
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The minimum weighted fraction of the input samples required to be at a
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leaf node.
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`ceil(min_samples_split * n_samples)` are the minimum
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number of samples for each split.
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.. versionchanged:: 0.18
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Added float values for percentages.
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min_samples_leaf : int, float, optional (default=1)
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The minimum number of samples required to be at a leaf node:
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@@ -999,6 +1010,9 @@ class RandomForestRegressor(ForestRegressor):
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`ceil(min_samples_leaf * n_samples)` are the minimum
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number of samples for each node.
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.. versionchanged:: 0.18
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Added float values for percentages.
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min_weight_fraction_leaf : float, optional (default=0.)
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The minimum weighted fraction of the input samples required to be at a
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leaf node.
@@ -1156,6 +1170,9 @@ class ExtraTreesClassifier(ForestClassifier):
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`ceil(min_samples_split * n_samples)` are the minimum
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number of samples for each split.
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.. versionchanged:: 0.18
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Added float values for percentages.
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min_samples_leaf : int, float, 8000 optional (default=1)
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The minimum number of samples required to be at a leaf node:
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@@ -1164,6 +1181,9 @@ class ExtraTreesClassifier(ForestClassifier):
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`ceil(min_samples_leaf * n_samples)` are the minimum
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number of samples for each node.
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.. versionchanged:: 0.18
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Added float values for percentages.
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min_weight_fraction_leaf : float, optional (default=0.)
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The minimum weighted fraction of the input samples required to be at a
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leaf node.
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`ceil(min_samples_split * n_samples)` are the minimum
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number of samples for each split.
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.. versionchanged:: 0.18
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Added float values for percentages.
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min_samples_leaf : int, float, optional (default=1)
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The minimum number of samples required to be at a leaf node:
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@@ -1368,6 +1391,9 @@ class ExtraTreesRegressor(ForestRegressor):
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`ceil(min_samples_leaf * n_samples)` are the minimum
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number of samples for each node.
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.. versionchanged:: 0.18
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Added float values for percentages.
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min_weight_fraction_leaf : float, optional (default=0.)
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The minimum weighted fraction of the input samples required to be at a
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leaf node.
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`ceil(min_samples_split * n_samples)` is the minimum
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number of samples for each split.
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.. versionchanged:: 0.18
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Added float values for percentages.
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min_samples_leaf : int, float, optional (default=1)
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The minimum number of samples required to be at a leaf node:
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`ceil(min_samples_leaf * n_samples)` is the minimum
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number of samples for each node.
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.. versionchanged:: 0.18
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Added float values for percentages.
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min_weight_fraction_leaf : float, optional (default=0.)
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The minimum weighted fraction of the input samples required to be at a
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leaf node.

‎sklearn/ensemble/gradient_boosting.py

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@@ -1315,6 +1315,9 @@ class GradientBoostingClassifier(BaseGradientBoosting, ClassifierMixin):
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`ceil(min_samples_split * n_samples)` are the minimum
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number of samples for each split.
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.. versionchanged:: 0.18
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min_samples_leaf : int, float, optional (default=1)
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The minimum number of samples required to be at a leaf node:
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`ceil(min_samples_leaf * n_samples)` are the minimum
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number of samples for each node.
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.. versionchanged:: 0.18
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Added float values for percentages.
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min_weight_fraction_leaf : float, optional (default=0.)
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The minimum weighted fraction of the input samples required to be at a
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`ceil(min_samples_split * n_samples)` are the minimum
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number of samples for each split.
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.. versionchanged:: 0.18
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Added float values for percentages.
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min_samples_leaf : int, float, optional (default=1)
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The minimum number of samples required to be at a leaf node:
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`ceil(min_samples_leaf * n_samples)` are the minimum
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number of samples for each node.
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.. versionchanged:: 0.18
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Added float values for percentages.
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min_weight_fraction_leaf : float, optional (default=0.)
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The minimum weighted fraction of the input samples required to be at a
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leaf node.

‎sklearn/ensemble/iforest.py

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@@ -105,6 +105,8 @@ class IsolationForest(BaseBagging):
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.. [2] Liu, Fei Tony, Ting, Kai Ming and Zhou, Zhi-Hua. "Isolation-based
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anomaly detection." ACM Transactions on Knowledge Discovery from
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Data (TKDD) 6.1 (2012): 3.
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.. versionadded:: 0.18
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"""
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def __init__(self,

‎sklearn/exceptions.py

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@@ -30,15 +30,26 @@ class NotFittedError(ValueError, AttributeError):
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... print(repr(e))
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... # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS
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NotFittedError('This LinearSVC instance is not fitted yet',)
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.. versionchanged:: 0.18
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Moved from sklearn.utils.validation.
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"""
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class ChangedBehaviorWarning(UserWarning):
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"""Warning class used to notify the user of any change in the behavior."""
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"""Warning class used to notify the user of any change in the behavior.
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.. versionchanged:: 0.18
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Moved from sklearn.base.
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"""
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class ConvergenceWarning(UserWarning):
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"""Custom warning to capture convergence problems"""
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"""Custom warning to capture convergence problems
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.. versionchanged:: 0.18
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Moved from sklearn.utils.
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"""
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class DataConversionWarning(UserWarning):
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- requests a non-copying operation, but a copy is required to meet the
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implementation's data-type expectations;
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- passes an input whose shape can be interpreted ambiguously.
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.. versionchanged:: 0.18
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Moved from sklearn.utils.validation.
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"""
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projection space, is higher than the number of features, which quantifies
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the dimensionality of the original source space, to imply that the
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dimensionality of the problem will not be reduced.
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.. versionchanged:: 0.18
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Moved from sklearn.utils.
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"""
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This warning notifies the user that the efficiency may not be optimal due
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to some reason which may be included as a part of the warning message.
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This may be subclassed into a more specific Warning class.
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.. versionadded:: 0.18
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"""
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FitFailedWarning("Classifier fit failed. The score on this train-test
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partition for these parameters will be set to 0.000000. Details:
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\\nValueError('Penalty term must be positive; got (C=-2)',)",)
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.. versionchanged:: 0.18
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Moved from sklearn.cross_validation.
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"""
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This warning is used to notify the user that BLAS was not used for dot
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operation and hence the efficiency may be affected.
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Moved from sklearn.utils.validation, extends EfficiencyWarning.
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"""
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class UndefinedMetricWarning(UserWarning):
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"""Warning used when the metric is invalid"""
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"""Warning used when the metric is invalid
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"""

‎sklearn/gaussian_process/gpc.py

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log_marginal_likelihood_value_: float
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The log-marginal-likelihood of ``self.kernel_.theta``
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.. versionadded:: 0.18
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"""
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def __init__(self, kernel=None, optimizer="fmin_l_bfgs_b",
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n_restarts_optimizer=0, max_iter_predict=100,
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n_classes_ : int
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The number of classes in the training data
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.. versionadded:: 0.18
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"""
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def __init__(self, kernel=None, optimizer="fmin_l_bfgs_b",
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n_restarts_optimizer=0, max_iter_predict=100,

‎sklearn/gaussian_process/gpr.py

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log_marginal_likelihood_value_: float
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The log-marginal-likelihood of ``self.kernel_.theta``
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.. versionadded:: 0.18
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"""
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def __init__(self, kernel=None, alpha=1e-10,
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optimizer="fmin_l_bfgs_b", n_restarts_optimizer=0,

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