From 60a1356f3144f807c5258fa82e29eade2c46c8ed Mon Sep 17 00:00:00 2001
From: Andreas Mueller <t3kcit@gmail.com>
Date: Tue, 27 Sep 2016 16:19:47 -0400
Subject: [PATCH] [MRG + 1] More versionadded everywhere! (#7403)
* insert versionadded versionchanged directives in docstrings for 0.18
indicate where exception classes were moved from
* moved versionadded in the proper places
---
sklearn/datasets/kddcup99.py | 3 +
sklearn/decomposition/kernel_pca.py | 6 ++
sklearn/decomposition/pca.py | 2 +
sklearn/ensemble/forest.py | 36 +++++++++++
sklearn/ensemble/gradient_boosting.py | 11 ++++
sklearn/ensemble/iforest.py | 3 +
sklearn/exceptions.py | 35 ++++++++++-
sklearn/gaussian_process/gpc.py | 5 ++
sklearn/gaussian_process/gpr.py | 3 +
sklearn/gaussian_process/kernels.py | 60 +++++++++++++++++--
sklearn/linear_model/base.py | 2 +
sklearn/metrics/classification.py | 2 +
sklearn/metrics/cluster/supervised.py | 2 +
.../neural_network/multilayer_perceptron.py | 8 +++
sklearn/tree/tree.py | 14 +++++
15 files changed, 184 insertions(+), 8 deletions(-)
diff --git a/sklearn/datasets/kddcup99.py b/sklearn/datasets/kddcup99.py
index 06b97da950..824809a80e 100644
--- a/sklearn/datasets/kddcup99.py
+++ b/sklearn/datasets/kddcup99.py
@@ -116,6 +116,8 @@ def fetch_kddcup99(subset=None, shuffle=False, random_state=None,
Targets str, 'normal.' or name of the anomaly type
================ ==========================================
+ .. versionadded:: 0.18
+
Parameters
----------
subset : None, 'SA', 'SF', 'http', 'smtp'
@@ -156,6 +158,7 @@ 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
+
"""
kddcup99 = _fetch_brute_kddcup99(shuffle=shuffle, percent10=percent10,
download_if_missing=download_if_missing)
diff --git a/sklearn/decomposition/kernel_pca.py b/sklearn/decomposition/kernel_pca.py
index 1b31562b46..49845e3268 100644
--- a/sklearn/decomposition/kernel_pca.py
+++ b/sklearn/decomposition/kernel_pca.py
@@ -78,15 +78,21 @@ class KernelPCA(BaseEstimator, TransformerMixin):
A pseudo random number generator used for the initialization of the
residuals when eigen_solver == 'arpack'.
+ .. versionadded:: 0.18
+
n_jobs : int, default=1
The number of parallel jobs to run.
If `-1`, then the number of jobs is set to the number of CPU cores.
+ .. versionadded:: 0.18
+
copy_X : boolean, default=True
If True, input X is copied and stored by the model in the `X_fit_`
attribute. If no further changes will be done to X, setting
`copy_X=False` saves memory by storing a reference.
+ .. versionadded:: 0.18
+
Attributes
----------
lambdas_ : array, (n_components,)
diff --git a/sklearn/decomposition/pca.py b/sklearn/decomposition/pca.py
index 2a6f0dd013..4aae66fa5d 100644
--- a/sklearn/decomposition/pca.py
+++ b/sklearn/decomposition/pca.py
@@ -194,6 +194,8 @@ class PCA(_BasePCA):
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
index 424b8266fe..ed0a5e0afe 100644
--- a/sklearn/ensemble/forest.py
+++ b/sklearn/ensemble/forest.py
@@ -73,6 +73,7 @@ __all__ = ["RandomForestClassifier",
MAX_INT = np.iinfo(np.int32).max
+
def _generate_sample_indices(random_state, n_samples):
"""Private function used to _parallel_build_trees function."""
random_instance = check_random_state(random_state)
@@ -80,6 +81,7 @@ def _generate_sample_indices(random_state, n_samples):
return sample_indices
+
def _generate_unsampled_indices(random_state, n_samples):
"""Private function used to forest._set_oob_score function."""
sample_indices = _generate_sample_indices(random_state, n_samples)
@@ -90,6 +92,7 @@ def _generate_unsampled_indices(random_state, n_samples):
return unsampled_indices
+
def _parallel_build_trees(tree, forest, X, y, sample_weight, tree_idx, n_trees,
verbose=0, class_weight=None):
"""Private function used to fit a single tree in parallel."""
@@ -181,6 +184,8 @@ class BaseForest(six.with_metaclass(ABCMeta, BaseEnsemble,
def decision_path(self, X):
"""Return the decision path in the forest
+ .. versionadded:: 0.18
+
Parameters
----------
X : array-like or sparse matrix, shape = [n_samples, n_features]
@@ -197,6 +202,7 @@ class BaseForest(six.with_metaclass(ABCMeta, BaseEnsemble,
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.
+
"""
X = self._validate_X_predict(X)
indicators = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
@@ -786,6 +792,9 @@ 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:
@@ -794,6 +803,9 @@ 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.
@@ -991,6 +1003,9 @@ 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:
@@ -999,6 +1014,9 @@ 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.
@@ -1156,6 +1174,9 @@ 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:
@@ -1164,6 +1185,9 @@ 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.
@@ -1360,6 +1384,9 @@ 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:
@@ -1368,6 +1395,9 @@ 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.
@@ -1511,6 +1541,9 @@ 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:
@@ -1519,6 +1552,9 @@ 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
index 4ea8ef8e4e..edb72c7f05 100644
--- a/sklearn/ensemble/gradient_boosting.py
+++ b/sklearn/ensemble/gradient_boosting.py
@@ -1315,6 +1315,9 @@ 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:
@@ -1323,6 +1326,8 @@ 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
@@ -1678,6 +1683,9 @@ 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:
@@ -1686,6 +1694,9 @@ 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
index 05ea557b58..7c2b0b66a9 100644
--- a/sklearn/ensemble/iforest.py
+++ b/sklearn/ensemble/iforest.py
@@ -44,6 +44,8 @@ class IsolationForest(BaseBagging):
Read more in the :ref:`User Guide <isolation_forest>`.
+ .. versionadded:: 0.18
+
Parameters
----------
n_estimators : int, optional (default=100)
@@ -105,6 +107,7 @@ 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.
+
"""
def __init__(self,
diff --git a/sklearn/exceptions.py b/sklearn/exceptions.py
index c830ef8116..70dda2a76b 100644
--- 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
index 3f637393d3..f5cec7bd89 100644
--- a/sklearn/gaussian_process/gpc.py
+++ b/sklearn/gaussian_process/gpc.py
@@ -45,6 +45,8 @@ class _BinaryGaussianProcessClassifierLaplace(BaseEstimator):
Currently, the implementation is restricted to using the logistic link
function.
+ .. versionadded:: 0.18
+
Parameters
----------
kernel : kernel object
@@ -138,6 +140,7 @@ class _BinaryGaussianProcessClassifierLaplace(BaseEstimator):
log_marginal_likelihood_value_: float
The log-marginal-likelihood of ``self.kernel_.theta``
+
"""
def __init__(self, kernel=None, optimizer="fmin_l_bfgs_b",
n_restarts_optimizer=0, max_iter_predict=100,
@@ -546,6 +549,8 @@ class GaussianProcessClassifier(BaseEstimator, ClassifierMixin):
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
index 24ff1b058a..4cf4cb07a7 100644
--- a/sklearn/gaussian_process/gpr.py
+++ b/sklearn/gaussian_process/gpr.py
@@ -35,6 +35,8 @@ class GaussianProcessRegressor(BaseEstimator, RegressorMixin):
Read more in the :ref:`User Guide <gaussian_process>`.
+ .. versionadded:: 0.18
+
Parameters
----------
kernel : kernel object
@@ -125,6 +127,7 @@ class GaussianProcessRegressor(BaseEstimator, RegressorMixin):
log_marginal_likelihood_value_: float
The log-marginal-likelihood of ``self.kernel_.theta``
+
"""
def __init__(self, kernel=None, alpha=1e-10,
optimizer="fmin_l_bfgs_b", n_restarts_optimizer=0,
diff --git a/sklearn/gaussian_process/kernels.py b/sklearn/gaussian_process/kernels.py
index 7b5aa005ad..c8466026e7 100644
--- a/sklearn/gaussian_process/kernels.py
+++ b/sklearn/gaussian_process/kernels.py
@@ -49,6 +49,8 @@ class Hyperparameter(namedtuple('Hyperparameter',
'n_elements', 'fixed'))):
"""A kernel hyperparameter's specification in form of a namedtuple.
+ .. versionadded:: 0.18
+
Attributes
----------
name : string
@@ -76,6 +78,7 @@ class Hyperparameter(namedtuple('Hyperparameter',
Whether the value of this hyperparameter is fixed, i.e., cannot be
changed during hyperparameter tuning. If None is passed, the "fixed" is
derived based on the given bounds.
+
"""
# A raw namedtuple is very memory efficient as it packs the attributes
# in a struct to get rid of the __dict__ of attributes in particular it
@@ -114,7 +117,10 @@ class Hyperparameter(namedtuple('Hyperparameter',
class Kernel(six.with_metaclass(ABCMeta)):
- """Base class for all kernels."""
+ """Base class for all kernels.
+
+ .. versionadded:: 0.18
+ """
def get_params(self, deep=True):
"""Get parameters of this kernel.
@@ -349,7 +355,10 @@ class Kernel(six.with_metaclass(ABCMeta)):
class NormalizedKernelMixin(object):
- """Mixin for kernels which are normalized: k(X, X)=1."""
+ """Mixin for kernels which are normalized: k(X, X)=1.
+
+ .. versionadded:: 0.18
+ """
def diag(self, X):
"""Returns the diagonal of the kernel k(X, X).
@@ -372,7 +381,10 @@ class NormalizedKernelMixin(object):
class StationaryKernelMixin(object):
- """Mixin for kernels which are stationary: k(X, Y)= f(X-Y)."""
+ """Mixin for kernels which are stationary: k(X, Y)= f(X-Y).
+
+ .. versionadded:: 0.18
+ """
def is_stationary(self):
"""Returns whether the kernel is stationary. """
@@ -380,7 +392,10 @@ class StationaryKernelMixin(object):
class CompoundKernel(Kernel):
- """Kernel which is composed of a set of other kernels."""
+ """Kernel which is composed of a set of other kernels.
+
+ .. versionadded:: 0.18
+ """
def __init__(self, kernels):
self.kernels = kernels
@@ -513,7 +528,10 @@ class CompoundKernel(Kernel):
class KernelOperator(Kernel):
- """Base class for all kernel operators. """
+ """Base class for all kernel operators.
+
+ .. versionadded:: 0.18
+ """
def __init__(self, k1, k2):
self.k1 = k1
@@ -619,6 +637,8 @@ class Sum(KernelOperator):
The resulting kernel is defined as
k_sum(X, Y) = k1(X, Y) + k2(X, Y)
+ .. versionadded:: 0.18
+
Parameters
----------
k1 : Kernel object
@@ -626,6 +646,7 @@ class Sum(KernelOperator):
k2 : Kernel object
The second base-kernel of the sum-kernel
+
"""
def __call__(self, X, Y=None, eval_gradient=False):
@@ -690,6 +711,8 @@ class Product(KernelOperator):
The resulting kernel is defined as
k_prod(X, Y) = k1(X, Y) * k2(X, Y)
+ .. versionadded:: 0.18
+
Parameters
----------
k1 : Kernel object
@@ -697,6 +720,7 @@ class Product(KernelOperator):
k2 : Kernel object
The second base-kernel of the product-kernel
+
"""
def __call__(self, X, Y=None, eval_gradient=False):
@@ -762,6 +786,8 @@ class Exponentiation(Kernel):
The resulting kernel is defined as
k_exp(X, Y) = k(X, Y) ** exponent
+ .. versionadded:: 0.18
+
Parameters
----------
kernel : Kernel object
@@ -920,6 +946,8 @@ class ConstantKernel(StationaryKernelMixin, Kernel):
k(x_1, x_2) = constant_value for all x_1, x_2
+ .. versionadded:: 0.18
+
Parameters
----------
constant_value : float, default: 1.0
@@ -928,6 +956,7 @@ class ConstantKernel(StationaryKernelMixin, Kernel):
constant_value_bounds : pair of floats >= 0, default: (1e-5, 1e5)
The lower and upper bound on constant_value
+
"""
def __init__(self, constant_value=1.0, constant_value_bounds=(1e-5, 1e5)):
self.constant_value = constant_value
@@ -1012,6 +1041,8 @@ class WhiteKernel(StationaryKernelMixin, Kernel):
k(x_1, x_2) = noise_level if x_1 == x_2 else 0
+ .. versionadded:: 0.18
+
Parameters
----------
noise_level : float, default: 1.0
@@ -1019,6 +1050,7 @@ class WhiteKernel(StationaryKernelMixin, Kernel):
noise_level_bounds : pair of floats >= 0, default: (1e-5, 1e5)
The lower and upper bound on noise_level
+
"""
def __init__(self, noise_level=1.0, noise_level_bounds=(1e-5, 1e5)):
self.noise_level = noise_level
@@ -1111,6 +1143,8 @@ class RBF(StationaryKernelMixin, NormalizedKernelMixin, Kernel):
kernel as covariance function have mean square derivatives of all orders,
and are thus very smooth.
+ .. versionadded:: 0.18
+
Parameters
-----------
length_scale : float or array with shape (n_features,), default: 1.0
@@ -1120,6 +1154,7 @@ class RBF(StationaryKernelMixin, NormalizedKernelMixin, Kernel):
length_scale_bounds : pair of floats >= 0, default: (1e-5, 1e5)
The lower and upper bound on length_scale
+
"""
def __init__(self, length_scale=1.0, length_scale_bounds=(1e-5, 1e5)):
self.length_scale = length_scale
@@ -1221,6 +1256,8 @@ class Matern(RBF):
See Rasmussen and Williams 2006, pp84 for details regarding the
different variants of the Matern kernel.
+ .. versionadded:: 0.18
+
Parameters
-----------
length_scale : float or array with shape (n_features,), default: 1.0
@@ -1242,6 +1279,7 @@ class Matern(RBF):
(appr. 10 times higher) since they require to evaluate the modified
Bessel function. Furthermore, in contrast to l, nu is kept fixed to
its initial value and not optimized.
+
"""
def __init__(self, length_scale=1.0, length_scale_bounds=(1e-5, 1e5),
nu=1.5):
@@ -1365,6 +1403,8 @@ class RationalQuadratic(StationaryKernelMixin, NormalizedKernelMixin, Kernel):
k(x_i, x_j) = (1 + d(x_i, x_j)^2 / (2*alpha * length_scale^2))^-alpha
+ .. versionadded:: 0.18
+
Parameters
----------
length_scale : float > 0, default: 1.0
@@ -1378,6 +1418,7 @@ class RationalQuadratic(StationaryKernelMixin, NormalizedKernelMixin, Kernel):
alpha_bounds : pair of floats >= 0, default: (1e-5, 1e5)
The lower and upper bound on alpha
+
"""
def __init__(self, length_scale=1.0, alpha=1.0,
length_scale_bounds=(1e-5, 1e5), alpha_bounds=(1e-5, 1e5)):
@@ -1473,6 +1514,8 @@ class ExpSineSquared(StationaryKernelMixin, NormalizedKernelMixin, Kernel):
k(x_i, x_j) = exp(-2 sin(\pi / periodicity * d(x_i, x_j)) / length_scale)^2
+ .. versionadded:: 0.18
+
Parameters
----------
length_scale : float > 0, default: 1.0
@@ -1486,6 +1529,7 @@ class ExpSineSquared(StationaryKernelMixin, NormalizedKernelMixin, Kernel):
periodicity_bounds : pair of floats >= 0, default: (1e-5, 1e5)
The lower and upper bound on periodicity
+
"""
def __init__(self, length_scale=1.0, periodicity=1.0,
length_scale_bounds=(1e-5, 1e5),
@@ -1587,6 +1631,8 @@ class DotProduct(Kernel):
The DotProduct kernel is commonly combined with exponentiation.
+ .. versionadded:: 0.18
+
Parameters
----------
sigma_0 : float >= 0, default: 1.0
@@ -1595,6 +1641,7 @@ class DotProduct(Kernel):
sigma_0_bounds : pair of floats >= 0, default: (1e-5, 1e5)
The lower and upper bound on l
+
"""
def __init__(self, sigma_0=1.0, sigma_0_bounds=(1e-5, 1e5)):
@@ -1703,6 +1750,8 @@ class PairwiseKernel(Kernel):
kernel parameters are set directly at initialization and are kept
fixed.
+ .. versionadded:: 0.18
+
Parameters
----------
gamma: float >= 0, default: 1.0
@@ -1724,6 +1773,7 @@ class PairwiseKernel(Kernel):
pairwise_kernels_kwargs : dict, default: None
All entries of this dict (if any) are passed as keyword arguments to
the pairwise kernel function.
+
"""
def __init__(self, gamma=1.0, gamma_bounds=(1e-5, 1e5), metric="linear",
diff --git a/sklearn/linear_model/base.py b/sklearn/linear_model/base.py
index f713593741..004aeac140 100644
--- a/sklearn/linear_model/base.py
+++ b/sklearn/linear_model/base.py
@@ -459,6 +459,8 @@ class LinearRegression(LinearModel, RegressorMixin):
array. If the target vector passed during the fit is 1-dimensional,
this is a (1,) shape array.
+ .. versionadded:: 0.18
+
intercept_ : array
Independent term in the linear model.
diff --git a/sklearn/metrics/classification.py b/sklearn/metrics/classification.py
index 7821060a95..60cdacea97 100644
--- a/sklearn/metrics/classification.py
+++ b/sklearn/metrics/classification.py
@@ -1459,6 +1459,8 @@ def hamming_loss(y_true, y_pred, labels=None, sample_weight=None,
sample_weight : array-like of shape = [n_samples], optional
Sample weights.
+ .. versionadded:: 0.18
+
classes : array, shape = [n_labels], optional
(deprecated) Integer array of labels. This parameter has been
renamed to ``labels`` in version 0.18 and will be removed in 0.20.
diff --git a/sklearn/metrics/cluster/supervised.py b/sklearn/metrics/cluster/supervised.py
index 3fc3373558..010f8bb928 100644
--- a/sklearn/metrics/cluster/supervised.py
+++ b/sklearn/metrics/cluster/supervised.py
@@ -73,6 +73,8 @@ def contingency_matrix(labels_true, labels_pred, eps=None, sparse=False):
.. versionadded:: 0.18
+ .. versionadded:: 0.18
+
Returns
-------
contingency : {array-like, sparse}, shape=[n_classes_true, n_classes_pred]
diff --git a/sklearn/neural_network/multilayer_perceptron.py b/sklearn/neural_network/multilayer_perceptron.py
index 9f7117eb1b..9f1d1320d1 100644
--- a/sklearn/neural_network/multilayer_perceptron.py
+++ b/sklearn/neural_network/multilayer_perceptron.py
@@ -41,6 +41,8 @@ class BaseMultilayerPerceptron(six.with_metaclass(ABCMeta, BaseEstimator)):
Warning: This class should not be used directly.
Use derived classes instead.
+
+ .. versionadded:: 0.18
"""
@abstractmethod
@@ -683,6 +685,8 @@ class MLPClassifier(BaseMultilayerPerceptron, ClassifierMixin):
This model optimizes the log-loss function using LBFGS or stochastic
gradient descent.
+ .. versionadded:: 0.18
+
Parameters
----------
hidden_layer_sizes : tuple, length = n_layers - 2, default (100,)
@@ -868,6 +872,7 @@ class MLPClassifier(BaseMultilayerPerceptron, ClassifierMixin):
Kingma, Diederik, and Jimmy Ba. "Adam: A method for stochastic
optimization." arXiv preprint arXiv:1412.6980 (2014).
+
"""
def __init__(self, hidden_layer_sizes=(100,), activation="relu",
solver='adam', alpha=0.0001,
@@ -1025,6 +1030,8 @@ class MLPRegressor(BaseMultilayerPerceptron, RegressorMixin):
This model optimizes the squared-loss using LBFGS or stochastic gradient
descent.
+ .. versionadded:: 0.18
+
Parameters
----------
hidden_layer_sizes : tuple, length = n_layers - 2, default (100,)
@@ -1207,6 +1214,7 @@ class MLPRegressor(BaseMultilayerPerceptron, RegressorMixin):
Kingma, Diederik, and Jimmy Ba. "Adam: A method for stochastic
optimization." arXiv preprint arXiv:1412.6980 (2014).
+
"""
def __init__(self, hidden_layer_sizes=(100,), activation="relu",
solver='adam', alpha=0.0001,
diff --git a/sklearn/tree/tree.py b/sklearn/tree/tree.py
index 1ec6789317..76458c72b3 100644
--- a/sklearn/tree/tree.py
+++ b/sklearn/tree/tree.py
@@ -486,6 +486,8 @@ class BaseDecisionTree(six.with_metaclass(ABCMeta, BaseEstimator,
def decision_path(self, X, check_input=True):
"""Return the decision path in the tree
+ .. versionadded:: 0.18
+
Parameters
----------
X : array_like or sparse matrix, shape = [n_samples, n_features]
@@ -575,6 +577,9 @@ class DecisionTreeClassifier(BaseDecisionTree, 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:
@@ -583,6 +588,9 @@ class DecisionTreeClassifier(BaseDecisionTree, 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
leaf node.
@@ -839,6 +847,9 @@ class DecisionTreeRegressor(BaseDecisionTree, 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:
@@ -847,6 +858,9 @@ class DecisionTreeRegressor(BaseDecisionTree, 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.
--
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