Upgrading scikit-learn 0.15 to 0.16
Unknown0.15.2 → 0.16.1
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API changes
Generated by DepDelta from published Python source. Not mixed with prose notes.
- added:
class CalibratedClassifierCV(BaseEstimator, ClassifierMixin) - added:
constructor CalibratedClassifierCV(base_estimator=…, method=…, cv=…) - added:
function calibration_curve(y_true, y_prob, normalize=…, n_bins=…) - added:
method CalibratedClassifierCV.fit(X, y, sample_weight=…) - added:
method CalibratedClassifierCV.predict(X) - added:
method CalibratedClassifierCV.predict_proba(X)
- added:
export Birch
- changed:
function affinity_propagation(S, preference=…, convergence_iter=…, max_iter=…, damping=…, copy=…, verbose=…) → function affinity_propagation(S, preference=…, convergence_iter=…, max_iter=…, damping=…, copy=…, verbose=…, return_n_iter=…) - changed:
method AffinityPropagation.fit(X) → method AffinityPropagation.fit(X, y=…)
- removed:
export SpectralBiclustering - removed:
export SpectralCoclustering - added:
class SpectralBiclustering(BaseSpectral) - added:
class SpectralCoclustering(BaseSpectral) - added:
constructor SpectralBiclustering(n_clusters=…, method=…, n_components=…, n_best=…, svd_method=…, n_svd_vecs=…, mini_batch=…, init=…, n_init=…, n_jobs=…, random_state=…) - added:
constructor SpectralCoclustering(n_clusters=…, svd_method=…, n_svd_vecs=…, mini_batch=…, init=…, n_init=…, n_jobs=…, random_state=…)
- removed:
class BaseSpectral(six.with_metaclass(ABCMeta, BaseEstimator, BiclusterMixin)) - removed:
class SpectralBiclustering(BaseSpectral) - removed:
class SpectralCoclustering(BaseSpectral) - removed:
constructor BaseSpectral(n_clusters=…, svd_method=…, n_svd_vecs=…, mini_batch=…, init=…, n_init=…, n_jobs=…, random_state=…) - removed:
constructor SpectralBiclustering(n_clusters=…, method=…, n_components=…, n_best=…, svd_method=…, n_svd_vecs=…, mini_batch=…, init=…, n_init=…, n_jobs=…, random_state=…) - removed:
constructor SpectralCoclustering(n_clusters=…, svd_method=…, n_svd_vecs=…, mini_batch=…, init=…, n_init=…, n_jobs=…, random_state=…) - removed:
method BaseSpectral.fit(X)
- removed:
function check_array_ndim(X) - removed:
function get_indicators(rows, columns, shape) - removed:
function get_indices(rows, columns) - removed:
function get_shape(rows, columns) - removed:
function get_submatrix(rows, columns, data)
- added:
class Birch(BaseEstimator, TransformerMixin, ClusterMixin) - added:
constructor Birch(threshold=…, branching_factor=…, n_clusters=…, compute_labels=…, copy=…) - added:
method Birch.fit(X, y=…) - added:
method Birch.partial_fit(X=…, y=…) - added:
method Birch.predict(X) - added:
method Birch.transform(X, y=…)
- changed:
function dbscan(X, eps=…, min_samples=…, metric=…, algorithm=…, leaf_size=…, p=…, random_state=…) → function dbscan(X, eps=…, min_samples=…, metric=…, algorithm=…, leaf_size=…, p=…, sample_weight=…, random_state=…) - changed:
method DBSCAN.fit(X) → method DBSCAN.fit(X, y=…, sample_weight=…) - added:
method DBSCAN.fit_predict(X, y=…, sample_weight=…)
- changed:
constructor Ward(n_clusters=…, memory=…, connectivity=…, copy=…, n_components=…, compute_full_tree=…, pooling_func=…) → constructor Ward(n_clusters=…, memory=…, connectivity=…, n_components=…, compute_full_tree=…, pooling_func=…) - changed:
function linkage_tree(X, connectivity=…, n_components=…, n_clusters=…, linkage=…, affinity=…) → function linkage_tree(X, connectivity=…, n_components=…, n_clusters=…, linkage=…, affinity=…, return_distance=…) - changed:
function ward_tree(X, connectivity=…, n_components=…, copy=…, n_clusters=…) → function ward_tree(X, connectivity=…, n_components=…, n_clusters=…, return_distance=…) - changed:
method AgglomerativeClustering.fit(X) → method AgglomerativeClustering.fit(X, y=…) - added:
property FeatureAgglomeration.fit_predict - added:
property WardAgglomeration.fit_predict
- changed:
function k_means(X, n_clusters, init=…, precompute_distances=…, n_init=…, max_iter=…, verbose=…, tol=…, random_state=…, copy_x=…, n_jobs=…) → function k_means(X, n_clusters, init=…, precompute_distances=…, n_init=…, max_iter=…, verbose=…, tol=…, random_state=…, copy_x=…, n_jobs=…, return_n_iter=…) - changed:
method KMeans.fit_predict(X) → method KMeans.fit_predict(X, y=…) - changed:
method KMeans.score(X) → method KMeans.score(X, y=…)
- changed:
function mean_shift(X, bandwidth=…, seeds=…, bin_seeding=…, min_bin_freq=…, cluster_all=…, max_iterations=…) → function mean_shift(X, bandwidth=…, seeds=…, bin_seeding=…, min_bin_freq=…, cluster_all=…, max_iter=…, max_iterations=…) - changed:
method MeanShift.fit(X) → method MeanShift.fit(X, y=…)
- changed:
method SpectralClustering.fit(X) → method SpectralClustering.fit(X, y=…)
- changed:
function graph_lasso(emp_cov, alpha, cov_init=…, mode=…, tol=…, max_iter=…, verbose=…, return_costs=…, eps=…) → function graph_lasso(emp_cov, alpha, cov_init=…, mode=…, tol=…, max_iter=…, verbose=…, return_costs=…, eps=…, return_n_iter=…)
- added:
property OutlierDetectionMixin.threshold
- added:
property PLSRegression.coefs
- changed:
constructor Bootstrap(n, n_iter=…, train_size=…, test_size=…, random_state=…, n_bootstraps=…) → constructor Bootstrap(n, n_iter=…, train_size=…, test_size=…, random_state=…) - changed:
function cross_val_score(estimator, X, y=…, scoring=…, cv=…, n_jobs=…, verbose=…, fit_params=…, score_func=…, pre_dispatch=…) → function cross_val_score(estimator, X, y=…, scoring=…, cv=…, n_jobs=…, verbose=…, fit_params=…, pre_dispatch=…) - changed:
function permutation_test_score(estimator, X, y, score_func=…, cv=…, n_permutations=…, n_jobs=…, labels=…, random_state=…, verbose=…, scoring=…) → function permutation_test_score(estimator, X, y, cv=…, n_permutations=…, n_jobs=…, labels=…, random_state=…, verbose=…, scoring=…) - added:
class PredefinedSplit(_PartitionIterator) - added:
constructor PredefinedSplit(test_fold, indices=…) - added:
function cross_val_predict(estimator, X, y=…, cv=…, n_jobs=…, verbose=…, fit_params=…, pre_dispatch=…)
- changed:
function load_files(container_path, description=…, categories=…, load_content=…, shuffle=…, encoding=…, charset=…, charset_error=…, decode_error=…, random_state=…) → function load_files(container_path, description=…, categories=…, load_content=…, shuffle=…, encoding=…, decode_error=…, random_state=…)
- changed:
function make_multilabel_classification(n_samples=…, n_features=…, n_classes=…, n_labels=…, length=…, allow_unlabeled=…, return_indicator=…, random_state=…) → function make_multilabel_classification(n_samples=…, n_features=…, n_classes=…, n_labels=…, length=…, allow_unlabeled=…, sparse=…, return_indicator=…, return_distributions=…, random_state=…)
- removed:
export ProbabilisticPCA - added:
export IncrementalPCA
- changed:
function dict_learning(X, n_components, alpha, max_iter=…, tol=…, method=…, n_jobs=…, dict_init=…, code_init=…, callback=…, verbose=…, random_state=…) → function dict_learning(X, n_components, alpha, max_iter=…, tol=…, method=…, n_jobs=…, dict_init=…, code_init=…, callback=…, verbose=…, random_state=…, return_n_iter=…) - changed:
function dict_learning_online(X, n_components=…, alpha=…, n_iter=…, return_code=…, dict_init=…, callback=…, batch_size=…, verbose=…, shuffle=…, n_jobs=…, method=…, iter_offset=…, random_state=…, return_inner_stats=…, inner_stats=…) → function dict_learning_online(X, n_components=…, alpha=…, n_iter=…, return_code=…, dict_init=…, callback=…, batch_size=…, verbose=…, shuffle=…, n_jobs=…, method=…, iter_offset=…, random_state=…, return_inner_stats=…, inner_stats=…, return_n_iter=…)
- changed:
constructor FactorAnalysis(n_components=…, tol=…, copy=…, max_iter=…, verbose=…, noise_variance_init=…, svd_method=…, iterated_power=…, random_state=…) → constructor FactorAnalysis(n_components=…, tol=…, copy=…, max_iter=…, noise_variance_init=…, svd_method=…, iterated_power=…, random_state=…)
- removed:
method FastICA.get_mixing_matrix() - removed:
property FastICA.sources_ - changed:
function fastica(X, n_components=…, algorithm=…, whiten=…, fun=…, fun_args=…, max_iter=…, tol=…, w_init=…, random_state=…, return_X_mean=…, compute_sources=…) → function fastica(X, n_components=…, algorithm=…, whiten=…, fun=…, fun_args=…, max_iter=…, tol=…, w_init=…, random_state=…, return_X_mean=…, compute_sources=…, return_n_iter=…)
- added:
class IncrementalPCA(_BasePCA) - added:
constructor IncrementalPCA(n_components=…, whiten=…, copy=…, batch_size=…) - added:
method IncrementalPCA.fit(X, y=…) - added:
method IncrementalPCA.partial_fit(X, y=…)
- removed:
class ProbabilisticPCA(PCA) - removed:
method ProbabilisticPCA.fit(X, y=…, homoscedastic=…) - removed:
method ProbabilisticPCA.score(X, y=…)
- removed:
property TruncatedSVD.n_iterations - changed:
constructor TruncatedSVD(n_components=…, algorithm=…, n_iter=…, random_state=…, tol=…, n_iterations=…) → constructor TruncatedSVD(n_components=…, algorithm=…, n_iter=…, random_state=…, tol=…)
- changed:
constructor DummyRegressor(strategy=…, constant=…) → constructor DummyRegressor(strategy=…, constant=…, quantile=…) - changed:
method DummyClassifier.fit(X, y) → method DummyClassifier.fit(X, y, sample_weight=…) - changed:
method DummyRegressor.fit(X, y) → method DummyRegressor.fit(X, y, sample_weight=…)
- changed:
constructor ExtraTreesClassifier(n_estimators=…, criterion=…, max_depth=…, min_samples_split=…, min_samples_leaf=…, max_features=…, max_leaf_nodes=…, bootstrap=…, oob_score=…, n_jobs=…, random_state=…, verbose=…, min_density=…, compute_importances=…) → constructor ExtraTreesClassifier(n_estimators=…, criterion=…, max_depth=…, min_samples_split=…, min_samples_leaf=…, min_weight_fraction_leaf=…, max_features=…, max_leaf_nodes=…, bootstrap=…, oob_score=…, n_jobs=…, random_state=…, verbose=…, warm_start=…, class_weight=…) - changed:
constructor ExtraTreesRegressor(n_estimators=…, criterion=…, max_depth=…, min_samples_split=…, min_samples_leaf=…, max_features=…, max_leaf_nodes=…, bootstrap=…, oob_score=…, n_jobs=…, random_state=…, verbose=…, min_density=…, compute_importances=…) → constructor ExtraTreesRegressor(n_estimators=…, criterion=…, max_depth=…, min_samples_split=…, min_samples_leaf=…, min_weight_fraction_leaf=…, max_features=…, max_leaf_nodes=…, bootstrap=…, oob_score=…, n_jobs=…, random_state=…, verbose=…, warm_start=…) - changed:
constructor RandomForestClassifier(n_estimators=…, criterion=…, max_depth=…, min_samples_split=…, min_samples_leaf=…, max_features=…, max_leaf_nodes=…, bootstrap=…, oob_score=…, n_jobs=…, random_state=…, verbose=…, min_density=…, compute_importances=…) → constructor RandomForestClassifier(n_estimators=…, criterion=…, max_depth=…, min_samples_split=…, min_samples_leaf=…, min_weight_fraction_leaf=…, max_features=…, max_leaf_nodes=…, bootstrap=…, oob_score=…, n_jobs=…, random_state=…, verbose=…, warm_start=…, class_weight=…) - changed:
constructor RandomForestRegressor(n_estimators=…, criterion=…, max_depth=…, min_samples_split=…, min_samples_leaf=…, max_features=…, max_leaf_nodes=…, bootstrap=…, oob_score=…, n_jobs=…, random_state=…, verbose=…, min_density=…, compute_importances=…) → constructor RandomForestRegressor(n_estimators=…, criterion=…, max_depth=…, min_samples_split=…, min_samples_leaf=…, min_weight_fraction_leaf=…, max_features=…, max_leaf_nodes=…, bootstrap=…, oob_score=…, n_jobs=…, random_state=…, verbose=…, warm_start=…) - added:
class RandomTreesEmbedding(BaseForest) - added:
constructor RandomTreesEmbedding(n_estimators=…, max_depth=…, min_samples_split=…, min_samples_leaf=…, min_weight_fraction_leaf=…, max_leaf_nodes=…, sparse_output=…, n_jobs=…, random_state=…, verbose=…, warm_start=…) - added:
method RandomTreesEmbedding.fit(X, y=…, sample_weight=…) - added:
method RandomTreesEmbedding.fit_transform(X, y=…, sample_weight=…) - added:
method RandomTreesEmbedding.transform(X)
- removed:
method GradientBoostingClassifier.fit(X, y, monitor=…) - removed:
method GradientBoostingRegressor.fit(X, y, monitor=…) - removed:
property BaseGradientBoosting.oob_score_ - changed:
class BinomialDeviance(LossFunction) → class BinomialDeviance(ClassificationLossFunction) - changed:
class MultinomialDeviance(LossFunction) → class MultinomialDeviance(ClassificationLossFunction) - changed:
constructor BaseGradientBoosting(loss, learning_rate, n_estimators, min_samples_split, min_samples_leaf, max_depth, init, subsample, max_features, random_state, alpha=…, verbose=…, max_leaf_nodes=…, warm_start=…) → constructor BaseGradientBoosting(loss, learning_rate, n_estimators, min_samples_split, min_samples_leaf, min_weight_fraction_leaf, max_depth, init, subsample, max_features, random_state, alpha=…, verbose=…, max_leaf_nodes=…, warm_start=…) - changed:
constructor GradientBoostingClassifier(loss=…, learning_rate=…, n_estimators=…, subsample=…, min_samples_split=…, min_samples_leaf=…, max_depth=…, init=…, random_state=…, max_features=…, verbose=…, max_leaf_nodes=…, warm_start=…) → constructor GradientBoostingClassifier(loss=…, learning_rate=…, n_estimators=…, subsample=…, min_samples_split=…, min_samples_leaf=…, min_weight_fraction_leaf=…, max_depth=…, init=…, random_state=…, max_features=…, verbose=…, max_leaf_nodes=…, warm_start=…) - changed:
constructor GradientBoostingRegressor(loss=…, learning_rate=…, n_estimators=…, subsample=…, min_samples_split=…, min_samples_leaf=…, max_depth=…, init=…, random_state=…, max_features=…, alpha=…, verbose=…, max_leaf_nodes=…, warm_start=…) → constructor GradientBoostingRegressor(loss=…, learning_rate=…, n_estimators=…, subsample=…, min_samples_split=…, min_samples_leaf=…, min_weight_fraction_leaf=…, max_depth=…, init=…, random_state=…, max_features=…, alpha=…, verbose=…, max_leaf_nodes=…, warm_start=…) - changed:
method BaseGradientBoosting.fit(X, y, monitor=…) → method BaseGradientBoosting.fit(X, y, sample_weight=…, monitor=…) - changed:
method HuberLossFunction.negative_gradient(y, pred, **kargs) → method HuberLossFunction.negative_gradient(y, pred, sample_weight=…, **kargs) - changed:
method LeastSquaresError.update_terminal_regions(tree, X, y, residual, y_pred, sample_mask, learning_rate=…, k=…) → method LeastSquaresError.update_terminal_regions(tree, X, y, residual, y_pred, sample_weight, sample_mask, learning_rate=…, k=…) - changed:
method LogOddsEstimator.fit(X, y) → method LogOddsEstimator.fit(X, y, sample_weight=…) - changed:
method LossFunction.init_estimator(X, y) → method LossFunction.init_estimator() - changed:
method LossFunction.update_terminal_regions(tree, X, y, residual, y_pred, sample_mask, learning_rate=…, k=…) → method LossFunction.update_terminal_regions(tree, X, y, residual, y_pred, sample_weight, sample_mask, learning_rate=…, k=…) - changed:
method MeanEstimator.fit(X, y) → method MeanEstimator.fit(X, y, sample_weight=…) - changed:
method MultinomialDeviance.negative_gradient(y, pred, k=…) → method MultinomialDeviance.negative_gradient(y, pred, k=…, **kwargs) - changed:
method PriorProbabilityEstimator.fit(X, y) → method PriorProbabilityEstimator.fit(X, y, sample_weight=…) - changed:
method QuantileEstimator.fit(X, y) → method QuantileEstimator.fit(X, y, sample_weight=…) - changed:
method ZeroEstimator.fit(X, y) → method ZeroEstimator.fit(X, y, sample_weight=…) - added:
class ClassificationLossFunction(six.with_metaclass(ABCMeta, LossFunction)) - added:
class ExponentialLoss(ClassificationLossFunction) - added:
class ScaledLogOddsEstimator(LogOddsEstimator) - added:
constructor ExponentialLoss(n_classes) - added:
field LogOddsEstimator.scale - added:
field ScaledLogOddsEstimator.scale - added:
method ExponentialLoss.init_estimator() - added:
method ExponentialLoss.negative_gradient(y, pred, **kargs) - added:
method GradientBoostingClassifier.predict_log_proba(X)
- changed:
class CustomizablePickler(_Pickler) → class CustomizablePickler(Pickler)
- changed:
constructor DictVectorizer(dtype=…, separator=…, sparse=…) → constructor DictVectorizer(dtype=…, separator=…, sparse=…, sort=…)
- changed:
constructor CountVectorizer(input=…, encoding=…, charset=…, decode_error=…, charset_error=…, strip_accents=…, lowercase=…, preprocessor=…, tokenizer=…, stop_words=…, token_pattern=…, ngram_range=…, analyzer=…, max_df=…, min_df=…, max_features=…, vocabulary=…, binary=…, dtype=…) → constructor CountVectorizer(input=…, encoding=…, decode_error=…, strip_accents=…, lowercase=…, preprocessor=…, tokenizer=…, stop_words=…, token_pattern=…, ngram_range=…, analyzer=…, max_df=…, min_df=…, max_features=…, vocabulary=…, binary=…, dtype=…) - changed:
constructor TfidfVectorizer(input=…, encoding=…, charset=…, decode_error=…, charset_error=…, strip_accents=…, lowercase=…, preprocessor=…, tokenizer=…, analyzer=…, stop_words=…, token_pattern=…, ngram_range=…, max_df=…, min_df=…, max_features=…, vocabulary=…, binary=…, dtype=…, norm=…, use_idf=…, smooth_idf=…, sublinear_tf=…) → constructor TfidfVectorizer(input=…, encoding=…, decode_error=…, strip_accents=…, lowercase=…, preprocessor=…, tokenizer=…, analyzer=…, stop_words=…, token_pattern=…, ngram_range=…, max_df=…, min_df=…, max_features=…, vocabulary=…, binary=…, dtype=…, norm=…, use_idf=…, smooth_idf=…, sublinear_tf=…)
- changed:
constructor RFECV(estimator, step=…, cv=…, scoring=…, loss_func=…, estimator_params=…, verbose=…) → constructor RFECV(estimator, step=…, cv=…, scoring=…, estimator_params=…, verbose=…) - added:
method RFE.predict_log_proba(X)
- removed:
export SelectorMixin
- changed:
constructor GridSearchCV(estimator, param_grid, scoring=…, loss_func=…, score_func=…, fit_params=…, n_jobs=…, iid=…, refit=…, cv=…, verbose=…, pre_dispatch=…) → constructor GridSearchCV(estimator, param_grid, scoring=…, loss_func=…, score_func=…, fit_params=…, n_jobs=…, iid=…, refit=…, cv=…, verbose=…, pre_dispatch=…, error_score=…) - changed:
constructor RandomizedSearchCV(estimator, param_distributions, n_iter=…, scoring=…, fit_params=…, n_jobs=…, iid=…, refit=…, cv=…, verbose=…, pre_dispatch=…, random_state=…) → constructor RandomizedSearchCV(estimator, param_distributions, n_iter=…, scoring=…, fit_params=…, n_jobs=…, iid=…, refit=…, cv=…, verbose=…, pre_dispatch=…, random_state=…, error_score=…) - changed:
function fit_grid_point(X, y, estimator, parameters, train, test, scorer, verbose, **fit_params) → function fit_grid_point(X, y, estimator, parameters, train, test, scorer, verbose, error_score=…, **fit_params)
- removed:
method IsotonicRegression.fit_transform(X, y, sample_weight=…, weight=…) - changed:
function isotonic_regression(y, sample_weight=…, y_min=…, y_max=…, weight=…, increasing=…) → function isotonic_regression(y, sample_weight=…, y_min=…, y_max=…, increasing=…) - changed:
method IsotonicRegression.fit(X, y, sample_weight=…, weight=…) → method IsotonicRegression.fit(X, y, sample_weight=…)
- added:
class KernelRidge(BaseEstimator, RegressorMixin) - added:
constructor KernelRidge(alpha=…, kernel=…, gamma=…, degree=…, coef0=…, kernel_params=…) - added:
method KernelRidge.fit(X, y=…, sample_weight=…) - added:
method KernelRidge.predict(X)
- removed:
method LDA.decision_function(X) - removed:
method LDA.predict(X) - changed:
class LDA(BaseEstimator, ClassifierMixin, TransformerMixin) → class LDA(BaseEstimator, LinearClassifierMixin, TransformerMixin) - changed:
constructor LDA(n_components=…, priors=…) → constructor LDA(solver=…, shrinkage=…, priors=…, n_components=…, store_covariance=…, tol=…)
- added:
export LogisticRegressionCV - added:
export TheilSenRegressor - added:
export logistic_regression_path
- changed:
constructor LinearRegression(fit_intercept=…, normalize=…, copy_X=…) → constructor LinearRegression(fit_intercept=…, normalize=…, copy_X=…, n_jobs=…)
- changed:
constructor ElasticNet(alpha=…, l1_ratio=…, fit_intercept=…, normalize=…, precompute=…, max_iter=…, copy_X=…, tol=…, warm_start=…, positive=…) → constructor ElasticNet(alpha=…, l1_ratio=…, fit_intercept=…, normalize=…, precompute=…, max_iter=…, copy_X=…, tol=…, warm_start=…, positive=…, random_state=…, selection=…) - changed:
constructor ElasticNetCV(l1_ratio=…, eps=…, n_alphas=…, alphas=…, fit_intercept=…, normalize=…, precompute=…, max_iter=…, tol=…, cv=…, copy_X=…, verbose=…, n_jobs=…, positive=…) → constructor ElasticNetCV(l1_ratio=…, eps=…, n_alphas=…, alphas=…, fit_intercept=…, normalize=…, precompute=…, max_iter=…, tol=…, cv=…, copy_X=…, verbose=…, n_jobs=…, positive=…, random_state=…, selection=…) - changed:
constructor Lasso(alpha=…, fit_intercept=…, normalize=…, precompute=…, copy_X=…, max_iter=…, tol=…, warm_start=…, positive=…) → constructor Lasso(alpha=…, fit_intercept=…, normalize=…, precompute=…, copy_X=…, max_iter=…, tol=…, warm_start=…, positive=…, random_state=…, selection=…) - changed:
constructor LassoCV(eps=…, n_alphas=…, alphas=…, fit_intercept=…, normalize=…, precompute=…, max_iter=…, tol=…, copy_X=…, cv=…, verbose=…, n_jobs=…, positive=…) → constructor LassoCV(eps=…, n_alphas=…, alphas=…, fit_intercept=…, normalize=…, precompute=…, max_iter=…, tol=…, copy_X=…, cv=…, verbose=…, n_jobs=…, positive=…, random_state=…, selection=…) - changed:
constructor LinearModelCV(eps=…, n_alphas=…, alphas=…, fit_intercept=…, normalize=…, precompute=…, max_iter=…, tol=…, copy_X=…, cv=…, verbose=…, n_jobs=…, positive=…) → constructor LinearModelCV(eps=…, n_alphas=…, alphas=…, fit_intercept=…, normalize=…, precompute=…, max_iter=…, tol=…, copy_X=…, cv=…, verbose=…, n_jobs=…, positive=…, random_state=…, selection=…) - changed:
constructor MultiTaskElasticNet(alpha=…, l1_ratio=…, fit_intercept=…, normalize=…, copy_X=…, max_iter=…, tol=…, warm_start=…) → constructor MultiTaskElasticNet(alpha=…, l1_ratio=…, fit_intercept=…, normalize=…, copy_X=…, max_iter=…, tol=…, warm_start=…, random_state=…, selection=…) - changed:
constructor MultiTaskElasticNetCV(l1_ratio=…, eps=…, n_alphas=…, alphas=…, fit_intercept=…, normalize=…, max_iter=…, tol=…, cv=…, copy_X=…, verbose=…, n_jobs=…) → constructor MultiTaskElasticNetCV(l1_ratio=…, eps=…, n_alphas=…, alphas=…, fit_intercept=…, normalize=…, max_iter=…, tol=…, cv=…, copy_X=…, verbose=…, n_jobs=…, random_state=…, selection=…) - changed:
constructor MultiTaskLasso(alpha=…, fit_intercept=…, normalize=…, copy_X=…, max_iter=…, tol=…, warm_start=…) → constructor MultiTaskLasso(alpha=…, fit_intercept=…, normalize=…, copy_X=…, max_iter=…, tol=…, warm_start=…, random_state=…, selection=…) - changed:
constructor MultiTaskLassoCV(eps=…, n_alphas=…, alphas=…, fit_intercept=…, normalize=…, max_iter=…, tol=…, copy_X=…, cv=…, verbose=…, n_jobs=…) → constructor MultiTaskLassoCV(eps=…, n_alphas=…, alphas=…, fit_intercept=…, normalize=…, max_iter=…, tol=…, copy_X=…, cv=…, verbose=…, n_jobs=…, random_state=…, selection=…) - changed:
function enet_path(X, y, l1_ratio=…, eps=…, n_alphas=…, alphas=…, precompute=…, Xy=…, fit_intercept=…, normalize=…, copy_X=…, coef_init=…, verbose=…, return_models=…, **params) → function enet_path(X, y, l1_ratio=…, eps=…, n_alphas=…, alphas=…, precompute=…, Xy=…, copy_X=…, coef_init=…, verbose=…, return_n_iter=…, positive=…, **params) - changed:
function lasso_path(X, y, eps=…, n_alphas=…, alphas=…, precompute=…, Xy=…, fit_intercept=…, normalize=…, copy_X=…, coef_init=…, verbose=…, return_models=…, **params) → function lasso_path(X, y, eps=…, n_alphas=…, alphas=…, precompute=…, Xy=…, copy_X=…, coef_init=…, verbose=…, return_n_iter=…, positive=…, **params)
- changed:
function lars_path(X, y, Xy=…, Gram=…, max_iter=…, alpha_min=…, method=…, copy_X=…, eps=…, copy_Gram=…, verbose=…, return_path=…) → function lars_path(X, y, Xy=…, Gram=…, max_iter=…, alpha_min=…, method=…, copy_X=…, eps=…, copy_Gram=…, verbose=…, return_path=…, return_n_iter=…)
- changed:
class LogisticRegression(BaseLibLinear, LinearClassifierMixin, _LearntSelectorMixin, SparseCoefMixin) → class LogisticRegression(BaseEstimator, LinearClassifierMixin, _LearntSelectorMixin, SparseCoefMixin) - changed:
constructor LogisticRegression(penalty=…, dual=…, tol=…, C=…, fit_intercept=…, intercept_scaling=…, class_weight=…, random_state=…) → constructor LogisticRegression(penalty=…, dual=…, tol=…, C=…, fit_intercept=…, intercept_scaling=…, class_weight=…, random_state=…, solver=…, max_iter=…, multi_class=…, verbose=…) - added:
class LogisticRegressionCV(LogisticRegression, BaseEstimator, LinearClassifierMixin, _LearntSelectorMixin) - added:
constructor LogisticRegressionCV(Cs=…, fit_intercept=…, cv=…, dual=…, penalty=…, scoring=…, solver=…, tol=…, max_iter=…, class_weight=…, n_jobs=…, verbose=…, refit=…, intercept_scaling=…, multi_class=…) - added:
function logistic_regression_path(X, y, pos_class=…, Cs=…, fit_intercept=…, max_iter=…, tol=…, verbose=…, solver=…, coef=…, copy=…, class_weight=…, dual=…, penalty=…, intercept_scaling=…, multi_class=…) - added:
method LogisticRegression.fit(X, y) - added:
method LogisticRegressionCV.fit(X, y)
- changed:
constructor OrthogonalMatchingPursuit(copy_X=…, copy_Gram=…, copy_Xy=…, n_nonzero_coefs=…, tol=…, fit_intercept=…, normalize=…, precompute=…, precompute_gram=…) → constructor OrthogonalMatchingPursuit(n_nonzero_coefs=…, tol=…, fit_intercept=…, normalize=…, precompute=…) - changed:
function orthogonal_mp(X, y, n_nonzero_coefs=…, tol=…, precompute=…, copy_X=…, return_path=…, precompute_gram=…) → function orthogonal_mp(X, y, n_nonzero_coefs=…, tol=…, precompute=…, copy_X=…, return_path=…, return_n_iter=…) - changed:
function orthogonal_mp_gram(Gram, Xy, n_nonzero_coefs=…, tol=…, norms_squared=…, copy_Gram=…, copy_Xy=…, return_path=…) → function orthogonal_mp_gram(Gram, Xy, n_nonzero_coefs=…, tol=…, norms_squared=…, copy_Gram=…, copy_Xy=…, return_path=…, return_n_iter=…) - changed:
method OrthogonalMatchingPursuit.fit(X, y, Gram=…, Xy=…) → method OrthogonalMatchingPursuit.fit(X, y)
- changed:
constructor RidgeClassifierCV(alphas=…, fit_intercept=…, normalize=…, scoring=…, score_func=…, loss_func=…, cv=…, class_weight=…) → constructor RidgeClassifierCV(alphas=…, fit_intercept=…, normalize=…, scoring=…, cv=…, class_weight=…) - changed:
function ridge_regression(X, y, alpha, sample_weight=…, solver=…, max_iter=…, tol=…) → function ridge_regression(X, y, alpha, sample_weight=…, solver=…, max_iter=…, tol=…, verbose=…) - changed:
method RidgeClassifierCV.fit(X, y, sample_weight=…, class_weight=…) → method RidgeClassifierCV.fit(X, y, sample_weight=…)
- changed:
constructor BaseSGD(loss, penalty=…, alpha=…, C=…, l1_ratio=…, fit_intercept=…, n_iter=…, shuffle=…, verbose=…, epsilon=…, random_state=…, learning_rate=…, eta0=…, power_t=…, warm_start=…) → constructor BaseSGD(loss, penalty=…, alpha=…, C=…, l1_ratio=…, fit_intercept=…, n_iter=…, shuffle=…, verbose=…, epsilon=…, random_state=…, learning_rate=…, eta0=…, power_t=…, warm_start=…, average=…) - changed:
constructor BaseSGDClassifier(loss=…, penalty=…, alpha=…, l1_ratio=…, fit_intercept=…, n_iter=…, shuffle=…, verbose=…, epsilon=…, n_jobs=…, random_state=…, learning_rate=…, eta0=…, power_t=…, class_weight=…, warm_start=…) → constructor BaseSGDClassifier(loss=…, penalty=…, alpha=…, l1_ratio=…, fit_intercept=…, n_iter=…, shuffle=…, verbose=…, epsilon=…, n_jobs=…, random_state=…, learning_rate=…, eta0=…, power_t=…, class_weight=…, warm_start=…, average=…) - changed:
constructor BaseSGDRegressor(loss=…, penalty=…, alpha=…, l1_ratio=…, fit_intercept=…, n_iter=…, shuffle=…, verbose=…, epsilon=…, random_state=…, learning_rate=…, eta0=…, power_t=…, warm_start=…) → constructor BaseSGDRegressor(loss=…, penalty=…, alpha=…, l1_ratio=…, fit_intercept=…, n_iter=…, shuffle=…, verbose=…, epsilon=…, random_state=…, learning_rate=…, eta0=…, power_t=…, warm_start=…, average=…) - changed:
constructor SGDClassifier(loss=…, penalty=…, alpha=…, l1_ratio=…, fit_intercept=…, n_iter=…, shuffle=…, verbose=…, epsilon=…, n_jobs=…, random_state=…, learning_rate=…, eta0=…, power_t=…, class_weight=…, warm_start=…) → constructor SGDClassifier(loss=…, penalty=…, alpha=…, l1_ratio=…, fit_intercept=…, n_iter=…, shuffle=…, verbose=…, epsilon=…, n_jobs=…, random_state=…, learning_rate=…, eta0=…, power_t=…, class_weight=…, warm_start=…, average=…) - changed:
constructor SGDRegressor(loss=…, penalty=…, alpha=…, l1_ratio=…, fit_intercept=…, n_iter=…, shuffle=…, verbose=…, epsilon=…, random_state=…, learning_rate=…, eta0=…, power_t=…, warm_start=…) → constructor SGDRegressor(loss=…, penalty=…, alpha=…, l1_ratio=…, fit_intercept=…, n_iter=…, shuffle=…, verbose=…, epsilon=…, random_state=…, learning_rate=…, eta0=…, power_t=…, warm_start=…, average=…)
- added:
class TheilSenRegressor(LinearModel, RegressorMixin) - added:
constructor TheilSenRegressor(fit_intercept=…, copy_X=…, max_subpopulation=…, n_subsamples=…, max_iter=…, tol=…, random_state=…, n_jobs=…, verbose=…) - added:
method TheilSenRegressor.fit(X, y)
- changed:
function smacof(similarities, metric=…, n_components=…, init=…, n_init=…, n_jobs=…, max_iter=…, verbose=…, eps=…, random_state=…) → function smacof(similarities, metric=…, n_components=…, init=…, n_init=…, n_jobs=…, max_iter=…, verbose=…, eps=…, random_state=…, return_n_iter=…) - changed:
method MDS.fit(X, init=…, y=…) → method MDS.fit(X, y=…, init=…) - changed:
method MDS.fit_transform(X, init=…, y=…) → method MDS.fit_transform(X, y=…, init=…)
- changed:
function spectral_embedding(adjacency, n_components=…, eigen_solver=…, random_state=…, eigen_tol=…, norm_laplacian=…, drop_first=…, mode=…) → function spectral_embedding(adjacency, n_components=…, eigen_solver=…, random_state=…, eigen_tol=…, norm_laplacian=…, drop_first=…)
- changed:
method TSNE.fit_transform(X) → method TSNE.fit_transform(X, y=…) - added:
method TSNE.fit(X, y=…)
- added:
export brier_score_loss - added:
export coverage_error - added:
export get_scorer - added:
export label_ranking_average_precision_score - added:
export median_absolute_error
- added:
class UndefinedMetricWarning(UserWarning)
- added:
function accuracy_score(y_true, y_pred, normalize=…, sample_weight=…) - added:
function brier_score_loss(y_true, y_prob, sample_weight=…, pos_label=…) - added:
function classification_report(y_true, y_pred, labels=…, target_names=…, sample_weight=…, digits=…) - added:
function confusion_matrix(y_true, y_pred, labels=…) - added:
function f1_score(y_true, y_pred, labels=…, pos_label=…, average=…, sample_weight=…) - added:
function fbeta_score(y_true, y_pred, beta, labels=…, pos_label=…, average=…, sample_weight=…) - added:
function hamming_loss(y_true, y_pred, classes=…) - added:
function hinge_loss(y_true, pred_decision, labels=…, sample_weight=…) - added:
function jaccard_similarity_score(y_true, y_pred, normalize=…, sample_weight=…) - added:
function log_loss(y_true, y_pred, eps=…, normalize=…, sample_weight=…) - added:
function matthews_corrcoef(y_true, y_pred) - added:
function precision_recall_fscore_support(y_true, y_pred, beta=…, labels=…, pos_label=…, average=…, warn_for=…, sample_weight=…) - added:
function precision_score(y_true, y_pred, labels=…, pos_label=…, average=…, sample_weight=…) - added:
function recall_score(y_true, y_pred, labels=…, pos_label=…, average=…, sample_weight=…) - added:
function zero_one_loss(y_true, y_pred, normalize=…, sample_weight=…)
- removed:
export consensus_score - added:
function consensus_score(a, b, similarity=…)
- removed:
function consensus_score(a, b, similarity=…)
- removed:
class UndefinedMetricWarning(UserWarning) - removed:
function accuracy_score(y_true, y_pred, normalize=…, sample_weight=…) - removed:
function auc(x, y, reorder=…) - removed:
function auc_score(y_true, y_score) - removed:
function average_precision_score(y_true, y_score, average=…, sample_weight=…) - removed:
function classification_report(y_true, y_pred, labels=…, target_names=…, sample_weight=…) - removed:
function confusion_matrix(y_true, y_pred, labels=…) - removed:
function explained_variance_score(y_true, y_pred, sample_weight=…) - removed:
function f1_score(y_true, y_pred, labels=…, pos_label=…, average=…, sample_weight=…) - removed:
function fbeta_score(y_true, y_pred, beta, labels=…, pos_label=…, average=…, sample_weight=…) - removed:
function hamming_loss(y_true, y_pred, classes=…) - removed:
function hinge_loss(y_true, pred_decision, pos_label=…, neg_label=…) - removed:
function jaccard_similarity_score(y_true, y_pred, normalize=…) - removed:
function log_loss(y_true, y_pred, eps=…, normalize=…) - removed:
function matthews_corrcoef(y_true, y_pred) - removed:
function mean_absolute_error(y_true, y_pred, sample_weight=…) - removed:
function mean_squared_error(y_true, y_pred, sample_weight=…) - removed:
function precision_recall_curve(y_true, probas_pred, pos_label=…, sample_weight=…) - removed:
function precision_recall_fscore_support(y_true, y_pred, beta=…, labels=…, pos_label=…, average=…, warn_for=…, sample_weight=…) - removed:
function precision_score(y_true, y_pred, labels=…, pos_label=…, average=…, sample_weight=…) - removed:
function r2_score(y_true, y_pred, sample_weight=…) - removed:
function recall_score(y_true, y_pred, labels=…, pos_label=…, average=…, sample_weight=…) - removed:
function roc_auc_score(y_true, y_score, average=…, sample_weight=…) - removed:
function roc_curve(y_true, y_score, pos_label=…, sample_weight=…) - removed:
function zero_one_loss(y_true, y_pred, normalize=…, sample_weight=…)
- added:
function auc(x, y, reorder=…) - added:
function average_precision_score(y_true, y_score, average=…, sample_weight=…) - added:
function coverage_error(y_true, y_score, sample_weight=…) - added:
function label_ranking_average_precision_score(y_true, y_score) - added:
function precision_recall_curve(y_true, probas_pred, pos_label=…, sample_weight=…) - added:
function roc_auc_score(y_true, y_score, average=…, sample_weight=…) - added:
function roc_curve(y_true, y_score, pos_label=…, sample_weight=…)
- added:
function explained_variance_score(y_true, y_pred, sample_weight=…) - added:
function mean_absolute_error(y_true, y_pred, sample_weight=…) - added:
function mean_squared_error(y_true, y_pred, sample_weight=…) - added:
function median_absolute_error(y_true, y_pred) - added:
function r2_score(y_true, y_pred, sample_weight=…)
- changed:
function check_scoring(estimator, scoring=…, allow_none=…, loss_func=…, score_func=…, score_overrides_loss=…) → function check_scoring(estimator, scoring=…, allow_none=…) - added:
variable median_absolute_error_scorer
- removed:
method DPGMM.eval(X) - removed:
method VBGMM.eval(X) - changed:
constructor DPGMM(n_components=…, covariance_type=…, alpha=…, random_state=…, thresh=…, verbose=…, min_covar=…, n_iter=…, params=…, init_params=…) → constructor DPGMM(n_components=…, covariance_type=…, alpha=…, random_state=…, thresh=…, tol=…, verbose=…, min_covar=…, n_iter=…, params=…, init_params=…) - changed:
constructor VBGMM(n_components=…, covariance_type=…, alpha=…, random_state=…, thresh=…, verbose=…, min_covar=…, n_iter=…, params=…, init_params=…) → constructor VBGMM(n_components=…, covariance_type=…, alpha=…, random_state=…, thresh=…, tol=…, verbose=…, min_covar=…, n_iter=…, params=…, init_params=…) - changed:
method DPGMM.fit(X) → method DPGMM.fit(X, y=…)
- removed:
method GMM.eval(X) - changed:
constructor GMM(n_components=…, covariance_type=…, random_state=…, thresh=…, min_covar=…, n_iter=…, n_init=…, params=…, init_params=…) → constructor GMM(n_components=…, covariance_type=…, random_state=…, thresh=…, tol=…, min_covar=…, n_iter=…, n_init=…, params=…, init_params=…) - changed:
method GMM.fit(X) → method GMM.fit(X, y=…) - changed:
method GMM.score(X) → method GMM.score(X, y=…)
- removed:
function fit_ecoc(estimator, X, y, code_size=…, random_state=…, n_jobs=…) - removed:
function fit_ovo(estimator, X, y, n_jobs=…) - removed:
function fit_ovr(estimator, X, y, n_jobs=…) - removed:
function predict_ecoc(estimators, classes, code_book, X) - removed:
function predict_ovo(estimators, classes, X) - removed:
function predict_ovr(estimators, label_binarizer, X) - removed:
function predict_proba_ovr(estimators, X, is_multilabel) - removed:
method OneVsRestClassifier.score(X, y) - added:
method OneVsOneClassifier.decision_function(X)
- added:
method GaussianNB.partial_fit(X, y, classes=…)
- added:
export LSHForest
- added:
class LSHForest(BaseEstimator, KNeighborsMixin, RadiusNeighborsMixin) - added:
constructor LSHForest(n_estimators=…, radius=…, n_candidates=…, n_neighbors=…, min_hash_match=…, radius_cutoff_ratio=…, random_state=…) - added:
method LSHForest.fit(X, y=…) - added:
method LSHForest.kneighbors(X, n_neighbors=…, return_distance=…) - added:
method LSHForest.partial_fit(X, y=…) - added:
method LSHForest.radius_neighbors(X, radius=…, return_distance=…)
- changed:
method KNeighborsMixin.kneighbors(X, n_neighbors=…, return_distance=…) → method KNeighborsMixin.kneighbors(X=…, n_neighbors=…, return_distance=…) - changed:
method KNeighborsMixin.kneighbors_graph(X, n_neighbors=…, mode=…) → method KNeighborsMixin.kneighbors_graph(X=…, n_neighbors=…, mode=…) - changed:
method RadiusNeighborsMixin.radius_neighbors(X, radius=…, return_distance=…) → method RadiusNeighborsMixin.radius_neighbors(X=…, radius=…, return_distance=…) - changed:
method RadiusNeighborsMixin.radius_neighbors_graph(X, radius=…, mode=…) → method RadiusNeighborsMixin.radius_neighbors_graph(X=…, radius=…, mode=…)
- changed:
function kneighbors_graph(X, n_neighbors, mode=…) → function kneighbors_graph(X, n_neighbors, mode=…, metric=…, p=…, metric_params=…, include_self=…) - changed:
function radius_neighbors_graph(X, radius, mode=…) → function radius_neighbors_graph(X, radius, mode=…, metric=…, p=…, metric_params=…, include_self=…)
- changed:
method KernelDensity.fit(X) → method KernelDensity.fit(X, y=…) - changed:
method KernelDensity.score(X) → method KernelDensity.score(X, y=…)
- changed:
method BernoulliRBM.partial_fit(X) → method BernoulliRBM.partial_fit(X, y=…)
- added:
property Pipeline.classes_
- removed:
export balance_weights
- changed:
constructor OneHotEncoder(n_values=…, categorical_features=…, dtype=…, sparse=…) → constructor OneHotEncoder(n_values=…, categorical_features=…, dtype=…, sparse=…, handle_unknown=…)
- removed:
property LabelBinarizer.multilabel - added:
class MultiLabelBinarizer(BaseEstimator, TransformerMixin) - added:
constructor MultiLabelBinarizer(classes=…, sparse_output=…) - added:
method MultiLabelBinarizer.fit(y) - added:
method MultiLabelBinarizer.fit_transform(y) - added:
method MultiLabelBinarizer.inverse_transform(yt) - added:
method MultiLabelBinarizer.transform(y)
- added:
export LinearSVR
- removed:
class BaseLibLinear(six.with_metaclass(ABCMeta, BaseEstimator)) - removed:
constructor BaseLibLinear(penalty=…, loss=…, dual=…, tol=…, C=…, multi_class=…, fit_intercept=…, intercept_scaling=…, class_weight=…, verbose=…, random_state=…) - removed:
method BaseLibLinear.fit(X, y) - removed:
property BaseLibLinear.classes_
- changed:
class LinearSVC(BaseLibLinear, LinearClassifierMixin, _LearntSelectorMixin, SparseCoefMixin) → class LinearSVC(BaseEstimator, LinearClassifierMixin, _LearntSelectorMixin, SparseCoefMixin) - changed:
constructor LinearSVC(penalty=…, loss=…, dual=…, tol=…, C=…, multi_class=…, fit_intercept=…, intercept_scaling=…, class_weight=…, verbose=…, random_state=…) → constructor LinearSVC(penalty=…, loss=…, dual=…, tol=…, C=…, multi_class=…, fit_intercept=…, intercept_scaling=…, class_weight=…, verbose=…, random_state=…, max_iter=…) - changed:
constructor NuSVR(nu=…, C=…, kernel=…, degree=…, gamma=…, coef0=…, shrinking=…, probability=…, tol=…, cache_size=…, verbose=…, max_iter=…, random_state=…) → constructor NuSVR(nu=…, C=…, kernel=…, degree=…, gamma=…, coef0=…, shrinking=…, tol=…, cache_size=…, verbose=…, max_iter=…) - changed:
constructor SVR(kernel=…, degree=…, gamma=…, coef0=…, tol=…, C=…, epsilon=…, shrinking=…, probability=…, cache_size=…, verbose=…, max_iter=…, random_state=…) → constructor SVR(kernel=…, degree=…, gamma=…, coef0=…, tol=…, C=…, epsilon=…, shrinking=…, cache_size=…, verbose=…, max_iter=…) - changed:
method OneClassSVM.fit(X, sample_weight=…, **params) → method OneClassSVM.fit(X, y=…, sample_weight=…, **params) - added:
class LinearSVR(LinearModel, RegressorMixin) - added:
constructor LinearSVR(epsilon=…, tol=…, C=…, loss=…, fit_intercept=…, intercept_scaling=…, dual=…, verbose=…, random_state=…, max_iter=…) - added:
method LinearSVC.fit(X, y) - added:
method LinearSVR.fit(X, y)
- changed:
function export_graphviz(decision_tree, out_file=…, feature_names=…, max_depth=…, close=…) → function export_graphviz(decision_tree, out_file=…, feature_names=…, max_depth=…)
- changed:
constructor DecisionTreeClassifier(criterion=…, splitter=…, max_depth=…, min_samples_split=…, min_samples_leaf=…, max_features=…, random_state=…, min_density=…, compute_importances=…, max_leaf_nodes=…) → constructor DecisionTreeClassifier(criterion=…, splitter=…, max_depth=…, min_samples_split=…, min_samples_leaf=…, min_weight_fraction_leaf=…, max_features=…, random_state=…, max_leaf_nodes=…, class_weight=…) - changed:
constructor DecisionTreeRegressor(criterion=…, splitter=…, max_depth=…, min_samples_split=…, min_samples_leaf=…, max_features=…, random_state=…, min_density=…, compute_importances=…, max_leaf_nodes=…) → constructor DecisionTreeRegressor(criterion=…, splitter=…, max_depth=…, min_samples_split=…, min_samples_leaf=…, min_weight_fraction_leaf=…, max_features=…, random_state=…, max_leaf_nodes=…) - changed:
constructor ExtraTreeClassifier(criterion=…, splitter=…, max_depth=…, min_samples_split=…, min_samples_leaf=…, max_features=…, random_state=…, min_density=…, compute_importances=…, max_leaf_nodes=…) → constructor ExtraTreeClassifier(criterion=…, splitter=…, max_depth=…, min_samples_split=…, min_samples_leaf=…, min_weight_fraction_leaf=…, max_features=…, random_state=…, max_leaf_nodes=…, class_weight=…) - changed:
constructor ExtraTreeRegressor(criterion=…, splitter=…, max_depth=…, min_samples_split=…, min_samples_leaf=…, max_features=…, random_state=…, min_density=…, compute_importances=…, max_leaf_nodes=…) → constructor ExtraTreeRegressor(criterion=…, splitter=…, max_depth=…, min_samples_split=…, min_samples_leaf=…, min_weight_fraction_leaf=…, max_features=…, random_state=…, max_leaf_nodes=…) - changed:
method DecisionTreeClassifier.predict_proba(X) → method DecisionTreeClassifier.predict_proba(X, check_input=…)
- removed:
export array2d - removed:
export atleast2d_or_csc - removed:
export atleast2d_or_csr - removed:
export check_arrays - removed:
export minimum_spanning_tree - removed:
export safe_asarray - added:
export check_X_y - added:
export check_array - added:
export check_consistent_length - added:
export check_symmetric - added:
export compute_sample_weight - added:
export indexable
- added:
function compute_sample_weight(class_weight, y, indices=…)
- added:
class NotAnArray(object) - added:
constructor NotAnArray(data) - added:
function check_class_weight_auto_classifiers(name, Classifier, X_train, y_train, X_test, y_test, weights) - added:
function check_class_weight_auto_linear_classifier(name, Classifier) - added:
function check_class_weight_classifiers(name, Classifier) - added:
function check_classifier_data_not_an_array(name, Estimator) - added:
function check_classifiers_classes(name, Classifier) - added:
function check_classifiers_input_shapes(name, Classifier) - added:
function check_classifiers_one_label(name, Classifier) - added:
function check_classifiers_pickle(name, Classifier) - added:
function check_classifiers_train(name, Classifier) - added:
function check_clusterer_compute_labels_predict(name, Clusterer) - added:
function check_clustering(name, Alg) - added:
function check_dtype_object(name, Estimator) - added:
function check_estimator_sparse_data(name, Estimator) - added:
function check_estimators_data_not_an_array(name, Estimator, X, y) - added:
function check_estimators_dtypes(name, Estimator) - added:
function check_estimators_empty_data_messages(name, Estimator) - added:
function check_estimators_nan_inf(name, Estimator) - added:
function check_estimators_overwrite_params(name, Estimator) - added:
function check_estimators_partial_fit_n_features(name, Alg) - added:
function check_estimators_unfitted(name, Estimator) - added:
function check_fit_score_takes_y(name, Estimator) - added:
function check_non_transformer_estimators_n_iter(name, estimator, multi_output=…) - added:
function check_parameters_default_constructible(name, Estimator) - added:
function check_pipeline_consistency(name, Estimator) - added:
function check_regressor_data_not_an_array(name, Estimator) - added:
function check_regressors_int(name, Regressor) - added:
function check_regressors_pickle(name, Regressor) - added:
function check_regressors_train(name, Regressor) - added:
function check_sparsify_coefficients(name, Estimator) - added:
function check_transformer(name, Transformer) - added:
function check_transformer_data_not_an_array(name, Transformer) - added:
function check_transformer_n_iter(name, estimator) - added:
function check_transformer_pickle(name, Transformer) - added:
function check_transformers_unfitted(name, Transformer) - added:
function multioutput_estimator_convert_y_2d(name, y) - added:
function set_fast_parameters(estimator) - added:
variable BOSTON - added:
variable CROSS_DECOMPOSITION
- changed:
function randomized_svd(M, n_components, n_oversamples=…, n_iter=…, transpose=…, flip_sign=…, random_state=…, n_iterations=…) → function randomized_svd(M, n_components, n_oversamples=…, n_iter=…, transpose=…, flip_sign=…, random_state=…) - changed:
function svd_flip(u, v) → function svd_flip(u, v, u_based_decision=…)
- added:
class partial(object) - added:
constructor partial(func, *args, **keywords) - added:
function bincount(x, weights=…, minlength=…) - added:
function frombuffer_empty(buf, dtype) - added:
variable frombuffer_empty - added:
variable sp_version
- added:
function if_delegate_has_method(delegate)
- added:
class ArraySlicingWrapper(object) - added:
class CheckingClassifier(BaseEstimator) - added:
class MockDataFrame(object) - added:
constructor ArraySlicingWrapper(array) - added:
constructor CheckingClassifier(check_y=…, check_X=…, foo_param=…) - added:
constructor MockDataFrame(array) - added:
method CheckingClassifier.fit(X, y) - added:
method CheckingClassifier.predict(T) - added:
method CheckingClassifier.score(X=…, Y=…)
- added:
function class_distribution(y, sample_weight=…)
- added:
function func(x) - added:
function func_grad_hess(x) - added:
function newton_cg(func_grad_hess, func, grad, x0, args=…, eps=…, tol=…, maxiter=…) - added:
variable A - added:
variable out - added:
variable x0
- removed:
function mean_variance_axis0(X) - added:
function count_nonzero(X, axis=…, sample_weight=…) - added:
function csc_median_axis_0(X) - added:
function mean_variance_axis(X, axis)
- removed:
export minimum_spanning_tree
- added:
variable rankdata
- removed:
function array2d(X, dtype=…, order=…, copy=…, force_all_finite=…) - removed:
function atleast2d_or_csc(X, dtype=…, order=…, copy=…, force_all_finite=…) - removed:
function atleast2d_or_csr(X, dtype=…, order=…, copy=…, force_all_finite=…) - removed:
function check_arrays(*arrays, **options) - removed:
function safe_asarray(X, dtype=…, order=…, copy=…, force_all_finite=…) - added:
class NotFittedError(ValueError, AttributeError) - added:
function check_X_y(X, y, accept_sparse=…, dtype=…, order=…, copy=…, force_all_finite=…, ensure_2d=…, allow_nd=…, multi_output=…, ensure_min_samples=…, ensure_min_features=…, y_numeric=…) - added:
function check_array(array, accept_sparse=…, dtype=…, order=…, copy=…, force_all_finite=…, ensure_2d=…, allow_nd=…, ensure_min_samples=…, ensure_min_features=…) - added:
function check_consistent_length(*arrays) - added:
function check_is_fitted(estimator, attributes, msg=…, all_or_any=…) - added:
function check_symmetric(array, tol=…, raise_warning=…, raise_exception=…) - added:
function has_fit_parameter(estimator, parameter) - added:
function indexable(*iterables)