Upgrading scikit-learn 0.16 to 0.17
Unknown0.16.1 → 0.17.1
UnknownUpgrade verdict
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- Their commits are listed with this comparison.
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API changes
Generated by DepDelta from published Python source. Not mixed with prose notes.
- added:
class ChangedBehaviorWarning(UserWarning) - added:
function is_regressor(estimator)
- removed:
export Ward - removed:
export WardAgglomeration
- removed:
class Ward(AgglomerativeClustering) - removed:
class WardAgglomeration(AgglomerationTransform, Ward) - removed:
constructor Ward(n_clusters=…, memory=…, connectivity=…, n_components=…, compute_full_tree=…, pooling_func=…) - removed:
field Ward.linkage - removed:
method WardAgglomeration.fit(X, y=…, **params) - removed:
property WardAgglomeration.fit_predict
- changed:
constructor MeanShift(bandwidth=…, seeds=…, bin_seeding=…, min_bin_freq=…, cluster_all=…) → constructor MeanShift(bandwidth=…, seeds=…, bin_seeding=…, min_bin_freq=…, cluster_all=…, n_jobs=…) - changed:
function mean_shift(X, bandwidth=…, seeds=…, bin_seeding=…, min_bin_freq=…, cluster_all=…, max_iter=…, max_iterations=…) → function mean_shift(X, bandwidth=…, seeds=…, bin_seeding=…, min_bin_freq=…, cluster_all=…, max_iter=…, max_iterations=…, n_jobs=…)
- changed:
constructor GraphLasso(alpha=…, mode=…, tol=…, max_iter=…, verbose=…, assume_centered=…) → constructor GraphLasso(alpha=…, mode=…, tol=…, enet_tol=…, max_iter=…, verbose=…, assume_centered=…) - changed:
constructor GraphLassoCV(alphas=…, n_refinements=…, cv=…, tol=…, max_iter=…, mode=…, n_jobs=…, verbose=…, assume_centered=…) → constructor GraphLassoCV(alphas=…, n_refinements=…, cv=…, tol=…, enet_tol=…, max_iter=…, mode=…, n_jobs=…, verbose=…, assume_centered=…) - changed:
function graph_lasso(emp_cov, alpha, cov_init=…, mode=…, tol=…, max_iter=…, verbose=…, return_costs=…, eps=…, return_n_iter=…) → function graph_lasso(emp_cov, alpha, cov_init=…, mode=…, tol=…, enet_tol=…, max_iter=…, verbose=…, return_costs=…, eps=…, return_n_iter=…) - changed:
function graph_lasso_path(X, alphas, cov_init=…, X_test=…, mode=…, tol=…, max_iter=…, verbose=…) → function graph_lasso_path(X, alphas, cov_init=…, X_test=…, mode=…, tol=…, enet_tol=…, max_iter=…, verbose=…)
- removed:
property PLSRegression.coefs
- removed:
class Bootstrap(object) - removed:
constructor Bootstrap(n, n_iter=…, train_size=…, test_size=…, random_state=…) - removed:
field Bootstrap.indices - changed:
constructor KFold(n, n_folds=…, indices=…, shuffle=…, random_state=…) → constructor KFold(n, n_folds=…, shuffle=…, random_state=…) - changed:
constructor LeaveOneLabelOut(labels, indices=…) → constructor LeaveOneLabelOut(labels) - changed:
constructor LeavePLabelOut(labels, p, indices=…) → constructor LeavePLabelOut(labels, p) - changed:
constructor LeavePOut(n, p, indices=…) → constructor LeavePOut(n, p) - changed:
constructor PredefinedSplit(test_fold, indices=…) → constructor PredefinedSplit(test_fold) - changed:
constructor StratifiedKFold(y, n_folds=…, indices=…, shuffle=…, random_state=…) → constructor StratifiedKFold(y, n_folds=…, shuffle=…, random_state=…) - changed:
constructor StratifiedShuffleSplit(y, n_iter=…, test_size=…, train_size=…, indices=…, random_state=…, n_iterations=…) → constructor StratifiedShuffleSplit(y, n_iter=…, test_size=…, train_size=…, random_state=…) - added:
class LabelKFold(_BaseKFold) - added:
class LabelShuffleSplit(ShuffleSplit) - added:
constructor LabelKFold(labels, n_folds=…) - added:
constructor LabelShuffleSplit(labels, n_iter=…, test_size=…, train_size=…, random_state=…)
- added:
export fetch_rcv1 - added:
export load_breast_cancer
- added:
function load_breast_cancer()
- added:
function fetch_rcv1(data_home=…, subset=…, download_if_missing=…, random_state=…, shuffle=…) - added:
variable URL - added:
variable URL_topics - added:
variable logger
- changed:
function dump_svmlight_file(X, y, f, zero_based=…, comment=…, query_id=…) → function dump_svmlight_file(X, y, f, zero_based=…, comment=…, query_id=…, multilabel=…)
- added:
export LatentDirichletAllocation
- changed:
function sparse_encode(X, dictionary, gram=…, cov=…, algorithm=…, n_nonzero_coefs=…, alpha=…, copy_cov=…, init=…, max_iter=…, n_jobs=…) → function sparse_encode(X, dictionary, gram=…, cov=…, algorithm=…, n_nonzero_coefs=…, alpha=…, copy_cov=…, init=…, max_iter=…, n_jobs=…, check_input=…, verbose=…)
- changed:
method IncrementalPCA.partial_fit(X, y=…) → method IncrementalPCA.partial_fit(X, y=…, check_input=…)
- removed:
function check_non_negative(X, whom) - removed:
method ProjectedGradientNMF.fit(X, y=…, **params) - removed:
method ProjectedGradientNMF.fit_transform(X, y=…) - removed:
method ProjectedGradientNMF.transform(X) - changed:
class NMF(ProjectedGradientNMF) → class NMF(BaseEstimator, TransformerMixin) - changed:
class ProjectedGradientNMF(BaseEstimator, TransformerMixin) → class ProjectedGradientNMF(NMF) - changed:
constructor ProjectedGradientNMF(n_components=…, init=…, sparseness=…, beta=…, eta=…, tol=…, max_iter=…, nls_max_iter=…, random_state=…) → constructor ProjectedGradientNMF(n_components=…, solver=…, init=…, tol=…, max_iter=…, random_state=…, alpha=…, l1_ratio=…, verbose=…, nls_max_iter=…, sparseness=…, beta=…, eta=…) - added:
constructor NMF(n_components=…, init=…, solver=…, tol=…, max_iter=…, random_state=…, alpha=…, l1_ratio=…, verbose=…, shuffle=…, nls_max_iter=…, sparseness=…, beta=…, eta=…) - added:
function non_negative_factorization(X, W=…, H=…, n_components=…, init=…, update_H=…, solver=…, tol=…, max_iter=…, alpha=…, l1_ratio=…, regularization=…, random_state=…, verbose=…, shuffle=…, nls_max_iter=…, sparseness=…, beta=…, eta=…) - added:
method NMF.fit(X, y=…, **params) - added:
method NMF.fit_transform(X, y=…, W=…, H=…) - added:
method NMF.transform(X)
- added:
class LatentDirichletAllocation(BaseEstimator, TransformerMixin) - added:
constructor LatentDirichletAllocation(n_topics=…, doc_topic_prior=…, topic_word_prior=…, learning_method=…, learning_decay=…, learning_offset=…, max_iter=…, batch_size=…, evaluate_every=…, total_samples=…, perp_tol=…, mean_change_tol=…, max_doc_update_iter=…, n_jobs=…, verbose=…, random_state=…) - added:
method LatentDirichletAllocation.fit(X, y=…) - added:
method LatentDirichletAllocation.partial_fit(X, y=…) - added:
method LatentDirichletAllocation.perplexity(X, doc_topic_distr=…, sub_sampling=…) - added:
method LatentDirichletAllocation.score(X, y=…) - added:
method LatentDirichletAllocation.transform(X) - added:
variable EPS
- added:
function configuration(parent_package=…, top_path=…)
- added:
class LinearDiscriminantAnalysis(BaseEstimator, LinearClassifierMixin, TransformerMixin) - added:
class QuadraticDiscriminantAnalysis(BaseEstimator, ClassifierMixin) - added:
constructor LinearDiscriminantAnalysis(solver=…, shrinkage=…, priors=…, n_components=…, store_covariance=…, tol=…) - added:
constructor QuadraticDiscriminantAnalysis(priors=…, reg_param=…, store_covariances=…, tol=…) - added:
method LinearDiscriminantAnalysis.fit(X, y, store_covariance=…, tol=…) - added:
method LinearDiscriminantAnalysis.predict_log_proba(X) - added:
method LinearDiscriminantAnalysis.predict_proba(X) - added:
method LinearDiscriminantAnalysis.transform(X) - added:
method QuadraticDiscriminantAnalysis.decision_function(X) - added:
method QuadraticDiscriminantAnalysis.fit(X, y, store_covariances=…, tol=…) - added:
method QuadraticDiscriminantAnalysis.predict(X) - added:
method QuadraticDiscriminantAnalysis.predict_log_proba(X) - added:
method QuadraticDiscriminantAnalysis.predict_proba(X)
- removed:
property DummyRegressor.y_mean_
- added:
export VotingClassifier
- changed:
constructor BaggingClassifier(base_estimator=…, n_estimators=…, max_samples=…, max_features=…, bootstrap=…, bootstrap_features=…, oob_score=…, n_jobs=…, random_state=…, verbose=…) → constructor BaggingClassifier(base_estimator=…, n_estimators=…, max_samples=…, max_features=…, bootstrap=…, bootstrap_features=…, oob_score=…, warm_start=…, n_jobs=…, random_state=…, verbose=…) - changed:
constructor BaggingRegressor(base_estimator=…, n_estimators=…, max_samples=…, max_features=…, bootstrap=…, bootstrap_features=…, oob_score=…, n_jobs=…, random_state=…, verbose=…) → constructor BaggingRegressor(base_estimator=…, n_estimators=…, max_samples=…, max_features=…, bootstrap=…, bootstrap_features=…, oob_score=…, warm_start=…, n_jobs=…, random_state=…, verbose=…)
- changed:
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=…) → 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=…, presort=…) - changed:
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=…) → 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=…, presort=…) - changed:
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=…) → 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=…, presort=…) - added:
method BaseGradientBoosting.apply(X) - added:
method GradientBoostingClassifier.decision_function(X) - added:
method GradientBoostingClassifier.staged_decision_function(X) - added:
method GradientBoostingRegressor.apply(X)
- added:
class VotingClassifier(BaseEstimator, ClassifierMixin, TransformerMixin) - added:
constructor VotingClassifier(estimators, voting=…, weights=…) - added:
method VotingClassifier.fit(X, y) - added:
method VotingClassifier.get_params(deep=…) - added:
method VotingClassifier.predict(X) - added:
method VotingClassifier.transform(X) - added:
property VotingClassifier.predict_proba
- added:
class BoundArguments(object) - added:
class Parameter(object) - added:
class Signature(object) - added:
constructor BoundArguments(signature, arguments) - added:
constructor Parameter(name, kind, default=…, annotation=…, _partial_kwarg=…) - added:
constructor Signature(parameters=…, return_annotation=…, __validate_parameters__=…) - added:
field Parameter.KEYWORD_ONLY - added:
field Parameter.POSITIONAL_ONLY - added:
field Parameter.POSITIONAL_OR_KEYWORD - added:
field Parameter.VAR_KEYWORD - added:
field Parameter.VAR_POSITIONAL - added:
field Parameter.empty - added:
field Signature.empty - added:
function signature(obj) - added:
method Parameter.replace(name=…, kind=…, annotation=…, default=…, _partial_kwarg=…) - added:
method Signature.bind(*args, **kwargs) - added:
method Signature.bind_partial(*args, **kwargs) - added:
method Signature.replace(parameters=…, return_annotation=…) - added:
property BoundArguments.args - added:
property BoundArguments.kwargs - added:
property BoundArguments.signature - added:
property Parameter.annotation - added:
property Parameter.default - added:
property Parameter.kind - added:
property Parameter.name - added:
property Signature.parameters - added:
property Signature.return_annotation - added:
static method Signature.from_function(func)
- removed:
variable PY3
- added:
function getfullargspec(func)
- added:
method Hasher.memoize(obj)
- changed:
constructor NDArrayWrapper(filename, subclass) → constructor NDArrayWrapper(filename, subclass, allow_mmap=…) - changed:
constructor NumpyPickler(filename, compress=…, cache_size=…) → constructor NumpyPickler(filename, compress=…, cache_size=…, protocol=…) - changed:
function dump(value, filename, compress=…, cache_size=…) → function dump(value, filename, compress=…, cache_size=…, protocol=…) - added:
field NumpyPickler.dispatch - added:
function hex_str(an_int)
- removed:
class CallBack(object) - removed:
class ImmediateApply(object) - removed:
class LockedIterator(object) - removed:
constructor CallBack(index, parallel) - removed:
constructor ImmediateApply(func, args, kwargs) - removed:
constructor LockedIterator(it) - removed:
method ImmediateApply.get() - removed:
method LockedIterator.next() - removed:
method Parallel.dispatch(func, args, kwargs) - changed:
constructor Parallel(n_jobs=…, backend=…, verbose=…, pre_dispatch=…, temp_folder=…, max_nbytes=…, mmap_mode=…) → constructor Parallel(n_jobs=…, backend=…, verbose=…, pre_dispatch=…, batch_size=…, temp_folder=…, max_nbytes=…, mmap_mode=…) - changed:
method Parallel.print_progress(index) → method Parallel.print_progress() - added:
class BatchCompletionCallBack(object) - added:
class BatchedCalls(object) - added:
class ImmediateComputeBatch(object) - added:
constructor BatchCompletionCallBack(dispatch_timestamp, batch_size, parallel) - added:
constructor BatchedCalls(iterator_slice) - added:
constructor ImmediateComputeBatch(batch) - added:
method ImmediateComputeBatch.get() - added:
method Parallel.dispatch_one_batch(iterator) - added:
variable DEFAULT_MP_CONTEXT - added:
variable MAX_IDEAL_BATCH_DURATION - added:
variable MIN_IDEAL_BATCH_DURATION - added:
variable method
- added:
variable WindowsError
- added:
class OrderedDict(dict) - added:
constructor OrderedDict(*args, **kwds) - added:
method OrderedDict.clear() - added:
method OrderedDict.copy() - added:
method OrderedDict.items() - added:
method OrderedDict.iteritems() - added:
method OrderedDict.iterkeys() - added:
method OrderedDict.itervalues() - added:
method OrderedDict.keys() - added:
method OrderedDict.pop(key, default=…) - added:
method OrderedDict.popitem(last=…) - added:
method OrderedDict.setdefault(key, default=…) - added:
method OrderedDict.update(*args, **kwds) - added:
method OrderedDict.values() - added:
method OrderedDict.viewitems() - added:
method OrderedDict.viewkeys() - added:
method OrderedDict.viewvalues() - added:
static method OrderedDict.fromkeys(iterable, value=…)
- added:
export SelectFromModel
- added:
class SelectFromModel(BaseEstimator, SelectorMixin) - added:
constructor SelectFromModel(estimator, threshold=…, prefit=…) - added:
method SelectFromModel.fit(X, y=…, **fit_params) - added:
method SelectFromModel.partial_fit(X, y=…, **fit_params)
- changed:
constructor GridSearchCV(estimator, param_grid, scoring=…, loss_func=…, score_func=…, fit_params=…, n_jobs=…, iid=…, refit=…, cv=…, verbose=…, pre_dispatch=…, error_score=…) → constructor GridSearchCV(estimator, param_grid, scoring=…, fit_params=…, n_jobs=…, iid=…, refit=…, cv=…, verbose=…, pre_dispatch=…, error_score=…)
- removed:
class GMMHMM(_BaseHMM) - removed:
class GaussianHMM(_BaseHMM) - removed:
class MultinomialHMM(_BaseHMM) - removed:
constructor GMMHMM(n_components=…, n_mix=…, startprob=…, transmat=…, startprob_prior=…, transmat_prior=…, algorithm=…, gmms=…, covariance_type=…, covars_prior=…, random_state=…, n_iter=…, thresh=…, params=…, init_params=…) - removed:
constructor GaussianHMM(n_components=…, covariance_type=…, startprob=…, transmat=…, startprob_prior=…, transmat_prior=…, algorithm=…, means_prior=…, means_weight=…, covars_prior=…, covars_weight=…, random_state=…, n_iter=…, thresh=…, params=…, init_params=…) - removed:
constructor MultinomialHMM(n_components=…, startprob=…, transmat=…, startprob_prior=…, transmat_prior=…, algorithm=…, random_state=…, n_iter=…, thresh=…, params=…, init_params=…) - removed:
field GaussianHMM.covars_ - removed:
field GaussianHMM.means_ - removed:
field MultinomialHMM.emissionprob_ - removed:
function normalize(A, axis=…) - removed:
method GaussianHMM.fit(obs) - removed:
method MultinomialHMM.fit(obs, **kwargs) - removed:
property GMMHMM.covariance_type - removed:
property GaussianHMM.covariance_type - removed:
variable decoder_algorithms
- removed:
class LDA(BaseEstimator, LinearClassifierMixin, TransformerMixin) - removed:
constructor LDA(solver=…, shrinkage=…, priors=…, n_components=…, store_covariance=…, tol=…) - removed:
method LDA.fit(X, y, store_covariance=…, tol=…) - removed:
method LDA.predict_log_proba(X) - removed:
method LDA.predict_proba(X) - removed:
method LDA.transform(X)
- changed:
method LinearRegression.fit(X, y, n_jobs=…) → method LinearRegression.fit(X, y, sample_weight=…) - added:
function make_dataset(X, y, sample_weight, random_state=…) - added:
property LinearRegression.residues_ - added:
variable SPARSE_INTERCEPT_DECAY
- changed:
function enet_path(X, y, l1_ratio=…, eps=…, n_alphas=…, alphas=…, precompute=…, Xy=…, copy_X=…, coef_init=…, verbose=…, return_n_iter=…, positive=…, **params) → function enet_path(X, y, l1_ratio=…, eps=…, n_alphas=…, alphas=…, precompute=…, Xy=…, copy_X=…, coef_init=…, verbose=…, return_n_iter=…, positive=…, check_input=…, **params) - changed:
method ElasticNet.fit(X, y) → method ElasticNet.fit(X, y, check_input=…)
- changed:
constructor Lars(fit_intercept=…, verbose=…, normalize=…, precompute=…, n_nonzero_coefs=…, eps=…, copy_X=…, fit_path=…) → constructor Lars(fit_intercept=…, verbose=…, normalize=…, precompute=…, n_nonzero_coefs=…, eps=…, copy_X=…, fit_path=…, positive=…) - changed:
constructor LarsCV(fit_intercept=…, verbose=…, max_iter=…, normalize=…, precompute=…, cv=…, max_n_alphas=…, n_jobs=…, eps=…, copy_X=…) → constructor LarsCV(fit_intercept=…, verbose=…, max_iter=…, normalize=…, precompute=…, cv=…, max_n_alphas=…, n_jobs=…, eps=…, copy_X=…, positive=…) - changed:
constructor LassoLars(alpha=…, fit_intercept=…, verbose=…, normalize=…, precompute=…, max_iter=…, eps=…, copy_X=…, fit_path=…) → constructor LassoLars(alpha=…, fit_intercept=…, verbose=…, normalize=…, precompute=…, max_iter=…, eps=…, copy_X=…, fit_path=…, positive=…) - changed:
constructor LassoLarsIC(criterion=…, fit_intercept=…, verbose=…, normalize=…, precompute=…, max_iter=…, eps=…, copy_X=…) → constructor LassoLarsIC(criterion=…, fit_intercept=…, verbose=…, normalize=…, precompute=…, max_iter=…, eps=…, copy_X=…, positive=…) - changed:
function lars_path(X, y, Xy=…, Gram=…, max_iter=…, alpha_min=…, method=…, copy_X=…, eps=…, copy_Gram=…, verbose=…, return_path=…, return_n_iter=…) → function lars_path(X, y, Xy=…, Gram=…, max_iter=…, alpha_min=…, method=…, copy_X=…, eps=…, copy_Gram=…, verbose=…, return_path=…, return_n_iter=…, positive=…)
- changed:
constructor LogisticRegression(penalty=…, dual=…, tol=…, C=…, fit_intercept=…, intercept_scaling=…, class_weight=…, random_state=…, solver=…, max_iter=…, multi_class=…, verbose=…) → constructor LogisticRegression(penalty=…, dual=…, tol=…, C=…, fit_intercept=…, intercept_scaling=…, class_weight=…, random_state=…, solver=…, max_iter=…, multi_class=…, verbose=…, warm_start=…, n_jobs=…) - changed:
constructor LogisticRegressionCV(Cs=…, fit_intercept=…, cv=…, dual=…, penalty=…, scoring=…, solver=…, tol=…, max_iter=…, class_weight=…, n_jobs=…, verbose=…, refit=…, intercept_scaling=…, multi_class=…) → constructor LogisticRegressionCV(Cs=…, fit_intercept=…, cv=…, dual=…, penalty=…, scoring=…, solver=…, tol=…, max_iter=…, class_weight=…, n_jobs=…, verbose=…, refit=…, intercept_scaling=…, multi_class=…, random_state=…) - changed:
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=…) → 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=…, random_state=…, check_input=…, max_squared_sum=…, sample_weight=…) - changed:
method LogisticRegression.fit(X, y) → method LogisticRegression.fit(X, y, sample_weight=…) - changed:
method LogisticRegressionCV.fit(X, y) → method LogisticRegressionCV.fit(X, y, sample_weight=…)
- changed:
constructor PassiveAggressiveClassifier(C=…, fit_intercept=…, n_iter=…, shuffle=…, verbose=…, loss=…, n_jobs=…, random_state=…, warm_start=…) → constructor PassiveAggressiveClassifier(C=…, fit_intercept=…, n_iter=…, shuffle=…, verbose=…, loss=…, n_jobs=…, random_state=…, warm_start=…, class_weight=…) - changed:
constructor PassiveAggressiveRegressor(C=…, fit_intercept=…, n_iter=…, shuffle=…, verbose=…, loss=…, epsilon=…, random_state=…, class_weight=…, warm_start=…) → constructor PassiveAggressiveRegressor(C=…, fit_intercept=…, n_iter=…, shuffle=…, verbose=…, loss=…, epsilon=…, random_state=…, warm_start=…)
- changed:
constructor Ridge(alpha=…, fit_intercept=…, normalize=…, copy_X=…, max_iter=…, tol=…, solver=…) → constructor Ridge(alpha=…, fit_intercept=…, normalize=…, copy_X=…, max_iter=…, tol=…, solver=…, random_state=…) - changed:
constructor RidgeClassifier(alpha=…, fit_intercept=…, normalize=…, copy_X=…, max_iter=…, tol=…, class_weight=…, solver=…) → constructor RidgeClassifier(alpha=…, fit_intercept=…, normalize=…, copy_X=…, max_iter=…, tol=…, class_weight=…, solver=…, random_state=…) - changed:
function ridge_regression(X, y, alpha, sample_weight=…, solver=…, max_iter=…, tol=…, verbose=…) → function ridge_regression(X, y, alpha, sample_weight=…, solver=…, max_iter=…, tol=…, verbose=…, random_state=…, return_n_iter=…, return_intercept=…) - changed:
method RidgeClassifier.fit(X, y) → method RidgeClassifier.fit(X, y, sample_weight=…)
- added:
function get_auto_step_size(max_squared_sum, alpha_scaled, loss, fit_intercept) - added:
function sag_solver(X, y, sample_weight=…, loss=…, alpha=…, max_iter=…, tol=…, verbose=…, random_state=…, check_input=…, max_squared_sum=…, warm_start_mem=…)
- removed:
variable SPARSE_INTERCEPT_DECAY - changed:
method BaseSGDClassifier.fit(X, y, coef_init=…, intercept_init=…, class_weight=…, sample_weight=…) → method BaseSGDClassifier.fit(X, y, coef_init=…, intercept_init=…, sample_weight=…)
- changed:
constructor TSNE(n_components=…, perplexity=…, early_exaggeration=…, learning_rate=…, n_iter=…, metric=…, init=…, verbose=…, random_state=…) → constructor TSNE(n_components=…, perplexity=…, early_exaggeration=…, learning_rate=…, n_iter=…, n_iter_without_progress=…, min_grad_norm=…, metric=…, init=…, verbose=…, random_state=…, method=…, angle=…)
- added:
export label_ranking_loss
- added:
function cohen_kappa_score(y1, y2, labels=…)
- changed:
function check_pairwise_arrays(X, Y) → function check_pairwise_arrays(X, Y, precomputed=…) - changed:
function cosine_similarity(X, Y=…) → function cosine_similarity(X, Y=…, dense_output=…) - changed:
function euclidean_distances(X, Y=…, Y_norm_squared=…, squared=…) → function euclidean_distances(X, Y=…, Y_norm_squared=…, squared=…, X_norm_squared=…) - added:
function laplacian_kernel(X, Y=…, gamma=…)
- changed:
function roc_curve(y_true, y_score, pos_label=…, sample_weight=…) → function roc_curve(y_true, y_score, pos_label=…, sample_weight=…, drop_intermediate=…) - added:
function label_ranking_loss(y_true, y_score, sample_weight=…)
- changed:
function explained_variance_score(y_true, y_pred, sample_weight=…) → function explained_variance_score(y_true, y_pred, sample_weight=…, multioutput=…) - changed:
function mean_absolute_error(y_true, y_pred, sample_weight=…) → function mean_absolute_error(y_true, y_pred, sample_weight=…, multioutput=…) - changed:
function mean_squared_error(y_true, y_pred, sample_weight=…) → function mean_squared_error(y_true, y_pred, sample_weight=…, multioutput=…) - changed:
function r2_score(y_true, y_pred, sample_weight=…) → function r2_score(y_true, y_pred, sample_weight=…, multioutput=…)
- removed:
method DPGMM.fit(X, y=…)
- changed:
constructor GMM(n_components=…, covariance_type=…, random_state=…, thresh=…, tol=…, 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=…, verbose=…) - added:
method GMM.fit_predict(X, y=…)
- changed:
method GaussianNB.fit(X, y) → method GaussianNB.fit(X, y, sample_weight=…) - changed:
method GaussianNB.partial_fit(X, y, classes=…) → method GaussianNB.partial_fit(X, y, classes=…, sample_weight=…)
- changed:
constructor KNeighborsClassifier(n_neighbors=…, weights=…, algorithm=…, leaf_size=…, p=…, metric=…, metric_params=…, **kwargs) → constructor KNeighborsClassifier(n_neighbors=…, weights=…, algorithm=…, leaf_size=…, p=…, metric=…, metric_params=…, n_jobs=…, **kwargs)
- changed:
constructor KNeighborsRegressor(n_neighbors=…, weights=…, algorithm=…, leaf_size=…, p=…, metric=…, metric_params=…, **kwargs) → constructor KNeighborsRegressor(n_neighbors=…, weights=…, algorithm=…, leaf_size=…, p=…, metric=…, metric_params=…, n_jobs=…, **kwargs)
- changed:
constructor NearestNeighbors(n_neighbors=…, radius=…, algorithm=…, leaf_size=…, metric=…, p=…, metric_params=…, **kwargs) → constructor NearestNeighbors(n_neighbors=…, radius=…, algorithm=…, leaf_size=…, metric=…, p=…, metric_params=…, n_jobs=…, **kwargs)
- added:
method Pipeline.fit_predict(X, y=…, **fit_params) - added:
property Pipeline.named_steps
- added:
export FunctionTransformer - added:
export MaxAbsScaler - added:
export RobustScaler - added:
export maxabs_scale - added:
export minmax_scale - added:
export robust_scale
- added:
class MaxAbsScaler(BaseEstimator, TransformerMixin) - added:
class RobustScaler(BaseEstimator, TransformerMixin) - added:
constructor MaxAbsScaler(copy=…) - added:
constructor RobustScaler(with_centering=…, with_scaling=…, copy=…) - added:
function maxabs_scale(X, axis=…, copy=…) - added:
function minmax_scale(X, feature_range=…, axis=…, copy=…) - added:
function robust_scale(X, axis=…, with_centering=…, with_scaling=…, copy=…) - added:
method MaxAbsScaler.fit(X, y=…) - added:
method MaxAbsScaler.inverse_transform(X) - added:
method MaxAbsScaler.partial_fit(X, y=…) - added:
method MaxAbsScaler.transform(X, y=…) - added:
method MinMaxScaler.partial_fit(X, y=…) - added:
method RobustScaler.fit(X, y=…) - added:
method RobustScaler.inverse_transform(X) - added:
method RobustScaler.transform(X, y=…) - added:
method StandardScaler.partial_fit(X, y=…) - added:
property MinMaxScaler.data_min - added:
property MinMaxScaler.data_range - added:
property StandardScaler.std_
- removed:
property LabelBinarizer.indicator_matrix_ - removed:
property LabelBinarizer.multilabel_ - changed:
function label_binarize(y, classes, neg_label=…, pos_label=…, sparse_output=…, multilabel=…) → function label_binarize(y, classes, neg_label=…, pos_label=…, sparse_output=…)
- removed:
class QDA(BaseEstimator, ClassifierMixin) - removed:
constructor QDA(priors=…, reg_param=…) - removed:
method QDA.decision_function(X) - removed:
method QDA.fit(X, y, store_covariances=…, tol=…) - removed:
method QDA.predict(X) - removed:
method QDA.predict_log_proba(X) - removed:
method QDA.predict_proba(X)
- changed:
class BaseSVC(BaseLibSVM, ClassifierMixin) → class BaseSVC(six.with_metaclass(ABCMeta, BaseLibSVM, ClassifierMixin)) - added:
constructor BaseSVC(impl, kernel, degree, gamma, coef0, tol, C, nu, shrinking, probability, cache_size, class_weight, verbose, max_iter, decision_function_shape, random_state) - added:
method BaseSVC.decision_function(X)
- changed:
constructor NuSVC(nu=…, kernel=…, degree=…, gamma=…, coef0=…, shrinking=…, probability=…, tol=…, cache_size=…, verbose=…, max_iter=…, random_state=…) → constructor NuSVC(nu=…, kernel=…, degree=…, gamma=…, coef0=…, shrinking=…, probability=…, tol=…, cache_size=…, class_weight=…, verbose=…, max_iter=…, decision_function_shape=…, random_state=…) - changed:
constructor SVC(C=…, kernel=…, degree=…, gamma=…, coef0=…, shrinking=…, probability=…, tol=…, cache_size=…, class_weight=…, verbose=…, max_iter=…, random_state=…) → constructor SVC(C=…, kernel=…, degree=…, gamma=…, coef0=…, shrinking=…, probability=…, tol=…, cache_size=…, class_weight=…, verbose=…, max_iter=…, decision_function_shape=…, random_state=…) - added:
method OneClassSVM.decision_function(X)
- changed:
function export_graphviz(decision_tree, out_file=…, feature_names=…, max_depth=…) → function export_graphviz(decision_tree, out_file=…, max_depth=…, feature_names=…, class_names=…, label=…, filled=…, leaves_parallel=…, impurity=…, node_ids=…, proportion=…, rotate=…, rounded=…, special_characters=…)
- changed:
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=…) → 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=…, presort=…) - changed:
constructor DecisionTreeRegressor(criterion=…, splitter=…, max_depth=…, min_samples_split=…, min_samples_leaf=…, min_weight_fraction_leaf=…, max_features=…, random_state=…, 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=…, presort=…)
- removed:
export warn_if_not_float
- removed:
function check_class_weight_auto_classifiers(name, Classifier, X_train, y_train, X_test, y_test, weights) - removed:
function check_class_weight_auto_linear_classifier(name, Classifier) - removed:
function check_classifiers_input_shapes(name, Classifier) - removed:
function check_classifiers_pickle(name, Classifier) - removed:
function check_regressors_pickle(name, Regressor) - removed:
function check_transformer(name, Transformer) - removed:
function check_transformer_pickle(name, Transformer) - removed:
function set_fast_parameters(estimator) - added:
function check_class_weight_balanced_classifiers(name, Classifier, X_train, y_train, X_test, y_test, weights) - added:
function check_class_weight_balanced_linear_classifier(name, Classifier) - added:
function check_classifiers_regression_target(name, Estimator) - added:
function check_estimator(Estimator) - added:
function check_estimators_fit_returns_self(name, Estimator) - added:
function check_estimators_pickle(name, Estimator) - added:
function check_fit1d_1feature(name, Estimator) - added:
function check_fit1d_1sample(name, Estimator) - added:
function check_fit2d_1feature(name, Estimator) - added:
function check_fit2d_1sample(name, Estimator) - added:
function check_fit2d_predict1d(name, Estimator) - added:
function check_get_params_invariance(name, estimator) - added:
function check_regressors_no_decision_function(name, Regressor) - added:
function check_supervised_y_2d(name, Estimator) - added:
function check_transformer_general(name, Transformer) - added:
function set_testing_parameters(estimator) - added:
variable DEPRECATED_TRANSFORM - added:
variable MULTI_OUTPUT
- removed:
function logistic_sigmoid(X, log=…, out=…) - added:
function softmax(X, copy=…)
- added:
function array_equal(a1, a2) - added:
function makedirs(name, mode=…, exist_ok=…) - added:
variable makedirs
- changed:
class CheckingClassifier(BaseEstimator) → class CheckingClassifier(BaseEstimator, ClassifierMixin)
- removed:
function is_label_indicator_matrix(y) - removed:
function is_sequence_of_sequences(y) - added:
function check_classification_targets(y)
- removed:
function func(x) - removed:
function func_grad_hess(x) - removed:
variable A - removed:
variable out - removed:
variable x0 - changed:
function newton_cg(func_grad_hess, func, grad, x0, args=…, eps=…, tol=…, maxiter=…) → function newton_cg(grad_hess, func, grad, x0, args=…, tol=…, maxiter=…, maxinner=…, line_search=…, warn=…)
- added:
function incr_mean_variance_axis(X, axis, last_mean, last_var, last_n)
- removed:
function warn_if_not_float(X, estimator=…) - changed:
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=…) → 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=…, warn_on_dtype=…, estimator=…) - changed:
function check_array(array, accept_sparse=…, dtype=…, order=…, copy=…, force_all_finite=…, ensure_2d=…, allow_nd=…, ensure_min_samples=…, ensure_min_features=…) → function check_array(array, accept_sparse=…, dtype=…, order=…, copy=…, force_all_finite=…, ensure_2d=…, allow_nd=…, ensure_min_samples=…, ensure_min_features=…, warn_on_dtype=…, estimator=…) - added:
function check_non_negative(X, whom) - added:
variable FLOAT_DTYPES