Upgrading scikit-learn 0.12 to 0.13
Unknown0.12.1 → 0.13.1
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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:
export clone - added:
variable test
- added:
function spectral_embedding(*args, **kwargs)
- removed:
class SpectralClustering(BaseEstimator, ClusterMixin) - removed:
constructor SpectralClustering(n_clusters=…, mode=…, random_state=…, n_init=…, gamma=…, affinity=…, n_neighbors=…, k=…, precomputed=…) - removed:
function spectral_clustering(affinity, n_clusters=…, n_components=…, mode=…, random_state=…, n_init=…, k=…) - removed:
function spectral_embedding(adjacency, n_components=…, mode=…, random_state=…) - removed:
method SpectralClustering.fit(X)
- changed:
method EmpiricalCovariance.fit(X) → method EmpiricalCovariance.fit(X, y=…) - changed:
method EmpiricalCovariance.score(X_test) → method EmpiricalCovariance.score(X_test, y=…)
- changed:
constructor EllipticEnvelope(store_precision=…, assume_centered=…, support_fraction=…, contamination=…) → constructor EllipticEnvelope(store_precision=…, assume_centered=…, support_fraction=…, contamination=…, random_state=…) - changed:
method EllipticEnvelope.fit(X) → method EllipticEnvelope.fit(X, y=…)
- changed:
method LedoitWolf.fit(X) → method LedoitWolf.fit(X, y=…) - changed:
method OAS.fit(X) → method OAS.fit(X, y=…) - changed:
method ShrunkCovariance.fit(X) → method ShrunkCovariance.fit(X, y=…)
- changed:
constructor Bootstrap(n, n_bootstraps=…, train_size=…, test_size=…, n_train=…, n_test=…, random_state=…) → constructor Bootstrap(n, n_iter=…, train_size=…, test_size=…, random_state=…, n_bootstraps=…) - changed:
constructor KFold(n, k, indices=…, shuffle=…, random_state=…) → constructor KFold(n, n_folds=…, indices=…, shuffle=…, random_state=…, k=…) - changed:
constructor ShuffleSplit(n, n_iterations=…, test_size=…, train_size=…, indices=…, random_state=…, test_fraction=…, train_fraction=…) → constructor ShuffleSplit(n, n_iter=…, test_size=…, train_size=…, indices=…, random_state=…, n_iterations=…) - changed:
constructor StratifiedKFold(y, k, indices=…) → constructor StratifiedKFold(y, n_folds=…, indices=…, k=…) - changed:
constructor StratifiedShuffleSplit(y, n_iterations=…, test_size=…, train_size=…, indices=…, random_state=…) → constructor StratifiedShuffleSplit(y, n_iter=…, test_size=…, train_size=…, indices=…, random_state=…, n_iterations=…) - changed:
function cross_val_score(estimator, X, y=…, score_func=…, cv=…, n_jobs=…, verbose=…) → function cross_val_score(estimator, X, y=…, score_func=…, cv=…, n_jobs=…, verbose=…, fit_params=…)
- added:
export fetch_california_housing - added:
export fetch_covtype
- added:
function fetch_covtype(data_home=…, download_if_missing=…, random_state=…, shuffle=…) - added:
variable URL - added:
variable logger
- added:
function fetch_olivetti_faces(data_home=…, shuffle=…, random_state=…, download_if_missing=…) - added:
variable DATA_URL - added:
variable MODULE_DOCS - added:
variable TARGET_FILENAME
- changed:
function dump_svmlight_file(X, y, f, zero_based=…, comment=…) → function dump_svmlight_file(X, y, f, zero_based=…, comment=…, query_id=…) - changed:
function load_svmlight_file(f, n_features=…, dtype=…, multilabel=…, zero_based=…) → function load_svmlight_file(f, n_features=…, dtype=…, multilabel=…, zero_based=…, query_id=…) - changed:
function load_svmlight_files(files, n_features=…, dtype=…, multilabel=…, zero_based=…) → function load_svmlight_files(files, n_features=…, dtype=…, multilabel=…, zero_based=…, query_id=…)
- added:
export FactorAnalysis
- changed:
constructor ProjectedGradientNMF(n_components=…, init=…, sparseness=…, beta=…, eta=…, tol=…, max_iter=…, nls_max_iter=…) → constructor ProjectedGradientNMF(n_components=…, init=…, sparseness=…, beta=…, eta=…, tol=…, max_iter=…, nls_max_iter=…, random_state=…) - added:
function safe_vstack(Xs)
- changed:
constructor MiniBatchSparsePCA(n_components=…, alpha=…, ridge_alpha=…, n_iter=…, callback=…, chunk_size=…, verbose=…, shuffle=…, n_jobs=…, method=…, random_state=…) → constructor MiniBatchSparsePCA(n_components=…, alpha=…, ridge_alpha=…, n_iter=…, callback=…, batch_size=…, verbose=…, shuffle=…, n_jobs=…, method=…, random_state=…, chunk_size=…)
- added:
class DummyClassifier(BaseEstimator, ClassifierMixin) - added:
class DummyRegressor(BaseEstimator, RegressorMixin) - added:
constructor DummyClassifier(strategy=…, random_state=…) - added:
method DummyClassifier.fit(X, y) - added:
method DummyClassifier.predict(X) - added:
method DummyClassifier.predict_log_proba(X) - added:
method DummyClassifier.predict_proba(X) - added:
method DummyRegressor.fit(X, y) - added:
method DummyRegressor.predict(X)
- added:
export RandomTreesEmbedding - added:
export forest - added:
export gradient_boosting - added:
export partial_dependence
- removed:
method BaseGradientBoosting.fit_stage(i, X, X_argsorted, y, y_pred, sample_mask) - changed:
constructor BaseGradientBoosting(loss, learn_rate, n_estimators, min_samples_split, min_samples_leaf, max_depth, init, subsample, max_features, random_state, alpha=…) → constructor BaseGradientBoosting(loss, learning_rate, n_estimators, min_samples_split, min_samples_leaf, max_depth, init, subsample, max_features, random_state, alpha=…, verbose=…, learn_rate=…) - changed:
constructor GradientBoostingClassifier(loss=…, learn_rate=…, n_estimators=…, subsample=…, min_samples_split=…, min_samples_leaf=…, max_depth=…, init=…, random_state=…, max_features=…) → constructor GradientBoostingClassifier(loss=…, learning_rate=…, n_estimators=…, subsample=…, min_samples_split=…, min_samples_leaf=…, max_depth=…, init=…, random_state=…, max_features=…, verbose=…, learn_rate=…) - changed:
constructor GradientBoostingRegressor(loss=…, learn_rate=…, n_estimators=…, subsample=…, min_samples_split=…, min_samples_leaf=…, max_depth=…, init=…, random_state=…, max_features=…, alpha=…) → constructor GradientBoostingRegressor(loss=…, learning_rate=…, n_estimators=…, subsample=…, min_samples_split=…, min_samples_leaf=…, max_depth=…, init=…, random_state=…, max_features=…, alpha=…, verbose=…, learn_rate=…) - changed:
method LeastSquaresError.update_terminal_regions(tree, X, y, residual, y_pred, sample_mask, learn_rate=…, k=…) → method LeastSquaresError.update_terminal_regions(tree, X, y, residual, y_pred, sample_mask, learning_rate=…, k=…) - changed:
method LossFunction.update_terminal_regions(tree, X, y, residual, y_pred, sample_mask, learn_rate=…, k=…) → method LossFunction.update_terminal_regions(tree, X, y, residual, y_pred, sample_mask, learning_rate=…, k=…)
- added:
function partial_dependence(gbrt, target_variables, grid=…, X=…, percentiles=…, grid_resolution=…) - added:
function plot_partial_dependence(gbrt, X, features, feature_names=…, label=…, n_cols=…, grid_resolution=…, percentiles=…, n_jobs=…, verbose=…, ax=…, line_kw=…, contour_kw=…, **fig_kw)
- added:
function eq_repr(value, repr=…) - added:
function fix_frame_records_filenames(records) - added:
function format_exc(etype, evalue, etb, context=…, tb_offset=…) - added:
function format_outer_frames(context=…, stack_start=…, stack_end=…, ignore_ipython=…) - added:
function format_records(records) - added:
function safe_repr(value) - added:
function uniq_stable(elems) - added:
variable INDENT - added:
variable PY3 - added:
variable generate_tokens
- removed:
variable StringIO - changed:
class Hasher(pickle.Pickler) → class Hasher(Pickler) - added:
method Hasher.save_set(set_items) - added:
variable Pickler
- added:
class JobLibCollisionWarning(UserWarning) - added:
class MemorizedFunc(Logger) - added:
class Memory(Logger) - added:
constructor MemorizedFunc(func, cachedir, ignore=…, mmap_mode=…, compress=…, verbose=…, timestamp=…) - added:
constructor Memory(cachedir, mmap_mode=…, compress=…, verbose=…) - added:
function extract_first_line(func_code) - added:
method MemorizedFunc.call(*args, **kwargs) - added:
method MemorizedFunc.clear(warn=…) - added:
method MemorizedFunc.format_call(*args, **kwds) - added:
method MemorizedFunc.format_signature(func, *args, **kwds) - added:
method MemorizedFunc.get_output_dir(*args, **kwargs) - added:
method MemorizedFunc.load_output(output_dir) - added:
method Memory.cache(func=…, ignore=…, verbose=…, mmap_mode=…) - added:
method Memory.clear(warn=…) - added:
method Memory.eval(func, *args, **kwargs) - added:
variable FIRST_LINE_TEXT
- added:
class NDArrayWrapper(object) - added:
class NumpyPickler(Pickler) - added:
class NumpyUnpickler(Unpickler) - added:
class ZNDArrayWrapper(NDArrayWrapper) - added:
class ZipNumpyUnpickler(NumpyUnpickler) - added:
constructor NDArrayWrapper(filename, subclass) - added:
constructor NumpyPickler(filename, compress=…, cache_size=…) - added:
constructor NumpyUnpickler(filename, file_handle, mmap_mode=…) - added:
constructor ZNDArrayWrapper(filename, init_args, state) - added:
constructor ZipNumpyUnpickler(filename, file_handle) - added:
field NumpyUnpickler.dispatch - added:
function asbytes(s) - added:
function dump(value, filename, compress=…, cache_size=…) - added:
function load(filename, mmap_mode=…) - added:
function read_zfile(file_handle) - added:
function write_zfile(file_handle, data, compress=…) - added:
method NDArrayWrapper.read(unpickler) - added:
method NumpyPickler.close() - added:
method NumpyPickler.save(obj) - added:
method NumpyUnpickler.load_build() - added:
method ZNDArrayWrapper.read(unpickler) - added:
variable Pickler - added:
variable Unpickler - added:
variable asbytes
- added:
class CallBack(object) - added:
class ImmediateApply(object) - added:
class Parallel(Logger) - added:
class SafeFunction(object) - added:
class WorkerInterrupt(Exception) - added:
constructor CallBack(index, parallel) - added:
constructor ImmediateApply(func, args, kwargs) - added:
constructor Parallel(n_jobs=…, verbose=…, pre_dispatch=…) - added:
constructor SafeFunction(func) - added:
function cpu_count() - added:
function delayed(function) - added:
method ImmediateApply.get() - added:
method Parallel.dispatch(func, args, kwargs) - added:
method Parallel.dispatch_next() - added:
method Parallel.print_progress(index) - added:
method Parallel.retrieve() - added:
variable multiprocessing
- added:
export FeatureHasher
- added:
class FeatureHasher(BaseEstimator, TransformerMixin) - added:
constructor FeatureHasher(n_features=…, input_type=…, dtype=…, non_negative=…) - added:
method FeatureHasher.fit(X=…, y=…) - added:
method FeatureHasher.transform(raw_X, y=…)
- added:
function configuration(parent_package=…, top_path=…)
- changed:
class GenericUnivariateSelect(_AbstractUnivariateFilter) → class GenericUnivariateSelect(_PvalueFilter) - changed:
class SelectFdr(_AbstractUnivariateFilter) → class SelectFdr(_PvalueFilter) - changed:
class SelectFpr(_AbstractUnivariateFilter) → class SelectFpr(_PvalueFilter) - changed:
class SelectFwe(_AbstractUnivariateFilter) → class SelectFwe(_PvalueFilter) - changed:
class SelectKBest(_AbstractUnivariateFilter) → class SelectKBest(_ScoreFilter) - changed:
class SelectPercentile(_AbstractUnivariateFilter) → class SelectPercentile(_ScoreFilter)
- added:
method MultinomialHMM.fit(obs, **kwargs)
- added:
class IsotonicRegression(BaseEstimator, TransformerMixin, RegressorMixin) - added:
constructor IsotonicRegression(y_min=…, y_max=…) - added:
function isotonic_regression(y, weight=…, y_min=…, y_max=…) - added:
method IsotonicRegression.fit(X, y, weight=…) - added:
method IsotonicRegression.fit_transform(X, y, weight=…) - added:
method IsotonicRegression.predict(T) - added:
method IsotonicRegression.transform(T)
- added:
class Nystroem(BaseEstimator, TransformerMixin) - added:
constructor Nystroem(kernel=…, gamma=…, coef0=…, degree=…, n_components=…, random_state=…) - added:
method Nystroem.fit(X, y=…) - added:
method Nystroem.transform(X)
- removed:
export sparse - added:
export PassiveAggressiveClassifier - added:
export PassiveAggressiveRegressor
- changed:
function center_data(X, y, fit_intercept, normalize=…, copy=…) → function center_data(X, y, fit_intercept, normalize=…, copy=…, sample_weight=…) - added:
class LinearClassifierMixin(ClassifierMixin) - added:
method LinearClassifierMixin.decision_function(X) - added:
method LinearClassifierMixin.predict(X)
- changed:
class LogisticRegression(BaseLibLinear, ClassifierMixin, SelectorMixin) → class LogisticRegression(BaseLibLinear, LinearClassifierMixin, SelectorMixin) - changed:
constructor LogisticRegression(penalty=…, dual=…, tol=…, C=…, fit_intercept=…, intercept_scaling=…, class_weight=…) → constructor LogisticRegression(penalty=…, dual=…, tol=…, C=…, fit_intercept=…, intercept_scaling=…, class_weight=…, random_state=…)
- added:
class PassiveAggressiveClassifier(BaseSGDClassifier) - added:
class PassiveAggressiveRegressor(BaseSGDRegressor) - added:
constructor PassiveAggressiveClassifier(C=…, fit_intercept=…, n_iter=…, shuffle=…, verbose=…, loss=…, n_jobs=…, random_state=…, warm_start=…) - added:
constructor PassiveAggressiveRegressor(C=…, fit_intercept=…, n_iter=…, shuffle=…, verbose=…, loss=…, epsilon=…, random_state=…, class_weight=…, warm_start=…) - added:
method PassiveAggressiveClassifier.fit(X, y, coef_init=…, intercept_init=…) - added:
method PassiveAggressiveClassifier.partial_fit(X, y, classes=…) - added:
method PassiveAggressiveRegressor.fit(X, y, coef_init=…, intercept_init=…) - added:
method PassiveAggressiveRegressor.partial_fit(X, y)
- changed:
class Perceptron(SGDClassifier) → class Perceptron(BaseSGDClassifier, SelectorMixin) - changed:
constructor Perceptron(penalty=…, alpha=…, fit_intercept=…, n_iter=…, shuffle=…, verbose=…, eta0=…, n_jobs=…, seed=…, class_weight=…, warm_start=…) → constructor Perceptron(penalty=…, alpha=…, fit_intercept=…, n_iter=…, shuffle=…, verbose=…, eta0=…, n_jobs=…, random_state=…, class_weight=…, warm_start=…, seed=…)
- removed:
method RidgeClassifier.predict(X) - removed:
method RidgeClassifierCV.predict(X) - changed:
class RidgeClassifier(_BaseRidge, ClassifierMixin) → class RidgeClassifier(LinearClassifierMixin, _BaseRidge) - changed:
class RidgeClassifierCV(_BaseRidgeCV, ClassifierMixin) → class RidgeClassifierCV(LinearClassifierMixin, _BaseRidgeCV) - changed:
constructor Ridge(alpha=…, fit_intercept=…, normalize=…, copy_X=…, tol=…) → constructor Ridge(alpha=…, fit_intercept=…, normalize=…, copy_X=…, max_iter=…, tol=…, solver=…) - changed:
constructor RidgeClassifier(alpha=…, fit_intercept=…, normalize=…, copy_X=…, tol=…, class_weight=…) → constructor RidgeClassifier(alpha=…, fit_intercept=…, normalize=…, copy_X=…, max_iter=…, tol=…, class_weight=…, solver=…) - changed:
function ridge_regression(X, y, alpha, sample_weight=…, solver=…, tol=…) → function ridge_regression(X, y, alpha, sample_weight=…, solver=…, max_iter=…, tol=…) - added:
method Ridge.fit(X, y, sample_weight=…, solver=…) - added:
property RidgeClassifier.classes_ - added:
property RidgeClassifierCV.classes_
- removed:
export ElasticNet - removed:
export Lasso - removed:
export LogisticRegression - removed:
export SGDClassifier - removed:
export SGDRegressor
- removed:
class ElasticNet(ElasticNet) - removed:
class Lasso(Lasso)
- removed:
class LogisticRegression(LogisticRegression)
- removed:
class SGDClassifier(SGDClassifier_) - removed:
class SGDRegressor(SGDRegressor_)
- removed:
constructor SGDClassifier(loss=…, penalty=…, alpha=…, rho=…, fit_intercept=…, n_iter=…, shuffle=…, verbose=…, epsilon=…, n_jobs=…, seed=…, learning_rate=…, eta0=…, power_t=…, class_weight=…, warm_start=…) - removed:
constructor SGDRegressor(loss=…, penalty=…, alpha=…, rho=…, fit_intercept=…, n_iter=…, shuffle=…, verbose=…, epsilon=…, p=…, seed=…, learning_rate=…, eta0=…, power_t=…, warm_start=…) - removed:
field SGDClassifier.loss_functions - removed:
field SGDRegressor.loss_functions - removed:
method BaseSGD.predict(X) - removed:
method SGDClassifier.decision_function(X) - removed:
method SGDClassifier.fit(X, y, coef_init=…, intercept_init=…, class_weight=…, sample_weight=…) - removed:
method SGDClassifier.partial_fit(X, y, classes=…, class_weight=…, sample_weight=…) - removed:
method SGDClassifier.predict(X) - removed:
method SGDRegressor.decision_function(X) - removed:
method SGDRegressor.fit(X, y, coef_init=…, intercept_init=…, sample_weight=…) - removed:
method SGDRegressor.partial_fit(X, y, sample_weight=…) - removed:
method SGDRegressor.predict(X) - removed:
property SGDClassifier.classes - changed:
class SGDClassifier(BaseSGD, ClassifierMixin, SelectorMixin) → class SGDClassifier(BaseSGDClassifier, SelectorMixin) - changed:
class SGDRegressor(BaseSGD, RegressorMixin, SelectorMixin) → class SGDRegressor(BaseSGDRegressor, SelectorMixin) - changed:
constructor BaseSGD(loss, penalty=…, alpha=…, rho=…, fit_intercept=…, n_iter=…, shuffle=…, verbose=…, epsilon=…, seed=…, 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=…, rho=…) - changed:
function fit_binary(est, i, X, y, n_iter, pos_weight, neg_weight, sample_weight) → function fit_binary(est, i, X, y, alpha, C, learning_rate, n_iter, pos_weight, neg_weight, sample_weight) - added:
class BaseSGDClassifier(BaseSGD, LinearClassifierMixin) - added:
class BaseSGDRegressor(BaseSGD, RegressorMixin) - added:
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=…, rho=…, seed=…) - added:
constructor BaseSGDRegressor(loss=…, penalty=…, alpha=…, l1_ratio=…, fit_intercept=…, n_iter=…, shuffle=…, verbose=…, epsilon=…, p=…, random_state=…, learning_rate=…, eta0=…, power_t=…, warm_start=…, rho=…) - added:
field BaseSGDClassifier.loss_functions - added:
field BaseSGDRegressor.loss_functions - added:
method BaseSGDClassifier.fit(X, y, coef_init=…, intercept_init=…, class_weight=…, sample_weight=…) - added:
method BaseSGDClassifier.partial_fit(X, y, classes=…, sample_weight=…) - added:
method BaseSGDRegressor.decision_function(X) - added:
method BaseSGDRegressor.fit(X, y, coef_init=…, intercept_init=…, sample_weight=…) - added:
method BaseSGDRegressor.partial_fit(X, y, sample_weight=…) - added:
method BaseSGDRegressor.predict(X) - added:
property BaseSGDClassifier.seed
- added:
export SpectralEmbedding - added:
export spectral_embedding
- changed:
constructor Isomap(n_neighbors=…, n_components=…, eigen_solver=…, tol=…, max_iter=…, path_method=…, neighbors_algorithm=…, out_dim=…) → constructor Isomap(n_neighbors=…, n_components=…, eigen_solver=…, tol=…, max_iter=…, path_method=…, neighbors_algorithm=…)
- changed:
constructor LocallyLinearEmbedding(n_neighbors=…, n_components=…, reg=…, eigen_solver=…, tol=…, max_iter=…, method=…, hessian_tol=…, modified_tol=…, neighbors_algorithm=…, random_state=…, out_dim=…) → constructor LocallyLinearEmbedding(n_neighbors=…, n_components=…, reg=…, eigen_solver=…, tol=…, max_iter=…, method=…, hessian_tol=…, modified_tol=…, neighbors_algorithm=…, random_state=…) - changed:
function locally_linear_embedding(X, n_neighbors, n_components, reg=…, eigen_solver=…, tol=…, max_iter=…, method=…, hessian_tol=…, modified_tol=…, random_state=…, out_dim=…) → function locally_linear_embedding(X, n_neighbors, n_components, reg=…, eigen_solver=…, tol=…, max_iter=…, method=…, hessian_tol=…, modified_tol=…, random_state=…)
- added:
class SpectralEmbedding(BaseEstimator, TransformerMixin) - added:
constructor SpectralEmbedding(n_components=…, affinity=…, gamma=…, random_state=…, eigen_solver=…, n_neighbors=…) - added:
function spectral_embedding(adjacency, n_components=…, eigen_solver=…, random_state=…, eigen_tol=…, norm_laplacian=…, drop_first=…, mode=…) - added:
method SpectralEmbedding.fit(X, y=…) - added:
method SpectralEmbedding.fit_transform(X, y=…)
- removed:
export mean_square_error - removed:
export zero_one - removed:
export zero_one_score - added:
export accuracy_score - added:
export mean_absolute_error - added:
export silhouette_samples - added:
export zero_one_loss
- added:
function configuration(parent_package=…, top_path=…)
- removed:
function expected_mutual_information(contingency, n_samples)
- removed:
function mean_square_error(y_true, y_pred) - changed:
function auc(x, y) → function auc(x, y, reorder=…) - changed:
function roc_curve(y_true, y_score) → function roc_curve(y_true, y_score, pos_label=…) - changed:
function zero_one(y_true, y_pred) → function zero_one(y_true, y_pred, normalize=…) - added:
function accuracy_score(y_true, y_pred) - added:
function mean_absolute_error(y_true, y_pred) - added:
function zero_one_loss(y_true, y_pred, normalize=…)
- added:
function additive_chi2_kernel(X, Y=…) - added:
function chi2_kernel(X, Y=…, gamma=…) - added:
function cosine_similarity(X, Y=…)
- added:
function configuration(parent_package=…, top_path=…)
- removed:
method GMM.decode(X) - removed:
method GMM.rvs(n_samples=…, random_state=…) - changed:
method GMM.fit(X, **kwargs) → method GMM.fit(X)
- changed:
constructor OneVsOneClassifier(estimator) → constructor OneVsOneClassifier(estimator, n_jobs=…) - changed:
constructor OneVsRestClassifier(estimator) → constructor OneVsRestClassifier(estimator, n_jobs=…) - changed:
constructor OutputCodeClassifier(estimator, code_size=…, random_state=…) → constructor OutputCodeClassifier(estimator, code_size=…, random_state=…, n_jobs=…) - changed:
function fit_ecoc(estimator, X, y, code_size=…, random_state=…) → function fit_ecoc(estimator, X, y, code_size=…, random_state=…, n_jobs=…) - changed:
function fit_ovo(estimator, X, y) → function fit_ovo(estimator, X, y, n_jobs=…) - changed:
function fit_ovr(estimator, X, y) → function fit_ovr(estimator, X, y, n_jobs=…) - added:
function predict_proba_ovr(estimators, X, is_multilabel) - added:
method OneVsRestClassifier.predict_proba(X)
- changed:
constructor BernoulliNB(alpha=…, binarize=…, fit_prior=…) → constructor BernoulliNB(alpha=…, binarize=…, fit_prior=…, class_prior=…) - changed:
constructor MultinomialNB(alpha=…, fit_prior=…) → constructor MultinomialNB(alpha=…, fit_prior=…, class_prior=…)
- added:
class FeatureUnion(BaseEstimator, TransformerMixin) - added:
constructor FeatureUnion(transformer_list, n_jobs=…, transformer_weights=…) - added:
method FeatureUnion.fit(X, y=…) - added:
method FeatureUnion.fit_transform(X, y=…, **fit_params) - added:
method FeatureUnion.get_feature_names() - added:
method FeatureUnion.get_params(deep=…) - added:
method FeatureUnion.transform(X)
- removed:
class Scaler(BaseEstimator, TransformerMixin) - removed:
constructor Scaler(copy=…, with_mean=…, with_std=…) - removed:
method Scaler.fit(X, y=…) - removed:
method Scaler.inverse_transform(X, copy=…) - removed:
method Scaler.transform(X, y=…, copy=…) - added:
class MinMaxScaler(BaseEstimator, TransformerMixin) - added:
class StandardScaler(BaseEstimator, TransformerMixin) - added:
constructor MinMaxScaler(feature_range=…, copy=…) - added:
constructor StandardScaler(copy=…, with_mean=…, with_std=…) - added:
method MinMaxScaler.fit(X, y=…) - added:
method MinMaxScaler.inverse_transform(X) - added:
method MinMaxScaler.transform(X) - added:
method StandardScaler.fit(X, y=…) - added:
method StandardScaler.inverse_transform(X, copy=…) - added:
method StandardScaler.transform(X, y=…, copy=…)
- added:
property QDA.rotations - added:
property QDA.scalings
- added:
class GaussianRandomProjection(BaseRandomProjection) - added:
class SparseRandomProjection(BaseRandomProjection) - added:
constructor GaussianRandomProjection(n_components=…, eps=…, random_state=…) - added:
constructor SparseRandomProjection(n_components=…, density=…, eps=…, dense_output=…, random_state=…) - added:
function johnson_lindenstrauss_min_dim(n_samples, eps=…)
- changed:
constructor BaseLabelPropagation(kernel=…, gamma=…, n_neighbors=…, alpha=…, max_iters=…, tol=…) → constructor BaseLabelPropagation(kernel=…, gamma=…, n_neighbors=…, alpha=…, max_iter=…, tol=…, max_iters=…) - changed:
constructor LabelSpreading(kernel=…, gamma=…, n_neighbors=…, alpha=…, max_iters=…, tol=…) → constructor LabelSpreading(kernel=…, gamma=…, n_neighbors=…, alpha=…, max_iter=…, tol=…, max_iters=…)
- changed:
class LinearSVC(BaseLibLinear, ClassifierMixin, SelectorMixin) → class LinearSVC(BaseLibLinear, LinearClassifierMixin, SelectorMixin) - changed:
constructor NuSVC(nu=…, kernel=…, degree=…, gamma=…, coef0=…, shrinking=…, probability=…, tol=…, cache_size=…, verbose=…) → constructor NuSVC(nu=…, kernel=…, degree=…, gamma=…, coef0=…, shrinking=…, probability=…, tol=…, cache_size=…, verbose=…, max_iter=…) - changed:
constructor NuSVR(nu=…, C=…, kernel=…, degree=…, gamma=…, coef0=…, shrinking=…, probability=…, tol=…, cache_size=…, verbose=…) → constructor NuSVR(nu=…, C=…, kernel=…, degree=…, gamma=…, coef0=…, shrinking=…, probability=…, tol=…, cache_size=…, verbose=…, max_iter=…) - changed:
constructor OneClassSVM(kernel=…, degree=…, gamma=…, coef0=…, tol=…, nu=…, shrinking=…, cache_size=…, verbose=…) → constructor OneClassSVM(kernel=…, degree=…, gamma=…, coef0=…, tol=…, nu=…, shrinking=…, cache_size=…, verbose=…, max_iter=…) - changed:
constructor SVC(C=…, kernel=…, degree=…, gamma=…, coef0=…, shrinking=…, probability=…, tol=…, cache_size=…, class_weight=…, verbose=…) → constructor SVC(C=…, kernel=…, degree=…, gamma=…, coef0=…, shrinking=…, probability=…, tol=…, cache_size=…, class_weight=…, verbose=…, max_iter=…) - changed:
constructor SVR(kernel=…, degree=…, gamma=…, coef0=…, tol=…, C=…, epsilon=…, shrinking=…, probability=…, cache_size=…, verbose=…) → constructor SVR(kernel=…, degree=…, gamma=…, coef0=…, tol=…, C=…, epsilon=…, shrinking=…, probability=…, cache_size=…, verbose=…, max_iter=…)
- changed:
constructor SparseBaseLibSVM(impl, kernel, degree, gamma, coef0, tol, C, nu, epsilon, shrinking, probability, cache_size, class_weight, verbose) → constructor SparseBaseLibSVM(impl, kernel, degree, gamma, coef0, tol, C, nu, epsilon, shrinking, probability, cache_size, class_weight, verbose, max_iter)
- changed:
class LinearSVC(LinearSVC) → class LinearSVC(GeneralLinearSVC) - changed:
constructor NuSVC(nu=…, kernel=…, degree=…, gamma=…, coef0=…, shrinking=…, probability=…, tol=…, cache_size=…, class_weight=…, verbose=…) → constructor NuSVC(nu=…, kernel=…, degree=…, gamma=…, coef0=…, shrinking=…, probability=…, tol=…, cache_size=…, class_weight=…, verbose=…, max_iter=…) - changed:
constructor NuSVR(nu=…, C=…, kernel=…, degree=…, gamma=…, coef0=…, shrinking=…, epsilon=…, probability=…, tol=…, cache_size=…, verbose=…) → constructor NuSVR(nu=…, C=…, kernel=…, degree=…, gamma=…, coef0=…, shrinking=…, epsilon=…, probability=…, tol=…, cache_size=…, verbose=…, max_iter=…) - changed:
constructor OneClassSVM(kernel=…, degree=…, gamma=…, coef0=…, tol=…, nu=…, shrinking=…, probability=…, cache_size=…, verbose=…) → constructor OneClassSVM(kernel=…, degree=…, gamma=…, coef0=…, tol=…, nu=…, shrinking=…, probability=…, cache_size=…, verbose=…, max_iter=…) - changed:
constructor SVC(C=…, kernel=…, degree=…, gamma=…, coef0=…, shrinking=…, probability=…, tol=…, cache_size=…, class_weight=…, verbose=…) → constructor SVC(C=…, kernel=…, degree=…, gamma=…, coef0=…, shrinking=…, probability=…, tol=…, cache_size=…, class_weight=…, verbose=…, max_iter=…) - changed:
constructor SVR(kernel=…, degree=…, gamma=…, coef0=…, tol=…, C=…, epsilon=…, shrinking=…, probability=…, cache_size=…, verbose=…) → constructor SVR(kernel=…, degree=…, gamma=…, coef0=…, tol=…, C=…, epsilon=…, shrinking=…, probability=…, cache_size=…, verbose=…, max_iter=…)
- removed:
class ConvergenceWarning(Warning) - removed:
class deprecated(object) - removed:
constructor deprecated(extra=…) - removed:
function gen_even_slices(n, n_packs) - removed:
function resample(*arrays, **options) - removed:
function safe_mask(X, mask) - removed:
function safe_sqr(X, copy=…) - removed:
function shuffle(*arrays, **options) - added:
export array2d - added:
export as_float_array - added:
export assert_all_finite - added:
export atleast2d_or_csc - added:
export atleast2d_or_csr - added:
export check_arrays - added:
export check_random_state - added:
export compute_class_weight - added:
export safe_asarray - added:
export warn_if_not_float
- added:
function compute_class_weight(class_weight, classes, y)
- changed:
function randomized_range_finder(A, size, n_iterations, random_state=…) → function randomized_range_finder(A, size, n_iter, random_state=…, n_iterations=…) - changed:
function randomized_svd(M, n_components, n_oversamples=…, n_iterations=…, transpose=…, random_state=…) → function randomized_svd(M, n_components, n_oversamples=…, n_iter=…, transpose=…, flip_sign=…, random_state=…, n_iterations=…) - added:
function cartesian(arrays, out=…) - added:
function svd_flip(u, s, v)
- added:
variable bincount