Upgrading scikit-learn 0.13 to 0.14
Unknown0.13.1 → 0.14.1
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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.
- removed:
variable test
- added:
class BiclusterMixin(object) - added:
method BiclusterMixin.get_indices(i) - added:
method BiclusterMixin.get_shape(i) - added:
method BiclusterMixin.get_submatrix(i, data) - added:
property BiclusterMixin.biclusters_
- added:
export SpectralBiclustering - added:
export SpectralCoclustering
- added:
class AffinityPropagation(BaseEstimator, ClusterMixin) - added:
constructor AffinityPropagation(damping=…, max_iter=…, convergence_iter=…, copy=…, preference=…, affinity=…, verbose=…) - added:
function affinity_propagation(S, preference=…, convergence_iter=…, max_iter=…, damping=…, copy=…, verbose=…) - added:
method AffinityPropagation.fit(X)
- added:
export SpectralBiclustering - added:
export SpectralCoclustering
- added:
class BaseSpectral(six.with_metaclass(ABCMeta, BaseEstimator, BiclusterMixin)) - added:
class SpectralBiclustering(BaseSpectral) - added:
class SpectralCoclustering(BaseSpectral) - added:
constructor BaseSpectral(n_clusters=…, svd_method=…, n_svd_vecs=…, mini_batch=…, init=…, n_init=…, n_jobs=…, random_state=…) - 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=…) - added:
method BaseSpectral.fit(X)
- added:
function check_array_ndim(X) - added:
function get_indicators(rows, columns, shape) - added:
function get_indices(rows, columns) - added:
function get_shape(rows, columns) - added:
function get_submatrix(rows, columns, data)
- changed:
constructor DBSCAN(eps=…, min_samples=…, metric=…, random_state=…) → constructor DBSCAN(eps=…, min_samples=…, metric=…, algorithm=…, leaf_size=…, p=…, random_state=…) - changed:
function dbscan(X, eps=…, min_samples=…, metric=…, random_state=…) → function dbscan(X, eps=…, min_samples=…, metric=…, algorithm=…, leaf_size=…, p=…, random_state=…) - changed:
method DBSCAN.fit(X, **params) → method DBSCAN.fit(X)
- added:
class KMeans(BaseEstimator, ClusterMixin, TransformerMixin) - added:
class MiniBatchKMeans(KMeans) - added:
constructor KMeans(n_clusters=…, init=…, n_init=…, max_iter=…, tol=…, precompute_distances=…, verbose=…, random_state=…, copy_x=…, n_jobs=…) - added:
constructor MiniBatchKMeans(n_clusters=…, init=…, max_iter=…, batch_size=…, verbose=…, compute_labels=…, random_state=…, tol=…, max_no_improvement=…, init_size=…, n_init=…, reassignment_ratio=…) - added:
function k_means(X, n_clusters, init=…, precompute_distances=…, n_init=…, max_iter=…, verbose=…, tol=…, random_state=…, copy_x=…, n_jobs=…) - added:
method KMeans.fit(X, y=…) - added:
method KMeans.fit_predict(X) - added:
method KMeans.fit_transform(X, y=…) - added:
method KMeans.predict(X) - added:
method KMeans.score(X) - added:
method KMeans.transform(X, y=…) - added:
method MiniBatchKMeans.fit(X, y=…) - added:
method MiniBatchKMeans.partial_fit(X, y=…)
- changed:
constructor MeanShift(bandwidth=…, seeds=…, bin_seeding=…, cluster_all=…) → constructor MeanShift(bandwidth=…, seeds=…, bin_seeding=…, min_bin_freq=…, cluster_all=…) - changed:
function mean_shift(X, bandwidth=…, seeds=…, bin_seeding=…, cluster_all=…, max_iterations=…) → function mean_shift(X, bandwidth=…, seeds=…, bin_seeding=…, min_bin_freq=…, cluster_all=…, max_iterations=…)
- added:
class SpectralClustering(BaseEstimator, ClusterMixin) - added:
constructor SpectralClustering(n_clusters=…, eigen_solver=…, random_state=…, n_init=…, gamma=…, affinity=…, n_neighbors=…, k=…, eigen_tol=…, assign_labels=…, mode=…, degree=…, coef0=…, kernel_params=…) - added:
function discretize(vectors, copy=…, max_svd_restarts=…, n_iter_max=…, random_state=…) - added:
function spectral_clustering(affinity, n_clusters=…, n_components=…, eigen_solver=…, random_state=…, n_init=…, k=…, eigen_tol=…, assign_labels=…, mode=…) - added:
method SpectralClustering.fit(X) - added:
property SpectralClustering.k - added:
property SpectralClustering.mode
- added:
class GraphLasso(EmpiricalCovariance) - added:
class GraphLassoCV(GraphLasso) - added:
constructor GraphLasso(alpha=…, mode=…, tol=…, max_iter=…, verbose=…) - added:
constructor GraphLassoCV(alphas=…, n_refinements=…, cv=…, tol=…, max_iter=…, mode=…, n_jobs=…, verbose=…) - added:
function alpha_max(emp_cov) - added:
function graph_lasso(emp_cov, alpha, cov_init=…, mode=…, tol=…, max_iter=…, verbose=…, return_costs=…, eps=…) - added:
function graph_lasso_path(X, alphas, cov_init=…, X_test=…, mode=…, tol=…, max_iter=…, verbose=…) - added:
method GraphLasso.fit(X, y=…) - added:
method GraphLassoCV.fit(X, y=…)
- added:
class MinCovDet(EmpiricalCovariance) - added:
constructor MinCovDet(store_precision=…, assume_centered=…, support_fraction=…, random_state=…) - added:
function c_step(X, n_support, remaining_iterations=…, initial_estimates=…, verbose=…, cov_computation_method=…, random_state=…) - added:
function fast_mcd(X, support_fraction=…, cov_computation_method=…, random_state=…) - added:
function select_candidates(X, n_support, n_trials, select=…, n_iter=…, verbose=…, cov_computation_method=…, random_state=…) - added:
method MinCovDet.correct_covariance(data) - added:
method MinCovDet.fit(X, y=…) - added:
method MinCovDet.reweight_covariance(data)
- added:
class CCA(_PLS) - added:
constructor CCA(n_components=…, scale=…, max_iter=…, tol=…, copy=…)
- added:
class PLSCanonical(_PLS) - added:
class PLSRegression(_PLS) - added:
class PLSSVD(BaseEstimator, TransformerMixin) - added:
constructor PLSCanonical(n_components=…, scale=…, algorithm=…, max_iter=…, tol=…, copy=…) - added:
constructor PLSRegression(n_components=…, scale=…, max_iter=…, tol=…, copy=…) - added:
constructor PLSSVD(n_components=…, scale=…, copy=…) - added:
method PLSSVD.fit(X, Y) - added:
method PLSSVD.fit_transform(X, y=…, **fit_params) - added:
method PLSSVD.transform(X, Y=…)
- removed:
constructor LeaveOneOut(n, indices=…) - removed:
constructor ShuffleSplit(n, n_iter=…, test_size=…, train_size=…, indices=…, random_state=…, n_iterations=…) - changed:
class KFold(object) → class KFold(_BaseKFold) - changed:
class LeaveOneLabelOut(object) → class LeaveOneLabelOut(_PartitionIterator) - changed:
class LeaveOneOut(object) → class LeaveOneOut(_PartitionIterator) - changed:
class LeavePLabelOut(object) → class LeavePLabelOut(_PartitionIterator) - changed:
class LeavePOut(object) → class LeavePOut(_PartitionIterator) - changed:
class ShuffleSplit(object) → class ShuffleSplit(BaseShuffleSplit) - changed:
class StratifiedKFold(object) → class StratifiedKFold(_BaseKFold) - changed:
class StratifiedShuffleSplit(object) → class StratifiedShuffleSplit(BaseShuffleSplit) - changed:
function cross_val_score(estimator, X, y=…, score_func=…, cv=…, n_jobs=…, verbose=…, fit_params=…) → function cross_val_score(estimator, X, y=…, scoring=…, cv=…, n_jobs=…, verbose=…, fit_params=…, score_func=…, pre_dispatch=…) - changed:
function permutation_test_score(estimator, X, y, score_func, cv=…, n_permutations=…, n_jobs=…, labels=…, random_state=…, verbose=…) → function permutation_test_score(estimator, X, y, score_func=…, cv=…, n_permutations=…, n_jobs=…, labels=…, random_state=…, verbose=…, scoring=…)
- removed:
function load_filenames(*args, **kwargs) - added:
export make_biclusters - added:
export make_checkerboard - added:
export make_gaussian_quantiles
- changed:
function load_files(container_path, description=…, categories=…, load_content=…, shuffle=…, charset=…, charse_error=…, random_state=…) → function load_files(container_path, description=…, categories=…, load_content=…, shuffle=…, encoding=…, charset=…, charset_error=…, decode_error=…, random_state=…)
- added:
function fetch_california_housing(data_home=…, download_if_missing=…) - added:
variable DATA_URL - added:
variable MODULE_DOCS - added:
variable TARGET_FILENAME
- added:
function setup_module(module) - added:
function teardown_module(module)
- added:
variable urlopen
- changed:
function make_multilabel_classification(n_samples=…, n_features=…, n_classes=…, n_labels=…, length=…, allow_unlabeled=…, random_state=…) → function make_multilabel_classification(n_samples=…, n_features=…, n_classes=…, n_labels=…, length=…, allow_unlabeled=…, return_indicator=…, random_state=…) - added:
function make_biclusters(shape, n_clusters, noise=…, minval=…, maxval=…, shuffle=…, random_state=…) - added:
function make_checkerboard(shape, n_clusters, noise=…, minval=…, maxval=…, shuffle=…, random_state=…) - added:
function make_gaussian_quantiles(mean=…, cov=…, n_samples=…, n_features=…, n_classes=…, shuffle=…, random_state=…) - added:
variable map - added:
variable zip
- added:
function construct_grids(batch) - added:
function fetch_species_distributions(data_home=…, download_if_missing=…) - added:
variable COVERAGES_URL - added:
variable DATA_ARCHIVE_NAME - added:
variable DIRECTORY_URL - added:
variable SAMPLES_URL
- added:
function download_20newsgroups(target_dir, cache_path) - added:
function fetch_20newsgroups(data_home=…, subset=…, categories=…, shuffle=…, random_state=…, remove=…, download_if_missing=…) - added:
function fetch_20newsgroups_vectorized(subset=…, remove=…, data_home=…) - added:
function strip_newsgroup_footer(text) - added:
function strip_newsgroup_header(text) - added:
function strip_newsgroup_quoting(text) - added:
variable ARCHIVE_NAME - added:
variable CACHE_NAME - added:
variable TEST_FOLDER - added:
variable TRAIN_FOLDER - added:
variable URL - added:
variable logger
- added:
export TruncatedSVD
- added:
class DictionaryLearning(BaseEstimator, SparseCodingMixin) - added:
class MiniBatchDictionaryLearning(BaseEstimator, SparseCodingMixin) - added:
class SparseCoder(BaseEstimator, SparseCodingMixin) - added:
class SparseCodingMixin(TransformerMixin) - added:
constructor DictionaryLearning(n_components=…, alpha=…, max_iter=…, tol=…, fit_algorithm=…, transform_algorithm=…, transform_n_nonzero_coefs=…, transform_alpha=…, n_jobs=…, code_init=…, dict_init=…, verbose=…, split_sign=…, random_state=…) - added:
constructor MiniBatchDictionaryLearning(n_components=…, alpha=…, n_iter=…, fit_algorithm=…, n_jobs=…, batch_size=…, shuffle=…, dict_init=…, transform_algorithm=…, transform_n_nonzero_coefs=…, transform_alpha=…, verbose=…, split_sign=…, random_state=…) - added:
constructor SparseCoder(dictionary, transform_algorithm=…, transform_n_nonzero_coefs=…, transform_alpha=…, split_sign=…, n_jobs=…) - added:
function dict_learning(X, n_components, alpha, max_iter=…, tol=…, method=…, n_jobs=…, dict_init=…, code_init=…, callback=…, verbose=…, random_state=…) - added:
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=…) - added:
function sparse_encode(X, dictionary, gram=…, cov=…, algorithm=…, n_nonzero_coefs=…, alpha=…, copy_cov=…, init=…, max_iter=…, n_jobs=…) - added:
method DictionaryLearning.fit(X, y=…) - added:
method MiniBatchDictionaryLearning.fit(X, y=…) - added:
method MiniBatchDictionaryLearning.partial_fit(X, y=…, iter_offset=…) - added:
method SparseCoder.fit(X, y=…) - added:
method SparseCodingMixin.transform(X, y=…)
- added:
class FactorAnalysis(BaseEstimator, TransformerMixin) - added:
constructor FactorAnalysis(n_components=…, tol=…, copy=…, max_iter=…, verbose=…, noise_variance_init=…) - added:
method FactorAnalysis.fit(X, y=…) - added:
method FactorAnalysis.get_covariance() - added:
method FactorAnalysis.score(X, y=…) - added:
method FactorAnalysis.transform(X)
- removed:
property FastICA.unmixing_matrix_ - changed:
constructor FastICA(n_components=…, algorithm=…, whiten=…, fun=…, fun_prime=…, fun_args=…, max_iter=…, tol=…, w_init=…, random_state=…) → constructor FastICA(n_components=…, algorithm=…, whiten=…, fun=…, fun_args=…, max_iter=…, tol=…, w_init=…, random_state=…) - changed:
function fastica(X, n_components=…, algorithm=…, whiten=…, fun=…, fun_prime=…, fun_args=…, max_iter=…, tol=…, w_init=…, random_state=…) → function fastica(X, n_components=…, algorithm=…, whiten=…, fun=…, fun_args=…, max_iter=…, tol=…, w_init=…, random_state=…, return_X_mean=…, compute_sources=…) - changed:
method FastICA.transform(X, y=…) → method FastICA.transform(X, y=…, copy=…) - added:
method FastICA.fit_transform(X, y=…) - added:
method FastICA.inverse_transform(X, copy=…) - added:
property FastICA.sources_
- changed:
constructor KernelPCA(n_components=…, kernel=…, gamma=…, degree=…, coef0=…, alpha=…, fit_inverse_transform=…, eigen_solver=…, tol=…, max_iter=…) → constructor KernelPCA(n_components=…, kernel=…, gamma=…, degree=…, coef0=…, kernel_params=…, alpha=…, fit_inverse_transform=…, eigen_solver=…, tol=…, max_iter=…, remove_zero_eig=…)
- removed:
property ProbabilisticPCA.dim - changed:
method PCA.fit(X, y=…, **params) → method PCA.fit(X, y=…) - changed:
method PCA.fit_transform(X, y=…, **params) → method PCA.fit_transform(X, y=…) - changed:
method RandomizedPCA.inverse_transform(X) → method RandomizedPCA.inverse_transform(X, y=…) - changed:
method RandomizedPCA.transform(X) → method RandomizedPCA.transform(X, y=…) - added:
method RandomizedPCA.fit_transform(X, y=…)
- changed:
constructor MiniBatchSparsePCA(n_components=…, alpha=…, ridge_alpha=…, n_iter=…, callback=…, batch_size=…, verbose=…, shuffle=…, n_jobs=…, method=…, random_state=…, chunk_size=…) → constructor MiniBatchSparsePCA(n_components=…, alpha=…, ridge_alpha=…, n_iter=…, callback=…, batch_size=…, verbose=…, shuffle=…, n_jobs=…, method=…, random_state=…)
- added:
class TruncatedSVD(BaseEstimator, TransformerMixin) - added:
constructor TruncatedSVD(n_components=…, algorithm=…, n_iterations=…, random_state=…, tol=…) - added:
method TruncatedSVD.fit(X, y=…) - added:
method TruncatedSVD.fit_transform(X, y=…) - added:
method TruncatedSVD.inverse_transform(X) - added:
method TruncatedSVD.transform(X)
- added:
export AdaBoostClassifier - added:
export AdaBoostRegressor - added:
export weight_boosting
- changed:
constructor ExtraTreesClassifier(n_estimators=…, criterion=…, max_depth=…, min_samples_split=…, min_samples_leaf=…, min_density=…, max_features=…, bootstrap=…, compute_importances=…, oob_score=…, n_jobs=…, random_state=…, verbose=…) → constructor ExtraTreesClassifier(n_estimators=…, criterion=…, max_depth=…, min_samples_split=…, min_samples_leaf=…, max_features=…, bootstrap=…, oob_score=…, n_jobs=…, random_state=…, verbose=…, min_density=…, compute_importances=…) - changed:
constructor ExtraTreesRegressor(n_estimators=…, criterion=…, max_depth=…, min_samples_split=…, min_samples_leaf=…, min_density=…, max_features=…, bootstrap=…, compute_importances=…, oob_score=…, n_jobs=…, random_state=…, verbose=…) → constructor ExtraTreesRegressor(n_estimators=…, criterion=…, max_depth=…, min_samples_split=…, min_samples_leaf=…, max_features=…, bootstrap=…, oob_score=…, n_jobs=…, random_state=…, verbose=…, min_density=…, compute_importances=…) - changed:
constructor RandomForestClassifier(n_estimators=…, criterion=…, max_depth=…, min_samples_split=…, min_samples_leaf=…, min_density=…, max_features=…, bootstrap=…, compute_importances=…, oob_score=…, n_jobs=…, random_state=…, verbose=…) → constructor RandomForestClassifier(n_estimators=…, criterion=…, max_depth=…, min_samples_split=…, min_samples_leaf=…, max_features=…, bootstrap=…, oob_score=…, n_jobs=…, random_state=…, verbose=…, min_density=…, compute_importances=…) - changed:
constructor RandomForestRegressor(n_estimators=…, criterion=…, max_depth=…, min_samples_split=…, min_samples_leaf=…, min_density=…, max_features=…, bootstrap=…, compute_importances=…, oob_score=…, n_jobs=…, random_state=…, verbose=…) → constructor RandomForestRegressor(n_estimators=…, criterion=…, max_depth=…, min_samples_split=…, min_samples_leaf=…, max_features=…, bootstrap=…, oob_score=…, n_jobs=…, random_state=…, verbose=…, min_density=…, compute_importances=…)
- changed:
class BaseGradientBoosting(BaseEnsemble) → class BaseGradientBoosting(six.with_metaclass(ABCMeta, BaseEnsemble)) - changed:
class LossFunction(object) → class LossFunction(six.with_metaclass(ABCMeta, object)) - changed:
class RegressionLossFunction(LossFunction) → class RegressionLossFunction(six.with_metaclass(ABCMeta, LossFunction)) - changed:
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=…) → constructor BaseGradientBoosting(loss, learning_rate, n_estimators, min_samples_split, min_samples_leaf, max_depth, init, subsample, max_features, random_state, alpha=…, verbose=…) - changed:
constructor GradientBoostingClassifier(loss=…, learning_rate=…, n_estimators=…, subsample=…, min_samples_split=…, min_samples_leaf=…, max_depth=…, init=…, random_state=…, max_features=…, verbose=…, learn_rate=…) → constructor GradientBoostingClassifier(loss=…, learning_rate=…, n_estimators=…, subsample=…, min_samples_split=…, min_samples_leaf=…, max_depth=…, init=…, random_state=…, max_features=…, verbose=…) - changed:
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=…) → constructor GradientBoostingRegressor(loss=…, learning_rate=…, n_estimators=…, subsample=…, min_samples_split=…, min_samples_leaf=…, max_depth=…, init=…, random_state=…, max_features=…, alpha=…, verbose=…) - added:
property BaseGradientBoosting.oob_score_
- added:
class AdaBoostClassifier(BaseWeightBoosting, ClassifierMixin) - added:
class AdaBoostRegressor(BaseWeightBoosting, RegressorMixin) - added:
constructor AdaBoostClassifier(base_estimator=…, n_estimators=…, learning_rate=…, algorithm=…, random_state=…) - added:
constructor AdaBoostRegressor(base_estimator=…, n_estimators=…, learning_rate=…, loss=…, random_state=…) - added:
method AdaBoostClassifier.decision_function(X) - added:
method AdaBoostClassifier.fit(X, y, sample_weight=…) - added:
method AdaBoostClassifier.predict(X) - added:
method AdaBoostClassifier.predict_log_proba(X) - added:
method AdaBoostClassifier.predict_proba(X) - added:
method AdaBoostClassifier.staged_decision_function(X) - added:
method AdaBoostClassifier.staged_predict(X) - added:
method AdaBoostClassifier.staged_predict_proba(X) - added:
method AdaBoostRegressor.fit(X, y, sample_weight=…) - added:
method AdaBoostRegressor.predict(X) - added:
method AdaBoostRegressor.staged_predict(X)
- added:
class Iterator(object) - added:
class MovedAttribute(_LazyDescr) - added:
class MovedModule(_LazyDescr) - added:
class X(object) - added:
constructor MovedAttribute(name, old_mod, new_mod, old_attr=…, new_attr=…) - added:
constructor MovedModule(name, old, new=…) - added:
function add_move(move) - added:
function b(s) - added:
function callable(obj) - added:
function exec_(_code_, _globs_=…, _locs_=…) - added:
function get_unbound_function(unbound) - added:
function int2byte(i) - added:
function iteritems(d) - added:
function iterkeys(d) - added:
function itervalues(d) - added:
function remove_move(name) - added:
function reraise(tp, value, tb=…) - added:
function u(s) - added:
function with_metaclass(meta, *bases) - added:
method Iterator.next() - added:
variable BytesIO - added:
variable Iterator - added:
variable MAXSIZE - added:
variable PY3 - added:
variable StringIO - added:
variable binary_type - added:
variable callable - added:
variable class_types - added:
variable exec_ - added:
variable get_function_code - added:
variable get_function_defaults - added:
variable get_method_function - added:
variable get_method_self - added:
variable int2byte - added:
variable integer_types - added:
variable moves - added:
variable string_types - added:
variable text_type
- added:
class CountVectorizer(BaseEstimator, VectorizerMixin) - added:
class TfidfTransformer(BaseEstimator, TransformerMixin) - added:
class TfidfVectorizer(CountVectorizer) - added:
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=…) - added:
constructor TfidfTransformer(norm=…, use_idf=…, smooth_idf=…, sublinear_tf=…) - added:
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=…) - added:
export ENGLISH_STOP_WORDS - added:
function strip_accents_ascii(s) - added:
function strip_accents_unicode(s) - added:
function strip_tags(s) - added:
method CountVectorizer.fit(raw_documents, y=…) - added:
method CountVectorizer.fit_transform(raw_documents, y=…) - added:
method CountVectorizer.get_feature_names() - added:
method CountVectorizer.inverse_transform(X) - added:
method CountVectorizer.transform(raw_documents) - added:
method TfidfTransformer.fit(X, y=…) - added:
method TfidfTransformer.transform(X, copy=…) - added:
method TfidfVectorizer.fit(raw_documents, y=…) - added:
method TfidfVectorizer.fit_transform(raw_documents, y=…) - added:
method TfidfVectorizer.transform(raw_documents, copy=…) - added:
property CountVectorizer.max_df_stop_words_ - added:
property TfidfTransformer.idf_ - added:
property TfidfVectorizer.norm - added:
property TfidfVectorizer.smooth_idf - added:
property TfidfVectorizer.sublinear_tf - added:
property TfidfVectorizer.use_idf
- added:
class SelectorMixin(six.with_metaclass(ABCMeta, TransformerMixin)) - added:
method SelectorMixin.get_support(indices=…) - added:
method SelectorMixin.inverse_transform(X) - added:
method SelectorMixin.transform(X)
- removed:
method RFE.transform(X) - changed:
class RFE(BaseEstimator, MetaEstimatorMixin) → class RFE(BaseEstimator, MetaEstimatorMixin, SelectorMixin) - changed:
constructor RFECV(estimator, step=…, cv=…, loss_func=…, estimator_params=…, verbose=…) → constructor RFECV(estimator, step=…, cv=…, scoring=…, loss_func=…, estimator_params=…, verbose=…)
- removed:
class SelectorMixin(TransformerMixin) - removed:
method SelectorMixin.transform(X, threshold=…) - added:
export SelectorMixin
- added:
class GaussianProcess(BaseEstimator, RegressorMixin) - added:
constructor GaussianProcess(regr=…, corr=…, beta0=…, storage_mode=…, verbose=…, theta0=…, thetaL=…, thetaU=…, optimizer=…, random_start=…, normalize=…, nugget=…, random_state=…) - added:
function l1_cross_distances(X) - added:
function solve_triangular(x, y, lower=…) - added:
method GaussianProcess.fit(X, y) - added:
method GaussianProcess.predict(X, eval_MSE=…, batch_size=…) - added:
method GaussianProcess.reduced_likelihood_function(theta=…) - added:
variable MACHINE_EPSILON - added:
variable solve_triangular
- added:
class GridSearchCV(BaseSearchCV) - added:
class ParameterGrid(object) - added:
class ParameterSampler(object) - added:
class RandomizedSearchCV(BaseSearchCV) - added:
constructor GridSearchCV(estimator, param_grid, scoring=…, loss_func=…, score_func=…, fit_params=…, n_jobs=…, iid=…, refit=…, cv=…, verbose=…, pre_dispatch=…) - added:
constructor ParameterGrid(param_grid) - added:
constructor ParameterSampler(param_distributions, n_iter, random_state=…) - added:
constructor RandomizedSearchCV(estimator, param_distributions, n_iter=…, scoring=…, fit_params=…, n_jobs=…, iid=…, refit=…, cv=…, verbose=…, pre_dispatch=…, random_state=…) - added:
function fit_grid_point(X, y, base_estimator, parameters, train, test, scorer, verbose, loss_func=…, **fit_params) - added:
method GridSearchCV.fit(X, y=…, **params) - added:
method RandomizedSearchCV.fit(X, y=…)
- changed:
constructor IsotonicRegression(y_min=…, y_max=…) → constructor IsotonicRegression(y_min=…, y_max=…, increasing=…) - changed:
function isotonic_regression(y, weight=…, y_min=…, y_max=…) → function isotonic_regression(y, sample_weight=…, y_min=…, y_max=…, weight=…, increasing=…) - changed:
method IsotonicRegression.fit(X, y, weight=…) → method IsotonicRegression.fit(X, y, sample_weight=…, weight=…) - changed:
method IsotonicRegression.fit_transform(X, y, weight=…) → method IsotonicRegression.fit_transform(X, y, sample_weight=…, weight=…)
- changed:
constructor Nystroem(kernel=…, gamma=…, coef0=…, degree=…, n_components=…, random_state=…) → constructor Nystroem(kernel=…, gamma=…, coef0=…, degree=…, kernel_params=…, n_components=…, random_state=…)
- added:
class LDA(BaseEstimator, ClassifierMixin, TransformerMixin) - added:
constructor LDA(n_components=…, priors=…) - added:
method LDA.decision_function(X) - added:
method LDA.fit(X, y, store_covariance=…, tol=…) - added:
method LDA.predict(X) - added:
method LDA.predict_log_proba(X) - added:
method LDA.predict_proba(X) - added:
method LDA.transform(X) - added:
property LDA.scaling
- added:
export OrthogonalMatchingPursuitCV
- changed:
class LinearModel(BaseEstimator) → class LinearModel(six.with_metaclass(ABCMeta, BaseEstimator)) - added:
class SparseCoefMixin(object) - added:
method SparseCoefMixin.densify() - added:
method SparseCoefMixin.sparsify()
- added:
class ARDRegression(LinearModel, RegressorMixin) - added:
class BayesianRidge(LinearModel, RegressorMixin) - added:
constructor ARDRegression(n_iter=…, tol=…, alpha_1=…, alpha_2=…, lambda_1=…, lambda_2=…, compute_score=…, threshold_lambda=…, fit_intercept=…, normalize=…, copy_X=…, verbose=…) - added:
constructor BayesianRidge(n_iter=…, tol=…, alpha_1=…, alpha_2=…, lambda_1=…, lambda_2=…, compute_score=…, fit_intercept=…, normalize=…, copy_X=…, verbose=…) - added:
method ARDRegression.fit(X, y) - added:
method BayesianRidge.fit(X, y)
- added:
class ElasticNet(LinearModel, RegressorMixin) - added:
class ElasticNetCV(LinearModelCV, RegressorMixin) - added:
class Lasso(ElasticNet) - added:
class LassoCV(LinearModelCV, RegressorMixin) - added:
class LinearModelCV(six.with_metaclass(ABCMeta, LinearModel)) - added:
class MultiTaskElasticNet(Lasso) - added:
class MultiTaskLasso(MultiTaskElasticNet) - added:
constructor ElasticNet(alpha=…, l1_ratio=…, fit_intercept=…, normalize=…, precompute=…, max_iter=…, copy_X=…, tol=…, warm_start=…, positive=…, rho=…) - added:
constructor ElasticNetCV(l1_ratio=…, eps=…, n_alphas=…, alphas=…, fit_intercept=…, normalize=…, precompute=…, max_iter=…, tol=…, cv=…, copy_X=…, verbose=…, n_jobs=…, rho=…) - added:
constructor Lasso(alpha=…, fit_intercept=…, normalize=…, precompute=…, copy_X=…, max_iter=…, tol=…, warm_start=…, positive=…) - added:
constructor LassoCV(eps=…, n_alphas=…, alphas=…, fit_intercept=…, normalize=…, precompute=…, max_iter=…, tol=…, copy_X=…, cv=…, verbose=…) - added:
constructor LinearModelCV(eps=…, n_alphas=…, alphas=…, fit_intercept=…, normalize=…, precompute=…, max_iter=…, tol=…, copy_X=…, cv=…, verbose=…) - added:
constructor MultiTaskElasticNet(alpha=…, l1_ratio=…, fit_intercept=…, normalize=…, copy_X=…, max_iter=…, tol=…, warm_start=…, rho=…) - added:
constructor MultiTaskLasso(alpha=…, fit_intercept=…, normalize=…, copy_X=…, max_iter=…, tol=…, warm_start=…) - added:
field ElasticNet.path - added:
field ElasticNetCV.path - added:
field Lasso.path - added:
field LassoCV.n_jobs - added:
field LassoCV.path - added:
function enet_path(X, y, l1_ratio=…, eps=…, n_alphas=…, alphas=…, precompute=…, Xy=…, fit_intercept=…, normalize=…, copy_X=…, verbose=…, rho=…, return_models=…, **params) - added:
function lasso_path(X, y, eps=…, n_alphas=…, alphas=…, precompute=…, Xy=…, fit_intercept=…, normalize=…, copy_X=…, verbose=…, return_models=…, **params) - added:
method ElasticNet.decision_function(X) - added:
method ElasticNet.fit(X, y, Xy=…, coef_init=…) - added:
method LinearModelCV.fit(X, y) - added:
method MultiTaskElasticNet.fit(X, y, Xy=…, coef_init=…) - added:
property ElasticNet.sparse_coef_ - added:
property ElasticNetCV.rho - added:
property LinearModelCV.rho_
- added:
class Lars(LinearModel, RegressorMixin) - added:
class LarsCV(Lars) - added:
class LassoLars(Lars) - added:
class LassoLarsCV(LarsCV) - added:
class LassoLarsIC(LassoLars) - added:
constructor Lars(fit_intercept=…, verbose=…, normalize=…, precompute=…, n_nonzero_coefs=…, eps=…, copy_X=…, fit_path=…) - added:
constructor LarsCV(fit_intercept=…, verbose=…, max_iter=…, normalize=…, precompute=…, cv=…, max_n_alphas=…, n_jobs=…, eps=…, copy_X=…) - added:
constructor LassoLars(alpha=…, fit_intercept=…, verbose=…, normalize=…, precompute=…, max_iter=…, eps=…, copy_X=…, fit_path=…) - added:
constructor LassoLarsIC(criterion=…, fit_intercept=…, verbose=…, normalize=…, precompute=…, max_iter=…, eps=…, copy_X=…) - added:
field LarsCV.method - added:
field LassoLarsCV.method - added:
function lars_path(X, y, Xy=…, Gram=…, max_iter=…, alpha_min=…, method=…, copy_X=…, eps=…, copy_Gram=…, verbose=…, return_path=…) - added:
method Lars.fit(X, y, Xy=…) - added:
method LarsCV.fit(X, y) - added:
method LassoLarsIC.fit(X, y, copy_X=…) - added:
property LarsCV.alpha
- changed:
class LogisticRegression(BaseLibLinear, LinearClassifierMixin, SelectorMixin) → class LogisticRegression(BaseLibLinear, LinearClassifierMixin, _LearntSelectorMixin, SparseCoefMixin)
- changed:
constructor OrthogonalMatchingPursuit(copy_X=…, copy_Gram=…, copy_Xy=…, n_nonzero_coefs=…, tol=…, fit_intercept=…, normalize=…, precompute_gram=…) → constructor OrthogonalMatchingPursuit(copy_X=…, copy_Gram=…, copy_Xy=…, n_nonzero_coefs=…, tol=…, fit_intercept=…, normalize=…, precompute=…, precompute_gram=…) - changed:
function orthogonal_mp(X, y, n_nonzero_coefs=…, tol=…, precompute_gram=…, copy_X=…) → function orthogonal_mp(X, y, n_nonzero_coefs=…, tol=…, precompute=…, copy_X=…, return_path=…, precompute_gram=…) - changed:
function orthogonal_mp_gram(Gram, Xy, n_nonzero_coefs=…, tol=…, norms_squared=…, copy_Gram=…, copy_Xy=…) → function orthogonal_mp_gram(Gram, Xy, n_nonzero_coefs=…, tol=…, norms_squared=…, copy_Gram=…, copy_Xy=…, return_path=…) - added:
class OrthogonalMatchingPursuitCV(LinearModel, RegressorMixin) - added:
constructor OrthogonalMatchingPursuitCV(copy=…, fit_intercept=…, normalize=…, max_iter=…, cv=…, n_jobs=…, verbose=…) - added:
method OrthogonalMatchingPursuitCV.fit(X, y)
- changed:
class Perceptron(BaseSGDClassifier, SelectorMixin) → class Perceptron(BaseSGDClassifier, _LearntSelectorMixin)
- changed:
class BaseRandomizedLinearModel(BaseEstimator, TransformerMixin) → class BaseRandomizedLinearModel(six.with_metaclass(ABCMeta, BaseEstimator, TransformerMixin))
- changed:
method Ridge.fit(X, y, sample_weight=…, solver=…) → method Ridge.fit(X, y, sample_weight=…) - changed:
method RidgeClassifier.fit(X, y, solver=…) → method RidgeClassifier.fit(X, y)
- changed:
class BaseSGD(BaseEstimator) → class BaseSGD(six.with_metaclass(ABCMeta, BaseEstimator, SparseCoefMixin)) - changed:
class BaseSGDClassifier(BaseSGD, LinearClassifierMixin) → class BaseSGDClassifier(six.with_metaclass(ABCMeta, BaseSGD, LinearClassifierMixin)) - changed:
class SGDClassifier(BaseSGDClassifier, SelectorMixin) → class SGDClassifier(BaseSGDClassifier, _LearntSelectorMixin) - changed:
class SGDRegressor(BaseSGDRegressor, SelectorMixin) → class SGDRegressor(BaseSGDRegressor, _LearntSelectorMixin) - changed:
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=…) → constructor BaseSGDRegressor(loss=…, penalty=…, alpha=…, l1_ratio=…, fit_intercept=…, n_iter=…, shuffle=…, verbose=…, epsilon=…, random_state=…, learning_rate=…, eta0=…, power_t=…, warm_start=…, rho=…) - added:
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=…, rho=…, seed=…) - added:
constructor SGDRegressor(loss=…, penalty=…, alpha=…, l1_ratio=…, fit_intercept=…, n_iter=…, shuffle=…, verbose=…, epsilon=…, random_state=…, learning_rate=…, eta0=…, power_t=…, warm_start=…, rho=…)
- added:
class MDS(BaseEstimator) - added:
constructor MDS(n_components=…, metric=…, n_init=…, max_iter=…, verbose=…, eps=…, n_jobs=…, random_state=…, dissimilarity=…) - added:
function smacof(similarities, metric=…, n_components=…, init=…, n_init=…, n_jobs=…, max_iter=…, verbose=…, eps=…, random_state=…) - added:
method MDS.fit(X, init=…, y=…) - added:
method MDS.fit_transform(X, init=…, y=…)
- removed:
class SpectralEmbedding(BaseEstimator, TransformerMixin) - removed:
constructor SpectralEmbedding(n_components=…, affinity=…, gamma=…, random_state=…, eigen_solver=…, n_neighbors=…) - removed:
function spectral_embedding(adjacency, n_components=…, eigen_solver=…, random_state=…, eigen_tol=…, norm_laplacian=…, drop_first=…, mode=…) - removed:
method SpectralEmbedding.fit(X, y=…) - removed:
method SpectralEmbedding.fit_transform(X, y=…)
- 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 auc_score - added:
export SCORERS - added:
export consensus_score - added:
export hamming_loss - added:
export jaccard_similarity_score - added:
export log_loss - added:
export make_scorer - added:
export roc_auc_score
- added:
export consensus_score
- added:
export consensus_score
- added:
function consensus_score(a, b, similarity=…)
- removed:
function unique_labels(*lists_of_labels) - changed:
function accuracy_score(y_true, y_pred) → function accuracy_score(y_true, y_pred, normalize=…) - changed:
function precision_recall_curve(y_true, probas_pred) → function precision_recall_curve(y_true, probas_pred, pos_label=…) - added:
function hamming_loss(y_true, y_pred, classes=…) - added:
function jaccard_similarity_score(y_true, y_pred, normalize=…) - added:
function log_loss(y_true, y_pred, eps=…, normalize=…) - added:
function roc_auc_score(y_true, y_score)
- removed:
variable kernel_params - removed:
variable pairwise_distance_functions - removed:
variable pairwise_kernel_functions - changed:
function manhattan_distances(X, Y=…, sum_over_features=…) → function manhattan_distances(X, Y=…, sum_over_features=…, size_threshold=…) - added:
variable KERNEL_PARAMS - added:
variable PAIRWISE_DISTANCE_FUNCTIONS - added:
variable PAIRWISE_KERNEL_FUNCTIONS
- added:
function make_scorer(score_func, greater_is_better=…, needs_proba=…, needs_threshold=…, **kwargs) - added:
variable SCORERS - added:
variable accuracy_scorer - added:
variable adjusted_rand_scorer - added:
variable average_precision_scorer - added:
variable f1_scorer - added:
variable log_loss_scorer - added:
variable mean_squared_error_scorer - added:
variable precision_scorer - added:
variable r2_scorer - added:
variable recall_scorer - added:
variable roc_auc_scorer
- added:
class DPGMM(GMM) - added:
class VBGMM(DPGMM) - added:
constructor DPGMM(n_components=…, covariance_type=…, alpha=…, random_state=…, thresh=…, verbose=…, min_covar=…, n_iter=…, params=…, init_params=…) - added:
constructor VBGMM(n_components=…, covariance_type=…, alpha=…, random_state=…, thresh=…, verbose=…, min_covar=…, n_iter=…, params=…, init_params=…) - added:
function digamma(x) - added:
function gammaln(x) - added:
function log_normalize(v, axis=…) - added:
function sqnorm(v) - added:
function wishart_log_det(a, b, detB, n_features) - added:
function wishart_logz(v, s, dets, n_features) - added:
method DPGMM.eval(X) - added:
method DPGMM.fit(X) - added:
method DPGMM.lower_bound(X, z) - added:
method DPGMM.score_samples(X) - added:
method VBGMM.eval(X) - added:
method VBGMM.score_samples(X)
- added:
method GMM.score_samples(X)
- added:
method OneVsRestClassifier.decision_function(X)
- added:
export DistanceMetric - added:
export KDTree - added:
export KernelDensity
- removed:
function warn_equidistant() - changed:
class NeighborsBase(BaseEstimator) → class NeighborsBase(six.with_metaclass(ABCMeta, BaseEstimator)) - added:
variable VALID_METRICS - added:
variable VALID_METRICS_SPARSE
- changed:
constructor KNeighborsClassifier(n_neighbors=…, weights=…, algorithm=…, leaf_size=…, warn_on_equidistant=…, p=…) → constructor KNeighborsClassifier(n_neighbors=…, weights=…, algorithm=…, leaf_size=…, p=…, metric=…, **kwargs) - changed:
constructor RadiusNeighborsClassifier(radius=…, weights=…, algorithm=…, leaf_size=…, p=…, outlier_label=…) → constructor RadiusNeighborsClassifier(radius=…, weights=…, algorithm=…, leaf_size=…, p=…, metric=…, outlier_label=…, **kwargs)
- added:
class KernelDensity(BaseEstimator) - added:
constructor KernelDensity(bandwidth=…, algorithm=…, kernel=…, metric=…, atol=…, rtol=…, breadth_first=…, leaf_size=…, metric_params=…) - added:
method KernelDensity.fit(X) - added:
method KernelDensity.sample(n_samples=…, random_state=…) - added:
method KernelDensity.score(X) - added:
method KernelDensity.score_samples(X) - added:
variable TREE_DICT - added:
variable VALID_KERNELS
- changed:
constructor KNeighborsRegressor(n_neighbors=…, weights=…, algorithm=…, leaf_size=…, warn_on_equidistant=…, p=…) → constructor KNeighborsRegressor(n_neighbors=…, weights=…, algorithm=…, leaf_size=…, p=…, metric=…, **kwargs) - changed:
constructor RadiusNeighborsRegressor(radius=…, weights=…, algorithm=…, leaf_size=…, p=…) → constructor RadiusNeighborsRegressor(radius=…, weights=…, algorithm=…, leaf_size=…, p=…, metric=…, **kwargs)
- changed:
constructor NearestNeighbors(n_neighbors=…, radius=…, algorithm=…, leaf_size=…, warn_on_equidistant=…, p=…) → constructor NearestNeighbors(n_neighbors=…, radius=…, algorithm=…, leaf_size=…, metric=…, **kwargs)
- added:
export BernoulliRBM
- added:
class BernoulliRBM(BaseEstimator, TransformerMixin) - added:
constructor BernoulliRBM(n_components=…, learning_rate=…, batch_size=…, n_iter=…, verbose=…, random_state=…) - added:
method BernoulliRBM.fit(X, y=…) - added:
method BernoulliRBM.gibbs(v) - added:
method BernoulliRBM.score_samples(v) - added:
method BernoulliRBM.transform(X)
- removed:
class CCA(_PLS) - removed:
class PLSCanonical(_PLS) - removed:
class PLSRegression(_PLS) - removed:
class PLSSVD(BaseEstimator, TransformerMixin) - removed:
constructor CCA(n_components=…, scale=…, max_iter=…, tol=…, copy=…) - removed:
constructor PLSCanonical(n_components=…, scale=…, algorithm=…, max_iter=…, tol=…, copy=…) - removed:
constructor PLSRegression(n_components=…, scale=…, max_iter=…, tol=…, copy=…) - removed:
constructor PLSSVD(n_components=…, scale=…, copy=…) - removed:
method PLSSVD.fit(X, Y) - removed:
method PLSSVD.fit_transform(X, y=…, **fit_params) - removed:
method PLSSVD.transform(X, Y=…)
- removed:
class Binarizer(BaseEstimator, TransformerMixin) - removed:
class KernelCenterer(BaseEstimator, TransformerMixin) - removed:
class LabelBinarizer(BaseEstimator, TransformerMixin) - removed:
class LabelEncoder(BaseEstimator, TransformerMixin) - removed:
class MinMaxScaler(BaseEstimator, TransformerMixin) - removed:
class Normalizer(BaseEstimator, TransformerMixin) - removed:
class StandardScaler(BaseEstimator, TransformerMixin) - removed:
constructor Binarizer(threshold=…, copy=…) - removed:
constructor LabelBinarizer(neg_label=…, pos_label=…) - removed:
constructor MinMaxScaler(feature_range=…, copy=…) - removed:
constructor Normalizer(norm=…, copy=…) - removed:
constructor StandardScaler(copy=…, with_mean=…, with_std=…) - removed:
function binarize(X, threshold=…, copy=…) - removed:
function normalize(X, norm=…, axis=…, copy=…) - removed:
function scale(X, axis=…, with_mean=…, with_std=…, copy=…) - removed:
method Binarizer.fit(X, y=…) - removed:
method Binarizer.transform(X, y=…, copy=…) - removed:
method KernelCenterer.fit(K, y=…) - removed:
method KernelCenterer.transform(K, y=…, copy=…) - removed:
method LabelBinarizer.fit(y) - removed:
method LabelBinarizer.inverse_transform(Y, threshold=…) - removed:
method LabelBinarizer.transform(y) - removed:
method LabelEncoder.fit(y) - removed:
method LabelEncoder.fit_transform(y) - removed:
method LabelEncoder.inverse_transform(y) - removed:
method LabelEncoder.transform(y) - removed:
method MinMaxScaler.fit(X, y=…) - removed:
method MinMaxScaler.inverse_transform(X) - removed:
method MinMaxScaler.transform(X) - removed:
method Normalizer.fit(X, y=…) - removed:
method Normalizer.transform(X, y=…, copy=…) - removed:
method StandardScaler.fit(X, y=…) - removed:
method StandardScaler.inverse_transform(X, copy=…) - removed:
method StandardScaler.transform(X, y=…, copy=…) - added:
export Binarizer - added:
export Imputer - added:
export KernelCenterer - added:
export LabelBinarizer - added:
export LabelEncoder - added:
export MinMaxScaler - added:
export Normalizer - added:
export OneHotEncoder - added:
export Scaler - added:
export StandardScaler - added:
export add_dummy_feature - added:
export binarize - added:
export label_binarize - added:
export normalize - added:
export scale
- added:
class Binarizer(BaseEstimator, TransformerMixin) - added:
class KernelCenterer(BaseEstimator, TransformerMixin) - added:
class MinMaxScaler(BaseEstimator, TransformerMixin) - added:
class Normalizer(BaseEstimator, TransformerMixin) - added:
class OneHotEncoder(BaseEstimator, TransformerMixin) - added:
class Scaler(StandardScaler) - added:
class StandardScaler(BaseEstimator, TransformerMixin) - added:
constructor Binarizer(threshold=…, copy=…) - added:
constructor MinMaxScaler(feature_range=…, copy=…) - added:
constructor Normalizer(norm=…, copy=…) - added:
constructor OneHotEncoder(n_values=…, categorical_features=…, dtype=…) - added:
constructor Scaler(copy=…, with_mean=…, with_std=…) - added:
constructor StandardScaler(copy=…, with_mean=…, with_std=…) - added:
function add_dummy_feature(X, value=…) - added:
function binarize(X, threshold=…, copy=…) - added:
function normalize(X, norm=…, axis=…, copy=…) - added:
function scale(X, axis=…, with_mean=…, with_std=…, copy=…) - added:
method Binarizer.fit(X, y=…) - added:
method Binarizer.transform(X, y=…, copy=…) - added:
method KernelCenterer.fit(K, y=…) - added:
method KernelCenterer.transform(K, y=…, copy=…) - added:
method MinMaxScaler.fit(X, y=…) - added:
method MinMaxScaler.inverse_transform(X) - added:
method MinMaxScaler.transform(X) - added:
method Normalizer.fit(X, y=…) - added:
method Normalizer.transform(X, y=…, copy=…) - added:
method OneHotEncoder.fit(X, y=…) - added:
method OneHotEncoder.fit_transform(X, y=…) - added:
method OneHotEncoder.transform(X) - added:
method StandardScaler.fit(X, y=…) - added:
method StandardScaler.inverse_transform(X, copy=…) - added:
method StandardScaler.transform(X, y=…, copy=…)
- added:
class Imputer(BaseEstimator, TransformerMixin) - added:
constructor Imputer(missing_values=…, strategy=…, axis=…, verbose=…, copy=…) - added:
method Imputer.fit(X, y=…) - added:
method Imputer.transform(X)
- added:
class LabelBinarizer(BaseEstimator, TransformerMixin) - added:
class LabelEncoder(BaseEstimator, TransformerMixin) - added:
constructor LabelBinarizer(neg_label=…, pos_label=…) - added:
function label_binarize(y, classes, multilabel=…, neg_label=…, pos_label=…) - added:
method LabelBinarizer.fit(y) - added:
method LabelBinarizer.inverse_transform(Y, threshold=…) - added:
method LabelBinarizer.transform(y) - added:
method LabelEncoder.fit(y) - added:
method LabelEncoder.fit_transform(y) - added:
method LabelEncoder.inverse_transform(y) - added:
method LabelEncoder.transform(y) - added:
property LabelBinarizer.multilabel
- removed:
property QDA.classes - changed:
constructor QDA(priors=…) → constructor QDA(priors=…, reg_param=…)
- changed:
class BaseLabelPropagation(BaseEstimator, ClassifierMixin) → class BaseLabelPropagation(six.with_metaclass(ABCMeta, BaseEstimator, ClassifierMixin)) - changed:
constructor BaseLabelPropagation(kernel=…, gamma=…, n_neighbors=…, alpha=…, max_iter=…, tol=…, max_iters=…) → constructor BaseLabelPropagation(kernel=…, gamma=…, n_neighbors=…, alpha=…, max_iter=…, tol=…) - changed:
constructor LabelSpreading(kernel=…, gamma=…, n_neighbors=…, alpha=…, max_iter=…, tol=…, max_iters=…) → constructor LabelSpreading(kernel=…, gamma=…, n_neighbors=…, alpha=…, max_iter=…, tol=…)
- removed:
export sparse
- added:
class BaseLibLinear(six.with_metaclass(ABCMeta, BaseEstimator)) - added:
class BaseLibSVM(six.with_metaclass(ABCMeta, BaseEstimator)) - added:
class BaseSVC(BaseLibSVM, ClassifierMixin) - added:
constructor BaseLibLinear(penalty=…, loss=…, dual=…, tol=…, C=…, multi_class=…, fit_intercept=…, intercept_scaling=…, class_weight=…, verbose=…, random_state=…) - added:
constructor BaseLibSVM(impl, kernel, degree, gamma, coef0, tol, C, nu, epsilon, shrinking, probability, cache_size, class_weight, verbose, max_iter, random_state) - added:
method BaseLibLinear.fit(X, y) - added:
method BaseLibSVM.decision_function(X) - added:
method BaseLibSVM.fit(X, y, sample_weight=…) - added:
method BaseLibSVM.predict(X) - added:
method BaseSVC.predict(X) - added:
method BaseSVC.predict_log_proba(X) - added:
method BaseSVC.predict_proba(X) - added:
property BaseLibLinear.classes_ - added:
property BaseLibLinear.label_ - added:
property BaseLibSVM.coef_ - added:
property BaseSVC.label_ - added:
variable LIBSVM_IMPL
- changed:
class LinearSVC(BaseLibLinear, LinearClassifierMixin, SelectorMixin) → class LinearSVC(BaseLibLinear, LinearClassifierMixin, _LearntSelectorMixin, SparseCoefMixin) - changed:
constructor NuSVC(nu=…, kernel=…, degree=…, gamma=…, coef0=…, shrinking=…, probability=…, tol=…, cache_size=…, verbose=…, max_iter=…) → constructor NuSVC(nu=…, kernel=…, degree=…, gamma=…, coef0=…, shrinking=…, probability=…, tol=…, cache_size=…, verbose=…, max_iter=…, random_state=…) - changed:
constructor NuSVR(nu=…, C=…, kernel=…, degree=…, gamma=…, coef0=…, shrinking=…, probability=…, tol=…, cache_size=…, verbose=…, max_iter=…) → constructor NuSVR(nu=…, C=…, kernel=…, degree=…, gamma=…, coef0=…, shrinking=…, probability=…, tol=…, cache_size=…, verbose=…, max_iter=…, random_state=…) - changed:
constructor OneClassSVM(kernel=…, degree=…, gamma=…, coef0=…, tol=…, nu=…, shrinking=…, cache_size=…, verbose=…, max_iter=…) → constructor OneClassSVM(kernel=…, degree=…, gamma=…, coef0=…, tol=…, nu=…, shrinking=…, cache_size=…, verbose=…, max_iter=…, random_state=…) - changed:
constructor SVC(C=…, kernel=…, degree=…, gamma=…, coef0=…, shrinking=…, probability=…, tol=…, cache_size=…, class_weight=…, verbose=…, max_iter=…) → constructor SVC(C=…, kernel=…, degree=…, gamma=…, coef0=…, shrinking=…, probability=…, tol=…, cache_size=…, class_weight=…, verbose=…, max_iter=…, random_state=…) - changed:
constructor SVR(kernel=…, degree=…, gamma=…, coef0=…, tol=…, C=…, epsilon=…, shrinking=…, probability=…, cache_size=…, verbose=…, max_iter=…) → constructor SVR(kernel=…, degree=…, gamma=…, coef0=…, tol=…, C=…, epsilon=…, shrinking=…, probability=…, cache_size=…, verbose=…, max_iter=…, random_state=…) - added:
constructor LinearSVC(penalty=…, loss=…, dual=…, tol=…, C=…, multi_class=…, fit_intercept=…, intercept_scaling=…, class_weight=…, verbose=…, random_state=…)
- removed:
export LinearSVC - removed:
export NuSVC - removed:
export NuSVR - removed:
export OneClassSVM - removed:
export SVC - removed:
export SVR - removed:
export libsvm
- removed:
class SparseBaseLibSVM(BaseLibSVM) - removed:
constructor SparseBaseLibSVM(impl, kernel, degree, gamma, coef0, tol, C, nu, epsilon, shrinking, probability, cache_size, class_weight, verbose, max_iter) - removed:
method SparseBaseLibSVM.fit(X, y, sample_weight=…)
- removed:
class LinearSVC(GeneralLinearSVC) - removed:
class NuSVC(SparseBaseLibSVM, BaseSVC) - removed:
class NuSVR(SparseBaseLibSVM, RegressorMixin) - removed:
class OneClassSVM(SparseBaseLibSVM) - removed:
class SVC(SparseBaseLibSVM, BaseSVC) - removed:
class SVR(SparseBaseLibSVM, RegressorMixin) - removed:
constructor NuSVC(nu=…, kernel=…, degree=…, gamma=…, coef0=…, shrinking=…, probability=…, tol=…, cache_size=…, class_weight=…, verbose=…, max_iter=…) - removed:
constructor NuSVR(nu=…, C=…, kernel=…, degree=…, gamma=…, coef0=…, shrinking=…, epsilon=…, probability=…, tol=…, cache_size=…, verbose=…, max_iter=…) - removed:
constructor OneClassSVM(kernel=…, degree=…, gamma=…, coef0=…, tol=…, nu=…, shrinking=…, probability=…, cache_size=…, verbose=…, max_iter=…) - removed:
constructor SVC(C=…, kernel=…, degree=…, gamma=…, coef0=…, shrinking=…, probability=…, tol=…, cache_size=…, class_weight=…, verbose=…, max_iter=…) - removed:
constructor SVR(kernel=…, degree=…, gamma=…, coef0=…, tol=…, C=…, epsilon=…, shrinking=…, probability=…, cache_size=…, verbose=…, max_iter=…) - removed:
method OneClassSVM.fit(X, sample_weight=…)
- added:
function export_graphviz(decision_tree, out_file=…, feature_names=…, max_depth=…, close=…)
- changed:
constructor DecisionTreeClassifier(criterion=…, max_depth=…, min_samples_split=…, min_samples_leaf=…, min_density=…, max_features=…, compute_importances=…, random_state=…) → constructor DecisionTreeClassifier(criterion=…, splitter=…, max_depth=…, min_samples_split=…, min_samples_leaf=…, max_features=…, random_state=…, min_density=…, compute_importances=…) - changed:
constructor DecisionTreeRegressor(criterion=…, max_depth=…, min_samples_split=…, min_samples_leaf=…, min_density=…, max_features=…, compute_importances=…, random_state=…) → constructor DecisionTreeRegressor(criterion=…, splitter=…, max_depth=…, min_samples_split=…, min_samples_leaf=…, max_features=…, random_state=…, min_density=…, compute_importances=…) - changed:
constructor ExtraTreeClassifier(criterion=…, max_depth=…, min_samples_split=…, min_samples_leaf=…, min_density=…, max_features=…, compute_importances=…, random_state=…) → constructor ExtraTreeClassifier(criterion=…, splitter=…, max_depth=…, min_samples_split=…, min_samples_leaf=…, max_features=…, random_state=…, min_density=…, compute_importances=…) - changed:
constructor ExtraTreeRegressor(criterion=…, max_depth=…, min_samples_split=…, min_samples_leaf=…, min_density=…, max_features=…, compute_importances=…, random_state=…) → constructor ExtraTreeRegressor(criterion=…, splitter=…, max_depth=…, min_samples_split=…, min_samples_leaf=…, max_features=…, random_state=…, min_density=…, compute_importances=…)
- added:
export column_or_1d - added:
export minimum_spanning_tree
- changed:
function compute_class_weight(class_weight, classes, y) → function compute_class_weight(class_weight, classes, y_ind)
- changed:
function svd_flip(u, s, v) → function svd_flip(u, v) - added:
function logistic_sigmoid(X, log=…, out=…) - added:
function make_nonnegative(X, min_value=…) - added:
function safe_min(X)
- added:
function divide(x1, x2, out=…, dtype=…) - added:
variable divide
- removed:
variable cs_graph_components - changed:
function graph_laplacian(graph, normed=…, return_diag=…) → function graph_laplacian(csgraph, normed=…, return_diag=…) - added:
variable connected_components
- added:
function linear_assignment(X)
- added:
function is_label_indicator_matrix(y) - added:
function is_multilabel(y) - added:
function is_sequence_of_sequences(y) - added:
function type_of_target(y) - added:
function unique_labels(*ys)
- removed:
export cs_graph_components - added:
export connected_components - added:
export minimum_spanning_tree
- removed:
function cs_graph_components(*args) - removed:
function swig_import_helper()
- changed:
function array2d(X, dtype=…, order=…, copy=…) → function array2d(X, dtype=…, order=…, copy=…, force_all_finite=…) - changed:
function atleast2d_or_csc(X, dtype=…, order=…, copy=…) → function atleast2d_or_csc(X, dtype=…, order=…, copy=…, force_all_finite=…) - changed:
function atleast2d_or_csr(X, dtype=…, order=…, copy=…) → function atleast2d_or_csr(X, dtype=…, order=…, copy=…, force_all_finite=…) - changed:
function safe_asarray(X, dtype=…, order=…) → function safe_asarray(X, dtype=…, order=…, copy=…) - added:
class DataConversionWarning(UserWarning) - added:
function column_or_1d(y, warn=…)