Upgrading scikit-learn 0.14 to 0.15
Unknown0.14.1 → 0.15.2
UnknownUpgrade verdict
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In plain terms
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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:
class SphinxClassDoc(SphinxDocString, ClassDoc) - removed:
class SphinxDocString(NumpyDocString) - removed:
class SphinxFunctionDoc(SphinxDocString, FunctionDoc) - removed:
class SphinxObjDoc(SphinxDocString) - removed:
constructor SphinxClassDoc(obj, doc=…, func_doc=…, config=…) - removed:
constructor SphinxDocString(docstring, config=…) - removed:
constructor SphinxFunctionDoc(obj, doc=…, config=…) - removed:
constructor SphinxObjDoc(obj, doc=…, config=…) - removed:
function get_doc_object(obj, what=…, doc=…, config=…)
- changed:
method ClassifierMixin.score(X, y) → method ClassifierMixin.score(X, y, sample_weight=…) - changed:
method RegressorMixin.score(X, y) → method RegressorMixin.score(X, y, sample_weight=…)
- removed:
function spectral_embedding(*args, **kwargs) - added:
export AgglomerativeClustering - added:
export FeatureAgglomeration - added:
export linkage_tree
- added:
method AffinityPropagation.predict(X)
- removed:
method Ward.fit(X) - changed:
class Ward(BaseEstimator, ClusterMixin) → class Ward(AgglomerativeClustering) - changed:
constructor Ward(n_clusters=…, memory=…, connectivity=…, copy=…, n_components=…, compute_full_tree=…) → constructor Ward(n_clusters=…, memory=…, connectivity=…, copy=…, n_components=…, compute_full_tree=…, pooling_func=…) - added:
class AgglomerativeClustering(BaseEstimator, ClusterMixin) - added:
class FeatureAgglomeration(AgglomerativeClustering, AgglomerationTransform) - added:
constructor AgglomerativeClustering(n_clusters=…, affinity=…, memory=…, connectivity=…, n_components=…, compute_full_tree=…, linkage=…, pooling_func=…) - added:
field Ward.linkage - added:
function linkage_tree(X, connectivity=…, n_components=…, n_clusters=…, linkage=…, affinity=…) - added:
method AgglomerativeClustering.fit(X) - added:
method FeatureAgglomeration.fit(X, y=…, **params) - added:
variable xrange
- added:
method MiniBatchKMeans.predict(X)
- added:
method MeanShift.predict(X)
- removed:
property SpectralClustering.k - removed:
property SpectralClustering.mode - changed:
constructor SpectralClustering(n_clusters=…, eigen_solver=…, random_state=…, n_init=…, gamma=…, affinity=…, n_neighbors=…, k=…, eigen_tol=…, assign_labels=…, mode=…, degree=…, coef0=…, kernel_params=…) → constructor SpectralClustering(n_clusters=…, eigen_solver=…, random_state=…, n_init=…, gamma=…, affinity=…, n_neighbors=…, eigen_tol=…, assign_labels=…, degree=…, coef0=…, kernel_params=…) - changed:
function spectral_clustering(affinity, n_clusters=…, n_components=…, eigen_solver=…, random_state=…, n_init=…, k=…, eigen_tol=…, assign_labels=…, mode=…) → function spectral_clustering(affinity, n_clusters=…, n_components=…, eigen_solver=…, random_state=…, n_init=…, eigen_tol=…, assign_labels=…)
- removed:
export EllipticEnvelop
- changed:
constructor GraphLasso(alpha=…, mode=…, tol=…, max_iter=…, verbose=…) → constructor GraphLasso(alpha=…, mode=…, tol=…, max_iter=…, verbose=…, assume_centered=…) - changed:
constructor GraphLassoCV(alphas=…, n_refinements=…, cv=…, tol=…, max_iter=…, mode=…, n_jobs=…, verbose=…) → constructor GraphLassoCV(alphas=…, n_refinements=…, cv=…, tol=…, max_iter=…, mode=…, n_jobs=…, verbose=…, assume_centered=…)
- removed:
class EllipticEnvelop(EllipticEnvelope)
- changed:
constructor KFold(n, n_folds=…, indices=…, shuffle=…, random_state=…, k=…) → constructor KFold(n, n_folds=…, indices=…, shuffle=…, random_state=…) - changed:
constructor StratifiedKFold(y, n_folds=…, indices=…, k=…) → constructor StratifiedKFold(y, n_folds=…, indices=…, shuffle=…, random_state=…)
- added:
variable PY2
- added:
export randomized_svd
- changed:
function dict_learning_online(X, n_components=…, alpha=…, n_iter=…, return_code=…, dict_init=…, callback=…, batch_size=…, verbose=…, shuffle=…, n_jobs=…, method=…, iter_offset=…, random_state=…) → function dict_learning_online(X, n_components=…, alpha=…, n_iter=…, return_code=…, dict_init=…, callback=…, batch_size=…, verbose=…, shuffle=…, n_jobs=…, method=…, iter_offset=…, random_state=…, return_inner_stats=…, inner_stats=…)
- changed:
constructor FactorAnalysis(n_components=…, tol=…, copy=…, max_iter=…, verbose=…, noise_variance_init=…) → constructor FactorAnalysis(n_components=…, tol=…, copy=…, max_iter=…, verbose=…, noise_variance_init=…, svd_method=…, iterated_power=…, random_state=…) - added:
method FactorAnalysis.get_precision() - added:
method FactorAnalysis.score_samples(X)
- added:
function trace_dot(X, Y)
- added:
method PCA.get_covariance() - added:
method PCA.get_precision() - added:
method PCA.score(X, y=…) - added:
method PCA.score_samples(X)
- changed:
constructor TruncatedSVD(n_components=…, algorithm=…, n_iterations=…, random_state=…, tol=…) → constructor TruncatedSVD(n_components=…, algorithm=…, n_iter=…, random_state=…, tol=…, n_iterations=…) - added:
property TruncatedSVD.n_iterations
- changed:
constructor DummyClassifier(strategy=…, random_state=…) → constructor DummyClassifier(strategy=…, random_state=…, constant=…) - added:
constructor DummyRegressor(strategy=…, constant=…) - added:
property DummyRegressor.y_mean_
- added:
export BaggingClassifier - added:
export BaggingRegressor - added:
export bagging
- added:
class BaggingClassifier(BaseBagging, ClassifierMixin) - added:
class BaggingRegressor(BaseBagging, RegressorMixin) - added:
constructor BaggingClassifier(base_estimator=…, n_estimators=…, max_samples=…, max_features=…, bootstrap=…, bootstrap_features=…, oob_score=…, n_jobs=…, random_state=…, verbose=…) - added:
constructor BaggingRegressor(base_estimator=…, n_estimators=…, max_samples=…, max_features=…, bootstrap=…, bootstrap_features=…, oob_score=…, n_jobs=…, random_state=…, verbose=…) - added:
method BaggingClassifier.decision_function(X) - added:
method BaggingClassifier.predict(X) - added:
method BaggingClassifier.predict_log_proba(X) - added:
method BaggingClassifier.predict_proba(X) - added:
method BaggingRegressor.predict(X)
- changed:
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=…) → constructor ExtraTreesClassifier(n_estimators=…, criterion=…, max_depth=…, min_samples_split=…, min_samples_leaf=…, max_features=…, max_leaf_nodes=…, bootstrap=…, oob_score=…, n_jobs=…, random_state=…, verbose=…, min_density=…, compute_importances=…) - changed:
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=…) → constructor ExtraTreesRegressor(n_estimators=…, criterion=…, max_depth=…, min_samples_split=…, min_samples_leaf=…, max_features=…, max_leaf_nodes=…, bootstrap=…, oob_score=…, n_jobs=…, random_state=…, verbose=…, min_density=…, compute_importances=…) - changed:
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=…) → constructor RandomForestClassifier(n_estimators=…, criterion=…, max_depth=…, min_samples_split=…, min_samples_leaf=…, max_features=…, max_leaf_nodes=…, bootstrap=…, oob_score=…, n_jobs=…, random_state=…, verbose=…, min_density=…, compute_importances=…) - changed:
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=…) → constructor RandomForestRegressor(n_estimators=…, criterion=…, max_depth=…, min_samples_split=…, min_samples_leaf=…, max_features=…, max_leaf_nodes=…, bootstrap=…, oob_score=…, n_jobs=…, random_state=…, verbose=…, min_density=…, compute_importances=…)
- changed:
class BaseGradientBoosting(six.with_metaclass(ABCMeta, BaseEnsemble)) → class BaseGradientBoosting(six.with_metaclass(ABCMeta, BaseEnsemble, _LearntSelectorMixin)) - changed:
constructor BaseGradientBoosting(loss, learning_rate, n_estimators, min_samples_split, min_samples_leaf, max_depth, init, subsample, max_features, random_state, alpha=…, verbose=…) → constructor BaseGradientBoosting(loss, learning_rate, n_estimators, min_samples_split, min_samples_leaf, max_depth, init, subsample, max_features, random_state, alpha=…, verbose=…, max_leaf_nodes=…, warm_start=…) - changed:
constructor GradientBoostingClassifier(loss=…, learning_rate=…, n_estimators=…, subsample=…, min_samples_split=…, min_samples_leaf=…, max_depth=…, init=…, random_state=…, max_features=…, verbose=…) → constructor GradientBoostingClassifier(loss=…, learning_rate=…, n_estimators=…, subsample=…, min_samples_split=…, min_samples_leaf=…, max_depth=…, init=…, random_state=…, max_features=…, verbose=…, max_leaf_nodes=…, warm_start=…) - changed:
constructor GradientBoostingRegressor(loss=…, learning_rate=…, n_estimators=…, subsample=…, min_samples_split=…, min_samples_leaf=…, max_depth=…, init=…, random_state=…, max_features=…, alpha=…, verbose=…) → constructor GradientBoostingRegressor(loss=…, learning_rate=…, n_estimators=…, subsample=…, min_samples_split=…, min_samples_leaf=…, max_depth=…, init=…, random_state=…, max_features=…, alpha=…, verbose=…, max_leaf_nodes=…, warm_start=…) - changed:
method BaseGradientBoosting.fit(X, y) → method BaseGradientBoosting.fit(X, y, monitor=…) - changed:
method GradientBoostingClassifier.fit(X, y) → method GradientBoostingClassifier.fit(X, y, monitor=…) - changed:
method GradientBoostingRegressor.fit(X, y) → method GradientBoostingRegressor.fit(X, y, monitor=…) - added:
class VerboseReporter(object) - added:
class ZeroEstimator(BaseEstimator) - added:
constructor VerboseReporter(verbose) - added:
method VerboseReporter.init(est, begin_at_stage=…) - added:
method VerboseReporter.update(j, est) - added:
method ZeroEstimator.fit(X, y) - added:
method ZeroEstimator.predict(X) - added:
variable INIT_ESTIMATORS
- added:
export MemorizedResult
- added:
function format_call(func, args, kwargs, object_name=…) - added:
function format_signature(func, *args, **kwargs)
- added:
function pformat(obj, indent=…, depth=…)
- removed:
method MemorizedFunc.get_output_dir(*args, **kwargs) - changed:
method MemorizedFunc.format_call(*args, **kwds) → method MemorizedFunc.format_call(*args, **kwargs) - changed:
method MemorizedFunc.format_signature(func, *args, **kwds) → method MemorizedFunc.format_signature(*args, **kwargs) - added:
class MemorizedResult(Logger) - added:
class NotMemorizedFunc(object) - added:
class NotMemorizedResult(object) - added:
constructor MemorizedResult(cachedir, func, argument_hash, mmap_mode=…, verbose=…, timestamp=…, metadata=…) - added:
constructor NotMemorizedFunc(func) - added:
constructor NotMemorizedResult(value) - added:
field MemorizedFunc.get_output_dir - added:
method MemorizedFunc.call_and_shelve(*args, **kwargs) - added:
method MemorizedResult.clear() - added:
method MemorizedResult.get() - added:
method NotMemorizedFunc.call_and_shelve(*args, **kwargs) - added:
method NotMemorizedFunc.clear(warn=…) - added:
method NotMemorizedResult.clear() - added:
method NotMemorizedResult.get()
- changed:
constructor JoblibException(message) → constructor JoblibException(*args)
- removed:
variable multiprocessing - changed:
constructor Parallel(n_jobs=…, verbose=…, pre_dispatch=…) → constructor Parallel(n_jobs=…, backend=…, verbose=…, pre_dispatch=…, temp_folder=…, max_nbytes=…, mmap_mode=…) - changed:
function delayed(function) → function delayed(function, check_pickle=…) - added:
class LockedIterator(object) - added:
constructor LockedIterator(it) - added:
method LockedIterator.next() - added:
variable JOBLIB_SPAWNED_PROCESS - added:
variable VALID_BACKENDS
- added:
class ArrayMemmapReducer(object) - added:
class CustomizablePickler(_Pickler) - added:
class CustomizablePicklingQueue(object) - added:
class MemmapingPool(PicklingPool) - added:
class PicklingPool(Pool) - added:
constructor ArrayMemmapReducer(max_nbytes, temp_folder, mmap_mode, verbose=…, context_id=…, prewarm=…) - added:
constructor CustomizablePickler(writer, reducers=…, protocol=…) - added:
constructor CustomizablePicklingQueue(context, reducers=…) - added:
constructor MemmapingPool(processes=…, temp_folder=…, max_nbytes=…, mmap_mode=…, forward_reducers=…, backward_reducers=…, verbose=…, context_id=…, prewarm=…, **kwargs) - added:
constructor PicklingPool(processes=…, forward_reducers=…, backward_reducers=…, **kwargs) - added:
function delete_folder(folder_path) - added:
function has_shareable_memory(a) - added:
function reduce_memmap(a) - added:
method CustomizablePickler.register(type, reduce_func) - added:
method CustomizablePicklingQueue.empty() - added:
method MemmapingPool.terminate() - added:
variable FILE_PERMISSIONS - added:
variable FOLDER_PERMISSIONS - added:
variable SYSTEM_SHARED_MEM_FS - added:
variable np
- changed:
function iteritems(d) → function iteritems(d, **kw) - changed:
function iterkeys(d) → function iterkeys(d, **kw) - changed:
function itervalues(d) → function itervalues(d, **kw) - added:
class Module_six_moves_urllib(types.ModuleType) - added:
class Module_six_moves_urllib_error(types.ModuleType) - added:
class Module_six_moves_urllib_parse(types.ModuleType) - added:
class Module_six_moves_urllib_request(types.ModuleType) - added:
class Module_six_moves_urllib_response(types.ModuleType) - added:
class Module_six_moves_urllib_robotparser(types.ModuleType) - added:
field Module_six_moves_urllib.error - added:
field Module_six_moves_urllib.parse - added:
field Module_six_moves_urllib.request - added:
field Module_six_moves_urllib.response - added:
field Module_six_moves_urllib.robotparser - added:
function add_metaclass(metaclass) - added:
function advance_iterator(it) - added:
function byte2int(bs) - added:
function create_bound_method(func, obj) - added:
function indexbytes(buf, i) - added:
function iterbytes(buf) - added:
function iterlists(d, **kw) - added:
function print_(*args, **kwargs) - added:
variable PY2 - added:
variable advance_iterator - added:
variable byte2int - added:
variable create_bound_method - added:
variable get_function_closure - added:
variable get_function_globals - added:
variable indexbytes - added:
variable iterbytes - added:
variable next - added:
variable print_ - added:
variable unichr
- removed:
property CountVectorizer.max_df_stop_words_ - added:
property TfidfVectorizer.idf_
- added:
export VarianceThreshold
- changed:
class GenericUnivariateSelect(_PvalueFilter) → class GenericUnivariateSelect(_BaseFilter) - changed:
class SelectFdr(_PvalueFilter) → class SelectFdr(_BaseFilter) - changed:
class SelectFpr(_PvalueFilter) → class SelectFpr(_BaseFilter) - changed:
class SelectFwe(_PvalueFilter) → class SelectFwe(_BaseFilter) - changed:
class SelectKBest(_ScoreFilter) → class SelectKBest(_BaseFilter) - changed:
class SelectPercentile(_ScoreFilter) → class SelectPercentile(_BaseFilter)
- added:
class VarianceThreshold(BaseEstimator, SelectorMixin) - added:
constructor VarianceThreshold(threshold=…) - added:
method VarianceThreshold.fit(X, y=…)
- removed:
function solve_triangular(x, y, lower=…) - removed:
variable solve_triangular
- changed:
function fit_grid_point(X, y, base_estimator, parameters, train, test, scorer, verbose, loss_func=…, **fit_params) → function fit_grid_point(X, y, estimator, parameters, train, test, scorer, verbose, **fit_params) - changed:
method GridSearchCV.fit(X, y=…, **params) → method GridSearchCV.fit(X, y=…)
- added:
method GaussianHMM.fit(obs)
- changed:
constructor IsotonicRegression(y_min=…, y_max=…, increasing=…) → constructor IsotonicRegression(y_min=…, y_max=…, increasing=…, out_of_bounds=…) - added:
function check_increasing(x, y)
- removed:
property LDA.scaling
- added:
function learning_curve(estimator, X, y, train_sizes=…, cv=…, scoring=…, exploit_incremental_learning=…, n_jobs=…, pre_dispatch=…, verbose=…) - added:
function validation_curve(estimator, X, y, param_name, param_range, cv=…, scoring=…, n_jobs=…, pre_dispatch=…, verbose=…)
- added:
export MultiTaskElasticNetCV - added:
export MultiTaskLassoCV - added:
export RANSACRegressor
- removed:
field LassoCV.n_jobs - removed:
property ElasticNetCV.rho - removed:
property LinearModelCV.rho_ - changed:
constructor ElasticNet(alpha=…, l1_ratio=…, fit_intercept=…, normalize=…, precompute=…, max_iter=…, copy_X=…, tol=…, warm_start=…, positive=…, rho=…) → constructor ElasticNet(alpha=…, l1_ratio=…, fit_intercept=…, normalize=…, precompute=…, max_iter=…, copy_X=…, tol=…, warm_start=…, positive=…) - changed:
constructor ElasticNetCV(l1_ratio=…, eps=…, n_alphas=…, alphas=…, fit_intercept=…, normalize=…, precompute=…, max_iter=…, tol=…, cv=…, copy_X=…, verbose=…, n_jobs=…, rho=…) → constructor ElasticNetCV(l1_ratio=…, eps=…, n_alphas=…, alphas=…, fit_intercept=…, normalize=…, precompute=…, max_iter=…, tol=…, cv=…, copy_X=…, verbose=…, n_jobs=…, positive=…) - changed:
constructor LassoCV(eps=…, n_alphas=…, alphas=…, fit_intercept=…, normalize=…, precompute=…, max_iter=…, tol=…, copy_X=…, cv=…, verbose=…) → constructor LassoCV(eps=…, n_alphas=…, alphas=…, fit_intercept=…, normalize=…, precompute=…, max_iter=…, tol=…, copy_X=…, cv=…, verbose=…, n_jobs=…, positive=…) - changed:
constructor LinearModelCV(eps=…, n_alphas=…, alphas=…, fit_intercept=…, normalize=…, precompute=…, max_iter=…, tol=…, copy_X=…, cv=…, verbose=…) → constructor LinearModelCV(eps=…, n_alphas=…, alphas=…, fit_intercept=…, normalize=…, precompute=…, max_iter=…, tol=…, copy_X=…, cv=…, verbose=…, n_jobs=…, positive=…) - changed:
constructor MultiTaskElasticNet(alpha=…, l1_ratio=…, fit_intercept=…, normalize=…, copy_X=…, max_iter=…, tol=…, warm_start=…, rho=…) → constructor MultiTaskElasticNet(alpha=…, l1_ratio=…, fit_intercept=…, normalize=…, copy_X=…, max_iter=…, tol=…, warm_start=…) - changed:
function enet_path(X, y, l1_ratio=…, eps=…, n_alphas=…, alphas=…, precompute=…, Xy=…, fit_intercept=…, normalize=…, copy_X=…, verbose=…, rho=…, return_models=…, **params) → function enet_path(X, y, l1_ratio=…, eps=…, n_alphas=…, alphas=…, precompute=…, Xy=…, fit_intercept=…, normalize=…, copy_X=…, coef_init=…, verbose=…, return_models=…, **params) - changed:
function lasso_path(X, y, eps=…, n_alphas=…, alphas=…, precompute=…, Xy=…, fit_intercept=…, normalize=…, copy_X=…, verbose=…, return_models=…, **params) → function lasso_path(X, y, eps=…, n_alphas=…, alphas=…, precompute=…, Xy=…, fit_intercept=…, normalize=…, copy_X=…, coef_init=…, verbose=…, return_models=…, **params) - changed:
method ElasticNet.fit(X, y, Xy=…, coef_init=…) → method ElasticNet.fit(X, y) - changed:
method MultiTaskElasticNet.fit(X, y, Xy=…, coef_init=…) → method MultiTaskElasticNet.fit(X, y) - added:
class MultiTaskElasticNetCV(LinearModelCV, RegressorMixin) - added:
class MultiTaskLassoCV(LinearModelCV, RegressorMixin) - added:
constructor MultiTaskElasticNetCV(l1_ratio=…, eps=…, n_alphas=…, alphas=…, fit_intercept=…, normalize=…, max_iter=…, tol=…, cv=…, copy_X=…, verbose=…, n_jobs=…) - added:
constructor MultiTaskLassoCV(eps=…, n_alphas=…, alphas=…, fit_intercept=…, normalize=…, max_iter=…, tol=…, copy_X=…, cv=…, verbose=…, n_jobs=…) - added:
field MultiTaskElasticNetCV.path - added:
field MultiTaskLassoCV.path
- added:
variable solve_triangular_args
- added:
variable solve_triangular_args
- changed:
constructor Perceptron(penalty=…, alpha=…, fit_intercept=…, n_iter=…, shuffle=…, verbose=…, eta0=…, n_jobs=…, random_state=…, class_weight=…, warm_start=…, seed=…) → constructor Perceptron(penalty=…, alpha=…, fit_intercept=…, n_iter=…, shuffle=…, verbose=…, eta0=…, n_jobs=…, random_state=…, class_weight=…, warm_start=…)
- added:
class RANSACRegressor(BaseEstimator, MetaEstimatorMixin, RegressorMixin) - added:
constructor RANSACRegressor(base_estimator=…, min_samples=…, residual_threshold=…, is_data_valid=…, is_model_valid=…, max_trials=…, stop_n_inliers=…, stop_score=…, stop_probability=…, residual_metric=…, random_state=…) - added:
method RANSACRegressor.fit(X, y) - added:
method RANSACRegressor.predict(X) - added:
method RANSACRegressor.score(X, y)
- changed:
constructor RidgeClassifierCV(alphas=…, fit_intercept=…, normalize=…, score_func=…, loss_func=…, cv=…, class_weight=…) → constructor RidgeClassifierCV(alphas=…, fit_intercept=…, normalize=…, scoring=…, score_func=…, loss_func=…, cv=…, class_weight=…)
- removed:
method SGDClassifier.predict_log_proba(X) - removed:
method SGDClassifier.predict_proba(X) - removed:
property BaseSGDClassifier.seed - changed:
constructor BaseSGD(loss, penalty=…, alpha=…, C=…, l1_ratio=…, fit_intercept=…, n_iter=…, shuffle=…, verbose=…, epsilon=…, random_state=…, learning_rate=…, eta0=…, power_t=…, warm_start=…, rho=…) → constructor BaseSGD(loss, penalty=…, alpha=…, C=…, l1_ratio=…, fit_intercept=…, n_iter=…, shuffle=…, verbose=…, epsilon=…, random_state=…, learning_rate=…, eta0=…, power_t=…, warm_start=…) - changed:
constructor BaseSGDClassifier(loss=…, penalty=…, alpha=…, l1_ratio=…, fit_intercept=…, n_iter=…, shuffle=…, verbose=…, epsilon=…, n_jobs=…, random_state=…, learning_rate=…, eta0=…, power_t=…, class_weight=…, warm_start=…, rho=…, seed=…) → 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=…) - changed:
constructor BaseSGDRegressor(loss=…, penalty=…, alpha=…, l1_ratio=…, fit_intercept=…, n_iter=…, shuffle=…, verbose=…, epsilon=…, 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=…) - changed:
constructor SGDClassifier(loss=…, penalty=…, alpha=…, l1_ratio=…, fit_intercept=…, n_iter=…, shuffle=…, verbose=…, epsilon=…, n_jobs=…, random_state=…, learning_rate=…, eta0=…, power_t=…, class_weight=…, warm_start=…, rho=…, seed=…) → 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=…) - changed:
constructor SGDRegressor(loss=…, penalty=…, alpha=…, l1_ratio=…, fit_intercept=…, n_iter=…, shuffle=…, verbose=…, epsilon=…, random_state=…, learning_rate=…, eta0=…, power_t=…, warm_start=…, rho=…) → constructor SGDRegressor(loss=…, penalty=…, alpha=…, l1_ratio=…, fit_intercept=…, n_iter=…, shuffle=…, verbose=…, epsilon=…, random_state=…, learning_rate=…, eta0=…, power_t=…, warm_start=…) - added:
property SGDClassifier.predict_log_proba - added:
property SGDClassifier.predict_proba
- added:
export TSNE
- added:
function configuration(parent_package=…, top_path=…)
- changed:
class SpectralEmbedding(BaseEstimator, TransformerMixin) → class SpectralEmbedding(BaseEstimator)
- added:
class TSNE(BaseEstimator) - added:
constructor TSNE(n_components=…, perplexity=…, early_exaggeration=…, learning_rate=…, n_iter=…, metric=…, init=…, verbose=…, random_state=…) - added:
function trustworthiness(X, X_embedded, n_neighbors=…, precomputed=…) - added:
method TSNE.fit_transform(X) - added:
variable MACHINE_EPSILON
- added:
export pairwise_distances_argmin - added:
export pairwise_distances_argmin_min
- removed:
function zero_one(y_true, y_pred, normalize=…) - removed:
function zero_one_score(y_true, y_pred) - changed:
function accuracy_score(y_true, y_pred, normalize=…) → function accuracy_score(y_true, y_pred, normalize=…, sample_weight=…) - changed:
function average_precision_score(y_true, y_score) → function average_precision_score(y_true, y_score, average=…, sample_weight=…) - changed:
function classification_report(y_true, y_pred, labels=…, target_names=…) → function classification_report(y_true, y_pred, labels=…, target_names=…, sample_weight=…) - changed:
function explained_variance_score(y_true, y_pred) → function explained_variance_score(y_true, y_pred, sample_weight=…) - changed:
function f1_score(y_true, y_pred, labels=…, pos_label=…, average=…) → function f1_score(y_true, y_pred, labels=…, pos_label=…, average=…, sample_weight=…) - changed:
function fbeta_score(y_true, y_pred, beta, labels=…, pos_label=…, average=…) → function fbeta_score(y_true, y_pred, beta, labels=…, pos_label=…, average=…, sample_weight=…) - changed:
function mean_absolute_error(y_true, y_pred) → function mean_absolute_error(y_true, y_pred, sample_weight=…) - changed:
function mean_squared_error(y_true, y_pred) → function mean_squared_error(y_true, y_pred, sample_weight=…) - changed:
function precision_recall_curve(y_true, probas_pred, pos_label=…) → function precision_recall_curve(y_true, probas_pred, pos_label=…, sample_weight=…) - changed:
function precision_recall_fscore_support(y_true, y_pred, beta=…, labels=…, pos_label=…, average=…) → function precision_recall_fscore_support(y_true, y_pred, beta=…, labels=…, pos_label=…, average=…, warn_for=…, sample_weight=…) - changed:
function precision_score(y_true, y_pred, labels=…, pos_label=…, average=…) → function precision_score(y_true, y_pred, labels=…, pos_label=…, average=…, sample_weight=…) - changed:
function r2_score(y_true, y_pred) → function r2_score(y_true, y_pred, sample_weight=…) - changed:
function recall_score(y_true, y_pred, labels=…, pos_label=…, average=…) → function recall_score(y_true, y_pred, labels=…, pos_label=…, average=…, sample_weight=…) - changed:
function roc_auc_score(y_true, y_score) → function roc_auc_score(y_true, y_score, average=…, sample_weight=…) - changed:
function roc_curve(y_true, y_score, pos_label=…) → function roc_curve(y_true, y_score, pos_label=…, sample_weight=…) - changed:
function zero_one_loss(y_true, y_pred, normalize=…) → function zero_one_loss(y_true, y_pred, normalize=…, sample_weight=…) - added:
class UndefinedMetricWarning(UserWarning)
- added:
function check_paired_arrays(X, Y) - added:
function cosine_distances(X, Y=…) - added:
function paired_cosine_distances(X, Y) - added:
function paired_distances(X, Y, metric=…, **kwds) - added:
function paired_euclidean_distances(X, Y) - added:
function paired_manhattan_distances(X, Y) - added:
function pairwise_distances_argmin(X, Y, axis=…, metric=…, batch_size=…, metric_kwargs=…) - added:
function pairwise_distances_argmin_min(X, Y, axis=…, metric=…, batch_size=…, metric_kwargs=…) - added:
variable PAIRED_DISTANCES
- added:
function check_scoring(estimator, scoring=…, allow_none=…, loss_func=…, score_func=…, score_overrides_loss=…) - added:
function get_scorer(scoring) - added:
variable mean_absolute_error_scorer
- removed:
function sqnorm(v)
- changed:
constructor KNeighborsClassifier(n_neighbors=…, weights=…, algorithm=…, leaf_size=…, p=…, metric=…, **kwargs) → constructor KNeighborsClassifier(n_neighbors=…, weights=…, algorithm=…, leaf_size=…, p=…, metric=…, metric_params=…, **kwargs) - changed:
constructor RadiusNeighborsClassifier(radius=…, weights=…, algorithm=…, leaf_size=…, p=…, metric=…, outlier_label=…, **kwargs) → constructor RadiusNeighborsClassifier(radius=…, weights=…, algorithm=…, leaf_size=…, p=…, metric=…, outlier_label=…, metric_params=…, **kwargs)
- changed:
constructor KNeighborsRegressor(n_neighbors=…, weights=…, algorithm=…, leaf_size=…, p=…, metric=…, **kwargs) → constructor KNeighborsRegressor(n_neighbors=…, weights=…, algorithm=…, leaf_size=…, p=…, metric=…, metric_params=…, **kwargs) - changed:
constructor RadiusNeighborsRegressor(radius=…, weights=…, algorithm=…, leaf_size=…, p=…, metric=…, **kwargs) → constructor RadiusNeighborsRegressor(radius=…, weights=…, algorithm=…, leaf_size=…, p=…, metric=…, metric_params=…, **kwargs)
- changed:
constructor NearestNeighbors(n_neighbors=…, radius=…, algorithm=…, leaf_size=…, metric=…, **kwargs) → constructor NearestNeighbors(n_neighbors=…, radius=…, algorithm=…, leaf_size=…, metric=…, p=…, metric_params=…, **kwargs)
- changed:
method BernoulliRBM.score_samples(v) → method BernoulliRBM.score_samples(X) - added:
method BernoulliRBM.partial_fit(X)
- removed:
export Scaler - added:
export MultiLabelBinarizer - added:
export PolynomialFeatures - added:
export balance_weights
- removed:
class Scaler(StandardScaler) - removed:
constructor Scaler(copy=…, with_mean=…, with_std=…) - changed:
constructor OneHotEncoder(n_values=…, categorical_features=…, dtype=…) → constructor OneHotEncoder(n_values=…, categorical_features=…, dtype=…, sparse=…)
- changed:
constructor LabelBinarizer(neg_label=…, pos_label=…) → constructor LabelBinarizer(neg_label=…, pos_label=…, sparse_output=…) - changed:
function label_binarize(y, classes, multilabel=…, neg_label=…, pos_label=…) → function label_binarize(y, classes, neg_label=…, pos_label=…, sparse_output=…, multilabel=…) - added:
property LabelBinarizer.indicator_matrix_ - added:
property LabelBinarizer.multilabel_
- removed:
property QDA.rotations - removed:
property QDA.scalings
- removed:
method BaseSVC.predict_log_proba(X) - removed:
method BaseSVC.predict_proba(X) - removed:
property BaseLibLinear.label_ - removed:
property BaseSVC.label_ - added:
property BaseSVC.predict_log_proba - added:
property BaseSVC.predict_proba
- changed:
constructor DecisionTreeClassifier(criterion=…, splitter=…, max_depth=…, min_samples_split=…, min_samples_leaf=…, max_features=…, random_state=…, min_density=…, compute_importances=…) → constructor DecisionTreeClassifier(criterion=…, splitter=…, max_depth=…, min_samples_split=…, min_samples_leaf=…, max_features=…, random_state=…, min_density=…, compute_importances=…, max_leaf_nodes=…) - changed:
constructor DecisionTreeRegressor(criterion=…, splitter=…, max_depth=…, min_samples_split=…, min_samples_leaf=…, max_features=…, random_state=…, min_density=…, compute_importances=…) → constructor DecisionTreeRegressor(criterion=…, splitter=…, max_depth=…, min_samples_split=…, min_samples_leaf=…, max_features=…, random_state=…, min_density=…, compute_importances=…, max_leaf_nodes=…) - changed:
constructor ExtraTreeClassifier(criterion=…, splitter=…, max_depth=…, min_samples_split=…, min_samples_leaf=…, max_features=…, random_state=…, min_density=…, compute_importances=…) → constructor ExtraTreeClassifier(criterion=…, splitter=…, max_depth=…, min_samples_split=…, min_samples_leaf=…, max_features=…, random_state=…, min_density=…, compute_importances=…, max_leaf_nodes=…) - changed:
constructor ExtraTreeRegressor(criterion=…, splitter=…, max_depth=…, min_samples_split=…, min_samples_leaf=…, max_features=…, random_state=…, min_density=…, compute_importances=…) → constructor ExtraTreeRegressor(criterion=…, splitter=…, max_depth=…, min_samples_split=…, min_samples_leaf=…, max_features=…, random_state=…, min_density=…, compute_importances=…, max_leaf_nodes=…)
- added:
function safe_indexing(X, indices)
- changed:
function compute_class_weight(class_weight, classes, y_ind) → function compute_class_weight(class_weight, classes, y)
- removed:
variable fast_logdet - changed:
function norm(v) → function norm(x) - changed:
function randomized_range_finder(A, size, n_iter, random_state=…, n_iterations=…) → function randomized_range_finder(A, size, n_iter, random_state=…) - added:
function fast_dot(A, B) - added:
function fast_logdet(A) - added:
function log_logistic(X, out=…) - added:
function row_norms(X, squared=…) - added:
function squared_norm(x) - added:
variable fast_dot
- removed:
class Counter(collections.defaultdict) - removed:
constructor Counter(iterable=…, **kwargs) - removed:
function count_nonzero(X) - removed:
function lsqr(X, y, tol=…) - removed:
function qr_economic(A, **kwargs) - removed:
function savemat(file_name, mdict, oned_as=…, **kwargs) - removed:
method Counter.most_common() - removed:
method Counter.update(other) - removed:
variable Counter - removed:
variable bincount - removed:
variable copysign - removed:
variable in1d - removed:
variable unique - added:
function argpartition(a, kth, axis=…, kind=…, order=…) - added:
function astype(array, dtype, copy=…) - added:
function combinations_with_replacement(iterable, r) - added:
function expit(x, out=…) - added:
function in1d(ar1, ar2, assume_unique=…, invert=…) - added:
function isclose(a, b, rtol=…, atol=…, equal_nan=…) - added:
function sparse_min_max(X, axis) - added:
variable astype
- added:
export sample_without_replacement - added:
function choice(a, size=…, replace=…, p=…, random_state=…)
- added:
function inplace_column_scale(X, scale) - added:
function inplace_csr_column_scale(X, scale) - added:
function inplace_csr_row_scale(X, scale) - added:
function inplace_row_scale(X, scale) - added:
function inplace_swap_column(X, m, n) - added:
function inplace_swap_row(X, m, n) - added:
function inplace_swap_row_csc(X, m, n) - added:
function inplace_swap_row_csr(X, m, n) - added:
function mean_variance_axis0(X) - added:
function min_max_axis(X, axis)
- changed:
function as_float_array(X, copy=…) → function as_float_array(X, copy=…, force_all_finite=…) - changed:
function safe_asarray(X, dtype=…, order=…, copy=…) → function safe_asarray(X, dtype=…, order=…, copy=…, force_all_finite=…) - added:
class NonBLASDotWarning(UserWarning)