Upgrading scikit-learn 0.17 to 0.18
Unknown0.17.1 → 0.18.2
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API changes
Generated by DepDelta from published Python source. Not mixed with prose notes.
- changed:
class ChangedBehaviorWarning(UserWarning) → class ChangedBehaviorWarning(_ChangedBehaviorWarning) - added:
class DensityMixin(object) - added:
method DensityMixin.score(X, y=…)
- changed:
constructor DBSCAN(eps=…, min_samples=…, metric=…, algorithm=…, leaf_size=…, p=…, random_state=…) → constructor DBSCAN(eps=…, min_samples=…, metric=…, algorithm=…, leaf_size=…, p=…, n_jobs=…) - changed:
function dbscan(X, eps=…, min_samples=…, metric=…, algorithm=…, leaf_size=…, p=…, sample_weight=…, random_state=…) → function dbscan(X, eps=…, min_samples=…, metric=…, algorithm=…, leaf_size=…, p=…, sample_weight=…, n_jobs=…)
- removed:
variable xrange - changed:
constructor AgglomerativeClustering(n_clusters=…, affinity=…, memory=…, connectivity=…, n_components=…, compute_full_tree=…, linkage=…, pooling_func=…) → constructor AgglomerativeClustering(n_clusters=…, affinity=…, memory=…, connectivity=…, compute_full_tree=…, linkage=…, pooling_func=…) - changed:
function ward_tree(X, connectivity=…, n_components=…, n_clusters=…, return_distance=…) → function ward_tree(X, connectivity=…, n_clusters=…, return_distance=…)
- changed:
constructor KMeans(n_clusters=…, init=…, n_init=…, max_iter=…, tol=…, precompute_distances=…, verbose=…, random_state=…, copy_x=…, n_jobs=…) → constructor KMeans(n_clusters=…, init=…, n_init=…, max_iter=…, tol=…, precompute_distances=…, verbose=…, random_state=…, copy_x=…, n_jobs=…, algorithm=…) - changed:
function k_means(X, n_clusters, init=…, precompute_distances=…, n_init=…, max_iter=…, verbose=…, tol=…, random_state=…, copy_x=…, n_jobs=…, return_n_iter=…) → function k_means(X, n_clusters, init=…, precompute_distances=…, n_init=…, max_iter=…, verbose=…, tol=…, random_state=…, copy_x=…, n_jobs=…, algorithm=…, return_n_iter=…)
- changed:
function estimate_bandwidth(X, quantile=…, n_samples=…, random_state=…) → function estimate_bandwidth(X, quantile=…, n_samples=…, random_state=…, n_jobs=…) - changed:
function mean_shift(X, bandwidth=…, seeds=…, bin_seeding=…, min_bin_freq=…, cluster_all=…, max_iter=…, max_iterations=…, n_jobs=…) → function mean_shift(X, bandwidth=…, seeds=…, bin_seeding=…, min_bin_freq=…, cluster_all=…, max_iter=…, n_jobs=…)
- changed:
constructor SpectralClustering(n_clusters=…, eigen_solver=…, random_state=…, n_init=…, gamma=…, affinity=…, n_neighbors=…, eigen_tol=…, assign_labels=…, 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=…, n_jobs=…)
- removed:
property OutlierDetectionMixin.threshold
- added:
export fetch_kddcup99
- changed:
function load_boston() → function load_boston(return_X_y=…) - changed:
function load_breast_cancer() → function load_breast_cancer(return_X_y=…) - changed:
function load_diabetes() → function load_diabetes(return_X_y=…) - changed:
function load_digits(n_class=…) → function load_digits(n_class=…, return_X_y=…) - changed:
function load_iris() → function load_iris(return_X_y=…) - changed:
function load_linnerud() → function load_linnerud(return_X_y=…)
- added:
function fetch_kddcup99(subset=…, shuffle=…, random_state=…, percent10=…, download_if_missing=…) - added:
variable URL - added:
variable URL10 - added:
variable logger
- added:
export non_negative_factorization
- changed:
constructor KernelPCA(n_components=…, kernel=…, gamma=…, degree=…, coef0=…, kernel_params=…, alpha=…, fit_inverse_transform=…, eigen_solver=…, tol=…, max_iter=…, remove_zero_eig=…) → constructor KernelPCA(n_components=…, kernel=…, gamma=…, degree=…, coef0=…, kernel_params=…, alpha=…, fit_inverse_transform=…, eigen_solver=…, tol=…, max_iter=…, remove_zero_eig=…, random_state=…, copy_X=…, n_jobs=…)
- added:
method NMF.inverse_transform(W) - added:
variable INTEGER_TYPES
- removed:
method PCA.get_covariance() - removed:
method PCA.get_precision() - removed:
method PCA.inverse_transform(X) - removed:
method PCA.transform(X) - changed:
class PCA(BaseEstimator, TransformerMixin) → class PCA(_BasePCA) - changed:
constructor PCA(n_components=…, copy=…, whiten=…) → constructor PCA(n_components=…, copy=…, whiten=…, svd_solver=…, tol=…, iterated_power=…, random_state=…)
- added:
export IsolationForest
- added:
variable MAX_RAND_SEED
- changed:
constructor ExtraTreesClassifier(n_estimators=…, criterion=…, max_depth=…, min_samples_split=…, min_samples_leaf=…, min_weight_fraction_leaf=…, max_features=…, max_leaf_nodes=…, bootstrap=…, oob_score=…, n_jobs=…, random_state=…, verbose=…, warm_start=…, class_weight=…) → constructor ExtraTreesClassifier(n_estimators=…, criterion=…, max_depth=…, min_samples_split=…, min_samples_leaf=…, min_weight_fraction_leaf=…, max_features=…, max_leaf_nodes=…, min_impurity_split=…, bootstrap=…, oob_score=…, n_jobs=…, random_state=…, verbose=…, warm_start=…, class_weight=…) - changed:
constructor ExtraTreesRegressor(n_estimators=…, criterion=…, max_depth=…, min_samples_split=…, min_samples_leaf=…, min_weight_fraction_leaf=…, max_features=…, max_leaf_nodes=…, bootstrap=…, oob_score=…, n_jobs=…, random_state=…, verbose=…, warm_start=…) → constructor ExtraTreesRegressor(n_estimators=…, criterion=…, max_depth=…, min_samples_split=…, min_samples_leaf=…, min_weight_fraction_leaf=…, max_features=…, max_leaf_nodes=…, min_impurity_split=…, bootstrap=…, oob_score=…, n_jobs=…, random_state=…, verbose=…, warm_start=…) - changed:
constructor RandomForestClassifier(n_estimators=…, criterion=…, max_depth=…, min_samples_split=…, min_samples_leaf=…, min_weight_fraction_leaf=…, max_features=…, max_leaf_nodes=…, bootstrap=…, oob_score=…, n_jobs=…, random_state=…, verbose=…, warm_start=…, class_weight=…) → constructor RandomForestClassifier(n_estimators=…, criterion=…, max_depth=…, min_samples_split=…, min_samples_leaf=…, min_weight_fraction_leaf=…, max_features=…, max_leaf_nodes=…, min_impurity_split=…, bootstrap=…, oob_score=…, n_jobs=…, random_state=…, verbose=…, warm_start=…, class_weight=…) - changed:
constructor RandomForestRegressor(n_estimators=…, criterion=…, max_depth=…, min_samples_split=…, min_samples_leaf=…, min_weight_fraction_leaf=…, max_features=…, max_leaf_nodes=…, bootstrap=…, oob_score=…, n_jobs=…, random_state=…, verbose=…, warm_start=…) → constructor RandomForestRegressor(n_estimators=…, criterion=…, max_depth=…, min_samples_split=…, min_samples_leaf=…, min_weight_fraction_leaf=…, max_features=…, max_leaf_nodes=…, min_impurity_split=…, bootstrap=…, oob_score=…, n_jobs=…, random_state=…, verbose=…, warm_start=…) - changed:
constructor RandomTreesEmbedding(n_estimators=…, max_depth=…, min_samples_split=…, min_samples_leaf=…, min_weight_fraction_leaf=…, max_leaf_nodes=…, sparse_output=…, n_jobs=…, random_state=…, verbose=…, warm_start=…) → constructor RandomTreesEmbedding(n_estimators=…, max_depth=…, min_samples_split=…, min_samples_leaf=…, min_weight_fraction_leaf=…, max_leaf_nodes=…, min_impurity_split=…, sparse_output=…, n_jobs=…, random_state=…, verbose=…, warm_start=…)
- changed:
constructor BaseGradientBoosting(loss, learning_rate, n_estimators, min_samples_split, min_samples_leaf, min_weight_fraction_leaf, max_depth, init, subsample, max_features, random_state, alpha=…, verbose=…, max_leaf_nodes=…, warm_start=…, presort=…) → constructor BaseGradientBoosting(loss, learning_rate, n_estimators, criterion, min_samples_split, min_samples_leaf, min_weight_fraction_leaf, max_depth, min_impurity_split, init, subsample, max_features, random_state, alpha=…, verbose=…, max_leaf_nodes=…, warm_start=…, presort=…) - changed:
constructor GradientBoostingClassifier(loss=…, learning_rate=…, n_estimators=…, subsample=…, min_samples_split=…, min_samples_leaf=…, min_weight_fraction_leaf=…, max_depth=…, init=…, random_state=…, max_features=…, verbose=…, max_leaf_nodes=…, warm_start=…, presort=…) → constructor GradientBoostingClassifier(loss=…, learning_rate=…, n_estimators=…, subsample=…, criterion=…, min_samples_split=…, min_samples_leaf=…, min_weight_fraction_leaf=…, max_depth=…, min_impurity_split=…, init=…, random_state=…, max_features=…, verbose=…, max_leaf_nodes=…, warm_start=…, presort=…) - changed:
constructor GradientBoostingRegressor(loss=…, learning_rate=…, n_estimators=…, subsample=…, min_samples_split=…, min_samples_leaf=…, min_weight_fraction_leaf=…, max_depth=…, init=…, random_state=…, max_features=…, alpha=…, verbose=…, max_leaf_nodes=…, warm_start=…, presort=…) → constructor GradientBoostingRegressor(loss=…, learning_rate=…, n_estimators=…, subsample=…, criterion=…, min_samples_split=…, min_samples_leaf=…, min_weight_fraction_leaf=…, max_depth=…, min_impurity_split=…, init=…, random_state=…, max_features=…, alpha=…, verbose=…, max_leaf_nodes=…, warm_start=…, presort=…)
- added:
class IsolationForest(BaseBagging) - added:
constructor IsolationForest(n_estimators=…, max_samples=…, contamination=…, max_features=…, bootstrap=…, n_jobs=…, random_state=…, verbose=…) - added:
method IsolationForest.decision_function(X) - added:
method IsolationForest.fit(X, y=…, sample_weight=…) - added:
method IsolationForest.predict(X)
- changed:
constructor VotingClassifier(estimators, voting=…, weights=…) → constructor VotingClassifier(estimators, voting=…, weights=…, n_jobs=…) - changed:
method VotingClassifier.fit(X, y) → method VotingClassifier.fit(X, y, sample_weight=…)
- added:
class ChangedBehaviorWarning(UserWarning) - added:
class ConvergenceWarning(UserWarning) - added:
class DataConversionWarning(UserWarning) - added:
class DataDimensionalityWarning(UserWarning) - added:
class EfficiencyWarning(UserWarning) - added:
class FitFailedWarning(RuntimeWarning) - added:
class NonBLASDotWarning(EfficiencyWarning) - added:
class NotFittedError(ValueError, AttributeError) - added:
class UndefinedMetricWarning(UserWarning)
- added:
export effective_n_jobs - added:
export parallel_backend - added:
export register_parallel_backend
- removed:
function memstr_to_kbytes(text) - added:
function memstr_to_bytes(text)
- added:
class WorkerInterrupt(Exception)
- removed:
class NDArrayWrapper(object) - removed:
class ZNDArrayWrapper(NDArrayWrapper) - removed:
class ZipNumpyUnpickler(NumpyUnpickler) - removed:
constructor NDArrayWrapper(filename, subclass, allow_mmap=…) - removed:
constructor ZNDArrayWrapper(filename, init_args, state) - removed:
constructor ZipNumpyUnpickler(filename, file_handle) - removed:
function asbytes(s) - removed:
function hex_str(an_int) - removed:
function read_zfile(file_handle) - removed:
function write_zfile(file_handle, data, compress=…) - removed:
method NDArrayWrapper.read(unpickler) - removed:
method NumpyPickler.close() - removed:
method ZNDArrayWrapper.read(unpickler) - removed:
variable Pickler - removed:
variable Unpickler - removed:
variable asbytes - changed:
constructor NumpyPickler(filename, compress=…, cache_size=…, protocol=…) → constructor NumpyPickler(fp, protocol=…) - changed:
function dump(value, filename, compress=…, cache_size=…, protocol=…) → function dump(value, filename, compress=…, protocol=…, cache_size=…) - added:
class NumpyArrayWrapper(object) - added:
constructor NumpyArrayWrapper(subclass, shape, order, dtype, allow_mmap=…) - added:
method NumpyArrayWrapper.read(unpickler) - added:
method NumpyArrayWrapper.read_array(unpickler) - added:
method NumpyArrayWrapper.read_mmap(unpickler) - added:
method NumpyArrayWrapper.write_array(array, pickler) - added:
variable Path
- added:
class NDArrayWrapper(object) - added:
class ZNDArrayWrapper(NDArrayWrapper) - added:
class ZipNumpyUnpickler(Unpickler) - added:
constructor NDArrayWrapper(filename, subclass, allow_mmap=…) - added:
constructor ZNDArrayWrapper(filename, init_args, state) - added:
constructor ZipNumpyUnpickler(filename, file_handle, mmap_mode=…) - added:
field ZipNumpyUnpickler.dispatch - added:
function asbytes(s) - added:
function hex_str(an_int) - added:
function load_compatibility(filename) - added:
function read_zfile(file_handle) - added:
function write_zfile(file_handle, data, compress=…) - added:
method NDArrayWrapper.read(unpickler) - added:
method ZNDArrayWrapper.read(unpickler) - added:
method ZipNumpyUnpickler.load_build() - added:
variable asbytes
- added:
class BinaryGzipFile(BinaryZlibFile) - added:
class BinaryZlibFile(io.BufferedIOBase) - added:
constructor BinaryZlibFile(filename, mode=…, compresslevel=…) - added:
field BinaryGzipFile.wbits - added:
field BinaryZlibFile.wbits - added:
method BinaryZlibFile.close() - added:
method BinaryZlibFile.fileno() - added:
method BinaryZlibFile.read(size=…) - added:
method BinaryZlibFile.readable() - added:
method BinaryZlibFile.readinto(b) - added:
method BinaryZlibFile.seek(offset, whence=…) - added:
method BinaryZlibFile.seekable() - added:
method BinaryZlibFile.tell() - added:
method BinaryZlibFile.writable() - added:
method BinaryZlibFile.write(data) - added:
property BinaryZlibFile.closed - added:
variable BUFFER_SIZE - added:
variable Pickler - added:
variable Unpickler - added:
variable lzma - added:
variable np - added:
variable xrange
- removed:
class ImmediateComputeBatch(object) - removed:
class SafeFunction(object) - removed:
class WorkerInterrupt(Exception) - removed:
constructor ImmediateComputeBatch(batch) - removed:
constructor SafeFunction(func) - removed:
method ImmediateComputeBatch.get() - removed:
variable JOBLIB_SPAWNED_PROCESS - removed:
variable MAX_IDEAL_BATCH_DURATION - removed:
variable MIN_IDEAL_BATCH_DURATION - removed:
variable VALID_BACKENDS - changed:
constructor Parallel(n_jobs=…, backend=…, verbose=…, pre_dispatch=…, batch_size=…, temp_folder=…, max_nbytes=…, mmap_mode=…) → constructor Parallel(n_jobs=…, backend=…, verbose=…, timeout=…, pre_dispatch=…, batch_size=…, temp_folder=…, max_nbytes=…, mmap_mode=…) - added:
function effective_n_jobs(n_jobs=…) - added:
function get_active_backend() - added:
function parallel_backend(backend, n_jobs=…, **backend_params) - added:
function register_parallel_backend(name, factory, make_default=…) - added:
variable BACKENDS - added:
variable DEFAULT_BACKEND - added:
variable DEFAULT_N_JOBS
- added:
export mutual_info_classif - added:
export mutual_info_regression
- added:
function mutual_info_classif(X, y, discrete_features=…, n_neighbors=…, copy=…, random_state=…) - added:
function mutual_info_regression(X, y, discrete_features=…, n_neighbors=…, copy=…, random_state=…)
- changed:
constructor RFE(estimator, n_features_to_select=…, step=…, estimator_params=…, verbose=…) → constructor RFE(estimator, n_features_to_select=…, step=…, verbose=…) - changed:
constructor RFECV(estimator, step=…, cv=…, scoring=…, estimator_params=…, verbose=…) → constructor RFECV(estimator, step=…, cv=…, scoring=…, verbose=…, n_jobs=…)
- added:
export GaussianProcessClassifier - added:
export GaussianProcessRegressor - added:
export kernels
- added:
class GaussianProcessClassifier(BaseEstimator, ClassifierMixin) - added:
constructor GaussianProcessClassifier(kernel=…, optimizer=…, n_restarts_optimizer=…, max_iter_predict=…, warm_start=…, copy_X_train=…, random_state=…, multi_class=…, n_jobs=…) - added:
method GaussianProcessClassifier.fit(X, y) - added:
method GaussianProcessClassifier.log_marginal_likelihood(theta=…, eval_gradient=…) - added:
method GaussianProcessClassifier.predict(X) - added:
method GaussianProcessClassifier.predict_proba(X) - added:
property GaussianProcessClassifier.kernel_ - added:
variable COEFS - added:
variable LAMBDAS
- added:
class GaussianProcessRegressor(BaseEstimator, RegressorMixin) - added:
constructor GaussianProcessRegressor(kernel=…, alpha=…, optimizer=…, n_restarts_optimizer=…, normalize_y=…, copy_X_train=…, random_state=…) - added:
method GaussianProcessRegressor.fit(X, y) - added:
method GaussianProcessRegressor.log_marginal_likelihood(theta=…, eval_gradient=…) - added:
method GaussianProcessRegressor.predict(X, return_std=…, return_cov=…) - added:
method GaussianProcessRegressor.sample_y(X, n_samples=…, random_state=…)
- added:
class CompoundKernel(Kernel) - added:
class ConstantKernel(StationaryKernelMixin, Kernel) - added:
class DotProduct(Kernel) - added:
class ExpSineSquared(StationaryKernelMixin, NormalizedKernelMixin, Kernel) - added:
class Exponentiation(Kernel) - added:
class Hyperparameter(namedtuple('Hyperparameter', ('name', 'value_type', 'bounds', 'n_elements', 'fixed'))) - added:
class Kernel(six.with_metaclass(ABCMeta)) - added:
class KernelOperator(Kernel) - added:
class Matern(RBF) - added:
class NormalizedKernelMixin(object) - added:
class PairwiseKernel(Kernel) - added:
class Product(KernelOperator) - added:
class RBF(StationaryKernelMixin, NormalizedKernelMixin, Kernel) - added:
class RationalQuadratic(StationaryKernelMixin, NormalizedKernelMixin, Kernel) - added:
class StationaryKernelMixin(object) - added:
class Sum(KernelOperator) - added:
class WhiteKernel(StationaryKernelMixin, Kernel) - added:
constructor CompoundKernel(kernels) - added:
constructor ConstantKernel(constant_value=…, constant_value_bounds=…) - added:
constructor DotProduct(sigma_0=…, sigma_0_bounds=…) - added:
constructor ExpSineSquared(length_scale=…, periodicity=…, length_scale_bounds=…, periodicity_bounds=…) - added:
constructor Exponentiation(kernel, exponent) - added:
constructor KernelOperator(k1, k2) - added:
constructor Matern(length_scale=…, length_scale_bounds=…, nu=…) - added:
constructor PairwiseKernel(gamma=…, gamma_bounds=…, metric=…, pairwise_kernels_kwargs=…) - added:
constructor RBF(length_scale=…, length_scale_bounds=…) - added:
constructor RationalQuadratic(length_scale=…, alpha=…, length_scale_bounds=…, alpha_bounds=…) - added:
constructor WhiteKernel(noise_level=…, noise_level_bounds=…) - added:
method CompoundKernel.diag(X) - added:
method CompoundKernel.get_params(deep=…) - added:
method CompoundKernel.is_stationary() - added:
method ConstantKernel.diag(X) - added:
method DotProduct.diag(X) - added:
method DotProduct.is_stationary() - added:
method Exponentiation.diag(X) - added:
method Exponentiation.get_params(deep=…) - added:
method Exponentiation.is_stationary() - added:
method Kernel.clone_with_theta(theta) - added:
method Kernel.diag(X) - added:
method Kernel.get_params(deep=…) - added:
method Kernel.is_stationary() - added:
method Kernel.set_params(**params) - added:
method KernelOperator.get_params(deep=…) - added:
method KernelOperator.is_stationary() - added:
method NormalizedKernelMixin.diag(X) - added:
method PairwiseKernel.diag(X) - added:
method PairwiseKernel.is_stationary() - added:
method Product.diag(X) - added:
method StationaryKernelMixin.is_stationary() - added:
method Sum.diag(X) - added:
method WhiteKernel.diag(X) - added:
property CompoundKernel.bounds - added:
property CompoundKernel.theta - added:
property ConstantKernel.hyperparameter_constant_value - added:
property DotProduct.hyperparameter_sigma_0 - added:
property ExpSineSquared.hyperparameter_length_scale - added:
property ExpSineSquared.hyperparameter_periodicity - added:
property Exponentiation.bounds - added:
property Exponentiation.hyperparameters - added:
property Exponentiation.theta - added:
property Kernel.bounds - added:
property Kernel.hyperparameters - added:
property Kernel.n_dims - added:
property Kernel.theta - added:
property KernelOperator.bounds - added:
property KernelOperator.hyperparameters - added:
property KernelOperator.theta - added:
property PairwiseKernel.hyperparameter_gamma - added:
property RBF.anisotropic - added:
property RBF.hyperparameter_length_scale - added:
property RationalQuadratic.hyperparameter_alpha - added:
property RationalQuadratic.hyperparameter_length_scale - added:
property WhiteKernel.hyperparameter_noise_level
- added:
property IsotonicRegression.X_ - added:
property IsotonicRegression.y_
- added:
class LDA(_LDA)
- changed:
function learning_curve(estimator, X, y, train_sizes=…, cv=…, scoring=…, exploit_incremental_learning=…, n_jobs=…, pre_dispatch=…, verbose=…) → function learning_curve(estimator, X, y, train_sizes=…, cv=…, scoring=…, exploit_incremental_learning=…, n_jobs=…, pre_dispatch=…, verbose=…, error_score=…)
- removed:
export Huber - added:
export HuberRegressor
- added:
class HuberRegressor(LinearModel, RegressorMixin, BaseEstimator) - added:
constructor HuberRegressor(epsilon=…, max_iter=…, alpha=…, warm_start=…, fit_intercept=…, tol=…) - added:
method HuberRegressor.fit(X, y, sample_weight=…)
- changed:
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=…) → 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=…, loss=…, random_state=…) - changed:
method RANSACRegressor.fit(X, y) → method RANSACRegressor.fit(X, y, sample_weight=…)
- changed:
constructor Isomap(n_neighbors=…, n_components=…, eigen_solver=…, tol=…, max_iter=…, path_method=…, neighbors_algorithm=…) → constructor Isomap(n_neighbors=…, n_components=…, eigen_solver=…, tol=…, max_iter=…, path_method=…, neighbors_algorithm=…, n_jobs=…)
- changed:
constructor LocallyLinearEmbedding(n_neighbors=…, n_components=…, reg=…, eigen_solver=…, tol=…, max_iter=…, method=…, hessian_tol=…, modified_tol=…, neighbors_algorithm=…, random_state=…) → constructor LocallyLinearEmbedding(n_neighbors=…, n_components=…, reg=…, eigen_solver=…, tol=…, max_iter=…, method=…, hessian_tol=…, modified_tol=…, neighbors_algorithm=…, random_state=…, n_jobs=…) - changed:
function barycenter_kneighbors_graph(X, n_neighbors, reg=…) → function barycenter_kneighbors_graph(X, n_neighbors, reg=…, n_jobs=…) - changed:
function locally_linear_embedding(X, n_neighbors, n_components, reg=…, eigen_solver=…, tol=…, max_iter=…, method=…, hessian_tol=…, modified_tol=…, random_state=…) → function locally_linear_embedding(X, n_neighbors, n_components, reg=…, eigen_solver=…, tol=…, max_iter=…, method=…, hessian_tol=…, modified_tol=…, random_state=…, n_jobs=…)
- changed:
constructor SpectralEmbedding(n_components=…, affinity=…, gamma=…, random_state=…, eigen_solver=…, n_neighbors=…) → constructor SpectralEmbedding(n_components=…, affinity=…, gamma=…, random_state=…, eigen_solver=…, n_neighbors=…, n_jobs=…)
- changed:
class UndefinedMetricWarning(UserWarning) → class UndefinedMetricWarning(_UndefinedMetricWarning)
- changed:
function cohen_kappa_score(y1, y2, labels=…) → function cohen_kappa_score(y1, y2, labels=…, weights=…) - changed:
function confusion_matrix(y_true, y_pred, labels=…) → function confusion_matrix(y_true, y_pred, labels=…, sample_weight=…) - changed:
function hamming_loss(y_true, y_pred, classes=…) → function hamming_loss(y_true, y_pred, labels=…, sample_weight=…, classes=…) - changed:
function log_loss(y_true, y_pred, eps=…, normalize=…, sample_weight=…) → function log_loss(y_true, y_pred, eps=…, normalize=…, sample_weight=…, labels=…) - changed:
function matthews_corrcoef(y_true, y_pred) → function matthews_corrcoef(y_true, y_pred, sample_weight=…)
- added:
export calinski_harabaz_score - added:
export fowlkes_mallows_score
- changed:
function contingency_matrix(labels_true, labels_pred, eps=…) → function contingency_matrix(labels_true, labels_pred, eps=…, sparse=…) - added:
function fowlkes_mallows_score(labels_true, labels_pred, sparse=…)
- added:
function calinski_harabaz_score(X, labels) - added:
function check_number_of_labels(n_labels, n_samples)
- changed:
function check_pairwise_arrays(X, Y, precomputed=…) → function check_pairwise_arrays(X, Y, precomputed=…, dtype=…) - added:
variable PAIRWISE_BOOLEAN_FUNCTIONS
- added:
variable deprecation_msg - added:
variable neg_log_loss_scorer - added:
variable neg_mean_absolute_error_scorer - added:
variable neg_mean_squared_error_scorer - added:
variable neg_median_absolute_error_scorer
- added:
export BayesianGaussianMixture - added:
export GaussianMixture
- added:
class BaseMixture(six.with_metaclass(ABCMeta, DensityMixin, BaseEstimator)) - added:
constructor BaseMixture(n_components, tol, reg_covar, max_iter, n_init, init_params, random_state, warm_start, verbose, verbose_interval) - added:
method BaseMixture.fit(X, y=…) - added:
method BaseMixture.predict(X, y=…) - added:
method BaseMixture.predict_proba(X) - added:
method BaseMixture.sample(n_samples=…) - added:
method BaseMixture.score(X, y=…) - added:
method BaseMixture.score_samples(X)
- added:
class BayesianGaussianMixture(BaseMixture) - added:
constructor BayesianGaussianMixture(n_components=…, covariance_type=…, tol=…, reg_covar=…, max_iter=…, n_init=…, init_params=…, weight_concentration_prior_type=…, weight_concentration_prior=…, mean_precision_prior=…, mean_prior=…, degrees_of_freedom_prior=…, covariance_prior=…, random_state=…, warm_start=…, verbose=…, verbose_interval=…)
- removed:
method DPGMM.lower_bound(X, z) - removed:
method DPGMM.score_samples(X) - changed:
class DPGMM(GMM) → class DPGMM(_DPGMMBase) - changed:
class VBGMM(DPGMM) → class VBGMM(_DPGMMBase) - changed:
constructor DPGMM(n_components=…, covariance_type=…, alpha=…, random_state=…, thresh=…, tol=…, verbose=…, min_covar=…, n_iter=…, params=…, init_params=…) → constructor DPGMM(n_components=…, covariance_type=…, alpha=…, random_state=…, tol=…, verbose=…, min_covar=…, n_iter=…, params=…, init_params=…) - changed:
constructor VBGMM(n_components=…, covariance_type=…, alpha=…, random_state=…, thresh=…, tol=…, verbose=…, min_covar=…, n_iter=…, params=…, init_params=…) → constructor VBGMM(n_components=…, covariance_type=…, alpha=…, random_state=…, tol=…, verbose=…, min_covar=…, n_iter=…, params=…, init_params=…)
- added:
class GaussianMixture(BaseMixture) - added:
constructor GaussianMixture(n_components=…, covariance_type=…, tol=…, reg_covar=…, max_iter=…, n_init=…, init_params=…, weights_init=…, means_init=…, precisions_init=…, random_state=…, warm_start=…, verbose=…, verbose_interval=…) - added:
method GaussianMixture.aic(X) - added:
method GaussianMixture.bic(X)
- removed:
method GMM.aic(X) - removed:
method GMM.bic(X) - removed:
method GMM.fit(X, y=…) - removed:
method GMM.fit_predict(X, y=…) - removed:
method GMM.predict(X) - removed:
method GMM.predict_proba(X) - removed:
method GMM.sample(n_samples=…, random_state=…) - removed:
method GMM.score(X, y=…) - removed:
method GMM.score_samples(X) - changed:
class GMM(BaseEstimator) → class GMM(_GMMBase) - changed:
constructor GMM(n_components=…, covariance_type=…, random_state=…, thresh=…, tol=…, min_covar=…, n_iter=…, n_init=…, params=…, init_params=…, verbose=…) → constructor GMM(n_components=…, covariance_type=…, random_state=…, tol=…, min_covar=…, n_iter=…, n_init=…, params=…, init_params=…, verbose=…)
- added:
export BaseCrossValidator - added:
export GridSearchCV - added:
export GroupKFold - added:
export GroupShuffleSplit - added:
export KFold - added:
export LeaveOneGroupOut - added:
export LeaveOneOut - added:
export LeavePGroupsOut - added:
export LeavePOut - added:
export ParameterGrid - added:
export ParameterSampler - added:
export PredefinedSplit - added:
export RandomizedSearchCV - added:
export ShuffleSplit - added:
export StratifiedKFold - added:
export StratifiedShuffleSplit - added:
export TimeSeriesSplit - added:
export check_cv - added:
export cross_val_predict - added:
export cross_val_score - added:
export fit_grid_point - added:
export learning_curve - added:
export permutation_test_score - added:
export train_test_split - added:
export validation_curve
- removed:
property OneVsRestClassifier.classes_ - added:
method OneVsOneClassifier.partial_fit(X, y, classes=…) - added:
method OneVsRestClassifier.partial_fit(X, y, classes=…) - added:
property OneVsOneClassifier.n_classes_ - added:
property OneVsRestClassifier.n_classes_
- added:
class MultiOutputClassifier(MultiOutputEstimator, ClassifierMixin) - added:
class MultiOutputRegressor(MultiOutputEstimator, RegressorMixin) - added:
constructor MultiOutputClassifier(estimator, n_jobs=…) - added:
constructor MultiOutputRegressor(estimator, n_jobs=…) - added:
method MultiOutputClassifier.predict_proba(X) - added:
method MultiOutputClassifier.score(X, y) - added:
method MultiOutputRegressor.score(X, y, sample_weight=…)
- added:
constructor GaussianNB(priors=…)
- removed:
class NeighborsWarning(UserWarning)
- changed:
function kneighbors_graph(X, n_neighbors, mode=…, metric=…, p=…, metric_params=…, include_self=…) → function kneighbors_graph(X, n_neighbors, mode=…, metric=…, p=…, metric_params=…, include_self=…, n_jobs=…) - changed:
function radius_neighbors_graph(X, radius, mode=…, metric=…, p=…, metric_params=…, include_self=…) → function radius_neighbors_graph(X, radius, mode=…, metric=…, p=…, metric_params=…, include_self=…, n_jobs=…)
- added:
export MLPClassifier - added:
export MLPRegressor
- added:
class BaseMultilayerPerceptron(six.with_metaclass(ABCMeta, BaseEstimator)) - added:
class MLPClassifier(BaseMultilayerPerceptron, ClassifierMixin) - added:
class MLPRegressor(BaseMultilayerPerceptron, RegressorMixin) - added:
constructor BaseMultilayerPerceptron(hidden_layer_sizes, activation, solver, alpha, batch_size, learning_rate, learning_rate_init, power_t, max_iter, loss, shuffle, random_state, tol, verbose, warm_start, momentum, nesterovs_momentum, early_stopping, validation_fraction, beta_1, beta_2, epsilon) - added:
constructor MLPClassifier(hidden_layer_sizes=…, activation=…, solver=…, alpha=…, batch_size=…, learning_rate=…, learning_rate_init=…, power_t=…, max_iter=…, shuffle=…, random_state=…, tol=…, verbose=…, warm_start=…, momentum=…, nesterovs_momentum=…, early_stopping=…, validation_fraction=…, beta_1=…, beta_2=…, epsilon=…) - added:
constructor MLPRegressor(hidden_layer_sizes=…, activation=…, solver=…, alpha=…, batch_size=…, learning_rate=…, learning_rate_init=…, power_t=…, max_iter=…, shuffle=…, random_state=…, tol=…, verbose=…, warm_start=…, momentum=…, nesterovs_momentum=…, early_stopping=…, validation_fraction=…, beta_1=…, beta_2=…, epsilon=…) - added:
method BaseMultilayerPerceptron.fit(X, y) - added:
method MLPClassifier.predict(X) - added:
method MLPClassifier.predict_log_proba(X) - added:
method MLPClassifier.predict_proba(X) - added:
method MLPRegressor.predict(X) - added:
property BaseMultilayerPerceptron.partial_fit - added:
property MLPClassifier.partial_fit
- removed:
method Pipeline.inverse_transform(X) - removed:
method Pipeline.transform(X) - changed:
class FeatureUnion(BaseEstimator, TransformerMixin) → class FeatureUnion(_BasePipeline, TransformerMixin) - changed:
class Pipeline(BaseEstimator) → class Pipeline(_BasePipeline) - added:
method FeatureUnion.set_params(**kwargs) - added:
method Pipeline.set_params(**kwargs) - added:
property Pipeline.inverse_transform - added:
property Pipeline.transform
- changed:
constructor RobustScaler(with_centering=…, with_scaling=…, copy=…) → constructor RobustScaler(with_centering=…, with_scaling=…, quantile_range=…, copy=…) - changed:
function normalize(X, norm=…, axis=…, copy=…) → function normalize(X, norm=…, axis=…, copy=…, return_norm=…) - changed:
function robust_scale(X, axis=…, with_centering=…, with_scaling=…, copy=…) → function robust_scale(X, axis=…, with_centering=…, with_scaling=…, quantile_range=…, copy=…)
- added:
class QDA(_QDA)
- changed:
constructor BaseLabelPropagation(kernel=…, gamma=…, n_neighbors=…, alpha=…, max_iter=…, tol=…) → constructor BaseLabelPropagation(kernel=…, gamma=…, n_neighbors=…, alpha=…, max_iter=…, tol=…, n_jobs=…) - changed:
constructor LabelSpreading(kernel=…, gamma=…, n_neighbors=…, alpha=…, max_iter=…, tol=…) → constructor LabelSpreading(kernel=…, gamma=…, n_neighbors=…, alpha=…, max_iter=…, tol=…, n_jobs=…)
- changed:
method LinearSVC.fit(X, y) → method LinearSVC.fit(X, y, sample_weight=…) - changed:
method LinearSVR.fit(X, y) → method LinearSVR.fit(X, y, sample_weight=…)
- added:
class Sentinel(object) - added:
variable SENTINEL
- changed:
constructor DecisionTreeClassifier(criterion=…, splitter=…, max_depth=…, min_samples_split=…, min_samples_leaf=…, min_weight_fraction_leaf=…, max_features=…, random_state=…, max_leaf_nodes=…, class_weight=…, presort=…) → constructor DecisionTreeClassifier(criterion=…, splitter=…, max_depth=…, min_samples_split=…, min_samples_leaf=…, min_weight_fraction_leaf=…, max_features=…, random_state=…, max_leaf_nodes=…, min_impurity_split=…, class_weight=…, presort=…) - changed:
constructor DecisionTreeRegressor(criterion=…, splitter=…, max_depth=…, min_samples_split=…, min_samples_leaf=…, min_weight_fraction_leaf=…, max_features=…, random_state=…, max_leaf_nodes=…, presort=…) → constructor DecisionTreeRegressor(criterion=…, splitter=…, max_depth=…, min_samples_split=…, min_samples_leaf=…, min_weight_fraction_leaf=…, max_features=…, random_state=…, max_leaf_nodes=…, min_impurity_split=…, presort=…) - changed:
constructor ExtraTreeClassifier(criterion=…, splitter=…, max_depth=…, min_samples_split=…, min_samples_leaf=…, min_weight_fraction_leaf=…, max_features=…, random_state=…, max_leaf_nodes=…, class_weight=…) → constructor ExtraTreeClassifier(criterion=…, splitter=…, max_depth=…, min_samples_split=…, min_samples_leaf=…, min_weight_fraction_leaf=…, max_features=…, random_state=…, max_leaf_nodes=…, min_impurity_split=…, class_weight=…) - changed:
constructor ExtraTreeRegressor(criterion=…, splitter=…, max_depth=…, min_samples_split=…, min_samples_leaf=…, min_weight_fraction_leaf=…, max_features=…, random_state=…, max_leaf_nodes=…) → constructor ExtraTreeRegressor(criterion=…, splitter=…, max_depth=…, min_samples_split=…, min_samples_leaf=…, min_weight_fraction_leaf=…, max_features=…, random_state=…, min_impurity_split=…, max_leaf_nodes=…) - added:
method DecisionTreeClassifier.fit(X, y, sample_weight=…, check_input=…, X_idx_sorted=…) - added:
method DecisionTreeRegressor.fit(X, y, sample_weight=…, check_input=…, X_idx_sorted=…)
- added:
function indices_to_mask(indices, mask_length)
- added:
class deprecated(object) - added:
constructor deprecated(extra=…)
- changed:
function check_non_transformer_estimators_n_iter(name, estimator, multi_output=…) → function check_non_transformer_estimators_n_iter(name, Estimator) - changed:
function check_transformer_n_iter(name, estimator) → function check_transformer_n_iter(name, Estimator) - added:
function check_dict_unchanged(name, Estimator) - added:
function check_supervised_y_no_nan(name, Estimator)
- changed:
function randomized_range_finder(A, size, n_iter, random_state=…) → function randomized_range_finder(A, size, n_iter, power_iteration_normalizer=…, random_state=…) - changed:
function randomized_svd(M, n_components, n_oversamples=…, n_iter=…, transpose=…, flip_sign=…, random_state=…) → function randomized_svd(M, n_components, n_oversamples=…, n_iter=…, power_iteration_normalizer=…, transpose=…, flip_sign=…, random_state=…) - added:
function stable_cumsum(arr, rtol=…, atol=…)
- removed:
function isclose(a, b, rtol=…, atol=…, equal_nan=…) - added:
class MaskedArray(np.ma.MaskedArray) - added:
function parallel_helper(obj, methodname, *args, **kwargs) - added:
function partition(a, kth, axis=…, kind=…, order=…) - added:
function rankdata(a, method=…)
- changed:
class DataConversionWarning(UserWarning) → class DataConversionWarning(_DataConversionWarning) - changed:
class NonBLASDotWarning(UserWarning) → class NonBLASDotWarning(_NonBLASDotWarning) - changed:
class NotFittedError(ValueError, AttributeError) → class NotFittedError(_NotFittedError)