Mercurial > repos > bgruening > sklearn_build_pipeline
changeset 8:913ee94945f3 draft
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/feature_selectors.py Tue May 14 18:06:37 2019 -0400 @@ -0,0 +1,357 @@ +""" +DyRFE +DyRFECV +MyPipeline +MyimbPipeline +check_feature_importances +""" +import numpy as np + +from imblearn import under_sampling, over_sampling, combine +from imblearn.pipeline import Pipeline as imbPipeline +from sklearn import (cluster, compose, decomposition, ensemble, + feature_extraction, feature_selection, + gaussian_process, kernel_approximation, + metrics, model_selection, naive_bayes, + neighbors, pipeline, preprocessing, + svm, linear_model, tree, discriminant_analysis) + +from sklearn.base import BaseEstimator +from sklearn.base import MetaEstimatorMixin, clone, is_classifier +from sklearn.feature_selection.rfe import _rfe_single_fit, RFE, RFECV +from sklearn.model_selection import check_cv +from sklearn.metrics.scorer import check_scoring +from sklearn.utils import check_X_y, safe_indexing, safe_sqr +from sklearn.utils._joblib import Parallel, delayed, effective_n_jobs + + +class DyRFE(RFE): + """ + Mainly used with DyRFECV + + Parameters + ---------- + estimator : object + A supervised learning estimator with a ``fit`` method that provides + information about feature importance either through a ``coef_`` + attribute or through a ``feature_importances_`` attribute. + n_features_to_select : int or None (default=None) + The number of features to select. If `None`, half of the features + are selected. + step : int, float or list, optional (default=1) + If greater than or equal to 1, then ``step`` corresponds to the + (integer) number of features to remove at each iteration. + If within (0.0, 1.0), then ``step`` corresponds to the percentage + (rounded down) of features to remove at each iteration. + If list, a series of steps of features to remove at each iteration. + Iterations stops when steps finish + verbose : int, (default=0) + Controls verbosity of output. + + """ + def __init__(self, estimator, n_features_to_select=None, step=1, + verbose=0): + super(DyRFE, self).__init__(estimator, n_features_to_select, + step, verbose) + + def _fit(self, X, y, step_score=None): + + if type(self.step) is not list: + return super(DyRFE, self)._fit(X, y, step_score) + + # dynamic step + X, y = check_X_y(X, y, "csc") + # Initialization + n_features = X.shape[1] + if self.n_features_to_select is None: + n_features_to_select = n_features // 2 + else: + n_features_to_select = self.n_features_to_select + + step = [] + for s in self.step: + if 0.0 < s < 1.0: + step.append(int(max(1, s * n_features))) + else: + step.append(int(s)) + if s <= 0: + raise ValueError("Step must be >0") + + support_ = np.ones(n_features, dtype=np.bool) + ranking_ = np.ones(n_features, dtype=np.int) + + if step_score: + self.scores_ = [] + + step_i = 0 + # Elimination + while np.sum(support_) > n_features_to_select and step_i < len(step): + + # if last step is 1, will keep loop + if step_i == len(step) - 1 and step[step_i] != 0: + step.append(step[step_i]) + + # Remaining features + features = np.arange(n_features)[support_] + + # Rank the remaining features + estimator = clone(self.estimator) + if self.verbose > 0: + print("Fitting estimator with %d features." % np.sum(support_)) + + estimator.fit(X[:, features], y) + + # Get coefs + if hasattr(estimator, 'coef_'): + coefs = estimator.coef_ + else: + coefs = getattr(estimator, 'feature_importances_', None) + if coefs is None: + raise RuntimeError('The classifier does not expose ' + '"coef_" or "feature_importances_" ' + 'attributes') + + # Get ranks + if coefs.ndim > 1: + ranks = np.argsort(safe_sqr(coefs).sum(axis=0)) + else: + ranks = np.argsort(safe_sqr(coefs)) + + # for sparse case ranks is matrix + ranks = np.ravel(ranks) + + # Eliminate the worse features + threshold =\ + min(step[step_i], np.sum(support_) - n_features_to_select) + + # Compute step score on the previous selection iteration + # because 'estimator' must use features + # that have not been eliminated yet + if step_score: + self.scores_.append(step_score(estimator, features)) + support_[features[ranks][:threshold]] = False + ranking_[np.logical_not(support_)] += 1 + + step_i += 1 + + # Set final attributes + features = np.arange(n_features)[support_] + self.estimator_ = clone(self.estimator) + self.estimator_.fit(X[:, features], y) + + # Compute step score when only n_features_to_select features left + if step_score: + self.scores_.append(step_score(self.estimator_, features)) + self.n_features_ = support_.sum() + self.support_ = support_ + self.ranking_ = ranking_ + + return self + + +class DyRFECV(RFECV, MetaEstimatorMixin): + """ + Compared with RFECV, DyRFECV offers flexiable `step` to eleminate + features, in the format of list, while RFECV supports only fixed number + of `step`. + + Parameters + ---------- + estimator : object + A supervised learning estimator with a ``fit`` method that provides + information about feature importance either through a ``coef_`` + attribute or through a ``feature_importances_`` attribute. + step : int or float, optional (default=1) + If greater than or equal to 1, then ``step`` corresponds to the + (integer) number of features to remove at each iteration. + If within (0.0, 1.0), then ``step`` corresponds to the percentage + (rounded down) of features to remove at each iteration. + If list, a series of step to remove at each iteration. iteration stopes + when finishing all steps + Note that the last iteration may remove fewer than ``step`` features in + order to reach ``min_features_to_select``. + min_features_to_select : int, (default=1) + The minimum number of features to be selected. This number of features + will always be scored, even if the difference between the original + feature count and ``min_features_to_select`` isn't divisible by + ``step``. + cv : int, cross-validation generator or an iterable, optional + Determines the cross-validation splitting strategy. + Possible inputs for cv are: + - None, to use the default 3-fold cross-validation, + - integer, to specify the number of folds. + - :term:`CV splitter`, + - An iterable yielding (train, test) splits as arrays of indices. + For integer/None inputs, if ``y`` is binary or multiclass, + :class:`sklearn.model_selection.StratifiedKFold` is used. If the + estimator is a classifier or if ``y`` is neither binary nor multiclass, + :class:`sklearn.model_selection.KFold` is used. + Refer :ref:`User Guide <cross_validation>` for the various + cross-validation strategies that can be used here. + .. versionchanged:: 0.20 + ``cv`` default value of None will change from 3-fold to 5-fold + in v0.22. + scoring : string, callable or None, optional, (default=None) + A string (see model evaluation documentation) or + a scorer callable object / function with signature + ``scorer(estimator, X, y)``. + verbose : int, (default=0) + Controls verbosity of output. + n_jobs : int or None, optional (default=None) + Number of cores to run in parallel while fitting across folds. + ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. + ``-1`` means using all processors. See :term:`Glossary <n_jobs>` + for more details. + """ + def __init__(self, estimator, step=1, min_features_to_select=1, cv='warn', + scoring=None, verbose=0, n_jobs=None): + super(DyRFECV, self).__init__( + estimator, step=step, + min_features_to_select=min_features_to_select, + cv=cv, scoring=scoring, verbose=verbose, + n_jobs=n_jobs) + + def fit(self, X, y, groups=None): + """Fit the RFE model and automatically tune the number of selected + features. + Parameters + ---------- + X : {array-like, sparse matrix}, shape = [n_samples, n_features] + Training vector, where `n_samples` is the number of samples and + `n_features` is the total number of features. + y : array-like, shape = [n_samples] + Target values (integers for classification, real numbers for + regression). + groups : array-like, shape = [n_samples], optional + Group labels for the samples used while splitting the dataset into + train/test set. + """ + if type(self.step) is not list: + return super(DyRFECV, self).fit(X, y, groups) + + X, y = check_X_y(X, y, "csr") + + # Initialization + cv = check_cv(self.cv, y, is_classifier(self.estimator)) + scorer = check_scoring(self.estimator, scoring=self.scoring) + n_features = X.shape[1] + + step = [] + for s in self.step: + if 0.0 < s < 1.0: + step.append(int(max(1, s * n_features))) + else: + step.append(int(s)) + if s <= 0: + raise ValueError("Step must be >0") + + # Build an RFE object, which will evaluate and score each possible + # feature count, down to self.min_features_to_select + rfe = DyRFE(estimator=self.estimator, + n_features_to_select=self.min_features_to_select, + step=self.step, verbose=self.verbose) + + # Determine the number of subsets of features by fitting across + # the train folds and choosing the "features_to_select" parameter + # that gives the least averaged error across all folds. + + # Note that joblib raises a non-picklable error for bound methods + # even if n_jobs is set to 1 with the default multiprocessing + # backend. + # This branching is done so that to + # make sure that user code that sets n_jobs to 1 + # and provides bound methods as scorers is not broken with the + # addition of n_jobs parameter in version 0.18. + + if effective_n_jobs(self.n_jobs) == 1: + parallel, func = list, _rfe_single_fit + else: + parallel = Parallel(n_jobs=self.n_jobs) + func = delayed(_rfe_single_fit) + + scores = parallel( + func(rfe, self.estimator, X, y, train, test, scorer) + for train, test in cv.split(X, y, groups)) + + scores = np.sum(scores, axis=0) + diff = int(scores.shape[0]) - len(step) + if diff > 0: + step = np.r_[step, [step[-1]] * diff] + scores_rev = scores[::-1] + argmax_idx = len(scores) - np.argmax(scores_rev) - 1 + n_features_to_select = max( + n_features - sum(step[:argmax_idx]), + self.min_features_to_select) + + # Re-execute an elimination with best_k over the whole set + rfe = DyRFE(estimator=self.estimator, + n_features_to_select=n_features_to_select, step=self.step, + verbose=self.verbose) + + rfe.fit(X, y) + + # Set final attributes + self.support_ = rfe.support_ + self.n_features_ = rfe.n_features_ + self.ranking_ = rfe.ranking_ + self.estimator_ = clone(self.estimator) + self.estimator_.fit(self.transform(X), y) + + # Fixing a normalization error, n is equal to get_n_splits(X, y) - 1 + # here, the scores are normalized by get_n_splits(X, y) + self.grid_scores_ = scores[::-1] / cv.get_n_splits(X, y, groups) + return self + + +class MyPipeline(pipeline.Pipeline): + """ + Extend pipeline object to have feature_importances_ attribute + """ + def fit(self, X, y=None, **fit_params): + super(MyPipeline, self).fit(X, y, **fit_params) + estimator = self.steps[-1][-1] + if hasattr(estimator, 'coef_'): + coefs = estimator.coef_ + else: + coefs = getattr(estimator, 'feature_importances_', None) + if coefs is None: + raise RuntimeError('The estimator in the pipeline does not expose ' + '"coef_" or "feature_importances_" ' + 'attributes') + self.feature_importances_ = coefs + return self + + +class MyimbPipeline(imbPipeline): + """ + Extend imblance pipeline object to have feature_importances_ attribute + """ + def fit(self, X, y=None, **fit_params): + super(MyimbPipeline, self).fit(X, y, **fit_params) + estimator = self.steps[-1][-1] + if hasattr(estimator, 'coef_'): + coefs = estimator.coef_ + else: + coefs = getattr(estimator, 'feature_importances_', None) + if coefs is None: + raise RuntimeError('The estimator in the pipeline does not expose ' + '"coef_" or "feature_importances_" ' + 'attributes') + self.feature_importances_ = coefs + return self + + +def check_feature_importances(estimator): + """ + For pipeline object which has no feature_importances_ property, + this function returns the same comfigured pipeline object with + attached the last estimator's feature_importances_. + """ + if estimator.__class__.__module__ == 'sklearn.pipeline': + pipeline_steps = estimator.get_params()['steps'] + estimator = MyPipeline(pipeline_steps) + elif estimator.__class__.__module__ == 'imblearn.pipeline': + pipeline_steps = estimator.get_params()['steps'] + estimator = MyimbPipeline(pipeline_steps) + else: + return estimator
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/iraps_classifier.py Tue May 14 18:06:37 2019 -0400 @@ -0,0 +1,569 @@ +""" +class IRAPSCore +class IRAPSClassifier +class BinarizeTargetClassifier +class BinarizeTargetRegressor +class _BinarizeTargetScorer +class _BinarizeTargetProbaScorer + +binarize_auc_scorer +binarize_average_precision_scorer + +binarize_accuracy_scorer +binarize_balanced_accuracy_scorer +binarize_precision_scorer +binarize_recall_scorer +""" + + +import numpy as np +import random +import warnings + +from abc import ABCMeta +from scipy.stats import ttest_ind +from sklearn import metrics +from sklearn.base import BaseEstimator, clone, RegressorMixin +from sklearn.externals import six +from sklearn.feature_selection.univariate_selection import _BaseFilter +from sklearn.metrics.scorer import _BaseScorer +from sklearn.pipeline import Pipeline +from sklearn.utils import as_float_array, check_X_y +from sklearn.utils._joblib import Parallel, delayed +from sklearn.utils.validation import (check_array, check_is_fitted, + check_memory, column_or_1d) + + +VERSION = '0.1.1' + + +class IRAPSCore(six.with_metaclass(ABCMeta, BaseEstimator)): + """ + Base class of IRAPSClassifier + From sklearn BaseEstimator: + get_params() + set_params() + + Parameters + ---------- + n_iter : int + sample count + + positive_thres : float + z_score shreshold to discretize positive target values + + negative_thres : float + z_score threshold to discretize negative target values + + verbose : int + 0 or geater, if not 0, print progress + + n_jobs : int, default=1 + The number of CPUs to use to do the computation. + + pre_dispatch : int, or string. + Controls the number of jobs that get dispatched during parallel + execution. Reducing this number can be useful to avoid an + explosion of memory consumption when more jobs get dispatched + than CPUs can process. This parameter can be: + - None, in which case all the jobs are immediately + created and spawned. Use this for lightweight and + fast-running jobs, to avoid delays due to on-demand + spawning of the jobs + - An int, giving the exact number of total jobs that are + spawned + - A string, giving an expression as a function of n_jobs, + as in '2*n_jobs' + + random_state : int or None + """ + + def __init__(self, n_iter=1000, positive_thres=-1, negative_thres=0, + verbose=0, n_jobs=1, pre_dispatch='2*n_jobs', + random_state=None): + """ + IRAPS turns towwards general Anomaly Detection + It comapares positive_thres with negative_thres, + and decide which portion is the positive target. + e.g.: + (positive_thres=-1, negative_thres=0) + => positive = Z_score of target < -1 + (positive_thres=1, negative_thres=0) + => positive = Z_score of target > 1 + + Note: The positive targets here is always the + abnormal minority group. + """ + self.n_iter = n_iter + self.positive_thres = positive_thres + self.negative_thres = negative_thres + self.verbose = verbose + self.n_jobs = n_jobs + self.pre_dispatch = pre_dispatch + self.random_state = random_state + + def fit(self, X, y): + """ + X: array-like (n_samples x n_features) + y: 1-d array-like (n_samples) + """ + X, y = check_X_y(X, y, ['csr', 'csc'], multi_output=False) + + def _stochastic_sampling(X, y, random_state=None, positive_thres=-1, + negative_thres=0): + # each iteration select a random number of random subset of + # training samples. this is somewhat different from the original + # IRAPS method, but effect is almost the same. + SAMPLE_SIZE = [0.25, 0.75] + n_samples = X.shape[0] + + if random_state is None: + n_select = random.randint(int(n_samples * SAMPLE_SIZE[0]), + int(n_samples * SAMPLE_SIZE[1])) + index = random.sample(list(range(n_samples)), n_select) + else: + n_select = random.Random(random_state).randint( + int(n_samples * SAMPLE_SIZE[0]), + int(n_samples * SAMPLE_SIZE[1])) + index = random.Random(random_state).sample( + list(range(n_samples)), n_select) + + X_selected, y_selected = X[index], y[index] + + # Spliting by z_scores. + y_selected = (y_selected - y_selected.mean()) / y_selected.std() + if positive_thres < negative_thres: + X_selected_positive = X_selected[y_selected < positive_thres] + X_selected_negative = X_selected[y_selected > negative_thres] + else: + X_selected_positive = X_selected[y_selected > positive_thres] + X_selected_negative = X_selected[y_selected < negative_thres] + + # For every iteration, at least 5 responders are selected + if X_selected_positive.shape[0] < 5: + warnings.warn("Warning: fewer than 5 positives were selected!") + return + + # p_values + _, p = ttest_ind(X_selected_positive, X_selected_negative, + axis=0, equal_var=False) + + # fold_change == mean change? + # TODO implement other normalization method + positive_mean = X_selected_positive.mean(axis=0) + negative_mean = X_selected_negative.mean(axis=0) + mean_change = positive_mean - negative_mean + # mean_change = np.select( + # [positive_mean > negative_mean, + # positive_mean < negative_mean], + # [positive_mean / negative_mean, + # -negative_mean / positive_mean]) + # mean_change could be adjusted by power of 2 + # mean_change = 2**mean_change \ + # if mean_change>0 else -2**abs(mean_change) + + return p, mean_change, negative_mean + + parallel = Parallel(n_jobs=self.n_jobs, verbose=self.verbose, + pre_dispatch=self.pre_dispatch) + if self.random_state is None: + res = parallel(delayed(_stochastic_sampling)( + X, y, random_state=None, + positive_thres=self.positive_thres, + negative_thres=self.negative_thres) + for i in range(self.n_iter)) + else: + res = parallel(delayed(_stochastic_sampling)( + X, y, random_state=seed, + positive_thres=self.positive_thres, + negative_thres=self.negative_thres) + for seed in range(self.random_state, + self.random_state+self.n_iter)) + res = [_ for _ in res if _] + if len(res) < 50: + raise ValueError("too few (%d) valid feature lists " + "were generated!" % len(res)) + pvalues = np.vstack([x[0] for x in res]) + fold_changes = np.vstack([x[1] for x in res]) + base_values = np.vstack([x[2] for x in res]) + + self.pvalues_ = np.asarray(pvalues) + self.fold_changes_ = np.asarray(fold_changes) + self.base_values_ = np.asarray(base_values) + + return self + + +def _iraps_core_fit(iraps_core, X, y): + return iraps_core.fit(X, y) + + +class IRAPSClassifier(six.with_metaclass(ABCMeta, _BaseFilter, + BaseEstimator, RegressorMixin)): + """ + Extend the bases of both sklearn feature_selector and classifier. + From sklearn BaseEstimator: + get_params() + set_params() + From sklearn _BaseFilter: + get_support() + fit_transform(X) + transform(X) + From sklearn RegressorMixin: + score(X, y): R2 + New: + predict(X) + predict_label(X) + get_signature() + Properties: + discretize_value + + Parameters + ---------- + iraps_core: object + p_thres: float, threshold for p_values + fc_thres: float, threshold for fold change or mean difference + occurrence: float, occurrence rate selected by set of p_thres and fc_thres + discretize: float, threshold of z_score to discretize target value + memory: None, str or joblib.Memory object + min_signature_features: int, the mininum number of features in a signature + """ + + def __init__(self, iraps_core, p_thres=1e-4, fc_thres=0.1, + occurrence=0.8, discretize=-1, memory=None, + min_signature_features=1): + self.iraps_core = iraps_core + self.p_thres = p_thres + self.fc_thres = fc_thres + self.occurrence = occurrence + self.discretize = discretize + self.memory = memory + self.min_signature_features = min_signature_features + + def fit(self, X, y): + memory = check_memory(self.memory) + cached_fit = memory.cache(_iraps_core_fit) + iraps_core = clone(self.iraps_core) + # allow pre-fitted iraps_core here + if not hasattr(iraps_core, 'pvalues_'): + iraps_core = cached_fit(iraps_core, X, y) + self.iraps_core_ = iraps_core + + pvalues = as_float_array(iraps_core.pvalues_, copy=True) + # why np.nan is here? + pvalues[np.isnan(pvalues)] = np.finfo(pvalues.dtype).max + + fold_changes = as_float_array(iraps_core.fold_changes_, copy=True) + fold_changes[np.isnan(fold_changes)] = 0.0 + + base_values = as_float_array(iraps_core.base_values_, copy=True) + + p_thres = self.p_thres + fc_thres = self.fc_thres + occurrence = self.occurrence + + mask_0 = np.zeros(pvalues.shape, dtype=np.int32) + # mark p_values less than the threashold + mask_0[pvalues <= p_thres] = 1 + # mark fold_changes only when greater than the threashold + mask_0[abs(fold_changes) < fc_thres] = 0 + + # count the occurrence and mask greater than the threshold + counts = mask_0.sum(axis=0) + occurrence_thres = int(occurrence * iraps_core.n_iter) + mask = np.zeros(counts.shape, dtype=bool) + mask[counts >= occurrence_thres] = 1 + + # generate signature + fold_changes[mask_0 == 0] = 0.0 + signature = fold_changes[:, mask].sum(axis=0) / counts[mask] + signature = np.vstack((signature, base_values[:, mask].mean(axis=0))) + # It's not clearn whether min_size could impact prediction + # performance + if signature is None\ + or signature.shape[1] < self.min_signature_features: + raise ValueError("The classifier got None signature or the number " + "of sinature feature is less than minimum!") + + self.signature_ = np.asarray(signature) + self.mask_ = mask + # TODO: support other discretize method: fixed value, upper + # third quater, etc. + self.discretize_value = y.mean() + y.std() * self.discretize + if iraps_core.negative_thres > iraps_core.positive_thres: + self.less_is_positive = True + else: + self.less_is_positive = False + + return self + + def _get_support_mask(self): + """ + return mask of feature selection indices + """ + check_is_fitted(self, 'mask_') + + return self.mask_ + + def get_signature(self): + """ + return signature + """ + check_is_fitted(self, 'signature_') + + return self.signature_ + + def predict(self, X): + """ + compute the correlation coefficient with irpas signature + """ + signature = self.get_signature() + + X = as_float_array(X) + X_transformed = self.transform(X) - signature[1] + corrcoef = np.array( + [np.corrcoef(signature[0], e)[0][1] for e in X_transformed]) + corrcoef[np.isnan(corrcoef)] = np.finfo(np.float32).min + + return corrcoef + + def predict_label(self, X, clf_cutoff=0.4): + return self.predict(X) >= clf_cutoff + + +class BinarizeTargetClassifier(BaseEstimator, RegressorMixin): + """ + Convert continuous target to binary labels (True and False) + and apply a classification estimator. + + Parameters + ---------- + classifier: object + Estimator object such as derived from sklearn `ClassifierMixin`. + + z_score: float, default=-1.0 + Threshold value based on z_score. Will be ignored when + fixed_value is set + + value: float, default=None + Threshold value + + less_is_positive: boolean, default=True + When target is less the threshold value, it will be converted + to True, False otherwise. + + Attributes + ---------- + classifier_: object + Fitted classifier + + discretize_value: float + The threshold value used to discretize True and False targets + """ + + def __init__(self, classifier, z_score=-1, value=None, + less_is_positive=True): + self.classifier = classifier + self.z_score = z_score + self.value = value + self.less_is_positive = less_is_positive + + def fit(self, X, y, sample_weight=None): + """ + Convert y to True and False labels and then fit the classifier + with X and new y + + Returns + ------ + self: object + """ + y = check_array(y, accept_sparse=False, force_all_finite=True, + ensure_2d=False, dtype='numeric') + y = column_or_1d(y) + + if self.value is None: + discretize_value = y.mean() + y.std() * self.z_score + else: + discretize_value = self.Value + self.discretize_value = discretize_value + + if self.less_is_positive: + y_trans = y < discretize_value + else: + y_trans = y > discretize_value + + self.classifier_ = clone(self.classifier) + + if sample_weight is not None: + self.classifier_.fit(X, y_trans, sample_weight=sample_weight) + else: + self.classifier_.fit(X, y_trans) + + if hasattr(self.classifier_, 'feature_importances_'): + self.feature_importances_ = self.classifier_.feature_importances_ + if hasattr(self.classifier_, 'coef_'): + self.coef_ = self.classifier_.coef_ + if hasattr(self.classifier_, 'n_outputs_'): + self.n_outputs_ = self.classifier_.n_outputs_ + if hasattr(self.classifier_, 'n_features_'): + self.n_features_ = self.classifier_.n_features_ + + return self + + def predict(self, X): + """ + Predict class probabilities of X. + """ + check_is_fitted(self, 'classifier_') + proba = self.classifier_.predict_proba(X) + return proba[:, 1] + + def predict_label(self, X): + """Predict class label of X + """ + check_is_fitted(self, 'classifier_') + return self.classifier_.predict(X) + + +class _BinarizeTargetProbaScorer(_BaseScorer): + """ + base class to make binarized target specific scorer + """ + + def __call__(self, clf, X, y, sample_weight=None): + clf_name = clf.__class__.__name__ + # support pipeline object + if isinstance(clf, Pipeline): + main_estimator = clf.steps[-1][-1] + # support stacking ensemble estimators + # TODO support nested pipeline/stacking estimators + elif clf_name in ['StackingCVClassifier', 'StackingClassifier']: + main_estimator = clf.meta_clf_ + elif clf_name in ['StackingCVRegressor', 'StackingRegressor']: + main_estimator = clf.meta_regr_ + else: + main_estimator = clf + + discretize_value = main_estimator.discretize_value + less_is_positive = main_estimator.less_is_positive + + if less_is_positive: + y_trans = y < discretize_value + else: + y_trans = y > discretize_value + + y_pred = clf.predict(X) + if sample_weight is not None: + return self._sign * self._score_func(y_trans, y_pred, + sample_weight=sample_weight, + **self._kwargs) + else: + return self._sign * self._score_func(y_trans, y_pred, + **self._kwargs) + + +# roc_auc +binarize_auc_scorer =\ + _BinarizeTargetProbaScorer(metrics.roc_auc_score, 1, {}) + +# average_precision_scorer +binarize_average_precision_scorer =\ + _BinarizeTargetProbaScorer(metrics.average_precision_score, 1, {}) + +# roc_auc_scorer +iraps_auc_scorer = binarize_auc_scorer + +# average_precision_scorer +iraps_average_precision_scorer = binarize_average_precision_scorer + + +class BinarizeTargetRegressor(BaseEstimator, RegressorMixin): + """ + Extend regression estimator to have discretize_value + + Parameters + ---------- + regressor: object + Estimator object such as derived from sklearn `RegressionMixin`. + + z_score: float, default=-1.0 + Threshold value based on z_score. Will be ignored when + fixed_value is set + + value: float, default=None + Threshold value + + less_is_positive: boolean, default=True + When target is less the threshold value, it will be converted + to True, False otherwise. + + Attributes + ---------- + regressor_: object + Fitted regressor + + discretize_value: float + The threshold value used to discretize True and False targets + """ + + def __init__(self, regressor, z_score=-1, value=None, + less_is_positive=True): + self.regressor = regressor + self.z_score = z_score + self.value = value + self.less_is_positive = less_is_positive + + def fit(self, X, y, sample_weight=None): + """ + Calculate the discretize_value fit the regressor with traning data + + Returns + ------ + self: object + """ + y = check_array(y, accept_sparse=False, force_all_finite=True, + ensure_2d=False, dtype='numeric') + y = column_or_1d(y) + + if self.value is None: + discretize_value = y.mean() + y.std() * self.z_score + else: + discretize_value = self.Value + self.discretize_value = discretize_value + + self.regressor_ = clone(self.regressor) + + if sample_weight is not None: + self.regressor_.fit(X, y, sample_weight=sample_weight) + else: + self.regressor_.fit(X, y) + + # attach classifier attributes + if hasattr(self.regressor_, 'feature_importances_'): + self.feature_importances_ = self.regressor_.feature_importances_ + if hasattr(self.regressor_, 'coef_'): + self.coef_ = self.regressor_.coef_ + if hasattr(self.regressor_, 'n_outputs_'): + self.n_outputs_ = self.regressor_.n_outputs_ + if hasattr(self.regressor_, 'n_features_'): + self.n_features_ = self.regressor_.n_features_ + + return self + + def predict(self, X): + """Predict target value of X + """ + check_is_fitted(self, 'regressor_') + y_pred = self.regressor_.predict(X) + if not np.all((y_pred >= 0) & (y_pred <= 1)): + y_pred = (y_pred - y_pred.min()) / (y_pred.max() - y_pred.min()) + if self.less_is_positive: + y_pred = 1 - y_pred + return y_pred + + +# roc_auc_scorer +regression_auc_scorer = binarize_auc_scorer + +# average_precision_scorer +regression_average_precision_scorer = binarize_average_precision_scorer
--- a/main_macros.xml Sun Dec 30 01:52:15 2018 -0500 +++ b/main_macros.xml Tue May 14 18:06:37 2019 -0400 @@ -1,14 +1,17 @@ <macros> - <token name="@VERSION@">1.0</token> + <token name="@VERSION@">1.0.0.4</token> <xml name="python_requirements"> <requirements> <requirement type="package" version="3.6">python</requirement> - <requirement type="package" version="0.20.2">scikit-learn</requirement> - <requirement type="package" version="0.23.4">pandas</requirement> + <requirement type="package" version="0.20.3">scikit-learn</requirement> + <requirement type="package" version="0.24.2">pandas</requirement> <requirement type="package" version="0.80">xgboost</requirement> <requirement type="package" version="0.9.13">asteval</requirement> - <yield /> + <requirement type="package" version="0.6">skrebate</requirement> + <requirement type="package" version="0.4.2">imbalanced-learn</requirement> + <requirement type="package" version="0.16.0">mlxtend</requirement> + <yield/> </requirements> </xml> @@ -352,10 +355,10 @@ <option value="all_columns">All columns</option> </param> <when value="by_index_number"> - <param name="@COL_NAME@" multiple="@MULTIPLE@" type="data_column" data_ref="@INFILE@" label="Select target column(s):"/> + <param name="@COL_NAME@" multiple="@MULTIPLE@" type="data_column" use_header_names="true" data_ref="@INFILE@" label="Select target column(s):"/> </when> <when value="all_but_by_index_number"> - <param name="@COL_NAME@" multiple="@MULTIPLE@" type="data_column" data_ref="@INFILE@" label="Select target column(s):"/> + <param name="@COL_NAME@" multiple="@MULTIPLE@" type="data_column" use_header_names="true" data_ref="@INFILE@" label="Select target column(s):"/> </when> <when value="by_header_name"> <param name="@COL_NAME@" type="text" value="" label="Type header name(s):" help="Comma-separated string. For example: target1,target2"/> @@ -428,7 +431,7 @@ <option value="sparse">sparse matrix</option> </param> <when value="tabular"> - <expand macro="samples_tabular" multiple1="true"/> + <expand macro="samples_tabular" multiple1="true" multiple2="false"/> </when> <when value="sparse"> <expand macro="sparse_target"/> @@ -823,6 +826,8 @@ <option value="StratifiedShuffleSplit">StratifiedShuffleSplit</option> <option value="TimeSeriesSplit">TimeSeriesSplit</option> <option value="PredefinedSplit">PredefinedSplit</option> + <option value="OrderedKFold">OrderedKFold</option> + <option value="RepeatedOrderedKFold">RepeatedOrderedKFold</option> <yield/> </xml> @@ -872,6 +877,16 @@ <when value="PredefinedSplit"> <param argument="test_fold" type="text" value="" area="true" label="test_fold" help="List, e.g., [0, 1, -1, 1], represents two test sets, [X[0]] and [X[1], X[3]], X[2] is excluded from any test set due to '-1'."/> </when> + <when value="OrderedKFold"> + <expand macro="cv_n_splits"/> + <expand macro="cv_shuffle"/> + <expand macro="random_state"/> + </when> + <when value="RepeatedOrderedKFold"> + <expand macro="cv_n_splits"/> + <param argument="n_repeats" type="integer" value="5"/> + <expand macro="random_state"/> + </when> <yield/> </xml> @@ -929,7 +944,13 @@ </xml> <xml name="cv_groups" > - <param argument="groups" type="text" value="" area="true" label="Groups" help="Group lables in a list. e.g., [1, 1, 2, 2, 3, 3, 3]"/> + <section name="groups_selector" title="Groups column selector" expanded="true"> + <param name="infile_g" type="data" format="tabular" label="Choose dataset containing groups info:"/> + <param name="header_g" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="False" label="Does the dataset contain header:" /> + <conditional name="column_selector_options_g"> + <expand macro="samples_column_selector_options" column_option="selected_column_selector_option_g" col_name="col_g" multiple="False" infile="infile_g"/> + </conditional> + </section> </xml> <xml name="feature_selection_algorithms"> @@ -943,6 +964,7 @@ <option value="SelectFromModel">SelectFromModel - Meta-transformer for selecting features based on importance weights</option> <option value="RFE">RFE - Feature ranking with recursive feature elimination</option> <option value="RFECV">RFECV - Feature ranking with recursive feature elimination and cross-validated selection of the best number of features</option> + <yield/> </xml> <xml name="feature_selection_algorithm_details"> @@ -991,7 +1013,7 @@ </when> <when value="VarianceThreshold"> <section name="options" title="Options" expanded="False"> - <param argument="threshold" type="float" value="" optional="True" label="Threshold" help="Features with a training-set variance lower than this threshold will be removed."/> + <param argument="threshold" type="float" value="0.0" optional="True" label="Threshold" help="Features with a training-set variance lower than this threshold will be removed."/> </section> </when> </xml> @@ -1047,13 +1069,47 @@ </when> </xml> - <xml name="feature_selection_RFECV"> + <xml name="feature_selection_RFECV_fs"> + <when value="RFECV"> + <yield/> + <section name="options" title="Advanced Options" expanded="False"> + <param argument="step" type="float" value="1" label="step" optional="true" help="Default = 1. " /> + <param argument="min_features_to_select" type="integer" value="1" optional="true" label="The minimum number of features to be selected"/> + <expand macro="cv"/> + <expand macro="scoring_selection"/> + <param argument="verbose" type="integer" value="0" label="verbose" help="Controls verbosity of output." /> + </section> + </when> + </xml> + + <xml name="feature_selection_RFECV_pipeline"> <when value="RFECV"> <yield/> <section name="options" title="Advanced Options" expanded="False"> <param argument="step" type="float" value="1" label="step" optional="true" help="Default = 1. " /> <param argument="min_features_to_select" type="integer" value="1" optional="true" label="The minimum number of features to be selected"/> <expand macro="cv_reduced"/> + <!-- TODO: group splitter support--> + <expand macro="scoring_selection"/> + <param argument="verbose" type="integer" value="0" label="verbose" help="Controls verbosity of output." /> + </section> + </when> + </xml> + + <xml name="feature_selection_DyRFECV_fs"> + <when value="DyRFECV"> + <yield/> + <section name="options" title="Advanced Options" expanded="False"> + <param argument="step" type="text" size="30" value="1" label="step" optional="true" help="Default = 1. Support float, int and list." > + <sanitizer> + <valid initial="default"> + <add value="["/> + <add value="]"/> + </valid> + </sanitizer> + </param> + <param argument="min_features_to_select" type="integer" value="1" optional="true" label="The minimum number of features to be selected"/> + <expand macro="cv"/> <expand macro="scoring_selection"/> <param argument="verbose" type="integer" value="0" label="verbose" help="Controls verbosity of output." /> </section> @@ -1061,7 +1117,7 @@ </xml> <xml name="feature_selection_pipeline"> - <!--compare to `feature_selection_fs`, no fitted estimator for SelectFromModel and no customer estimator for RFE and RFECV--> + <!--compare to `feature_selection_fs`, no fitted estimator for SelectFromModel and no custom estimator for RFE and RFECV--> <conditional name="fs_algorithm_selector"> <param name="selected_algorithm" type="select" label="Select a feature selection algorithm"> <expand macro="feature_selection_algorithms"/> @@ -1071,23 +1127,29 @@ <expand macro="feature_selection_RFE"> <expand macro="estimator_selector_all"/> </expand> - <expand macro="feature_selection_RFECV"> + <expand macro="feature_selection_RFECV_pipeline"> <expand macro="estimator_selector_all"/> </expand> + <!-- TODO: add DyRFECV to pipeline--> </conditional> </xml> <xml name="feature_selection_fs"> <conditional name="fs_algorithm_selector"> <param name="selected_algorithm" type="select" label="Select a feature selection algorithm"> - <expand macro="feature_selection_algorithms"/> + <expand macro="feature_selection_algorithms"> + <option value="DyRFECV">DyRFECV - Extended RFECV with changeable steps</option> + </expand> </param> <expand macro="feature_selection_algorithm_details"/> <expand macro="feature_selection_SelectFromModel"/> <expand macro="feature_selection_RFE"> <expand macro="estimator_selector_fs"/> </expand> - <expand macro="feature_selection_RFECV"> + <expand macro="feature_selection_RFECV_fs"> + <expand macro="estimator_selector_fs"/> + </expand> + <expand macro="feature_selection_DyRFECV_fs"> <expand macro="estimator_selector_fs"/> </expand> </conditional> @@ -1105,7 +1167,7 @@ <xml name="model_validation_common_options"> <expand macro="cv"/> - <expand macro="verbose"/> + <!-- expand macro="verbose"/> --> <yield/> </xml> @@ -1139,6 +1201,8 @@ <option value="neg_mean_squared_log_error">Regression -- 'neg_mean_squared_log_error'</option> <option value="neg_median_absolute_error">Regression -- 'neg_median_absolute_error'</option> <option value="r2">Regression -- 'r2'</option> + <option value="binarize_auc_scorer">anomaly detection -- binarize_auc_scorer</option> + <option value="binarize_average_precision_scorer">anomaly detection -- binarize_average_precision_scorer</option> </param> <when value="default"/> <when value="accuracy"><expand macro="secondary_scoring_selection_classification"/></when> @@ -1167,6 +1231,8 @@ <when value="neg_mean_squared_log_error"><expand macro="secondary_scoring_selection_regression"/></when> <when value="neg_median_absolute_error"><expand macro="secondary_scoring_selection_regression"/></when> <when value="r2"><expand macro="secondary_scoring_selection_regression"/></when> + <when value="binarize_auc_scorer"><expand macro="secondary_scoring_selection_anormaly"/></when> + <when value="binarize_average_precision_scorer"><expand macro="secondary_scoring_selection_anormaly"/></when> </conditional> </xml> @@ -1206,63 +1272,48 @@ </param> </xml> + <xml name="secondary_scoring_selection_anormaly"> + <param name="secondary_scoring" type="select" multiple="true" label="Additional scoring used in multi-metric mode:" help="If the same metric with the primary is chosen, the metric will be ignored."> + <option value="binarize_auc_scorer">anomaly detection -- binarize_auc_scorer</option> + <option value="binarize_average_precision_scorer">anomaly detection -- binarize_average_precision_scorer</option> + </param> + </xml> + <xml name="pre_dispatch" token_type="hidden" token_default_value="all" token_help="Number of predispatched jobs for parallel execution"> <param argument="pre_dispatch" type="@TYPE@" value="@DEFAULT_VALUE@" optional="true" label="pre_dispatch" help="@HELP@"/> </xml> <xml name="search_cv_estimator"> - <param name="infile_pipeline" type="data" format="zip" label="Choose the dataset containing pipeline object:"/> + <param name="infile_estimator" type="data" format="zip" label="Choose the dataset containing pipeline/estimator object"/> <section name="search_params_builder" title="Search parameters Builder" expanded="true"> - <repeat name="param_set" min="1" max="20" title="Parameter setting for search:"> - <conditional name="search_param_selector"> - <param name="selected_param_type" type="select" label="Choose the transformation the parameter belongs to"> - <option value="final_estimator_p" selected="true">Final estimator</option> - <option value="prep_1_p">Pre-processing step #1</option> - <option value="prep_2_p">Pre-processing step #2</option> - <option value="prep_3_p">Pre-processing step #3</option> - <option value="prep_4_p">Pre-processing step #4</option> - <option value="prep_5_p">Pre-processing step #5</option> + <param name="infile_params" type="data" format="tabular" label="Choose the dataset containing parameter names"/> + <repeat name="param_set" min="1" max="30" title="Parameter settings for search:"> + <param name="sp_name" type="select" label="Choose a parameter name (with current value)"> + <options from_dataset="infile_params" startswith="@"> + <column name="name" index="2"/> + <column name="value" index="1"/> + <filter type="unique_value" name="unique_param" column="1"/> + <filter type="sort_by" name="sorted_param" column="2"/> + </options> </param> - <when value="final_estimator_p"> - <expand macro="search_param_input" /> - </when> - <when value="prep_1_p"> - <expand macro="search_param_input" label="Pre_processing component #1 parameter:" help="One parameter per box. For example: with_centering: [True, False]."/> - </when> - <when value="prep_2_p"> - <expand macro="search_param_input" label="Pre_processing component #2 parameter:" help="One parameter per box. For example: k: [3, 5, 7, 9]. See bottom for more examples"/> - </when> - <when value="prep_3_p"> - <expand macro="search_param_input" label="Pre_processing component #3 parameter:" help="One parameter per box. For example: n_components: [1, 10, 100, 1000]. See bottom for more examples"/> - </when> - <when value="prep_4_p"> - <expand macro="search_param_input" label="Pre_processing component #4 parameter:" help="One parameter per box. For example: n_components: [1, 10, 100, 1000]. See bottom for more examples"/> - </when> - <when value="prep_5_p"> - <expand macro="search_param_input" label="Pre_processing component #5 parameter:" help="One parameter per box. For example: affinity: ['euclidean', 'l1', 'l2', 'manhattan']. See bottom for more examples"/> - </when> - </conditional> + <param name="sp_list" type="text" value="" optional="true" label="Search list" help="list or array-like, for example: [1, 10, 100, 1000], [True, False] and ['auto', 'sqrt', None]. See `help` section for more examples"> + <sanitizer> + <valid initial="default"> + <add value="'"/> + <add value="""/> + <add value="["/> + <add value="]"/> + </valid> + </sanitizer> + </param> </repeat> </section> </xml> - <xml name="search_param_input" token_label="Estimator parameter:" token_help="One parameter per box. For example: C: [1, 10, 100, 1000]. See bottom for more examples"> - <param name="search_p" type="text" value="" optional="true" label="@LABEL@" help="@HELP@"> - <sanitizer> - <valid initial="default"> - <add value="'"/> - <add value="""/> - <add value="["/> - <add value="]"/> - </valid> - </sanitizer> - </param> - </xml> - <xml name="search_cv_options"> <expand macro="scoring_selection"/> <expand macro="model_validation_common_options"/> - <expand macro="pre_dispatch" value="2*n_jobs" help="Controls the number of jobs that get dispatched during parallel execution"/> + <!--expand macro="pre_dispatch" default_value="2*n_jobs" help="Controls the number of jobs that get dispatched during parallel execution"/--> <param argument="iid" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="true" label="iid" help="If True, data is identically distributed across the folds"/> <param argument="refit" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="true" label="refit" help="Refit an estimator using the best found parameters on the whole dataset."/> <param argument="error_score" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="true" label="Raise fit error:" help="If false, the metric score is assigned to NaN if an error occurs in estimator fitting and FitFailedWarning is raised."/> @@ -1403,12 +1454,12 @@ <conditional name="estimator_selector"> <param name="selected_module" type="select" label="Choose the module that contains target estimator:" > <expand macro="estimator_module_options"> - <option value="customer_estimator">Load a customer estimator</option> + <option value="custom_estimator">Load a custom estimator</option> </expand> </param> <expand macro="estimator_suboptions"> - <when value="customer_estimator"> - <param name="c_estimator" type="data" format="zip" label="Choose the dataset containing the customer estimator or pipeline:"/> + <when value="custom_estimator"> + <param name="c_estimator" type="data" format="zip" label="Choose the dataset containing the custom estimator or pipeline:"/> </when> </expand> </conditional> @@ -1591,6 +1642,7 @@ <option value="over_sampling.SMOTENC">over_sampling.SMOTENC</option> <option value="combine.SMOTEENN">combine.SMOTEENN</option> <option value="combine.SMOTETomek">combine.SMOTETomek</option> + <option value="Z_RandomOverSampler">Z_RandomOverSampler - for regression</option> </param> <when value="under_sampling.ClusterCentroids"> <expand macro="estimator_params_text" @@ -1668,6 +1720,33 @@ <expand macro="estimator_params_text" help="Default(=blank): sampling_strategy='auto', random_state=None, smote=None, tomek=None."/> </when> + <when value="Z_RandomOverSampler"> + <expand macro="estimator_params_text" + help="Default(=blank): sampling_strategy='auto', random_state=None, negative_thres=0, positive_thres=-1."/> + </when> + </conditional> + </xml> + + <xml name="stacking_ensemble_inputs"> + <section name="options" title="Advanced Options" expanded="false"> + <yield/> + <param argument="use_features_in_secondary" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="false"/> + <param argument="store_train_meta_features" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="false"/> + </section> + </xml> + + <xml name="stacking_base_estimator"> + <conditional name="estimator_selector"> + <param name="selected_module" type="select" label="Choose the module that contains target estimator:" > + <expand macro="estimator_module_options"> + <option value="custom_estimator">Load a custom estimator</option> + </expand> + </param> + <expand macro="estimator_suboptions"> + <when value="custom_estimator"> + <param name="c_estimator" type="data" format="zip" label="Choose the dataset containing the custom estimator or pipeline"/> + </when> + </expand> </conditional> </xml>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/model_validations.py Tue May 14 18:06:37 2019 -0400 @@ -0,0 +1,252 @@ +""" +class +----- +OrderedKFold +RepeatedOrderedKold + + +function +-------- +train_test_split +""" + +import numpy as np +import warnings + +from itertools import chain +from math import ceil, floor +from sklearn.model_selection import (GroupShuffleSplit, ShuffleSplit, + StratifiedShuffleSplit) +from sklearn.model_selection._split import _BaseKFold, _RepeatedSplits +from sklearn.utils import check_random_state, indexable, safe_indexing +from sklearn.utils.validation import _num_samples, check_array + + +def _validate_shuffle_split(n_samples, test_size, train_size, + default_test_size=None): + """ + Validation helper to check if the test/test sizes are meaningful wrt to the + size of the data (n_samples) + """ + if test_size is None and train_size is None: + test_size = default_test_size + + test_size_type = np.asarray(test_size).dtype.kind + train_size_type = np.asarray(train_size).dtype.kind + + if (test_size_type == 'i' and (test_size >= n_samples or test_size <= 0) + or test_size_type == 'f' and (test_size <= 0 or test_size >= 1)): + raise ValueError('test_size={0} should be either positive and smaller' + ' than the number of samples {1} or a float in the ' + '(0, 1) range'.format(test_size, n_samples)) + + if (train_size_type == 'i' and (train_size >= n_samples or train_size <= 0) + or train_size_type == 'f' and (train_size <= 0 or train_size >= 1)): + raise ValueError('train_size={0} should be either positive and smaller' + ' than the number of samples {1} or a float in the ' + '(0, 1) range'.format(train_size, n_samples)) + + if train_size is not None and train_size_type not in ('i', 'f'): + raise ValueError("Invalid value for train_size: {}".format(train_size)) + if test_size is not None and test_size_type not in ('i', 'f'): + raise ValueError("Invalid value for test_size: {}".format(test_size)) + + if (train_size_type == 'f' and test_size_type == 'f' and + train_size + test_size > 1): + raise ValueError( + 'The sum of test_size and train_size = {}, should be in the (0, 1)' + ' range. Reduce test_size and/or train_size.' + .format(train_size + test_size)) + + if test_size_type == 'f': + n_test = ceil(test_size * n_samples) + elif test_size_type == 'i': + n_test = float(test_size) + + if train_size_type == 'f': + n_train = floor(train_size * n_samples) + elif train_size_type == 'i': + n_train = float(train_size) + + if train_size is None: + n_train = n_samples - n_test + elif test_size is None: + n_test = n_samples - n_train + + if n_train + n_test > n_samples: + raise ValueError('The sum of train_size and test_size = %d, ' + 'should be smaller than the number of ' + 'samples %d. Reduce test_size and/or ' + 'train_size.' % (n_train + n_test, n_samples)) + + n_train, n_test = int(n_train), int(n_test) + + if n_train == 0: + raise ValueError( + 'With n_samples={}, test_size={} and train_size={}, the ' + 'resulting train set will be empty. Adjust any of the ' + 'aforementioned parameters.'.format(n_samples, test_size, + train_size) + ) + + return n_train, n_test + + +def train_test_split(*arrays, **options): + """Extend sklearn.model_selection.train_test_slit to have group split. + + Parameters + ---------- + *arrays : sequence of indexables with same length / shape[0] + Allowed inputs are lists, numpy arrays, scipy-sparse + matrices or pandas dataframes. + + test_size : float, int or None, optional (default=None) + If float, should be between 0.0 and 1.0 and represent the proportion + of the dataset to include in the test split. If int, represents the + absolute number of test samples. If None, the value is set to the + complement of the train size. If ``train_size`` is also None, it will + be set to 0.25. + + train_size : float, int, or None, (default=None) + If float, should be between 0.0 and 1.0 and represent the + proportion of the dataset to include in the train split. If + int, represents the absolute number of train samples. If None, + the value is automatically set to the complement of the test size. + + random_state : int, RandomState instance or None, optional (default=None) + If int, random_state is the seed used by the random number generator; + If RandomState instance, random_state is the random number generator; + If None, the random number generator is the RandomState instance used + by `np.random`. + + shuffle : None or str (default='simple') + How to shuffle the data before splitting. + None, no shuffle. + For str, one of 'simple', 'stratified' and 'group', corresponding to + `ShuffleSplit`, `StratifiedShuffleSplit` and `GroupShuffleSplit`, + respectively. + + labels : array-like or None (default=None) + Ignored if shuffle is None or 'simple'. + When shuffle='stratified', this array is used as class labels. + When shuffle='group', this array is used as groups. + + Returns + ------- + splitting : list, length=2 * len(arrays) + List containing train-test split of inputs. + + """ + n_arrays = len(arrays) + if n_arrays == 0: + raise ValueError("At least one array required as input") + test_size = options.pop('test_size', None) + train_size = options.pop('train_size', None) + random_state = options.pop('random_state', None) + shuffle = options.pop('shuffle', 'simple') + labels = options.pop('labels', None) + + if options: + raise TypeError("Invalid parameters passed: %s" % str(options)) + + arrays = indexable(*arrays) + + n_samples = _num_samples(arrays[0]) + if shuffle == 'group': + if labels is None: + raise ValueError("When shuffle='group', " + "labels should not be None!") + labels = check_array(labels, ensure_2d=False, dtype=None) + uniques = np.unique(labels) + n_samples = uniques.size + + n_train, n_test = _validate_shuffle_split(n_samples, test_size, train_size, + default_test_size=0.25) + + shuffle_options = dict(test_size=n_test, + train_size=n_train, + random_state=random_state) + + if shuffle is None: + if labels is not None: + warnings.warn("The `labels` is ignored for " + "shuffle being None!") + + train = np.arange(n_train) + test = np.arange(n_train, n_train + n_test) + + elif shuffle == 'simple': + if labels is not None: + warnings.warn("The `labels` is not needed and therefore " + "ignored for ShuffleSplit, as shuffle='simple'!") + + cv = ShuffleSplit(**shuffle_options) + train, test = next(cv.split(X=arrays[0], y=None)) + + elif shuffle == 'stratified': + cv = StratifiedShuffleSplit(**shuffle_options) + train, test = next(cv.split(X=arrays[0], y=labels)) + + elif shuffle == 'group': + cv = GroupShuffleSplit(**shuffle_options) + train, test = next(cv.split(X=arrays[0], y=None, groups=labels)) + + else: + raise ValueError("The argument `shuffle` only supports None, " + "'simple', 'stratified' and 'group', but got `%s`!" + % shuffle) + + return list(chain.from_iterable((safe_indexing(a, train), + safe_indexing(a, test)) for a in arrays)) + + +class OrderedKFold(_BaseKFold): + """ + Split into K fold based on ordered target value + + Parameters + ---------- + n_splits : int, default=3 + Number of folds. Must be at least 2. + shuffle: bool + random_state: None or int + """ + + def __init__(self, n_splits=3, shuffle=False, random_state=None): + super(OrderedKFold, self).__init__(n_splits, shuffle, random_state) + + def _iter_test_indices(self, X, y, groups=None): + n_samples = _num_samples(X) + n_splits = self.n_splits + y = np.asarray(y) + sorted_index = np.argsort(y) + if self.shuffle: + current = 0 + rng = check_random_state(self.random_state) + for i in range(n_samples // int(n_splits)): + start, stop = current, current + n_splits + rng.shuffle(sorted_index[start:stop]) + current = stop + rng.shuffle(sorted_index[current:]) + + for i in range(n_splits): + yield sorted_index[i:n_samples:n_splits] + + +class RepeatedOrderedKFold(_RepeatedSplits): + """ Repeated OrderedKFold runs mutiple times with different randomization. + + Parameters + ---------- + n_splits : int, default=5 + Number of folds. Must be at least 2. + + n_repeats : int, default=5 + Number of times cross-validator to be repeated. + + random_state: int, RandomState instance or None. Optional + """ + def __init__(self, n_splits=5, n_repeats=5, random_state=None): + super(RepeatedOrderedKFold, self).__init__( + OrderedKFold, n_repeats, random_state, n_splits=n_splits)
--- a/pipeline.xml Sun Dec 30 01:52:15 2018 -0500 +++ b/pipeline.xml Tue May 14 18:06:37 2019 -0400 @@ -3,10 +3,7 @@ <macros> <import>main_macros.xml</import> </macros> - <expand macro="python_requirements"> - <requirement type="package" version="0.6">skrebate</requirement> - <requirement type="package" version="0.4.2">imbalanced-learn</requirement> - </expand> + <expand macro="python_requirements"/> <expand macro="macro_stdio"/> <version_command>echo "@VERSION@"</version_command> <command> @@ -18,19 +15,28 @@ <inputs name="inputs" /> <configfile name="sklearn_pipeline_script"> <![CDATA[ +import imblearn import json +import pickle import pprint import skrebate -import imblearn -from imblearn import under_sampling, over_sampling, combine -from imblearn.pipeline import Pipeline as imbPipeline -from sklearn import (preprocessing, svm, linear_model, ensemble, naive_bayes, - tree, neighbors, decomposition, kernel_approximation, cluster) -from sklearn.pipeline import Pipeline +import sys +import warnings +from mlxtend import classifier, regressor +from sklearn import ( + cluster, compose, decomposition, ensemble, feature_extraction, + feature_selection, gaussian_process, kernel_approximation, metrics, + model_selection, naive_bayes, neighbors, pipeline, preprocessing, + svm, linear_model, tree, discriminant_analysis) +from sklearn.pipeline import make_pipeline +from imblearn.pipeline import make_pipeline as imb_make_pipeline -with open('$__tool_directory__/sk_whitelist.json', 'r') as f: - sk_whitelist = json.load(f) -exec(open('$__tool_directory__/utils.py').read(), globals()) +sys.path.insert(0, '$__tool_directory__') + +from utils import SafeEval, feature_selector, get_estimator, try_get_attr +from preprocessors import Z_RandomOverSampler + +N_JOBS = int(__import__('os').environ.get('GALAXY_SLOTS', 1)) warnings.filterwarnings('ignore') @@ -40,11 +46,16 @@ with open(input_json_path, 'r') as param_handler: params = json.load(param_handler) -#if $final_estimator.estimator_selector.selected_module == 'customer_estimator': +#if $final_estimator.estimator_selector.selected_module == 'custom_estimator': params['final_estimator']['estimator_selector']['c_estimator'] =\ '$final_estimator.estimator_selector.c_estimator' #end if +#if $final_estimator.estimator_selector.selected_module == 'binarize_target': +params['final_estimator']['estimator_selector']['wrapped_estimator'] =\ + '$final_estimator.estimator_selector.wrapped_estimator' +#end if + pipeline_steps = [] def get_component(input_json, check_none=False): @@ -53,7 +64,8 @@ if not check_none: return None, False else: - sys.exit("The pre-processing component type can't be None when the number of components is greater than 1.") + sys.exit("The pre-processing component type can't be None " + "when the number of components is greater than 1.") if input_json['component_type'] == 'pre_processor': preprocessor = input_json['pre_processors']['selected_pre_processor'] pre_processor_options = input_json['pre_processors']['options'] @@ -97,6 +109,8 @@ algorithm = input_json['imblearn_selector']['select_algorithm'] if algorithm == 'over_sampling.SMOTENC': obj = over_sampling.SMOTENC(categorical_features=[]) + elif algorithm == 'Z_RandomOverSampler': + obj = Z_RandomOverSampler() else: globals = algorithm.split('.') mod, klass = globals[0], globals[1] @@ -105,6 +119,26 @@ if options != '': options = safe_eval( 'dict(' + options + ')' ) obj.set_params(**options) + elif input_json['component_type'] == 'IRAPS': + iraps_core = try_get_attr('iraps_classifier','IRAPSCore')() + core_params = input_json['text_params'].strip() + if core_params != '': + try: + params = safe_eval('dict(' + core_params + ')') + except ValueError: + sys.exit("Unsupported parameter input: `%s`" % core_params) + iraps_core.set_params(**params) + options = {} + if input_json['p_thres'] is not None: + options['p_thres'] = input_json['p_thres'] + if input_json['fc_thres'] is not None: + options['fc_thres'] = input_json['fc_thres'] + if input_json['occurrence'] is not None: + options['occurrence'] = input_json['occurrence'] + if input_json['discretize'] is not None: + options['discretize'] = input_json['discretize'] + IRAPSClassifier = try_get_attr('iraps_classifier','IRAPSClassifier') + obj = IRAPSClassifier(iraps_core, **options) if 'n_jobs' in obj.get_params(): obj.set_params( n_jobs=N_JOBS ) return obj, is_imblearn @@ -113,36 +147,41 @@ if len(params['pipeline_component']) == 1: step_obj, is_imblearn = get_component( params['pipeline_component'][0]['component_selector']) if step_obj: - pipeline_steps.append( ('preprocessing_1', step_obj) ) + pipeline_steps.append( step_obj ) if is_imblearn: has_imblearn = True else: for i, c in enumerate(params['pipeline_component']): step_obj, is_imblearn = get_component( c['component_selector'], check_none=True ) - pipeline_steps.append( ('preprocessing_' + str(i+1), step_obj) ) + pipeline_steps.append( step_obj ) if is_imblearn: has_imblearn = True -# Set up final estimator and add to pipeline. +## Set up final estimator and add to pipeline. estimator_json = params['final_estimator']['estimator_selector'] if estimator_json['selected_module'] == 'none': if len(pipeline_steps) == 0: sys.exit("No pipeline steps specified!") - else: # turn the last pre-process component to final estimator - pipeline_steps[-1] = ('estimator', pipeline_steps[-1][-1]) + ## else: turn the last pre-process component to final estimator else: estimator = get_estimator(estimator_json) - pipeline_steps.append( ('estimator', estimator) ) + pipeline_steps.append( estimator ) +#if $output_type == 'Final_Estimator_Builder': +with open('$outfile', 'wb') as out_handler: + final_est = pipeline_steps[-1] + print(final_est) + pickle.dump(final_est, out_handler, pickle.HIGHEST_PROTOCOL) +#else: if has_imblearn: - pipeline = imbPipeline(pipeline_steps) + pipeline = imb_make_pipeline(*pipeline_steps) else: - pipeline = Pipeline(pipeline_steps) + pipeline = make_pipeline(*pipeline_steps) pprint.pprint(pipeline.named_steps) with open('$outfile', 'wb') as out_handler: pickle.dump(pipeline, out_handler, pickle.HIGHEST_PROTOCOL) - +#end if ]]> </configfile> </configfiles> @@ -158,6 +197,7 @@ <option value="FeatureAgglomeration">Agglomerate Features</option> <option value="skrebate">SK-rebate feature selection</option> <option value="imblearn">imbalanced-learn sampling</option> + <option value="IRAPS">IRAPS -- feature selector and classifier</option> </param> <when value="None"/> <when value="pre_processor"> @@ -184,27 +224,51 @@ <when value="imblearn"> <expand macro="imbalanced_learn_sampling"/> </when> + <when value="IRAPS"> + <expand macro="estimator_params_text" + label="Type in parameter settings for IRAPSCore if different from default:" + help="Default(=blank): n_iter=1000, responsive_thres=-1, resistant_thres=0, random_state=None. No double quotes"/> + <param argument="p_thres" type="float" value="0.001" label="P value threshold" help="Float. default=0.001"/> + <param argument="fc_thres" type="float" value="0.1" label="fold change threshold" help="Float. default=0.1"/> + <param argument="occurrence" type="float" value="0.7" label="reservation factor" help="Float. default=0.7"/> + <param argument="discretize" type="float" value="-1" label="The z_score threshold to discretize target value" help="Float. default=-1"/> + </when> </conditional> </repeat> <section name="final_estimator" title="Final Estimator" expanded="true"> <conditional name="estimator_selector"> <param name="selected_module" type="select" label="Choose the module that contains target estimator:" > <expand macro="estimator_module_options"> - <option value="customer_estimator">Load a customer estimator</option> + <option value="binarize_target">Binarize Target Classifier or Regressor</option> + <option value="custom_estimator">Load a custom estimator</option> <option value="none">none -- The last component of pre-processing step will turn to a final estimator</option> </expand> </param> <expand macro="estimator_suboptions"> - <when value="customer_estimator"> - <param name="c_estimator" type="data" format="zip" label="Choose the dataset containing the customer estimator or pipeline:"/> + <when value="binarize_target"> + <param name="clf_or_regr" type="select" label="Classifier or Regressor:"> + <option value="BinarizeTargetClassifier">BinarizeTargetClassifier</option> + <option value="BinarizeTargetRegressor">BinarizeTargetRegressor</option> + </param> + <param name="wrapped_estimator" type="data" format="zip" label="Choose the dataset containing the wrapped estimator or pipeline"/> + <param name='z_score' type="float" value="-1" optional="false" label="Discrize target values using z_score"/> + <param name='value' type="float" value="" optional="true" label="Discretize target values using a fixed value instead" help="Optional. default: None."/> + <param name="less_is_positive" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="true" label="Are the detecting values smaller than others?"/> + </when> + <when value="custom_estimator"> + <param name="c_estimator" type="data" format="zip" label="Choose the dataset containing the custom estimator or pipeline"/> </when> <when value="none"/> </expand> </conditional> </section> + <param name="output_type" type="select" label="Output the final estimator instead?"> + <option value="Pipeline_Builder" selected="true">Pipeline</option> + <option value="Final_Estimator_Builder">Final Estimator</option> + </param> </inputs> <outputs> - <data format="zip" name="outfile"/> + <data format="zip" name="outfile" label="${output_type}"/> </outputs> <tests> <test> @@ -313,7 +377,7 @@ </conditional> <param name="selected_module" value="ensemble"/> <param name="selected_estimator" value="AdaBoostClassifier"/> - <output name="outfile" file="pipeline08" compare="sim_size" delta="5"/> + <output name="outfile" file="pipeline08" compare="sim_size" delta="20"/> </test> <test> <conditional name="component_selector"> @@ -373,6 +437,41 @@ </section> <output name="outfile" file="pipeline12" compare="sim_size" delta="5"/> </test> + <test> + <conditional name="component_selector"> + <param name="component_type" value="None"/> + </conditional> + <param name="selected_module" value="ensemble"/> + <param name="selected_estimator" value="RandomForestClassifier"/> + <param name="output_type" value="Final_Estimator_Builder"/> + <output name="outfile" file="RandomForestClassifier.zip" compare="sim_size" delta="5"/> + </test> + <test> + <conditional name="component_selector"> + <param name="component_type" value="IRAPS"/> + </conditional> + <section name="final_estimator"> + <conditional name="estimator_selector"> + <param name="selected_module" value="none"/> + </conditional> + </section> + <param name="output_type" value="Final_Estimator_Builder"/> + <output name="outfile" file="pipeline14" compare="sim_size" delta="5"/> + </test> + <test> + <conditional name="component_selector"> + <param name="component_type" value="None"/> + </conditional> + <section name="final_estimator"> + <conditional name="estimator_selector"> + <param name="selected_module" value="binarize_target"/> + <param name="clf_or_regr" value="BinarizeTargetClassifier"/> + <param name="wrapped_estimator" value="RandomForestClassifier.zip" ftype="zip"/> + </conditional> + </section> + <param name="output_type" value="Final_Estimator_Builder"/> + <output name="outfile" file="pipeline15" compare="sim_size" delta="5"/> + </test> </tests> <help> <![CDATA[
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/pk_whitelist.json Tue May 14 18:06:37 2019 -0400 @@ -0,0 +1,768 @@ +{ "SK_NAMES": [ + "sklearn._ASSUME_FINITE", "sklearn._isotonic._inplace_contiguous_isotonic_regression", + "sklearn._isotonic._make_unique", "sklearn.base.BaseEstimator", + "sklearn.base.BiclusterMixin", "sklearn.base.ClassifierMixin", + "sklearn.base.ClusterMixin", "sklearn.base.DensityMixin", + "sklearn.base.MetaEstimatorMixin", "sklearn.base.RegressorMixin", + "sklearn.base.TransformerMixin", "sklearn.base._first_and_last_element", + "sklearn.base._pprint", "sklearn.base.clone", + "sklearn.base.is_classifier", "sklearn.base.is_regressor", + "sklearn.clone", "sklearn.cluster.AffinityPropagation", + "sklearn.cluster.AgglomerativeClustering", "sklearn.cluster.Birch", + "sklearn.cluster.DBSCAN", "sklearn.cluster.FeatureAgglomeration", + "sklearn.cluster.KMeans", "sklearn.cluster.MeanShift", + "sklearn.cluster.MiniBatchKMeans", "sklearn.cluster.SpectralBiclustering", + "sklearn.cluster.SpectralClustering", "sklearn.cluster.SpectralCoclustering", + "sklearn.cluster._dbscan_inner.dbscan_inner", "sklearn.cluster._feature_agglomeration.AgglomerationTransform", + "sklearn.cluster._hierarchical.WeightedEdge", "sklearn.cluster._hierarchical._get_parents", + "sklearn.cluster._hierarchical._hc_get_descendent", "sklearn.cluster._hierarchical.average_merge", + "sklearn.cluster._hierarchical.compute_ward_dist", "sklearn.cluster._hierarchical.hc_get_heads", + "sklearn.cluster._hierarchical.max_merge", "sklearn.cluster._k_means._assign_labels_array", + "sklearn.cluster._k_means._assign_labels_csr", "sklearn.cluster._k_means._centers_dense", + "sklearn.cluster._k_means._centers_sparse", "sklearn.cluster._k_means._mini_batch_update_csr", + "sklearn.cluster._k_means_elkan.k_means_elkan", "sklearn.cluster.affinity_propagation", + "sklearn.cluster.affinity_propagation_.AffinityPropagation", "sklearn.cluster.affinity_propagation_.affinity_propagation", + "sklearn.cluster.bicluster.BaseSpectral", "sklearn.cluster.bicluster.SpectralBiclustering", + "sklearn.cluster.bicluster.SpectralCoclustering", "sklearn.cluster.bicluster._bistochastic_normalize", + "sklearn.cluster.bicluster._log_normalize", "sklearn.cluster.bicluster._scale_normalize", + "sklearn.cluster.birch.Birch", "sklearn.cluster.birch._CFNode", + "sklearn.cluster.birch._CFSubcluster", "sklearn.cluster.birch._iterate_sparse_X", + "sklearn.cluster.birch._split_node", "sklearn.cluster.dbscan", + "sklearn.cluster.dbscan_.DBSCAN", "sklearn.cluster.dbscan_.dbscan", + "sklearn.cluster.estimate_bandwidth", "sklearn.cluster.get_bin_seeds", + "sklearn.cluster.hierarchical.AgglomerativeClustering", "sklearn.cluster.hierarchical.FeatureAgglomeration", + "sklearn.cluster.hierarchical._TREE_BUILDERS", "sklearn.cluster.hierarchical._average_linkage", + "sklearn.cluster.hierarchical._complete_linkage", "sklearn.cluster.hierarchical._fix_connectivity", + "sklearn.cluster.hierarchical._hc_cut", "sklearn.cluster.hierarchical.linkage_tree", + "sklearn.cluster.hierarchical.ward_tree", "sklearn.cluster.k_means", + "sklearn.cluster.k_means_.FLOAT_DTYPES", "sklearn.cluster.k_means_.KMeans", + "sklearn.cluster.k_means_.MiniBatchKMeans", "sklearn.cluster.k_means_._init_centroids", + "sklearn.cluster.k_means_._k_init", "sklearn.cluster.k_means_._kmeans_single_elkan", + "sklearn.cluster.k_means_._kmeans_single_lloyd", "sklearn.cluster.k_means_._labels_inertia", + "sklearn.cluster.k_means_._labels_inertia_precompute_dense", "sklearn.cluster.k_means_._mini_batch_convergence", + "sklearn.cluster.k_means_._mini_batch_step", "sklearn.cluster.k_means_._tolerance", + "sklearn.cluster.k_means_._validate_center_shape", "sklearn.cluster.k_means_.k_means", + "sklearn.cluster.k_means_.string_types", "sklearn.cluster.linkage_tree", + "sklearn.cluster.mean_shift", "sklearn.cluster.mean_shift_.MeanShift", + "sklearn.cluster.mean_shift_._mean_shift_single_seed", "sklearn.cluster.mean_shift_.estimate_bandwidth", + "sklearn.cluster.mean_shift_.get_bin_seeds", "sklearn.cluster.mean_shift_.mean_shift", + "sklearn.cluster.spectral.SpectralClustering", "sklearn.cluster.spectral.discretize", + "sklearn.cluster.spectral.spectral_clustering", "sklearn.cluster.spectral_clustering", + "sklearn.cluster.ward_tree", "sklearn.config_context", "sklearn.compose.TransformedTargetRegressor", + "sklearn.compose._target.TransformedTargetRegressor", "sklearn.compose.ColumnTransformer", + "sklearn.compose._column_transformer.ColumnTransformer", "sklearn.compose.make_column_transformer", + "sklearn.compose._column_transformer.make_column_transformer", + "sklearn.covariance.EllipticEnvelope", "sklearn.covariance.EmpiricalCovariance", + "sklearn.covariance.GraphLasso", "sklearn.covariance.GraphLassoCV", + "sklearn.covariance.LedoitWolf", "sklearn.covariance.MinCovDet", + "sklearn.covariance.OAS", "sklearn.covariance.ShrunkCovariance", + "sklearn.covariance.empirical_covariance", "sklearn.covariance.empirical_covariance_.EmpiricalCovariance", + "sklearn.covariance.empirical_covariance_.empirical_covariance", "sklearn.covariance.empirical_covariance_.log_likelihood", + "sklearn.covariance.fast_mcd", "sklearn.covariance.graph_lasso", + "sklearn.covariance.graph_lasso_.GraphLasso", "sklearn.covariance.graph_lasso_.GraphLassoCV", + "sklearn.covariance.graph_lasso_._dual_gap", "sklearn.covariance.graph_lasso_._objective", + "sklearn.covariance.graph_lasso_.alpha_max", "sklearn.covariance.graph_lasso_.graph_lasso", + "sklearn.covariance.graph_lasso_.graph_lasso_path", "sklearn.covariance.ledoit_wolf", + "sklearn.covariance.ledoit_wolf_shrinkage", "sklearn.covariance.log_likelihood", + "sklearn.covariance.oas", "sklearn.covariance.outlier_detection.EllipticEnvelope", + "sklearn.covariance.robust_covariance.MinCovDet", "sklearn.covariance.robust_covariance._c_step", + "sklearn.covariance.robust_covariance.c_step", "sklearn.covariance.robust_covariance.fast_mcd", + "sklearn.covariance.robust_covariance.select_candidates", "sklearn.covariance.shrunk_covariance", + "sklearn.covariance.shrunk_covariance_.LedoitWolf", "sklearn.covariance.shrunk_covariance_.OAS", + "sklearn.covariance.shrunk_covariance_.ShrunkCovariance", "sklearn.covariance.shrunk_covariance_.ledoit_wolf", + "sklearn.covariance.shrunk_covariance_.ledoit_wolf_shrinkage", "sklearn.covariance.shrunk_covariance_.oas", + "sklearn.covariance.shrunk_covariance_.shrunk_covariance", "sklearn.decomposition.DictionaryLearning", + "sklearn.decomposition.FactorAnalysis", "sklearn.decomposition.FastICA", + "sklearn.decomposition.IncrementalPCA", "sklearn.decomposition.KernelPCA", + "sklearn.decomposition.LatentDirichletAllocation", "sklearn.decomposition.MiniBatchDictionaryLearning", + "sklearn.decomposition.MiniBatchSparsePCA", "sklearn.decomposition.NMF", + "sklearn.decomposition.PCA", "sklearn.decomposition.RandomizedPCA", + "sklearn.decomposition.SparseCoder", "sklearn.decomposition.SparsePCA", + "sklearn.decomposition.TruncatedSVD", "sklearn.decomposition._online_lda._dirichlet_expectation_1d", + "sklearn.decomposition._online_lda._dirichlet_expectation_2d", "sklearn.decomposition._online_lda.mean_change", + "sklearn.decomposition.base._BasePCA", "sklearn.decomposition.cdnmf_fast._update_cdnmf_fast", + "sklearn.decomposition.dict_learning", "sklearn.decomposition.dict_learning_online", + "sklearn.decomposition.factor_analysis.FactorAnalysis", "sklearn.decomposition.fastica", + "sklearn.decomposition.fastica_.FLOAT_DTYPES", "sklearn.decomposition.fastica_.FastICA", + "sklearn.decomposition.fastica_._cube", "sklearn.decomposition.fastica_._exp", + "sklearn.decomposition.fastica_._gs_decorrelation", "sklearn.decomposition.fastica_._ica_def", + "sklearn.decomposition.fastica_._ica_par", "sklearn.decomposition.fastica_._logcosh", + "sklearn.decomposition.fastica_._sym_decorrelation", "sklearn.decomposition.fastica_.fastica", + "sklearn.decomposition.fastica_.string_types", "sklearn.decomposition.incremental_pca.IncrementalPCA", + "sklearn.decomposition.kernel_pca.KernelPCA", "sklearn.decomposition.nmf.EPSILON", + "sklearn.decomposition.nmf.INTEGER_TYPES", "sklearn.decomposition.nmf.NMF", + "sklearn.decomposition.nmf._beta_divergence", "sklearn.decomposition.nmf._beta_loss_to_float", + "sklearn.decomposition.nmf._check_init", "sklearn.decomposition.nmf._check_string_param", + "sklearn.decomposition.nmf._compute_regularization", "sklearn.decomposition.nmf._fit_coordinate_descent", + "sklearn.decomposition.nmf._fit_multiplicative_update", "sklearn.decomposition.nmf._initialize_nmf", + "sklearn.decomposition.nmf._multiplicative_update_h", "sklearn.decomposition.nmf._multiplicative_update_w", + "sklearn.decomposition.nmf._special_sparse_dot", "sklearn.decomposition.nmf._update_coordinate_descent", + "sklearn.decomposition.nmf.non_negative_factorization", "sklearn.decomposition.nmf.norm", + "sklearn.decomposition.nmf.trace_dot", "sklearn.decomposition.non_negative_factorization", + "sklearn.decomposition.online_lda.EPS", "sklearn.decomposition.online_lda.LatentDirichletAllocation", + "sklearn.decomposition.online_lda._update_doc_distribution", "sklearn.decomposition.online_lda.gammaln", + "sklearn.decomposition.pca.PCA", "sklearn.decomposition.pca.RandomizedPCA", + "sklearn.decomposition.pca._assess_dimension_", "sklearn.decomposition.pca._infer_dimension_", + "sklearn.decomposition.pca.gammaln", "sklearn.decomposition.sparse_encode", + "sklearn.decomposition.sparse_pca.MiniBatchSparsePCA", "sklearn.decomposition.sparse_pca.SparsePCA", + "sklearn.decomposition.truncated_svd.TruncatedSVD", "sklearn.discriminant_analysis.LinearDiscriminantAnalysis", + "sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis", "sklearn.discriminant_analysis._class_cov", + "sklearn.discriminant_analysis._class_means", "sklearn.discriminant_analysis._cov", + "sklearn.discriminant_analysis.string_types", "sklearn.ensemble.AdaBoostClassifier", + "sklearn.ensemble.AdaBoostRegressor", "sklearn.ensemble.BaggingClassifier", + "sklearn.ensemble.BaggingRegressor", "sklearn.ensemble.BaseEnsemble", + "sklearn.ensemble.ExtraTreesClassifier", "sklearn.ensemble.ExtraTreesRegressor", + "sklearn.ensemble.GradientBoostingClassifier", "sklearn.ensemble.GradientBoostingRegressor", + "sklearn.ensemble.IsolationForest", "sklearn.ensemble.RandomForestClassifier", + "sklearn.ensemble.RandomForestRegressor", "sklearn.ensemble.RandomTreesEmbedding", + "sklearn.ensemble.VotingClassifier", "sklearn.ensemble._gradient_boosting._partial_dependence_tree", + "sklearn.ensemble._gradient_boosting._predict_regression_tree_stages_sparse", "sklearn.ensemble._gradient_boosting._random_sample_mask", + "sklearn.ensemble._gradient_boosting.predict_stage", "sklearn.ensemble._gradient_boosting.predict_stages", + "sklearn.ensemble.bagging.BaggingClassifier", "sklearn.ensemble.bagging.BaggingRegressor", + "sklearn.ensemble.bagging.BaseBagging", "sklearn.ensemble.bagging.MAX_INT", + "sklearn.ensemble.bagging._generate_bagging_indices", "sklearn.ensemble.bagging._generate_indices", + "sklearn.ensemble.bagging._parallel_build_estimators", "sklearn.ensemble.bagging._parallel_decision_function", + "sklearn.ensemble.bagging._parallel_predict_log_proba", "sklearn.ensemble.bagging._parallel_predict_proba", + "sklearn.ensemble.bagging._parallel_predict_regression", "sklearn.ensemble.base.BaseEnsemble", + "sklearn.ensemble.base.MAX_RAND_SEED", "sklearn.ensemble.base._partition_estimators", + "sklearn.ensemble.base._set_random_states", "sklearn.ensemble.forest.BaseForest", + "sklearn.ensemble.forest.ExtraTreesClassifier", "sklearn.ensemble.forest.ExtraTreesRegressor", + "sklearn.ensemble.forest.ForestClassifier", "sklearn.ensemble.forest.ForestRegressor", + "sklearn.ensemble.forest.MAX_INT", "sklearn.ensemble.forest.RandomForestClassifier", + "sklearn.ensemble.forest.RandomForestRegressor", "sklearn.ensemble.forest.RandomTreesEmbedding", + "sklearn.ensemble.forest._generate_sample_indices", "sklearn.ensemble.forest._generate_unsampled_indices", + "sklearn.ensemble.forest._parallel_build_trees", "sklearn.ensemble.forest.accumulate_prediction", + "sklearn.ensemble.gradient_boosting.BaseGradientBoosting", "sklearn.ensemble.gradient_boosting.BinomialDeviance", + "sklearn.ensemble.gradient_boosting.ClassificationLossFunction", "sklearn.ensemble.gradient_boosting.ExponentialLoss", + "sklearn.ensemble.gradient_boosting.GradientBoostingClassifier", "sklearn.ensemble.gradient_boosting.GradientBoostingRegressor", + "sklearn.ensemble.gradient_boosting.HuberLossFunction", "sklearn.ensemble.gradient_boosting.INIT_ESTIMATORS", + "sklearn.ensemble.gradient_boosting.LOSS_FUNCTIONS", "sklearn.ensemble.gradient_boosting.LeastAbsoluteError", + "sklearn.ensemble.gradient_boosting.LeastSquaresError", "sklearn.ensemble.gradient_boosting.LogOddsEstimator", + "sklearn.ensemble.gradient_boosting.LossFunction", "sklearn.ensemble.gradient_boosting.MeanEstimator", + "sklearn.ensemble.gradient_boosting.MultinomialDeviance", "sklearn.ensemble.gradient_boosting.PriorProbabilityEstimator", + "sklearn.ensemble.gradient_boosting.QuantileEstimator", "sklearn.ensemble.gradient_boosting.QuantileLossFunction", + "sklearn.ensemble.gradient_boosting.RegressionLossFunction", "sklearn.ensemble.gradient_boosting.ScaledLogOddsEstimator", + "sklearn.ensemble.gradient_boosting.TREE_LEAF", "sklearn.ensemble.gradient_boosting.VerboseReporter", + "sklearn.ensemble.gradient_boosting.ZeroEstimator", "sklearn.ensemble.gradient_boosting.expit", + "sklearn.ensemble.iforest.INTEGER_TYPES", "sklearn.ensemble.iforest.IsolationForest", + "sklearn.ensemble.iforest._average_path_length", "sklearn.ensemble.iforest.euler_gamma", + "sklearn.ensemble.partial_dependence._grid_from_X", "sklearn.ensemble.partial_dependence.partial_dependence", + "sklearn.ensemble.partial_dependence.plot_partial_dependence", "sklearn.ensemble.voting_classifier.VotingClassifier", + "sklearn.ensemble.voting_classifier._parallel_fit_estimator", "sklearn.ensemble.weight_boosting.AdaBoostClassifier", + "sklearn.ensemble.weight_boosting.AdaBoostRegressor", "sklearn.ensemble.weight_boosting.BaseWeightBoosting", + "sklearn.ensemble.weight_boosting._samme_proba", "sklearn.ensemble.weight_boosting.inner1d", + "sklearn.feature_extraction.DictVectorizer", "sklearn.feature_extraction.FeatureHasher", + "sklearn.feature_extraction._hashing.transform", "sklearn.feature_extraction.dict_vectorizer.DictVectorizer", + "sklearn.feature_extraction.dict_vectorizer._tosequence", "sklearn.feature_extraction.grid_to_graph", + "sklearn.feature_extraction.hashing.FeatureHasher", "sklearn.feature_extraction.hashing._iteritems", + "sklearn.feature_extraction.image.PatchExtractor", "sklearn.feature_extraction.image._compute_gradient_3d", + "sklearn.feature_extraction.image._compute_n_patches", "sklearn.feature_extraction.image._make_edges_3d", + "sklearn.feature_extraction.image._mask_edges_weights", "sklearn.feature_extraction.image._to_graph", + "sklearn.feature_extraction.image.extract_patches", "sklearn.feature_extraction.image.extract_patches_2d", + "sklearn.feature_extraction.image.grid_to_graph", "sklearn.feature_extraction.image.img_to_graph", + "sklearn.feature_extraction.image.reconstruct_from_patches_2d", "sklearn.feature_extraction.img_to_graph", + "sklearn.feature_extraction.stop_words.ENGLISH_STOP_WORDS", "sklearn.feature_extraction.text.CountVectorizer", + "sklearn.feature_extraction.text.ENGLISH_STOP_WORDS", "sklearn.feature_extraction.text.HashingVectorizer", + "sklearn.feature_extraction.text.TfidfTransformer", "sklearn.feature_extraction.text.TfidfVectorizer", + "sklearn.feature_extraction.text.VectorizerMixin", "sklearn.feature_extraction.text._check_stop_list", + "sklearn.feature_extraction.text._document_frequency", "sklearn.feature_extraction.text._make_int_array", + "sklearn.feature_extraction.text.strip_accents_ascii", "sklearn.feature_extraction.text.strip_accents_unicode", + "sklearn.feature_extraction.text.strip_tags", "sklearn.feature_selection.GenericUnivariateSelect", + "sklearn.feature_selection.RFE", "sklearn.feature_selection.RFECV", + "sklearn.feature_selection.SelectFdr", "sklearn.feature_selection.SelectFpr", + "sklearn.feature_selection.SelectFromModel", "sklearn.feature_selection.SelectFwe", + "sklearn.feature_selection.SelectKBest", "sklearn.feature_selection.SelectPercentile", + "sklearn.feature_selection.VarianceThreshold", "sklearn.feature_selection.base.SelectorMixin", + "sklearn.feature_selection.chi2", "sklearn.feature_selection.f_classif", + "sklearn.feature_selection.f_oneway", "sklearn.feature_selection.f_regression", + "sklearn.feature_selection.from_model.SelectFromModel", "sklearn.feature_selection.from_model._calculate_threshold", + "sklearn.feature_selection.from_model._get_feature_importances", "sklearn.feature_selection.mutual_info_._compute_mi", + "sklearn.feature_selection.mutual_info_._compute_mi_cc", "sklearn.feature_selection.mutual_info_._compute_mi_cd", + "sklearn.feature_selection.mutual_info_._estimate_mi", "sklearn.feature_selection.mutual_info_._iterate_columns", + "sklearn.feature_selection.mutual_info_.digamma", "sklearn.feature_selection.mutual_info_.mutual_info_classif", + "sklearn.feature_selection.mutual_info_.mutual_info_regression", "sklearn.feature_selection.mutual_info_classif", + "sklearn.feature_selection.mutual_info_regression", "sklearn.feature_selection.rfe.RFE", + "sklearn.feature_selection.rfe.RFECV", "sklearn.feature_selection.rfe._rfe_single_fit", + "sklearn.feature_selection.univariate_selection.GenericUnivariateSelect", "sklearn.feature_selection.univariate_selection.SelectFdr", + "sklearn.feature_selection.univariate_selection.SelectFpr", "sklearn.feature_selection.univariate_selection.SelectFwe", + "sklearn.feature_selection.univariate_selection.SelectKBest", "sklearn.feature_selection.univariate_selection.SelectPercentile", + "sklearn.feature_selection.univariate_selection._BaseFilter", "sklearn.feature_selection.univariate_selection._chisquare", + "sklearn.feature_selection.univariate_selection._clean_nans", "sklearn.feature_selection.univariate_selection.chi2", + "sklearn.feature_selection.univariate_selection.f_classif", "sklearn.feature_selection.univariate_selection.f_oneway", + "sklearn.feature_selection.univariate_selection.f_regression", "sklearn.feature_selection.variance_threshold.VarianceThreshold", + "sklearn.gaussian_process.GaussianProcess", "sklearn.gaussian_process.GaussianProcessClassifier", + "sklearn.gaussian_process.GaussianProcessRegressor", "sklearn.gaussian_process.correlation_models.absolute_exponential", + "sklearn.gaussian_process.correlation_models.cubic", "sklearn.gaussian_process.correlation_models.generalized_exponential", + "sklearn.gaussian_process.correlation_models.linear", "sklearn.gaussian_process.correlation_models.pure_nugget", + "sklearn.gaussian_process.correlation_models.squared_exponential", "sklearn.gaussian_process.gaussian_process.GaussianProcess", + "sklearn.gaussian_process.gaussian_process.MACHINE_EPSILON", "sklearn.gaussian_process.gaussian_process.l1_cross_distances", + "sklearn.gaussian_process.gpc.COEFS", "sklearn.gaussian_process.gpc.GaussianProcessClassifier", + "sklearn.gaussian_process.gpc.LAMBDAS", "sklearn.gaussian_process.gpc._BinaryGaussianProcessClassifierLaplace", + "sklearn.gaussian_process.gpc.erf", "sklearn.gaussian_process.gpc.expit", + "sklearn.gaussian_process.gpr.GaussianProcessRegressor", "sklearn.gaussian_process.kernels.CompoundKernel", + "sklearn.gaussian_process.kernels.ConstantKernel", "sklearn.gaussian_process.kernels.DotProduct", + "sklearn.gaussian_process.kernels.ExpSineSquared", "sklearn.gaussian_process.kernels.Exponentiation", + "sklearn.gaussian_process.kernels.Hyperparameter", "sklearn.gaussian_process.kernels.Kernel", + "sklearn.gaussian_process.kernels.KernelOperator", "sklearn.gaussian_process.kernels.Matern", + "sklearn.gaussian_process.kernels.NormalizedKernelMixin", "sklearn.gaussian_process.kernels.PairwiseKernel", + "sklearn.gaussian_process.kernels.Product", "sklearn.gaussian_process.kernels.RBF", + "sklearn.gaussian_process.kernels.RationalQuadratic", "sklearn.gaussian_process.kernels.StationaryKernelMixin", + "sklearn.gaussian_process.kernels.Sum", "sklearn.gaussian_process.kernels.WhiteKernel", + "sklearn.gaussian_process.kernels._approx_fprime", "sklearn.gaussian_process.kernels._check_length_scale", + "sklearn.gaussian_process.kernels.gamma", "sklearn.gaussian_process.kernels.kv", + "sklearn.gaussian_process.regression_models.constant", "sklearn.gaussian_process.regression_models.linear", + "sklearn.gaussian_process.regression_models.quadratic", "sklearn.get_config", + "sklearn.isotonic.IsotonicRegression", "sklearn.isotonic.check_increasing", + "sklearn.isotonic.isotonic_regression", "sklearn.kernel_approximation.AdditiveChi2Sampler", + "sklearn.kernel_approximation.KERNEL_PARAMS", "sklearn.kernel_approximation.Nystroem", + "sklearn.kernel_approximation.RBFSampler", "sklearn.kernel_approximation.SkewedChi2Sampler", + "sklearn.kernel_ridge.KernelRidge", "sklearn.linear_model.ARDRegression", + "sklearn.linear_model.BayesianRidge", "sklearn.linear_model.ElasticNet", + "sklearn.linear_model.ElasticNetCV", "sklearn.linear_model.Hinge", + "sklearn.linear_model.Huber", "sklearn.linear_model.HuberRegressor", + "sklearn.linear_model.Lars", "sklearn.linear_model.LarsCV", + "sklearn.linear_model.Lasso", "sklearn.linear_model.LassoCV", + "sklearn.linear_model.LassoLars", "sklearn.linear_model.LassoLarsCV", + "sklearn.linear_model.LassoLarsIC", "sklearn.linear_model.LinearRegression", + "sklearn.linear_model.Log", "sklearn.linear_model.LogisticRegression", + "sklearn.linear_model.LogisticRegressionCV", "sklearn.linear_model.ModifiedHuber", + "sklearn.linear_model.MultiTaskElasticNet", "sklearn.linear_model.MultiTaskElasticNetCV", + "sklearn.linear_model.MultiTaskLasso", "sklearn.linear_model.MultiTaskLassoCV", + "sklearn.linear_model.OrthogonalMatchingPursuit", "sklearn.linear_model.OrthogonalMatchingPursuitCV", + "sklearn.linear_model.PassiveAggressiveClassifier", "sklearn.linear_model.PassiveAggressiveRegressor", + "sklearn.linear_model.Perceptron", "sklearn.linear_model.RANSACRegressor", + "sklearn.linear_model.RandomizedLasso", "sklearn.linear_model.RandomizedLogisticRegression", + "sklearn.linear_model.Ridge", "sklearn.linear_model.RidgeCV", + "sklearn.linear_model.RidgeClassifier", "sklearn.linear_model.RidgeClassifierCV", + "sklearn.linear_model.SGDClassifier", "sklearn.linear_model.SGDRegressor", + "sklearn.linear_model.SquaredLoss", "sklearn.linear_model.TheilSenRegressor", + "sklearn.linear_model.base.FLOAT_DTYPES", "sklearn.linear_model.base.LinearClassifierMixin", + "sklearn.linear_model.base.LinearModel", "sklearn.linear_model.base.LinearRegression", + "sklearn.linear_model.base.SPARSE_INTERCEPT_DECAY", "sklearn.linear_model.base.SparseCoefMixin", + "sklearn.linear_model.base._pre_fit", "sklearn.linear_model.base._preprocess_data", + "sklearn.linear_model.base._rescale_data", "sklearn.linear_model.base.center_data", + "sklearn.linear_model.base.make_dataset", "sklearn.linear_model.base.sparse_center_data", + "sklearn.linear_model.bayes.ARDRegression", "sklearn.linear_model.bayes.BayesianRidge", + "sklearn.linear_model.cd_fast.enet_coordinate_descent", "sklearn.linear_model.cd_fast.enet_coordinate_descent_gram", + "sklearn.linear_model.cd_fast.enet_coordinate_descent_multi_task", "sklearn.linear_model.cd_fast.sparse_enet_coordinate_descent", + "sklearn.linear_model.coordinate_descent.ElasticNet", "sklearn.linear_model.coordinate_descent.ElasticNetCV", + "sklearn.linear_model.coordinate_descent.Lasso", "sklearn.linear_model.coordinate_descent.LassoCV", + "sklearn.linear_model.coordinate_descent.LinearModelCV", "sklearn.linear_model.coordinate_descent.MultiTaskElasticNet", + "sklearn.linear_model.coordinate_descent.MultiTaskElasticNetCV", "sklearn.linear_model.coordinate_descent.MultiTaskLasso", + "sklearn.linear_model.coordinate_descent.MultiTaskLassoCV", "sklearn.linear_model.coordinate_descent._alpha_grid", + "sklearn.linear_model.coordinate_descent._path_residuals", "sklearn.linear_model.coordinate_descent.enet_path", + "sklearn.linear_model.coordinate_descent.lasso_path", "sklearn.linear_model.enet_path", + "sklearn.linear_model.huber.HuberRegressor", "sklearn.linear_model.huber._huber_loss_and_gradient", + "sklearn.linear_model.lars_path", "sklearn.linear_model.lasso_path", + "sklearn.linear_model.lasso_stability_path", "sklearn.linear_model.least_angle.Lars", + "sklearn.linear_model.least_angle.LarsCV", "sklearn.linear_model.least_angle.LassoLars", + "sklearn.linear_model.least_angle.LassoLarsCV", "sklearn.linear_model.least_angle.LassoLarsIC", + "sklearn.linear_model.least_angle._check_copy_and_writeable", "sklearn.linear_model.least_angle._lars_path_residues", + "sklearn.linear_model.least_angle.lars_path", "sklearn.linear_model.least_angle.solve_triangular_args", + "sklearn.linear_model.least_angle.string_types", "sklearn.linear_model.logistic.LogisticRegression", + "sklearn.linear_model.logistic.LogisticRegressionCV", "sklearn.linear_model.logistic.SCORERS", + "sklearn.linear_model.logistic._check_solver_option", "sklearn.linear_model.logistic._intercept_dot", + "sklearn.linear_model.logistic._log_reg_scoring_path", "sklearn.linear_model.logistic._logistic_grad_hess", + "sklearn.linear_model.logistic._logistic_loss", "sklearn.linear_model.logistic._logistic_loss_and_grad", + "sklearn.linear_model.logistic._multinomial_grad_hess", "sklearn.linear_model.logistic._multinomial_loss", + "sklearn.linear_model.logistic._multinomial_loss_grad", "sklearn.linear_model.logistic.expit", + "sklearn.linear_model.logistic.logistic_regression_path", "sklearn.linear_model.logistic_regression_path", + "sklearn.linear_model.omp.OrthogonalMatchingPursuit", "sklearn.linear_model.omp.OrthogonalMatchingPursuitCV", + "sklearn.linear_model.omp._cholesky_omp", "sklearn.linear_model.omp._gram_omp", + "sklearn.linear_model.omp._omp_path_residues", "sklearn.linear_model.omp.orthogonal_mp", + "sklearn.linear_model.omp.orthogonal_mp_gram", "sklearn.linear_model.omp.premature", + "sklearn.linear_model.omp.solve_triangular_args", "sklearn.linear_model.orthogonal_mp", + "sklearn.linear_model.orthogonal_mp_gram", "sklearn.linear_model.passive_aggressive.DEFAULT_EPSILON", + "sklearn.linear_model.passive_aggressive.PassiveAggressiveClassifier", "sklearn.linear_model.passive_aggressive.PassiveAggressiveRegressor", + "sklearn.linear_model.perceptron.Perceptron", "sklearn.linear_model.randomized_l1.BaseRandomizedLinearModel", + "sklearn.linear_model.randomized_l1.RandomizedLasso", "sklearn.linear_model.randomized_l1.RandomizedLogisticRegression", + "sklearn.linear_model.randomized_l1._lasso_stability_path", "sklearn.linear_model.randomized_l1._randomized_lasso", + "sklearn.linear_model.randomized_l1._randomized_logistic", "sklearn.linear_model.randomized_l1._resample_model", + "sklearn.linear_model.randomized_l1.lasso_stability_path", "sklearn.linear_model.ransac.RANSACRegressor", + "sklearn.linear_model.ransac._EPSILON", "sklearn.linear_model.ransac._dynamic_max_trials", + "sklearn.linear_model.ridge.Ridge", "sklearn.linear_model.ridge.RidgeCV", + "sklearn.linear_model.ridge.RidgeClassifier", "sklearn.linear_model.ridge.RidgeClassifierCV", + "sklearn.linear_model.ridge._BaseRidge", "sklearn.linear_model.ridge._BaseRidgeCV", + "sklearn.linear_model.ridge._RidgeGCV", "sklearn.linear_model.ridge._solve_cholesky", + "sklearn.linear_model.ridge._solve_cholesky_kernel", "sklearn.linear_model.ridge._solve_lsqr", + "sklearn.linear_model.ridge._solve_sparse_cg", "sklearn.linear_model.ridge._solve_svd", + "sklearn.linear_model.ridge.ridge_regression", "sklearn.linear_model.ridge_regression", + "sklearn.linear_model.sag.get_auto_step_size", "sklearn.linear_model.sag.sag", + "sklearn.linear_model.sag.sag_solver", "sklearn.linear_model.sag_fast.MultinomialLogLoss", + "sklearn.linear_model.sag_fast._multinomial_grad_loss_all_samples", "sklearn.linear_model.sag_fast.sag", + "sklearn.linear_model.sgd_fast.Classification", "sklearn.linear_model.sgd_fast.EpsilonInsensitive", + "sklearn.linear_model.sgd_fast.Hinge", "sklearn.linear_model.sgd_fast.Huber", + "sklearn.linear_model.sgd_fast.Log", "sklearn.linear_model.sgd_fast.LossFunction", + "sklearn.linear_model.sgd_fast.ModifiedHuber", "sklearn.linear_model.sgd_fast.Regression", + "sklearn.linear_model.sgd_fast.SquaredEpsilonInsensitive", "sklearn.linear_model.sgd_fast.SquaredHinge", + "sklearn.linear_model.sgd_fast.SquaredLoss", "sklearn.linear_model.sgd_fast._plain_sgd", + "sklearn.linear_model.sgd_fast.average_sgd", "sklearn.linear_model.sgd_fast.plain_sgd", + "sklearn.linear_model.stochastic_gradient.BaseSGD", "sklearn.linear_model.stochastic_gradient.BaseSGDClassifier", + "sklearn.linear_model.stochastic_gradient.BaseSGDRegressor", "sklearn.linear_model.stochastic_gradient.DEFAULT_EPSILON", + "sklearn.linear_model.stochastic_gradient.LEARNING_RATE_TYPES", "sklearn.linear_model.stochastic_gradient.PENALTY_TYPES", + "sklearn.linear_model.stochastic_gradient.SGDClassifier", "sklearn.linear_model.stochastic_gradient.SGDRegressor", + "sklearn.linear_model.stochastic_gradient._prepare_fit_binary", "sklearn.linear_model.stochastic_gradient.fit_binary", + "sklearn.linear_model.theil_sen.TheilSenRegressor", "sklearn.linear_model.theil_sen._EPSILON", + "sklearn.linear_model.theil_sen._breakdown_point", "sklearn.linear_model.theil_sen._lstsq", + "sklearn.linear_model.theil_sen._modified_weiszfeld_step", "sklearn.linear_model.theil_sen._spatial_median", + "sklearn.linear_model.theil_sen.binom", "sklearn.manifold.Isomap", + "sklearn.manifold.LocallyLinearEmbedding", "sklearn.manifold.MDS", + "sklearn.manifold.SpectralEmbedding", "sklearn.manifold.TSNE", + "sklearn.manifold._barnes_hut_tsne.gradient", "sklearn.manifold._utils._binary_search_perplexity", + "sklearn.manifold.isomap.Isomap", "sklearn.manifold.locally_linear.FLOAT_DTYPES", + "sklearn.manifold.locally_linear.LocallyLinearEmbedding", "sklearn.manifold.locally_linear.barycenter_kneighbors_graph", + "sklearn.manifold.locally_linear.barycenter_weights", "sklearn.manifold.locally_linear.locally_linear_embedding", + "sklearn.manifold.locally_linear.null_space", "sklearn.manifold.locally_linear_embedding", + "sklearn.manifold.mds.MDS", "sklearn.manifold.mds._smacof_single", + "sklearn.manifold.mds.smacof", "sklearn.manifold.smacof", + "sklearn.manifold.spectral_embedding", "sklearn.manifold.spectral_embedding_.SpectralEmbedding", + "sklearn.manifold.spectral_embedding_._graph_connected_component", "sklearn.manifold.spectral_embedding_._graph_is_connected", + "sklearn.manifold.spectral_embedding_._set_diag", "sklearn.manifold.spectral_embedding_.spectral_embedding", + "sklearn.manifold.t_sne.MACHINE_EPSILON", "sklearn.manifold.t_sne.TSNE", + "sklearn.manifold.t_sne._gradient_descent", "sklearn.manifold.t_sne._joint_probabilities", + "sklearn.manifold.t_sne._joint_probabilities_nn", "sklearn.manifold.t_sne._kl_divergence", + "sklearn.manifold.t_sne._kl_divergence_bh", "sklearn.manifold.t_sne.string_types", + "sklearn.manifold.t_sne.trustworthiness", "sklearn.metrics.SCORERS", + "sklearn.metrics.accuracy_score", "sklearn.metrics.adjusted_mutual_info_score", + "sklearn.metrics.adjusted_rand_score", "sklearn.metrics.auc", + "sklearn.metrics.average_precision_score", "sklearn.metrics.base._average_binary_score", + "sklearn.metrics.brier_score_loss", "sklearn.metrics.calinski_harabaz_score", + "sklearn.metrics.classification._check_binary_probabilistic_predictions", "sklearn.metrics.classification._check_targets", + "sklearn.metrics.classification._prf_divide", "sklearn.metrics.classification._weighted_sum", + "sklearn.metrics.classification.accuracy_score", "sklearn.metrics.classification.brier_score_loss", + "sklearn.metrics.classification.classification_report", "sklearn.metrics.classification.cohen_kappa_score", + "sklearn.metrics.classification.confusion_matrix", "sklearn.metrics.classification.f1_score", + "sklearn.metrics.classification.fbeta_score", "sklearn.metrics.classification.hamming_loss", + "sklearn.metrics.classification.hinge_loss", "sklearn.metrics.classification.jaccard_similarity_score", + "sklearn.metrics.classification.log_loss", "sklearn.metrics.classification.matthews_corrcoef", + "sklearn.metrics.classification.precision_recall_fscore_support", "sklearn.metrics.classification.precision_score", + "sklearn.metrics.classification.recall_score", "sklearn.metrics.classification.zero_one_loss", + "sklearn.metrics.classification_report", "sklearn.metrics.cluster.adjusted_mutual_info_score", + "sklearn.metrics.cluster.adjusted_rand_score", "sklearn.metrics.cluster.bicluster._check_rows_and_columns", + "sklearn.metrics.cluster.bicluster._jaccard", "sklearn.metrics.cluster.bicluster._pairwise_similarity", + "sklearn.metrics.cluster.bicluster.consensus_score", "sklearn.metrics.cluster.calinski_harabaz_score", + "sklearn.metrics.cluster.completeness_score", "sklearn.metrics.cluster.consensus_score", + "sklearn.metrics.cluster.contingency_matrix", "sklearn.metrics.cluster.entropy", + "sklearn.metrics.cluster.expected_mutual_info_fast.expected_mutual_information", "sklearn.metrics.cluster.expected_mutual_info_fast.gammaln", + "sklearn.metrics.cluster.expected_mutual_information", "sklearn.metrics.cluster.fowlkes_mallows_score", + "sklearn.metrics.cluster.homogeneity_completeness_v_measure", "sklearn.metrics.cluster.homogeneity_score", + "sklearn.metrics.cluster.mutual_info_score", "sklearn.metrics.cluster.normalized_mutual_info_score", + "sklearn.metrics.cluster.silhouette_samples", "sklearn.metrics.cluster.silhouette_score", + "sklearn.metrics.cluster.supervised.adjusted_mutual_info_score", "sklearn.metrics.cluster.supervised.adjusted_rand_score", + "sklearn.metrics.cluster.supervised.check_clusterings", "sklearn.metrics.cluster.supervised.comb2", + "sklearn.metrics.cluster.supervised.completeness_score", "sklearn.metrics.cluster.supervised.contingency_matrix", + "sklearn.metrics.cluster.supervised.entropy", "sklearn.metrics.cluster.supervised.fowlkes_mallows_score", + "sklearn.metrics.cluster.supervised.homogeneity_completeness_v_measure", "sklearn.metrics.cluster.supervised.homogeneity_score", + "sklearn.metrics.cluster.supervised.mutual_info_score", "sklearn.metrics.cluster.supervised.normalized_mutual_info_score", + "sklearn.metrics.cluster.supervised.v_measure_score", "sklearn.metrics.cluster.unsupervised.calinski_harabaz_score", + "sklearn.metrics.cluster.unsupervised.check_number_of_labels", "sklearn.metrics.cluster.unsupervised.silhouette_samples", + "sklearn.metrics.cluster.unsupervised.silhouette_score", "sklearn.metrics.cluster.v_measure_score", + "sklearn.metrics.cohen_kappa_score", "sklearn.metrics.completeness_score", + "sklearn.metrics.confusion_matrix", "sklearn.metrics.consensus_score", + "sklearn.metrics.coverage_error", "sklearn.metrics.euclidean_distances", + "sklearn.metrics.explained_variance_score", "sklearn.metrics.f1_score", + "sklearn.metrics.fbeta_score", "sklearn.metrics.fowlkes_mallows_score", + "sklearn.metrics.get_scorer", "sklearn.metrics.hamming_loss", + "sklearn.metrics.hinge_loss", "sklearn.metrics.homogeneity_completeness_v_measure", + "sklearn.metrics.homogeneity_score", "sklearn.metrics.jaccard_similarity_score", + "sklearn.metrics.label_ranking_average_precision_score", "sklearn.metrics.label_ranking_loss", + "sklearn.metrics.log_loss", "sklearn.metrics.make_scorer", + "sklearn.metrics.matthews_corrcoef", "sklearn.metrics.mean_absolute_error", + "sklearn.metrics.mean_squared_error", "sklearn.metrics.mean_squared_log_error", + "sklearn.metrics.median_absolute_error", "sklearn.metrics.mutual_info_score", + "sklearn.metrics.normalized_mutual_info_score", "sklearn.metrics.pairwise.KERNEL_PARAMS", + "sklearn.metrics.pairwise.PAIRED_DISTANCES", "sklearn.metrics.pairwise.PAIRWISE_BOOLEAN_FUNCTIONS", + "sklearn.metrics.pairwise.PAIRWISE_DISTANCE_FUNCTIONS", "sklearn.metrics.pairwise.PAIRWISE_KERNEL_FUNCTIONS", + "sklearn.metrics.pairwise._VALID_METRICS", "sklearn.metrics.pairwise._chi2_kernel_fast", + "sklearn.metrics.pairwise._pairwise_callable", "sklearn.metrics.pairwise._parallel_pairwise", + "sklearn.metrics.pairwise._return_float_dtype", "sklearn.metrics.pairwise._sparse_manhattan", + "sklearn.metrics.pairwise.additive_chi2_kernel", "sklearn.metrics.pairwise.check_paired_arrays", + "sklearn.metrics.pairwise.check_pairwise_arrays", "sklearn.metrics.pairwise.chi2_kernel", + "sklearn.metrics.pairwise.cosine_distances", "sklearn.metrics.pairwise.cosine_similarity", + "sklearn.metrics.pairwise.distance_metrics", "sklearn.metrics.pairwise.euclidean_distances", + "sklearn.metrics.pairwise.kernel_metrics", "sklearn.metrics.pairwise.laplacian_kernel", + "sklearn.metrics.pairwise.linear_kernel", "sklearn.metrics.pairwise.manhattan_distances", + "sklearn.metrics.pairwise.paired_cosine_distances", "sklearn.metrics.pairwise.paired_distances", + "sklearn.metrics.pairwise.paired_euclidean_distances", "sklearn.metrics.pairwise.paired_manhattan_distances", + "sklearn.metrics.pairwise.pairwise_distances", "sklearn.metrics.pairwise.pairwise_distances_argmin", + "sklearn.metrics.pairwise.pairwise_distances_argmin_min", "sklearn.metrics.pairwise.pairwise_kernels", + "sklearn.metrics.pairwise.polynomial_kernel", "sklearn.metrics.pairwise.rbf_kernel", + "sklearn.metrics.pairwise.sigmoid_kernel", "sklearn.metrics.pairwise_distances", + "sklearn.metrics.pairwise_distances_argmin", "sklearn.metrics.pairwise_distances_argmin_min", + "sklearn.metrics.pairwise_fast._chi2_kernel_fast", "sklearn.metrics.pairwise_fast._sparse_manhattan", + "sklearn.metrics.pairwise_kernels", "sklearn.metrics.precision_recall_curve", + "sklearn.metrics.precision_recall_fscore_support", "sklearn.metrics.precision_score", + "sklearn.metrics.r2_score", "sklearn.metrics.ranking._binary_clf_curve", + "sklearn.metrics.ranking.auc", "sklearn.metrics.ranking.average_precision_score", + "sklearn.metrics.ranking.coverage_error", "sklearn.metrics.ranking.label_ranking_average_precision_score", + "sklearn.metrics.ranking.label_ranking_loss", "sklearn.metrics.ranking.precision_recall_curve", + "sklearn.metrics.ranking.roc_auc_score", "sklearn.metrics.ranking.roc_curve", + "sklearn.metrics.recall_score", "sklearn.metrics.regression._check_reg_targets", + "sklearn.metrics.regression.explained_variance_score", "sklearn.metrics.regression.mean_absolute_error", + "sklearn.metrics.regression.mean_squared_error", "sklearn.metrics.regression.mean_squared_log_error", + "sklearn.metrics.regression.median_absolute_error", "sklearn.metrics.regression.r2_score", + "sklearn.metrics.regression.string_types", "sklearn.metrics.roc_auc_score", + "sklearn.metrics.roc_curve", "sklearn.metrics.scorer.SCORERS", + "sklearn.metrics.scorer._BaseScorer", "sklearn.metrics.scorer._PredictScorer", + "sklearn.metrics.scorer._ProbaScorer", "sklearn.metrics.scorer._ThresholdScorer", + "sklearn.metrics.scorer._check_multimetric_scoring", "sklearn.metrics.scorer._passthrough_scorer", + "sklearn.metrics.scorer.accuracy_scorer", "sklearn.metrics.scorer.adjusted_mutual_info_scorer", + "sklearn.metrics.scorer.adjusted_rand_scorer", "sklearn.metrics.scorer.average", + "sklearn.metrics.scorer.average_precision_scorer", "sklearn.metrics.scorer.check_scoring", + "sklearn.metrics.scorer.completeness_scorer", "sklearn.metrics.scorer.deprecation_msg", + "sklearn.metrics.scorer.explained_variance_scorer", "sklearn.metrics.scorer.f1_scorer", + "sklearn.metrics.scorer.fowlkes_mallows_scorer", "sklearn.metrics.scorer.get_scorer", + "sklearn.metrics.scorer.homogeneity_scorer", "sklearn.metrics.scorer.log_loss_scorer", + "sklearn.metrics.scorer.make_scorer", "sklearn.metrics.scorer.mean_absolute_error_scorer", + "sklearn.metrics.scorer.mean_squared_error_scorer", "sklearn.metrics.scorer.median_absolute_error_scorer", + "sklearn.metrics.scorer.mutual_info_scorer", "sklearn.metrics.scorer.name", + "sklearn.metrics.scorer.neg_log_loss_scorer", "sklearn.metrics.scorer.neg_mean_absolute_error_scorer", + "sklearn.metrics.scorer.neg_mean_squared_error_scorer", "sklearn.metrics.scorer.neg_mean_squared_log_error_scorer", + "sklearn.metrics.scorer.neg_median_absolute_error_scorer", "sklearn.metrics.scorer.normalized_mutual_info_scorer", + "sklearn.metrics.scorer.precision_scorer", "sklearn.metrics.scorer.qualified_name", + "sklearn.metrics.scorer.r2_scorer", "sklearn.metrics.scorer.recall_scorer", + "sklearn.metrics.scorer.roc_auc_scorer", "sklearn.metrics.scorer.v_measure_scorer", + "sklearn.metrics.silhouette_samples", "sklearn.metrics.silhouette_score", + "sklearn.metrics.v_measure_score", "sklearn.metrics.zero_one_loss", + "sklearn.model_selection.BaseCrossValidator", "sklearn.model_selection.GridSearchCV", + "sklearn.model_selection.GroupKFold", "sklearn.model_selection.GroupShuffleSplit", + "sklearn.model_selection.KFold", "sklearn.model_selection.LeaveOneGroupOut", + "sklearn.model_selection.LeaveOneOut", "sklearn.model_selection.LeavePGroupsOut", + "sklearn.model_selection.LeavePOut", "sklearn.model_selection.ParameterGrid", + "sklearn.model_selection.ParameterSampler", "sklearn.model_selection.PredefinedSplit", + "sklearn.model_selection.RandomizedSearchCV", "sklearn.model_selection.RepeatedKFold", + "sklearn.model_selection.RepeatedStratifiedKFold", "sklearn.model_selection.ShuffleSplit", + "sklearn.model_selection.StratifiedKFold", "sklearn.model_selection.StratifiedShuffleSplit", + "sklearn.model_selection.TimeSeriesSplit", "sklearn.model_selection._search.BaseSearchCV", + "sklearn.model_selection._search.GridSearchCV", "sklearn.model_selection._search.ParameterGrid", + "sklearn.model_selection._search.ParameterSampler", "sklearn.model_selection._search.RandomizedSearchCV", + "sklearn.model_selection._search._CVScoreTuple", "sklearn.model_selection._search._check_param_grid", + "sklearn.model_selection._search.fit_grid_point", "sklearn.model_selection._search.sp_version", + "sklearn.model_selection._split.BaseCrossValidator", "sklearn.model_selection._split.BaseShuffleSplit", + "sklearn.model_selection._split.GroupKFold", "sklearn.model_selection._split.GroupShuffleSplit", + "sklearn.model_selection._split.KFold", "sklearn.model_selection._split.LeaveOneGroupOut", + "sklearn.model_selection._split.LeaveOneOut", "sklearn.model_selection._split.LeavePGroupsOut", + "sklearn.model_selection._split.LeavePOut", "sklearn.model_selection._split.PredefinedSplit", + "sklearn.model_selection._split.RepeatedKFold", "sklearn.model_selection._split.RepeatedStratifiedKFold", + "sklearn.model_selection._split.ShuffleSplit", "sklearn.model_selection._split.StratifiedKFold", + "sklearn.model_selection._split.StratifiedShuffleSplit", "sklearn.model_selection._split.TimeSeriesSplit", + "sklearn.model_selection._split._BaseKFold", "sklearn.model_selection._split._CVIterableWrapper", + "sklearn.model_selection._split._RepeatedSplits", "sklearn.model_selection._split._approximate_mode", + "sklearn.model_selection._split._build_repr", "sklearn.model_selection._split._validate_shuffle_split", + "sklearn.model_selection._split._validate_shuffle_split_init", "sklearn.model_selection._split.check_cv", + "sklearn.model_selection._split.train_test_split", "sklearn.model_selection._validation._aggregate_score_dicts", + "sklearn.model_selection._validation._check_is_permutation", "sklearn.model_selection._validation._fit_and_predict", + "sklearn.model_selection._validation._fit_and_score", "sklearn.model_selection._validation._incremental_fit_estimator", + "sklearn.model_selection._validation._index_param_value", "sklearn.model_selection._validation._multimetric_score", + "sklearn.model_selection._validation._permutation_test_score", "sklearn.model_selection._validation._score", + "sklearn.model_selection._validation._shuffle", "sklearn.model_selection._validation._translate_train_sizes", + "sklearn.model_selection._validation.cross_val_predict", "sklearn.model_selection._validation.cross_val_score", + "sklearn.model_selection._validation.cross_validate", "sklearn.model_selection._validation.learning_curve", + "sklearn.model_selection._validation.permutation_test_score", "sklearn.model_selection._validation.validation_curve", + "sklearn.model_selection.check_cv", "sklearn.model_selection.cross_val_predict", + "sklearn.model_selection.cross_val_score", "sklearn.model_selection.cross_validate", + "sklearn.model_selection.fit_grid_point", "sklearn.model_selection.learning_curve", + "sklearn.model_selection.permutation_test_score", "sklearn.model_selection.train_test_split", + "sklearn.model_selection.validation_curve", "sklearn.multiclass.OneVsOneClassifier", + "sklearn.multiclass.OneVsRestClassifier", "sklearn.multiclass.OutputCodeClassifier", + "sklearn.multiclass._ConstantPredictor", "sklearn.multiclass._check_estimator", + "sklearn.multiclass._fit_binary", "sklearn.multiclass._fit_ovo_binary", + "sklearn.multiclass._partial_fit_binary", "sklearn.multiclass._partial_fit_ovo_binary", + "sklearn.multiclass._predict_binary", "sklearn.naive_bayes.BaseDiscreteNB", + "sklearn.naive_bayes.BaseNB", "sklearn.naive_bayes.BernoulliNB", + "sklearn.naive_bayes.GaussianNB", "sklearn.naive_bayes.MultinomialNB", + "sklearn.naive_bayes._ALPHA_MIN", "sklearn.neighbors.BallTree", + "sklearn.neighbors.DistanceMetric", "sklearn.neighbors.KDTree", + "sklearn.neighbors.KNeighborsClassifier", "sklearn.neighbors.KNeighborsRegressor", + "sklearn.neighbors.KernelDensity", "sklearn.neighbors.LSHForest", + "sklearn.neighbors.LocalOutlierFactor", "sklearn.neighbors.NearestCentroid", + "sklearn.neighbors.NearestNeighbors", "sklearn.neighbors.RadiusNeighborsClassifier", + "sklearn.neighbors.RadiusNeighborsRegressor", "sklearn.neighbors.approximate.GaussianRandomProjectionHash", + "sklearn.neighbors.approximate.HASH_DTYPE", "sklearn.neighbors.approximate.LSHForest", + "sklearn.neighbors.approximate.MAX_HASH_SIZE", "sklearn.neighbors.approximate.ProjectionToHashMixin", + "sklearn.neighbors.approximate._array_of_arrays", "sklearn.neighbors.approximate._find_longest_prefix_match", + "sklearn.neighbors.approximate._find_matching_indices", "sklearn.neighbors.ball_tree.BallTree", + "sklearn.neighbors.ball_tree.BinaryTree", "sklearn.neighbors.ball_tree.CLASS_DOC", + "sklearn.neighbors.ball_tree.DOC_DICT", "sklearn.neighbors.ball_tree.NeighborsHeap", + "sklearn.neighbors.ball_tree.NodeData", "sklearn.neighbors.ball_tree.NodeHeap", + "sklearn.neighbors.ball_tree.NodeHeapData", "sklearn.neighbors.ball_tree.VALID_METRICS", + "sklearn.neighbors.ball_tree.VALID_METRIC_IDS", "sklearn.neighbors.ball_tree.kernel_norm", + "sklearn.neighbors.ball_tree.load_heap", "sklearn.neighbors.ball_tree.newObj", + "sklearn.neighbors.ball_tree.nodeheap_sort", "sklearn.neighbors.ball_tree.offsets", + "sklearn.neighbors.ball_tree.simultaneous_sort", "sklearn.neighbors.base.KNeighborsMixin", + "sklearn.neighbors.base.NeighborsBase", "sklearn.neighbors.base.PAIRWISE_DISTANCE_FUNCTIONS", + "sklearn.neighbors.base.RadiusNeighborsMixin", "sklearn.neighbors.base.SupervisedFloatMixin", + "sklearn.neighbors.base.SupervisedIntegerMixin", "sklearn.neighbors.base.UnsupervisedMixin", + "sklearn.neighbors.base.VALID_METRICS", "sklearn.neighbors.base.VALID_METRICS_SPARSE", + "sklearn.neighbors.base._check_weights", "sklearn.neighbors.base._get_weights", + "sklearn.neighbors.classification.KNeighborsClassifier", "sklearn.neighbors.classification.RadiusNeighborsClassifier", + "sklearn.neighbors.dist_metrics.BrayCurtisDistance", "sklearn.neighbors.dist_metrics.CanberraDistance", + "sklearn.neighbors.dist_metrics.ChebyshevDistance", "sklearn.neighbors.dist_metrics.DiceDistance", + "sklearn.neighbors.dist_metrics.DistanceMetric", "sklearn.neighbors.dist_metrics.EuclideanDistance", + "sklearn.neighbors.dist_metrics.HammingDistance", "sklearn.neighbors.dist_metrics.HaversineDistance", + "sklearn.neighbors.dist_metrics.JaccardDistance", "sklearn.neighbors.dist_metrics.KulsinskiDistance", + "sklearn.neighbors.dist_metrics.METRIC_MAPPING", "sklearn.neighbors.dist_metrics.MahalanobisDistance", + "sklearn.neighbors.dist_metrics.ManhattanDistance", "sklearn.neighbors.dist_metrics.MatchingDistance", + "sklearn.neighbors.dist_metrics.MinkowskiDistance", "sklearn.neighbors.dist_metrics.PyFuncDistance", + "sklearn.neighbors.dist_metrics.RogersTanimotoDistance", "sklearn.neighbors.dist_metrics.RussellRaoDistance", + "sklearn.neighbors.dist_metrics.SEuclideanDistance", "sklearn.neighbors.dist_metrics.SokalMichenerDistance", + "sklearn.neighbors.dist_metrics.SokalSneathDistance", "sklearn.neighbors.dist_metrics.WMinkowskiDistance", + "sklearn.neighbors.dist_metrics.get_valid_metric_ids", "sklearn.neighbors.dist_metrics.newObj", + "sklearn.neighbors.graph._check_params", "sklearn.neighbors.graph._query_include_self", + "sklearn.neighbors.graph.kneighbors_graph", "sklearn.neighbors.graph.radius_neighbors_graph", + "sklearn.neighbors.kd_tree.BinaryTree", "sklearn.neighbors.kd_tree.CLASS_DOC", + "sklearn.neighbors.kd_tree.DOC_DICT", "sklearn.neighbors.kd_tree.KDTree", + "sklearn.neighbors.kd_tree.NeighborsHeap", "sklearn.neighbors.kd_tree.NodeData", + "sklearn.neighbors.kd_tree.NodeHeap", "sklearn.neighbors.kd_tree.NodeHeapData", + "sklearn.neighbors.kd_tree.VALID_METRICS", "sklearn.neighbors.kd_tree.VALID_METRIC_IDS", + "sklearn.neighbors.kd_tree.kernel_norm", "sklearn.neighbors.kd_tree.load_heap", + "sklearn.neighbors.kd_tree.newObj", "sklearn.neighbors.kd_tree.nodeheap_sort", + "sklearn.neighbors.kd_tree.offsets", "sklearn.neighbors.kd_tree.simultaneous_sort", + "sklearn.neighbors.kde.KernelDensity", "sklearn.neighbors.kde.TREE_DICT", + "sklearn.neighbors.kde.VALID_KERNELS", "sklearn.neighbors.kde.gammainc", + "sklearn.neighbors.kneighbors_graph", "sklearn.neighbors.lof.LocalOutlierFactor", + "sklearn.neighbors.nearest_centroid.NearestCentroid", "sklearn.neighbors.quad_tree.CELL_DTYPE", + "sklearn.neighbors.quad_tree._QuadTree", "sklearn.neighbors.radius_neighbors_graph", + "sklearn.neighbors.regression.KNeighborsRegressor", "sklearn.neighbors.regression.RadiusNeighborsRegressor", + "sklearn.neighbors.unsupervised.NearestNeighbors", "sklearn.pipeline.FeatureUnion", + "sklearn.pipeline.Pipeline", "sklearn.pipeline._fit_one_transformer", + "sklearn.pipeline._fit_transform_one", "sklearn.pipeline._name_estimators", + "sklearn.pipeline._transform_one", "sklearn.pipeline.make_pipeline", + "sklearn.pipeline.make_union", "sklearn.preprocessing.Binarizer", + "sklearn.preprocessing.FunctionTransformer", "sklearn.preprocessing.Imputer", + "sklearn.preprocessing.KernelCenterer", "sklearn.preprocessing.LabelBinarizer", + "sklearn.preprocessing.LabelEncoder", "sklearn.preprocessing.MaxAbsScaler", + "sklearn.preprocessing.MinMaxScaler", "sklearn.preprocessing.MultiLabelBinarizer", + "sklearn.preprocessing.Normalizer", "sklearn.preprocessing.OneHotEncoder", + "sklearn.preprocessing.PolynomialFeatures", "sklearn.preprocessing.QuantileTransformer", + "sklearn.preprocessing.RobustScaler", "sklearn.preprocessing.StandardScaler", + "sklearn.preprocessing._function_transformer.FunctionTransformer", "sklearn.preprocessing._function_transformer._identity", + "sklearn.preprocessing._function_transformer.string_types", "sklearn.preprocessing.add_dummy_feature", + "sklearn.preprocessing.binarize", "sklearn.preprocessing.data.BOUNDS_THRESHOLD", + "sklearn.preprocessing.data.Binarizer", "sklearn.preprocessing.data.FLOAT_DTYPES", + "sklearn.preprocessing.data.KernelCenterer", "sklearn.preprocessing.data.MaxAbsScaler", + "sklearn.preprocessing.data.MinMaxScaler", "sklearn.preprocessing.data.Normalizer", + "sklearn.preprocessing.data.OneHotEncoder", "sklearn.preprocessing.data.PolynomialFeatures", + "sklearn.preprocessing.data.QuantileTransformer", "sklearn.preprocessing.data.RobustScaler", + "sklearn.preprocessing.data.StandardScaler", "sklearn.preprocessing.data._handle_zeros_in_scale", + "sklearn.preprocessing.data._transform_selected", "sklearn.preprocessing.data.add_dummy_feature", + "sklearn.preprocessing.data.binarize", "sklearn.preprocessing.data.maxabs_scale", + "sklearn.preprocessing.data.minmax_scale", "sklearn.preprocessing.data.normalize", + "sklearn.preprocessing.data.quantile_transform", "sklearn.preprocessing.data.robust_scale", + "sklearn.preprocessing.data.scale", "sklearn.preprocessing.data.string_types", + "sklearn.preprocessing.imputation.FLOAT_DTYPES", "sklearn.preprocessing.imputation.Imputer", + "sklearn.preprocessing.imputation._get_mask", "sklearn.preprocessing.imputation._most_frequent", + "sklearn.preprocessing.label.LabelBinarizer", "sklearn.preprocessing.label.LabelEncoder", + "sklearn.preprocessing.label.MultiLabelBinarizer", "sklearn.preprocessing.label._inverse_binarize_multiclass", + "sklearn.preprocessing.label._inverse_binarize_thresholding", "sklearn.preprocessing.label.label_binarize", + "sklearn.preprocessing.label_binarize", "sklearn.preprocessing.maxabs_scale", + "sklearn.preprocessing.minmax_scale", "sklearn.preprocessing.normalize", + "sklearn.preprocessing.quantile_transform", "sklearn.preprocessing.robust_scale", + "sklearn.preprocessing.scale", "sklearn.random_projection.BaseRandomProjection", + "sklearn.random_projection.GaussianRandomProjection", "sklearn.random_projection.SparseRandomProjection", + "sklearn.random_projection._check_density", "sklearn.random_projection._check_input_size", + "sklearn.random_projection.gaussian_random_matrix", "sklearn.random_projection.johnson_lindenstrauss_min_dim", + "sklearn.random_projection.sparse_random_matrix", "sklearn.set_config", + "sklearn.setup_module", "sklearn.svm.LinearSVC", + "sklearn.svm.LinearSVR", "sklearn.svm.NuSVC", + "sklearn.svm.NuSVR", "sklearn.svm.OneClassSVM", + "sklearn.svm.SVC", "sklearn.svm.SVR", + "sklearn.svm.base.BaseLibSVM", "sklearn.svm.base.BaseSVC", + "sklearn.svm.base.LIBSVM_IMPL", "sklearn.svm.base._fit_liblinear", + "sklearn.svm.base._get_liblinear_solver_type", "sklearn.svm.base._one_vs_one_coef", + "sklearn.svm.bounds.l1_min_c", "sklearn.svm.classes.LinearSVC", + "sklearn.svm.classes.LinearSVR", "sklearn.svm.classes.NuSVC", + "sklearn.svm.classes.NuSVR", "sklearn.svm.classes.OneClassSVM", + "sklearn.svm.classes.SVC", "sklearn.svm.classes.SVR", + "sklearn.svm.l1_min_c", "sklearn.svm.liblinear.set_verbosity_wrap", + "sklearn.svm.liblinear.train_wrap", "sklearn.svm.libsvm.LIBSVM_KERNEL_TYPES", + "sklearn.svm.libsvm.cross_validation", "sklearn.svm.libsvm.decision_function", + "sklearn.svm.libsvm.fit", "sklearn.svm.libsvm.predict", + "sklearn.svm.libsvm.predict_proba", "sklearn.svm.libsvm.set_verbosity_wrap", + "sklearn.svm.libsvm_sparse.libsvm_sparse_decision_function", "sklearn.svm.libsvm_sparse.libsvm_sparse_predict", + "sklearn.svm.libsvm_sparse.libsvm_sparse_predict_proba", "sklearn.svm.libsvm_sparse.libsvm_sparse_train", + "sklearn.svm.libsvm_sparse.set_verbosity_wrap", "sklearn.tree.DecisionTreeClassifier", + "sklearn.tree.DecisionTreeRegressor", "sklearn.tree.ExtraTreeClassifier", + "sklearn.tree.ExtraTreeRegressor", "sklearn.tree._criterion.ClassificationCriterion", + "sklearn.tree._criterion.Criterion", "sklearn.tree._criterion.Entropy", + "sklearn.tree._criterion.FriedmanMSE", "sklearn.tree._criterion.Gini", + "sklearn.tree._criterion.MAE", "sklearn.tree._criterion.MSE", + "sklearn.tree._criterion.RegressionCriterion", "sklearn.tree._splitter.BaseDenseSplitter", + "sklearn.tree._splitter.BaseSparseSplitter", "sklearn.tree._splitter.BestSparseSplitter", + "sklearn.tree._splitter.BestSplitter", "sklearn.tree._splitter.RandomSparseSplitter", + "sklearn.tree._splitter.RandomSplitter", "sklearn.tree._splitter.Splitter", + "sklearn.tree._tree.BestFirstTreeBuilder", "sklearn.tree._tree.DepthFirstTreeBuilder", + "sklearn.tree._tree.NODE_DTYPE", "sklearn.tree._tree.TREE_LEAF", + "sklearn.tree._tree.TREE_UNDEFINED", "sklearn.tree._tree.Tree", + "sklearn.tree._tree.TreeBuilder", "sklearn.tree._utils.PriorityHeap", + "sklearn.tree._utils.Stack", "sklearn.tree._utils.WeightedMedianCalculator", + "sklearn.tree._utils.WeightedPQueue", "sklearn.tree._utils._realloc_test", + "sklearn.tree.export.SENTINEL", "sklearn.tree.export.Sentinel", + "sklearn.tree.export._color_brew", "sklearn.tree.export.export_graphviz", + "sklearn.tree.export_graphviz", "sklearn.tree.tree.BaseDecisionTree", + "sklearn.tree.tree.CRITERIA_CLF", "sklearn.tree.tree.CRITERIA_REG", + "sklearn.tree.tree.DENSE_SPLITTERS", "sklearn.tree.tree.DecisionTreeClassifier", + "sklearn.tree.tree.DecisionTreeRegressor", "sklearn.tree.tree.ExtraTreeClassifier", + "sklearn.tree.tree.ExtraTreeRegressor", "sklearn.tree.tree.SPARSE_SPLITTERS", + "sklearn.utils.Bunch", "sklearn.utils._get_n_jobs", + "sklearn.utils._logistic_sigmoid._log_logistic_sigmoid", "sklearn.utils._random._sample_without_replacement_check_input", + "sklearn.utils._random._sample_without_replacement_with_pool", "sklearn.utils._random._sample_without_replacement_with_reservoir_sampling", + "sklearn.utils._random._sample_without_replacement_with_tracking_selection", "sklearn.utils._random.sample_without_replacement", + "sklearn.utils.arrayfuncs.cholesky_delete", "sklearn.utils.arrayfuncs.min_pos", + "sklearn.utils.as_float_array", "sklearn.utils.assert_all_finite", + "sklearn.utils.axis0_safe_slice", "sklearn.utils.check_X_y", + "sklearn.utils.check_array", "sklearn.utils.check_consistent_length", + "sklearn.utils.check_random_state", "sklearn.utils.check_symmetric", + "sklearn.utils.class_weight.compute_class_weight", "sklearn.utils.class_weight.compute_sample_weight", + "sklearn.utils.column_or_1d", "sklearn.utils.compute_class_weight", + "sklearn.utils.compute_sample_weight", "sklearn.utils.deprecated", + "sklearn.utils.deprecation.DeprecationDict", "sklearn.utils.deprecation._is_deprecated", + "sklearn.utils.deprecation.deprecated", "sklearn.utils.extmath._deterministic_vector_sign_flip", + "sklearn.utils.extmath._impose_f_order", "sklearn.utils.extmath._incremental_mean_and_var", + "sklearn.utils.extmath.cartesian", "sklearn.utils.extmath.density", + "sklearn.utils.extmath.fast_dot", "sklearn.utils.extmath.fast_logdet", + "sklearn.utils.extmath.log_logistic", "sklearn.utils.extmath.logsumexp", + "sklearn.utils.extmath.make_nonnegative", "sklearn.utils.extmath.norm", + "sklearn.utils.extmath.np_version", "sklearn.utils.extmath.pinvh", + "sklearn.utils.extmath.randomized_range_finder", "sklearn.utils.extmath.randomized_svd", + "sklearn.utils.extmath.row_norms", "sklearn.utils.extmath.safe_min", + "sklearn.utils.extmath.safe_sparse_dot", "sklearn.utils.extmath.softmax", + "sklearn.utils.extmath.squared_norm", "sklearn.utils.extmath.stable_cumsum", + "sklearn.utils.extmath.svd_flip", "sklearn.utils.extmath.weighted_mode", + "sklearn.utils.fast_dict.IntFloatDict", "sklearn.utils.fast_dict.argmin", + "sklearn.utils.fixes._parse_version", "sklearn.utils.fixes.divide", + "sklearn.utils.fixes.euler_gamma", "sklearn.utils.fixes.makedirs", + "sklearn.utils.fixes.np_version", "sklearn.utils.fixes.parallel_helper", + "sklearn.utils.fixes.sp_version", "sklearn.utils.fixes.sparse_min_max", + "sklearn.utils.gen_batches", "sklearn.utils.gen_even_slices", + "sklearn.utils.graph.connected_components", "sklearn.utils.graph.graph_laplacian", + "sklearn.utils.graph.graph_shortest_path", "sklearn.utils.graph.single_source_shortest_path_length", + "sklearn.utils.graph_shortest_path.graph_shortest_path", "sklearn.utils.indexable", + "sklearn.utils.indices_to_mask", "sklearn.utils.linear_assignment_._HungarianState", + "sklearn.utils.linear_assignment_._hungarian", "sklearn.utils.linear_assignment_._step1", + "sklearn.utils.linear_assignment_._step3", "sklearn.utils.linear_assignment_._step4", + "sklearn.utils.linear_assignment_._step5", "sklearn.utils.linear_assignment_._step6", + "sklearn.utils.linear_assignment_.linear_assignment", "sklearn.utils.metaestimators._BaseComposition", + "sklearn.utils.metaestimators._IffHasAttrDescriptor", "sklearn.utils.metaestimators._safe_split", + "sklearn.utils.metaestimators.if_delegate_has_method", "sklearn.utils.multiclass._FN_UNIQUE_LABELS", + "sklearn.utils.multiclass._check_partial_fit_first_call", "sklearn.utils.multiclass._is_integral_float", + "sklearn.utils.multiclass._ovr_decision_function", "sklearn.utils.multiclass._unique_indicator", + "sklearn.utils.multiclass._unique_multiclass", "sklearn.utils.multiclass.check_classification_targets", + "sklearn.utils.multiclass.class_distribution", "sklearn.utils.multiclass.is_multilabel", + "sklearn.utils.multiclass.string_types", "sklearn.utils.multiclass.type_of_target", + "sklearn.utils.multiclass.unique_labels", "sklearn.utils.murmurhash.murmurhash3_32", + "sklearn.utils.murmurhash.murmurhash3_bytes_array_s32", "sklearn.utils.murmurhash.murmurhash3_bytes_array_u32", + "sklearn.utils.murmurhash.murmurhash3_bytes_s32", "sklearn.utils.murmurhash.murmurhash3_bytes_u32", + "sklearn.utils.murmurhash.murmurhash3_int_s32", "sklearn.utils.murmurhash.murmurhash3_int_u32", + "sklearn.utils.murmurhash3_32", "sklearn.utils.optimize._LineSearchError", + "sklearn.utils.optimize._cg", "sklearn.utils.optimize._line_search_wolfe12", + "sklearn.utils.optimize.newton_cg", "sklearn.utils.random.choice", + "sklearn.utils.random.random_choice_csc", "sklearn.utils.resample", + "sklearn.utils.safe_indexing", "sklearn.utils.safe_mask", + "sklearn.utils.safe_sqr", "sklearn.utils.seq_dataset.ArrayDataset", + "sklearn.utils.seq_dataset.CSRDataset", "sklearn.utils.seq_dataset.SequentialDataset", + "sklearn.utils.shuffle", "sklearn.utils.sparsefuncs._csc_mean_var_axis0", + "sklearn.utils.sparsefuncs._csr_mean_var_axis0", "sklearn.utils.sparsefuncs._get_elem_at_rank", + "sklearn.utils.sparsefuncs._get_median", "sklearn.utils.sparsefuncs._incr_mean_var_axis0", + "sklearn.utils.sparsefuncs._raise_error_wrong_axis", "sklearn.utils.sparsefuncs._raise_typeerror", + "sklearn.utils.sparsefuncs.count_nonzero", "sklearn.utils.sparsefuncs.csc_median_axis_0", + "sklearn.utils.sparsefuncs.incr_mean_variance_axis", "sklearn.utils.sparsefuncs.inplace_column_scale", + "sklearn.utils.sparsefuncs.inplace_csr_column_scale", "sklearn.utils.sparsefuncs.inplace_csr_row_scale", + "sklearn.utils.sparsefuncs.inplace_row_scale", "sklearn.utils.sparsefuncs.inplace_swap_column", + "sklearn.utils.sparsefuncs.inplace_swap_row", "sklearn.utils.sparsefuncs.inplace_swap_row_csc", + "sklearn.utils.sparsefuncs.inplace_swap_row_csr", "sklearn.utils.sparsefuncs.mean_variance_axis", + "sklearn.utils.sparsefuncs.min_max_axis", "sklearn.utils.sparsefuncs_fast._csc_mean_variance_axis0", + "sklearn.utils.sparsefuncs_fast._csr_mean_variance_axis0", "sklearn.utils.sparsefuncs_fast._csr_row_norms", + "sklearn.utils.sparsefuncs_fast._incr_mean_variance_axis0", "sklearn.utils.sparsefuncs_fast._inplace_csr_row_normalize_l1", + "sklearn.utils.sparsefuncs_fast._inplace_csr_row_normalize_l2", "sklearn.utils.sparsefuncs_fast.assign_rows_csr", + "sklearn.utils.sparsefuncs_fast.csc_mean_variance_axis0", "sklearn.utils.sparsefuncs_fast.csr_mean_variance_axis0", + "sklearn.utils.sparsefuncs_fast.csr_row_norms", "sklearn.utils.sparsefuncs_fast.incr_mean_variance_axis0", + "sklearn.utils.sparsefuncs_fast.inplace_csr_row_normalize_l1", "sklearn.utils.sparsefuncs_fast.inplace_csr_row_normalize_l2", + "sklearn.utils.stats._weighted_percentile", "sklearn.utils.stats.rankdata", + "sklearn.utils.tosequence", "sklearn.utils.validation.FLOAT_DTYPES", + "sklearn.utils.validation._assert_all_finite", "sklearn.utils.validation._ensure_sparse_format", + "sklearn.utils.validation._is_arraylike", "sklearn.utils.validation._num_samples", + "sklearn.utils.validation._shape_repr", "sklearn.utils.validation.as_float_array", + "sklearn.utils.validation.assert_all_finite", "sklearn.utils.validation.check_X_y", + "sklearn.utils.validation.check_array", "sklearn.utils.validation.check_consistent_length", + "sklearn.utils.validation.check_is_fitted", "sklearn.utils.validation.check_memory", + "sklearn.utils.validation.check_non_negative", "sklearn.utils.validation.check_random_state", + "sklearn.utils.validation.check_symmetric", "sklearn.utils.validation.column_or_1d", + "sklearn.utils.validation.has_fit_parameter", "sklearn.utils.validation.indexable", + "sklearn.utils.weight_vector.WeightVector" +], + + "SKR_NAMES": [ + "skrebate.MultiSURF", "skrebate.MultiSURFstar", + "skrebate.ReliefF", "skrebate.SURF", + "skrebate.SURFstar", "skrebate.TuRF", + "skrebate.multisurf.MultiSURF", "skrebate.multisurfstar.MultiSURFstar", + "skrebate.relieff.ReliefF", "skrebate.scoring_utils.MultiSURF_compute_scores", + "skrebate.scoring_utils.MultiSURFstar_compute_scores", "skrebate.scoring_utils.ReliefF_compute_scores", + "skrebate.scoring_utils.SURF_compute_scores", "skrebate.scoring_utils.SURFstar_compute_scores", + "skrebate.scoring_utils.compute_score", "skrebate.scoring_utils.get_row_missing", + "skrebate.scoring_utils.ramp_function", "skrebate.surf.SURF", + "skrebate.surfstar.SURFstar", "skrebate.turf.TuRF" + ], + + "XGB_NAMES": [ + "xgboost.Booster", "xgboost.DMatrix", + "xgboost.VERSION_FILE", "xgboost.XGBClassifier", + "xgboost.XGBModel", "xgboost.XGBRegressor", + "xgboost.callback._fmt_metric", "xgboost.callback._get_callback_context", + "xgboost.callback.early_stop", "xgboost.callback.print_evaluation", + "xgboost.callback.record_evaluation", "xgboost.callback.reset_learning_rate", + "xgboost.compat.PANDAS_INSTALLED", "xgboost.compat.PY3", + "xgboost.compat.SKLEARN_INSTALLED", "xgboost.compat.STRING_TYPES", + "xgboost.compat.py_str", "xgboost.core.Booster", + "xgboost.core.CallbackEnv", "xgboost.core.DMatrix", + "xgboost.core.EarlyStopException", "xgboost.core.PANDAS_DTYPE_MAPPER", + "xgboost.core.PANDAS_INSTALLED", "xgboost.core.PY3", + "xgboost.core.STRING_TYPES", "xgboost.core.XGBoostError", + "xgboost.core._check_call", "xgboost.core._load_lib", + "xgboost.core._maybe_pandas_data", "xgboost.core._maybe_pandas_label", + "xgboost.core.c_array", "xgboost.core.c_str", + "xgboost.core.ctypes2buffer", "xgboost.core.ctypes2numpy", + "xgboost.core.from_cstr_to_pystr", "xgboost.core.from_pystr_to_cstr", + "xgboost.cv", "xgboost.f", + "xgboost.libpath.XGBoostLibraryNotFound", "xgboost.libpath.find_lib_path", + "xgboost.plot_importance", "xgboost.plot_tree", + "xgboost.plotting._EDGEPAT", "xgboost.plotting._EDGEPAT2", + "xgboost.plotting._LEAFPAT", "xgboost.plotting._NODEPAT", + "xgboost.plotting._parse_edge", "xgboost.plotting._parse_node", + "xgboost.plotting.plot_importance", "xgboost.plotting.plot_tree", + "xgboost.plotting.to_graphviz", "xgboost.rabit.DTYPE_ENUM__", + "xgboost.rabit.STRING_TYPES", "xgboost.rabit._init_rabit", + "xgboost.rabit.allreduce", "xgboost.rabit.broadcast", + "xgboost.rabit.finalize", "xgboost.rabit.get_processor_name", + "xgboost.rabit.get_rank", "xgboost.rabit.get_world_size", + "xgboost.rabit.init", "xgboost.rabit.tracker_print", + "xgboost.rabit.version_number", "xgboost.sklearn.SKLEARN_INSTALLED", + "xgboost.sklearn.XGBClassifier", "xgboost.sklearn.XGBModel", + "xgboost.sklearn.XGBRegressor", "xgboost.sklearn._objective_decorator", + "xgboost.to_graphviz", "xgboost.train", + "xgboost.training.CVPack", "xgboost.training.SKLEARN_INSTALLED", + "xgboost.training.STRING_TYPES", "xgboost.training._train_internal", + "xgboost.training.aggcv", "xgboost.training.cv", + "xgboost.training.mknfold", "xgboost.training.train" + ], + + + "NUMPY_NAMES": [ + "numpy.core.multiarray._reconstruct", "numpy.ndarray", + "numpy.dtype", "numpy.core.multiarray.scalar", "numpy.random.__RandomState_ctor", + "numpy.ma.core._mareconstruct", "numpy.ma.core.MaskedArray" + ], + + "IMBLEARN_NAMES":[ + "imblearn.pipeline.Pipeline", "imblearn.over_sampling._random_over_sampler.RandomOverSampler", + "imblearn.under_sampling._prototype_selection._edited_nearest_neighbours.EditedNearestNeighbours" + ], + + "MLXTEND_NAMES":[ + "mlxtend.classifier.stacking_cv_classification.StackingCVClassifier", + "mlxtend.classifier.stacking_classification.StackingClassifier", + "mlxtend.regressor.stacking_cv_regression.StackingCVRegressor", + "mlxtend.regressor.stacking_regression.StackingRegressor" + ] +} \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/preprocessors.py Tue May 14 18:06:37 2019 -0400 @@ -0,0 +1,184 @@ +""" +Z_RandomOverSampler +""" + +import imblearn +import numpy as np + +from collections import Counter +from imblearn.over_sampling.base import BaseOverSampler +from imblearn.over_sampling import RandomOverSampler +from imblearn.pipeline import Pipeline as imbPipeline +from imblearn.utils import check_target_type +from scipy import sparse +from sklearn.base import BaseEstimator, TransformerMixin +from sklearn.preprocessing.data import _handle_zeros_in_scale +from sklearn.utils import check_array, safe_indexing +from sklearn.utils.fixes import nanpercentile +from sklearn.utils.validation import (check_is_fitted, check_X_y, + FLOAT_DTYPES) + + +class Z_RandomOverSampler(BaseOverSampler): + + def __init__(self, sampling_strategy='auto', + return_indices=False, + random_state=None, + ratio=None, + negative_thres=0, + positive_thres=-1): + super(Z_RandomOverSampler, self).__init__( + sampling_strategy=sampling_strategy, ratio=ratio) + self.random_state = random_state + self.return_indices = return_indices + self.negative_thres = negative_thres + self.positive_thres = positive_thres + + @staticmethod + def _check_X_y(X, y): + y, binarize_y = check_target_type(y, indicate_one_vs_all=True) + X, y = check_X_y(X, y, accept_sparse=['csr', 'csc'], dtype=None) + return X, y, binarize_y + + def _fit_resample(self, X, y): + n_samples = X.shape[0] + + # convert y to z_score + y_z = (y - y.mean()) / y.std() + + index0 = np.arange(n_samples) + index_negative = index0[y_z > self.negative_thres] + index_positive = index0[y_z <= self.positive_thres] + index_unclassified = [x for x in index0 + if x not in index_negative + and x not in index_positive] + + y_z[index_negative] = 0 + y_z[index_positive] = 1 + y_z[index_unclassified] = -1 + + ros = RandomOverSampler( + sampling_strategy=self.sampling_strategy, + random_state=self.random_state, + ratio=self.ratio) + _, _ = ros.fit_resample(X, y_z) + sample_indices = ros.sample_indices_ + + print("Before sampler: %s. Total after: %s" + % (Counter(y_z), sample_indices.shape)) + + self.sample_indices_ = np.array(sample_indices) + + if self.return_indices: + return (safe_indexing(X, sample_indices), + safe_indexing(y, sample_indices), + sample_indices) + return (safe_indexing(X, sample_indices), + safe_indexing(y, sample_indices)) + + +def _get_quantiles(X, quantile_range): + """ + Calculate column percentiles for 2d array + + Parameters + ---------- + X : array-like, shape [n_samples, n_features] + """ + quantiles = [] + for feature_idx in range(X.shape[1]): + if sparse.issparse(X): + column_nnz_data = X.data[ + X.indptr[feature_idx]: X.indptr[feature_idx + 1]] + column_data = np.zeros(shape=X.shape[0], dtype=X.dtype) + column_data[:len(column_nnz_data)] = column_nnz_data + else: + column_data = X[:, feature_idx] + quantiles.append(nanpercentile(column_data, quantile_range)) + + quantiles = np.transpose(quantiles) + + return quantiles + + +class TDMScaler(BaseEstimator, TransformerMixin): + """ + Scale features using Training Distribution Matching (TDM) algorithm + + References + ---------- + .. [1] Thompson JA, Tan J and Greene CS (2016) Cross-platform + normalization of microarray and RNA-seq data for machine + learning applications. PeerJ 4, e1621. + """ + + def __init__(self, q_lower=25.0, q_upper=75.0, ): + self.q_lower = q_lower + self.q_upper = q_upper + + def fit(self, X, y=None): + """ + Parameters + ---------- + X : array-like, shape [n_samples, n_features] + """ + X = check_array(X, copy=True, estimator=self, dtype=FLOAT_DTYPES, + force_all_finite=True) + + if not 0 <= self.q_lower <= self.q_upper <= 100: + raise ValueError("Invalid quantile parameter values: " + "q_lower %s, q_upper: %s" + % (str(self.q_lower), str(self.q_upper))) + + # TODO sparse data + quantiles = nanpercentile(X, (self.q_lower, self.q_upper)) + iqr = quantiles[1] - quantiles[0] + + self.q_lower_ = quantiles[0] + self.q_upper_ = quantiles[1] + self.iqr_ = _handle_zeros_in_scale(iqr, copy=False) + + self.max_ = np.nanmax(X) + self.min_ = np.nanmin(X) + + return self + + def transform(self, X): + """ + Parameters + ---------- + X : {array-like, sparse matrix} + The data used to scale along the specified axis. + """ + check_is_fitted(self, 'iqr_', 'max_') + X = check_array(X, copy=True, estimator=self, dtype=FLOAT_DTYPES, + force_all_finite=True) + + # TODO sparse data + train_upper_scale = (self.max_ - self.q_upper_) / self.iqr_ + train_lower_scale = (self.q_lower_ - self.min_) / self.iqr_ + + test_quantiles = nanpercentile(X, (self.q_lower, self.q_upper)) + test_iqr = _handle_zeros_in_scale( + test_quantiles[1] - test_quantiles[0], copy=False) + + test_upper_bound = test_quantiles[1] + train_upper_scale * test_iqr + test_lower_bound = test_quantiles[0] - train_lower_scale * test_iqr + + test_min = np.nanmin(X) + if test_lower_bound < test_min: + test_lower_bound = test_min + + X[X > test_upper_bound] = test_upper_bound + X[X < test_lower_bound] = test_lower_bound + + X = (X - test_lower_bound) / (test_upper_bound - test_lower_bound)\ + * (self.max_ - self.min_) + self.min_ + + return X + + def inverse_transform(self, X): + """ + Scale the data back to the original state + """ + raise NotImplementedError("Inverse transformation is not implemented!")
--- a/search_model_validation.py Sun Dec 30 01:52:15 2018 -0500 +++ b/search_model_validation.py Tue May 14 18:06:37 2019 -0400 @@ -1,7 +1,8 @@ +import argparse +import collections import imblearn import json import numpy as np -import os import pandas import pickle import skrebate @@ -9,93 +10,124 @@ import sys import xgboost import warnings +import iraps_classifier +import model_validations +import preprocessors +import feature_selectors from imblearn import under_sampling, over_sampling, combine -from imblearn.pipeline import Pipeline as imbPipeline -from sklearn import (cluster, compose, decomposition, ensemble, feature_extraction, - feature_selection, gaussian_process, kernel_approximation, metrics, - model_selection, naive_bayes, neighbors, pipeline, preprocessing, - svm, linear_model, tree, discriminant_analysis) +from scipy.io import mmread +from mlxtend import classifier, regressor +from sklearn import (cluster, compose, decomposition, ensemble, + feature_extraction, feature_selection, + gaussian_process, kernel_approximation, metrics, + model_selection, naive_bayes, neighbors, + pipeline, preprocessing, svm, linear_model, + tree, discriminant_analysis) from sklearn.exceptions import FitFailedWarning from sklearn.externals import joblib -from utils import get_cv, get_scoring, get_X_y, load_model, read_columns, SafeEval +from sklearn.model_selection._validation import _score + +from utils import (SafeEval, get_cv, get_scoring, get_X_y, + load_model, read_columns) +from model_validations import train_test_split -N_JOBS = int(os.environ.get('GALAXY_SLOTS', 1)) +N_JOBS = int(__import__('os').environ.get('GALAXY_SLOTS', 1)) +CACHE_DIR = './cached' +NON_SEARCHABLE = ('n_jobs', 'pre_dispatch', 'memory', 'steps', + 'nthread', 'verbose') -def get_search_params(params_builder): +def _eval_search_params(params_builder): search_params = {} - safe_eval = SafeEval(load_scipy=True, load_numpy=True) - safe_eval_es = SafeEval(load_estimators=True) for p in params_builder['param_set']: - search_p = p['search_param_selector']['search_p'] - if search_p.strip() == '': + search_list = p['sp_list'].strip() + if search_list == '': continue - param_type = p['search_param_selector']['selected_param_type'] + + param_name = p['sp_name'] + if param_name.lower().endswith(NON_SEARCHABLE): + print("Warning: `%s` is not eligible for search and was " + "omitted!" % param_name) + continue - lst = search_p.split(':') - assert (len(lst) == 2), "Error, make sure there is one and only one colon in search parameter input." - literal = lst[1].strip() - param_name = lst[0].strip() - if param_name: - if param_name.lower() == 'n_jobs': - sys.exit("Parameter `%s` is invalid for search." %param_name) - elif not param_name.endswith('-'): - ev = safe_eval(literal) - if param_type == 'final_estimator_p': - search_params['estimator__' + param_name] = ev - else: - search_params['preprocessing_' + param_type[5:6] + '__' + param_name] = ev - else: - # only for estimator eval, add `-` to the end of param - #TODO maybe add regular express check - ev = safe_eval_es(literal) - for obj in ev: - if 'n_jobs' in obj.get_params(): - obj.set_params( n_jobs=N_JOBS ) - if param_type == 'final_estimator_p': - search_params['estimator__' + param_name[:-1]] = ev - else: - search_params['preprocessing_' + param_type[5:6] + '__' + param_name[:-1]] = ev - elif param_type != 'final_estimator_p': - #TODO regular express check ? - ev = safe_eval_es(literal) - preprocessors = [preprocessing.StandardScaler(), preprocessing.Binarizer(), preprocessing.Imputer(), - preprocessing.MaxAbsScaler(), preprocessing.Normalizer(), preprocessing.MinMaxScaler(), - preprocessing.PolynomialFeatures(),preprocessing.RobustScaler(), - feature_selection.SelectKBest(), feature_selection.GenericUnivariateSelect(), - feature_selection.SelectPercentile(), feature_selection.SelectFpr(), feature_selection.SelectFdr(), - feature_selection.SelectFwe(), feature_selection.VarianceThreshold(), - decomposition.FactorAnalysis(random_state=0), decomposition.FastICA(random_state=0), decomposition.IncrementalPCA(), - decomposition.KernelPCA(random_state=0, n_jobs=N_JOBS), decomposition.LatentDirichletAllocation(random_state=0, n_jobs=N_JOBS), - decomposition.MiniBatchDictionaryLearning(random_state=0, n_jobs=N_JOBS), - decomposition.MiniBatchSparsePCA(random_state=0, n_jobs=N_JOBS), decomposition.NMF(random_state=0), - decomposition.PCA(random_state=0), decomposition.SparsePCA(random_state=0, n_jobs=N_JOBS), - decomposition.TruncatedSVD(random_state=0), - kernel_approximation.Nystroem(random_state=0), kernel_approximation.RBFSampler(random_state=0), - kernel_approximation.AdditiveChi2Sampler(), kernel_approximation.SkewedChi2Sampler(random_state=0), - cluster.FeatureAgglomeration(), - skrebate.ReliefF(n_jobs=N_JOBS), skrebate.SURF(n_jobs=N_JOBS), skrebate.SURFstar(n_jobs=N_JOBS), - skrebate.MultiSURF(n_jobs=N_JOBS), skrebate.MultiSURFstar(n_jobs=N_JOBS), - imblearn.under_sampling.ClusterCentroids(random_state=0, n_jobs=N_JOBS), - imblearn.under_sampling.CondensedNearestNeighbour(random_state=0, n_jobs=N_JOBS), - imblearn.under_sampling.EditedNearestNeighbours(random_state=0, n_jobs=N_JOBS), - imblearn.under_sampling.RepeatedEditedNearestNeighbours(random_state=0, n_jobs=N_JOBS), - imblearn.under_sampling.AllKNN(random_state=0, n_jobs=N_JOBS), - imblearn.under_sampling.InstanceHardnessThreshold(random_state=0, n_jobs=N_JOBS), - imblearn.under_sampling.NearMiss(random_state=0, n_jobs=N_JOBS), - imblearn.under_sampling.NeighbourhoodCleaningRule(random_state=0, n_jobs=N_JOBS), - imblearn.under_sampling.OneSidedSelection(random_state=0, n_jobs=N_JOBS), - imblearn.under_sampling.RandomUnderSampler(random_state=0), - imblearn.under_sampling.TomekLinks(random_state=0, n_jobs=N_JOBS), - imblearn.over_sampling.ADASYN(random_state=0, n_jobs=N_JOBS), - imblearn.over_sampling.RandomOverSampler(random_state=0), - imblearn.over_sampling.SMOTE(random_state=0, n_jobs=N_JOBS), - imblearn.over_sampling.SVMSMOTE(random_state=0, n_jobs=N_JOBS), - imblearn.over_sampling.BorderlineSMOTE(random_state=0, n_jobs=N_JOBS), - imblearn.over_sampling.SMOTENC(categorical_features=[], random_state=0, n_jobs=N_JOBS), - imblearn.combine.SMOTEENN(random_state=0), imblearn.combine.SMOTETomek(random_state=0)] + if not search_list.startswith(':'): + safe_eval = SafeEval(load_scipy=True, load_numpy=True) + ev = safe_eval(search_list) + search_params[param_name] = ev + else: + # Have `:` before search list, asks for estimator evaluatio + safe_eval_es = SafeEval(load_estimators=True) + search_list = search_list[1:].strip() + # TODO maybe add regular express check + ev = safe_eval_es(search_list) + preprocessors = ( + preprocessing.StandardScaler(), preprocessing.Binarizer(), + preprocessing.Imputer(), preprocessing.MaxAbsScaler(), + preprocessing.Normalizer(), preprocessing.MinMaxScaler(), + preprocessing.PolynomialFeatures(), + preprocessing.RobustScaler(), feature_selection.SelectKBest(), + feature_selection.GenericUnivariateSelect(), + feature_selection.SelectPercentile(), + feature_selection.SelectFpr(), feature_selection.SelectFdr(), + feature_selection.SelectFwe(), + feature_selection.VarianceThreshold(), + decomposition.FactorAnalysis(random_state=0), + decomposition.FastICA(random_state=0), + decomposition.IncrementalPCA(), + decomposition.KernelPCA(random_state=0, n_jobs=N_JOBS), + decomposition.LatentDirichletAllocation( + random_state=0, n_jobs=N_JOBS), + decomposition.MiniBatchDictionaryLearning( + random_state=0, n_jobs=N_JOBS), + decomposition.MiniBatchSparsePCA( + random_state=0, n_jobs=N_JOBS), + decomposition.NMF(random_state=0), + decomposition.PCA(random_state=0), + decomposition.SparsePCA(random_state=0, n_jobs=N_JOBS), + decomposition.TruncatedSVD(random_state=0), + kernel_approximation.Nystroem(random_state=0), + kernel_approximation.RBFSampler(random_state=0), + kernel_approximation.AdditiveChi2Sampler(), + kernel_approximation.SkewedChi2Sampler(random_state=0), + cluster.FeatureAgglomeration(), + skrebate.ReliefF(n_jobs=N_JOBS), + skrebate.SURF(n_jobs=N_JOBS), + skrebate.SURFstar(n_jobs=N_JOBS), + skrebate.MultiSURF(n_jobs=N_JOBS), + skrebate.MultiSURFstar(n_jobs=N_JOBS), + imblearn.under_sampling.ClusterCentroids( + random_state=0, n_jobs=N_JOBS), + imblearn.under_sampling.CondensedNearestNeighbour( + random_state=0, n_jobs=N_JOBS), + imblearn.under_sampling.EditedNearestNeighbours( + random_state=0, n_jobs=N_JOBS), + imblearn.under_sampling.RepeatedEditedNearestNeighbours( + random_state=0, n_jobs=N_JOBS), + imblearn.under_sampling.AllKNN(random_state=0, n_jobs=N_JOBS), + imblearn.under_sampling.InstanceHardnessThreshold( + random_state=0, n_jobs=N_JOBS), + imblearn.under_sampling.NearMiss( + random_state=0, n_jobs=N_JOBS), + imblearn.under_sampling.NeighbourhoodCleaningRule( + random_state=0, n_jobs=N_JOBS), + imblearn.under_sampling.OneSidedSelection( + random_state=0, n_jobs=N_JOBS), + imblearn.under_sampling.RandomUnderSampler( + random_state=0), + imblearn.under_sampling.TomekLinks( + random_state=0, n_jobs=N_JOBS), + imblearn.over_sampling.ADASYN(random_state=0, n_jobs=N_JOBS), + imblearn.over_sampling.RandomOverSampler(random_state=0), + imblearn.over_sampling.SMOTE(random_state=0, n_jobs=N_JOBS), + imblearn.over_sampling.SVMSMOTE(random_state=0, n_jobs=N_JOBS), + imblearn.over_sampling.BorderlineSMOTE( + random_state=0, n_jobs=N_JOBS), + imblearn.over_sampling.SMOTENC( + categorical_features=[], random_state=0, n_jobs=N_JOBS), + imblearn.combine.SMOTEENN(random_state=0), + imblearn.combine.SMOTETomek(random_state=0)) newlist = [] for obj in ev: if obj is None: @@ -114,87 +146,102 @@ newlist.extend(preprocessors[31:36]) elif obj == 'imb_all': newlist.extend(preprocessors[36:55]) - elif type(obj) is int and -1 < obj < len(preprocessors): + elif type(obj) is int and -1 < obj < len(preprocessors): newlist.append(preprocessors[obj]) - elif hasattr(obj, 'get_params'): # user object + elif hasattr(obj, 'get_params'): # user uploaded object if 'n_jobs' in obj.get_params(): - newlist.append( obj.set_params(n_jobs=N_JOBS) ) + newlist.append(obj.set_params(n_jobs=N_JOBS)) else: newlist.append(obj) else: - sys.exit("Unsupported preprocessor type: %r" %(obj)) - search_params['preprocessing_' + param_type[5:6]] = newlist - else: - sys.exit("Parameter name of the final estimator can't be skipped!") + sys.exit("Unsupported estimator type: %r" % (obj)) + + search_params[param_name] = newlist return search_params -if __name__ == '__main__': +def main(inputs, infile_estimator, infile1, infile2, + outfile_result, outfile_object=None, groups=None): + """ + Parameter + --------- + inputs : str + File path to galaxy tool parameter + + infile_estimator : str + File path to estimator + + infile1 : str + File path to dataset containing features + + infile2 : str + File path to dataset containing target values + + outfile_result : str + File path to save the results, either cv_results or test result + + outfile_object : str, optional + File path to save searchCV object + + groups : str + File path to dataset containing groups labels + """ warnings.simplefilter('ignore') - input_json_path = sys.argv[1] - with open(input_json_path, 'r') as param_handler: + with open(inputs, 'r') as param_handler: params = json.load(param_handler) - - infile_pipeline = sys.argv[2] - infile1 = sys.argv[3] - infile2 = sys.argv[4] - outfile_result = sys.argv[5] - if len(sys.argv) > 6: - outfile_estimator = sys.argv[6] - else: - outfile_estimator = None + if groups: + (params['search_schemes']['options']['cv_selector'] + ['groups_selector']['infile_g']) = groups params_builder = params['search_schemes']['search_params_builder'] input_type = params['input_options']['selected_input'] if input_type == 'tabular': header = 'infer' if params['input_options']['header1'] else None - column_option = params['input_options']['column_selector_options_1']['selected_column_selector_option'] - if column_option in ['by_index_number', 'all_but_by_index_number', 'by_header_name', 'all_but_by_header_name']: + column_option = (params['input_options']['column_selector_options_1'] + ['selected_column_selector_option']) + if column_option in ['by_index_number', 'all_but_by_index_number', + 'by_header_name', 'all_but_by_header_name']: c = params['input_options']['column_selector_options_1']['col1'] else: c = None X = read_columns( infile1, - c = c, - c_option = column_option, + c=c, + c_option=column_option, sep='\t', header=header, - parse_dates=True - ) + parse_dates=True).astype(float) else: X = mmread(open(infile1, 'r')) header = 'infer' if params['input_options']['header2'] else None - column_option = params['input_options']['column_selector_options_2']['selected_column_selector_option2'] - if column_option in ['by_index_number', 'all_but_by_index_number', 'by_header_name', 'all_but_by_header_name']: + column_option = (params['input_options']['column_selector_options_2'] + ['selected_column_selector_option2']) + if column_option in ['by_index_number', 'all_but_by_index_number', + 'by_header_name', 'all_but_by_header_name']: c = params['input_options']['column_selector_options_2']['col2'] else: c = None y = read_columns( infile2, - c = c, - c_option = column_option, + c=c, + c_option=column_option, sep='\t', header=header, - parse_dates=True - ) + parse_dates=True) y = y.ravel() optimizer = params['search_schemes']['selected_search_scheme'] optimizer = getattr(model_selection, optimizer) options = params['search_schemes']['options'] + splitter, groups = get_cv(options.pop('cv_selector')) - if groups is None: - options['cv'] = splitter - elif groups == '': - options['cv'] = list( splitter.split(X, y, groups=None) ) - else: - options['cv'] = list( splitter.split(X, y, groups=groups) ) + options['cv'] = splitter options['n_jobs'] = N_JOBS primary_scoring = options['scoring']['primary_scoring'] options['scoring'] = get_scoring(options['scoring']) @@ -203,32 +250,117 @@ else: options['error_score'] = np.NaN if options['refit'] and isinstance(options['scoring'], dict): - options['refit'] = 'primary' + options['refit'] = primary_scoring if 'pre_dispatch' in options and options['pre_dispatch'] == '': options['pre_dispatch'] = None - with open(infile_pipeline, 'rb') as pipeline_handler: - pipeline = load_model(pipeline_handler) + with open(infile_estimator, 'rb') as estimator_handler: + estimator = load_model(estimator_handler) + + memory = joblib.Memory(location=CACHE_DIR, verbose=0) + # cache iraps_core fits could increase search speed significantly + if estimator.__class__.__name__ == 'IRAPSClassifier': + estimator.set_params(memory=memory) + else: + for p, v in estimator.get_params().items(): + if p.endswith('memory'): + if len(p) > 8 and p[:-8].endswith('irapsclassifier'): + # cache iraps_core fits could increase search + # speed significantly + new_params = {p: memory} + estimator.set_params(**new_params) + elif v: + new_params = {p, None} + estimator.set_params(**new_params) + elif p.endswith('n_jobs'): + new_params = {p: 1} + estimator.set_params(**new_params) + + param_grid = _eval_search_params(params_builder) + searcher = optimizer(estimator, param_grid, **options) - search_params = get_search_params(params_builder) - searcher = optimizer(pipeline, search_params, **options) + # do train_test_split + do_train_test_split = params['train_test_split'].pop('do_split') + if do_train_test_split == 'yes': + # make sure refit is choosen + if not options['refit']: + raise ValueError("Refit must be `True` for shuffle splitting!") + split_options = params['train_test_split'] + + # splits + if split_options['shuffle'] == 'stratified': + split_options['labels'] = y + X, X_test, y, y_test = train_test_split(X, y, **split_options) + elif split_options['shuffle'] == 'group': + if not groups: + raise ValueError("No group based CV option was " + "choosen for group shuffle!") + split_options['labels'] = groups + X, X_test, y, y_test, groups, _ =\ + train_test_split(X, y, **split_options) + else: + if split_options['shuffle'] == 'None': + split_options['shuffle'] = None + X, X_test, y, y_test =\ + train_test_split(X, y, **split_options) + # end train_test_split if options['error_score'] == 'raise': - searcher.fit(X, y) + searcher.fit(X, y, groups=groups) else: warnings.simplefilter('always', FitFailedWarning) with warnings.catch_warnings(record=True) as w: try: - searcher.fit(X, y) + searcher.fit(X, y, groups=groups) except ValueError: pass for warning in w: print(repr(warning.message)) - cv_result = pandas.DataFrame(searcher.cv_results_) - cv_result.rename(inplace=True, columns={'mean_test_primary': 'mean_test_'+primary_scoring, 'rank_test_primary': 'rank_test_'+primary_scoring}) - cv_result.to_csv(path_or_buf=outfile_result, sep='\t', header=True, index=False) + if do_train_test_split == 'no': + # save results + cv_results = pandas.DataFrame(searcher.cv_results_) + cv_results = cv_results[sorted(cv_results.columns)] + cv_results.to_csv(path_or_buf=outfile_result, sep='\t', + header=True, index=False) + + # output test result using best_estimator_ + else: + best_estimator_ = searcher.best_estimator_ + if isinstance(options['scoring'], collections.Mapping): + is_multimetric = True + else: + is_multimetric = False - if outfile_estimator: - with open(outfile_estimator, 'wb') as output_handler: - pickle.dump(searcher.best_estimator_, output_handler, pickle.HIGHEST_PROTOCOL) + test_score = _score(best_estimator_, X_test, + y_test, options['scoring'], + is_multimetric=is_multimetric) + if not is_multimetric: + test_score = {primary_scoring: test_score} + for key, value in test_score.items(): + test_score[key] = [value] + result_df = pandas.DataFrame(test_score) + result_df.to_csv(path_or_buf=outfile_result, sep='\t', + header=True, index=False) + + memory.clear(warn=False) + + if outfile_object: + with open(outfile_object, 'wb') as output_handler: + pickle.dump(searcher, output_handler, pickle.HIGHEST_PROTOCOL) + + +if __name__ == '__main__': + aparser = argparse.ArgumentParser() + aparser.add_argument("-i", "--inputs", dest="inputs", required=True) + aparser.add_argument("-e", "--estimator", dest="infile_estimator") + aparser.add_argument("-X", "--infile1", dest="infile1") + aparser.add_argument("-y", "--infile2", dest="infile2") + aparser.add_argument("-r", "--outfile_result", dest="outfile_result") + aparser.add_argument("-o", "--outfile_object", dest="outfile_object") + aparser.add_argument("-g", "--groups", dest="groups") + args = aparser.parse_args() + + main(args.inputs, args.infile_estimator, args.infile1, args.infile2, + args.outfile_result, outfile_object=args.outfile_object, + groups=args.groups)
--- a/sk_whitelist.json Sun Dec 30 01:52:15 2018 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,761 +0,0 @@ -{ "SK_NAMES": [ - "sklearn._ASSUME_FINITE", "sklearn._isotonic._inplace_contiguous_isotonic_regression", - "sklearn._isotonic._make_unique", "sklearn.base.BaseEstimator", - "sklearn.base.BiclusterMixin", "sklearn.base.ClassifierMixin", - "sklearn.base.ClusterMixin", "sklearn.base.DensityMixin", - "sklearn.base.MetaEstimatorMixin", "sklearn.base.RegressorMixin", - "sklearn.base.TransformerMixin", "sklearn.base._first_and_last_element", - "sklearn.base._pprint", "sklearn.base.clone", - "sklearn.base.is_classifier", "sklearn.base.is_regressor", - "sklearn.clone", "sklearn.cluster.AffinityPropagation", - "sklearn.cluster.AgglomerativeClustering", "sklearn.cluster.Birch", - "sklearn.cluster.DBSCAN", "sklearn.cluster.FeatureAgglomeration", - "sklearn.cluster.KMeans", "sklearn.cluster.MeanShift", - "sklearn.cluster.MiniBatchKMeans", "sklearn.cluster.SpectralBiclustering", - "sklearn.cluster.SpectralClustering", "sklearn.cluster.SpectralCoclustering", - "sklearn.cluster._dbscan_inner.dbscan_inner", "sklearn.cluster._feature_agglomeration.AgglomerationTransform", - "sklearn.cluster._hierarchical.WeightedEdge", "sklearn.cluster._hierarchical._get_parents", - "sklearn.cluster._hierarchical._hc_get_descendent", "sklearn.cluster._hierarchical.average_merge", - "sklearn.cluster._hierarchical.compute_ward_dist", "sklearn.cluster._hierarchical.hc_get_heads", - "sklearn.cluster._hierarchical.max_merge", "sklearn.cluster._k_means._assign_labels_array", - "sklearn.cluster._k_means._assign_labels_csr", "sklearn.cluster._k_means._centers_dense", - "sklearn.cluster._k_means._centers_sparse", "sklearn.cluster._k_means._mini_batch_update_csr", - "sklearn.cluster._k_means_elkan.k_means_elkan", "sklearn.cluster.affinity_propagation", - "sklearn.cluster.affinity_propagation_.AffinityPropagation", "sklearn.cluster.affinity_propagation_.affinity_propagation", - "sklearn.cluster.bicluster.BaseSpectral", "sklearn.cluster.bicluster.SpectralBiclustering", - "sklearn.cluster.bicluster.SpectralCoclustering", "sklearn.cluster.bicluster._bistochastic_normalize", - "sklearn.cluster.bicluster._log_normalize", "sklearn.cluster.bicluster._scale_normalize", - "sklearn.cluster.birch.Birch", "sklearn.cluster.birch._CFNode", - "sklearn.cluster.birch._CFSubcluster", "sklearn.cluster.birch._iterate_sparse_X", - "sklearn.cluster.birch._split_node", "sklearn.cluster.dbscan", - "sklearn.cluster.dbscan_.DBSCAN", "sklearn.cluster.dbscan_.dbscan", - "sklearn.cluster.estimate_bandwidth", "sklearn.cluster.get_bin_seeds", - "sklearn.cluster.hierarchical.AgglomerativeClustering", "sklearn.cluster.hierarchical.FeatureAgglomeration", - "sklearn.cluster.hierarchical._TREE_BUILDERS", "sklearn.cluster.hierarchical._average_linkage", - "sklearn.cluster.hierarchical._complete_linkage", "sklearn.cluster.hierarchical._fix_connectivity", - "sklearn.cluster.hierarchical._hc_cut", "sklearn.cluster.hierarchical.linkage_tree", - "sklearn.cluster.hierarchical.ward_tree", "sklearn.cluster.k_means", - "sklearn.cluster.k_means_.FLOAT_DTYPES", "sklearn.cluster.k_means_.KMeans", - "sklearn.cluster.k_means_.MiniBatchKMeans", "sklearn.cluster.k_means_._init_centroids", - "sklearn.cluster.k_means_._k_init", "sklearn.cluster.k_means_._kmeans_single_elkan", - "sklearn.cluster.k_means_._kmeans_single_lloyd", "sklearn.cluster.k_means_._labels_inertia", - "sklearn.cluster.k_means_._labels_inertia_precompute_dense", "sklearn.cluster.k_means_._mini_batch_convergence", - "sklearn.cluster.k_means_._mini_batch_step", "sklearn.cluster.k_means_._tolerance", - "sklearn.cluster.k_means_._validate_center_shape", "sklearn.cluster.k_means_.k_means", - "sklearn.cluster.k_means_.string_types", "sklearn.cluster.linkage_tree", - "sklearn.cluster.mean_shift", "sklearn.cluster.mean_shift_.MeanShift", - "sklearn.cluster.mean_shift_._mean_shift_single_seed", "sklearn.cluster.mean_shift_.estimate_bandwidth", - "sklearn.cluster.mean_shift_.get_bin_seeds", "sklearn.cluster.mean_shift_.mean_shift", - "sklearn.cluster.spectral.SpectralClustering", "sklearn.cluster.spectral.discretize", - "sklearn.cluster.spectral.spectral_clustering", "sklearn.cluster.spectral_clustering", - "sklearn.cluster.ward_tree", "sklearn.config_context", "sklearn.compose.TransformedTargetRegressor", - "sklearn.compose._target.TransformedTargetRegressor", "sklearn.compose.ColumnTransformer", - "sklearn.compose._column_transformer.ColumnTransformer", "sklearn.compose.make_column_transformer", - "sklearn.compose._column_transformer.make_column_transformer", - "sklearn.covariance.EllipticEnvelope", "sklearn.covariance.EmpiricalCovariance", - "sklearn.covariance.GraphLasso", "sklearn.covariance.GraphLassoCV", - "sklearn.covariance.LedoitWolf", "sklearn.covariance.MinCovDet", - "sklearn.covariance.OAS", "sklearn.covariance.ShrunkCovariance", - "sklearn.covariance.empirical_covariance", "sklearn.covariance.empirical_covariance_.EmpiricalCovariance", - "sklearn.covariance.empirical_covariance_.empirical_covariance", "sklearn.covariance.empirical_covariance_.log_likelihood", - "sklearn.covariance.fast_mcd", "sklearn.covariance.graph_lasso", - "sklearn.covariance.graph_lasso_.GraphLasso", "sklearn.covariance.graph_lasso_.GraphLassoCV", - "sklearn.covariance.graph_lasso_._dual_gap", "sklearn.covariance.graph_lasso_._objective", - "sklearn.covariance.graph_lasso_.alpha_max", "sklearn.covariance.graph_lasso_.graph_lasso", - "sklearn.covariance.graph_lasso_.graph_lasso_path", "sklearn.covariance.ledoit_wolf", - "sklearn.covariance.ledoit_wolf_shrinkage", "sklearn.covariance.log_likelihood", - "sklearn.covariance.oas", "sklearn.covariance.outlier_detection.EllipticEnvelope", - "sklearn.covariance.robust_covariance.MinCovDet", "sklearn.covariance.robust_covariance._c_step", - "sklearn.covariance.robust_covariance.c_step", "sklearn.covariance.robust_covariance.fast_mcd", - "sklearn.covariance.robust_covariance.select_candidates", "sklearn.covariance.shrunk_covariance", - "sklearn.covariance.shrunk_covariance_.LedoitWolf", "sklearn.covariance.shrunk_covariance_.OAS", - "sklearn.covariance.shrunk_covariance_.ShrunkCovariance", "sklearn.covariance.shrunk_covariance_.ledoit_wolf", - "sklearn.covariance.shrunk_covariance_.ledoit_wolf_shrinkage", "sklearn.covariance.shrunk_covariance_.oas", - "sklearn.covariance.shrunk_covariance_.shrunk_covariance", "sklearn.decomposition.DictionaryLearning", - "sklearn.decomposition.FactorAnalysis", "sklearn.decomposition.FastICA", - "sklearn.decomposition.IncrementalPCA", "sklearn.decomposition.KernelPCA", - "sklearn.decomposition.LatentDirichletAllocation", "sklearn.decomposition.MiniBatchDictionaryLearning", - "sklearn.decomposition.MiniBatchSparsePCA", "sklearn.decomposition.NMF", - "sklearn.decomposition.PCA", "sklearn.decomposition.RandomizedPCA", - "sklearn.decomposition.SparseCoder", "sklearn.decomposition.SparsePCA", - "sklearn.decomposition.TruncatedSVD", "sklearn.decomposition._online_lda._dirichlet_expectation_1d", - "sklearn.decomposition._online_lda._dirichlet_expectation_2d", "sklearn.decomposition._online_lda.mean_change", - "sklearn.decomposition.base._BasePCA", "sklearn.decomposition.cdnmf_fast._update_cdnmf_fast", - "sklearn.decomposition.dict_learning", "sklearn.decomposition.dict_learning_online", - "sklearn.decomposition.factor_analysis.FactorAnalysis", "sklearn.decomposition.fastica", - "sklearn.decomposition.fastica_.FLOAT_DTYPES", "sklearn.decomposition.fastica_.FastICA", - "sklearn.decomposition.fastica_._cube", "sklearn.decomposition.fastica_._exp", - "sklearn.decomposition.fastica_._gs_decorrelation", "sklearn.decomposition.fastica_._ica_def", - "sklearn.decomposition.fastica_._ica_par", "sklearn.decomposition.fastica_._logcosh", - "sklearn.decomposition.fastica_._sym_decorrelation", "sklearn.decomposition.fastica_.fastica", - "sklearn.decomposition.fastica_.string_types", "sklearn.decomposition.incremental_pca.IncrementalPCA", - "sklearn.decomposition.kernel_pca.KernelPCA", "sklearn.decomposition.nmf.EPSILON", - "sklearn.decomposition.nmf.INTEGER_TYPES", "sklearn.decomposition.nmf.NMF", - "sklearn.decomposition.nmf._beta_divergence", "sklearn.decomposition.nmf._beta_loss_to_float", - "sklearn.decomposition.nmf._check_init", "sklearn.decomposition.nmf._check_string_param", - "sklearn.decomposition.nmf._compute_regularization", "sklearn.decomposition.nmf._fit_coordinate_descent", - "sklearn.decomposition.nmf._fit_multiplicative_update", "sklearn.decomposition.nmf._initialize_nmf", - "sklearn.decomposition.nmf._multiplicative_update_h", "sklearn.decomposition.nmf._multiplicative_update_w", - "sklearn.decomposition.nmf._special_sparse_dot", "sklearn.decomposition.nmf._update_coordinate_descent", - "sklearn.decomposition.nmf.non_negative_factorization", "sklearn.decomposition.nmf.norm", - "sklearn.decomposition.nmf.trace_dot", "sklearn.decomposition.non_negative_factorization", - "sklearn.decomposition.online_lda.EPS", "sklearn.decomposition.online_lda.LatentDirichletAllocation", - "sklearn.decomposition.online_lda._update_doc_distribution", "sklearn.decomposition.online_lda.gammaln", - "sklearn.decomposition.pca.PCA", "sklearn.decomposition.pca.RandomizedPCA", - "sklearn.decomposition.pca._assess_dimension_", "sklearn.decomposition.pca._infer_dimension_", - "sklearn.decomposition.pca.gammaln", "sklearn.decomposition.sparse_encode", - "sklearn.decomposition.sparse_pca.MiniBatchSparsePCA", "sklearn.decomposition.sparse_pca.SparsePCA", - "sklearn.decomposition.truncated_svd.TruncatedSVD", "sklearn.discriminant_analysis.LinearDiscriminantAnalysis", - "sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis", "sklearn.discriminant_analysis._class_cov", - "sklearn.discriminant_analysis._class_means", "sklearn.discriminant_analysis._cov", - "sklearn.discriminant_analysis.string_types", "sklearn.ensemble.AdaBoostClassifier", - "sklearn.ensemble.AdaBoostRegressor", "sklearn.ensemble.BaggingClassifier", - "sklearn.ensemble.BaggingRegressor", "sklearn.ensemble.BaseEnsemble", - "sklearn.ensemble.ExtraTreesClassifier", "sklearn.ensemble.ExtraTreesRegressor", - "sklearn.ensemble.GradientBoostingClassifier", "sklearn.ensemble.GradientBoostingRegressor", - "sklearn.ensemble.IsolationForest", "sklearn.ensemble.RandomForestClassifier", - "sklearn.ensemble.RandomForestRegressor", "sklearn.ensemble.RandomTreesEmbedding", - "sklearn.ensemble.VotingClassifier", "sklearn.ensemble._gradient_boosting._partial_dependence_tree", - "sklearn.ensemble._gradient_boosting._predict_regression_tree_stages_sparse", "sklearn.ensemble._gradient_boosting._random_sample_mask", - "sklearn.ensemble._gradient_boosting.predict_stage", "sklearn.ensemble._gradient_boosting.predict_stages", - "sklearn.ensemble.bagging.BaggingClassifier", "sklearn.ensemble.bagging.BaggingRegressor", - "sklearn.ensemble.bagging.BaseBagging", "sklearn.ensemble.bagging.MAX_INT", - "sklearn.ensemble.bagging._generate_bagging_indices", "sklearn.ensemble.bagging._generate_indices", - "sklearn.ensemble.bagging._parallel_build_estimators", "sklearn.ensemble.bagging._parallel_decision_function", - "sklearn.ensemble.bagging._parallel_predict_log_proba", "sklearn.ensemble.bagging._parallel_predict_proba", - "sklearn.ensemble.bagging._parallel_predict_regression", "sklearn.ensemble.base.BaseEnsemble", - "sklearn.ensemble.base.MAX_RAND_SEED", "sklearn.ensemble.base._partition_estimators", - "sklearn.ensemble.base._set_random_states", "sklearn.ensemble.forest.BaseForest", - "sklearn.ensemble.forest.ExtraTreesClassifier", "sklearn.ensemble.forest.ExtraTreesRegressor", - "sklearn.ensemble.forest.ForestClassifier", "sklearn.ensemble.forest.ForestRegressor", - "sklearn.ensemble.forest.MAX_INT", "sklearn.ensemble.forest.RandomForestClassifier", - "sklearn.ensemble.forest.RandomForestRegressor", "sklearn.ensemble.forest.RandomTreesEmbedding", - "sklearn.ensemble.forest._generate_sample_indices", "sklearn.ensemble.forest._generate_unsampled_indices", - "sklearn.ensemble.forest._parallel_build_trees", "sklearn.ensemble.forest.accumulate_prediction", - "sklearn.ensemble.gradient_boosting.BaseGradientBoosting", "sklearn.ensemble.gradient_boosting.BinomialDeviance", - "sklearn.ensemble.gradient_boosting.ClassificationLossFunction", "sklearn.ensemble.gradient_boosting.ExponentialLoss", - "sklearn.ensemble.gradient_boosting.GradientBoostingClassifier", "sklearn.ensemble.gradient_boosting.GradientBoostingRegressor", - "sklearn.ensemble.gradient_boosting.HuberLossFunction", "sklearn.ensemble.gradient_boosting.INIT_ESTIMATORS", - "sklearn.ensemble.gradient_boosting.LOSS_FUNCTIONS", "sklearn.ensemble.gradient_boosting.LeastAbsoluteError", - "sklearn.ensemble.gradient_boosting.LeastSquaresError", "sklearn.ensemble.gradient_boosting.LogOddsEstimator", - "sklearn.ensemble.gradient_boosting.LossFunction", "sklearn.ensemble.gradient_boosting.MeanEstimator", - "sklearn.ensemble.gradient_boosting.MultinomialDeviance", "sklearn.ensemble.gradient_boosting.PriorProbabilityEstimator", - "sklearn.ensemble.gradient_boosting.QuantileEstimator", "sklearn.ensemble.gradient_boosting.QuantileLossFunction", - "sklearn.ensemble.gradient_boosting.RegressionLossFunction", "sklearn.ensemble.gradient_boosting.ScaledLogOddsEstimator", - "sklearn.ensemble.gradient_boosting.TREE_LEAF", "sklearn.ensemble.gradient_boosting.VerboseReporter", - "sklearn.ensemble.gradient_boosting.ZeroEstimator", "sklearn.ensemble.gradient_boosting.expit", - "sklearn.ensemble.iforest.INTEGER_TYPES", "sklearn.ensemble.iforest.IsolationForest", - "sklearn.ensemble.iforest._average_path_length", "sklearn.ensemble.iforest.euler_gamma", - "sklearn.ensemble.partial_dependence._grid_from_X", "sklearn.ensemble.partial_dependence.partial_dependence", - "sklearn.ensemble.partial_dependence.plot_partial_dependence", "sklearn.ensemble.voting_classifier.VotingClassifier", - "sklearn.ensemble.voting_classifier._parallel_fit_estimator", "sklearn.ensemble.weight_boosting.AdaBoostClassifier", - "sklearn.ensemble.weight_boosting.AdaBoostRegressor", "sklearn.ensemble.weight_boosting.BaseWeightBoosting", - "sklearn.ensemble.weight_boosting._samme_proba", "sklearn.ensemble.weight_boosting.inner1d", - "sklearn.feature_extraction.DictVectorizer", "sklearn.feature_extraction.FeatureHasher", - "sklearn.feature_extraction._hashing.transform", "sklearn.feature_extraction.dict_vectorizer.DictVectorizer", - "sklearn.feature_extraction.dict_vectorizer._tosequence", "sklearn.feature_extraction.grid_to_graph", - "sklearn.feature_extraction.hashing.FeatureHasher", "sklearn.feature_extraction.hashing._iteritems", - "sklearn.feature_extraction.image.PatchExtractor", "sklearn.feature_extraction.image._compute_gradient_3d", - "sklearn.feature_extraction.image._compute_n_patches", "sklearn.feature_extraction.image._make_edges_3d", - "sklearn.feature_extraction.image._mask_edges_weights", "sklearn.feature_extraction.image._to_graph", - "sklearn.feature_extraction.image.extract_patches", "sklearn.feature_extraction.image.extract_patches_2d", - "sklearn.feature_extraction.image.grid_to_graph", "sklearn.feature_extraction.image.img_to_graph", - "sklearn.feature_extraction.image.reconstruct_from_patches_2d", "sklearn.feature_extraction.img_to_graph", - "sklearn.feature_extraction.stop_words.ENGLISH_STOP_WORDS", "sklearn.feature_extraction.text.CountVectorizer", - "sklearn.feature_extraction.text.ENGLISH_STOP_WORDS", "sklearn.feature_extraction.text.HashingVectorizer", - "sklearn.feature_extraction.text.TfidfTransformer", "sklearn.feature_extraction.text.TfidfVectorizer", - "sklearn.feature_extraction.text.VectorizerMixin", "sklearn.feature_extraction.text._check_stop_list", - "sklearn.feature_extraction.text._document_frequency", "sklearn.feature_extraction.text._make_int_array", - "sklearn.feature_extraction.text.strip_accents_ascii", "sklearn.feature_extraction.text.strip_accents_unicode", - "sklearn.feature_extraction.text.strip_tags", "sklearn.feature_selection.GenericUnivariateSelect", - "sklearn.feature_selection.RFE", "sklearn.feature_selection.RFECV", - "sklearn.feature_selection.SelectFdr", "sklearn.feature_selection.SelectFpr", - "sklearn.feature_selection.SelectFromModel", "sklearn.feature_selection.SelectFwe", - "sklearn.feature_selection.SelectKBest", "sklearn.feature_selection.SelectPercentile", - "sklearn.feature_selection.VarianceThreshold", "sklearn.feature_selection.base.SelectorMixin", - "sklearn.feature_selection.chi2", "sklearn.feature_selection.f_classif", - "sklearn.feature_selection.f_oneway", "sklearn.feature_selection.f_regression", - "sklearn.feature_selection.from_model.SelectFromModel", "sklearn.feature_selection.from_model._calculate_threshold", - "sklearn.feature_selection.from_model._get_feature_importances", "sklearn.feature_selection.mutual_info_._compute_mi", - "sklearn.feature_selection.mutual_info_._compute_mi_cc", "sklearn.feature_selection.mutual_info_._compute_mi_cd", - "sklearn.feature_selection.mutual_info_._estimate_mi", "sklearn.feature_selection.mutual_info_._iterate_columns", - "sklearn.feature_selection.mutual_info_.digamma", "sklearn.feature_selection.mutual_info_.mutual_info_classif", - "sklearn.feature_selection.mutual_info_.mutual_info_regression", "sklearn.feature_selection.mutual_info_classif", - "sklearn.feature_selection.mutual_info_regression", "sklearn.feature_selection.rfe.RFE", - "sklearn.feature_selection.rfe.RFECV", "sklearn.feature_selection.rfe._rfe_single_fit", - "sklearn.feature_selection.univariate_selection.GenericUnivariateSelect", "sklearn.feature_selection.univariate_selection.SelectFdr", - "sklearn.feature_selection.univariate_selection.SelectFpr", "sklearn.feature_selection.univariate_selection.SelectFwe", - "sklearn.feature_selection.univariate_selection.SelectKBest", "sklearn.feature_selection.univariate_selection.SelectPercentile", - "sklearn.feature_selection.univariate_selection._BaseFilter", "sklearn.feature_selection.univariate_selection._chisquare", - "sklearn.feature_selection.univariate_selection._clean_nans", "sklearn.feature_selection.univariate_selection.chi2", - "sklearn.feature_selection.univariate_selection.f_classif", "sklearn.feature_selection.univariate_selection.f_oneway", - "sklearn.feature_selection.univariate_selection.f_regression", "sklearn.feature_selection.variance_threshold.VarianceThreshold", - "sklearn.gaussian_process.GaussianProcess", "sklearn.gaussian_process.GaussianProcessClassifier", - "sklearn.gaussian_process.GaussianProcessRegressor", "sklearn.gaussian_process.correlation_models.absolute_exponential", - "sklearn.gaussian_process.correlation_models.cubic", "sklearn.gaussian_process.correlation_models.generalized_exponential", - "sklearn.gaussian_process.correlation_models.linear", "sklearn.gaussian_process.correlation_models.pure_nugget", - "sklearn.gaussian_process.correlation_models.squared_exponential", "sklearn.gaussian_process.gaussian_process.GaussianProcess", - "sklearn.gaussian_process.gaussian_process.MACHINE_EPSILON", "sklearn.gaussian_process.gaussian_process.l1_cross_distances", - "sklearn.gaussian_process.gpc.COEFS", "sklearn.gaussian_process.gpc.GaussianProcessClassifier", - "sklearn.gaussian_process.gpc.LAMBDAS", "sklearn.gaussian_process.gpc._BinaryGaussianProcessClassifierLaplace", - "sklearn.gaussian_process.gpc.erf", "sklearn.gaussian_process.gpc.expit", - "sklearn.gaussian_process.gpr.GaussianProcessRegressor", "sklearn.gaussian_process.kernels.CompoundKernel", - "sklearn.gaussian_process.kernels.ConstantKernel", "sklearn.gaussian_process.kernels.DotProduct", - "sklearn.gaussian_process.kernels.ExpSineSquared", "sklearn.gaussian_process.kernels.Exponentiation", - "sklearn.gaussian_process.kernels.Hyperparameter", "sklearn.gaussian_process.kernels.Kernel", - "sklearn.gaussian_process.kernels.KernelOperator", "sklearn.gaussian_process.kernels.Matern", - "sklearn.gaussian_process.kernels.NormalizedKernelMixin", "sklearn.gaussian_process.kernels.PairwiseKernel", - "sklearn.gaussian_process.kernels.Product", "sklearn.gaussian_process.kernels.RBF", - "sklearn.gaussian_process.kernels.RationalQuadratic", "sklearn.gaussian_process.kernels.StationaryKernelMixin", - "sklearn.gaussian_process.kernels.Sum", "sklearn.gaussian_process.kernels.WhiteKernel", - "sklearn.gaussian_process.kernels._approx_fprime", "sklearn.gaussian_process.kernels._check_length_scale", - "sklearn.gaussian_process.kernels.gamma", "sklearn.gaussian_process.kernels.kv", - "sklearn.gaussian_process.regression_models.constant", "sklearn.gaussian_process.regression_models.linear", - "sklearn.gaussian_process.regression_models.quadratic", "sklearn.get_config", - "sklearn.isotonic.IsotonicRegression", "sklearn.isotonic.check_increasing", - "sklearn.isotonic.isotonic_regression", "sklearn.kernel_approximation.AdditiveChi2Sampler", - "sklearn.kernel_approximation.KERNEL_PARAMS", "sklearn.kernel_approximation.Nystroem", - "sklearn.kernel_approximation.RBFSampler", "sklearn.kernel_approximation.SkewedChi2Sampler", - "sklearn.kernel_ridge.KernelRidge", "sklearn.linear_model.ARDRegression", - "sklearn.linear_model.BayesianRidge", "sklearn.linear_model.ElasticNet", - "sklearn.linear_model.ElasticNetCV", "sklearn.linear_model.Hinge", - "sklearn.linear_model.Huber", "sklearn.linear_model.HuberRegressor", - "sklearn.linear_model.Lars", "sklearn.linear_model.LarsCV", - "sklearn.linear_model.Lasso", "sklearn.linear_model.LassoCV", - "sklearn.linear_model.LassoLars", "sklearn.linear_model.LassoLarsCV", - "sklearn.linear_model.LassoLarsIC", "sklearn.linear_model.LinearRegression", - "sklearn.linear_model.Log", "sklearn.linear_model.LogisticRegression", - "sklearn.linear_model.LogisticRegressionCV", "sklearn.linear_model.ModifiedHuber", - "sklearn.linear_model.MultiTaskElasticNet", "sklearn.linear_model.MultiTaskElasticNetCV", - "sklearn.linear_model.MultiTaskLasso", "sklearn.linear_model.MultiTaskLassoCV", - "sklearn.linear_model.OrthogonalMatchingPursuit", "sklearn.linear_model.OrthogonalMatchingPursuitCV", - "sklearn.linear_model.PassiveAggressiveClassifier", "sklearn.linear_model.PassiveAggressiveRegressor", - "sklearn.linear_model.Perceptron", "sklearn.linear_model.RANSACRegressor", - "sklearn.linear_model.RandomizedLasso", "sklearn.linear_model.RandomizedLogisticRegression", - "sklearn.linear_model.Ridge", "sklearn.linear_model.RidgeCV", - "sklearn.linear_model.RidgeClassifier", "sklearn.linear_model.RidgeClassifierCV", - "sklearn.linear_model.SGDClassifier", "sklearn.linear_model.SGDRegressor", - "sklearn.linear_model.SquaredLoss", "sklearn.linear_model.TheilSenRegressor", - "sklearn.linear_model.base.FLOAT_DTYPES", "sklearn.linear_model.base.LinearClassifierMixin", - "sklearn.linear_model.base.LinearModel", "sklearn.linear_model.base.LinearRegression", - "sklearn.linear_model.base.SPARSE_INTERCEPT_DECAY", "sklearn.linear_model.base.SparseCoefMixin", - "sklearn.linear_model.base._pre_fit", "sklearn.linear_model.base._preprocess_data", - "sklearn.linear_model.base._rescale_data", "sklearn.linear_model.base.center_data", - "sklearn.linear_model.base.make_dataset", "sklearn.linear_model.base.sparse_center_data", - "sklearn.linear_model.bayes.ARDRegression", "sklearn.linear_model.bayes.BayesianRidge", - "sklearn.linear_model.cd_fast.enet_coordinate_descent", "sklearn.linear_model.cd_fast.enet_coordinate_descent_gram", - "sklearn.linear_model.cd_fast.enet_coordinate_descent_multi_task", "sklearn.linear_model.cd_fast.sparse_enet_coordinate_descent", - "sklearn.linear_model.coordinate_descent.ElasticNet", "sklearn.linear_model.coordinate_descent.ElasticNetCV", - "sklearn.linear_model.coordinate_descent.Lasso", "sklearn.linear_model.coordinate_descent.LassoCV", - "sklearn.linear_model.coordinate_descent.LinearModelCV", "sklearn.linear_model.coordinate_descent.MultiTaskElasticNet", - "sklearn.linear_model.coordinate_descent.MultiTaskElasticNetCV", "sklearn.linear_model.coordinate_descent.MultiTaskLasso", - "sklearn.linear_model.coordinate_descent.MultiTaskLassoCV", "sklearn.linear_model.coordinate_descent._alpha_grid", - "sklearn.linear_model.coordinate_descent._path_residuals", "sklearn.linear_model.coordinate_descent.enet_path", - "sklearn.linear_model.coordinate_descent.lasso_path", "sklearn.linear_model.enet_path", - "sklearn.linear_model.huber.HuberRegressor", "sklearn.linear_model.huber._huber_loss_and_gradient", - "sklearn.linear_model.lars_path", "sklearn.linear_model.lasso_path", - "sklearn.linear_model.lasso_stability_path", "sklearn.linear_model.least_angle.Lars", - "sklearn.linear_model.least_angle.LarsCV", "sklearn.linear_model.least_angle.LassoLars", - "sklearn.linear_model.least_angle.LassoLarsCV", "sklearn.linear_model.least_angle.LassoLarsIC", - "sklearn.linear_model.least_angle._check_copy_and_writeable", "sklearn.linear_model.least_angle._lars_path_residues", - "sklearn.linear_model.least_angle.lars_path", "sklearn.linear_model.least_angle.solve_triangular_args", - "sklearn.linear_model.least_angle.string_types", "sklearn.linear_model.logistic.LogisticRegression", - "sklearn.linear_model.logistic.LogisticRegressionCV", "sklearn.linear_model.logistic.SCORERS", - "sklearn.linear_model.logistic._check_solver_option", "sklearn.linear_model.logistic._intercept_dot", - "sklearn.linear_model.logistic._log_reg_scoring_path", "sklearn.linear_model.logistic._logistic_grad_hess", - "sklearn.linear_model.logistic._logistic_loss", "sklearn.linear_model.logistic._logistic_loss_and_grad", - "sklearn.linear_model.logistic._multinomial_grad_hess", "sklearn.linear_model.logistic._multinomial_loss", - "sklearn.linear_model.logistic._multinomial_loss_grad", "sklearn.linear_model.logistic.expit", - "sklearn.linear_model.logistic.logistic_regression_path", "sklearn.linear_model.logistic_regression_path", - "sklearn.linear_model.omp.OrthogonalMatchingPursuit", "sklearn.linear_model.omp.OrthogonalMatchingPursuitCV", - "sklearn.linear_model.omp._cholesky_omp", "sklearn.linear_model.omp._gram_omp", - "sklearn.linear_model.omp._omp_path_residues", "sklearn.linear_model.omp.orthogonal_mp", - "sklearn.linear_model.omp.orthogonal_mp_gram", "sklearn.linear_model.omp.premature", - "sklearn.linear_model.omp.solve_triangular_args", "sklearn.linear_model.orthogonal_mp", - "sklearn.linear_model.orthogonal_mp_gram", "sklearn.linear_model.passive_aggressive.DEFAULT_EPSILON", - "sklearn.linear_model.passive_aggressive.PassiveAggressiveClassifier", "sklearn.linear_model.passive_aggressive.PassiveAggressiveRegressor", - "sklearn.linear_model.perceptron.Perceptron", "sklearn.linear_model.randomized_l1.BaseRandomizedLinearModel", - "sklearn.linear_model.randomized_l1.RandomizedLasso", "sklearn.linear_model.randomized_l1.RandomizedLogisticRegression", - "sklearn.linear_model.randomized_l1._lasso_stability_path", "sklearn.linear_model.randomized_l1._randomized_lasso", - "sklearn.linear_model.randomized_l1._randomized_logistic", "sklearn.linear_model.randomized_l1._resample_model", - "sklearn.linear_model.randomized_l1.lasso_stability_path", "sklearn.linear_model.ransac.RANSACRegressor", - "sklearn.linear_model.ransac._EPSILON", "sklearn.linear_model.ransac._dynamic_max_trials", - "sklearn.linear_model.ridge.Ridge", "sklearn.linear_model.ridge.RidgeCV", - "sklearn.linear_model.ridge.RidgeClassifier", "sklearn.linear_model.ridge.RidgeClassifierCV", - "sklearn.linear_model.ridge._BaseRidge", "sklearn.linear_model.ridge._BaseRidgeCV", - "sklearn.linear_model.ridge._RidgeGCV", "sklearn.linear_model.ridge._solve_cholesky", - "sklearn.linear_model.ridge._solve_cholesky_kernel", "sklearn.linear_model.ridge._solve_lsqr", - "sklearn.linear_model.ridge._solve_sparse_cg", "sklearn.linear_model.ridge._solve_svd", - "sklearn.linear_model.ridge.ridge_regression", "sklearn.linear_model.ridge_regression", - "sklearn.linear_model.sag.get_auto_step_size", "sklearn.linear_model.sag.sag", - "sklearn.linear_model.sag.sag_solver", "sklearn.linear_model.sag_fast.MultinomialLogLoss", - "sklearn.linear_model.sag_fast._multinomial_grad_loss_all_samples", "sklearn.linear_model.sag_fast.sag", - "sklearn.linear_model.sgd_fast.Classification", "sklearn.linear_model.sgd_fast.EpsilonInsensitive", - "sklearn.linear_model.sgd_fast.Hinge", "sklearn.linear_model.sgd_fast.Huber", - "sklearn.linear_model.sgd_fast.Log", "sklearn.linear_model.sgd_fast.LossFunction", - "sklearn.linear_model.sgd_fast.ModifiedHuber", "sklearn.linear_model.sgd_fast.Regression", - "sklearn.linear_model.sgd_fast.SquaredEpsilonInsensitive", "sklearn.linear_model.sgd_fast.SquaredHinge", - "sklearn.linear_model.sgd_fast.SquaredLoss", "sklearn.linear_model.sgd_fast._plain_sgd", - "sklearn.linear_model.sgd_fast.average_sgd", "sklearn.linear_model.sgd_fast.plain_sgd", - "sklearn.linear_model.stochastic_gradient.BaseSGD", "sklearn.linear_model.stochastic_gradient.BaseSGDClassifier", - "sklearn.linear_model.stochastic_gradient.BaseSGDRegressor", "sklearn.linear_model.stochastic_gradient.DEFAULT_EPSILON", - "sklearn.linear_model.stochastic_gradient.LEARNING_RATE_TYPES", "sklearn.linear_model.stochastic_gradient.PENALTY_TYPES", - "sklearn.linear_model.stochastic_gradient.SGDClassifier", "sklearn.linear_model.stochastic_gradient.SGDRegressor", - "sklearn.linear_model.stochastic_gradient._prepare_fit_binary", "sklearn.linear_model.stochastic_gradient.fit_binary", - "sklearn.linear_model.theil_sen.TheilSenRegressor", "sklearn.linear_model.theil_sen._EPSILON", - "sklearn.linear_model.theil_sen._breakdown_point", "sklearn.linear_model.theil_sen._lstsq", - "sklearn.linear_model.theil_sen._modified_weiszfeld_step", "sklearn.linear_model.theil_sen._spatial_median", - "sklearn.linear_model.theil_sen.binom", "sklearn.manifold.Isomap", - "sklearn.manifold.LocallyLinearEmbedding", "sklearn.manifold.MDS", - "sklearn.manifold.SpectralEmbedding", "sklearn.manifold.TSNE", - "sklearn.manifold._barnes_hut_tsne.gradient", "sklearn.manifold._utils._binary_search_perplexity", - "sklearn.manifold.isomap.Isomap", "sklearn.manifold.locally_linear.FLOAT_DTYPES", - "sklearn.manifold.locally_linear.LocallyLinearEmbedding", "sklearn.manifold.locally_linear.barycenter_kneighbors_graph", - "sklearn.manifold.locally_linear.barycenter_weights", "sklearn.manifold.locally_linear.locally_linear_embedding", - "sklearn.manifold.locally_linear.null_space", "sklearn.manifold.locally_linear_embedding", - "sklearn.manifold.mds.MDS", "sklearn.manifold.mds._smacof_single", - "sklearn.manifold.mds.smacof", "sklearn.manifold.smacof", - "sklearn.manifold.spectral_embedding", "sklearn.manifold.spectral_embedding_.SpectralEmbedding", - "sklearn.manifold.spectral_embedding_._graph_connected_component", "sklearn.manifold.spectral_embedding_._graph_is_connected", - "sklearn.manifold.spectral_embedding_._set_diag", "sklearn.manifold.spectral_embedding_.spectral_embedding", - "sklearn.manifold.t_sne.MACHINE_EPSILON", "sklearn.manifold.t_sne.TSNE", - "sklearn.manifold.t_sne._gradient_descent", "sklearn.manifold.t_sne._joint_probabilities", - "sklearn.manifold.t_sne._joint_probabilities_nn", "sklearn.manifold.t_sne._kl_divergence", - "sklearn.manifold.t_sne._kl_divergence_bh", "sklearn.manifold.t_sne.string_types", - "sklearn.manifold.t_sne.trustworthiness", "sklearn.metrics.SCORERS", - "sklearn.metrics.accuracy_score", "sklearn.metrics.adjusted_mutual_info_score", - "sklearn.metrics.adjusted_rand_score", "sklearn.metrics.auc", - "sklearn.metrics.average_precision_score", "sklearn.metrics.base._average_binary_score", - "sklearn.metrics.brier_score_loss", "sklearn.metrics.calinski_harabaz_score", - "sklearn.metrics.classification._check_binary_probabilistic_predictions", "sklearn.metrics.classification._check_targets", - "sklearn.metrics.classification._prf_divide", "sklearn.metrics.classification._weighted_sum", - "sklearn.metrics.classification.accuracy_score", "sklearn.metrics.classification.brier_score_loss", - "sklearn.metrics.classification.classification_report", "sklearn.metrics.classification.cohen_kappa_score", - "sklearn.metrics.classification.confusion_matrix", "sklearn.metrics.classification.f1_score", - "sklearn.metrics.classification.fbeta_score", "sklearn.metrics.classification.hamming_loss", - "sklearn.metrics.classification.hinge_loss", "sklearn.metrics.classification.jaccard_similarity_score", - "sklearn.metrics.classification.log_loss", "sklearn.metrics.classification.matthews_corrcoef", - "sklearn.metrics.classification.precision_recall_fscore_support", "sklearn.metrics.classification.precision_score", - "sklearn.metrics.classification.recall_score", "sklearn.metrics.classification.zero_one_loss", - "sklearn.metrics.classification_report", "sklearn.metrics.cluster.adjusted_mutual_info_score", - "sklearn.metrics.cluster.adjusted_rand_score", "sklearn.metrics.cluster.bicluster._check_rows_and_columns", - "sklearn.metrics.cluster.bicluster._jaccard", "sklearn.metrics.cluster.bicluster._pairwise_similarity", - "sklearn.metrics.cluster.bicluster.consensus_score", "sklearn.metrics.cluster.calinski_harabaz_score", - "sklearn.metrics.cluster.completeness_score", "sklearn.metrics.cluster.consensus_score", - "sklearn.metrics.cluster.contingency_matrix", "sklearn.metrics.cluster.entropy", - "sklearn.metrics.cluster.expected_mutual_info_fast.expected_mutual_information", "sklearn.metrics.cluster.expected_mutual_info_fast.gammaln", - "sklearn.metrics.cluster.expected_mutual_information", "sklearn.metrics.cluster.fowlkes_mallows_score", - "sklearn.metrics.cluster.homogeneity_completeness_v_measure", "sklearn.metrics.cluster.homogeneity_score", - "sklearn.metrics.cluster.mutual_info_score", "sklearn.metrics.cluster.normalized_mutual_info_score", - "sklearn.metrics.cluster.silhouette_samples", "sklearn.metrics.cluster.silhouette_score", - "sklearn.metrics.cluster.supervised.adjusted_mutual_info_score", "sklearn.metrics.cluster.supervised.adjusted_rand_score", - "sklearn.metrics.cluster.supervised.check_clusterings", "sklearn.metrics.cluster.supervised.comb2", - "sklearn.metrics.cluster.supervised.completeness_score", "sklearn.metrics.cluster.supervised.contingency_matrix", - "sklearn.metrics.cluster.supervised.entropy", "sklearn.metrics.cluster.supervised.fowlkes_mallows_score", - "sklearn.metrics.cluster.supervised.homogeneity_completeness_v_measure", "sklearn.metrics.cluster.supervised.homogeneity_score", - "sklearn.metrics.cluster.supervised.mutual_info_score", "sklearn.metrics.cluster.supervised.normalized_mutual_info_score", - "sklearn.metrics.cluster.supervised.v_measure_score", "sklearn.metrics.cluster.unsupervised.calinski_harabaz_score", - "sklearn.metrics.cluster.unsupervised.check_number_of_labels", "sklearn.metrics.cluster.unsupervised.silhouette_samples", - "sklearn.metrics.cluster.unsupervised.silhouette_score", "sklearn.metrics.cluster.v_measure_score", - "sklearn.metrics.cohen_kappa_score", "sklearn.metrics.completeness_score", - "sklearn.metrics.confusion_matrix", "sklearn.metrics.consensus_score", - "sklearn.metrics.coverage_error", "sklearn.metrics.euclidean_distances", - "sklearn.metrics.explained_variance_score", "sklearn.metrics.f1_score", - "sklearn.metrics.fbeta_score", "sklearn.metrics.fowlkes_mallows_score", - "sklearn.metrics.get_scorer", "sklearn.metrics.hamming_loss", - "sklearn.metrics.hinge_loss", "sklearn.metrics.homogeneity_completeness_v_measure", - "sklearn.metrics.homogeneity_score", "sklearn.metrics.jaccard_similarity_score", - "sklearn.metrics.label_ranking_average_precision_score", "sklearn.metrics.label_ranking_loss", - "sklearn.metrics.log_loss", "sklearn.metrics.make_scorer", - "sklearn.metrics.matthews_corrcoef", "sklearn.metrics.mean_absolute_error", - "sklearn.metrics.mean_squared_error", "sklearn.metrics.mean_squared_log_error", - "sklearn.metrics.median_absolute_error", "sklearn.metrics.mutual_info_score", - "sklearn.metrics.normalized_mutual_info_score", "sklearn.metrics.pairwise.KERNEL_PARAMS", - "sklearn.metrics.pairwise.PAIRED_DISTANCES", "sklearn.metrics.pairwise.PAIRWISE_BOOLEAN_FUNCTIONS", - "sklearn.metrics.pairwise.PAIRWISE_DISTANCE_FUNCTIONS", "sklearn.metrics.pairwise.PAIRWISE_KERNEL_FUNCTIONS", - "sklearn.metrics.pairwise._VALID_METRICS", "sklearn.metrics.pairwise._chi2_kernel_fast", - "sklearn.metrics.pairwise._pairwise_callable", "sklearn.metrics.pairwise._parallel_pairwise", - "sklearn.metrics.pairwise._return_float_dtype", "sklearn.metrics.pairwise._sparse_manhattan", - "sklearn.metrics.pairwise.additive_chi2_kernel", "sklearn.metrics.pairwise.check_paired_arrays", - "sklearn.metrics.pairwise.check_pairwise_arrays", "sklearn.metrics.pairwise.chi2_kernel", - "sklearn.metrics.pairwise.cosine_distances", "sklearn.metrics.pairwise.cosine_similarity", - "sklearn.metrics.pairwise.distance_metrics", "sklearn.metrics.pairwise.euclidean_distances", - "sklearn.metrics.pairwise.kernel_metrics", "sklearn.metrics.pairwise.laplacian_kernel", - "sklearn.metrics.pairwise.linear_kernel", "sklearn.metrics.pairwise.manhattan_distances", - "sklearn.metrics.pairwise.paired_cosine_distances", "sklearn.metrics.pairwise.paired_distances", - "sklearn.metrics.pairwise.paired_euclidean_distances", "sklearn.metrics.pairwise.paired_manhattan_distances", - "sklearn.metrics.pairwise.pairwise_distances", "sklearn.metrics.pairwise.pairwise_distances_argmin", - "sklearn.metrics.pairwise.pairwise_distances_argmin_min", "sklearn.metrics.pairwise.pairwise_kernels", - "sklearn.metrics.pairwise.polynomial_kernel", "sklearn.metrics.pairwise.rbf_kernel", - "sklearn.metrics.pairwise.sigmoid_kernel", "sklearn.metrics.pairwise_distances", - "sklearn.metrics.pairwise_distances_argmin", "sklearn.metrics.pairwise_distances_argmin_min", - "sklearn.metrics.pairwise_fast._chi2_kernel_fast", "sklearn.metrics.pairwise_fast._sparse_manhattan", - "sklearn.metrics.pairwise_kernels", "sklearn.metrics.precision_recall_curve", - "sklearn.metrics.precision_recall_fscore_support", "sklearn.metrics.precision_score", - "sklearn.metrics.r2_score", "sklearn.metrics.ranking._binary_clf_curve", - "sklearn.metrics.ranking.auc", "sklearn.metrics.ranking.average_precision_score", - "sklearn.metrics.ranking.coverage_error", "sklearn.metrics.ranking.label_ranking_average_precision_score", - "sklearn.metrics.ranking.label_ranking_loss", "sklearn.metrics.ranking.precision_recall_curve", - "sklearn.metrics.ranking.roc_auc_score", "sklearn.metrics.ranking.roc_curve", - "sklearn.metrics.recall_score", "sklearn.metrics.regression._check_reg_targets", - "sklearn.metrics.regression.explained_variance_score", "sklearn.metrics.regression.mean_absolute_error", - "sklearn.metrics.regression.mean_squared_error", "sklearn.metrics.regression.mean_squared_log_error", - "sklearn.metrics.regression.median_absolute_error", "sklearn.metrics.regression.r2_score", - "sklearn.metrics.regression.string_types", "sklearn.metrics.roc_auc_score", - "sklearn.metrics.roc_curve", "sklearn.metrics.scorer.SCORERS", - "sklearn.metrics.scorer._BaseScorer", "sklearn.metrics.scorer._PredictScorer", - "sklearn.metrics.scorer._ProbaScorer", "sklearn.metrics.scorer._ThresholdScorer", - "sklearn.metrics.scorer._check_multimetric_scoring", "sklearn.metrics.scorer._passthrough_scorer", - "sklearn.metrics.scorer.accuracy_scorer", "sklearn.metrics.scorer.adjusted_mutual_info_scorer", - "sklearn.metrics.scorer.adjusted_rand_scorer", "sklearn.metrics.scorer.average", - "sklearn.metrics.scorer.average_precision_scorer", "sklearn.metrics.scorer.check_scoring", - "sklearn.metrics.scorer.completeness_scorer", "sklearn.metrics.scorer.deprecation_msg", - "sklearn.metrics.scorer.explained_variance_scorer", "sklearn.metrics.scorer.f1_scorer", - "sklearn.metrics.scorer.fowlkes_mallows_scorer", "sklearn.metrics.scorer.get_scorer", - "sklearn.metrics.scorer.homogeneity_scorer", "sklearn.metrics.scorer.log_loss_scorer", - "sklearn.metrics.scorer.make_scorer", "sklearn.metrics.scorer.mean_absolute_error_scorer", - "sklearn.metrics.scorer.mean_squared_error_scorer", "sklearn.metrics.scorer.median_absolute_error_scorer", - "sklearn.metrics.scorer.mutual_info_scorer", "sklearn.metrics.scorer.name", - "sklearn.metrics.scorer.neg_log_loss_scorer", "sklearn.metrics.scorer.neg_mean_absolute_error_scorer", - "sklearn.metrics.scorer.neg_mean_squared_error_scorer", "sklearn.metrics.scorer.neg_mean_squared_log_error_scorer", - "sklearn.metrics.scorer.neg_median_absolute_error_scorer", "sklearn.metrics.scorer.normalized_mutual_info_scorer", - "sklearn.metrics.scorer.precision_scorer", "sklearn.metrics.scorer.qualified_name", - "sklearn.metrics.scorer.r2_scorer", "sklearn.metrics.scorer.recall_scorer", - "sklearn.metrics.scorer.roc_auc_scorer", "sklearn.metrics.scorer.v_measure_scorer", - "sklearn.metrics.silhouette_samples", "sklearn.metrics.silhouette_score", - "sklearn.metrics.v_measure_score", "sklearn.metrics.zero_one_loss", - "sklearn.model_selection.BaseCrossValidator", "sklearn.model_selection.GridSearchCV", - "sklearn.model_selection.GroupKFold", "sklearn.model_selection.GroupShuffleSplit", - "sklearn.model_selection.KFold", "sklearn.model_selection.LeaveOneGroupOut", - "sklearn.model_selection.LeaveOneOut", "sklearn.model_selection.LeavePGroupsOut", - "sklearn.model_selection.LeavePOut", "sklearn.model_selection.ParameterGrid", - "sklearn.model_selection.ParameterSampler", "sklearn.model_selection.PredefinedSplit", - "sklearn.model_selection.RandomizedSearchCV", "sklearn.model_selection.RepeatedKFold", - "sklearn.model_selection.RepeatedStratifiedKFold", "sklearn.model_selection.ShuffleSplit", - "sklearn.model_selection.StratifiedKFold", "sklearn.model_selection.StratifiedShuffleSplit", - "sklearn.model_selection.TimeSeriesSplit", "sklearn.model_selection._search.BaseSearchCV", - "sklearn.model_selection._search.GridSearchCV", "sklearn.model_selection._search.ParameterGrid", - "sklearn.model_selection._search.ParameterSampler", "sklearn.model_selection._search.RandomizedSearchCV", - "sklearn.model_selection._search._CVScoreTuple", "sklearn.model_selection._search._check_param_grid", - "sklearn.model_selection._search.fit_grid_point", "sklearn.model_selection._search.sp_version", - "sklearn.model_selection._split.BaseCrossValidator", "sklearn.model_selection._split.BaseShuffleSplit", - "sklearn.model_selection._split.GroupKFold", "sklearn.model_selection._split.GroupShuffleSplit", - "sklearn.model_selection._split.KFold", "sklearn.model_selection._split.LeaveOneGroupOut", - "sklearn.model_selection._split.LeaveOneOut", "sklearn.model_selection._split.LeavePGroupsOut", - "sklearn.model_selection._split.LeavePOut", "sklearn.model_selection._split.PredefinedSplit", - "sklearn.model_selection._split.RepeatedKFold", "sklearn.model_selection._split.RepeatedStratifiedKFold", - "sklearn.model_selection._split.ShuffleSplit", "sklearn.model_selection._split.StratifiedKFold", - "sklearn.model_selection._split.StratifiedShuffleSplit", "sklearn.model_selection._split.TimeSeriesSplit", - "sklearn.model_selection._split._BaseKFold", "sklearn.model_selection._split._CVIterableWrapper", - "sklearn.model_selection._split._RepeatedSplits", "sklearn.model_selection._split._approximate_mode", - "sklearn.model_selection._split._build_repr", "sklearn.model_selection._split._validate_shuffle_split", - "sklearn.model_selection._split._validate_shuffle_split_init", "sklearn.model_selection._split.check_cv", - "sklearn.model_selection._split.train_test_split", "sklearn.model_selection._validation._aggregate_score_dicts", - "sklearn.model_selection._validation._check_is_permutation", "sklearn.model_selection._validation._fit_and_predict", - "sklearn.model_selection._validation._fit_and_score", "sklearn.model_selection._validation._incremental_fit_estimator", - "sklearn.model_selection._validation._index_param_value", "sklearn.model_selection._validation._multimetric_score", - "sklearn.model_selection._validation._permutation_test_score", "sklearn.model_selection._validation._score", - "sklearn.model_selection._validation._shuffle", "sklearn.model_selection._validation._translate_train_sizes", - "sklearn.model_selection._validation.cross_val_predict", "sklearn.model_selection._validation.cross_val_score", - "sklearn.model_selection._validation.cross_validate", "sklearn.model_selection._validation.learning_curve", - "sklearn.model_selection._validation.permutation_test_score", "sklearn.model_selection._validation.validation_curve", - "sklearn.model_selection.check_cv", "sklearn.model_selection.cross_val_predict", - "sklearn.model_selection.cross_val_score", "sklearn.model_selection.cross_validate", - "sklearn.model_selection.fit_grid_point", "sklearn.model_selection.learning_curve", - "sklearn.model_selection.permutation_test_score", "sklearn.model_selection.train_test_split", - "sklearn.model_selection.validation_curve", "sklearn.multiclass.OneVsOneClassifier", - "sklearn.multiclass.OneVsRestClassifier", "sklearn.multiclass.OutputCodeClassifier", - "sklearn.multiclass._ConstantPredictor", "sklearn.multiclass._check_estimator", - "sklearn.multiclass._fit_binary", "sklearn.multiclass._fit_ovo_binary", - "sklearn.multiclass._partial_fit_binary", "sklearn.multiclass._partial_fit_ovo_binary", - "sklearn.multiclass._predict_binary", "sklearn.naive_bayes.BaseDiscreteNB", - "sklearn.naive_bayes.BaseNB", "sklearn.naive_bayes.BernoulliNB", - "sklearn.naive_bayes.GaussianNB", "sklearn.naive_bayes.MultinomialNB", - "sklearn.naive_bayes._ALPHA_MIN", "sklearn.neighbors.BallTree", - "sklearn.neighbors.DistanceMetric", "sklearn.neighbors.KDTree", - "sklearn.neighbors.KNeighborsClassifier", "sklearn.neighbors.KNeighborsRegressor", - "sklearn.neighbors.KernelDensity", "sklearn.neighbors.LSHForest", - "sklearn.neighbors.LocalOutlierFactor", "sklearn.neighbors.NearestCentroid", - "sklearn.neighbors.NearestNeighbors", "sklearn.neighbors.RadiusNeighborsClassifier", - "sklearn.neighbors.RadiusNeighborsRegressor", "sklearn.neighbors.approximate.GaussianRandomProjectionHash", - "sklearn.neighbors.approximate.HASH_DTYPE", "sklearn.neighbors.approximate.LSHForest", - "sklearn.neighbors.approximate.MAX_HASH_SIZE", "sklearn.neighbors.approximate.ProjectionToHashMixin", - "sklearn.neighbors.approximate._array_of_arrays", "sklearn.neighbors.approximate._find_longest_prefix_match", - "sklearn.neighbors.approximate._find_matching_indices", "sklearn.neighbors.ball_tree.BallTree", - "sklearn.neighbors.ball_tree.BinaryTree", "sklearn.neighbors.ball_tree.CLASS_DOC", - "sklearn.neighbors.ball_tree.DOC_DICT", "sklearn.neighbors.ball_tree.NeighborsHeap", - "sklearn.neighbors.ball_tree.NodeData", "sklearn.neighbors.ball_tree.NodeHeap", - "sklearn.neighbors.ball_tree.NodeHeapData", "sklearn.neighbors.ball_tree.VALID_METRICS", - "sklearn.neighbors.ball_tree.VALID_METRIC_IDS", "sklearn.neighbors.ball_tree.kernel_norm", - "sklearn.neighbors.ball_tree.load_heap", "sklearn.neighbors.ball_tree.newObj", - "sklearn.neighbors.ball_tree.nodeheap_sort", "sklearn.neighbors.ball_tree.offsets", - "sklearn.neighbors.ball_tree.simultaneous_sort", "sklearn.neighbors.base.KNeighborsMixin", - "sklearn.neighbors.base.NeighborsBase", "sklearn.neighbors.base.PAIRWISE_DISTANCE_FUNCTIONS", - "sklearn.neighbors.base.RadiusNeighborsMixin", "sklearn.neighbors.base.SupervisedFloatMixin", - "sklearn.neighbors.base.SupervisedIntegerMixin", "sklearn.neighbors.base.UnsupervisedMixin", - "sklearn.neighbors.base.VALID_METRICS", "sklearn.neighbors.base.VALID_METRICS_SPARSE", - "sklearn.neighbors.base._check_weights", "sklearn.neighbors.base._get_weights", - "sklearn.neighbors.classification.KNeighborsClassifier", "sklearn.neighbors.classification.RadiusNeighborsClassifier", - "sklearn.neighbors.dist_metrics.BrayCurtisDistance", "sklearn.neighbors.dist_metrics.CanberraDistance", - "sklearn.neighbors.dist_metrics.ChebyshevDistance", "sklearn.neighbors.dist_metrics.DiceDistance", - "sklearn.neighbors.dist_metrics.DistanceMetric", "sklearn.neighbors.dist_metrics.EuclideanDistance", - "sklearn.neighbors.dist_metrics.HammingDistance", "sklearn.neighbors.dist_metrics.HaversineDistance", - "sklearn.neighbors.dist_metrics.JaccardDistance", "sklearn.neighbors.dist_metrics.KulsinskiDistance", - "sklearn.neighbors.dist_metrics.METRIC_MAPPING", "sklearn.neighbors.dist_metrics.MahalanobisDistance", - "sklearn.neighbors.dist_metrics.ManhattanDistance", "sklearn.neighbors.dist_metrics.MatchingDistance", - "sklearn.neighbors.dist_metrics.MinkowskiDistance", "sklearn.neighbors.dist_metrics.PyFuncDistance", - "sklearn.neighbors.dist_metrics.RogersTanimotoDistance", "sklearn.neighbors.dist_metrics.RussellRaoDistance", - "sklearn.neighbors.dist_metrics.SEuclideanDistance", "sklearn.neighbors.dist_metrics.SokalMichenerDistance", - "sklearn.neighbors.dist_metrics.SokalSneathDistance", "sklearn.neighbors.dist_metrics.WMinkowskiDistance", - "sklearn.neighbors.dist_metrics.get_valid_metric_ids", "sklearn.neighbors.dist_metrics.newObj", - "sklearn.neighbors.graph._check_params", "sklearn.neighbors.graph._query_include_self", - "sklearn.neighbors.graph.kneighbors_graph", "sklearn.neighbors.graph.radius_neighbors_graph", - "sklearn.neighbors.kd_tree.BinaryTree", "sklearn.neighbors.kd_tree.CLASS_DOC", - "sklearn.neighbors.kd_tree.DOC_DICT", "sklearn.neighbors.kd_tree.KDTree", - "sklearn.neighbors.kd_tree.NeighborsHeap", "sklearn.neighbors.kd_tree.NodeData", - "sklearn.neighbors.kd_tree.NodeHeap", "sklearn.neighbors.kd_tree.NodeHeapData", - "sklearn.neighbors.kd_tree.VALID_METRICS", "sklearn.neighbors.kd_tree.VALID_METRIC_IDS", - "sklearn.neighbors.kd_tree.kernel_norm", "sklearn.neighbors.kd_tree.load_heap", - "sklearn.neighbors.kd_tree.newObj", "sklearn.neighbors.kd_tree.nodeheap_sort", - "sklearn.neighbors.kd_tree.offsets", "sklearn.neighbors.kd_tree.simultaneous_sort", - "sklearn.neighbors.kde.KernelDensity", "sklearn.neighbors.kde.TREE_DICT", - "sklearn.neighbors.kde.VALID_KERNELS", "sklearn.neighbors.kde.gammainc", - "sklearn.neighbors.kneighbors_graph", "sklearn.neighbors.lof.LocalOutlierFactor", - "sklearn.neighbors.nearest_centroid.NearestCentroid", "sklearn.neighbors.quad_tree.CELL_DTYPE", - "sklearn.neighbors.quad_tree._QuadTree", "sklearn.neighbors.radius_neighbors_graph", - "sklearn.neighbors.regression.KNeighborsRegressor", "sklearn.neighbors.regression.RadiusNeighborsRegressor", - "sklearn.neighbors.unsupervised.NearestNeighbors", "sklearn.pipeline.FeatureUnion", - "sklearn.pipeline.Pipeline", "sklearn.pipeline._fit_one_transformer", - "sklearn.pipeline._fit_transform_one", "sklearn.pipeline._name_estimators", - "sklearn.pipeline._transform_one", "sklearn.pipeline.make_pipeline", - "sklearn.pipeline.make_union", "sklearn.preprocessing.Binarizer", - "sklearn.preprocessing.FunctionTransformer", "sklearn.preprocessing.Imputer", - "sklearn.preprocessing.KernelCenterer", "sklearn.preprocessing.LabelBinarizer", - "sklearn.preprocessing.LabelEncoder", "sklearn.preprocessing.MaxAbsScaler", - "sklearn.preprocessing.MinMaxScaler", "sklearn.preprocessing.MultiLabelBinarizer", - "sklearn.preprocessing.Normalizer", "sklearn.preprocessing.OneHotEncoder", - "sklearn.preprocessing.PolynomialFeatures", "sklearn.preprocessing.QuantileTransformer", - "sklearn.preprocessing.RobustScaler", "sklearn.preprocessing.StandardScaler", - "sklearn.preprocessing._function_transformer.FunctionTransformer", "sklearn.preprocessing._function_transformer._identity", - "sklearn.preprocessing._function_transformer.string_types", "sklearn.preprocessing.add_dummy_feature", - "sklearn.preprocessing.binarize", "sklearn.preprocessing.data.BOUNDS_THRESHOLD", - "sklearn.preprocessing.data.Binarizer", "sklearn.preprocessing.data.FLOAT_DTYPES", - "sklearn.preprocessing.data.KernelCenterer", "sklearn.preprocessing.data.MaxAbsScaler", - "sklearn.preprocessing.data.MinMaxScaler", "sklearn.preprocessing.data.Normalizer", - "sklearn.preprocessing.data.OneHotEncoder", "sklearn.preprocessing.data.PolynomialFeatures", - "sklearn.preprocessing.data.QuantileTransformer", "sklearn.preprocessing.data.RobustScaler", - "sklearn.preprocessing.data.StandardScaler", "sklearn.preprocessing.data._handle_zeros_in_scale", - "sklearn.preprocessing.data._transform_selected", "sklearn.preprocessing.data.add_dummy_feature", - "sklearn.preprocessing.data.binarize", "sklearn.preprocessing.data.maxabs_scale", - "sklearn.preprocessing.data.minmax_scale", "sklearn.preprocessing.data.normalize", - "sklearn.preprocessing.data.quantile_transform", "sklearn.preprocessing.data.robust_scale", - "sklearn.preprocessing.data.scale", "sklearn.preprocessing.data.string_types", - "sklearn.preprocessing.imputation.FLOAT_DTYPES", "sklearn.preprocessing.imputation.Imputer", - "sklearn.preprocessing.imputation._get_mask", "sklearn.preprocessing.imputation._most_frequent", - "sklearn.preprocessing.label.LabelBinarizer", "sklearn.preprocessing.label.LabelEncoder", - "sklearn.preprocessing.label.MultiLabelBinarizer", "sklearn.preprocessing.label._inverse_binarize_multiclass", - "sklearn.preprocessing.label._inverse_binarize_thresholding", "sklearn.preprocessing.label.label_binarize", - "sklearn.preprocessing.label_binarize", "sklearn.preprocessing.maxabs_scale", - "sklearn.preprocessing.minmax_scale", "sklearn.preprocessing.normalize", - "sklearn.preprocessing.quantile_transform", "sklearn.preprocessing.robust_scale", - "sklearn.preprocessing.scale", "sklearn.random_projection.BaseRandomProjection", - "sklearn.random_projection.GaussianRandomProjection", "sklearn.random_projection.SparseRandomProjection", - "sklearn.random_projection._check_density", "sklearn.random_projection._check_input_size", - "sklearn.random_projection.gaussian_random_matrix", "sklearn.random_projection.johnson_lindenstrauss_min_dim", - "sklearn.random_projection.sparse_random_matrix", "sklearn.set_config", - "sklearn.setup_module", "sklearn.svm.LinearSVC", - "sklearn.svm.LinearSVR", "sklearn.svm.NuSVC", - "sklearn.svm.NuSVR", "sklearn.svm.OneClassSVM", - "sklearn.svm.SVC", "sklearn.svm.SVR", - "sklearn.svm.base.BaseLibSVM", "sklearn.svm.base.BaseSVC", - "sklearn.svm.base.LIBSVM_IMPL", "sklearn.svm.base._fit_liblinear", - "sklearn.svm.base._get_liblinear_solver_type", "sklearn.svm.base._one_vs_one_coef", - "sklearn.svm.bounds.l1_min_c", "sklearn.svm.classes.LinearSVC", - "sklearn.svm.classes.LinearSVR", "sklearn.svm.classes.NuSVC", - "sklearn.svm.classes.NuSVR", "sklearn.svm.classes.OneClassSVM", - "sklearn.svm.classes.SVC", "sklearn.svm.classes.SVR", - "sklearn.svm.l1_min_c", "sklearn.svm.liblinear.set_verbosity_wrap", - "sklearn.svm.liblinear.train_wrap", "sklearn.svm.libsvm.LIBSVM_KERNEL_TYPES", - "sklearn.svm.libsvm.cross_validation", "sklearn.svm.libsvm.decision_function", - "sklearn.svm.libsvm.fit", "sklearn.svm.libsvm.predict", - "sklearn.svm.libsvm.predict_proba", "sklearn.svm.libsvm.set_verbosity_wrap", - "sklearn.svm.libsvm_sparse.libsvm_sparse_decision_function", "sklearn.svm.libsvm_sparse.libsvm_sparse_predict", - "sklearn.svm.libsvm_sparse.libsvm_sparse_predict_proba", "sklearn.svm.libsvm_sparse.libsvm_sparse_train", - "sklearn.svm.libsvm_sparse.set_verbosity_wrap", "sklearn.tree.DecisionTreeClassifier", - "sklearn.tree.DecisionTreeRegressor", "sklearn.tree.ExtraTreeClassifier", - "sklearn.tree.ExtraTreeRegressor", "sklearn.tree._criterion.ClassificationCriterion", - "sklearn.tree._criterion.Criterion", "sklearn.tree._criterion.Entropy", - "sklearn.tree._criterion.FriedmanMSE", "sklearn.tree._criterion.Gini", - "sklearn.tree._criterion.MAE", "sklearn.tree._criterion.MSE", - "sklearn.tree._criterion.RegressionCriterion", "sklearn.tree._splitter.BaseDenseSplitter", - "sklearn.tree._splitter.BaseSparseSplitter", "sklearn.tree._splitter.BestSparseSplitter", - "sklearn.tree._splitter.BestSplitter", "sklearn.tree._splitter.RandomSparseSplitter", - "sklearn.tree._splitter.RandomSplitter", "sklearn.tree._splitter.Splitter", - "sklearn.tree._tree.BestFirstTreeBuilder", "sklearn.tree._tree.DepthFirstTreeBuilder", - "sklearn.tree._tree.NODE_DTYPE", "sklearn.tree._tree.TREE_LEAF", - "sklearn.tree._tree.TREE_UNDEFINED", "sklearn.tree._tree.Tree", - "sklearn.tree._tree.TreeBuilder", "sklearn.tree._utils.PriorityHeap", - "sklearn.tree._utils.Stack", "sklearn.tree._utils.WeightedMedianCalculator", - "sklearn.tree._utils.WeightedPQueue", "sklearn.tree._utils._realloc_test", - "sklearn.tree.export.SENTINEL", "sklearn.tree.export.Sentinel", - "sklearn.tree.export._color_brew", "sklearn.tree.export.export_graphviz", - "sklearn.tree.export_graphviz", "sklearn.tree.tree.BaseDecisionTree", - "sklearn.tree.tree.CRITERIA_CLF", "sklearn.tree.tree.CRITERIA_REG", - "sklearn.tree.tree.DENSE_SPLITTERS", "sklearn.tree.tree.DecisionTreeClassifier", - "sklearn.tree.tree.DecisionTreeRegressor", "sklearn.tree.tree.ExtraTreeClassifier", - "sklearn.tree.tree.ExtraTreeRegressor", "sklearn.tree.tree.SPARSE_SPLITTERS", - "sklearn.utils.Bunch", "sklearn.utils._get_n_jobs", - "sklearn.utils._logistic_sigmoid._log_logistic_sigmoid", "sklearn.utils._random._sample_without_replacement_check_input", - "sklearn.utils._random._sample_without_replacement_with_pool", "sklearn.utils._random._sample_without_replacement_with_reservoir_sampling", - "sklearn.utils._random._sample_without_replacement_with_tracking_selection", "sklearn.utils._random.sample_without_replacement", - "sklearn.utils.arrayfuncs.cholesky_delete", "sklearn.utils.arrayfuncs.min_pos", - "sklearn.utils.as_float_array", "sklearn.utils.assert_all_finite", - "sklearn.utils.axis0_safe_slice", "sklearn.utils.check_X_y", - "sklearn.utils.check_array", "sklearn.utils.check_consistent_length", - "sklearn.utils.check_random_state", "sklearn.utils.check_symmetric", - "sklearn.utils.class_weight.compute_class_weight", "sklearn.utils.class_weight.compute_sample_weight", - "sklearn.utils.column_or_1d", "sklearn.utils.compute_class_weight", - "sklearn.utils.compute_sample_weight", "sklearn.utils.deprecated", - "sklearn.utils.deprecation.DeprecationDict", "sklearn.utils.deprecation._is_deprecated", - "sklearn.utils.deprecation.deprecated", "sklearn.utils.extmath._deterministic_vector_sign_flip", - "sklearn.utils.extmath._impose_f_order", "sklearn.utils.extmath._incremental_mean_and_var", - "sklearn.utils.extmath.cartesian", "sklearn.utils.extmath.density", - "sklearn.utils.extmath.fast_dot", "sklearn.utils.extmath.fast_logdet", - "sklearn.utils.extmath.log_logistic", "sklearn.utils.extmath.logsumexp", - "sklearn.utils.extmath.make_nonnegative", "sklearn.utils.extmath.norm", - "sklearn.utils.extmath.np_version", "sklearn.utils.extmath.pinvh", - "sklearn.utils.extmath.randomized_range_finder", "sklearn.utils.extmath.randomized_svd", - "sklearn.utils.extmath.row_norms", "sklearn.utils.extmath.safe_min", - "sklearn.utils.extmath.safe_sparse_dot", "sklearn.utils.extmath.softmax", - "sklearn.utils.extmath.squared_norm", "sklearn.utils.extmath.stable_cumsum", - "sklearn.utils.extmath.svd_flip", "sklearn.utils.extmath.weighted_mode", - "sklearn.utils.fast_dict.IntFloatDict", "sklearn.utils.fast_dict.argmin", - "sklearn.utils.fixes._parse_version", "sklearn.utils.fixes.divide", - "sklearn.utils.fixes.euler_gamma", "sklearn.utils.fixes.makedirs", - "sklearn.utils.fixes.np_version", "sklearn.utils.fixes.parallel_helper", - "sklearn.utils.fixes.sp_version", "sklearn.utils.fixes.sparse_min_max", - "sklearn.utils.gen_batches", "sklearn.utils.gen_even_slices", - "sklearn.utils.graph.connected_components", "sklearn.utils.graph.graph_laplacian", - "sklearn.utils.graph.graph_shortest_path", "sklearn.utils.graph.single_source_shortest_path_length", - "sklearn.utils.graph_shortest_path.graph_shortest_path", "sklearn.utils.indexable", - "sklearn.utils.indices_to_mask", "sklearn.utils.linear_assignment_._HungarianState", - "sklearn.utils.linear_assignment_._hungarian", "sklearn.utils.linear_assignment_._step1", - "sklearn.utils.linear_assignment_._step3", "sklearn.utils.linear_assignment_._step4", - "sklearn.utils.linear_assignment_._step5", "sklearn.utils.linear_assignment_._step6", - "sklearn.utils.linear_assignment_.linear_assignment", "sklearn.utils.metaestimators._BaseComposition", - "sklearn.utils.metaestimators._IffHasAttrDescriptor", "sklearn.utils.metaestimators._safe_split", - "sklearn.utils.metaestimators.if_delegate_has_method", "sklearn.utils.multiclass._FN_UNIQUE_LABELS", - "sklearn.utils.multiclass._check_partial_fit_first_call", "sklearn.utils.multiclass._is_integral_float", - "sklearn.utils.multiclass._ovr_decision_function", "sklearn.utils.multiclass._unique_indicator", - "sklearn.utils.multiclass._unique_multiclass", "sklearn.utils.multiclass.check_classification_targets", - "sklearn.utils.multiclass.class_distribution", "sklearn.utils.multiclass.is_multilabel", - "sklearn.utils.multiclass.string_types", "sklearn.utils.multiclass.type_of_target", - "sklearn.utils.multiclass.unique_labels", "sklearn.utils.murmurhash.murmurhash3_32", - "sklearn.utils.murmurhash.murmurhash3_bytes_array_s32", "sklearn.utils.murmurhash.murmurhash3_bytes_array_u32", - "sklearn.utils.murmurhash.murmurhash3_bytes_s32", "sklearn.utils.murmurhash.murmurhash3_bytes_u32", - "sklearn.utils.murmurhash.murmurhash3_int_s32", "sklearn.utils.murmurhash.murmurhash3_int_u32", - "sklearn.utils.murmurhash3_32", "sklearn.utils.optimize._LineSearchError", - "sklearn.utils.optimize._cg", "sklearn.utils.optimize._line_search_wolfe12", - "sklearn.utils.optimize.newton_cg", "sklearn.utils.random.choice", - "sklearn.utils.random.random_choice_csc", "sklearn.utils.resample", - "sklearn.utils.safe_indexing", "sklearn.utils.safe_mask", - "sklearn.utils.safe_sqr", "sklearn.utils.seq_dataset.ArrayDataset", - "sklearn.utils.seq_dataset.CSRDataset", "sklearn.utils.seq_dataset.SequentialDataset", - "sklearn.utils.shuffle", "sklearn.utils.sparsefuncs._csc_mean_var_axis0", - "sklearn.utils.sparsefuncs._csr_mean_var_axis0", "sklearn.utils.sparsefuncs._get_elem_at_rank", - "sklearn.utils.sparsefuncs._get_median", "sklearn.utils.sparsefuncs._incr_mean_var_axis0", - "sklearn.utils.sparsefuncs._raise_error_wrong_axis", "sklearn.utils.sparsefuncs._raise_typeerror", - "sklearn.utils.sparsefuncs.count_nonzero", "sklearn.utils.sparsefuncs.csc_median_axis_0", - "sklearn.utils.sparsefuncs.incr_mean_variance_axis", "sklearn.utils.sparsefuncs.inplace_column_scale", - "sklearn.utils.sparsefuncs.inplace_csr_column_scale", "sklearn.utils.sparsefuncs.inplace_csr_row_scale", - "sklearn.utils.sparsefuncs.inplace_row_scale", "sklearn.utils.sparsefuncs.inplace_swap_column", - "sklearn.utils.sparsefuncs.inplace_swap_row", "sklearn.utils.sparsefuncs.inplace_swap_row_csc", - "sklearn.utils.sparsefuncs.inplace_swap_row_csr", "sklearn.utils.sparsefuncs.mean_variance_axis", - "sklearn.utils.sparsefuncs.min_max_axis", "sklearn.utils.sparsefuncs_fast._csc_mean_variance_axis0", - "sklearn.utils.sparsefuncs_fast._csr_mean_variance_axis0", "sklearn.utils.sparsefuncs_fast._csr_row_norms", - "sklearn.utils.sparsefuncs_fast._incr_mean_variance_axis0", "sklearn.utils.sparsefuncs_fast._inplace_csr_row_normalize_l1", - "sklearn.utils.sparsefuncs_fast._inplace_csr_row_normalize_l2", "sklearn.utils.sparsefuncs_fast.assign_rows_csr", - "sklearn.utils.sparsefuncs_fast.csc_mean_variance_axis0", "sklearn.utils.sparsefuncs_fast.csr_mean_variance_axis0", - "sklearn.utils.sparsefuncs_fast.csr_row_norms", "sklearn.utils.sparsefuncs_fast.incr_mean_variance_axis0", - "sklearn.utils.sparsefuncs_fast.inplace_csr_row_normalize_l1", "sklearn.utils.sparsefuncs_fast.inplace_csr_row_normalize_l2", - "sklearn.utils.stats._weighted_percentile", "sklearn.utils.stats.rankdata", - "sklearn.utils.tosequence", "sklearn.utils.validation.FLOAT_DTYPES", - "sklearn.utils.validation._assert_all_finite", "sklearn.utils.validation._ensure_sparse_format", - "sklearn.utils.validation._is_arraylike", "sklearn.utils.validation._num_samples", - "sklearn.utils.validation._shape_repr", "sklearn.utils.validation.as_float_array", - "sklearn.utils.validation.assert_all_finite", "sklearn.utils.validation.check_X_y", - "sklearn.utils.validation.check_array", "sklearn.utils.validation.check_consistent_length", - "sklearn.utils.validation.check_is_fitted", "sklearn.utils.validation.check_memory", - "sklearn.utils.validation.check_non_negative", "sklearn.utils.validation.check_random_state", - "sklearn.utils.validation.check_symmetric", "sklearn.utils.validation.column_or_1d", - "sklearn.utils.validation.has_fit_parameter", "sklearn.utils.validation.indexable", - "sklearn.utils.weight_vector.WeightVector" -], - - "SKR_NAMES": [ - "skrebate.MultiSURF", "skrebate.MultiSURFstar", - "skrebate.ReliefF", "skrebate.SURF", - "skrebate.SURFstar", "skrebate.TuRF", - "skrebate.multisurf.MultiSURF", "skrebate.multisurfstar.MultiSURFstar", - "skrebate.relieff.ReliefF", "skrebate.scoring_utils.MultiSURF_compute_scores", - "skrebate.scoring_utils.MultiSURFstar_compute_scores", "skrebate.scoring_utils.ReliefF_compute_scores", - "skrebate.scoring_utils.SURF_compute_scores", "skrebate.scoring_utils.SURFstar_compute_scores", - "skrebate.scoring_utils.compute_score", "skrebate.scoring_utils.get_row_missing", - "skrebate.scoring_utils.ramp_function", "skrebate.surf.SURF", - "skrebate.surfstar.SURFstar", "skrebate.turf.TuRF" - ], - - "XGB_NAMES": [ - "xgboost.Booster", "xgboost.DMatrix", - "xgboost.VERSION_FILE", "xgboost.XGBClassifier", - "xgboost.XGBModel", "xgboost.XGBRegressor", - "xgboost.callback._fmt_metric", "xgboost.callback._get_callback_context", - "xgboost.callback.early_stop", "xgboost.callback.print_evaluation", - "xgboost.callback.record_evaluation", "xgboost.callback.reset_learning_rate", - "xgboost.compat.PANDAS_INSTALLED", "xgboost.compat.PY3", - "xgboost.compat.SKLEARN_INSTALLED", "xgboost.compat.STRING_TYPES", - "xgboost.compat.py_str", "xgboost.core.Booster", - "xgboost.core.CallbackEnv", "xgboost.core.DMatrix", - "xgboost.core.EarlyStopException", "xgboost.core.PANDAS_DTYPE_MAPPER", - "xgboost.core.PANDAS_INSTALLED", "xgboost.core.PY3", - "xgboost.core.STRING_TYPES", "xgboost.core.XGBoostError", - "xgboost.core._check_call", "xgboost.core._load_lib", - "xgboost.core._maybe_pandas_data", "xgboost.core._maybe_pandas_label", - "xgboost.core.c_array", "xgboost.core.c_str", - "xgboost.core.ctypes2buffer", "xgboost.core.ctypes2numpy", - "xgboost.core.from_cstr_to_pystr", "xgboost.core.from_pystr_to_cstr", - "xgboost.cv", "xgboost.f", - "xgboost.libpath.XGBoostLibraryNotFound", "xgboost.libpath.find_lib_path", - "xgboost.plot_importance", "xgboost.plot_tree", - "xgboost.plotting._EDGEPAT", "xgboost.plotting._EDGEPAT2", - "xgboost.plotting._LEAFPAT", "xgboost.plotting._NODEPAT", - "xgboost.plotting._parse_edge", "xgboost.plotting._parse_node", - "xgboost.plotting.plot_importance", "xgboost.plotting.plot_tree", - "xgboost.plotting.to_graphviz", "xgboost.rabit.DTYPE_ENUM__", - "xgboost.rabit.STRING_TYPES", "xgboost.rabit._init_rabit", - "xgboost.rabit.allreduce", "xgboost.rabit.broadcast", - "xgboost.rabit.finalize", "xgboost.rabit.get_processor_name", - "xgboost.rabit.get_rank", "xgboost.rabit.get_world_size", - "xgboost.rabit.init", "xgboost.rabit.tracker_print", - "xgboost.rabit.version_number", "xgboost.sklearn.SKLEARN_INSTALLED", - "xgboost.sklearn.XGBClassifier", "xgboost.sklearn.XGBModel", - "xgboost.sklearn.XGBRegressor", "xgboost.sklearn._objective_decorator", - "xgboost.to_graphviz", "xgboost.train", - "xgboost.training.CVPack", "xgboost.training.SKLEARN_INSTALLED", - "xgboost.training.STRING_TYPES", "xgboost.training._train_internal", - "xgboost.training.aggcv", "xgboost.training.cv", - "xgboost.training.mknfold", "xgboost.training.train" - ], - - - "NUMPY_NAMES": [ - "numpy.core.multiarray._reconstruct", "numpy.ndarray", - "numpy.dtype", "numpy.core.multiarray.scalar", - "numpy.random.__RandomState_ctor" - ], - - "IMBLEARN_NAMES":[ - "imblearn.pipeline.Pipeline", "imblearn.over_sampling._random_over_sampler.RandomOverSampler", - "imblearn.under_sampling._prototype_selection._edited_nearest_neighbours.EditedNearestNeighbours" - ] -} \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/stacking_ensembles.py Tue May 14 18:06:37 2019 -0400 @@ -0,0 +1,128 @@ +import argparse +import json +import pandas as pd +import pickle +import xgboost +import warnings +from sklearn import (cluster, compose, decomposition, ensemble, + feature_extraction, feature_selection, + gaussian_process, kernel_approximation, metrics, + model_selection, naive_bayes, neighbors, + pipeline, preprocessing, svm, linear_model, + tree, discriminant_analysis) +from sklearn.model_selection._split import check_cv +from feature_selectors import (DyRFE, DyRFECV, + MyPipeline, MyimbPipeline) +from iraps_classifier import (IRAPSCore, IRAPSClassifier, + BinarizeTargetClassifier, + BinarizeTargetRegressor) +from preprocessors import Z_RandomOverSampler +from utils import load_model, get_cv, get_estimator, get_search_params + +from mlxtend.regressor import StackingCVRegressor, StackingRegressor +from mlxtend.classifier import StackingCVClassifier, StackingClassifier + + +warnings.filterwarnings('ignore') + +N_JOBS = int(__import__('os').environ.get('GALAXY_SLOTS', 1)) + + +def main(inputs_path, output_obj, base_paths=None, meta_path=None, + outfile_params=None): + """ + Parameter + --------- + inputs_path : str + File path for Galaxy parameters + + output_obj : str + File path for ensemble estimator ouput + + base_paths : str + File path or paths concatenated by comma. + + meta_path : str + File path + + outfile_params : str + File path for params output + """ + with open(inputs_path, 'r') as param_handler: + params = json.load(param_handler) + + base_estimators = [] + for idx, base_file in enumerate(base_paths.split(',')): + if base_file and base_file != 'None': + with open(base_file, 'rb') as handler: + model = load_model(handler) + else: + estimator_json = (params['base_est_builder'][idx] + ['estimator_selector']) + model = get_estimator(estimator_json) + base_estimators.append(model) + + if meta_path: + with open(meta_path, 'rb') as f: + meta_estimator = load_model(f) + else: + estimator_json = params['meta_estimator']['estimator_selector'] + meta_estimator = get_estimator(estimator_json) + + options = params['algo_selection']['options'] + + cv_selector = options.pop('cv_selector', None) + if cv_selector: + splitter, groups = get_cv(cv_selector) + options['cv'] = splitter + # set n_jobs + options['n_jobs'] = N_JOBS + + if params['algo_selection']['estimator_type'] == 'StackingCVClassifier': + ensemble_estimator = StackingCVClassifier( + classifiers=base_estimators, + meta_classifier=meta_estimator, + **options) + + elif params['algo_selection']['estimator_type'] == 'StackingClassifier': + ensemble_estimator = StackingClassifier( + classifiers=base_estimators, + meta_classifier=meta_estimator, + **options) + + elif params['algo_selection']['estimator_type'] == 'StackingCVRegressor': + ensemble_estimator = StackingCVRegressor( + regressors=base_estimators, + meta_regressor=meta_estimator, + **options) + + else: + ensemble_estimator = StackingRegressor( + regressors=base_estimators, + meta_regressor=meta_estimator, + **options) + + print(ensemble_estimator) + for base_est in base_estimators: + print(base_est) + + with open(output_obj, 'wb') as out_handler: + pickle.dump(ensemble_estimator, out_handler, pickle.HIGHEST_PROTOCOL) + + if params['get_params'] and outfile_params: + results = get_search_params(ensemble_estimator) + df = pd.DataFrame(results, columns=['', 'Parameter', 'Value']) + df.to_csv(outfile_params, sep='\t', index=False) + + +if __name__ == '__main__': + aparser = argparse.ArgumentParser() + aparser.add_argument("-b", "--bases", dest="bases") + aparser.add_argument("-m", "--meta", dest="meta") + aparser.add_argument("-i", "--inputs", dest="inputs") + aparser.add_argument("-o", "--outfile", dest="outfile") + aparser.add_argument("-p", "--outfile_params", dest="outfile_params") + args = aparser.parse_args() + + main(args.inputs, args.outfile, base_paths=args.bases, + meta_path=args.meta, outfile_params=args.outfile_params)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/best_params_.txt Tue May 14 18:06:37 2019 -0400 @@ -0,0 +1,1 @@ +{'estimator__n_estimators': 100} \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/best_score_.tabular Tue May 14 18:06:37 2019 -0400 @@ -0,0 +1,2 @@ +best_score_ +0.7976348550293088
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/feature_importances_.tabular Tue May 14 18:06:37 2019 -0400 @@ -0,0 +1,11 @@ +feature_importances_ +0.15959252 +0.20373514 +0.22071308 +0.06281833 +0.098471984 +0.06960951 +0.13073005 +0.027164686 +0.022071308 +0.0050933785
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/feature_selection_result13 Tue May 14 18:06:37 2019 -0400 @@ -0,0 +1,262 @@ +temp_1 average forecast_noaa friend +69.0 69.7 65.0 88.0 +59.0 58.1 57.0 66.0 +88.0 77.3 75.0 70.0 +65.0 64.7 63.0 58.0 +50.0 47.5 44.0 58.0 +51.0 48.2 45.0 63.0 +52.0 48.6 45.0 41.0 +78.0 76.7 75.0 66.0 +35.0 45.2 43.0 38.0 +40.0 46.1 45.0 36.0 +47.0 45.3 41.0 58.0 +72.0 76.3 76.0 88.0 +76.0 74.4 73.0 72.0 +39.0 45.3 45.0 46.0 +78.0 72.2 70.0 84.0 +71.0 67.3 63.0 85.0 +48.0 47.7 44.0 61.0 +72.0 77.0 77.0 68.0 +57.0 54.7 50.0 70.0 +40.0 45.1 44.0 39.0 +54.0 47.6 47.0 53.0 +58.0 53.2 52.0 71.0 +68.0 58.6 58.0 54.0 +65.0 55.3 55.0 65.0 +47.0 48.8 46.0 51.0 +44.0 45.6 43.0 42.0 +64.0 67.1 64.0 69.0 +62.0 57.1 57.0 67.0 +66.0 65.7 64.0 74.0 +70.0 71.8 67.0 90.0 +57.0 54.2 54.0 70.0 +50.0 50.5 46.0 57.0 +55.0 51.8 49.0 71.0 +55.0 49.5 46.0 67.0 +42.0 45.2 41.0 47.0 +65.0 60.1 57.0 41.0 +63.0 65.6 63.0 73.0 +48.0 47.3 45.0 28.0 +42.0 46.3 44.0 62.0 +51.0 46.2 45.0 38.0 +64.0 68.0 65.0 64.0 +75.0 74.6 74.0 63.0 +52.0 46.7 42.0 39.0 +67.0 68.6 66.0 80.0 +68.0 68.7 65.0 56.0 +54.0 55.0 53.0 42.0 +62.0 56.8 52.0 70.0 +76.0 76.1 76.0 61.0 +73.0 73.1 71.0 93.0 +52.0 50.3 50.0 35.0 +70.0 73.9 71.0 68.0 +77.0 77.4 75.0 62.0 +60.0 56.6 52.0 72.0 +52.0 53.3 50.0 54.0 +79.0 75.0 71.0 85.0 +76.0 57.2 53.0 74.0 +66.0 66.5 64.0 85.0 +57.0 61.8 58.0 62.0 +66.0 57.4 57.0 60.0 +61.0 58.4 58.0 41.0 +55.0 53.1 52.0 65.0 +48.0 48.1 46.0 54.0 +49.0 49.2 46.0 63.0 +65.0 66.7 64.0 73.0 +60.0 62.5 58.0 56.0 +56.0 53.0 53.0 36.0 +59.0 57.4 56.0 44.0 +44.0 45.7 41.0 35.0 +82.0 63.2 62.0 83.0 +64.0 67.0 65.0 76.0 +43.0 45.5 41.0 46.0 +64.0 55.7 51.0 57.0 +63.0 52.7 49.0 49.0 +70.0 70.6 67.0 79.0 +71.0 52.4 48.0 42.0 +76.0 73.5 69.0 85.0 +68.0 62.1 58.0 55.0 +39.0 45.3 44.0 39.0 +71.0 70.7 70.0 52.0 +69.0 71.7 68.0 89.0 +74.0 71.5 71.0 82.0 +81.0 64.1 62.0 81.0 +51.0 49.3 49.0 34.0 +45.0 46.8 44.0 61.0 +87.0 76.8 73.0 73.0 +71.0 73.8 71.0 86.0 +55.0 60.3 56.0 77.0 +80.0 76.9 72.0 81.0 +67.0 69.0 65.0 76.0 +61.0 61.4 60.0 78.0 +46.0 46.6 43.0 65.0 +39.0 45.1 42.0 51.0 +67.0 68.3 67.0 61.0 +52.0 47.8 43.0 50.0 +67.0 69.8 68.0 87.0 +75.0 71.2 67.0 77.0 +68.0 73.3 73.0 79.0 +92.0 68.2 65.0 71.0 +67.0 72.8 69.0 56.0 +44.0 45.8 43.0 56.0 +61.0 61.0 56.0 73.0 +65.0 53.4 49.0 41.0 +68.0 73.0 72.0 70.0 +87.0 62.1 62.0 69.0 +117.0 54.8 51.0 62.0 +80.0 76.4 75.0 66.0 +57.0 51.0 47.0 46.0 +67.0 63.6 61.0 68.0 +58.0 54.0 51.0 56.0 +65.0 56.2 53.0 41.0 +52.0 48.6 45.0 47.0 +59.0 55.3 52.0 39.0 +57.0 53.9 53.0 35.0 +81.0 59.2 56.0 66.0 +75.0 77.1 76.0 75.0 +76.0 77.4 76.0 95.0 +57.0 64.8 61.0 53.0 +69.0 74.2 72.0 86.0 +77.0 66.8 66.0 64.0 +55.0 49.9 47.0 55.0 +49.0 46.8 45.0 53.0 +54.0 52.7 48.0 57.0 +55.0 51.2 49.0 42.0 +56.0 55.6 53.0 45.0 +68.0 74.6 72.0 77.0 +54.0 53.4 49.0 44.0 +67.0 69.0 69.0 87.0 +49.0 46.9 45.0 33.0 +49.0 49.1 47.0 45.0 +56.0 48.5 48.0 49.0 +73.0 71.0 66.0 78.0 +66.0 66.4 65.0 60.0 +69.0 66.5 66.0 62.0 +82.0 64.5 64.0 65.0 +90.0 76.7 75.0 65.0 +51.0 50.7 49.0 43.0 +77.0 57.1 57.0 41.0 +60.0 61.4 58.0 58.0 +74.0 72.8 71.0 87.0 +85.0 77.2 73.0 74.0 +68.0 62.8 61.0 64.0 +56.0 49.5 46.0 37.0 +71.0 56.2 55.0 45.0 +62.0 59.5 57.0 40.0 +83.0 77.3 76.0 76.0 +64.0 65.4 62.0 56.0 +56.0 48.4 45.0 54.0 +41.0 45.1 42.0 31.0 +65.0 66.2 66.0 67.0 +65.0 53.7 49.0 38.0 +40.0 46.0 46.0 41.0 +45.0 45.6 43.0 29.0 +52.0 48.4 48.0 58.0 +63.0 51.7 50.0 63.0 +52.0 47.6 47.0 44.0 +60.0 57.9 55.0 77.0 +81.0 75.7 73.0 89.0 +75.0 75.8 74.0 77.0 +59.0 51.4 48.0 64.0 +73.0 77.1 77.0 94.0 +75.0 77.3 73.0 66.0 +60.0 58.5 56.0 59.0 +75.0 71.3 68.0 56.0 +59.0 57.6 56.0 40.0 +53.0 49.1 47.0 56.0 +79.0 77.2 76.0 60.0 +57.0 52.1 49.0 46.0 +75.0 67.6 64.0 77.0 +71.0 69.4 67.0 81.0 +53.0 50.2 50.0 42.0 +46.0 48.8 48.0 56.0 +81.0 76.9 72.0 70.0 +49.0 48.9 47.0 29.0 +57.0 48.4 44.0 34.0 +60.0 58.8 54.0 53.0 +67.0 73.7 72.0 64.0 +61.0 64.1 62.0 60.0 +66.0 69.5 66.0 85.0 +64.0 51.9 50.0 55.0 +66.0 65.7 62.0 49.0 +64.0 52.2 52.0 49.0 +71.0 65.2 61.0 56.0 +75.0 63.8 62.0 60.0 +48.0 46.4 46.0 47.0 +53.0 52.5 48.0 70.0 +49.0 47.1 46.0 65.0 +85.0 68.5 67.0 81.0 +62.0 49.4 48.0 30.0 +50.0 47.0 42.0 58.0 +58.0 55.9 51.0 39.0 +72.0 77.2 74.0 95.0 +55.0 50.7 50.0 34.0 +74.0 72.3 70.0 91.0 +85.0 77.3 77.0 77.0 +73.0 77.3 77.0 93.0 +52.0 47.4 44.0 39.0 +67.0 67.6 64.0 62.0 +45.0 45.1 45.0 35.0 +46.0 47.2 46.0 41.0 +66.0 60.6 60.0 57.0 +71.0 77.0 75.0 86.0 +70.0 69.3 66.0 79.0 +58.0 49.9 46.0 53.0 +72.0 77.1 76.0 65.0 +74.0 75.4 74.0 71.0 +65.0 64.5 63.0 49.0 +77.0 58.8 55.0 39.0 +59.0 50.9 49.0 35.0 +45.0 45.7 41.0 61.0 +53.0 50.5 49.0 46.0 +53.0 54.9 54.0 72.0 +79.0 77.3 73.0 79.0 +49.0 49.0 44.0 44.0 +63.0 62.9 62.0 78.0 +69.0 56.5 54.0 45.0 +60.0 50.8 47.0 46.0 +64.0 62.5 60.0 73.0 +79.0 71.0 66.0 64.0 +55.0 47.0 43.0 58.0 +73.0 56.0 54.0 41.0 +60.0 59.1 57.0 62.0 +67.0 70.2 67.0 77.0 +42.0 45.2 45.0 58.0 +60.0 65.0 62.0 55.0 +57.0 49.8 47.0 30.0 +35.0 45.2 44.0 36.0 +75.0 70.3 66.0 84.0 +61.0 51.1 48.0 65.0 +51.0 50.6 46.0 59.0 +71.0 71.9 67.0 70.0 +74.0 75.3 74.0 71.0 +48.0 45.4 44.0 42.0 +74.0 74.9 70.0 60.0 +76.0 70.8 68.0 57.0 +58.0 51.6 47.0 37.0 +51.0 50.4 48.0 43.0 +72.0 72.6 68.0 78.0 +76.0 67.2 64.0 74.0 +52.0 47.9 47.0 60.0 +53.0 48.2 48.0 53.0 +65.0 69.1 65.0 83.0 +58.0 58.1 58.0 43.0 +77.0 75.6 74.0 56.0 +61.0 52.9 51.0 35.0 +67.0 65.3 64.0 54.0 +54.0 49.3 46.0 58.0 +79.0 67.4 65.0 58.0 +77.0 64.3 63.0 67.0 +71.0 67.7 64.0 55.0 +58.0 57.7 54.0 61.0 +68.0 55.9 55.0 56.0 +40.0 45.4 45.0 49.0 +80.0 77.3 75.0 71.0 +74.0 62.3 59.0 61.0 +57.0 45.5 42.0 57.0 +52.0 47.8 43.0 57.0 +71.0 75.1 71.0 95.0 +49.0 53.6 49.0 70.0 +89.0 59.0 59.0 61.0 +60.0 60.2 56.0 78.0 +59.0 58.3 58.0 40.0
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/get_params.tabular Tue May 14 18:06:37 2019 -0400 @@ -0,0 +1,6 @@ + Parameter Value +@ copy_X copy_X: True +@ fit_intercept fit_intercept: True +* n_jobs n_jobs: 1 +@ normalize normalize: False + Note: @, params eligible for search in searchcv tool.
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/get_params01.tabular Tue May 14 18:06:37 2019 -0400 @@ -0,0 +1,30 @@ + Parameter Value +* memory memory: None +* steps "steps: [('robustscaler', RobustScaler(copy=True, quantile_range=(25.0, 75.0), with_centering=True, + with_scaling=True)), ('selectkbest', SelectKBest(k=10, score_func=<function f_classif at 0x111ef0158>)), ('svr', SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, + gamma='auto_deprecated', kernel='linear', max_iter=-1, shrinking=True, + tol=0.001, verbose=False))]" +@ robustscaler "robustscaler: RobustScaler(copy=True, quantile_range=(25.0, 75.0), with_centering=True, + with_scaling=True)" +@ selectkbest selectkbest: SelectKBest(k=10, score_func=<function f_classif at 0x111ef0158>) +@ svr "svr: SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, + gamma='auto_deprecated', kernel='linear', max_iter=-1, shrinking=True, + tol=0.001, verbose=False)" +@ robustscaler__copy robustscaler__copy: True +@ robustscaler__quantile_range robustscaler__quantile_range: (25.0, 75.0) +@ robustscaler__with_centering robustscaler__with_centering: True +@ robustscaler__with_scaling robustscaler__with_scaling: True +@ selectkbest__k selectkbest__k: 10 +@ selectkbest__score_func selectkbest__score_func: <function f_classif at 0x111ef0158> +@ svr__C svr__C: 1.0 +@ svr__cache_size svr__cache_size: 200 +@ svr__coef0 svr__coef0: 0.0 +@ svr__degree svr__degree: 3 +@ svr__epsilon svr__epsilon: 0.1 +@ svr__gamma svr__gamma: 'auto_deprecated' +@ svr__kernel svr__kernel: 'linear' +@ svr__max_iter svr__max_iter: -1 +@ svr__shrinking svr__shrinking: True +@ svr__tol svr__tol: 0.001 +* svr__verbose svr__verbose: False + Note: @, searchable params in searchcv too.
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/get_params02.tabular Tue May 14 18:06:37 2019 -0400 @@ -0,0 +1,33 @@ + Parameter Value +* memory memory: None +* steps "steps: [('robustscaler', RobustScaler(copy=True, quantile_range=(25.0, 75.0), with_centering=True, + with_scaling=True)), ('lassocv', LassoCV(alphas=None, copy_X=True, cv='warn', eps=0.001, fit_intercept=True, + max_iter=1000, n_alphas=100, n_jobs=1, normalize=False, positive=False, + precompute='auto', random_state=None, selection='cyclic', tol=0.0001, + verbose=False))]" +@ robustscaler "robustscaler: RobustScaler(copy=True, quantile_range=(25.0, 75.0), with_centering=True, + with_scaling=True)" +@ lassocv "lassocv: LassoCV(alphas=None, copy_X=True, cv='warn', eps=0.001, fit_intercept=True, + max_iter=1000, n_alphas=100, n_jobs=1, normalize=False, positive=False, + precompute='auto', random_state=None, selection='cyclic', tol=0.0001, + verbose=False)" +@ robustscaler__copy robustscaler__copy: True +@ robustscaler__quantile_range robustscaler__quantile_range: (25.0, 75.0) +@ robustscaler__with_centering robustscaler__with_centering: True +@ robustscaler__with_scaling robustscaler__with_scaling: True +@ lassocv__alphas lassocv__alphas: None +@ lassocv__copy_X lassocv__copy_X: True +@ lassocv__cv lassocv__cv: 'warn' +@ lassocv__eps lassocv__eps: 0.001 +@ lassocv__fit_intercept lassocv__fit_intercept: True +@ lassocv__max_iter lassocv__max_iter: 1000 +@ lassocv__n_alphas lassocv__n_alphas: 100 +* lassocv__n_jobs lassocv__n_jobs: 1 +@ lassocv__normalize lassocv__normalize: False +@ lassocv__positive lassocv__positive: False +@ lassocv__precompute lassocv__precompute: 'auto' +@ lassocv__random_state lassocv__random_state: None +@ lassocv__selection lassocv__selection: 'cyclic' +@ lassocv__tol lassocv__tol: 0.0001 +* lassocv__verbose lassocv__verbose: False + Note: @, searchable params in searchcv too.
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/get_params03.tabular Tue May 14 18:06:37 2019 -0400 @@ -0,0 +1,43 @@ + Parameter Value +* memory memory: None +* steps "steps: [('robustscaler', RobustScaler(copy=True, quantile_range=(25.0, 75.0), with_centering=True, + with_scaling=True)), ('xgbclassifier', XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1, + colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0, + max_depth=3, min_child_weight=1, missing=nan, n_estimators=100, + n_jobs=1, nthread=None, objective='binary:logistic', random_state=0, + reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None, + silent=True, subsample=1))]" +@ robustscaler "robustscaler: RobustScaler(copy=True, quantile_range=(25.0, 75.0), with_centering=True, + with_scaling=True)" +@ xgbclassifier "xgbclassifier: XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1, + colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0, + max_depth=3, min_child_weight=1, missing=nan, n_estimators=100, + n_jobs=1, nthread=None, objective='binary:logistic', random_state=0, + reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None, + silent=True, subsample=1)" +@ robustscaler__copy robustscaler__copy: True +@ robustscaler__quantile_range robustscaler__quantile_range: (25.0, 75.0) +@ robustscaler__with_centering robustscaler__with_centering: True +@ robustscaler__with_scaling robustscaler__with_scaling: True +@ xgbclassifier__base_score xgbclassifier__base_score: 0.5 +@ xgbclassifier__booster xgbclassifier__booster: 'gbtree' +@ xgbclassifier__colsample_bylevel xgbclassifier__colsample_bylevel: 1 +@ xgbclassifier__colsample_bytree xgbclassifier__colsample_bytree: 1 +@ xgbclassifier__gamma xgbclassifier__gamma: 0 +@ xgbclassifier__learning_rate xgbclassifier__learning_rate: 0.1 +@ xgbclassifier__max_delta_step xgbclassifier__max_delta_step: 0 +@ xgbclassifier__max_depth xgbclassifier__max_depth: 3 +@ xgbclassifier__min_child_weight xgbclassifier__min_child_weight: 1 +@ xgbclassifier__missing xgbclassifier__missing: nan +@ xgbclassifier__n_estimators xgbclassifier__n_estimators: 100 +* xgbclassifier__n_jobs xgbclassifier__n_jobs: 1 +* xgbclassifier__nthread xgbclassifier__nthread: None +@ xgbclassifier__objective xgbclassifier__objective: 'binary:logistic' +@ xgbclassifier__random_state xgbclassifier__random_state: 0 +@ xgbclassifier__reg_alpha xgbclassifier__reg_alpha: 0 +@ xgbclassifier__reg_lambda xgbclassifier__reg_lambda: 1 +@ xgbclassifier__scale_pos_weight xgbclassifier__scale_pos_weight: 1 +@ xgbclassifier__seed xgbclassifier__seed: None +@ xgbclassifier__silent xgbclassifier__silent: True +@ xgbclassifier__subsample xgbclassifier__subsample: 1 + Note: @, searchable params in searchcv too.
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/get_params04.tabular Tue May 14 18:06:37 2019 -0400 @@ -0,0 +1,39 @@ + Parameter Value +* memory memory: None +* steps "steps: [('selectfrommodel', SelectFromModel(estimator=AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None, + learning_rate=1.0, n_estimators=50, random_state=None), + max_features=None, norm_order=1, prefit=False, threshold=None)), ('linearsvc', LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True, + intercept_scaling=1, loss='squared_hinge', max_iter=1000, + multi_class='ovr', penalty='l2', random_state=None, tol=0.0001, + verbose=0))]" +@ selectfrommodel "selectfrommodel: SelectFromModel(estimator=AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None, + learning_rate=1.0, n_estimators=50, random_state=None), + max_features=None, norm_order=1, prefit=False, threshold=None)" +@ linearsvc "linearsvc: LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True, + intercept_scaling=1, loss='squared_hinge', max_iter=1000, + multi_class='ovr', penalty='l2', random_state=None, tol=0.0001, + verbose=0)" +@ selectfrommodel__estimator__algorithm selectfrommodel__estimator__algorithm: 'SAMME.R' +@ selectfrommodel__estimator__base_estimator selectfrommodel__estimator__base_estimator: None +@ selectfrommodel__estimator__learning_rate selectfrommodel__estimator__learning_rate: 1.0 +@ selectfrommodel__estimator__n_estimators selectfrommodel__estimator__n_estimators: 50 +@ selectfrommodel__estimator__random_state selectfrommodel__estimator__random_state: None +@ selectfrommodel__estimator "selectfrommodel__estimator: AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None, + learning_rate=1.0, n_estimators=50, random_state=None)" +@ selectfrommodel__max_features selectfrommodel__max_features: None +@ selectfrommodel__norm_order selectfrommodel__norm_order: 1 +@ selectfrommodel__prefit selectfrommodel__prefit: False +@ selectfrommodel__threshold selectfrommodel__threshold: None +@ linearsvc__C linearsvc__C: 1.0 +@ linearsvc__class_weight linearsvc__class_weight: None +@ linearsvc__dual linearsvc__dual: True +@ linearsvc__fit_intercept linearsvc__fit_intercept: True +@ linearsvc__intercept_scaling linearsvc__intercept_scaling: 1 +@ linearsvc__loss linearsvc__loss: 'squared_hinge' +@ linearsvc__max_iter linearsvc__max_iter: 1000 +@ linearsvc__multi_class linearsvc__multi_class: 'ovr' +@ linearsvc__penalty linearsvc__penalty: 'l2' +@ linearsvc__random_state linearsvc__random_state: None +@ linearsvc__tol linearsvc__tol: 0.0001 +* linearsvc__verbose linearsvc__verbose: 0 + Note: @, searchable params in searchcv too.
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/get_params05.tabular Tue May 14 18:06:37 2019 -0400 @@ -0,0 +1,31 @@ + Parameter Value +* memory memory: None +* steps "steps: [('randomforestregressor', RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None, + max_features='auto', max_leaf_nodes=None, + min_impurity_decrease=0.0, min_impurity_split=None, + min_samples_leaf=1, min_samples_split=2, + min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=1, + oob_score=False, random_state=42, verbose=0, warm_start=False))]" +@ randomforestregressor "randomforestregressor: RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None, + max_features='auto', max_leaf_nodes=None, + min_impurity_decrease=0.0, min_impurity_split=None, + min_samples_leaf=1, min_samples_split=2, + min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=1, + oob_score=False, random_state=42, verbose=0, warm_start=False)" +@ randomforestregressor__bootstrap randomforestregressor__bootstrap: True +@ randomforestregressor__criterion randomforestregressor__criterion: 'mse' +@ randomforestregressor__max_depth randomforestregressor__max_depth: None +@ randomforestregressor__max_features randomforestregressor__max_features: 'auto' +@ randomforestregressor__max_leaf_nodes randomforestregressor__max_leaf_nodes: None +@ randomforestregressor__min_impurity_decrease randomforestregressor__min_impurity_decrease: 0.0 +@ randomforestregressor__min_impurity_split randomforestregressor__min_impurity_split: None +@ randomforestregressor__min_samples_leaf randomforestregressor__min_samples_leaf: 1 +@ randomforestregressor__min_samples_split randomforestregressor__min_samples_split: 2 +@ randomforestregressor__min_weight_fraction_leaf randomforestregressor__min_weight_fraction_leaf: 0.0 +@ randomforestregressor__n_estimators randomforestregressor__n_estimators: 100 +* randomforestregressor__n_jobs randomforestregressor__n_jobs: 1 +@ randomforestregressor__oob_score randomforestregressor__oob_score: False +@ randomforestregressor__random_state randomforestregressor__random_state: 42 +* randomforestregressor__verbose randomforestregressor__verbose: 0 +@ randomforestregressor__warm_start randomforestregressor__warm_start: False + Note: @, searchable params in searchcv too.
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/get_params06.tabular Tue May 14 18:06:37 2019 -0400 @@ -0,0 +1,22 @@ + Parameter Value +* memory memory: None +* steps "steps: [('pca', PCA(copy=True, iterated_power='auto', n_components=None, random_state=None, + svd_solver='auto', tol=0.0, whiten=False)), ('adaboostregressor', AdaBoostRegressor(base_estimator=None, learning_rate=1.0, loss='linear', + n_estimators=50, random_state=None))]" +@ pca "pca: PCA(copy=True, iterated_power='auto', n_components=None, random_state=None, + svd_solver='auto', tol=0.0, whiten=False)" +@ adaboostregressor "adaboostregressor: AdaBoostRegressor(base_estimator=None, learning_rate=1.0, loss='linear', + n_estimators=50, random_state=None)" +@ pca__copy pca__copy: True +@ pca__iterated_power pca__iterated_power: 'auto' +@ pca__n_components pca__n_components: None +@ pca__random_state pca__random_state: None +@ pca__svd_solver pca__svd_solver: 'auto' +@ pca__tol pca__tol: 0.0 +@ pca__whiten pca__whiten: False +@ adaboostregressor__base_estimator adaboostregressor__base_estimator: None +@ adaboostregressor__learning_rate adaboostregressor__learning_rate: 1.0 +@ adaboostregressor__loss adaboostregressor__loss: 'linear' +@ adaboostregressor__n_estimators adaboostregressor__n_estimators: 50 +@ adaboostregressor__random_state adaboostregressor__random_state: None + Note: @, searchable params in searchcv too.
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/get_params07.tabular Tue May 14 18:06:37 2019 -0400 @@ -0,0 +1,16 @@ + Parameter Value +* memory memory: None +* steps "steps: [('rbfsampler', RBFSampler(gamma=2.0, n_components=10, random_state=None)), ('adaboostclassifier', AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None, + learning_rate=1.0, n_estimators=50, random_state=None))]" +@ rbfsampler rbfsampler: RBFSampler(gamma=2.0, n_components=10, random_state=None) +@ adaboostclassifier "adaboostclassifier: AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None, + learning_rate=1.0, n_estimators=50, random_state=None)" +@ rbfsampler__gamma rbfsampler__gamma: 2.0 +@ rbfsampler__n_components rbfsampler__n_components: 10 +@ rbfsampler__random_state rbfsampler__random_state: None +@ adaboostclassifier__algorithm adaboostclassifier__algorithm: 'SAMME.R' +@ adaboostclassifier__base_estimator adaboostclassifier__base_estimator: None +@ adaboostclassifier__learning_rate adaboostclassifier__learning_rate: 1.0 +@ adaboostclassifier__n_estimators adaboostclassifier__n_estimators: 50 +@ adaboostclassifier__random_state adaboostclassifier__random_state: None + Note: @, searchable params in searchcv too.
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/get_params08.tabular Tue May 14 18:06:37 2019 -0400 @@ -0,0 +1,24 @@ + Parameter Value +* memory memory: None +* steps "steps: [('featureagglomeration', FeatureAgglomeration(affinity='euclidean', compute_full_tree='auto', + connectivity=None, linkage='ward', memory=None, n_clusters=3, + pooling_func=<function mean at 0x1123f1620>)), ('adaboostclassifier', AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None, + learning_rate=1.0, n_estimators=50, random_state=None))]" +@ featureagglomeration "featureagglomeration: FeatureAgglomeration(affinity='euclidean', compute_full_tree='auto', + connectivity=None, linkage='ward', memory=None, n_clusters=3, + pooling_func=<function mean at 0x1123f1620>)" +@ adaboostclassifier "adaboostclassifier: AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None, + learning_rate=1.0, n_estimators=50, random_state=None)" +@ featureagglomeration__affinity featureagglomeration__affinity: 'euclidean' +@ featureagglomeration__compute_full_tree featureagglomeration__compute_full_tree: 'auto' +@ featureagglomeration__connectivity featureagglomeration__connectivity: None +@ featureagglomeration__linkage featureagglomeration__linkage: 'ward' +* featureagglomeration__memory featureagglomeration__memory: None +@ featureagglomeration__n_clusters featureagglomeration__n_clusters: 3 +@ featureagglomeration__pooling_func featureagglomeration__pooling_func: <function mean at 0x1123f1620> +@ adaboostclassifier__algorithm adaboostclassifier__algorithm: 'SAMME.R' +@ adaboostclassifier__base_estimator adaboostclassifier__base_estimator: None +@ adaboostclassifier__learning_rate adaboostclassifier__learning_rate: 1.0 +@ adaboostclassifier__n_estimators adaboostclassifier__n_estimators: 50 +@ adaboostclassifier__random_state adaboostclassifier__random_state: None + Note: @, searchable params in searchcv too.
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/get_params09.tabular Tue May 14 18:06:37 2019 -0400 @@ -0,0 +1,39 @@ + Parameter Value +* memory memory: None +* steps "steps: [('relieff', ReliefF(discrete_threshold=10, n_features_to_select=3, n_jobs=1, + n_neighbors=100, verbose=False)), ('randomforestregressor', RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None, + max_features='auto', max_leaf_nodes=None, + min_impurity_decrease=0.0, min_impurity_split=None, + min_samples_leaf=1, min_samples_split=2, + min_weight_fraction_leaf=0.0, n_estimators='warn', n_jobs=1, + oob_score=False, random_state=None, verbose=0, warm_start=False))]" +@ relieff "relieff: ReliefF(discrete_threshold=10, n_features_to_select=3, n_jobs=1, + n_neighbors=100, verbose=False)" +@ randomforestregressor "randomforestregressor: RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None, + max_features='auto', max_leaf_nodes=None, + min_impurity_decrease=0.0, min_impurity_split=None, + min_samples_leaf=1, min_samples_split=2, + min_weight_fraction_leaf=0.0, n_estimators='warn', n_jobs=1, + oob_score=False, random_state=None, verbose=0, warm_start=False)" +@ relieff__discrete_threshold relieff__discrete_threshold: 10 +@ relieff__n_features_to_select relieff__n_features_to_select: 3 +* relieff__n_jobs relieff__n_jobs: 1 +@ relieff__n_neighbors relieff__n_neighbors: 100 +* relieff__verbose relieff__verbose: False +@ randomforestregressor__bootstrap randomforestregressor__bootstrap: True +@ randomforestregressor__criterion randomforestregressor__criterion: 'mse' +@ randomforestregressor__max_depth randomforestregressor__max_depth: None +@ randomforestregressor__max_features randomforestregressor__max_features: 'auto' +@ randomforestregressor__max_leaf_nodes randomforestregressor__max_leaf_nodes: None +@ randomforestregressor__min_impurity_decrease randomforestregressor__min_impurity_decrease: 0.0 +@ randomforestregressor__min_impurity_split randomforestregressor__min_impurity_split: None +@ randomforestregressor__min_samples_leaf randomforestregressor__min_samples_leaf: 1 +@ randomforestregressor__min_samples_split randomforestregressor__min_samples_split: 2 +@ randomforestregressor__min_weight_fraction_leaf randomforestregressor__min_weight_fraction_leaf: 0.0 +@ randomforestregressor__n_estimators randomforestregressor__n_estimators: 'warn' +* randomforestregressor__n_jobs randomforestregressor__n_jobs: 1 +@ randomforestregressor__oob_score randomforestregressor__oob_score: False +@ randomforestregressor__random_state randomforestregressor__random_state: None +* randomforestregressor__verbose randomforestregressor__verbose: 0 +@ randomforestregressor__warm_start randomforestregressor__warm_start: False + Note: @, searchable params in searchcv too.
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/get_params10.tabular Tue May 14 18:06:37 2019 -0400 @@ -0,0 +1,12 @@ + Parameter Value +* memory memory: None +* steps "steps: [('adaboostregressor', AdaBoostRegressor(base_estimator=None, learning_rate=1.0, loss='linear', + n_estimators=50, random_state=None))]" +@ adaboostregressor "adaboostregressor: AdaBoostRegressor(base_estimator=None, learning_rate=1.0, loss='linear', + n_estimators=50, random_state=None)" +@ adaboostregressor__base_estimator adaboostregressor__base_estimator: None +@ adaboostregressor__learning_rate adaboostregressor__learning_rate: 1.0 +@ adaboostregressor__loss adaboostregressor__loss: 'linear' +@ adaboostregressor__n_estimators adaboostregressor__n_estimators: 50 +@ adaboostregressor__random_state adaboostregressor__random_state: None + Note: @, params eligible for search in searchcv tool.
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/get_params11.tabular Tue May 14 18:06:37 2019 -0400 @@ -0,0 +1,46 @@ + Parameter Value +* memory memory: None +* steps "steps: [('editednearestneighbours', EditedNearestNeighbours(kind_sel='all', n_jobs=1, n_neighbors=3, + random_state=None, ratio=None, return_indices=False, + sampling_strategy='auto')), ('randomforestclassifier', RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini', + max_depth=None, max_features='auto', max_leaf_nodes=None, + min_impurity_decrease=0.0, min_impurity_split=None, + min_samples_leaf=1, min_samples_split=2, + min_weight_fraction_leaf=0.0, n_estimators='warn', n_jobs=1, + oob_score=False, random_state=None, verbose=0, + warm_start=False))]" +@ editednearestneighbours "editednearestneighbours: EditedNearestNeighbours(kind_sel='all', n_jobs=1, n_neighbors=3, + random_state=None, ratio=None, return_indices=False, + sampling_strategy='auto')" +@ randomforestclassifier "randomforestclassifier: RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini', + max_depth=None, max_features='auto', max_leaf_nodes=None, + min_impurity_decrease=0.0, min_impurity_split=None, + min_samples_leaf=1, min_samples_split=2, + min_weight_fraction_leaf=0.0, n_estimators='warn', n_jobs=1, + oob_score=False, random_state=None, verbose=0, + warm_start=False)" +@ editednearestneighbours__kind_sel editednearestneighbours__kind_sel: 'all' +* editednearestneighbours__n_jobs editednearestneighbours__n_jobs: 1 +@ editednearestneighbours__n_neighbors editednearestneighbours__n_neighbors: 3 +@ editednearestneighbours__random_state editednearestneighbours__random_state: None +@ editednearestneighbours__ratio editednearestneighbours__ratio: None +@ editednearestneighbours__return_indices editednearestneighbours__return_indices: False +@ editednearestneighbours__sampling_strategy editednearestneighbours__sampling_strategy: 'auto' +@ randomforestclassifier__bootstrap randomforestclassifier__bootstrap: True +@ randomforestclassifier__class_weight randomforestclassifier__class_weight: None +@ randomforestclassifier__criterion randomforestclassifier__criterion: 'gini' +@ randomforestclassifier__max_depth randomforestclassifier__max_depth: None +@ randomforestclassifier__max_features randomforestclassifier__max_features: 'auto' +@ randomforestclassifier__max_leaf_nodes randomforestclassifier__max_leaf_nodes: None +@ randomforestclassifier__min_impurity_decrease randomforestclassifier__min_impurity_decrease: 0.0 +@ randomforestclassifier__min_impurity_split randomforestclassifier__min_impurity_split: None +@ randomforestclassifier__min_samples_leaf randomforestclassifier__min_samples_leaf: 1 +@ randomforestclassifier__min_samples_split randomforestclassifier__min_samples_split: 2 +@ randomforestclassifier__min_weight_fraction_leaf randomforestclassifier__min_weight_fraction_leaf: 0.0 +@ randomforestclassifier__n_estimators randomforestclassifier__n_estimators: 'warn' +* randomforestclassifier__n_jobs randomforestclassifier__n_jobs: 1 +@ randomforestclassifier__oob_score randomforestclassifier__oob_score: False +@ randomforestclassifier__random_state randomforestclassifier__random_state: None +* randomforestclassifier__verbose randomforestclassifier__verbose: 0 +@ randomforestclassifier__warm_start randomforestclassifier__warm_start: False + Note: @, searchable params in searchcv too.
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/get_params12.tabular Tue May 14 18:06:37 2019 -0400 @@ -0,0 +1,47 @@ + Parameter Value +* memory memory: None +* steps "steps: [('rfe', RFE(estimator=XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1, + colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0, + max_depth=3, min_child_weight=1, missing=nan, n_estimators=100, + n_jobs=1, nthread=None, objective='reg:linear', random_state=0, + reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None, + silent=True, subsample=1), + n_features_to_select=None, step=1, verbose=0))]" +@ rfe "rfe: RFE(estimator=XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1, + colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0, + max_depth=3, min_child_weight=1, missing=nan, n_estimators=100, + n_jobs=1, nthread=None, objective='reg:linear', random_state=0, + reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None, + silent=True, subsample=1), + n_features_to_select=None, step=1, verbose=0)" +@ rfe__estimator__base_score rfe__estimator__base_score: 0.5 +@ rfe__estimator__booster rfe__estimator__booster: 'gbtree' +@ rfe__estimator__colsample_bylevel rfe__estimator__colsample_bylevel: 1 +@ rfe__estimator__colsample_bytree rfe__estimator__colsample_bytree: 1 +@ rfe__estimator__gamma rfe__estimator__gamma: 0 +@ rfe__estimator__learning_rate rfe__estimator__learning_rate: 0.1 +@ rfe__estimator__max_delta_step rfe__estimator__max_delta_step: 0 +@ rfe__estimator__max_depth rfe__estimator__max_depth: 3 +@ rfe__estimator__min_child_weight rfe__estimator__min_child_weight: 1 +@ rfe__estimator__missing rfe__estimator__missing: nan +@ rfe__estimator__n_estimators rfe__estimator__n_estimators: 100 +* rfe__estimator__n_jobs rfe__estimator__n_jobs: 1 +* rfe__estimator__nthread rfe__estimator__nthread: None +@ rfe__estimator__objective rfe__estimator__objective: 'reg:linear' +@ rfe__estimator__random_state rfe__estimator__random_state: 0 +@ rfe__estimator__reg_alpha rfe__estimator__reg_alpha: 0 +@ rfe__estimator__reg_lambda rfe__estimator__reg_lambda: 1 +@ rfe__estimator__scale_pos_weight rfe__estimator__scale_pos_weight: 1 +@ rfe__estimator__seed rfe__estimator__seed: None +@ rfe__estimator__silent rfe__estimator__silent: True +@ rfe__estimator__subsample rfe__estimator__subsample: 1 +@ rfe__estimator "rfe__estimator: XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1, + colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0, + max_depth=3, min_child_weight=1, missing=nan, n_estimators=100, + n_jobs=1, nthread=None, objective='reg:linear', random_state=0, + reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None, + silent=True, subsample=1)" +@ rfe__n_features_to_select rfe__n_features_to_select: None +@ rfe__step rfe__step: 1 +* rfe__verbose rfe__verbose: 0 + Note: @, searchable params in searchcv too.
--- a/test-data/mv_result01.tabular Sun Dec 30 01:52:15 2018 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,3 +0,0 @@ -0.9452947345848994 -0.9926363525448115 --0.4384003222944141
--- a/test-data/mv_result02.tabular Sun Dec 30 01:52:15 2018 -0500 +++ b/test-data/mv_result02.tabular Tue May 14 18:06:37 2019 -0400 @@ -1,10 +1,11 @@ -1.6957921248350636 --0.9248588846061156 --0.48640795813792376 -0.647707440306449 -0.32740690920811427 --0.8229559569886034 -1.2150108977866847 -0.14723254190255275 -0.6053186541119763 -0.3972102859168325 +Predicted +1.578912095858962 +-1.199072894940544 +-0.7173258906076226 +0.3255908318822695 +0.21919344304093213 +-0.6841926371423699 +1.1144698671662865 +0.19379531649046616 +0.9405094785593062 +1.2581284896870837
--- a/test-data/mv_result03.tabular Sun Dec 30 01:52:15 2018 -0500 +++ b/test-data/mv_result03.tabular Tue May 14 18:06:37 2019 -0400 @@ -1,3 +1,6 @@ -0.9452947345848994 -0.9926363525448115 --0.4384003222944141 +train_sizes_abs mean_train_scores std_train_scores mean_test_scores std_test_scores +17 0.9668700841937653 0.00277836829836518 0.7008862995946905 0.03857541198731935 +56 0.9730008602419361 0.006839342612121988 0.7963376762427242 0.004846330083938778 +95 0.9728783377589098 0.0037790183626530663 0.814592845745573 0.020457691766770824 +134 0.9739086338111185 0.001627343246847077 0.7985540571195479 0.03954641079310707 +174 0.9726218628287785 0.0032867750457225182 0.8152971572131146 0.04280261115004303
--- a/test-data/mv_result04.tabular Sun Dec 30 01:52:15 2018 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,5 +0,0 @@ -17 -56 -95 -134 -174
--- a/test-data/mv_result05.tabular Sun Dec 30 01:52:15 2018 -0500 +++ b/test-data/mv_result05.tabular Tue May 14 18:06:37 2019 -0400 @@ -1,1 +1,262 @@ -0.4998435882784322 +Predicted +70.16 +62.06 +83.04 +62.84 +48.63 +51.25 +54.98 +80.3 +42.84 +41.52 +43.83 +73.15 +74.22 +42.88 +74.93 +72.9 +53.74 +78.86 +59.0 +40.28 +54.52 +58.34 +62.74 +62.35 +49.15 +41.92 +65.59 +59.91 +66.49 +72.08 +60.44 +53.84 +54.82 +52.66 +42.37 +61.3 +63.14 +50.62 +42.75 +47.39 +67.8 +73.58 +49.97 +67.04 +67.45 +54.67 +64.87 +77.23 +73.52 +53.55 +70.53 +77.98 +61.99 +53.08 +78.12 +66.55 +63.95 +60.57 +61.6 +60.37 +55.29 +54.31 +52.54 +65.31 +61.51 +57.3 +60.02 +43.64 +74.78 +68.26 +42.72 +61.26 +61.25 +71.58 +61.03 +70.53 +70.25 +43.4 +71.39 +72.31 +72.7 +72.11 +53.55 +43.4 +80.6 +73.72 +58.86 +76.71 +68.36 +60.26 +48.56 +38.96 +69.67 +52.9 +67.63 +75.12 +70.92 +70.89 +67.05 +43.89 +59.94 +62.98 +71.1 +79.22 +77.31 +79.06 +61.11 +66.32 +54.7 +61.1 +54.59 +58.7 +59.6 +73.79 +72.69 +81.83 +61.08 +69.21 +74.8 +54.37 +50.85 +53.07 +58.53 +55.44 +72.62 +54.14 +68.12 +48.81 +50.11 +56.06 +73.63 +63.29 +71.0 +74.87 +81.24 +54.67 +66.96 +61.37 +74.84 +76.71 +69.27 +56.53 +71.91 +58.74 +77.83 +64.57 +51.93 +42.84 +64.11 +59.47 +42.46 +43.79 +51.75 +63.98 +54.71 +64.95 +79.72 +72.12 +60.66 +79.3 +71.26 +59.9 +74.25 +59.68 +52.37 +78.52 +58.52 +71.98 +71.77 +54.48 +48.96 +81.42 +54.08 +53.52 +64.38 +70.79 +63.95 +67.48 +61.76 +66.15 +62.1 +75.68 +69.72 +43.8 +56.27 +53.38 +81.31 +57.54 +48.15 +59.47 +78.01 +56.39 +72.33 +78.8 +78.66 +52.01 +66.68 +48.56 +47.75 +65.67 +77.93 +72.68 +58.0 +77.83 +73.37 +65.39 +69.79 +55.98 +46.35 +54.31 +55.58 +79.69 +52.76 +62.62 +66.54 +60.29 +62.57 +74.86 +48.05 +65.09 +65.02 +67.84 +41.86 +62.28 +57.05 +43.68 +72.0 +63.04 +54.41 +73.37 +75.11 +42.65 +73.16 +71.68 +58.61 +53.54 +73.33 +72.16 +49.96 +54.78 +64.24 +60.13 +76.46 +61.53 +68.36 +53.1 +71.33 +76.12 +70.86 +61.35 +67.12 +43.25 +80.2 +71.16 +58.63 +52.37 +74.93 +53.34 +76.41 +63.87 +59.97
--- a/test-data/mv_result06.tabular Sun Dec 30 01:52:15 2018 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,5 +0,0 @@ -0.07547169811320754 0.10344827586206896 0.10294117647058823 -0.07547169811320754 0.10344827586206896 0.10294117647058823 -0.07547169811320754 0.10344827586206896 0.10294117647058823 -0.07547169811320754 0.10344827586206896 0.10294117647058823 -0.07547169811320754 0.10344827586206896 0.10294117647058823
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/named_steps.txt Tue May 14 18:06:37 2019 -0400 @@ -0,0 +1,6 @@ +{'preprocessing_1': SelectKBest(k=10, score_func=<function f_regression at 0x113310ea0>), 'estimator': XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1, + colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0, + max_depth=3, min_child_weight=1, missing=nan, n_estimators=100, + n_jobs=1, nthread=None, objective='reg:linear', random_state=10, + reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None, + silent=True, subsample=1)} \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/ranking_.tabular Tue May 14 18:06:37 2019 -0400 @@ -0,0 +1,18 @@ +ranking_ +17 +7 +4 +5 +2 +1 +9 +6 +8 +3 +10 +15 +14 +11 +13 +12 +16
--- a/utils.py Sun Dec 30 01:52:15 2018 -0500 +++ b/utils.py Tue May 14 18:06:37 2019 -0400 @@ -1,80 +1,134 @@ +import ast import json +import imblearn import numpy as np -import os import pandas import pickle import re import scipy import sklearn +import skrebate import sys import warnings import xgboost +from collections import Counter from asteval import Interpreter, make_symbol_table -from sklearn import (cluster, compose, decomposition, ensemble, feature_extraction, - feature_selection, gaussian_process, kernel_approximation, metrics, - model_selection, naive_bayes, neighbors, pipeline, preprocessing, - svm, linear_model, tree, discriminant_analysis) +from imblearn import under_sampling, over_sampling, combine +from imblearn.pipeline import Pipeline as imbPipeline +from mlxtend import regressor, classifier +from scipy.io import mmread +from sklearn import ( + cluster, compose, decomposition, ensemble, feature_extraction, + feature_selection, gaussian_process, kernel_approximation, metrics, + model_selection, naive_bayes, neighbors, pipeline, preprocessing, + svm, linear_model, tree, discriminant_analysis) + +try: + import iraps_classifier +except ImportError: + pass try: - import skrebate -except ModuleNotFoundError: + import model_validations +except ImportError: + pass + +try: + import feature_selectors +except ImportError: pass - -N_JOBS = int(os.environ.get('GALAXY_SLOTS', 1)) +try: + import preprocessors +except ImportError: + pass -try: - sk_whitelist -except NameError: - sk_whitelist = None +# handle pickle white list file +WL_FILE = __import__('os').path.join( + __import__('os').path.dirname(__file__), 'pk_whitelist.json') + +N_JOBS = int(__import__('os').environ.get('GALAXY_SLOTS', 1)) -class SafePickler(pickle.Unpickler): +class _SafePickler(pickle.Unpickler, object): """ - Used to safely deserialize scikit-learn model objects serialized by cPickle.dump + Used to safely deserialize scikit-learn model objects Usage: - eg.: SafePickler.load(pickled_file_object) + eg.: _SafePickler.load(pickled_file_object) """ - def find_class(self, module, name): + def __init__(self, file): + super(_SafePickler, self).__init__(file) + # load global white list + with open(WL_FILE, 'r') as f: + self.pk_whitelist = json.load(f) - # sk_whitelist could be read from tool - global sk_whitelist - if not sk_whitelist: - whitelist_file = os.path.join(os.path.dirname(__file__), 'sk_whitelist.json') - with open(whitelist_file, 'r') as f: - sk_whitelist = json.load(f) + self.bad_names = ( + 'and', 'as', 'assert', 'break', 'class', 'continue', + 'def', 'del', 'elif', 'else', 'except', 'exec', + 'finally', 'for', 'from', 'global', 'if', 'import', + 'in', 'is', 'lambda', 'not', 'or', 'pass', 'print', + 'raise', 'return', 'try', 'system', 'while', 'with', + 'True', 'False', 'None', 'eval', 'execfile', '__import__', + '__package__', '__subclasses__', '__bases__', '__globals__', + '__code__', '__closure__', '__func__', '__self__', '__module__', + '__dict__', '__class__', '__call__', '__get__', + '__getattribute__', '__subclasshook__', '__new__', + '__init__', 'func_globals', 'func_code', 'func_closure', + 'im_class', 'im_func', 'im_self', 'gi_code', 'gi_frame', + '__asteval__', 'f_locals', '__mro__') - bad_names = ('and', 'as', 'assert', 'break', 'class', 'continue', - 'def', 'del', 'elif', 'else', 'except', 'exec', - 'finally', 'for', 'from', 'global', 'if', 'import', - 'in', 'is', 'lambda', 'not', 'or', 'pass', 'print', - 'raise', 'return', 'try', 'system', 'while', 'with', - 'True', 'False', 'None', 'eval', 'execfile', '__import__', - '__package__', '__subclasses__', '__bases__', '__globals__', - '__code__', '__closure__', '__func__', '__self__', '__module__', - '__dict__', '__class__', '__call__', '__get__', - '__getattribute__', '__subclasshook__', '__new__', - '__init__', 'func_globals', 'func_code', 'func_closure', - 'im_class', 'im_func', 'im_self', 'gi_code', 'gi_frame', - '__asteval__', 'f_locals', '__mro__') - good_names = ['copy_reg._reconstructor', '__builtin__.object'] + # unclassified good globals + self.good_names = [ + 'copy_reg._reconstructor', '__builtin__.object', + '__builtin__.bytearray', 'builtins.object', + 'builtins.bytearray', 'keras.engine.sequential.Sequential', + 'keras.engine.sequential.Model'] + + # custom module in Galaxy-ML + self.custom_modules = [ + '__main__', 'keras_galaxy_models', 'feature_selectors', + 'preprocessors', 'iraps_classifier', 'model_validations'] + # override + def find_class(self, module, name): + # balack list first + if name in self.bad_names: + raise pickle.UnpicklingError("global '%s.%s' is forbidden" + % (module, name)) + + # custom module in Galaxy-ML + if module in self.custom_modules: + cutom_module = sys.modules.get(module, None) + if cutom_module: + return getattr(cutom_module, name) + else: + raise pickle.UnpicklingError("Module %s' is not imported" + % module) + + # For objects from outside libraries, it's necessary to verify + # both module and name. Currently only a blacklist checker + # is working. + # TODO: replace with a whitelist checker. + good_names = self.good_names + pk_whitelist = self.pk_whitelist if re.match(r'^[a-zA-Z_][a-zA-Z0-9_]*$', name): fullname = module + '.' + name if (fullname in good_names)\ - or ( ( module.startswith('sklearn.') - or module.startswith('xgboost.') - or module.startswith('skrebate.') - or module.startswith('imblearn') - or module.startswith('numpy.') - or module == 'numpy' - ) - and (name not in bad_names) - ): - # TODO: replace with a whitelist checker - if fullname not in sk_whitelist['SK_NAMES'] + sk_whitelist['SKR_NAMES'] + sk_whitelist['XGB_NAMES'] + sk_whitelist['NUMPY_NAMES'] + sk_whitelist['IMBLEARN_NAMES'] + good_names: - print("Warning: global %s is not in pickler whitelist yet and will loss support soon. Contact tool author or leave a message at github.com" % fullname) + or (module.startswith(('sklearn.', 'xgboost.', 'skrebate.', + 'imblearn.', 'mlxtend.', 'numpy.')) + or module == 'numpy'): + if fullname not in (pk_whitelist['SK_NAMES'] + + pk_whitelist['SKR_NAMES'] + + pk_whitelist['XGB_NAMES'] + + pk_whitelist['NUMPY_NAMES'] + + pk_whitelist['IMBLEARN_NAMES'] + + pk_whitelist['MLXTEND_NAMES'] + + good_names): + # raise pickle.UnpicklingError + print("Warning: global %s is not in pickler whitelist " + "yet and will loss support soon. Contact tool " + "author or leave a message at github.com" % fullname) mod = sys.modules[module] return getattr(mod, name) @@ -82,10 +136,15 @@ def load_model(file): - return SafePickler(file).load() + """Load pickled object with `_SafePicker` + """ + return _SafePickler(file).load() -def read_columns(f, c=None, c_option='by_index_number', return_df=False, **args): +def read_columns(f, c=None, c_option='by_index_number', + return_df=False, **args): + """Return array from a tabular dataset by various columns selection + """ data = pandas.read_csv(f, **args) if c_option == 'by_index_number': cols = list(map(lambda x: x - 1, c)) @@ -106,10 +165,21 @@ return y -## generate an instance for one of sklearn.feature_selection classes -def feature_selector(inputs): +def feature_selector(inputs, X=None, y=None): + """generate an instance of sklearn.feature_selection classes + + Parameters + ---------- + inputs : dict + From galaxy tool parameters. + X : array + Containing training features. + y : array or list + Target values. + """ selector = inputs['selected_algorithm'] - selector = getattr(sklearn.feature_selection, selector) + if selector != 'DyRFECV': + selector = getattr(sklearn.feature_selection, selector) options = inputs['options'] if inputs['selected_algorithm'] == 'SelectFromModel': @@ -128,27 +198,60 @@ else: estimator_json = inputs['model_inputter']['estimator_selector'] estimator = get_estimator(estimator_json) + check_feature_importances = try_get_attr( + 'feature_selectors', 'check_feature_importances') + estimator = check_feature_importances(estimator) new_selector = selector(estimator, **options) elif inputs['selected_algorithm'] == 'RFE': - estimator = get_estimator(inputs['estimator_selector']) step = options.get('step', None) if step and step >= 1.0: options['step'] = int(step) + estimator = get_estimator(inputs["estimator_selector"]) + check_feature_importances = try_get_attr( + 'feature_selectors', 'check_feature_importances') + estimator = check_feature_importances(estimator) new_selector = selector(estimator, **options) elif inputs['selected_algorithm'] == 'RFECV': options['scoring'] = get_scoring(options['scoring']) options['n_jobs'] = N_JOBS splitter, groups = get_cv(options.pop('cv_selector')) - # TODO support group cv splitters - options['cv'] = splitter + if groups is None: + options['cv'] = splitter + else: + options['cv'] = list(splitter.split(X, y, groups=groups)) step = options.get('step', None) if step and step >= 1.0: options['step'] = int(step) estimator = get_estimator(inputs['estimator_selector']) + check_feature_importances = try_get_attr( + 'feature_selectors', 'check_feature_importances') + estimator = check_feature_importances(estimator) new_selector = selector(estimator, **options) + elif inputs['selected_algorithm'] == 'DyRFECV': + options['scoring'] = get_scoring(options['scoring']) + options['n_jobs'] = N_JOBS + splitter, groups = get_cv(options.pop('cv_selector')) + if groups is None: + options['cv'] = splitter + else: + options['cv'] = list(splitter.split(X, y, groups=groups)) + step = options.get('step') + if not step or step == 'None': + step = None + else: + step = ast.literal_eval(step) + options['step'] = step + estimator = get_estimator(inputs["estimator_selector"]) + check_feature_importances = try_get_attr( + 'feature_selectors', 'check_feature_importances') + estimator = check_feature_importances(estimator) + DyRFECV = try_get_attr('feature_selectors', 'DyRFECV') + + new_selector = DyRFECV(estimator, **options) + elif inputs['selected_algorithm'] == 'VarianceThreshold': new_selector = selector(**options) @@ -161,12 +264,20 @@ def get_X_y(params, file1, file2): - input_type = params['selected_tasks']['selected_algorithms']['input_options']['selected_input'] + """Return machine learning inputs X, y from tabluar inputs + """ + input_type = (params['selected_tasks']['selected_algorithms'] + ['input_options']['selected_input']) if input_type == 'tabular': - header = 'infer' if params['selected_tasks']['selected_algorithms']['input_options']['header1'] else None - column_option = params['selected_tasks']['selected_algorithms']['input_options']['column_selector_options_1']['selected_column_selector_option'] - if column_option in ['by_index_number', 'all_but_by_index_number', 'by_header_name', 'all_but_by_header_name']: - c = params['selected_tasks']['selected_algorithms']['input_options']['column_selector_options_1']['col1'] + header = 'infer' if (params['selected_tasks']['selected_algorithms'] + ['input_options']['header1']) else None + column_option = (params['selected_tasks']['selected_algorithms'] + ['input_options']['column_selector_options_1'] + ['selected_column_selector_option']) + if column_option in ['by_index_number', 'all_but_by_index_number', + 'by_header_name', 'all_but_by_header_name']: + c = (params['selected_tasks']['selected_algorithms'] + ['input_options']['column_selector_options_1']['col1']) else: c = None X = read_columns( @@ -175,15 +286,19 @@ c_option=column_option, sep='\t', header=header, - parse_dates=True - ) + parse_dates=True).astype(float) else: X = mmread(file1) - header = 'infer' if params['selected_tasks']['selected_algorithms']['input_options']['header2'] else None - column_option = params['selected_tasks']['selected_algorithms']['input_options']['column_selector_options_2']['selected_column_selector_option2'] - if column_option in ['by_index_number', 'all_but_by_index_number', 'by_header_name', 'all_but_by_header_name']: - c = params['selected_tasks']['selected_algorithms']['input_options']['column_selector_options_2']['col2'] + header = 'infer' if (params['selected_tasks']['selected_algorithms'] + ['input_options']['header2']) else None + column_option = (params['selected_tasks']['selected_algorithms'] + ['input_options']['column_selector_options_2'] + ['selected_column_selector_option2']) + if column_option in ['by_index_number', 'all_but_by_index_number', + 'by_header_name', 'all_but_by_header_name']: + c = (params['selected_tasks']['selected_algorithms'] + ['input_options']['column_selector_options_2']['col2']) else: c = None y = read_columns( @@ -192,15 +307,17 @@ c_option=column_option, sep='\t', header=header, - parse_dates=True - ) + parse_dates=True) y = y.ravel() + return X, y class SafeEval(Interpreter): - - def __init__(self, load_scipy=False, load_numpy=False, load_estimators=False): + """Customized symbol table for safely literal eval + """ + def __init__(self, load_scipy=False, load_numpy=False, + load_estimators=False): # File opening and other unneeded functions could be dropped unwanted = ['open', 'type', 'dir', 'id', 'str', 'repr'] @@ -208,7 +325,8 @@ # Allowed symbol table. Add more if needed. new_syms = { 'np_arange': getattr(np, 'arange'), - 'ensemble_ExtraTreesClassifier': getattr(ensemble, 'ExtraTreesClassifier') + 'ensemble_ExtraTreesClassifier': + getattr(ensemble, 'ExtraTreesClassifier') } syms = make_symbol_table(use_numpy=False, **new_syms) @@ -216,80 +334,109 @@ if load_scipy: scipy_distributions = scipy.stats.distributions.__dict__ for k, v in scipy_distributions.items(): - if isinstance(v, (scipy.stats.rv_continuous, scipy.stats.rv_discrete)): + if isinstance(v, (scipy.stats.rv_continuous, + scipy.stats.rv_discrete)): syms['scipy_stats_' + k] = v if load_numpy: - from_numpy_random = ['beta', 'binomial', 'bytes', 'chisquare', 'choice', 'dirichlet', 'division', - 'exponential', 'f', 'gamma', 'geometric', 'gumbel', 'hypergeometric', - 'laplace', 'logistic', 'lognormal', 'logseries', 'mtrand', 'multinomial', - 'multivariate_normal', 'negative_binomial', 'noncentral_chisquare', 'noncentral_f', - 'normal', 'pareto', 'permutation', 'poisson', 'power', 'rand', 'randint', - 'randn', 'random', 'random_integers', 'random_sample', 'ranf', 'rayleigh', - 'sample', 'seed', 'set_state', 'shuffle', 'standard_cauchy', 'standard_exponential', - 'standard_gamma', 'standard_normal', 'standard_t', 'triangular', 'uniform', - 'vonmises', 'wald', 'weibull', 'zipf'] + from_numpy_random = [ + 'beta', 'binomial', 'bytes', 'chisquare', 'choice', + 'dirichlet', 'division', 'exponential', 'f', 'gamma', + 'geometric', 'gumbel', 'hypergeometric', 'laplace', + 'logistic', 'lognormal', 'logseries', 'mtrand', + 'multinomial', 'multivariate_normal', 'negative_binomial', + 'noncentral_chisquare', 'noncentral_f', 'normal', 'pareto', + 'permutation', 'poisson', 'power', 'rand', 'randint', + 'randn', 'random', 'random_integers', 'random_sample', + 'ranf', 'rayleigh', 'sample', 'seed', 'set_state', + 'shuffle', 'standard_cauchy', 'standard_exponential', + 'standard_gamma', 'standard_normal', 'standard_t', + 'triangular', 'uniform', 'vonmises', 'wald', 'weibull', 'zipf'] for f in from_numpy_random: syms['np_random_' + f] = getattr(np.random, f) if load_estimators: estimator_table = { - 'sklearn_svm' : getattr(sklearn, 'svm'), - 'sklearn_tree' : getattr(sklearn, 'tree'), - 'sklearn_ensemble' : getattr(sklearn, 'ensemble'), - 'sklearn_neighbors' : getattr(sklearn, 'neighbors'), - 'sklearn_naive_bayes' : getattr(sklearn, 'naive_bayes'), - 'sklearn_linear_model' : getattr(sklearn, 'linear_model'), - 'sklearn_cluster' : getattr(sklearn, 'cluster'), - 'sklearn_decomposition' : getattr(sklearn, 'decomposition'), - 'sklearn_preprocessing' : getattr(sklearn, 'preprocessing'), - 'sklearn_feature_selection' : getattr(sklearn, 'feature_selection'), - 'sklearn_kernel_approximation' : getattr(sklearn, 'kernel_approximation'), + 'sklearn_svm': getattr(sklearn, 'svm'), + 'sklearn_tree': getattr(sklearn, 'tree'), + 'sklearn_ensemble': getattr(sklearn, 'ensemble'), + 'sklearn_neighbors': getattr(sklearn, 'neighbors'), + 'sklearn_naive_bayes': getattr(sklearn, 'naive_bayes'), + 'sklearn_linear_model': getattr(sklearn, 'linear_model'), + 'sklearn_cluster': getattr(sklearn, 'cluster'), + 'sklearn_decomposition': getattr(sklearn, 'decomposition'), + 'sklearn_preprocessing': getattr(sklearn, 'preprocessing'), + 'sklearn_feature_selection': + getattr(sklearn, 'feature_selection'), + 'sklearn_kernel_approximation': + getattr(sklearn, 'kernel_approximation'), 'skrebate_ReliefF': getattr(skrebate, 'ReliefF'), 'skrebate_SURF': getattr(skrebate, 'SURF'), 'skrebate_SURFstar': getattr(skrebate, 'SURFstar'), 'skrebate_MultiSURF': getattr(skrebate, 'MultiSURF'), 'skrebate_MultiSURFstar': getattr(skrebate, 'MultiSURFstar'), 'skrebate_TuRF': getattr(skrebate, 'TuRF'), - 'xgboost_XGBClassifier' : getattr(xgboost, 'XGBClassifier'), - 'xgboost_XGBRegressor' : getattr(xgboost, 'XGBRegressor') + 'xgboost_XGBClassifier': getattr(xgboost, 'XGBClassifier'), + 'xgboost_XGBRegressor': getattr(xgboost, 'XGBRegressor'), + 'imblearn_over_sampling': getattr(imblearn, 'over_sampling'), + 'imblearn_combine': getattr(imblearn, 'combine') } syms.update(estimator_table) for key in unwanted: syms.pop(key, None) - super(SafeEval, self).__init__(symtable=syms, use_numpy=False, minimal=False, - no_if=True, no_for=True, no_while=True, no_try=True, - no_functiondef=True, no_ifexp=True, no_listcomp=False, - no_augassign=False, no_assert=True, no_delete=True, - no_raise=True, no_print=True) - + super(SafeEval, self).__init__( + symtable=syms, use_numpy=False, minimal=False, + no_if=True, no_for=True, no_while=True, no_try=True, + no_functiondef=True, no_ifexp=True, no_listcomp=False, + no_augassign=False, no_assert=True, no_delete=True, + no_raise=True, no_print=True) def get_estimator(estimator_json): - + """Return a sklearn or compatible estimator from Galaxy tool inputs + """ estimator_module = estimator_json['selected_module'] - if estimator_module == 'customer_estimator': + if estimator_module == 'custom_estimator': c_estimator = estimator_json['c_estimator'] with open(c_estimator, 'rb') as model_handler: new_model = load_model(model_handler) return new_model + if estimator_module == "binarize_target": + wrapped_estimator = estimator_json['wrapped_estimator'] + with open(wrapped_estimator, 'rb') as model_handler: + wrapped_estimator = load_model(model_handler) + options = {} + if estimator_json['z_score'] is not None: + options['z_score'] = estimator_json['z_score'] + if estimator_json['value'] is not None: + options['value'] = estimator_json['value'] + options['less_is_positive'] = estimator_json['less_is_positive'] + if estimator_json['clf_or_regr'] == 'BinarizeTargetClassifier': + klass = try_get_attr('iraps_classifier', + 'BinarizeTargetClassifier') + else: + klass = try_get_attr('iraps_classifier', + 'BinarizeTargetRegressor') + return klass(wrapped_estimator, **options) + estimator_cls = estimator_json['selected_estimator'] if estimator_module == 'xgboost': - cls = getattr(xgboost, estimator_cls) + klass = getattr(xgboost, estimator_cls) else: module = getattr(sklearn, estimator_module) - cls = getattr(module, estimator_cls) + klass = getattr(module, estimator_cls) - estimator = cls() + estimator = klass() estimator_params = estimator_json['text_params'].strip() if estimator_params != '': try: + safe_eval = SafeEval() params = safe_eval('dict(' + estimator_params + ')') except ValueError: sys.exit("Unsupported parameter input: `%s`" % estimator_params) @@ -301,9 +448,13 @@ def get_cv(cv_json): - """ - cv_json: - e.g.: + """ Return CV splitter from Galaxy tool inputs + + Parameters + ---------- + cv_json : dict + From Galaxy tool inputs. + e.g.: { 'selected_cv': 'StratifiedKFold', 'n_splits': 3, @@ -315,15 +466,25 @@ if cv == 'default': return cv_json['n_splits'], None - groups = cv_json.pop('groups', None) - if groups: - groups = groups.strip() - if groups != '': - if groups.startswith('__ob__'): - groups = groups[6:] - if groups.endswith('__cb__'): - groups = groups[:-6] - groups = [int(x.strip()) for x in groups.split(',')] + groups = cv_json.pop('groups_selector', None) + if groups is not None: + infile_g = groups['infile_g'] + header = 'infer' if groups['header_g'] else None + column_option = (groups['column_selector_options_g'] + ['selected_column_selector_option_g']) + if column_option in ['by_index_number', 'all_but_by_index_number', + 'by_header_name', 'all_but_by_header_name']: + c = groups['column_selector_options_g']['col_g'] + else: + c = None + groups = read_columns( + infile_g, + c=c, + c_option=column_option, + sep='\t', + header=header, + parse_dates=True) + groups = groups.ravel() for k, v in cv_json.items(): if v == '': @@ -341,7 +502,12 @@ if test_size and test_size > 1.0: cv_json['test_size'] = int(test_size) - cv_class = getattr(model_selection, cv) + if cv == 'OrderedKFold': + cv_class = try_get_attr('model_validations', 'OrderedKFold') + elif cv == 'RepeatedOrderedKFold': + cv_class = try_get_attr('model_validations', 'RepeatedOrderedKFold') + else: + cv_class = getattr(model_selection, cv) splitter = cv_class(**cv_json) return splitter, groups @@ -349,6 +515,9 @@ # needed when sklearn < v0.20 def balanced_accuracy_score(y_true, y_pred): + """Compute balanced accuracy score, which is now available in + scikit-learn from v0.20.0. + """ C = metrics.confusion_matrix(y_true, y_pred) with np.errstate(divide='ignore', invalid='ignore'): per_class = np.diag(C) / C.sum(axis=1) @@ -360,21 +529,71 @@ def get_scoring(scoring_json): - + """Return single sklearn scorer class + or multiple scoers in dictionary + """ if scoring_json['primary_scoring'] == 'default': return None my_scorers = metrics.SCORERS + my_scorers['binarize_auc_scorer'] =\ + try_get_attr('iraps_classifier', 'binarize_auc_scorer') + my_scorers['binarize_average_precision_scorer'] =\ + try_get_attr('iraps_classifier', 'binarize_average_precision_scorer') if 'balanced_accuracy' not in my_scorers: - my_scorers['balanced_accuracy'] = metrics.make_scorer(balanced_accuracy_score) + my_scorers['balanced_accuracy'] =\ + metrics.make_scorer(balanced_accuracy_score) if scoring_json['secondary_scoring'] != 'None'\ - and scoring_json['secondary_scoring'] != scoring_json['primary_scoring']: - scoring = {} - scoring['primary'] = my_scorers[scoring_json['primary_scoring']] + and scoring_json['secondary_scoring'] !=\ + scoring_json['primary_scoring']: + return_scoring = {} + primary_scoring = scoring_json['primary_scoring'] + return_scoring[primary_scoring] = my_scorers[primary_scoring] for scorer in scoring_json['secondary_scoring'].split(','): if scorer != scoring_json['primary_scoring']: - scoring[scorer] = my_scorers[scorer] - return scoring + return_scoring[scorer] = my_scorers[scorer] + return return_scoring return my_scorers[scoring_json['primary_scoring']] + + +def get_search_params(estimator): + """Format the output of `estimator.get_params()` + """ + params = estimator.get_params() + results = [] + for k, v in params.items(): + # params below won't be shown for search in the searchcv tool + keywords = ('n_jobs', 'pre_dispatch', 'memory', 'steps', + 'nthread', 'verbose') + if k.endswith(keywords): + results.append(['*', k, k+": "+repr(v)]) + else: + results.append(['@', k, k+": "+repr(v)]) + results.append( + ["", "Note:", + "@, params eligible for search in searchcv tool."]) + + return results + + +def try_get_attr(module, name): + """try to get attribute from a custom module + + Parameters + ---------- + module : str + Module name + name : str + Attribute (class/function) name. + + Returns + ------- + class or function + """ + mod = sys.modules.get(module, None) + if mod: + return getattr(mod, name) + else: + raise Exception("No module named %s." % module)