# HG changeset patch # User bgruening # Date 1557870424 14400 # Node ID 92918723f751568d68261306263b4dd61d42ce8f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7 diff -r 000000000000 -r 92918723f751 README.rst --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/README.rst Tue May 14 17:47:04 2019 -0400 @@ -0,0 +1,146 @@ +Galaxy wrapper for scikit-learn library +*************************************** + +Contents +======== + +- `What is scikit-learn?`_ + - `Scikit-learn main package groups`_ + - `Tools offered by this wrapper`_ + +- `Machine learning workflows`_ + - `Supervised learning workflows`_ + - `Unsupervised learning workflows`_ + + +____________________________ + + +.. _What is scikit-learn?: + +What is scikit-learn? +===================== + +Scikit-learn is an open-source machine learning library for the Python programming language. It offers various algorithms for performing supervised and unsupervised learning as well as data preprocessing and transformation, model selection and evaluation, and dataset utilities. It is built upon SciPy (Scientific Python) library. + +Scikit-learn source code can be accessed at https://github.com/scikit-learn/scikit-learn. +Detailed installation instructions can be found at http://scikit-learn.org/stable/install.html + + +.. _Scikit-learn main package groups: + +Scikit-learn main package groups +================================ + +Scikit-learn provides the users with several main groups of related operations. +These are: + +- Classification + - Identifying to which category an object belongs. +- Regression + - Predicting a continuous-valued attribute associated with an object. +- Clustering + - Automatic grouping of similar objects into sets. +- Preprocessing + - Feature extraction and normalization. +- Model selection and evaluation + - Comparing, validating and choosing parameters and models. +- Dimensionality reduction + - Reducing the number of random variables to consider. + +Each group consists of a number of well-known algorithms from the category. For example, one can find hierarchical, spectral, kmeans, and other clustering methods in sklearn.cluster package. + + +.. _Tools offered by this wrapper: + +Available tools in the current wrapper +====================================== + +The current release of the wrapper offers a subset of the packages from scikit-learn library. You can find: + +- A subset of classification metric functions +- Linear and quadratic discriminant classifiers +- Random forest and Ada boost classifiers and regressors +- All the clustering methods +- All support vector machine classifiers +- A subset of data preprocessing estimator classes +- Pairwise metric measurement functions + +In addition, several tools for performing matrix operations, generating problem-specific datasets, and encoding text and extracting features have been prepared to help the user with more advanced operations. + +.. _Machine learning workflows: + +Machine learning workflows +========================== + +Machine learning is about processes. No matter what machine learning algorithm we use, we can apply typical workflows and dataflows to produce more robust models and better predictions. +Here we discuss supervised and unsupervised learning workflows. + +.. _Supervised learning workflows: + +Supervised machine learning workflows +===================================== + +**What is supervised learning?** + +In this machine learning task, given sample data which are labeled, the aim is to build a model which can predict the labels for new observations. +In practice, there are five steps which we can go through to start from raw input data and end up getting reasonable predictions for new samples: + +1. Preprocess the data:: + + * Change the collected data into the proper format and datatype. + * Adjust the data quality by filling the missing values, performing + required scaling and normalizations, etc. + * Extract features which are the most meaningfull for the learning task. + * Split the ready dataset into training and test samples. + +2. Choose an algorithm:: + + * These factors help one to choose a learning algorithm: + - Nature of the data (e.g. linear vs. nonlinear data) + - Structure of the predicted output (e.g. binary vs. multilabel classification) + - Memory and time usage of the training + - Predictive accuracy on new data + - Interpretability of the predictions + +3. Choose a validation method + + Every machine learning model should be evaluated before being put into practicical use. + There are numerous performance metrics to evaluate machine learning models. + For supervised learning, usually classification or regression metrics are used. + + A validation method helps to evaluate the performance metrics of a trained model in order + to optimize its performance or ultimately switch to a more efficient model. + Cross-validation is a known validation method. + +4. Fit a model + + Given the learning algorithm, validation method, and performance metric(s) + repeat the following steps:: + + * Train the model. + * Evaluate based on metrics. + * Optimize unitl satisfied. + +5. Use fitted model for prediction:: + + This is a final evaluation in which, the optimized model is used to make predictions + on unseen (here test) samples. After this, the model is put into production. + +.. _Unsupervised learning workflows: + +Unsupervised machine learning workflows +======================================= + +**What is unsupervised learning?** + +Unlike supervised learning and more liklely in real life, here the initial data is not labeled. +The task is to extract the structure from the data and group the samples based on their similarities. +Clustering and dimensionality reduction are two famous examples of unsupervised learning tasks. + +In this case, the workflow is as follows:: + + * Preprocess the data (without splitting to train and test). + * Train a model. + * Evaluate and tune parameters. + * Analyse the model and test on real data. diff -r 000000000000 -r 92918723f751 estimator_attributes.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/estimator_attributes.xml Tue May 14 17:47:04 2019 -0400 @@ -0,0 +1,179 @@ + + get important attributes from an estimator or scikit object + + main_macros.xml + + + + echo "@VERSION@" + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff -r 000000000000 -r 92918723f751 feature_selectors.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/feature_selectors.py Tue May 14 17:47:04 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 ` 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 ` + 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 diff -r 000000000000 -r 92918723f751 iraps_classifier.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/iraps_classifier.py Tue May 14 17:47:04 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 diff -r 000000000000 -r 92918723f751 main_macros.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/main_macros.xml Tue May 14 17:47:04 2019 -0400 @@ -0,0 +1,1865 @@ + + 1.0.0.4 + + + + python + scikit-learn + pandas + xgboost + asteval + skrebate + imbalanced-learn + mlxtend + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ + + +
+ + + + + + + + + + + + + + + + + + + + + + + selected_tasks['selected_task'] == 'load' + + + selected_tasks['selected_task'] == 'train' + + + + + + + + 10.5281/zenodo.15094 + + + + + + + @article{scikit-learn, + title={Scikit-learn: Machine Learning in {P}ython}, + author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. + and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. + and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and + Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.}, + journal={Journal of Machine Learning Research}, + volume={12}, + pages={2825--2830}, + year={2011} + } + + + + + + + + + @Misc{, + author = {Eric Jones and Travis Oliphant and Pearu Peterson and others}, + title = {{SciPy}: Open source scientific tools for {Python}}, + year = {2001--}, + url = "http://www.scipy.org/", + note = {[Online; accessed 2016-04-09]} + } + + + + + + + @article{DBLP:journals/corr/abs-1711-08477, + author = {Ryan J. Urbanowicz and + Randal S. Olson and + Peter Schmitt and + Melissa Meeker and + Jason H. Moore}, + title = {Benchmarking Relief-Based Feature Selection Methods}, + journal = {CoRR}, + volume = {abs/1711.08477}, + year = {2017}, + url = {http://arxiv.org/abs/1711.08477}, + archivePrefix = {arXiv}, + eprint = {1711.08477}, + timestamp = {Mon, 13 Aug 2018 16:46:04 +0200}, + biburl = {https://dblp.org/rec/bib/journals/corr/abs-1711-08477}, + bibsource = {dblp computer science bibliography, https://dblp.org} + } + + + + + + @inproceedings{Chen:2016:XST:2939672.2939785, + author = {Chen, Tianqi and Guestrin, Carlos}, + title = {{XGBoost}: A Scalable Tree Boosting System}, + booktitle = {Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining}, + series = {KDD '16}, + year = {2016}, + isbn = {978-1-4503-4232-2}, + location = {San Francisco, California, USA}, + pages = {785--794}, + numpages = {10}, + url = {http://doi.acm.org/10.1145/2939672.2939785}, + doi = {10.1145/2939672.2939785}, + acmid = {2939785}, + publisher = {ACM}, + address = {New York, NY, USA}, + keywords = {large-scale machine learning}, + } + + + + + + @article{JMLR:v18:16-365, + author = {Guillaume Lema{{\^i}}tre and Fernando Nogueira and Christos K. Aridas}, + title = {Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning}, + journal = {Journal of Machine Learning Research}, + year = {2017}, + volume = {18}, + number = {17}, + pages = {1-5}, + url = {http://jmlr.org/papers/v18/16-365.html} + } + + + + diff -r 000000000000 -r 92918723f751 model_validations.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/model_validations.py Tue May 14 17:47:04 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) diff -r 000000000000 -r 92918723f751 pk_whitelist.json --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/pk_whitelist.json Tue May 14 17:47:04 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", + 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"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 diff -r 000000000000 -r 92918723f751 preprocessors.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/preprocessors.py Tue May 14 17:47:04 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!") diff -r 000000000000 -r 92918723f751 search_model_validation.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/search_model_validation.py Tue May 14 17:47:04 2019 -0400 @@ -0,0 +1,366 @@ +import argparse +import collections +import imblearn +import json +import numpy as np +import pandas +import pickle +import skrebate +import sklearn +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 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 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(__import__('os').environ.get('GALAXY_SLOTS', 1)) +CACHE_DIR = './cached' +NON_SEARCHABLE = ('n_jobs', 'pre_dispatch', 'memory', 'steps', + 'nthread', 'verbose') + + +def _eval_search_params(params_builder): + search_params = {} + + for p in params_builder['param_set']: + search_list = p['sp_list'].strip() + if search_list == '': + continue + + 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 + + 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: + newlist.append(None) + elif obj == 'all_0': + newlist.extend(preprocessors[0:36]) + elif obj == 'sk_prep_all': # no KernalCenter() + newlist.extend(preprocessors[0:8]) + elif obj == 'fs_all': + newlist.extend(preprocessors[8:15]) + elif obj == 'decomp_all': + newlist.extend(preprocessors[15:26]) + elif obj == 'k_appr_all': + newlist.extend(preprocessors[26:30]) + elif obj == 'reb_all': + newlist.extend(preprocessors[31:36]) + elif obj == 'imb_all': + newlist.extend(preprocessors[36:55]) + elif type(obj) is int and -1 < obj < len(preprocessors): + newlist.append(preprocessors[obj]) + elif hasattr(obj, 'get_params'): # user uploaded object + if 'n_jobs' in obj.get_params(): + newlist.append(obj.set_params(n_jobs=N_JOBS)) + else: + newlist.append(obj) + else: + sys.exit("Unsupported estimator type: %r" % (obj)) + + search_params[param_name] = newlist + + return search_params + + +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') + + with open(inputs, 'r') as param_handler: + params = json.load(param_handler) + 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']: + c = params['input_options']['column_selector_options_1']['col1'] + else: + c = None + X = read_columns( + infile1, + c=c, + c_option=column_option, + sep='\t', + header=header, + 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']: + c = params['input_options']['column_selector_options_2']['col2'] + else: + c = None + y = read_columns( + infile2, + c=c, + c_option=column_option, + sep='\t', + header=header, + 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')) + options['cv'] = splitter + options['n_jobs'] = N_JOBS + primary_scoring = options['scoring']['primary_scoring'] + options['scoring'] = get_scoring(options['scoring']) + if options['error_score']: + options['error_score'] = 'raise' + else: + options['error_score'] = np.NaN + if options['refit'] and isinstance(options['scoring'], dict): + options['refit'] = primary_scoring + if 'pre_dispatch' in options and options['pre_dispatch'] == '': + options['pre_dispatch'] = None + + 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) + + # 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, groups=groups) + else: + warnings.simplefilter('always', FitFailedWarning) + with warnings.catch_warnings(record=True) as w: + try: + searcher.fit(X, y, groups=groups) + except ValueError: + pass + for warning in w: + print(repr(warning.message)) + + 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 + + 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) diff -r 000000000000 -r 92918723f751 stacking_ensembles.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/stacking_ensembles.py Tue May 14 17:47:04 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) diff -r 000000000000 -r 92918723f751 test-data/GridSearchCV.zip Binary file test-data/GridSearchCV.zip has changed diff -r 000000000000 -r 92918723f751 test-data/LinearRegression01.zip Binary file test-data/LinearRegression01.zip has changed diff -r 000000000000 -r 92918723f751 test-data/LinearRegression02.zip Binary file test-data/LinearRegression02.zip has changed diff -r 000000000000 -r 92918723f751 test-data/RF01704.fasta --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/RF01704.fasta Tue May 14 17:47:04 2019 -0400 @@ -0,0 +1,4 @@ +>CP000097.1/1411351-1411410 +CAACGUUCACCUCACAUUUGUGAGGCGCAGACAACCCAGGCCAAGGAACGGGGACCUGGA +>ACNY01000002.1/278641-278580 +GAUCGUUCACUUCGCAUCGCGCGAAGCGCAGUUCGCCUCAGGCCAUGGAACGGGGACCUGAG diff -r 000000000000 -r 92918723f751 test-data/RFE.zip Binary file test-data/RFE.zip has changed diff -r 000000000000 -r 92918723f751 test-data/RandomForestClassifier.zip Binary file test-data/RandomForestClassifier.zip has changed diff -r 000000000000 -r 92918723f751 test-data/RandomForestRegressor01.zip Binary file test-data/RandomForestRegressor01.zip has changed diff -r 000000000000 -r 92918723f751 test-data/StackingCVRegressor01.zip Binary file test-data/StackingCVRegressor01.zip has changed diff -r 000000000000 -r 92918723f751 test-data/StackingCVRegressor02.zip Binary file test-data/StackingCVRegressor02.zip has changed diff -r 000000000000 -r 92918723f751 test-data/XGBRegressor01.zip Binary file test-data/XGBRegressor01.zip has changed diff -r 000000000000 -r 92918723f751 test-data/abc_model01 Binary file test-data/abc_model01 has changed diff -r 000000000000 -r 92918723f751 test-data/abc_result01 --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/abc_result01 Tue May 14 17:47:04 2019 -0400 @@ -0,0 +1,6 @@ +0 1 2 3 predicted +3.68258022948 2.82110345641 -3.9901407239999998 -1.9523364774 1 +0.015942057224 -0.7119585943469999 0.125502976978 -0.972218263337 0 +2.0869076882499997 0.929399321468 -2.1292408448400004 -1.9971402218799998 1 +1.4132105208399999 0.523750660422 -1.4210539291 -1.49298569451 1 +0.7683140439399999 1.38267855169 -0.989045048734 0.649504257894 1 diff -r 000000000000 -r 92918723f751 test-data/abr_model01 Binary file test-data/abr_model01 has changed diff -r 000000000000 -r 92918723f751 test-data/abr_result01 --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/abr_result01 Tue May 14 17:47:04 2019 -0400 @@ -0,0 +1,6 @@ +0 1 2 3 4 predicted +86.97021227350001 1.00532111569 -1.01739601979 -0.613139481654 0.641846874331 0.323842059244 +91.2021798817 -0.6215229712070001 1.11914889596 0.390012184498 1.28956938152 1.1503117056799999 +-47.4101632272 -0.638416457964 -0.7327774684530001 -0.8640261049779999 -1.06109770116 -0.7191695359690001 +61.712804630200004 -1.0999480057700002 -0.739679672932 0.585657963012 1.4890682753600002 1.1503117056799999 +-206.998295124 0.130238853011 0.70574123041 1.3320656526399999 -1.3322092373799999 -0.7191695359690001 diff -r 000000000000 -r 92918723f751 test-data/accuracy_score.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/accuracy_score.txt Tue May 14 17:47:04 2019 -0400 @@ -0,0 +1,2 @@ +accuracy_score : +0.8461538461538461 diff -r 000000000000 -r 92918723f751 test-data/auc.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/auc.txt Tue May 14 17:47:04 2019 -0400 @@ -0,0 +1,2 @@ +auc : +2.5 diff -r 000000000000 -r 92918723f751 test-data/average_precision_score.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/average_precision_score.txt Tue May 14 17:47:04 2019 -0400 @@ -0,0 +1,2 @@ +average_precision_score : +1.0 diff -r 000000000000 -r 92918723f751 test-data/best_estimator_.zip Binary file test-data/best_estimator_.zip has changed diff -r 000000000000 -r 92918723f751 test-data/best_params_.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/best_params_.txt Tue May 14 17:47:04 2019 -0400 @@ -0,0 +1,1 @@ +{'estimator__n_estimators': 100} \ No newline at end of file diff -r 000000000000 -r 92918723f751 test-data/best_score_.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/best_score_.tabular Tue May 14 17:47:04 2019 -0400 @@ -0,0 +1,2 @@ +best_score_ +0.7976348550293088 diff -r 000000000000 -r 92918723f751 test-data/blobs.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/blobs.txt Tue May 14 17:47:04 2019 -0400 @@ -0,0 +1,101 @@ +0 1 0 +0.33681845896740 -3.40287961299073 0 +-9.48324265575857 -8.66266051536995 2 +-1.93336328496076 5.70953908146890 1 +-10.03182405989413 -5.57834393458362 2 +0.54186077661701 -4.37693628326930 0 +-8.12962929067378 -7.05554320549807 2 +-0.73082578569427 7.32375551699482 1 +-1.84142532007015 6.20630466830832 1 +0.41007185031668 -3.99744881000119 0 +-8.73509589323240 -5.49090535208751 2 +1.84941962277054 -3.91839345672969 0 +-9.15256068848857 -9.17805648051067 2 +-3.21429939077830 5.75926163957071 1 +0.28450378549664 -3.61576522643830 0 +-0.92907484922306 5.79099955373578 1 +0.36692524194483 6.51861929622910 1 +1.59909917965412 -3.07105617297875 0 +-9.71270568435724 -7.91707651499009 2 +-10.08040443063205 -6.55135324108655 2 +1.10594345774293 -4.41906374949547 0 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b/test-data/gbc_result01 Tue May 14 17:47:04 2019 -0400 @@ -0,0 +1,6 @@ +0 1 2 3 predicted +3.68258022948 2.82110345641 -3.9901407239999998 -1.9523364774 1 +0.015942057224 -0.7119585943469999 0.125502976978 -0.972218263337 0 +2.0869076882499997 0.929399321468 -2.1292408448400004 -1.9971402218799998 1 +1.4132105208399999 0.523750660422 -1.4210539291 -1.49298569451 1 +0.7683140439399999 1.38267855169 -0.989045048734 0.649504257894 1 diff -r 000000000000 -r 92918723f751 test-data/gbr_model01 Binary file test-data/gbr_model01 has changed diff -r 000000000000 -r 92918723f751 test-data/gbr_prediction_result01.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/gbr_prediction_result01.tabular Tue May 14 17:47:04 2019 -0400 @@ -0,0 +1,88 @@ +year month day temp_2 temp_1 average forecast_noaa forecast_acc forecast_under friend week_Fri week_Mon week_Sat week_Sun week_Thurs week_Tues week_Wed predicted +2016 9 29 69 68 66.1 63 71 68 57 0 0 0 0 1 0 0 69.58390938468499 +2016 4 27 59 60 60.7 59 65 60 50 0 0 0 0 0 0 1 62.52052253790107 +2016 11 28 53 48 48.0 46 48 49 44 0 1 0 0 0 0 0 51.680887055498296 +2016 10 12 60 62 61.0 60 63 63 52 0 0 0 0 0 0 1 61.246237852679315 +2016 6 19 67 65 70.4 69 73 70 58 0 0 0 1 0 0 0 65.03047512424794 +2016 5 7 68 77 63.0 61 65 63 83 0 0 1 0 0 0 0 71.59883326872612 +2016 7 25 75 80 77.1 75 82 76 81 0 1 0 0 0 0 0 78.5487110206859 +2016 8 15 90 83 76.6 76 79 75 70 0 1 0 0 0 0 0 80.77545676519121 +2016 10 28 58 60 55.6 52 56 55 52 1 0 0 0 0 0 0 62.11231551486949 +2016 6 5 80 81 68.0 64 70 66 54 0 0 0 1 0 0 0 72.42798354934989 +2016 3 19 58 63 54.2 54 59 54 62 0 0 1 0 0 0 0 61.63169537788603 +2016 6 7 92 86 68.3 67 69 70 58 0 0 0 0 0 1 0 74.4731129283374 +2016 12 10 41 36 45.9 44 48 44 65 0 0 1 0 0 0 0 39.39391240070939 +2016 4 23 73 64 59.9 56 63 59 57 0 0 1 0 0 0 0 62.93072314757922 +2016 6 24 75 68 71.5 67 73 73 65 1 0 0 0 0 0 0 73.42248151259705 +2016 2 9 51 57 49.4 45 52 49 57 0 0 0 0 0 1 0 55.106926049453094 +2016 11 10 71 65 52.2 52 54 51 38 0 0 0 0 1 0 0 62.931939262865185 +2016 3 21 61 55 54.5 52 56 55 52 0 1 0 0 0 0 0 56.54303204039889 +2016 2 28 60 57 51.3 48 56 53 66 0 0 0 1 0 0 0 57.5819236251605 +2016 6 28 78 85 72.4 72 76 74 67 0 0 0 0 0 1 0 77.87772901898535 +2016 10 6 63 66 63.3 62 67 63 55 0 0 0 0 1 0 0 64.65839290042257 +2016 2 17 55 56 50.0 45 51 49 46 0 0 0 0 0 0 1 54.26509333618539 +2016 6 15 66 60 69.7 65 73 71 69 0 0 0 0 0 0 1 66.15190585447276 +2016 10 15 60 60 59.9 59 62 59 46 0 0 1 0 0 0 0 62.135403207035466 +2016 3 26 54 57 55.2 53 57 55 54 0 0 1 0 0 0 0 59.148716891180484 +2016 1 26 51 54 48.3 44 53 50 61 0 0 0 0 0 1 0 53.05069255536133 +2016 5 23 59 66 66.1 63 68 68 66 0 1 0 0 0 0 0 64.85734973368784 +2016 1 10 48 50 46.5 45 48 48 49 0 0 0 1 0 0 0 45.06961558051259 +2016 5 22 66 59 65.9 62 66 65 80 0 0 0 1 0 0 0 60.46222634728846 +2016 7 15 75 77 76.0 74 80 78 75 1 0 0 0 0 0 0 82.42822449858019 +2016 4 22 81 73 59.7 59 64 60 59 1 0 0 0 0 0 0 72.82325656081416 +2016 4 29 61 64 61.2 61 65 61 49 1 0 0 0 0 0 0 65.00954748796826 +2016 1 23 52 57 48.0 45 49 50 37 0 0 1 0 0 0 0 50.836039030817304 +2016 8 16 83 84 76.5 72 78 78 90 0 0 0 0 0 1 0 82.12928759095375 +2016 8 1 76 73 77.4 76 78 79 65 0 1 0 0 0 0 0 72.22807576891064 +2016 2 27 61 60 51.2 51 53 53 61 0 0 1 0 0 0 0 61.680080402280524 +2016 2 12 56 55 49.6 49 52 48 33 1 0 0 0 0 0 0 54.563346197441135 +2016 1 31 52 48 48.7 47 52 49 61 0 0 0 1 0 0 0 51.05906646453181 +2016 9 5 67 68 73.5 71 75 73 54 0 1 0 0 0 0 0 68.96231670707674 +2016 12 20 39 46 45.1 45 49 45 62 0 0 0 0 0 1 0 41.12571355242521 +2016 5 1 61 68 61.6 60 65 60 75 0 0 0 1 0 0 0 66.15287588548186 +2016 3 28 59 51 55.5 55 57 55 47 0 1 0 0 0 0 0 59.11011722462772 +2016 4 21 81 81 59.4 55 61 59 55 0 0 0 0 1 0 0 74.41085058157081 +2016 1 6 40 44 46.1 43 49 48 40 0 0 0 0 0 0 1 41.20470505512009 +2016 10 21 58 62 57.8 56 60 59 44 1 0 0 0 0 0 0 61.62578223843827 +2016 5 2 68 77 61.9 60 66 61 59 0 1 0 0 0 0 0 72.48517225879384 +2016 3 1 53 54 51.5 48 56 50 53 0 0 0 0 0 1 0 53.70588500948454 +2016 7 21 78 82 76.8 73 81 78 84 0 0 0 0 1 0 0 82.7108327367616 +2016 3 17 51 53 53.9 49 58 52 62 0 0 0 0 1 0 0 53.251174797156146 +2016 12 6 46 40 46.4 44 50 45 56 0 0 0 0 0 1 0 42.363067913515295 +2016 12 21 46 51 45.1 44 50 46 39 0 0 0 0 0 0 1 45.6445314453422 +2016 1 4 44 41 45.9 44 48 46 53 0 1 0 0 0 0 0 42.214387828919136 +2016 10 2 67 63 64.9 62 69 66 82 0 0 0 1 0 0 0 62.736396078841445 +2016 5 28 65 64 66.8 64 69 65 64 0 0 1 0 0 0 0 63.947755881441275 +2016 9 11 74 77 72.1 69 75 71 70 0 0 0 1 0 0 0 73.98460722074996 +2016 10 25 62 61 56.5 53 60 55 70 0 0 0 0 0 1 0 61.917230159710556 +2016 2 18 56 57 50.1 47 55 49 34 0 0 0 0 1 0 0 55.720840480421955 +2016 11 1 117 59 54.5 51 59 55 61 0 0 0 0 0 1 0 61.52527009553642 +2016 3 16 49 51 53.7 52 54 55 65 0 0 0 0 0 0 1 54.86875365404632 +2016 4 26 55 59 60.5 56 61 62 75 0 0 0 0 0 1 0 61.34654097192005 +2016 6 10 67 65 68.8 67 71 67 73 1 0 0 0 0 0 0 65.38427016260138 +2016 2 3 46 51 48.9 48 49 50 40 0 0 0 0 0 0 1 49.75042424691725 +2016 3 7 64 60 52.4 49 57 53 71 0 1 0 0 0 0 0 61.08886411894317 +2016 9 18 75 68 70.0 66 73 71 90 0 0 0 1 0 0 0 70.7844532497458 +2016 3 20 63 61 54.3 51 56 55 50 0 0 0 1 0 0 0 59.66542877819202 +2016 4 6 60 57 56.8 53 59 57 64 0 0 0 0 0 0 1 59.301283011436794 +2016 7 2 73 76 73.3 70 77 73 84 0 0 1 0 0 0 0 71.22373270826222 +2016 7 5 71 68 74.0 72 77 74 62 0 0 0 0 0 1 0 69.18347305115272 +2016 7 19 80 73 76.6 76 78 77 90 0 0 0 0 0 1 0 77.46150755171419 +2016 12 9 40 41 46.0 43 51 44 54 1 0 0 0 0 0 0 41.72540550328788 +2016 6 29 85 79 72.6 68 76 74 81 0 0 0 0 0 0 1 76.10594345672801 +2016 3 22 55 56 54.6 51 55 54 64 0 0 0 0 0 1 0 58.39058086785531 +2016 4 3 71 63 56.3 54 61 56 64 0 0 0 1 0 0 0 60.14340322699943 +2016 1 17 48 54 47.4 45 51 46 47 0 0 0 1 0 0 0 50.26292708961779 +2016 3 10 54 55 52.8 49 55 53 50 0 0 0 0 1 0 0 55.522605642512985 +2016 5 9 82 63 63.4 59 66 62 64 0 1 0 0 0 0 0 61.00788720614107 +2016 1 8 51 45 46.3 43 47 46 34 1 0 0 0 0 0 0 44.83434926564482 +2016 8 11 72 76 76.9 74 81 75 80 0 0 0 0 1 0 0 74.70250254902773 +2016 12 29 47 48 45.3 43 50 45 65 0 0 0 0 1 0 0 49.53438043623214 +2016 11 23 54 54 49.1 48 52 49 38 0 0 0 0 0 0 1 51.467278500089826 +2016 11 19 52 55 50.0 50 54 49 56 0 0 1 0 0 0 0 53.781953941654095 +2016 4 7 57 68 56.9 52 61 55 38 0 0 0 0 1 0 0 68.59176558339176 +2016 6 4 71 80 67.9 63 72 66 76 0 0 1 0 0 0 0 72.73805569547436 +2016 6 17 67 71 70.0 66 74 69 54 1 0 0 0 0 0 0 74.00873400230815 +2016 10 5 61 63 63.7 61 66 65 48 0 0 0 0 0 0 1 63.553834877849695 +2016 3 4 55 59 51.9 47 56 53 45 1 0 0 0 0 0 0 57.389419897063036 +2016 12 22 51 49 45.1 42 47 46 38 0 0 0 0 1 0 0 44.218563783534144 diff -r 000000000000 -r 92918723f751 test-data/get_params.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/get_params.tabular Tue May 14 17:47:04 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. diff -r 000000000000 -r 92918723f751 test-data/get_params01.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/get_params01.tabular Tue May 14 17:47:04 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=)), ('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=) +@ 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: +@ 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. diff -r 000000000000 -r 92918723f751 test-data/get_params02.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/get_params02.tabular Tue May 14 17:47:04 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. diff -r 000000000000 -r 92918723f751 test-data/get_params03.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/get_params03.tabular Tue May 14 17:47:04 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. diff -r 000000000000 -r 92918723f751 test-data/get_params04.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/get_params04.tabular Tue May 14 17:47:04 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. diff -r 000000000000 -r 92918723f751 test-data/get_params05.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/get_params05.tabular Tue May 14 17:47:04 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. diff -r 000000000000 -r 92918723f751 test-data/get_params06.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/get_params06.tabular Tue May 14 17:47:04 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. diff -r 000000000000 -r 92918723f751 test-data/get_params07.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/get_params07.tabular Tue May 14 17:47:04 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. diff -r 000000000000 -r 92918723f751 test-data/get_params08.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/get_params08.tabular Tue May 14 17:47:04 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=)), ('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=)" +@ 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: +@ 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. diff -r 000000000000 -r 92918723f751 test-data/get_params09.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/get_params09.tabular Tue May 14 17:47:04 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. diff -r 000000000000 -r 92918723f751 test-data/get_params10.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/get_params10.tabular Tue May 14 17:47:04 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. diff -r 000000000000 -r 92918723f751 test-data/get_params11.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/get_params11.tabular Tue May 14 17:47:04 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. diff -r 000000000000 -r 92918723f751 test-data/get_params12.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/get_params12.tabular Tue May 14 17:47:04 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. diff -r 000000000000 -r 92918723f751 test-data/glm_model01 Binary file test-data/glm_model01 has changed diff -r 000000000000 -r 92918723f751 test-data/glm_model02 Binary file test-data/glm_model02 has changed diff -r 000000000000 -r 92918723f751 test-data/glm_model03 Binary file test-data/glm_model03 has changed diff -r 000000000000 -r 92918723f751 test-data/glm_model04 Binary file test-data/glm_model04 has changed diff -r 000000000000 -r 92918723f751 test-data/glm_model05 Binary file test-data/glm_model05 has changed diff -r 000000000000 -r 92918723f751 test-data/glm_model06 Binary file test-data/glm_model06 has changed diff -r 000000000000 -r 92918723f751 test-data/glm_model07 Binary file test-data/glm_model07 has changed diff -r 000000000000 -r 92918723f751 test-data/glm_model08 Binary file test-data/glm_model08 has changed diff -r 000000000000 -r 92918723f751 test-data/glm_result01 --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/glm_result01 Tue May 14 17:47:04 2019 -0400 @@ -0,0 +1,5 @@ +86.97021227350001 1.00532111569 -1.01739601979 -0.613139481654 0.641846874331 3703215242836.872 +91.2021798817 -0.6215229712070001 1.11914889596 0.390012184498 1.28956938152 3875943636708.156 +-47.4101632272 -0.638416457964 -0.7327774684530001 -0.8640261049779999 -1.06109770116 -2071574726112.0168 +61.712804630200004 -1.0999480057700002 -0.739679672932 0.585657963012 1.4890682753600002 2642119730255.405 +-206.998295124 0.130238853011 0.70574123041 1.3320656526399999 -1.3322092373799999 -8851040854159.11 diff -r 000000000000 -r 92918723f751 test-data/glm_result02 --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/glm_result02 Tue May 14 17:47:04 2019 -0400 @@ -0,0 +1,5 @@ +3.68258022948 2.82110345641 -3.9901407239999998 -1.9523364774 1 +0.015942057224 -0.7119585943469999 0.125502976978 -0.972218263337 0 +2.0869076882499997 0.929399321468 -2.1292408448400004 -1.9971402218799998 0 +1.4132105208399999 0.523750660422 -1.4210539291 -1.49298569451 0 +0.7683140439399999 1.38267855169 -0.989045048734 0.649504257894 1 diff -r 000000000000 -r 92918723f751 test-data/glm_result03 --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/glm_result03 Tue May 14 17:47:04 2019 -0400 @@ -0,0 +1,5 @@ +3.68258022948 2.82110345641 -3.9901407239999998 -1.9523364774 1 +0.015942057224 -0.7119585943469999 0.125502976978 -0.972218263337 0 +2.0869076882499997 0.929399321468 -2.1292408448400004 -1.9971402218799998 0 +1.4132105208399999 0.523750660422 -1.4210539291 -1.49298569451 0 +0.7683140439399999 1.38267855169 -0.989045048734 0.649504257894 1 diff -r 000000000000 -r 92918723f751 test-data/glm_result04 --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/glm_result04 Tue May 14 17:47:04 2019 -0400 @@ -0,0 +1,5 @@ +86.97021227350001 1.00532111569 -1.01739601979 -0.613139481654 0.641846874331 0.5282637592226301 +91.2021798817 -0.6215229712070001 1.11914889596 0.390012184498 1.28956938152 0.5180352211818147 +-47.4101632272 -0.638416457964 -0.7327774684530001 -0.8640261049779999 -1.06109770116 0.012682414140451959 +61.712804630200004 -1.0999480057700002 -0.739679672932 0.585657963012 1.4890682753600002 0.1869842234155321 +-206.998295124 0.130238853011 0.70574123041 1.3320656526399999 -1.3322092373799999 -1.6599360904302456 diff -r 000000000000 -r 92918723f751 test-data/glm_result05 --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/glm_result05 Tue May 14 17:47:04 2019 -0400 @@ -0,0 +1,5 @@ +3.68258022948 2.82110345641 -3.9901407239999998 -1.9523364774 1 +0.015942057224 -0.7119585943469999 0.125502976978 -0.972218263337 0 +2.0869076882499997 0.929399321468 -2.1292408448400004 -1.9971402218799998 1 +1.4132105208399999 0.523750660422 -1.4210539291 -1.49298569451 1 +0.7683140439399999 1.38267855169 -0.989045048734 0.649504257894 1 diff -r 000000000000 -r 92918723f751 test-data/glm_result06 --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/glm_result06 Tue May 14 17:47:04 2019 -0400 @@ -0,0 +1,5 @@ +3.68258022948 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-1.3322092373799999 -1.7330414645145749 diff -r 000000000000 -r 92918723f751 test-data/glm_result08 --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/glm_result08 Tue May 14 17:47:04 2019 -0400 @@ -0,0 +1,5 @@ +3.68258022948 2.82110345641 -3.9901407239999998 -1.9523364774 0 +0.015942057224 -0.7119585943469999 0.125502976978 -0.972218263337 0 +2.0869076882499997 0.929399321468 -2.1292408448400004 -1.9971402218799998 0 +1.4132105208399999 0.523750660422 -1.4210539291 -1.49298569451 0 +0.7683140439399999 1.38267855169 -0.989045048734 0.649504257894 1 diff -r 000000000000 -r 92918723f751 test-data/hamming_loss.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/hamming_loss.txt Tue May 14 17:47:04 2019 -0400 @@ -0,0 +1,2 @@ +hamming_loss : +0.15384615384615385 diff -r 000000000000 -r 92918723f751 test-data/hastie.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/hastie.txt Tue May 14 17:47:04 2019 -0400 @@ -0,0 +1,12001 @@ +0 1 2 3 4 5 6 7 8 9 0 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-r 92918723f751 test-data/y.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/y.tabular Tue May 14 17:47:04 2019 -0400 @@ -0,0 +1,39 @@ +0 0 0.0 1.0 2.0 0 0 -2.76903910779 -0.777269253713 2.08028572913 +2 2 0.0 0.0 1.0 1 1 -1.46032791667 0.555654963057 -1.54234795893 +1 1 0.0 1.0 0.0 1 1 1.72939677275 -1.3402943146 -7.95375106924 +0 0 1.0 0.0 0.0 0 0 -3.15016545997 0.19568758864 1.40593056786 +2 2 0.0 0.0 1.0 1 1 1.21845859294 -0.677633363546 -6.62421395692 +0 0 1.0 0.0 0.0 0 0 -3.25263252854 -0.908498631085 2.74671790479 +2 2 0.0 0.0 1.0 1 1 1.38291089706 -0.924165117418 -6.87449092795 +0 0 1.0 0.0 0.0 0 0 -1.7423603376 -0.326034812837 -1.12743832183 +1 2 0.0 1.0 0.0 1 1 -1.88141734237 0.0471879612496 -0.990485600884 +1 2 0.0 0.9 0.1 1 1 -1.32547081613 -0.193430743286 -1.66958283068 +1 1 0.0 1.0 0.0 1 1 -2.7799666645 0.485621555351 1.21494093967 +2 2 0.0 0.2 0.8 1 1 -1.60125339649 -0.493901618129 -1.20213785254 +1 2 0.0 1.0 0.0 1 1 -1.86658623206 0.162709340336 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-r 92918723f751 utils.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/utils.py Tue May 14 17:47:04 2019 -0400 @@ -0,0 +1,599 @@ +import ast +import json +import imblearn +import numpy as np +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 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 model_validations +except ImportError: + pass + +try: + import feature_selectors +except ImportError: + pass + +try: + import preprocessors +except ImportError: + pass + +# 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, object): + """ + Used to safely deserialize scikit-learn model objects + Usage: + eg.: _SafePickler.load(pickled_file_object) + """ + 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) + + 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__') + + # 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.', '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) + + raise pickle.UnpicklingError("global '%s' is forbidden" % fullname) + + +def load_model(file): + """Load pickled object with `_SafePicker` + """ + return _SafePickler(file).load() + + +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)) + data = data.iloc[:, cols] + if c_option == 'all_but_by_index_number': + cols = list(map(lambda x: x - 1, c)) + data.drop(data.columns[cols], axis=1, inplace=True) + if c_option == 'by_header_name': + cols = [e.strip() for e in c.split(',')] + data = data[cols] + if c_option == 'all_but_by_header_name': + cols = [e.strip() for e in c.split(',')] + data.drop(cols, axis=1, inplace=True) + y = data.values + if return_df: + return y, data + else: + return y + + +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'] + if selector != 'DyRFECV': + selector = getattr(sklearn.feature_selection, selector) + options = inputs['options'] + + if inputs['selected_algorithm'] == 'SelectFromModel': + if not options['threshold'] or options['threshold'] == 'None': + options['threshold'] = None + else: + try: + options['threshold'] = float(options['threshold']) + except ValueError: + pass + if inputs['model_inputter']['input_mode'] == 'prefitted': + model_file = inputs['model_inputter']['fitted_estimator'] + with open(model_file, 'rb') as model_handler: + fitted_estimator = load_model(model_handler) + new_selector = selector(fitted_estimator, prefit=True, **options) + 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': + 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')) + 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) + + else: + score_func = inputs['score_func'] + score_func = getattr(sklearn.feature_selection, score_func) + new_selector = selector(score_func, **options) + + return new_selector + + +def get_X_y(params, file1, file2): + """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']) + else: + c = None + X = read_columns( + file1, + c=c, + c_option=column_option, + sep='\t', + header=header, + 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']) + else: + c = None + y = read_columns( + file2, + c=c, + c_option=column_option, + sep='\t', + header=header, + parse_dates=True) + y = y.ravel() + + return X, y + + +class SafeEval(Interpreter): + """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'] + + # Allowed symbol table. Add more if needed. + new_syms = { + 'np_arange': getattr(np, 'arange'), + 'ensemble_ExtraTreesClassifier': + getattr(ensemble, 'ExtraTreesClassifier') + } + + syms = make_symbol_table(use_numpy=False, **new_syms) + + 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)): + 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'] + 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'), + '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'), + '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) + + +def get_estimator(estimator_json): + """Return a sklearn or compatible estimator from Galaxy tool inputs + """ + estimator_module = estimator_json['selected_module'] + + 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': + klass = getattr(xgboost, estimator_cls) + else: + module = getattr(sklearn, estimator_module) + klass = getattr(module, 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) + estimator.set_params(**params) + if 'n_jobs' in estimator.get_params(): + estimator.set_params(n_jobs=N_JOBS) + + return estimator + + +def get_cv(cv_json): + """ Return CV splitter from Galaxy tool inputs + + Parameters + ---------- + cv_json : dict + From Galaxy tool inputs. + e.g.: + { + 'selected_cv': 'StratifiedKFold', + 'n_splits': 3, + 'shuffle': True, + 'random_state': 0 + } + """ + cv = cv_json.pop('selected_cv') + if cv == 'default': + return cv_json['n_splits'], None + + 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 == '': + cv_json[k] = None + + test_fold = cv_json.get('test_fold', None) + if test_fold: + if test_fold.startswith('__ob__'): + test_fold = test_fold[6:] + if test_fold.endswith('__cb__'): + test_fold = test_fold[:-6] + cv_json['test_fold'] = [int(x.strip()) for x in test_fold.split(',')] + + test_size = cv_json.get('test_size', None) + if test_size and test_size > 1.0: + cv_json['test_size'] = int(test_size) + + 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 + + +# 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) + if np.any(np.isnan(per_class)): + warnings.warn('y_pred contains classes not in y_true') + per_class = per_class[~np.isnan(per_class)] + score = np.mean(per_class) + return score + + +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) + + if scoring_json['secondary_scoring'] != 'None'\ + 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']: + 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)