# HG changeset patch
# User bgruening
# Date 1565349916 14400
# Node ID 0b88494bdcac14162e9ceac7d61ea56794591cc7
# Parent  231e9a9849e88a2d5a13aafba654ec3d38dd8e68
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty

diff -r 231e9a9849e8 -r 0b88494bdcac feature_selection.xml
--- a/feature_selection.xml	Tue Jul 09 19:34:06 2019 -0400
+++ b/feature_selection.xml	Fri Aug 09 07:25:16 2019 -0400
@@ -1,4 +1,4 @@
-<tool id="sklearn_feature_selection" name="Feature Selection" version="@VERSION@.1">
+<tool id="sklearn_feature_selection" name="Feature Selection" version="@VERSION@">
     <description>module, including univariate filter selection methods and recursive feature elimination algorithm</description>
     <macros>
         <import>main_macros.xml</import>
@@ -31,8 +31,9 @@
 from imblearn.pipeline import Pipeline as imbPipeline
 from sklearn.pipeline import Pipeline
 
-sys.path.insert(0, '$__tool_directory__')
-from utils import SafeEval, feature_selector, read_columns
+from galaxy_ml.utils import (SafeEval, feature_selector,
+                             read_columns, get_module)
+
 
 warnings.simplefilter('ignore')
 
@@ -71,25 +72,43 @@
 
 ## Read features
 features_has_header = params['input_options']['header1']
-input_type = params['input_options']['selected_input']
-if input_type == 'tabular':
-    header = 'infer' if features_has_header 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, input_df = read_columns(
-            '$input_options.infile1',
-            c = c,
-            c_option = column_option,
-            return_df = True,
-            sep='\t',
-            header=header,
-            parse_dates=True)
-    X = X.astype(float)
+#if $input_options.selected_input == 'tabular'
+header = 'infer' if features_has_header else None
+column_option = params['input_options']['column_selector_options_1']['selected_column_selector_option']
+if column_option in ['by_index_number', 'all_but_by_index_number', 'by_header_name', 'all_but_by_header_name']:
+    c = params['input_options']['column_selector_options_1']['col1']
 else:
-    X = mmread('$input_options.infile1')
+    c = None
+X, input_df = read_columns(
+        '$input_options.infile1',
+        c = c,
+        c_option = column_option,
+        return_df = True,
+        sep='\t',
+        header=header,
+        parse_dates=True)
+X = X.astype(float)
+#elif $input_options.selected_input == 'seq_fasta'
+fasta_file = '$input_options.fasta_file'
+pyfaidx = get_module('pyfaidx')
+sequences = pyfaidx.Fasta(fasta_file)
+n_seqs = len(sequences.keys())
+X = np.arange(n_seqs)[:, np.newaxis]
+for param in estimator_params.keys():
+    if param.endswith('fasta_path'):
+        estimator.set_params(
+            **{param: fasta_file})
+else:
+    raise ValueError(
+        "The selected estimator doesn't support "
+        "fasta file input! Please consider using "
+        "KerasGBatchClassifier with "
+        "FastaDNABatchGenerator/FastaProteinBatchGenerator "
+        "or having GenomeOneHotEncoder/ProteinOneHotEncoder "
+        "in pipeline!")
+#elif $input_options.selected_input == 'sparse'
+X = mmread('$input_options.infile1')
+#end if
 
 ## Read labels
 header = 'infer' if params['input_options']['header2'] else None
diff -r 231e9a9849e8 -r 0b88494bdcac feature_selectors.py
--- a/feature_selectors.py	Tue Jul 09 19:34:06 2019 -0400
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,357 +0,0 @@
-"""
-DyRFE
-DyRFECV
-MyPipeline
-MyimbPipeline
-check_feature_importances
-"""
-import numpy as np
-
-from imblearn import under_sampling, over_sampling, combine
-from imblearn.pipeline import Pipeline as imbPipeline
-from sklearn import (cluster, compose, decomposition, ensemble,
-                     feature_extraction, feature_selection,
-                     gaussian_process, kernel_approximation,
-                     metrics, model_selection, naive_bayes,
-                     neighbors, pipeline, preprocessing,
-                     svm, linear_model, tree, discriminant_analysis)
-
-from sklearn.base import BaseEstimator
-from sklearn.base import MetaEstimatorMixin, clone, is_classifier
-from sklearn.feature_selection.rfe import _rfe_single_fit, RFE, RFECV
-from sklearn.model_selection import check_cv
-from sklearn.metrics.scorer import check_scoring
-from sklearn.utils import check_X_y, safe_indexing, safe_sqr
-from sklearn.utils._joblib import Parallel, delayed, effective_n_jobs
-
-
-class DyRFE(RFE):
-    """
-    Mainly used with DyRFECV
-
-    Parameters
-    ----------
-    estimator : object
-        A supervised learning estimator with a ``fit`` method that provides
-        information about feature importance either through a ``coef_``
-        attribute or through a ``feature_importances_`` attribute.
-    n_features_to_select : int or None (default=None)
-        The number of features to select. If `None`, half of the features
-        are selected.
-    step : int, float or list, optional (default=1)
-        If greater than or equal to 1, then ``step`` corresponds to the
-        (integer) number of features to remove at each iteration.
-        If within (0.0, 1.0), then ``step`` corresponds to the percentage
-        (rounded down) of features to remove at each iteration.
-        If list, a series of steps of features to remove at each iteration.
-        Iterations stops when steps finish
-    verbose : int, (default=0)
-        Controls verbosity of output.
-
-    """
-    def __init__(self, estimator, n_features_to_select=None, step=1,
-                 verbose=0):
-        super(DyRFE, self).__init__(estimator, n_features_to_select,
-                                    step, verbose)
-
-    def _fit(self, X, y, step_score=None):
-
-        if type(self.step) is not list:
-            return super(DyRFE, self)._fit(X, y, step_score)
-
-        # dynamic step
-        X, y = check_X_y(X, y, "csc")
-        # Initialization
-        n_features = X.shape[1]
-        if self.n_features_to_select is None:
-            n_features_to_select = n_features // 2
-        else:
-            n_features_to_select = self.n_features_to_select
-
-        step = []
-        for s in self.step:
-            if 0.0 < s < 1.0:
-                step.append(int(max(1, s * n_features)))
-            else:
-                step.append(int(s))
-            if s <= 0:
-                raise ValueError("Step must be >0")
-
-        support_ = np.ones(n_features, dtype=np.bool)
-        ranking_ = np.ones(n_features, dtype=np.int)
-
-        if step_score:
-            self.scores_ = []
-
-        step_i = 0
-        # Elimination
-        while np.sum(support_) > n_features_to_select and step_i < len(step):
-
-            # if last step is 1, will keep loop
-            if step_i == len(step) - 1 and step[step_i] != 0:
-                step.append(step[step_i])
-
-            # Remaining features
-            features = np.arange(n_features)[support_]
-
-            # Rank the remaining features
-            estimator = clone(self.estimator)
-            if self.verbose > 0:
-                print("Fitting estimator with %d features." % np.sum(support_))
-
-            estimator.fit(X[:, features], y)
-
-            # Get coefs
-            if hasattr(estimator, 'coef_'):
-                coefs = estimator.coef_
-            else:
-                coefs = getattr(estimator, 'feature_importances_', None)
-            if coefs is None:
-                raise RuntimeError('The classifier does not expose '
-                                   '"coef_" or "feature_importances_" '
-                                   'attributes')
-
-            # Get ranks
-            if coefs.ndim > 1:
-                ranks = np.argsort(safe_sqr(coefs).sum(axis=0))
-            else:
-                ranks = np.argsort(safe_sqr(coefs))
-
-            # for sparse case ranks is matrix
-            ranks = np.ravel(ranks)
-
-            # Eliminate the worse features
-            threshold =\
-                min(step[step_i], np.sum(support_) - n_features_to_select)
-
-            # Compute step score on the previous selection iteration
-            # because 'estimator' must use features
-            # that have not been eliminated yet
-            if step_score:
-                self.scores_.append(step_score(estimator, features))
-            support_[features[ranks][:threshold]] = False
-            ranking_[np.logical_not(support_)] += 1
-
-            step_i += 1
-
-        # Set final attributes
-        features = np.arange(n_features)[support_]
-        self.estimator_ = clone(self.estimator)
-        self.estimator_.fit(X[:, features], y)
-
-        # Compute step score when only n_features_to_select features left
-        if step_score:
-            self.scores_.append(step_score(self.estimator_, features))
-        self.n_features_ = support_.sum()
-        self.support_ = support_
-        self.ranking_ = ranking_
-
-        return self
-
-
-class DyRFECV(RFECV, MetaEstimatorMixin):
-    """
-    Compared with RFECV, DyRFECV offers flexiable `step` to eleminate
-    features, in the format of list, while RFECV supports only fixed number
-    of `step`.
-
-    Parameters
-    ----------
-    estimator : object
-        A supervised learning estimator with a ``fit`` method that provides
-        information about feature importance either through a ``coef_``
-        attribute or through a ``feature_importances_`` attribute.
-    step : int or float, optional (default=1)
-        If greater than or equal to 1, then ``step`` corresponds to the
-        (integer) number of features to remove at each iteration.
-        If within (0.0, 1.0), then ``step`` corresponds to the percentage
-        (rounded down) of features to remove at each iteration.
-        If list, a series of step to remove at each iteration. iteration stopes
-        when finishing all steps
-        Note that the last iteration may remove fewer than ``step`` features in
-        order to reach ``min_features_to_select``.
-    min_features_to_select : int, (default=1)
-        The minimum number of features to be selected. This number of features
-        will always be scored, even if the difference between the original
-        feature count and ``min_features_to_select`` isn't divisible by
-        ``step``.
-    cv : int, cross-validation generator or an iterable, optional
-        Determines the cross-validation splitting strategy.
-        Possible inputs for cv are:
-        - None, to use the default 3-fold cross-validation,
-        - integer, to specify the number of folds.
-        - :term:`CV splitter`,
-        - An iterable yielding (train, test) splits as arrays of indices.
-        For integer/None inputs, if ``y`` is binary or multiclass,
-        :class:`sklearn.model_selection.StratifiedKFold` is used. If the
-        estimator is a classifier or if ``y`` is neither binary nor multiclass,
-        :class:`sklearn.model_selection.KFold` is used.
-        Refer :ref:`User Guide <cross_validation>` for the various
-        cross-validation strategies that can be used here.
-        .. versionchanged:: 0.20
-            ``cv`` default value of None will change from 3-fold to 5-fold
-            in v0.22.
-    scoring : string, callable or None, optional, (default=None)
-        A string (see model evaluation documentation) or
-        a scorer callable object / function with signature
-        ``scorer(estimator, X, y)``.
-    verbose : int, (default=0)
-        Controls verbosity of output.
-    n_jobs : int or None, optional (default=None)
-        Number of cores to run in parallel while fitting across folds.
-        ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
-        ``-1`` means using all processors. See :term:`Glossary <n_jobs>`
-        for more details.
-    """
-    def __init__(self, estimator, step=1, min_features_to_select=1, cv='warn',
-                 scoring=None, verbose=0, n_jobs=None):
-        super(DyRFECV, self).__init__(
-            estimator, step=step,
-            min_features_to_select=min_features_to_select,
-            cv=cv, scoring=scoring, verbose=verbose,
-            n_jobs=n_jobs)
-
-    def fit(self, X, y, groups=None):
-        """Fit the RFE model and automatically tune the number of selected
-           features.
-        Parameters
-        ----------
-        X : {array-like, sparse matrix}, shape = [n_samples, n_features]
-            Training vector, where `n_samples` is the number of samples and
-            `n_features` is the total number of features.
-        y : array-like, shape = [n_samples]
-            Target values (integers for classification, real numbers for
-            regression).
-        groups : array-like, shape = [n_samples], optional
-            Group labels for the samples used while splitting the dataset into
-            train/test set.
-        """
-        if type(self.step) is not list:
-            return super(DyRFECV, self).fit(X, y, groups)
-
-        X, y = check_X_y(X, y, "csr")
-
-        # Initialization
-        cv = check_cv(self.cv, y, is_classifier(self.estimator))
-        scorer = check_scoring(self.estimator, scoring=self.scoring)
-        n_features = X.shape[1]
-
-        step = []
-        for s in self.step:
-            if 0.0 < s < 1.0:
-                step.append(int(max(1, s * n_features)))
-            else:
-                step.append(int(s))
-            if s <= 0:
-                raise ValueError("Step must be >0")
-
-        # Build an RFE object, which will evaluate and score each possible
-        # feature count, down to self.min_features_to_select
-        rfe = DyRFE(estimator=self.estimator,
-                    n_features_to_select=self.min_features_to_select,
-                    step=self.step, verbose=self.verbose)
-
-        # Determine the number of subsets of features by fitting across
-        # the train folds and choosing the "features_to_select" parameter
-        # that gives the least averaged error across all folds.
-
-        # Note that joblib raises a non-picklable error for bound methods
-        # even if n_jobs is set to 1 with the default multiprocessing
-        # backend.
-        # This branching is done so that to
-        # make sure that user code that sets n_jobs to 1
-        # and provides bound methods as scorers is not broken with the
-        # addition of n_jobs parameter in version 0.18.
-
-        if effective_n_jobs(self.n_jobs) == 1:
-            parallel, func = list, _rfe_single_fit
-        else:
-            parallel = Parallel(n_jobs=self.n_jobs)
-            func = delayed(_rfe_single_fit)
-
-        scores = parallel(
-            func(rfe, self.estimator, X, y, train, test, scorer)
-            for train, test in cv.split(X, y, groups))
-
-        scores = np.sum(scores, axis=0)
-        diff = int(scores.shape[0]) - len(step)
-        if diff > 0:
-            step = np.r_[step, [step[-1]] * diff]
-        scores_rev = scores[::-1]
-        argmax_idx = len(scores) - np.argmax(scores_rev) - 1
-        n_features_to_select = max(
-            n_features - sum(step[:argmax_idx]),
-            self.min_features_to_select)
-
-        # Re-execute an elimination with best_k over the whole set
-        rfe = DyRFE(estimator=self.estimator,
-                    n_features_to_select=n_features_to_select, step=self.step,
-                    verbose=self.verbose)
-
-        rfe.fit(X, y)
-
-        # Set final attributes
-        self.support_ = rfe.support_
-        self.n_features_ = rfe.n_features_
-        self.ranking_ = rfe.ranking_
-        self.estimator_ = clone(self.estimator)
-        self.estimator_.fit(self.transform(X), y)
-
-        # Fixing a normalization error, n is equal to get_n_splits(X, y) - 1
-        # here, the scores are normalized by get_n_splits(X, y)
-        self.grid_scores_ = scores[::-1] / cv.get_n_splits(X, y, groups)
-        return self
-
-
-class MyPipeline(pipeline.Pipeline):
-    """
-    Extend pipeline object to have feature_importances_ attribute
-    """
-    def fit(self, X, y=None, **fit_params):
-        super(MyPipeline, self).fit(X, y, **fit_params)
-        estimator = self.steps[-1][-1]
-        if hasattr(estimator, 'coef_'):
-            coefs = estimator.coef_
-        else:
-            coefs = getattr(estimator, 'feature_importances_', None)
-        if coefs is None:
-            raise RuntimeError('The estimator in the pipeline does not expose '
-                               '"coef_" or "feature_importances_" '
-                               'attributes')
-        self.feature_importances_ = coefs
-        return self
-
-
-class MyimbPipeline(imbPipeline):
-    """
-    Extend imblance pipeline object to have feature_importances_ attribute
-    """
-    def fit(self, X, y=None, **fit_params):
-        super(MyimbPipeline, self).fit(X, y, **fit_params)
-        estimator = self.steps[-1][-1]
-        if hasattr(estimator, 'coef_'):
-            coefs = estimator.coef_
-        else:
-            coefs = getattr(estimator, 'feature_importances_', None)
-        if coefs is None:
-            raise RuntimeError('The estimator in the pipeline does not expose '
-                               '"coef_" or "feature_importances_" '
-                               'attributes')
-        self.feature_importances_ = coefs
-        return self
-
-
-def check_feature_importances(estimator):
-    """
-    For pipeline object which has no feature_importances_ property,
-    this function returns the same comfigured pipeline object with
-    attached the last estimator's feature_importances_.
-    """
-    if estimator.__class__.__module__ == 'sklearn.pipeline':
-        pipeline_steps = estimator.get_params()['steps']
-        estimator = MyPipeline(pipeline_steps)
-    elif estimator.__class__.__module__ == 'imblearn.pipeline':
-        pipeline_steps = estimator.get_params()['steps']
-        estimator = MyimbPipeline(pipeline_steps)
-    else:
-        return estimator
diff -r 231e9a9849e8 -r 0b88494bdcac iraps_classifier.py
--- a/iraps_classifier.py	Tue Jul 09 19:34:06 2019 -0400
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,569 +0,0 @@
-"""
-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 231e9a9849e8 -r 0b88494bdcac keras_deep_learning.py
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/keras_deep_learning.py	Fri Aug 09 07:25:16 2019 -0400
@@ -0,0 +1,359 @@
+import argparse
+import json
+import keras
+import pandas as pd
+import pickle
+import six
+import warnings
+
+from ast import literal_eval
+from keras.models import Sequential, Model
+from galaxy_ml.utils import try_get_attr, get_search_params
+
+
+def _handle_shape(literal):
+    """Eval integer or list/tuple of integers from string
+
+    Parameters:
+    -----------
+    literal : str.
+    """
+    literal = literal.strip()
+    if not literal:
+        return None
+    try:
+        return literal_eval(literal)
+    except NameError as e:
+        print(e)
+        return literal
+
+
+def _handle_regularizer(literal):
+    """Construct regularizer from string literal
+
+    Parameters
+    ----------
+    literal : str. E.g. '(0.1, 0)'
+    """
+    literal = literal.strip()
+    if not literal:
+        return None
+
+    l1, l2 = literal_eval(literal)
+
+    if not l1 and not l2:
+        return None
+
+    if l1 is None:
+        l1 = 0.
+    if l2 is None:
+        l2 = 0.
+
+    return keras.regularizers.l1_l2(l1=l1, l2=l2)
+
+
+def _handle_constraint(config):
+    """Construct constraint from galaxy tool parameters.
+    Suppose correct dictionary format
+
+    Parameters
+    ----------
+    config : dict. E.g.
+        "bias_constraint":
+            {"constraint_options":
+                {"max_value":1.0,
+                "min_value":0.0,
+                "axis":"[0, 1, 2]"
+                },
+            "constraint_type":
+                "MinMaxNorm"
+            }
+    """
+    constraint_type = config['constraint_type']
+    if constraint_type == 'None':
+        return None
+
+    klass = getattr(keras.constraints, constraint_type)
+    options = config.get('constraint_options', {})
+    if 'axis' in options:
+        options['axis'] = literal_eval(options['axis'])
+
+    return klass(**options)
+
+
+def _handle_lambda(literal):
+    return None
+
+
+def _handle_layer_parameters(params):
+    """Access to handle all kinds of parameters
+    """
+    for key, value in six.iteritems(params):
+        if value == 'None':
+            params[key] = None
+            continue
+
+        if type(value) in [int, float, bool]\
+                or (type(value) is str and value.isalpha()):
+            continue
+
+        if key in ['input_shape', 'noise_shape', 'shape', 'batch_shape',
+                   'target_shape', 'dims', 'kernel_size', 'strides',
+                   'dilation_rate', 'output_padding', 'cropping', 'size',
+                   'padding', 'pool_size', 'axis', 'shared_axes']:
+            params[key] = _handle_shape(value)
+
+        elif key.endswith('_regularizer'):
+            params[key] = _handle_regularizer(value)
+
+        elif key.endswith('_constraint'):
+            params[key] = _handle_constraint(value)
+
+        elif key == 'function':  # No support for lambda/function eval
+            params.pop(key)
+
+    return params
+
+
+def get_sequential_model(config):
+    """Construct keras Sequential model from Galaxy tool parameters
+
+    Parameters:
+    -----------
+    config : dictionary, galaxy tool parameters loaded by JSON
+    """
+    model = Sequential()
+    input_shape = _handle_shape(config['input_shape'])
+    layers = config['layers']
+    for layer in layers:
+        options = layer['layer_selection']
+        layer_type = options.pop('layer_type')
+        klass = getattr(keras.layers, layer_type)
+        other_options = options.pop('layer_options', {})
+        options.update(other_options)
+
+        # parameters needs special care
+        options = _handle_layer_parameters(options)
+
+        # add input_shape to the first layer only
+        if not getattr(model, '_layers') and input_shape is not None:
+            options['input_shape'] = input_shape
+
+        model.add(klass(**options))
+
+    return model
+
+
+def get_functional_model(config):
+    """Construct keras functional model from Galaxy tool parameters
+
+    Parameters
+    -----------
+    config : dictionary, galaxy tool parameters loaded by JSON
+    """
+    layers = config['layers']
+    all_layers = []
+    for layer in layers:
+        options = layer['layer_selection']
+        layer_type = options.pop('layer_type')
+        klass = getattr(keras.layers, layer_type)
+        inbound_nodes = options.pop('inbound_nodes', None)
+        other_options = options.pop('layer_options', {})
+        options.update(other_options)
+
+        # parameters needs special care
+        options = _handle_layer_parameters(options)
+        # merge layers
+        if 'merging_layers' in options:
+            idxs = literal_eval(options.pop('merging_layers'))
+            merging_layers = [all_layers[i-1] for i in idxs]
+            new_layer = klass(**options)(merging_layers)
+        # non-input layers
+        elif inbound_nodes is not None:
+            new_layer = klass(**options)(all_layers[inbound_nodes-1])
+        # input layers
+        else:
+            new_layer = klass(**options)
+
+        all_layers.append(new_layer)
+
+    input_indexes = _handle_shape(config['input_layers'])
+    input_layers = [all_layers[i-1] for i in input_indexes]
+
+    output_indexes = _handle_shape(config['output_layers'])
+    output_layers = [all_layers[i-1] for i in output_indexes]
+
+    return Model(inputs=input_layers, outputs=output_layers)
+
+
+def get_batch_generator(config):
+    """Construct keras online data generator from Galaxy tool parameters
+
+    Parameters
+    -----------
+    config : dictionary, galaxy tool parameters loaded by JSON
+    """
+    generator_type = config.pop('generator_type')
+    klass = try_get_attr('galaxy_ml.preprocessors', generator_type)
+
+    if generator_type == 'GenomicIntervalBatchGenerator':
+        config['ref_genome_path'] = 'to_be_determined'
+        config['intervals_path'] = 'to_be_determined'
+        config['target_path'] = 'to_be_determined'
+        config['features'] = 'to_be_determined'
+    else:
+        config['fasta_path'] = 'to_be_determined'
+
+    return klass(**config)
+
+
+def config_keras_model(inputs, outfile):
+    """ config keras model layers and output JSON
+
+    Parameters
+    ----------
+    inputs : dict
+        loaded galaxy tool parameters from `keras_model_config`
+        tool.
+    outfile : str
+        Path to galaxy dataset containing keras model JSON.
+    """
+    model_type = inputs['model_selection']['model_type']
+    layers_config = inputs['model_selection']
+
+    if model_type == 'sequential':
+        model = get_sequential_model(layers_config)
+    else:
+        model = get_functional_model(layers_config)
+
+    json_string = model.to_json()
+
+    with open(outfile, 'w') as f:
+        f.write(json_string)
+
+
+def build_keras_model(inputs, outfile, model_json, infile_weights=None,
+                      batch_mode=False, outfile_params=None):
+    """ for `keras_model_builder` tool
+
+    Parameters
+    ----------
+    inputs : dict
+        loaded galaxy tool parameters from `keras_model_builder` tool.
+    outfile : str
+        Path to galaxy dataset containing the keras_galaxy model output.
+    model_json : str
+        Path to dataset containing keras model JSON.
+    infile_weights : str or None
+        If string, path to dataset containing model weights.
+    batch_mode : bool, default=False
+        Whether to build online batch classifier.
+    outfile_params : str, default=None
+        File path to search parameters output.
+    """
+    with open(model_json, 'r') as f:
+        json_model = json.load(f)
+
+    config = json_model['config']
+
+    options = {}
+
+    if json_model['class_name'] == 'Sequential':
+        options['model_type'] = 'sequential'
+        klass = Sequential
+    elif json_model['class_name'] == 'Model':
+        options['model_type'] = 'functional'
+        klass = Model
+    else:
+        raise ValueError("Unknow Keras model class: %s"
+                         % json_model['class_name'])
+
+    # load prefitted model
+    if inputs['mode_selection']['mode_type'] == 'prefitted':
+        estimator = klass.from_config(config)
+        estimator.load_weights(infile_weights)
+    # build train model
+    else:
+        cls_name = inputs['mode_selection']['learning_type']
+        klass = try_get_attr('galaxy_ml.keras_galaxy_models', cls_name)
+
+        options['loss'] = (inputs['mode_selection']
+                           ['compile_params']['loss'])
+        options['optimizer'] =\
+            (inputs['mode_selection']['compile_params']
+             ['optimizer_selection']['optimizer_type']).lower()
+
+        options.update((inputs['mode_selection']['compile_params']
+                        ['optimizer_selection']['optimizer_options']))
+
+        train_metrics = (inputs['mode_selection']['compile_params']
+                         ['metrics']).split(',')
+        if train_metrics[-1] == 'none':
+            train_metrics = train_metrics[:-1]
+        options['metrics'] = train_metrics
+
+        options.update(inputs['mode_selection']['fit_params'])
+        options['seed'] = inputs['mode_selection']['random_seed']
+
+        if batch_mode:
+            generator = get_batch_generator(inputs['mode_selection']
+                                            ['generator_selection'])
+            options['data_batch_generator'] = generator
+            options['prediction_steps'] = \
+                inputs['mode_selection']['prediction_steps']
+            options['class_positive_factor'] = \
+                inputs['mode_selection']['class_positive_factor']
+        estimator = klass(config, **options)
+        if outfile_params:
+            hyper_params = get_search_params(estimator)
+            # TODO: remove this after making `verbose` tunable
+            for h_param in hyper_params:
+                if h_param[1].endswith('verbose'):
+                    h_param[0] = '@'
+            df = pd.DataFrame(hyper_params, columns=['', 'Parameter', 'Value'])
+            df.to_csv(outfile_params, sep='\t', index=False)
+
+    print(repr(estimator))
+    # save model by pickle
+    with open(outfile, 'wb') as f:
+        pickle.dump(estimator, f, pickle.HIGHEST_PROTOCOL)
+
+
+if __name__ == '__main__':
+    warnings.simplefilter('ignore')
+
+    aparser = argparse.ArgumentParser()
+    aparser.add_argument("-i", "--inputs", dest="inputs", required=True)
+    aparser.add_argument("-m", "--model_json", dest="model_json")
+    aparser.add_argument("-t", "--tool_id", dest="tool_id")
+    aparser.add_argument("-w", "--infile_weights", dest="infile_weights")
+    aparser.add_argument("-o", "--outfile", dest="outfile")
+    aparser.add_argument("-p", "--outfile_params", dest="outfile_params")
+    args = aparser.parse_args()
+
+    input_json_path = args.inputs
+    with open(input_json_path, 'r') as param_handler:
+        inputs = json.load(param_handler)
+
+    tool_id = args.tool_id
+    outfile = args.outfile
+    outfile_params = args.outfile_params
+    model_json = args.model_json
+    infile_weights = args.infile_weights
+
+    # for keras_model_config tool
+    if tool_id == 'keras_model_config':
+        config_keras_model(inputs, outfile)
+
+    # for keras_model_builder tool
+    else:
+        batch_mode = False
+        if tool_id == 'keras_batch_models':
+            batch_mode = True
+
+        build_keras_model(inputs=inputs,
+                          model_json=model_json,
+                          infile_weights=infile_weights,
+                          batch_mode=batch_mode,
+                          outfile=outfile,
+                          outfile_params=outfile_params)
diff -r 231e9a9849e8 -r 0b88494bdcac keras_macros.xml
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/keras_macros.xml	Fri Aug 09 07:25:16 2019 -0400
@@ -0,0 +1,1228 @@
+<macros>
+  <token name="@KERAS_VERSION@">0.4.0</token>
+
+  <xml name="macro_stdio">
+    <stdio>
+        <exit_code range="1:" level="fatal" description="Error occurred. Please check Tool Standard Error"/>
+    </stdio>
+  </xml>
+
+  <xml name="keras_optimizer_common" token_lr="0.01">
+    <section name="optimizer_options" title="Optimizer Advanced Options" expanded="false">
+      <param argument="lr" type="float" value="@LR@" optional="true" label="Learning rate" help="float >= 0"/>
+      <yield/>
+      <!--param argument="clipnorm" type="float" value="" optional="true" label="clipnorm" help="float >= 0"/-->
+      <!--param argument="clipvalue" type="float" value="" optional="true" label="clipvalue" help="float >= 0"/-->
+    </section>
+  </xml>
+
+  <xml name="keras_optimizer_common_more" token_lr="0.001">
+    <expand macro="keras_optimizer_common" lr="@LR@">
+      <param argument="epsilon" type="float" value="" label="epsilon" optional="true" help="Fuzz factor. If `None`, defaults to `K.epsilon()`"/>
+      <param argument="decay" type="float" value="0" optional="true" label="decay" help="Learning rate decay over each update."/>
+      <yield/>
+    </expand>
+  </xml>
+
+  <xml name="keras_activations">
+    <param argument="activation" type="select" label="Activation function">
+      <option value="linear" selected="true">None / linear (default)</option>
+      <option value="softmax">softmax</option>
+      <option value="elu">elu</option>
+      <option value="selu">selu</option>
+      <option value="softplus">softplus</option>
+      <option value="softsign">softsign</option>
+      <option value="relu">relu</option>
+      <option value="tanh">tanh</option>
+      <option value="sigmoid">sigmoid</option>
+      <option value="hard_sigmoid">hard_sigmoid</option>
+      <option value="exponential">tanh</option>
+    </param>
+  </xml>
+
+  <xml name="keras_initializers" token_argument="kernel_initializer" token_default_kernel="false" token_default_bias="false" token_default_embeddings="false">
+    <param argument="@ARGUMENT@" type="select" label="@ARGUMENT@">
+      <option value="zeros" selected="@DEFAULT_BIAS@">zero / zeros / Zeros</option>
+      <option value="ones">one / ones / Ones</option>
+      <option value="constant">constant / Constant</option>
+      <option value="random_normal">normal / random_normal / RandomNormal</option>
+      <option value="random_uniform" selected="@DEFAULT_EMBEDDINGS@">uniform / random_uniform / RandomUniform</option>
+      <option value="truncated_normal">truncated_normal / TruncatedNormal</option>
+      <option value="orthogonal">orthogonal / Orthogonal</option>
+      <option value="identity">identity / Identity</option>
+      <option value="glorot_normal">glorot_normal</option>
+      <option value="glorot_uniform" selected="@DEFAULT_KERNEL@">glorot_uniform</option>
+      <option value="he_normal">he_normal</option>
+      <option value="he_uniform">he_uniform</option>
+      <option value="lecun_normal">lecun_normal</option>
+      <option value="lecun_uniform">lecun_uniform</option>
+    </param>
+  </xml>
+
+  <xml name="keras_regularizers" token_argument="kernel_regularizer">
+    <param argument="@ARGUMENT@" type="text" value="(0. , 0.)" optional="true" label="@ARGUMENT@"
+            help="(l1, l2). l1/l2: float; L1/l2 regularization factor. (0., 0.) is equivalent to `None`"/>
+  </xml>
+
+  <xml name="keras_constraints_options">
+    <section name="constraint_options" title="Constraint Advanced Options" expanded="false">
+      <yield/>
+      <param argument="axis" type="text" value="0" help="Integer or list of integers. axis along which to calculate weight norms">
+        <sanitizer>
+          <valid initial="default">
+            <add value="["/>
+            <add value="]"/>
+          </valid>
+        </sanitizer>
+      </param>
+    </section>
+  </xml>
+
+  <xml name="keras_constraints" token_argument="kernel_constraint">
+    <conditional name="@ARGUMENT@">
+      <param argument="constraint_type" type="select" label="@ARGUMENT@">
+        <option value="None" selected="true">None (default)</option>
+        <option value="MaxNorm">maxnorm / max_norm / MaxNorm</option>
+        <option value="NonNeg">nonneg / non_neg / NonNeg</option>
+        <option value="UnitNorm">unitnorm / unit_norm / UnitNorm</option>
+        <option value="MinMaxNorm">min_max_norm / MinMaxNorm</option>
+      </param>
+      <when value="None"/>
+      <when value="MaxNorm">
+        <expand macro="keras_constraints_options">
+          <param argument="max_value" type="float" value="2" help="the maximum norm for the incoming weights."/>
+        </expand>
+      </when>
+      <when value="NonNeg">
+      </when>
+      <when value="UnitNorm">
+        <expand macro="keras_constraints_options"/>
+      </when>
+      <when value="MinMaxNorm">
+        <expand macro="keras_constraints_options">
+          <param argument="min_value" type="float" value="0." help="the minimum norm for the incoming weights."/>
+          <param argument="max_value" type="float" value="1." help="the maximum norm for the incoming weights."/>
+          <param argument="max_value" type="float" value="1." help="rate for enforcing the constraint."/>
+        </expand>
+      </when>
+    </conditional>
+  </xml>
+
+  <xml name="keras_layer_types_core">
+    <option value="Dense">Dense</option>
+    <option value="Activation">Activation</option>
+    <option value="Dropout">Dropout</option>
+    <option value="Flatten">Flatten</option>
+    <option value="Reshape">Reshape</option>
+    <option value="Permute">Permute</option>
+    <option value="RepeatVector">RepeatVector</option>
+    <!--option value="Lambda">Lambda - Not supported</option-->
+    <option value="ActivityRegularization">ActivityRegularization</option>
+    <option value="Masking">Masking</option>
+    <option value="SpatialDropout1D">SpatialDropout1D</option>
+    <option value="SpatialDropout2D">SpatialDropout2D</option>
+    <option value="SpatialDropout3D">SpatialDropout3D</option>
+  </xml>
+
+  <xml name="keras_layer_types_Convolutional">
+    <option value="Conv1D">Conv1D</option>
+    <option value="Conv2D">Conv2D</option>
+    <option value="SeparableConv1D">SeparableConv1D</option>
+    <option value="SeparableConv2D">SeparableConv2D</option>
+    <option value="DepthwiseConv2D">DepthwiseConv2D</option>
+    <option value="Conv2DTranspose">Conv2DTranspose</option>
+    <option value="Conv3D">Conv3D</option>
+    <option value="Conv3DTranspose">Conv3DTranspose</option>
+    <option value="Cropping1D">Cropping1D</option>
+    <option value="Cropping2D">Cropping2D</option>
+    <option value="Cropping3D">Cropping3D</option>
+    <option value="UpSampling1D">UpSampling1D</option>
+    <option value="UpSampling2D">UpSampling2D</option>
+    <option value="UpSampling3D">UpSampling3D</option>
+    <option value="ZeroPadding1D">ZeroPadding1D</option>
+    <option value="ZeroPadding2D">ZeroPadding2D</option>
+    <option value="ZeroPadding3D">ZeroPadding3D</option>
+  </xml>
+
+  <xml name="keras_layer_types_Pooling">
+    <option value="MaxPooling1D">MaxPooling1D</option>
+    <option value="MaxPooling2D">MaxPooling2D</option>
+    <option value="MaxPooling3D">MaxPooling3D</option>
+    <option value="AveragePooling1D">AveragePooling1D</option>
+    <option value="AveragePooling2D">AveragePooling2D</option>
+    <option value="AveragePooling3D">AveragePooling3D</option>
+    <option value="GlobalMaxPooling1D">GlobalMaxPooling1D</option>
+    <option value="GlobalAveragePooling1D">GlobalAveragePooling1D</option>
+    <option value="GlobalMaxPooling2D">GlobalMaxPooling2D</option>
+    <option value="GlobalAveragePooling2D">GlobalAveragePooling2D</option>
+    <option value="GlobalMaxPooling3D">GlobalMaxPooling3D</option>
+    <option value="GlobalAveragePooling3D">GlobalAveragePooling3D</option>
+  </xml>
+
+  <xml name="keras_layer_types_locally_connected">
+    <option value="LocallyConnected1D">LocallyConnected1D</option>
+    <option value="LocallyConnected2D">LocallyConnected2D</option>
+  </xml>
+
+  <xml name="keras_layer_types_recurrent">
+    <option value="RNN">RNN</option>
+    <option value="Masking">Masking</option>
+    <option value="SimpleRNN">SimpleRNN</option>
+    <option value="GRU">GRU</option>
+    <option value="LSTM">LSTM</option>
+    <option value="ConvLSTM2D">ConvLSTM2D</option>
+    <option value="ConvLSTM2DCell">ConvLSTM2DCell</option>
+    <option value="SimpleRNNCell">SimpleRNNCell</option>
+    <option value="GRUCell">GRUCell</option>
+    <option value="LSTMCell">LSTMCell</option>
+    <option value="CuDNNGRU">CuDNNGRU</option>
+    <option value="CuDNNLSTM">Dense</option>
+  </xml>
+
+  <xml name="keras_layer_types_embedding">
+    <option value="Embedding">Embedding</option>
+  </xml>
+
+  <xml name="keras_layer_types_advanced_activations">
+    <option value="LeakyReLU">LeakyReLU</option>
+    <option value="PReLU">PReLU</option>
+    <option value="ELU">ELU</option>
+    <option value="ThresholdedReLU">ThresholdedReLU</option>
+    <option value="Softmax">Softmax</option>
+    <option value="ReLU">ReLU</option>
+  </xml>
+
+  <xml name="keras_layer_types_normalization">
+    <option value="BatchNormalization">BatchNormalization</option>
+  </xml>
+
+  <xml name="keras_layer_types_noise">
+    <option value="GaussianNoise">GaussianNoise</option>
+    <option value="GaussianDropout">GaussianDropout</option>
+    <option value="AlphaDropout">AlphaDropout</option>    
+  </xml>
+
+  <xml name="keras_layer_types_merge">
+    <option value="Add">Add</option>
+    <option value="Subtract">Subtract</option>
+    <option value="Multiply">Multiply</option>
+    <option value="Average">Average</option>
+    <option value="Maximum">Maximum</option>
+    <option value="Minimum">Minimum</option>
+    <option value="Concatenate">Concatenate</option>
+    <option value="Dot">Dot</option>
+  </xml>
+
+  <!--Core Layers-->
+
+  <xml name="layer_Dense">
+    <param argument="units" type="integer" value="" optional="false" label="units" help="Positive integer, dimensionality of the output space."/>
+    <section name="layer_options" title="Layer Advanced Options" expanded="false">
+      <expand macro="keras_activations"/>
+      <param argument="use_bias" type="boolean" truevalue="booltrue" falsevalue="boolfalse" optional="true" checked="true" />
+      <expand macro="keras_initializers" default_kernel="true"/>
+      <expand macro="keras_initializers" argument="bias_initializer" default_bias="true"/>
+      <expand macro="keras_regularizers"/>
+      <expand macro="keras_regularizers" argument="bias_regularizer"/>
+      <expand macro="keras_regularizers" argument="activity_regularizer"/>
+      <expand macro="keras_constraints"/>
+      <expand macro="keras_constraints" argument="bias_constraint"/>
+    </section>
+    <yield/>
+  </xml>
+
+  <xml name="layer_Dropout">
+    <param argument="rate" type="float" value="" min="0." max="1.0" help="Fraction of the input units to drop."/>
+    <param argument="noise_shape" type="text" value="" help="1D integer tensor representing the shape of the
+            binary dropout mask that will be multiplied with the input."/>
+    <param argument="seed" type="integer" value="" min="0" optional="true" help="A Python integer to use as random seed."/>
+    <yield/>
+  </xml>
+
+  <xml name="layer_Flatten">
+    <param argument="data_format" type="select" help="The ordering of the dimensions in the inputs.">
+      <option value="channels_last">channels_last</option>
+      <option value="channels_first">channels_first</option>
+    </param>
+    <yield/>
+  </xml>
+
+  <xml name="layer_Input">
+    <param argument="shape" type="text" value="" help="A shape tuple (integer), not including the batch size.For instance, `shape=(32,)`"/>
+    <!--param argument="batch_shape" type="text" value="" optional="true" help="A shape tuple (integer), including the batch size.For instance, `batch_shape=(10, 32)`"/-->
+    <param argument="name" type="text" value="" optional="true" help="An optional string name for the layer. Unique. autogenerated if it isn't provided."/>
+    <param argument="dtype" type="select" help="The data type expected by the input">
+      <option value="float32" selected="true">float32</option>
+      <option value="float64">float64</option>
+      <option value="int32">int32</option>
+      <option value="int64">int64</option>
+      <!--TODO add more DTYPEs-->
+    </param>
+    <param argument="sparse" type="boolean" truevalue="booltrue" falsevalue="boolfalse" optional="true" checked="false" />
+    <!--param argument="tensor" type="data" format="zip" help="Optional existing tensor to wrap into the `Input` layer."/ -->
+    <yield/>
+  </xml>
+
+  <xml name="layer_Reshape">
+    <param argument="target_shape" type="text" value="" help="Tuple of integers. Does not include the batch axis."/>
+    <yield/>
+  </xml>
+
+  <xml name="layer_Permute">
+    <param argument="dims" type="text" value="" help="Tuple of integers. Permutation pattern, does not include the samples dimension. 
+            Indexing starts at 1. For instance, (2, 1) permutes the first and second dimension of the input."/>
+    <yield/>
+  </xml>
+
+  <xml name="layer_RepeatVector">
+    <param argument="n" type="integer" value="" help="repetition factor"/>
+  </xml>
+
+  <xml name="layer_Lambda">
+    <param argument="function" type="text" value="lambda x: " help="The function to be evaluated. Only lambda function is supported!"/>
+    <yield/>
+  </xml>
+
+  <xml name="layer_ActivityRegularization">
+    <param argument="l1" type="float" value="0." min="0." help="L1 regularization factor (positive float)."/>
+    <param argument="l2" type="float" value="0." min="0." help="L2 regularization factor (positive float)."/>
+    <yield/>
+  </xml>
+
+  <xml name="layer_Masking">
+    <param argument="mask_value" type="float" value="0." help="Masks a sequence by using a mask value to skip timesteps."/>
+    <yield/>
+  </xml>
+
+  <xml name="layer_SpatialDropout1D">
+    <param argument="rate" type="float" value="" min="0." max="1." help="Fraction of the input units to drop."/>
+    <yield/>
+  </xml>
+
+  <xml name="layer_SpatialDropout2D">
+    <param argument="rate" type="float" value="" min="0." max="1." help="Fraction of the input units to drop."/>
+    <param argument="data_format" type="select">
+      <option value="channels_last" selected="true">channels_last - the channels dimension (the depth) is at index 3</option>
+      <option value="channels_first">channels_first - the channels dimension (the depth) is at index 1</option>
+    </param>
+    <yield/>
+  </xml>
+
+  <xml name="layer_SpatialDropout3D">
+    <param argument="rate" type="float" value="" min="0." max="1." help="Fraction of the input units to drop."/>
+    <param argument="data_format" type="select">
+      <option value="channels_last" selected="true">channels_last - the channels dimension (the depth) is at index 4</option>
+      <option value="channels_first">channels_first - the channels dimension (the depth) is at index 1</option>
+    </param>
+    <yield/>
+  </xml>
+
+  <!--Convolutional Layers-->
+
+  <xml name="layer_Conv1D">
+    <param argument="filters" type="integer" value="" min="0" help="the dimensionality of the output space (i.e. the number of output filters in the convolution)."/>
+    <param argument="kernel_size" type="text" value="" help="An integer or tuple/list of a single integer, specifying the length of the 1D convolution window."/>
+    <param argument="strides" type="text" value="1" help="An integer or tuple/list of a single integer, specifying the stride length of the convolution."/>
+    <section name="layer_options" title="Layer Advanced Options" expanded="false">
+      <param argument="padding" type="select" help="">
+        <option value="valid" selected="true">valid - no padding</option>
+        <option value="same">same - output has the same length as the original input</option>
+        <option value="causal">causal - causal (dilated) convolutions</option>
+      </param>
+      <param argument="data_format" type="select" help="The ordering of the dimensions in the inputs.">
+        <option value="channels_last" selected="true">channels_last - inputs with shape  (batch, steps, channels)</option>
+        <option value="channels_first">channels_first - inputs with shape (batch, channels, steps)</option>
+      </param>
+      <param argument="dilation_rate" type="text" value="1" help="an integer or tuple/list of a single integer, specifying the dilation rate to use for dilated convolution."/>
+      <expand macro="keras_activations"/>
+      <param argument="use_bias" type="boolean" truevalue="booltrue" falsevalue="boolfalse" optional="true" checked="true"/>
+      <expand macro="keras_initializers" default_kernel="true"/>\
+      <expand macro="keras_initializers" argument="bias_initializer" default_bias="true"/>
+      <expand macro="keras_regularizers"/>
+      <expand macro="keras_regularizers" argument="bias_regularizer"/>
+      <expand macro="keras_regularizers" argument="activity_regularizer"/>
+      <expand macro="keras_constraints"/>
+      <expand macro="keras_constraints" argument="bias_constraint"/>
+    </section>
+    <yield/>
+  </xml>
+
+  <xml name="layer_Conv2D">
+    <param argument="filters" type="integer" value="" min="0" help="the dimensionality of the output space (i.e. the number of output filters in the convolution)."/>
+    <param argument="kernel_size" type="text" value="" help="An integer or tuple/list of 2 integers, specifying the height and width of the 2D convolution window."/>
+    <param argument="strides" type="text" value="1" help="An integer or tuple/list of 2 integers, specifying the strides of the convolution along the height and width."/>
+    <section name="layer_options" title="Layer Advanced Options" expanded="false">
+      <param argument="padding" type="select" help="">
+        <option value="valid" selected="true">valid - no padding</option>
+        <option value="same">same - output has the same length as the original input</option>
+        <!--option value="causal">causal - causal (dilated) convolutions</option-->
+      </param>
+      <param argument="data_format" type="select" help="The ordering of the dimensions in the inputs.">
+        <option value="channels_last" selected="true">channels_last - inputs with shape (batch, height, width, channels)</option>
+        <option value="channels_first">channels_first - inputs with shape (batch, channels, height, width)</option>
+      </param>
+      <param argument="dilation_rate" type="text" value="1" help="an integer or tuple/list of 2 integers, specifying the dilation rate to use for dilated convolution."/>
+      <expand macro="keras_activations"/>
+      <param argument="use_bias" type="boolean" truevalue="booltrue" falsevalue="boolfalse" optional="true" checked="true"/>
+      <expand macro="keras_initializers" default_kernel="true"/>\
+      <expand macro="keras_initializers" argument="bias_initializer" default_bias="true"/>
+      <expand macro="keras_regularizers"/>
+      <expand macro="keras_regularizers" argument="bias_regularizer"/>
+      <expand macro="keras_regularizers" argument="activity_regularizer"/>
+      <expand macro="keras_constraints"/>
+      <expand macro="keras_constraints" argument="bias_constraint"/>
+    </section>
+    <yield/>
+  </xml>
+
+  <xml name="layer_SeparableConv1D">
+    <param argument="filters" type="integer" value="" min="0" help="the dimensionality of the output space (i.e. the number of output filters in the convolution)."/>
+    <param argument="kernel_size" type="text" value="" help="An integer or tuple/list of a single integer, specifying the length of the 1D convolution window."/>
+    <param argument="strides" type="text" value="1" help="An integer or tuple/list of single integer, specifying the stride length of the convolution. "/>
+    <section name="layer_options" title="Layer Advanced Options" expanded="false">
+      <param argument="padding" type="select" help="">
+        <option value="valid" selected="true">valid - no padding</option>
+        <option value="same">same - output has the same length as the original input</option>
+        <!--option value="causal">causal - causal (dilated) convolutions</option-->
+      </param>
+      <param argument="data_format" type="select" help="The ordering of the dimensions in the inputs.">
+        <option value="channels_last" selected="true">channels_last - inputs with shape  (batch, steps, channels)</option>
+        <option value="channels_first">channels_first - inputs with shape (batch, channels, steps)</option>
+      </param>
+      <param argument="dilation_rate" type="text" value="1" help="an integer or tuple/list of a single integer, specifying the dilation rate to use for dilated convolution. "/>
+      <param argument="depth_multiplier" type="integer" value="1" help="The number of depthwise convolution output channels for each input channel."/>
+      <expand macro="keras_activations"/>
+      <param argument="use_bias" type="boolean" truevalue="booltrue" falsevalue="boolfalse" optional="true" checked="true"/>
+      <expand macro="keras_initializers" argument="depthwise_initializer" default_kernel="true"/>
+      <expand macro="keras_initializers" argument="pointwise_initializer" default_kernel="true"/>
+      <expand macro="keras_initializers" argument="bias_initializer" default_bias="true"/>
+      <expand macro="keras_regularizers" argument="depthwise_regularizer"/>
+      <expand macro="keras_regularizers" argument="pointwise_regularizer"/>
+      <expand macro="keras_regularizers" argument="bias_regularizer"/>
+      <expand macro="keras_regularizers" argument="activity_regularizer"/>
+      <expand macro="keras_constraints" argument="depthwise_constraint"/>
+      <expand macro="keras_constraints" argument="pointwise_constraint"/>
+      <expand macro="keras_constraints" argument="bias_constraint"/>
+    </section>
+    <yield/>
+  </xml>
+
+  <xml name="layer_SeparableConv2D">
+    <param argument="filters" type="integer" value="" min="0" help="the dimensionality of the output space (i.e. the number of output filters in the convolution)."/>
+    <param argument="kernel_size" type="text" value="" help="An integer or tuple/list of 2 integers, specifying the height and width of the 2D convolution window."/>
+    <param argument="strides" type="text" value="1" help="An integer or tuple/list of 2 integers, specifying the strides of the convolution along the height and width."/>
+    <section name="layer_options" title="Layer Advanced Options" expanded="false">
+      <param argument="padding" type="select" help="">
+        <option value="valid" selected="true">valid - no padding</option>
+        <option value="same">same - output has the same length as the original input</option>
+        <!--option value="causal">causal - causal (dilated) convolutions</option-->
+      </param>
+      <param argument="data_format" type="select" help="The ordering of the dimensions in the inputs.">
+        <option value="channels_last" selected="true">channels_last - inputs with shape (batch, height, width, channels)</option>
+        <option value="channels_first">channels_first - inputs with shape (batch, channels, height, width)</option>
+      </param>
+      <param argument="dilation_rate" type="text" value="1" help="an integer or tuple/list of 2 integers, specifying the dilation rate to use for dilated convolution."/>
+      <param argument="depth_multiplier" type="integer" value="1" help="The number of depthwise convolution output channels for each input channel."/>
+      <expand macro="keras_activations"/>
+      <param argument="use_bias" type="boolean" truevalue="booltrue" falsevalue="boolfalse" optional="true" checked="true"/>
+      <expand macro="keras_initializers" argument="depthwise_initializer" default_kernel="true"/>
+      <expand macro="keras_initializers" argument="pointwise_initializer" default_kernel="true"/>
+      <expand macro="keras_initializers" argument="bias_initializer" default_bias="true"/>
+      <expand macro="keras_regularizers" argument="depthwise_regularizer"/>
+      <expand macro="keras_regularizers" argument="pointwise_regularizer"/>
+      <expand macro="keras_regularizers" argument="bias_regularizer"/>
+      <expand macro="keras_regularizers" argument="activity_regularizer"/>
+      <expand macro="keras_constraints" argument="depthwise_constraint"/>
+      <expand macro="keras_constraints" argument="pointwise_constraint"/>
+      <expand macro="keras_constraints" argument="bias_constraint"/>
+    </section>
+    <yield/>
+  </xml>
+
+  <xml name="layer_DepthwiseConv2D">
+    <param argument="filters" type="integer" value="" min="0" help="the dimensionality of the output space (i.e. the number of output filters in the convolution)."/>
+    <param argument="kernel_size" type="text" value="" help="An integer or tuple/list of 2 integers, specifying the height and width of the 2D convolution window."/>
+    <param argument="strides" type="text" value="1" help="An integer or tuple/list of 2 integers, specifying the strides of the convolution along the height and width."/>
+    <section name="layer_options" title="Layer Advanced Options" expanded="false">
+      <param argument="padding" type="select" help="">
+        <option value="valid" selected="true">valid - no padding</option>
+        <option value="same">same - output has the same length as the original input</option>
+        <!--option value="causal">causal - causal (dilated) convolutions</option-->
+      </param>
+      <param argument="data_format" type="select" help="The ordering of the dimensions in the inputs.">
+        <option value="channels_last" selected="true">channels_last - inputs with shape (batch, height, width, channels)</option>
+        <option value="channels_first">channels_first - inputs with shape (batch, channels, height, width)</option>
+      </param>
+      <param argument="dilation_rate" type="text" value="1" help="an integer or tuple/list of 2 integers, specifying the dilation rate to use for dilated convolution."/>
+      <param argument="depth_multiplier" type="integer" value="1" help="The number of depthwise convolution output channels for each input channel."/>
+      <expand macro="keras_activations"/>
+      <param argument="use_bias" type="boolean" truevalue="booltrue" falsevalue="boolfalse" optional="true" checked="true"/>
+      <expand macro="keras_initializers" argument="depthwise_initializer" default_kernel="true"/>
+      <expand macro="keras_initializers" argument="bias_initializer" default_bias="true"/>
+      <expand macro="keras_regularizers" argument="depthwise_regularizer"/>
+      <expand macro="keras_regularizers" argument="pointwise_regularizer"/>
+      <expand macro="keras_regularizers" argument="bias_regularizer"/>
+      <expand macro="keras_regularizers" argument="activity_regularizer"/>
+      <expand macro="keras_constraints" argument="depthwise_constraint"/>
+      <expand macro="keras_constraints" argument="bias_constraint"/>
+    </section>
+    <yield/>
+  </xml>
+
+  <xml name="layer_Conv2DTranspose">
+    <param argument="filters" type="integer" value="" min="0" help="the dimensionality of the output space (i.e. the number of output filters in the convolution)."/>
+    <param argument="kernel_size" type="text" value="" help="An integer or tuple/list of 2 integers, specifying the height and width of the 2D convolution window."/>
+    <param argument="strides" type="text" value="1" help="An integer or tuple/list of 2 integers, specifying the strides of the convolution along the height and width."/>
+    <section name="layer_options" title="Layer Advanced Options" expanded="false">
+      <param argument="padding" type="select" help="">
+        <option value="valid" selected="true">valid - no padding</option>
+        <option value="same">same - output has the same length as the original input</option>
+        <!--option value="causal">causal - causal (dilated) convolutions</option-->
+      </param>
+      <param argument="output_padding" type="text" value="" help="An integer or tuple/list of 2 integers, specifying the amount of padding along the height and width of the output tensor."/>
+      <param argument="data_format" type="select" help="The ordering of the dimensions in the inputs.">
+        <option value="channels_last" selected="true">channels_last - inputs with shape (batch, height, width, channels)</option>
+        <option value="channels_first">channels_first - inputs with shape (batch, channels, height, width)</option>
+      </param>
+      <param argument="dilation_rate" type="text" value="1" help="an integer or tuple/list of 2 integers, specifying the dilation rate to use for dilated convolution."/>
+      <expand macro="keras_activations"/>
+      <param argument="use_bias" type="boolean" truevalue="booltrue" falsevalue="boolfalse" optional="true" checked="true"/>
+      <expand macro="keras_initializers" default_kernel="true"/>
+      <expand macro="keras_initializers" argument="bias_initializer" default_bias="true"/>
+      <expand macro="keras_regularizers" />
+      <expand macro="keras_regularizers" argument="bias_regularizer"/>
+      <expand macro="keras_regularizers" argument="activity_regularizer"/>
+      <expand macro="keras_constraints"/>
+      <expand macro="keras_constraints" argument="bias_constraint"/>
+    </section>
+    <yield/>
+  </xml>
+
+  <xml name="layer_Conv3D">
+    <param argument="filters" type="integer" value="" min="0" help="the dimensionality of the output space (i.e. the number of output filters in the convolution)."/>
+    <param argument="kernel_size" type="text" value="" help="An integer or tuple/list of 3 integers, specifying the depth, height and width of the 3D convolution window."/>
+    <param argument="strides" type="text" value="1" help="An integer or tuple/list of 3 integers, specifying the strides of the convolution along each spatial dimension."/>
+    <section name="layer_options" title="Layer Advanced Options" expanded="false">
+      <param argument="padding" type="select" help="">
+        <option value="valid" selected="true">valid - no padding</option>
+        <option value="same">same - output has the same length as the original input</option>
+        <!--option value="causal">causal - causal (dilated) convolutions</option-->
+      </param>
+      <param argument="data_format" type="select" help="The ordering of the dimensions in the inputs.">
+        <option value="channels_last" selected="true">channels_last - inputs with shape (batch, spatial_dim1, spatial_dim2, spatial_dim3, channels)</option>
+        <option value="channels_first">channels_first - inputs with shape (batch, channels, spatial_dim1, spatial_dim2, spatial_dim3)</option>
+      </param>
+      <param argument="dilation_rate" type="text" value="1" help="an integer or tuple/list of 3 integers, specifying the dilation rate to use for dilated convolution."/>
+      <expand macro="keras_activations"/>
+      <param argument="use_bias" type="boolean" truevalue="booltrue" falsevalue="boolfalse" optional="true" checked="true"/>
+      <expand macro="keras_initializers" default_kernel="true"/>
+      <expand macro="keras_initializers" argument="bias_initializer" default_bias="true"/>
+      <expand macro="keras_regularizers" />
+      <expand macro="keras_regularizers" argument="bias_regularizer"/>
+      <expand macro="keras_regularizers" argument="activity_regularizer"/>
+      <expand macro="keras_constraints"/>
+      <expand macro="keras_constraints" argument="bias_constraint"/>
+    </section>
+    <yield/>
+  </xml>
+
+  <xml name="layer_Conv3DTranspose">
+    <param argument="filters" type="integer" value="" min="0" help="the dimensionality of the output space (i.e. the number of output filters in the convolution)."/>
+    <param argument="kernel_size" type="text" value="" help="An integer or tuple/list of 3 integers, specifying the depth, height and width of the 3D convolution window."/>
+    <param argument="strides" type="text" value="1" help="An integer or tuple/list of 3 integers, specifying the strides of the convolution along each spatial dimension."/>
+    <section name="layer_options" title="Layer Advanced Options" expanded="false">
+      <param argument="padding" type="select" help="">
+        <option value="valid" selected="true">valid - no padding</option>
+        <option value="same">same - output has the same length as the original input</option>
+        <!--option value="causal">causal - causal (dilated) convolutions</option-->
+      </param>
+      <param argument="output_padding" type="text" value="" help="An integer or tuple/list of 3 integers, specifying the amount of padding along the depth, height, and width. "/>
+      <param argument="data_format" type="select" help="The ordering of the dimensions in the inputs.">
+        <option value="channels_last" selected="true">channels_last - inputs with shape (batch, spatial_dim1, spatial_dim2, spatial_dim3, channels)</option>
+        <option value="channels_first">channels_first - inputs with shape (batch, channels, spatial_dim1, spatial_dim2, spatial_dim3)</option>
+      </param>
+      <param argument="dilation_rate" type="text" value="1" help="an integer or tuple/list of 3 integers, specifying the dilation rate to use for dilated convolution."/>
+      <expand macro="keras_activations"/>
+      <param argument="use_bias" type="boolean" truevalue="booltrue" falsevalue="boolfalse" optional="true" checked="true"/>
+      <expand macro="keras_initializers" default_kernel="true"/>
+      <expand macro="keras_initializers" argument="bias_initializer" default_bias="true"/>
+      <expand macro="keras_regularizers" />
+      <expand macro="keras_regularizers" argument="bias_regularizer"/>
+      <expand macro="keras_regularizers" argument="activity_regularizer"/>
+      <expand macro="keras_constraints"/>
+      <expand macro="keras_constraints" argument="bias_constraint"/>
+    </section>
+    <yield/>
+  </xml>
+
+  <xml name="layer_Cropping1D">
+    <param argument="cropping" type="text" value="(1, 1)" help="int or tuple of int (length 2) How many units should be trimmed off at the beginning and end of the cropping dimension (axis 1).
+              If a single int is provided, the same value will be used for both."/>
+    <yield/>
+  </xml>
+
+  <xml name="layer_Cropping2D">
+    <param argument="cropping" type="text" value="((0, 0), (0, 0))" help="int, or tuple of 2 ints, or tuple of 2 tuples of 2 ints."/>
+    <param argument="data_format" type="select" help="The ordering of the dimensions in the inputs.">
+        <option value="channels_last" selected="true">channels_last - inputs with shape (batch, height, width, channels)</option>
+        <option value="channels_first">channels_first - inputs with shape (batch, channels, height, width)</option>
+    </param>
+    <yield/>
+  </xml>
+
+  <xml name="layer_Cropping3D">
+    <param argument="cropping" type="text" value="((1, 1), (1, 1), (1, 1))" help="int, or tuple of 3 ints, or tuple of 3 tuples of 2 ints."/>
+    <param argument="data_format" type="select" help="The ordering of the dimensions in the inputs.">
+        <option value="channels_last" selected="true">channels_last - inputs with shape  (batch, spatial_dim1, spatial_dim2, spatial_dim3, channels)</option>
+        <option value="channels_first">channels_first - inputs with shape (batch, channels, spatial_dim1, spatial_dim2, spatial_dim3)</option>
+    </param>
+    <yield/>
+  </xml>
+
+  <xml name="layer_UpSampling1D">
+    <param argument="size" type="integer" value="2" min="0" help="integer. Upsampling factor."/>
+    <yield/>
+  </xml>
+
+  <xml name="layer_UpSampling2D">
+    <param argument="size" type="text" value="(2, 2)" help="int, or tuple of 2 integers. The upsampling factors for rows and columns."/>
+    <param argument="data_format" type="select" help="The ordering of the dimensions in the inputs.">
+        <option value="channels_last" selected="true">channels_last - inputs with shape (batch, height, width, channels)</option>
+        <option value="channels_first">channels_first - inputs with shape (batch, channels, height, width)</option>
+    </param>
+    <param argument="interpolation" type="select">
+      <option value="nearest" selected="true">nearest</option>
+      <option value="bilinear">bilinear</option>
+    </param>
+    <yield/>
+  </xml>
+
+  <xml name="layer_UpSampling3D">
+    <param argument="size" type="text" value="(2, 2, 2)" help="int, or tuple of 3 integers. The upsampling factors for dim1, dim2 and dim3."/>
+    <param argument="data_format" type="select" help="The ordering of the dimensions in the inputs.">
+        <option value="channels_last" selected="true">channels_last - inputs with shape (batch, spatial_dim1, spatial_dim2, spatial_dim3, channels)</option>
+        <option value="channels_first">channels_first - inputs with shape (batch, channels, spatial_dim1, spatial_dim2, spatial_dim3)</option>
+    </param>
+    <yield/>
+  </xml>
+
+  <xml name="layer_ZeroPadding1D">
+    <param argument="padding" type="text" value="1" help="int, or tuple of int (length 2)"/>
+    <yield/>
+  </xml>
+
+  <xml name="layer_ZeroPadding2D">
+    <param argument="padding" type="text" value="(1, 1)" help="int, or tuple of 2 ints, or tuple of 2 tuples of 2 ints."/>
+    <param argument="data_format" type="select" help="The ordering of the dimensions in the inputs.">
+        <option value="channels_last" selected="true">channels_last - inputs with shape (batch, height, width, channels)</option>
+        <option value="channels_first">channels_first - inputs with shape (batch, channels, height, width)</option>
+    </param>
+    <yield/>
+  </xml>
+
+  <xml name="layer_ZeroPadding3D">
+    <param argument="padding" type="text" value="(1, 1, 1)" help="int, or tuple of 3 ints, or tuple of 3 tuples of 2 ints."/>
+    <param argument="data_format" type="select" help="The ordering of the dimensions in the inputs.">
+        <option value="channels_last" selected="true">channels_last - inputs with shape  (batch, spatial_dim1, spatial_dim2, spatial_dim3, channels)</option>
+        <option value="channels_first">channels_first - inputs with shape (batch, channels, spatial_dim1, spatial_dim2, spatial_dim3)</option>
+    </param>
+    <yield/>
+  </xml>
+
+  <!--Pooling Layers-->
+
+  <xml name="layer_MaxPooling1D">
+    <param name="pool_size" type="integer" value="2" help="Integer, size of the max pooling windows."/>
+    <param name="strides" type="integer" value="" help="Integer, or None. Factor by which to downscale. E.g. 2 will halve the input. If None, it will default to pool_size."/>
+    <param argument="padding" type="select" >
+      <option value="valid" selected="true">valid - no padding</option>
+      <option value="same">same - output has the same length as the original input</option>
+      <!--option value="causal">causal - causal (dilated) convolutions</option-->
+    </param>
+    <param argument="data_format" type="select" help="The ordering of the dimensions in the inputs.">
+      <option value="channels_last" selected="true">channels_last - inputs with shape  (batch, steps, channels)</option>
+      <option value="channels_first">channels_first - inputs with shape (batch, channels, steps)</option>
+    </param>
+    <yield/>
+  </xml>
+
+  <xml name="layer_MaxPooling2D">
+    <param name="pool_size" type="text" value="(2, 2)" help="integer or tuple of 2 integers, factors by which to downscale (vertical, horizontal)"/>
+    <param name="strides" type="text" value="" help="Integer, tuple of 2 integers, or None. Strides values. If None, it will default to pool_size."/>
+    <param argument="padding" type="select" >
+      <option value="valid" selected="true">valid - no padding</option>
+      <option value="same">same - output has the same length as the original input</option>
+      <!--option value="causal">causal - causal (dilated) convolutions</option-->
+    </param>
+    <param argument="data_format" type="select" help="The ordering of the dimensions in the inputs.">
+        <option value="channels_last" selected="true">channels_last - inputs with shape (batch, height, width, channels)</option>
+        <option value="channels_first">channels_first - inputs with shape (batch, channels, height, width)</option>
+    </param>
+    <yield/>
+  </xml>
+
+  <xml name="layer_MaxPooling3D">
+    <param name="pool_size" type="text" value="(2, 2, 2)" help="tuple of 3 integers, factors by which to downscale (dim1, dim2, dim3). 
+                                                                (2, 2, 2) will halve the size of the 3D input in each dimension."/>
+    <param name="strides" type="text" value="" help="tuple of 3 integers, or None. If None, it will default to pool_size."/>
+    <param argument="padding" type="select" >
+      <option value="valid" selected="true">valid - no padding</option>
+      <option value="same">same - output has the same length as the original input</option>
+      <!--option value="causal">causal - causal (dilated) convolutions</option-->
+    </param>
+    <param argument="data_format" type="select" help="The ordering of the dimensions in the inputs.">
+      <option value="channels_last" selected="true">channels_last - inputs with shape (batch, spatial_dim1, spatial_dim2, spatial_dim3, channels)</option>
+      <option value="channels_first">channels_first - inputs with shape (batch, channels, spatial_dim1, spatial_dim2, spatial_dim3)</option>
+    </param>
+    <yield/>
+  </xml>
+
+  <xml name="layer_AveragePooling1D">
+    <param name="pool_size" type="integer" value="2" help="Integer, size of the max pooling windows."/>
+    <param name="strides" type="integer" value="" help="Integer, or None. Factor by which to downscale. E.g. 2 will halve the input. If None, it will default to pool_size."/>
+    <param argument="padding" type="select" >
+      <option value="valid" selected="true">valid - no padding</option>
+      <option value="same">same - output has the same length as the original input</option>
+      <!--option value="causal">causal - causal (dilated) convolutions</option-->
+    </param>
+    <param argument="data_format" type="select" help="The ordering of the dimensions in the inputs.">
+      <option value="channels_last" selected="true">channels_last - inputs with shape  (batch, steps, channels)</option>
+      <option value="channels_first">channels_first - inputs with shape (batch, channels, steps)</option>
+    </param>
+    <yield/>
+  </xml>
+
+  <xml name="layer_AveragePooling2D">
+    <param name="pool_size" type="text" value="(2, 2)" help="integer or tuple of 2 integers, factors by which to downscale (vertical, horizontal)"/>
+    <param name="strides" type="text" value="" help=" Integer, tuple of 2 integers, or None. Strides values. If None, it will default to pool_size."/>
+    <param argument="padding" type="select" >
+      <option value="valid" selected="true">valid - no padding</option>
+      <option value="same">same - output has the same length as the original input</option>
+      <!--option value="causal">causal - causal (dilated) convolutions</option-->
+    </param>
+    <param argument="data_format" type="select" help="The ordering of the dimensions in the inputs.">
+        <option value="channels_last" selected="true">channels_last - inputs with shape (batch, height, width, channels)</option>
+        <option value="channels_first">channels_first - inputs with shape (batch, channels, height, width)</option>
+    </param>
+    <yield/>
+  </xml>
+
+  <xml name="layer_AveragePooling3D">
+    <param name="pool_size" type="text" value="(2, 2, 2)" help="tuple of 3 integers, factors by which to downscale (dim1, dim2, dim3). 
+                                                                (2, 2, 2) will halve the size of the 3D input in each dimension."/>
+    <param name="strides" type="text" value="" help="tuple of 3 integers, or None. If None, it will default to pool_size."/>
+    <param argument="padding" type="select" >
+      <option value="valid" selected="true">valid - no padding</option>
+      <option value="same">same - output has the same length as the original input</option>
+      <!--option value="causal">causal - causal (dilated) convolutions</option-->
+    </param>
+    <param argument="data_format" type="select" help="The ordering of the dimensions in the inputs.">
+      <option value="channels_last" selected="true">channels_last - inputs with shape (batch, spatial_dim1, spatial_dim2, spatial_dim3, channels)</option>
+      <option value="channels_first">channels_first - inputs with shape (batch, channels, spatial_dim1, spatial_dim2, spatial_dim3)</option>
+    </param>
+    <yield/>
+  </xml>
+
+  <xml name="layer_GlobalMaxPooling1D">
+    <param argument="data_format" type="select" help="The ordering of the dimensions in the inputs.">
+      <option value="channels_last" selected="true">channels_last - inputs with shape  (batch, steps, channels)</option>
+      <option value="channels_first">channels_first - inputs with shape (batch, channels, steps)</option>
+    </param>
+    <yield/>
+  </xml>
+
+  <xml name="layer_GlobalMaxPooling2D">
+    <param argument="data_format" type="select" help="The ordering of the dimensions in the inputs.">
+        <option value="channels_last" selected="true">channels_last - inputs with shape (batch, height, width, channels)</option>
+        <option value="channels_first">channels_first - inputs with shape (batch, channels, height, width)</option>
+    </param>
+    <yield/>
+  </xml>
+
+  <xml name="layer_GlobalMaxPooling3D">
+    <param argument="data_format" type="select" help="The ordering of the dimensions in the inputs.">
+      <option value="channels_last" selected="true">channels_last - inputs with shape (batch, spatial_dim1, spatial_dim2, spatial_dim3, channels)</option>
+      <option value="channels_first">channels_first - inputs with shape (batch, channels, spatial_dim1, spatial_dim2, spatial_dim3)</option>
+    </param>
+    <yield/>
+  </xml>
+
+  <xml name="layer_GlobalAveragePooling1D">
+    <param argument="data_format" type="select" help="The ordering of the dimensions in the inputs.">
+      <option value="channels_last" selected="true">channels_last - inputs with shape  (batch, steps, channels)</option>
+      <option value="channels_first">channels_first - inputs with shape (batch, channels, steps)</option>
+    </param>
+    <yield/>
+  </xml>
+
+  <xml name="layer_GlobalAveragePooling2D">
+    <param argument="data_format" type="select" help="The ordering of the dimensions in the inputs.">
+        <option value="channels_last" selected="true">channels_last - inputs with shape (batch, height, width, channels)</option>
+        <option value="channels_first">channels_first - inputs with shape (batch, channels, height, width)</option>
+    </param>
+    <yield/>
+  </xml>
+
+  <xml name="layer_GlobalAveragePooling3D">
+    <param argument="data_format" type="select" help="The ordering of the dimensions in the inputs.">
+      <option value="channels_last" selected="true">channels_last - inputs with shape (batch, spatial_dim1, spatial_dim2, spatial_dim3, channels)</option>
+      <option value="channels_first">channels_first - inputs with shape (batch, channels, spatial_dim1, spatial_dim2, spatial_dim3)</option>
+    </param>
+    <yield/>
+  </xml>
+
+  <!--Locally-connected Layers-->
+
+  <xml name="layer_LocallyConnected1D">
+    <param argument="filters" type="integer" value="" min="0" help="the dimensionality of the output space (i.e. the number of output filters in the convolution)."/>
+    <param argument="kernel_size" type="text" value="" help="An integer or tuple/list of a single integer, specifying the length of the 1D convolution window."/>
+    <param argument="strides" type="text" value="1" help="An integer or tuple/list of a single integer, specifying the stride length of the convolution."/>
+    <section name="layer_options" title="Layer Advanced Options" expanded="false">
+      <param argument="padding" type="select" help="">
+        <option value="valid" selected="true">valid - no padding</option>
+      </param>
+      <param argument="data_format" type="select" help="The ordering of the dimensions in the inputs.">
+        <option value="channels_last" selected="true">channels_last - inputs with shape  (batch, steps, channels)</option>
+        <option value="channels_first">channels_first - inputs with shape (batch, channels, steps)</option>
+      </param>
+      <expand macro="keras_activations"/>
+      <param argument="use_bias" type="boolean" truevalue="booltrue" falsevalue="boolfalse" optional="true" checked="true"/>
+      <expand macro="keras_initializers" default_kernel="true"/>\
+      <expand macro="keras_initializers" argument="bias_initializer" default_bias="true"/>
+      <expand macro="keras_regularizers"/>
+      <expand macro="keras_regularizers" argument="bias_regularizer"/>
+      <expand macro="keras_regularizers" argument="activity_regularizer"/>
+      <expand macro="keras_constraints"/>
+      <expand macro="keras_constraints" argument="bias_constraint"/>
+    </section>
+    <yield/>
+  </xml>
+
+  <xml name="layer_LocallyConnected2D">
+    <param argument="filters" type="integer" value="" min="0" help="the dimensionality of the output space (i.e. the number of output filters in the convolution)."/>
+    <param argument="kernel_size" type="text" value="" help="An integer or tuple/list of 2 integers, specifying the height and width of the 2D convolution window."/>
+    <param argument="strides" type="text" value="(1, 1)" help="An integer or tuple/list of 2 integers, specifying the strides of the convolution along the height and width."/>
+    <section name="layer_options" title="Layer Advanced Options" expanded="false">
+      <param argument="padding" type="select" help="">
+        <option value="valid" selected="true">valid - no padding</option>
+        <option value="same">same - output has the same length as the original input</option>
+        <!--option value="causal">causal - causal (dilated) convolutions</option-->
+      </param>
+      <param argument="data_format" type="select" help="The ordering of the dimensions in the inputs.">
+        <option value="channels_last" selected="true">channels_last - inputs with shape (batch, height, width, channels)</option>
+        <option value="channels_first">channels_first - inputs with shape (batch, channels, height, width)</option>
+      </param>
+      <expand macro="keras_activations"/>
+      <param argument="use_bias" type="boolean" truevalue="booltrue" falsevalue="boolfalse" optional="true" checked="true"/>
+      <expand macro="keras_initializers" default_kernel="true"/>\
+      <expand macro="keras_initializers" argument="bias_initializer" default_bias="true"/>
+      <expand macro="keras_regularizers"/>
+      <expand macro="keras_regularizers" argument="bias_regularizer"/>
+      <expand macro="keras_regularizers" argument="activity_regularizer"/>
+      <expand macro="keras_constraints"/>
+      <expand macro="keras_constraints" argument="bias_constraint"/>
+    </section>
+    <yield/>
+  </xml>
+
+  <!--Recurrent Layers>
+
+  <xml name="layer_RNN">
+    <param argument="cell" >
+    <section name="layer_options" title="Layer Advanced Options" expanded="false">
+      <param argument="return_sequences" type="boolean" truevalue="booltrue" falsevalue="boolfalse" optional="true" checked="false" />
+      <param argument="return_state" type="boolean" truevalue="booltrue" falsevalue="boolfalse" optional="true" checked="false" />
+      <param argument="go_backwards" type="boolean" truevalue="booltrue" falsevalue="boolfalse" optional="true" checked="false" />
+      <param argument="stateful" type="boolean" truevalue="booltrue" falsevalue="boolfalse" optional="true" checked="false" />
+      <param argument="unroll" type="boolean" truevalue="booltrue" falsevalue="boolfalse" optional="true" checked="false" />
+      <param argument="input_dim" >
+      <param argument="input_length" >
+    </section>
+    <yield/>
+  </xml-->
+
+  <xml name="layer_LSTM">
+    <param argument="units" type="integer" value="" min="1" help="Positive integer, dimensionality of the output space."/>
+    <section name="layer_options" title="Layer Advanced Options" expanded="false">
+      <expand macro="keras_activations"/>
+    </section>
+    <yield/>
+  </xml>
+
+  <!--Embedding Layers-->
+
+  <xml name="layer_Embedding">
+    <param argument="input_dim" type="integer" value="" min="0" help="int > 0. Size of the vocabulary, i.e. maximum integer index + 1."/>
+    <param argument="output_dim" type="integer" value="" min="0" help="int >= 0. Dimension of the dense embedding."/>
+    <section name="layer_options" title="Layer Advanced Options" expanded="false">
+      <expand macro="keras_initializers" argument="embeddings_initializer" default_embeddings="true"/>
+      <expand macro="keras_regularizers" argument="embeddings_regularizer"/>
+      <expand macro="keras_regularizers" argument="activity_regularizer"/>
+      <expand macro="keras_constraints" argument="embeddings_constraint"/>
+      <param argument="mask_zero" type="boolean" truevalue="booltrue" falsevalue="boolfalse" optional="true" checked="false"/>
+      <param argument="input_length" type="integer" value="" optional="true" min="0" help="Length of input sequences. Required if connecting Flatten then Dense layers upstream"/>
+    </section>
+    <yield/>
+  </xml>
+
+  <!--Merge Layers-->
+
+  <xml name="layer_merge">
+    <param name="merging_layers" type="text" value="[ ]" label="Type the layer index numbers in a list" help="List of numbers">
+      <sanitizer>
+        <valid initial="default">
+          <add value="["/>
+          <add value="]"/>
+        </valid>
+      </sanitizer>
+    </param>
+    <yield/>
+  </xml>
+
+  <xml name="layer_Concatenate" token_type="integer" token_default_value="-1" token_help="Axis along which to concatenate.">
+    <expand macro="layer_merge">
+      <param argument="axis" type="@TYPE@" value="@DEFAULT_VALUE@" help="@HELP@"/>
+    </expand>
+    <yield/>
+  </xml>
+
+  <xml name="layer_Dot">
+    <expand macro="layer_Concatenate" type="text" default_value="" help="Integer or tuple of integers, axis or axes along which to take the dot product.">
+      <param argument="normalize" type="boolean" truevalue="booltrue" falsevalue="boolfalse" optional="true" checked="false" label="Whether to L2-normalize samples along the dot product axis before taking the dot product?"/>
+    </expand>
+    <yield/>
+  </xml>
+
+  <!--Advanced Activations Layers-->
+
+  <xml name="layer_LeakyReLU">
+    <param argument="alpha" type="float" value="0.3" min="0." help="float >= 0. Negative slope coefficient."/>
+    <yield/>
+  </xml>
+
+  <xml name="layer_PReLU">
+    <expand macro="keras_initializers" argument="alpha_initializer" default_bias="true"/>
+    <expand macro="keras_regularizers" argument="alpha_regularizer"/>
+    <expand macro="keras_constraints" argument="alpha_constraint"/>
+    <param argument="shared_axes" type="text" value="" help="the axes along which to share learnable parameters for the activation function. E.g. [1, 2]">
+      <sanitizer>
+        <valid initial="default">
+          <add value="["/>
+          <add value="]"/>
+        </valid>
+      </sanitizer>
+    </param>
+    <yield/>
+  </xml>
+
+  <xml name="layer_ELU">
+    <param argument="alpha" type="float" value="1.0" help="scale for the negative factor."/>
+    <yield/>
+  </xml>
+
+  <xml name="layer_ThresholdedReLU">
+    <param argument="theta" type="float" value="1.0" help="float >= 0. Threshold location of activation."/>
+    <yield/>
+  </xml>
+
+  <xml name="layer_Softmax">
+    <param argument="axis" type="integer" value="-1" help="Integer, axis along which the softmax normalization is applied."/>
+    <yield/>
+  </xml>
+
+  <xml name="layer_ReLU">
+    <param argument="max_value" type="float" value="" min="0." help="float >= 0. Maximum activation value."/>
+    <param argument="negative_slope" type="float" value="0." min="0." help="float >= 0. Negative slope coefficient."/>
+    <param argument="max_value" type="float" value="0." help="float. Threshold value for thresholded activation."/>
+    <yield/>
+  </xml>
+
+  <!--Normalization Layers-->
+
+  <!--Noise layers-->
+
+  <xml name="inbound_nodes_index">
+    <param name="inbound_nodes" type="integer" value="" label="Type the index number of input layer"
+          help="Find the index number at the left top corner of layer configuration block"/>
+  </xml>
+
+
+  <!-- Keras CallBacks -->
+
+  <xml name="keras_callbacks">
+    <repeat name="callbacks" min="1" max="5" title="callback">
+      <conditional name="callback_selection">
+        <param name="callback_type" type="select" label="Choose a callback">
+          <option value="None" selected="true">None</option>
+          <option value="EarlyStopping">EarlyStopping -- Stop training when a monitored quantity has stopped improving</option>
+          <!--option value="KerasTensorBoard">TensorBoard</option-->
+          <!--option value="KerasLearningRateScheduler">LearningRateScheduler</option-->
+          <!--option value="RemoteMonitor">RemoteMonitor - Stream events to a server</option> -->
+          <!--option value="ModelCheckpoint">ModelCheckpoint - Save the model after every epoch</option>-->
+          <option value="TerminateOnNaN">TerminateOnNaN -- Terminates training when a NaN loss is encountered.</option>
+          <option value="ReduceLROnPlateau">ReduceLROnPlateau -- Reduce learning rate when a metric has stopped improving</option>
+          <option value="CSVLogger">CSVLogger -- Streams epoch results to a csv file</option>
+        </param>
+        <when value="None"/>
+        <when value="EarlyStopping">
+          <expand macro="keras_callback_common_options">
+            <param argument="min_delta" type="float" value="0" optional="true" help="Minimum change in the monitored quantity to qualify as an improvement."/>
+            <param argument="patience" type="integer" value="0" min="0" help="Number of epochs with no improvement after which training will be stopped."/>
+            <param argument="baseline" type="float" value="" optional="true" help="Baseline value for the monitored quantity to reach. Training will stop if the model doesn't show improvement over the baseline."/>
+            <param argument="restore_best_weights" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="false" help="Whether to restore model weights from the epoch with the best value of the monitored quantity"/>
+          </expand>
+        </when>
+        <!--when value="TensorBoard">
+          <param argument="histogram_freq" />
+          <param argument="batch_size" />
+          <param argument="write_graph" />
+          <param argument="write_grads" />
+          <param argument="write_images" />
+          <param argument="embeddings_layer_names" />
+          <param argument="embeddings_metadata" />
+          <param argument="embeddings_data" />
+          <param argument="update_freq" />
+        </when-->
+        <!--when value="RemoteMonitor">
+          <param argument="root" type="text" value="http://localhost:9000" help="Root url of the target server."/>
+          <param argument="path" type="text" value="/publish/epoch/end/" help="Path relative to root to which the events will be sent. E.g., root + '/publish/epoch/end/'"/>
+          <param argument="field" type="text" value="data" optional="true" help="JSON field under which the data will be stored. The field is used only if the payload is sent within a form (i.e. send_as_json is set to False)."/>
+          <param argument="headers" type="text" value="" optional="true" help="Dictionary; optional custom HTTP headers.">
+            <sanitizer>
+              <valid initial="default">
+                <add value="{"/>
+                <add value="}"/>
+              </valid>
+            </sanitizer>
+          </param>
+          <param argument="send_as_json" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="false" help="Whether the request should be send as application/json."/>
+        </when>
+        <when value="ModelCheckpoint">
+          <expand macro="keras_callback_common_options">
+            <param argument="save_best_only" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="false" help="If True, the latest best model according to the quantity monitored will not be overwritten."/>
+            <param argument="save_weights_only" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="true" help="If True, then only the model's weights will be saved, else the full model is saved."/>
+            <param argument="period" type="integer" value="1" min="1" help="Interval (number of epochs) between checkpoints."/>
+          </expand>
+        </when>-->
+        <when value="TerminateOnNaN"/>
+        <when value="ReduceLROnPlateau">
+          <expand macro="keras_callback_common_options">
+            <param argument="factor" type="float" value="0.1" help="Factor by which the learning rate will be reduced. new_lr = lr * factor"/>
+            <param argument="patience" type="integer" value="10" help="Number of epochs with no improvement after which learning rate will be reduced."/>
+            <param argument="min_delta" type="float" value="0.0001" help="Threshold for measuring the new optimum, to only focus on significant changes."/>
+            <param argument="cooldown" type="integer" value="0" help="Number of epochs to wait before resuming normal operation after lr has been reduced."/>
+            <param argument="min_lr" type="float" value="0" help="Lower bound on the learning rate."/>
+          </expand>
+        </when>
+        <when value="CSVLogger"/>
+      </conditional>
+    </repeat>
+  </xml>
+
+  <xml name="keras_callback_common_options">
+    <param argument="monitor" type="select" help="Quantity to be monitored.">
+      <option value="val_loss" selected="true">val_loss</option>
+      <option value="loss">loss</option>
+    </param>
+    <param argument="mode" type="select">
+      <option value="auto" selected="true">auto -- the direction is automatically inferred from the name of the monitored quantity</option>
+      <option value="min">min -- training will stop when the quantity monitored has stopped decreasing</option>
+      <option value="max">max -- training will stop when the quantity monitored has stopped increasing</option>
+    </param>
+    <yield/>
+  </xml>
+
+  <!--Batch online data generators-->
+
+  <xml name="params_fasta_dna_batch_generator">
+    <param argument="seq_length" type="integer" value="1000" optional="true" help="Integer. Sequence length or number of bases."/>
+    <param argument="shuffle" type="boolean" truevalue="booltrue" falsevalue="boolfalse" optional="true" checked="true" help="Whether to shuffle the data between epochs."/>
+    <param argument="seed" type="integer" value="" optional="true" help="Integer, random seed for data shuffling"/>
+  </xml>
+
+  <xml name="params_fasta_protein_batch_generator">
+    <expand macro="params_fasta_dna_batch_generator"/>
+  </xml>
+
+  <xml name="params_genomic_interval_batch_generator">
+    <expand macro="params_fasta_dna_batch_generator"/>
+    <param argument="blacklist_regions" type="select" help="Tabix-indexed list of regions from which we should not output sequences">
+      <option value="hg38" selected="true">hg38</option>
+      <option value="hg19">hg19</option>
+    </param>
+    <param argument="center_bin_to_predict" type="integer" value="200" optional="true" help="Query the tabix-indexed file for a region of length."/>
+    <param argument="feature_thresholds" type="float" value="0.5"  optional="true" help="Threshold values to determine target value."/>
+    <param argument="random_state" type="integer" value="" optional="true" help="Random seed number, to control the sample position in each invertal."/>
+  </xml>
+
+  <xml name="params_image_batch_generator">
+  </xml>
+
+
+  <!--composite params macro-->
+
+  <xml name="keras_compile_params_section">
+    <section name="compile_params" title="Compile Parameters" expanded="true">
+      <param name="loss" type="select" label="Select a loss function">
+        <option value="binary_crossentropy" selected="true">binary_crossentropy</option>
+        <option value="mean_squared_error">mse / MSE/ mean_squared_error</option>
+        <option value="mean_absolute_error">mae / MAE / mean_absolute_error</option>
+        <option value="mean_absolute_percentage_error">mape / MAPE / mean_absolute_percentage_error</option>
+        <option value="mean_squared_logarithmic_error">msle / MSLE / mean_squared_logarithmic_error</option>
+        <option value="squared_hinge">squared_hinge</option>
+        <option value="hinge">hinge</option>
+        <option value="categorical_hinge">categorical_hinge</option>
+        <option value="logcosh">logcosh</option>
+        <option value="categorical_crossentropy">categorical_crossentropy</option>
+        <option value="sparse_categorical_crossentropy">sparse_categorical_crossentropy</option>
+        <option value="kullback_leibler_divergence">kld / KLD / kullback_leibler_divergence</option>
+        <option value="poisson">poisson</option>
+        <option value="cosine_proximity">cosine / cosine_proximity</option>
+      </param>
+      <conditional name="optimizer_selection">
+        <param name="optimizer_type" type="select" label="Select an optimizer">
+          <option value="SGD" selected="true">SGD - Stochastic gradient descent optimizer </option>
+          <option value="RMSprop">RMSprop - RMSProp optimizer </option>
+          <option value="Adagrad">Adagrad - Adagrad optimizer </option>
+          <option value="Adadelta">Adadelta - Adadelta optimizer </option>
+          <option value="Adam">Adam - Adam optimizer </option>
+          <option value="Adamax">Adamax - A variant of Adam based on the infinity norm </option>
+          <option value="Nadam">Nadam - Nesterov Adam optimizer </option>
+        </param>
+        <when value="SGD">
+          <expand macro="keras_optimizer_common">
+            <param argument="momentum" type="float" value="0" optional="true" label="Momentum"
+                help="float >= 0. Parameter that accelerates SGD in the relevant direction and dampens oscillations."/>
+            <param argument="decay" type="float" value="0" label="Decay" optional="true" help="float &gt;= 0. Learning rate decay over each update."/>
+            <param argument="nesterov" type="boolean" truevalue="booltrue" falsevalue="boolfalse" optional="true" checked="false" label="Whether to apply Nesterov momentum"/>
+          </expand>
+        </when>
+        <when value="RMSprop">
+          <expand macro="keras_optimizer_common_more" lr="0.001">
+            <param argument="rho" type="float" value="0.9" optional="true" label="rho" help="float &gt;= 0."/>
+          </expand>
+        </when>
+        <when value="Adagrad">
+          <expand macro="keras_optimizer_common_more" lr="0.001"/>
+        </when>
+        <when value="Adadelta">
+          <expand macro="keras_optimizer_common_more" lr="1.0">
+            <param argument="rho" type="float" value="0.95" optional="true" label="rho" help="float &gt;= 0."/>
+          </expand>
+        </when>
+        <when value="Adam">
+          <expand macro="keras_optimizer_common_more" lr="0.001">
+            <param argument="beta_1" type="float" value="0.9" optional="true" label="beta_1" help="float, 0 &lt; beta &lt; 1. Generally close to 1."/>
+            <param argument="beta_2" type="float" value="0.999" optional="true" label="beta_2" help="float, 0 &lt; beta &lt; 1. Generally close to 1."/>
+            <param argument="amsgrad" type="boolean" truevalue="booltrue" falsevalue="boolfalse" optional="true" checked="false" label="Whether to apply the AMSGrad variant?" 
+                help="Refer to paper `On the Convergence of Adam and Beyond`"/>
+          </expand>
+        </when>
+        <when value="Adamax">
+          <expand macro="keras_optimizer_common_more" lr="0.002">
+            <param argument="beta_1" type="float" value="0.9" optional="true" label="beta_1" help="float, 0 &lt; beta &lt; 1. Generally close to 1."/>
+            <param argument="beta_2" type="float" value="0.999" optional="true" label="beta_2" help="float, 0 &lt; beta &lt; 1. Generally close to 1."/>
+          </expand>
+        </when>
+        <when value="Nadam">
+          <expand macro="keras_optimizer_common" lr="0.002">
+            <param argument="beta_1" type="float" value="0.9" optional="true" label="beta_1" help="float, 0 &lt; beta &lt; 1. Generally close to 1."/>
+            <param argument="beta_2" type="float" value="0.999" optional="true" label="beta_2" help="float, 0 &lt; beta &lt; 1. Generally close to 1."/>
+            <param argument="epsilon" type="float" value="" label="epsilon" optional="true" help="Fuzz factor. If `None`, defaults to `K.epsilon()`"/>
+            <param argument="schedule_decay" type="float" value="0.004" optional="true" label="schedule_decay" help="float, 0 &lt; beta &lt; 1."/>
+          </expand>
+        </when>
+      </conditional>
+      <param name="metrics" type="select" optional="true" multiple="true" label="Select metrics">
+        <option value="acc" selected="true">acc / accruracy</option>
+        <option value="binary_accuracy">binary_accuracy</option>
+        <option value="categorical_accuracy">categorical_accuracy</option>
+        <option value="sparse_categorical_accuracy">sparse_categorical_accuracy</option>
+        <option value="mse">mse / MSE / mean_squared_error</option>
+        <option value="mae">mae / MAE / mean_absolute_error</option>
+        <option value="mae">mape / MAPE / mean_absolute_percentage_error</option>
+        <option value="cosine_proximity">cosine_proximity</option>
+        <option value="cosine">cosine</option>
+        <option value="none">none</option>
+      </param>
+    </section>
+  </xml>
+
+  <xml name="keras_fit_params_section">
+    <section name="fit_params" title="Fit Parameters" expanded="true">
+      <param name="epochs" type="integer" value="1" min="1" label="epochs"/>
+      <param name="batch_size" type="integer" value="32" optional="true" label="batch_size" help="Integer or blank for 32"/>
+      <param name="steps_per_epoch" type="integer" value="" optional="true" label="steps_per_epoch" help="The number of steps (batches of samples) before declaring one epoch finished and starting the next epoch. The default None is equal to the number of samples in your dataset divided by the batch size, or 1 if that cannot be determined."/>
+      <param name="validation_steps" type="integer" value="" optional="true" label="validation_steps" help="Default None. Total number of steps (batches of samples) to validate before stopping." />
+      <!--`validation_freq` will be available in next keras version-->
+      <!--param name="validation_freq" type="integer" value="1" optional="true" label="validation_freq" help="Integer only at current moment. If an integer, specifies how many training epochs to run before a new validation run is performed."/-->
+      <expand macro="keras_callbacks"/>
+    </section>
+  </xml>
+
+ <!--Citation-->
+  <xml name="keras_citation">
+    <citation type="bibtex">
+      @misc{chollet2015keras,
+        title={Keras},
+        url={https://keras.io},
+        author={Chollet, Fran\c{c}ois and others},
+        year={2015},
+        howpublished={https://keras.io},
+      }
+    </citation>
+  </xml>
+
+  <xml name="tensorflow_citation">
+    <citation type="bibtex">
+      @misc{tensorflow2015-whitepaper,
+        title={ {TensorFlow}: Large-Scale Machine Learning on Heterogeneous Systems},
+        url={https://www.tensorflow.org/},
+        note={Software available from tensorflow.org},
+        author={
+            Mart\'{\i}n~Abadi and
+            Ashish~Agarwal and
+            Paul~Barham and
+            Eugene~Brevdo and
+            Zhifeng~Chen and
+            Craig~Citro and
+            Greg~S.~Corrado and
+            Andy~Davis and
+            Jeffrey~Dean and
+            Matthieu~Devin and
+            Sanjay~Ghemawat and
+            Ian~Goodfellow and
+            Andrew~Harp and
+            Geoffrey~Irving and
+            Michael~Isard and
+            Yangqing Jia and
+            Rafal~Jozefowicz and
+            Lukasz~Kaiser and
+            Manjunath~Kudlur and
+            Josh~Levenberg and
+            Dandelion~Man\'{e} and
+            Rajat~Monga and
+            Sherry~Moore and
+            Derek~Murray and
+            Chris~Olah and
+            Mike~Schuster and
+            Jonathon~Shlens and
+            Benoit~Steiner and
+            Ilya~Sutskever and
+            Kunal~Talwar and
+            Paul~Tucker and
+            Vincent~Vanhoucke and
+            Vijay~Vasudevan and
+            Fernanda~Vi\'{e}gas and
+            Oriol~Vinyals and
+            Pete~Warden and
+            Martin~Wattenberg and
+            Martin~Wicke and
+            Yuan~Yu and
+            Xiaoqiang~Zheng},
+          year={2015},
+      }
+    </citation>
+  </xml>
+
+</macros>
\ No newline at end of file
diff -r 231e9a9849e8 -r 0b88494bdcac main_macros.xml
--- a/main_macros.xml	Tue Jul 09 19:34:06 2019 -0400
+++ b/main_macros.xml	Fri Aug 09 07:25:16 2019 -0400
@@ -1,16 +1,12 @@
 <macros>
-  <token name="@VERSION@">1.0.0.4</token>
+  <token name="@VERSION@">1.0.7.10</token>
+
+  <token name="@ENSEMBLE_VERSION@">0.2.0</token>
 
   <xml name="python_requirements">
       <requirements>
           <requirement type="package" version="3.6">python</requirement>
-          <requirement type="package" version="0.20.3">scikit-learn</requirement>
-          <requirement type="package" version="0.24.2">pandas</requirement>
-          <requirement type="package" version="0.80">xgboost</requirement>
-          <requirement type="package" version="0.9.13">asteval</requirement>
-          <requirement type="package" version="0.6">skrebate</requirement>
-          <requirement type="package" version="0.4.2">imbalanced-learn</requirement>
-          <requirement type="package" version="0.16.0">mlxtend</requirement>
+          <requirement type="package" version="0.7.10">Galaxy-ML</requirement>
           <yield/>
       </requirements>
   </xml>
@@ -420,8 +416,7 @@
 
   <xml name="sparse_target" token_label1="Select a sparse matrix:" token_label2="Select the tabular containing true labels:" token_multiple="False" token_format1="txt" token_format2="tabular" token_help1="" token_help2="">
     <param name="infile1" type="data" format="@FORMAT1@" label="@LABEL1@" help="@HELP1@"/>
-    <param name="infile2" type="data" format="@FORMAT2@" label="@LABEL2@" help="@HELP2@"/>
-    <param name="col2" multiple="@MULTIPLE@" type="data_column" data_ref="infile2" label="Select target column(s):"/>
+    <expand macro="input_tabular_target"/>
   </xml>
 
   <xml name="sl_mixed_input">
@@ -429,6 +424,8 @@
       <param name="selected_input" type="select" label="Select input type:">
           <option value="tabular" selected="true">tabular data</option>
           <option value="sparse">sparse matrix</option>
+          <option value="seq_fasta">sequnences in a fasta file</option>
+          <option value="refseq_and_interval">reference genome and intervals</option>
       </param>
       <when value="tabular">
           <expand macro="samples_tabular" multiple1="true" multiple2="false"/>
@@ -436,6 +433,36 @@
       <when value="sparse">
           <expand macro="sparse_target"/>
       </when>
+      <when value="seq_fasta">
+          <expand macro="inputs_seq_fasta"/>
+      </when>
+      <when value="refseq_and_interval">
+          <expand macro="inputs_refseq_and_interval"/>
+      </when>
+    </conditional>
+  </xml>
+
+  <xml name="input_tabular_target">
+    <param name="infile2" type="data" format="tabular" label="Dataset containing class labels or target values:"/>
+    <param name="header2" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="false" label="Does the dataset contain header:" />
+    <conditional name="column_selector_options_2">
+      <expand macro="samples_column_selector_options" column_option="selected_column_selector_option2" col_name="col2" multiple="false" infile="infile2"/>
+    </conditional>
+  </xml>
+
+  <xml name="inputs_seq_fasta">
+    <param name="fasta_path" type="data" format="fasta" label="Dataset containing fasta genomic/protein sequences" help="Sequences will be one-hot encoded to arrays."/>
+    <expand macro="input_tabular_target"/>
+  </xml>
+
+  <xml name="inputs_refseq_and_interval">
+    <param name="ref_genome_file" type="data" format="fasta" label="Dataset containing reference genomic sequence"/>
+    <param name="interval_file" type="data" format="interval" label="Dataset containing sequence intervals for training" help="interval. Sequences will be retrieved from the reference genome and one-hot encoded to training arrays."/>
+    <param name="target_file" type="data" format="bed" label="Dataset containing positions and features for target values." help="bed. The file will be compressed with `bgzip` and then indexed using `tabix`."/>
+    <param name="infile2" type="data" format="tabular" label="Dataset containing the feature list for prediction"/>
+    <param name="header2" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="false" label="Does the dataset contain header:" />
+    <conditional name="column_selector_options_2">
+      <expand macro="samples_column_selector_options" column_option="selected_column_selector_option2" col_name="col2" multiple="true" infile="infile2"/>
     </conditional>
   </xml>
 
@@ -705,7 +732,6 @@
     <param name="selected_pre_processor" type="select" label="Select a preprocessor:">
       <option value="StandardScaler" selected="true">Standard Scaler (Standardizes features by removing the mean and scaling to unit variance)</option>
       <option value="Binarizer">Binarizer (Binarizes data)</option>
-      <option value="Imputer">Imputer (Completes missing values)</option>
       <option value="MaxAbsScaler">Max Abs Scaler (Scales features by their maximum absolute value)</option>
       <option value="Normalizer">Normalizer (Normalizes samples individually to unit norm)</option>
       <yield/>
@@ -731,25 +757,6 @@
                 help="Feature values below or equal to this are replaced by 0, above it by 1. Threshold may not be less than 0 for operations on sparse matrices. "/>
         </section>
     </when>
-    <when value="Imputer">
-      <section name="options" title="Advanced Options" expanded="False">
-          <param argument="copy" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="true"
-            label="Use a copy of data for precomputing imputation" help=" "/>
-          <param argument="strategy" type="select" optional="true" label="Imputation strategy" help=" ">
-              <option value="mean" selected="true">Replace missing values using the mean along the axis</option>
-              <option value="median">Replace missing values using the median along the axis</option>
-              <option value="most_frequent">Replace missing using the most frequent value along the axis</option>
-          </param>
-          <param argument="missing_values" type="text" optional="true" value="NaN"
-                label="Placeholder for missing values" help="For missing values encoded as numpy.nan, use the string value “NaN”"/>
-          <!--param argument="axis" type="boolean" optional="true" truevalue="1" falsevalue="0"
-                label="Impute along axis = 1" help="If fasle, axis = 0 is selected for imputation. "/> -->
-          <!--param argument="axis" type="select" optional="true" label="The axis along which to impute" help=" ">
-              <option value="0" selected="true">Impute along columns</option>
-              <option value="1">Impute along rows</option>
-          </param-->
-      </section>
-    </when>
     <when value="StandardScaler">
       <section name="options" title="Advanced Options" expanded="False">
         <param argument="copy" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="true"
@@ -788,7 +795,7 @@
       </when>
       <when value="MinMaxScaler">
           <section name="options" title="Advanced Options" expanded="False">
-              <!--feature_range-->
+              <param argument="feature_range" type="text" value="(0, 1)" optional="true" help="Desired range of transformed data. None or tuple (min, max). None equals to (0, 1)"/>
               <param argument="copy" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolflase" checked="true"
                   label="Use a copy of data for precomputing normalization" help=" "/>
           </section>
@@ -922,9 +929,9 @@
     </conditional>
   </xml>
 
-  <xml name="cv_reduced">
+  <xml name="cv_reduced" token_label="Select the cv splitter">
     <conditional name="cv_selector">
-      <param name="selected_cv" type="select" label="Select the cv splitter:">
+      <param name="selected_cv" type="select" label="@LABEL@">
         <expand macro="cv_splitter"/>
       </param>
       <expand macro="cv_splitter_options"/>
@@ -932,7 +939,7 @@
   </xml>
 
   <xml name="cv_n_splits" token_value="3" token_help="Number of folds. Must be at least 2.">
-    <param argument="n_splits" type="integer" value="@VALUE@" min="2" label="n_splits" help="@HELP@"/>
+    <param argument="n_splits" type="integer" value="@VALUE@" min="1" label="n_splits" help="@HELP@"/>
   </xml>
 
   <xml name="cv_shuffle">
@@ -953,6 +960,40 @@
     </section>
   </xml>
 
+  <xml name="train_test_split_params">
+    <conditional name="split_algos">
+      <param name="shuffle" type="select" label="Select the splitting method">
+        <option value="None">No shuffle</option>
+        <option value="simple" selected="true">ShuffleSplit</option>
+        <option value="stratified">StratifiedShuffleSplit -- target values serve as class labels</option>
+        <option value="group">GroupShuffleSplit or split by group names</option>
+      </param>
+      <when value="None">
+        <expand macro="train_test_split_test_size"/>
+      </when>
+      <when value="simple">
+        <expand macro="train_test_split_test_size"/>
+        <expand macro="random_state"/>
+      </when>
+      <when value="stratified">
+        <expand macro="train_test_split_test_size"/>
+        <expand macro="random_state"/>
+      </when>
+      <when value="group">
+        <expand macro="train_test_split_test_size" optional="true"/>
+        <expand macro="random_state"/>
+        <param argument="group_names" type="text" value="" optional="true" label="Type in group names instead"
+        help="For example: chr6, chr7. This parameter is optional. If used, it will override the holdout size and random seed."/>
+        <yield/>
+      </when>
+    </conditional>
+    <!--param argument="train_size" type="float" optional="True" value="" label="Train size:"/>-->
+  </xml>
+
+  <xml name="train_test_split_test_size" token_optional="false">
+    <param name="test_size" type="float" value="0.2" optional="@OPTIONAL@" label="Holdout size" help="Leass than 1, for preportion; greater than 1 (integer), for number of samples."/>
+  </xml>
+
   <xml name="feature_selection_algorithms">
     <option value="SelectKBest" selected="true">SelectKBest - Select features according to the k highest scores</option>
     <option value="GenericUnivariateSelect">GenericUnivariateSelect - Univariate feature selector with configurable strategy</option>
@@ -1167,7 +1208,7 @@
 
   <xml name="model_validation_common_options">
     <expand macro="cv"/>
-    <!-- expand macro="verbose"/> -->
+    <expand macro="verbose"/>
     <yield/>
   </xml>
 
@@ -1286,14 +1327,13 @@
   <xml name="search_cv_estimator">
     <param name="infile_estimator" type="data" format="zip" label="Choose the dataset containing pipeline/estimator object"/>
     <section name="search_params_builder" title="Search parameters Builder" expanded="true">
-      <param name="infile_params" type="data" format="tabular" label="Choose the dataset containing parameter names"/>
+      <param name="infile_params" type="data" format="tabular" optional="true" label="Choose the dataset containing parameter names" help="This dataset could be the output of `get_params` in the `Estimator Attributes` tool."/>
       <repeat name="param_set" min="1" max="30" title="Parameter settings for search:">
-          <param name="sp_name" type="select" label="Choose a parameter name (with current value)">
+          <param name="sp_name" type="select" optional="true" label="Choose a parameter name (with current value)">
             <options from_dataset="infile_params" startswith="@">
               <column name="name" index="2"/>
               <column name="value" index="1"/>
               <filter type="unique_value" name="unique_param" column="1"/>
-              <filter type="sort_by" name="sorted_param" column="2"/>
             </options>
           </param>
           <param name="sp_list" type="text" value="" optional="true" label="Search list" help="list or array-like, for example: [1, 10, 100, 1000], [True, False] and ['auto', 'sqrt', None]. See `help` section for more examples">
@@ -1310,6 +1350,30 @@
     </section>
   </xml>
 
+  <xml name="estimator_and_hyperparameter">
+    <param name="infile_estimator" type="data" format="zip" label="Choose the dataset containing pipeline/estimator object"/>
+    <section name="hyperparams_swapping" title="Hyperparameter Swapping" expanded="false">
+      <param name="infile_params" type="data" format="tabular" optional="true" label="Choose the dataset containing hyperparameters for the pipeline/estimator above" help="This dataset could be the output of `get_params` in the `Estimator Attributes` tool."/>
+      <repeat name="param_set" min="1" max="30" title="New hyperparameter setting">
+          <param name="sp_name" type="select" optional="true" label="Choose a parameter name (with current value)">
+            <options from_dataset="infile_params" startswith="@">
+              <column name="name" index="2"/>
+              <column name="value" index="1"/>
+              <filter type="unique_value" name="unique_param" column="1"/>
+            </options>
+          </param>
+          <param name="sp_value" type="text" value="" optional="true" label="New value" help="Supports int, float, boolean, single quoted string, and selected object constructor. Similar to the `Parameter settings for search` section in `searchcv` tool except that only single value is expected here.">
+            <sanitizer>
+              <valid initial="default">
+                <add value="&apos;"/>
+                <add value="&quot;"/>
+              </valid>
+            </sanitizer>
+          </param>
+      </repeat>
+    </section>
+  </xml>
+
   <xml name="search_cv_options">
       <expand macro="scoring_selection"/>
       <expand macro="model_validation_common_options"/>
@@ -1750,6 +1814,40 @@
     </conditional>
   </xml>
 
+  <xml name="stacking_voting_weights">
+    <section name="options" title="Advanced Options" expanded="false">
+        <param argument="weights" type="text" value="[]" optional="true" help="Sequence of weights (float or int). Uses uniform weights if None (`[]`).">
+          <sanitizer>
+            <valid initial="default">
+              <add value="["/>
+              <add value="]"/>
+            </valid>
+          </sanitizer>
+        </param>
+        <yield/>
+    </section>
+  </xml>
+
+  <xml name="preprocessors_sequence_encoders">
+    <conditional name="encoder_selection">
+        <param name="encoder_type" type="select" label="Choose the sequence encoder class">
+            <option value="GenomeOneHotEncoder">GenomeOneHotEncoder</option>
+            <option value="ProteinOneHotEncoder">ProteinOneHotEncoder</option>
+        </param>
+        <when value="GenomeOneHotEncoder">
+            <expand macro="preprocessors_sequence_encoder_arguments"/>
+        </when>
+        <when value="ProteinOneHotEncoder">
+            <expand macro="preprocessors_sequence_encoder_arguments"/>
+        </when>
+    </conditional>
+  </xml>
+
+  <xml name="preprocessors_sequence_encoder_arguments">
+    <param argument="seq_length" type="integer" value="" min="0" optional="true" help="Integer. Sequence length"/>
+    <param argument="padding" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="true" help="Whether to pad or truncate sequence to meet the sequence length."/>
+  </xml>
+
   <!-- Outputs -->
 
   <xml name="output">
@@ -1847,7 +1945,7 @@
     </citation>
   </xml>
 
-    <xml name="imblearn_citation">
+  <xml name="imblearn_citation">
     <citation type="bibtex">
       @article{JMLR:v18:16-365,
         author  = {Guillaume  Lema{{\^i}}tre and Fernando Nogueira and Christos K. Aridas},
@@ -1862,4 +1960,19 @@
     </citation>
   </xml>
 
+  <xml name="selene_citation">
+    <citation type="bibtex">
+      @article{chen2019selene,
+        title={Selene: a PyTorch-based deep learning library for sequence data},
+        author={Chen, Kathleen M and Cofer, Evan M and Zhou, Jian and Troyanskaya, Olga G},
+        journal={Nature methods},
+        volume={16},
+        number={4},
+        pages={315},
+        year={2019},
+        publisher={Nature Publishing Group}
+      }
+    </citation>
+  </xml>
+
 </macros>
diff -r 231e9a9849e8 -r 0b88494bdcac model_prediction.py
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/model_prediction.py	Fri Aug 09 07:25:16 2019 -0400
@@ -0,0 +1,205 @@
+import argparse
+import json
+import numpy as np
+import pandas as pd
+import warnings
+
+from scipy.io import mmread
+from sklearn.pipeline import Pipeline
+
+from galaxy_ml.utils import (load_model, read_columns,
+                             get_module, try_get_attr)
+
+
+N_JOBS = int(__import__('os').environ.get('GALAXY_SLOTS', 1))
+
+
+def main(inputs, infile_estimator, outfile_predict,
+         infile_weights=None, infile1=None,
+         fasta_path=None, ref_seq=None,
+         vcf_path=None):
+    """
+    Parameter
+    ---------
+    inputs : str
+        File path to galaxy tool parameter
+
+    infile_estimator : strgit
+        File path to trained estimator input
+
+    outfile_predict : str
+        File path to save the prediction results, tabular
+
+    infile_weights : str
+        File path to weights input
+
+    infile1 : str
+        File path to dataset containing features
+
+    fasta_path : str
+        File path to dataset containing fasta file
+
+    ref_seq : str
+        File path to dataset containing the reference genome sequence.
+
+    vcf_path : str
+        File path to dataset containing variants info.
+    """
+    warnings.filterwarnings('ignore')
+
+    with open(inputs, 'r') as param_handler:
+        params = json.load(param_handler)
+
+    # load model
+    with open(infile_estimator, 'rb') as est_handler:
+        estimator = load_model(est_handler)
+
+    main_est = estimator
+    if isinstance(estimator, Pipeline):
+        main_est = estimator.steps[-1][-1]
+    if hasattr(main_est, 'config') and hasattr(main_est, 'load_weights'):
+        if not infile_weights or infile_weights == 'None':
+            raise ValueError("The selected model skeleton asks for weights, "
+                             "but dataset for weights wan not selected!")
+        main_est.load_weights(infile_weights)
+
+    # handle data input
+    input_type = params['input_options']['selected_input']
+    # tabular 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
+
+        df = pd.read_csv(infile1, sep='\t', header=header, parse_dates=True)
+
+        X = read_columns(df, c=c, c_option=column_option).astype(float)
+
+        if params['method'] == 'predict':
+            preds = estimator.predict(X)
+        else:
+            preds = estimator.predict_proba(X)
+
+    # sparse input
+    elif input_type == 'sparse':
+        X = mmread(open(infile1, 'r'))
+        if params['method'] == 'predict':
+            preds = estimator.predict(X)
+        else:
+            preds = estimator.predict_proba(X)
+
+    # fasta input
+    elif input_type == 'seq_fasta':
+        if not hasattr(estimator, 'data_batch_generator'):
+            raise ValueError(
+                "To do prediction on sequences in fasta input, "
+                "the estimator must be a `KerasGBatchClassifier`"
+                "equipped with data_batch_generator!")
+        pyfaidx = get_module('pyfaidx')
+        sequences = pyfaidx.Fasta(fasta_path)
+        n_seqs = len(sequences.keys())
+        X = np.arange(n_seqs)[:, np.newaxis]
+        seq_length = estimator.data_batch_generator.seq_length
+        batch_size = getattr(estimator, 'batch_size', 32)
+        steps = (n_seqs + batch_size - 1) // batch_size
+
+        seq_type = params['input_options']['seq_type']
+        klass = try_get_attr(
+            'galaxy_ml.preprocessors', seq_type)
+
+        pred_data_generator = klass(
+            fasta_path, seq_length=seq_length)
+
+        if params['method'] == 'predict':
+            preds = estimator.predict(
+                X, data_generator=pred_data_generator, steps=steps)
+        else:
+            preds = estimator.predict_proba(
+                X, data_generator=pred_data_generator, steps=steps)
+
+    # vcf input
+    elif input_type == 'variant_effect':
+        klass = try_get_attr('galaxy_ml.preprocessors',
+                             'GenomicVariantBatchGenerator')
+
+        options = params['input_options']
+        options.pop('selected_input')
+        if options['blacklist_regions'] == 'none':
+            options['blacklist_regions'] = None
+
+        pred_data_generator = klass(
+            ref_genome_path=ref_seq, vcf_path=vcf_path, **options)
+
+        pred_data_generator.fit()
+
+        preds = estimator.model_.predict_generator(
+            pred_data_generator.flow(batch_size=32),
+            workers=N_JOBS,
+            use_multiprocessing=True)
+
+        if preds.min() < 0. or preds.max() > 1.:
+            warnings.warn('Network returning invalid probability values. '
+                          'The last layer might not normalize predictions '
+                          'into probabilities '
+                          '(like softmax or sigmoid would).')
+
+        if params['method'] == 'predict_proba' and preds.shape[1] == 1:
+            # first column is probability of class 0 and second is of class 1
+            preds = np.hstack([1 - preds, preds])
+
+        elif params['method'] == 'predict':
+            if preds.shape[-1] > 1:
+                # if the last activation is `softmax`, the sum of all
+                # probibilities will 1, the classification is considered as
+                # multi-class problem, otherwise, we take it as multi-label.
+                act = getattr(estimator.model_.layers[-1], 'activation', None)
+                if act and act.__name__ == 'softmax':
+                    classes = preds.argmax(axis=-1)
+                else:
+                    preds = (preds > 0.5).astype('int32')
+            else:
+                classes = (preds > 0.5).astype('int32')
+
+            preds = estimator.classes_[classes]
+    # end input
+
+    # output
+    if input_type == 'variant_effect':   # TODO: save in batchs
+        rval = pd.DataFrame(preds)
+        meta = pd.DataFrame(
+            pred_data_generator.variants,
+            columns=['chrom', 'pos', 'name', 'ref', 'alt', 'strand'])
+
+        rval = pd.concat([meta, rval], axis=1)
+
+    elif len(preds.shape) == 1:
+        rval = pd.DataFrame(preds, columns=['Predicted'])
+    else:
+        rval = pd.DataFrame(preds)
+
+    rval.to_csv(outfile_predict, sep='\t',
+                header=True, index=False)
+
+
+if __name__ == '__main__':
+    aparser = argparse.ArgumentParser()
+    aparser.add_argument("-i", "--inputs", dest="inputs", required=True)
+    aparser.add_argument("-e", "--infile_estimator", dest="infile_estimator")
+    aparser.add_argument("-w", "--infile_weights", dest="infile_weights")
+    aparser.add_argument("-X", "--infile1", dest="infile1")
+    aparser.add_argument("-O", "--outfile_predict", dest="outfile_predict")
+    aparser.add_argument("-f", "--fasta_path", dest="fasta_path")
+    aparser.add_argument("-r", "--ref_seq", dest="ref_seq")
+    aparser.add_argument("-v", "--vcf_path", dest="vcf_path")
+    args = aparser.parse_args()
+
+    main(args.inputs, args.infile_estimator, args.outfile_predict,
+         infile_weights=args.infile_weights, infile1=args.infile1,
+         fasta_path=args.fasta_path, ref_seq=args.ref_seq,
+         vcf_path=args.vcf_path)
diff -r 231e9a9849e8 -r 0b88494bdcac model_validations.py
--- a/model_validations.py	Tue Jul 09 19:34:06 2019 -0400
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,252 +0,0 @@
-"""
-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 231e9a9849e8 -r 0b88494bdcac pk_whitelist.json
--- a/pk_whitelist.json	Tue Jul 09 19:34:06 2019 -0400
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,768 +0,0 @@
-{ "SK_NAMES": [
-    "sklearn._ASSUME_FINITE", "sklearn._isotonic._inplace_contiguous_isotonic_regression",
-    "sklearn._isotonic._make_unique", "sklearn.base.BaseEstimator",
-    "sklearn.base.BiclusterMixin", "sklearn.base.ClassifierMixin",
-    "sklearn.base.ClusterMixin", "sklearn.base.DensityMixin",
-    "sklearn.base.MetaEstimatorMixin", "sklearn.base.RegressorMixin",
-    "sklearn.base.TransformerMixin", "sklearn.base._first_and_last_element",
-    "sklearn.base._pprint", "sklearn.base.clone",
-    "sklearn.base.is_classifier", "sklearn.base.is_regressor",
-    "sklearn.clone", "sklearn.cluster.AffinityPropagation",
-    "sklearn.cluster.AgglomerativeClustering", "sklearn.cluster.Birch",
-    "sklearn.cluster.DBSCAN", "sklearn.cluster.FeatureAgglomeration",
-    "sklearn.cluster.KMeans", "sklearn.cluster.MeanShift",
-    "sklearn.cluster.MiniBatchKMeans", "sklearn.cluster.SpectralBiclustering",
-    "sklearn.cluster.SpectralClustering", "sklearn.cluster.SpectralCoclustering",
-    "sklearn.cluster._dbscan_inner.dbscan_inner", "sklearn.cluster._feature_agglomeration.AgglomerationTransform",
-    "sklearn.cluster._hierarchical.WeightedEdge", "sklearn.cluster._hierarchical._get_parents",
-    "sklearn.cluster._hierarchical._hc_get_descendent", "sklearn.cluster._hierarchical.average_merge",
-    "sklearn.cluster._hierarchical.compute_ward_dist", "sklearn.cluster._hierarchical.hc_get_heads",
-    "sklearn.cluster._hierarchical.max_merge", "sklearn.cluster._k_means._assign_labels_array",
-    "sklearn.cluster._k_means._assign_labels_csr", "sklearn.cluster._k_means._centers_dense",
-    "sklearn.cluster._k_means._centers_sparse", "sklearn.cluster._k_means._mini_batch_update_csr",
-    "sklearn.cluster._k_means_elkan.k_means_elkan", "sklearn.cluster.affinity_propagation",
-    "sklearn.cluster.affinity_propagation_.AffinityPropagation", "sklearn.cluster.affinity_propagation_.affinity_propagation",
-    "sklearn.cluster.bicluster.BaseSpectral", "sklearn.cluster.bicluster.SpectralBiclustering",
-    "sklearn.cluster.bicluster.SpectralCoclustering", "sklearn.cluster.bicluster._bistochastic_normalize",
-    "sklearn.cluster.bicluster._log_normalize", "sklearn.cluster.bicluster._scale_normalize",
-    "sklearn.cluster.birch.Birch", "sklearn.cluster.birch._CFNode",
-    "sklearn.cluster.birch._CFSubcluster", "sklearn.cluster.birch._iterate_sparse_X",
-    "sklearn.cluster.birch._split_node", "sklearn.cluster.dbscan",
-    "sklearn.cluster.dbscan_.DBSCAN", "sklearn.cluster.dbscan_.dbscan",
-    "sklearn.cluster.estimate_bandwidth", "sklearn.cluster.get_bin_seeds",
-    "sklearn.cluster.hierarchical.AgglomerativeClustering", "sklearn.cluster.hierarchical.FeatureAgglomeration",
-    "sklearn.cluster.hierarchical._TREE_BUILDERS", "sklearn.cluster.hierarchical._average_linkage",
-    "sklearn.cluster.hierarchical._complete_linkage", "sklearn.cluster.hierarchical._fix_connectivity",
-    "sklearn.cluster.hierarchical._hc_cut", "sklearn.cluster.hierarchical.linkage_tree",
-    "sklearn.cluster.hierarchical.ward_tree", "sklearn.cluster.k_means",
-    "sklearn.cluster.k_means_.FLOAT_DTYPES", "sklearn.cluster.k_means_.KMeans",
-    "sklearn.cluster.k_means_.MiniBatchKMeans", "sklearn.cluster.k_means_._init_centroids",
-    "sklearn.cluster.k_means_._k_init", "sklearn.cluster.k_means_._kmeans_single_elkan",
-    "sklearn.cluster.k_means_._kmeans_single_lloyd", "sklearn.cluster.k_means_._labels_inertia",
-    "sklearn.cluster.k_means_._labels_inertia_precompute_dense", "sklearn.cluster.k_means_._mini_batch_convergence",
-    "sklearn.cluster.k_means_._mini_batch_step", "sklearn.cluster.k_means_._tolerance",
-    "sklearn.cluster.k_means_._validate_center_shape", "sklearn.cluster.k_means_.k_means",
-    "sklearn.cluster.k_means_.string_types", "sklearn.cluster.linkage_tree",
-    "sklearn.cluster.mean_shift", "sklearn.cluster.mean_shift_.MeanShift",
-    "sklearn.cluster.mean_shift_._mean_shift_single_seed", "sklearn.cluster.mean_shift_.estimate_bandwidth",
-    "sklearn.cluster.mean_shift_.get_bin_seeds", "sklearn.cluster.mean_shift_.mean_shift",
-    "sklearn.cluster.spectral.SpectralClustering", "sklearn.cluster.spectral.discretize",
-    "sklearn.cluster.spectral.spectral_clustering", "sklearn.cluster.spectral_clustering",
-    "sklearn.cluster.ward_tree", "sklearn.config_context", "sklearn.compose.TransformedTargetRegressor",
-    "sklearn.compose._target.TransformedTargetRegressor", "sklearn.compose.ColumnTransformer",
-    "sklearn.compose._column_transformer.ColumnTransformer", "sklearn.compose.make_column_transformer",
-    "sklearn.compose._column_transformer.make_column_transformer",
-    "sklearn.covariance.EllipticEnvelope", "sklearn.covariance.EmpiricalCovariance",
-    "sklearn.covariance.GraphLasso", "sklearn.covariance.GraphLassoCV",
-    "sklearn.covariance.LedoitWolf", "sklearn.covariance.MinCovDet",
-    "sklearn.covariance.OAS", "sklearn.covariance.ShrunkCovariance",
-    "sklearn.covariance.empirical_covariance", "sklearn.covariance.empirical_covariance_.EmpiricalCovariance",
-    "sklearn.covariance.empirical_covariance_.empirical_covariance", "sklearn.covariance.empirical_covariance_.log_likelihood",
-    "sklearn.covariance.fast_mcd", "sklearn.covariance.graph_lasso",
-    "sklearn.covariance.graph_lasso_.GraphLasso", "sklearn.covariance.graph_lasso_.GraphLassoCV",
-    "sklearn.covariance.graph_lasso_._dual_gap", "sklearn.covariance.graph_lasso_._objective",
-    "sklearn.covariance.graph_lasso_.alpha_max", "sklearn.covariance.graph_lasso_.graph_lasso",
-    "sklearn.covariance.graph_lasso_.graph_lasso_path", "sklearn.covariance.ledoit_wolf",
-    "sklearn.covariance.ledoit_wolf_shrinkage", "sklearn.covariance.log_likelihood",
-    "sklearn.covariance.oas", "sklearn.covariance.outlier_detection.EllipticEnvelope",
-    "sklearn.covariance.robust_covariance.MinCovDet", "sklearn.covariance.robust_covariance._c_step",
-    "sklearn.covariance.robust_covariance.c_step", "sklearn.covariance.robust_covariance.fast_mcd",
-    "sklearn.covariance.robust_covariance.select_candidates", "sklearn.covariance.shrunk_covariance",
-    "sklearn.covariance.shrunk_covariance_.LedoitWolf", "sklearn.covariance.shrunk_covariance_.OAS",
-    "sklearn.covariance.shrunk_covariance_.ShrunkCovariance", "sklearn.covariance.shrunk_covariance_.ledoit_wolf",
-    "sklearn.covariance.shrunk_covariance_.ledoit_wolf_shrinkage", "sklearn.covariance.shrunk_covariance_.oas",
-    "sklearn.covariance.shrunk_covariance_.shrunk_covariance", "sklearn.decomposition.DictionaryLearning",
-    "sklearn.decomposition.FactorAnalysis", "sklearn.decomposition.FastICA",
-    "sklearn.decomposition.IncrementalPCA", "sklearn.decomposition.KernelPCA",
-    "sklearn.decomposition.LatentDirichletAllocation", "sklearn.decomposition.MiniBatchDictionaryLearning",
-    "sklearn.decomposition.MiniBatchSparsePCA", "sklearn.decomposition.NMF",
-    "sklearn.decomposition.PCA", "sklearn.decomposition.RandomizedPCA",
-    "sklearn.decomposition.SparseCoder", "sklearn.decomposition.SparsePCA",
-    "sklearn.decomposition.TruncatedSVD", "sklearn.decomposition._online_lda._dirichlet_expectation_1d",
-    "sklearn.decomposition._online_lda._dirichlet_expectation_2d", "sklearn.decomposition._online_lda.mean_change",
-    "sklearn.decomposition.base._BasePCA", "sklearn.decomposition.cdnmf_fast._update_cdnmf_fast",
-    "sklearn.decomposition.dict_learning", "sklearn.decomposition.dict_learning_online",
-    "sklearn.decomposition.factor_analysis.FactorAnalysis", "sklearn.decomposition.fastica",
-    "sklearn.decomposition.fastica_.FLOAT_DTYPES", "sklearn.decomposition.fastica_.FastICA",
-    "sklearn.decomposition.fastica_._cube", "sklearn.decomposition.fastica_._exp",
-    "sklearn.decomposition.fastica_._gs_decorrelation", "sklearn.decomposition.fastica_._ica_def",
-    "sklearn.decomposition.fastica_._ica_par", "sklearn.decomposition.fastica_._logcosh",
-    "sklearn.decomposition.fastica_._sym_decorrelation", "sklearn.decomposition.fastica_.fastica",
-    "sklearn.decomposition.fastica_.string_types", "sklearn.decomposition.incremental_pca.IncrementalPCA",
-    "sklearn.decomposition.kernel_pca.KernelPCA", "sklearn.decomposition.nmf.EPSILON",
-    "sklearn.decomposition.nmf.INTEGER_TYPES", "sklearn.decomposition.nmf.NMF",
-    "sklearn.decomposition.nmf._beta_divergence", "sklearn.decomposition.nmf._beta_loss_to_float",
-    "sklearn.decomposition.nmf._check_init", "sklearn.decomposition.nmf._check_string_param",
-    "sklearn.decomposition.nmf._compute_regularization", "sklearn.decomposition.nmf._fit_coordinate_descent",
-    "sklearn.decomposition.nmf._fit_multiplicative_update", "sklearn.decomposition.nmf._initialize_nmf",
-    "sklearn.decomposition.nmf._multiplicative_update_h", "sklearn.decomposition.nmf._multiplicative_update_w",
-    "sklearn.decomposition.nmf._special_sparse_dot", "sklearn.decomposition.nmf._update_coordinate_descent",
-    "sklearn.decomposition.nmf.non_negative_factorization", "sklearn.decomposition.nmf.norm",
-    "sklearn.decomposition.nmf.trace_dot", "sklearn.decomposition.non_negative_factorization",
-    "sklearn.decomposition.online_lda.EPS", "sklearn.decomposition.online_lda.LatentDirichletAllocation",
-    "sklearn.decomposition.online_lda._update_doc_distribution", "sklearn.decomposition.online_lda.gammaln",
-    "sklearn.decomposition.pca.PCA", "sklearn.decomposition.pca.RandomizedPCA",
-    "sklearn.decomposition.pca._assess_dimension_", "sklearn.decomposition.pca._infer_dimension_",
-    "sklearn.decomposition.pca.gammaln", "sklearn.decomposition.sparse_encode",
-    "sklearn.decomposition.sparse_pca.MiniBatchSparsePCA", "sklearn.decomposition.sparse_pca.SparsePCA",
-    "sklearn.decomposition.truncated_svd.TruncatedSVD", "sklearn.discriminant_analysis.LinearDiscriminantAnalysis",
-    "sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis", "sklearn.discriminant_analysis._class_cov",
-    "sklearn.discriminant_analysis._class_means", "sklearn.discriminant_analysis._cov",
-    "sklearn.discriminant_analysis.string_types", "sklearn.ensemble.AdaBoostClassifier",
-    "sklearn.ensemble.AdaBoostRegressor", "sklearn.ensemble.BaggingClassifier",
-    "sklearn.ensemble.BaggingRegressor", "sklearn.ensemble.BaseEnsemble",
-    "sklearn.ensemble.ExtraTreesClassifier", "sklearn.ensemble.ExtraTreesRegressor",
-    "sklearn.ensemble.GradientBoostingClassifier", "sklearn.ensemble.GradientBoostingRegressor",
-    "sklearn.ensemble.IsolationForest", "sklearn.ensemble.RandomForestClassifier",
-    "sklearn.ensemble.RandomForestRegressor", "sklearn.ensemble.RandomTreesEmbedding",
-    "sklearn.ensemble.VotingClassifier", "sklearn.ensemble._gradient_boosting._partial_dependence_tree",
-    "sklearn.ensemble._gradient_boosting._predict_regression_tree_stages_sparse", "sklearn.ensemble._gradient_boosting._random_sample_mask",
-    "sklearn.ensemble._gradient_boosting.predict_stage", "sklearn.ensemble._gradient_boosting.predict_stages",
-    "sklearn.ensemble.bagging.BaggingClassifier", "sklearn.ensemble.bagging.BaggingRegressor",
-    "sklearn.ensemble.bagging.BaseBagging", "sklearn.ensemble.bagging.MAX_INT",
-    "sklearn.ensemble.bagging._generate_bagging_indices", "sklearn.ensemble.bagging._generate_indices",
-    "sklearn.ensemble.bagging._parallel_build_estimators", "sklearn.ensemble.bagging._parallel_decision_function",
-    "sklearn.ensemble.bagging._parallel_predict_log_proba", "sklearn.ensemble.bagging._parallel_predict_proba",
-    "sklearn.ensemble.bagging._parallel_predict_regression", "sklearn.ensemble.base.BaseEnsemble",
-    "sklearn.ensemble.base.MAX_RAND_SEED", "sklearn.ensemble.base._partition_estimators",
-    "sklearn.ensemble.base._set_random_states", "sklearn.ensemble.forest.BaseForest",
-    "sklearn.ensemble.forest.ExtraTreesClassifier", "sklearn.ensemble.forest.ExtraTreesRegressor",
-    "sklearn.ensemble.forest.ForestClassifier", "sklearn.ensemble.forest.ForestRegressor",
-    "sklearn.ensemble.forest.MAX_INT", "sklearn.ensemble.forest.RandomForestClassifier",
-    "sklearn.ensemble.forest.RandomForestRegressor", "sklearn.ensemble.forest.RandomTreesEmbedding",
-    "sklearn.ensemble.forest._generate_sample_indices", "sklearn.ensemble.forest._generate_unsampled_indices",
-    "sklearn.ensemble.forest._parallel_build_trees", "sklearn.ensemble.forest.accumulate_prediction",
-    "sklearn.ensemble.gradient_boosting.BaseGradientBoosting", "sklearn.ensemble.gradient_boosting.BinomialDeviance",
-    "sklearn.ensemble.gradient_boosting.ClassificationLossFunction", "sklearn.ensemble.gradient_boosting.ExponentialLoss",
-    "sklearn.ensemble.gradient_boosting.GradientBoostingClassifier", "sklearn.ensemble.gradient_boosting.GradientBoostingRegressor",
-    "sklearn.ensemble.gradient_boosting.HuberLossFunction", "sklearn.ensemble.gradient_boosting.INIT_ESTIMATORS",
-    "sklearn.ensemble.gradient_boosting.LOSS_FUNCTIONS", "sklearn.ensemble.gradient_boosting.LeastAbsoluteError",
-    "sklearn.ensemble.gradient_boosting.LeastSquaresError", "sklearn.ensemble.gradient_boosting.LogOddsEstimator",
-    "sklearn.ensemble.gradient_boosting.LossFunction", "sklearn.ensemble.gradient_boosting.MeanEstimator",
-    "sklearn.ensemble.gradient_boosting.MultinomialDeviance", "sklearn.ensemble.gradient_boosting.PriorProbabilityEstimator",
-    "sklearn.ensemble.gradient_boosting.QuantileEstimator", "sklearn.ensemble.gradient_boosting.QuantileLossFunction",
-    "sklearn.ensemble.gradient_boosting.RegressionLossFunction", "sklearn.ensemble.gradient_boosting.ScaledLogOddsEstimator",
-    "sklearn.ensemble.gradient_boosting.TREE_LEAF", "sklearn.ensemble.gradient_boosting.VerboseReporter",
-    "sklearn.ensemble.gradient_boosting.ZeroEstimator", "sklearn.ensemble.gradient_boosting.expit",
-    "sklearn.ensemble.iforest.INTEGER_TYPES", "sklearn.ensemble.iforest.IsolationForest",
-    "sklearn.ensemble.iforest._average_path_length", "sklearn.ensemble.iforest.euler_gamma",
-    "sklearn.ensemble.partial_dependence._grid_from_X", "sklearn.ensemble.partial_dependence.partial_dependence",
-    "sklearn.ensemble.partial_dependence.plot_partial_dependence", "sklearn.ensemble.voting_classifier.VotingClassifier",
-    "sklearn.ensemble.voting_classifier._parallel_fit_estimator", "sklearn.ensemble.weight_boosting.AdaBoostClassifier",
-    "sklearn.ensemble.weight_boosting.AdaBoostRegressor", "sklearn.ensemble.weight_boosting.BaseWeightBoosting",
-    "sklearn.ensemble.weight_boosting._samme_proba", "sklearn.ensemble.weight_boosting.inner1d",
-    "sklearn.feature_extraction.DictVectorizer", "sklearn.feature_extraction.FeatureHasher",
-    "sklearn.feature_extraction._hashing.transform", "sklearn.feature_extraction.dict_vectorizer.DictVectorizer",
-    "sklearn.feature_extraction.dict_vectorizer._tosequence", "sklearn.feature_extraction.grid_to_graph",
-    "sklearn.feature_extraction.hashing.FeatureHasher", "sklearn.feature_extraction.hashing._iteritems",
-    "sklearn.feature_extraction.image.PatchExtractor", "sklearn.feature_extraction.image._compute_gradient_3d",
-    "sklearn.feature_extraction.image._compute_n_patches", "sklearn.feature_extraction.image._make_edges_3d",
-    "sklearn.feature_extraction.image._mask_edges_weights", "sklearn.feature_extraction.image._to_graph",
-    "sklearn.feature_extraction.image.extract_patches", "sklearn.feature_extraction.image.extract_patches_2d",
-    "sklearn.feature_extraction.image.grid_to_graph", "sklearn.feature_extraction.image.img_to_graph",
-    "sklearn.feature_extraction.image.reconstruct_from_patches_2d", "sklearn.feature_extraction.img_to_graph",
-    "sklearn.feature_extraction.stop_words.ENGLISH_STOP_WORDS", "sklearn.feature_extraction.text.CountVectorizer",
-    "sklearn.feature_extraction.text.ENGLISH_STOP_WORDS", "sklearn.feature_extraction.text.HashingVectorizer",
-    "sklearn.feature_extraction.text.TfidfTransformer", "sklearn.feature_extraction.text.TfidfVectorizer",
-    "sklearn.feature_extraction.text.VectorizerMixin", "sklearn.feature_extraction.text._check_stop_list",
-    "sklearn.feature_extraction.text._document_frequency", "sklearn.feature_extraction.text._make_int_array",
-    "sklearn.feature_extraction.text.strip_accents_ascii", "sklearn.feature_extraction.text.strip_accents_unicode",
-    "sklearn.feature_extraction.text.strip_tags", "sklearn.feature_selection.GenericUnivariateSelect",
-    "sklearn.feature_selection.RFE", "sklearn.feature_selection.RFECV",
-    "sklearn.feature_selection.SelectFdr", "sklearn.feature_selection.SelectFpr",
-    "sklearn.feature_selection.SelectFromModel", "sklearn.feature_selection.SelectFwe",
-    "sklearn.feature_selection.SelectKBest", "sklearn.feature_selection.SelectPercentile",
-    "sklearn.feature_selection.VarianceThreshold", "sklearn.feature_selection.base.SelectorMixin",
-    "sklearn.feature_selection.chi2", "sklearn.feature_selection.f_classif",
-    "sklearn.feature_selection.f_oneway", "sklearn.feature_selection.f_regression",
-    "sklearn.feature_selection.from_model.SelectFromModel", "sklearn.feature_selection.from_model._calculate_threshold",
-    "sklearn.feature_selection.from_model._get_feature_importances", "sklearn.feature_selection.mutual_info_._compute_mi",
-    "sklearn.feature_selection.mutual_info_._compute_mi_cc", "sklearn.feature_selection.mutual_info_._compute_mi_cd",
-    "sklearn.feature_selection.mutual_info_._estimate_mi", "sklearn.feature_selection.mutual_info_._iterate_columns",
-    "sklearn.feature_selection.mutual_info_.digamma", "sklearn.feature_selection.mutual_info_.mutual_info_classif",
-    "sklearn.feature_selection.mutual_info_.mutual_info_regression", "sklearn.feature_selection.mutual_info_classif",
-    "sklearn.feature_selection.mutual_info_regression", "sklearn.feature_selection.rfe.RFE",
-    "sklearn.feature_selection.rfe.RFECV", "sklearn.feature_selection.rfe._rfe_single_fit",
-    "sklearn.feature_selection.univariate_selection.GenericUnivariateSelect", "sklearn.feature_selection.univariate_selection.SelectFdr",
-    "sklearn.feature_selection.univariate_selection.SelectFpr", "sklearn.feature_selection.univariate_selection.SelectFwe",
-    "sklearn.feature_selection.univariate_selection.SelectKBest", "sklearn.feature_selection.univariate_selection.SelectPercentile",
-    "sklearn.feature_selection.univariate_selection._BaseFilter", "sklearn.feature_selection.univariate_selection._chisquare",
-    "sklearn.feature_selection.univariate_selection._clean_nans", "sklearn.feature_selection.univariate_selection.chi2",
-    "sklearn.feature_selection.univariate_selection.f_classif", "sklearn.feature_selection.univariate_selection.f_oneway",
-    "sklearn.feature_selection.univariate_selection.f_regression", "sklearn.feature_selection.variance_threshold.VarianceThreshold",
-    "sklearn.gaussian_process.GaussianProcess", "sklearn.gaussian_process.GaussianProcessClassifier",
-    "sklearn.gaussian_process.GaussianProcessRegressor", "sklearn.gaussian_process.correlation_models.absolute_exponential",
-    "sklearn.gaussian_process.correlation_models.cubic", "sklearn.gaussian_process.correlation_models.generalized_exponential",
-    "sklearn.gaussian_process.correlation_models.linear", "sklearn.gaussian_process.correlation_models.pure_nugget",
-    "sklearn.gaussian_process.correlation_models.squared_exponential", "sklearn.gaussian_process.gaussian_process.GaussianProcess",
-    "sklearn.gaussian_process.gaussian_process.MACHINE_EPSILON", "sklearn.gaussian_process.gaussian_process.l1_cross_distances",
-    "sklearn.gaussian_process.gpc.COEFS", "sklearn.gaussian_process.gpc.GaussianProcessClassifier",
-    "sklearn.gaussian_process.gpc.LAMBDAS", "sklearn.gaussian_process.gpc._BinaryGaussianProcessClassifierLaplace",
-    "sklearn.gaussian_process.gpc.erf", "sklearn.gaussian_process.gpc.expit",
-    "sklearn.gaussian_process.gpr.GaussianProcessRegressor", "sklearn.gaussian_process.kernels.CompoundKernel",
-    "sklearn.gaussian_process.kernels.ConstantKernel", "sklearn.gaussian_process.kernels.DotProduct",
-    "sklearn.gaussian_process.kernels.ExpSineSquared", "sklearn.gaussian_process.kernels.Exponentiation",
-    "sklearn.gaussian_process.kernels.Hyperparameter", "sklearn.gaussian_process.kernels.Kernel",
-    "sklearn.gaussian_process.kernels.KernelOperator", "sklearn.gaussian_process.kernels.Matern",
-    "sklearn.gaussian_process.kernels.NormalizedKernelMixin", "sklearn.gaussian_process.kernels.PairwiseKernel",
-    "sklearn.gaussian_process.kernels.Product", "sklearn.gaussian_process.kernels.RBF",
-    "sklearn.gaussian_process.kernels.RationalQuadratic", "sklearn.gaussian_process.kernels.StationaryKernelMixin",
-    "sklearn.gaussian_process.kernels.Sum", "sklearn.gaussian_process.kernels.WhiteKernel",
-    "sklearn.gaussian_process.kernels._approx_fprime", "sklearn.gaussian_process.kernels._check_length_scale",
-    "sklearn.gaussian_process.kernels.gamma", "sklearn.gaussian_process.kernels.kv",
-    "sklearn.gaussian_process.regression_models.constant", "sklearn.gaussian_process.regression_models.linear",
-    "sklearn.gaussian_process.regression_models.quadratic", "sklearn.get_config",
-    "sklearn.isotonic.IsotonicRegression", "sklearn.isotonic.check_increasing",
-    "sklearn.isotonic.isotonic_regression", "sklearn.kernel_approximation.AdditiveChi2Sampler",
-    "sklearn.kernel_approximation.KERNEL_PARAMS", "sklearn.kernel_approximation.Nystroem",
-    "sklearn.kernel_approximation.RBFSampler", "sklearn.kernel_approximation.SkewedChi2Sampler",
-    "sklearn.kernel_ridge.KernelRidge", "sklearn.linear_model.ARDRegression",
-    "sklearn.linear_model.BayesianRidge", "sklearn.linear_model.ElasticNet",
-    "sklearn.linear_model.ElasticNetCV", "sklearn.linear_model.Hinge",
-    "sklearn.linear_model.Huber", "sklearn.linear_model.HuberRegressor",
-    "sklearn.linear_model.Lars", "sklearn.linear_model.LarsCV",
-    "sklearn.linear_model.Lasso", "sklearn.linear_model.LassoCV",
-    "sklearn.linear_model.LassoLars", "sklearn.linear_model.LassoLarsCV",
-    "sklearn.linear_model.LassoLarsIC", "sklearn.linear_model.LinearRegression",
-    "sklearn.linear_model.Log", "sklearn.linear_model.LogisticRegression",
-    "sklearn.linear_model.LogisticRegressionCV", "sklearn.linear_model.ModifiedHuber",
-    "sklearn.linear_model.MultiTaskElasticNet", "sklearn.linear_model.MultiTaskElasticNetCV",
-    "sklearn.linear_model.MultiTaskLasso", "sklearn.linear_model.MultiTaskLassoCV",
-    "sklearn.linear_model.OrthogonalMatchingPursuit", "sklearn.linear_model.OrthogonalMatchingPursuitCV",
-    "sklearn.linear_model.PassiveAggressiveClassifier", "sklearn.linear_model.PassiveAggressiveRegressor",
-    "sklearn.linear_model.Perceptron", "sklearn.linear_model.RANSACRegressor",
-    "sklearn.linear_model.RandomizedLasso", "sklearn.linear_model.RandomizedLogisticRegression",
-    "sklearn.linear_model.Ridge", "sklearn.linear_model.RidgeCV",
-    "sklearn.linear_model.RidgeClassifier", "sklearn.linear_model.RidgeClassifierCV",
-    "sklearn.linear_model.SGDClassifier", "sklearn.linear_model.SGDRegressor",
-    "sklearn.linear_model.SquaredLoss", "sklearn.linear_model.TheilSenRegressor",
-    "sklearn.linear_model.base.FLOAT_DTYPES", "sklearn.linear_model.base.LinearClassifierMixin",
-    "sklearn.linear_model.base.LinearModel", "sklearn.linear_model.base.LinearRegression",
-    "sklearn.linear_model.base.SPARSE_INTERCEPT_DECAY", "sklearn.linear_model.base.SparseCoefMixin",
-    "sklearn.linear_model.base._pre_fit", "sklearn.linear_model.base._preprocess_data",
-    "sklearn.linear_model.base._rescale_data", "sklearn.linear_model.base.center_data",
-    "sklearn.linear_model.base.make_dataset", "sklearn.linear_model.base.sparse_center_data",
-    "sklearn.linear_model.bayes.ARDRegression", "sklearn.linear_model.bayes.BayesianRidge",
-    "sklearn.linear_model.cd_fast.enet_coordinate_descent", "sklearn.linear_model.cd_fast.enet_coordinate_descent_gram",
-    "sklearn.linear_model.cd_fast.enet_coordinate_descent_multi_task", "sklearn.linear_model.cd_fast.sparse_enet_coordinate_descent",
-    "sklearn.linear_model.coordinate_descent.ElasticNet", "sklearn.linear_model.coordinate_descent.ElasticNetCV",
-    "sklearn.linear_model.coordinate_descent.Lasso", "sklearn.linear_model.coordinate_descent.LassoCV",
-    "sklearn.linear_model.coordinate_descent.LinearModelCV", "sklearn.linear_model.coordinate_descent.MultiTaskElasticNet",
-    "sklearn.linear_model.coordinate_descent.MultiTaskElasticNetCV", "sklearn.linear_model.coordinate_descent.MultiTaskLasso",
-    "sklearn.linear_model.coordinate_descent.MultiTaskLassoCV", "sklearn.linear_model.coordinate_descent._alpha_grid",
-    "sklearn.linear_model.coordinate_descent._path_residuals", "sklearn.linear_model.coordinate_descent.enet_path",
-    "sklearn.linear_model.coordinate_descent.lasso_path", "sklearn.linear_model.enet_path",
-    "sklearn.linear_model.huber.HuberRegressor", "sklearn.linear_model.huber._huber_loss_and_gradient",
-    "sklearn.linear_model.lars_path", "sklearn.linear_model.lasso_path",
-    "sklearn.linear_model.lasso_stability_path", "sklearn.linear_model.least_angle.Lars",
-    "sklearn.linear_model.least_angle.LarsCV", "sklearn.linear_model.least_angle.LassoLars",
-    "sklearn.linear_model.least_angle.LassoLarsCV", "sklearn.linear_model.least_angle.LassoLarsIC",
-    "sklearn.linear_model.least_angle._check_copy_and_writeable", "sklearn.linear_model.least_angle._lars_path_residues",
-    "sklearn.linear_model.least_angle.lars_path", "sklearn.linear_model.least_angle.solve_triangular_args",
-    "sklearn.linear_model.least_angle.string_types", "sklearn.linear_model.logistic.LogisticRegression",
-    "sklearn.linear_model.logistic.LogisticRegressionCV", "sklearn.linear_model.logistic.SCORERS",
-    "sklearn.linear_model.logistic._check_solver_option", "sklearn.linear_model.logistic._intercept_dot",
-    "sklearn.linear_model.logistic._log_reg_scoring_path", "sklearn.linear_model.logistic._logistic_grad_hess",
-    "sklearn.linear_model.logistic._logistic_loss", "sklearn.linear_model.logistic._logistic_loss_and_grad",
-    "sklearn.linear_model.logistic._multinomial_grad_hess", "sklearn.linear_model.logistic._multinomial_loss",
-    "sklearn.linear_model.logistic._multinomial_loss_grad", "sklearn.linear_model.logistic.expit",
-    "sklearn.linear_model.logistic.logistic_regression_path", "sklearn.linear_model.logistic_regression_path",
-    "sklearn.linear_model.omp.OrthogonalMatchingPursuit", "sklearn.linear_model.omp.OrthogonalMatchingPursuitCV",
-    "sklearn.linear_model.omp._cholesky_omp", "sklearn.linear_model.omp._gram_omp",
-    "sklearn.linear_model.omp._omp_path_residues", "sklearn.linear_model.omp.orthogonal_mp",
-    "sklearn.linear_model.omp.orthogonal_mp_gram", "sklearn.linear_model.omp.premature",
-    "sklearn.linear_model.omp.solve_triangular_args", "sklearn.linear_model.orthogonal_mp",
-    "sklearn.linear_model.orthogonal_mp_gram", "sklearn.linear_model.passive_aggressive.DEFAULT_EPSILON",
-    "sklearn.linear_model.passive_aggressive.PassiveAggressiveClassifier", "sklearn.linear_model.passive_aggressive.PassiveAggressiveRegressor",
-    "sklearn.linear_model.perceptron.Perceptron", "sklearn.linear_model.randomized_l1.BaseRandomizedLinearModel",
-    "sklearn.linear_model.randomized_l1.RandomizedLasso", "sklearn.linear_model.randomized_l1.RandomizedLogisticRegression",
-    "sklearn.linear_model.randomized_l1._lasso_stability_path", "sklearn.linear_model.randomized_l1._randomized_lasso",
-    "sklearn.linear_model.randomized_l1._randomized_logistic", "sklearn.linear_model.randomized_l1._resample_model",
-    "sklearn.linear_model.randomized_l1.lasso_stability_path", "sklearn.linear_model.ransac.RANSACRegressor",
-    "sklearn.linear_model.ransac._EPSILON", "sklearn.linear_model.ransac._dynamic_max_trials",
-    "sklearn.linear_model.ridge.Ridge", "sklearn.linear_model.ridge.RidgeCV",
-    "sklearn.linear_model.ridge.RidgeClassifier", "sklearn.linear_model.ridge.RidgeClassifierCV",
-    "sklearn.linear_model.ridge._BaseRidge", "sklearn.linear_model.ridge._BaseRidgeCV",
-    "sklearn.linear_model.ridge._RidgeGCV", "sklearn.linear_model.ridge._solve_cholesky",
-    "sklearn.linear_model.ridge._solve_cholesky_kernel", "sklearn.linear_model.ridge._solve_lsqr",
-    "sklearn.linear_model.ridge._solve_sparse_cg", "sklearn.linear_model.ridge._solve_svd",
-    "sklearn.linear_model.ridge.ridge_regression", "sklearn.linear_model.ridge_regression",
-    "sklearn.linear_model.sag.get_auto_step_size", "sklearn.linear_model.sag.sag",
-    "sklearn.linear_model.sag.sag_solver", "sklearn.linear_model.sag_fast.MultinomialLogLoss",
-    "sklearn.linear_model.sag_fast._multinomial_grad_loss_all_samples", "sklearn.linear_model.sag_fast.sag",
-    "sklearn.linear_model.sgd_fast.Classification", "sklearn.linear_model.sgd_fast.EpsilonInsensitive",
-    "sklearn.linear_model.sgd_fast.Hinge", "sklearn.linear_model.sgd_fast.Huber",
-    "sklearn.linear_model.sgd_fast.Log", "sklearn.linear_model.sgd_fast.LossFunction",
-    "sklearn.linear_model.sgd_fast.ModifiedHuber", "sklearn.linear_model.sgd_fast.Regression",
-    "sklearn.linear_model.sgd_fast.SquaredEpsilonInsensitive", "sklearn.linear_model.sgd_fast.SquaredHinge",
-    "sklearn.linear_model.sgd_fast.SquaredLoss", "sklearn.linear_model.sgd_fast._plain_sgd",
-    "sklearn.linear_model.sgd_fast.average_sgd", "sklearn.linear_model.sgd_fast.plain_sgd",
-    "sklearn.linear_model.stochastic_gradient.BaseSGD", "sklearn.linear_model.stochastic_gradient.BaseSGDClassifier",
-    "sklearn.linear_model.stochastic_gradient.BaseSGDRegressor", "sklearn.linear_model.stochastic_gradient.DEFAULT_EPSILON",
-    "sklearn.linear_model.stochastic_gradient.LEARNING_RATE_TYPES", "sklearn.linear_model.stochastic_gradient.PENALTY_TYPES",
-    "sklearn.linear_model.stochastic_gradient.SGDClassifier", "sklearn.linear_model.stochastic_gradient.SGDRegressor",
-    "sklearn.linear_model.stochastic_gradient._prepare_fit_binary", "sklearn.linear_model.stochastic_gradient.fit_binary",
-    "sklearn.linear_model.theil_sen.TheilSenRegressor", "sklearn.linear_model.theil_sen._EPSILON",
-    "sklearn.linear_model.theil_sen._breakdown_point", "sklearn.linear_model.theil_sen._lstsq",
-    "sklearn.linear_model.theil_sen._modified_weiszfeld_step", "sklearn.linear_model.theil_sen._spatial_median",
-    "sklearn.linear_model.theil_sen.binom", "sklearn.manifold.Isomap",
-    "sklearn.manifold.LocallyLinearEmbedding", "sklearn.manifold.MDS",
-    "sklearn.manifold.SpectralEmbedding", "sklearn.manifold.TSNE",
-    "sklearn.manifold._barnes_hut_tsne.gradient", "sklearn.manifold._utils._binary_search_perplexity",
-    "sklearn.manifold.isomap.Isomap", "sklearn.manifold.locally_linear.FLOAT_DTYPES",
-    "sklearn.manifold.locally_linear.LocallyLinearEmbedding", "sklearn.manifold.locally_linear.barycenter_kneighbors_graph",
-    "sklearn.manifold.locally_linear.barycenter_weights", "sklearn.manifold.locally_linear.locally_linear_embedding",
-    "sklearn.manifold.locally_linear.null_space", "sklearn.manifold.locally_linear_embedding",
-    "sklearn.manifold.mds.MDS", "sklearn.manifold.mds._smacof_single",
-    "sklearn.manifold.mds.smacof", "sklearn.manifold.smacof",
-    "sklearn.manifold.spectral_embedding", "sklearn.manifold.spectral_embedding_.SpectralEmbedding",
-    "sklearn.manifold.spectral_embedding_._graph_connected_component", "sklearn.manifold.spectral_embedding_._graph_is_connected",
-    "sklearn.manifold.spectral_embedding_._set_diag", "sklearn.manifold.spectral_embedding_.spectral_embedding",
-    "sklearn.manifold.t_sne.MACHINE_EPSILON", "sklearn.manifold.t_sne.TSNE",
-    "sklearn.manifold.t_sne._gradient_descent", "sklearn.manifold.t_sne._joint_probabilities",
-    "sklearn.manifold.t_sne._joint_probabilities_nn", "sklearn.manifold.t_sne._kl_divergence",
-    "sklearn.manifold.t_sne._kl_divergence_bh", "sklearn.manifold.t_sne.string_types",
-    "sklearn.manifold.t_sne.trustworthiness", "sklearn.metrics.SCORERS",
-    "sklearn.metrics.accuracy_score", "sklearn.metrics.adjusted_mutual_info_score",
-    "sklearn.metrics.adjusted_rand_score", "sklearn.metrics.auc",
-    "sklearn.metrics.average_precision_score", "sklearn.metrics.base._average_binary_score",
-    "sklearn.metrics.brier_score_loss", "sklearn.metrics.calinski_harabaz_score",
-    "sklearn.metrics.classification._check_binary_probabilistic_predictions", "sklearn.metrics.classification._check_targets",
-    "sklearn.metrics.classification._prf_divide", "sklearn.metrics.classification._weighted_sum",
-    "sklearn.metrics.classification.accuracy_score", "sklearn.metrics.classification.brier_score_loss",
-    "sklearn.metrics.classification.classification_report", "sklearn.metrics.classification.cohen_kappa_score",
-    "sklearn.metrics.classification.confusion_matrix", "sklearn.metrics.classification.f1_score",
-    "sklearn.metrics.classification.fbeta_score", "sklearn.metrics.classification.hamming_loss",
-    "sklearn.metrics.classification.hinge_loss", "sklearn.metrics.classification.jaccard_similarity_score",
-    "sklearn.metrics.classification.log_loss", "sklearn.metrics.classification.matthews_corrcoef",
-    "sklearn.metrics.classification.precision_recall_fscore_support", "sklearn.metrics.classification.precision_score",
-    "sklearn.metrics.classification.recall_score", "sklearn.metrics.classification.zero_one_loss",
-    "sklearn.metrics.classification_report", "sklearn.metrics.cluster.adjusted_mutual_info_score",
-    "sklearn.metrics.cluster.adjusted_rand_score", "sklearn.metrics.cluster.bicluster._check_rows_and_columns",
-    "sklearn.metrics.cluster.bicluster._jaccard", "sklearn.metrics.cluster.bicluster._pairwise_similarity",
-    "sklearn.metrics.cluster.bicluster.consensus_score", "sklearn.metrics.cluster.calinski_harabaz_score",
-    "sklearn.metrics.cluster.completeness_score", "sklearn.metrics.cluster.consensus_score",
-    "sklearn.metrics.cluster.contingency_matrix", "sklearn.metrics.cluster.entropy",
-    "sklearn.metrics.cluster.expected_mutual_info_fast.expected_mutual_information", "sklearn.metrics.cluster.expected_mutual_info_fast.gammaln",
-    "sklearn.metrics.cluster.expected_mutual_information", "sklearn.metrics.cluster.fowlkes_mallows_score",
-    "sklearn.metrics.cluster.homogeneity_completeness_v_measure", "sklearn.metrics.cluster.homogeneity_score",
-    "sklearn.metrics.cluster.mutual_info_score", "sklearn.metrics.cluster.normalized_mutual_info_score",
-    "sklearn.metrics.cluster.silhouette_samples", "sklearn.metrics.cluster.silhouette_score",
-    "sklearn.metrics.cluster.supervised.adjusted_mutual_info_score", "sklearn.metrics.cluster.supervised.adjusted_rand_score",
-    "sklearn.metrics.cluster.supervised.check_clusterings", "sklearn.metrics.cluster.supervised.comb2",
-    "sklearn.metrics.cluster.supervised.completeness_score", "sklearn.metrics.cluster.supervised.contingency_matrix",
-    "sklearn.metrics.cluster.supervised.entropy", "sklearn.metrics.cluster.supervised.fowlkes_mallows_score",
-    "sklearn.metrics.cluster.supervised.homogeneity_completeness_v_measure", "sklearn.metrics.cluster.supervised.homogeneity_score",
-    "sklearn.metrics.cluster.supervised.mutual_info_score", "sklearn.metrics.cluster.supervised.normalized_mutual_info_score",
-    "sklearn.metrics.cluster.supervised.v_measure_score", "sklearn.metrics.cluster.unsupervised.calinski_harabaz_score",
-    "sklearn.metrics.cluster.unsupervised.check_number_of_labels", "sklearn.metrics.cluster.unsupervised.silhouette_samples",
-    "sklearn.metrics.cluster.unsupervised.silhouette_score", "sklearn.metrics.cluster.v_measure_score",
-    "sklearn.metrics.cohen_kappa_score", "sklearn.metrics.completeness_score",
-    "sklearn.metrics.confusion_matrix", "sklearn.metrics.consensus_score",
-    "sklearn.metrics.coverage_error", "sklearn.metrics.euclidean_distances",
-    "sklearn.metrics.explained_variance_score", "sklearn.metrics.f1_score",
-    "sklearn.metrics.fbeta_score", "sklearn.metrics.fowlkes_mallows_score",
-    "sklearn.metrics.get_scorer", "sklearn.metrics.hamming_loss",
-    "sklearn.metrics.hinge_loss", "sklearn.metrics.homogeneity_completeness_v_measure",
-    "sklearn.metrics.homogeneity_score", "sklearn.metrics.jaccard_similarity_score",
-    "sklearn.metrics.label_ranking_average_precision_score", "sklearn.metrics.label_ranking_loss",
-    "sklearn.metrics.log_loss", "sklearn.metrics.make_scorer",
-    "sklearn.metrics.matthews_corrcoef", "sklearn.metrics.mean_absolute_error",
-    "sklearn.metrics.mean_squared_error", "sklearn.metrics.mean_squared_log_error",
-    "sklearn.metrics.median_absolute_error", "sklearn.metrics.mutual_info_score",
-    "sklearn.metrics.normalized_mutual_info_score", "sklearn.metrics.pairwise.KERNEL_PARAMS",
-    "sklearn.metrics.pairwise.PAIRED_DISTANCES", "sklearn.metrics.pairwise.PAIRWISE_BOOLEAN_FUNCTIONS",
-    "sklearn.metrics.pairwise.PAIRWISE_DISTANCE_FUNCTIONS", "sklearn.metrics.pairwise.PAIRWISE_KERNEL_FUNCTIONS",
-    "sklearn.metrics.pairwise._VALID_METRICS", "sklearn.metrics.pairwise._chi2_kernel_fast",
-    "sklearn.metrics.pairwise._pairwise_callable", "sklearn.metrics.pairwise._parallel_pairwise",
-    "sklearn.metrics.pairwise._return_float_dtype", "sklearn.metrics.pairwise._sparse_manhattan",
-    "sklearn.metrics.pairwise.additive_chi2_kernel", "sklearn.metrics.pairwise.check_paired_arrays",
-    "sklearn.metrics.pairwise.check_pairwise_arrays", "sklearn.metrics.pairwise.chi2_kernel",
-    "sklearn.metrics.pairwise.cosine_distances", "sklearn.metrics.pairwise.cosine_similarity",
-    "sklearn.metrics.pairwise.distance_metrics", "sklearn.metrics.pairwise.euclidean_distances",
-    "sklearn.metrics.pairwise.kernel_metrics", "sklearn.metrics.pairwise.laplacian_kernel",
-    "sklearn.metrics.pairwise.linear_kernel", "sklearn.metrics.pairwise.manhattan_distances",
-    "sklearn.metrics.pairwise.paired_cosine_distances", "sklearn.metrics.pairwise.paired_distances",
-    "sklearn.metrics.pairwise.paired_euclidean_distances", "sklearn.metrics.pairwise.paired_manhattan_distances",
-    "sklearn.metrics.pairwise.pairwise_distances", "sklearn.metrics.pairwise.pairwise_distances_argmin",
-    "sklearn.metrics.pairwise.pairwise_distances_argmin_min", "sklearn.metrics.pairwise.pairwise_kernels",
-    "sklearn.metrics.pairwise.polynomial_kernel", "sklearn.metrics.pairwise.rbf_kernel",
-    "sklearn.metrics.pairwise.sigmoid_kernel", "sklearn.metrics.pairwise_distances",
-    "sklearn.metrics.pairwise_distances_argmin", "sklearn.metrics.pairwise_distances_argmin_min",
-    "sklearn.metrics.pairwise_fast._chi2_kernel_fast", "sklearn.metrics.pairwise_fast._sparse_manhattan",
-    "sklearn.metrics.pairwise_kernels", "sklearn.metrics.precision_recall_curve",
-    "sklearn.metrics.precision_recall_fscore_support", "sklearn.metrics.precision_score",
-    "sklearn.metrics.r2_score", "sklearn.metrics.ranking._binary_clf_curve",
-    "sklearn.metrics.ranking.auc", "sklearn.metrics.ranking.average_precision_score",
-    "sklearn.metrics.ranking.coverage_error", "sklearn.metrics.ranking.label_ranking_average_precision_score",
-    "sklearn.metrics.ranking.label_ranking_loss", "sklearn.metrics.ranking.precision_recall_curve",
-    "sklearn.metrics.ranking.roc_auc_score", "sklearn.metrics.ranking.roc_curve",
-    "sklearn.metrics.recall_score", "sklearn.metrics.regression._check_reg_targets",
-    "sklearn.metrics.regression.explained_variance_score", "sklearn.metrics.regression.mean_absolute_error",
-    "sklearn.metrics.regression.mean_squared_error", "sklearn.metrics.regression.mean_squared_log_error",
-    "sklearn.metrics.regression.median_absolute_error", "sklearn.metrics.regression.r2_score",
-    "sklearn.metrics.regression.string_types", "sklearn.metrics.roc_auc_score",
-    "sklearn.metrics.roc_curve", "sklearn.metrics.scorer.SCORERS",
-    "sklearn.metrics.scorer._BaseScorer", "sklearn.metrics.scorer._PredictScorer",
-    "sklearn.metrics.scorer._ProbaScorer", "sklearn.metrics.scorer._ThresholdScorer",
-    "sklearn.metrics.scorer._check_multimetric_scoring", "sklearn.metrics.scorer._passthrough_scorer",
-    "sklearn.metrics.scorer.accuracy_scorer", "sklearn.metrics.scorer.adjusted_mutual_info_scorer",
-    "sklearn.metrics.scorer.adjusted_rand_scorer", "sklearn.metrics.scorer.average",
-    "sklearn.metrics.scorer.average_precision_scorer", "sklearn.metrics.scorer.check_scoring",
-    "sklearn.metrics.scorer.completeness_scorer", "sklearn.metrics.scorer.deprecation_msg",
-    "sklearn.metrics.scorer.explained_variance_scorer", "sklearn.metrics.scorer.f1_scorer",
-    "sklearn.metrics.scorer.fowlkes_mallows_scorer", "sklearn.metrics.scorer.get_scorer",
-    "sklearn.metrics.scorer.homogeneity_scorer", "sklearn.metrics.scorer.log_loss_scorer",
-    "sklearn.metrics.scorer.make_scorer", "sklearn.metrics.scorer.mean_absolute_error_scorer",
-    "sklearn.metrics.scorer.mean_squared_error_scorer", "sklearn.metrics.scorer.median_absolute_error_scorer",
-    "sklearn.metrics.scorer.mutual_info_scorer", "sklearn.metrics.scorer.name",
-    "sklearn.metrics.scorer.neg_log_loss_scorer", "sklearn.metrics.scorer.neg_mean_absolute_error_scorer",
-    "sklearn.metrics.scorer.neg_mean_squared_error_scorer", "sklearn.metrics.scorer.neg_mean_squared_log_error_scorer",
-    "sklearn.metrics.scorer.neg_median_absolute_error_scorer", "sklearn.metrics.scorer.normalized_mutual_info_scorer",
-    "sklearn.metrics.scorer.precision_scorer", "sklearn.metrics.scorer.qualified_name",
-    "sklearn.metrics.scorer.r2_scorer", "sklearn.metrics.scorer.recall_scorer",
-    "sklearn.metrics.scorer.roc_auc_scorer", "sklearn.metrics.scorer.v_measure_scorer",
-    "sklearn.metrics.silhouette_samples", "sklearn.metrics.silhouette_score",
-    "sklearn.metrics.v_measure_score", "sklearn.metrics.zero_one_loss",
-    "sklearn.model_selection.BaseCrossValidator", "sklearn.model_selection.GridSearchCV",
-    "sklearn.model_selection.GroupKFold", "sklearn.model_selection.GroupShuffleSplit",
-    "sklearn.model_selection.KFold", "sklearn.model_selection.LeaveOneGroupOut",
-    "sklearn.model_selection.LeaveOneOut", "sklearn.model_selection.LeavePGroupsOut",
-    "sklearn.model_selection.LeavePOut", "sklearn.model_selection.ParameterGrid",
-    "sklearn.model_selection.ParameterSampler", "sklearn.model_selection.PredefinedSplit",
-    "sklearn.model_selection.RandomizedSearchCV", "sklearn.model_selection.RepeatedKFold",
-    "sklearn.model_selection.RepeatedStratifiedKFold", "sklearn.model_selection.ShuffleSplit",
-    "sklearn.model_selection.StratifiedKFold", "sklearn.model_selection.StratifiedShuffleSplit",
-    "sklearn.model_selection.TimeSeriesSplit", "sklearn.model_selection._search.BaseSearchCV",
-    "sklearn.model_selection._search.GridSearchCV", "sklearn.model_selection._search.ParameterGrid",
-    "sklearn.model_selection._search.ParameterSampler", "sklearn.model_selection._search.RandomizedSearchCV",
-    "sklearn.model_selection._search._CVScoreTuple", "sklearn.model_selection._search._check_param_grid",
-    "sklearn.model_selection._search.fit_grid_point", "sklearn.model_selection._search.sp_version",
-    "sklearn.model_selection._split.BaseCrossValidator", "sklearn.model_selection._split.BaseShuffleSplit",
-    "sklearn.model_selection._split.GroupKFold", "sklearn.model_selection._split.GroupShuffleSplit",
-    "sklearn.model_selection._split.KFold", "sklearn.model_selection._split.LeaveOneGroupOut",
-    "sklearn.model_selection._split.LeaveOneOut", "sklearn.model_selection._split.LeavePGroupsOut",
-    "sklearn.model_selection._split.LeavePOut", "sklearn.model_selection._split.PredefinedSplit",
-    "sklearn.model_selection._split.RepeatedKFold", "sklearn.model_selection._split.RepeatedStratifiedKFold",
-    "sklearn.model_selection._split.ShuffleSplit", "sklearn.model_selection._split.StratifiedKFold",
-    "sklearn.model_selection._split.StratifiedShuffleSplit", "sklearn.model_selection._split.TimeSeriesSplit",
-    "sklearn.model_selection._split._BaseKFold", "sklearn.model_selection._split._CVIterableWrapper",
-    "sklearn.model_selection._split._RepeatedSplits", "sklearn.model_selection._split._approximate_mode",
-    "sklearn.model_selection._split._build_repr", "sklearn.model_selection._split._validate_shuffle_split",
-    "sklearn.model_selection._split._validate_shuffle_split_init", "sklearn.model_selection._split.check_cv",
-    "sklearn.model_selection._split.train_test_split", "sklearn.model_selection._validation._aggregate_score_dicts",
-    "sklearn.model_selection._validation._check_is_permutation", "sklearn.model_selection._validation._fit_and_predict",
-    "sklearn.model_selection._validation._fit_and_score", "sklearn.model_selection._validation._incremental_fit_estimator",
-    "sklearn.model_selection._validation._index_param_value", "sklearn.model_selection._validation._multimetric_score",
-    "sklearn.model_selection._validation._permutation_test_score", "sklearn.model_selection._validation._score",
-    "sklearn.model_selection._validation._shuffle", "sklearn.model_selection._validation._translate_train_sizes",
-    "sklearn.model_selection._validation.cross_val_predict", "sklearn.model_selection._validation.cross_val_score",
-    "sklearn.model_selection._validation.cross_validate", "sklearn.model_selection._validation.learning_curve",
-    "sklearn.model_selection._validation.permutation_test_score", "sklearn.model_selection._validation.validation_curve",
-    "sklearn.model_selection.check_cv", "sklearn.model_selection.cross_val_predict",
-    "sklearn.model_selection.cross_val_score", "sklearn.model_selection.cross_validate",
-    "sklearn.model_selection.fit_grid_point", "sklearn.model_selection.learning_curve",
-    "sklearn.model_selection.permutation_test_score", "sklearn.model_selection.train_test_split",
-    "sklearn.model_selection.validation_curve", "sklearn.multiclass.OneVsOneClassifier",
-    "sklearn.multiclass.OneVsRestClassifier", "sklearn.multiclass.OutputCodeClassifier",
-    "sklearn.multiclass._ConstantPredictor", "sklearn.multiclass._check_estimator",
-    "sklearn.multiclass._fit_binary", "sklearn.multiclass._fit_ovo_binary",
-    "sklearn.multiclass._partial_fit_binary", "sklearn.multiclass._partial_fit_ovo_binary",
-    "sklearn.multiclass._predict_binary", "sklearn.naive_bayes.BaseDiscreteNB",
-    "sklearn.naive_bayes.BaseNB", "sklearn.naive_bayes.BernoulliNB",
-    "sklearn.naive_bayes.GaussianNB", "sklearn.naive_bayes.MultinomialNB",
-    "sklearn.naive_bayes._ALPHA_MIN", "sklearn.neighbors.BallTree",
-    "sklearn.neighbors.DistanceMetric", "sklearn.neighbors.KDTree",
-    "sklearn.neighbors.KNeighborsClassifier", "sklearn.neighbors.KNeighborsRegressor",
-    "sklearn.neighbors.KernelDensity", "sklearn.neighbors.LSHForest",
-    "sklearn.neighbors.LocalOutlierFactor", "sklearn.neighbors.NearestCentroid",
-    "sklearn.neighbors.NearestNeighbors", "sklearn.neighbors.RadiusNeighborsClassifier",
-    "sklearn.neighbors.RadiusNeighborsRegressor", "sklearn.neighbors.approximate.GaussianRandomProjectionHash",
-    "sklearn.neighbors.approximate.HASH_DTYPE", "sklearn.neighbors.approximate.LSHForest",
-    "sklearn.neighbors.approximate.MAX_HASH_SIZE", "sklearn.neighbors.approximate.ProjectionToHashMixin",
-    "sklearn.neighbors.approximate._array_of_arrays", "sklearn.neighbors.approximate._find_longest_prefix_match",
-    "sklearn.neighbors.approximate._find_matching_indices", "sklearn.neighbors.ball_tree.BallTree",
-    "sklearn.neighbors.ball_tree.BinaryTree", "sklearn.neighbors.ball_tree.CLASS_DOC",
-    "sklearn.neighbors.ball_tree.DOC_DICT", "sklearn.neighbors.ball_tree.NeighborsHeap",
-    "sklearn.neighbors.ball_tree.NodeData", "sklearn.neighbors.ball_tree.NodeHeap",
-    "sklearn.neighbors.ball_tree.NodeHeapData", "sklearn.neighbors.ball_tree.VALID_METRICS",
-    "sklearn.neighbors.ball_tree.VALID_METRIC_IDS", "sklearn.neighbors.ball_tree.kernel_norm",
-    "sklearn.neighbors.ball_tree.load_heap", "sklearn.neighbors.ball_tree.newObj",
-    "sklearn.neighbors.ball_tree.nodeheap_sort", "sklearn.neighbors.ball_tree.offsets",
-    "sklearn.neighbors.ball_tree.simultaneous_sort", "sklearn.neighbors.base.KNeighborsMixin",
-    "sklearn.neighbors.base.NeighborsBase", "sklearn.neighbors.base.PAIRWISE_DISTANCE_FUNCTIONS",
-    "sklearn.neighbors.base.RadiusNeighborsMixin", "sklearn.neighbors.base.SupervisedFloatMixin",
-    "sklearn.neighbors.base.SupervisedIntegerMixin", "sklearn.neighbors.base.UnsupervisedMixin",
-    "sklearn.neighbors.base.VALID_METRICS", "sklearn.neighbors.base.VALID_METRICS_SPARSE",
-    "sklearn.neighbors.base._check_weights", "sklearn.neighbors.base._get_weights",
-    "sklearn.neighbors.classification.KNeighborsClassifier", "sklearn.neighbors.classification.RadiusNeighborsClassifier",
-    "sklearn.neighbors.dist_metrics.BrayCurtisDistance", "sklearn.neighbors.dist_metrics.CanberraDistance",
-    "sklearn.neighbors.dist_metrics.ChebyshevDistance", "sklearn.neighbors.dist_metrics.DiceDistance",
-    "sklearn.neighbors.dist_metrics.DistanceMetric", "sklearn.neighbors.dist_metrics.EuclideanDistance",
-    "sklearn.neighbors.dist_metrics.HammingDistance", "sklearn.neighbors.dist_metrics.HaversineDistance",
-    "sklearn.neighbors.dist_metrics.JaccardDistance", "sklearn.neighbors.dist_metrics.KulsinskiDistance",
-    "sklearn.neighbors.dist_metrics.METRIC_MAPPING", "sklearn.neighbors.dist_metrics.MahalanobisDistance",
-    "sklearn.neighbors.dist_metrics.ManhattanDistance", "sklearn.neighbors.dist_metrics.MatchingDistance",
-    "sklearn.neighbors.dist_metrics.MinkowskiDistance", "sklearn.neighbors.dist_metrics.PyFuncDistance",
-    "sklearn.neighbors.dist_metrics.RogersTanimotoDistance", "sklearn.neighbors.dist_metrics.RussellRaoDistance",
-    "sklearn.neighbors.dist_metrics.SEuclideanDistance", "sklearn.neighbors.dist_metrics.SokalMichenerDistance",
-    "sklearn.neighbors.dist_metrics.SokalSneathDistance", "sklearn.neighbors.dist_metrics.WMinkowskiDistance",
-    "sklearn.neighbors.dist_metrics.get_valid_metric_ids", "sklearn.neighbors.dist_metrics.newObj",
-    "sklearn.neighbors.graph._check_params", "sklearn.neighbors.graph._query_include_self",
-    "sklearn.neighbors.graph.kneighbors_graph", "sklearn.neighbors.graph.radius_neighbors_graph",
-    "sklearn.neighbors.kd_tree.BinaryTree", "sklearn.neighbors.kd_tree.CLASS_DOC",
-    "sklearn.neighbors.kd_tree.DOC_DICT", "sklearn.neighbors.kd_tree.KDTree",
-    "sklearn.neighbors.kd_tree.NeighborsHeap", "sklearn.neighbors.kd_tree.NodeData",
-    "sklearn.neighbors.kd_tree.NodeHeap", "sklearn.neighbors.kd_tree.NodeHeapData",
-    "sklearn.neighbors.kd_tree.VALID_METRICS", "sklearn.neighbors.kd_tree.VALID_METRIC_IDS",
-    "sklearn.neighbors.kd_tree.kernel_norm", "sklearn.neighbors.kd_tree.load_heap",
-    "sklearn.neighbors.kd_tree.newObj", "sklearn.neighbors.kd_tree.nodeheap_sort",
-    "sklearn.neighbors.kd_tree.offsets", "sklearn.neighbors.kd_tree.simultaneous_sort",
-    "sklearn.neighbors.kde.KernelDensity", "sklearn.neighbors.kde.TREE_DICT",
-    "sklearn.neighbors.kde.VALID_KERNELS", "sklearn.neighbors.kde.gammainc",
-    "sklearn.neighbors.kneighbors_graph", "sklearn.neighbors.lof.LocalOutlierFactor",
-    "sklearn.neighbors.nearest_centroid.NearestCentroid", "sklearn.neighbors.quad_tree.CELL_DTYPE",
-    "sklearn.neighbors.quad_tree._QuadTree", "sklearn.neighbors.radius_neighbors_graph",
-    "sklearn.neighbors.regression.KNeighborsRegressor", "sklearn.neighbors.regression.RadiusNeighborsRegressor",
-    "sklearn.neighbors.unsupervised.NearestNeighbors", "sklearn.pipeline.FeatureUnion",
-    "sklearn.pipeline.Pipeline", "sklearn.pipeline._fit_one_transformer",
-    "sklearn.pipeline._fit_transform_one", "sklearn.pipeline._name_estimators",
-    "sklearn.pipeline._transform_one", "sklearn.pipeline.make_pipeline",
-    "sklearn.pipeline.make_union", "sklearn.preprocessing.Binarizer",
-    "sklearn.preprocessing.FunctionTransformer", "sklearn.preprocessing.Imputer",
-    "sklearn.preprocessing.KernelCenterer", "sklearn.preprocessing.LabelBinarizer",
-    "sklearn.preprocessing.LabelEncoder", "sklearn.preprocessing.MaxAbsScaler",
-    "sklearn.preprocessing.MinMaxScaler", "sklearn.preprocessing.MultiLabelBinarizer",
-    "sklearn.preprocessing.Normalizer", "sklearn.preprocessing.OneHotEncoder",
-    "sklearn.preprocessing.PolynomialFeatures", "sklearn.preprocessing.QuantileTransformer",
-    "sklearn.preprocessing.RobustScaler", "sklearn.preprocessing.StandardScaler",
-    "sklearn.preprocessing._function_transformer.FunctionTransformer", "sklearn.preprocessing._function_transformer._identity",
-    "sklearn.preprocessing._function_transformer.string_types", "sklearn.preprocessing.add_dummy_feature",
-    "sklearn.preprocessing.binarize", "sklearn.preprocessing.data.BOUNDS_THRESHOLD",
-    "sklearn.preprocessing.data.Binarizer", "sklearn.preprocessing.data.FLOAT_DTYPES",
-    "sklearn.preprocessing.data.KernelCenterer", "sklearn.preprocessing.data.MaxAbsScaler",
-    "sklearn.preprocessing.data.MinMaxScaler", "sklearn.preprocessing.data.Normalizer",
-    "sklearn.preprocessing.data.OneHotEncoder", "sklearn.preprocessing.data.PolynomialFeatures",
-    "sklearn.preprocessing.data.QuantileTransformer", "sklearn.preprocessing.data.RobustScaler",
-    "sklearn.preprocessing.data.StandardScaler", "sklearn.preprocessing.data._handle_zeros_in_scale",
-    "sklearn.preprocessing.data._transform_selected", "sklearn.preprocessing.data.add_dummy_feature",
-    "sklearn.preprocessing.data.binarize", "sklearn.preprocessing.data.maxabs_scale",
-    "sklearn.preprocessing.data.minmax_scale", "sklearn.preprocessing.data.normalize",
-    "sklearn.preprocessing.data.quantile_transform", "sklearn.preprocessing.data.robust_scale",
-    "sklearn.preprocessing.data.scale", "sklearn.preprocessing.data.string_types",
-    "sklearn.preprocessing.imputation.FLOAT_DTYPES", "sklearn.preprocessing.imputation.Imputer",
-    "sklearn.preprocessing.imputation._get_mask", "sklearn.preprocessing.imputation._most_frequent",
-    "sklearn.preprocessing.label.LabelBinarizer", "sklearn.preprocessing.label.LabelEncoder",
-    "sklearn.preprocessing.label.MultiLabelBinarizer", "sklearn.preprocessing.label._inverse_binarize_multiclass",
-    "sklearn.preprocessing.label._inverse_binarize_thresholding", "sklearn.preprocessing.label.label_binarize",
-    "sklearn.preprocessing.label_binarize", "sklearn.preprocessing.maxabs_scale",
-    "sklearn.preprocessing.minmax_scale", "sklearn.preprocessing.normalize",
-    "sklearn.preprocessing.quantile_transform", "sklearn.preprocessing.robust_scale",
-    "sklearn.preprocessing.scale", "sklearn.random_projection.BaseRandomProjection",
-    "sklearn.random_projection.GaussianRandomProjection", "sklearn.random_projection.SparseRandomProjection",
-    "sklearn.random_projection._check_density", "sklearn.random_projection._check_input_size",
-    "sklearn.random_projection.gaussian_random_matrix", "sklearn.random_projection.johnson_lindenstrauss_min_dim",
-    "sklearn.random_projection.sparse_random_matrix", "sklearn.set_config",
-    "sklearn.setup_module", "sklearn.svm.LinearSVC",
-    "sklearn.svm.LinearSVR", "sklearn.svm.NuSVC",
-    "sklearn.svm.NuSVR", "sklearn.svm.OneClassSVM",
-    "sklearn.svm.SVC", "sklearn.svm.SVR",
-    "sklearn.svm.base.BaseLibSVM", "sklearn.svm.base.BaseSVC",
-    "sklearn.svm.base.LIBSVM_IMPL", "sklearn.svm.base._fit_liblinear",
-    "sklearn.svm.base._get_liblinear_solver_type", "sklearn.svm.base._one_vs_one_coef",
-    "sklearn.svm.bounds.l1_min_c", "sklearn.svm.classes.LinearSVC",
-    "sklearn.svm.classes.LinearSVR", "sklearn.svm.classes.NuSVC",
-    "sklearn.svm.classes.NuSVR", "sklearn.svm.classes.OneClassSVM",
-    "sklearn.svm.classes.SVC", "sklearn.svm.classes.SVR",
-    "sklearn.svm.l1_min_c", "sklearn.svm.liblinear.set_verbosity_wrap",
-    "sklearn.svm.liblinear.train_wrap", "sklearn.svm.libsvm.LIBSVM_KERNEL_TYPES",
-    "sklearn.svm.libsvm.cross_validation", "sklearn.svm.libsvm.decision_function",
-    "sklearn.svm.libsvm.fit", "sklearn.svm.libsvm.predict",
-    "sklearn.svm.libsvm.predict_proba", "sklearn.svm.libsvm.set_verbosity_wrap",
-    "sklearn.svm.libsvm_sparse.libsvm_sparse_decision_function", "sklearn.svm.libsvm_sparse.libsvm_sparse_predict",
-    "sklearn.svm.libsvm_sparse.libsvm_sparse_predict_proba", "sklearn.svm.libsvm_sparse.libsvm_sparse_train",
-    "sklearn.svm.libsvm_sparse.set_verbosity_wrap", "sklearn.tree.DecisionTreeClassifier",
-    "sklearn.tree.DecisionTreeRegressor", "sklearn.tree.ExtraTreeClassifier",
-    "sklearn.tree.ExtraTreeRegressor", "sklearn.tree._criterion.ClassificationCriterion",
-    "sklearn.tree._criterion.Criterion", "sklearn.tree._criterion.Entropy",
-    "sklearn.tree._criterion.FriedmanMSE", "sklearn.tree._criterion.Gini",
-    "sklearn.tree._criterion.MAE", "sklearn.tree._criterion.MSE",
-    "sklearn.tree._criterion.RegressionCriterion", "sklearn.tree._splitter.BaseDenseSplitter",
-    "sklearn.tree._splitter.BaseSparseSplitter", "sklearn.tree._splitter.BestSparseSplitter",
-    "sklearn.tree._splitter.BestSplitter", "sklearn.tree._splitter.RandomSparseSplitter",
-    "sklearn.tree._splitter.RandomSplitter", "sklearn.tree._splitter.Splitter",
-    "sklearn.tree._tree.BestFirstTreeBuilder", "sklearn.tree._tree.DepthFirstTreeBuilder",
-    "sklearn.tree._tree.NODE_DTYPE", "sklearn.tree._tree.TREE_LEAF",
-    "sklearn.tree._tree.TREE_UNDEFINED", "sklearn.tree._tree.Tree",
-    "sklearn.tree._tree.TreeBuilder", "sklearn.tree._utils.PriorityHeap",
-    "sklearn.tree._utils.Stack", "sklearn.tree._utils.WeightedMedianCalculator",
-    "sklearn.tree._utils.WeightedPQueue", "sklearn.tree._utils._realloc_test",
-    "sklearn.tree.export.SENTINEL", "sklearn.tree.export.Sentinel",
-    "sklearn.tree.export._color_brew", "sklearn.tree.export.export_graphviz",
-    "sklearn.tree.export_graphviz", "sklearn.tree.tree.BaseDecisionTree",
-    "sklearn.tree.tree.CRITERIA_CLF", "sklearn.tree.tree.CRITERIA_REG",
-    "sklearn.tree.tree.DENSE_SPLITTERS", "sklearn.tree.tree.DecisionTreeClassifier",
-    "sklearn.tree.tree.DecisionTreeRegressor", "sklearn.tree.tree.ExtraTreeClassifier",
-    "sklearn.tree.tree.ExtraTreeRegressor", "sklearn.tree.tree.SPARSE_SPLITTERS",
-    "sklearn.utils.Bunch", "sklearn.utils._get_n_jobs",
-    "sklearn.utils._logistic_sigmoid._log_logistic_sigmoid", "sklearn.utils._random._sample_without_replacement_check_input",
-    "sklearn.utils._random._sample_without_replacement_with_pool", "sklearn.utils._random._sample_without_replacement_with_reservoir_sampling",
-    "sklearn.utils._random._sample_without_replacement_with_tracking_selection", "sklearn.utils._random.sample_without_replacement",
-    "sklearn.utils.arrayfuncs.cholesky_delete", "sklearn.utils.arrayfuncs.min_pos",
-    "sklearn.utils.as_float_array", "sklearn.utils.assert_all_finite",
-    "sklearn.utils.axis0_safe_slice", "sklearn.utils.check_X_y",
-    "sklearn.utils.check_array", "sklearn.utils.check_consistent_length",
-    "sklearn.utils.check_random_state", "sklearn.utils.check_symmetric",
-    "sklearn.utils.class_weight.compute_class_weight", "sklearn.utils.class_weight.compute_sample_weight",
-    "sklearn.utils.column_or_1d", "sklearn.utils.compute_class_weight",
-    "sklearn.utils.compute_sample_weight", "sklearn.utils.deprecated",
-    "sklearn.utils.deprecation.DeprecationDict", "sklearn.utils.deprecation._is_deprecated",
-    "sklearn.utils.deprecation.deprecated", "sklearn.utils.extmath._deterministic_vector_sign_flip",
-    "sklearn.utils.extmath._impose_f_order", "sklearn.utils.extmath._incremental_mean_and_var",
-    "sklearn.utils.extmath.cartesian", "sklearn.utils.extmath.density",
-    "sklearn.utils.extmath.fast_dot", "sklearn.utils.extmath.fast_logdet",
-    "sklearn.utils.extmath.log_logistic", "sklearn.utils.extmath.logsumexp",
-    "sklearn.utils.extmath.make_nonnegative", "sklearn.utils.extmath.norm",
-    "sklearn.utils.extmath.np_version", "sklearn.utils.extmath.pinvh",
-    "sklearn.utils.extmath.randomized_range_finder", "sklearn.utils.extmath.randomized_svd",
-    "sklearn.utils.extmath.row_norms", "sklearn.utils.extmath.safe_min",
-    "sklearn.utils.extmath.safe_sparse_dot", "sklearn.utils.extmath.softmax",
-    "sklearn.utils.extmath.squared_norm", "sklearn.utils.extmath.stable_cumsum",
-    "sklearn.utils.extmath.svd_flip", "sklearn.utils.extmath.weighted_mode",
-    "sklearn.utils.fast_dict.IntFloatDict", "sklearn.utils.fast_dict.argmin",
-    "sklearn.utils.fixes._parse_version", "sklearn.utils.fixes.divide",
-    "sklearn.utils.fixes.euler_gamma", "sklearn.utils.fixes.makedirs",
-    "sklearn.utils.fixes.np_version", "sklearn.utils.fixes.parallel_helper",
-    "sklearn.utils.fixes.sp_version", "sklearn.utils.fixes.sparse_min_max",
-    "sklearn.utils.gen_batches", "sklearn.utils.gen_even_slices",
-    "sklearn.utils.graph.connected_components", "sklearn.utils.graph.graph_laplacian",
-    "sklearn.utils.graph.graph_shortest_path", "sklearn.utils.graph.single_source_shortest_path_length",
-    "sklearn.utils.graph_shortest_path.graph_shortest_path", "sklearn.utils.indexable",
-    "sklearn.utils.indices_to_mask", "sklearn.utils.linear_assignment_._HungarianState",
-    "sklearn.utils.linear_assignment_._hungarian", "sklearn.utils.linear_assignment_._step1",
-    "sklearn.utils.linear_assignment_._step3", "sklearn.utils.linear_assignment_._step4",
-    "sklearn.utils.linear_assignment_._step5", "sklearn.utils.linear_assignment_._step6",
-    "sklearn.utils.linear_assignment_.linear_assignment", "sklearn.utils.metaestimators._BaseComposition",
-    "sklearn.utils.metaestimators._IffHasAttrDescriptor", "sklearn.utils.metaestimators._safe_split",
-    "sklearn.utils.metaestimators.if_delegate_has_method", "sklearn.utils.multiclass._FN_UNIQUE_LABELS",
-    "sklearn.utils.multiclass._check_partial_fit_first_call", "sklearn.utils.multiclass._is_integral_float",
-    "sklearn.utils.multiclass._ovr_decision_function", "sklearn.utils.multiclass._unique_indicator",
-    "sklearn.utils.multiclass._unique_multiclass", "sklearn.utils.multiclass.check_classification_targets",
-    "sklearn.utils.multiclass.class_distribution", "sklearn.utils.multiclass.is_multilabel",
-    "sklearn.utils.multiclass.string_types", "sklearn.utils.multiclass.type_of_target",
-    "sklearn.utils.multiclass.unique_labels", "sklearn.utils.murmurhash.murmurhash3_32",
-    "sklearn.utils.murmurhash.murmurhash3_bytes_array_s32", "sklearn.utils.murmurhash.murmurhash3_bytes_array_u32",
-    "sklearn.utils.murmurhash.murmurhash3_bytes_s32", "sklearn.utils.murmurhash.murmurhash3_bytes_u32",
-    "sklearn.utils.murmurhash.murmurhash3_int_s32", "sklearn.utils.murmurhash.murmurhash3_int_u32",
-    "sklearn.utils.murmurhash3_32", "sklearn.utils.optimize._LineSearchError",
-    "sklearn.utils.optimize._cg", "sklearn.utils.optimize._line_search_wolfe12",
-    "sklearn.utils.optimize.newton_cg", "sklearn.utils.random.choice",
-    "sklearn.utils.random.random_choice_csc", "sklearn.utils.resample",
-    "sklearn.utils.safe_indexing", "sklearn.utils.safe_mask",
-    "sklearn.utils.safe_sqr", "sklearn.utils.seq_dataset.ArrayDataset",
-    "sklearn.utils.seq_dataset.CSRDataset", "sklearn.utils.seq_dataset.SequentialDataset",
-    "sklearn.utils.shuffle", "sklearn.utils.sparsefuncs._csc_mean_var_axis0",
-    "sklearn.utils.sparsefuncs._csr_mean_var_axis0", "sklearn.utils.sparsefuncs._get_elem_at_rank",
-    "sklearn.utils.sparsefuncs._get_median", "sklearn.utils.sparsefuncs._incr_mean_var_axis0",
-    "sklearn.utils.sparsefuncs._raise_error_wrong_axis", "sklearn.utils.sparsefuncs._raise_typeerror",
-    "sklearn.utils.sparsefuncs.count_nonzero", "sklearn.utils.sparsefuncs.csc_median_axis_0",
-    "sklearn.utils.sparsefuncs.incr_mean_variance_axis", "sklearn.utils.sparsefuncs.inplace_column_scale",
-    "sklearn.utils.sparsefuncs.inplace_csr_column_scale", "sklearn.utils.sparsefuncs.inplace_csr_row_scale",
-    "sklearn.utils.sparsefuncs.inplace_row_scale", "sklearn.utils.sparsefuncs.inplace_swap_column",
-    "sklearn.utils.sparsefuncs.inplace_swap_row", "sklearn.utils.sparsefuncs.inplace_swap_row_csc",
-    "sklearn.utils.sparsefuncs.inplace_swap_row_csr", "sklearn.utils.sparsefuncs.mean_variance_axis",
-    "sklearn.utils.sparsefuncs.min_max_axis", "sklearn.utils.sparsefuncs_fast._csc_mean_variance_axis0",
-    "sklearn.utils.sparsefuncs_fast._csr_mean_variance_axis0", "sklearn.utils.sparsefuncs_fast._csr_row_norms",
-    "sklearn.utils.sparsefuncs_fast._incr_mean_variance_axis0", "sklearn.utils.sparsefuncs_fast._inplace_csr_row_normalize_l1",
-    "sklearn.utils.sparsefuncs_fast._inplace_csr_row_normalize_l2", "sklearn.utils.sparsefuncs_fast.assign_rows_csr",
-    "sklearn.utils.sparsefuncs_fast.csc_mean_variance_axis0", "sklearn.utils.sparsefuncs_fast.csr_mean_variance_axis0",
-    "sklearn.utils.sparsefuncs_fast.csr_row_norms", "sklearn.utils.sparsefuncs_fast.incr_mean_variance_axis0",
-    "sklearn.utils.sparsefuncs_fast.inplace_csr_row_normalize_l1", "sklearn.utils.sparsefuncs_fast.inplace_csr_row_normalize_l2",
-    "sklearn.utils.stats._weighted_percentile", "sklearn.utils.stats.rankdata",
-    "sklearn.utils.tosequence", "sklearn.utils.validation.FLOAT_DTYPES",
-    "sklearn.utils.validation._assert_all_finite", "sklearn.utils.validation._ensure_sparse_format",
-    "sklearn.utils.validation._is_arraylike", "sklearn.utils.validation._num_samples",
-    "sklearn.utils.validation._shape_repr", "sklearn.utils.validation.as_float_array",
-    "sklearn.utils.validation.assert_all_finite", "sklearn.utils.validation.check_X_y",
-    "sklearn.utils.validation.check_array", "sklearn.utils.validation.check_consistent_length",
-    "sklearn.utils.validation.check_is_fitted", "sklearn.utils.validation.check_memory",
-    "sklearn.utils.validation.check_non_negative", "sklearn.utils.validation.check_random_state",
-    "sklearn.utils.validation.check_symmetric", "sklearn.utils.validation.column_or_1d",
-    "sklearn.utils.validation.has_fit_parameter", "sklearn.utils.validation.indexable",
-    "sklearn.utils.weight_vector.WeightVector"
-],
-
-  "SKR_NAMES": [
-    "skrebate.MultiSURF", "skrebate.MultiSURFstar",
-    "skrebate.ReliefF", "skrebate.SURF",
-    "skrebate.SURFstar", "skrebate.TuRF",
-    "skrebate.multisurf.MultiSURF", "skrebate.multisurfstar.MultiSURFstar",
-    "skrebate.relieff.ReliefF", "skrebate.scoring_utils.MultiSURF_compute_scores",
-    "skrebate.scoring_utils.MultiSURFstar_compute_scores", "skrebate.scoring_utils.ReliefF_compute_scores",
-    "skrebate.scoring_utils.SURF_compute_scores", "skrebate.scoring_utils.SURFstar_compute_scores",
-    "skrebate.scoring_utils.compute_score", "skrebate.scoring_utils.get_row_missing",
-    "skrebate.scoring_utils.ramp_function", "skrebate.surf.SURF",
-    "skrebate.surfstar.SURFstar", "skrebate.turf.TuRF"
-  ],
-
-  "XGB_NAMES": [
-    "xgboost.Booster", "xgboost.DMatrix",
-    "xgboost.VERSION_FILE", "xgboost.XGBClassifier",
-    "xgboost.XGBModel", "xgboost.XGBRegressor",
-    "xgboost.callback._fmt_metric", "xgboost.callback._get_callback_context",
-    "xgboost.callback.early_stop", "xgboost.callback.print_evaluation",
-    "xgboost.callback.record_evaluation", "xgboost.callback.reset_learning_rate",
-    "xgboost.compat.PANDAS_INSTALLED", "xgboost.compat.PY3",
-    "xgboost.compat.SKLEARN_INSTALLED", "xgboost.compat.STRING_TYPES",
-    "xgboost.compat.py_str", "xgboost.core.Booster",
-    "xgboost.core.CallbackEnv", "xgboost.core.DMatrix",
-    "xgboost.core.EarlyStopException", "xgboost.core.PANDAS_DTYPE_MAPPER",
-    "xgboost.core.PANDAS_INSTALLED", "xgboost.core.PY3",
-    "xgboost.core.STRING_TYPES", "xgboost.core.XGBoostError",
-    "xgboost.core._check_call", "xgboost.core._load_lib",
-    "xgboost.core._maybe_pandas_data", "xgboost.core._maybe_pandas_label",
-    "xgboost.core.c_array", "xgboost.core.c_str",
-    "xgboost.core.ctypes2buffer", "xgboost.core.ctypes2numpy",
-    "xgboost.core.from_cstr_to_pystr", "xgboost.core.from_pystr_to_cstr",
-    "xgboost.cv", "xgboost.f",
-    "xgboost.libpath.XGBoostLibraryNotFound", "xgboost.libpath.find_lib_path",
-    "xgboost.plot_importance", "xgboost.plot_tree",
-    "xgboost.plotting._EDGEPAT", "xgboost.plotting._EDGEPAT2",
-    "xgboost.plotting._LEAFPAT", "xgboost.plotting._NODEPAT",
-    "xgboost.plotting._parse_edge", "xgboost.plotting._parse_node",
-    "xgboost.plotting.plot_importance", "xgboost.plotting.plot_tree",
-    "xgboost.plotting.to_graphviz", "xgboost.rabit.DTYPE_ENUM__",
-    "xgboost.rabit.STRING_TYPES", "xgboost.rabit._init_rabit",
-    "xgboost.rabit.allreduce", "xgboost.rabit.broadcast",
-    "xgboost.rabit.finalize", "xgboost.rabit.get_processor_name",
-    "xgboost.rabit.get_rank", "xgboost.rabit.get_world_size",
-    "xgboost.rabit.init", "xgboost.rabit.tracker_print",
-    "xgboost.rabit.version_number", "xgboost.sklearn.SKLEARN_INSTALLED",
-    "xgboost.sklearn.XGBClassifier", "xgboost.sklearn.XGBModel",
-    "xgboost.sklearn.XGBRegressor", "xgboost.sklearn._objective_decorator",
-    "xgboost.to_graphviz", "xgboost.train",
-    "xgboost.training.CVPack", "xgboost.training.SKLEARN_INSTALLED",
-    "xgboost.training.STRING_TYPES", "xgboost.training._train_internal",
-    "xgboost.training.aggcv", "xgboost.training.cv",
-    "xgboost.training.mknfold", "xgboost.training.train"
-  ],
-
-
-  "NUMPY_NAMES": [
-    "numpy.core.multiarray._reconstruct", "numpy.ndarray",
-    "numpy.dtype", "numpy.core.multiarray.scalar", "numpy.random.__RandomState_ctor",
-    "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 231e9a9849e8 -r 0b88494bdcac preprocessors.py
--- a/preprocessors.py	Tue Jul 09 19:34:06 2019 -0400
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,184 +0,0 @@
-"""
-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 231e9a9849e8 -r 0b88494bdcac search_model_validation.py
--- a/search_model_validation.py	Tue Jul 09 19:34:06 2019 -0400
+++ b/search_model_validation.py	Fri Aug 09 07:25:16 2019 -0400
@@ -1,22 +1,20 @@
 import argparse
 import collections
 import imblearn
+import joblib
 import json
 import numpy as np
-import pandas
+import pandas as pd
 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.base import clone
 from sklearn import (cluster, compose, decomposition, ensemble,
                      feature_extraction, feature_selection,
                      gaussian_process, kernel_approximation, metrics,
@@ -24,18 +22,23 @@
                      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 sklearn.model_selection._validation import _score, cross_validate
+from sklearn.model_selection import _search, _validation
 
-from utils import (SafeEval, get_cv, get_scoring, get_X_y,
-                   load_model, read_columns)
-from model_validations import train_test_split
+from galaxy_ml.utils import (SafeEval, get_cv, get_scoring, load_model,
+                             read_columns, try_get_attr, get_module)
+
 
+_fit_and_score = try_get_attr('galaxy_ml.model_validations', '_fit_and_score')
+setattr(_search, '_fit_and_score', _fit_and_score)
+setattr(_validation, '_fit_and_score', _fit_and_score)
 
 N_JOBS = int(__import__('os').environ.get('GALAXY_SLOTS', 1))
 CACHE_DIR = './cached'
-NON_SEARCHABLE = ('n_jobs', 'pre_dispatch', 'memory', 'steps',
-                  'nthread', 'verbose')
+NON_SEARCHABLE = ('n_jobs', 'pre_dispatch', 'memory', '_path',
+                  'nthread', 'callbacks')
+ALLOWED_CALLBACKS = ('EarlyStopping', 'TerminateOnNaN', 'ReduceLROnPlateau',
+                     'CSVLogger', 'None')
 
 
 def _eval_search_params(params_builder):
@@ -62,9 +65,9 @@
             search_list = search_list[1:].strip()
             # TODO maybe add regular express check
             ev = safe_eval_es(search_list)
-            preprocessors = (
+            preprocessings = (
                 preprocessing.StandardScaler(), preprocessing.Binarizer(),
-                preprocessing.Imputer(), preprocessing.MaxAbsScaler(),
+                preprocessing.MaxAbsScaler(),
                 preprocessing.Normalizer(), preprocessing.MinMaxScaler(),
                 preprocessing.PolynomialFeatures(),
                 preprocessing.RobustScaler(), feature_selection.SelectKBest(),
@@ -133,21 +136,21 @@
                 if obj is None:
                     newlist.append(None)
                 elif obj == 'all_0':
-                    newlist.extend(preprocessors[0:36])
+                    newlist.extend(preprocessings[0:35])
                 elif obj == 'sk_prep_all':      # no KernalCenter()
-                    newlist.extend(preprocessors[0:8])
+                    newlist.extend(preprocessings[0:7])
                 elif obj == 'fs_all':
-                    newlist.extend(preprocessors[8:15])
+                    newlist.extend(preprocessings[7:14])
                 elif obj == 'decomp_all':
-                    newlist.extend(preprocessors[15:26])
+                    newlist.extend(preprocessings[14:25])
                 elif obj == 'k_appr_all':
-                    newlist.extend(preprocessors[26:30])
+                    newlist.extend(preprocessings[25:29])
                 elif obj == 'reb_all':
-                    newlist.extend(preprocessors[31:36])
+                    newlist.extend(preprocessings[30:35])
                 elif obj == 'imb_all':
-                    newlist.extend(preprocessors[36:55])
-                elif type(obj) is int and -1 < obj < len(preprocessors):
-                    newlist.append(preprocessors[obj])
+                    newlist.extend(preprocessings[35:54])
+                elif type(obj) is int and -1 < obj < len(preprocessings):
+                    newlist.append(preprocessings[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))
@@ -162,7 +165,10 @@
 
 
 def main(inputs, infile_estimator, infile1, infile2,
-         outfile_result, outfile_object=None, groups=None):
+         outfile_result, outfile_object=None,
+         outfile_weights=None, groups=None,
+         ref_seq=None, intervals=None, targets=None,
+         fasta_path=None):
     """
     Parameter
     ---------
@@ -184,21 +190,40 @@
     outfile_object : str, optional
         File path to save searchCV object
 
+    outfile_weights : str, optional
+        File path to save model weights
+
     groups : str
         File path to dataset containing groups labels
+
+    ref_seq : str
+        File path to dataset containing genome sequence file
+
+    intervals : str
+        File path to dataset containing interval file
+
+    targets : str
+        File path to dataset compressed target bed file
+
+    fasta_path : str
+        File path to dataset containing fasta file
     """
-
     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']
 
+    with open(infile_estimator, 'rb') as estimator_handler:
+        estimator = load_model(estimator_handler)
+    estimator_params = estimator.get_params()
+
+    # store read dataframe object
+    loaded_df = {}
+
     input_type = params['input_options']['selected_input']
+    # tabular input
     if input_type == 'tabular':
         header = 'infer' if params['input_options']['header1'] else None
         column_option = (params['input_options']['column_selector_options_1']
@@ -208,16 +233,48 @@
             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:
+
+        df_key = infile1 + repr(header)
+        df = pd.read_csv(infile1, sep='\t', header=header,
+                         parse_dates=True)
+        loaded_df[df_key] = df
+
+        X = read_columns(df, c=c, c_option=column_option).astype(float)
+    # sparse input
+    elif input_type == 'sparse':
         X = mmread(open(infile1, 'r'))
 
+    # fasta_file input
+    elif input_type == 'seq_fasta':
+        pyfaidx = get_module('pyfaidx')
+        sequences = pyfaidx.Fasta(fasta_path)
+        n_seqs = len(sequences.keys())
+        X = np.arange(n_seqs)[:, np.newaxis]
+        for param in estimator_params.keys():
+            if param.endswith('fasta_path'):
+                estimator.set_params(
+                    **{param: fasta_path})
+                break
+        else:
+            raise ValueError(
+                "The selected estimator doesn't support "
+                "fasta file input! Please consider using "
+                "KerasGBatchClassifier with "
+                "FastaDNABatchGenerator/FastaProteinBatchGenerator "
+                "or having GenomeOneHotEncoder/ProteinOneHotEncoder "
+                "in pipeline!")
+
+    elif input_type == 'refseq_and_interval':
+        path_params = {
+            'data_batch_generator__ref_genome_path': ref_seq,
+            'data_batch_generator__intervals_path': intervals,
+            'data_batch_generator__target_path': targets
+        }
+        estimator.set_params(**path_params)
+        n_intervals = sum(1 for line in open(intervals))
+        X = np.arange(n_intervals)[:, np.newaxis]
+
+    # Get target y
     header = 'infer' if params['input_options']['header2'] else None
     column_option = (params['input_options']['column_selector_options_2']
                      ['selected_column_selector_option2'])
@@ -226,6 +283,15 @@
         c = params['input_options']['column_selector_options_2']['col2']
     else:
         c = None
+
+    df_key = infile2 + repr(header)
+    if df_key in loaded_df:
+        infile2 = loaded_df[df_key]
+    else:
+        infile2 = pd.read_csv(infile2, sep='\t',
+                              header=header, parse_dates=True)
+        loaded_df[df_key] = infile2
+
     y = read_columns(
             infile2,
             c=c,
@@ -233,13 +299,47 @@
             sep='\t',
             header=header,
             parse_dates=True)
-    y = y.ravel()
+    if len(y.shape) == 2 and y.shape[1] == 1:
+        y = y.ravel()
+    if input_type == 'refseq_and_interval':
+        estimator.set_params(
+            data_batch_generator__features=y.ravel().tolist())
+        y = None
+    # end y
 
     optimizer = params['search_schemes']['selected_search_scheme']
     optimizer = getattr(model_selection, optimizer)
 
+    # handle gridsearchcv options
     options = params['search_schemes']['options']
 
+    if groups:
+        header = 'infer' if (options['cv_selector']['groups_selector']
+                                    ['header_g']) else None
+        column_option = (options['cv_selector']['groups_selector']
+                                ['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 = (options['cv_selector']['groups_selector']
+                        ['column_selector_options_g']['col_g'])
+        else:
+            c = None
+
+        df_key = groups + repr(header)
+        if df_key in loaded_df:
+            groups = loaded_df[df_key]
+
+        groups = read_columns(
+                groups,
+                c=c,
+                c_option=column_option,
+                sep='\t',
+                header=header,
+                parse_dates=True)
+        groups = groups.ravel()
+        options['cv_selector']['groups_selector'] = groups
+
     splitter, groups = get_cv(options.pop('cv_selector'))
     options['cv'] = splitter
     options['n_jobs'] = N_JOBS
@@ -254,100 +354,199 @@
     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)
+    # del loaded_df
+    del loaded_df
 
+    # handle memory
     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():
+        # For iraps buried in pipeline
+        for p, v in estimator_params.items():
             if p.endswith('memory'):
+                # for case of `__irapsclassifier__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)
+                # security reason, we don't want memory being
+                # modified unexpectedly
                 elif v:
                     new_params = {p, None}
                     estimator.set_params(**new_params)
+            # For now, 1 CPU is suggested for iprasclassifier
             elif p.endswith('n_jobs'):
                 new_params = {p: 1}
                 estimator.set_params(**new_params)
+            # for security reason, types of callbacks are limited
+            elif p.endswith('callbacks'):
+                for cb in v:
+                    cb_type = cb['callback_selection']['callback_type']
+                    if cb_type not in ALLOWED_CALLBACKS:
+                        raise ValueError(
+                            "Prohibited callback type: %s!" % cb_type)
 
     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']
+    # do nested split
+    split_mode = params['outer_split'].pop('split_mode')
+    # nested CV, outer cv using cross_validate
+    if split_mode == 'nested_cv':
+        outer_cv, _ = get_cv(params['outer_split']['cv_selector'])
 
-        # 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)
+        if options['error_score'] == 'raise':
+            rval = cross_validate(
+                searcher, X, y, scoring=options['scoring'],
+                cv=outer_cv, n_jobs=N_JOBS, verbose=0,
+                error_score=options['error_score'])
         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
+            warnings.simplefilter('always', FitFailedWarning)
+            with warnings.catch_warnings(record=True) as w:
+                try:
+                    rval = cross_validate(
+                        searcher, X, y,
+                        scoring=options['scoring'],
+                        cv=outer_cv, n_jobs=N_JOBS,
+                        verbose=0,
+                        error_score=options['error_score'])
+                except ValueError:
+                    pass
+                for warning in w:
+                    print(repr(warning.message))
 
-    if options['error_score'] == 'raise':
-        searcher.fit(X, y, groups=groups)
+        keys = list(rval.keys())
+        for k in keys:
+            if k.startswith('test'):
+                rval['mean_' + k] = np.mean(rval[k])
+                rval['std_' + k] = np.std(rval[k])
+            if k.endswith('time'):
+                rval.pop(k)
+        rval = pd.DataFrame(rval)
+        rval = rval[sorted(rval.columns)]
+        rval.to_csv(path_or_buf=outfile_result, sep='\t',
+                    header=True, index=False)
     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 split_mode == 'train_test_split':
+            train_test_split = try_get_attr(
+                'galaxy_ml.model_validations', 'train_test_split')
+            # make sure refit is choosen
+            # this could be True for sklearn models, but not the case for
+            # deep learning models
+            if not options['refit'] and \
+                    not all(hasattr(estimator, attr)
+                            for attr in ('config', 'model_type')):
+                warnings.warn("Refit is change to `True` for nested "
+                              "validation!")
+                setattr(searcher, 'refit', True)
+            split_options = params['outer_split']
 
-    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)
+            # 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 groups is None:
+                    raise ValueError("No group based CV option was "
+                                     "choosen for group shuffle!")
+                split_options['labels'] = groups
+                if y is None:
+                    X, X_test, groups, _ =\
+                        train_test_split(X, groups, **split_options)
+                else:
+                    X, X_test, y, y_test, groups, _ =\
+                        train_test_split(X, y, groups, **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
 
-    # output test result using best_estimator_
-    else:
-        best_estimator_ = searcher.best_estimator_
-        if isinstance(options['scoring'], collections.Mapping):
-            is_multimetric = True
+        # shared by both train_test_split and non-split
+        if options['error_score'] == 'raise':
+            searcher.fit(X, y, groups=groups)
         else:
-            is_multimetric = False
+            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))
+
+        # no outer split
+        if split_mode == 'no':
+            # save results
+            cv_results = pd.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)
 
-        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)
+        # train_test_split, output test result using best_estimator_
+        # or rebuild the trained estimator using weights if applicable.
+        else:
+            scorer_ = searcher.scorer_
+            if isinstance(scorer_, collections.Mapping):
+                is_multimetric = True
+            else:
+                is_multimetric = False
+
+            best_estimator_ = getattr(searcher, 'best_estimator_', None)
+            if not best_estimator_:
+                raise ValueError("GridSearchCV object has no "
+                                 "`best_estimator_` when `refit`=False!")
+
+            if best_estimator_.__class__.__name__ == 'KerasGBatchClassifier' \
+                    and hasattr(estimator.data_batch_generator, 'target_path'):
+                test_score = best_estimator_.evaluate(
+                    X_test, scorer=scorer_, is_multimetric=is_multimetric)
+            else:
+                test_score = _score(best_estimator_, X_test,
+                                    y_test, scorer_,
+                                    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 = pd.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:
+        best_estimator_ = getattr(searcher, 'best_estimator_', None)
+        if not best_estimator_:
+            warnings.warn("GridSearchCV object has no attribute "
+                          "'best_estimator_', because either it's "
+                          "nested gridsearch or `refit` is False!")
+            return
+
+        main_est = best_estimator_
+        if isinstance(best_estimator_, pipeline.Pipeline):
+            main_est = best_estimator_.steps[-1][-1]
+
+        if hasattr(main_est, 'model_') \
+                and hasattr(main_est, 'save_weights'):
+            if outfile_weights:
+                main_est.save_weights(outfile_weights)
+            del main_est.model_
+            del main_est.fit_params
+            del main_est.model_class_
+            del main_est.validation_data
+            if getattr(main_est, 'data_generator_', None):
+                del main_est.data_generator_
+                del main_est.data_batch_generator
+
         with open(outfile_object, 'wb') as output_handler:
-            pickle.dump(searcher, output_handler, pickle.HIGHEST_PROTOCOL)
+            pickle.dump(best_estimator_, output_handler,
+                        pickle.HIGHEST_PROTOCOL)
 
 
 if __name__ == '__main__':
@@ -356,11 +555,18 @@
     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_result", dest="outfile_result")
     aparser.add_argument("-o", "--outfile_object", dest="outfile_object")
+    aparser.add_argument("-w", "--outfile_weights", dest="outfile_weights")
     aparser.add_argument("-g", "--groups", dest="groups")
+    aparser.add_argument("-r", "--ref_seq", dest="ref_seq")
+    aparser.add_argument("-b", "--intervals", dest="intervals")
+    aparser.add_argument("-t", "--targets", dest="targets")
+    aparser.add_argument("-f", "--fasta_path", dest="fasta_path")
     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)
+         outfile_weights=args.outfile_weights, groups=args.groups,
+         ref_seq=args.ref_seq, intervals=args.intervals,
+         targets=args.targets, fasta_path=args.fasta_path)
diff -r 231e9a9849e8 -r 0b88494bdcac stacking_ensembles.py
--- a/stacking_ensembles.py	Tue Jul 09 19:34:06 2019 -0400
+++ b/stacking_ensembles.py	Fri Aug 09 07:25:16 2019 -0400
@@ -1,26 +1,17 @@
 import argparse
+import ast
 import json
+import mlxtend.regressor
+import mlxtend.classifier
 import pandas as pd
 import pickle
-import xgboost
+import sklearn
+import sys
 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 sklearn import ensemble
 
-from mlxtend.regressor import StackingCVRegressor, StackingRegressor
-from mlxtend.classifier import StackingCVClassifier, StackingClassifier
+from galaxy_ml.utils import (load_model, get_cv, get_estimator,
+                          get_search_params)
 
 
 warnings.filterwarnings('ignore')
@@ -51,6 +42,8 @@
     with open(inputs_path, 'r') as param_handler:
         params = json.load(param_handler)
 
+    estimator_type = params['algo_selection']['estimator_type']
+    # get base estimators
     base_estimators = []
     for idx, base_file in enumerate(base_paths.split(',')):
         if base_file and base_file != 'None':
@@ -60,14 +53,23 @@
             estimator_json = (params['base_est_builder'][idx]
                               ['estimator_selector'])
             model = get_estimator(estimator_json)
-        base_estimators.append(model)
+
+        if estimator_type.startswith('sklearn'):
+            named = model.__class__.__name__.lower()
+            named = 'base_%d_%s' % (idx, named)
+            base_estimators.append((named, model))
+        else:
+            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)
+    # get meta estimator, if applicable
+    if estimator_type.startswith('mlxtend'):
+        if meta_path:
+            with open(meta_path, 'rb') as f:
+                meta_estimator = load_model(f)
+        else:
+            estimator_json = (params['algo_selection']
+                              ['meta_estimator']['estimator_selector'])
+            meta_estimator = get_estimator(estimator_json)
 
     options = params['algo_selection']['options']
 
@@ -78,26 +80,26 @@
         # set n_jobs
         options['n_jobs'] = N_JOBS
 
-    if params['algo_selection']['estimator_type'] == 'StackingCVClassifier':
-        ensemble_estimator = StackingCVClassifier(
+    weights = options.pop('weights', None)
+    if weights:
+        options['weights'] = ast.literal_eval(weights)
+
+    mod_and_name = estimator_type.split('_')
+    mod = sys.modules[mod_and_name[0]]
+    klass = getattr(mod, mod_and_name[1])
+
+    if estimator_type.startswith('sklearn'):
+        options['n_jobs'] = N_JOBS
+        ensemble_estimator = klass(base_estimators, **options)
+
+    elif mod == mlxtend.classifier:
+        ensemble_estimator = klass(
             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(
+        ensemble_estimator = klass(
             regressors=base_estimators,
             meta_regressor=meta_estimator,
             **options)
diff -r 231e9a9849e8 -r 0b88494bdcac test-data/RandomForestClassifier.zip
Binary file test-data/RandomForestClassifier.zip has changed
diff -r 231e9a9849e8 -r 0b88494bdcac test-data/StackingCVRegressor01.zip
Binary file test-data/StackingCVRegressor01.zip has changed
diff -r 231e9a9849e8 -r 0b88494bdcac test-data/StackingRegressor02.zip
Binary file test-data/StackingRegressor02.zip has changed
diff -r 231e9a9849e8 -r 0b88494bdcac test-data/StackingVoting03.zip
Binary file test-data/StackingVoting03.zip has changed
diff -r 231e9a9849e8 -r 0b88494bdcac test-data/deepsear_1feature.json
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/deepsear_1feature.json	Fri Aug 09 07:25:16 2019 -0400
@@ -0,0 +1,1 @@
+{"class_name": "Sequential", "config": {"name": "sequential_1", "layers": [{"class_name": "Conv1D", "config": {"name": "conv1d_1", "trainable": true, "batch_input_shape": [null, 1000, 4], "dtype": "float32", "filters": 320, "kernel_size": [8], "strides": [1], "padding": "valid", "data_format": "channels_last", "dilation_rate": [1], "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "MaxPooling1D", "config": {"name": "max_pooling1d_1", "trainable": true, "strides": [4], "pool_size": [4], "padding": "valid", "data_format": "channels_last"}}, {"class_name": "Dropout", "config": {"name": "dropout_1", "trainable": true, "rate": 0.2, "noise_shape": null, "seed": 999}}, {"class_name": "Conv1D", "config": {"name": "conv1d_2", "trainable": true, "filters": 480, "kernel_size": [8], "strides": [1], "padding": "valid", "data_format": "channels_last", "dilation_rate": [1], "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "MaxPooling1D", "config": {"name": "max_pooling1d_2", "trainable": true, "strides": [4], "pool_size": [4], "padding": "valid", "data_format": "channels_last"}}, {"class_name": "Dropout", "config": {"name": "dropout_2", "trainable": true, "rate": 0.2, "noise_shape": null, "seed": 999}}, {"class_name": "Conv1D", "config": {"name": "conv1d_3", "trainable": true, "filters": 960, "kernel_size": [8], "strides": [1], "padding": "valid", "data_format": "channels_last", "dilation_rate": [1], "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Dropout", "config": {"name": "dropout_3", "trainable": true, "rate": 0.5, "noise_shape": null, "seed": 999}}, {"class_name": "Reshape", "config": {"name": "reshape_1", "trainable": true, "target_shape": [50880]}}, {"class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "units": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Dense", "config": {"name": "dense_2", "trainable": true, "units": 1, "activation": "sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}]}, "keras_version": "2.2.4", "backend": "tensorflow"}
diff -r 231e9a9849e8 -r 0b88494bdcac test-data/fitted_keras_g_regressor01.zip
Binary file test-data/fitted_keras_g_regressor01.zip has changed
diff -r 231e9a9849e8 -r 0b88494bdcac test-data/keras01.json
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/keras01.json	Fri Aug 09 07:25:16 2019 -0400
@@ -0,0 +1,1 @@
+{"class_name": "Sequential", "config": {"name": "sequential_1", "layers": [{"class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "batch_input_shape": [null, 784], "dtype": "float32", "units": 32, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Activation", "config": {"name": "activation_1", "trainable": true, "activation": "relu"}}, {"class_name": "Dense", "config": {"name": "dense_2", "trainable": true, "units": 10, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Activation", "config": {"name": "activation_2", "trainable": true, "activation": "softmax"}}]}, "keras_version": "2.2.4", "backend": "tensorflow"}
\ No newline at end of file
diff -r 231e9a9849e8 -r 0b88494bdcac test-data/keras02.json
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/keras02.json	Fri Aug 09 07:25:16 2019 -0400
@@ -0,0 +1,1 @@
+{"class_name": "Model", "config": {"name": "model_1", "layers": [{"name": "main_input", "class_name": "InputLayer", "config": {"batch_input_shape": [null, 100], "dtype": "int32", "sparse": false, "name": "main_input"}, "inbound_nodes": []}, {"name": "embedding_1", "class_name": "Embedding", "config": {"name": "embedding_1", "trainable": true, "batch_input_shape": [null, 100], "dtype": "float32", "input_dim": 10000, "output_dim": 512, "embeddings_initializer": {"class_name": "RandomUniform", "config": {"minval": -0.05, "maxval": 0.05, "seed": null}}, "embeddings_regularizer": null, "activity_regularizer": null, "embeddings_constraint": null, "mask_zero": false, "input_length": 100}, "inbound_nodes": [[["main_input", 0, 0, {}]]]}, {"name": "lstm_1", "class_name": "LSTM", "config": {"name": "lstm_1", "trainable": true, "return_sequences": false, "return_state": false, "go_backwards": false, "stateful": false, "unroll": false, "units": 32, "activation": "linear", "recurrent_activation": "hard_sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "recurrent_initializer": {"class_name": "Orthogonal", "config": {"gain": 1.0, "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "unit_forget_bias": true, "kernel_regularizer": null, "recurrent_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "recurrent_constraint": null, "bias_constraint": null, "dropout": 0.0, "recurrent_dropout": 0.0, "implementation": 1}, "inbound_nodes": [[["embedding_1", 0, 0, {}]]]}, {"name": "dense_1", "class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "units": 1, "activation": "sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["lstm_1", 0, 0, {}]]]}, {"name": "aux_input", "class_name": "InputLayer", "config": {"batch_input_shape": [null, 5], "dtype": "float32", "sparse": false, "name": "aux_input"}, "inbound_nodes": []}, {"name": "concatenate_1", "class_name": "Concatenate", "config": {"name": "concatenate_1", "trainable": true, "axis": -1}, "inbound_nodes": [[["dense_1", 0, 0, {}], ["aux_input", 0, 0, {}]]]}, {"name": "dense_2", "class_name": "Dense", "config": {"name": "dense_2", "trainable": true, "units": 64, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["concatenate_1", 0, 0, {}]]]}, {"name": "dense_3", "class_name": "Dense", "config": {"name": "dense_3", "trainable": true, "units": 64, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["dense_2", 0, 0, {}]]]}, {"name": "dense_4", "class_name": "Dense", "config": {"name": "dense_4", "trainable": true, "units": 64, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["dense_3", 0, 0, {}]]]}, {"name": "dense_5", "class_name": "Dense", "config": {"name": "dense_5", "trainable": true, "units": 1, "activation": "sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["dense_4", 0, 0, {}]]]}], "input_layers": [["main_input", 0, 0], ["aux_input", 0, 0]], "output_layers": [["dense_1", 0, 0], ["dense_5", 0, 0]]}, "keras_version": "2.2.4", "backend": "tensorflow"}
\ No newline at end of file
diff -r 231e9a9849e8 -r 0b88494bdcac test-data/keras03.json
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/keras03.json	Fri Aug 09 07:25:16 2019 -0400
@@ -0,0 +1,1 @@
+{"class_name": "Sequential", "config": {"name": "sequential_1", "layers": [{"class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "batch_input_shape": [null, 17], "dtype": "float32", "units": 100, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": 0}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Dropout", "config": {"name": "dropout_1", "trainable": true, "rate": 0.1, "noise_shape": null, "seed": 0}}, {"class_name": "Dense", "config": {"name": "dense_2", "trainable": true, "units": 1, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": 0}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}]}, "keras_version": "2.2.4", "backend": "tensorflow"}
\ No newline at end of file
diff -r 231e9a9849e8 -r 0b88494bdcac test-data/keras04.json
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/keras04.json	Fri Aug 09 07:25:16 2019 -0400
@@ -0,0 +1,1 @@
+{"class_name": "Sequential", "config": {"name": "sequential_1", "layers": [{"class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "batch_input_shape": [null, 17], "dtype": "float32", "units": 32, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Activation", "config": {"name": "activation_1", "trainable": true, "activation": "linear"}}, {"class_name": "Dense", "config": {"name": "dense_2", "trainable": true, "units": 1, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Activation", "config": {"name": "activation_2", "trainable": true, "activation": "linear"}}]}, "keras_version": "2.2.4", "backend": "tensorflow"}
\ No newline at end of file
diff -r 231e9a9849e8 -r 0b88494bdcac test-data/keras_batch_model01
Binary file test-data/keras_batch_model01 has changed
diff -r 231e9a9849e8 -r 0b88494bdcac test-data/keras_batch_model02
Binary file test-data/keras_batch_model02 has changed
diff -r 231e9a9849e8 -r 0b88494bdcac test-data/keras_batch_model03
Binary file test-data/keras_batch_model03 has changed
diff -r 231e9a9849e8 -r 0b88494bdcac test-data/keras_batch_params01.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/keras_batch_params01.tabular	Fri Aug 09 07:25:16 2019 -0400
@@ -0,0 +1,94 @@
+	Parameter	Value
+@	amsgrad	amsgrad: None
+@	batch_size	batch_size: 32
+@	beta_1	beta_1: None
+@	beta_2	beta_2: None
+@	callbacks	callbacks: [{'callback_selection': {'callback_type': 'None'}}]
+@	class_positive_factor	class_positive_factor: 1.0
+@	config	config: {'name': 'sequential_1', 'layers': [{'class_name': 'Dense', 'config': {'name': 'dense_1', 'trainable
+@	data_batch_generator	"data_batch_generator: FastaDNABatchGenerator(fasta_path='to_be_determined', seed=999,
+            seq_length=1000, shuffle=True)"
+@	decay	decay: 0.0
+@	epochs	epochs: 100
+@	epsilon	epsilon: None
+@	layers_0_Dense	layers_0_Dense: {'class_name': 'Dense', 'config': {'name': 'dense_1', 'trainable': True, 'batch_input_shape': [None,
+@	layers_1_Activation	layers_1_Activation: {'class_name': 'Activation', 'config': {'name': 'activation_1', 'trainable': True, 'activation': 're
+@	layers_2_Dense	layers_2_Dense: {'class_name': 'Dense', 'config': {'name': 'dense_2', 'trainable': True, 'units': 10, 'activation': 
+@	layers_3_Activation	layers_3_Activation: {'class_name': 'Activation', 'config': {'name': 'activation_2', 'trainable': True, 'activation': 'so
+@	loss	loss: 'binary_crossentropy'
+@	lr	lr: 0.01
+@	metrics	metrics: ['acc']
+@	model_type	model_type: 'sequential'
+@	momentum	momentum: 0.0
+*	n_jobs	n_jobs: 1
+@	nesterov	nesterov: False
+@	optimizer	optimizer: 'sgd'
+@	prediction_steps	prediction_steps: None
+@	rho	rho: None
+@	schedule_decay	schedule_decay: None
+@	seed	seed: None
+@	steps_per_epoch	steps_per_epoch: None
+@	validation_data	validation_data: None
+@	validation_steps	validation_steps: None
+@	verbose	verbose: 0
+*	data_batch_generator__fasta_path	data_batch_generator__fasta_path: 'to_be_determined'
+@	data_batch_generator__seed	data_batch_generator__seed: 999
+@	data_batch_generator__seq_length	data_batch_generator__seq_length: 1000
+@	data_batch_generator__shuffle	data_batch_generator__shuffle: True
+*	layers_0_Dense__class_name	layers_0_Dense__class_name: 'Dense'
+@	layers_0_Dense__config	layers_0_Dense__config: {'name': 'dense_1', 'trainable': True, 'batch_input_shape': [None, 784], 'dtype': 'float32', 'units'
+@	layers_0_Dense__config__activation	layers_0_Dense__config__activation: 'linear'
+@	layers_0_Dense__config__activity_regularizer	layers_0_Dense__config__activity_regularizer: None
+@	layers_0_Dense__config__batch_input_shape	layers_0_Dense__config__batch_input_shape: [None, 784]
+@	layers_0_Dense__config__bias_constraint	layers_0_Dense__config__bias_constraint: None
+@	layers_0_Dense__config__bias_initializer	layers_0_Dense__config__bias_initializer: {'class_name': 'Zeros', 'config': {}}
+*	layers_0_Dense__config__bias_initializer__class_name	layers_0_Dense__config__bias_initializer__class_name: 'Zeros'
+@	layers_0_Dense__config__bias_initializer__config	layers_0_Dense__config__bias_initializer__config: {}
+@	layers_0_Dense__config__bias_regularizer	layers_0_Dense__config__bias_regularizer: None
+@	layers_0_Dense__config__dtype	layers_0_Dense__config__dtype: 'float32'
+@	layers_0_Dense__config__kernel_constraint	layers_0_Dense__config__kernel_constraint: None
+@	layers_0_Dense__config__kernel_initializer	layers_0_Dense__config__kernel_initializer: {'class_name': 'VarianceScaling', 'config': {'scale': 1.0, 'mode': 'fan_avg', 'distribution': 'unifo
+*	layers_0_Dense__config__kernel_initializer__class_name	layers_0_Dense__config__kernel_initializer__class_name: 'VarianceScaling'
+@	layers_0_Dense__config__kernel_initializer__config	layers_0_Dense__config__kernel_initializer__config: {'scale': 1.0, 'mode': 'fan_avg', 'distribution': 'uniform', 'seed': None}
+@	layers_0_Dense__config__kernel_initializer__config__distribution	layers_0_Dense__config__kernel_initializer__config__distribution: 'uniform'
+@	layers_0_Dense__config__kernel_initializer__config__mode	layers_0_Dense__config__kernel_initializer__config__mode: 'fan_avg'
+@	layers_0_Dense__config__kernel_initializer__config__scale	layers_0_Dense__config__kernel_initializer__config__scale: 1.0
+@	layers_0_Dense__config__kernel_initializer__config__seed	layers_0_Dense__config__kernel_initializer__config__seed: None
+@	layers_0_Dense__config__kernel_regularizer	layers_0_Dense__config__kernel_regularizer: None
+*	layers_0_Dense__config__name	layers_0_Dense__config__name: 'dense_1'
+@	layers_0_Dense__config__trainable	layers_0_Dense__config__trainable: True
+@	layers_0_Dense__config__units	layers_0_Dense__config__units: 32
+@	layers_0_Dense__config__use_bias	layers_0_Dense__config__use_bias: True
+*	layers_1_Activation__class_name	layers_1_Activation__class_name: 'Activation'
+@	layers_1_Activation__config	layers_1_Activation__config: {'name': 'activation_1', 'trainable': True, 'activation': 'relu'}
+@	layers_1_Activation__config__activation	layers_1_Activation__config__activation: 'relu'
+*	layers_1_Activation__config__name	layers_1_Activation__config__name: 'activation_1'
+@	layers_1_Activation__config__trainable	layers_1_Activation__config__trainable: True
+*	layers_2_Dense__class_name	layers_2_Dense__class_name: 'Dense'
+@	layers_2_Dense__config	layers_2_Dense__config: {'name': 'dense_2', 'trainable': True, 'units': 10, 'activation': 'linear', 'use_bias': True, 'kerne
+@	layers_2_Dense__config__activation	layers_2_Dense__config__activation: 'linear'
+@	layers_2_Dense__config__activity_regularizer	layers_2_Dense__config__activity_regularizer: None
+@	layers_2_Dense__config__bias_constraint	layers_2_Dense__config__bias_constraint: None
+@	layers_2_Dense__config__bias_initializer	layers_2_Dense__config__bias_initializer: {'class_name': 'Zeros', 'config': {}}
+*	layers_2_Dense__config__bias_initializer__class_name	layers_2_Dense__config__bias_initializer__class_name: 'Zeros'
+@	layers_2_Dense__config__bias_initializer__config	layers_2_Dense__config__bias_initializer__config: {}
+@	layers_2_Dense__config__bias_regularizer	layers_2_Dense__config__bias_regularizer: None
+@	layers_2_Dense__config__kernel_constraint	layers_2_Dense__config__kernel_constraint: None
+@	layers_2_Dense__config__kernel_initializer	layers_2_Dense__config__kernel_initializer: {'class_name': 'VarianceScaling', 'config': {'scale': 1.0, 'mode': 'fan_avg', 'distribution': 'unifo
+*	layers_2_Dense__config__kernel_initializer__class_name	layers_2_Dense__config__kernel_initializer__class_name: 'VarianceScaling'
+@	layers_2_Dense__config__kernel_initializer__config	layers_2_Dense__config__kernel_initializer__config: {'scale': 1.0, 'mode': 'fan_avg', 'distribution': 'uniform', 'seed': None}
+@	layers_2_Dense__config__kernel_initializer__config__distribution	layers_2_Dense__config__kernel_initializer__config__distribution: 'uniform'
+@	layers_2_Dense__config__kernel_initializer__config__mode	layers_2_Dense__config__kernel_initializer__config__mode: 'fan_avg'
+@	layers_2_Dense__config__kernel_initializer__config__scale	layers_2_Dense__config__kernel_initializer__config__scale: 1.0
+@	layers_2_Dense__config__kernel_initializer__config__seed	layers_2_Dense__config__kernel_initializer__config__seed: None
+@	layers_2_Dense__config__kernel_regularizer	layers_2_Dense__config__kernel_regularizer: None
+*	layers_2_Dense__config__name	layers_2_Dense__config__name: 'dense_2'
+@	layers_2_Dense__config__trainable	layers_2_Dense__config__trainable: True
+@	layers_2_Dense__config__units	layers_2_Dense__config__units: 10
+@	layers_2_Dense__config__use_bias	layers_2_Dense__config__use_bias: True
+*	layers_3_Activation__class_name	layers_3_Activation__class_name: 'Activation'
+@	layers_3_Activation__config	layers_3_Activation__config: {'name': 'activation_2', 'trainable': True, 'activation': 'softmax'}
+@	layers_3_Activation__config__activation	layers_3_Activation__config__activation: 'softmax'
+*	layers_3_Activation__config__name	layers_3_Activation__config__name: 'activation_2'
+@	layers_3_Activation__config__trainable	layers_3_Activation__config__trainable: True
+	Note:	@, params eligible for search in searchcv tool.
diff -r 231e9a9849e8 -r 0b88494bdcac test-data/keras_model01
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diff -r 231e9a9849e8 -r 0b88494bdcac test-data/keras_model02
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diff -r 231e9a9849e8 -r 0b88494bdcac test-data/keras_model04
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diff -r 231e9a9849e8 -r 0b88494bdcac test-data/keras_params04.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/keras_params04.tabular	Fri Aug 09 07:25:16 2019 -0400
@@ -0,0 +1,85 @@
+	Parameter	Value
+@	amsgrad	amsgrad: False
+@	batch_size	batch_size: 32
+@	beta_1	beta_1: 0.9
+@	beta_2	beta_2: 0.999
+@	callbacks	callbacks: [{'callback_selection': {'callback_type': 'None'}}]
+@	config	config: {'name': 'sequential_1', 'layers': [{'class_name': 'Dense', 'config': {'name': 'dense_1', 'trainable
+@	decay	decay: 0.0
+@	epochs	epochs: 100
+@	epsilon	epsilon: None
+@	layers_0_Dense	layers_0_Dense: {'class_name': 'Dense', 'config': {'name': 'dense_1', 'trainable': True, 'batch_input_shape': [None,
+@	layers_1_Activation	layers_1_Activation: {'class_name': 'Activation', 'config': {'name': 'activation_1', 'trainable': True, 'activation': 'li
+@	layers_2_Dense	layers_2_Dense: {'class_name': 'Dense', 'config': {'name': 'dense_2', 'trainable': True, 'units': 1, 'activation': '
+@	layers_3_Activation	layers_3_Activation: {'class_name': 'Activation', 'config': {'name': 'activation_2', 'trainable': True, 'activation': 'li
+@	loss	loss: 'mean_squared_error'
+@	lr	lr: 0.001
+@	metrics	metrics: ['mse']
+@	model_type	model_type: 'sequential'
+@	momentum	momentum: None
+@	nesterov	nesterov: None
+@	optimizer	optimizer: 'adam'
+@	rho	rho: None
+@	schedule_decay	schedule_decay: None
+@	seed	seed: 42
+@	steps_per_epoch	steps_per_epoch: None
+@	validation_data	validation_data: None
+@	validation_steps	validation_steps: None
+@	verbose	verbose: 0
+*	layers_0_Dense__class_name	layers_0_Dense__class_name: 'Dense'
+@	layers_0_Dense__config	layers_0_Dense__config: {'name': 'dense_1', 'trainable': True, 'batch_input_shape': [None, 17], 'dtype': 'float32', 'units':
+@	layers_0_Dense__config__activation	layers_0_Dense__config__activation: 'linear'
+@	layers_0_Dense__config__activity_regularizer	layers_0_Dense__config__activity_regularizer: None
+@	layers_0_Dense__config__batch_input_shape	layers_0_Dense__config__batch_input_shape: [None, 17]
+@	layers_0_Dense__config__bias_constraint	layers_0_Dense__config__bias_constraint: None
+@	layers_0_Dense__config__bias_initializer	layers_0_Dense__config__bias_initializer: {'class_name': 'Zeros', 'config': {}}
+*	layers_0_Dense__config__bias_initializer__class_name	layers_0_Dense__config__bias_initializer__class_name: 'Zeros'
+@	layers_0_Dense__config__bias_initializer__config	layers_0_Dense__config__bias_initializer__config: {}
+@	layers_0_Dense__config__bias_regularizer	layers_0_Dense__config__bias_regularizer: None
+@	layers_0_Dense__config__dtype	layers_0_Dense__config__dtype: 'float32'
+@	layers_0_Dense__config__kernel_constraint	layers_0_Dense__config__kernel_constraint: None
+@	layers_0_Dense__config__kernel_initializer	layers_0_Dense__config__kernel_initializer: {'class_name': 'VarianceScaling', 'config': {'scale': 1.0, 'mode': 'fan_avg', 'distribution': 'unifo
+*	layers_0_Dense__config__kernel_initializer__class_name	layers_0_Dense__config__kernel_initializer__class_name: 'VarianceScaling'
+@	layers_0_Dense__config__kernel_initializer__config	layers_0_Dense__config__kernel_initializer__config: {'scale': 1.0, 'mode': 'fan_avg', 'distribution': 'uniform', 'seed': None}
+@	layers_0_Dense__config__kernel_initializer__config__distribution	layers_0_Dense__config__kernel_initializer__config__distribution: 'uniform'
+@	layers_0_Dense__config__kernel_initializer__config__mode	layers_0_Dense__config__kernel_initializer__config__mode: 'fan_avg'
+@	layers_0_Dense__config__kernel_initializer__config__scale	layers_0_Dense__config__kernel_initializer__config__scale: 1.0
+@	layers_0_Dense__config__kernel_initializer__config__seed	layers_0_Dense__config__kernel_initializer__config__seed: None
+@	layers_0_Dense__config__kernel_regularizer	layers_0_Dense__config__kernel_regularizer: None
+*	layers_0_Dense__config__name	layers_0_Dense__config__name: 'dense_1'
+@	layers_0_Dense__config__trainable	layers_0_Dense__config__trainable: True
+@	layers_0_Dense__config__units	layers_0_Dense__config__units: 32
+@	layers_0_Dense__config__use_bias	layers_0_Dense__config__use_bias: True
+*	layers_1_Activation__class_name	layers_1_Activation__class_name: 'Activation'
+@	layers_1_Activation__config	layers_1_Activation__config: {'name': 'activation_1', 'trainable': True, 'activation': 'linear'}
+@	layers_1_Activation__config__activation	layers_1_Activation__config__activation: 'linear'
+*	layers_1_Activation__config__name	layers_1_Activation__config__name: 'activation_1'
+@	layers_1_Activation__config__trainable	layers_1_Activation__config__trainable: True
+*	layers_2_Dense__class_name	layers_2_Dense__class_name: 'Dense'
+@	layers_2_Dense__config	layers_2_Dense__config: {'name': 'dense_2', 'trainable': True, 'units': 1, 'activation': 'linear', 'use_bias': True, 'kernel
+@	layers_2_Dense__config__activation	layers_2_Dense__config__activation: 'linear'
+@	layers_2_Dense__config__activity_regularizer	layers_2_Dense__config__activity_regularizer: None
+@	layers_2_Dense__config__bias_constraint	layers_2_Dense__config__bias_constraint: None
+@	layers_2_Dense__config__bias_initializer	layers_2_Dense__config__bias_initializer: {'class_name': 'Zeros', 'config': {}}
+*	layers_2_Dense__config__bias_initializer__class_name	layers_2_Dense__config__bias_initializer__class_name: 'Zeros'
+@	layers_2_Dense__config__bias_initializer__config	layers_2_Dense__config__bias_initializer__config: {}
+@	layers_2_Dense__config__bias_regularizer	layers_2_Dense__config__bias_regularizer: None
+@	layers_2_Dense__config__kernel_constraint	layers_2_Dense__config__kernel_constraint: None
+@	layers_2_Dense__config__kernel_initializer	layers_2_Dense__config__kernel_initializer: {'class_name': 'VarianceScaling', 'config': {'scale': 1.0, 'mode': 'fan_avg', 'distribution': 'unifo
+*	layers_2_Dense__config__kernel_initializer__class_name	layers_2_Dense__config__kernel_initializer__class_name: 'VarianceScaling'
+@	layers_2_Dense__config__kernel_initializer__config	layers_2_Dense__config__kernel_initializer__config: {'scale': 1.0, 'mode': 'fan_avg', 'distribution': 'uniform', 'seed': None}
+@	layers_2_Dense__config__kernel_initializer__config__distribution	layers_2_Dense__config__kernel_initializer__config__distribution: 'uniform'
+@	layers_2_Dense__config__kernel_initializer__config__mode	layers_2_Dense__config__kernel_initializer__config__mode: 'fan_avg'
+@	layers_2_Dense__config__kernel_initializer__config__scale	layers_2_Dense__config__kernel_initializer__config__scale: 1.0
+@	layers_2_Dense__config__kernel_initializer__config__seed	layers_2_Dense__config__kernel_initializer__config__seed: None
+@	layers_2_Dense__config__kernel_regularizer	layers_2_Dense__config__kernel_regularizer: None
+*	layers_2_Dense__config__name	layers_2_Dense__config__name: 'dense_2'
+@	layers_2_Dense__config__trainable	layers_2_Dense__config__trainable: True
+@	layers_2_Dense__config__units	layers_2_Dense__config__units: 1
+@	layers_2_Dense__config__use_bias	layers_2_Dense__config__use_bias: True
+*	layers_3_Activation__class_name	layers_3_Activation__class_name: 'Activation'
+@	layers_3_Activation__config	layers_3_Activation__config: {'name': 'activation_2', 'trainable': True, 'activation': 'linear'}
+@	layers_3_Activation__config__activation	layers_3_Activation__config__activation: 'linear'
+*	layers_3_Activation__config__name	layers_3_Activation__config__name: 'activation_2'
+@	layers_3_Activation__config__trainable	layers_3_Activation__config__trainable: True
+	Note:	@, params eligible for search in searchcv tool.
diff -r 231e9a9849e8 -r 0b88494bdcac test-data/keras_prefitted01.zip
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diff -r 231e9a9849e8 -r 0b88494bdcac test-data/model_pred01.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/model_pred01.tabular	Fri Aug 09 07:25:16 2019 -0400
@@ -0,0 +1,262 @@
+Predicted
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diff -r 231e9a9849e8 -r 0b88494bdcac test-data/model_pred02.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/model_pred02.tabular	Fri Aug 09 07:25:16 2019 -0400
@@ -0,0 +1,262 @@
+Predicted
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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/prp_result10	Fri Aug 09 07:25:16 2019 -0400
@@ -0,0 +1,262 @@
+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
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+-1.0	0.6363636363636365	-0.1333333333333333	0.14814814814814792	-0.3902439024390243	-0.06501547987616085	-0.16666666666666696	0.02857142857142847	-0.08571428571428585	0.4925373134328359	1.0	-1.0	-1.0	-1.0	-1.0	-1.0	-1.0
+-1.0	-0.4545454545454546	-0.06666666666666665	-0.11111111111111116	-0.41463414634146334	-0.18266253869969074	-0.0555555555555558	-0.1428571428571428	-0.08571428571428585	-0.6417910447761195	1.0	-1.0	-1.0	-1.0	-1.0	-1.0	-1.0
diff -r 231e9a9849e8 -r 0b88494bdcac test-data/regression_groups.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/regression_groups.tabular	Fri Aug 09 07:25:16 2019 -0400
@@ -0,0 +1,262 @@
+groups
+train
+train
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diff -r 231e9a9849e8 -r 0b88494bdcac test-data/searchCV01
Binary file test-data/searchCV01 has changed
diff -r 231e9a9849e8 -r 0b88494bdcac test-data/searchCV02
Binary file test-data/searchCV02 has changed
diff -r 231e9a9849e8 -r 0b88494bdcac test-data/train_test_eval01.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/train_test_eval01.tabular	Fri Aug 09 07:25:16 2019 -0400
@@ -0,0 +1,2 @@
+neg_mean_absolute_error	r2
+-5.29904520286704	0.6841931628349759
diff -r 231e9a9849e8 -r 0b88494bdcac test-data/train_test_eval03.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/train_test_eval03.tabular	Fri Aug 09 07:25:16 2019 -0400
@@ -0,0 +1,2 @@
+neg_mean_absolute_error	r2
+-4.811320754716981	0.7343422874316201
diff -r 231e9a9849e8 -r 0b88494bdcac test-data/train_test_eval_model01
Binary file test-data/train_test_eval_model01 has changed
diff -r 231e9a9849e8 -r 0b88494bdcac test-data/train_test_eval_weights01.h5
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diff -r 231e9a9849e8 -r 0b88494bdcac test-data/train_test_eval_weights02.h5
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diff -r 231e9a9849e8 -r 0b88494bdcac train_test_eval.py
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/train_test_eval.py	Fri Aug 09 07:25:16 2019 -0400
@@ -0,0 +1,433 @@
+import argparse
+import joblib
+import json
+import numpy as np
+import pandas as pd
+import pickle
+import warnings
+from itertools import chain
+from scipy.io import mmread
+from sklearn.base import clone
+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.metrics.scorer import _check_multimetric_scoring
+from sklearn.model_selection._validation import _score, cross_validate
+from sklearn.model_selection import _search, _validation
+from sklearn.utils import indexable, safe_indexing
+
+from galaxy_ml.model_validations import train_test_split
+from galaxy_ml.utils import (SafeEval, get_scoring, load_model,
+                             read_columns, try_get_attr, get_module)
+
+
+_fit_and_score = try_get_attr('galaxy_ml.model_validations', '_fit_and_score')
+setattr(_search, '_fit_and_score', _fit_and_score)
+setattr(_validation, '_fit_and_score', _fit_and_score)
+
+N_JOBS = int(__import__('os').environ.get('GALAXY_SLOTS', 1))
+CACHE_DIR = './cached'
+NON_SEARCHABLE = ('n_jobs', 'pre_dispatch', 'memory', '_path',
+                  'nthread', 'callbacks')
+ALLOWED_CALLBACKS = ('EarlyStopping', 'TerminateOnNaN', 'ReduceLROnPlateau',
+                     'CSVLogger', 'None')
+
+
+def _eval_swap_params(params_builder):
+    swap_params = {}
+
+    for p in params_builder['param_set']:
+        swap_value = p['sp_value'].strip()
+        if swap_value == '':
+            continue
+
+        param_name = p['sp_name']
+        if param_name.lower().endswith(NON_SEARCHABLE):
+            warnings.warn("Warning: `%s` is not eligible for search and was "
+                          "omitted!" % param_name)
+            continue
+
+        if not swap_value.startswith(':'):
+            safe_eval = SafeEval(load_scipy=True, load_numpy=True)
+            ev = safe_eval(swap_value)
+        else:
+            # Have `:` before search list, asks for estimator evaluatio
+            safe_eval_es = SafeEval(load_estimators=True)
+            swap_value = swap_value[1:].strip()
+            # TODO maybe add regular express check
+            ev = safe_eval_es(swap_value)
+
+        swap_params[param_name] = ev
+
+    return swap_params
+
+
+def train_test_split_none(*arrays, **kwargs):
+    """extend train_test_split to take None arrays
+    and support split by group names.
+    """
+    nones = []
+    new_arrays = []
+    for idx, arr in enumerate(arrays):
+        if arr is None:
+            nones.append(idx)
+        else:
+            new_arrays.append(arr)
+
+    if kwargs['shuffle'] == 'None':
+        kwargs['shuffle'] = None
+
+    group_names = kwargs.pop('group_names', None)
+
+    if group_names is not None and group_names.strip():
+        group_names = [name.strip() for name in
+                       group_names.split(',')]
+        new_arrays = indexable(*new_arrays)
+        groups = kwargs['labels']
+        n_samples = new_arrays[0].shape[0]
+        index_arr = np.arange(n_samples)
+        test = index_arr[np.isin(groups, group_names)]
+        train = index_arr[~np.isin(groups, group_names)]
+        rval = list(chain.from_iterable(
+            (safe_indexing(a, train),
+             safe_indexing(a, test)) for a in new_arrays))
+    else:
+        rval = train_test_split(*new_arrays, **kwargs)
+
+    for pos in nones:
+        rval[pos * 2: 2] = [None, None]
+
+    return rval
+
+
+def main(inputs, infile_estimator, infile1, infile2,
+         outfile_result, outfile_object=None,
+         outfile_weights=None, groups=None,
+         ref_seq=None, intervals=None, targets=None,
+         fasta_path=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
+
+    outfile_weights : str, optional
+        File path to save deep learning model weights
+
+    groups : str
+        File path to dataset containing groups labels
+
+    ref_seq : str
+        File path to dataset containing genome sequence file
+
+    intervals : str
+        File path to dataset containing interval file
+
+    targets : str
+        File path to dataset compressed target bed file
+
+    fasta_path : str
+        File path to dataset containing fasta file
+    """
+    warnings.simplefilter('ignore')
+
+    with open(inputs, 'r') as param_handler:
+        params = json.load(param_handler)
+
+    #  load estimator
+    with open(infile_estimator, 'rb') as estimator_handler:
+        estimator = load_model(estimator_handler)
+
+    # swap hyperparameter
+    swapping = params['experiment_schemes']['hyperparams_swapping']
+    swap_params = _eval_swap_params(swapping)
+    estimator.set_params(**swap_params)
+
+    estimator_params = estimator.get_params()
+
+    # store read dataframe object
+    loaded_df = {}
+
+    input_type = params['input_options']['selected_input']
+    # tabular 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
+
+        df_key = infile1 + repr(header)
+        df = pd.read_csv(infile1, sep='\t', header=header,
+                         parse_dates=True)
+        loaded_df[df_key] = df
+
+        X = read_columns(df, c=c, c_option=column_option).astype(float)
+    # sparse input
+    elif input_type == 'sparse':
+        X = mmread(open(infile1, 'r'))
+
+    # fasta_file input
+    elif input_type == 'seq_fasta':
+        pyfaidx = get_module('pyfaidx')
+        sequences = pyfaidx.Fasta(fasta_path)
+        n_seqs = len(sequences.keys())
+        X = np.arange(n_seqs)[:, np.newaxis]
+        for param in estimator_params.keys():
+            if param.endswith('fasta_path'):
+                estimator.set_params(
+                    **{param: fasta_path})
+                break
+        else:
+            raise ValueError(
+                "The selected estimator doesn't support "
+                "fasta file input! Please consider using "
+                "KerasGBatchClassifier with "
+                "FastaDNABatchGenerator/FastaProteinBatchGenerator "
+                "or having GenomeOneHotEncoder/ProteinOneHotEncoder "
+                "in pipeline!")
+
+    elif input_type == 'refseq_and_interval':
+        path_params = {
+            'data_batch_generator__ref_genome_path': ref_seq,
+            'data_batch_generator__intervals_path': intervals,
+            'data_batch_generator__target_path': targets
+        }
+        estimator.set_params(**path_params)
+        n_intervals = sum(1 for line in open(intervals))
+        X = np.arange(n_intervals)[:, np.newaxis]
+
+    # Get target y
+    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
+
+    df_key = infile2 + repr(header)
+    if df_key in loaded_df:
+        infile2 = loaded_df[df_key]
+    else:
+        infile2 = pd.read_csv(infile2, sep='\t',
+                              header=header, parse_dates=True)
+        loaded_df[df_key] = infile2
+
+    y = read_columns(
+            infile2,
+            c=c,
+            c_option=column_option,
+            sep='\t',
+            header=header,
+            parse_dates=True)
+    if len(y.shape) == 2 and y.shape[1] == 1:
+        y = y.ravel()
+    if input_type == 'refseq_and_interval':
+        estimator.set_params(
+            data_batch_generator__features=y.ravel().tolist())
+        y = None
+    # end y
+
+    # load groups
+    if groups:
+        groups_selector = (params['experiment_schemes']['test_split']
+                                 ['split_algos']).pop('groups_selector')
+
+        header = 'infer' if groups_selector['header_g'] else None
+        column_option = \
+            (groups_selector['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_selector['column_selector_options_g']['col_g']
+        else:
+            c = None
+
+        df_key = groups + repr(header)
+        if df_key in loaded_df:
+            groups = loaded_df[df_key]
+
+        groups = read_columns(
+                groups,
+                c=c,
+                c_option=column_option,
+                sep='\t',
+                header=header,
+                parse_dates=True)
+        groups = groups.ravel()
+
+    # del loaded_df
+    del loaded_df
+
+    # handle memory
+    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 iraps buried in pipeline
+        new_params = {}
+        for p, v in estimator_params.items():
+            if p.endswith('memory'):
+                # for case of `__irapsclassifier__memory`
+                if len(p) > 8 and p[:-8].endswith('irapsclassifier'):
+                    # cache iraps_core fits could increase search
+                    # speed significantly
+                    new_params[p] = memory
+                # security reason, we don't want memory being
+                # modified unexpectedly
+                elif v:
+                    new_params[p] = None
+            # handle n_jobs
+            elif p.endswith('n_jobs'):
+                # For now, 1 CPU is suggested for iprasclassifier
+                if len(p) > 8 and p[:-8].endswith('irapsclassifier'):
+                    new_params[p] = 1
+                else:
+                    new_params[p] = N_JOBS
+            # for security reason, types of callback are limited
+            elif p.endswith('callbacks'):
+                for cb in v:
+                    cb_type = cb['callback_selection']['callback_type']
+                    if cb_type not in ALLOWED_CALLBACKS:
+                        raise ValueError(
+                            "Prohibited callback type: %s!" % cb_type)
+
+        estimator.set_params(**new_params)
+
+    # handle scorer, convert to scorer dict
+    scoring = params['experiment_schemes']['metrics']['scoring']
+    scorer = get_scoring(scoring)
+    scorer, _ = _check_multimetric_scoring(estimator, scoring=scorer)
+
+    # handle test (first) split
+    test_split_options = (params['experiment_schemes']
+                                ['test_split']['split_algos'])
+
+    if test_split_options['shuffle'] == 'group':
+        test_split_options['labels'] = groups
+    if test_split_options['shuffle'] == 'stratified':
+        if y is not None:
+            test_split_options['labels'] = y
+        else:
+            raise ValueError("Stratified shuffle split is not "
+                             "applicable on empty target values!")
+
+    X_train, X_test, y_train, y_test, groups_train, groups_test = \
+        train_test_split_none(X, y, groups, **test_split_options)
+
+    exp_scheme = params['experiment_schemes']['selected_exp_scheme']
+
+    # handle validation (second) split
+    if exp_scheme == 'train_val_test':
+        val_split_options = (params['experiment_schemes']
+                                   ['val_split']['split_algos'])
+
+        if val_split_options['shuffle'] == 'group':
+            val_split_options['labels'] = groups_train
+        if val_split_options['shuffle'] == 'stratified':
+            if y_train is not None:
+                val_split_options['labels'] = y_train
+            else:
+                raise ValueError("Stratified shuffle split is not "
+                                 "applicable on empty target values!")
+
+        X_train, X_val, y_train, y_val, groups_train, groups_val = \
+            train_test_split_none(X_train, y_train, groups_train,
+                                  **val_split_options)
+
+    # train and eval
+    if hasattr(estimator, 'validation_data'):
+        if exp_scheme == 'train_val_test':
+            estimator.fit(X_train, y_train,
+                          validation_data=(X_val, y_val))
+        else:
+            estimator.fit(X_train, y_train,
+                          validation_data=(X_test, y_test))
+    else:
+        estimator.fit(X_train, y_train)
+
+    if hasattr(estimator, 'evaluate'):
+        scores = estimator.evaluate(X_test, y_test=y_test,
+                                    scorer=scorer,
+                                    is_multimetric=True)
+    else:
+        scores = _score(estimator, X_test, y_test, scorer,
+                        is_multimetric=True)
+    # handle output
+    for name, score in scores.items():
+        scores[name] = [score]
+    df = pd.DataFrame(scores)
+    df = df[sorted(df.columns)]
+    df.to_csv(path_or_buf=outfile_result, sep='\t',
+              header=True, index=False)
+
+    memory.clear(warn=False)
+
+    if outfile_object:
+        main_est = estimator
+        if isinstance(estimator, pipeline.Pipeline):
+            main_est = estimator.steps[-1][-1]
+
+        if hasattr(main_est, 'model_') \
+                and hasattr(main_est, 'save_weights'):
+            if outfile_weights:
+                main_est.save_weights(outfile_weights)
+            del main_est.model_
+            del main_est.fit_params
+            del main_est.model_class_
+            del main_est.validation_data
+            if getattr(main_est, 'data_generator_', None):
+                del main_est.data_generator_
+                del main_est.data_batch_generator
+
+        with open(outfile_object, 'wb') as output_handler:
+            pickle.dump(estimator, 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("-O", "--outfile_result", dest="outfile_result")
+    aparser.add_argument("-o", "--outfile_object", dest="outfile_object")
+    aparser.add_argument("-w", "--outfile_weights", dest="outfile_weights")
+    aparser.add_argument("-g", "--groups", dest="groups")
+    aparser.add_argument("-r", "--ref_seq", dest="ref_seq")
+    aparser.add_argument("-b", "--intervals", dest="intervals")
+    aparser.add_argument("-t", "--targets", dest="targets")
+    aparser.add_argument("-f", "--fasta_path", dest="fasta_path")
+    args = aparser.parse_args()
+
+    main(args.inputs, args.infile_estimator, args.infile1, args.infile2,
+         args.outfile_result, outfile_object=args.outfile_object,
+         outfile_weights=args.outfile_weights, groups=args.groups,
+         ref_seq=args.ref_seq, intervals=args.intervals,
+         targets=args.targets, fasta_path=args.fasta_path)
diff -r 231e9a9849e8 -r 0b88494bdcac utils.py
--- a/utils.py	Tue Jul 09 19:34:06 2019 -0400
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,599 +0,0 @@
-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)