diff search_model_validation.py @ 0:03f61bb3ca43 draft

"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/search_model_validation.py	Mon Dec 16 05:36:53 2019 -0500
@@ -0,0 +1,707 @@
+import argparse
+import collections
+import imblearn
+import joblib
+import json
+import numpy as np
+import os
+import pandas as pd
+import pickle
+import skrebate
+import sys
+import warnings
+from scipy.io import mmread
+from sklearn import (cluster, decomposition, feature_selection,
+                     kernel_approximation, model_selection, preprocessing)
+from sklearn.exceptions import FitFailedWarning
+from sklearn.model_selection._validation import _score, cross_validate
+from sklearn.model_selection import _search, _validation
+from sklearn.pipeline import Pipeline
+
+from galaxy_ml.utils import (SafeEval, get_cv, get_scoring, load_model,
+                             read_columns, try_get_attr, get_module,
+                             clean_params, get_main_estimator)
+
+
+_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(os.environ.get('GALAXY_SLOTS', 1))
+# handle  disk cache
+CACHE_DIR = os.path.join(os.getcwd(), 'cached')
+del os
+NON_SEARCHABLE = ('n_jobs', 'pre_dispatch', 'memory', '_path',
+                  'nthread', 'callbacks')
+
+
+def _eval_search_params(params_builder):
+    search_params = {}
+
+    for p in params_builder['param_set']:
+        search_list = p['sp_list'].strip()
+        if search_list == '':
+            continue
+
+        param_name = p['sp_name']
+        if param_name.lower().endswith(NON_SEARCHABLE):
+            print("Warning: `%s` is not eligible for search and was "
+                  "omitted!" % param_name)
+            continue
+
+        if not search_list.startswith(':'):
+            safe_eval = SafeEval(load_scipy=True, load_numpy=True)
+            ev = safe_eval(search_list)
+            search_params[param_name] = ev
+        else:
+            # Have `:` before search list, asks for estimator evaluatio
+            safe_eval_es = SafeEval(load_estimators=True)
+            search_list = search_list[1:].strip()
+            # TODO maybe add regular express check
+            ev = safe_eval_es(search_list)
+            preprocessings = (
+                preprocessing.StandardScaler(), preprocessing.Binarizer(),
+                preprocessing.MaxAbsScaler(),
+                preprocessing.Normalizer(), preprocessing.MinMaxScaler(),
+                preprocessing.PolynomialFeatures(),
+                preprocessing.RobustScaler(), feature_selection.SelectKBest(),
+                feature_selection.GenericUnivariateSelect(),
+                feature_selection.SelectPercentile(),
+                feature_selection.SelectFpr(), feature_selection.SelectFdr(),
+                feature_selection.SelectFwe(),
+                feature_selection.VarianceThreshold(),
+                decomposition.FactorAnalysis(random_state=0),
+                decomposition.FastICA(random_state=0),
+                decomposition.IncrementalPCA(),
+                decomposition.KernelPCA(random_state=0, n_jobs=N_JOBS),
+                decomposition.LatentDirichletAllocation(
+                    random_state=0, n_jobs=N_JOBS),
+                decomposition.MiniBatchDictionaryLearning(
+                    random_state=0, n_jobs=N_JOBS),
+                decomposition.MiniBatchSparsePCA(
+                    random_state=0, n_jobs=N_JOBS),
+                decomposition.NMF(random_state=0),
+                decomposition.PCA(random_state=0),
+                decomposition.SparsePCA(random_state=0, n_jobs=N_JOBS),
+                decomposition.TruncatedSVD(random_state=0),
+                kernel_approximation.Nystroem(random_state=0),
+                kernel_approximation.RBFSampler(random_state=0),
+                kernel_approximation.AdditiveChi2Sampler(),
+                kernel_approximation.SkewedChi2Sampler(random_state=0),
+                cluster.FeatureAgglomeration(),
+                skrebate.ReliefF(n_jobs=N_JOBS),
+                skrebate.SURF(n_jobs=N_JOBS),
+                skrebate.SURFstar(n_jobs=N_JOBS),
+                skrebate.MultiSURF(n_jobs=N_JOBS),
+                skrebate.MultiSURFstar(n_jobs=N_JOBS),
+                imblearn.under_sampling.ClusterCentroids(
+                    random_state=0, n_jobs=N_JOBS),
+                imblearn.under_sampling.CondensedNearestNeighbour(
+                    random_state=0, n_jobs=N_JOBS),
+                imblearn.under_sampling.EditedNearestNeighbours(
+                    random_state=0, n_jobs=N_JOBS),
+                imblearn.under_sampling.RepeatedEditedNearestNeighbours(
+                    random_state=0, n_jobs=N_JOBS),
+                imblearn.under_sampling.AllKNN(random_state=0, n_jobs=N_JOBS),
+                imblearn.under_sampling.InstanceHardnessThreshold(
+                    random_state=0, n_jobs=N_JOBS),
+                imblearn.under_sampling.NearMiss(
+                    random_state=0, n_jobs=N_JOBS),
+                imblearn.under_sampling.NeighbourhoodCleaningRule(
+                    random_state=0, n_jobs=N_JOBS),
+                imblearn.under_sampling.OneSidedSelection(
+                    random_state=0, n_jobs=N_JOBS),
+                imblearn.under_sampling.RandomUnderSampler(
+                    random_state=0),
+                imblearn.under_sampling.TomekLinks(
+                    random_state=0, n_jobs=N_JOBS),
+                imblearn.over_sampling.ADASYN(random_state=0, n_jobs=N_JOBS),
+                imblearn.over_sampling.RandomOverSampler(random_state=0),
+                imblearn.over_sampling.SMOTE(random_state=0, n_jobs=N_JOBS),
+                imblearn.over_sampling.SVMSMOTE(random_state=0, n_jobs=N_JOBS),
+                imblearn.over_sampling.BorderlineSMOTE(
+                    random_state=0, n_jobs=N_JOBS),
+                imblearn.over_sampling.SMOTENC(
+                    categorical_features=[], random_state=0, n_jobs=N_JOBS),
+                imblearn.combine.SMOTEENN(random_state=0),
+                imblearn.combine.SMOTETomek(random_state=0))
+            newlist = []
+            for obj in ev:
+                if obj is None:
+                    newlist.append(None)
+                elif obj == 'all_0':
+                    newlist.extend(preprocessings[0:35])
+                elif obj == 'sk_prep_all':      # no KernalCenter()
+                    newlist.extend(preprocessings[0:7])
+                elif obj == 'fs_all':
+                    newlist.extend(preprocessings[7:14])
+                elif obj == 'decomp_all':
+                    newlist.extend(preprocessings[14:25])
+                elif obj == 'k_appr_all':
+                    newlist.extend(preprocessings[25:29])
+                elif obj == 'reb_all':
+                    newlist.extend(preprocessings[30:35])
+                elif obj == 'imb_all':
+                    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))
+                    else:
+                        newlist.append(obj)
+                else:
+                    sys.exit("Unsupported estimator type: %r" % (obj))
+
+            search_params[param_name] = newlist
+
+    return search_params
+
+
+def _handle_X_y(estimator, params, infile1, infile2, loaded_df={},
+                ref_seq=None, intervals=None, targets=None,
+                fasta_path=None):
+    """read inputs
+
+    Params
+    -------
+    estimator : estimator object
+    params : dict
+        Galaxy tool parameter inputs
+    infile1 : str
+        File path to dataset containing features
+    infile2 : str
+        File path to dataset containing target values
+    loaded_df : dict
+        Contains loaded DataFrame objects with file path as keys
+    ref_seq : str
+        File path to dataset containing genome sequence file
+    interval : 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
+
+
+    Returns
+    -------
+    estimator : estimator object after setting new attributes
+    X : numpy array
+    y : numpy array
+    """
+    estimator_params = estimator.get_params()
+
+    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)
+
+        if df_key in loaded_df:
+            infile1 = loaded_df[df_key]
+
+        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
+
+    return estimator, X, y
+
+
+def _do_outer_cv(searcher, X, y, outer_cv, scoring, error_score='raise',
+                 outfile=None):
+    """Do outer cross-validation for nested CV
+
+    Parameters
+    ----------
+    searcher : object
+        SearchCV object
+    X : numpy array
+        Containing features
+    y : numpy array
+        Target values or labels
+    outer_cv : int or CV splitter
+        Control the cv splitting
+    scoring : object
+        Scorer
+    error_score: str, float or numpy float
+        Whether to raise fit error or return an value
+    outfile : str
+        File path to store the restuls
+    """
+    if error_score == 'raise':
+        rval = cross_validate(
+            searcher, X, y, scoring=scoring,
+            cv=outer_cv, n_jobs=N_JOBS, verbose=0,
+            error_score=error_score)
+    else:
+        warnings.simplefilter('always', FitFailedWarning)
+        with warnings.catch_warnings(record=True) as w:
+            try:
+                rval = cross_validate(
+                    searcher, X, y,
+                    scoring=scoring,
+                    cv=outer_cv, n_jobs=N_JOBS,
+                    verbose=0,
+                    error_score=error_score)
+            except ValueError:
+                pass
+            for warning in w:
+                print(repr(warning.message))
+
+    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, sep='\t', header=True, index=False)
+
+
+def _do_train_test_split_val(searcher, X, y, params, error_score='raise',
+                             primary_scoring=None, groups=None,
+                             outfile=None):
+    """ do train test split, searchCV validates on the train and then use
+    the best_estimator_ to evaluate on the test
+
+    Returns
+    --------
+    Fitted SearchCV object
+    """
+    train_test_split = try_get_attr(
+        'galaxy_ml.model_validations', 'train_test_split')
+    split_options = params['outer_split']
+
+    # splits
+    if split_options['shuffle'] == 'stratified':
+        split_options['labels'] = y
+        X, X_test, y, y_test = train_test_split(X, y, **split_options)
+    elif split_options['shuffle'] == 'group':
+        if 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)
+
+    if error_score == 'raise':
+        searcher.fit(X, y, groups=groups)
+    else:
+        warnings.simplefilter('always', FitFailedWarning)
+        with warnings.catch_warnings(record=True) as w:
+            try:
+                searcher.fit(X, y, groups=groups)
+            except ValueError:
+                pass
+            for warning in w:
+                print(repr(warning.message))
+
+    scorer_ = searcher.scorer_
+    if isinstance(scorer_, collections.Mapping):
+        is_multimetric = True
+    else:
+        is_multimetric = False
+
+    best_estimator_ = getattr(searcher, 'best_estimator_')
+
+    # TODO Solve deep learning models in pipeline
+    if best_estimator_.__class__.__name__ == 'KerasGBatchClassifier':
+        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, sep='\t', header=True,
+                     index=False)
+
+    return searcher
+
+
+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 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')
+
+    # store read dataframe object
+    loaded_df = {}
+
+    with open(inputs, 'r') as param_handler:
+        params = json.load(param_handler)
+
+    # Override the refit parameter
+    params['search_schemes']['options']['refit'] = True \
+        if params['save'] != 'nope' else False
+
+    with open(infile_estimator, 'rb') as estimator_handler:
+        estimator = load_model(estimator_handler)
+
+    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)
+
+        groups = pd.read_csv(groups, sep='\t', header=header,
+                             parse_dates=True)
+        loaded_df[df_key] = groups
+
+        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
+    primary_scoring = options['scoring']['primary_scoring']
+    options['scoring'] = get_scoring(options['scoring'])
+    if options['error_score']:
+        options['error_score'] = 'raise'
+    else:
+        options['error_score'] = np.NaN
+    if options['refit'] and isinstance(options['scoring'], dict):
+        options['refit'] = primary_scoring
+    if 'pre_dispatch' in options and options['pre_dispatch'] == '':
+        options['pre_dispatch'] = None
+
+    params_builder = params['search_schemes']['search_params_builder']
+    param_grid = _eval_search_params(params_builder)
+
+    estimator = clean_params(estimator)
+
+    # save the SearchCV object without fit
+    if params['save'] == 'save_no_fit':
+        searcher = optimizer(estimator, param_grid, **options)
+        print(searcher)
+        with open(outfile_object, 'wb') as output_handler:
+            pickle.dump(searcher, output_handler,
+                        pickle.HIGHEST_PROTOCOL)
+        return 0
+
+    # read inputs and loads new attributes, like paths
+    estimator, X, y = _handle_X_y(estimator, params, infile1, infile2,
+                                  loaded_df=loaded_df, ref_seq=ref_seq,
+                                  intervals=intervals, targets=targets,
+                                  fasta_path=fasta_path)
+
+    # cache iraps_core fits could increase search speed significantly
+    memory = joblib.Memory(location=CACHE_DIR, verbose=0)
+    main_est = get_main_estimator(estimator)
+    if main_est.__class__.__name__ == 'IRAPSClassifier':
+        main_est.set_params(memory=memory)
+
+    searcher = optimizer(estimator, param_grid, **options)
+
+    split_mode = params['outer_split'].pop('split_mode')
+
+    if split_mode == 'nested_cv':
+        # 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)
+
+        outer_cv, _ = get_cv(params['outer_split']['cv_selector'])
+        # nested CV, outer cv using cross_validate
+        if options['error_score'] == 'raise':
+            rval = cross_validate(
+                searcher, X, y, scoring=options['scoring'],
+                cv=outer_cv, n_jobs=N_JOBS,
+                verbose=options['verbose'],
+                return_estimator=(params['save'] == 'save_estimator'),
+                error_score=options['error_score'],
+                return_train_score=True)
+        else:
+            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=options['verbose'],
+                        return_estimator=(params['save'] == 'save_estimator'),
+                        error_score=options['error_score'],
+                        return_train_score=True)
+                except ValueError:
+                    pass
+                for warning in w:
+                    print(repr(warning.message))
+
+        fitted_searchers = rval.pop('estimator', [])
+        if fitted_searchers:
+            import os
+            pwd = os.getcwd()
+            save_dir = os.path.join(pwd, 'cv_results_in_folds')
+            try:
+                os.mkdir(save_dir)
+                for idx, obj in enumerate(fitted_searchers):
+                    target_name = 'cv_results_' + '_' + 'split%d' % idx
+                    target_path = os.path.join(pwd, save_dir, target_name)
+                    cv_results_ = getattr(obj, 'cv_results_', None)
+                    if not cv_results_:
+                        print("%s is not available" % target_name)
+                        continue
+                    cv_results_ = pd.DataFrame(cv_results_)
+                    cv_results_ = cv_results_[sorted(cv_results_.columns)]
+                    cv_results_.to_csv(target_path, sep='\t', header=True,
+                                       index=False)
+            except Exception as e:
+                print(e)
+            finally:
+                del os
+
+        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)
+
+        return 0
+
+        # deprecate train test split mode
+        """searcher = _do_train_test_split_val(
+            searcher, X, y, params,
+            primary_scoring=primary_scoring,
+            error_score=options['error_score'],
+            groups=groups,
+            outfile=outfile_result)"""
+
+    # no outer split
+    else:
+        searcher.set_params(n_jobs=N_JOBS)
+        if options['error_score'] == 'raise':
+            searcher.fit(X, y, groups=groups)
+        else:
+            warnings.simplefilter('always', FitFailedWarning)
+            with warnings.catch_warnings(record=True) as w:
+                try:
+                    searcher.fit(X, y, groups=groups)
+                except ValueError:
+                    pass
+                for warning in w:
+                    print(repr(warning.message))
+
+        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)
+
+    memory.clear(warn=False)
+
+    # output best estimator, and weights if applicable
+    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
+
+        # clean prams
+        best_estimator_ = clean_params(best_estimator_)
+
+        main_est = get_main_estimator(best_estimator_)
+
+        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_
+
+        with open(outfile_object, 'wb') as output_handler:
+            print("Best estimator is saved: %s " % repr(best_estimator_))
+            pickle.dump(best_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)