comparison search_model_validation.py @ 35:eeaf989f1024 draft

"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"

34:2d032cff49eb

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),

                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

    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

    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

    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

        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("-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)

35:eeaf989f1024

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),

                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

    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

    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

    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

        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"]
    # get_scoring() expects secondary_scoring to be a comma separated string (not a list)
    # Check if secondary_scoring is specified
    secondary_scoring = options["scoring"].get("secondary_scoring", None)
    if secondary_scoring is not None:
        # If secondary_scoring is specified, convert the list into comman separated string
        options["scoring"]["secondary_scoring"] = ",".join(options["scoring"]["secondary_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)
        # 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)"""
        return 0

    # 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("-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,
    )