comparison train_test_eval.py @ 35:1e99cfb71f40 draft

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

34:7068b5fcd623

import argparse
import joblib
import json
import numpy as np
import os
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(os.environ.get('GALAXY_SLOTS', 1))
CACHE_DIR = os.path.join(os.getcwd(), 'cached')
del os
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)

        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

        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_

        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("-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)

35:1e99cfb71f40

import argparse
import json
import os
import pickle
import warnings

from itertools import chain

import joblib
import numpy as np
import pandas as pd
from galaxy_ml.model_validations import train_test_split
from galaxy_ml.utils import (
    get_module,
    get_scoring,
    load_model,
    read_columns,
    SafeEval,
    try_get_attr,
)
from scipy.io import mmread
from sklearn import pipeline
from sklearn.metrics.scorer import _check_multimetric_scoring
from sklearn.model_selection._validation import _score
from sklearn.model_selection import _search, _validation
from sklearn.model_selection._validation import _score
from sklearn.utils import indexable, safe_indexing


_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))
CACHE_DIR = os.path.join(os.getcwd(), "cached")
del os
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)

        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

        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
    # Check if scoring is specified
    scoring = params["experiment_schemes"]["metrics"].get("scoring", None)
    if scoring is not None:
        # get_scoring() expects secondary_scoring to be a comma separated string (not a list)
        # Check if secondary_scoring is specified
        secondary_scoring = scoring.get("secondary_scoring", None)
        if secondary_scoring is not None:
            # If secondary_scoring is specified, convert the list into comman separated string
            scoring["secondary_scoring"] = ",".join(scoring["secondary_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)
            if getattr(main_est, "model_", None):
                del main_est.model_
            if getattr(main_est, "fit_params", None):
                del main_est.fit_params
            if getattr(main_est, "model_class_", None):
                del main_est.model_class_
            if getattr(main_est, "validation_data", None):
                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:
            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("-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,
    )