diff keras_train_and_eval.py @ 3:0a1812986bc3 draft

planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 9981e25b00de29ed881b2229a173a8c812ded9bb

--- a/keras_train_and_eval.py	Mon Dec 16 10:07:37 2019 +0000
+++ b/keras_train_and_eval.py	Wed Aug 09 11:10:37 2023 +0000
@@ -1,56 +1,69 @@
 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.pipeline import Pipeline
-from sklearn.metrics.scorer import _check_multimetric_scoring
-from sklearn import model_selection
-from sklearn.model_selection._validation import _score
-from sklearn.model_selection import _search, _validation
-from sklearn.utils import indexable, safe_indexing
 
-from galaxy_ml.externals.selene_sdk.utils import compute_score
+import joblib
+import numpy as np
+import pandas as pd
+from galaxy_ml.keras_galaxy_models import (
+    _predict_generator,
+    KerasGBatchClassifier,
+)
+from galaxy_ml.model_persist import dump_model_to_h5, load_model_from_h5
 from galaxy_ml.model_validations import train_test_split
-from galaxy_ml.keras_galaxy_models import _predict_generator
-from galaxy_ml.utils import (SafeEval, get_scoring, load_model,
-                             read_columns, try_get_attr, get_module,
-                             clean_params, get_main_estimator)
-
+from galaxy_ml.utils import (
+    clean_params,
+    gen_compute_scores,
+    get_main_estimator,
+    get_module,
+    get_scoring,
+    read_columns,
+    SafeEval
+)
+from scipy.io import mmread
+from sklearn.metrics._scorer import _check_multimetric_scoring
+from sklearn.model_selection._validation import _score
+from sklearn.utils import _safe_indexing, indexable
 
-_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')
+N_JOBS = int(os.environ.get("GALAXY_SLOTS", 1))
+CACHE_DIR = os.path.join(os.getcwd(), "cached")
+NON_SEARCHABLE = (
+    "n_jobs",
+    "pre_dispatch",
+    "memory",
+    "_path",
+    "_dir",
+    "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 == '':
+    for p in params_builder["param_set"]:
+        swap_value = p["sp_value"].strip()
+        if swap_value == "":
             continue
 
-        param_name = p['sp_name']
+        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)
+            warnings.warn(
+                "Warning: `%s` is not eligible for search and was "
+                "omitted!" % param_name
+            )
             continue
 
-        if not swap_value.startswith(':'):
+        if not swap_value.startswith(":"):
             safe_eval = SafeEval(load_scipy=True, load_numpy=True)
             ev = safe_eval(swap_value)
         else:
@@ -77,23 +90,24 @@
         else:
             new_arrays.append(arr)
 
-    if kwargs['shuffle'] == 'None':
-        kwargs['shuffle'] = None
+    if kwargs["shuffle"] == "None":
+        kwargs["shuffle"] = None
 
-    group_names = kwargs.pop('group_names', 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(',')]
+        group_names = [name.strip() for name in group_names.split(",")]
         new_arrays = indexable(*new_arrays)
-        groups = kwargs['labels']
+        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))
+        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)
 
@@ -103,125 +117,140 @@
     return rval
 
 
-def _evaluate(y_true, pred_probas, scorer, is_multimetric=True):
-    """ output scores based on input scorer
+def _evaluate_keras_and_sklearn_scores(
+    estimator,
+    data_generator,
+    X,
+    y=None,
+    sk_scoring=None,
+    steps=None,
+    batch_size=32,
+    return_predictions=False,
+):
+    """output scores for bother keras and sklearn metrics
 
     Parameters
-    ----------
-    y_true : array
-        True label or target values
-    pred_probas : array
-        Prediction values, probability for classification problem
-    scorer : dict
-        dict of `sklearn.metrics.scorer.SCORER`
-    is_multimetric : bool, default is True
+    -----------
+    estimator : object
+        Fitted `galaxy_ml.keras_galaxy_models.KerasGBatchClassifier`.
+    data_generator : object
+        From `galaxy_ml.preprocessors.ImageDataFrameBatchGenerator`.
+    X : 2-D array
+        Contains indecies of images that need to be evaluated.
+    y : None
+        Target value.
+    sk_scoring : dict
+        Galaxy tool input parameters.
+    steps : integer or None
+        Evaluation/prediction steps before stop.
+    batch_size : integer
+        Number of samples in a batch
+    return_predictions : bool, default is False
+        Whether to return predictions and true labels.
     """
-    if y_true.ndim == 1 or y_true.shape[-1] == 1:
-        pred_probas = pred_probas.ravel()
-        pred_labels = (pred_probas > 0.5).astype('int32')
-        targets = y_true.ravel().astype('int32')
-        if not is_multimetric:
-            preds = pred_labels if scorer.__class__.__name__ == \
-                '_PredictScorer' else pred_probas
-            score = scorer._score_func(targets, preds, **scorer._kwargs)
+    scores = {}
 
-            return score
-        else:
-            scores = {}
-            for name, one_scorer in scorer.items():
-                preds = pred_labels if one_scorer.__class__.__name__\
-                    == '_PredictScorer' else pred_probas
-                score = one_scorer._score_func(targets, preds,
-                                               **one_scorer._kwargs)
-                scores[name] = score
-
-    # TODO: multi-class metrics
-    # multi-label
+    generator = data_generator.flow(X, y=y, batch_size=batch_size)
+    # keras metrics evaluation
+    # handle scorer, convert to scorer dict
+    generator.reset()
+    score_results = estimator.model_.evaluate_generator(generator, steps=steps)
+    metrics_names = estimator.model_.metrics_names
+    if not isinstance(metrics_names, list):
+        scores[metrics_names] = score_results
     else:
-        pred_labels = (pred_probas > 0.5).astype('int32')
-        targets = y_true.astype('int32')
-        if not is_multimetric:
-            preds = pred_labels if scorer.__class__.__name__ == \
-                '_PredictScorer' else pred_probas
-            score, _ = compute_score(preds, targets,
-                                     scorer._score_func)
-            return score
-        else:
-            scores = {}
-            for name, one_scorer in scorer.items():
-                preds = pred_labels if one_scorer.__class__.__name__\
-                    == '_PredictScorer' else pred_probas
-                score, _ = compute_score(preds, targets,
-                                         one_scorer._score_func)
-                scores[name] = score
+        scores = dict(zip(metrics_names, score_results))
+
+    if sk_scoring["primary_scoring"] == "default" and not return_predictions:
+        return scores
+
+    generator.reset()
+    predictions, y_true = _predict_generator(estimator.model_, generator, steps=steps)
 
-    return scores
+    # for sklearn metrics
+    if sk_scoring["primary_scoring"] != "default":
+        scorer = get_scoring(sk_scoring)
+        if not isinstance(scorer, (dict, list)):
+            scorer = [sk_scoring["primary_scoring"]]
+        scorer = _check_multimetric_scoring(estimator, scoring=scorer)
+        sk_scores = gen_compute_scores(y_true, predictions, scorer)
+        scores.update(sk_scores)
+
+    if return_predictions:
+        return scores, predictions, y_true
+    else:
+        return scores, None, None
 
 
-def main(inputs, infile_estimator, infile1, infile2,
-         outfile_result, outfile_object=None,
-         outfile_weights=None, outfile_y_true=None,
-         outfile_y_preds=None, groups=None,
-         ref_seq=None, intervals=None, targets=None,
-         fasta_path=None):
+def main(
+    inputs,
+    infile_estimator,
+    infile1,
+    infile2,
+    outfile_result,
+    outfile_object=None,
+    outfile_y_true=None,
+    outfile_y_preds=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 galaxy tool parameter.
 
     infile_estimator : str
-        File path to estimator
+        File path to estimator.
 
     infile1 : str
-        File path to dataset containing features
+        File path to dataset containing features.
 
     infile2 : str
-        File path to dataset containing target values
+        File path to dataset containing target values.
 
     outfile_result : str
-        File path to save the results, either cv_results or test result
+        File path to save the results, either cv_results or test result.
 
     outfile_object : str, optional
-        File path to save searchCV object
-
-    outfile_weights : str, optional
-        File path to save deep learning model weights
+        File path to save searchCV object.
 
     outfile_y_true : str, optional
-        File path to target values for prediction
+        File path to target values for prediction.
 
     outfile_y_preds : str, optional
-        File path to save deep learning model weights
+        File path to save predictions.
 
     groups : str
-        File path to dataset containing groups labels
+        File path to dataset containing groups labels.
 
     ref_seq : str
-        File path to dataset containing genome sequence file
+        File path to dataset containing genome sequence file.
 
     intervals : str
-        File path to dataset containing interval file
+        File path to dataset containing interval file.
 
     targets : str
-        File path to dataset compressed target bed file
+        File path to dataset compressed target bed file.
 
     fasta_path : str
-        File path to dataset containing fasta file
+        File path to dataset containing fasta file.
     """
-    warnings.simplefilter('ignore')
+    warnings.simplefilter("ignore")
 
-    with open(inputs, 'r') as param_handler:
+    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)
+    estimator = load_model_from_h5(infile_estimator)
 
     estimator = clean_params(estimator)
 
     # swap hyperparameter
-    swapping = params['experiment_schemes']['hyperparams_swapping']
+    swapping = params["experiment_schemes"]["hyperparams_swapping"]
     swap_params = _eval_swap_params(swapping)
     estimator.set_params(**swap_params)
 
@@ -230,38 +259,41 @@
     # store read dataframe object
     loaded_df = {}
 
-    input_type = params['input_options']['selected_input']
+    input_type = params["input_options"]["selected_input"]
     # tabular input
-    if input_type == 'tabular':
-        header = 'infer' if params['input_options']['header1'] else None
-        column_option = (params['input_options']['column_selector_options_1']
-                         ['selected_column_selector_option'])
-        if column_option in ['by_index_number', 'all_but_by_index_number',
-                             'by_header_name', 'all_but_by_header_name']:
-            c = params['input_options']['column_selector_options_1']['col1']
+    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)
+        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'))
+    elif input_type == "sparse":
+        X = mmread(open(infile1, "r"))
 
     # fasta_file input
-    elif input_type == 'seq_fasta':
-        pyfaidx = get_module('pyfaidx')
+    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})
+            if param.endswith("fasta_path"):
+                estimator.set_params(**{param: fasta_path})
                 break
         else:
             raise ValueError(
@@ -270,25 +302,31 @@
                 "KerasGBatchClassifier with "
                 "FastaDNABatchGenerator/FastaProteinBatchGenerator "
                 "or having GenomeOneHotEncoder/ProteinOneHotEncoder "
-                "in pipeline!")
+                "in pipeline!"
+            )
 
-    elif input_type == 'refseq_and_interval':
+    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
+            "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']
+    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
 
@@ -296,37 +334,41 @@
     if df_key in loaded_df:
         infile2 = loaded_df[df_key]
     else:
-        infile2 = pd.read_csv(infile2, sep='\t',
-                              header=header, parse_dates=True)
+        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)
+        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())
+    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')
+        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']
+        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
 
@@ -335,12 +377,13 @@
             groups = loaded_df[df_key]
 
         groups = read_columns(
-                groups,
-                c=c,
-                c_option=column_option,
-                sep='\t',
-                header=header,
-                parse_dates=True)
+            groups,
+            c=c,
+            c_option=column_option,
+            sep="\t",
+            header=header,
+            parse_dates=True,
+        )
         groups = groups.ravel()
 
     # del loaded_df
@@ -349,121 +392,134 @@
     # 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':
+    if main_est.__class__.__name__ == "IRAPSClassifier":
         main_est.set_params(memory=memory)
 
     # handle scorer, convert to scorer dict
-    scoring = params['experiment_schemes']['metrics']['scoring']
+    scoring = params["experiment_schemes"]["metrics"]["scoring"]
     scorer = get_scoring(scoring)
-    scorer, _ = _check_multimetric_scoring(estimator, scoring=scorer)
+    if not isinstance(scorer, (dict, list)):
+        scorer = [scoring["primary_scoring"]]
+    scorer = _check_multimetric_scoring(estimator, scoring=scorer)
 
     # handle test (first) split
-    test_split_options = (params['experiment_schemes']
-                                ['test_split']['split_algos'])
+    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 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
+            test_split_options["labels"] = y
         else:
-            raise ValueError("Stratified shuffle split is not "
-                             "applicable on empty target values!")
+            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)
+    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']
+    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 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 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
+                val_split_options["labels"] = y_train
             else:
-                raise ValueError("Stratified shuffle split is not "
-                                 "applicable on empty target values!")
+                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)
+        (
+            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))
+    if hasattr(estimator, "config") and hasattr(estimator, "model_type"):
+        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))
+            estimator.fit(X_train, y_train, validation_data=(X_test, y_test))
     else:
         estimator.fit(X_train, y_train)
 
-    if hasattr(estimator, 'evaluate'):
+    if isinstance(estimator, KerasGBatchClassifier):
+        scores = {}
         steps = estimator.prediction_steps
         batch_size = estimator.batch_size
-        generator = estimator.data_generator_.flow(X_test, y=y_test,
-                                                   batch_size=batch_size)
-        predictions, y_true = _predict_generator(estimator.model_, generator,
-                                                 steps=steps)
-        scores = _evaluate(y_true, predictions, scorer, is_multimetric=True)
+        data_generator = estimator.data_generator_
+
+        scores, predictions, y_true = _evaluate_keras_and_sklearn_scores(
+            estimator,
+            data_generator,
+            X_test,
+            y=y_test,
+            sk_scoring=scoring,
+            steps=steps,
+            batch_size=batch_size,
+            return_predictions=bool(outfile_y_true),
+        )
 
     else:
-        if hasattr(estimator, 'predict_proba'):
+        scores = {}
+        if hasattr(estimator, "model_") and hasattr(estimator.model_, "metrics_names"):
+            batch_size = estimator.batch_size
+            score_results = estimator.model_.evaluate(
+                X_test, y=y_test, batch_size=batch_size, verbose=0
+            )
+            metrics_names = estimator.model_.metrics_names
+            if not isinstance(metrics_names, list):
+                scores[metrics_names] = score_results
+            else:
+                scores = dict(zip(metrics_names, score_results))
+
+        if hasattr(estimator, "predict_proba"):
             predictions = estimator.predict_proba(X_test)
         else:
             predictions = estimator.predict(X_test)
 
         y_true = y_test
-        scores = _score(estimator, X_test, y_test, scorer,
-                        is_multimetric=True)
+        sk_scores = _score(estimator, X_test, y_test, scorer)
+        scores.update(sk_scores)
+
+    # handle output
     if outfile_y_true:
         try:
-            pd.DataFrame(y_true).to_csv(outfile_y_true, sep='\t',
-                                        index=False)
+            pd.DataFrame(y_true).to_csv(outfile_y_true, sep="\t", index=False)
             pd.DataFrame(predictions).astype(np.float32).to_csv(
-                outfile_y_preds, sep='\t', index=False,
-                float_format='%g', chunksize=10000)
+                outfile_y_preds,
+                sep="\t",
+                index=False,
+                float_format="%g",
+                chunksize=10000,
+            )
         except Exception as e:
             print("Error in saving predictions: %s" % e)
-
     # 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)
+    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):
-            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)
+        dump_model_to_h5(estimator, outfile_object)
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     aparser = argparse.ArgumentParser()
     aparser.add_argument("-i", "--inputs", dest="inputs", required=True)
     aparser.add_argument("-e", "--estimator", dest="infile_estimator")
@@ -471,7 +527,6 @@
     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("-l", "--outfile_y_true", dest="outfile_y_true")
     aparser.add_argument("-p", "--outfile_y_preds", dest="outfile_y_preds")
     aparser.add_argument("-g", "--groups", dest="groups")
@@ -481,11 +536,18 @@
     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,
-         outfile_y_true=args.outfile_y_true,
-         outfile_y_preds=args.outfile_y_preds,
-         groups=args.groups,
-         ref_seq=args.ref_seq, intervals=args.intervals,
-         targets=args.targets, fasta_path=args.fasta_path)
+    main(
+        args.inputs,
+        args.infile_estimator,
+        args.infile1,
+        args.infile2,
+        args.outfile_result,
+        outfile_object=args.outfile_object,
+        outfile_y_true=args.outfile_y_true,
+        outfile_y_preds=args.outfile_y_preds,
+        groups=args.groups,
+        ref_seq=args.ref_seq,
+        intervals=args.intervals,
+        targets=args.targets,
+        fasta_path=args.fasta_path,
+    )