--- a/train_test_eval.py	Thu Oct 01 19:58:28 2020 +0000
+++ b/train_test_eval.py	Tue Apr 13 17:25:00 2021 +0000
@@ -1,59 +1,66 @@
 import argparse
-import joblib
 import json
-import numpy as np
 import os
-import pandas as pd
 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.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 import pipeline
 from sklearn.metrics.scorer import _check_multimetric_scoring
-from sklearn.model_selection._validation import _score, cross_validate
+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
 
-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)
 
-_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')
+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')
+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 == '':
+    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:
@@ -80,23 +87,24 @@
         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)
 
@@ -106,11 +114,20 @@
     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):
+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
     ---------
@@ -150,17 +167,17 @@
     fasta_path : str
         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:
+    with open(infile_estimator, "rb") as estimator_handler:
         estimator = load_model(estimator_handler)
 
     # 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)
 
@@ -169,38 +186,41 @@
     # 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(
@@ -209,25 +229,31 @@
                 "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
 
@@ -235,37 +261,39 @@
     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)
+    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())
+    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
 
@@ -273,13 +301,12 @@
         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 = read_columns(groups,
+                              c=c,
+                              c_option=column_option,
+                              sep='\t',
+                              header=header,
+                              parse_dates=True)
         groups = groups.ravel()
 
     # del loaded_df
@@ -288,15 +315,15 @@
     # handle memory
     memory = joblib.Memory(location=CACHE_DIR, verbose=0)
     # cache iraps_core fits could increase search speed significantly
-    if estimator.__class__.__name__ == 'IRAPSClassifier':
+    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'):
+            if p.endswith("memory"):
                 # for case of `__irapsclassifier__memory`
-                if len(p) > 8 and p[:-8].endswith('irapsclassifier'):
+                if len(p) > 8 and p[:-8].endswith("irapsclassifier"):
                     # cache iraps_core fits could increase search
                     # speed significantly
                     new_params[p] = memory
@@ -305,88 +332,98 @@
                 elif v:
                     new_params[p] = None
             # handle n_jobs
-            elif p.endswith('n_jobs'):
+            elif p.endswith("n_jobs"):
                 # For now, 1 CPU is suggested for iprasclassifier
-                if len(p) > 8 and p[:-8].endswith('irapsclassifier'):
+                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'):
+            elif p.endswith("callbacks"):
                 for cb in v:
-                    cb_type = cb['callback_selection']['callback_type']
+                    cb_type = cb["callback_selection"]["callback_type"]
                     if cb_type not in ALLOWED_CALLBACKS:
-                        raise ValueError(
-                            "Prohibited callback type: %s!" % cb_type)
+                        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']
+    # 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'])
+    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, "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))
+            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)
+    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)
+        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)
+    df.to_csv(path_or_buf=outfile_result, sep="\t", header=True, index=False)
 
     memory.clear(warn=False)
 
@@ -395,23 +432,25 @@
         if isinstance(estimator, pipeline.Pipeline):
             main_est = estimator.steps[-1][-1]
 
-        if hasattr(main_est, 'model_') \
-                and hasattr(main_est, 'save_weights'):
+        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):
+            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)
+        with open(outfile_object, "wb") as output_handler:
+            pickle.dump(estimator, output_handler, pickle.HIGHEST_PROTOCOL)
 
 
-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")
@@ -427,8 +466,17 @@
     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)
+    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,
+    )