--- a/stacking_ensembles.py	Fri Oct 02 08:45:21 2020 +0000
+++ b/stacking_ensembles.py	Tue Apr 13 18:49:35 2021 +0000
@@ -5,22 +5,17 @@
 import mlxtend.classifier
 import pandas as pd
 import pickle
-import sklearn
 import sys
 import warnings
-from sklearn import ensemble
-
-from galaxy_ml.utils import (load_model, get_cv, get_estimator,
-                             get_search_params)
+from galaxy_ml.utils import load_model, get_cv, get_estimator, get_search_params
 
 
-warnings.filterwarnings('ignore')
+warnings.filterwarnings("ignore")
 
-N_JOBS = int(__import__('os').environ.get('GALAXY_SLOTS', 1))
+N_JOBS = int(__import__("os").environ.get("GALAXY_SLOTS", 1))
 
 
-def main(inputs_path, output_obj, base_paths=None, meta_path=None,
-         outfile_params=None):
+def main(inputs_path, output_obj, base_paths=None, meta_path=None, outfile_params=None):
     """
     Parameter
     ---------
@@ -39,87 +34,79 @@
     outfile_params : str
         File path for params output
     """
-    with open(inputs_path, 'r') as param_handler:
+    with open(inputs_path, "r") as param_handler:
         params = json.load(param_handler)
 
-    estimator_type = params['algo_selection']['estimator_type']
+    estimator_type = params["algo_selection"]["estimator_type"]
     # get base estimators
     base_estimators = []
-    for idx, base_file in enumerate(base_paths.split(',')):
-        if base_file and base_file != 'None':
-            with open(base_file, 'rb') as handler:
+    for idx, base_file in enumerate(base_paths.split(",")):
+        if base_file and base_file != "None":
+            with open(base_file, "rb") as handler:
                 model = load_model(handler)
         else:
-            estimator_json = (params['base_est_builder'][idx]
-                              ['estimator_selector'])
+            estimator_json = params["base_est_builder"][idx]["estimator_selector"]
             model = get_estimator(estimator_json)
 
-        if estimator_type.startswith('sklearn'):
+        if estimator_type.startswith("sklearn"):
             named = model.__class__.__name__.lower()
-            named = 'base_%d_%s' % (idx, named)
+            named = "base_%d_%s" % (idx, named)
             base_estimators.append((named, model))
         else:
             base_estimators.append(model)
 
     # get meta estimator, if applicable
-    if estimator_type.startswith('mlxtend'):
+    if estimator_type.startswith("mlxtend"):
         if meta_path:
-            with open(meta_path, 'rb') as f:
+            with open(meta_path, "rb") as f:
                 meta_estimator = load_model(f)
         else:
-            estimator_json = (params['algo_selection']
-                              ['meta_estimator']['estimator_selector'])
+            estimator_json = params["algo_selection"]["meta_estimator"]["estimator_selector"]
             meta_estimator = get_estimator(estimator_json)
 
-    options = params['algo_selection']['options']
+    options = params["algo_selection"]["options"]
 
-    cv_selector = options.pop('cv_selector', None)
+    cv_selector = options.pop("cv_selector", None)
     if cv_selector:
-        splitter, groups = get_cv(cv_selector)
-        options['cv'] = splitter
+        splitter, _groups = get_cv(cv_selector)
+        options["cv"] = splitter
         # set n_jobs
-        options['n_jobs'] = N_JOBS
+        options["n_jobs"] = N_JOBS
 
-    weights = options.pop('weights', None)
+    weights = options.pop("weights", None)
     if weights:
         weights = ast.literal_eval(weights)
         if weights:
-            options['weights'] = weights
+            options["weights"] = weights
 
-    mod_and_name = estimator_type.split('_')
+    mod_and_name = estimator_type.split("_")
     mod = sys.modules[mod_and_name[0]]
     klass = getattr(mod, mod_and_name[1])
 
-    if estimator_type.startswith('sklearn'):
-        options['n_jobs'] = N_JOBS
+    if estimator_type.startswith("sklearn"):
+        options["n_jobs"] = N_JOBS
         ensemble_estimator = klass(base_estimators, **options)
 
     elif mod == mlxtend.classifier:
-        ensemble_estimator = klass(
-            classifiers=base_estimators,
-            meta_classifier=meta_estimator,
-            **options)
+        ensemble_estimator = klass(classifiers=base_estimators, meta_classifier=meta_estimator, **options)
 
     else:
-        ensemble_estimator = klass(
-            regressors=base_estimators,
-            meta_regressor=meta_estimator,
-            **options)
+        ensemble_estimator = klass(regressors=base_estimators, meta_regressor=meta_estimator, **options)
 
     print(ensemble_estimator)
     for base_est in base_estimators:
         print(base_est)
 
-    with open(output_obj, 'wb') as out_handler:
+    with open(output_obj, "wb") as out_handler:
         pickle.dump(ensemble_estimator, out_handler, pickle.HIGHEST_PROTOCOL)
 
-    if params['get_params'] and outfile_params:
+    if params["get_params"] and outfile_params:
         results = get_search_params(ensemble_estimator)
-        df = pd.DataFrame(results, columns=['', 'Parameter', 'Value'])
-        df.to_csv(outfile_params, sep='\t', index=False)
+        df = pd.DataFrame(results, columns=["", "Parameter", "Value"])
+        df.to_csv(outfile_params, sep="\t", index=False)
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     aparser = argparse.ArgumentParser()
     aparser.add_argument("-b", "--bases", dest="bases")
     aparser.add_argument("-m", "--meta", dest="meta")
@@ -128,5 +115,10 @@
     aparser.add_argument("-p", "--outfile_params", dest="outfile_params")
     args = aparser.parse_args()
 
-    main(args.inputs, args.outfile, base_paths=args.bases,
-         meta_path=args.meta, outfile_params=args.outfile_params)
+    main(
+        args.inputs,
+        args.outfile,
+        base_paths=args.bases,
+        meta_path=args.meta,
+        outfile_params=args.outfile_params,
+    )