--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/stacking_ensembles.py	Tue May 14 18:09:43 2019 -0400
@@ -0,0 +1,128 @@
+import argparse
+import json
+import pandas as pd
+import pickle
+import xgboost
+import warnings
+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.model_selection._split import check_cv
+from feature_selectors import (DyRFE, DyRFECV,
+                               MyPipeline, MyimbPipeline)
+from iraps_classifier import (IRAPSCore, IRAPSClassifier,
+                              BinarizeTargetClassifier,
+                              BinarizeTargetRegressor)
+from preprocessors import Z_RandomOverSampler
+from utils import load_model, get_cv, get_estimator, get_search_params
+
+from mlxtend.regressor import StackingCVRegressor, StackingRegressor
+from mlxtend.classifier import StackingCVClassifier, StackingClassifier
+
+
+warnings.filterwarnings('ignore')
+
+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):
+    """
+    Parameter
+    ---------
+    inputs_path : str
+        File path for Galaxy parameters
+
+    output_obj : str
+        File path for ensemble estimator ouput
+
+    base_paths : str
+        File path or paths concatenated by comma.
+
+    meta_path : str
+        File path
+
+    outfile_params : str
+        File path for params output
+    """
+    with open(inputs_path, 'r') as param_handler:
+        params = json.load(param_handler)
+
+    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:
+                model = load_model(handler)
+        else:
+            estimator_json = (params['base_est_builder'][idx]
+                              ['estimator_selector'])
+            model = get_estimator(estimator_json)
+        base_estimators.append(model)
+
+    if meta_path:
+        with open(meta_path, 'rb') as f:
+            meta_estimator = load_model(f)
+    else:
+        estimator_json = params['meta_estimator']['estimator_selector']
+        meta_estimator = get_estimator(estimator_json)
+
+    options = params['algo_selection']['options']
+
+    cv_selector = options.pop('cv_selector', None)
+    if cv_selector:
+        splitter, groups = get_cv(cv_selector)
+        options['cv'] = splitter
+        # set n_jobs
+        options['n_jobs'] = N_JOBS
+
+    if params['algo_selection']['estimator_type'] == 'StackingCVClassifier':
+        ensemble_estimator = StackingCVClassifier(
+            classifiers=base_estimators,
+            meta_classifier=meta_estimator,
+            **options)
+
+    elif params['algo_selection']['estimator_type'] == 'StackingClassifier':
+        ensemble_estimator = StackingClassifier(
+            classifiers=base_estimators,
+            meta_classifier=meta_estimator,
+            **options)
+
+    elif params['algo_selection']['estimator_type'] == 'StackingCVRegressor':
+        ensemble_estimator = StackingCVRegressor(
+            regressors=base_estimators,
+            meta_regressor=meta_estimator,
+            **options)
+
+    else:
+        ensemble_estimator = StackingRegressor(
+            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:
+        pickle.dump(ensemble_estimator, out_handler, pickle.HIGHEST_PROTOCOL)
+
+    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)
+
+
+if __name__ == '__main__':
+    aparser = argparse.ArgumentParser()
+    aparser.add_argument("-b", "--bases", dest="bases")
+    aparser.add_argument("-m", "--meta", dest="meta")
+    aparser.add_argument("-i", "--inputs", dest="inputs")
+    aparser.add_argument("-o", "--outfile", dest="outfile")
+    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)