--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/stacking_ensembles.py	Sat May 01 01:24:32 2021 +0000
@@ -0,0 +1,131 @@
+import argparse
+import ast
+import json
+import pickle
+import sys
+import warnings
+
+import mlxtend.classifier
+import mlxtend.regressor
+import pandas as pd
+from galaxy_ml.utils import (get_cv, get_estimator, get_search_params,
+                             load_model)
+
+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)
+
+    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:
+                model = load_model(handler)
+        else:
+            estimator_json = params["base_est_builder"][idx]["estimator_selector"]
+            model = get_estimator(estimator_json)
+
+        if estimator_type.startswith("sklearn"):
+            named = model.__class__.__name__.lower()
+            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 meta_path:
+            with open(meta_path, "rb") as f:
+                meta_estimator = load_model(f)
+        else:
+            estimator_json = params["algo_selection"]["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
+
+    weights = options.pop("weights", None)
+    if weights:
+        weights = ast.literal_eval(weights)
+        if weights:
+            options["weights"] = weights
+
+    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
+        ensemble_estimator = klass(base_estimators, **options)
+
+    elif mod == mlxtend.classifier:
+        ensemble_estimator = klass(
+            classifiers=base_estimators, meta_classifier=meta_estimator, **options
+        )
+
+    else:
+        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:
+        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,
+    )