--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/simple_model_fit.py	Sat May 01 01:24:32 2021 +0000
@@ -0,0 +1,194 @@
+import argparse
+import json
+import pickle
+
+import pandas as pd
+from galaxy_ml.utils import load_model, read_columns
+from scipy.io import mmread
+from sklearn.pipeline import Pipeline
+
+N_JOBS = int(__import__("os").environ.get("GALAXY_SLOTS", 1))
+
+
+# TODO import from galaxy_ml.utils in future versions
+def clean_params(estimator, n_jobs=None):
+    """clean unwanted hyperparameter settings
+
+    If n_jobs is not None, set it into the estimator, if applicable
+
+    Return
+    ------
+    Cleaned estimator object
+    """
+    ALLOWED_CALLBACKS = (
+        "EarlyStopping",
+        "TerminateOnNaN",
+        "ReduceLROnPlateau",
+        "CSVLogger",
+        "None",
+    )
+
+    estimator_params = estimator.get_params()
+
+    for name, p in estimator_params.items():
+        # all potential unauthorized file write
+        if name == "memory" or name.endswith("__memory") or name.endswith("_path"):
+            new_p = {name: None}
+            estimator.set_params(**new_p)
+        elif n_jobs is not None and (name == "n_jobs" or name.endswith("__n_jobs")):
+            new_p = {name: n_jobs}
+            estimator.set_params(**new_p)
+        elif name.endswith("callbacks"):
+            for cb in p:
+                cb_type = cb["callback_selection"]["callback_type"]
+                if cb_type not in ALLOWED_CALLBACKS:
+                    raise ValueError("Prohibited callback type: %s!" % cb_type)
+
+    return estimator
+
+
+def _get_X_y(params, infile1, infile2):
+    """read from inputs and output X and y
+
+    Parameters
+    ----------
+    params : dict
+        Tool inputs parameter
+    infile1 : str
+        File path to dataset containing features
+    infile2 : str
+        File path to dataset containing target values
+
+    """
+    # store read dataframe object
+    loaded_df = {}
+
+    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"]
+        else:
+            c = None
+
+        df_key = infile1 + repr(header)
+        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"))
+
+    # 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"]
+    else:
+        c = None
+
+    df_key = infile2 + repr(header)
+    if df_key in loaded_df:
+        infile2 = loaded_df[df_key]
+    else:
+        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
+    )
+    if len(y.shape) == 2 and y.shape[1] == 1:
+        y = y.ravel()
+
+    return X, y
+
+
+def main(inputs, infile_estimator, infile1, infile2, out_object, out_weights=None):
+    """main
+
+    Parameters
+    ----------
+    inputs : str
+        File path to galaxy tool parameter
+
+    infile_estimator : str
+        File paths of input estimator
+
+    infile1 : str
+        File path to dataset containing features
+
+    infile2 : str
+        File path to dataset containing target labels
+
+    out_object : str
+        File path for output of fitted model or skeleton
+
+    out_weights : str
+        File path for output of weights
+
+    """
+    with open(inputs, "r") as param_handler:
+        params = json.load(param_handler)
+
+    # load model
+    with open(infile_estimator, "rb") as est_handler:
+        estimator = load_model(est_handler)
+    estimator = clean_params(estimator, n_jobs=N_JOBS)
+
+    X_train, y_train = _get_X_y(params, infile1, infile2)
+
+    estimator.fit(X_train, y_train)
+
+    main_est = estimator
+    if isinstance(main_est, Pipeline):
+        main_est = main_est.steps[-1][-1]
+    if hasattr(main_est, "model_") and hasattr(main_est, "save_weights"):
+        if out_weights:
+            main_est.save_weights(out_weights)
+        del main_est.model_
+        del main_est.fit_params
+        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(out_object, "wb") as output_handler:
+        pickle.dump(estimator, output_handler, pickle.HIGHEST_PROTOCOL)
+
+
+if __name__ == "__main__":
+    aparser = argparse.ArgumentParser()
+    aparser.add_argument("-i", "--inputs", dest="inputs", required=True)
+    aparser.add_argument("-X", "--infile_estimator", dest="infile_estimator")
+    aparser.add_argument("-y", "--infile1", dest="infile1")
+    aparser.add_argument("-g", "--infile2", dest="infile2")
+    aparser.add_argument("-o", "--out_object", dest="out_object")
+    aparser.add_argument("-t", "--out_weights", dest="out_weights")
+    args = aparser.parse_args()
+
+    main(
+        args.inputs,
+        args.infile_estimator,
+        args.infile1,
+        args.infile2,
+        args.out_object,
+        args.out_weights,
+    )