--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/fitted_model_eval.py	Fri Nov 01 17:30:30 2019 -0400
@@ -0,0 +1,160 @@
+import argparse
+import json
+import pandas as pd
+import warnings
+
+from scipy.io import mmread
+from sklearn.pipeline import Pipeline
+from sklearn.metrics.scorer import _check_multimetric_scoring
+from sklearn.model_selection._validation import _score
+from galaxy_ml.utils import get_scoring, load_model, read_columns
+
+
+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, outfile_eval,
+         infile_weights=None, infile1=None,
+         infile2=None):
+    """
+    Parameter
+    ---------
+    inputs : str
+        File path to galaxy tool parameter
+
+    infile_estimator : strgit
+        File path to trained estimator input
+
+    outfile_eval : str
+        File path to save the evalulation results, tabular
+
+    infile_weights : str
+        File path to weights input
+
+    infile1 : str
+        File path to dataset containing features
+
+    infile2 : str
+        File path to dataset containing target values
+    """
+    warnings.filterwarnings('ignore')
+
+    with open(inputs, 'r') as param_handler:
+        params = json.load(param_handler)
+
+    X_test, y_test = _get_X_y(params, infile1, infile2)
+
+    # load model
+    with open(infile_estimator, 'rb') as est_handler:
+        estimator = load_model(est_handler)
+
+    main_est = estimator
+    if isinstance(estimator, Pipeline):
+        main_est = estimator.steps[-1][-1]
+    if hasattr(main_est, 'config') and hasattr(main_est, 'load_weights'):
+        if not infile_weights or infile_weights == 'None':
+            raise ValueError("The selected model skeleton asks for weights, "
+                             "but no dataset for weights was provided!")
+        main_est.load_weights(infile_weights)
+
+    # handle scorer, convert to scorer dict
+    scoring = params['scoring']
+    scorer = get_scoring(scoring)
+    scorer, _ = _check_multimetric_scoring(estimator, scoring=scorer)
+
+    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)
+
+    # 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_eval, sep='\t',
+              header=True, index=False)
+
+
+if __name__ == '__main__':
+    aparser = argparse.ArgumentParser()
+    aparser.add_argument("-i", "--inputs", dest="inputs", required=True)
+    aparser.add_argument("-e", "--infile_estimator", dest="infile_estimator")
+    aparser.add_argument("-w", "--infile_weights", dest="infile_weights")
+    aparser.add_argument("-X", "--infile1", dest="infile1")
+    aparser.add_argument("-y", "--infile2", dest="infile2")
+    aparser.add_argument("-O", "--outfile_eval", dest="outfile_eval")
+    args = aparser.parse_args()
+
+    main(args.inputs, args.infile_estimator, args.outfile_eval,
+         infile_weights=args.infile_weights, infile1=args.infile1,
+         infile2=args.infile2)