diff fitted_model_eval.py @ 35:eeaf989f1024 draft

"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"

--- a/fitted_model_eval.py	Thu Oct 01 20:44:13 2020 +0000
+++ b/fitted_model_eval.py	Tue Apr 13 18:09:01 2021 +0000
@@ -11,7 +11,7 @@
 
 
 def _get_X_y(params, infile1, infile2):
-    """ read from inputs and output X and y
+    """read from inputs and output X and y
 
     Parameters
     ----------
@@ -26,35 +26,40 @@
     # store read dataframe object
     loaded_df = {}
 
-    input_type = params['input_options']['selected_input']
+    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']
+    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)
+        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'))
+    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']
+    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
 
@@ -62,26 +67,24 @@
     if df_key in loaded_df:
         infile2 = loaded_df[df_key]
     else:
-        infile2 = pd.read_csv(infile2, sep='\t',
-                              header=header, parse_dates=True)
+        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)
+    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):
+def main(
+    inputs,
+    infile_estimator,
+    outfile_eval,
+    infile_weights=None,
+    infile1=None,
+    infile2=None,
+):
     """
     Parameter
     ---------
@@ -103,49 +106,55 @@
     infile2 : str
         File path to dataset containing target values
     """
-    warnings.filterwarnings('ignore')
+    warnings.filterwarnings("ignore")
 
-    with open(inputs, 'r') as param_handler:
+    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:
+    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!")
+    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']
+    # Check if scoring is specified
+    scoring = params["scoring"]
+    if scoring is not None:
+        # get_scoring() expects secondary_scoring to be a comma separated string (not a list)
+        # Check if secondary_scoring is specified
+        secondary_scoring = scoring.get("secondary_scoring", None)
+        if secondary_scoring is not None:
+            # If secondary_scoring is specified, convert the list into comman separated string
+            scoring["secondary_scoring"] = ",".join(scoring["secondary_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)
+    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)
+        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)
+    df.to_csv(path_or_buf=outfile_eval, sep="\t", header=True, index=False)
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     aparser = argparse.ArgumentParser()
     aparser.add_argument("-i", "--inputs", dest="inputs", required=True)
     aparser.add_argument("-e", "--infile_estimator", dest="infile_estimator")
@@ -155,6 +164,11 @@
     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)
+    main(
+        args.inputs,
+        args.infile_estimator,
+        args.outfile_eval,
+        infile_weights=args.infile_weights,
+        infile1=args.infile1,
+        infile2=args.infile2,
+    )