Mercurial > repos > bgruening > sklearn_train_test_eval
diff fitted_model_eval.py @ 11:caf7d2b71a48 draft
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ea12f973df4b97a2691d9e4ce6bf6fae59d57717"
author | bgruening |
---|---|
date | Sat, 01 May 2021 01:47:26 +0000 |
parents | a9e0b963b7bb |
children | 2eb5c017958d |
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--- a/fitted_model_eval.py Tue Apr 13 22:04:06 2021 +0000 +++ b/fitted_model_eval.py Sat May 01 01:47:26 2021 +0000 @@ -30,7 +30,9 @@ # 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"] + 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", @@ -52,7 +54,9 @@ # 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"] + 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", @@ -70,7 +74,9 @@ 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() @@ -123,7 +129,8 @@ 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!" + "The selected model skeleton asks for weights, " + "but no dataset for weights was provided!" ) main_est.load_weights(infile_weights) @@ -142,7 +149,9 @@ 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) + 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)