Mercurial > repos > bgruening > sklearn_train_test_eval
comparison fitted_model_eval.py @ 11:caf7d2b71a48 draft
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ea12f973df4b97a2691d9e4ce6bf6fae59d57717"
author | bgruening |
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date | Sat, 01 May 2021 01:47:26 +0000 |
parents | a9e0b963b7bb |
children | 2eb5c017958d |
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10:a9e0b963b7bb | 11:caf7d2b71a48 |
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28 | 28 |
29 input_type = params["input_options"]["selected_input"] | 29 input_type = params["input_options"]["selected_input"] |
30 # tabular input | 30 # tabular input |
31 if input_type == "tabular": | 31 if input_type == "tabular": |
32 header = "infer" if params["input_options"]["header1"] else None | 32 header = "infer" if params["input_options"]["header1"] else None |
33 column_option = params["input_options"]["column_selector_options_1"]["selected_column_selector_option"] | 33 column_option = params["input_options"]["column_selector_options_1"][ |
34 "selected_column_selector_option" | |
35 ] | |
34 if column_option in [ | 36 if column_option in [ |
35 "by_index_number", | 37 "by_index_number", |
36 "all_but_by_index_number", | 38 "all_but_by_index_number", |
37 "by_header_name", | 39 "by_header_name", |
38 "all_but_by_header_name", | 40 "all_but_by_header_name", |
50 elif input_type == "sparse": | 52 elif input_type == "sparse": |
51 X = mmread(open(infile1, "r")) | 53 X = mmread(open(infile1, "r")) |
52 | 54 |
53 # Get target y | 55 # Get target y |
54 header = "infer" if params["input_options"]["header2"] else None | 56 header = "infer" if params["input_options"]["header2"] else None |
55 column_option = params["input_options"]["column_selector_options_2"]["selected_column_selector_option2"] | 57 column_option = params["input_options"]["column_selector_options_2"][ |
58 "selected_column_selector_option2" | |
59 ] | |
56 if column_option in [ | 60 if column_option in [ |
57 "by_index_number", | 61 "by_index_number", |
58 "all_but_by_index_number", | 62 "all_but_by_index_number", |
59 "by_header_name", | 63 "by_header_name", |
60 "all_but_by_header_name", | 64 "all_but_by_header_name", |
68 infile2 = loaded_df[df_key] | 72 infile2 = loaded_df[df_key] |
69 else: | 73 else: |
70 infile2 = pd.read_csv(infile2, sep="\t", header=header, parse_dates=True) | 74 infile2 = pd.read_csv(infile2, sep="\t", header=header, parse_dates=True) |
71 loaded_df[df_key] = infile2 | 75 loaded_df[df_key] = infile2 |
72 | 76 |
73 y = read_columns(infile2, c=c, c_option=column_option, sep="\t", header=header, parse_dates=True) | 77 y = read_columns( |
78 infile2, c=c, c_option=column_option, sep="\t", header=header, parse_dates=True | |
79 ) | |
74 if len(y.shape) == 2 and y.shape[1] == 1: | 80 if len(y.shape) == 2 and y.shape[1] == 1: |
75 y = y.ravel() | 81 y = y.ravel() |
76 | 82 |
77 return X, y | 83 return X, y |
78 | 84 |
121 if isinstance(estimator, Pipeline): | 127 if isinstance(estimator, Pipeline): |
122 main_est = estimator.steps[-1][-1] | 128 main_est = estimator.steps[-1][-1] |
123 if hasattr(main_est, "config") and hasattr(main_est, "load_weights"): | 129 if hasattr(main_est, "config") and hasattr(main_est, "load_weights"): |
124 if not infile_weights or infile_weights == "None": | 130 if not infile_weights or infile_weights == "None": |
125 raise ValueError( | 131 raise ValueError( |
126 "The selected model skeleton asks for weights, " "but no dataset for weights was provided!" | 132 "The selected model skeleton asks for weights, " |
133 "but no dataset for weights was provided!" | |
127 ) | 134 ) |
128 main_est.load_weights(infile_weights) | 135 main_est.load_weights(infile_weights) |
129 | 136 |
130 # handle scorer, convert to scorer dict | 137 # handle scorer, convert to scorer dict |
131 # Check if scoring is specified | 138 # Check if scoring is specified |
140 | 147 |
141 scorer = get_scoring(scoring) | 148 scorer = get_scoring(scoring) |
142 scorer, _ = _check_multimetric_scoring(estimator, scoring=scorer) | 149 scorer, _ = _check_multimetric_scoring(estimator, scoring=scorer) |
143 | 150 |
144 if hasattr(estimator, "evaluate"): | 151 if hasattr(estimator, "evaluate"): |
145 scores = estimator.evaluate(X_test, y_test=y_test, scorer=scorer, is_multimetric=True) | 152 scores = estimator.evaluate( |
153 X_test, y_test=y_test, scorer=scorer, is_multimetric=True | |
154 ) | |
146 else: | 155 else: |
147 scores = _score(estimator, X_test, y_test, scorer, is_multimetric=True) | 156 scores = _score(estimator, X_test, y_test, scorer, is_multimetric=True) |
148 | 157 |
149 # handle output | 158 # handle output |
150 for name, score in scores.items(): | 159 for name, score in scores.items(): |