Mercurial > repos > bgruening > keras_model_builder

comparison fitted_model_eval.py @ 8:449a757be9c9 draft

Find changesets by keywords (author, files, the commit message), revision number or hash, or revset expression.
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
author bgruening
date Tue, 13 Apr 2021 18:29:36 +0000
parents 4bd0bd2add7f
children e3b420d0b71a
comparison
equal deleted inserted replaced
7:053570bac5ea 8:449a757be9c9
9 from sklearn.model_selection._validation import _score 9 from sklearn.model_selection._validation import _score
10 from galaxy_ml.utils import get_scoring, load_model, read_columns 10 from galaxy_ml.utils import get_scoring, load_model, read_columns
11 11
12 12
13 def _get_X_y(params, infile1, infile2): 13 def _get_X_y(params, infile1, infile2):
14 """ read from inputs and output X and y 14 """read from inputs and output X and y
15 15
16 Parameters 16 Parameters
17 ---------- 17 ----------
18 params : dict 18 params : dict
19 Tool inputs parameter 19 Tool inputs parameter
24 24
25 """ 25 """
26 # store read dataframe object 26 # store read dataframe object
27 loaded_df = {} 27 loaded_df = {}
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'] 33 column_option = params["input_options"]["column_selector_options_1"]["selected_column_selector_option"]
34 ['selected_column_selector_option']) 34 if column_option in [
35 if column_option in ['by_index_number', 'all_but_by_index_number', 35 "by_index_number",
36 'by_header_name', 'all_but_by_header_name']: 36 "all_but_by_index_number",
37 c = params['input_options']['column_selector_options_1']['col1'] 37 "by_header_name",
38 "all_but_by_header_name",
39 ]:
40 c = params["input_options"]["column_selector_options_1"]["col1"]
38 else: 41 else:
39 c = None 42 c = None
40 43
41 df_key = infile1 + repr(header) 44 df_key = infile1 + repr(header)
42 df = pd.read_csv(infile1, sep='\t', header=header, 45 df = pd.read_csv(infile1, sep="\t", header=header, parse_dates=True)
43 parse_dates=True)
44 loaded_df[df_key] = df 46 loaded_df[df_key] = df
45 47
46 X = read_columns(df, c=c, c_option=column_option).astype(float) 48 X = read_columns(df, c=c, c_option=column_option).astype(float)
47 # sparse input 49 # sparse input
48 elif input_type == 'sparse': 50 elif input_type == "sparse":
49 X = mmread(open(infile1, 'r')) 51 X = mmread(open(infile1, "r"))
50 52
51 # Get target y 53 # Get target y
52 header = 'infer' if params['input_options']['header2'] else None 54 header = "infer" if params["input_options"]["header2"] else None
53 column_option = (params['input_options']['column_selector_options_2'] 55 column_option = params["input_options"]["column_selector_options_2"]["selected_column_selector_option2"]
54 ['selected_column_selector_option2']) 56 if column_option in [
55 if column_option in ['by_index_number', 'all_but_by_index_number', 57 "by_index_number",
56 'by_header_name', 'all_but_by_header_name']: 58 "all_but_by_index_number",
57 c = params['input_options']['column_selector_options_2']['col2'] 59 "by_header_name",
60 "all_but_by_header_name",
61 ]:
62 c = params["input_options"]["column_selector_options_2"]["col2"]
58 else: 63 else:
59 c = None 64 c = None
60 65
61 df_key = infile2 + repr(header) 66 df_key = infile2 + repr(header)
62 if df_key in loaded_df: 67 if df_key in loaded_df:
63 infile2 = loaded_df[df_key] 68 infile2 = loaded_df[df_key]
64 else: 69 else:
65 infile2 = pd.read_csv(infile2, sep='\t', 70 infile2 = pd.read_csv(infile2, sep="\t", header=header, parse_dates=True)
66 header=header, parse_dates=True)
67 loaded_df[df_key] = infile2 71 loaded_df[df_key] = infile2
68 72
69 y = read_columns( 73 y = read_columns(infile2, c=c, c_option=column_option, sep="\t", header=header, parse_dates=True)
70 infile2,
71 c=c,
72 c_option=column_option,
73 sep='\t',
74 header=header,
75 parse_dates=True)
76 if len(y.shape) == 2 and y.shape[1] == 1: 74 if len(y.shape) == 2 and y.shape[1] == 1:
77 y = y.ravel() 75 y = y.ravel()
78 76
79 return X, y 77 return X, y
80 78
81 79
82 def main(inputs, infile_estimator, outfile_eval, 80 def main(
83 infile_weights=None, infile1=None, 81 inputs,
84 infile2=None): 82 infile_estimator,
83 outfile_eval,
84 infile_weights=None,
85 infile1=None,
86 infile2=None,
87 ):
85 """ 88 """
86 Parameter 89 Parameter
87 --------- 90 ---------
88 inputs : str 91 inputs : str
89 File path to galaxy tool parameter 92 File path to galaxy tool parameter
101 File path to dataset containing features 104 File path to dataset containing features
102 105
103 infile2 : str 106 infile2 : str
104 File path to dataset containing target values 107 File path to dataset containing target values
105 """ 108 """
106 warnings.filterwarnings('ignore') 109 warnings.filterwarnings("ignore")
107 110
108 with open(inputs, 'r') as param_handler: 111 with open(inputs, "r") as param_handler:
109 params = json.load(param_handler) 112 params = json.load(param_handler)
110 113
111 X_test, y_test = _get_X_y(params, infile1, infile2) 114 X_test, y_test = _get_X_y(params, infile1, infile2)
112 115
113 # load model 116 # load model
114 with open(infile_estimator, 'rb') as est_handler: 117 with open(infile_estimator, "rb") as est_handler:
115 estimator = load_model(est_handler) 118 estimator = load_model(est_handler)
116 119
117 main_est = estimator 120 main_est = estimator
118 if isinstance(estimator, Pipeline): 121 if isinstance(estimator, Pipeline):
119 main_est = estimator.steps[-1][-1] 122 main_est = estimator.steps[-1][-1]
120 if hasattr(main_est, 'config') and hasattr(main_est, 'load_weights'): 123 if hasattr(main_est, "config") and hasattr(main_est, "load_weights"):
121 if not infile_weights or infile_weights == 'None': 124 if not infile_weights or infile_weights == "None":
122 raise ValueError("The selected model skeleton asks for weights, " 125 raise ValueError(
123 "but no dataset for weights was provided!") 126 "The selected model skeleton asks for weights, " "but no dataset for weights was provided!"
127 )
124 main_est.load_weights(infile_weights) 128 main_est.load_weights(infile_weights)
125 129
126 # handle scorer, convert to scorer dict 130 # handle scorer, convert to scorer dict
127 scoring = params['scoring'] 131 # Check if scoring is specified
132 scoring = params["scoring"]
133 if scoring is not None:
134 # get_scoring() expects secondary_scoring to be a comma separated string (not a list)
135 # Check if secondary_scoring is specified
136 secondary_scoring = scoring.get("secondary_scoring", None)
137 if secondary_scoring is not None:
138 # If secondary_scoring is specified, convert the list into comman separated string
139 scoring["secondary_scoring"] = ",".join(scoring["secondary_scoring"])
140
128 scorer = get_scoring(scoring) 141 scorer = get_scoring(scoring)
129 scorer, _ = _check_multimetric_scoring(estimator, scoring=scorer) 142 scorer, _ = _check_multimetric_scoring(estimator, scoring=scorer)
130 143
131 if hasattr(estimator, 'evaluate'): 144 if hasattr(estimator, "evaluate"):
132 scores = estimator.evaluate(X_test, y_test=y_test, 145 scores = estimator.evaluate(X_test, y_test=y_test, scorer=scorer, is_multimetric=True)
133 scorer=scorer,
134 is_multimetric=True)
135 else: 146 else:
136 scores = _score(estimator, X_test, y_test, scorer, 147 scores = _score(estimator, X_test, y_test, scorer, is_multimetric=True)
137 is_multimetric=True)
138 148
139 # handle output 149 # handle output
140 for name, score in scores.items(): 150 for name, score in scores.items():
141 scores[name] = [score] 151 scores[name] = [score]
142 df = pd.DataFrame(scores) 152 df = pd.DataFrame(scores)
143 df = df[sorted(df.columns)] 153 df = df[sorted(df.columns)]
144 df.to_csv(path_or_buf=outfile_eval, sep='\t', 154 df.to_csv(path_or_buf=outfile_eval, sep="\t", header=True, index=False)
145 header=True, index=False)
146 155
147 156
148 if __name__ == '__main__': 157 if __name__ == "__main__":
149 aparser = argparse.ArgumentParser() 158 aparser = argparse.ArgumentParser()
150 aparser.add_argument("-i", "--inputs", dest="inputs", required=True) 159 aparser.add_argument("-i", "--inputs", dest="inputs", required=True)
151 aparser.add_argument("-e", "--infile_estimator", dest="infile_estimator") 160 aparser.add_argument("-e", "--infile_estimator", dest="infile_estimator")
152 aparser.add_argument("-w", "--infile_weights", dest="infile_weights") 161 aparser.add_argument("-w", "--infile_weights", dest="infile_weights")
153 aparser.add_argument("-X", "--infile1", dest="infile1") 162 aparser.add_argument("-X", "--infile1", dest="infile1")
154 aparser.add_argument("-y", "--infile2", dest="infile2") 163 aparser.add_argument("-y", "--infile2", dest="infile2")
155 aparser.add_argument("-O", "--outfile_eval", dest="outfile_eval") 164 aparser.add_argument("-O", "--outfile_eval", dest="outfile_eval")
156 args = aparser.parse_args() 165 args = aparser.parse_args()
157 166
158 main(args.inputs, args.infile_estimator, args.outfile_eval, 167 main(
159 infile_weights=args.infile_weights, infile1=args.infile1, 168 args.inputs,
160 infile2=args.infile2) 169 args.infile_estimator,
170 args.outfile_eval,
171 infile_weights=args.infile_weights,
172 infile1=args.infile1,
173 infile2=args.infile2,
174 )