Mercurial > repos > bgruening > sklearn_to_categorical
comparison train_test_split.py @ 0:59e8b4328c82 draft
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 208a8d348e7c7a182cfbe1b6f17868146428a7e2"
author | bgruening |
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date | Tue, 13 Apr 2021 22:40:10 +0000 |
parents | |
children | f93f0cdbaf18 |
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-1:000000000000 | 0:59e8b4328c82 |
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1 import argparse | |
2 import json | |
3 import warnings | |
4 | |
5 import pandas as pd | |
6 from galaxy_ml.model_validations import train_test_split | |
7 from galaxy_ml.utils import get_cv, read_columns | |
8 | |
9 | |
10 def _get_single_cv_split(params, array, infile_labels=None, infile_groups=None): | |
11 """output (train, test) subset from a cv splitter | |
12 | |
13 Parameters | |
14 ---------- | |
15 params : dict | |
16 Galaxy tool inputs | |
17 array : pandas DataFrame object | |
18 The target dataset to split | |
19 infile_labels : str | |
20 File path to dataset containing target values | |
21 infile_groups : str | |
22 File path to dataset containing group values | |
23 """ | |
24 y = None | |
25 groups = None | |
26 | |
27 nth_split = params["mode_selection"]["nth_split"] | |
28 | |
29 # read groups | |
30 if infile_groups: | |
31 header = "infer" if (params["mode_selection"]["cv_selector"]["groups_selector"]["header_g"]) else None | |
32 column_option = params["mode_selection"]["cv_selector"]["groups_selector"]["column_selector_options_g"][ | |
33 "selected_column_selector_option_g" | |
34 ] | |
35 if column_option in [ | |
36 "by_index_number", | |
37 "all_but_by_index_number", | |
38 "by_header_name", | |
39 "all_but_by_header_name", | |
40 ]: | |
41 c = params["mode_selection"]["cv_selector"]["groups_selector"]["column_selector_options_g"]["col_g"] | |
42 else: | |
43 c = None | |
44 | |
45 groups = read_columns( | |
46 infile_groups, | |
47 c=c, | |
48 c_option=column_option, | |
49 sep="\t", | |
50 header=header, | |
51 parse_dates=True, | |
52 ) | |
53 groups = groups.ravel() | |
54 | |
55 params["mode_selection"]["cv_selector"]["groups_selector"] = groups | |
56 | |
57 # read labels | |
58 if infile_labels: | |
59 target_input = params["mode_selection"]["cv_selector"].pop("target_input") | |
60 header = "infer" if target_input["header1"] else None | |
61 col_index = target_input["col"][0] - 1 | |
62 df = pd.read_csv(infile_labels, sep="\t", header=header, parse_dates=True) | |
63 y = df.iloc[:, col_index].values | |
64 | |
65 # construct the cv splitter object | |
66 splitter, groups = get_cv(params["mode_selection"]["cv_selector"]) | |
67 | |
68 total_n_splits = splitter.get_n_splits(array.values, y=y, groups=groups) | |
69 if nth_split > total_n_splits: | |
70 raise ValueError("Total number of splits is {}, but got `nth_split` " "= {}".format(total_n_splits, nth_split)) | |
71 | |
72 i = 1 | |
73 for train_index, test_index in splitter.split(array.values, y=y, groups=groups): | |
74 # suppose nth_split >= 1 | |
75 if i == nth_split: | |
76 break | |
77 else: | |
78 i += 1 | |
79 | |
80 train = array.iloc[train_index, :] | |
81 test = array.iloc[test_index, :] | |
82 | |
83 return train, test | |
84 | |
85 | |
86 def main( | |
87 inputs, | |
88 infile_array, | |
89 outfile_train, | |
90 outfile_test, | |
91 infile_labels=None, | |
92 infile_groups=None, | |
93 ): | |
94 """ | |
95 Parameter | |
96 --------- | |
97 inputs : str | |
98 File path to galaxy tool parameter | |
99 | |
100 infile_array : str | |
101 File paths of input arrays separated by comma | |
102 | |
103 infile_labels : str | |
104 File path to dataset containing labels | |
105 | |
106 infile_groups : str | |
107 File path to dataset containing groups | |
108 | |
109 outfile_train : str | |
110 File path to dataset containing train split | |
111 | |
112 outfile_test : str | |
113 File path to dataset containing test split | |
114 """ | |
115 warnings.simplefilter("ignore") | |
116 | |
117 with open(inputs, "r") as param_handler: | |
118 params = json.load(param_handler) | |
119 | |
120 input_header = params["header0"] | |
121 header = "infer" if input_header else None | |
122 array = pd.read_csv(infile_array, sep="\t", header=header, parse_dates=True) | |
123 | |
124 # train test split | |
125 if params["mode_selection"]["selected_mode"] == "train_test_split": | |
126 options = params["mode_selection"]["options"] | |
127 shuffle_selection = options.pop("shuffle_selection") | |
128 options["shuffle"] = shuffle_selection["shuffle"] | |
129 if infile_labels: | |
130 header = "infer" if shuffle_selection["header1"] else None | |
131 col_index = shuffle_selection["col"][0] - 1 | |
132 df = pd.read_csv(infile_labels, sep="\t", header=header, parse_dates=True) | |
133 labels = df.iloc[:, col_index].values | |
134 options["labels"] = labels | |
135 | |
136 train, test = train_test_split(array, **options) | |
137 | |
138 # cv splitter | |
139 else: | |
140 train, test = _get_single_cv_split(params, array, infile_labels=infile_labels, infile_groups=infile_groups) | |
141 | |
142 print("Input shape: %s" % repr(array.shape)) | |
143 print("Train shape: %s" % repr(train.shape)) | |
144 print("Test shape: %s" % repr(test.shape)) | |
145 train.to_csv(outfile_train, sep="\t", header=input_header, index=False) | |
146 test.to_csv(outfile_test, sep="\t", header=input_header, index=False) | |
147 | |
148 | |
149 if __name__ == "__main__": | |
150 aparser = argparse.ArgumentParser() | |
151 aparser.add_argument("-i", "--inputs", dest="inputs", required=True) | |
152 aparser.add_argument("-X", "--infile_array", dest="infile_array") | |
153 aparser.add_argument("-y", "--infile_labels", dest="infile_labels") | |
154 aparser.add_argument("-g", "--infile_groups", dest="infile_groups") | |
155 aparser.add_argument("-o", "--outfile_train", dest="outfile_train") | |
156 aparser.add_argument("-t", "--outfile_test", dest="outfile_test") | |
157 args = aparser.parse_args() | |
158 | |
159 main( | |
160 args.inputs, | |
161 args.infile_array, | |
162 args.outfile_train, | |
163 args.outfile_test, | |
164 args.infile_labels, | |
165 args.infile_groups, | |
166 ) |