Mercurial > repos > bgruening > keras_batch_models

annotate train_test_split.py @ 9:5369fdfec6a6 draft

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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 208a8d348e7c7a182cfbe1b6f17868146428a7e2"
author bgruening
date Tue, 13 Apr 2021 20:37:11 +0000
parents 8edfc7381344
children 33af12059f42
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1 import argparse
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2 import json
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3 import warnings
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5 import pandas as pd
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6 from galaxy_ml.model_validations import train_test_split
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7 from galaxy_ml.utils import get_cv, read_columns
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10 def _get_single_cv_split(params, array, infile_labels=None, infile_groups=None):
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11 """output (train, test) subset from a cv splitter
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13 Parameters
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14 ----------
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15 params : dict
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16 Galaxy tool inputs
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17 array : pandas DataFrame object
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18 The target dataset to split
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19 infile_labels : str
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20 File path to dataset containing target values
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21 infile_groups : str
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22 File path to dataset containing group values
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23 """
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24 y = None
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25 groups = None
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27 nth_split = params["mode_selection"]["nth_split"]
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29 # read groups
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30 if infile_groups:
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31 header = "infer" if (params["mode_selection"]["cv_selector"]["groups_selector"]["header_g"]) else None
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32 column_option = params["mode_selection"]["cv_selector"]["groups_selector"]["column_selector_options_g"][
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33 "selected_column_selector_option_g"
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34 ]
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35 if column_option in [
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36 "by_index_number",
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37 "all_but_by_index_number",
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38 "by_header_name",
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39 "all_but_by_header_name",
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40 ]:
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41 c = params["mode_selection"]["cv_selector"]["groups_selector"]["column_selector_options_g"]["col_g"]
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42 else:
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43 c = None
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44
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45 groups = read_columns(
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46 infile_groups,
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47 c=c,
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48 c_option=column_option,
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49 sep="\t",
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50 header=header,
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51 parse_dates=True,
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52 )
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53 groups = groups.ravel()
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54
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55 params["mode_selection"]["cv_selector"]["groups_selector"] = groups
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57 # read labels
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58 if infile_labels:
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59 target_input = params["mode_selection"]["cv_selector"].pop("target_input")
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60 header = "infer" if target_input["header1"] else None
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61 col_index = target_input["col"][0] - 1
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62 df = pd.read_csv(infile_labels, sep="\t", header=header, parse_dates=True)
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63 y = df.iloc[:, col_index].values
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64
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65 # construct the cv splitter object
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66 splitter, groups = get_cv(params["mode_selection"]["cv_selector"])
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68 total_n_splits = splitter.get_n_splits(array.values, y=y, groups=groups)
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69 if nth_split > total_n_splits:
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70 raise ValueError("Total number of splits is {}, but got `nth_split` " "= {}".format(total_n_splits, nth_split))
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72 i = 1
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73 for train_index, test_index in splitter.split(array.values, y=y, groups=groups):
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74 # suppose nth_split >= 1
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75 if i == nth_split:
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76 break
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77 else:
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78 i += 1
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80 train = array.iloc[train_index, :]
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81 test = array.iloc[test_index, :]
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82
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83 return train, test
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85
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86 def main(
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87 inputs,
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88 infile_array,
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89 outfile_train,
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90 outfile_test,
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91 infile_labels=None,
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92 infile_groups=None,
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93 ):
3
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94 """
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95 Parameter
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96 ---------
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97 inputs : str
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98 File path to galaxy tool parameter
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99
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100 infile_array : str
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101 File paths of input arrays separated by comma
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102
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103 infile_labels : str
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104 File path to dataset containing labels
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105
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106 infile_groups : str
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107 File path to dataset containing groups
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108
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109 outfile_train : str
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110 File path to dataset containing train split
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111
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112 outfile_test : str
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113 File path to dataset containing test split
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114 """
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115 warnings.simplefilter("ignore")
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116
8
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117 with open(inputs, "r") as param_handler:
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118 params = json.load(param_handler)
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119
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120 input_header = params["header0"]
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121 header = "infer" if input_header else None
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122 array = pd.read_csv(infile_array, sep="\t", header=header, parse_dates=True)
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123
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124 # train test split
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125 if params["mode_selection"]["selected_mode"] == "train_test_split":
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126 options = params["mode_selection"]["options"]
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127 shuffle_selection = options.pop("shuffle_selection")
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128 options["shuffle"] = shuffle_selection["shuffle"]
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129 if infile_labels:
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130 header = "infer" if shuffle_selection["header1"] else None
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131 col_index = shuffle_selection["col"][0] - 1
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132 df = pd.read_csv(infile_labels, sep="\t", header=header, parse_dates=True)
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133 labels = df.iloc[:, col_index].values
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134 options["labels"] = labels
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135
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136 train, test = train_test_split(array, **options)
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137
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138 # cv splitter
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139 else:
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140 train, test = _get_single_cv_split(params, array, infile_labels=infile_labels, infile_groups=infile_groups)
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141
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142 print("Input shape: %s" % repr(array.shape))
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143 print("Train shape: %s" % repr(train.shape))
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144 print("Test shape: %s" % repr(test.shape))
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145 train.to_csv(outfile_train, sep="\t", header=input_header, index=False)
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146 test.to_csv(outfile_test, sep="\t", header=input_header, index=False)
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147
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148
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149 if __name__ == "__main__":
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150 aparser = argparse.ArgumentParser()
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151 aparser.add_argument("-i", "--inputs", dest="inputs", required=True)
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152 aparser.add_argument("-X", "--infile_array", dest="infile_array")
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153 aparser.add_argument("-y", "--infile_labels", dest="infile_labels")
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154 aparser.add_argument("-g", "--infile_groups", dest="infile_groups")
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155 aparser.add_argument("-o", "--outfile_train", dest="outfile_train")
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156 aparser.add_argument("-t", "--outfile_test", dest="outfile_test")
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157 args = aparser.parse_args()
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158
8
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159 main(
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160 args.inputs,
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161 args.infile_array,
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162 args.outfile_train,
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163 args.outfile_test,
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164 args.infile_labels,
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165 args.infile_groups,
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166 )