Mercurial > repos > bgruening > sklearn_estimator_attributes

annotate train_test_split.py @ 6:ad297a5397a0 draft

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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d6333e7294e67be5968a41f404b66699cad4ae53"
author bgruening
date Thu, 07 Nov 2019 05:34:32 -0500
parents f4a7c3aa1e10
children 27fabe5feedc
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1 import argparse
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2 import json
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3 import pandas as pd
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4 import warnings
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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,
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11 infile_groups=None):
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12 """ output (train, test) subset from a cv splitter
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14 Parameters
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15 ----------
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16 params : dict
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17 Galaxy tool inputs
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18 array : pandas DataFrame object
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19 The target dataset to split
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20 infile_labels : str
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21 File path to dataset containing target values
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22 infile_groups : str
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23 File path to dataset containing group values
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24 """
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25 y = None
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26 groups = None
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28 nth_split = params['mode_selection']['nth_split']
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29
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30 # read groups
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31 if infile_groups:
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32 header = 'infer' if (params['mode_selection']['cv_selector']
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33 ['groups_selector']['header_g']) else None
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34 column_option = (params['mode_selection']['cv_selector']
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35 ['groups_selector']['column_selector_options_g']
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36 ['selected_column_selector_option_g'])
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37 if column_option in ['by_index_number', 'all_but_by_index_number',
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38 'by_header_name', 'all_but_by_header_name']:
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39 c = (params['mode_selection']['cv_selector']['groups_selector']
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40 ['column_selector_options_g']['col_g'])
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41 else:
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42 c = None
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43
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44 groups = read_columns(infile_groups, c=c, c_option=column_option,
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45 sep='\t', header=header, parse_dates=True)
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46 groups = groups.ravel()
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47
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48 params['mode_selection']['cv_selector']['groups_selector'] = groups
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49
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50 # read labels
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51 if infile_labels:
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52 target_input = (params['mode_selection']
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53 ['cv_selector'].pop('target_input'))
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54 header = 'infer' if target_input['header1'] else None
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55 col_index = target_input['col'][0] - 1
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56 df = pd.read_csv(infile_labels, sep='\t', header=header,
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57 parse_dates=True)
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58 y = df.iloc[:, col_index].values
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59
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60 # construct the cv splitter object
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61 splitter, groups = get_cv(params['mode_selection']['cv_selector'])
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62
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63 total_n_splits = splitter.get_n_splits(array.values, y=y, groups=groups)
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64 if nth_split > total_n_splits:
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65 raise ValueError("Total number of splits is {}, but got `nth_split` "
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66 "= {}".format(total_n_splits, nth_split))
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67
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68 i = 1
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69 for train_index, test_index in splitter.split(array.values, y=y, groups=groups):
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70 # suppose nth_split >= 1
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71 if i == nth_split:
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72 break
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73 else:
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74 i += 1
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75
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76 train = array.iloc[train_index, :]
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77 test = array.iloc[test_index, :]
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78
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79 return train, test
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80
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81
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82 def main(inputs, infile_array, outfile_train, outfile_test,
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83 infile_labels=None, infile_groups=None):
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84 """
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85 Parameter
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86 ---------
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87 inputs : str
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88 File path to galaxy tool parameter
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89
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90 infile_array : str
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91 File paths of input arrays separated by comma
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92
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93 infile_labels : str
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94 File path to dataset containing labels
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95
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96 infile_groups : str
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97 File path to dataset containing groups
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98
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99 outfile_train : str
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100 File path to dataset containing train split
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101
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102 outfile_test : str
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103 File path to dataset containing test split
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104 """
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105 warnings.simplefilter('ignore')
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106
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107 with open(inputs, 'r') as param_handler:
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108 params = json.load(param_handler)
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109
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110 input_header = params['header0']
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111 header = 'infer' if input_header else None
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112 array = pd.read_csv(infile_array, sep='\t', header=header,
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113 parse_dates=True)
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114
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115 # train test split
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116 if params['mode_selection']['selected_mode'] == 'train_test_split':
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117 options = params['mode_selection']['options']
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118 shuffle_selection = options.pop('shuffle_selection')
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119 options['shuffle'] = shuffle_selection['shuffle']
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120 if infile_labels:
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121 header = 'infer' if shuffle_selection['header1'] else None
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122 col_index = shuffle_selection['col'][0] - 1
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123 df = pd.read_csv(infile_labels, sep='\t', header=header,
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124 parse_dates=True)
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125 labels = df.iloc[:, col_index].values
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126 options['labels'] = labels
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127
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128 train, test = train_test_split(array, **options)
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129
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130 # cv splitter
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131 else:
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132 train, test = _get_single_cv_split(params, array,
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133 infile_labels=infile_labels,
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134 infile_groups=infile_groups)
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135
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136 print("Input shape: %s" % repr(array.shape))
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137 print("Train shape: %s" % repr(train.shape))
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138 print("Test shape: %s" % repr(test.shape))
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139 train.to_csv(outfile_train, sep='\t', header=input_header, index=False)
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140 test.to_csv(outfile_test, sep='\t', header=input_header, index=False)
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141
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142
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143 if __name__ == '__main__':
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144 aparser = argparse.ArgumentParser()
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145 aparser.add_argument("-i", "--inputs", dest="inputs", required=True)
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146 aparser.add_argument("-X", "--infile_array", dest="infile_array")
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147 aparser.add_argument("-y", "--infile_labels", dest="infile_labels")
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148 aparser.add_argument("-g", "--infile_groups", dest="infile_groups")
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149 aparser.add_argument("-o", "--outfile_train", dest="outfile_train")
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150 aparser.add_argument("-t", "--outfile_test", dest="outfile_test")
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151 args = aparser.parse_args()
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152
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153 main(args.inputs, args.infile_array, args.outfile_train,
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154 args.outfile_test, args.infile_labels, args.infile_groups)