Mercurial > repos > bgruening > sklearn_data_preprocess

annotate train_test_split.py @ 37:1bef885255e0 draft

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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ea12f973df4b97a2691d9e4ce6bf6fae59d57717"
author bgruening
date Sat, 01 May 2021 01:41:14 +0000
parents b75cae00f980
children a16f33c6ca64
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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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26
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27 nth_split = params["mode_selection"]["nth_split"]
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28
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29 # read groups
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30 if infile_groups:
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31 header = (
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32 "infer"
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33 if (params["mode_selection"]["cv_selector"]["groups_selector"]["header_g"])
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34 else None
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35 )
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36 column_option = params["mode_selection"]["cv_selector"]["groups_selector"][
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37 "column_selector_options_g"
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38 ]["selected_column_selector_option_g"]
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39 if column_option in [
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40 "by_index_number",
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41 "all_but_by_index_number",
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42 "by_header_name",
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43 "all_but_by_header_name",
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44 ]:
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45 c = params["mode_selection"]["cv_selector"]["groups_selector"][
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46 "column_selector_options_g"
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47 ]["col_g"]
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48 else:
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49 c = None
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50
35
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51 groups = read_columns(
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52 infile_groups,
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53 c=c,
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54 c_option=column_option,
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55 sep="\t",
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56 header=header,
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57 parse_dates=True,
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58 )
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59 groups = groups.ravel()
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60
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61 params["mode_selection"]["cv_selector"]["groups_selector"] = groups
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62
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63 # read labels
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64 if infile_labels:
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65 target_input = params["mode_selection"]["cv_selector"].pop("target_input")
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66 header = "infer" if target_input["header1"] else None
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67 col_index = target_input["col"][0] - 1
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68 df = pd.read_csv(infile_labels, sep="\t", header=header, parse_dates=True)
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69 y = df.iloc[:, col_index].values
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70
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71 # construct the cv splitter object
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72 splitter, groups = get_cv(params["mode_selection"]["cv_selector"])
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74 total_n_splits = splitter.get_n_splits(array.values, y=y, groups=groups)
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75 if nth_split > total_n_splits:
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76 raise ValueError(
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77 "Total number of splits is {}, but got `nth_split` "
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78 "= {}".format(total_n_splits, nth_split)
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79 )
29
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80
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81 i = 1
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82 for train_index, test_index in splitter.split(array.values, y=y, groups=groups):
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83 # suppose nth_split >= 1
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84 if i == nth_split:
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85 break
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86 else:
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87 i += 1
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88
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89 train = array.iloc[train_index, :]
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90 test = array.iloc[test_index, :]
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91
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92 return train, test
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93
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94
35
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95 def main(
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96 inputs,
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97 infile_array,
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98 outfile_train,
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99 outfile_test,
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100 infile_labels=None,
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101 infile_groups=None,
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102 ):
29
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103 """
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104 Parameter
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105 ---------
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106 inputs : str
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107 File path to galaxy tool parameter
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108
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109 infile_array : str
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110 File paths of input arrays separated by comma
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111
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112 infile_labels : str
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113 File path to dataset containing labels
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114
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115 infile_groups : str
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116 File path to dataset containing groups
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117
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118 outfile_train : str
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119 File path to dataset containing train split
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120
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121 outfile_test : str
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122 File path to dataset containing test split
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123 """
35
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124 warnings.simplefilter("ignore")
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125
35
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126 with open(inputs, "r") as param_handler:
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127 params = json.load(param_handler)
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128
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129 input_header = params["header0"]
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130 header = "infer" if input_header else None
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131 array = pd.read_csv(infile_array, sep="\t", header=header, parse_dates=True)
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132
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133 # train test split
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134 if params["mode_selection"]["selected_mode"] == "train_test_split":
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135 options = params["mode_selection"]["options"]
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136 shuffle_selection = options.pop("shuffle_selection")
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137 options["shuffle"] = shuffle_selection["shuffle"]
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138 if infile_labels:
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139 header = "infer" if shuffle_selection["header1"] else None
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140 col_index = shuffle_selection["col"][0] - 1
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141 df = pd.read_csv(infile_labels, sep="\t", header=header, parse_dates=True)
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142 labels = df.iloc[:, col_index].values
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143 options["labels"] = labels
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144
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145 train, test = train_test_split(array, **options)
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146
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147 # cv splitter
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148 else:
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149 train, test = _get_single_cv_split(
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150 params, array, infile_labels=infile_labels, infile_groups=infile_groups
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151 )
29
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152
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153 print("Input shape: %s" % repr(array.shape))
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154 print("Train shape: %s" % repr(train.shape))
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155 print("Test shape: %s" % repr(test.shape))
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156 train.to_csv(outfile_train, sep="\t", header=input_header, index=False)
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157 test.to_csv(outfile_test, sep="\t", header=input_header, index=False)
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158
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159
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160 if __name__ == "__main__":
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161 aparser = argparse.ArgumentParser()
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162 aparser.add_argument("-i", "--inputs", dest="inputs", required=True)
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163 aparser.add_argument("-X", "--infile_array", dest="infile_array")
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164 aparser.add_argument("-y", "--infile_labels", dest="infile_labels")
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165 aparser.add_argument("-g", "--infile_groups", dest="infile_groups")
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166 aparser.add_argument("-o", "--outfile_train", dest="outfile_train")
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167 aparser.add_argument("-t", "--outfile_test", dest="outfile_test")
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168 args = aparser.parse_args()
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169
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170 main(
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171 args.inputs,
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172 args.infile_array,
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173 args.outfile_train,
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174 args.outfile_test,
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175 args.infile_labels,
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176 args.infile_groups,
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177 )