Mercurial > repos > bgruening > sklearn_train_test_split

annotate fitted_model_eval.py @ 12:b6f5443c4f58 draft

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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 9981e25b00de29ed881b2229a173a8c812ded9bb
author bgruening
date Wed, 09 Aug 2023 13:53:33 +0000
parents 5da2217cd788
children
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0985b0dd6f1a "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit eb703290e2589561ea215c84aa9f71bcfe1712c6"
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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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4
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5 import pandas as pd
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6 from galaxy_ml.model_persist import load_model_from_h5
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7 from galaxy_ml.utils import clean_params, get_scoring, read_columns
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8 from scipy.io import mmread
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9 from sklearn.metrics._scorer import _check_multimetric_scoring
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10 from sklearn.model_selection._validation import _score
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12
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13 def _get_X_y(params, infile1, infile2):
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14 """read from inputs and output X and y
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16 Parameters
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17 ----------
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18 params : dict
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19 Tool inputs parameter
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20 infile1 : str
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21 File path to dataset containing features
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22 infile2 : str
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23 File path to dataset containing target values
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24
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25 """
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26 # store read dataframe object
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27 loaded_df = {}
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28
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29 input_type = params["input_options"]["selected_input"]
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30 # tabular input
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31 if input_type == "tabular":
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32 header = "infer" if params["input_options"]["header1"] else None
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33 column_option = params["input_options"]["column_selector_options_1"][
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34 "selected_column_selector_option"
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35 ]
6
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36 if column_option in [
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37 "by_index_number",
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38 "all_but_by_index_number",
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39 "by_header_name",
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40 "all_but_by_header_name",
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41 ]:
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42 c = params["input_options"]["column_selector_options_1"]["col1"]
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43 else:
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44 c = None
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45
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46 df_key = infile1 + repr(header)
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47 df = pd.read_csv(infile1, sep="\t", header=header, parse_dates=True)
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48 loaded_df[df_key] = df
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49
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50 X = read_columns(df, c=c, c_option=column_option).astype(float)
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51 # sparse input
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52 elif input_type == "sparse":
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53 X = mmread(open(infile1, "r"))
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54
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55 # Get target y
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56 header = "infer" if params["input_options"]["header2"] else None
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57 column_option = params["input_options"]["column_selector_options_2"][
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58 "selected_column_selector_option2"
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59 ]
6
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60 if column_option in [
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61 "by_index_number",
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62 "all_but_by_index_number",
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63 "by_header_name",
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64 "all_but_by_header_name",
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65 ]:
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66 c = params["input_options"]["column_selector_options_2"]["col2"]
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67 else:
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68 c = None
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69
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70 df_key = infile2 + repr(header)
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71 if df_key in loaded_df:
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72 infile2 = loaded_df[df_key]
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73 else:
6
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74 infile2 = pd.read_csv(infile2, sep="\t", header=header, parse_dates=True)
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75 loaded_df[df_key] = infile2
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76
7
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77 y = read_columns(
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78 infile2,
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79 c=c,
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80 c_option=column_option,
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81 sep="\t",
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82 header=header,
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83 parse_dates=True,
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84 )
0
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85 if len(y.shape) == 2 and y.shape[1] == 1:
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86 y = y.ravel()
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87
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88 return X, y
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89
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90
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91 def main(inputs, infile_estimator, outfile_eval, infile1=None, infile2=None):
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92 """
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93 Parameter
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94 ---------
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95 inputs : str
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96 File path to galaxy tool parameter
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97
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98 infile_estimator : strgit
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99 File path to trained estimator input
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100
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101 outfile_eval : str
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102 File path to save the evalulation results, tabular
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103
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104 infile1 : str
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105 File path to dataset containing features
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106
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107 infile2 : str
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108 File path to dataset containing target values
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109 """
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110 warnings.filterwarnings("ignore")
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111
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112 with open(inputs, "r") as param_handler:
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113 params = json.load(param_handler)
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114
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115 X_test, y_test = _get_X_y(params, infile1, infile2)
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116
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117 # load model
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118 estimator = load_model_from_h5(infile_estimator)
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119 estimator = clean_params(estimator)
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120
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121 # handle scorer, convert to scorer dict
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122 scoring = params["scoring"]
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123 scorer = get_scoring(scoring)
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124 if not isinstance(scorer, (dict, list)):
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125 scorer = [scoring["primary_scoring"]]
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126 scorer = _check_multimetric_scoring(estimator, scoring=scorer)
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127
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128 if hasattr(estimator, "evaluate"):
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129 scores = estimator.evaluate(X_test, y_test=y_test, scorer=scorer)
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130 else:
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131 scores = _score(estimator, X_test, y_test, scorer)
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132
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133 # handle output
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134 for name, score in scores.items():
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135 scores[name] = [score]
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136 df = pd.DataFrame(scores)
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137 df = df[sorted(df.columns)]
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138 df.to_csv(path_or_buf=outfile_eval, sep="\t", header=True, index=False)
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139
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140
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141 if __name__ == "__main__":
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142 aparser = argparse.ArgumentParser()
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143 aparser.add_argument("-i", "--inputs", dest="inputs", required=True)
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144 aparser.add_argument("-e", "--infile_estimator", dest="infile_estimator")
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145 aparser.add_argument("-X", "--infile1", dest="infile1")
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146 aparser.add_argument("-y", "--infile2", dest="infile2")
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147 aparser.add_argument("-O", "--outfile_eval", dest="outfile_eval")
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148 args = aparser.parse_args()
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149
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150 main(
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151 args.inputs,
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152 args.infile_estimator,
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153 args.outfile_eval,
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154 infile1=args.infile1,
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155 infile2=args.infile2,
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156 )