Mercurial > repos > bgruening > sklearn_numeric_clustering

annotate fitted_model_eval.py @ 29:9ff214ce6ec2 draft

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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit eb703290e2589561ea215c84aa9f71bcfe1712c6"
author bgruening
date Fri, 01 Nov 2019 17:34:29 -0400
parents
children 816b65d52c33
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29
9ff214ce6ec2 "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 pandas as pd
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4 import warnings
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5
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6 from scipy.io import mmread
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7 from sklearn.pipeline import Pipeline
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8 from sklearn.metrics.scorer import _check_multimetric_scoring
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9 from sklearn.model_selection._validation import _score
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10 from galaxy_ml.utils import get_scoring, load_model, read_columns
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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 if column_option in ['by_index_number', 'all_but_by_index_number',
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36 'by_header_name', 'all_but_by_header_name']:
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37 c = params['input_options']['column_selector_options_1']['col1']
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38 else:
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39 c = None
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40
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41 df_key = infile1 + repr(header)
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42 df = pd.read_csv(infile1, sep='\t', header=header,
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43 parse_dates=True)
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44 loaded_df[df_key] = df
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45
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46 X = read_columns(df, c=c, c_option=column_option).astype(float)
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47 # sparse input
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48 elif input_type == 'sparse':
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49 X = mmread(open(infile1, 'r'))
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50
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51 # Get target y
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52 header = 'infer' if params['input_options']['header2'] else None
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53 column_option = (params['input_options']['column_selector_options_2']
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54 ['selected_column_selector_option2'])
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55 if column_option in ['by_index_number', 'all_but_by_index_number',
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56 'by_header_name', 'all_but_by_header_name']:
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57 c = params['input_options']['column_selector_options_2']['col2']
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58 else:
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59 c = None
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60
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61 df_key = infile2 + repr(header)
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62 if df_key in loaded_df:
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63 infile2 = loaded_df[df_key]
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64 else:
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65 infile2 = pd.read_csv(infile2, sep='\t',
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66 header=header, parse_dates=True)
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67 loaded_df[df_key] = infile2
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68
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69 y = read_columns(
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70 infile2,
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71 c=c,
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72 c_option=column_option,
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73 sep='\t',
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74 header=header,
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75 parse_dates=True)
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76 if len(y.shape) == 2 and y.shape[1] == 1:
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77 y = y.ravel()
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78
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79 return X, y
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80
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81
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82 def main(inputs, infile_estimator, outfile_eval,
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83 infile_weights=None, infile1=None,
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84 infile2=None):
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85 """
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86 Parameter
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87 ---------
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88 inputs : str
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89 File path to galaxy tool parameter
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90
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91 infile_estimator : strgit
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92 File path to trained estimator input
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93
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94 outfile_eval : str
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95 File path to save the evalulation results, tabular
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96
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97 infile_weights : str
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98 File path to weights input
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99
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100 infile1 : str
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101 File path to dataset containing features
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102
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103 infile2 : str
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104 File path to dataset containing target values
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105 """
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106 warnings.filterwarnings('ignore')
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107
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108 with open(inputs, 'r') as param_handler:
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109 params = json.load(param_handler)
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110
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111 X_test, y_test = _get_X_y(params, infile1, infile2)
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112
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113 # load model
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114 with open(infile_estimator, 'rb') as est_handler:
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115 estimator = load_model(est_handler)
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116
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117 main_est = estimator
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118 if isinstance(estimator, Pipeline):
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119 main_est = estimator.steps[-1][-1]
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120 if hasattr(main_est, 'config') and hasattr(main_est, 'load_weights'):
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121 if not infile_weights or infile_weights == 'None':
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122 raise ValueError("The selected model skeleton asks for weights, "
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123 "but no dataset for weights was provided!")
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124 main_est.load_weights(infile_weights)
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125
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126 # handle scorer, convert to scorer dict
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127 scoring = params['scoring']
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128 scorer = get_scoring(scoring)
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129 scorer, _ = _check_multimetric_scoring(estimator, scoring=scorer)
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130
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131 if hasattr(estimator, 'evaluate'):
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132 scores = estimator.evaluate(X_test, y_test=y_test,
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133 scorer=scorer,
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134 is_multimetric=True)
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135 else:
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136 scores = _score(estimator, X_test, y_test, scorer,
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137 is_multimetric=True)
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138
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139 # handle output
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140 for name, score in scores.items():
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141 scores[name] = [score]
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142 df = pd.DataFrame(scores)
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143 df = df[sorted(df.columns)]
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144 df.to_csv(path_or_buf=outfile_eval, sep='\t',
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145 header=True, index=False)
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146
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147
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148 if __name__ == '__main__':
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149 aparser = argparse.ArgumentParser()
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150 aparser.add_argument("-i", "--inputs", dest="inputs", required=True)
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151 aparser.add_argument("-e", "--infile_estimator", dest="infile_estimator")
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152 aparser.add_argument("-w", "--infile_weights", dest="infile_weights")
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153 aparser.add_argument("-X", "--infile1", dest="infile1")
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154 aparser.add_argument("-y", "--infile2", dest="infile2")
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155 aparser.add_argument("-O", "--outfile_eval", dest="outfile_eval")
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156 args = aparser.parse_args()
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157
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158 main(args.inputs, args.infile_estimator, args.outfile_eval,
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159 infile_weights=args.infile_weights, infile1=args.infile1,
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160 infile2=args.infile2)