Mercurial > repos > bgruening > sklearn_numeric_clustering

annotate fitted_model_eval.py @ 35:e7f047a9dca9 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 22:08:10 +0000
parents 816b65d52c33
children 73e7f1c76ece
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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.utils import get_scoring, load_model, read_columns
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7 from scipy.io import mmread
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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 sklearn.pipeline import Pipeline
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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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25 """
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26 # store read dataframe object
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27 loaded_df = {}
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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"]["selected_column_selector_option"]
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34 if column_option in [
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35 "by_index_number",
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36 "all_but_by_index_number",
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37 "by_header_name",
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38 "all_but_by_header_name",
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39 ]:
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40 c = params["input_options"]["column_selector_options_1"]["col1"]
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41 else:
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42 c = None
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44 df_key = infile1 + repr(header)
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45 df = pd.read_csv(infile1, sep="\t", header=header, parse_dates=True)
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46 loaded_df[df_key] = df
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47
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48 X = read_columns(df, c=c, c_option=column_option).astype(float)
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49 # sparse input
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50 elif input_type == "sparse":
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51 X = mmread(open(infile1, "r"))
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53 # Get target y
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54 header = "infer" if params["input_options"]["header2"] else None
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55 column_option = params["input_options"]["column_selector_options_2"]["selected_column_selector_option2"]
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56 if column_option in [
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57 "by_index_number",
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58 "all_but_by_index_number",
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59 "by_header_name",
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60 "all_but_by_header_name",
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61 ]:
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62 c = params["input_options"]["column_selector_options_2"]["col2"]
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63 else:
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64 c = None
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65
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66 df_key = infile2 + repr(header)
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67 if df_key in loaded_df:
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68 infile2 = loaded_df[df_key]
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69 else:
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70 infile2 = pd.read_csv(infile2, sep="\t", header=header, parse_dates=True)
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71 loaded_df[df_key] = infile2
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72
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73 y = read_columns(infile2, c=c, c_option=column_option, sep="\t", header=header, parse_dates=True)
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74 if len(y.shape) == 2 and y.shape[1] == 1:
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75 y = y.ravel()
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76
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77 return X, y
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79
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80 def main(
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81 inputs,
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82 infile_estimator,
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83 outfile_eval,
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84 infile_weights=None,
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85 infile1=None,
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86 infile2=None,
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87 ):
29
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88 """
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89 Parameter
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90 ---------
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91 inputs : str
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92 File path to galaxy tool parameter
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93
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94 infile_estimator : strgit
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95 File path to trained estimator input
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96
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97 outfile_eval : str
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98 File path to save the evalulation results, tabular
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99
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100 infile_weights : str
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101 File path to weights input
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102
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103 infile1 : str
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104 File path to dataset containing features
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105
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106 infile2 : str
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107 File path to dataset containing target values
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108 """
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109 warnings.filterwarnings("ignore")
29
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110
34
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111 with open(inputs, "r") as param_handler:
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112 params = json.load(param_handler)
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113
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114 X_test, y_test = _get_X_y(params, infile1, infile2)
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115
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116 # load model
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117 with open(infile_estimator, "rb") as est_handler:
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118 estimator = load_model(est_handler)
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119
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120 main_est = estimator
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121 if isinstance(estimator, Pipeline):
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122 main_est = estimator.steps[-1][-1]
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123 if hasattr(main_est, "config") and hasattr(main_est, "load_weights"):
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124 if not infile_weights or infile_weights == "None":
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125 raise ValueError(
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126 "The selected model skeleton asks for weights, " "but no dataset for weights was provided!"
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127 )
29
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128 main_est.load_weights(infile_weights)
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129
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130 # handle scorer, convert to scorer dict
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131 # Check if scoring is specified
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132 scoring = params["scoring"]
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133 if scoring is not None:
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134 # get_scoring() expects secondary_scoring to be a comma separated string (not a list)
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135 # Check if secondary_scoring is specified
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136 secondary_scoring = scoring.get("secondary_scoring", None)
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137 if secondary_scoring is not None:
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138 # If secondary_scoring is specified, convert the list into comman separated string
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139 scoring["secondary_scoring"] = ",".join(scoring["secondary_scoring"])
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140
29
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141 scorer = get_scoring(scoring)
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142 scorer, _ = _check_multimetric_scoring(estimator, scoring=scorer)
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143
34
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144 if hasattr(estimator, "evaluate"):
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145 scores = estimator.evaluate(X_test, y_test=y_test, scorer=scorer, is_multimetric=True)
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146 else:
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147 scores = _score(estimator, X_test, y_test, scorer, is_multimetric=True)
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148
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149 # handle output
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150 for name, score in scores.items():
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151 scores[name] = [score]
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152 df = pd.DataFrame(scores)
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153 df = df[sorted(df.columns)]
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154 df.to_csv(path_or_buf=outfile_eval, sep="\t", header=True, index=False)
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155
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156
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157 if __name__ == "__main__":
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158 aparser = argparse.ArgumentParser()
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159 aparser.add_argument("-i", "--inputs", dest="inputs", required=True)
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160 aparser.add_argument("-e", "--infile_estimator", dest="infile_estimator")
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161 aparser.add_argument("-w", "--infile_weights", dest="infile_weights")
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162 aparser.add_argument("-X", "--infile1", dest="infile1")
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163 aparser.add_argument("-y", "--infile2", dest="infile2")
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164 aparser.add_argument("-O", "--outfile_eval", dest="outfile_eval")
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165 args = aparser.parse_args()
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166
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167 main(
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168 args.inputs,
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169 args.infile_estimator,
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170 args.outfile_eval,
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171 infile_weights=args.infile_weights,
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172 infile1=args.infile1,
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173 infile2=args.infile2,
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174 )