Mercurial > repos > bgruening > sklearn_numeric_clustering

annotate simple_model_fit.py @ 45:838ca001438d draft

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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5eca9041ce0154eded5aec07195502d5eb3cdd4f
author bgruening
date Fri, 03 Nov 2023 23:25:01 +0000
parents 06d772036a62
children
Ignore whitespace changes - Everywhere: Within whitespace: At end of lines:
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1 import argparse
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2 import json
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3
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4 import pandas as pd
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5 from galaxy_ml.model_persist import dump_model_to_h5, load_model_from_h5
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6 from galaxy_ml.utils import read_columns
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7 from scipy.io import mmread
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8 from sklearn.pipeline import Pipeline
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9
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10 N_JOBS = int(__import__("os").environ.get("GALAXY_SLOTS", 1))
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13 # TODO import from galaxy_ml.utils in future versions
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14 def clean_params(estimator, n_jobs=None):
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15 """clean unwanted hyperparameter settings
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17 If n_jobs is not None, set it into the estimator, if applicable
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19 Return
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20 ------
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21 Cleaned estimator object
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22 """
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23 ALLOWED_CALLBACKS = (
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24 "EarlyStopping",
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25 "TerminateOnNaN",
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26 "ReduceLROnPlateau",
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27 "CSVLogger",
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28 "None",
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29 )
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31 estimator_params = estimator.get_params()
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33 for name, p in estimator_params.items():
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34 # all potential unauthorized file write
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35 if name == "memory" or name.endswith("__memory") or name.endswith("_path"):
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36 new_p = {name: None}
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37 estimator.set_params(**new_p)
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38 elif n_jobs is not None and (name == "n_jobs" or name.endswith("__n_jobs")):
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39 new_p = {name: n_jobs}
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40 estimator.set_params(**new_p)
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41 elif name.endswith("callbacks"):
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42 for cb in p:
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43 cb_type = cb["callback_selection"]["callback_type"]
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44 if cb_type not in ALLOWED_CALLBACKS:
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45 raise ValueError("Prohibited callback type: %s!" % cb_type)
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47 return estimator
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49
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50 def _get_X_y(params, infile1, infile2):
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51 """read from inputs and output X and y
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53 Parameters
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54 ----------
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55 params : dict
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56 Tool inputs parameter
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57 infile1 : str
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58 File path to dataset containing features
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59 infile2 : str
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60 File path to dataset containing target values
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62 """
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63 # store read dataframe object
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64 loaded_df = {}
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65
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66 input_type = params["input_options"]["selected_input"]
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67 # tabular input
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68 if input_type == "tabular":
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69 header = "infer" if params["input_options"]["header1"] else None
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70 column_option = params["input_options"]["column_selector_options_1"][
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71 "selected_column_selector_option"
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72 ]
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73 if column_option in [
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74 "by_index_number",
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75 "all_but_by_index_number",
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76 "by_header_name",
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77 "all_but_by_header_name",
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78 ]:
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79 c = params["input_options"]["column_selector_options_1"]["col1"]
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80 else:
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81 c = None
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83 df_key = infile1 + repr(header)
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84 df = pd.read_csv(infile1, sep="\t", header=header, parse_dates=True)
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85 loaded_df[df_key] = df
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87 X = read_columns(df, c=c, c_option=column_option).astype(float)
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88 # sparse input
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89 elif input_type == "sparse":
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90 X = mmread(open(infile1, "r"))
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91
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92 # Get target y
34
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93 header = "infer" if params["input_options"]["header2"] else None
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94 column_option = params["input_options"]["column_selector_options_2"][
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95 "selected_column_selector_option2"
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96 ]
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97 if column_option in [
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98 "by_index_number",
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99 "all_but_by_index_number",
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100 "by_header_name",
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101 "all_but_by_header_name",
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102 ]:
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103 c = params["input_options"]["column_selector_options_2"]["col2"]
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104 else:
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105 c = None
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106
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107 df_key = infile2 + repr(header)
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108 if df_key in loaded_df:
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109 infile2 = loaded_df[df_key]
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110 else:
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111 infile2 = pd.read_csv(infile2, sep="\t", header=header, parse_dates=True)
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112 loaded_df[df_key] = infile2
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113
36
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114 y = read_columns(
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115 infile2, c=c, c_option=column_option, sep="\t", header=header, parse_dates=True
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116 )
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117 if len(y.shape) == 2 and y.shape[1] == 1:
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118 y = y.ravel()
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119
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120 return X, y
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121
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122
34
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123 def main(inputs, infile_estimator, infile1, infile2, out_object, out_weights=None):
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124 """main
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125
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126 Parameters
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127 ----------
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128 inputs : str
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129 File path to galaxy tool parameter
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130
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131 infile_estimator : str
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132 File paths of input estimator
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133
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134 infile1 : str
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135 File path to dataset containing features
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136
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137 infile2 : str
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138 File path to dataset containing target labels
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139
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140 out_object : str
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141 File path for output of fitted model or skeleton
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142
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143 out_weights : str
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144 File path for output of weights
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145
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146 """
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147 with open(inputs, "r") as param_handler:
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148 params = json.load(param_handler)
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149
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150 # load model
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151 estimator = load_model_from_h5(infile_estimator)
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152
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153 estimator = clean_params(estimator)
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154
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155 X_train, y_train = _get_X_y(params, infile1, infile2)
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156
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157 estimator.fit(X_train, y_train)
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158
29
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159 main_est = estimator
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160 if isinstance(main_est, Pipeline):
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161 main_est = main_est.steps[-1][-1]
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162 if hasattr(main_est, "model_") and hasattr(main_est, "save_weights"):
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163 if out_weights:
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164 main_est.save_weights(out_weights)
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165 del main_est.model_
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166 del main_est.fit_params
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167 del main_est.model_class_
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168 if getattr(main_est, "validation_data", None):
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169 del main_est.validation_data
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170 if getattr(main_est, "data_generator_", None):
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171 del main_est.data_generator_
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172
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173 dump_model_to_h5(estimator, out_object)
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174
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175
34
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176 if __name__ == "__main__":
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177 aparser = argparse.ArgumentParser()
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178 aparser.add_argument("-i", "--inputs", dest="inputs", required=True)
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179 aparser.add_argument("-X", "--infile_estimator", dest="infile_estimator")
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180 aparser.add_argument("-y", "--infile1", dest="infile1")
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181 aparser.add_argument("-g", "--infile2", dest="infile2")
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182 aparser.add_argument("-o", "--out_object", dest="out_object")
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183 aparser.add_argument("-t", "--out_weights", dest="out_weights")
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184 args = aparser.parse_args()
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185
34
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186 main(
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187 args.inputs,
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188 args.infile_estimator,
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189 args.infile1,
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190 args.infile2,
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191 args.out_object,
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192 args.out_weights,
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193 )