Mercurial > repos > bgruening > sklearn_numeric_clustering

annotate simple_model_fit.py @ 35:e38a2675db5e 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 17:03:46 -0400
parents
children 836ba896e2be
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35
e38a2675db5e "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 pickle
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5
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6 from galaxy_ml.utils import load_model, read_columns
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7 from sklearn.pipeline import Pipeline
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8
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9
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10 def _get_X_y(params, infile1, infile2):
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11 """ read from inputs and output X and y
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12
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13 Parameters
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14 ----------
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15 params : dict
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16 Tool inputs parameter
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17 infile1 : str
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18 File path to dataset containing features
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19 infile2 : str
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20 File path to dataset containing target values
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21
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22 """
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23 # store read dataframe object
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24 loaded_df = {}
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25
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26 input_type = params['input_options']['selected_input']
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27 # tabular input
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28 if input_type == 'tabular':
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29 header = 'infer' if params['input_options']['header1'] else None
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30 column_option = (params['input_options']['column_selector_options_1']
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31 ['selected_column_selector_option'])
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32 if column_option in ['by_index_number', 'all_but_by_index_number',
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33 'by_header_name', 'all_but_by_header_name']:
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34 c = params['input_options']['column_selector_options_1']['col1']
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35 else:
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36 c = None
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37
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38 df_key = infile1 + repr(header)
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39 df = pd.read_csv(infile1, sep='\t', header=header,
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40 parse_dates=True)
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41 loaded_df[df_key] = df
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42
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43 X = read_columns(df, c=c, c_option=column_option).astype(float)
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44 # sparse input
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45 elif input_type == 'sparse':
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46 X = mmread(open(infile1, 'r'))
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47
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48 # Get target y
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49 header = 'infer' if params['input_options']['header2'] else None
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50 column_option = (params['input_options']['column_selector_options_2']
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51 ['selected_column_selector_option2'])
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52 if column_option in ['by_index_number', 'all_but_by_index_number',
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53 'by_header_name', 'all_but_by_header_name']:
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54 c = params['input_options']['column_selector_options_2']['col2']
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55 else:
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56 c = None
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57
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58 df_key = infile2 + repr(header)
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59 if df_key in loaded_df:
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60 infile2 = loaded_df[df_key]
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61 else:
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62 infile2 = pd.read_csv(infile2, sep='\t',
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63 header=header, parse_dates=True)
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64 loaded_df[df_key] = infile2
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65
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66 y = read_columns(
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67 infile2,
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68 c=c,
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69 c_option=column_option,
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70 sep='\t',
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71 header=header,
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72 parse_dates=True)
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73 if len(y.shape) == 2 and y.shape[1] == 1:
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74 y = y.ravel()
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75
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76 return X, y
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77
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78
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79 def main(inputs, infile_estimator, infile1, infile2, out_object,
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80 out_weights=None):
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81 """ main
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82
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83 Parameters
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84 ----------
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85 inputs : str
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86 File path to galaxy tool parameter
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87
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88 infile_estimator : str
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89 File paths of input estimator
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90
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91 infile1 : str
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92 File path to dataset containing features
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93
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94 infile2 : str
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95 File path to dataset containing target labels
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96
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97 out_object : str
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98 File path for output of fitted model or skeleton
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99
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100 out_weights : str
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101 File path for output of weights
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102
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103 """
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104 with open(inputs, 'r') as param_handler:
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105 params = json.load(param_handler)
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106
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107 # load model
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108 with open(infile_estimator, 'rb') as est_handler:
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109 estimator = load_model(est_handler)
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110
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111 X_train, y_train = _get_X_y(params, infile1, infile2)
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112
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113 estimator.fit(X_train, y_train)
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114
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115 main_est = estimator
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116 if isinstance(main_est, Pipeline):
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117 main_est = main_est.steps[-1][-1]
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118 if hasattr(main_est, 'model_') \
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119 and hasattr(main_est, 'save_weights'):
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120 if out_weights:
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121 main_est.save_weights(out_weights)
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122 del main_est.model_
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123 del main_est.fit_params
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124 del main_est.model_class_
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125 del main_est.validation_data
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126 if getattr(main_est, 'data_generator_', None):
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127 del main_est.data_generator_
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128
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129 with open(out_object, 'wb') as output_handler:
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130 pickle.dump(estimator, output_handler,
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131 pickle.HIGHEST_PROTOCOL)
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132
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133
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134 if __name__ == '__main__':
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135 aparser = argparse.ArgumentParser()
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136 aparser.add_argument("-i", "--inputs", dest="inputs", required=True)
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137 aparser.add_argument("-X", "--infile_estimator", dest="infile_estimator")
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138 aparser.add_argument("-y", "--infile1", dest="infile1")
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139 aparser.add_argument("-g", "--infile2", dest="infile2")
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140 aparser.add_argument("-o", "--out_object", dest="out_object")
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141 aparser.add_argument("-t", "--out_weights", dest="out_weights")
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142 args = aparser.parse_args()
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143
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144 main(args.inputs, args.infile_estimator, args.infile1,
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145 args.infile2, args.out_object, args.out_weights)