Mercurial > repos > bgruening > sklearn_train_test_eval

annotate model_prediction.py @ 0:68aaa903052a draft

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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
author bgruening
date Fri, 09 Aug 2019 07:09:06 -0400
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children cc49634df38f
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68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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1 import argparse
68aaa903052a planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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2 import json
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3 import numpy as np
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4 import pandas as pd
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5 import warnings
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7 from scipy.io import mmread
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8 from sklearn.pipeline import Pipeline
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10 from galaxy_ml.utils import (load_model, read_columns,
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11 get_module, try_get_attr)
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14 N_JOBS = int(__import__('os').environ.get('GALAXY_SLOTS', 1))
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17 def main(inputs, infile_estimator, outfile_predict,
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18 infile_weights=None, infile1=None,
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19 fasta_path=None, ref_seq=None,
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20 vcf_path=None):
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21 """
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22 Parameter
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23 ---------
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24 inputs : str
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25 File path to galaxy tool parameter
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27 infile_estimator : strgit
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28 File path to trained estimator input
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30 outfile_predict : str
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31 File path to save the prediction results, tabular
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33 infile_weights : str
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34 File path to weights input
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36 infile1 : str
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37 File path to dataset containing features
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38
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39 fasta_path : str
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40 File path to dataset containing fasta file
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42 ref_seq : str
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43 File path to dataset containing the reference genome sequence.
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44
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45 vcf_path : str
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46 File path to dataset containing variants info.
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47 """
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48 warnings.filterwarnings('ignore')
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49
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50 with open(inputs, 'r') as param_handler:
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51 params = json.load(param_handler)
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52
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53 # load model
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54 with open(infile_estimator, 'rb') as est_handler:
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55 estimator = load_model(est_handler)
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56
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57 main_est = estimator
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58 if isinstance(estimator, Pipeline):
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59 main_est = estimator.steps[-1][-1]
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60 if hasattr(main_est, 'config') and hasattr(main_est, 'load_weights'):
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61 if not infile_weights or infile_weights == 'None':
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62 raise ValueError("The selected model skeleton asks for weights, "
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63 "but dataset for weights wan not selected!")
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64 main_est.load_weights(infile_weights)
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65
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66 # handle data input
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67 input_type = params['input_options']['selected_input']
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68 # tabular input
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69 if input_type == 'tabular':
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70 header = 'infer' if params['input_options']['header1'] else None
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71 column_option = (params['input_options']
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72 ['column_selector_options_1']
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73 ['selected_column_selector_option'])
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74 if column_option in ['by_index_number', 'all_but_by_index_number',
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75 'by_header_name', 'all_but_by_header_name']:
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76 c = params['input_options']['column_selector_options_1']['col1']
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77 else:
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78 c = None
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79
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80 df = pd.read_csv(infile1, sep='\t', header=header, parse_dates=True)
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81
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82 X = read_columns(df, c=c, c_option=column_option).astype(float)
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83
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84 if params['method'] == 'predict':
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85 preds = estimator.predict(X)
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86 else:
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87 preds = estimator.predict_proba(X)
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88
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89 # sparse input
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90 elif input_type == 'sparse':
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91 X = mmread(open(infile1, 'r'))
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92 if params['method'] == 'predict':
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93 preds = estimator.predict(X)
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94 else:
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95 preds = estimator.predict_proba(X)
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96
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97 # fasta input
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98 elif input_type == 'seq_fasta':
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99 if not hasattr(estimator, 'data_batch_generator'):
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100 raise ValueError(
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101 "To do prediction on sequences in fasta input, "
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102 "the estimator must be a `KerasGBatchClassifier`"
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103 "equipped with data_batch_generator!")
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104 pyfaidx = get_module('pyfaidx')
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105 sequences = pyfaidx.Fasta(fasta_path)
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106 n_seqs = len(sequences.keys())
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107 X = np.arange(n_seqs)[:, np.newaxis]
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108 seq_length = estimator.data_batch_generator.seq_length
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109 batch_size = getattr(estimator, 'batch_size', 32)
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110 steps = (n_seqs + batch_size - 1) // batch_size
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111
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112 seq_type = params['input_options']['seq_type']
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113 klass = try_get_attr(
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114 'galaxy_ml.preprocessors', seq_type)
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115
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116 pred_data_generator = klass(
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117 fasta_path, seq_length=seq_length)
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118
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119 if params['method'] == 'predict':
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120 preds = estimator.predict(
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121 X, data_generator=pred_data_generator, steps=steps)
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122 else:
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123 preds = estimator.predict_proba(
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124 X, data_generator=pred_data_generator, steps=steps)
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125
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126 # vcf input
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127 elif input_type == 'variant_effect':
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128 klass = try_get_attr('galaxy_ml.preprocessors',
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129 'GenomicVariantBatchGenerator')
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130
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131 options = params['input_options']
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132 options.pop('selected_input')
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133 if options['blacklist_regions'] == 'none':
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134 options['blacklist_regions'] = None
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135
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136 pred_data_generator = klass(
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137 ref_genome_path=ref_seq, vcf_path=vcf_path, **options)
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138
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139 pred_data_generator.fit()
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140
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141 preds = estimator.model_.predict_generator(
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142 pred_data_generator.flow(batch_size=32),
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143 workers=N_JOBS,
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144 use_multiprocessing=True)
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145
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146 if preds.min() < 0. or preds.max() > 1.:
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147 warnings.warn('Network returning invalid probability values. '
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148 'The last layer might not normalize predictions '
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149 'into probabilities '
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150 '(like softmax or sigmoid would).')
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151
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152 if params['method'] == 'predict_proba' and preds.shape[1] == 1:
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153 # first column is probability of class 0 and second is of class 1
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154 preds = np.hstack([1 - preds, preds])
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155
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156 elif params['method'] == 'predict':
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157 if preds.shape[-1] > 1:
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158 # if the last activation is `softmax`, the sum of all
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159 # probibilities will 1, the classification is considered as
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160 # multi-class problem, otherwise, we take it as multi-label.
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161 act = getattr(estimator.model_.layers[-1], 'activation', None)
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162 if act and act.__name__ == 'softmax':
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163 classes = preds.argmax(axis=-1)
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164 else:
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165 preds = (preds > 0.5).astype('int32')
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166 else:
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167 classes = (preds > 0.5).astype('int32')
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168
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169 preds = estimator.classes_[classes]
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170 # end input
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171
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172 # output
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173 if input_type == 'variant_effect': # TODO: save in batchs
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174 rval = pd.DataFrame(preds)
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175 meta = pd.DataFrame(
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176 pred_data_generator.variants,
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177 columns=['chrom', 'pos', 'name', 'ref', 'alt', 'strand'])
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178
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179 rval = pd.concat([meta, rval], axis=1)
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180
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181 elif len(preds.shape) == 1:
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182 rval = pd.DataFrame(preds, columns=['Predicted'])
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183 else:
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184 rval = pd.DataFrame(preds)
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185
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186 rval.to_csv(outfile_predict, sep='\t',
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187 header=True, index=False)
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188
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189
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190 if __name__ == '__main__':
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191 aparser = argparse.ArgumentParser()
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192 aparser.add_argument("-i", "--inputs", dest="inputs", required=True)
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193 aparser.add_argument("-e", "--infile_estimator", dest="infile_estimator")
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194 aparser.add_argument("-w", "--infile_weights", dest="infile_weights")
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195 aparser.add_argument("-X", "--infile1", dest="infile1")
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196 aparser.add_argument("-O", "--outfile_predict", dest="outfile_predict")
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197 aparser.add_argument("-f", "--fasta_path", dest="fasta_path")
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198 aparser.add_argument("-r", "--ref_seq", dest="ref_seq")
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199 aparser.add_argument("-v", "--vcf_path", dest="vcf_path")
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200 args = aparser.parse_args()
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201
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202 main(args.inputs, args.infile_estimator, args.outfile_predict,
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203 infile_weights=args.infile_weights, infile1=args.infile1,
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204 fasta_path=args.fasta_path, ref_seq=args.ref_seq,
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205 vcf_path=args.vcf_path)