Mercurial > repos > bgruening > sklearn_train_test_eval

annotate model_prediction.py @ 5:2b8406e74f9e draft

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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
author bgruening
date Mon, 16 Dec 2019 05:14:47 -0500
parents cc49634df38f
children ead7adad8d0e
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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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41
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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
5
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139 pred_data_generator.set_processing_attrs()
0
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140
1
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141 variants = pred_data_generator.variants
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142
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143 # predict 1600 sample at once then write to file
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144 gen_flow = pred_data_generator.flow(batch_size=1600)
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145
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146 file_writer = open(outfile_predict, 'w')
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147 header_row = '\t'.join(['chrom', 'pos', 'name', 'ref',
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148 'alt', 'strand'])
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149 file_writer.write(header_row)
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150 header_done = False
0
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151
1
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152 steps_done = 0
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153
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154 # TODO: multiple threading
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155 try:
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156 while steps_done < len(gen_flow):
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157 index_array = next(gen_flow.index_generator)
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158 batch_X = gen_flow._get_batches_of_transformed_samples(
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159 index_array)
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160
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161 if params['method'] == 'predict':
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162 batch_preds = estimator.predict(
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163 batch_X,
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164 # The presence of `pred_data_generator` below is to
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165 # override model carrying data_generator if there
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166 # is any.
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167 data_generator=pred_data_generator)
0
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168 else:
1
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169 batch_preds = estimator.predict_proba(
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170 batch_X,
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171 # The presence of `pred_data_generator` below is to
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172 # override model carrying data_generator if there
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173 # is any.
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174 data_generator=pred_data_generator)
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175
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176 if batch_preds.ndim == 1:
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177 batch_preds = batch_preds[:, np.newaxis]
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178
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179 batch_meta = variants[index_array]
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180 batch_out = np.column_stack([batch_meta, batch_preds])
0
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181
1
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182 if not header_done:
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183 heads = np.arange(batch_preds.shape[-1]).astype(str)
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184 heads_str = '\t'.join(heads)
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185 file_writer.write("\t%s\n" % heads_str)
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186 header_done = True
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187
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188 for row in batch_out:
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189 row_str = '\t'.join(row)
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190 file_writer.write("%s\n" % row_str)
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191
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192 steps_done += 1
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193
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194 finally:
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195 file_writer.close()
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196 # TODO: make api `pred_data_generator.close()`
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197 pred_data_generator.close()
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198 return 0
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199 # end input
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200
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201 # output
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202 if len(preds.shape) == 1:
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203 rval = pd.DataFrame(preds, columns=['Predicted'])
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204 else:
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205 rval = pd.DataFrame(preds)
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206
1
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207 rval.to_csv(outfile_predict, sep='\t', header=True, index=False)
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208
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209
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210 if __name__ == '__main__':
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211 aparser = argparse.ArgumentParser()
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212 aparser.add_argument("-i", "--inputs", dest="inputs", required=True)
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213 aparser.add_argument("-e", "--infile_estimator", dest="infile_estimator")
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214 aparser.add_argument("-w", "--infile_weights", dest="infile_weights")
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215 aparser.add_argument("-X", "--infile1", dest="infile1")
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216 aparser.add_argument("-O", "--outfile_predict", dest="outfile_predict")
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217 aparser.add_argument("-f", "--fasta_path", dest="fasta_path")
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218 aparser.add_argument("-r", "--ref_seq", dest="ref_seq")
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219 aparser.add_argument("-v", "--vcf_path", dest="vcf_path")
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220 args = aparser.parse_args()
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221
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222 main(args.inputs, args.infile_estimator, args.outfile_predict,
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223 infile_weights=args.infile_weights, infile1=args.infile1,
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224 fasta_path=args.fasta_path, ref_seq=args.ref_seq,
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225 vcf_path=args.vcf_path)