Mercurial > repos > bgruening > sklearn_stacking_ensemble_models

annotate model_prediction.py @ 6:aae4725f152b draft

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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d6333e7294e67be5968a41f404b66699cad4ae53"
author bgruening
date Thu, 07 Nov 2019 05:15:47 -0500
parents 963e449636d3
children 00819b7f2f55
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1 import argparse
e18d9b17c322 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c035d399196b3bef9982db4f8e47331411dbb20e
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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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963e449636d3 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ba6a47bdf76bbf4cb276206ac1a8cbf61332fd16"
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5 import tabix
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6 import warnings
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8 from scipy.io import mmread
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9 from sklearn.pipeline import Pipeline
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963e449636d3 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ba6a47bdf76bbf4cb276206ac1a8cbf61332fd16"
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11 from galaxy_ml.externals.selene_sdk.sequences import Genome
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12 from galaxy_ml.utils import (load_model, read_columns,
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13 get_module, try_get_attr)
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16 N_JOBS = int(__import__('os').environ.get('GALAXY_SLOTS', 1))
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19 def main(inputs, infile_estimator, outfile_predict,
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20 infile_weights=None, infile1=None,
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21 fasta_path=None, ref_seq=None,
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22 vcf_path=None):
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23 """
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24 Parameter
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25 ---------
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26 inputs : str
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27 File path to galaxy tool parameter
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29 infile_estimator : strgit
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30 File path to trained estimator input
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31
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32 outfile_predict : str
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33 File path to save the prediction results, tabular
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35 infile_weights : str
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36 File path to weights input
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37
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38 infile1 : str
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39 File path to dataset containing features
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40
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41 fasta_path : str
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42 File path to dataset containing fasta file
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44 ref_seq : str
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45 File path to dataset containing the reference genome sequence.
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47 vcf_path : str
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48 File path to dataset containing variants info.
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49 """
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50 warnings.filterwarnings('ignore')
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51
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52 with open(inputs, 'r') as param_handler:
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53 params = json.load(param_handler)
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54
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55 # load model
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56 with open(infile_estimator, 'rb') as est_handler:
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57 estimator = load_model(est_handler)
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58
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59 main_est = estimator
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60 if isinstance(estimator, Pipeline):
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61 main_est = estimator.steps[-1][-1]
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62 if hasattr(main_est, 'config') and hasattr(main_est, 'load_weights'):
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63 if not infile_weights or infile_weights == 'None':
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64 raise ValueError("The selected model skeleton asks for weights, "
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65 "but dataset for weights wan not selected!")
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66 main_est.load_weights(infile_weights)
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67
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68 # handle data input
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69 input_type = params['input_options']['selected_input']
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70 # tabular input
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71 if input_type == 'tabular':
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72 header = 'infer' if params['input_options']['header1'] else None
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73 column_option = (params['input_options']
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74 ['column_selector_options_1']
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75 ['selected_column_selector_option'])
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76 if column_option in ['by_index_number', 'all_but_by_index_number',
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77 'by_header_name', 'all_but_by_header_name']:
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78 c = params['input_options']['column_selector_options_1']['col1']
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79 else:
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80 c = None
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81
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82 df = pd.read_csv(infile1, sep='\t', header=header, parse_dates=True)
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83
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84 X = read_columns(df, c=c, c_option=column_option).astype(float)
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85
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86 if params['method'] == 'predict':
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87 preds = estimator.predict(X)
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88 else:
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89 preds = estimator.predict_proba(X)
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90
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91 # sparse input
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92 elif input_type == 'sparse':
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93 X = mmread(open(infile1, 'r'))
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94 if params['method'] == 'predict':
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95 preds = estimator.predict(X)
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96 else:
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97 preds = estimator.predict_proba(X)
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98
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99 # fasta input
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100 elif input_type == 'seq_fasta':
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101 if not hasattr(estimator, 'data_batch_generator'):
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102 raise ValueError(
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103 "To do prediction on sequences in fasta input, "
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104 "the estimator must be a `KerasGBatchClassifier`"
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105 "equipped with data_batch_generator!")
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106 pyfaidx = get_module('pyfaidx')
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107 sequences = pyfaidx.Fasta(fasta_path)
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108 n_seqs = len(sequences.keys())
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109 X = np.arange(n_seqs)[:, np.newaxis]
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110 seq_length = estimator.data_batch_generator.seq_length
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111 batch_size = getattr(estimator, 'batch_size', 32)
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112 steps = (n_seqs + batch_size - 1) // batch_size
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113
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114 seq_type = params['input_options']['seq_type']
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115 klass = try_get_attr(
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116 'galaxy_ml.preprocessors', seq_type)
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117
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118 pred_data_generator = klass(
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119 fasta_path, seq_length=seq_length)
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120
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121 if params['method'] == 'predict':
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122 preds = estimator.predict(
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123 X, data_generator=pred_data_generator, steps=steps)
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124 else:
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125 preds = estimator.predict_proba(
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126 X, data_generator=pred_data_generator, steps=steps)
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127
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128 # vcf input
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129 elif input_type == 'variant_effect':
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130 klass = try_get_attr('galaxy_ml.preprocessors',
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131 'GenomicVariantBatchGenerator')
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132
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133 options = params['input_options']
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134 options.pop('selected_input')
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135 if options['blacklist_regions'] == 'none':
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136 options['blacklist_regions'] = None
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137
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138 pred_data_generator = klass(
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139 ref_genome_path=ref_seq, vcf_path=vcf_path, **options)
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140
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141 pred_data_generator.fit()
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142
3
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143 variants = pred_data_generator.variants
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144 # TODO : remove the following block after galaxy-ml v0.7.13
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145 blacklist_tabix = getattr(pred_data_generator.reference_genome_,
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146 '_blacklist_tabix', None)
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147 clean_variants = []
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148 if blacklist_tabix:
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149 start_radius = pred_data_generator.start_radius_
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150 end_radius = pred_data_generator.end_radius_
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151
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152 for chrom, pos, name, ref, alt, strand in variants:
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153 center = pos + len(ref) // 2
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154 start = center - start_radius
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155 end = center + end_radius
2
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156
3
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157 if isinstance(pred_data_generator.reference_genome_, Genome):
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158 if "chr" not in chrom:
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159 chrom = "chr" + chrom
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160 if "MT" in chrom:
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161 chrom = chrom[:-1]
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162 try:
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163 rows = blacklist_tabix.query(chrom, start, end)
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164 found = 0
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165 for row in rows:
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166 found = 1
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167 break
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168 if found:
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169 continue
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170 except tabix.TabixError:
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171 pass
2
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172
3
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173 clean_variants.append((chrom, pos, name, ref, alt, strand))
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174 else:
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175 clean_variants = variants
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176
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177 setattr(pred_data_generator, 'variants', clean_variants)
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178
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179 variants = np.array(clean_variants)
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180 # predict 1600 sample at once then write to file
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181 gen_flow = pred_data_generator.flow(batch_size=1600)
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182
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183 file_writer = open(outfile_predict, 'w')
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184 header_row = '\t'.join(['chrom', 'pos', 'name', 'ref',
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185 'alt', 'strand'])
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186 file_writer.write(header_row)
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187 header_done = False
2
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188
3
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189 steps_done = 0
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190
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191 # TODO: multiple threading
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192 try:
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193 while steps_done < len(gen_flow):
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194 index_array = next(gen_flow.index_generator)
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195 batch_X = gen_flow._get_batches_of_transformed_samples(
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196 index_array)
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197
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198 if params['method'] == 'predict':
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199 batch_preds = estimator.predict(
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200 batch_X,
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201 # The presence of `pred_data_generator` below is to
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202 # override model carrying data_generator if there
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203 # is any.
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204 data_generator=pred_data_generator)
2
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205 else:
3
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206 batch_preds = estimator.predict_proba(
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207 batch_X,
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208 # The presence of `pred_data_generator` below is to
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209 # override model carrying data_generator if there
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210 # is any.
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211 data_generator=pred_data_generator)
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212
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213 if batch_preds.ndim == 1:
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214 batch_preds = batch_preds[:, np.newaxis]
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215
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216 batch_meta = variants[index_array]
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217 batch_out = np.column_stack([batch_meta, batch_preds])
2
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218
3
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219 if not header_done:
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220 heads = np.arange(batch_preds.shape[-1]).astype(str)
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221 heads_str = '\t'.join(heads)
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222 file_writer.write("\t%s\n" % heads_str)
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223 header_done = True
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224
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225 for row in batch_out:
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226 row_str = '\t'.join(row)
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227 file_writer.write("%s\n" % row_str)
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228
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229 steps_done += 1
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230
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231 finally:
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232 file_writer.close()
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233 # TODO: make api `pred_data_generator.close()`
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234 pred_data_generator.close()
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235 return 0
2
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236 # end input
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237
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238 # output
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239 if len(preds.shape) == 1:
2
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240 rval = pd.DataFrame(preds, columns=['Predicted'])
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241 else:
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242 rval = pd.DataFrame(preds)
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243
3
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244 rval.to_csv(outfile_predict, sep='\t', header=True, index=False)
2
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245
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246
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247 if __name__ == '__main__':
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248 aparser = argparse.ArgumentParser()
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249 aparser.add_argument("-i", "--inputs", dest="inputs", required=True)
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250 aparser.add_argument("-e", "--infile_estimator", dest="infile_estimator")
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251 aparser.add_argument("-w", "--infile_weights", dest="infile_weights")
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252 aparser.add_argument("-X", "--infile1", dest="infile1")
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253 aparser.add_argument("-O", "--outfile_predict", dest="outfile_predict")
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254 aparser.add_argument("-f", "--fasta_path", dest="fasta_path")
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255 aparser.add_argument("-r", "--ref_seq", dest="ref_seq")
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256 aparser.add_argument("-v", "--vcf_path", dest="vcf_path")
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257 args = aparser.parse_args()
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258
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259 main(args.inputs, args.infile_estimator, args.outfile_predict,
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260 infile_weights=args.infile_weights, infile1=args.infile1,
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261 fasta_path=args.fasta_path, ref_seq=args.ref_seq,
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262 vcf_path=args.vcf_path)