Mercurial > repos > bgruening > sklearn_discriminant_classifier

annotate model_prediction.py @ 40:4335cec181ba draft

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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit f031d8ddfb73cec24572648666ac44ee47f08aad
author bgruening
date Thu, 11 Aug 2022 08:53:29 +0000
parents e76f6dfea5c9
children d769d83ec796
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1 import argparse
9bb505eafac9 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 warnings
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4
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5 import numpy as np
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6 import pandas as pd
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7 from galaxy_ml.utils import get_module, load_model, read_columns, try_get_attr
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8 from scipy.io import mmread
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9 from sklearn.pipeline import Pipeline
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11 N_JOBS = int(__import__("os").environ.get("GALAXY_SLOTS", 1))
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14 def main(
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15 inputs,
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16 infile_estimator,
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17 outfile_predict,
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18 infile_weights=None,
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19 infile1=None,
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20 fasta_path=None,
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21 ref_seq=None,
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22 vcf_path=None,
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23 ):
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24 """
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25 Parameter
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26 ---------
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27 inputs : str
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28 File path to galaxy tool parameter
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30 infile_estimator : strgit
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31 File path to trained estimator input
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33 outfile_predict : str
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34 File path to save the prediction results, tabular
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36 infile_weights : str
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37 File path to weights input
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39 infile1 : str
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40 File path to dataset containing features
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42 fasta_path : str
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43 File path to dataset containing fasta file
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45 ref_seq : str
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46 File path to dataset containing the reference genome sequence.
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48 vcf_path : str
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49 File path to dataset containing variants info.
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50 """
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51 warnings.filterwarnings("ignore")
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52
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53 with open(inputs, "r") as param_handler:
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54 params = json.load(param_handler)
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56 # load model
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57 with open(infile_estimator, "rb") as est_handler:
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58 estimator = load_model(est_handler)
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60 main_est = estimator
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61 if isinstance(estimator, Pipeline):
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62 main_est = estimator.steps[-1][-1]
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63 if hasattr(main_est, "config") and hasattr(main_est, "load_weights"):
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64 if not infile_weights or infile_weights == "None":
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65 raise ValueError(
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66 "The selected model skeleton asks for weights, "
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67 "but dataset for weights wan not selected!"
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68 )
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69 main_est.load_weights(infile_weights)
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71 # handle data input
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72 input_type = params["input_options"]["selected_input"]
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73 # tabular input
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74 if input_type == "tabular":
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75 header = "infer" if params["input_options"]["header1"] else None
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76 column_option = params["input_options"]["column_selector_options_1"][
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77 "selected_column_selector_option"
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78 ]
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79 if column_option in [
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80 "by_index_number",
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81 "all_but_by_index_number",
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82 "by_header_name",
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83 "all_but_by_header_name",
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84 ]:
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85 c = params["input_options"]["column_selector_options_1"]["col1"]
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86 else:
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87 c = None
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88
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89 df = pd.read_csv(infile1, sep="\t", header=header, parse_dates=True)
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90
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91 X = read_columns(df, c=c, c_option=column_option).astype(float)
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92
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93 if params["method"] == "predict":
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94 preds = estimator.predict(X)
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95 else:
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96 preds = estimator.predict_proba(X)
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97
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98 # sparse input
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99 elif input_type == "sparse":
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100 X = mmread(open(infile1, "r"))
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101 if params["method"] == "predict":
26
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102 preds = estimator.predict(X)
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103 else:
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104 preds = estimator.predict_proba(X)
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105
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106 # fasta input
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107 elif input_type == "seq_fasta":
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108 if not hasattr(estimator, "data_batch_generator"):
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109 raise ValueError(
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110 "To do prediction on sequences in fasta input, "
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111 "the estimator must be a `KerasGBatchClassifier`"
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112 "equipped with data_batch_generator!"
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113 )
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114 pyfaidx = get_module("pyfaidx")
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115 sequences = pyfaidx.Fasta(fasta_path)
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116 n_seqs = len(sequences.keys())
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117 X = np.arange(n_seqs)[:, np.newaxis]
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118 seq_length = estimator.data_batch_generator.seq_length
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119 batch_size = getattr(estimator, "batch_size", 32)
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120 steps = (n_seqs + batch_size - 1) // batch_size
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121
35
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122 seq_type = params["input_options"]["seq_type"]
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123 klass = try_get_attr("galaxy_ml.preprocessors", seq_type)
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124
35
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125 pred_data_generator = klass(fasta_path, seq_length=seq_length)
26
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126
35
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127 if params["method"] == "predict":
37
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128 preds = estimator.predict(
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129 X, data_generator=pred_data_generator, steps=steps
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130 )
26
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131 else:
37
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132 preds = estimator.predict_proba(
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133 X, data_generator=pred_data_generator, steps=steps
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134 )
26
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135
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136 # vcf input
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137 elif input_type == "variant_effect":
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138 klass = try_get_attr("galaxy_ml.preprocessors", "GenomicVariantBatchGenerator")
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139
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140 options = params["input_options"]
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141 options.pop("selected_input")
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142 if options["blacklist_regions"] == "none":
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143 options["blacklist_regions"] = None
26
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144
37
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145 pred_data_generator = klass(
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146 ref_genome_path=ref_seq, vcf_path=vcf_path, **options
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147 )
26
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148
31
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149 pred_data_generator.set_processing_attrs()
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150
27
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151 variants = pred_data_generator.variants
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152
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153 # predict 1600 sample at once then write to file
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154 gen_flow = pred_data_generator.flow(batch_size=1600)
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155
35
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156 file_writer = open(outfile_predict, "w")
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157 header_row = "\t".join(["chrom", "pos", "name", "ref", "alt", "strand"])
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158 file_writer.write(header_row)
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diff changeset
159 header_done = False
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160
27
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diff changeset
161 steps_done = 0
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162
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163 # TODO: multiple threading
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164 try:
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165 while steps_done < len(gen_flow):
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166 index_array = next(gen_flow.index_generator)
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167 batch_X = gen_flow._get_batches_of_transformed_samples(index_array)
27
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168
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169 if params["method"] == "predict":
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170 batch_preds = estimator.predict(
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171 batch_X,
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172 # The presence of `pred_data_generator` below is to
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173 # override model carrying data_generator if there
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174 # is any.
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175 data_generator=pred_data_generator,
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176 )
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177 else:
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178 batch_preds = estimator.predict_proba(
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179 batch_X,
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180 # The presence of `pred_data_generator` below is to
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181 # override model carrying data_generator if there
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182 # is any.
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183 data_generator=pred_data_generator,
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184 )
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185
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186 if batch_preds.ndim == 1:
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187 batch_preds = batch_preds[:, np.newaxis]
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188
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189 batch_meta = variants[index_array]
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190 batch_out = np.column_stack([batch_meta, batch_preds])
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191
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192 if not header_done:
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193 heads = np.arange(batch_preds.shape[-1]).astype(str)
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194 heads_str = "\t".join(heads)
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195 file_writer.write("\t%s\n" % heads_str)
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196 header_done = True
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197
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198 for row in batch_out:
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199 row_str = "\t".join(row)
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200 file_writer.write("%s\n" % row_str)
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201
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202 steps_done += 1
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203
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204 finally:
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205 file_writer.close()
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206 # TODO: make api `pred_data_generator.close()`
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207 pred_data_generator.close()
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208 return 0
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209 # end input
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210
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211 # output
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212 if len(preds.shape) == 1:
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213 rval = pd.DataFrame(preds, columns=["Predicted"])
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214 else:
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215 rval = pd.DataFrame(preds)
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216
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217 rval.to_csv(outfile_predict, sep="\t", header=True, index=False)
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218
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219
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220 if __name__ == "__main__":
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221 aparser = argparse.ArgumentParser()
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222 aparser.add_argument("-i", "--inputs", dest="inputs", required=True)
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223 aparser.add_argument("-e", "--infile_estimator", dest="infile_estimator")
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224 aparser.add_argument("-w", "--infile_weights", dest="infile_weights")
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225 aparser.add_argument("-X", "--infile1", dest="infile1")
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226 aparser.add_argument("-O", "--outfile_predict", dest="outfile_predict")
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227 aparser.add_argument("-f", "--fasta_path", dest="fasta_path")
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228 aparser.add_argument("-r", "--ref_seq", dest="ref_seq")
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229 aparser.add_argument("-v", "--vcf_path", dest="vcf_path")
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230 args = aparser.parse_args()
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231
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232 main(
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233 args.inputs,
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234 args.infile_estimator,
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235 args.outfile_predict,
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236 infile_weights=args.infile_weights,
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237 infile1=args.infile1,
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238 fasta_path=args.fasta_path,
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239 ref_seq=args.ref_seq,
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240 vcf_path=args.vcf_path,
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241 )