Mercurial > repos > bgruening > sklearn_feature_selection

annotate model_prediction.py @ 40:96bb4c49e30e draft default tip

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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 57a0433defa3cbc37ab34fbb0ebcfaeb680db8d5
author bgruening
date Sun, 05 Nov 2023 15:34:49 +0000
parents 61edd9e5c17f
children
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0b88494bdcac planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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1 import argparse
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2 import json
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3 import warnings
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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.model_persist import load_model_from_h5
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8 from galaxy_ml.utils import (clean_params, get_module, read_columns,
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9 try_get_attr)
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10 from scipy.io import mmread
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12 N_JOBS = int(__import__("os").environ.get("GALAXY_SLOTS", 1))
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15 def main(
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16 inputs,
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17 infile_estimator,
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18 outfile_predict,
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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 : str
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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 infile1 : str
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37 File path to dataset containing features
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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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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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53 # load model
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54 estimator = load_model_from_h5(infile_estimator)
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55 estimator = clean_params(estimator)
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57 # handle data input
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58 input_type = params["input_options"]["selected_input"]
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59 # tabular input
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60 if input_type == "tabular":
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61 header = "infer" if params["input_options"]["header1"] else None
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62 column_option = params["input_options"]["column_selector_options_1"][
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63 "selected_column_selector_option"
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64 ]
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65 if column_option in [
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66 "by_index_number",
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67 "all_but_by_index_number",
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68 "by_header_name",
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69 "all_but_by_header_name",
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70 ]:
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71 c = params["input_options"]["column_selector_options_1"]["col1"]
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72 else:
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73 c = None
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74
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75 df = pd.read_csv(infile1, sep="\t", header=header, parse_dates=True)
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77 X = read_columns(df, c=c, c_option=column_option).astype(float)
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78
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79 if params["method"] == "predict":
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80 preds = estimator.predict(X)
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81 else:
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82 preds = estimator.predict_proba(X)
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84 # sparse input
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85 elif input_type == "sparse":
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86 X = mmread(open(infile1, "r"))
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87 if params["method"] == "predict":
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88 preds = estimator.predict(X)
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89 else:
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90 preds = estimator.predict_proba(X)
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91
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92 # fasta input
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93 elif input_type == "seq_fasta":
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94 if not hasattr(estimator, "data_batch_generator"):
20
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95 raise ValueError(
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96 "To do prediction on sequences in fasta input, "
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97 "the estimator must be a `KerasGBatchClassifier`"
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98 "equipped with data_batch_generator!"
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99 )
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100 pyfaidx = get_module("pyfaidx")
20
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101 sequences = pyfaidx.Fasta(fasta_path)
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102 n_seqs = len(sequences.keys())
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103 X = np.arange(n_seqs)[:, np.newaxis]
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104 seq_length = estimator.data_batch_generator.seq_length
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105 batch_size = getattr(estimator, "batch_size", 32)
20
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106 steps = (n_seqs + batch_size - 1) // batch_size
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107
29
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108 seq_type = params["input_options"]["seq_type"]
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109 klass = try_get_attr("galaxy_ml.preprocessors", seq_type)
20
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110
29
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111 pred_data_generator = klass(fasta_path, seq_length=seq_length)
20
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112
29
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113 if params["method"] == "predict":
31
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114 preds = estimator.predict(
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115 X, data_generator=pred_data_generator, steps=steps
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116 )
20
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117 else:
31
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118 preds = estimator.predict_proba(
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119 X, data_generator=pred_data_generator, steps=steps
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120 )
20
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121
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122 # vcf input
29
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123 elif input_type == "variant_effect":
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124 klass = try_get_attr("galaxy_ml.preprocessors", "GenomicVariantBatchGenerator")
20
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125
29
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126 options = params["input_options"]
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127 options.pop("selected_input")
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128 if options["blacklist_regions"] == "none":
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129 options["blacklist_regions"] = None
20
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130
31
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131 pred_data_generator = klass(
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132 ref_genome_path=ref_seq, vcf_path=vcf_path, **options
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133 )
20
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134
25
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135 pred_data_generator.set_processing_attrs()
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136
21
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137 variants = pred_data_generator.variants
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138
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139 # predict 1600 sample at once then write to file
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140 gen_flow = pred_data_generator.flow(batch_size=1600)
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141
29
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142 file_writer = open(outfile_predict, "w")
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143 header_row = "\t".join(["chrom", "pos", "name", "ref", "alt", "strand"])
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144 file_writer.write(header_row)
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145 header_done = False
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146
21
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147 steps_done = 0
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148
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149 # TODO: multiple threading
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150 try:
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151 while steps_done < len(gen_flow):
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152 index_array = next(gen_flow.index_generator)
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153 batch_X = gen_flow._get_batches_of_transformed_samples(index_array)
21
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154
29
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155 if params["method"] == "predict":
21
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156 batch_preds = estimator.predict(
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157 batch_X,
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158 # The presence of `pred_data_generator` below is to
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159 # override model carrying data_generator if there
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160 # is any.
29
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161 data_generator=pred_data_generator,
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162 )
20
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163 else:
21
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164 batch_preds = estimator.predict_proba(
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165 batch_X,
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166 # The presence of `pred_data_generator` below is to
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167 # override model carrying data_generator if there
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168 # is any.
29
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169 data_generator=pred_data_generator,
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170 )
21
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171
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172 if batch_preds.ndim == 1:
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173 batch_preds = batch_preds[:, np.newaxis]
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174
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175 batch_meta = variants[index_array]
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176 batch_out = np.column_stack([batch_meta, batch_preds])
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177
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178 if not header_done:
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179 heads = np.arange(batch_preds.shape[-1]).astype(str)
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180 heads_str = "\t".join(heads)
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181 file_writer.write("\t%s\n" % heads_str)
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182 header_done = True
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183
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184 for row in batch_out:
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185 row_str = "\t".join(row)
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186 file_writer.write("%s\n" % row_str)
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187
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188 steps_done += 1
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189
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190 finally:
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191 file_writer.close()
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192 # TODO: make api `pred_data_generator.close()`
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193 pred_data_generator.close()
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194 return 0
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195 # end input
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196
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197 # output
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198 if len(preds.shape) == 1:
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199 rval = pd.DataFrame(preds, columns=["Predicted"])
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200 else:
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201 rval = pd.DataFrame(preds)
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202
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203 rval.to_csv(outfile_predict, sep="\t", header=True, index=False)
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204
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205
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206 if __name__ == "__main__":
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207 aparser = argparse.ArgumentParser()
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208 aparser.add_argument("-i", "--inputs", dest="inputs", required=True)
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209 aparser.add_argument("-e", "--infile_estimator", dest="infile_estimator")
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210 aparser.add_argument("-X", "--infile1", dest="infile1")
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211 aparser.add_argument("-O", "--outfile_predict", dest="outfile_predict")
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212 aparser.add_argument("-f", "--fasta_path", dest="fasta_path")
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213 aparser.add_argument("-r", "--ref_seq", dest="ref_seq")
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214 aparser.add_argument("-v", "--vcf_path", dest="vcf_path")
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215 args = aparser.parse_args()
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216
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217 main(
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218 args.inputs,
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219 args.infile_estimator,
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220 args.outfile_predict,
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221 infile1=args.infile1,
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222 fasta_path=args.fasta_path,
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223 ref_seq=args.ref_seq,
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224 vcf_path=args.vcf_path,
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225 )