Mercurial > repos > bgruening > sklearn_build_pipeline

annotate model_prediction.py @ 19:4de3d598c116 draft

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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
author bgruening
date Tue, 13 Apr 2021 18:57:31 +0000
parents 3f3c6dc38f3e
children 664666bd14ec
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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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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 scipy.io import mmread
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8 from sklearn.pipeline import Pipeline
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10 from galaxy_ml.utils import (get_module, load_model,
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11 read_columns, 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(
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18 inputs,
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19 infile_estimator,
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20 outfile_predict,
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21 infile_weights=None,
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22 infile1=None,
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23 fasta_path=None,
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24 ref_seq=None,
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25 vcf_path=None,
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26 ):
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27 """
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28 Parameter
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29 ---------
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30 inputs : str
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31 File path to galaxy tool parameter
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33 infile_estimator : strgit
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34 File path to trained estimator input
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36 outfile_predict : str
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37 File path to save the prediction results, tabular
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39 infile_weights : str
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40 File path to weights input
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42 infile1 : str
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43 File path to dataset containing features
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45 fasta_path : str
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46 File path to dataset containing fasta file
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48 ref_seq : str
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49 File path to dataset containing the reference genome sequence.
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51 vcf_path : str
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52 File path to dataset containing variants info.
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53 """
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54 warnings.filterwarnings("ignore")
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55
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56 with open(inputs, "r") as param_handler:
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57 params = json.load(param_handler)
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59 # load model
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60 with open(infile_estimator, "rb") as est_handler:
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61 estimator = load_model(est_handler)
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63 main_est = estimator
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64 if isinstance(estimator, Pipeline):
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65 main_est = estimator.steps[-1][-1]
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66 if hasattr(main_est, "config") and hasattr(main_est, "load_weights"):
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67 if not infile_weights or infile_weights == "None":
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68 raise ValueError(
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69 "The selected model skeleton asks for weights, " "but dataset for weights wan not selected!"
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70 )
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71 main_est.load_weights(infile_weights)
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73 # handle data input
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74 input_type = params["input_options"]["selected_input"]
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75 # tabular input
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76 if input_type == "tabular":
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77 header = "infer" if params["input_options"]["header1"] else None
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78 column_option = params["input_options"]["column_selector_options_1"]["selected_column_selector_option"]
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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":
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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
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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
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125 pred_data_generator = klass(fasta_path, seq_length=seq_length)
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126
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127 if params["method"] == "predict":
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128 preds = estimator.predict(X, data_generator=pred_data_generator, steps=steps)
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129 else:
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130 preds = estimator.predict_proba(X, data_generator=pred_data_generator, steps=steps)
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131
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132 # vcf input
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133 elif input_type == "variant_effect":
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134 klass = try_get_attr("galaxy_ml.preprocessors", "GenomicVariantBatchGenerator")
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135
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136 options = params["input_options"]
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137 options.pop("selected_input")
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138 if options["blacklist_regions"] == "none":
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139 options["blacklist_regions"] = None
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140
19
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141 pred_data_generator = klass(ref_genome_path=ref_seq, vcf_path=vcf_path, **options)
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142
15
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143 pred_data_generator.set_processing_attrs()
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144
11
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145 variants = pred_data_generator.variants
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146
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147 # predict 1600 sample at once then write to file
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148 gen_flow = pred_data_generator.flow(batch_size=1600)
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149
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150 file_writer = open(outfile_predict, "w")
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151 header_row = "\t".join(["chrom", "pos", "name", "ref", "alt", "strand"])
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152 file_writer.write(header_row)
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153 header_done = False
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154
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155 steps_done = 0
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156
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157 # TODO: multiple threading
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158 try:
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159 while steps_done < len(gen_flow):
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160 index_array = next(gen_flow.index_generator)
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161 batch_X = gen_flow._get_batches_of_transformed_samples(index_array)
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162
19
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163 if params["method"] == "predict":
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164 batch_preds = estimator.predict(
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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.
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169 data_generator=pred_data_generator,
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170 )
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171 else:
11
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172 batch_preds = estimator.predict_proba(
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173 batch_X,
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174 # The presence of `pred_data_generator` below is to
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175 # override model carrying data_generator if there
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176 # is any.
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177 data_generator=pred_data_generator,
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178 )
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179
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180 if batch_preds.ndim == 1:
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181 batch_preds = batch_preds[:, np.newaxis]
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182
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183 batch_meta = variants[index_array]
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184 batch_out = np.column_stack([batch_meta, batch_preds])
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185
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186 if not header_done:
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187 heads = np.arange(batch_preds.shape[-1]).astype(str)
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188 heads_str = "\t".join(heads)
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189 file_writer.write("\t%s\n" % heads_str)
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190 header_done = True
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191
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192 for row in batch_out:
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193 row_str = "\t".join(row)
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194 file_writer.write("%s\n" % row_str)
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195
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196 steps_done += 1
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197
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198 finally:
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199 file_writer.close()
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200 # TODO: make api `pred_data_generator.close()`
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201 pred_data_generator.close()
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202 return 0
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203 # end input
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204
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205 # output
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206 if len(preds.shape) == 1:
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207 rval = pd.DataFrame(preds, columns=["Predicted"])
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208 else:
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209 rval = pd.DataFrame(preds)
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210
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211 rval.to_csv(outfile_predict, sep="\t", header=True, index=False)
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212
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213
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214 if __name__ == "__main__":
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215 aparser = argparse.ArgumentParser()
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216 aparser.add_argument("-i", "--inputs", dest="inputs", required=True)
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217 aparser.add_argument("-e", "--infile_estimator", dest="infile_estimator")
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218 aparser.add_argument("-w", "--infile_weights", dest="infile_weights")
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219 aparser.add_argument("-X", "--infile1", dest="infile1")
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220 aparser.add_argument("-O", "--outfile_predict", dest="outfile_predict")
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221 aparser.add_argument("-f", "--fasta_path", dest="fasta_path")
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222 aparser.add_argument("-r", "--ref_seq", dest="ref_seq")
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223 aparser.add_argument("-v", "--vcf_path", dest="vcf_path")
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224 args = aparser.parse_args()
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225
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226 main(
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227 args.inputs,
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228 args.infile_estimator,
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229 args.outfile_predict,
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230 infile_weights=args.infile_weights,
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231 infile1=args.infile1,
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232 fasta_path=args.fasta_path,
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233 ref_seq=args.ref_seq,
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234 vcf_path=args.vcf_path,
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235 )