Mercurial > repos > bgruening > sklearn_discriminant_classifier

annotate model_prediction.py @ 36:420a4bf99244 draft

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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 208a8d348e7c7a182cfbe1b6f17868146428a7e2"
author bgruening
date Tue, 13 Apr 2021 22:38:08 +0000
parents eeaf989f1024
children e76f6dfea5c9
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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 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, " "but dataset for weights wan not selected!"
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67 )
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68 main_est.load_weights(infile_weights)
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70 # handle data input
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71 input_type = params["input_options"]["selected_input"]
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72 # tabular input
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73 if input_type == "tabular":
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74 header = "infer" if params["input_options"]["header1"] else None
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75 column_option = params["input_options"]["column_selector_options_1"]["selected_column_selector_option"]
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76 if column_option in [
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77 "by_index_number",
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78 "all_but_by_index_number",
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79 "by_header_name",
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80 "all_but_by_header_name",
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81 ]:
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82 c = params["input_options"]["column_selector_options_1"]["col1"]
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83 else:
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84 c = None
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85
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86 df = pd.read_csv(infile1, sep="\t", header=header, parse_dates=True)
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88 X = read_columns(df, c=c, c_option=column_option).astype(float)
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89
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90 if params["method"] == "predict":
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91 preds = estimator.predict(X)
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92 else:
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93 preds = estimator.predict_proba(X)
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94
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95 # sparse input
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96 elif input_type == "sparse":
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97 X = mmread(open(infile1, "r"))
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98 if params["method"] == "predict":
26
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99 preds = estimator.predict(X)
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100 else:
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101 preds = estimator.predict_proba(X)
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102
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103 # fasta input
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104 elif input_type == "seq_fasta":
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105 if not hasattr(estimator, "data_batch_generator"):
26
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106 raise ValueError(
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107 "To do prediction on sequences in fasta input, "
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108 "the estimator must be a `KerasGBatchClassifier`"
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109 "equipped with data_batch_generator!"
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110 )
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111 pyfaidx = get_module("pyfaidx")
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112 sequences = pyfaidx.Fasta(fasta_path)
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113 n_seqs = len(sequences.keys())
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114 X = np.arange(n_seqs)[:, np.newaxis]
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115 seq_length = estimator.data_batch_generator.seq_length
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116 batch_size = getattr(estimator, "batch_size", 32)
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117 steps = (n_seqs + batch_size - 1) // batch_size
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118
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119 seq_type = params["input_options"]["seq_type"]
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120 klass = try_get_attr("galaxy_ml.preprocessors", seq_type)
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121
35
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122 pred_data_generator = klass(fasta_path, seq_length=seq_length)
26
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123
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124 if params["method"] == "predict":
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125 preds = estimator.predict(X, data_generator=pred_data_generator, steps=steps)
26
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126 else:
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127 preds = estimator.predict_proba(X, data_generator=pred_data_generator, steps=steps)
26
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128
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129 # vcf input
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130 elif input_type == "variant_effect":
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131 klass = try_get_attr("galaxy_ml.preprocessors", "GenomicVariantBatchGenerator")
26
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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(ref_genome_path=ref_seq, vcf_path=vcf_path, **options)
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139
31
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140 pred_data_generator.set_processing_attrs()
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141
27
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142 variants = pred_data_generator.variants
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143
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144 # predict 1600 sample at once then write to file
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145 gen_flow = pred_data_generator.flow(batch_size=1600)
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146
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147 file_writer = open(outfile_predict, "w")
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148 header_row = "\t".join(["chrom", "pos", "name", "ref", "alt", "strand"])
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149 file_writer.write(header_row)
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150 header_done = False
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151
27
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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(index_array)
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159
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160 if params["method"] == "predict":
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161 batch_preds = estimator.predict(
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162 batch_X,
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163 # The presence of `pred_data_generator` below is to
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164 # override model carrying data_generator if there
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165 # is any.
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166 data_generator=pred_data_generator,
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167 )
26
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168 else:
27
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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
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177 if batch_preds.ndim == 1:
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178 batch_preds = batch_preds[:, np.newaxis]
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179
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180 batch_meta = variants[index_array]
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181 batch_out = np.column_stack([batch_meta, batch_preds])
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182
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183 if not header_done:
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184 heads = np.arange(batch_preds.shape[-1]).astype(str)
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185 heads_str = "\t".join(heads)
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186 file_writer.write("\t%s\n" % heads_str)
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187 header_done = True
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188
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189 for row in batch_out:
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190 row_str = "\t".join(row)
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191 file_writer.write("%s\n" % row_str)
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192
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193 steps_done += 1
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194
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195 finally:
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196 file_writer.close()
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197 # TODO: make api `pred_data_generator.close()`
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198 pred_data_generator.close()
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199 return 0
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200 # end input
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201
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202 # output
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203 if len(preds.shape) == 1:
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204 rval = pd.DataFrame(preds, columns=["Predicted"])
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205 else:
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206 rval = pd.DataFrame(preds)
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207
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208 rval.to_csv(outfile_predict, sep="\t", header=True, index=False)
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209
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210
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211 if __name__ == "__main__":
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212 aparser = argparse.ArgumentParser()
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213 aparser.add_argument("-i", "--inputs", dest="inputs", required=True)
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214 aparser.add_argument("-e", "--infile_estimator", dest="infile_estimator")
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215 aparser.add_argument("-w", "--infile_weights", dest="infile_weights")
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216 aparser.add_argument("-X", "--infile1", dest="infile1")
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217 aparser.add_argument("-O", "--outfile_predict", dest="outfile_predict")
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218 aparser.add_argument("-f", "--fasta_path", dest="fasta_path")
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219 aparser.add_argument("-r", "--ref_seq", dest="ref_seq")
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220 aparser.add_argument("-v", "--vcf_path", dest="vcf_path")
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221 args = aparser.parse_args()
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222
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223 main(
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224 args.inputs,
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225 args.infile_estimator,
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226 args.outfile_predict,
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227 infile_weights=args.infile_weights,
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228 infile1=args.infile1,
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229 fasta_path=args.fasta_path,
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230 ref_seq=args.ref_seq,
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231 vcf_path=args.vcf_path,
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232 )