Mercurial > repos > bgruening > sklearn_pairwise_metrics

annotate train_test_eval.py @ 35:dbbe397b64ad 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 17:36:50 +0000
parents bc17040617c0
children cd4328a02798
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1 import argparse
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2 import json
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3 import os
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4 import pickle
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5 import warnings
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6
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7 from itertools import chain
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8
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9 import joblib
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10 import numpy as np
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11 import pandas as pd
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12 from galaxy_ml.model_validations import train_test_split
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13 from galaxy_ml.utils import (
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14 get_module,
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15 get_scoring,
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16 load_model,
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17 read_columns,
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18 SafeEval,
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19 try_get_attr,
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20 )
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21 from scipy.io import mmread
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22 from sklearn import pipeline
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23 from sklearn.metrics.scorer import _check_multimetric_scoring
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24 from sklearn.model_selection._validation import _score
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25 from sklearn.model_selection import _search, _validation
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26 from sklearn.model_selection._validation import _score
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27 from sklearn.utils import indexable, safe_indexing
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30 _fit_and_score = try_get_attr("galaxy_ml.model_validations", "_fit_and_score")
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31 setattr(_search, "_fit_and_score", _fit_and_score)
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32 setattr(_validation, "_fit_and_score", _fit_and_score)
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34 N_JOBS = int(os.environ.get("GALAXY_SLOTS", 1))
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35 CACHE_DIR = os.path.join(os.getcwd(), "cached")
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36 del os
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37 NON_SEARCHABLE = ("n_jobs", "pre_dispatch", "memory", "_path", "nthread", "callbacks")
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38 ALLOWED_CALLBACKS = (
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39 "EarlyStopping",
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40 "TerminateOnNaN",
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41 "ReduceLROnPlateau",
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42 "CSVLogger",
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43 "None",
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44 )
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47 def _eval_swap_params(params_builder):
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48 swap_params = {}
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49
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50 for p in params_builder["param_set"]:
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51 swap_value = p["sp_value"].strip()
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52 if swap_value == "":
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53 continue
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54
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55 param_name = p["sp_name"]
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56 if param_name.lower().endswith(NON_SEARCHABLE):
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57 warnings.warn(
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58 "Warning: `%s` is not eligible for search and was "
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59 "omitted!" % param_name
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60 )
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61 continue
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62
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63 if not swap_value.startswith(":"):
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64 safe_eval = SafeEval(load_scipy=True, load_numpy=True)
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65 ev = safe_eval(swap_value)
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66 else:
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67 # Have `:` before search list, asks for estimator evaluatio
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68 safe_eval_es = SafeEval(load_estimators=True)
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69 swap_value = swap_value[1:].strip()
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70 # TODO maybe add regular express check
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71 ev = safe_eval_es(swap_value)
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73 swap_params[param_name] = ev
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75 return swap_params
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78 def train_test_split_none(*arrays, **kwargs):
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79 """extend train_test_split to take None arrays
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80 and support split by group names.
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81 """
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82 nones = []
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83 new_arrays = []
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84 for idx, arr in enumerate(arrays):
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85 if arr is None:
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86 nones.append(idx)
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87 else:
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88 new_arrays.append(arr)
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89
35
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90 if kwargs["shuffle"] == "None":
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91 kwargs["shuffle"] = None
26
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92
35
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93 group_names = kwargs.pop("group_names", None)
26
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94
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95 if group_names is not None and group_names.strip():
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96 group_names = [name.strip() for name in group_names.split(",")]
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97 new_arrays = indexable(*new_arrays)
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98 groups = kwargs["labels"]
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99 n_samples = new_arrays[0].shape[0]
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100 index_arr = np.arange(n_samples)
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101 test = index_arr[np.isin(groups, group_names)]
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102 train = index_arr[~np.isin(groups, group_names)]
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103 rval = list(
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104 chain.from_iterable(
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105 (safe_indexing(a, train), safe_indexing(a, test)) for a in new_arrays
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106 )
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107 )
26
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108 else:
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109 rval = train_test_split(*new_arrays, **kwargs)
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110
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111 for pos in nones:
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112 rval[pos * 2: 2] = [None, None]
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113
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114 return rval
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115
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116
35
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117 def main(
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118 inputs,
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119 infile_estimator,
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120 infile1,
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121 infile2,
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122 outfile_result,
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123 outfile_object=None,
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124 outfile_weights=None,
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125 groups=None,
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126 ref_seq=None,
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127 intervals=None,
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128 targets=None,
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129 fasta_path=None,
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130 ):
26
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131 """
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132 Parameter
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133 ---------
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134 inputs : str
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135 File path to galaxy tool parameter
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136
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137 infile_estimator : str
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138 File path to estimator
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139
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140 infile1 : str
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141 File path to dataset containing features
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142
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143 infile2 : str
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144 File path to dataset containing target values
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145
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146 outfile_result : str
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147 File path to save the results, either cv_results or test result
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148
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149 outfile_object : str, optional
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150 File path to save searchCV object
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151
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152 outfile_weights : str, optional
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153 File path to save deep learning model weights
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154
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155 groups : str
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156 File path to dataset containing groups labels
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157
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158 ref_seq : str
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159 File path to dataset containing genome sequence file
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160
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161 intervals : str
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162 File path to dataset containing interval file
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163
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164 targets : str
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165 File path to dataset compressed target bed file
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166
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167 fasta_path : str
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168 File path to dataset containing fasta file
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169 """
35
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170 warnings.simplefilter("ignore")
26
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171
35
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172 with open(inputs, "r") as param_handler:
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173 params = json.load(param_handler)
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174
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175 # load estimator
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176 with open(infile_estimator, "rb") as estimator_handler:
26
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177 estimator = load_model(estimator_handler)
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178
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179 # swap hyperparameter
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180 swapping = params["experiment_schemes"]["hyperparams_swapping"]
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181 swap_params = _eval_swap_params(swapping)
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182 estimator.set_params(**swap_params)
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183
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184 estimator_params = estimator.get_params()
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185
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186 # store read dataframe object
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187 loaded_df = {}
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188
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189 input_type = params["input_options"]["selected_input"]
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190 # tabular input
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191 if input_type == "tabular":
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192 header = "infer" if params["input_options"]["header1"] else None
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193 column_option = params["input_options"]["column_selector_options_1"][
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194 "selected_column_selector_option"
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195 ]
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196 if column_option in [
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197 "by_index_number",
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198 "all_but_by_index_number",
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199 "by_header_name",
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200 "all_but_by_header_name",
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201 ]:
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202 c = params["input_options"]["column_selector_options_1"]["col1"]
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203 else:
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204 c = None
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205
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206 df_key = infile1 + repr(header)
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207 df = pd.read_csv(infile1, sep="\t", header=header, parse_dates=True)
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208 loaded_df[df_key] = df
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209
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210 X = read_columns(df, c=c, c_option=column_option).astype(float)
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211 # sparse input
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212 elif input_type == "sparse":
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213 X = mmread(open(infile1, "r"))
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214
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215 # fasta_file input
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216 elif input_type == "seq_fasta":
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217 pyfaidx = get_module("pyfaidx")
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218 sequences = pyfaidx.Fasta(fasta_path)
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219 n_seqs = len(sequences.keys())
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220 X = np.arange(n_seqs)[:, np.newaxis]
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221 for param in estimator_params.keys():
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222 if param.endswith("fasta_path"):
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223 estimator.set_params(**{param: fasta_path})
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224 break
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225 else:
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226 raise ValueError(
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227 "The selected estimator doesn't support "
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228 "fasta file input! Please consider using "
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229 "KerasGBatchClassifier with "
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230 "FastaDNABatchGenerator/FastaProteinBatchGenerator "
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231 "or having GenomeOneHotEncoder/ProteinOneHotEncoder "
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232 "in pipeline!"
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233 )
26
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234
35
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235 elif input_type == "refseq_and_interval":
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236 path_params = {
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237 "data_batch_generator__ref_genome_path": ref_seq,
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238 "data_batch_generator__intervals_path": intervals,
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239 "data_batch_generator__target_path": targets,
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240 }
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241 estimator.set_params(**path_params)
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242 n_intervals = sum(1 for line in open(intervals))
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243 X = np.arange(n_intervals)[:, np.newaxis]
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244
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245 # Get target y
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246 header = "infer" if params["input_options"]["header2"] else None
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247 column_option = params["input_options"]["column_selector_options_2"][
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248 "selected_column_selector_option2"
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249 ]
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250 if column_option in [
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251 "by_index_number",
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252 "all_but_by_index_number",
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253 "by_header_name",
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254 "all_but_by_header_name",
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255 ]:
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256 c = params["input_options"]["column_selector_options_2"]["col2"]
26
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257 else:
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258 c = None
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259
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260 df_key = infile2 + repr(header)
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261 if df_key in loaded_df:
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262 infile2 = loaded_df[df_key]
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263 else:
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264 infile2 = pd.read_csv(infile2, sep="\t", header=header, parse_dates=True)
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265 loaded_df[df_key] = infile2
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266
35
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267 y = read_columns(infile2,
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268 c=c,
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269 c_option=column_option,
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270 sep='\t',
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271 header=header,
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272 parse_dates=True)
26
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273 if len(y.shape) == 2 and y.shape[1] == 1:
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274 y = y.ravel()
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275 if input_type == "refseq_and_interval":
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276 estimator.set_params(data_batch_generator__features=y.ravel().tolist())
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277 y = None
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278 # end y
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279
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280 # load groups
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281 if groups:
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282 groups_selector = (
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283 params["experiment_schemes"]["test_split"]["split_algos"]
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284 ).pop("groups_selector")
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285
35
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286 header = "infer" if groups_selector["header_g"] else None
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287 column_option = groups_selector["column_selector_options_g"][
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288 "selected_column_selector_option_g"
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289 ]
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290 if column_option in [
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291 "by_index_number",
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292 "all_but_by_index_number",
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293 "by_header_name",
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294 "all_but_by_header_name",
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295 ]:
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296 c = groups_selector["column_selector_options_g"]["col_g"]
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297 else:
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298 c = None
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299
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300 df_key = groups + repr(header)
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301 if df_key in loaded_df:
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302 groups = loaded_df[df_key]
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303
35
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304 groups = read_columns(groups,
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305 c=c,
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306 c_option=column_option,
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307 sep='\t',
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308 header=header,
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309 parse_dates=True)
26
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310 groups = groups.ravel()
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311
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312 # del loaded_df
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313 del loaded_df
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314
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315 # handle memory
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316 memory = joblib.Memory(location=CACHE_DIR, verbose=0)
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317 # cache iraps_core fits could increase search speed significantly
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318 if estimator.__class__.__name__ == "IRAPSClassifier":
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319 estimator.set_params(memory=memory)
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320 else:
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321 # For iraps buried in pipeline
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322 new_params = {}
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323 for p, v in estimator_params.items():
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324 if p.endswith("memory"):
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325 # for case of `__irapsclassifier__memory`
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326 if len(p) > 8 and p[:-8].endswith("irapsclassifier"):
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327 # cache iraps_core fits could increase search
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328 # speed significantly
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329 new_params[p] = memory
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330 # security reason, we don't want memory being
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331 # modified unexpectedly
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332 elif v:
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333 new_params[p] = None
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334 # handle n_jobs
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335 elif p.endswith("n_jobs"):
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336 # For now, 1 CPU is suggested for iprasclassifier
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337 if len(p) > 8 and p[:-8].endswith("irapsclassifier"):
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338 new_params[p] = 1
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339 else:
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340 new_params[p] = N_JOBS
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341 # for security reason, types of callback are limited
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342 elif p.endswith("callbacks"):
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343 for cb in v:
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344 cb_type = cb["callback_selection"]["callback_type"]
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345 if cb_type not in ALLOWED_CALLBACKS:
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346 raise ValueError("Prohibited callback type: %s!" % cb_type)
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347
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348 estimator.set_params(**new_params)
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349
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350 # handle scorer, convert to scorer dict
35
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351 # Check if scoring is specified
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352 scoring = params["experiment_schemes"]["metrics"].get("scoring", None)
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diff changeset
353 if scoring is not None:
dbbe397b64ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
354 # get_scoring() expects secondary_scoring to be a comma separated string (not a list)
dbbe397b64ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
355 # Check if secondary_scoring is specified
dbbe397b64ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
356 secondary_scoring = scoring.get("secondary_scoring", None)
dbbe397b64ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
357 if secondary_scoring is not None:
dbbe397b64ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
358 # If secondary_scoring is specified, convert the list into comman separated string
dbbe397b64ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
359 scoring["secondary_scoring"] = ",".join(scoring["secondary_scoring"])
26
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
360 scorer = get_scoring(scoring)
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
361 scorer, _ = _check_multimetric_scoring(estimator, scoring=scorer)
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
362
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
363 # handle test (first) split
35
dbbe397b64ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
364 test_split_options = params["experiment_schemes"]["test_split"]["split_algos"]
26
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
365
35
dbbe397b64ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
366 if test_split_options["shuffle"] == "group":
dbbe397b64ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
367 test_split_options["labels"] = groups
dbbe397b64ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
368 if test_split_options["shuffle"] == "stratified":
26
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
369 if y is not None:
35
dbbe397b64ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
370 test_split_options["labels"] = y
26
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
371 else:
35
dbbe397b64ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
372 raise ValueError(
dbbe397b64ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
373 "Stratified shuffle split is not " "applicable on empty target values!"
dbbe397b64ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
374 )
26
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
375
35
dbbe397b64ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
376 X_train, X_test, y_train, y_test, groups_train, _groups_test = train_test_split_none(
dbbe397b64ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
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diff changeset
377 X, y, groups, **test_split_options
dbbe397b64ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
378 )
26
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
379
35
dbbe397b64ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
380 exp_scheme = params["experiment_schemes"]["selected_exp_scheme"]
26
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
381
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
382 # handle validation (second) split
35
dbbe397b64ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
383 if exp_scheme == "train_val_test":
dbbe397b64ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
384 val_split_options = params["experiment_schemes"]["val_split"]["split_algos"]
26
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
385
35
dbbe397b64ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
386 if val_split_options["shuffle"] == "group":
dbbe397b64ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
387 val_split_options["labels"] = groups_train
dbbe397b64ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
388 if val_split_options["shuffle"] == "stratified":
26
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
389 if y_train is not None:
35
dbbe397b64ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
390 val_split_options["labels"] = y_train
26
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
391 else:
35
dbbe397b64ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
392 raise ValueError(
dbbe397b64ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
393 "Stratified shuffle split is not "
dbbe397b64ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
394 "applicable on empty target values!"
dbbe397b64ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
395 )
26
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
396
35
dbbe397b64ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
397 (
dbbe397b64ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
398 X_train,
dbbe397b64ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
399 X_val,
dbbe397b64ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
400 y_train,
dbbe397b64ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
401 y_val,
dbbe397b64ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
402 groups_train,
dbbe397b64ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
403 _groups_val,
dbbe397b64ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
404 ) = train_test_split_none(X_train, y_train, groups_train, **val_split_options)
26
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
405
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
406 # train and eval
35
dbbe397b64ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
407 if hasattr(estimator, "validation_data"):
dbbe397b64ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
408 if exp_scheme == "train_val_test":
dbbe397b64ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
409 estimator.fit(X_train, y_train, validation_data=(X_val, y_val))
26
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
410 else:
35
dbbe397b64ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
411 estimator.fit(X_train, y_train, validation_data=(X_test, y_test))
26
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
412 else:
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
413 estimator.fit(X_train, y_train)
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
414
35
dbbe397b64ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
415 if hasattr(estimator, "evaluate"):
dbbe397b64ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
416 scores = estimator.evaluate(
dbbe397b64ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
417 X_test, y_test=y_test, scorer=scorer, is_multimetric=True
dbbe397b64ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
418 )
26
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
419 else:
35
dbbe397b64ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
420 scores = _score(estimator, X_test, y_test, scorer, is_multimetric=True)
26
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
421 # handle output
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
422 for name, score in scores.items():
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
423 scores[name] = [score]
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
424 df = pd.DataFrame(scores)
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
425 df = df[sorted(df.columns)]
35
dbbe397b64ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
426 df.to_csv(path_or_buf=outfile_result, sep="\t", header=True, index=False)
26
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
427
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
428 memory.clear(warn=False)
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
429
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
430 if outfile_object:
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
431 main_est = estimator
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
432 if isinstance(estimator, pipeline.Pipeline):
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
433 main_est = estimator.steps[-1][-1]
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
434
35
dbbe397b64ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
435 if hasattr(main_est, "model_") and hasattr(main_est, "save_weights"):
26
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
436 if outfile_weights:
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
437 main_est.save_weights(outfile_weights)
35
dbbe397b64ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
438 if getattr(main_est, "model_", None):
dbbe397b64ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
439 del main_est.model_
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440 if getattr(main_est, "fit_params", None):
dbbe397b64ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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441 del main_est.fit_params
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442 if getattr(main_est, "model_class_", None):
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443 del main_est.model_class_
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444 if getattr(main_est, "validation_data", None):
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445 del main_est.validation_data
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446 if getattr(main_est, "data_generator_", None):
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447 del main_est.data_generator_
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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448
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449 with open(outfile_object, "wb") as output_handler:
dbbe397b64ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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450 pickle.dump(estimator, output_handler, pickle.HIGHEST_PROTOCOL)
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451
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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452
35
dbbe397b64ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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453 if __name__ == "__main__":
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454 aparser = argparse.ArgumentParser()
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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455 aparser.add_argument("-i", "--inputs", dest="inputs", required=True)
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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456 aparser.add_argument("-e", "--estimator", dest="infile_estimator")
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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457 aparser.add_argument("-X", "--infile1", dest="infile1")
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458 aparser.add_argument("-y", "--infile2", dest="infile2")
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459 aparser.add_argument("-O", "--outfile_result", dest="outfile_result")
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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460 aparser.add_argument("-o", "--outfile_object", dest="outfile_object")
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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461 aparser.add_argument("-w", "--outfile_weights", dest="outfile_weights")
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462 aparser.add_argument("-g", "--groups", dest="groups")
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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463 aparser.add_argument("-r", "--ref_seq", dest="ref_seq")
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464 aparser.add_argument("-b", "--intervals", dest="intervals")
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465 aparser.add_argument("-t", "--targets", dest="targets")
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466 aparser.add_argument("-f", "--fasta_path", dest="fasta_path")
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467 args = aparser.parse_args()
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468
35
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469 main(
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470 args.inputs,
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471 args.infile_estimator,
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472 args.infile1,
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473 args.infile2,
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474 args.outfile_result,
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475 outfile_object=args.outfile_object,
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476 outfile_weights=args.outfile_weights,
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477 groups=args.groups,
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478 ref_seq=args.ref_seq,
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479 intervals=args.intervals,
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480 targets=args.targets,
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481 fasta_path=args.fasta_path,
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482 )