Mercurial > repos > bgruening > sklearn_numeric_clustering

annotate train_test_eval.py @ 35:e7f047a9dca9 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 22:08:10 +0000
parents 816b65d52c33
children 73e7f1c76ece
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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 from itertools import chain
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8 import joblib
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9 import numpy as np
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10 import pandas as pd
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11 from galaxy_ml.model_validations import train_test_split
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12 from galaxy_ml.utils import (
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13 get_module,
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14 get_scoring,
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15 load_model,
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16 read_columns,
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17 SafeEval,
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18 try_get_attr,
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19 )
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20 from scipy.io import mmread
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21 from sklearn import pipeline
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22 from sklearn.metrics.scorer import _check_multimetric_scoring
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23 from sklearn.model_selection import _search, _validation
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24 from sklearn.model_selection._validation import _score
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25 from sklearn.utils import indexable, safe_indexing
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28 _fit_and_score = try_get_attr("galaxy_ml.model_validations", "_fit_and_score")
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29 setattr(_search, "_fit_and_score", _fit_and_score)
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30 setattr(_validation, "_fit_and_score", _fit_and_score)
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32 N_JOBS = int(os.environ.get("GALAXY_SLOTS", 1))
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33 CACHE_DIR = os.path.join(os.getcwd(), "cached")
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34 del os
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35 NON_SEARCHABLE = ("n_jobs", "pre_dispatch", "memory", "_path", "nthread", "callbacks")
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36 ALLOWED_CALLBACKS = (
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37 "EarlyStopping",
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38 "TerminateOnNaN",
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39 "ReduceLROnPlateau",
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40 "CSVLogger",
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41 "None",
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42 )
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45 def _eval_swap_params(params_builder):
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46 swap_params = {}
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47
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48 for p in params_builder["param_set"]:
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49 swap_value = p["sp_value"].strip()
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50 if swap_value == "":
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51 continue
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53 param_name = p["sp_name"]
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54 if param_name.lower().endswith(NON_SEARCHABLE):
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55 warnings.warn(
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56 "Warning: `%s` is not eligible for search and was "
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57 "omitted!" % param_name
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58 )
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59 continue
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60
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61 if not swap_value.startswith(":"):
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62 safe_eval = SafeEval(load_scipy=True, load_numpy=True)
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63 ev = safe_eval(swap_value)
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64 else:
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65 # Have `:` before search list, asks for estimator evaluatio
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66 safe_eval_es = SafeEval(load_estimators=True)
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67 swap_value = swap_value[1:].strip()
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68 # TODO maybe add regular express check
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69 ev = safe_eval_es(swap_value)
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71 swap_params[param_name] = ev
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73 return swap_params
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76 def train_test_split_none(*arrays, **kwargs):
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77 """extend train_test_split to take None arrays
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78 and support split by group names.
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79 """
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80 nones = []
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81 new_arrays = []
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82 for idx, arr in enumerate(arrays):
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83 if arr is None:
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84 nones.append(idx)
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85 else:
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86 new_arrays.append(arr)
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87
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88 if kwargs["shuffle"] == "None":
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89 kwargs["shuffle"] = None
26
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90
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91 group_names = kwargs.pop("group_names", None)
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92
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93 if group_names is not None and group_names.strip():
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94 group_names = [name.strip() for name in group_names.split(",")]
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95 new_arrays = indexable(*new_arrays)
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96 groups = kwargs["labels"]
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97 n_samples = new_arrays[0].shape[0]
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98 index_arr = np.arange(n_samples)
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99 test = index_arr[np.isin(groups, group_names)]
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100 train = index_arr[~np.isin(groups, group_names)]
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101 rval = list(
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102 chain.from_iterable(
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103 (safe_indexing(a, train), safe_indexing(a, test)) for a in new_arrays
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104 )
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105 )
26
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106 else:
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107 rval = train_test_split(*new_arrays, **kwargs)
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108
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109 for pos in nones:
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110 rval[pos * 2: 2] = [None, None]
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111
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112 return rval
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113
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114
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115 def main(
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116 inputs,
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117 infile_estimator,
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118 infile1,
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119 infile2,
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120 outfile_result,
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121 outfile_object=None,
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122 outfile_weights=None,
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123 groups=None,
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124 ref_seq=None,
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125 intervals=None,
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126 targets=None,
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127 fasta_path=None,
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128 ):
26
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129 """
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130 Parameter
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131 ---------
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132 inputs : str
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133 File path to galaxy tool parameter
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134
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135 infile_estimator : str
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136 File path to estimator
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137
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138 infile1 : str
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139 File path to dataset containing features
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140
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141 infile2 : str
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142 File path to dataset containing target values
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143
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144 outfile_result : str
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145 File path to save the results, either cv_results or test result
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146
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147 outfile_object : str, optional
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148 File path to save searchCV object
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149
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150 outfile_weights : str, optional
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151 File path to save deep learning model weights
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152
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153 groups : str
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154 File path to dataset containing groups labels
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155
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156 ref_seq : str
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157 File path to dataset containing genome sequence file
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158
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159 intervals : str
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160 File path to dataset containing interval file
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161
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162 targets : str
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163 File path to dataset compressed target bed file
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164
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165 fasta_path : str
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166 File path to dataset containing fasta file
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167 """
34
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168 warnings.simplefilter("ignore")
26
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169
34
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170 with open(inputs, "r") as param_handler:
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171 params = json.load(param_handler)
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172
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173 # load estimator
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174 with open(infile_estimator, "rb") as estimator_handler:
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175 estimator = load_model(estimator_handler)
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176
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177 # swap hyperparameter
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178 swapping = params["experiment_schemes"]["hyperparams_swapping"]
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179 swap_params = _eval_swap_params(swapping)
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180 estimator.set_params(**swap_params)
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181
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182 estimator_params = estimator.get_params()
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183
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184 # store read dataframe object
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185 loaded_df = {}
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186
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187 input_type = params["input_options"]["selected_input"]
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188 # tabular input
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189 if input_type == "tabular":
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190 header = "infer" if params["input_options"]["header1"] else None
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191 column_option = params["input_options"]["column_selector_options_1"][
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192 "selected_column_selector_option"
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193 ]
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194 if column_option in [
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195 "by_index_number",
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196 "all_but_by_index_number",
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197 "by_header_name",
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198 "all_but_by_header_name",
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199 ]:
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200 c = params["input_options"]["column_selector_options_1"]["col1"]
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201 else:
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202 c = None
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203
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204 df_key = infile1 + repr(header)
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205 df = pd.read_csv(infile1, sep="\t", header=header, parse_dates=True)
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206 loaded_df[df_key] = df
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207
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208 X = read_columns(df, c=c, c_option=column_option).astype(float)
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209 # sparse input
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210 elif input_type == "sparse":
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211 X = mmread(open(infile1, "r"))
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212
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213 # fasta_file input
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214 elif input_type == "seq_fasta":
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215 pyfaidx = get_module("pyfaidx")
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216 sequences = pyfaidx.Fasta(fasta_path)
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217 n_seqs = len(sequences.keys())
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218 X = np.arange(n_seqs)[:, np.newaxis]
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219 for param in estimator_params.keys():
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220 if param.endswith("fasta_path"):
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221 estimator.set_params(**{param: fasta_path})
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222 break
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223 else:
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224 raise ValueError(
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225 "The selected estimator doesn't support "
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226 "fasta file input! Please consider using "
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227 "KerasGBatchClassifier with "
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228 "FastaDNABatchGenerator/FastaProteinBatchGenerator "
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229 "or having GenomeOneHotEncoder/ProteinOneHotEncoder "
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230 "in pipeline!"
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231 )
26
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232
34
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233 elif input_type == "refseq_and_interval":
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234 path_params = {
34
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235 "data_batch_generator__ref_genome_path": ref_seq,
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236 "data_batch_generator__intervals_path": intervals,
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237 "data_batch_generator__target_path": targets,
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238 }
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239 estimator.set_params(**path_params)
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240 n_intervals = sum(1 for line in open(intervals))
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241 X = np.arange(n_intervals)[:, np.newaxis]
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242
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243 # Get target y
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244 header = "infer" if params["input_options"]["header2"] else None
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245 column_option = params["input_options"]["column_selector_options_2"][
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246 "selected_column_selector_option2"
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247 ]
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248 if column_option in [
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249 "by_index_number",
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250 "all_but_by_index_number",
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251 "by_header_name",
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252 "all_but_by_header_name",
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253 ]:
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254 c = params["input_options"]["column_selector_options_2"]["col2"]
26
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255 else:
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256 c = None
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257
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258 df_key = infile2 + repr(header)
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259 if df_key in loaded_df:
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260 infile2 = loaded_df[df_key]
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261 else:
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262 infile2 = pd.read_csv(infile2, sep="\t", header=header, parse_dates=True)
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263 loaded_df[df_key] = infile2
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264
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265 y = read_columns(infile2,
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266 c=c,
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267 c_option=column_option,
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268 sep='\t',
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269 header=header,
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270 parse_dates=True)
26
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271 if len(y.shape) == 2 and y.shape[1] == 1:
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272 y = y.ravel()
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273 if input_type == "refseq_and_interval":
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274 estimator.set_params(data_batch_generator__features=y.ravel().tolist())
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275 y = None
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276 # end y
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277
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278 # load groups
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279 if groups:
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280 groups_selector = (
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281 params["experiment_schemes"]["test_split"]["split_algos"]
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282 ).pop("groups_selector")
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283
34
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284 header = "infer" if groups_selector["header_g"] else None
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285 column_option = groups_selector["column_selector_options_g"][
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286 "selected_column_selector_option_g"
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287 ]
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288 if column_option in [
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289 "by_index_number",
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290 "all_but_by_index_number",
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291 "by_header_name",
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292 "all_but_by_header_name",
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293 ]:
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294 c = groups_selector["column_selector_options_g"]["col_g"]
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295 else:
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296 c = None
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297
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298 df_key = groups + repr(header)
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299 if df_key in loaded_df:
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300 groups = loaded_df[df_key]
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301
34
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302 groups = read_columns(groups,
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303 c=c,
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304 c_option=column_option,
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305 sep='\t',
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306 header=header,
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307 parse_dates=True)
26
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308 groups = groups.ravel()
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309
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310 # del loaded_df
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311 del loaded_df
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312
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313 # handle memory
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314 memory = joblib.Memory(location=CACHE_DIR, verbose=0)
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315 # cache iraps_core fits could increase search speed significantly
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316 if estimator.__class__.__name__ == "IRAPSClassifier":
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317 estimator.set_params(memory=memory)
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318 else:
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319 # For iraps buried in pipeline
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320 new_params = {}
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321 for p, v in estimator_params.items():
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322 if p.endswith("memory"):
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323 # for case of `__irapsclassifier__memory`
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324 if len(p) > 8 and p[:-8].endswith("irapsclassifier"):
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325 # cache iraps_core fits could increase search
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326 # speed significantly
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327 new_params[p] = memory
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328 # security reason, we don't want memory being
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329 # modified unexpectedly
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330 elif v:
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331 new_params[p] = None
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332 # handle n_jobs
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333 elif p.endswith("n_jobs"):
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334 # For now, 1 CPU is suggested for iprasclassifier
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335 if len(p) > 8 and p[:-8].endswith("irapsclassifier"):
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336 new_params[p] = 1
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337 else:
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338 new_params[p] = N_JOBS
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339 # for security reason, types of callback are limited
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340 elif p.endswith("callbacks"):
26
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341 for cb in v:
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342 cb_type = cb["callback_selection"]["callback_type"]
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343 if cb_type not in ALLOWED_CALLBACKS:
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344 raise ValueError("Prohibited callback type: %s!" % cb_type)
26
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345
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346 estimator.set_params(**new_params)
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347
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348 # handle scorer, convert to scorer dict
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diff changeset
349 # Check if scoring is specified
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
350 scoring = params["experiment_schemes"]["metrics"].get("scoring", None)
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
351 if scoring is not None:
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
352 # get_scoring() expects secondary_scoring to be a comma separated string (not a list)
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
353 # Check if secondary_scoring is specified
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
354 secondary_scoring = scoring.get("secondary_scoring", None)
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
355 if secondary_scoring is not None:
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
356 # If secondary_scoring is specified, convert the list into comman separated string
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
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diff changeset
357 scoring["secondary_scoring"] = ",".join(scoring["secondary_scoring"])
26
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
358 scorer = get_scoring(scoring)
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
359 scorer, _ = _check_multimetric_scoring(estimator, scoring=scorer)
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
360
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
361 # handle test (first) split
34
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
362 test_split_options = params["experiment_schemes"]["test_split"]["split_algos"]
26
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
363
34
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
364 if test_split_options["shuffle"] == "group":
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
365 test_split_options["labels"] = groups
816b65d52c33 "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"] == "stratified":
26
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
367 if y is not None:
34
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
368 test_split_options["labels"] = y
26
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
369 else:
34
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
370 raise ValueError(
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
371 "Stratified shuffle split is not " "applicable on empty target values!"
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
372 )
26
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
373
34
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
374 X_train, X_test, y_train, y_test, groups_train, _groups_test = train_test_split_none(
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
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diff changeset
375 X, y, groups, **test_split_options
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
376 )
26
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
377
34
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
378 exp_scheme = params["experiment_schemes"]["selected_exp_scheme"]
26
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
379
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
380 # handle validation (second) split
34
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
381 if exp_scheme == "train_val_test":
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
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diff changeset
382 val_split_options = params["experiment_schemes"]["val_split"]["split_algos"]
26
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
383
34
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
384 if val_split_options["shuffle"] == "group":
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
385 val_split_options["labels"] = groups_train
816b65d52c33 "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"] == "stratified":
26
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
387 if y_train is not None:
34
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
388 val_split_options["labels"] = y_train
26
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
389 else:
34
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
390 raise ValueError(
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
391 "Stratified shuffle split is not "
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
392 "applicable on empty target values!"
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
393 )
26
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
394
34
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
395 (
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
396 X_train,
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
397 X_val,
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
398 y_train,
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
399 y_val,
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
400 groups_train,
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
401 _groups_val,
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
402 ) = train_test_split_none(X_train, y_train, groups_train, **val_split_options)
26
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
403
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
404 # train and eval
34
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
405 if hasattr(estimator, "validation_data"):
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
406 if exp_scheme == "train_val_test":
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
407 estimator.fit(X_train, y_train, validation_data=(X_val, y_val))
26
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
408 else:
34
816b65d52c33 "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_test, y_test))
26
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
410 else:
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
411 estimator.fit(X_train, y_train)
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
412
34
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
413 if hasattr(estimator, "evaluate"):
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
414 scores = estimator.evaluate(
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
415 X_test, y_test=y_test, scorer=scorer, is_multimetric=True
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
416 )
26
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
417 else:
34
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
418 scores = _score(estimator, X_test, y_test, scorer, is_multimetric=True)
26
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
419 # handle output
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
420 for name, score in scores.items():
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
421 scores[name] = [score]
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
422 df = pd.DataFrame(scores)
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
423 df = df[sorted(df.columns)]
34
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
424 df.to_csv(path_or_buf=outfile_result, sep="\t", header=True, index=False)
26
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
425
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
426 memory.clear(warn=False)
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
427
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
428 if outfile_object:
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
429 main_est = estimator
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
430 if isinstance(estimator, pipeline.Pipeline):
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
431 main_est = estimator.steps[-1][-1]
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
432
34
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
433 if hasattr(main_est, "model_") and hasattr(main_est, "save_weights"):
26
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
434 if outfile_weights:
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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435 main_est.save_weights(outfile_weights)
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bgruening
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436 if getattr(main_est, "model_", None):
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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437 del main_est.model_
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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438 if getattr(main_est, "fit_params", None):
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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439 del main_est.fit_params
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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440 if getattr(main_est, "model_class_", None):
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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441 del main_est.model_class_
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
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442 if getattr(main_est, "validation_data", None):
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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443 del main_est.validation_data
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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444 if getattr(main_est, "data_generator_", None):
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37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
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445 del main_est.data_generator_
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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446
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816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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447 with open(outfile_object, "wb") as output_handler:
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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448 pickle.dump(estimator, output_handler, pickle.HIGHEST_PROTOCOL)
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37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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449
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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450
34
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
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451 if __name__ == "__main__":
26
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
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452 aparser = argparse.ArgumentParser()
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
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453 aparser.add_argument("-i", "--inputs", dest="inputs", required=True)
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
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454 aparser.add_argument("-e", "--estimator", dest="infile_estimator")
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
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455 aparser.add_argument("-X", "--infile1", dest="infile1")
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
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456 aparser.add_argument("-y", "--infile2", dest="infile2")
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
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457 aparser.add_argument("-O", "--outfile_result", dest="outfile_result")
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
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458 aparser.add_argument("-o", "--outfile_object", dest="outfile_object")
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
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459 aparser.add_argument("-w", "--outfile_weights", dest="outfile_weights")
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
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460 aparser.add_argument("-g", "--groups", dest="groups")
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
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461 aparser.add_argument("-r", "--ref_seq", dest="ref_seq")
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
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462 aparser.add_argument("-b", "--intervals", dest="intervals")
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
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463 aparser.add_argument("-t", "--targets", dest="targets")
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
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464 aparser.add_argument("-f", "--fasta_path", dest="fasta_path")
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
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465 args = aparser.parse_args()
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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466
34
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parents: 31
diff changeset
467 main(
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
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diff changeset
468 args.inputs,
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
469 args.infile_estimator,
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
470 args.infile1,
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
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471 args.infile2,
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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diff changeset
472 args.outfile_result,
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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diff changeset
473 outfile_object=args.outfile_object,
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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474 outfile_weights=args.outfile_weights,
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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parents: 31
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475 groups=args.groups,
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
476 ref_seq=args.ref_seq,
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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parents: 31
diff changeset
477 intervals=args.intervals,
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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478 targets=args.targets,
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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diff changeset
479 fasta_path=args.fasta_path,
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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480 )