Mercurial > repos > bgruening > sklearn_numeric_clustering

annotate train_test_eval.py @ 36:73e7f1c76ece draft

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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ea12f973df4b97a2691d9e4ce6bf6fae59d57717"
author bgruening
date Sat, 01 May 2021 00:48:46 +0000
parents e7f047a9dca9
children 06d772036a62
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1 import argparse
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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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 (get_module, get_scoring, load_model,
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13 read_columns, SafeEval, try_get_attr)
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14 from scipy.io import mmread
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15 from sklearn import pipeline
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16 from sklearn.metrics.scorer import _check_multimetric_scoring
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17 from sklearn.model_selection import _search, _validation
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18 from sklearn.model_selection._validation import _score
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19 from sklearn.utils import indexable, safe_indexing
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20
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21 _fit_and_score = try_get_attr("galaxy_ml.model_validations", "_fit_and_score")
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22 setattr(_search, "_fit_and_score", _fit_and_score)
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23 setattr(_validation, "_fit_and_score", _fit_and_score)
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25 N_JOBS = int(os.environ.get("GALAXY_SLOTS", 1))
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26 CACHE_DIR = os.path.join(os.getcwd(), "cached")
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27 del os
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28 NON_SEARCHABLE = ("n_jobs", "pre_dispatch", "memory", "_path", "nthread", "callbacks")
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29 ALLOWED_CALLBACKS = (
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30 "EarlyStopping",
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31 "TerminateOnNaN",
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32 "ReduceLROnPlateau",
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33 "CSVLogger",
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34 "None",
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35 )
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38 def _eval_swap_params(params_builder):
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39 swap_params = {}
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41 for p in params_builder["param_set"]:
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42 swap_value = p["sp_value"].strip()
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43 if swap_value == "":
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44 continue
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46 param_name = p["sp_name"]
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47 if param_name.lower().endswith(NON_SEARCHABLE):
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48 warnings.warn(
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49 "Warning: `%s` is not eligible for search and was "
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50 "omitted!" % param_name
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51 )
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52 continue
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53
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54 if not swap_value.startswith(":"):
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55 safe_eval = SafeEval(load_scipy=True, load_numpy=True)
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56 ev = safe_eval(swap_value)
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57 else:
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58 # Have `:` before search list, asks for estimator evaluatio
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59 safe_eval_es = SafeEval(load_estimators=True)
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60 swap_value = swap_value[1:].strip()
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61 # TODO maybe add regular express check
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62 ev = safe_eval_es(swap_value)
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64 swap_params[param_name] = ev
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66 return swap_params
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69 def train_test_split_none(*arrays, **kwargs):
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70 """extend train_test_split to take None arrays
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71 and support split by group names.
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72 """
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73 nones = []
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74 new_arrays = []
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75 for idx, arr in enumerate(arrays):
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76 if arr is None:
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77 nones.append(idx)
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78 else:
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79 new_arrays.append(arr)
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80
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81 if kwargs["shuffle"] == "None":
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82 kwargs["shuffle"] = None
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83
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84 group_names = kwargs.pop("group_names", None)
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86 if group_names is not None and group_names.strip():
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87 group_names = [name.strip() for name in group_names.split(",")]
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88 new_arrays = indexable(*new_arrays)
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89 groups = kwargs["labels"]
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90 n_samples = new_arrays[0].shape[0]
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91 index_arr = np.arange(n_samples)
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92 test = index_arr[np.isin(groups, group_names)]
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93 train = index_arr[~np.isin(groups, group_names)]
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94 rval = list(
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95 chain.from_iterable(
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96 (safe_indexing(a, train), safe_indexing(a, test)) for a in new_arrays
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97 )
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98 )
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99 else:
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100 rval = train_test_split(*new_arrays, **kwargs)
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101
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102 for pos in nones:
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103 rval[pos * 2: 2] = [None, None]
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104
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105 return rval
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106
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107
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108 def main(
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109 inputs,
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110 infile_estimator,
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111 infile1,
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112 infile2,
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113 outfile_result,
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114 outfile_object=None,
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115 outfile_weights=None,
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116 groups=None,
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117 ref_seq=None,
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118 intervals=None,
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119 targets=None,
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120 fasta_path=None,
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121 ):
26
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122 """
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123 Parameter
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124 ---------
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125 inputs : str
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126 File path to galaxy tool parameter
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127
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128 infile_estimator : str
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129 File path to estimator
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130
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131 infile1 : str
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132 File path to dataset containing features
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133
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134 infile2 : str
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135 File path to dataset containing target values
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136
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137 outfile_result : str
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138 File path to save the results, either cv_results or test result
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139
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140 outfile_object : str, optional
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141 File path to save searchCV object
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142
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143 outfile_weights : str, optional
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144 File path to save deep learning model weights
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145
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146 groups : str
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147 File path to dataset containing groups labels
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148
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149 ref_seq : str
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150 File path to dataset containing genome sequence file
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151
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152 intervals : str
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153 File path to dataset containing interval file
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154
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155 targets : str
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156 File path to dataset compressed target bed file
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157
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158 fasta_path : str
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159 File path to dataset containing fasta file
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160 """
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161 warnings.simplefilter("ignore")
26
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162
34
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163 with open(inputs, "r") as param_handler:
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164 params = json.load(param_handler)
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165
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166 # load estimator
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167 with open(infile_estimator, "rb") as estimator_handler:
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168 estimator = load_model(estimator_handler)
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169
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170 # swap hyperparameter
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171 swapping = params["experiment_schemes"]["hyperparams_swapping"]
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172 swap_params = _eval_swap_params(swapping)
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173 estimator.set_params(**swap_params)
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174
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175 estimator_params = estimator.get_params()
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176
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177 # store read dataframe object
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178 loaded_df = {}
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179
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180 input_type = params["input_options"]["selected_input"]
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181 # tabular input
34
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182 if input_type == "tabular":
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183 header = "infer" if params["input_options"]["header1"] else None
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184 column_option = params["input_options"]["column_selector_options_1"][
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185 "selected_column_selector_option"
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186 ]
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187 if column_option in [
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188 "by_index_number",
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189 "all_but_by_index_number",
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190 "by_header_name",
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191 "all_but_by_header_name",
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192 ]:
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193 c = params["input_options"]["column_selector_options_1"]["col1"]
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194 else:
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195 c = None
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196
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197 df_key = infile1 + repr(header)
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198 df = pd.read_csv(infile1, sep="\t", header=header, parse_dates=True)
26
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199 loaded_df[df_key] = df
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200
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201 X = read_columns(df, c=c, c_option=column_option).astype(float)
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202 # sparse input
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203 elif input_type == "sparse":
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204 X = mmread(open(infile1, "r"))
26
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205
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206 # fasta_file input
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207 elif input_type == "seq_fasta":
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208 pyfaidx = get_module("pyfaidx")
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209 sequences = pyfaidx.Fasta(fasta_path)
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210 n_seqs = len(sequences.keys())
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211 X = np.arange(n_seqs)[:, np.newaxis]
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212 for param in estimator_params.keys():
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213 if param.endswith("fasta_path"):
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214 estimator.set_params(**{param: fasta_path})
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215 break
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216 else:
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217 raise ValueError(
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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218 "The selected estimator doesn't support "
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219 "fasta file input! Please consider using "
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220 "KerasGBatchClassifier with "
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221 "FastaDNABatchGenerator/FastaProteinBatchGenerator "
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222 "or having GenomeOneHotEncoder/ProteinOneHotEncoder "
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223 "in pipeline!"
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224 )
26
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225
34
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226 elif input_type == "refseq_and_interval":
26
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227 path_params = {
34
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228 "data_batch_generator__ref_genome_path": ref_seq,
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229 "data_batch_generator__intervals_path": intervals,
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230 "data_batch_generator__target_path": targets,
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231 }
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232 estimator.set_params(**path_params)
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233 n_intervals = sum(1 for line in open(intervals))
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234 X = np.arange(n_intervals)[:, np.newaxis]
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235
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236 # Get target y
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237 header = "infer" if params["input_options"]["header2"] else None
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238 column_option = params["input_options"]["column_selector_options_2"][
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239 "selected_column_selector_option2"
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240 ]
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241 if column_option in [
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242 "by_index_number",
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243 "all_but_by_index_number",
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244 "by_header_name",
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245 "all_but_by_header_name",
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246 ]:
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247 c = params["input_options"]["column_selector_options_2"]["col2"]
26
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248 else:
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249 c = None
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250
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251 df_key = infile2 + repr(header)
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252 if df_key in loaded_df:
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253 infile2 = loaded_df[df_key]
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254 else:
34
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255 infile2 = pd.read_csv(infile2, sep="\t", header=header, parse_dates=True)
26
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256 loaded_df[df_key] = infile2
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257
36
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258 y = read_columns(
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259 infile2, c=c, c_option=column_option, sep="\t", header=header, parse_dates=True
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260 )
26
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261 if len(y.shape) == 2 and y.shape[1] == 1:
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262 y = y.ravel()
34
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263 if input_type == "refseq_and_interval":
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264 estimator.set_params(data_batch_generator__features=y.ravel().tolist())
26
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265 y = None
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266 # end y
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267
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268 # load groups
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269 if groups:
34
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270 groups_selector = (
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271 params["experiment_schemes"]["test_split"]["split_algos"]
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272 ).pop("groups_selector")
26
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273
34
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274 header = "infer" if groups_selector["header_g"] else None
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275 column_option = groups_selector["column_selector_options_g"][
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276 "selected_column_selector_option_g"
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277 ]
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278 if column_option in [
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279 "by_index_number",
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280 "all_but_by_index_number",
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281 "by_header_name",
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282 "all_but_by_header_name",
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283 ]:
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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284 c = groups_selector["column_selector_options_g"]["col_g"]
26
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285 else:
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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286 c = None
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287
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288 df_key = groups + repr(header)
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289 if df_key in loaded_df:
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290 groups = loaded_df[df_key]
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291
36
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292 groups = read_columns(
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293 groups,
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294 c=c,
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295 c_option=column_option,
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296 sep="\t",
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297 header=header,
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298 parse_dates=True,
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299 )
26
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300 groups = groups.ravel()
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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301
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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302 # del loaded_df
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303 del loaded_df
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304
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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305 # handle memory
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306 memory = joblib.Memory(location=CACHE_DIR, verbose=0)
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307 # cache iraps_core fits could increase search speed significantly
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308 if estimator.__class__.__name__ == "IRAPSClassifier":
26
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309 estimator.set_params(memory=memory)
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310 else:
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311 # For iraps buried in pipeline
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312 new_params = {}
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313 for p, v in estimator_params.items():
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314 if p.endswith("memory"):
26
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315 # for case of `__irapsclassifier__memory`
34
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316 if len(p) > 8 and p[:-8].endswith("irapsclassifier"):
26
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317 # cache iraps_core fits could increase search
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318 # speed significantly
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319 new_params[p] = memory
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320 # security reason, we don't want memory being
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321 # modified unexpectedly
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322 elif v:
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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323 new_params[p] = None
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324 # handle n_jobs
34
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325 elif p.endswith("n_jobs"):
26
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326 # For now, 1 CPU is suggested for iprasclassifier
34
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327 if len(p) > 8 and p[:-8].endswith("irapsclassifier"):
26
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328 new_params[p] = 1
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329 else:
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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330 new_params[p] = N_JOBS
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331 # for security reason, types of callback are limited
34
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332 elif p.endswith("callbacks"):
26
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333 for cb in v:
34
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334 cb_type = cb["callback_selection"]["callback_type"]
26
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335 if cb_type not in ALLOWED_CALLBACKS:
34
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336 raise ValueError("Prohibited callback type: %s!" % cb_type)
26
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337
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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338 estimator.set_params(**new_params)
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339
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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340 # handle scorer, convert to scorer dict
34
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341 # Check if scoring is specified
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342 scoring = params["experiment_schemes"]["metrics"].get("scoring", None)
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343 if scoring is not None:
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344 # 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"
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345 # Check if secondary_scoring is specified
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346 secondary_scoring = scoring.get("secondary_scoring", None)
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347 if secondary_scoring is not None:
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348 # If secondary_scoring is specified, convert the list into comman separated string
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349 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
350 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
351 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
352
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
353 # 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
354 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
355
34
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
356 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
357 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
358 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
359 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
360 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
361 else:
34
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
362 raise ValueError(
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
363 "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
364 )
26
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
365
36
73e7f1c76ece "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ea12f973df4b97a2691d9e4ce6bf6fae59d57717"
bgruening
parents: 35
diff changeset
366 (
73e7f1c76ece "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ea12f973df4b97a2691d9e4ce6bf6fae59d57717"
bgruening
parents: 35
diff changeset
367 X_train,
73e7f1c76ece "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ea12f973df4b97a2691d9e4ce6bf6fae59d57717"
bgruening
parents: 35
diff changeset
368 X_test,
73e7f1c76ece "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ea12f973df4b97a2691d9e4ce6bf6fae59d57717"
bgruening
parents: 35
diff changeset
369 y_train,
73e7f1c76ece "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ea12f973df4b97a2691d9e4ce6bf6fae59d57717"
bgruening
parents: 35
diff changeset
370 y_test,
73e7f1c76ece "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ea12f973df4b97a2691d9e4ce6bf6fae59d57717"
bgruening
parents: 35
diff changeset
371 groups_train,
73e7f1c76ece "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ea12f973df4b97a2691d9e4ce6bf6fae59d57717"
bgruening
parents: 35
diff changeset
372 _groups_test,
73e7f1c76ece "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ea12f973df4b97a2691d9e4ce6bf6fae59d57717"
bgruening
parents: 35
diff changeset
373 ) = train_test_split_none(X, y, groups, **test_split_options)
26
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
374
34
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
375 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
376
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
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diff changeset
377 # 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
378 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
379 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
380
34
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
381 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
382 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
383 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
384 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
385 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
386 else:
34
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
387 raise ValueError(
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
388 "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
389 "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
390 )
26
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
391
34
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
392 (
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
393 X_train,
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
394 X_val,
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
395 y_train,
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
396 y_val,
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
397 groups_train,
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
398 _groups_val,
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
399 ) = 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
400
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
401 # 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
402 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
403 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
404 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
405 else:
34
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
406 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
407 else:
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
408 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
409
34
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
410 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
411 scores = estimator.evaluate(
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
412 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
413 )
26
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
414 else:
34
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
415 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
416 # handle output
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
417 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
418 scores[name] = [score]
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
419 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
420 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
421 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
422
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
423 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
424
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
425 if outfile_object:
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
426 main_est = estimator
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
427 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
428 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
429
34
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
430 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
431 if outfile_weights:
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
432 main_est.save_weights(outfile_weights)
34
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
433 if getattr(main_est, "model_", None):
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
434 del main_est.model_
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
435 if getattr(main_est, "fit_params", None):
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
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436 del main_est.fit_params
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
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437 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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parents: 31
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438 del main_est.model_class_
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
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439 if getattr(main_est, "validation_data", None):
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
440 del main_est.validation_data
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
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441 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
parents:
diff changeset
442 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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443
34
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
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444 with open(outfile_object, "wb") as output_handler:
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
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445 pickle.dump(estimator, output_handler, pickle.HIGHEST_PROTOCOL)
26
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
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446
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
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447
34
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
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448 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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449 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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450 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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451 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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452 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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453 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
parents:
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454 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
parents:
diff changeset
455 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
parents:
diff changeset
456 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
parents:
diff changeset
457 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
parents:
diff changeset
458 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
parents:
diff changeset
459 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
parents:
diff changeset
460 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
parents:
diff changeset
461 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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diff changeset
462 args = aparser.parse_args()
37e193b3fdd7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff changeset
463
34
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
464 main(
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
465 args.inputs,
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
466 args.infile_estimator,
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
467 args.infile1,
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
468 args.infile2,
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
469 args.outfile_result,
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
470 outfile_object=args.outfile_object,
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
471 outfile_weights=args.outfile_weights,
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
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diff changeset
472 groups=args.groups,
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
473 ref_seq=args.ref_seq,
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
474 intervals=args.intervals,
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
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diff changeset
475 targets=args.targets,
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents: 31
diff changeset
476 fasta_path=args.fasta_path,
816b65d52c33 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
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diff changeset
477 )