Mercurial > repos > bgruening > sklearn_pairwise_metrics

annotate search_model_validation.py @ 27:20b7295ae6e9 draft

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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ba6a47bdf76bbf4cb276206ac1a8cbf61332fd16"
author bgruening
date Fri, 13 Sep 2019 12:21:45 -0400
parents e84c0db80565
children bc17040617c0
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1 import argparse
86020dbc8ef7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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2 import collections
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3 import imblearn
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4 import joblib
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5 import json
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6 import numpy as np
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7 import pandas as pd
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8 import pickle
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9 import skrebate
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10 import sklearn
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11 import sys
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12 import xgboost
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13 import warnings
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14 from imblearn import under_sampling, over_sampling, combine
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15 from scipy.io import mmread
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16 from mlxtend import classifier, regressor
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17 from sklearn.base import clone
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18 from sklearn import (cluster, compose, decomposition, ensemble,
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19 feature_extraction, feature_selection,
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20 gaussian_process, kernel_approximation, metrics,
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21 model_selection, naive_bayes, neighbors,
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22 pipeline, preprocessing, svm, linear_model,
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23 tree, discriminant_analysis)
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24 from sklearn.exceptions import FitFailedWarning
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25 from sklearn.model_selection._validation import _score, cross_validate
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26 from sklearn.model_selection import _search, _validation
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28 from galaxy_ml.utils import (SafeEval, get_cv, get_scoring, load_model,
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29 read_columns, try_get_attr, get_module)
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32 _fit_and_score = try_get_attr('galaxy_ml.model_validations', '_fit_and_score')
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33 setattr(_search, '_fit_and_score', _fit_and_score)
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34 setattr(_validation, '_fit_and_score', _fit_and_score)
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36 N_JOBS = int(__import__('os').environ.get('GALAXY_SLOTS', 1))
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37 CACHE_DIR = './cached'
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38 NON_SEARCHABLE = ('n_jobs', 'pre_dispatch', 'memory', '_path',
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39 'nthread', 'callbacks')
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40 ALLOWED_CALLBACKS = ('EarlyStopping', 'TerminateOnNaN', 'ReduceLROnPlateau',
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41 'CSVLogger', 'None')
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44 def _eval_search_params(params_builder):
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45 search_params = {}
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47 for p in params_builder['param_set']:
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48 search_list = p['sp_list'].strip()
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49 if search_list == '':
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50 continue
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52 param_name = p['sp_name']
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53 if param_name.lower().endswith(NON_SEARCHABLE):
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54 print("Warning: `%s` is not eligible for search and was "
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55 "omitted!" % param_name)
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56 continue
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58 if not search_list.startswith(':'):
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59 safe_eval = SafeEval(load_scipy=True, load_numpy=True)
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60 ev = safe_eval(search_list)
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61 search_params[param_name] = ev
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62 else:
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63 # Have `:` before search list, asks for estimator evaluatio
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64 safe_eval_es = SafeEval(load_estimators=True)
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65 search_list = search_list[1:].strip()
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66 # TODO maybe add regular express check
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67 ev = safe_eval_es(search_list)
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68 preprocessings = (
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69 preprocessing.StandardScaler(), preprocessing.Binarizer(),
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70 preprocessing.MaxAbsScaler(),
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71 preprocessing.Normalizer(), preprocessing.MinMaxScaler(),
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72 preprocessing.PolynomialFeatures(),
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73 preprocessing.RobustScaler(), feature_selection.SelectKBest(),
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74 feature_selection.GenericUnivariateSelect(),
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75 feature_selection.SelectPercentile(),
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76 feature_selection.SelectFpr(), feature_selection.SelectFdr(),
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77 feature_selection.SelectFwe(),
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78 feature_selection.VarianceThreshold(),
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79 decomposition.FactorAnalysis(random_state=0),
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80 decomposition.FastICA(random_state=0),
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81 decomposition.IncrementalPCA(),
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82 decomposition.KernelPCA(random_state=0, n_jobs=N_JOBS),
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83 decomposition.LatentDirichletAllocation(
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84 random_state=0, n_jobs=N_JOBS),
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85 decomposition.MiniBatchDictionaryLearning(
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86 random_state=0, n_jobs=N_JOBS),
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87 decomposition.MiniBatchSparsePCA(
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88 random_state=0, n_jobs=N_JOBS),
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89 decomposition.NMF(random_state=0),
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90 decomposition.PCA(random_state=0),
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91 decomposition.SparsePCA(random_state=0, n_jobs=N_JOBS),
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92 decomposition.TruncatedSVD(random_state=0),
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93 kernel_approximation.Nystroem(random_state=0),
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94 kernel_approximation.RBFSampler(random_state=0),
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95 kernel_approximation.AdditiveChi2Sampler(),
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96 kernel_approximation.SkewedChi2Sampler(random_state=0),
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97 cluster.FeatureAgglomeration(),
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98 skrebate.ReliefF(n_jobs=N_JOBS),
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99 skrebate.SURF(n_jobs=N_JOBS),
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100 skrebate.SURFstar(n_jobs=N_JOBS),
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101 skrebate.MultiSURF(n_jobs=N_JOBS),
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102 skrebate.MultiSURFstar(n_jobs=N_JOBS),
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103 imblearn.under_sampling.ClusterCentroids(
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104 random_state=0, n_jobs=N_JOBS),
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105 imblearn.under_sampling.CondensedNearestNeighbour(
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106 random_state=0, n_jobs=N_JOBS),
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107 imblearn.under_sampling.EditedNearestNeighbours(
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108 random_state=0, n_jobs=N_JOBS),
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109 imblearn.under_sampling.RepeatedEditedNearestNeighbours(
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110 random_state=0, n_jobs=N_JOBS),
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111 imblearn.under_sampling.AllKNN(random_state=0, n_jobs=N_JOBS),
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112 imblearn.under_sampling.InstanceHardnessThreshold(
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113 random_state=0, n_jobs=N_JOBS),
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114 imblearn.under_sampling.NearMiss(
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115 random_state=0, n_jobs=N_JOBS),
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116 imblearn.under_sampling.NeighbourhoodCleaningRule(
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117 random_state=0, n_jobs=N_JOBS),
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118 imblearn.under_sampling.OneSidedSelection(
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119 random_state=0, n_jobs=N_JOBS),
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120 imblearn.under_sampling.RandomUnderSampler(
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121 random_state=0),
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122 imblearn.under_sampling.TomekLinks(
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123 random_state=0, n_jobs=N_JOBS),
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124 imblearn.over_sampling.ADASYN(random_state=0, n_jobs=N_JOBS),
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125 imblearn.over_sampling.RandomOverSampler(random_state=0),
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126 imblearn.over_sampling.SMOTE(random_state=0, n_jobs=N_JOBS),
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127 imblearn.over_sampling.SVMSMOTE(random_state=0, n_jobs=N_JOBS),
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128 imblearn.over_sampling.BorderlineSMOTE(
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129 random_state=0, n_jobs=N_JOBS),
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130 imblearn.over_sampling.SMOTENC(
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131 categorical_features=[], random_state=0, n_jobs=N_JOBS),
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132 imblearn.combine.SMOTEENN(random_state=0),
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133 imblearn.combine.SMOTETomek(random_state=0))
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134 newlist = []
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135 for obj in ev:
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136 if obj is None:
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137 newlist.append(None)
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138 elif obj == 'all_0':
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139 newlist.extend(preprocessings[0:35])
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140 elif obj == 'sk_prep_all': # no KernalCenter()
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141 newlist.extend(preprocessings[0:7])
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142 elif obj == 'fs_all':
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143 newlist.extend(preprocessings[7:14])
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144 elif obj == 'decomp_all':
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145 newlist.extend(preprocessings[14:25])
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146 elif obj == 'k_appr_all':
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147 newlist.extend(preprocessings[25:29])
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148 elif obj == 'reb_all':
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149 newlist.extend(preprocessings[30:35])
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150 elif obj == 'imb_all':
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151 newlist.extend(preprocessings[35:54])
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152 elif type(obj) is int and -1 < obj < len(preprocessings):
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153 newlist.append(preprocessings[obj])
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154 elif hasattr(obj, 'get_params'): # user uploaded object
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155 if 'n_jobs' in obj.get_params():
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156 newlist.append(obj.set_params(n_jobs=N_JOBS))
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157 else:
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158 newlist.append(obj)
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159 else:
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160 sys.exit("Unsupported estimator type: %r" % (obj))
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161
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162 search_params[param_name] = newlist
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163
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164 return search_params
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165
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166
24
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167 def main(inputs, infile_estimator, infile1, infile2,
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168 outfile_result, outfile_object=None,
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169 outfile_weights=None, groups=None,
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170 ref_seq=None, intervals=None, targets=None,
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171 fasta_path=None):
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172 """
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173 Parameter
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174 ---------
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175 inputs : str
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176 File path to galaxy tool parameter
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177
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178 infile_estimator : str
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179 File path to estimator
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180
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181 infile1 : str
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182 File path to dataset containing features
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183
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184 infile2 : str
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185 File path to dataset containing target values
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186
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187 outfile_result : str
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188 File path to save the results, either cv_results or test result
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189
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190 outfile_object : str, optional
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191 File path to save searchCV object
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192
26
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193 outfile_weights : str, optional
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194 File path to save model weights
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195
24
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196 groups : str
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197 File path to dataset containing groups labels
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198
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199 ref_seq : str
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200 File path to dataset containing genome sequence file
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201
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202 intervals : str
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203 File path to dataset containing interval file
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204
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205 targets : str
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206 File path to dataset compressed target bed file
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207
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208 fasta_path : str
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209 File path to dataset containing fasta file
24
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210 """
23
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211 warnings.simplefilter('ignore')
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212
24
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213 with open(inputs, 'r') as param_handler:
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214 params = json.load(param_handler)
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215
27
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216 # conflict param checker
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217 if params['outer_split']['split_mode'] == 'nested_cv' \
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218 and params['save'] != 'nope':
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219 raise ValueError("Save best estimator is not possible for nested CV!")
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220
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221 if not (params['search_schemes']['options']['refit']) \
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222 and params['save'] != 'nope':
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223 raise ValueError("Save best estimator is not possible when refit "
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224 "is False!")
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225
23
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226 params_builder = params['search_schemes']['search_params_builder']
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227
26
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228 with open(infile_estimator, 'rb') as estimator_handler:
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229 estimator = load_model(estimator_handler)
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230 estimator_params = estimator.get_params()
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231
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232 # store read dataframe object
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233 loaded_df = {}
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234
23
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235 input_type = params['input_options']['selected_input']
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236 # tabular input
23
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237 if input_type == 'tabular':
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238 header = 'infer' if params['input_options']['header1'] else None
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239 column_option = (params['input_options']['column_selector_options_1']
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240 ['selected_column_selector_option'])
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241 if column_option in ['by_index_number', 'all_but_by_index_number',
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242 'by_header_name', 'all_but_by_header_name']:
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243 c = params['input_options']['column_selector_options_1']['col1']
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244 else:
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245 c = None
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246
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247 df_key = infile1 + repr(header)
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248 df = pd.read_csv(infile1, sep='\t', header=header,
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249 parse_dates=True)
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250 loaded_df[df_key] = df
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251
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252 X = read_columns(df, c=c, c_option=column_option).astype(float)
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253 # sparse input
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254 elif input_type == 'sparse':
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255 X = mmread(open(infile1, 'r'))
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256
26
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257 # fasta_file input
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258 elif input_type == 'seq_fasta':
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259 pyfaidx = get_module('pyfaidx')
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260 sequences = pyfaidx.Fasta(fasta_path)
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261 n_seqs = len(sequences.keys())
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262 X = np.arange(n_seqs)[:, np.newaxis]
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263 for param in estimator_params.keys():
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264 if param.endswith('fasta_path'):
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265 estimator.set_params(
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266 **{param: fasta_path})
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267 break
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268 else:
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269 raise ValueError(
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270 "The selected estimator doesn't support "
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271 "fasta file input! Please consider using "
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272 "KerasGBatchClassifier with "
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273 "FastaDNABatchGenerator/FastaProteinBatchGenerator "
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274 "or having GenomeOneHotEncoder/ProteinOneHotEncoder "
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275 "in pipeline!")
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276
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277 elif input_type == 'refseq_and_interval':
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278 path_params = {
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279 'data_batch_generator__ref_genome_path': ref_seq,
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280 'data_batch_generator__intervals_path': intervals,
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281 'data_batch_generator__target_path': targets
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282 }
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283 estimator.set_params(**path_params)
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284 n_intervals = sum(1 for line in open(intervals))
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285 X = np.arange(n_intervals)[:, np.newaxis]
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286
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287 # Get target y
23
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288 header = 'infer' if params['input_options']['header2'] else None
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289 column_option = (params['input_options']['column_selector_options_2']
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290 ['selected_column_selector_option2'])
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291 if column_option in ['by_index_number', 'all_but_by_index_number',
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292 'by_header_name', 'all_but_by_header_name']:
23
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293 c = params['input_options']['column_selector_options_2']['col2']
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294 else:
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295 c = None
26
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296
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297 df_key = infile2 + repr(header)
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298 if df_key in loaded_df:
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299 infile2 = loaded_df[df_key]
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300 else:
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301 infile2 = pd.read_csv(infile2, sep='\t',
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302 header=header, parse_dates=True)
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303 loaded_df[df_key] = infile2
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304
23
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305 y = read_columns(
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306 infile2,
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307 c=c,
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308 c_option=column_option,
23
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309 sep='\t',
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310 header=header,
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311 parse_dates=True)
26
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312 if len(y.shape) == 2 and y.shape[1] == 1:
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313 y = y.ravel()
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314 if input_type == 'refseq_and_interval':
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315 estimator.set_params(
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316 data_batch_generator__features=y.ravel().tolist())
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317 y = None
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318 # end y
23
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319
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320 optimizer = params['search_schemes']['selected_search_scheme']
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321 optimizer = getattr(model_selection, optimizer)
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322
26
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323 # handle gridsearchcv options
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324 options = params['search_schemes']['options']
24
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325
26
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326 if groups:
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327 header = 'infer' if (options['cv_selector']['groups_selector']
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328 ['header_g']) else None
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329 column_option = (options['cv_selector']['groups_selector']
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330 ['column_selector_options_g']
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331 ['selected_column_selector_option_g'])
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332 if column_option in ['by_index_number', 'all_but_by_index_number',
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333 'by_header_name', 'all_but_by_header_name']:
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334 c = (options['cv_selector']['groups_selector']
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335 ['column_selector_options_g']['col_g'])
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336 else:
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337 c = None
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338
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339 df_key = groups + repr(header)
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340 if df_key in loaded_df:
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341 groups = loaded_df[df_key]
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342
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343 groups = read_columns(
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344 groups,
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345 c=c,
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346 c_option=column_option,
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347 sep='\t',
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348 header=header,
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349 parse_dates=True)
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350 groups = groups.ravel()
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351 options['cv_selector']['groups_selector'] = groups
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352
23
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353 splitter, groups = get_cv(options.pop('cv_selector'))
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354 options['cv'] = splitter
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355 options['n_jobs'] = N_JOBS
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356 primary_scoring = options['scoring']['primary_scoring']
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357 options['scoring'] = get_scoring(options['scoring'])
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358 if options['error_score']:
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359 options['error_score'] = 'raise'
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360 else:
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361 options['error_score'] = np.NaN
8338f71ed0c4 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 57f4407e278a615f47a377a3328782b1d8e0b54d
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362 if options['refit'] and isinstance(options['scoring'], dict):
24
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diff changeset
363 options['refit'] = primary_scoring
23
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364 if 'pre_dispatch' in options and options['pre_dispatch'] == '':
8338f71ed0c4 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 57f4407e278a615f47a377a3328782b1d8e0b54d
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365 options['pre_dispatch'] = None
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366
26
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diff changeset
367 # del loaded_df
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368 del loaded_df
24
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diff changeset
369
26
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370 # handle memory
24
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371 memory = joblib.Memory(location=CACHE_DIR, verbose=0)
86020dbc8ef7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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372 # cache iraps_core fits could increase search speed significantly
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373 if estimator.__class__.__name__ == 'IRAPSClassifier':
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374 estimator.set_params(memory=memory)
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diff changeset
375 else:
26
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376 # For iraps buried in pipeline
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377 for p, v in estimator_params.items():
24
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378 if p.endswith('memory'):
26
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379 # for case of `__irapsclassifier__memory`
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380 if len(p) > 8 and p[:-8].endswith('irapsclassifier'):
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381 # cache iraps_core fits could increase search
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382 # speed significantly
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383 new_params = {p: memory}
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diff changeset
384 estimator.set_params(**new_params)
26
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385 # security reason, we don't want memory being
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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diff changeset
386 # modified unexpectedly
24
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diff changeset
387 elif v:
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diff changeset
388 new_params = {p, None}
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diff changeset
389 estimator.set_params(**new_params)
26
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diff changeset
390 # For now, 1 CPU is suggested for iprasclassifier
24
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diff changeset
391 elif p.endswith('n_jobs'):
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392 new_params = {p: 1}
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diff changeset
393 estimator.set_params(**new_params)
26
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diff changeset
394 # for security reason, types of callbacks are limited
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395 elif p.endswith('callbacks'):
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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diff changeset
396 for cb in v:
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diff changeset
397 cb_type = cb['callback_selection']['callback_type']
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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diff changeset
398 if cb_type not in ALLOWED_CALLBACKS:
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diff changeset
399 raise ValueError(
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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diff changeset
400 "Prohibited callback type: %s!" % cb_type)
24
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diff changeset
401
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diff changeset
402 param_grid = _eval_search_params(params_builder)
86020dbc8ef7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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diff changeset
403 searcher = optimizer(estimator, param_grid, **options)
23
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diff changeset
404
26
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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diff changeset
405 # do nested split
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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diff changeset
406 split_mode = params['outer_split'].pop('split_mode')
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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407 # nested CV, outer cv using cross_validate
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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diff changeset
408 if split_mode == 'nested_cv':
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diff changeset
409 outer_cv, _ = get_cv(params['outer_split']['cv_selector'])
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diff changeset
410
26
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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diff changeset
411 if options['error_score'] == 'raise':
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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diff changeset
412 rval = cross_validate(
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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diff changeset
413 searcher, X, y, scoring=options['scoring'],
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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diff changeset
414 cv=outer_cv, n_jobs=N_JOBS, verbose=0,
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diff changeset
415 error_score=options['error_score'])
24
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diff changeset
416 else:
26
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diff changeset
417 warnings.simplefilter('always', FitFailedWarning)
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418 with warnings.catch_warnings(record=True) as w:
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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diff changeset
419 try:
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diff changeset
420 rval = cross_validate(
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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diff changeset
421 searcher, X, y,
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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diff changeset
422 scoring=options['scoring'],
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diff changeset
423 cv=outer_cv, n_jobs=N_JOBS,
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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diff changeset
424 verbose=0,
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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diff changeset
425 error_score=options['error_score'])
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diff changeset
426 except ValueError:
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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diff changeset
427 pass
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diff changeset
428 for warning in w:
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429 print(repr(warning.message))
23
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diff changeset
430
26
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431 keys = list(rval.keys())
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432 for k in keys:
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diff changeset
433 if k.startswith('test'):
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diff changeset
434 rval['mean_' + k] = np.mean(rval[k])
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diff changeset
435 rval['std_' + k] = np.std(rval[k])
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diff changeset
436 if k.endswith('time'):
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diff changeset
437 rval.pop(k)
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438 rval = pd.DataFrame(rval)
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diff changeset
439 rval = rval[sorted(rval.columns)]
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440 rval.to_csv(path_or_buf=outfile_result, sep='\t',
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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diff changeset
441 header=True, index=False)
23
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diff changeset
442 else:
26
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diff changeset
443 if split_mode == 'train_test_split':
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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diff changeset
444 train_test_split = try_get_attr(
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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445 'galaxy_ml.model_validations', 'train_test_split')
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446 # make sure refit is choosen
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447 # this could be True for sklearn models, but not the case for
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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448 # deep learning models
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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449 if not options['refit'] and \
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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450 not all(hasattr(estimator, attr)
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451 for attr in ('config', 'model_type')):
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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diff changeset
452 warnings.warn("Refit is change to `True` for nested "
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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453 "validation!")
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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454 setattr(searcher, 'refit', True)
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455 split_options = params['outer_split']
23
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diff changeset
456
26
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457 # splits
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458 if split_options['shuffle'] == 'stratified':
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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459 split_options['labels'] = y
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460 X, X_test, y, y_test = train_test_split(X, y, **split_options)
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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461 elif split_options['shuffle'] == 'group':
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462 if groups is None:
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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463 raise ValueError("No group based CV option was "
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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464 "choosen for group shuffle!")
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465 split_options['labels'] = groups
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466 if y is None:
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467 X, X_test, groups, _ =\
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468 train_test_split(X, groups, **split_options)
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469 else:
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470 X, X_test, y, y_test, groups, _ =\
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471 train_test_split(X, y, groups, **split_options)
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472 else:
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473 if split_options['shuffle'] == 'None':
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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474 split_options['shuffle'] = None
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475 X, X_test, y, y_test =\
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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476 train_test_split(X, y, **split_options)
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477 # end train_test_split
24
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478
26
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479 # shared by both train_test_split and non-split
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480 if options['error_score'] == 'raise':
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481 searcher.fit(X, y, groups=groups)
24
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482 else:
26
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483 warnings.simplefilter('always', FitFailedWarning)
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484 with warnings.catch_warnings(record=True) as w:
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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diff changeset
485 try:
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486 searcher.fit(X, y, groups=groups)
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487 except ValueError:
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diff changeset
488 pass
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diff changeset
489 for warning in w:
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490 print(repr(warning.message))
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diff changeset
491
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492 # no outer split
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493 if split_mode == 'no':
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494 # save results
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495 cv_results = pd.DataFrame(searcher.cv_results_)
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496 cv_results = cv_results[sorted(cv_results.columns)]
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497 cv_results.to_csv(path_or_buf=outfile_result, sep='\t',
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498 header=True, index=False)
23
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499
26
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500 # train_test_split, output test result using best_estimator_
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501 # or rebuild the trained estimator using weights if applicable.
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502 else:
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503 scorer_ = searcher.scorer_
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504 if isinstance(scorer_, collections.Mapping):
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505 is_multimetric = True
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506 else:
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507 is_multimetric = False
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diff changeset
508
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509 best_estimator_ = getattr(searcher, 'best_estimator_', None)
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diff changeset
510 if not best_estimator_:
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511 raise ValueError("GridSearchCV object has no "
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512 "`best_estimator_` when `refit`=False!")
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diff changeset
513
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514 if best_estimator_.__class__.__name__ == 'KerasGBatchClassifier' \
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515 and hasattr(estimator.data_batch_generator, 'target_path'):
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diff changeset
516 test_score = best_estimator_.evaluate(
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diff changeset
517 X_test, scorer=scorer_, is_multimetric=is_multimetric)
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518 else:
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519 test_score = _score(best_estimator_, X_test,
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diff changeset
520 y_test, scorer_,
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521 is_multimetric=is_multimetric)
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diff changeset
522
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523 if not is_multimetric:
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524 test_score = {primary_scoring: test_score}
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525 for key, value in test_score.items():
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526 test_score[key] = [value]
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527 result_df = pd.DataFrame(test_score)
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528 result_df.to_csv(path_or_buf=outfile_result, sep='\t',
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529 header=True, index=False)
24
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diff changeset
530
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531 memory.clear(warn=False)
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532
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533 if outfile_object:
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534 best_estimator_ = getattr(searcher, 'best_estimator_', None)
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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535 if not best_estimator_:
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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536 warnings.warn("GridSearchCV object has no attribute "
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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537 "'best_estimator_', because either it's "
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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538 "nested gridsearch or `refit` is False!")
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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539 return
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540
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541 main_est = best_estimator_
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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542 if isinstance(best_estimator_, pipeline.Pipeline):
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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543 main_est = best_estimator_.steps[-1][-1]
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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diff changeset
544
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545 if hasattr(main_est, 'model_') \
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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546 and hasattr(main_est, 'save_weights'):
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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547 if outfile_weights:
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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548 main_est.save_weights(outfile_weights)
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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549 del main_est.model_
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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550 del main_est.fit_params
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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551 del main_est.model_class_
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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552 del main_est.validation_data
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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553 if getattr(main_est, 'data_generator_', None):
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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554 del main_est.data_generator_
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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555
24
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556 with open(outfile_object, 'wb') as output_handler:
26
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557 pickle.dump(best_estimator_, output_handler,
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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558 pickle.HIGHEST_PROTOCOL)
24
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diff changeset
559
86020dbc8ef7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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diff changeset
560
86020dbc8ef7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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diff changeset
561 if __name__ == '__main__':
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diff changeset
562 aparser = argparse.ArgumentParser()
86020dbc8ef7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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diff changeset
563 aparser.add_argument("-i", "--inputs", dest="inputs", required=True)
86020dbc8ef7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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564 aparser.add_argument("-e", "--estimator", dest="infile_estimator")
86020dbc8ef7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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diff changeset
565 aparser.add_argument("-X", "--infile1", dest="infile1")
86020dbc8ef7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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diff changeset
566 aparser.add_argument("-y", "--infile2", dest="infile2")
26
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diff changeset
567 aparser.add_argument("-O", "--outfile_result", dest="outfile_result")
24
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diff changeset
568 aparser.add_argument("-o", "--outfile_object", dest="outfile_object")
26
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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diff changeset
569 aparser.add_argument("-w", "--outfile_weights", dest="outfile_weights")
24
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diff changeset
570 aparser.add_argument("-g", "--groups", dest="groups")
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diff changeset
571 aparser.add_argument("-r", "--ref_seq", dest="ref_seq")
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diff changeset
572 aparser.add_argument("-b", "--intervals", dest="intervals")
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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diff changeset
573 aparser.add_argument("-t", "--targets", dest="targets")
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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diff changeset
574 aparser.add_argument("-f", "--fasta_path", dest="fasta_path")
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diff changeset
575 args = aparser.parse_args()
86020dbc8ef7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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diff changeset
576
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diff changeset
577 main(args.inputs, args.infile_estimator, args.infile1, args.infile2,
86020dbc8ef7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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diff changeset
578 args.outfile_result, outfile_object=args.outfile_object,
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diff changeset
579 outfile_weights=args.outfile_weights, groups=args.groups,
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diff changeset
580 ref_seq=args.ref_seq, intervals=args.intervals,
e84c0db80565 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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diff changeset
581 targets=args.targets, fasta_path=args.fasta_path)