Mercurial > repos > bgruening > sklearn_ensemble

annotate utils.py @ 23:39ae276e75d9 draft

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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 57f4407e278a615f47a377a3328782b1d8e0b54d
author bgruening
date Sun, 30 Dec 2018 01:56:11 -0500
parents 9ce3e347506c
children e94395c672bd
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39ae276e75d9 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 57f4407e278a615f47a377a3328782b1d8e0b54d
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diff changeset
1 import json
39ae276e75d9 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 57f4407e278a615f47a377a3328782b1d8e0b54d
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2 import numpy as np
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3 import os
4570575d060c planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d00173591e4a783a4c1cb2664e4bb192ab5414f7
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4 import pandas
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39ae276e75d9 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 57f4407e278a615f47a377a3328782b1d8e0b54d
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5 import pickle
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6 import re
4570575d060c planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d00173591e4a783a4c1cb2664e4bb192ab5414f7
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7 import scipy
4570575d060c planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d00173591e4a783a4c1cb2664e4bb192ab5414f7
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8 import sklearn
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9 import sys
39ae276e75d9 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 57f4407e278a615f47a377a3328782b1d8e0b54d
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10 import warnings
39ae276e75d9 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 57f4407e278a615f47a377a3328782b1d8e0b54d
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11 import xgboost
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13 from asteval import Interpreter, make_symbol_table
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39ae276e75d9 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 57f4407e278a615f47a377a3328782b1d8e0b54d
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14 from sklearn import (cluster, compose, decomposition, ensemble, feature_extraction,
39ae276e75d9 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 57f4407e278a615f47a377a3328782b1d8e0b54d
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15 feature_selection, gaussian_process, kernel_approximation, metrics,
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16 model_selection, naive_bayes, neighbors, pipeline, preprocessing,
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17 svm, linear_model, tree, discriminant_analysis)
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19 try:
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20 import skrebate
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21 except ModuleNotFoundError:
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22 pass
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25 N_JOBS = int(os.environ.get('GALAXY_SLOTS', 1))
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27 try:
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28 sk_whitelist
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29 except NameError:
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30 sk_whitelist = None
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33 class SafePickler(pickle.Unpickler):
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34 """
038cecaa9e7c planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 8cf3d813ec755166ee0bd517b4ecbbd4f84d4df1
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35 Used to safely deserialize scikit-learn model objects serialized by cPickle.dump
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36 Usage:
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37 eg.: SafePickler.load(pickled_file_object)
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38 """
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39 def find_class(self, module, name):
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41 # sk_whitelist could be read from tool
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42 global sk_whitelist
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43 if not sk_whitelist:
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44 whitelist_file = os.path.join(os.path.dirname(__file__), 'sk_whitelist.json')
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45 with open(whitelist_file, 'r') as f:
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46 sk_whitelist = json.load(f)
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48 bad_names = ('and', 'as', 'assert', 'break', 'class', 'continue',
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49 'def', 'del', 'elif', 'else', 'except', 'exec',
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50 'finally', 'for', 'from', 'global', 'if', 'import',
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51 'in', 'is', 'lambda', 'not', 'or', 'pass', 'print',
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52 'raise', 'return', 'try', 'system', 'while', 'with',
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53 'True', 'False', 'None', 'eval', 'execfile', '__import__',
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54 '__package__', '__subclasses__', '__bases__', '__globals__',
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55 '__code__', '__closure__', '__func__', '__self__', '__module__',
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56 '__dict__', '__class__', '__call__', '__get__',
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57 '__getattribute__', '__subclasshook__', '__new__',
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58 '__init__', 'func_globals', 'func_code', 'func_closure',
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59 'im_class', 'im_func', 'im_self', 'gi_code', 'gi_frame',
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60 '__asteval__', 'f_locals', '__mro__')
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61 good_names = ['copy_reg._reconstructor', '__builtin__.object']
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63 if re.match(r'^[a-zA-Z_][a-zA-Z0-9_]*$', name):
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64 fullname = module + '.' + name
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65 if (fullname in good_names)\
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66 or ( ( module.startswith('sklearn.')
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67 or module.startswith('xgboost.')
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68 or module.startswith('skrebate.')
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69 or module.startswith('imblearn')
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70 or module.startswith('numpy.')
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71 or module == 'numpy'
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72 )
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73 and (name not in bad_names)
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74 ):
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75 # TODO: replace with a whitelist checker
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76 if fullname not in sk_whitelist['SK_NAMES'] + sk_whitelist['SKR_NAMES'] + sk_whitelist['XGB_NAMES'] + sk_whitelist['NUMPY_NAMES'] + sk_whitelist['IMBLEARN_NAMES'] + good_names:
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77 print("Warning: global %s is not in pickler whitelist yet and will loss support soon. Contact tool author or leave a message at github.com" % fullname)
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78 mod = sys.modules[module]
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79 return getattr(mod, name)
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81 raise pickle.UnpicklingError("global '%s' is forbidden" % fullname)
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84 def load_model(file):
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85 return SafePickler(file).load()
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88 def read_columns(f, c=None, c_option='by_index_number', return_df=False, **args):
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89 data = pandas.read_csv(f, **args)
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90 if c_option == 'by_index_number':
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91 cols = list(map(lambda x: x - 1, c))
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92 data = data.iloc[:, cols]
19
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93 if c_option == 'all_but_by_index_number':
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94 cols = list(map(lambda x: x - 1, c))
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95 data.drop(data.columns[cols], axis=1, inplace=True)
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96 if c_option == 'by_header_name':
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97 cols = [e.strip() for e in c.split(',')]
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98 data = data[cols]
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99 if c_option == 'all_but_by_header_name':
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100 cols = [e.strip() for e in c.split(',')]
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101 data.drop(cols, axis=1, inplace=True)
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102 y = data.values
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103 if return_df:
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104 return y, data
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105 else:
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106 return y
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107
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108
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109 ## generate an instance for one of sklearn.feature_selection classes
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110 def feature_selector(inputs):
23
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111 selector = inputs['selected_algorithm']
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112 selector = getattr(sklearn.feature_selection, selector)
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113 options = inputs['options']
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114
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115 if inputs['selected_algorithm'] == 'SelectFromModel':
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116 if not options['threshold'] or options['threshold'] == 'None':
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117 options['threshold'] = None
23
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118 else:
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119 try:
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120 options['threshold'] = float(options['threshold'])
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121 except ValueError:
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122 pass
19
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123 if inputs['model_inputter']['input_mode'] == 'prefitted':
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124 model_file = inputs['model_inputter']['fitted_estimator']
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125 with open(model_file, 'rb') as model_handler:
21
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126 fitted_estimator = load_model(model_handler)
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127 new_selector = selector(fitted_estimator, prefit=True, **options)
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128 else:
23
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129 estimator_json = inputs['model_inputter']['estimator_selector']
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130 estimator = get_estimator(estimator_json)
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131 new_selector = selector(estimator, **options)
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132
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133 elif inputs['selected_algorithm'] == 'RFE':
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134 estimator = get_estimator(inputs['estimator_selector'])
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135 step = options.get('step', None)
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136 if step and step >= 1.0:
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137 options['step'] = int(step)
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138 new_selector = selector(estimator, **options)
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139
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140 elif inputs['selected_algorithm'] == 'RFECV':
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141 options['scoring'] = get_scoring(options['scoring'])
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142 options['n_jobs'] = N_JOBS
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143 splitter, groups = get_cv(options.pop('cv_selector'))
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144 # TODO support group cv splitters
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145 options['cv'] = splitter
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146 step = options.get('step', None)
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147 if step and step >= 1.0:
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148 options['step'] = int(step)
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149 estimator = get_estimator(inputs['estimator_selector'])
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150 new_selector = selector(estimator, **options)
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151
23
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152 elif inputs['selected_algorithm'] == 'VarianceThreshold':
19
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153 new_selector = selector(**options)
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154
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155 else:
23
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156 score_func = inputs['score_func']
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157 score_func = getattr(sklearn.feature_selection, score_func)
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158 new_selector = selector(score_func, **options)
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159
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160 return new_selector
21
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161
19
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162
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163 def get_X_y(params, file1, file2):
23
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164 input_type = params['selected_tasks']['selected_algorithms']['input_options']['selected_input']
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165 if input_type == 'tabular':
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166 header = 'infer' if params['selected_tasks']['selected_algorithms']['input_options']['header1'] else None
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167 column_option = params['selected_tasks']['selected_algorithms']['input_options']['column_selector_options_1']['selected_column_selector_option']
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168 if column_option in ['by_index_number', 'all_but_by_index_number', 'by_header_name', 'all_but_by_header_name']:
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169 c = params['selected_tasks']['selected_algorithms']['input_options']['column_selector_options_1']['col1']
19
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170 else:
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171 c = None
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172 X = read_columns(
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173 file1,
21
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174 c=c,
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175 c_option=column_option,
19
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176 sep='\t',
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177 header=header,
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178 parse_dates=True
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179 )
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180 else:
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181 X = mmread(file1)
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182
23
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183 header = 'infer' if params['selected_tasks']['selected_algorithms']['input_options']['header2'] else None
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184 column_option = params['selected_tasks']['selected_algorithms']['input_options']['column_selector_options_2']['selected_column_selector_option2']
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185 if column_option in ['by_index_number', 'all_but_by_index_number', 'by_header_name', 'all_but_by_header_name']:
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186 c = params['selected_tasks']['selected_algorithms']['input_options']['column_selector_options_2']['col2']
19
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187 else:
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188 c = None
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189 y = read_columns(
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190 file2,
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191 c=c,
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192 c_option=column_option,
19
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193 sep='\t',
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194 header=header,
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195 parse_dates=True
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196 )
21
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197 y = y.ravel()
19
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198 return X, y
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199
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200
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201 class SafeEval(Interpreter):
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202
23
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203 def __init__(self, load_scipy=False, load_numpy=False, load_estimators=False):
19
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204
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205 # File opening and other unneeded functions could be dropped
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206 unwanted = ['open', 'type', 'dir', 'id', 'str', 'repr']
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207
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208 # Allowed symbol table. Add more if needed.
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209 new_syms = {
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210 'np_arange': getattr(np, 'arange'),
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211 'ensemble_ExtraTreesClassifier': getattr(ensemble, 'ExtraTreesClassifier')
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212 }
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213
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214 syms = make_symbol_table(use_numpy=False, **new_syms)
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215
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216 if load_scipy:
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217 scipy_distributions = scipy.stats.distributions.__dict__
20
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218 for k, v in scipy_distributions.items():
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219 if isinstance(v, (scipy.stats.rv_continuous, scipy.stats.rv_discrete)):
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220 syms['scipy_stats_' + k] = v
19
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221
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222 if load_numpy:
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223 from_numpy_random = ['beta', 'binomial', 'bytes', 'chisquare', 'choice', 'dirichlet', 'division',
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224 'exponential', 'f', 'gamma', 'geometric', 'gumbel', 'hypergeometric',
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225 'laplace', 'logistic', 'lognormal', 'logseries', 'mtrand', 'multinomial',
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226 'multivariate_normal', 'negative_binomial', 'noncentral_chisquare', 'noncentral_f',
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227 'normal', 'pareto', 'permutation', 'poisson', 'power', 'rand', 'randint',
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228 'randn', 'random', 'random_integers', 'random_sample', 'ranf', 'rayleigh',
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229 'sample', 'seed', 'set_state', 'shuffle', 'standard_cauchy', 'standard_exponential',
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230 'standard_gamma', 'standard_normal', 'standard_t', 'triangular', 'uniform',
21
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231 'vonmises', 'wald', 'weibull', 'zipf']
19
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232 for f in from_numpy_random:
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233 syms['np_random_' + f] = getattr(np.random, f)
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234
23
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235 if load_estimators:
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236 estimator_table = {
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237 'sklearn_svm' : getattr(sklearn, 'svm'),
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238 'sklearn_tree' : getattr(sklearn, 'tree'),
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239 'sklearn_ensemble' : getattr(sklearn, 'ensemble'),
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240 'sklearn_neighbors' : getattr(sklearn, 'neighbors'),
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241 'sklearn_naive_bayes' : getattr(sklearn, 'naive_bayes'),
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242 'sklearn_linear_model' : getattr(sklearn, 'linear_model'),
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243 'sklearn_cluster' : getattr(sklearn, 'cluster'),
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244 'sklearn_decomposition' : getattr(sklearn, 'decomposition'),
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245 'sklearn_preprocessing' : getattr(sklearn, 'preprocessing'),
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246 'sklearn_feature_selection' : getattr(sklearn, 'feature_selection'),
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247 'sklearn_kernel_approximation' : getattr(sklearn, 'kernel_approximation'),
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248 'skrebate_ReliefF': getattr(skrebate, 'ReliefF'),
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249 'skrebate_SURF': getattr(skrebate, 'SURF'),
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250 'skrebate_SURFstar': getattr(skrebate, 'SURFstar'),
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251 'skrebate_MultiSURF': getattr(skrebate, 'MultiSURF'),
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252 'skrebate_MultiSURFstar': getattr(skrebate, 'MultiSURFstar'),
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253 'skrebate_TuRF': getattr(skrebate, 'TuRF'),
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254 'xgboost_XGBClassifier' : getattr(xgboost, 'XGBClassifier'),
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255 'xgboost_XGBRegressor' : getattr(xgboost, 'XGBRegressor')
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256 }
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257 syms.update(estimator_table)
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258
19
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259 for key in unwanted:
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260 syms.pop(key, None)
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261
21
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262 super(SafeEval, self).__init__(symtable=syms, use_numpy=False, minimal=False,
19
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263 no_if=True, no_for=True, no_while=True, no_try=True,
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264 no_functiondef=True, no_ifexp=True, no_listcomp=False,
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265 no_augassign=False, no_assert=True, no_delete=True,
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266 no_raise=True, no_print=True)
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267
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268
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269
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270 def get_estimator(estimator_json):
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271
19
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272 estimator_module = estimator_json['selected_module']
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273
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274 if estimator_module == 'customer_estimator':
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275 c_estimator = estimator_json['c_estimator']
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276 with open(c_estimator, 'rb') as model_handler:
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277 new_model = load_model(model_handler)
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278 return new_model
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279
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280 estimator_cls = estimator_json['selected_estimator']
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281
23
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282 if estimator_module == 'xgboost':
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283 cls = getattr(xgboost, estimator_cls)
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284 else:
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285 module = getattr(sklearn, estimator_module)
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286 cls = getattr(module, estimator_cls)
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287
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288 estimator = cls()
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289
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290 estimator_params = estimator_json['text_params'].strip()
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291 if estimator_params != '':
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292 try:
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293 params = safe_eval('dict(' + estimator_params + ')')
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294 except ValueError:
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295 sys.exit("Unsupported parameter input: `%s`" % estimator_params)
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296 estimator.set_params(**params)
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297 if 'n_jobs' in estimator.get_params():
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298 estimator.set_params(n_jobs=N_JOBS)
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299
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300 return estimator
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301
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302
23
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303 def get_cv(cv_json):
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304 """
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305 cv_json:
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306 e.g.:
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307 {
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308 'selected_cv': 'StratifiedKFold',
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309 'n_splits': 3,
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310 'shuffle': True,
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311 'random_state': 0
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312 }
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313 """
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314 cv = cv_json.pop('selected_cv')
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315 if cv == 'default':
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316 return cv_json['n_splits'], None
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317
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318 groups = cv_json.pop('groups', None)
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319 if groups:
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320 groups = groups.strip()
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321 if groups != '':
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322 if groups.startswith('__ob__'):
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323 groups = groups[6:]
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324 if groups.endswith('__cb__'):
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325 groups = groups[:-6]
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326 groups = [int(x.strip()) for x in groups.split(',')]
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327
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328 for k, v in cv_json.items():
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329 if v == '':
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330 cv_json[k] = None
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331
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332 test_fold = cv_json.get('test_fold', None)
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333 if test_fold:
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334 if test_fold.startswith('__ob__'):
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335 test_fold = test_fold[6:]
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336 if test_fold.endswith('__cb__'):
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337 test_fold = test_fold[:-6]
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338 cv_json['test_fold'] = [int(x.strip()) for x in test_fold.split(',')]
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339
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340 test_size = cv_json.get('test_size', None)
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341 if test_size and test_size > 1.0:
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342 cv_json['test_size'] = int(test_size)
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343
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344 cv_class = getattr(model_selection, cv)
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345 splitter = cv_class(**cv_json)
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346
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347 return splitter, groups
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348
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349
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350 # needed when sklearn < v0.20
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351 def balanced_accuracy_score(y_true, y_pred):
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352 C = metrics.confusion_matrix(y_true, y_pred)
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353 with np.errstate(divide='ignore', invalid='ignore'):
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354 per_class = np.diag(C) / C.sum(axis=1)
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355 if np.any(np.isnan(per_class)):
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356 warnings.warn('y_pred contains classes not in y_true')
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357 per_class = per_class[~np.isnan(per_class)]
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358 score = np.mean(per_class)
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359 return score
19
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360
4570575d060c planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d00173591e4a783a4c1cb2664e4bb192ab5414f7
bgruening
parents:
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361
4570575d060c planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d00173591e4a783a4c1cb2664e4bb192ab5414f7
bgruening
parents:
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362 def get_scoring(scoring_json):
4570575d060c planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d00173591e4a783a4c1cb2664e4bb192ab5414f7
bgruening
parents:
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363
23
39ae276e75d9 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 57f4407e278a615f47a377a3328782b1d8e0b54d
bgruening
parents: 21
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364 if scoring_json['primary_scoring'] == 'default':
19
4570575d060c planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d00173591e4a783a4c1cb2664e4bb192ab5414f7
bgruening
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365 return None
4570575d060c planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d00173591e4a783a4c1cb2664e4bb192ab5414f7
bgruening
parents:
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366
4570575d060c planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d00173591e4a783a4c1cb2664e4bb192ab5414f7
bgruening
parents:
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367 my_scorers = metrics.SCORERS
4570575d060c planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d00173591e4a783a4c1cb2664e4bb192ab5414f7
bgruening
parents:
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368 if 'balanced_accuracy' not in my_scorers:
4570575d060c planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d00173591e4a783a4c1cb2664e4bb192ab5414f7
bgruening
parents:
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369 my_scorers['balanced_accuracy'] = metrics.make_scorer(balanced_accuracy_score)
4570575d060c planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d00173591e4a783a4c1cb2664e4bb192ab5414f7
bgruening
parents:
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370
4570575d060c planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d00173591e4a783a4c1cb2664e4bb192ab5414f7
bgruening
parents:
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371 if scoring_json['secondary_scoring'] != 'None'\
4570575d060c planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d00173591e4a783a4c1cb2664e4bb192ab5414f7
bgruening
parents:
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372 and scoring_json['secondary_scoring'] != scoring_json['primary_scoring']:
4570575d060c planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d00173591e4a783a4c1cb2664e4bb192ab5414f7
bgruening
parents:
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373 scoring = {}
21
9ce3e347506c planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 2a058459e6daf0486871f93845f00fdb4a4eaca1
bgruening
parents: 20
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374 scoring['primary'] = my_scorers[scoring_json['primary_scoring']]
19
4570575d060c planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d00173591e4a783a4c1cb2664e4bb192ab5414f7
bgruening
parents:
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375 for scorer in scoring_json['secondary_scoring'].split(','):
4570575d060c planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d00173591e4a783a4c1cb2664e4bb192ab5414f7
bgruening
parents:
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376 if scorer != scoring_json['primary_scoring']:
4570575d060c planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d00173591e4a783a4c1cb2664e4bb192ab5414f7
bgruening
parents:
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377 scoring[scorer] = my_scorers[scorer]
4570575d060c planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d00173591e4a783a4c1cb2664e4bb192ab5414f7
bgruening
parents:
diff changeset
378 return scoring
4570575d060c planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d00173591e4a783a4c1cb2664e4bb192ab5414f7
bgruening
parents:
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379
21
9ce3e347506c planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 2a058459e6daf0486871f93845f00fdb4a4eaca1
bgruening
parents: 20
diff changeset
380 return my_scorers[scoring_json['primary_scoring']]