Mercurial > repos > bgruening > sklearn_numeric_clustering

annotate utils.py @ 27:a62c8c1f2ef7 draft

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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 2a058459e6daf0486871f93845f00fdb4a4eaca1
author bgruening
date Sat, 29 Sep 2018 07:18:42 -0400
parents 6bebd53612d4
children c156b85a6389
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64200dc3d769 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d00173591e4a783a4c1cb2664e4bb192ab5414f7
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1 import sys
64200dc3d769 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d00173591e4a783a4c1cb2664e4bb192ab5414f7
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2 import os
64200dc3d769 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d00173591e4a783a4c1cb2664e4bb192ab5414f7
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3 import pandas
64200dc3d769 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d00173591e4a783a4c1cb2664e4bb192ab5414f7
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4 import re
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5 import pickle
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6 import warnings
64200dc3d769 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d00173591e4a783a4c1cb2664e4bb192ab5414f7
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7 import numpy as np
64200dc3d769 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d00173591e4a783a4c1cb2664e4bb192ab5414f7
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8 import xgboost
64200dc3d769 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d00173591e4a783a4c1cb2664e4bb192ab5414f7
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9 import scipy
64200dc3d769 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d00173591e4a783a4c1cb2664e4bb192ab5414f7
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10 import sklearn
64200dc3d769 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d00173591e4a783a4c1cb2664e4bb192ab5414f7
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11 from asteval import Interpreter, make_symbol_table
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12 from sklearn import (cluster, decomposition, ensemble, feature_extraction, feature_selection,
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a62c8c1f2ef7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 2a058459e6daf0486871f93845f00fdb4a4eaca1
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13 gaussian_process, kernel_approximation, metrics,
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14 model_selection, naive_bayes, neighbors, pipeline, preprocessing,
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15 svm, linear_model, tree, discriminant_analysis)
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17 N_JOBS = int(os.environ.get('GALAXY_SLOTS', 1))
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a62c8c1f2ef7 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 2a058459e6daf0486871f93845f00fdb4a4eaca1
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20 class SafePickler(pickle.Unpickler):
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21 """
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22 Used to safely deserialize scikit-learn model objects serialized by cPickle.dump
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23 Usage:
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24 eg.: SafePickler.load(pickled_file_object)
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25 """
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26 def find_class(self, module, name):
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28 bad_names = ('and', 'as', 'assert', 'break', 'class', 'continue',
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29 'def', 'del', 'elif', 'else', 'except', 'exec',
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30 'finally', 'for', 'from', 'global', 'if', 'import',
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31 'in', 'is', 'lambda', 'not', 'or', 'pass', 'print',
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32 'raise', 'return', 'try', 'system', 'while', 'with',
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33 'True', 'False', 'None', 'eval', 'execfile', '__import__',
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34 '__package__', '__subclasses__', '__bases__', '__globals__',
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35 '__code__', '__closure__', '__func__', '__self__', '__module__',
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36 '__dict__', '__class__', '__call__', '__get__',
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37 '__getattribute__', '__subclasshook__', '__new__',
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38 '__init__', 'func_globals', 'func_code', 'func_closure',
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39 'im_class', 'im_func', 'im_self', 'gi_code', 'gi_frame',
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40 '__asteval__', 'f_locals', '__mro__')
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41 good_names = ['copy_reg._reconstructor', '__builtin__.object']
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42
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43 if re.match(r'^[a-zA-Z_][a-zA-Z0-9_]*$', name):
6bebd53612d4 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 8cf3d813ec755166ee0bd517b4ecbbd4f84d4df1
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44 fullname = module + '.' + name
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45 if (fullname in good_names)\
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46 or ( ( module.startswith('sklearn.')
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47 or module.startswith('xgboost.')
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48 or module.startswith('skrebate.')
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49 or module.startswith('numpy.')
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50 or module == 'numpy'
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51 )
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52 and (name not in bad_names)
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53 ):
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54 # TODO: replace with a whitelist checker
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55 if fullname not in sk_whitelist['SK_NAMES'] + sk_whitelist['SKR_NAMES'] + sk_whitelist['XGB_NAMES'] + sk_whitelist['NUMPY_NAMES'] + good_names:
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56 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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57 mod = sys.modules[module]
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58 return getattr(mod, name)
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59
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60 raise pickle.UnpicklingError("global '%s' is forbidden" % fullname)
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61
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62
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63 def load_model(file):
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64 return SafePickler(file).load()
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65
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66
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67 def read_columns(f, c=None, c_option='by_index_number', return_df=False, **args):
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68 data = pandas.read_csv(f, **args)
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69 if c_option == 'by_index_number':
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70 cols = list(map(lambda x: x - 1, c))
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71 data = data.iloc[:, cols]
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72 if c_option == 'all_but_by_index_number':
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73 cols = list(map(lambda x: x - 1, c))
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74 data.drop(data.columns[cols], axis=1, inplace=True)
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75 if c_option == 'by_header_name':
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76 cols = [e.strip() for e in c.split(',')]
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77 data = data[cols]
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78 if c_option == 'all_but_by_header_name':
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79 cols = [e.strip() for e in c.split(',')]
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80 data.drop(cols, axis=1, inplace=True)
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81 y = data.values
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82 if return_df:
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83 return y, data
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84 else:
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85 return y
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86 return y
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87
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88
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89 ## generate an instance for one of sklearn.feature_selection classes
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90 def feature_selector(inputs):
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91 selector = inputs["selected_algorithm"]
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92 selector = getattr(sklearn.feature_selection, selector)
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93 options = inputs["options"]
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94
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95 if inputs['selected_algorithm'] == 'SelectFromModel':
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96 if not options['threshold'] or options['threshold'] == 'None':
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97 options['threshold'] = None
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98 if inputs['model_inputter']['input_mode'] == 'prefitted':
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99 model_file = inputs['model_inputter']['fitted_estimator']
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100 with open(model_file, 'rb') as model_handler:
27
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101 fitted_estimator = load_model(model_handler)
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102 new_selector = selector(fitted_estimator, prefit=True, **options)
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103 else:
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104 estimator_json = inputs['model_inputter']["estimator_selector"]
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105 estimator = get_estimator(estimator_json)
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106 new_selector = selector(estimator, **options)
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107
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108 elif inputs['selected_algorithm'] == 'RFE':
27
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109 estimator = get_estimator(inputs["estimator_selector"])
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110 new_selector = selector(estimator, **options)
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111
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112 elif inputs['selected_algorithm'] == 'RFECV':
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113 options['scoring'] = get_scoring(options['scoring'])
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114 options['n_jobs'] = N_JOBS
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115 options['cv'] = get_cv(options['cv'].strip())
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116 estimator = get_estimator(inputs["estimator_selector"])
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117 new_selector = selector(estimator, **options)
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118
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119 elif inputs['selected_algorithm'] == "VarianceThreshold":
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120 new_selector = selector(**options)
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121
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122 else:
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123 score_func = inputs["score_func"]
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124 score_func = getattr(sklearn.feature_selection, score_func)
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125 new_selector = selector(score_func, **options)
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126
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127 return new_selector
27
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128
25
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129
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130 def get_X_y(params, file1, file2):
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131 input_type = params["selected_tasks"]["selected_algorithms"]["input_options"]["selected_input"]
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132 if input_type == "tabular":
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133 header = 'infer' if params["selected_tasks"]["selected_algorithms"]["input_options"]["header1"] else None
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134 column_option = params["selected_tasks"]["selected_algorithms"]["input_options"]["column_selector_options_1"]["selected_column_selector_option"]
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135 if column_option in ["by_index_number", "all_but_by_index_number", "by_header_name", "all_but_by_header_name"]:
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136 c = params["selected_tasks"]["selected_algorithms"]["input_options"]["column_selector_options_1"]["col1"]
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137 else:
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138 c = None
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139 X = read_columns(
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140 file1,
27
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141 c=c,
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142 c_option=column_option,
25
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143 sep='\t',
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144 header=header,
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145 parse_dates=True
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146 )
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147 else:
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148 X = mmread(file1)
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149
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150 header = 'infer' if params["selected_tasks"]["selected_algorithms"]["input_options"]["header2"] else None
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151 column_option = params["selected_tasks"]["selected_algorithms"]["input_options"]["column_selector_options_2"]["selected_column_selector_option2"]
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152 if column_option in ["by_index_number", "all_but_by_index_number", "by_header_name", "all_but_by_header_name"]:
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153 c = params["selected_tasks"]["selected_algorithms"]["input_options"]["column_selector_options_2"]["col2"]
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154 else:
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155 c = None
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156 y = read_columns(
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157 file2,
27
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158 c=c,
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159 c_option=column_option,
25
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160 sep='\t',
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161 header=header,
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162 parse_dates=True
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163 )
27
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164 y = y.ravel()
25
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165 return X, y
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166
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167
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168 class SafeEval(Interpreter):
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169
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170 def __init__(self, load_scipy=False, load_numpy=False):
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171
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172 # File opening and other unneeded functions could be dropped
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173 unwanted = ['open', 'type', 'dir', 'id', 'str', 'repr']
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174
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175 # Allowed symbol table. Add more if needed.
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176 new_syms = {
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177 'np_arange': getattr(np, 'arange'),
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178 'ensemble_ExtraTreesClassifier': getattr(ensemble, 'ExtraTreesClassifier')
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179 }
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180
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181 syms = make_symbol_table(use_numpy=False, **new_syms)
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182
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183 if load_scipy:
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184 scipy_distributions = scipy.stats.distributions.__dict__
26
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185 for k, v in scipy_distributions.items():
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186 if isinstance(v, (scipy.stats.rv_continuous, scipy.stats.rv_discrete)):
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187 syms['scipy_stats_' + k] = v
25
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188
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189 if load_numpy:
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190 from_numpy_random = ['beta', 'binomial', 'bytes', 'chisquare', 'choice', 'dirichlet', 'division',
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191 'exponential', 'f', 'gamma', 'geometric', 'gumbel', 'hypergeometric',
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192 'laplace', 'logistic', 'lognormal', 'logseries', 'mtrand', 'multinomial',
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193 'multivariate_normal', 'negative_binomial', 'noncentral_chisquare', 'noncentral_f',
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194 'normal', 'pareto', 'permutation', 'poisson', 'power', 'rand', 'randint',
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195 'randn', 'random', 'random_integers', 'random_sample', 'ranf', 'rayleigh',
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196 'sample', 'seed', 'set_state', 'shuffle', 'standard_cauchy', 'standard_exponential',
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197 'standard_gamma', 'standard_normal', 'standard_t', 'triangular', 'uniform',
27
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198 'vonmises', 'wald', 'weibull', 'zipf']
25
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199 for f in from_numpy_random:
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200 syms['np_random_' + f] = getattr(np.random, f)
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201
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202 for key in unwanted:
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203 syms.pop(key, None)
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204
27
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205 super(SafeEval, self).__init__(symtable=syms, use_numpy=False, minimal=False,
25
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206 no_if=True, no_for=True, no_while=True, no_try=True,
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207 no_functiondef=True, no_ifexp=True, no_listcomp=False,
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208 no_augassign=False, no_assert=True, no_delete=True,
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209 no_raise=True, no_print=True)
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210
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211
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212 def get_search_params(params_builder):
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213 search_params = {}
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214 safe_eval = SafeEval(load_scipy=True, load_numpy=True)
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215
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216 for p in params_builder['param_set']:
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217 search_p = p['search_param_selector']['search_p']
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218 if search_p.strip() == '':
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219 continue
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220 param_type = p['search_param_selector']['selected_param_type']
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221
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222 lst = search_p.split(":")
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223 assert (len(lst) == 2), "Error, make sure there is one and only one colon in search parameter input."
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224 literal = lst[1].strip()
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225 ev = safe_eval(literal)
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226 if param_type == "final_estimator_p":
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227 search_params["estimator__" + lst[0].strip()] = ev
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228 else:
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229 search_params["preprocessing_" + param_type[5:6] + "__" + lst[0].strip()] = ev
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230
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231 return search_params
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232
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233
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234 def get_estimator(estimator_json):
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235 estimator_module = estimator_json['selected_module']
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236 estimator_cls = estimator_json['selected_estimator']
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237
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238 if estimator_module == "xgboost":
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239 cls = getattr(xgboost, estimator_cls)
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240 else:
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241 module = getattr(sklearn, estimator_module)
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242 cls = getattr(module, estimator_cls)
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243
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244 estimator = cls()
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245
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246 estimator_params = estimator_json['text_params'].strip()
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247 if estimator_params != "":
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248 try:
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249 params = safe_eval('dict(' + estimator_params + ')')
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250 except ValueError:
27
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diff changeset
251 sys.exit("Unsupported parameter input: `%s`" % estimator_params)
25
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252 estimator.set_params(**params)
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253 if 'n_jobs' in estimator.get_params():
27
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254 estimator.set_params(n_jobs=N_JOBS)
25
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255
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256 return estimator
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257
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258
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259 def get_cv(literal):
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260 safe_eval = SafeEval()
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261 if literal == "":
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262 return None
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263 if literal.isdigit():
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264 return int(literal)
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265 m = re.match(r'^(?P<method>\w+)\((?P<args>.*)\)$', literal)
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266 if m:
27
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267 my_class = getattr(model_selection, m.group('method'))
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268 args = safe_eval('dict('+ m.group('args') + ')')
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269 return my_class(**args)
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270 sys.exit("Unsupported CV input: %s" % literal)
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271
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272
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273 def get_scoring(scoring_json):
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274 def balanced_accuracy_score(y_true, y_pred):
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275 C = metrics.confusion_matrix(y_true, y_pred)
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276 with np.errstate(divide='ignore', invalid='ignore'):
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277 per_class = np.diag(C) / C.sum(axis=1)
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278 if np.any(np.isnan(per_class)):
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279 warnings.warn('y_pred contains classes not in y_true')
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280 per_class = per_class[~np.isnan(per_class)]
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281 score = np.mean(per_class)
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282 return score
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283
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284 if scoring_json['primary_scoring'] == "default":
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285 return None
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286
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287 my_scorers = metrics.SCORERS
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288 if 'balanced_accuracy' not in my_scorers:
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289 my_scorers['balanced_accuracy'] = metrics.make_scorer(balanced_accuracy_score)
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290
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291 if scoring_json['secondary_scoring'] != 'None'\
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292 and scoring_json['secondary_scoring'] != scoring_json['primary_scoring']:
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293 scoring = {}
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294 scoring['primary'] = my_scorers[scoring_json['primary_scoring']]
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295 for scorer in scoring_json['secondary_scoring'].split(','):
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296 if scorer != scoring_json['primary_scoring']:
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297 scoring[scorer] = my_scorers[scorer]
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298 return scoring
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299
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300 return my_scorers[scoring_json['primary_scoring']]