Mercurial > repos > bgruening > sklearn_nn_classifier

annotate utils.py @ 6:e972a913e61a draft

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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 8cf3d813ec755166ee0bd517b4ecbbd4f84d4df1
author bgruening
date Thu, 23 Aug 2018 16:15:30 -0400
parents 753ebd417b17
children 5072ac474cd5
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753ebd417b17 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d00173591e4a783a4c1cb2664e4bb192ab5414f7
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1 import sys
753ebd417b17 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d00173591e4a783a4c1cb2664e4bb192ab5414f7
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2 import os
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3 import pandas
753ebd417b17 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 cPickle as pickle
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6 import warnings
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7 import numpy as np
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8 import xgboost
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9 import scipy
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10 import sklearn
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11 import ast
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12 from asteval import Interpreter, make_symbol_table
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13 from sklearn import (cluster, decomposition, ensemble, feature_extraction, feature_selection,
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14 gaussian_process, kernel_approximation, linear_model, metrics,
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15 model_selection, naive_bayes, neighbors, pipeline, preprocessing,
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16 svm, linear_model, tree, discriminant_analysis)
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18 N_JOBS = int( os.environ.get('GALAXY_SLOTS', 1) )
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19
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20 class SafePickler(object):
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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 @classmethod
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27 def find_class(self, module, name):
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28
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29 bad_names = ('and', 'as', 'assert', 'break', 'class', 'continue',
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30 'def', 'del', 'elif', 'else', 'except', 'exec',
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31 'finally', 'for', 'from', 'global', 'if', 'import',
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32 'in', 'is', 'lambda', 'not', 'or', 'pass', 'print',
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33 'raise', 'return', 'try', 'system', 'while', 'with',
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34 'True', 'False', 'None', 'eval', 'execfile', '__import__',
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35 '__package__', '__subclasses__', '__bases__', '__globals__',
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36 '__code__', '__closure__', '__func__', '__self__', '__module__',
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37 '__dict__', '__class__', '__call__', '__get__',
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38 '__getattribute__', '__subclasshook__', '__new__',
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39 '__init__', 'func_globals', 'func_code', 'func_closure',
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40 'im_class', 'im_func', 'im_self', 'gi_code', 'gi_frame',
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41 '__asteval__', 'f_locals', '__mro__')
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42 good_names = ('copy_reg._reconstructor', '__builtin__.object')
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43
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44 if re.match(r'^[a-zA-Z_][a-zA-Z0-9_]*$', name):
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45 fullname = module + '.' + name
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46 if (fullname in good_names)\
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47 or ( ( module.startswith('sklearn.')
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48 or module.startswith('xgboost.')
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49 or module.startswith('skrebate.')
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50 or module.startswith('numpy.')
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51 or module == 'numpy'
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52 )
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53 and (name not in bad_names)
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54 ) :
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55 # TODO: replace with a whitelist checker
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56 if fullname not in SK_NAMES + SKR_NAMES + XGB_NAMES + NUMPY_NAMES + good_names:
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57 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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58 mod = sys.modules[module]
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59 return getattr(mod, name)
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60
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61 raise pickle.UnpicklingError("global '%s' is forbidden" % fullname)
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62
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63 @classmethod
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64 def load(self, file):
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65 obj = pickle.Unpickler(file)
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66 obj.find_global = self.find_class
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67 return obj.load()
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68
5
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69 def read_columns(f, c=None, c_option='by_index_number', return_df=False, **args):
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70 data = pandas.read_csv(f, **args)
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71 if c_option == 'by_index_number':
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72 cols = list(map(lambda x: x - 1, c))
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73 data = data.iloc[:,cols]
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74 if c_option == 'all_but_by_index_number':
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75 cols = list(map(lambda x: x - 1, c))
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76 data.drop(data.columns[cols], axis=1, inplace=True)
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77 if c_option == 'by_header_name':
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78 cols = [e.strip() for e in c.split(',')]
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79 data = data[cols]
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80 if c_option == 'all_but_by_header_name':
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81 cols = [e.strip() for e in c.split(',')]
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82 data.drop(cols, axis=1, inplace=True)
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83 y = data.values
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84 if return_df:
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85 return y, data
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86 else:
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87 return y
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88 return y
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89
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90
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91 ## generate an instance for one of sklearn.feature_selection classes
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92 def feature_selector(inputs):
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93 selector = inputs["selected_algorithm"]
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94 selector = getattr(sklearn.feature_selection, selector)
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95 options = inputs["options"]
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96
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97 if inputs['selected_algorithm'] == 'SelectFromModel':
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98 if not options['threshold'] or options['threshold'] == 'None':
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99 options['threshold'] = None
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100 if inputs['model_inputter']['input_mode'] == 'prefitted':
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101 model_file = inputs['model_inputter']['fitted_estimator']
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102 with open(model_file, 'rb') as model_handler:
6
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103 fitted_estimator = SafePickler.load(model_handler)
5
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104 new_selector = selector(fitted_estimator, prefit=True, **options)
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105 else:
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106 estimator_json = inputs['model_inputter']["estimator_selector"]
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107 estimator = get_estimator(estimator_json)
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108 new_selector = selector(estimator, **options)
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109
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110 elif inputs['selected_algorithm'] == 'RFE':
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111 estimator=get_estimator(inputs["estimator_selector"])
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112 new_selector = selector(estimator, **options)
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113
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114 elif inputs['selected_algorithm'] == 'RFECV':
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115 options['scoring'] = get_scoring(options['scoring'])
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116 options['n_jobs'] = N_JOBS
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117 options['cv'] = get_cv( options['cv'].strip() )
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118 estimator=get_estimator(inputs["estimator_selector"])
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119 new_selector = selector(estimator, **options)
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120
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121 elif inputs['selected_algorithm'] == "VarianceThreshold":
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122 new_selector = selector(**options)
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123
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124 else:
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125 score_func = inputs["score_func"]
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126 score_func = getattr(sklearn.feature_selection, score_func)
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127 new_selector = selector(score_func, **options)
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128
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129 return new_selector
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130
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131
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132 def get_X_y(params, file1, file2):
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133 input_type = params["selected_tasks"]["selected_algorithms"]["input_options"]["selected_input"]
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134 if input_type=="tabular":
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135 header = 'infer' if params["selected_tasks"]["selected_algorithms"]["input_options"]["header1"] else None
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136 column_option = params["selected_tasks"]["selected_algorithms"]["input_options"]["column_selector_options_1"]["selected_column_selector_option"]
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137 if column_option in ["by_index_number", "all_but_by_index_number", "by_header_name", "all_but_by_header_name"]:
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138 c = params["selected_tasks"]["selected_algorithms"]["input_options"]["column_selector_options_1"]["col1"]
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139 else:
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140 c = None
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141 X = read_columns(
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142 file1,
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143 c = c,
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144 c_option = column_option,
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145 sep='\t',
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146 header=header,
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147 parse_dates=True
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148 )
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149 else:
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150 X = mmread(file1)
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151
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152 header = 'infer' if params["selected_tasks"]["selected_algorithms"]["input_options"]["header2"] else None
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153 column_option = params["selected_tasks"]["selected_algorithms"]["input_options"]["column_selector_options_2"]["selected_column_selector_option2"]
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154 if column_option in ["by_index_number", "all_but_by_index_number", "by_header_name", "all_but_by_header_name"]:
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155 c = params["selected_tasks"]["selected_algorithms"]["input_options"]["column_selector_options_2"]["col2"]
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156 else:
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157 c = None
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158 y = read_columns(
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159 file2,
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160 c = c,
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161 c_option = column_option,
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162 sep='\t',
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163 header=header,
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164 parse_dates=True
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165 )
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166 y=y.ravel()
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167 return X, y
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168
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169
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170 class SafeEval(Interpreter):
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171
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172 def __init__(self, load_scipy=False, load_numpy=False):
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173
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174 # File opening and other unneeded functions could be dropped
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175 unwanted = ['open', 'type', 'dir', 'id', 'str', 'repr']
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176
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177 # Allowed symbol table. Add more if needed.
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178 new_syms = {
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179 'np_arange': getattr(np, 'arange'),
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180 'ensemble_ExtraTreesClassifier': getattr(ensemble, 'ExtraTreesClassifier')
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181 }
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182
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183 syms = make_symbol_table(use_numpy=False, **new_syms)
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184
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185 if load_scipy:
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186 scipy_distributions = scipy.stats.distributions.__dict__
6
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187 for k, v in scipy_distributions.items():
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188 if isinstance(v, (scipy.stats.rv_continuous, scipy.stats.rv_discrete)):
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189 syms['scipy_stats_' + k] = v
5
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190
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191 if load_numpy:
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192 from_numpy_random = ['beta', 'binomial', 'bytes', 'chisquare', 'choice', 'dirichlet', 'division',
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193 'exponential', 'f', 'gamma', 'geometric', 'gumbel', 'hypergeometric',
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194 'laplace', 'logistic', 'lognormal', 'logseries', 'mtrand', 'multinomial',
753ebd417b17 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d00173591e4a783a4c1cb2664e4bb192ab5414f7
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195 'multivariate_normal', 'negative_binomial', 'noncentral_chisquare', 'noncentral_f',
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196 'normal', 'pareto', 'permutation', 'poisson', 'power', 'rand', 'randint',
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197 'randn', 'random', 'random_integers', 'random_sample', 'ranf', 'rayleigh',
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198 'sample', 'seed', 'set_state', 'shuffle', 'standard_cauchy', 'standard_exponential',
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199 'standard_gamma', 'standard_normal', 'standard_t', 'triangular', 'uniform',
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200 'vonmises', 'wald', 'weibull', 'zipf' ]
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201 for f in from_numpy_random:
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202 syms['np_random_' + f] = getattr(np.random, f)
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203
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204 for key in unwanted:
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205 syms.pop(key, None)
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206
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207 super(SafeEval, self).__init__( symtable=syms, use_numpy=False, minimal=False,
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208 no_if=True, no_for=True, no_while=True, no_try=True,
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209 no_functiondef=True, no_ifexp=True, no_listcomp=False,
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210 no_augassign=False, no_assert=True, no_delete=True,
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211 no_raise=True, no_print=True)
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212
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213
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214 def get_search_params(params_builder):
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215 search_params = {}
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216 safe_eval = SafeEval(load_scipy=True, load_numpy=True)
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217
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218 for p in params_builder['param_set']:
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219 search_p = p['search_param_selector']['search_p']
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220 if search_p.strip() == '':
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221 continue
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222 param_type = p['search_param_selector']['selected_param_type']
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223
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224 lst = search_p.split(":")
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225 assert (len(lst) == 2), "Error, make sure there is one and only one colon in search parameter input."
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226 literal = lst[1].strip()
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227 ev = safe_eval(literal)
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228 if param_type == "final_estimator_p":
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229 search_params["estimator__" + lst[0].strip()] = ev
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230 else:
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231 search_params["preprocessing_" + param_type[5:6] + "__" + lst[0].strip()] = ev
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232
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233 return search_params
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234
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235
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236 def get_estimator(estimator_json):
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237 estimator_module = estimator_json['selected_module']
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238 estimator_cls = estimator_json['selected_estimator']
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239
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240 if estimator_module == "xgboost":
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241 cls = getattr(xgboost, estimator_cls)
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242 else:
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243 module = getattr(sklearn, estimator_module)
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244 cls = getattr(module, estimator_cls)
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245
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246 estimator = cls()
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247
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248 estimator_params = estimator_json['text_params'].strip()
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249 if estimator_params != "":
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250 try:
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251 params = safe_eval('dict(' + estimator_params + ')')
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252 except ValueError:
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253 sys.exit("Unsupported parameter input: `%s`" %estimator_params)
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254 estimator.set_params(**params)
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255 if 'n_jobs' in estimator.get_params():
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256 estimator.set_params( n_jobs=N_JOBS )
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257
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258 return estimator
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259
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260
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261 def get_cv(literal):
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262 safe_eval = SafeEval()
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263 if literal == "":
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264 return None
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265 if literal.isdigit():
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266 return int(literal)
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267 m = re.match(r'^(?P<method>\w+)\((?P<args>.*)\)$', literal)
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268 if m:
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269 my_class = getattr( model_selection, m.group('method') )
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270 args = safe_eval( 'dict('+ m.group('args') + ')' )
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271 return my_class( **args )
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272 sys.exit("Unsupported CV input: %s" %literal)
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273
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275 def get_scoring(scoring_json):
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276 def balanced_accuracy_score(y_true, y_pred):
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277 C = metrics.confusion_matrix(y_true, y_pred)
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278 with np.errstate(divide='ignore', invalid='ignore'):
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279 per_class = np.diag(C) / C.sum(axis=1)
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280 if np.any(np.isnan(per_class)):
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281 warnings.warn('y_pred contains classes not in y_true')
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282 per_class = per_class[~np.isnan(per_class)]
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283 score = np.mean(per_class)
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284 return score
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285
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286 if scoring_json['primary_scoring'] == "default":
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287 return None
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288
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289 my_scorers = metrics.SCORERS
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290 if 'balanced_accuracy' not in my_scorers:
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291 my_scorers['balanced_accuracy'] = metrics.make_scorer(balanced_accuracy_score)
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292
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293 if scoring_json['secondary_scoring'] != 'None'\
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294 and scoring_json['secondary_scoring'] != scoring_json['primary_scoring']:
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295 scoring = {}
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296 scoring['primary'] = my_scorers[ scoring_json['primary_scoring'] ]
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297 for scorer in scoring_json['secondary_scoring'].split(','):
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298 if scorer != scoring_json['primary_scoring']:
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299 scoring[scorer] = my_scorers[scorer]
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300 return scoring
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302 return my_scorers[ scoring_json['primary_scoring'] ]
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