Mercurial > repos > bgruening > sklearn_nn_classifier

annotate utils.py @ 5:753ebd417b17 draft

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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d00173591e4a783a4c1cb2664e4bb192ab5414f7
author bgruening
date Fri, 17 Aug 2018 12:27:46 -0400
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children e972a913e61a
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5
753ebd417b17 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d00173591e4a783a4c1cb2664e4bb192ab5414f7
bgruening
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diff changeset
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
753ebd417b17 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d00173591e4a783a4c1cb2664e4bb192ab5414f7
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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
753ebd417b17 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d00173591e4a783a4c1cb2664e4bb192ab5414f7
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5 import pickle
753ebd417b17 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d00173591e4a783a4c1cb2664e4bb192ab5414f7
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6 import warnings
753ebd417b17 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d00173591e4a783a4c1cb2664e4bb192ab5414f7
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7 import numpy as np
753ebd417b17 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d00173591e4a783a4c1cb2664e4bb192ab5414f7
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8 import xgboost
753ebd417b17 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d00173591e4a783a4c1cb2664e4bb192ab5414f7
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9 import scipy
753ebd417b17 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d00173591e4a783a4c1cb2664e4bb192ab5414f7
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10 import sklearn
753ebd417b17 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d00173591e4a783a4c1cb2664e4bb192ab5414f7
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11 import ast
753ebd417b17 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d00173591e4a783a4c1cb2664e4bb192ab5414f7
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12 from asteval import Interpreter, make_symbol_table
753ebd417b17 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d00173591e4a783a4c1cb2664e4bb192ab5414f7
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13 from sklearn import metrics, model_selection, ensemble, svm, linear_model, naive_bayes, tree, neighbors
753ebd417b17 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d00173591e4a783a4c1cb2664e4bb192ab5414f7
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14
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15 N_JOBS = int( os.environ.get('GALAXY_SLOTS', 1) )
753ebd417b17 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d00173591e4a783a4c1cb2664e4bb192ab5414f7
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16
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17 def read_columns(f, c=None, c_option='by_index_number', return_df=False, **args):
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18 data = pandas.read_csv(f, **args)
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19 if c_option == 'by_index_number':
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20 cols = list(map(lambda x: x - 1, c))
753ebd417b17 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d00173591e4a783a4c1cb2664e4bb192ab5414f7
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21 data = data.iloc[:,cols]
753ebd417b17 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d00173591e4a783a4c1cb2664e4bb192ab5414f7
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22 if c_option == 'all_but_by_index_number':
753ebd417b17 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d00173591e4a783a4c1cb2664e4bb192ab5414f7
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23 cols = list(map(lambda x: x - 1, c))
753ebd417b17 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d00173591e4a783a4c1cb2664e4bb192ab5414f7
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24 data.drop(data.columns[cols], axis=1, inplace=True)
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25 if c_option == 'by_header_name':
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26 cols = [e.strip() for e in c.split(',')]
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27 data = data[cols]
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28 if c_option == 'all_but_by_header_name':
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29 cols = [e.strip() for e in c.split(',')]
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30 data.drop(cols, axis=1, inplace=True)
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31 y = data.values
753ebd417b17 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d00173591e4a783a4c1cb2664e4bb192ab5414f7
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32 if return_df:
753ebd417b17 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d00173591e4a783a4c1cb2664e4bb192ab5414f7
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33 return y, data
753ebd417b17 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d00173591e4a783a4c1cb2664e4bb192ab5414f7
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34 else:
753ebd417b17 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d00173591e4a783a4c1cb2664e4bb192ab5414f7
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35 return y
753ebd417b17 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d00173591e4a783a4c1cb2664e4bb192ab5414f7
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36 return y
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37
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38
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39 ## generate an instance for one of sklearn.feature_selection classes
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40 def feature_selector(inputs):
753ebd417b17 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d00173591e4a783a4c1cb2664e4bb192ab5414f7
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41 selector = inputs["selected_algorithm"]
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42 selector = getattr(sklearn.feature_selection, selector)
753ebd417b17 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d00173591e4a783a4c1cb2664e4bb192ab5414f7
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43 options = inputs["options"]
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44
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45 if inputs['selected_algorithm'] == 'SelectFromModel':
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46 if not options['threshold'] or options['threshold'] == 'None':
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47 options['threshold'] = None
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48 if inputs['model_inputter']['input_mode'] == 'prefitted':
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49 model_file = inputs['model_inputter']['fitted_estimator']
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50 with open(model_file, 'rb') as model_handler:
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51 fitted_estimator = pickle.load(model_handler)
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52 new_selector = selector(fitted_estimator, prefit=True, **options)
753ebd417b17 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d00173591e4a783a4c1cb2664e4bb192ab5414f7
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53 else:
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54 estimator_json = inputs['model_inputter']["estimator_selector"]
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55 estimator = get_estimator(estimator_json)
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56 new_selector = selector(estimator, **options)
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57
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58 elif inputs['selected_algorithm'] == 'RFE':
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59 estimator=get_estimator(inputs["estimator_selector"])
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60 new_selector = selector(estimator, **options)
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61
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62 elif inputs['selected_algorithm'] == 'RFECV':
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63 options['scoring'] = get_scoring(options['scoring'])
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64 options['n_jobs'] = N_JOBS
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65 options['cv'] = get_cv( options['cv'].strip() )
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66 estimator=get_estimator(inputs["estimator_selector"])
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67 new_selector = selector(estimator, **options)
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68
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69 elif inputs['selected_algorithm'] == "VarianceThreshold":
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70 new_selector = selector(**options)
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71
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72 else:
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73 score_func = inputs["score_func"]
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74 score_func = getattr(sklearn.feature_selection, score_func)
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75 new_selector = selector(score_func, **options)
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76
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77 return new_selector
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78
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79
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80 def get_X_y(params, file1, file2):
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81 input_type = params["selected_tasks"]["selected_algorithms"]["input_options"]["selected_input"]
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82 if input_type=="tabular":
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83 header = 'infer' if params["selected_tasks"]["selected_algorithms"]["input_options"]["header1"] else None
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84 column_option = params["selected_tasks"]["selected_algorithms"]["input_options"]["column_selector_options_1"]["selected_column_selector_option"]
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85 if column_option in ["by_index_number", "all_but_by_index_number", "by_header_name", "all_but_by_header_name"]:
753ebd417b17 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d00173591e4a783a4c1cb2664e4bb192ab5414f7
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86 c = params["selected_tasks"]["selected_algorithms"]["input_options"]["column_selector_options_1"]["col1"]
753ebd417b17 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d00173591e4a783a4c1cb2664e4bb192ab5414f7
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87 else:
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88 c = None
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89 X = read_columns(
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90 file1,
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91 c = c,
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92 c_option = column_option,
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93 sep='\t',
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94 header=header,
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95 parse_dates=True
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96 )
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97 else:
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98 X = mmread(file1)
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99
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100 header = 'infer' if params["selected_tasks"]["selected_algorithms"]["input_options"]["header2"] else None
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101 column_option = params["selected_tasks"]["selected_algorithms"]["input_options"]["column_selector_options_2"]["selected_column_selector_option2"]
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102 if column_option in ["by_index_number", "all_but_by_index_number", "by_header_name", "all_but_by_header_name"]:
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103 c = params["selected_tasks"]["selected_algorithms"]["input_options"]["column_selector_options_2"]["col2"]
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104 else:
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105 c = None
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106 y = read_columns(
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107 file2,
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108 c = c,
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109 c_option = column_option,
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110 sep='\t',
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111 header=header,
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112 parse_dates=True
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113 )
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114 y=y.ravel()
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115 return X, y
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116
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117
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118 class SafeEval(Interpreter):
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119
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120 def __init__(self, load_scipy=False, load_numpy=False):
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121
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122 # File opening and other unneeded functions could be dropped
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123 unwanted = ['open', 'type', 'dir', 'id', 'str', 'repr']
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124
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125 # Allowed symbol table. Add more if needed.
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126 new_syms = {
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127 'np_arange': getattr(np, 'arange'),
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128 'ensemble_ExtraTreesClassifier': getattr(ensemble, 'ExtraTreesClassifier')
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129 }
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130
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131 syms = make_symbol_table(use_numpy=False, **new_syms)
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132
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133 if load_scipy:
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134 scipy_distributions = scipy.stats.distributions.__dict__
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135 for key in scipy_distributions.keys():
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136 if isinstance(scipy_distributions[key], (scipy.stats.rv_continuous, scipy.stats.rv_discrete)):
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137 syms['scipy_stats_' + key] = scipy_distributions[key]
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138
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139 if load_numpy:
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140 from_numpy_random = ['beta', 'binomial', 'bytes', 'chisquare', 'choice', 'dirichlet', 'division',
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141 'exponential', 'f', 'gamma', 'geometric', 'gumbel', 'hypergeometric',
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142 'laplace', 'logistic', 'lognormal', 'logseries', 'mtrand', 'multinomial',
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143 'multivariate_normal', 'negative_binomial', 'noncentral_chisquare', 'noncentral_f',
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144 'normal', 'pareto', 'permutation', 'poisson', 'power', 'rand', 'randint',
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145 'randn', 'random', 'random_integers', 'random_sample', 'ranf', 'rayleigh',
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146 'sample', 'seed', 'set_state', 'shuffle', 'standard_cauchy', 'standard_exponential',
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147 'standard_gamma', 'standard_normal', 'standard_t', 'triangular', 'uniform',
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148 'vonmises', 'wald', 'weibull', 'zipf' ]
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149 for f in from_numpy_random:
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150 syms['np_random_' + f] = getattr(np.random, f)
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151
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152 for key in unwanted:
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153 syms.pop(key, None)
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154
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155 super(SafeEval, self).__init__( symtable=syms, use_numpy=False, minimal=False,
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156 no_if=True, no_for=True, no_while=True, no_try=True,
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157 no_functiondef=True, no_ifexp=True, no_listcomp=False,
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158 no_augassign=False, no_assert=True, no_delete=True,
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159 no_raise=True, no_print=True)
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160
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161
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162 def get_search_params(params_builder):
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163 search_params = {}
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164 safe_eval = SafeEval(load_scipy=True, load_numpy=True)
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165
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166 for p in params_builder['param_set']:
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167 search_p = p['search_param_selector']['search_p']
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168 if search_p.strip() == '':
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169 continue
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170 param_type = p['search_param_selector']['selected_param_type']
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171
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172 lst = search_p.split(":")
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173 assert (len(lst) == 2), "Error, make sure there is one and only one colon in search parameter input."
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174 literal = lst[1].strip()
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175 ev = safe_eval(literal)
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176 if param_type == "final_estimator_p":
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177 search_params["estimator__" + lst[0].strip()] = ev
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178 else:
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179 search_params["preprocessing_" + param_type[5:6] + "__" + lst[0].strip()] = ev
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180
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181 return search_params
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182
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183
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184 def get_estimator(estimator_json):
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185 estimator_module = estimator_json['selected_module']
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186 estimator_cls = estimator_json['selected_estimator']
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187
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188 if estimator_module == "xgboost":
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189 cls = getattr(xgboost, estimator_cls)
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190 else:
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191 module = getattr(sklearn, estimator_module)
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192 cls = getattr(module, estimator_cls)
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193
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194 estimator = cls()
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195
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196 estimator_params = estimator_json['text_params'].strip()
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197 if estimator_params != "":
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198 try:
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199 params = safe_eval('dict(' + estimator_params + ')')
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200 except ValueError:
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201 sys.exit("Unsupported parameter input: `%s`" %estimator_params)
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202 estimator.set_params(**params)
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203 if 'n_jobs' in estimator.get_params():
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204 estimator.set_params( n_jobs=N_JOBS )
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205
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206 return estimator
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207
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208
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209 def get_cv(literal):
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210 safe_eval = SafeEval()
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211 if literal == "":
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212 return None
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213 if literal.isdigit():
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214 return int(literal)
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215 m = re.match(r'^(?P<method>\w+)\((?P<args>.*)\)$', literal)
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216 if m:
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217 my_class = getattr( model_selection, m.group('method') )
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218 args = safe_eval( 'dict('+ m.group('args') + ')' )
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219 return my_class( **args )
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220 sys.exit("Unsupported CV input: %s" %literal)
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221
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222
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223 def get_scoring(scoring_json):
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224 def balanced_accuracy_score(y_true, y_pred):
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225 C = metrics.confusion_matrix(y_true, y_pred)
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226 with np.errstate(divide='ignore', invalid='ignore'):
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227 per_class = np.diag(C) / C.sum(axis=1)
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228 if np.any(np.isnan(per_class)):
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229 warnings.warn('y_pred contains classes not in y_true')
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230 per_class = per_class[~np.isnan(per_class)]
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231 score = np.mean(per_class)
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232 return score
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233
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234 if scoring_json['primary_scoring'] == "default":
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235 return None
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236
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237 my_scorers = metrics.SCORERS
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238 if 'balanced_accuracy' not in my_scorers:
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239 my_scorers['balanced_accuracy'] = metrics.make_scorer(balanced_accuracy_score)
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240
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241 if scoring_json['secondary_scoring'] != 'None'\
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242 and scoring_json['secondary_scoring'] != scoring_json['primary_scoring']:
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243 scoring = {}
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244 scoring['primary'] = my_scorers[ scoring_json['primary_scoring'] ]
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245 for scorer in scoring_json['secondary_scoring'].split(','):
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246 if scorer != scoring_json['primary_scoring']:
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247 scoring[scorer] = my_scorers[scorer]
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248 return scoring
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249
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250 return my_scorers[ scoring_json['primary_scoring'] ]
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251