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annotate feature_selection.xml @ 2:2eb90e73f0d5 draft

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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 79fe42239dcf077b13f85cbcd6c6e30d7e1e4832
author bgruening
date Tue, 22 May 2018 19:31:59 -0400
parents 092199a095dd
children 3a1acc39b39b
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1 <tool id="sklearn_feature_selection" name="Feature Selection" version="@VERSION@.1">
0
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2 <description>module, including univariate filter selection methods and recursive feature elimination algorithm</description>
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3 <macros>
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4 <import>main_macros.xml</import>
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5 </macros>
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6 <expand macro="python_requirements"/>
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7 <expand macro="macro_stdio"/>
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8 <version_command>echo "@VERSION@"</version_command>
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9 <command>
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10 <![CDATA[
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11 python "$feature_selection_script" '$inputs'
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12 ]]>
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13 </command>
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14 <configfiles>
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15 <inputs name="inputs" />
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16 <configfile name="feature_selection_script">
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17 <![CDATA[
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18 import sys
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19 import json
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20 import pandas
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21 import pickle
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22 import numpy as np
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23 import sklearn.feature_selection
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24 from sklearn import svm, linear_model, ensemble
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25
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26 @COLUMNS_FUNCTION@
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27
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28 input_json_path = sys.argv[1]
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29 params = json.load(open(input_json_path, "r"))
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30
2
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31 ## Read features
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32 features_has_header = params["input_options"]["header1"]
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33 input_type = params["input_options"]["selected_input"]
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34 if input_type=="tabular":
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35 header = 'infer' if features_has_header else None
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36 header = 'infer' if params["input_options"]["header1"] else None
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37 X, input_df = read_columns(
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38 "$input_options.infile1",
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39 "$input_options.col1",
2
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40 return_df = True,
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41 sep='\t',
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42 header=header,
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43 parse_dates=True
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44 )
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45 else:
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46 X = mmread(open("$input_options.infile1", 'r'))
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47
2
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48 ## Read labels
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49 header = 'infer' if params["input_options"]["header2"] else None
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50 y = read_columns(
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51 "$input_options.infile2",
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52 "$input_options.col2",
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53 sep='\t',
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54 header=header,
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55 parse_dates=True
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56 )
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57 y=y.ravel()
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58
2
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59 ## Create feature selector
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60 selector = params["feature_selection_algorithms"]["selected_algorithm"]
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61 selector = getattr(sklearn.feature_selection, selector)
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62 options = params["feature_selection_algorithms"]["options"]
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63
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64 if params['feature_selection_algorithms']['selected_algorithm'] == 'SelectFromModel':
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65 if not options['threshold'] or options['threshold'] == 'None':
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66 options['threshold'] = None
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67 if 'extra_estimator' in params['feature_selection_algorithms'] and params['feature_selection_algorithms']['extra_estimator']['has_estimator'] == 'no_load':
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68 fitted_estimator = pickle.load(open("params['feature_selection_algorithms']['extra_estimator']['fitted_estimator']", 'r'))
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69 new_selector = selector(fitted_estimator, prefit=True, **options)
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70 else:
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71 estimator=params["feature_selection_algorithms"]["estimator"]
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72 if params["feature_selection_algorithms"]["extra_estimator"]["has_estimator"]=='no':
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73 estimator=params["feature_selection_algorithms"]["extra_estimator"]["new_estimator"]
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74 estimator=eval(estimator.replace('__dq__', '"').replace("__sq__","'"))
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75 new_selector = selector(estimator, **options)
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76 new_selector.fit(X, y)
0
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77
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78 elif params['feature_selection_algorithms']['selected_algorithm'] in ['RFE', 'RFECV']:
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79 if 'scoring' in options and (not options['scoring'] or options['scoring'] == 'None'):
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80 options['scoring'] = None
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81 estimator=params["feature_selection_algorithms"]["estimator"]
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82 if params["feature_selection_algorithms"]["extra_estimator"]["has_estimator"]=='no':
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83 estimator=params["feature_selection_algorithms"]["extra_estimator"]["new_estimator"]
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84 estimator=eval(estimator.replace('__dq__', '"').replace("__sq__","'"))
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85 new_selector = selector(estimator, **options)
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86 new_selector.fit(X, y)
0
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87
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88 elif params['feature_selection_algorithms']['selected_algorithm'] == "VarianceThreshold":
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89 new_selector = selector(**options)
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90 new_selector.fit(X, y)
0
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91
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92 else:
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93 score_func = params["feature_selection_algorithms"]["score_func"]
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94 score_func = getattr(sklearn.feature_selection, score_func)
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95 new_selector = selector(score_func, **options)
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96 new_selector.fit(X, y)
0
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97
2
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98 ## Transform to select features
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99 selected_names = None
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100 if "$select_methods.selected_method" == "fit_transform":
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101 res = new_selector.transform(X)
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102 if features_has_header:
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103 selected_names = input_df.columns[new_selector.get_support(indices=True)]
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104 else:
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105 res = new_selector.get_support(params["select_methods"]["indices"])
0
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106
2
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107 res = pandas.DataFrame(res, columns = selected_names)
0
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108 res.to_csv(path_or_buf="$outfile", sep='\t', index=False)
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109
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110
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111 ]]>
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112 </configfile>
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113 </configfiles>
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114 <inputs>
2
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115 <expand macro="feature_selection_all" />
0
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116 <expand macro="feature_selection_methods" />
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117 <expand macro="sl_mixed_input"/>
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118 </inputs>
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119 <outputs>
2
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120 <data format="tabular" name="outfile"/>
0
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121 </outputs>
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122 <tests>
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123 <test>
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124 <param name="selected_algorithm" value="SelectFromModel"/>
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125 <param name="has_estimator" value="no"/>
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126 <param name="new_estimator" value="ensemble.RandomForestRegressor(n_estimators = 1000, random_state = 42)"/>
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127 <param name="infile1" value="regression_X.tabular" ftype="tabular"/>
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128 <param name="header1" value="True"/>
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129 <param name="col1" value="1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17"/>
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130 <param name="infile2" value="regression_y.tabular" ftype="tabular"/>
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131 <param name="col2" value="1"/>
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132 <param name="header2" value="True"/>
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133 <output name="outfile" file="feature_selection_result01"/>
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134 </test>
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135 <test>
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136 <param name="selected_algorithm" value="GenericUnivariateSelect"/>
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137 <param name="param" value="20"/>
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138 <param name="infile1" value="regression_X.tabular" ftype="tabular"/>
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139 <param name="header1" value="True"/>
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140 <param name="col1" value="1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17"/>
092199a095dd planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 7a31960686122d7e53054fef4996525f04ebd254
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141 <param name="infile2" value="regression_y.tabular" ftype="tabular"/>
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142 <param name="col2" value="1"/>
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143 <param name="header2" value="True"/>
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144 <output name="outfile" file="feature_selection_result02"/>
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145 </test>
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146 <test>
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147 <param name="selected_algorithm" value="SelectPercentile"/>
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148 <param name="infile1" value="regression_X.tabular" ftype="tabular"/>
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149 <param name="header1" value="True"/>
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150 <param name="col1" value="1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17"/>
092199a095dd planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 7a31960686122d7e53054fef4996525f04ebd254
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151 <param name="infile2" value="regression_y.tabular" ftype="tabular"/>
092199a095dd planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 7a31960686122d7e53054fef4996525f04ebd254
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diff changeset
152 <param name="col2" value="1"/>
092199a095dd planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 7a31960686122d7e53054fef4996525f04ebd254
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153 <param name="header2" value="True"/>
092199a095dd planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 7a31960686122d7e53054fef4996525f04ebd254
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154 <output name="outfile" file="feature_selection_result03"/>
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155 </test>
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156 <test>
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157 <param name="selected_algorithm" value="SelectKBest"/>
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158 <param name="infile1" value="regression_X.tabular" ftype="tabular"/>
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159 <param name="header1" value="True"/>
092199a095dd planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 7a31960686122d7e53054fef4996525f04ebd254
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160 <param name="col1" value="1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17"/>
092199a095dd planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 7a31960686122d7e53054fef4996525f04ebd254
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161 <param name="infile2" value="regression_y.tabular" ftype="tabular"/>
092199a095dd planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 7a31960686122d7e53054fef4996525f04ebd254
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162 <param name="col2" value="1"/>
092199a095dd planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 7a31960686122d7e53054fef4996525f04ebd254
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163 <param name="header2" value="True"/>
092199a095dd planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 7a31960686122d7e53054fef4996525f04ebd254
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164 <output name="outfile" file="feature_selection_result04"/>
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165 </test>
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166 <test>
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167 <param name="selected_algorithm" value="SelectFpr"/>
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168 <param name="alpha" value="0.05"/>
092199a095dd planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 7a31960686122d7e53054fef4996525f04ebd254
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169 <param name="infile1" value="regression_X.tabular" ftype="tabular"/>
092199a095dd planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 7a31960686122d7e53054fef4996525f04ebd254
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170 <param name="header1" value="True"/>
092199a095dd planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 7a31960686122d7e53054fef4996525f04ebd254
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171 <param name="col1" value="1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17"/>
092199a095dd planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 7a31960686122d7e53054fef4996525f04ebd254
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172 <param name="infile2" value="regression_y.tabular" ftype="tabular"/>
092199a095dd planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 7a31960686122d7e53054fef4996525f04ebd254
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diff changeset
173 <param name="col2" value="1"/>
092199a095dd planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 7a31960686122d7e53054fef4996525f04ebd254
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diff changeset
174 <param name="header2" value="True"/>
092199a095dd planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 7a31960686122d7e53054fef4996525f04ebd254
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175 <output name="outfile" file="feature_selection_result05"/>
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176 </test>
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177 <test>
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178 <param name="selected_algorithm" value="SelectFdr"/>
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179 <param name="alpha" value="0.05"/>
092199a095dd planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 7a31960686122d7e53054fef4996525f04ebd254
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180 <param name="infile1" value="regression_X.tabular" ftype="tabular"/>
092199a095dd planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 7a31960686122d7e53054fef4996525f04ebd254
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181 <param name="header1" value="True"/>
092199a095dd planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 7a31960686122d7e53054fef4996525f04ebd254
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182 <param name="col1" value="1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17"/>
092199a095dd planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 7a31960686122d7e53054fef4996525f04ebd254
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183 <param name="infile2" value="regression_y.tabular" ftype="tabular"/>
092199a095dd planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 7a31960686122d7e53054fef4996525f04ebd254
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184 <param name="col2" value="1"/>
092199a095dd planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 7a31960686122d7e53054fef4996525f04ebd254
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185 <param name="header2" value="True"/>
092199a095dd planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 7a31960686122d7e53054fef4996525f04ebd254
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186 <output name="outfile" file="feature_selection_result06"/>
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187 </test>
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188 <test>
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189 <param name="selected_algorithm" value="SelectFwe"/>
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190 <param name="alpha" value="0.05"/>
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191 <param name="infile1" value="regression_X.tabular" ftype="tabular"/>
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192 <param name="header1" value="True"/>
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193 <param name="col1" value="1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17"/>
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194 <param name="infile2" value="regression_y.tabular" ftype="tabular"/>
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195 <param name="col2" value="1"/>
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196 <param name="header2" value="True"/>
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197 <output name="outfile" file="feature_selection_result07"/>
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198 </test>
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199 <test>
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200 <param name="selected_algorithm" value="RFE"/>
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201 <param name="has_estimator" value="no"/>
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202 <param name="new_estimator" value="ensemble.RandomForestRegressor(n_estimators = 1000, random_state = 42)"/>
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203 <param name="infile1" value="regression_X.tabular" ftype="tabular"/>
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204 <param name="header1" value="True"/>
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205 <param name="col1" value="1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17"/>
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206 <param name="infile2" value="regression_y.tabular" ftype="tabular"/>
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207 <param name="col2" value="1"/>
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208 <param name="header2" value="True"/>
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209 <output name="outfile" file="feature_selection_result08"/>
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210 </test>
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211 <test>
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212 <param name="selected_algorithm" value="RFECV"/>
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213 <param name="has_estimator" value="no"/>
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214 <param name="new_estimator" value="ensemble.RandomForestRegressor(n_estimators = 1000, random_state = 42)"/>
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215 <param name="infile1" value="regression_X.tabular" ftype="tabular"/>
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216 <param name="header1" value="True"/>
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217 <param name="col1" value="1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17"/>
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218 <param name="infile2" value="regression_y.tabular" ftype="tabular"/>
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219 <param name="col2" value="1"/>
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220 <param name="header2" value="True"/>
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221 <output name="outfile" file="feature_selection_result09"/>
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222 </test>
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223 <test>
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224 <param name="selected_algorithm" value="VarianceThreshold"/>
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225 <param name="threshold" value="0.1"/>
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226 <param name="infile1" value="regression_X.tabular" ftype="tabular"/>
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227 <param name="header1" value="True"/>
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228 <param name="col1" value="1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17"/>
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229 <param name="infile2" value="regression_y.tabular" ftype="tabular"/>
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230 <param name="col2" value="1"/>
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231 <param name="header2" value="True"/>
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232 <output name="outfile" file="feature_selection_result10"/>
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233 </test>
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234 </tests>
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235 <help>
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236 <![CDATA[
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237 **What it does**
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238 This tool provides several loss, score, and utility functions to measure classification performance. Some metrics might require probability estimates of the positive class, confidence values, or binary decisions values. This tool is based on
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239 sklearn.metrics package.
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240 For information about classification metric functions and their parameter settings please refer to `Scikit-learn classification metrics`_.
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241
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242 .. _`Scikit-learn classification metrics`: http://scikit-learn.org/stable/modules/model_evaluation.html#classification-metrics
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243 ]]>
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244 </help>
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245 <expand macro="sklearn_citation"/>
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246 </tool>