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comparison model_validation.xml @ 17:cf9aa11b91c8 draft

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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
author bgruening
date Wed, 15 May 2019 07:42:07 -0400
parents 86e1e2874460
children efbec977a47d
comparison
equal deleted inserted replaced
16:86e1e2874460 17:cf9aa11b91c8
13 </command> 13 </command>
14 <configfiles> 14 <configfiles>
15 <inputs name="inputs" /> 15 <inputs name="inputs" />
16 <configfile name="sklearn_model_validation_script"> 16 <configfile name="sklearn_model_validation_script">
17 <![CDATA[ 17 <![CDATA[
18 import imblearn
19 import json
20 import numpy as np
21 import pandas as pd
22 import pickle
23 import pprint
24 import skrebate
18 import sys 25 import sys
19 import os 26 import warnings
20 import json 27 import xgboost
21 import pandas 28 from mlxtend import classifier, regressor
22 import numpy as np 29 from sklearn import (
23 from sklearn import preprocessing, model_selection, svm, linear_model, ensemble, naive_bayes, tree, neighbors 30 cluster, compose, decomposition, ensemble, feature_extraction,
24 from sklearn.pipeline import Pipeline 31 feature_selection, gaussian_process, kernel_approximation, metrics,
25 32 model_selection, naive_bayes, neighbors, pipeline, preprocessing,
26 exec(open('$__tool_directory__/utils.py').read(), globals()) 33 svm, linear_model, tree, discriminant_analysis)
34
35 sys.path.insert(0, '$__tool_directory__')
36 from utils import SafeEval, get_cv, get_scoring, load_model, read_columns
37
38 N_JOBS = int(__import__('os').environ.get('GALAXY_SLOTS', 1))
27 39
28 warnings.filterwarnings('ignore') 40 warnings.filterwarnings('ignore')
29 41
30 safe_eval = SafeEval() 42 safe_eval = SafeEval()
31 43
32 input_json_path = sys.argv[1] 44 input_json_path = sys.argv[1]
33 with open(input_json_path, 'r') as param_handler: 45 with open(input_json_path, 'r') as param_handler:
34 params = json.load(param_handler) 46 params = json.load(param_handler)
47
48 #if $model_validation_functions.options.cv_selector.selected_cv\
49 in ['GroupKFold', 'GroupShuffleSplit', 'LeaveOneGroupOut', 'LeavePGroupsOut']:
50 params['model_validation_functions']['options']['cv_selector']['groups_selector']['infile_g'] =\
51 '$model_validation_functions.options.cv_selector.groups_selector.infile_g'
52 #end if
35 53
36 input_type = params['input_options']['selected_input'] 54 input_type = params['input_options']['selected_input']
37 if input_type == 'tabular': 55 if input_type == 'tabular':
38 header = 'infer' if params['input_options']['header1'] else None 56 header = 'infer' if params['input_options']['header1'] else None
39 column_option = params['input_options']['column_selector_options_1']['selected_column_selector_option'] 57 column_option = params['input_options']['column_selector_options_1']['selected_column_selector_option']
45 '$input_options.infile1', 63 '$input_options.infile1',
46 c = c, 64 c = c,
47 c_option = column_option, 65 c_option = column_option,
48 sep='\t', 66 sep='\t',
49 header=header, 67 header=header,
50 parse_dates=True 68 parse_dates=True).astype(float)
51 )
52 else: 69 else:
53 X = mmread('$input_options.infile1') 70 X = mmread('$input_options.infile1')
54 71
55 header = 'infer' if params['input_options']['header2'] else None 72 header = 'infer' if params['input_options']['header2'] else None
56 column_option = params['input_options']['column_selector_options_2']['selected_column_selector_option2'] 73 column_option = params['input_options']['column_selector_options_2']['selected_column_selector_option2']
62 '$input_options.infile2', 79 '$input_options.infile2',
63 c = c, 80 c = c,
64 c_option = column_option, 81 c_option = column_option,
65 sep='\t', 82 sep='\t',
66 header=header, 83 header=header,
67 parse_dates=True 84 parse_dates=True)
68 ) 85 y = y.ravel()
69 y=y.ravel() 86
70 87 ## handle options
71 options = params['model_validation_functions']['options'] 88 options = params['model_validation_functions']['options']
72 splitter, groups = get_cv( options.pop('cv_selector') ) 89 splitter, groups = get_cv( options.pop('cv_selector') )
73 if groups is None: 90 options['cv'] = splitter
74 options['cv'] = splitter 91 options['groups'] = groups
75 elif groups == '':
76 options['cv'] = list( splitter.split(X, y, groups=None) )
77 else:
78 options['cv'] = list( splitter.split(X, y, groups=groups) )
79 options['n_jobs'] = N_JOBS 92 options['n_jobs'] = N_JOBS
80 if 'scoring' in options: 93 if 'scoring' in options:
94 primary_scoring = options['scoring']['primary_scoring']
81 options['scoring'] = get_scoring(options['scoring']) 95 options['scoring'] = get_scoring(options['scoring'])
82 if 'pre_dispatch' in options and options['pre_dispatch'] == '': 96 if 'pre_dispatch' in options and options['pre_dispatch'] == '':
83 options['pre_dispatch'] = None 97 options['pre_dispatch'] = None
84 98
85 pipeline_steps = [] 99 ## load pipeline
86 100 with open('$infile_pipeline', 'rb') as pipeline_handler:
87 ## Set up pre_processor and add to pipeline steps. 101 pipeline = load_model(pipeline_handler)
88 if params['pre_processing']['do_pre_processing'] == 'Yes':
89 preprocessor = params['pre_processing']['pre_processors']['selected_pre_processor']
90 pre_processor_options = params['pre_processing']['pre_processors']['options']
91 my_class = getattr(preprocessing, preprocessor)
92 pipeline_steps.append( ('pre_processor', my_class(**pre_processor_options)) )
93
94 ## Set up feature selector and add to pipeline steps.
95 if params['feature_selection']['do_feature_selection'] == 'Yes':
96 feature_selector = feature_selector(params['feature_selection']['fs_algorithm_selector'])
97 pipeline_steps.append( ('feature_selector', feature_selector) )
98
99 ## Set up estimator and add to pipeline.
100 estimator_json = params['model_validation_functions']['estimator_selector']
101 estimator = get_estimator(estimator_json)
102
103 pipeline_steps.append( ('estimator', estimator) )
104
105 pipeline = Pipeline(pipeline_steps)
106 102
107 ## Set up validator, run pipeline through validator and return results. 103 ## Set up validator, run pipeline through validator and return results.
108 104
109 validator = params['model_validation_functions']['selected_function'] 105 validator = params['model_validation_functions']['selected_function']
110 validator = getattr(model_selection, validator) 106 validator = getattr(model_selection, validator)
111 107
112 selected_function = params['model_validation_functions']['selected_function'] 108 selected_function = params['model_validation_functions']['selected_function']
113 rval_type = params['model_validation_functions'].get('return_type', None)
114 109
115 if selected_function == 'cross_validate': 110 if selected_function == 'cross_validate':
116 res = validator(pipeline, X, y, **options) 111 res = validator(pipeline, X, y, **options)
117 rval = res[rval_type] 112 rval = pd.DataFrame(res)
113 col_rename = {}
114 for col in rval.columns:
115 if col.endswith('_primary'):
116 col_rename[col] = col[:-7] + primary_scoring
117 rval.rename(inplace=True, columns=col_rename)
118 elif selected_function == 'cross_val_predict':
119 predicted = validator(pipeline, X, y, **options)
120 if len(predicted.shape) == 1:
121 rval = pd.DataFrame(predicted, columns=['Predicted'])
122 else:
123 rval = pd.DataFrame(predicted)
118 elif selected_function == 'learning_curve': 124 elif selected_function == 'learning_curve':
119 options['train_sizes'] = eval(options['train_sizes']) 125 try:
126 train_sizes = safe_eval(options['train_sizes'])
127 except:
128 sys.exit("Unsupported train_sizes input! Supports int/float in tuple and array-like structure.")
129 if type(train_sizes) is tuple:
130 train_sizes = np.linspace(*train_sizes)
131 options['train_sizes'] = train_sizes
120 train_sizes_abs, train_scores, test_scores = validator(pipeline, X, y, **options) 132 train_sizes_abs, train_scores, test_scores = validator(pipeline, X, y, **options)
121 rval = eval(rval_type) 133 rval = pd.DataFrame(dict(
134 train_sizes_abs = train_sizes_abs,
135 mean_train_scores = np.mean(train_scores, axis=1),
136 std_train_scores = np.std(train_scores, axis=1),
137 mean_test_scores = np.mean(test_scores, axis=1),
138 std_test_scores = np.std(test_scores, axis=1)))
139 rval = rval[['train_sizes_abs', 'mean_train_scores', 'std_train_scores',
140 'mean_test_scores', 'std_test_scores']]
122 elif selected_function == 'permutation_test_score': 141 elif selected_function == 'permutation_test_score':
123 score, permutation_scores, pvalue = validator(pipeline, X, y, **options) 142 score, permutation_scores, pvalue = validator(pipeline, X, y, **options)
124 rval = eval(rval_type) 143 permutation_scores_df = pd.DataFrame(dict(
125 if rval_type in ['score', 'pvalue']: 144 permutation_scores = permutation_scores))
126 rval = [rval] 145 score_df = pd.DataFrame(dict(
127 elif selected_function == 'validation_curve': 146 score = [score],
128 options['param_name'] = 'estimator__' + options['param_name'] 147 pvalue = [pvalue]))
129 options['param_range'] = eval(options['param_range']) 148 rval = pd.concat([score_df[['score', 'pvalue']], permutation_scores_df], axis=1)
130 train_scores, test_scores = validator(pipeline, X, y, **options) 149
131 rval = eval(rval_type) 150 rval.to_csv(path_or_buf='$outfile', sep='\t', header=True, index=False)
132 else:
133 rval = validator(pipeline, X, y, **options)
134
135 rval = pandas.DataFrame(rval)
136 rval.to_csv(path_or_buf='$outfile', sep='\t', header=False, index=False)
137 151
138 ]]> 152 ]]>
139 </configfile> 153 </configfile>
140 </configfiles> 154 </configfiles>
141 <inputs> 155 <inputs>
142 <conditional name="pre_processing"> 156 <param name="infile_pipeline" type="data" format="zip" label="Choose the dataset containing model/pipeline object"/>
143 <param name="do_pre_processing" type="select" label="Do pre_processing?">
144 <option value="No" selected="true"/>
145 <option value="Yes"/>
146 </param>
147 <when value="No"/>
148 <when value="Yes">
149 <conditional name="pre_processors">
150 <expand macro="sparse_preprocessors_ext" />
151 <expand macro="sparse_preprocessor_options_ext" />
152 </conditional>
153 </when>
154 </conditional>
155 <conditional name="feature_selection">
156 <param name="do_feature_selection" type="select" label="Do feature selection?">
157 <option value="No" selected="true"/>
158 <option value="Yes"/>
159 </param>
160 <when value="No"/>
161 <when value="Yes">
162 <expand macro="feature_selection_pipeline"/>
163 </when>
164 </conditional>
165 <conditional name="model_validation_functions"> 157 <conditional name="model_validation_functions">
166 <param name="selected_function" type="select" label="Select a model validation function"> 158 <param name="selected_function" type="select" label="Select a model validation function">
167 <option value="cross_validate">cross_validate - Evaluate metric(s) by cross-validation and also record fit/score times</option> 159 <option value="cross_validate">cross_validate - Evaluate metric(s) by cross-validation and also record fit/score times</option>
168 <option value="cross_val_predict">cross_val_predict - Generate cross-validated estimates for each input data point</option> 160 <option value="cross_val_predict">cross_val_predict - Generate cross-validated estimates for each input data point</option>
169 <option value="cross_val_score">cross_val_score - Evaluate a score by cross-validation</option>
170 <option value="learning_curve">learning_curve - Learning curve</option> 161 <option value="learning_curve">learning_curve - Learning curve</option>
171 <option value="permutation_test_score">permutation_test_score - Evaluate the significance of a cross-validated score with permutations</option> 162 <option value="permutation_test_score">permutation_test_score - Evaluate the significance of a cross-validated score with permutations</option>
172 <option value="validation_curve">validation_curve - Validation curve</option> 163 <option value="validation_curve">validation_curve - Use grid search with one parameter instead</option>
173 </param> 164 </param>
174 <when value="cross_validate"> 165 <when value="cross_validate">
175 <expand macro="estimator_selector_all" />
176 <section name="options" title="Other Options" expanded="false"> 166 <section name="options" title="Other Options" expanded="false">
177 <!--groups--> 167 <expand macro="scoring_selection"/>
178 <expand macro="model_validation_common_options"/> 168 <expand macro="model_validation_common_options"/>
179 <expand macro="scoring_selection"/> 169 <!--param argument="return_train_score" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="true" help="Whether to include train scores."/> -->
170 <!--param argument="return_estimator" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="false" help="Whether to return the estimators fitted on each split."/> -->
171 <!--param argument="error_score" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="true" label="Raise fit error:" help="If false, the metric score is assigned to NaN if an error occurs in estimator fitting and FitFailedWarning is raised."/> -->
180 <!--fit_params--> 172 <!--fit_params-->
181 <expand macro="pre_dispatch"/> 173 <expand macro="pre_dispatch"/>
182 </section> 174 </section>
183 <param name="return_type" type="select" label="Select a return type">
184 <option value="test_score" selected="true">test_score</option>
185 <option value="train_score">train_score</option>
186 <option value="fit_time">fit_time</option>
187 <option value="score_time">score_time</option>
188 </param>
189 </when> 175 </when>
190 <when value="cross_val_predict"> 176 <when value="cross_val_predict">
191 <expand macro="estimator_selector_all" />
192 <section name="options" title="Other Options" expanded="false"> 177 <section name="options" title="Other Options" expanded="false">
193 <!--groups-->
194 <expand macro="model_validation_common_options" /> 178 <expand macro="model_validation_common_options" />
195 <!--fit_params--> 179 <!--fit_params-->
196 <expand macro="pre_dispatch" value="2*n_jobs’" help="Controls the number of jobs that get dispatched during parallel execution"/> 180 <expand macro="pre_dispatch" value="2*n_jobs’" help="Controls the number of jobs that get dispatched during parallel execution"/>
197 <param argument="method" type="select" label="Invokes the passed method name of the passed estimator"> 181 <param argument="method" type="select" label="Invokes the passed method name of the passed estimator">
198 <option value="predict" selected="true">predict</option> 182 <option value="predict" selected="true">predict</option>
199 <option value="predict_proba">predict_proba</option> 183 <option value="predict_proba">predict_proba</option>
200 </param> 184 </param>
201 </section> 185 </section>
202 </when> 186 </when>
203 <when value="cross_val_score"> 187 <when value="learning_curve">
204 <expand macro="estimator_selector_all" />
205 <section name="options" title="Other Options" expanded="false"> 188 <section name="options" title="Other Options" expanded="false">
206 <!--groups--> 189 <expand macro="scoring_selection"/>
207 <expand macro="model_validation_common_options"/> 190 <expand macro="model_validation_common_options"/>
208 <expand macro="scoring_selection"/> 191 <param argument="train_sizes" type="text" value="(0.1, 1.0, 5)" label="train_sizes"
209 <!--fit_params--> 192 help="Relative or absolute numbers of training examples that will be used to generate the learning curve. Supports 1) tuple, to be evaled by np.linspace, e.g. (0.1, 1.0, 5); 2) array-like, e.g. [0.1 , 0.325, 0.55 , 0.775, 1.]">
193 <sanitizer>
194 <valid initial="default">
195 <add value="["/>
196 <add value="]"/>
197 </valid>
198 </sanitizer>
199 </param>
200 <param argument="exploit_incremental_learning" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="false" help="Whether to apply incremental learning to speed up fitting of the estimator if supported"/>
210 <expand macro="pre_dispatch"/> 201 <expand macro="pre_dispatch"/>
202 <expand macro="shuffle" checked="false" label="shuffle" help="Whether to shuffle training data before taking prefixes"/>
203 <expand macro="random_state" help_text="If int, the seed used by the random number generator. Used when `shuffle` is True"/>
211 </section> 204 </section>
212 </when> 205 </when>
213 <when value="learning_curve"> 206 <when value="permutation_test_score">
214 <expand macro="estimator_selector_all" />
215 <section name="options" title="Other Options" expanded="false"> 207 <section name="options" title="Other Options" expanded="false">
216 <!--groups--> 208 <expand macro="scoring_selection"/>
217 <expand macro="model_validation_common_options"/> 209 <expand macro="model_validation_common_options"/>
218 <param argument="train_sizes" type="text" value="np.linspace(0.1, 1.0, 5)" label="train_sizes" help="Relative or absolute numbers of training examples that will be used to generate the learning curve"/>
219 <expand macro="scoring_selection"/>
220 <param argument="exploit_incremental_learning" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="false" label="exploit_incremental_learning" help="Whether to apply incremental learning to speed up fitting of the estimator if supported"/>
221 <expand macro="pre_dispatch"/>
222 <expand macro="shuffle" checked="false" label="shuffle" help="Whether to shuffle training data before taking prefixes"/>
223 <expand macro="random_state"/>
224 </section>
225 <param name="return_type" type="select" label="Select a return type">
226 <option value="train_sizes_abs" selected="true">train_sizes_abs</option>
227 <option value="train_scores">train_scores</option>
228 <option value="test_scores">test_scores</option>
229 </param>
230 </when>
231 <when value="permutation_test_score">
232 <expand macro="estimator_selector_all" />
233 <section name="options" title="Other Options" expanded="false">
234 <!--groups-->
235 <expand macro="model_validation_common_options"/>
236 <expand macro="scoring_selection"/>
237 <param name="n_permutations" type="integer" value="100" optional="true" label="n_permutations" help="Number of times to permute y"/> 210 <param name="n_permutations" type="integer" value="100" optional="true" label="n_permutations" help="Number of times to permute y"/>
238 <expand macro="random_state"/> 211 <expand macro="random_state"/>
239 </section> 212 </section>
240 <param name="return_type" type="select" label="Select a return type">
241 <option value="score" selected="true">score</option>
242 <option value="permutation_scores">permutation_scores</option>
243 <option value="pvalue">pvalue</option>
244 </param>
245 </when> 213 </when>
246 <when value="validation_curve"> 214 <when value="validation_curve"/>
247 <expand macro="estimator_selector_all" />
248 <section name="options" title="Other Options" expanded="false">
249 <param name="param_name" type="text" value="gamma" label="param_name" help="Name of the parameter that will be varied"/>
250 <param name="param_range" type="text" value="np.logspace(-6, -1, 5)" label="param_range" help="The values of the parameter that will be evaluated."/>
251 <!--groups-->
252 <expand macro="model_validation_common_options"/>
253 <expand macro="scoring_selection"/>
254 <expand macro="pre_dispatch"/>
255 </section>
256 <param name="return_type" type="select" label="Select a return type">
257 <option value="train_scores" selected="true">train_scores</option>
258 <option value="test_scores">test_scores</option>
259 </param>
260 </when>
261 </conditional> 215 </conditional>
262 <expand macro="sl_mixed_input"/> 216 <expand macro="sl_mixed_input"/>
263 </inputs> 217 </inputs>
264 <outputs> 218 <outputs>
265 <data format="tabular" name="outfile"/> 219 <data format="tabular" name="outfile"/>
266 </outputs> 220 </outputs>
267 <tests> 221 <tests>
268 <test> 222 <test>
223 <param name="infile_pipeline" value="pipeline02"/>
269 <param name="selected_function" value="cross_validate"/> 224 <param name="selected_function" value="cross_validate"/>
270 <param name="selected_module" value="linear_model"/>
271 <param name="selected_estimator" value="LassoCV"/>
272 <param name="infile1" value="regression_train.tabular" ftype="tabular"/> 225 <param name="infile1" value="regression_train.tabular" ftype="tabular"/>
273 <param name="col1" value="1,2,3,4,5"/> 226 <param name="col1" value="1,2,3,4,5"/>
274 <param name="infile2" value="regression_train.tabular" ftype="tabular"/> 227 <param name="infile2" value="regression_train.tabular" ftype="tabular"/>
275 <param name="col2" value="6"/> 228 <param name="col2" value="6"/>
276 <output name="outfile" file="mv_result01.tabular"/> 229 <output name="outfile">
277 </test> 230 <assert_contents>
278 <test> 231 <has_n_columns n="4"/>
232 <has_text text="0.9999961390418067"/>
233 <has_text text="0.9944541531269271"/>
234 <has_text text="0.9999193322454393"/>
235 </assert_contents>
236 </output>
237 </test>
238 <test>
239 <param name="infile_pipeline" value="pipeline02"/>
279 <param name="selected_function" value="cross_val_predict"/> 240 <param name="selected_function" value="cross_val_predict"/>
280 <param name="selected_module" value="linear_model"/>
281 <param name="selected_estimator" value="LassoCV"/>
282 <param name="infile1" value="regression_train.tabular" ftype="tabular"/> 241 <param name="infile1" value="regression_train.tabular" ftype="tabular"/>
283 <param name="col1" value="1,2,3,4,5"/> 242 <param name="col1" value="1,2,3,4,5"/>
284 <param name="infile2" value="regression_train.tabular" ftype="tabular"/> 243 <param name="infile2" value="regression_train.tabular" ftype="tabular"/>
285 <param name="col2" value="6"/> 244 <param name="col2" value="6"/>
286 <output name="outfile" file="mv_result02.tabular"/> 245 <output name="outfile" file="mv_result02.tabular" lines_diff="4"/>
287 </test> 246 </test>
288 <test> 247 <test>
289 <param name="selected_function" value="cross_val_score"/> 248 <param name="infile_pipeline" value="pipeline05"/>
290 <param name="selected_module" value="linear_model"/>
291 <param name="selected_estimator" value="LassoCV"/>
292 <param name="infile1" value="regression_train.tabular" ftype="tabular"/>
293 <param name="col1" value="1,2,3,4,5"/>
294 <param name="infile2" value="regression_train.tabular" ftype="tabular"/>
295 <param name="col2" value="6"/>
296 <output name="outfile" file="mv_result03.tabular"/>
297 </test>
298 <test>
299 <param name="selected_function" value="learning_curve"/> 249 <param name="selected_function" value="learning_curve"/>
300 <param name="selected_module" value="linear_model"/>
301 <param name="selected_estimator" value="LassoCV"/>
302 <param name="infile1" value="regression_X.tabular" ftype="tabular"/> 250 <param name="infile1" value="regression_X.tabular" ftype="tabular"/>
303 <param name="header1" value="true" /> 251 <param name="header1" value="true" />
304 <param name="col1" value="1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17"/> 252 <param name="col1" value="1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17"/>
305 <param name="infile2" value="regression_y.tabular" ftype="tabular"/> 253 <param name="infile2" value="regression_y.tabular" ftype="tabular"/>
306 <param name="header2" value="true" /> 254 <param name="header2" value="true" />
307 <param name="col2" value="1"/> 255 <param name="col2" value="1"/>
308 <output name="outfile" file="mv_result04.tabular"/> 256 <output name="outfile" file="mv_result03.tabular"/>
309 </test> 257 </test>
310 <test> 258 <test>
259 <param name="infile_pipeline" value="pipeline05"/>
311 <param name="selected_function" value="permutation_test_score"/> 260 <param name="selected_function" value="permutation_test_score"/>
312 <param name="selected_module" value="linear_model"/>
313 <param name="selected_estimator" value="LassoCV"/>
314 <param name="infile1" value="regression_train.tabular" ftype="tabular"/> 261 <param name="infile1" value="regression_train.tabular" ftype="tabular"/>
315 <param name="col1" value="1,2,3,4,5"/> 262 <param name="col1" value="1,2,3,4,5"/>
316 <param name="infile2" value="regression_train.tabular" ftype="tabular"/> 263 <param name="infile2" value="regression_train.tabular" ftype="tabular"/>
317 <param name="col2" value="6"/> 264 <param name="col2" value="6"/>
265 <output name="outfile">
266 <assert_contents>
267 <has_n_columns n="3"/>
268 <has_text text="0.25697059258228816"/>
269 </assert_contents>
270 </output>
271 </test>
272 <test>
273 <param name="infile_pipeline" value="pipeline05"/>
274 <param name="selected_function" value="cross_val_predict"/>
275 <section name="groups_selector">
276 <param name="infile_groups" value="regression_y.tabular" ftype="tabular"/>
277 <param name="header_g" value="true"/>
278 <param name="selected_column_selector_option_g" value="by_index_number"/>
279 <param name="col_g" value="1"/>
280 </section>
281 <param name="selected_cv" value="GroupKFold"/>
282 <param name="infile1" value="regression_X.tabular" ftype="tabular"/>
283 <param name="header1" value="true"/>
284 <param name="col1" value="1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17"/>
285 <param name="infile2" value="regression_y.tabular" ftype="tabular"/>
286 <param name="header2" value="true"/>
287 <param name="col2" value="1"/>
318 <output name="outfile" file="mv_result05.tabular"/> 288 <output name="outfile" file="mv_result05.tabular"/>
319 </test>
320 <test>
321 <param name="selected_function" value="validation_curve"/>
322 <param name="selected_module" value="svm"/>
323 <param name="selected_estimator" value="SVC"/>
324 <param name="text_params" value="kernel='linear'"/>
325 <param name="infile1" value="regression_X.tabular" ftype="tabular"/>
326 <param name="header1" value="true" />
327 <param name="selected_column_selector_option" value="all_columns"/>
328 <param name="infile2" value="regression_y.tabular" ftype="tabular"/>
329 <param name="header2" value="true" />
330 <param name="col2" value="1"/>
331 <param name="return_type" value="test_scores"/>
332 <output name="outfile" file="mv_result06.tabular"/>
333 </test> 289 </test>
334 </tests> 290 </tests>
335 <help> 291 <help>
336 <![CDATA[ 292 <![CDATA[
337 **What it does** 293 **What it does**