Mercurial > repos > bgruening > sklearn_model_validation
comparison main_macros.xml @ 2:dd502cb0d567 draft
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 4ed8c4f6ef9ece81797a398b17a99bbaf49a6978
author | bgruening |
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date | Wed, 30 May 2018 08:27:01 -0400 |
parents | 02eadaaa4bf7 |
children | 424d8d21744d |
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1:02eadaaa4bf7 | 2:dd502cb0d567 |
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12 if return_df: | 12 if return_df: |
13 return y, data | 13 return y, data |
14 else: | 14 else: |
15 return y | 15 return y |
16 return y | 16 return y |
17 </token> | |
18 | |
19 ## generate an instance for one of sklearn.feature_selection classes | |
20 ## must call "@COLUMNS_FUNCTION@" | |
21 <token name="@FEATURE_SELECTOR_FUNCTION@"> | |
22 def feature_selector(inputs): | |
23 selector = inputs["selected_algorithm"] | |
24 selector = getattr(sklearn.feature_selection, selector) | |
25 options = inputs["options"] | |
26 | |
27 if inputs['selected_algorithm'] == 'SelectFromModel': | |
28 if not options['threshold'] or options['threshold'] == 'None': | |
29 options['threshold'] = None | |
30 if 'extra_estimator' in inputs and inputs['extra_estimator']['has_estimator'] == 'no_load': | |
31 fitted_estimator = pickle.load(open("inputs['extra_estimator']['fitted_estimator']", 'r')) | |
32 new_selector = selector(fitted_estimator, prefit=True, **options) | |
33 else: | |
34 estimator=inputs["estimator"] | |
35 if inputs["extra_estimator"]["has_estimator"]=='no': | |
36 estimator=inputs["extra_estimator"]["new_estimator"] | |
37 estimator=eval(estimator.replace('__dq__', '"').replace("__sq__","'")) | |
38 new_selector = selector(estimator, **options) | |
39 | |
40 elif inputs['selected_algorithm'] in ['RFE', 'RFECV']: | |
41 if 'scoring' in options and (not options['scoring'] or options['scoring'] == 'None'): | |
42 options['scoring'] = None | |
43 estimator=inputs["estimator"] | |
44 if inputs["extra_estimator"]["has_estimator"]=='no': | |
45 estimator=inputs["extra_estimator"]["new_estimator"] | |
46 estimator=eval(estimator.replace('__dq__', '"').replace("__sq__","'")) | |
47 new_selector = selector(estimator, **options) | |
48 | |
49 elif inputs['selected_algorithm'] == "VarianceThreshold": | |
50 new_selector = selector(**options) | |
51 | |
52 else: | |
53 score_func = inputs["score_func"] | |
54 score_func = getattr(sklearn.feature_selection, score_func) | |
55 new_selector = selector(score_func, **options) | |
56 | |
57 return new_selector | |
17 </token> | 58 </token> |
18 | 59 |
19 <xml name="python_requirements"> | 60 <xml name="python_requirements"> |
20 <requirements> | 61 <requirements> |
21 <requirement type="package" version="2.7">python</requirement> | 62 <requirement type="package" version="2.7">python</requirement> |
792 label="Use a copy of data for precomputing row normalization" help=" "/> | 833 label="Use a copy of data for precomputing row normalization" help=" "/> |
793 </section> | 834 </section> |
794 </when> | 835 </when> |
795 <yield/> | 836 <yield/> |
796 </xml> | 837 </xml> |
838 <xml name="estimator_input_no_fit"> | |
839 <expand macro="feature_selection_estimator" /> | |
840 <conditional name="extra_estimator"> | |
841 <expand macro="feature_selection_extra_estimator" /> | |
842 <expand macro="feature_selection_estimator_choices" /> | |
843 </conditional> | |
844 </xml> | |
797 <xml name="feature_selection_all"> | 845 <xml name="feature_selection_all"> |
798 <conditional name="feature_selection_algorithms"> | 846 <conditional name="feature_selection_algorithms"> |
799 <param name="selected_algorithm" type="select" label="Select a feature selection algorithm"> | 847 <param name="selected_algorithm" type="select" label="Select a feature selection algorithm"> |
800 <option value="SelectFromModel" selected="true">SelectFromModel - Meta-transformer for selecting features based on importance weights</option> | 848 <option value="SelectFromModel" selected="true">SelectFromModel - Meta-transformer for selecting features based on importance weights</option> |
801 <option value="GenericUnivariateSelect" selected="true">GenericUnivariateSelect - Univariate feature selector with configurable strategy</option> | 849 <option value="GenericUnivariateSelect" selected="true">GenericUnivariateSelect - Univariate feature selector with configurable strategy</option> |
973 | 1021 |
974 <xml name="scoring"> | 1022 <xml name="scoring"> |
975 <param argument="scoring" type="text" value="" optional="true" label="scoring" help="A metric used to evaluate the estimator"/> | 1023 <param argument="scoring" type="text" value="" optional="true" label="scoring" help="A metric used to evaluate the estimator"/> |
976 </xml> | 1024 </xml> |
977 | 1025 |
978 <xml name="pre_dispatch"> | 1026 <xml name="pre_dispatch" token_type="text" token_default_value="all" token_help="Number of predispatched jobs for parallel execution"> |
979 <param argument="pre_dispatch" type="text" value="all" optional="true" label="pre_dispatch" help="Number of predispatched jobs for parallel execution"/> | 1027 <param argument="pre_dispatch" type="@TYPE@" value="@DEFAULT_VALUE@" optional="true" label="pre_dispatch" help="@HELP@"/> |
980 </xml> | 1028 </xml> |
981 | 1029 |
982 <!-- Outputs --> | 1030 <!-- Outputs --> |
983 | 1031 |
984 <xml name="output"> | 1032 <xml name="output"> |