Mercurial > repos > bgruening > sklearn_pairwise_metrics
changeset 10:d39bac038704 draft
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 7a31960686122d7e53054fef4996525f04ebd254
author | bgruening |
---|---|
date | Thu, 12 Apr 2018 08:18:10 -0400 |
parents | efdf8df169a1 |
children | 051c24dcc560 |
files | main_macros.xml test-data/feature_selection_result01 test-data/feature_selection_result02 test-data/feature_selection_result03 test-data/feature_selection_result04 test-data/feature_selection_result05 test-data/feature_selection_result06 test-data/feature_selection_result07 test-data/feature_selection_result08 test-data/feature_selection_result09 test-data/feature_selection_result10 |
diffstat | 11 files changed, 2667 insertions(+), 0 deletions(-) [+] |
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--- a/main_macros.xml Tue Apr 10 15:14:23 2018 -0400 +++ b/main_macros.xml Thu Apr 12 08:18:10 2018 -0400 @@ -787,6 +787,53 @@ </when> <yield/> </xml> + <xml name="feature_selection_score_function"> + <param argument="score_func" type="select" label="Select a score function"> + <option value="chi2">chi2 - Compute chi-squared stats between each non-negative feature and class</option> + <option value="f_classif">f_classif - Compute the ANOVA F-value for the provided sample</option> + <option value="f_regression">f_regression - Univariate linear regression tests</option> + <option value="mutual_info_classif">mutual_info_classif - Estimate mutual information for a discrete target variable</option> + <option value="mutual_info_regression">mutual_info_regression - Estimate mutual information for a continuous target variable</option> + </param> + </xml> + <xml name="feature_selection_estimator"> + <param argument="estimator" type="select" label="Select an estimator" help="The base estimator from which the transformer is built."> + <option value="svm.SVR(kernel="linear")">svm.SVR(kernel="linear")</option> + <option value="svm.SVC(kernel="linear")">svm.SVC(kernel="linear")</option> + <option value="svm.LinearSVC(penalty="l1", dual=False, tol=1e-3)">svm.LinearSVC(penalty="l1", dual=False, tol=1e-3)</option> + <option value="linear_model.LassoCV()">linear_model.LassoCV()</option> + <option value="ensemble.RandomForestRegressor(n_estimators = 1000, random_state = 42)">ensemble.RandomForestRegressor(n_estimators = 1000, random_state = 42)</option> + </param> + </xml> + <xml name="feature_selection_extra_estimator"> + <param name="has_estimator" type="select" label="Does your estimator on the list above?"> + <option value="yes">Yes, my estimator is on the list</option> + <option value="no">No, I need make a new estimator</option> + <yield/> + </param> + </xml> + <xml name="feature_selection_estimator_choices"> + <when value="yes"> + </when> + <when value="no"> + <param name="new_estimator" type="text" value="" label="Make a new estimator" /> + </when> + <yield/> + </xml> + <xml name="feature_selection_methods"> + <conditional name="select_methods"> + <param name="selected_method" type="select" label="Select an operation"> + <option value="fit_transform">fit_transform - Fit to data, then transform it</option> + <option value="get_support">get_support - Get a mask, or integer index, of the features selected</option> + </param> + <when value="fit_transform"> + <!--**fit_params--> + </when> + <when value="get_support"> + <param name="indices" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="false" label="Indices" help="If True, the return value will be an array of integers, rather than a boolean mask."/> + </when> + </conditional> + </xml> <!-- Outputs -->
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