Mercurial > repos > bgruening > sklearn_model_fit

diff test-data/get_params08.tabular @ 0:5e1241fd6afd draft

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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit eb703290e2589561ea215c84aa9f71bcfe1712c6"
author bgruening
date Fri, 01 Nov 2019 16:43:07 -0400
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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/get_params08.tabular	Fri Nov 01 16:43:07 2019 -0400
@@ -0,0 +1,24 @@
+	Parameter	Value
+*	memory	memory: None
+*	steps	"steps: [('featureagglomeration', FeatureAgglomeration(affinity='euclidean', compute_full_tree='auto',
+           connectivity=None, linkage='ward', memory=None, n_clusters=3,
+           pooling_func=<function mean at 0x1123f1620>)), ('adaboostclassifier', AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
+          learning_rate=1.0, n_estimators=50, random_state=None))]"
+@	featureagglomeration	"featureagglomeration: FeatureAgglomeration(affinity='euclidean', compute_full_tree='auto',
+           connectivity=None, linkage='ward', memory=None, n_clusters=3,
+           pooling_func=<function mean at 0x1123f1620>)"
+@	adaboostclassifier	"adaboostclassifier: AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
+          learning_rate=1.0, n_estimators=50, random_state=None)"
+@	featureagglomeration__affinity	featureagglomeration__affinity: 'euclidean'
+@	featureagglomeration__compute_full_tree	featureagglomeration__compute_full_tree: 'auto'
+@	featureagglomeration__connectivity	featureagglomeration__connectivity: None
+@	featureagglomeration__linkage	featureagglomeration__linkage: 'ward'
+*	featureagglomeration__memory	featureagglomeration__memory: None
+@	featureagglomeration__n_clusters	featureagglomeration__n_clusters: 3
+@	featureagglomeration__pooling_func	featureagglomeration__pooling_func: <function mean at 0x1123f1620>
+@	adaboostclassifier__algorithm	adaboostclassifier__algorithm: 'SAMME.R'
+@	adaboostclassifier__base_estimator	adaboostclassifier__base_estimator: None
+@	adaboostclassifier__learning_rate	adaboostclassifier__learning_rate: 1.0
+@	adaboostclassifier__n_estimators	adaboostclassifier__n_estimators: 50
+@	adaboostclassifier__random_state	adaboostclassifier__random_state: None
+	Note:	@, searchable params in searchcv too.