Mercurial > repos > bgruening > sklearn_regression_metrics

diff test-data/get_params04.tabular @ 17:40ee30b5e456 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 07:38:45 -0400
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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/get_params04.tabular	Wed May 15 07:38:45 2019 -0400
@@ -0,0 +1,39 @@
+	Parameter	Value
+*	memory	memory: None
+*	steps	"steps: [('selectfrommodel', SelectFromModel(estimator=AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
+          learning_rate=1.0, n_estimators=50, random_state=None),
+        max_features=None, norm_order=1, prefit=False, threshold=None)), ('linearsvc', LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,
+     intercept_scaling=1, loss='squared_hinge', max_iter=1000,
+     multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,
+     verbose=0))]"
+@	selectfrommodel	"selectfrommodel: SelectFromModel(estimator=AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
+          learning_rate=1.0, n_estimators=50, random_state=None),
+        max_features=None, norm_order=1, prefit=False, threshold=None)"
+@	linearsvc	"linearsvc: LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,
+     intercept_scaling=1, loss='squared_hinge', max_iter=1000,
+     multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,
+     verbose=0)"
+@	selectfrommodel__estimator__algorithm	selectfrommodel__estimator__algorithm: 'SAMME.R'
+@	selectfrommodel__estimator__base_estimator	selectfrommodel__estimator__base_estimator: None
+@	selectfrommodel__estimator__learning_rate	selectfrommodel__estimator__learning_rate: 1.0
+@	selectfrommodel__estimator__n_estimators	selectfrommodel__estimator__n_estimators: 50
+@	selectfrommodel__estimator__random_state	selectfrommodel__estimator__random_state: None
+@	selectfrommodel__estimator	"selectfrommodel__estimator: AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
+          learning_rate=1.0, n_estimators=50, random_state=None)"
+@	selectfrommodel__max_features	selectfrommodel__max_features: None
+@	selectfrommodel__norm_order	selectfrommodel__norm_order: 1
+@	selectfrommodel__prefit	selectfrommodel__prefit: False
+@	selectfrommodel__threshold	selectfrommodel__threshold: None
+@	linearsvc__C	linearsvc__C: 1.0
+@	linearsvc__class_weight	linearsvc__class_weight: None
+@	linearsvc__dual	linearsvc__dual: True
+@	linearsvc__fit_intercept	linearsvc__fit_intercept: True
+@	linearsvc__intercept_scaling	linearsvc__intercept_scaling: 1
+@	linearsvc__loss	linearsvc__loss: 'squared_hinge'
+@	linearsvc__max_iter	linearsvc__max_iter: 1000
+@	linearsvc__multi_class	linearsvc__multi_class: 'ovr'
+@	linearsvc__penalty	linearsvc__penalty: 'l2'
+@	linearsvc__random_state	linearsvc__random_state: None
+@	linearsvc__tol	linearsvc__tol: 0.0001
+*	linearsvc__verbose	linearsvc__verbose: 0
+	Note:	@, searchable params in searchcv too.