Mercurial > repos > bgruening > sklearn_stacking_ensemble_models

diff test-data/get_params09.tabular @ 0:fcc5eaaec401 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 07:25:29 -0400
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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/get_params09.tabular	Wed May 15 07:25:29 2019 -0400
@@ -0,0 +1,39 @@
+	Parameter	Value
+*	memory	memory: None
+*	steps	"steps: [('relieff', ReliefF(discrete_threshold=10, n_features_to_select=3, n_jobs=1,
+    n_neighbors=100, verbose=False)), ('randomforestregressor', RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,
+           max_features='auto', max_leaf_nodes=None,
+           min_impurity_decrease=0.0, min_impurity_split=None,
+           min_samples_leaf=1, min_samples_split=2,
+           min_weight_fraction_leaf=0.0, n_estimators='warn', n_jobs=1,
+           oob_score=False, random_state=None, verbose=0, warm_start=False))]"
+@	relieff	"relieff: ReliefF(discrete_threshold=10, n_features_to_select=3, n_jobs=1,
+    n_neighbors=100, verbose=False)"
+@	randomforestregressor	"randomforestregressor: RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,
+           max_features='auto', max_leaf_nodes=None,
+           min_impurity_decrease=0.0, min_impurity_split=None,
+           min_samples_leaf=1, min_samples_split=2,
+           min_weight_fraction_leaf=0.0, n_estimators='warn', n_jobs=1,
+           oob_score=False, random_state=None, verbose=0, warm_start=False)"
+@	relieff__discrete_threshold	relieff__discrete_threshold: 10
+@	relieff__n_features_to_select	relieff__n_features_to_select: 3
+*	relieff__n_jobs	relieff__n_jobs: 1
+@	relieff__n_neighbors	relieff__n_neighbors: 100
+*	relieff__verbose	relieff__verbose: False
+@	randomforestregressor__bootstrap	randomforestregressor__bootstrap: True
+@	randomforestregressor__criterion	randomforestregressor__criterion: 'mse'
+@	randomforestregressor__max_depth	randomforestregressor__max_depth: None
+@	randomforestregressor__max_features	randomforestregressor__max_features: 'auto'
+@	randomforestregressor__max_leaf_nodes	randomforestregressor__max_leaf_nodes: None
+@	randomforestregressor__min_impurity_decrease	randomforestregressor__min_impurity_decrease: 0.0
+@	randomforestregressor__min_impurity_split	randomforestregressor__min_impurity_split: None
+@	randomforestregressor__min_samples_leaf	randomforestregressor__min_samples_leaf: 1
+@	randomforestregressor__min_samples_split	randomforestregressor__min_samples_split: 2
+@	randomforestregressor__min_weight_fraction_leaf	randomforestregressor__min_weight_fraction_leaf: 0.0
+@	randomforestregressor__n_estimators	randomforestregressor__n_estimators: 'warn'
+*	randomforestregressor__n_jobs	randomforestregressor__n_jobs: 1
+@	randomforestregressor__oob_score	randomforestregressor__oob_score: False
+@	randomforestregressor__random_state	randomforestregressor__random_state: None
+*	randomforestregressor__verbose	randomforestregressor__verbose: 0
+@	randomforestregressor__warm_start	randomforestregressor__warm_start: False
+	Note:	@, searchable params in searchcv too.