Mercurial > repos > bgruening > sklearn_model_fit

diff test-data/get_params03.tabular @ 0:734c66aa945a 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 17:18:28 -0400
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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/get_params03.tabular	Fri Nov 01 17:18:28 2019 -0400
@@ -0,0 +1,43 @@
+	Parameter	Value
+*	memory	memory: None
+*	steps	"steps: [('robustscaler', RobustScaler(copy=True, quantile_range=(25.0, 75.0), with_centering=True,
+       with_scaling=True)), ('xgbclassifier', XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,
+       colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0,
+       max_depth=3, min_child_weight=1, missing=nan, n_estimators=100,
+       n_jobs=1, nthread=None, objective='binary:logistic', random_state=0,
+       reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,
+       silent=True, subsample=1))]"
+@	robustscaler	"robustscaler: RobustScaler(copy=True, quantile_range=(25.0, 75.0), with_centering=True,
+       with_scaling=True)"
+@	xgbclassifier	"xgbclassifier: XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,
+       colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0,
+       max_depth=3, min_child_weight=1, missing=nan, n_estimators=100,
+       n_jobs=1, nthread=None, objective='binary:logistic', random_state=0,
+       reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,
+       silent=True, subsample=1)"
+@	robustscaler__copy	robustscaler__copy: True
+@	robustscaler__quantile_range	robustscaler__quantile_range: (25.0, 75.0)
+@	robustscaler__with_centering	robustscaler__with_centering: True
+@	robustscaler__with_scaling	robustscaler__with_scaling: True
+@	xgbclassifier__base_score	xgbclassifier__base_score: 0.5
+@	xgbclassifier__booster	xgbclassifier__booster: 'gbtree'
+@	xgbclassifier__colsample_bylevel	xgbclassifier__colsample_bylevel: 1
+@	xgbclassifier__colsample_bytree	xgbclassifier__colsample_bytree: 1
+@	xgbclassifier__gamma	xgbclassifier__gamma: 0
+@	xgbclassifier__learning_rate	xgbclassifier__learning_rate: 0.1
+@	xgbclassifier__max_delta_step	xgbclassifier__max_delta_step: 0
+@	xgbclassifier__max_depth	xgbclassifier__max_depth: 3
+@	xgbclassifier__min_child_weight	xgbclassifier__min_child_weight: 1
+@	xgbclassifier__missing	xgbclassifier__missing: nan
+@	xgbclassifier__n_estimators	xgbclassifier__n_estimators: 100
+*	xgbclassifier__n_jobs	xgbclassifier__n_jobs: 1
+*	xgbclassifier__nthread	xgbclassifier__nthread: None
+@	xgbclassifier__objective	xgbclassifier__objective: 'binary:logistic'
+@	xgbclassifier__random_state	xgbclassifier__random_state: 0
+@	xgbclassifier__reg_alpha	xgbclassifier__reg_alpha: 0
+@	xgbclassifier__reg_lambda	xgbclassifier__reg_lambda: 1
+@	xgbclassifier__scale_pos_weight	xgbclassifier__scale_pos_weight: 1
+@	xgbclassifier__seed	xgbclassifier__seed: None
+@	xgbclassifier__silent	xgbclassifier__silent: True
+@	xgbclassifier__subsample	xgbclassifier__subsample: 1
+	Note:	@, searchable params in searchcv too.