diff test-data/pipeline_params18 @ 2:cf54bae8ad42 draft

"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/pipeline_params18	Mon Dec 16 05:18:07 2019 -0500
@@ -0,0 +1,89 @@
+	Parameter	Value
+*	memory	memory: None
+@	powertransformer	powertransformer: PowerTransformer(copy=True, method='yeo-johnson', standardize=True)
+*	steps	"steps: [('powertransformer', PowerTransformer(copy=True, method='yeo-johnson', standardize=True)), ('transformedtargetregressor', TransformedTargetRegressor(check_inverse=True, func=None, inverse_func=None,
+                           regressor=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=10,
+                                                           verbose=0,
+                                                           warm_start=False),
+                           transformer=QuantileTransformer(copy=True,
+                                                           ignore_implicit_zeros=False,
+                                                           n_quantiles=1000,
+                                                           output_distribution='uniform',
+                                                           random_state=10,
+                                                           subsample=100000)))]"
+@	transformedtargetregressor	"transformedtargetregressor: TransformedTargetRegressor(check_inverse=True, func=None, inverse_func=None,
+                           regressor=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=10,
+                                                           verbose=0,
+                                                           warm_start=False),
+                           transformer=QuantileTransformer(copy=True,
+                                                           ignore_implicit_zeros=False,
+                                                           n_quantiles=1000,
+                                                           output_distribution='uniform',
+                                                           random_state=10,
+                                                           subsample=100000))"
+*	verbose	verbose: False
+@	powertransformer__copy	powertransformer__copy: True
+@	powertransformer__method	powertransformer__method: 'yeo-johnson'
+@	powertransformer__standardize	powertransformer__standardize: True
+@	transformedtargetregressor__check_inverse	transformedtargetregressor__check_inverse: True
+@	transformedtargetregressor__func	transformedtargetregressor__func: None
+@	transformedtargetregressor__inverse_func	transformedtargetregressor__inverse_func: None
+@	transformedtargetregressor__regressor	"transformedtargetregressor__regressor: 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=10, verbose=0,
+                      warm_start=False)"
+@	transformedtargetregressor__regressor__bootstrap	transformedtargetregressor__regressor__bootstrap: True
+@	transformedtargetregressor__regressor__criterion	transformedtargetregressor__regressor__criterion: 'mse'
+@	transformedtargetregressor__regressor__max_depth	transformedtargetregressor__regressor__max_depth: None
+@	transformedtargetregressor__regressor__max_features	transformedtargetregressor__regressor__max_features: 'auto'
+@	transformedtargetregressor__regressor__max_leaf_nodes	transformedtargetregressor__regressor__max_leaf_nodes: None
+@	transformedtargetregressor__regressor__min_impurity_decrease	transformedtargetregressor__regressor__min_impurity_decrease: 0.0
+@	transformedtargetregressor__regressor__min_impurity_split	transformedtargetregressor__regressor__min_impurity_split: None
+@	transformedtargetregressor__regressor__min_samples_leaf	transformedtargetregressor__regressor__min_samples_leaf: 1
+@	transformedtargetregressor__regressor__min_samples_split	transformedtargetregressor__regressor__min_samples_split: 2
+@	transformedtargetregressor__regressor__min_weight_fraction_leaf	transformedtargetregressor__regressor__min_weight_fraction_leaf: 0.0
+@	transformedtargetregressor__regressor__n_estimators	transformedtargetregressor__regressor__n_estimators: 'warn'
+*	transformedtargetregressor__regressor__n_jobs	transformedtargetregressor__regressor__n_jobs: 1
+@	transformedtargetregressor__regressor__oob_score	transformedtargetregressor__regressor__oob_score: False
+@	transformedtargetregressor__regressor__random_state	transformedtargetregressor__regressor__random_state: 10
+*	transformedtargetregressor__regressor__verbose	transformedtargetregressor__regressor__verbose: 0
+@	transformedtargetregressor__regressor__warm_start	transformedtargetregressor__regressor__warm_start: False
+@	transformedtargetregressor__transformer	"transformedtargetregressor__transformer: QuantileTransformer(copy=True, ignore_implicit_zeros=False, n_quantiles=1000,
+                    output_distribution='uniform', random_state=10,
+                    subsample=100000)"
+@	transformedtargetregressor__transformer__copy	transformedtargetregressor__transformer__copy: True
+@	transformedtargetregressor__transformer__ignore_implicit_zeros	transformedtargetregressor__transformer__ignore_implicit_zeros: False
+@	transformedtargetregressor__transformer__n_quantiles	transformedtargetregressor__transformer__n_quantiles: 1000
+@	transformedtargetregressor__transformer__output_distribution	transformedtargetregressor__transformer__output_distribution: 'uniform'
+@	transformedtargetregressor__transformer__random_state	transformedtargetregressor__transformer__random_state: 10
+@	transformedtargetregressor__transformer__subsample	transformedtargetregressor__transformer__subsample: 100000
+	Note:	@, params eligible for search in searchcv tool.