Mercurial > repos > goeckslab > tabular_learner
diff tabular_learner.xml @ 10:49f73a3c12f3 draft default tip
planemo upload for repository https://github.com/goeckslab/gleam commit 1ffd143e57fa952ee9dd84fc141771520aea0791
| author | goeckslab |
|---|---|
| date | Wed, 26 Nov 2025 17:49:36 +0000 |
| parents | e7dd78077b72 |
| children |
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--- a/tabular_learner.xml Sat Nov 08 14:20:19 2025 +0000 +++ b/tabular_learner.xml Wed Nov 26 17:49:36 2025 +0000 @@ -6,7 +6,7 @@ <expand macro="python_requirements" /> <command> <![CDATA[ - python $__tool_directory__/pycaret_train.py --input_file '$input_file' --target_col '$target_feature' --output_dir '.' --random_seed '$random_seed' + python $__tool_directory__/pycaret_train.py --input_file '$input_file' --target_col '$target_feature' --output_dir '.' --random_seed '$random_seed' --n-jobs \${GALAXY_SLOTS:-1} #if $model_type == "classification" #if $classification_models --models '$classification_models' @@ -199,18 +199,18 @@ <test> <param name="input_file" value="pcr.tsv"/> <param name="target_feature" value="11"/> - <param name="model_type" value="classification"/> - <param name="best_model_metric" value="F1"/> + <param name="model_selection|model_type" value="classification"/> + <param name="model_selection|best_model_metric" value="F1"/> <param name="random_seed" value="42"/> - <param name="customize_defaults" value="true"/> - <param name="train_size" value="0.8"/> - <param name="normalize" value="true"/> - <param name="feature_selection" value="true"/> - <param name="enable_cross_validation" value="true"/> - <param name="cross_validation_folds" value="5"/> - <param name="remove_outliers" value="true"/> - <param name="remove_multicollinearity" value="true"/> - <param name="probability_threshold" value="0.4" /> + <param name="advanced_settings|customize_defaults" value="true"/> + <param name="advanced_settings|train_size" value="0.8"/> + <param name="advanced_settings|normalize" value="true"/> + <param name="advanced_settings|feature_selection" value="true"/> + <param name="advanced_settings|cross_validation|enable_cross_validation" value="true"/> + <param name="advanced_settings|cross_validation|cross_validation_folds" value="5"/> + <param name="advanced_settings|remove_outliers" value="true"/> + <param name="advanced_settings|remove_multicollinearity" value="true"/> + <param name="advanced_settings|probability_threshold" value="0.4" /> <output name="model" file="expected_model_classification_customized.h5" compare="sim_size"/> <output name="comparison_result"> <assert_contents> @@ -226,16 +226,16 @@ <test> <param name="input_file" value="pcr.tsv"/> <param name="target_feature" value="11"/> - <param name="model_type" value="classification"/> + <param name="model_selection|model_type" value="classification"/> <param name="random_seed" value="42"/> - <param name="customize_defaults" value="true"/> - <param name="train_size" value="0.8"/> - <param name="normalize" value="true"/> - <param name="feature_selection" value="true"/> - <param name="enable_cross_validation" value="false"/> - <param name="remove_outliers" value="true"/> - <param name="remove_multicollinearity" value="true"/> - <param name="probability_threshold" value="0.6" /> + <param name="advanced_settings|customize_defaults" value="true"/> + <param name="advanced_settings|train_size" value="0.8"/> + <param name="advanced_settings|normalize" value="true"/> + <param name="advanced_settings|feature_selection" value="true"/> + <param name="advanced_settings|cross_validation|enable_cross_validation" value="false"/> + <param name="advanced_settings|remove_outliers" value="true"/> + <param name="advanced_settings|remove_multicollinearity" value="true"/> + <param name="advanced_settings|probability_threshold" value="0.6" /> <output name="model" file="expected_model_classification_customized_cross_off.h5" compare="sim_size"/> <output name="comparison_result"> <assert_contents> @@ -249,7 +249,7 @@ <test> <param name="input_file" value="pcr.tsv"/> <param name="target_feature" value="11"/> - <param name="model_type" value="classification"/> + <param name="model_selection|model_type" value="classification"/> <param name="random_seed" value="42"/> <param name="tune_model" value="true"/> <output name="model" file="expected_model_classification.h5" compare="sim_size"/> @@ -260,12 +260,18 @@ <has_text text="Feature Importance" /> </assert_contents> </output> - <output name="best_model_csv" value="expected_best_model_classification.csv" /> + <output name="best_model_csv"> + <assert_contents> + <has_text text="n_estimators,100" /> + <has_text text="num_leaves,31" /> + <has_text text="learning_rate,0.1" /> + </assert_contents> + </output> </test> <test> <param name="input_file" value="pcr.tsv"/> <param name="target_feature" value="11"/> - <param name="model_type" value="classification"/> + <param name="model_selection|model_type" value="classification"/> <param name="random_seed" value="42"/> <output name="model" file="expected_model_classification.h5" compare="sim_size"/> <output name="comparison_result"> @@ -275,13 +281,19 @@ <has_text text="Feature Importance" /> </assert_contents> </output> - <output name="best_model_csv" value="expected_best_model_classification.csv" /> + <output name="best_model_csv"> + <assert_contents> + <has_text text="n_estimators,100" /> + <has_text text="num_leaves,31" /> + <has_text text="learning_rate,0.1" /> + </assert_contents> + </output> </test> <test> <param name="input_file" value="auto-mpg.tsv"/> <param name="target_feature" value="1"/> - <param name="model_type" value="regression"/> - <param name="best_model_metric" value="RMSE"/> + <param name="model_selection|model_type" value="regression"/> + <param name="model_selection|best_model_metric" value="RMSE"/> <param name="random_seed" value="42"/> <output name="model" file="expected_model_regression.h5" compare="sim_size" /> <output name="comparison_result">