Mercurial > repos > goeckslab > tabular_learner

comparison tabular_learner.xml @ 2:77c88226bfde draft

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planemo upload for repository https://github.com/goeckslab/gleam commit 06c0da44ac93256dfb616a6b40276b5485a71e8e
author goeckslab
date Wed, 02 Jul 2025 18:59:39 +0000
parents 209b663a4f62
children f6a65e05d6ec
comparison
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1:f69ed50c9768 2:77c88226bfde
47 #end if 47 #end if
48 #end if 48 #end if
49 #if $test_file 49 #if $test_file
50 --test_file '$test_file' 50 --test_file '$test_file'
51 #end if 51 #end if
52 --model_type '$model_type' 52 --model_type '$model_type'
53 ]]> 53 ]]>
54 </command> 54 </command>
55 <inputs> 55 <inputs>
56 <param name="input_file" type="data" format="csv,tabular" label="Tabular Input Dataset" /> 56 <param name="input_file" type="data" format="csv,tabular" label="Tabular Input Dataset" />
57 <param name="test_file" type="data" format="csv,tabular" optional="true" label="Tabular Test Dataset" 57 <param name="test_file" type="data" format="csv,tabular" optional="true" label="Tabular Test Dataset"
167 <param name="enable_cross_validation" value="true"/> 167 <param name="enable_cross_validation" value="true"/>
168 <param name="cross_validation_folds" value="5"/> 168 <param name="cross_validation_folds" value="5"/>
169 <param name="remove_outliers" value="true"/> 169 <param name="remove_outliers" value="true"/>
170 <param name="remove_multicollinearity" value="true"/> 170 <param name="remove_multicollinearity" value="true"/>
171 <output name="model" file="expected_model_classification_customized.h5" compare="sim_size"/> 171 <output name="model" file="expected_model_classification_customized.h5" compare="sim_size"/>
172 <output name="comparison_result" file="expected_comparison_result_classification_customized.html" compare="sim_size" /> 172 <output name="comparison_result">
173 <assert_contents>
174 <has_text text="Validation Result Summary" />
175 <has_text text="Test Results" />
176 <has_text text="Feature Importance" />
177 </assert_contents>
178 </output>
173 <output name="best_model_csv" value="expected_best_model_classification_customized.csv" /> 179 <output name="best_model_csv" value="expected_best_model_classification_customized.csv" />
174 </test> 180 </test>
175 <test> 181 <test>
176 <param name="input_file" value="pcr.tsv"/> 182 <param name="input_file" value="pcr.tsv"/>
177 <param name="target_feature" value="11"/> 183 <param name="target_feature" value="11"/>
178 <param name="model_type" value="classification"/> 184 <param name="model_type" value="classification"/>
179 <param name="random_seed" value="42"/> 185 <param name="random_seed" value="42"/>
180 <output name="model" file="expected_model_classification.h5" compare="sim_size"/> 186 <output name="model" file="expected_model_classification.h5" compare="sim_size"/>
181 <output name="comparison_result" file="expected_comparison_result_classification.html" compare="sim_size" /> 187 <output name="comparison_result">
188 <assert_contents>
189 <has_text text="Validation Result Summary" />
190 <has_text text="Test Results" />
191 <has_text text="Feature Importance" />
192 </assert_contents>
193 </output>
182 <output name="best_model_csv" value="expected_best_model_classification.csv" /> 194 <output name="best_model_csv" value="expected_best_model_classification.csv" />
183 </test> 195 </test>
184 <test> 196 <test>
185 <param name="input_file" value="auto-mpg.tsv"/> 197 <param name="input_file" value="auto-mpg.tsv"/>
186 <param name="target_feature" value="1"/> 198 <param name="target_feature" value="1"/>
187 <param name="model_type" value="regression"/> 199 <param name="model_type" value="regression"/>
188 <param name="random_seed" value="42"/> 200 <param name="random_seed" value="42"/>
189 <output name="model" file="expected_model_regression.h5" compare="sim_size" /> 201 <output name="model" file="expected_model_regression.h5" compare="sim_size" />
190 <output name="comparison_result" file="expected_comparison_result_regression.html" compare="sim_size" /> 202 <output name="comparison_result">
203 <assert_contents>
204 <has_text text="Validation Result Summary" />
205 <has_text text="Test Results" />
206 <has_text text="Feature Importance" />
207 </assert_contents>
208 </output>
191 <output name="best_model_csv" value="expected_best_model_regression.csv" /> 209 <output name="best_model_csv" value="expected_best_model_regression.csv" />
192 </test> 210 </test>
193 </tests> 211 </tests>
194 <help> 212 <help>
195 This tool uses PyCaret to train and evaluate machine learning models. 213 This tool uses PyCaret to train and evaluate machine learning models.