Mercurial > repos > goeckslab > tabular_learner

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planemo upload for repository https://github.com/goeckslab/gleam commit f632803cda732005bdcf3ac3e8fe7a807a82c1d9
author goeckslab
date Mon, 19 Jan 2026 05:54:52 +0000
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1 <tool id="tabular_learner" name="Tabular Learner" version="@TABULAR_LEARNER_VERSION@" profile="@PROFILE@">
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2 <description>applies and evaluates multiple machine learning models on a tabular dataset</description>
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3 <macros>
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4 <import>pycaret_macros.xml</import>
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5 </macros>
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6 <expand macro="python_requirements" />
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7 <command>
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8 <![CDATA[
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9 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}
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10 #if $sample_id_selector.use_sample_id == "yes"
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11 --sample-id-column '$sample_id_selector.sample_id_column'
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12 #end if
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13 #if $model_selection.model_type == "classification"
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14 #if $model_selection.classification_models
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15 --models '$model_selection.classification_models'
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16 #end if
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17 #end if
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18 #if $model_selection.model_type == "regression"
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19 #if $model_selection.regression_models
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20 --models '$model_selection.regression_models'
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21 #end if
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22 #end if
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23 #if $tune_model
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24 --tune_model
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25 #end if
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26 #if $advanced_settings.customize_defaults == "true"
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27 #if $advanced_settings.train_size
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28 --train_size '$advanced_settings.train_size'
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29 #end if
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30 #if $advanced_settings.normalize
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31 --normalize
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32 #end if
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33 #if $advanced_settings.feature_selection
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34 --feature_selection
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35 #end if
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36 #if $advanced_settings.cross_validation.enable_cross_validation == "true"
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37 --cross_validation
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38 #if $advanced_settings.cross_validation.cross_validation_folds
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39 --cross_validation_folds '$advanced_settings.cross_validation.cross_validation_folds'
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40 #end if
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41 #end if
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42 #if $advanced_settings.cross_validation.enable_cross_validation == "false"
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43 --no_cross_validation
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44 #end if
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45 #if $advanced_settings.remove_outliers
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46 --remove_outliers
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47 #end if
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48 #if $advanced_settings.remove_multicollinearity
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49 --remove_multicollinearity
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50 #end if
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51 #if $advanced_settings.polynomial_features
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52 --polynomial_features
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53 #end if
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54 #if $advanced_settings.fix_imbalance
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55 --fix_imbalance
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56 #end if
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57 #if $advanced_settings.probability_threshold
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58 --probability_threshold '$advanced_settings.probability_threshold'
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59 #end if
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60 #end if
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61 #if $test_data_choice.has_test_file == "yes"
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62 --test_file '$test_data_choice.test_file'
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63 #end if
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64 --model_type '$model_selection.model_type'
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65 #if $model_selection.best_model_metric
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66 --best_model_metric '$model_selection.best_model_metric'
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67 #end if
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68 ]]>
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69 </command>
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70 <inputs>
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71 <param name="input_file" type="data" format="csv,tabular" label="Tabular Input Dataset" />
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72 <conditional name="test_data_choice">
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73 <param name="has_test_file" type="select" label="Do you have a separate test dataset?">
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74 <option value="no" selected="true">No</option>
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75 <option value="yes">Yes</option>
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76 </param>
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77 <when value="yes">
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78 <param name="test_file" type="data" format="csv,tabular" label="Tabular Test Dataset"
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79 help="If a test dataset is provided, the input dataset will be split into training and validation sets only.
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80 If not, the tool will split your input into training, validation, and test automatically." />
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81 </when>
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82 <when value="no">
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83 <!-- Nothing extra shown -->
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84 </when>
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85 </conditional>
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86 <param name="target_feature" multiple="false" type="data_column" use_header_names="true" data_ref="input_file" label="Select the target column:" />
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87 <conditional name="sample_id_selector">
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88 <param name="use_sample_id" type="select" label="Use a sample ID column for leakage-aware splitting?" help="Select yes to choose a column that groups related records (e.g., patient_id or slide_id).">
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89 <option value="no" selected="true">No column selected</option>
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90 <option value="yes">Yes</option>
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91 </param>
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92 <when value="yes">
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93 <param name="sample_id_column" type="data_column" data_ref="input_file" use_header_names="true" label="Sample ID column" help="All rows with the same ID stay in the same split to reduce leakage. Used for group-aware splitting when no separate test file is provided, and for group-aware cross-validation when enabled." />
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94 </when>
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95 <when value="no">
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96 <!-- No sample ID column -->
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97 </when>
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98 </conditional>
0
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99 <conditional name="model_selection">
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100 <param name="model_type" type="select" label="Task">
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101 <option value="classification">classification</option>
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102 <option value="regression">regression</option>
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103 </param>
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104 <when value="classification">
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105 <param name="classification_models" type="select" multiple="true" label="Only Select Classification Models if you don't want to compare all models">
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106 <option value="lr">Logistic Regression</option>
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107 <option value="knn">K Neighbors Classifier</option>
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108 <option value="nb">Naive Bayes</option>
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109 <option value="dt">Decision Tree Classifier</option>
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110 <option value="svm">SVM - Linear Kernel</option>
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111 <option value="rbfsvm">SVM - Radial Kernel</option>
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112 <option value="gpc">Gaussian Process Classifier</option>
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113 <option value="mlp">MLP Classifier</option>
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114 <option value="ridge">Ridge Classifier</option>
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115 <option value="rf">Random Forest Classifier</option>
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116 <option value="qda">Quadratic Discriminant Analysis</option>
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117 <option value="ada">Ada Boost Classifier</option>
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118 <option value="gbc">Gradient Boosting Classifier</option>
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119 <option value="lda">Linear Discriminant Analysis</option>
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120 <option value="et">Extra Trees Classifier</option>
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121 <option value="xgboost">Extreme Gradient Boosting</option>
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122 <option value="lightgbm">Light Gradient Boosting Machine</option>
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123 <option value="catboost">CatBoost Classifier</option>
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124 </param>
9
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125 <param name="best_model_metric" type="select" label="Select metric to pick the best model" help="PyCaret will rank models by this metric. Default is Accuracy.">
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126 <option value="Accuracy" selected="true">Accuracy</option>
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127 <option value="AUC">ROC-AUC</option>
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128 <option value="Precision">Precision</option>
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129 <option value="Recall">Recall</option>
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130 <option value="F1">F1</option>
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131 <option value="Kappa">Cohen’s Kappa</option>
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132 <option value="Log Loss">Log Loss (lower is better)</option>
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133 <option value="PR-AUC-Weighted">PR-AUC (weighted)</option>
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134 </param>
0
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135 </when>
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136 <when value="regression">
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137 <param name="regression_models" type="select" multiple="true" label="Only Select Regression Models if you don't want to compare all models">
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138 <option value="lr">Linear Regression</option>
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139 <option value="lasso">Lasso Regression</option>
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140 <option value="ridge">Ridge Regression</option>
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141 <option value="en">Elastic Net</option>
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142 <option value="lar">Least Angle Regression</option>
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143 <option value="llar">Lasso Least Angle Regression</option>
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144 <option value="omp">Orthogonal Matching Pursuit</option>
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145 <option value="br">Bayesian Ridge</option>
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146 <option value="ard">Automatic Relevance Determination</option>
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147 <option value="par">Passive Aggressive Regressor</option>
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148 <option value="ransac">Random Sample Consensus</option>
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149 <option value="tr">TheilSen Regressor</option>
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150 <option value="huber">Huber Regressor</option>
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151 <option value="kr">Kernel Ridge</option>
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152 <option value="svm">Support Vector Regression</option>
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153 <option value="knn">K Neighbors Regressor</option>
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154 <option value="dt">Decision Tree Regressor</option>
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155 <option value="rf">Random Forest Regressor</option>
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156 <option value="et">Extra Trees Regressor</option>
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157 <option value="ada">AdaBoost Regressor</option>
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158 <option value="gbr">Gradient Boosting Regressor</option>
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159 <option value="mlp">MLP Regressor</option>
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160 <option value="xgboost">Extreme Gradient Boosting</option>
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161 <option value="lightgbm">Light Gradient Boosting Machine</option>
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162 <option value="catboost">CatBoost Regressor</option>
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163 </param>
9
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164 <param name="best_model_metric" type="select" label="Select metric to pick the best model" help="PyCaret will rank models by this metric. Default is R².">
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165 <option value="R2" selected="true">R²</option>
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166 <option value="MAE">MAE</option>
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167 <option value="MSE">MSE</option>
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168 <option value="RMSE">RMSE</option>
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169 <option value="RMSLE">RMSLE</option>
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170 <option value="MAPE">MAPE</option>
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171 </param>
0
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172 </when>
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173 </conditional>
4
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174 <param name="tune_model" type="boolean" truevalue="True" falsevalue="False" label="Tune hyperparameters" help="Hyperparameter tuning on the best model" />
0
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175 <param name="random_seed" type="integer" value="42" label="Random Seed" help="Random seed for reproducibility." />
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176 <conditional name="advanced_settings">
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177 <param name="customize_defaults" type="select" label="Customize Default Settings?" help="Select yes if you want to customize the default settings of the experiment.">
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178 <option value="false" selected="true">No</option>
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179 <option value="true">Yes</option>
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180 </param>
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181 <when value="true">
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182 <param name="train_size" type="float" value="0.7" min="0.1" max="0.9" label="Train Size" help="Proportion of the input dataset to include in the train split." />
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183 <param name="normalize" type="boolean" truevalue="True" falsevalue="False" checked="false" label="Normalize Data" help="Whether to normalize data before training." />
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184 <param name="feature_selection" type="boolean" truevalue="True" falsevalue="False" checked="false" label="Feature Selection" help="Whether to perform feature selection." />
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185 <conditional name="cross_validation">
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186 <param name="enable_cross_validation" type="select" label="Enable Cross Validation?" help="Select whether to enable cross-validation." >
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187 <option value="false" >No</option>
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188 <option value="true" selected="true">Yes</option>
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189 </param>
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190 <when value="true">
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191 <param name="cross_validation_folds" type="integer" value="10" min="2" max="20" label="Cross Validation Folds" help="Number of folds to use for cross-validation." />
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192 </when>
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193 <when value="false">
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194 <!-- No additional parameters to show if the user selects 'No' -->
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195 </when>
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196 </conditional>
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197 <param name="remove_outliers" type="boolean" truevalue="True" falsevalue="False" checked="false" label="Remove Outliers" help="Whether to remove outliers from the input dataset before training." />
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198 <param name="remove_multicollinearity" type="boolean" truevalue="True" falsevalue="False" checked="false" label="Remove Multicollinearity" help="Whether to remove multicollinear features before training." />
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199 <param name="polynomial_features" type="boolean" truevalue="True" falsevalue="False" checked="false" label="Polynomial Features" help="Whether to create polynomial features before training." />
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200 <param name="fix_imbalance" type="boolean" truevalue="True" falsevalue="False" checked="false" label="Fix Imbalance" help="ONLY for classfication! Whether to use SMOTE or similar methods to fix imbalance in the input dataset." />
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201 <param name="probability_threshold" type="float" min="0.0" max="1.0" value="0.5" label="Classification Probability Threshold" help="Only applies to classification. Probability above which a prediction is considered positive. Default is 0.5." />
0
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202 </when>
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203 <when value="false">
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204 <!-- No additional parameters to show if the user selects 'No' -->
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205 </when>
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206 </conditional>
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207 </inputs>
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208 <outputs>
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209 <data name="model" format="h5" from_work_dir="pycaret_model.h5" label="${tool.name} best model on ${on_string}" />
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210 <data name="best_model_csv" format="csv" from_work_dir="best_model.csv" label="${tool.name} The parameters of the best model on ${on_string}" hidden="true" />
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211 <data name="comparison_result" format="html" from_work_dir="comparison_result.html" label="${tool.name} analysis report on ${on_string}" />
0
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212 </outputs>
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213 <tests>
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214 <test>
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215 <param name="input_file" value="pcr.tsv"/>
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216 <param name="target_feature" value="11"/>
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217 <param name="model_selection|model_type" value="classification"/>
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218 <param name="model_selection|best_model_metric" value="F1"/>
0
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219 <param name="random_seed" value="42"/>
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220 <param name="advanced_settings|customize_defaults" value="true"/>
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221 <param name="advanced_settings|train_size" value="0.8"/>
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222 <param name="advanced_settings|normalize" value="true"/>
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223 <param name="advanced_settings|feature_selection" value="true"/>
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224 <param name="advanced_settings|cross_validation|enable_cross_validation" value="true"/>
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225 <param name="advanced_settings|cross_validation|cross_validation_folds" value="5"/>
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226 <param name="advanced_settings|remove_outliers" value="true"/>
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227 <param name="advanced_settings|remove_multicollinearity" value="true"/>
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228 <param name="advanced_settings|probability_threshold" value="0.4" />
0
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229 <output name="model" file="expected_model_classification_customized.h5" compare="sim_size"/>
2
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230 <output name="comparison_result">
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231 <assert_contents>
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232 <has_text text="Validation Summary" />
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233 <has_text text="Test Summary" />
2
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234 <has_text text="Feature Importance" />
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235 <has_text text="Best Model Metric" />
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236 <has_text text="F1" />
2
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237 </assert_contents>
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238 </output>
0
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239 <output name="best_model_csv" value="expected_best_model_classification_customized.csv" />
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240 </test>
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241 <test>
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242 <param name="input_file" value="pcr.tsv"/>
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243 <param name="target_feature" value="11"/>
10
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244 <param name="model_selection|model_type" value="classification"/>
0
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245 <param name="random_seed" value="42"/>
10
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246 <param name="advanced_settings|customize_defaults" value="true"/>
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247 <param name="advanced_settings|train_size" value="0.8"/>
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248 <param name="advanced_settings|normalize" value="true"/>
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249 <param name="advanced_settings|feature_selection" value="true"/>
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250 <param name="advanced_settings|cross_validation|enable_cross_validation" value="false"/>
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251 <param name="advanced_settings|remove_outliers" value="true"/>
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252 <param name="advanced_settings|remove_multicollinearity" value="true"/>
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253 <param name="advanced_settings|probability_threshold" value="0.6" />
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254 <output name="model" file="expected_model_classification_customized_cross_off.h5" compare="sim_size"/>
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255 <output name="comparison_result">
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256 <assert_contents>
4
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257 <has_text text="Validation Summary" />
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258 <has_text text="Test Summary" />
3
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259 <has_text text="Feature Importance" />
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260 </assert_contents>
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261 </output>
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262 <output name="best_model_csv" value="expected_best_model_classification_customized_cross_off.csv" />
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263 </test>
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264 <test>
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265 <param name="input_file" value="pcr.tsv"/>
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266 <param name="target_feature" value="11"/>
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267 <param name="model_selection|model_type" value="classification"/>
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268 <param name="random_seed" value="42"/>
4
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269 <param name="tune_model" value="true"/>
0
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270 <output name="model" file="expected_model_classification.h5" compare="sim_size"/>
2
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271 <output name="comparison_result">
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272 <assert_contents>
4
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273 <has_text text="Validation Summary" />
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274 <has_text text="Test Summary" />
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275 <has_text text="Feature Importance" />
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276 </assert_contents>
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277 </output>
10
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278 <output name="best_model_csv">
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279 <assert_contents>
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280 <has_text text="n_estimators,100" />
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281 <has_text text="num_leaves,31" />
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282 <has_text text="learning_rate,0.1" />
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283 </assert_contents>
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284 </output>
4
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285 </test>
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286 <test>
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287 <param name="input_file" value="pcr.tsv"/>
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288 <param name="target_feature" value="11"/>
10
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289 <param name="model_selection|model_type" value="classification"/>
4
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290 <param name="random_seed" value="42"/>
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291 <output name="model" file="expected_model_classification.h5" compare="sim_size"/>
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292 <output name="comparison_result">
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293 <assert_contents>
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294 <has_text text="Validation Summary" />
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295 <has_text text="Test Summary" />
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296 <has_text text="Feature Importance" />
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297 </assert_contents>
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298 </output>
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299 <output name="best_model_csv">
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300 <assert_contents>
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301 <has_text text="n_estimators,100" />
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302 <has_text text="num_leaves,31" />
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303 <has_text text="learning_rate,0.1" />
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304 </assert_contents>
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305 </output>
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306 </test>
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307 <test>
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308 <param name="input_file" value="auto-mpg.tsv"/>
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309 <param name="target_feature" value="1"/>
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310 <param name="model_selection|model_type" value="regression"/>
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311 <param name="model_selection|best_model_metric" value="RMSE"/>
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312 <param name="random_seed" value="42"/>
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313 <output name="model" file="expected_model_regression.h5" compare="sim_size" />
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314 <output name="comparison_result">
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315 <assert_contents>
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316 <has_text text="Validation Summary" />
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317 <has_text text="Test Summary" />
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318 <has_text text="Feature Importance" />
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319 <has_text text="Best Model Metric" />
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320 <has_text text="RMSE" />
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321 </assert_contents>
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322 </output>
0
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323 <output name="best_model_csv" value="expected_best_model_regression.csv" />
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324 </test>
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325 </tests>
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326 <help>
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327 This tool uses PyCaret to train and evaluate machine learning models.
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328 It compares different models on a dataset and provides the best model based on the performance metrics.
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329 You can optionally select a sample ID column to keep related records in the same split and reduce data leakage when the tool creates splits internally.
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330
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331 **Outputs**
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332
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333 - **Model**: The best model trained on the dataset in h5 format.
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334
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335
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336 - **Comparison Result**: The comparison result of different models in html format.
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337 It contains the performance metrics of different models, plots of the best model
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338 on the testing set (or part of the training set if a separate test set is not uploaded), and feature analysis plots.
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339
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340 </help>
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341 <expand macro="macro_citations" />
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342 </tool>