comparison pipeline.xml @ 19:4de3d598c116 draft

"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"

18:9c3e1d3235c8

<tool id="sklearn_build_pipeline" name="Pipeline Builder" version="@VERSION@">
    <description>an all-in-one platform to build pipeline, single estimator, preprocessor and custom wrappers</description>
    <macros>
        <import>main_macros.xml</import>
    </macros>
    <expand macro="python_requirements"/>
    <expand macro="macro_stdio"/>
    <version_command>echo "@VERSION@"</version_command>
    <command>
        <![CDATA[
        python "$sklearn_pipeline_script" '$inputs'
        ]]>

                    <option value="skrebate">SK-rebate Feature Selection</option>
                    <option value="imblearn">Imbalanced-learn Sampling</option>
                    <option value="IRAPS">IRAPS -- feature selector and classifier</option>
                    <option value="preprocessors">Bio-sequence Encoders</option>
                </param>
                <when value="None"/>
                <when value="pre_processor">
                    <conditional name="pre_processors">
                        <expand macro="sparse_preprocessors_ext" />
                        <expand macro="sparse_preprocessor_options_ext" />
                    </conditional>
                </when>
                <when value="feature_selection">
                    <expand macro="feature_selection_pipeline"/>
                </when>
                <when value="decomposition">
                    <expand macro="matrix_decomposition_all"/>
                </when>
                <when value="kernel_approximation">
                    <expand macro="kernel_approximation_all"/>
                </when>
                <when value="FeatureAgglomeration">
                    <expand macro="FeatureAgglomeration"/>
                </when>
                <when value="skrebate">
                    <expand macro="skrebate"/>
                </when>
                <when value="imblearn">
                    <expand macro="imbalanced_learn_sampling"/>
                </when>
                <when value="IRAPS">
                    <expand macro="estimator_params_text"
                        label="Type in parameter settings for IRAPSCore if different from default:"
                        help="Default(=blank): n_iter=1000, responsive_thres=-1, resistant_thres=0, random_state=None. No double quotes"/>
                    <param argument="p_thres" type="float" value="0.001" label="P value threshold" help="Float. default=0.001"/>
                    <param argument="fc_thres" type="float" value="0.1" label="fold change threshold" help="Float. default=0.1"/>
                    <param argument="occurrence" type="float" value="0.7" label="reservation factor" help="Float. default=0.7"/>
                    <param argument="discretize" type="float" value="-1" label="The z_score threshold to discretize target value" help="Float. default=-1"/>
                </when>
                <when value="preprocessors">
                    <expand macro="preprocessors_sequence_encoders"/>
                </when>
            </conditional>
        </repeat>
        <section name="final_estimator" title="Final Estimator" expanded="true">
            <conditional name="estimator_selector">
                <param name="selected_module" type="select" label="Choose the module that contains target estimator:" >
                    <expand macro="estimator_module_options">
                        <option value="sklearn.compose">sklearn.compose</option>
                        <option value="binarize_target">Binarize Target Classifier or Regressor</option>
                        <option value="custom_estimator">Load a custom estimator</option>
                        <option value="none">none -- The last component of pre-processing step will turn to a final estimator</option>

                <expand macro="estimator_suboptions">
                    <when value="sklearn.compose">
                        <param name="selected_estimator" type="select" label="Choose estimator class:">
                            <option value="TransformedTargetRegressor" selected="true">TransformedTargetRegressor</option>
                        </param>
                        <param name="regressor" type="data" format="zip" label="Choose the dataset containing the wrapped regressor"/>
                        <param name="transformer" type="data" format="zip" label="Choose the dataset containing transformer"/>
                    </when>
                    <when value="binarize_target">
                        <param name="clf_or_regr" type="select" label="Classifier or Regressor:">
                            <option value="BinarizeTargetClassifier">BinarizeTargetClassifier</option>
                            <option value="BinarizeTargetRegressor">BinarizeTargetRegressor</option>
                        </param>
                        <param name="wrapped_estimator" type="data" format="zip" label="Choose the dataset containing the wrapped estimator or pipeline"/>
                        <param name='z_score' type="float" value="-1" optional="false" label="Discrize target values using z_score"/>
                        <param name='value' type="float" value="" optional="true" label="Discretize target values using a fixed value instead" help="Optional. default: None."/>
                        <param name="less_is_positive" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="true" label="Are the detecting values smaller than others?"/>
                    </when>
                    <when value="custom_estimator">
                        <param name="c_estimator" type="data" format="zip" label="Choose the dataset containing the custom estimator or pipeline"/>
                    </when>
                    <when value="none"/>
                </expand>
            </conditional>
        </section>
        <!--param name="output_type" type="select" label="Output the final estimator instead?">
            <option value="Pipeline_Builder" selected="true">Pipeline</option>
            <option value="Final_Estimator_Builder">Final Estimator</option>
        </param>-->
        <param name="get_params" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="false" label="Output parameters for searchCV?"
                help="Optional. Tunable parameters could be obtained through `estimator_attributes` tool."/>
    </inputs>
    <outputs>
        <data format="zip" name="outfile" label="New Pipleline/Estimator"/>
        <data format="tabular" name="outfile_params" label="get_params for Pipleline/Estimator">
            <filter>get_params</filter>
        </data>
    </outputs>
    <tests>
        <test>
            <conditional name="component_selector">
                <param name="component_type" value="pre_processor"/>
                <conditional name="pre_processors">
                    <param name="selected_pre_processor" value="QuantileTransformer"/>
                    <section name="options">
                        <param name="random_state" value="10"/>
                    </section>
                </conditional>
            </conditional>
            <section name="final_estimator">
                <conditional name="estimator_selector">
                    <param name="selected_module" value="none"/>
                </conditional>
            </section>
            <output name="outfile" file="pipeline17" compare="sim_size" delta="5"/>
        </test>
        <test>
            <conditional name="component_selector">
                <param name="component_type" value="pre_processor"/>
                <conditional name="pre_processors">
                    <param name="selected_pre_processor" value="PowerTransformer"/>
                </conditional>
            </conditional>
            <section name="final_estimator">
                <conditional name="estimator_selector">
                    <param name="selected_module" value="sklearn.compose"/>
                    <param name="regressor" value="RandomForestRegressor01.zip" ftype="zip"/>
                    <param name="transformer" value="pipeline17" ftype="zip"/>
                </conditional>
            </section>
            <param name="get_params" value="true"/>
            <output name="outfile_params" file="pipeline_params18" ftype="tabular"/>
        </test>
        <test>
            <repeat name="pipeline_component">
                <conditional name="component_selector">
                    <param name="component_type" value="pre_processor"/>
                    <conditional name="pre_processors">
                        <param name="selected_pre_processor" value="RobustScaler"/>
                    </conditional>
                </conditional>
            </repeat>
            <repeat name="pipeline_component">
                <conditional name="component_selector">
                    <param name="component_type" value="feature_selection"/>
                    <conditional name="fs_algorithm_selector">
                        <param name="selected_algorithm" value="SelectKBest"/>
                        <param name="score_func" value="f_classif"/>
                    </conditional>
                </conditional>
            </repeat>
            <param name="selected_module" value="svm"/>
            <param name="selected_estimator" value="SVR"/>
            <param name="text_params" value="kernel='linear'"/>
            <output name="outfile" file="pipeline01" compare="sim_size" delta="5"/>
        </test>
        <test>
            <conditional name="component_selector">
                <param name="component_type" value="pre_processor"/>
                <conditional name="pre_processors">
                    <param name="selected_pre_processor" value="RobustScaler"/>
                </conditional>
            </conditional>
            <param name="selected_module" value="linear_model"/>
            <param name="selected_estimator" value="LassoCV"/>
            <output name="outfile" file="pipeline02" compare="sim_size" delta="5"/>
        </test>
        <test>
            <conditional name="component_selector">
                <param name="component_type" value="pre_processor"/>
                <conditional name="pre_processors">
                    <param name="selected_pre_processor" value="RobustScaler"/>
                </conditional>
            </conditional>
            <param name="selected_module" value="xgboost"/>
            <param name="selected_estimator" value="XGBClassifier"/>
            <output name="outfile" file="pipeline03" compare="sim_size" delta="5"/>
        </test>
        <test>
            <conditional name="component_selector">
                <param name="component_type" value="feature_selection"/>
                <conditional name="fs_algorithm_selector">
                    <param name="selected_algorithm" value="SelectFromModel"/>
                    <conditional name="model_inputter">
                        <conditional name="estimator_selector">
                            <param name="selected_module" value="ensemble"/>
                            <param name="selected_estimator" value="AdaBoostClassifier"/>
                        </conditional>
                    </conditional>
                </conditional>
            </conditional>
            <section name="final_estimator">
                <param name="selected_module" value="svm"/>
                <param name="selected_estimator" value="LinearSVC"/>
            </section>
            <output name="outfile" file="pipeline04" compare="sim_size" delta="5"/>
        </test>
        <test>
            <conditional name="component_selector">
                <param name="component_type" value="None"/>
            </conditional>
            <param name="selected_module" value="ensemble"/>
            <param name="selected_estimator" value="RandomForestRegressor"/>
            <param name="text_params" value="n_estimators=100, random_state=42"/>
            <param name="get_params" value="true"/>
            <output name="outfile" file="pipeline05" compare="sim_size" delta="5"/>
            <output name="outfile_params" file="pipeline_params05.tabular" ftype="tabular"/>
        </test>
        <test>
            <conditional name="component_selector">
                <param name="component_type" value="decomposition"/>
                    <conditional name="matrix_decomposition_selector">
                        <param name="select_algorithm" value="PCA"/>
                    </conditional>
            </conditional>
            <param name="selected_module" value="ensemble"/>
            <param name="selected_estimator" value="AdaBoostRegressor"/>
            <output name="outfile" file="pipeline06" compare="sim_size" delta="5"/>
        </test>
        <test>
            <conditional name="component_selector">
                <param name="component_type" value="kernel_approximation"/>
                    <conditional name="kernel_approximation_selector">
                        <param name="select_algorithm" value="RBFSampler"/>
                        <param name="text_params" value="n_components=10, gamma=2.0"/>
                    </conditional>
            </conditional>
            <param name="selected_module" value="ensemble"/>
            <param name="selected_estimator" value="AdaBoostClassifier"/>
            <output name="outfile" file="pipeline07" compare="sim_size" delta="5"/>
        </test>
        <test>
            <conditional name="component_selector">
                <param name="component_type" value="FeatureAgglomeration"/>
                    <conditional name="FeatureAgglomeration_selector">
                        <param name="select_algorithm" value="FeatureAgglomeration"/>
                        <param name="text_params" value="n_clusters=3, affinity='euclidean'"/>
                    </conditional>
            </conditional>
            <param name="selected_module" value="ensemble"/>
            <param name="selected_estimator" value="AdaBoostClassifier"/>
            <output name="outfile" file="pipeline08" compare="sim_size" delta="20"/>
        </test>
        <test>
            <conditional name="component_selector">
                <param name="component_type" value="skrebate"/>
                    <conditional name="skrebate_selector">
                        <param name="select_algorithm" value="ReliefF"/>
                        <param name="text_params" value="n_features_to_select=3, n_neighbors=100"/>
                    </conditional>
            </conditional>
            <param name="selected_module" value="ensemble"/>
            <param name="selected_estimator" value="RandomForestRegressor"/>
            <output name="outfile" file="pipeline09" compare="sim_size" delta="5"/>
        </test>
        <test>
            <conditional name="component_selector">
                <param name="component_type" value="imblearn"/>
                <conditional name="imblearn_selector">
                    <param name="select_algorithm" value="under_sampling.EditedNearestNeighbours"/>
                </conditional>
            </conditional>
            <param name="selected_module" value="ensemble"/>
            <param name="selected_estimator" value="RandomForestClassifier"/>
            <output name="outfile" file="pipeline11" compare="sim_size" delta="5"/>
        </test>
        <test expect_failure="true">
            <conditional name="component_selector">
                <param name="component_type" value="None"/>
            </conditional>
            <param name="selected_module" value="ensemble"/>
            <param name="selected_estimator" value="RandomForestRegressor"/>
            <param name="text_params" value="n_estimators=__import__('os').system('ls ~')"/>
        </test>
        <test>
            <conditional name="component_selector">
                <param name="component_type" value="feature_selection"/>
                <conditional name="fs_algorithm_selector">
                    <param name="selected_algorithm" value="RFE"/>
                    <conditional name="estimator_selector">
                        <param name="selected_module" value="xgboost"/>
                        <param name="selected_estimator" value="XGBRegressor"/>
                        <param name="text_params" value="random_state=0"/>
                    </conditional>
                </conditional>
            </conditional>
            <section name="final_estimator">
                <conditional name="estimator_selector">
                    <param name="selected_module" value="none"/>
                </conditional>
            </section>
            <output name="outfile" file="pipeline12" compare="sim_size" delta="5"/>
        </test>
        <test>
            <conditional name="component_selector">
                <param name="component_type" value="None"/>
            </conditional>
            <param name="selected_module" value="ensemble"/>
            <param name="selected_estimator" value="RandomForestClassifier"/>
            <output name="outfile" file="RandomForestClassifier.zip" compare="sim_size" delta="5"/>
        </test>
        <test>
            <conditional name="component_selector">
                <param name="component_type" value="IRAPS"/>
            </conditional>
            <section name="final_estimator">
                <conditional name="estimator_selector">
                    <param name="selected_module" value="none"/>
                </conditional>
            </section>
            <output name="outfile" file="pipeline14" compare="sim_size" delta="5"/>
        </test>
        <test>
            <conditional name="component_selector">
                <param name="component_type" value="None"/>
            </conditional>
            <section name="final_estimator">
                <conditional name="estimator_selector">
                    <param name="selected_module" value="binarize_target"/>
                    <param name="clf_or_regr" value="BinarizeTargetClassifier"/>
                    <param name="wrapped_estimator" value="RandomForestClassifier.zip" ftype="zip"/>
                </conditional>
            </section>
            <output name="outfile" file="pipeline15" compare="sim_size" delta="5"/>
        </test>
        <test>
            <conditional name="component_selector">
                <param name="component_type" value="preprocessors"/>
                <conditional name="encoder_selection">
                    <param name="encoder_type" value="GenomeOneHotEncoder"/>
                    <param name="seq_length" value="1000"/>
                    <param name="padding" value="True"/>
                </conditional>
            </conditional>
            <section name="final_estimator">
                <conditional name="estimator_selector">
                    <param name="selected_module" value="custom_estimator"/>
                    <param name="c_estimator" value="keras_model02" ftype="zip"/>
                </conditional>
            </section>
            <output name="outfile" file="pipeline16" compare="sim_size" delta="5"/>
        </test>
    </tests>
    <help>
        <![CDATA[
**What it does**

.. _`BinarizeTargetRegressor`: https://goeckslab.github.io/Galaxy-ML/APIs/binarize-target/#binarizetargetregressor

        ]]>
    </help>
    <expand macro="sklearn_citation">
        <expand macro="skrebate_citation"/>
        <expand macro="xgboost_citation"/>
        <expand macro="imblearn_citation"/>
    </expand>
</tool>

19:4de3d598c116

<tool id="sklearn_build_pipeline" name="Pipeline Builder" version="@VERSION@" profile="20.05">
    <description>an all-in-one platform to build pipeline, single estimator, preprocessor and custom wrappers</description>
    <macros>
        <import>main_macros.xml</import>
    </macros>
    <expand macro="python_requirements" />
    <expand macro="macro_stdio" />
    <version_command>echo "@VERSION@"</version_command>
    <command>
        <![CDATA[
        python "$sklearn_pipeline_script" '$inputs'
        ]]>

                    <option value="skrebate">SK-rebate Feature Selection</option>
                    <option value="imblearn">Imbalanced-learn Sampling</option>
                    <option value="IRAPS">IRAPS -- feature selector and classifier</option>
                    <option value="preprocessors">Bio-sequence Encoders</option>
                </param>
                <when value="None" />
                <when value="pre_processor">
                    <conditional name="pre_processors">
                        <expand macro="sparse_preprocessors_ext" />
                        <expand macro="sparse_preprocessor_options_ext" />
                    </conditional>
                </when>
                <when value="feature_selection">
                    <expand macro="feature_selection_pipeline" />
                </when>
                <when value="decomposition">
                    <expand macro="matrix_decomposition_all" />
                </when>
                <when value="kernel_approximation">
                    <expand macro="kernel_approximation_all" />
                </when>
                <when value="FeatureAgglomeration">
                    <expand macro="FeatureAgglomeration" />
                </when>
                <when value="skrebate">
                    <expand macro="skrebate" />
                </when>
                <when value="imblearn">
                    <expand macro="imbalanced_learn_sampling" />
                </when>
                <when value="IRAPS">
                    <expand macro="estimator_params_text" label="Type in parameter settings for IRAPSCore if different from default:" help="Default(=blank): n_iter=1000, responsive_thres=-1, resistant_thres=0, random_state=None. No double quotes" />
                    <param argument="p_thres" type="float" value="0.001" label="P value threshold" help="Float. default=0.001" />
                    <param argument="fc_thres" type="float" value="0.1" label="fold change threshold" help="Float. default=0.1" />
                    <param argument="occurrence" type="float" value="0.7" label="reservation factor" help="Float. default=0.7" />
                    <param argument="discretize" type="float" value="-1" label="The z_score threshold to discretize target value" help="Float. default=-1" />
                </when>
                <when value="preprocessors">
                    <expand macro="preprocessors_sequence_encoders" />
                </when>
            </conditional>
        </repeat>
        <section name="final_estimator" title="Final Estimator" expanded="true">
            <conditional name="estimator_selector">
                <param name="selected_module" type="select" label="Choose the module that contains target estimator:">
                    <expand macro="estimator_module_options">
                        <option value="sklearn.compose">sklearn.compose</option>
                        <option value="binarize_target">Binarize Target Classifier or Regressor</option>
                        <option value="custom_estimator">Load a custom estimator</option>
                        <option value="none">none -- The last component of pre-processing step will turn to a final estimator</option>

                <expand macro="estimator_suboptions">
                    <when value="sklearn.compose">
                        <param name="selected_estimator" type="select" label="Choose estimator class:">
                            <option value="TransformedTargetRegressor" selected="true">TransformedTargetRegressor</option>
                        </param>
                        <param name="regressor" type="data" format="zip" label="Choose the dataset containing the wrapped regressor" />
                        <param name="transformer" type="data" format="zip" label="Choose the dataset containing transformer" />
                    </when>
                    <when value="binarize_target">
                        <param name="clf_or_regr" type="select" label="Classifier or Regressor:">
                            <option value="BinarizeTargetClassifier">BinarizeTargetClassifier</option>
                            <option value="BinarizeTargetRegressor">BinarizeTargetRegressor</option>
                        </param>
                        <param name="wrapped_estimator" type="data" format="zip" label="Choose the dataset containing the wrapped estimator or pipeline" />
                        <param name='z_score' type="float" value="-1" optional="false" label="Discrize target values using z_score" />
                        <param name='value' type="float" value="" optional="true" label="Discretize target values using a fixed value instead" help="Optional. default: None." />
                        <param name="less_is_positive" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="true" label="Are the detecting values smaller than others?" />
                    </when>
                    <when value="custom_estimator">
                        <param name="c_estimator" type="data" format="zip" label="Choose the dataset containing the custom estimator or pipeline" />
                    </when>
                    <when value="none" />
                </expand>
            </conditional>
        </section>
        <!--param name="output_type" type="select" label="Output the final estimator instead?">
            <option value="Pipeline_Builder" selected="true">Pipeline</option>
            <option value="Final_Estimator_Builder">Final Estimator</option>
        </param>-->
        <param name="get_params" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="false" label="Output parameters for searchCV?" help="Optional. Tunable parameters could be obtained through `estimator_attributes` tool." />
    </inputs>
    <outputs>
        <data format="zip" name="outfile" label="New Pipleline/Estimator" />
        <data format="tabular" name="outfile_params" label="get_params for Pipleline/Estimator">
            <filter>get_params</filter>
        </data>
    </outputs>
    <tests>
        <test>
            <conditional name="component_selector">
                <param name="component_type" value="pre_processor" />
                <conditional name="pre_processors">
                    <param name="selected_pre_processor" value="QuantileTransformer" />
                    <section name="options">
                        <param name="random_state" value="10" />
                    </section>
                </conditional>
            </conditional>
            <section name="final_estimator">
                <conditional name="estimator_selector">
                    <param name="selected_module" value="none" />
                </conditional>
            </section>
            <output name="outfile" file="pipeline17" compare="sim_size" delta="30" />
        </test>
        <test>
            <conditional name="component_selector">
                <param name="component_type" value="pre_processor" />
                <conditional name="pre_processors">
                    <param name="selected_pre_processor" value="PowerTransformer" />
                </conditional>
            </conditional>
            <section name="final_estimator">
                <conditional name="estimator_selector">
                    <param name="selected_module" value="sklearn.compose" />
                    <param name="regressor" value="RandomForestRegressor01.zip" ftype="zip" />
                    <param name="transformer" value="pipeline17" ftype="zip" />
                </conditional>
            </section>
            <param name="get_params" value="true" />
            <output name="outfile_params" file="pipeline_params18" ftype="tabular" />
        </test>
        <test>
            <repeat name="pipeline_component">
                <conditional name="component_selector">
                    <param name="component_type" value="pre_processor" />
                    <conditional name="pre_processors">
                        <param name="selected_pre_processor" value="RobustScaler" />
                    </conditional>
                </conditional>
            </repeat>
            <repeat name="pipeline_component">
                <conditional name="component_selector">
                    <param name="component_type" value="feature_selection" />
                    <conditional name="fs_algorithm_selector">
                        <param name="selected_algorithm" value="SelectKBest" />
                        <param name="score_func" value="f_classif" />
                    </conditional>
                </conditional>
            </repeat>
            <param name="selected_module" value="svm" />
            <param name="selected_estimator" value="SVR" />
            <param name="text_params" value="kernel='linear'" />
            <output name="outfile" file="pipeline01" compare="sim_size" delta="30" />
        </test>
        <test>
            <conditional name="component_selector">
                <param name="component_type" value="pre_processor" />
                <conditional name="pre_processors">
                    <param name="selected_pre_processor" value="RobustScaler" />
                </conditional>
            </conditional>
            <param name="selected_module" value="linear_model" />
            <param name="selected_estimator" value="LassoCV" />
            <output name="outfile" file="pipeline02" compare="sim_size" delta="30" />
        </test>
        <test>
            <conditional name="component_selector">
                <param name="component_type" value="pre_processor" />
                <conditional name="pre_processors">
                    <param name="selected_pre_processor" value="RobustScaler" />
                </conditional>
            </conditional>
            <param name="selected_module" value="xgboost" />
            <param name="selected_estimator" value="XGBClassifier" />
            <output name="outfile" file="pipeline03" compare="sim_size" delta="30" />
        </test>
        <test>
            <conditional name="component_selector">
                <param name="component_type" value="feature_selection" />
                <conditional name="fs_algorithm_selector">
                    <param name="selected_algorithm" value="SelectFromModel" />
                    <conditional name="model_inputter">
                        <conditional name="estimator_selector">
                            <param name="selected_module" value="ensemble" />
                            <param name="selected_estimator" value="AdaBoostClassifier" />
                        </conditional>
                    </conditional>
                </conditional>
            </conditional>
            <section name="final_estimator">
                <param name="selected_module" value="svm" />
                <param name="selected_estimator" value="LinearSVC" />
            </section>
            <output name="outfile" file="pipeline04" compare="sim_size" delta="30" />
        </test>
        <test>
            <conditional name="component_selector">
                <param name="component_type" value="None" />
            </conditional>
            <param name="selected_module" value="ensemble" />
            <param name="selected_estimator" value="RandomForestRegressor" />
            <param name="text_params" value="n_estimators=100, random_state=42" />
            <param name="get_params" value="true" />
            <output name="outfile" file="pipeline05" compare="sim_size" delta="30" />
            <output name="outfile_params" file="pipeline_params05.tabular" ftype="tabular" />
        </test>
        <test>
            <conditional name="component_selector">
                <param name="component_type" value="decomposition" />
                <conditional name="matrix_decomposition_selector">
                    <param name="select_algorithm" value="PCA" />
                </conditional>
            </conditional>
            <param name="selected_module" value="ensemble" />
            <param name="selected_estimator" value="AdaBoostRegressor" />
            <output name="outfile" file="pipeline06" compare="sim_size" delta="30" />
        </test>
        <test>
            <conditional name="component_selector">
                <param name="component_type" value="kernel_approximation" />
                <conditional name="kernel_approximation_selector">
                    <param name="select_algorithm" value="RBFSampler" />
                    <param name="text_params" value="n_components=10, gamma=2.0" />
                </conditional>
            </conditional>
            <param name="selected_module" value="ensemble" />
            <param name="selected_estimator" value="AdaBoostClassifier" />
            <output name="outfile" file="pipeline07" compare="sim_size" delta="30" />
        </test>
        <test>
            <conditional name="component_selector">
                <param name="component_type" value="FeatureAgglomeration" />
                <conditional name="FeatureAgglomeration_selector">
                    <param name="select_algorithm" value="FeatureAgglomeration" />
                    <param name="text_params" value="n_clusters=3, affinity='euclidean'" />
                </conditional>
            </conditional>
            <param name="selected_module" value="ensemble" />
            <param name="selected_estimator" value="AdaBoostClassifier" />
            <output name="outfile" file="pipeline08" compare="sim_size" delta="30" />
        </test>
        <test>
            <conditional name="component_selector">
                <param name="component_type" value="skrebate" />
                <conditional name="skrebate_selector">
                    <param name="select_algorithm" value="ReliefF" />
                    <param name="text_params" value="n_features_to_select=3, n_neighbors=100" />
                </conditional>
            </conditional>
            <param name="selected_module" value="ensemble" />
            <param name="selected_estimator" value="RandomForestRegressor" />
            <output name="outfile" file="pipeline09" compare="sim_size" delta="30" />
        </test>
        <test>
            <conditional name="component_selector">
                <param name="component_type" value="imblearn" />
                <conditional name="imblearn_selector">
                    <param name="select_algorithm" value="under_sampling.EditedNearestNeighbours" />
                </conditional>
            </conditional>
            <param name="selected_module" value="ensemble" />
            <param name="selected_estimator" value="RandomForestClassifier" />
            <output name="outfile" file="pipeline11" compare="sim_size" delta="30" />
        </test>
        <test expect_failure="true">
            <conditional name="component_selector">
                <param name="component_type" value="None" />
            </conditional>
            <param name="selected_module" value="ensemble" />
            <param name="selected_estimator" value="RandomForestRegressor" />
            <param name="text_params" value="n_estimators=__import__('os').system('ls ~')" />
        </test>
        <test>
            <conditional name="component_selector">
                <param name="component_type" value="feature_selection" />
                <conditional name="fs_algorithm_selector">
                    <param name="selected_algorithm" value="RFE" />
                    <conditional name="estimator_selector">
                        <param name="selected_module" value="xgboost" />
                        <param name="selected_estimator" value="XGBRegressor" />
                        <param name="text_params" value="random_state=0" />
                    </conditional>
                </conditional>
            </conditional>
            <section name="final_estimator">
                <conditional name="estimator_selector">
                    <param name="selected_module" value="none" />
                </conditional>
            </section>
            <output name="outfile" file="pipeline12" compare="sim_size" delta="30" />
        </test>
        <test>
            <conditional name="component_selector">
                <param name="component_type" value="None" />
            </conditional>
            <param name="selected_module" value="ensemble" />
            <param name="selected_estimator" value="RandomForestClassifier" />
            <output name="outfile" file="RandomForestClassifier.zip" compare="sim_size" delta="30" />
        </test>
        <test>
            <conditional name="component_selector">
                <param name="component_type" value="IRAPS" />
            </conditional>
            <section name="final_estimator">
                <conditional name="estimator_selector">
                    <param name="selected_module" value="none" />
                </conditional>
            </section>
            <output name="outfile" file="pipeline14" compare="sim_size" delta="30" />
        </test>
        <test>
            <conditional name="component_selector">
                <param name="component_type" value="None" />
            </conditional>
            <section name="final_estimator">
                <conditional name="estimator_selector">
                    <param name="selected_module" value="binarize_target" />
                    <param name="clf_or_regr" value="BinarizeTargetClassifier" />
                    <param name="wrapped_estimator" value="RandomForestClassifier.zip" ftype="zip" />
                </conditional>
            </section>
            <output name="outfile" file="pipeline15" compare="sim_size" delta="30" />
        </test>
        <test>
            <conditional name="component_selector">
                <param name="component_type" value="preprocessors" />
                <conditional name="encoder_selection">
                    <param name="encoder_type" value="GenomeOneHotEncoder" />
                    <param name="seq_length" value="1000" />
                    <param name="padding" value="True" />
                </conditional>
            </conditional>
            <section name="final_estimator">
                <conditional name="estimator_selector">
                    <param name="selected_module" value="custom_estimator" />
                    <param name="c_estimator" value="keras_model02" ftype="zip" />
                </conditional>
            </section>
            <output name="outfile" file="pipeline16" compare="sim_size" delta="30" />
        </test>
    </tests>
    <help>
        <![CDATA[
**What it does**

.. _`BinarizeTargetRegressor`: https://goeckslab.github.io/Galaxy-ML/APIs/binarize-target/#binarizetargetregressor

        ]]>
    </help>
    <expand macro="sklearn_citation">
        <expand macro="skrebate_citation" />
        <expand macro="xgboost_citation" />
        <expand macro="imblearn_citation" />
    </expand>
</tool>