comparison pipeline.xml @ 25:118e230e85ce draft

planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 9981e25b00de29ed881b2229a173a8c812ded9bb

24:b1eda492f063

<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" />

        <configfile name="sklearn_pipeline_script">
            <![CDATA[
import imblearn
import json
import pandas as pd
import pickle
import pprint
import skrebate
import sys
import warnings
from sklearn import (

    feature_selection, gaussian_process, kernel_approximation, metrics,
    model_selection, naive_bayes, neighbors, pipeline, preprocessing,
    svm, linear_model, tree, discriminant_analysis)
from sklearn.pipeline import make_pipeline
from imblearn.pipeline import make_pipeline as imb_make_pipeline
from galaxy_ml.utils import (SafeEval, feature_selector, get_estimator,
                             try_get_attr, get_search_params, load_model)

## TODO remove following imports after scikit-learn v0.22
from sklearn.experimental import enable_hist_gradient_boosting


N_JOBS = int(__import__('os').environ.get('GALAXY_SLOTS', 1))

warnings.filterwarnings('ignore')

elif estimator_json['selected_module'] == 'sklearn.compose':
    #if $final_estimator.estimator_selector.selected_module == 'sklearn.compose':
    regressor_path = '$final_estimator.estimator_selector.regressor'
    transformer_path = '$final_estimator.estimator_selector.transformer'
    #end if
    with open(regressor_path, 'rb') as f:
        regressor = load_model(f)
    with open(transformer_path, 'rb') as f:
        transformer = load_model(f)
    estimator = compose.TransformedTargetRegressor(regressor=regressor, transformer=transformer)
    pipeline_steps.append( estimator )
else:
    estimator = get_estimator(estimator_json)
    pipeline_steps.append( estimator )

        out_obj = imb_make_pipeline(*pipeline_steps)
    else:
        out_obj = make_pipeline(*pipeline_steps)
    pprint.pprint(out_obj.named_steps)

with open('$outfile', 'wb') as out_handler:
    pickle.dump(out_obj, out_handler, pickle.HIGHEST_PROTOCOL)

#if $get_params
results = get_search_params(out_obj)
df = pd.DataFrame(results, columns=['', 'Parameter', 'Value'])
df.to_csv('$outfile_params', sep='\t', index=False)
#end if
            ]]>
        </configfile>
    </configfiles>
    <inputs>
        <repeat name="pipeline_component" min="1" max="5" title="Pre-processing step">

                </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>
            </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" />

            <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">

                </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>
            </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">

            </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>

            <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">

                </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`_

**Output**

- Pickled pipeline/estimator object

- Hyperparameter of the ojbect (optional)


.. _`Scikit-learn pipeline Pipeline`: http://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html
.. _`svm`: http://scikit-learn.org/stable/modules/classes.html#module-sklearn.svm
.. _`linear_model`: http://scikit-learn.org/stable/modules/classes.html#module-sklearn.linear_model

25:118e230e85ce

<tool id="sklearn_build_pipeline" name="Pipeline Builder" version="@VERSION@" profile="@PROFILE@">
    <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" />

        <configfile name="sklearn_pipeline_script">
            <![CDATA[
import imblearn
import json
import pandas as pd
import pprint
import skrebate
import sys
import warnings
from sklearn import (

    feature_selection, gaussian_process, kernel_approximation, metrics,
    model_selection, naive_bayes, neighbors, pipeline, preprocessing,
    svm, linear_model, tree, discriminant_analysis)
from sklearn.pipeline import make_pipeline
from imblearn.pipeline import make_pipeline as imb_make_pipeline
from galaxy_ml.model_persist import dump_model_to_h5, load_model_from_h5
from galaxy_ml.utils import (SafeEval, feature_selector, get_estimator,
                             try_get_attr, get_search_params)


N_JOBS = int(__import__('os').environ.get('GALAXY_SLOTS', 1))

warnings.filterwarnings('ignore')

elif estimator_json['selected_module'] == 'sklearn.compose':
    #if $final_estimator.estimator_selector.selected_module == 'sklearn.compose':
    regressor_path = '$final_estimator.estimator_selector.regressor'
    transformer_path = '$final_estimator.estimator_selector.transformer'
    #end if
    regressor = load_model_from_h5(regressor_path)
    transformer = load_model_from_h5(transformer_path)
    estimator = compose.TransformedTargetRegressor(regressor=regressor, transformer=transformer)
    pipeline_steps.append( estimator )
else:
    estimator = get_estimator(estimator_json)
    pipeline_steps.append( estimator )

        out_obj = imb_make_pipeline(*pipeline_steps)
    else:
        out_obj = make_pipeline(*pipeline_steps)
    pprint.pprint(out_obj.named_steps)

dump_model_to_h5(out_obj, '$outfile', verbose=0)

            ]]>
        </configfile>
    </configfiles>
    <inputs>
        <repeat name="pipeline_component" min="1" max="5" title="Pre-processing step">

                </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>
            </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="h5mlm" label="Choose the dataset containing the wrapped regressor" />
                        <param name="transformer" type="data" format="h5mlm" 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="h5mlm" 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="h5mlm" 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>-->
    </inputs>
    <outputs>
        <data format="h5mlm" name="outfile" label="New Pipleline/Estimator" />
    </outputs>
    <tests>
        <test>
            <conditional name="component_selector">
                <param name="component_type" value="pre_processor" />

            <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">

                </conditional>
            </conditional>
            <section name="final_estimator">
                <conditional name="estimator_selector">
                    <param name="selected_module" value="sklearn.compose" />
                    <param name="regressor" value="RandomForestRegressor01.h5mlm" ftype="h5mlm" />
                    <param name="transformer" value="pipeline17" ftype="h5mlm" />
                </conditional>
            </section>
            <output name="outfile" file="pipeline18" compare="sim_size" delta="5" />
        </test>
        <test>
            <repeat name="pipeline_component">
                <conditional name="component_selector">
                    <param name="component_type" value="pre_processor" />

                </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">

            </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" />
            <output name="outfile" file="pipeline05" compare="sim_size" delta="5" />
        </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>

            <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.h5mlm" 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.h5mlm" ftype="h5mlm" />
                </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">

                </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="h5mlm" />
                </conditional>
            </section>
            <output name="outfile" file="pipeline16" compare="sim_size" delta="5" />
        </test>
    </tests>
    <help>
        <![CDATA[
**What it does**

- `BinarizeTargetRegressor`_

**Output**

- Pipeline/estimator object

- Hyperparameter of the object (optional)


.. _`Scikit-learn pipeline Pipeline`: http://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html
.. _`svm`: http://scikit-learn.org/stable/modules/classes.html#module-sklearn.svm
.. _`linear_model`: http://scikit-learn.org/stable/modules/classes.html#module-sklearn.linear_model