comparison search_model_validation.xml @ 10:82b6104d4682 draft

planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty

9:21d3e08b1a48

        <import>main_macros.xml</import>
    </macros>
    <expand macro="python_requirements"/>
    <expand macro="macro_stdio"/>
    <version_command>echo "@VERSION@"</version_command>
    <command>
        <![CDATA[
        python '$__tool_directory__/search_model_validation.py'
            --inputs '$inputs'
            --estimator '$search_schemes.infile_estimator'
            --infile1 '$input_options.infile1'
            --infile2 '$input_options.infile2'
            --outfile_result '$outfile_result'
            #if $save
            --outfile_object '$outfile_object'
            #end if
            #if $search_schemes.options.cv_selector.selected_cv in ['GroupKFold', 'GroupShuffleSplit', 'LeaveOneGroupOut', 'LeavePGroupsOut']
            --groups '$inputs,$search_schemes.options.cv_selector.groups_selector.infile_g'
            #end if

        ]]>
    </command>
    <configfiles>
        <inputs name="inputs" />

                    <param argument="n_iter" type="integer" value="10" label="Number of parameter settings that are sampled"/>
                    <expand macro="random_state"/>
                </section>
            </when>
        </conditional>
        <param name="save" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="true" label="Save the searchCV object"/>
        <expand macro="sl_mixed_input"/>
        <conditional name="train_test_split">
            <param name="do_split" type="select" label="Whether to hold a portion of samples for test exclusively?" help="train_test_split">
                <option value="no">Nope</option>
                <option value="yes">Yes - I do</option>
            </param>
            <when value='no'/>
            <when value='yes'>
                <param argument="test_size" type="float" optional="True" value="0.25" label="Test size:"/>
                <param argument="train_size" type="float" optional="True" value="" label="Train size:"/>
                <param argument="random_state" type="integer" optional="True" value="" label="Random seed number:"/>
                <param argument="shuffle" type="select">
                    <option value="None">None - No shuffle</option>
                    <option value="simple">Shuffle -- for regression problems</option>
                    <option value="stratified">StratifiedShuffle -- will use the target values as class labels</option>
                    <option value="group">GroupShuffle -- make sure group CV option is choosen</option>
                </param>
            </when>
        </conditional>
    </inputs>
    <outputs>
        <data format="tabular" name="outfile_result"/>
        <data format="zip" name="outfile_object" label="${search_schemes.selected_search_scheme} on ${on_string}">
            <filter>save</filter>
        </data>
    </outputs>
    <tests>
        <test>
            <param name="selected_search_scheme" value="GridSearchCV"/>

            <param name="header1" value="true" />
            <param name="selected_column_selector_option" value="all_columns"/>
            <param name="infile2" value="regression_y.tabular" ftype="tabular"/>
            <param name="header2" value="true" />
            <param name="selected_column_selector_option2" value="all_columns"/>
            <output name="outfile_object" file="searchCV01" compare="sim_size" delta="10"/>
        </test>
        <test>
            <param name="selected_search_scheme" value="GridSearchCV"/>
            <param name="infile_estimator" value="pipeline06" ftype="zip"/>

            <param name="header1" value="true" />
            <param name="selected_column_selector_option" value="all_columns"/>
            <param name="infile2" value="regression_y.tabular" ftype="tabular"/>
            <param name="header2" value="true" />
            <param name="selected_column_selector_option2" value="all_columns"/>
            <output name="outfile_object" file="searchCV02" compare="sim_size" delta="10"/>
        </test>
        <test>
            <param name="selected_search_scheme" value="GridSearchCV"/>
            <param name="infile_estimator" value="pipeline03" ftype="zip"/>

        <test>
            <param name="selected_search_scheme" value="GridSearchCV"/>
            <param name="infile_estimator" value="pipeline09" ftype="zip"/>
            <param name="infile_params" value="get_params09.tabular" ftype="tabular"/>
            <repeat name="param_set">
                <param name="sp_list" value=": [None,'sk_prep_all', 8, 14, skrebate_ReliefF(n_features_to_select=12)]"/>
                <param name="sp_name" value="relieff"/>
            </repeat>
            <repeat name="param_set">
                <param name="sp_list" value="[10]"/>
                <param name="sp_name" value="randomforestregressor__random_state"/>

                    <has_n_columns n="13"/>
                    <has_text text="0.8149439619875293"/>
                </assert_contents>
            </output>
        </test>
        <!--test>
            <conditional name="search_schemes">
                <param name="selected_search_scheme" value="GridSearchCV"/>
                <param name="infile_estimator" value="pipeline05" ftype="zip"/>
                <section name="search_params_builder">
                    <param name="infile_params" value="get_params05.tabular" ftype="tabular"/>

            <param name="header1" value="true" />
            <param name="selected_column_selector_option" value="all_columns"/>
            <param name="infile2" value="regression_y.tabular" ftype="tabular"/>
            <param name="header2" value="true" />
            <param name="selected_column_selector_option2" value="all_columns"/>
            <output name="outfile_result">
                <assert_contents>
                    <has_n_columns n="1"/>
                    <has_text text="0.7986842219788204" />
                </assert_contents>
            </output>
        </test-->
    </tests>
    <help>
        <![CDATA[
**What it does**
Searches optimized parameter settings for an estimator or pipeline through either exhaustive grid cross validation search or Randomized cross validation search.

**Hot number/keyword for preprocessors**::

    0   sklearn_preprocessing.StandardScaler(copy=True, with_mean=True, with_std=True)
    1   sklearn_preprocessing.Binarizer(copy=True, threshold=0.0)
    2   sklearn_preprocessing.Imputer(axis=0, copy=True, missing_values='NaN', strategy='mean', verbose=0)
    3   sklearn_preprocessing.MaxAbsScaler(copy=True)
    4   sklearn_preprocessing.Normalizer(copy=True, norm='l2')
    5   sklearn_preprocessing.MinMaxScaler(copy=True, feature_range=(0, 1))
    6   sklearn_preprocessing.PolynomialFeatures(degree=2, include_bias=True, interaction_only=False)
    7   sklearn_preprocessing.RobustScaler(copy=True, quantile_range=(25.0, 75.0), with_centering=True, with_scaling=True)
    8   sklearn_feature_selection.SelectKBest(k=10, score_func=<function f_classif at 0x113806d90>)
    9   sklearn_feature_selection.GenericUnivariateSelect(mode='percentile', param=1e-05, score_func=<function f_classif at 0x113806d90>)
    10  sklearn_feature_selection.SelectPercentile(percentile=10, score_func=<function f_classif at 0x113806d90>)
    11  sklearn_feature_selection.SelectFpr(alpha=0.05, score_func=<function f_classif at 0x113806d90>)
    12  sklearn_feature_selection.SelectFdr(alpha=0.05, score_func=<function f_classif at 0x113806d90>)
    13  sklearn_feature_selection.SelectFwe(alpha=0.05, score_func=<function f_classif at 0x113806d90>)
    14  sklearn_feature_selection.VarianceThreshold(threshold=0.0)
    15  sklearn_decomposition.FactorAnalysis(copy=True, iterated_power=3, max_iter=1000, n_components=None,
        noise_variance_init=None, random_state=0, svd_method='randomized', tol=0.01)
    16  sklearn_decomposition.FastICA(algorithm='parallel', fun='logcosh', fun_args=None,
        max_iter=200, n_components=None, random_state=0, tol=0.0001, w_init=None, whiten=True)
    17  sklearn_decomposition.IncrementalPCA(batch_size=None, copy=True, n_components=None, whiten=False)
    18  sklearn_decomposition.KernelPCA(alpha=1.0, coef0=1, copy_X=True, degree=3, eigen_solver='auto',
        fit_inverse_transform=False, gamma=None, kernel='linear', kernel_params=None, max_iter=None,
        n_components=None, random_state=0, remove_zero_eig=False, tol=0)
    19  sklearn_decomposition.LatentDirichletAllocation(batch_size=128, doc_topic_prior=None, evaluate_every=-1, learning_decay=0.7,
        learning_method=None, learning_offset=10.0, max_doc_update_iter=100, max_iter=10, mean_change_tol=0.001, n_components=10,
        n_topics=None, perp_tol=0.1, random_state=0, topic_word_prior=None, total_samples=1000000.0, verbose=0)
    20  sklearn_decomposition.MiniBatchDictionaryLearning(alpha=1, batch_size=3, dict_init=None, fit_algorithm='lars',
        n_components=None, n_iter=1000, random_state=0, shuffle=True, split_sign=False, transform_algorithm='omp',
        transform_alpha=None, transform_n_nonzero_coefs=None, verbose=False)
    21  sklearn_decomposition.MiniBatchSparsePCA(alpha=1, batch_size=3, callback=None, method='lars', n_components=None,
        n_iter=100, random_state=0, ridge_alpha=0.01, shuffle=True, verbose=False)
    22  sklearn_decomposition.NMF(alpha=0.0, beta_loss='frobenius', init=None, l1_ratio=0.0, max_iter=200,
        n_components=None, random_state=0, shuffle=False, solver='cd', tol=0.0001, verbose=0)
    23  sklearn_decomposition.PCA(copy=True, iterated_power='auto', n_components=None, random_state=0, svd_solver='auto', tol=0.0, whiten=False)
    24  sklearn_decomposition.SparsePCA(U_init=None, V_init=None, alpha=1, max_iter=1000, method='lars',
        n_components=None, random_state=0, ridge_alpha=0.01, tol=1e-08, verbose=False)
    25  sklearn_decomposition.TruncatedSVD(algorithm='randomized', n_components=2, n_iter=5, random_state=0, tol=0.0)
    26  sklearn_kernel_approximation.Nystroem(coef0=None, degree=None, gamma=None, kernel='rbf',
        kernel_params=None, n_components=100, random_state=0)
    27  sklearn_kernel_approximation.RBFSampler(gamma=1.0, n_components=100, random_state=0)
    28  sklearn_kernel_approximation.AdditiveChi2Sampler(sample_interval=None, sample_steps=2)
    29  sklearn_kernel_approximation.SkewedChi2Sampler(n_components=100, random_state=0, skewedness=1.0)
    30  sklearn_cluster.FeatureAgglomeration(affinity='euclidean', compute_full_tree='auto', connectivity=None,
        linkage='ward', memory=None, n_clusters=2, pooling_func=<function mean at 0x113078ae8>)
    31  skrebate_ReliefF(discrete_threshold=10, n_features_to_select=10, n_neighbors=100, verbose=False)
    32  skrebate_SURF(discrete_threshold=10, n_features_to_select=10, verbose=False)
    33  skrebate_SURFstar(discrete_threshold=10, n_features_to_select=10, verbose=False)
    34  skrebate_MultiSURF(discrete_threshold=10, n_features_to_select=10, verbose=False)
    35  skrebate_MultiSURFstar(discrete_threshold=10, n_features_to_select=10, verbose=False)
    'sk_prep_all':   All sklearn preprocessing estimators, i.e., 0-7
    'fs_all':        All feature_selection estimators, i.e., 8-14
    'decomp_all':    All decomposition estimators, i.e., 15-25
    'k_appr_all':    All kernel_approximation estimators, i.e., 26-29
    'reb_all':       All skrebate estimators, i.e., 31-35
    'all_0':         All except the imbalanced-learn samplers, i.e., 0-35
    'imb_all':       All imbalanced-learn sampling methods, i.e., 36-54.
                     **CAUTION**: Mix of imblearn and other preprocessors may not work.
     None:           opt out of preprocessor

Support mix (CAUTION: Mix of imblearn and other preprocessors may not work), e.g.::

     : [None, 'sk_prep_all', 22, 'k_appr_all', sklearn_feature_selection.SelectKBest(k=50)]



**Whether to do train_test_split?**

10:82b6104d4682

        <import>main_macros.xml</import>
    </macros>
    <expand macro="python_requirements"/>
    <expand macro="macro_stdio"/>
    <version_command>echo "@VERSION@"</version_command>
    <command detect_errors="aggressive">
        <![CDATA[
        export HDF5_USE_FILE_LOCKING='FALSE';
        #if $input_options.selected_input == 'refseq_and_interval'
        bgzip -c '$input_options.target_file' > '${target_file.element_identifier}.gz' &&
        tabix -p bed '${target_file.element_identifier}.gz' &&
        #end if
        python '$__tool_directory__/search_model_validation.py'
            --inputs '$inputs'
            --estimator '$search_schemes.infile_estimator'
            #if $input_options.selected_input == 'seq_fasta'
            --fasta_path '$input_options.fasta_path'
            #elif $input_options.selected_input == 'refseq_and_interval'
            --ref_seq '$input_options.ref_genome_file'
            --interval '$input_options.interval_file'
            --targets "`pwd`/${target_file.element_identifier}.gz"
            #else
            --infile1 '$input_options.infile1'
            #end if
            --infile2 '$input_options.infile2'
            --outfile_result "`pwd`/tmp_outfile_result"
            #if $save != 'nope'
            --outfile_object '$outfile_object'
            #end if
            #if $save == 'save_weights'
            --outfile_weights '$outfile_weights'
            #end if
            #if $search_schemes.options.cv_selector.selected_cv in ['GroupKFold', 'GroupShuffleSplit', 'LeaveOneGroupOut', 'LeavePGroupsOut']
            --groups '$search_schemes.options.cv_selector.groups_selector.infile_g'
            #end if
            >'$outfile_result' && cp tmp_outfile_result '$outfile_result';

        ]]>
    </command>
    <configfiles>
        <inputs name="inputs" />

                    <param argument="n_iter" type="integer" value="10" label="Number of parameter settings that are sampled"/>
                    <expand macro="random_state"/>
                </section>
            </when>
        </conditional>
        <expand macro="sl_mixed_input"/>
        <conditional name="outer_split">
            <param name="split_mode" type="select" label="Whether to hold a portion of samples for test exclusively?" help="Nested CV or train_test_split">
                <option value="no" selected="true">Nope</option>
                <option value="train_test_split">Yes - do a single train test split</option>
                <option value="nested_cv">Yes - do nested CV</option>
            </param>
            <when value='no'/>
            <when value='train_test_split'>
                <param argument="test_size" type="float" optional="True" value="0.25" label="Test size:"/>
                <!--param argument="train_size" type="float" optional="True" value="" label="Train size:"/>-->
                <param argument="random_state" type="integer" optional="True" value="" label="Random seed number:"/>
                <param argument="shuffle" type="select">
                    <option value="None">None - No shuffle</option>
                    <option value="simple">Shuffle -- for regression problems</option>
                    <option value="stratified">StratifiedShuffle -- will use the target values as class labels</option>
                    <option value="group">GroupShuffle -- make sure group CV option is choosen</option>
                </param>
            </when>
            <when value="nested_cv">
                <expand macro="cv_reduced" label="Select the outer cv splitter"/>
            </when>
        </conditional>
        <param name="save" type="select" label="Save best estimator?" help="For security reason, deep learning models will be saved into two datasets, model skeleton and weights.">
            <option value="nope" selected="true">Nope, save is unnecessary</option>
            <option value="save_estimator">Fitted estimator (excluding deep learning)</option>
            <option value="save_weights">Model skeleton and weights, for deep learning exclusively</option>
        </param>
    </inputs>
    <outputs>
        <data format="tabular" name="outfile_result"/>
        <data format="zip" name="outfile_object" label="Fitted estimator or estimator skeleton on ${on_string}">
            <filter>save != 'nope'</filter>
        </data>
        <data format="h5" name="outfile_weights" label="Weights trained on ${on_string}">
            <filter>save == 'save_weights'</filter>
        </data>
    </outputs>
    <tests>
        <test>
            <param name="selected_search_scheme" value="GridSearchCV"/>

            <param name="header1" value="true" />
            <param name="selected_column_selector_option" value="all_columns"/>
            <param name="infile2" value="regression_y.tabular" ftype="tabular"/>
            <param name="header2" value="true" />
            <param name="selected_column_selector_option2" value="all_columns"/>
            <param name="save" value="save_estimator"/>
            <output name="outfile_object" file="searchCV01" compare="sim_size" delta="10"/>
        </test>
        <test>
            <param name="selected_search_scheme" value="GridSearchCV"/>
            <param name="infile_estimator" value="pipeline06" ftype="zip"/>

            <param name="header1" value="true" />
            <param name="selected_column_selector_option" value="all_columns"/>
            <param name="infile2" value="regression_y.tabular" ftype="tabular"/>
            <param name="header2" value="true" />
            <param name="selected_column_selector_option2" value="all_columns"/>
            <param name="save" value="save_estimator"/>
            <output name="outfile_object" file="searchCV02" compare="sim_size" delta="10"/>
        </test>
        <test>
            <param name="selected_search_scheme" value="GridSearchCV"/>
            <param name="infile_estimator" value="pipeline03" ftype="zip"/>

        <test>
            <param name="selected_search_scheme" value="GridSearchCV"/>
            <param name="infile_estimator" value="pipeline09" ftype="zip"/>
            <param name="infile_params" value="get_params09.tabular" ftype="tabular"/>
            <repeat name="param_set">
                <param name="sp_list" value=": [None,'sk_prep_all', 7, 13, skrebate_ReliefF(n_features_to_select=12)]"/>
                <param name="sp_name" value="relieff"/>
            </repeat>
            <repeat name="param_set">
                <param name="sp_list" value="[10]"/>
                <param name="sp_name" value="randomforestregressor__random_state"/>

                    <has_n_columns n="13"/>
                    <has_text text="0.8149439619875293"/>
                </assert_contents>
            </output>
        </test>
        <test>
            <conditional name="search_schemes">
                <param name="selected_search_scheme" value="GridSearchCV"/>
                <param name="infile_estimator" value="pipeline05" ftype="zip"/>
                <section name="search_params_builder">
                    <param name="infile_params" value="get_params05.tabular" ftype="tabular"/>

            <param name="header1" value="true" />
            <param name="selected_column_selector_option" value="all_columns"/>
            <param name="infile2" value="regression_y.tabular" ftype="tabular"/>
            <param name="header2" value="true" />
            <param name="selected_column_selector_option2" value="all_columns"/>
            <conditional name="outer_split">
                <param name="split_mode" value="train_test_split"/>
                <param name="shuffle" value="simple"/>
                <param name="random_state" value="123"/>
            </conditional>
            <output name="outfile_result">
                <assert_contents>
                    <has_n_columns n="1"/>
                    <has_text text="0.8124083594523798"/>
                </assert_contents>
            </output>
        </test>
        <test>
            <conditional name="search_schemes">
                <param name="selected_search_scheme" value="GridSearchCV"/>
                <param name="infile_estimator" value="pipeline05" ftype="zip"/>
                <section name="search_params_builder">
                    <param name="infile_params" value="get_params05.tabular" ftype="tabular"/>
                    <repeat name="param_set">
                        <param name="sp_list" value="[10, 50, 100, 300]"/>
                        <param name="sp_name" value="randomforestregressor__n_estimators"/>
                    </repeat>
                </section>
            </conditional>
            <param name="infile1" value="regression_X.tabular" ftype="tabular"/>
            <param name="header1" value="true" />
            <param name="selected_column_selector_option" value="all_columns"/>
            <param name="infile2" value="regression_y.tabular" ftype="tabular"/>
            <param name="header2" value="true" />
            <param name="selected_column_selector_option2" value="all_columns"/>
            <conditional name="outer_split">
                <param name="split_mode" value="nested_cv"/>
                <conditional name="cv_selector">
                    <param name='selected_cv' value="KFold"/>
                    <param name="n_splits" value="3"/>
                    <param name="shuffle" value="true" />
                    <param name="random_state" value="123"/>
                </conditional>
            </conditional>
            <output name="outfile_result">
                <assert_contents>
                    <has_n_columns n="4"/>
                    <has_text text="0.8044418936007722" />
                </assert_contents>
            </output>
        </test>
    </tests>
    <help>
        <![CDATA[
**What it does**
Searches optimized parameter settings for an estimator or pipeline through either exhaustive grid cross validation search or Randomized cross validation search.

**Hot number/keyword for preprocessors**::

    0   sklearn_preprocessing.StandardScaler(copy=True, with_mean=True, with_std=True)
    1   sklearn_preprocessing.Binarizer(copy=True, threshold=0.0)
    2   sklearn_preprocessing.MaxAbsScaler(copy=True)
    3   sklearn_preprocessing.Normalizer(copy=True, norm='l2')
    4   sklearn_preprocessing.MinMaxScaler(copy=True, feature_range=(0, 1))
    5   sklearn_preprocessing.PolynomialFeatures(degree=2, include_bias=True, interaction_only=False)
    6   sklearn_preprocessing.RobustScaler(copy=True, quantile_range=(25.0, 75.0), with_centering=True, with_scaling=True)
    7   sklearn_feature_selection.SelectKBest(k=10, score_func=<function f_classif at 0x113806d90>)
    8   sklearn_feature_selection.GenericUnivariateSelect(mode='percentile', param=1e-05, score_func=<function f_classif at 0x113806d90>)
    9  sklearn_feature_selection.SelectPercentile(percentile=10, score_func=<function f_classif at 0x113806d90>)
    10  sklearn_feature_selection.SelectFpr(alpha=0.05, score_func=<function f_classif at 0x113806d90>)
    11  sklearn_feature_selection.SelectFdr(alpha=0.05, score_func=<function f_classif at 0x113806d90>)
    12  sklearn_feature_selection.SelectFwe(alpha=0.05, score_func=<function f_classif at 0x113806d90>)
    13  sklearn_feature_selection.VarianceThreshold(threshold=0.0)
    14  sklearn_decomposition.FactorAnalysis(copy=True, iterated_power=3, max_iter=1000, n_components=None,
        noise_variance_init=None, random_state=0, svd_method='randomized', tol=0.01)
    15  sklearn_decomposition.FastICA(algorithm='parallel', fun='logcosh', fun_args=None,
        max_iter=200, n_components=None, random_state=0, tol=0.0001, w_init=None, whiten=True)
    16  sklearn_decomposition.IncrementalPCA(batch_size=None, copy=True, n_components=None, whiten=False)
    17  sklearn_decomposition.KernelPCA(alpha=1.0, coef0=1, copy_X=True, degree=3, eigen_solver='auto',
        fit_inverse_transform=False, gamma=None, kernel='linear', kernel_params=None, max_iter=None,
        n_components=None, random_state=0, remove_zero_eig=False, tol=0)
    18  sklearn_decomposition.LatentDirichletAllocation(batch_size=128, doc_topic_prior=None, evaluate_every=-1, learning_decay=0.7,
        learning_method=None, learning_offset=10.0, max_doc_update_iter=100, max_iter=10, mean_change_tol=0.001, n_components=10,
        n_topics=None, perp_tol=0.1, random_state=0, topic_word_prior=None, total_samples=1000000.0, verbose=0)
    19  sklearn_decomposition.MiniBatchDictionaryLearning(alpha=1, batch_size=3, dict_init=None, fit_algorithm='lars',
        n_components=None, n_iter=1000, random_state=0, shuffle=True, split_sign=False, transform_algorithm='omp',
        transform_alpha=None, transform_n_nonzero_coefs=None, verbose=False)
    20  sklearn_decomposition.MiniBatchSparsePCA(alpha=1, batch_size=3, callback=None, method='lars', n_components=None,
        n_iter=100, random_state=0, ridge_alpha=0.01, shuffle=True, verbose=False)
    21  sklearn_decomposition.NMF(alpha=0.0, beta_loss='frobenius', init=None, l1_ratio=0.0, max_iter=200,
        n_components=None, random_state=0, shuffle=False, solver='cd', tol=0.0001, verbose=0)
    22  sklearn_decomposition.PCA(copy=True, iterated_power='auto', n_components=None, random_state=0, svd_solver='auto', tol=0.0, whiten=False)
    23  sklearn_decomposition.SparsePCA(U_init=None, V_init=None, alpha=1, max_iter=1000, method='lars',
        n_components=None, random_state=0, ridge_alpha=0.01, tol=1e-08, verbose=False)
    24  sklearn_decomposition.TruncatedSVD(algorithm='randomized', n_components=2, n_iter=5, random_state=0, tol=0.0)
    25  sklearn_kernel_approximation.Nystroem(coef0=None, degree=None, gamma=None, kernel='rbf',
        kernel_params=None, n_components=100, random_state=0)
    26  sklearn_kernel_approximation.RBFSampler(gamma=1.0, n_components=100, random_state=0)
    27  sklearn_kernel_approximation.AdditiveChi2Sampler(sample_interval=None, sample_steps=2)
    28  sklearn_kernel_approximation.SkewedChi2Sampler(n_components=100, random_state=0, skewedness=1.0)
    29  sklearn_cluster.FeatureAgglomeration(affinity='euclidean', compute_full_tree='auto', connectivity=None,
        linkage='ward', memory=None, n_clusters=2, pooling_func=<function mean at 0x113078ae8>)
    30  skrebate_ReliefF(discrete_threshold=10, n_features_to_select=10, n_neighbors=100, verbose=False)
    31  skrebate_SURF(discrete_threshold=10, n_features_to_select=10, verbose=False)
    32  skrebate_SURFstar(discrete_threshold=10, n_features_to_select=10, verbose=False)
    33  skrebate_MultiSURF(discrete_threshold=10, n_features_to_select=10, verbose=False)
    34  skrebate_MultiSURFstar(discrete_threshold=10, n_features_to_select=10, verbose=False)
    'sk_prep_all':   All sklearn preprocessing estimators, i.e., 0-6
    'fs_all':        All feature_selection estimators, i.e., 7-13
    'decomp_all':    All decomposition estimators, i.e., 14-24
    'k_appr_all':    All kernel_approximation estimators, i.e., 25-28
    'reb_all':       All skrebate estimators, i.e., 30-34
    'all_0':         All except the imbalanced-learn samplers, i.e., 0-34
    'imb_all':       All imbalanced-learn sampling methods, i.e., 35-53.
                     **CAUTION**: Mix of imblearn and other preprocessors may not work.
     None:           opt out of preprocessor

Support mix (CAUTION: Mix of imblearn and other preprocessors may not work), e.g.::

     : [None, 'sk_prep_all', 21, 'k_appr_all', sklearn_feature_selection.SelectKBest(k=50)]



**Whether to do train_test_split?**