comparison main_macros.xml @ 0:03f61bb3ca43 draft

"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"

0:03f61bb3ca43

<macros>
  <token name="@VERSION@">1.0.8.1</token>

  <xml name="python_requirements">
      <requirements>
          <requirement type="package" version="3.6">python</requirement>
          <requirement type="package" version="0.8.1">Galaxy-ML</requirement>
          <yield/>
      </requirements>
  </xml>

  <xml name="macro_stdio">
    <stdio>
        <exit_code range="1:" level="fatal" description="Error occurred. Please check Tool Standard Error"/>
    </stdio>
  </xml>


  <!--Generic interface-->

  <xml name="sl_Conditional" token_train="tabular" token_data="tabular" token_model="txt">
    <conditional name="selected_tasks">
        <param name="selected_task" type="select" label="Select a Classification Task">
            <option value="train" selected="true">Train a model</option>
            <option value="load">Load a model and predict</option>
        </param>
        <when value="load">
            <param name="infile_model" type="data" format="@MODEL@" label="Models" help="Select a model file."/>
            <param name="infile_data" type="data" format="@DATA@" label="Data (tabular)" help="Select the dataset you want to classify."/>
            <param name="header" type="boolean" optional="True" truevalue="booltrue" falsevalue="boolfalse" checked="False" label="Does the dataset contain header:" />
            <conditional name="prediction_options">
                <param name="prediction_option" type="select" label="Select the type of prediction">
                    <option value="predict">Predict class labels</option>
                    <option value="advanced">Include advanced options</option>
                </param>
                <when value="predict">
                </when>
                <when value="advanced">
                </when>
            </conditional>
        </when>
        <when value="train">
            <conditional name="selected_algorithms">
                <yield />
            </conditional>
        </when>
    </conditional>
  </xml>

  <xml name="advanced_section">
    <section name="options" title="Advanced Options" expanded="False">
      <yield />
    </section>
  </xml>


  <!--Generalized Linear Models-->
  <xml name="loss" token_help=" " token_select="false">
    <param argument="loss" type="select" label="Loss function"  help="@HELP@">
        <option value="squared_loss" selected="@SELECT@">squared loss</option>
        <option value="huber">huber</option>
        <option value="epsilon_insensitive">epsilon insensitive</option>
        <option value="squared_epsilon_insensitive">squared epsilon insensitive</option>
        <yield/>
    </param>
  </xml>

  <xml name="penalty" token_help=" ">
    <param argument="penalty" type="select" label="Penalty (regularization term)"  help="@HELP@">
        <option value="l2" selected="true">l2</option>
        <option value="l1">l1</option>
        <option value="elasticnet">elastic net</option>
        <option value="none">none</option>
        <yield/>
    </param>
  </xml>

  <xml name="l1_ratio" token_default_value="0.15" token_help=" ">
    <param argument="l1_ratio" type="float" value="@DEFAULT_VALUE@" label="Elastic Net mixing parameter" help="@HELP@"/>
  </xml>

  <xml name="epsilon" token_default_value="0.1" token_help="Used if loss is ‘huber’, ‘epsilon_insensitive’, or ‘squared_epsilon_insensitive’. ">
    <param argument="epsilon" type="float" value="@DEFAULT_VALUE@" label="Epsilon (epsilon-sensitive loss functions only)" help="@HELP@"/>
  </xml>

  <xml name="learning_rate_s" token_help=" " token_selected1="false" token_selected2="false">
    <param argument="learning_rate" type="select" optional="true" label="Learning rate schedule"  help="@HELP@">
        <option value="optimal" selected="@SELECTED1@">optimal</option>
        <option value="constant">constant</option>
        <option value="invscaling" selected="@SELECTED2@">inverse scaling</option>
        <yield/>
    </param>
  </xml>

  <xml name="eta0" token_default_value="0.0" token_help="Used with ‘constant’ or ‘invscaling’ schedules. ">
    <param argument="eta0" type="float" value="@DEFAULT_VALUE@" label="Initial learning rate" help="@HELP@"/>
  </xml>

  <xml name="power_t" token_default_value="0.5" token_help=" ">
    <param argument="power_t" type="float" value="@DEFAULT_VALUE@" label="Exponent for inverse scaling learning rate" help="@HELP@"/>
  </xml>

  <xml name="normalize" token_checked="false" token_help=" ">
    <param argument="normalize" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="@CHECKED@" label="Normalize samples before training" help=" "/>
  </xml>

  <xml name="copy_X" token_checked="true" token_help=" ">
    <param argument="copy_X" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="@CHECKED@" label="Use a copy of samples" help="If false, samples would be overwritten. "/>
  </xml>

  <xml name="ridge_params">
    <expand macro="normalize"/>
    <expand macro="alpha" default_value="1.0"/>
    <expand macro="fit_intercept"/>
    <expand macro="max_iter" default_value=""/>
    <expand macro="tol" default_value="0.001" help_text="Precision of the solution. "/>
    <!--class_weight-->
    <expand macro="copy_X"/>
    <param argument="solver" type="select" value="" label="Solver to use in the computational routines" help=" ">
        <option value="auto" selected="true">auto</option>
        <option value="svd">svd</option>
        <option value="cholesky">cholesky</option>
        <option value="lsqr">lsqr</option>
        <option value="sparse_cg">sparse_cg</option>
        <option value="sag">sag</option>
    </param>
    <expand macro="random_state"/>
  </xml>

  <!--Ensemble methods-->
  <xml name="n_estimators" token_default_value="10" token_help=" ">
    <param argument="n_estimators" type="integer" optional="true" value="@DEFAULT_VALUE@" label="Number of trees in the forest" help="@HELP@"/>
  </xml>

  <xml name="max_depth" token_default_value="" token_help=" ">
    <param argument="max_depth" type="integer" optional="true" value="@DEFAULT_VALUE@" label="Maximum depth of the tree" help="@HELP@"/>
  </xml>

  <xml name="min_samples_split" token_type="integer" token_default_value="2" token_help=" ">
    <param argument="min_samples_split" type="@TYPE@" optional="true" value="@DEFAULT_VALUE@" label="Minimum number of samples required to split an internal node" help="@HELP@"/>
  </xml>

  <xml name="min_samples_leaf" token_type="integer" token_default_value="1" token_label="Minimum number of samples in newly created leaves" token_help=" ">
    <param argument="min_samples_leaf" type="@TYPE@" optional="true" value="@DEFAULT_VALUE@" label="@LABEL@" help="@HELP@"/>
  </xml>

  <xml name="min_weight_fraction_leaf" token_default_value="0.0" token_help=" ">
    <param argument="min_weight_fraction_leaf" type="float" optional="true" value="@DEFAULT_VALUE@" label="Minimum weighted fraction of the input samples required to be at a leaf node" help="@HELP@"/>
  </xml>

  <xml name="max_leaf_nodes" token_default_value="" token_help=" ">
    <param argument="max_leaf_nodes" type="integer" optional="true" value="@DEFAULT_VALUE@" label="Maximum number of leaf nodes in best-first method" help="@HELP@"/>
  </xml>

  <xml name="min_impurity_decrease" token_default_value="0" token_help=" ">
    <param argument="min_impurity_decrease" type="float" value="@DEFAULT_VALUE@" optional="true" label="The threshold value of impurity for stopping node splitting" help="@HELP@"/>
  </xml>

  <xml name="bootstrap" token_checked="true" token_help=" ">
    <param argument="bootstrap" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolflase" checked="@CHECKED@" label="Use bootstrap samples for building trees." help="@HELP@"/>
  </xml>

  <xml name="criterion" token_help=" ">
    <param argument="criterion" type="select" label="Function to measure the quality of a split"  help=" ">
        <option value="gini" selected="true">Gini impurity</option>
        <option value="entropy">Information gain</option>
        <yield/>
    </param>
  </xml>

  <xml name="criterion2" token_help="">
    <param argument="criterion" type="select" label="Function to measure the quality of a split" >
      <option value="mse">mse - mean squared error</option>
      <option value="mae">mae - mean absolute error</option>
      <yield/>
    </param>
  </xml>

  <xml name="oob_score" token_checked="false" token_help=" ">
    <param argument="oob_score" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="@CHECKED@" label="Use out-of-bag samples to estimate the generalization error" help="@HELP@"/>
  </xml>

  <xml name="max_features">
    <conditional name="select_max_features">
      <param argument="max_features" type="select" label="max_features">
        <option value="auto" selected="true">auto - max_features=n_features</option>
        <option value="sqrt">sqrt - max_features=sqrt(n_features)</option>
        <option value="log2">log2 - max_features=log2(n_features)</option>
        <option value="number_input">I want to type the number in or input None type</option>
      </param>
      <when value="auto">
      </when>
      <when value="sqrt">
      </when>
      <when value="log2">
      </when>
      <when value="number_input">
        <param name="num_max_features" type="float" value="" optional="true" label="Input max_features number:" help="If int, consider the number of features at each split; If float, then max_features is a percentage and int(max_features * n_features) features are considered at each split."/>
      </when>
    </conditional>
  </xml>

  <xml name="verbose" token_default_value="0" token_help="If 1 then it prints progress and performance once in a while. If greater than 1 then it prints progress and performance for every tree.">
    <param argument="verbose" type="integer" value="@DEFAULT_VALUE@" optional="true" label="Enable verbose output" help="@HELP@"/>
  </xml>

  <xml name="learning_rate" token_default_value="1.0" token_help=" ">
    <param argument="learning_rate" type="float" optional="true" value="@DEFAULT_VALUE@" label="Learning rate" help="@HELP@"/>
  </xml>

  <xml name="subsample" token_help=" ">
    <param argument="subsample" type="float" value="1.0" optional="true" label="The fraction of samples to be used for fitting the individual base learners" help="@HELP@"/>
  </xml>

  <xml name="presort">
    <param argument="presort" type="select" label="Whether to presort the data to speed up the finding of best splits in fitting" >
      <option value="auto" selected="true">auto</option>
      <option value="true">true</option>
      <option value="false">false</option>
    </param>
  </xml>

  <!--Parameters-->
  <xml name="tol" token_default_value="0.0" token_help_text="Early stopping heuristics based on the relative center changes. Set to default (0.0) to disable this convergence detection.">
        <param argument="tol" type="float" optional="true" value="@DEFAULT_VALUE@" label="Tolerance" help="@HELP_TEXT@"/>
  </xml>

  <xml name="n_clusters" token_default_value="8">
    <param argument="n_clusters" type="integer" optional="true" value="@DEFAULT_VALUE@" label="Number of clusters" help=" "/>
  </xml>

  <xml name="fit_intercept" token_checked="true">
    <param argument="fit_intercept" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="@CHECKED@" label="Estimate the intercept" help="If false, the data is assumed to be already centered."/>
  </xml>

  <xml name="n_iter_no_change" token_default_value="5" token_help_text="Number of iterations with no improvement to wait before early stopping. ">
    <param argument="n_iter_no_change" type="integer" optional="true" value="@DEFAULT_VALUE@" label="Number of iterations" help="@HELP_TEXT@"/>
  </xml>

  <xml name="shuffle" token_checked="true" token_help_text=" " token_label="Shuffle data after each iteration">
    <param argument="shuffle" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="@CHECKED@" label="@LABEL@" help="@HELP_TEXT@"/>
  </xml>

  <xml name="random_state" token_default_value="" token_help_text="Integer number. The seed of the pseudo random number generator to use when shuffling the data. A fixed seed allows reproducible results. default=None.">
    <param argument="random_state" type="integer" optional="true" value="@DEFAULT_VALUE@" label="Random seed number" help="@HELP_TEXT@"/>
  </xml>

  <xml name="warm_start" token_checked="true" token_help_text="When set to True, reuse the solution of the previous call to fit as initialization,otherwise, just erase the previous solution.">
    <param argument="warm_start" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="@CHECKED@" label="Perform warm start" help="@HELP_TEXT@"/>
  </xml>

  <xml name="C" token_default_value="1.0" token_help_text="Penalty parameter C of the error term.">
    <param argument="C" type="float" optional="true" value="@DEFAULT_VALUE@" label="Penalty parameter" help="@HELP_TEXT@"/>
  </xml>

  <!--xml name="class_weight" token_default_value="" token_help_text="">
    <param argument="class_weight" type="" optional="true" value="@DEFAULT_VALUE@" label="" help="@HELP_TEXT@"/>
  </xml-->

  <xml name="alpha" token_default_value="0.0001" token_help_text="Constant that multiplies the regularization term if regularization is used. ">
    <param argument="alpha" type="float" optional="true" value="@DEFAULT_VALUE@" label="Regularization coefficient" help="@HELP_TEXT@"/>
  </xml>

  <xml name="n_samples" token_default_value="100" token_help_text="The total number of points equally divided among clusters.">
    <param argument="n_samples" type="integer" optional="true" value="@DEFAULT_VALUE@" label="Number of samples" help="@HELP_TEXT@"/>
  </xml>

  <xml name="n_features" token_default_value="2" token_help_text="Number of different numerical properties produced for each sample.">
    <param argument="n_features" type="integer" optional="true" value="@DEFAULT_VALUE@" label="Number of features" help="@HELP_TEXT@"/>
  </xml>

  <xml name="noise" token_default_value="0.0" token_help_text="Floating point number. ">
    <param argument="noise" type="float" optional="true" value="@DEFAULT_VALUE@" label="Standard deviation of the Gaussian noise added to the data" help="@HELP_TEXT@"/>
  </xml>

  <xml name="C" token_default_value="1.0" token_help_text="Penalty parameter C of the error term. ">
      <param argument="C" type="float" optional="true" value="@DEFAULT_VALUE@" label="Penalty parameter" help="@HELP_TEXT@"/>
  </xml>

  <xml name="max_iter" token_default_value="300" token_label="Maximum number of iterations per single run" token_help_text=" ">
      <param argument="max_iter" type="integer" optional="true" value="@DEFAULT_VALUE@" label="@LABEL@" help="@HELP_TEXT@"/>
  </xml>

  <xml name="n_init" token_default_value="10" >
      <param argument="n_init" type="integer" optional="true" value="@DEFAULT_VALUE@" label="Number of runs with different centroid seeds" help=" "/>
  </xml>

  <xml name="init">
      <param argument="init" type="select" label="Centroid initialization method"  help="''k-means++'' selects initial cluster centers that speed up convergence. ''random'' chooses k observations (rows) at random from data as initial centroids.">
          <option value="k-means++">k-means++</option>
          <option value="random">random</option>
      </param>
  </xml>

  <xml name="gamma" token_default_value="1.0" token_label="Scaling parameter" token_help_text=" ">
    <param argument="gamma" type="float" optional="true" value="@DEFAULT_VALUE@" label="@LABEL@" help="@HELP_TEXT@"/>
  </xml>

  <xml name="degree" token_default_value="3" token_label="Degree of the polynomial" token_help_text=" ">
    <param argument="degree" type="integer" optional="true" value="@DEFAULT_VALUE@" label="@LABEL@" help="@HELP_TEXT@"/>
  </xml>

  <xml name="coef0" token_default_value="1" token_label="Zero coefficient" token_help_text=" ">
    <param argument="coef0" type="integer" optional="true" value="@DEFAULT_VALUE@" label="@LABEL@" help="@HELP_TEXT@"/>
  </xml>

  <xml name="pos_label" token_default_value="">
    <param argument="pos_label" type="integer" optional="true" value="@DEFAULT_VALUE@" label="Label of the positive class" help=" "/>
  </xml>

  <xml name="average">
    <param argument="average" type="select" optional="true" label="Averaging type" help=" ">
      <option value="micro">Calculate metrics globally by counting the total true positives, false negatives and false positives. (micro)</option>
      <option value="samples">Calculate metrics for each instance, and find their average. Only meaningful for multilabel. (samples)</option>
      <option value="macro">Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account. (macro)</option>
      <option value="weighted">Calculate metrics for each label, and find their average, weighted by support (the number of true instances for each label). This alters ‘macro’ to account for label imbalance; it can result in an F-score that is not between precision and recall. (weighted)</option>
      <option value="None">None</option>
      <yield/>
    </param>
  </xml>

  <xml name="beta">
    <param argument="beta" type="float" value="1.0" label="The strength of recall versus precision in the F-score" help=" "/>
  </xml>


  <!--Data interface-->

  <xml name="samples_tabular" token_label1="Training samples dataset:" token_multiple1="false" token_multiple2="false">
    <param name="infile1" type="data" format="tabular" label="@LABEL1@"/>
    <param name="header1" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="False" label="Does the dataset contain header:" />
    <conditional name="column_selector_options_1">
      <expand macro="samples_column_selector_options" multiple="@MULTIPLE1@"/>
    </conditional>
    <param name="infile2" type="data" format="tabular" label="Dataset containing class labels or target values:"/>
    <param name="header2" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="False" label="Does the dataset contain header:" />
    <conditional name="column_selector_options_2">
      <expand macro="samples_column_selector_options" column_option="selected_column_selector_option2" col_name="col2" multiple="@MULTIPLE2@" infile="infile2"/>
    </conditional>
    <yield/>
  </xml>

  <xml name="samples_column_selector_options" token_column_option="selected_column_selector_option" token_col_name="col1" token_multiple="False" token_infile="infile1">
    <param name="@COLUMN_OPTION@" type="select" label="Choose how to select data by column:">
      <option value="by_index_number" selected="true">Select columns by column index number(s)</option>
      <option value="all_but_by_index_number">All columns EXCLUDING some by column index number(s)</option>
      <option value="by_header_name">Select columns by column header name(s)</option>
      <option value="all_but_by_header_name">All columns EXCLUDING some by column header name(s)</option>
      <option value="all_columns">All columns</option>
    </param>
    <when value="by_index_number">
      <param name="@COL_NAME@" multiple="@MULTIPLE@" type="data_column" use_header_names="true" data_ref="@INFILE@" label="Select target column(s):"/>
    </when>
    <when value="all_but_by_index_number">
      <param name="@COL_NAME@" multiple="@MULTIPLE@" type="data_column" use_header_names="true" data_ref="@INFILE@" label="Select target column(s):"/>
    </when>
    <when value="by_header_name">
      <param name="@COL_NAME@" type="text" value="" label="Type header name(s):" help="Comma-separated string. For example: target1,target2"/>
    </when>
    <when value="all_but_by_header_name">
      <param name="@COL_NAME@" type="text" value="" label="Type header name(s):" help="Comma-separated string. For example: target1,target2"/>
    </when>
    <when value="all_columns">
    </when>
  </xml>

  <xml name="clf_inputs_extended" token_label1=" " token_label2=" " token_multiple="False">
    <conditional name="true_columns">
      <param name="selected_input1" type="select" label="Select the input type of true labels dataset:">
          <option value="tabular" selected="true">Tabular</option>
          <option value="sparse">Sparse</option>
      </param>
      <when value="tabular">
        <param name="infile1" type="data" label="@LABEL1@"/>
        <param name="col1" type="data_column" data_ref="infile1" label="Select the target column:"/>
      </when>
      <when value="sparse">
          <param name="infile1" type="data" format="txt" label="@LABEL1@"/>
      </when>
    </conditional>
    <conditional name="predicted_columns">
      <param name="selected_input2" type="select" label="Select the input type of predicted labels dataset:">
          <option value="tabular" selected="true">Tabular</option>
          <option value="sparse">Sparse</option>
      </param>
      <when value="tabular">
        <param name="infile2" type="data" label="@LABEL2@"/>
        <param name="col2" multiple="@MULTIPLE@" type="data_column" data_ref="infile2" label="Select target column(s):"/>
      </when>
      <when value="sparse">
          <param name="infile2" type="data" format="txt" label="@LABEL1@"/>
      </when>
    </conditional>
  </xml>

  <xml name="clf_inputs" token_label1="Dataset containing true labels (tabular):" token_label2="Dataset containing predicted values (tabular):" token_multiple1="False" token_multiple="False">
    <param name="infile1" type="data" format="tabular" label="@LABEL1@"/>
    <param name="header1" type="boolean" optional="True" truevalue="booltrue" falsevalue="boolfalse" checked="False" label="Does the dataset contain header:" />
    <conditional name="column_selector_options_1">
      <expand macro="samples_column_selector_options" multiple="@MULTIPLE1@"/>
    </conditional>
    <param name="infile2" type="data" format="tabular" label="@LABEL2@"/>
    <param name="header2" type="boolean" optional="True" truevalue="booltrue" falsevalue="boolfalse" checked="False" label="Does the dataset contain header:" />
    <conditional name="column_selector_options_2">
      <expand macro="samples_column_selector_options" column_option="selected_column_selector_option2" col_name="col2" multiple="@MULTIPLE@" infile="infile2"/>
    </conditional>
  </xml>

  <xml name="multiple_input" token_name="input_files" token_max_num="10" token_format="txt" token_label="Sparse matrix file (.mtx, .txt)" token_help_text="Specify a sparse matrix file in .txt format.">
    <repeat name="@NAME@" min="1" max="@MAX_NUM@" title="Select input file(s):">
        <param name="input" type="data" format="@FORMAT@" label="@LABEL@" help="@HELP_TEXT@"/>
    </repeat>
  </xml>

  <xml name="sparse_target" token_label1="Select a sparse matrix:" token_label2="Select the tabular containing true labels:" token_multiple="False" token_format1="txt" token_format2="tabular" token_help1="" token_help2="">
    <param name="infile1" type="data" format="@FORMAT1@" label="@LABEL1@" help="@HELP1@"/>
    <expand macro="input_tabular_target"/>
  </xml>

  <xml name="sl_mixed_input">
    <conditional name="input_options">
        <expand macro="data_input_options"/>
        <expand macro="data_input_whens"/>
    </conditional>
  </xml>

  <xml name="sl_mixed_input_plus_sequence">
    <conditional name="input_options">
        <expand macro="data_input_options">
            <option value="seq_fasta">sequnences in a fasta file</option>
            <option value="refseq_and_interval">reference genome and intervals</option>
        </expand>
        <expand macro="data_input_whens">
            <when value="seq_fasta">
                <expand macro="inputs_seq_fasta"/>
            </when>
            <when value="refseq_and_interval">
                <expand macro="inputs_refseq_and_interval"/>
            </when>
        </expand>
    </conditional>
  </xml>

  <xml name="data_input_options">
    <param name="selected_input" type="select" label="Select input type:">
        <option value="tabular" selected="true">tabular data</option>
        <option value="sparse">sparse matrix</option>
        <yield/>
    </param>
  </xml>

  <xml name="data_input_whens">
    <when value="tabular">
        <expand macro="samples_tabular" multiple1="true" multiple2="false"/>
    </when>
    <when value="sparse">
        <expand macro="sparse_target"/>
    </when>
    <yield/>
  </xml>

  <xml name="input_tabular_target">
    <param name="infile2" type="data" format="tabular" label="Dataset containing class labels or target values:"/>
    <param name="header2" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="false" label="Does the dataset contain header:" />
    <conditional name="column_selector_options_2">
      <expand macro="samples_column_selector_options" column_option="selected_column_selector_option2" col_name="col2" multiple="false" infile="infile2"/>
    </conditional>
  </xml>

  <xml name="inputs_seq_fasta">
    <param name="fasta_path" type="data" format="fasta" label="Dataset containing fasta genomic/protein sequences" help="Sequences will be one-hot encoded to arrays."/>
    <expand macro="input_tabular_target"/>
  </xml>

  <xml name="inputs_refseq_and_interval">
    <param name="ref_genome_file" type="data" format="fasta" label="Dataset containing reference genomic sequence"/>
    <param name="interval_file" type="data" format="interval" label="Dataset containing sequence intervals for training" help="interval. Sequences will be retrieved from the reference genome and one-hot encoded to training arrays."/>
    <param name="target_file" type="data" format="bed" label="Dataset containing positions and features for target values." help="bed. The file will be compressed with `bgzip` and then indexed using `tabix`."/>
    <param name="infile2" type="data" format="tabular" label="Dataset containing the feature list for prediction"/>
    <param name="header2" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="false" label="Does the dataset contain header:" />
    <conditional name="column_selector_options_2">
      <expand macro="samples_column_selector_options" column_option="selected_column_selector_option2" col_name="col2" multiple="true" infile="infile2"/>
    </conditional>
  </xml>

  <!--Advanced options-->
  <xml name="nn_advanced_options">
    <section name="options" title="Advanced Options" expanded="False">
      <yield/>
      <param argument="weights" type="select" label="Weight function" help="Used in prediction.">
          <option value="uniform" selected="true">Uniform weights. All points in each neighborhood are weighted equally. (Uniform)</option>
          <option value="distance">Weight points by the inverse of their distance. (Distance)</option>
      </param>
      <param argument="algorithm" type="select" label="Neighbor selection algorithm" help=" ">
          <option value="auto" selected="true">Auto</option>
          <option value="ball_tree">BallTree</option>
          <option value="kd_tree">KDTree</option>
          <option value="brute">Brute-force</option>
      </param>
      <param argument="leaf_size" type="integer" value="30" label="Leaf size" help="Used with BallTree and KDTree. Affects the time and memory usage of the constructed tree."/>
      <!--param name="metric"-->
      <!--param name="p"-->
      <!--param name="metric_params"-->
    </section>
  </xml>

  <xml name="svc_advanced_options">
    <section name="options" title="Advanced Options" expanded="False">
        <yield/>
        <param argument="kernel" type="select" optional="true" label="Kernel type" help="Kernel type to be used in the algorithm. If none is given, ‘rbf’ will be used.">
            <option value="rbf" selected="true">rbf</option>
            <option value="linear">linear</option>
            <option value="poly">poly</option>
            <option value="sigmoid">sigmoid</option>
            <option value="precomputed">precomputed</option>
        </param>
        <param argument="degree" type="integer" optional="true" value="3" label="Degree of the polynomial (polynomial kernel only)" help="Ignored by other kernels. dafault : 3 "/>
        <!--TODO: param argument="gamma" float, optional (default=’auto’) -->
        <param argument="coef0" type="float" optional="true" value="0.0" label="Zero coefficient (polynomial and sigmoid kernels only)"
            help="Independent term in kernel function. dafault: 0.0 "/>
        <param argument="shrinking" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="true"
            label="Use the shrinking heuristic" help=" "/>
        <param argument="probability" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="false"
            label="Enable probability estimates. " help="This must be enabled prior to calling fit, and will slow down that method."/>
        <!-- param argument="cache_size"-->
        <!--expand macro="class_weight"/-->
        <expand macro="tol" default_value="0.001" help_text="Tolerance for stopping criterion. "/>
        <expand macro="max_iter" default_value="-1" label="Solver maximum number of iterations" help_text="Hard limit on iterations within solver, or -1 for no limit."/>
        <!--param argument="decision_function_shape"-->
        <expand macro="random_state" help_text="Integer number. The seed of the pseudo random number generator to use when shuffling the data for probability estimation. A fixed seed allows reproducible results."/>
    </section>
  </xml>

  <xml name="spectral_clustering_advanced_options">
    <section name="options" title="Advanced Options" expanded="False">
        <expand macro="n_clusters"/>
        <param argument="eigen_solver" type="select" value="" label="Eigen solver" help="The eigenvalue decomposition strategy to use.">
            <option value="arpack" selected="true">arpack</option>
            <option value="lobpcg">lobpcg</option>
            <option value="amg">amg</option>
            <!--None-->
        </param>
        <expand macro="random_state"/>
        <expand macro="n_init"/>
        <param argument="gamma" type="float" optional="true" value="1.0" label="Kernel scaling factor" help="Scaling factor of RBF, polynomial, exponential chi^2 and sigmoid affinity kernel. Ignored for affinity=''nearest_neighbors''."/>
        <param argument="affinity" type="select" label="Affinity" help="Affinity kernel to use. ">
            <option value="rbf" selected="true">RBF</option>
            <option value="precomputed">precomputed</option>
            <option value="nearest_neighbors">Nearset neighbors</option>
        </param>
        <param argument="n_neighbors" type="integer" optional="true" value="10" label="Number of neighbors" help="Number of neighbors to use when constructing the affinity matrix using the nearest neighbors method. Ignored for affinity=''rbf''"/>
        <!--param argument="eigen_tol"-->
        <param argument="assign_labels" type="select" label="Assign labels" help="The strategy to use to assign labels in the embedding space.">
            <option value="kmeans" selected="true">kmeans</option>
            <option value="discretize">discretize</option>
        </param>
        <param argument="degree" type="integer" optional="true" value="3"
            label="Degree of the polynomial (polynomial kernel only)" help="Ignored by other kernels. dafault : 3 "/>
        <param argument="coef0" type="integer" optional="true" value="1"
            label="Zero coefficient (polynomial and sigmoid kernels only)" help="Ignored by other kernels. dafault : 1 "/>
        <!--param argument="kernel_params"-->
    </section>
  </xml>

  <xml name="minibatch_kmeans_advanced_options">
    <section name="options" title="Advanced Options" expanded="False">
        <expand macro="n_clusters"/>
        <expand macro="init"/>
        <expand macro="n_init" default_value="3"/>
        <expand macro="max_iter" default_value="100"/>
        <expand macro="tol" help_text="Early stopping heuristics based on normalized center change. To disable set to 0.0 ."/>
        <expand macro="random_state"/>
        <param argument="batch_size" type="integer" optional="true" value="100" label="Batch size" help="Size of the mini batches."/>
        <!--param argument="compute_labels"-->
        <param argument="max_no_improvement" type="integer" optional="true" value="10" label="Maximum number of improvement attempts" help="
        Convergence detection based on inertia (the consecutive number of mini batches that doe not yield an improvement on the smoothed inertia).
        To disable, set max_no_improvement to None. "/>
        <param argument="init_size" type="integer" optional="true" value="" label="Number of random initialization samples" help="Number of samples to randomly sample for speeding up the initialization . ( default: 3 * batch_size )"/>
        <param argument="reassignment_ratio" type="float" optional="true" value="0.01" label="Re-assignment ratio" help="Controls the fraction of the maximum number of counts for a center to be reassigned. Higher values yield better clustering results."/>
    </section>
  </xml>

  <xml name="kmeans_advanced_options">
    <section name="options" title="Advanced Options" expanded="False">
      <expand macro="n_clusters"/>
      <expand macro="init"/>
      <expand macro="n_init"/>
      <expand macro="max_iter"/>
      <expand macro="tol" default_value="0.0001" help_text="Relative tolerance with regards to inertia to declare convergence."/>
      <!--param argument="precompute_distances"/-->
      <expand macro="random_state"/>
      <param argument="copy_x" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="true" label="Use a copy of data for precomputing distances" help="Mofifying the original data introduces small numerical differences caused by subtracting and then adding the data mean."/>
      <expand macro="kmeans_algorithm"/>
    </section>
  </xml>

  <xml name="kmeans_algorithm">
    <param argument="algorithm" type="select" label="K-means algorithm to use:">
      <option value="auto" selected="true">auto</option>
      <option value="full">full</option>
      <option value="elkan">elkan</option>
    </param>
  </xml>

  <xml name="birch_advanced_options">
    <section name="options" title="Advanced Options" expanded="False">
      <param argument="threshold" type="float" optional="true" value="0.5" label="Subcluster radius threshold" help="The radius of the subcluster obtained by merging a new sample; the closest subcluster should be less than the threshold to avoid a new subcluster."/>
      <param argument="branching_factor" type="integer" optional="true" value="50" label="Maximum number of subclusters per branch" help="Maximum number of CF subclusters in each node."/>
      <expand macro="n_clusters" default_value="3"/>
      <!--param argument="compute_labels"/-->
    </section>
  </xml>

  <xml name="dbscan_advanced_options">
    <section name="options" title="Advanced Options" expanded="False">
      <param argument="eps" type="float" optional="true" value="0.5" label="Maximum neighborhood distance" help="The maximum distance between two samples for them to be considered as in the same neighborhood."/>
      <param argument="min_samples" type="integer" optional="true" value="5" label="Minimal core point density" help="The number of samples (or total weight) in a neighborhood for a point (including the point itself) to be considered as a core point."/>
      <param argument="metric" type="text" optional="true" value="euclidean" label="Metric" help="The metric to use when calculating distance between instances in a feature array."/>
      <param argument="algorithm" type="select" label="Pointwise distance computation algorithm" help="The algorithm to be used by the NearestNeighbors module to compute pointwise distances and find nearest neighbors.">
          <option value="auto" selected="true">auto</option>
          <option value="ball_tree">ball_tree</option>
          <option value="kd_tree">kd_tree</option>
          <option value="brute">brute</option>
      </param>
      <param argument="leaf_size" type="integer" optional="true" value="30" label="Leaf size" help="Leaf size passed to BallTree or cKDTree. Memory and time efficieny factor in tree constrution and querying."/>
    </section>
  </xml>

  <xml name="clustering_algorithms_options">
    <conditional name="algorithm_options">
      <param name="selected_algorithm" type="select" label="Clustering Algorithm">
          <option value="KMeans" selected="true">KMeans</option>
          <option value="SpectralClustering">Spectral Clustering</option>
          <option value="MiniBatchKMeans">Mini Batch KMeans</option>
          <option value="DBSCAN">DBSCAN</option>
          <option value="Birch">Birch</option>
      </param>
      <when value="KMeans">
          <expand macro="kmeans_advanced_options"/>
      </when>
      <when value="DBSCAN">
          <expand macro="dbscan_advanced_options"/>
      </when>
      <when value="Birch">
          <expand macro="birch_advanced_options"/>
      </when>
      <when value="SpectralClustering">
          <expand macro="spectral_clustering_advanced_options"/>
      </when>
      <when value="MiniBatchKMeans">
          <expand macro="minibatch_kmeans_advanced_options"/>
      </when>
    </conditional>
  </xml>

  <xml name="distance_metrics">
    <param argument="metric" type="select" label="Distance metric" help=" ">
      <option value="euclidean" selected="true">euclidean</option>
      <option value="cityblock">cityblock</option>
      <option value="cosine">cosine</option>
      <option value="l1">l1</option>
      <option value="l2">l2</option>
      <option value="manhattan">manhattan</option>
      <yield/>
    </param>
  </xml>

  <xml name="distance_nonsparse_metrics">
    <option value="braycurtis">braycurtis</option>
    <option value="canberra">canberra</option>
    <option value="chebyshev">chebyshev</option>
    <option value="correlation">correlation</option>
    <option value="dice">dice</option>
    <option value="hamming">hamming</option>
    <option value="jaccard">jaccard</option>
    <option value="kulsinski">kulsinski</option>
    <option value="mahalanobis">mahalanobis</option>
    <option value="matching">matching</option>
    <option value="minkowski">minkowski</option>
    <option value="rogerstanimoto">rogerstanimoto</option>
    <option value="russellrao">russellrao</option>
    <option value="seuclidean">seuclidean</option>
    <option value="sokalmichener">sokalmichener</option>
    <option value="sokalsneath">sokalsneath</option>
    <option value="sqeuclidean">sqeuclidean</option>
    <option value="yule">yule</option>
  </xml>

  <xml name="pairwise_kernel_metrics">
    <param argument="metric" type="select" label="Pirwise Kernel metric" help=" ">
      <option value="rbf" selected="true">rbf</option>
      <option value="sigmoid">sigmoid</option>
      <option value="polynomial">polynomial</option>
      <option value="linear" selected="true">linear</option>
      <option value="chi2">chi2</option>
      <option value="additive_chi2">additive_chi2</option>
    </param>
  </xml>

  <xml name="sparse_pairwise_metric_functions">
    <param name="selected_metric_function" type="select" label="Select the pairwise metric you want to compute:">
      <option value="euclidean_distances" selected="true">Euclidean distance matrix</option>
      <option value="pairwise_distances">Distance matrix</option>
      <option value="pairwise_distances_argmin">Minimum distances between one point and a set of points</option>
      <yield/>
    </param>
  </xml>

  <xml name="pairwise_metric_functions">
    <option value="additive_chi2_kernel" >Additive chi-squared kernel</option>
    <option value="chi2_kernel">Exponential chi-squared kernel</option>
    <option value="linear_kernel">Linear kernel</option>
    <option value="manhattan_distances">L1 distances</option>
    <option value="pairwise_kernels">Kernel</option>
    <option value="polynomial_kernel">Polynomial kernel</option>
    <option value="rbf_kernel">Gaussian (rbf) kernel</option>
    <option value="laplacian_kernel">Laplacian kernel</option>
  </xml>

  <xml name="sparse_pairwise_condition">
    <when value="pairwise_distances">
      <section name="options" title="Advanced Options" expanded="False">
          <expand macro="distance_metrics">
              <yield/>
          </expand>
      </section>
    </when>
    <when value="euclidean_distances">
      <section name="options" title="Advanced Options" expanded="False">
          <param argument="squared" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="false"
            label="Return squared Euclidean distances" help=" "/>
      </section>
    </when>
  </xml>

  <xml name="argmin_distance_condition">
    <when value="pairwise_distances_argmin">
      <section name="options" title="Advanced Options" expanded="False">
          <param argument="axis" type="integer" optional="true" value="1" label="Axis" help="Axis along which the argmin and distances are to be computed."/>
          <expand macro="distance_metrics">
              <yield/>
          </expand>
          <param argument="batch_size" type="integer" optional="true" value="500" label="Batch size" help="Number of rows to be processed in each batch run."/>
      </section>
    </when>
  </xml>

  <xml name="sparse_preprocessors">
    <param name="selected_pre_processor" type="select" label="Select a preprocessor:">
      <option value="StandardScaler" selected="true">Standard Scaler (Standardizes features by removing the mean and scaling to unit variance)</option>
      <option value="Binarizer">Binarizer (Binarizes data)</option>
      <option value="MaxAbsScaler">Max Abs Scaler (Scales features by their maximum absolute value)</option>
      <option value="Normalizer">Normalizer (Normalizes samples individually to unit norm)</option>
      <yield/>
    </param>
  </xml>

  <xml name="sparse_preprocessors_ext">
    <expand macro="sparse_preprocessors">
      <option value="KernelCenterer">Kernel Centerer (Centers a kernel matrix)</option>
      <option value="MinMaxScaler">Minmax Scaler (Scales features to a range)</option>
      <option value="PolynomialFeatures">Polynomial Features (Generates polynomial and interaction features)</option>
      <option value="RobustScaler">Robust Scaler (Scales features using outlier-invariance statistics)</option>
      <option value="QuantileTransformer">QuantileTransformer (Transform features using quantiles information)</option>
      <option value="PowerTransformer">PowerTransformer (Apply a power transform featurewise to make data more Gaussian-like)</option>
      <option value="KBinsDiscretizer">KBinsDiscretizer (Bin continuous data into intervals.)</option>
    </expand>
  </xml>

  <xml name="sparse_preprocessor_options">
    <when value="Binarizer">
        <section name="options" title="Advanced Options" expanded="False">
            <param argument="copy" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="true"
                label="Use a copy of data for precomputing binarization" help=" "/>
            <param argument="threshold" type="float" optional="true" value="0.0"
                label="Threshold"
                help="Feature values below or equal to this are replaced by 0, above it by 1. Threshold may not be less than 0 for operations on sparse matrices. "/>
        </section>
    </when>
    <when value="StandardScaler">
      <section name="options" title="Advanced Options" expanded="False">
        <param argument="copy" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="true"
            label="Use a copy of data for performing inplace scaling" help=" "/>
        <param argument="with_mean" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="true"
            label="Center the data before scaling" help=" "/>
        <param argument="with_std" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="true"
            label="Scale the data to unit variance (or unit standard deviation)" help=" "/>
      </section>
    </when>
    <when value="MaxAbsScaler">
      <section name="options" title="Advanced Options" expanded="False">
        <param argument="copy" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="true"
            label="Use a copy of data for precomputing scaling" help=" "/>
      </section>
    </when>
    <when value="Normalizer">
      <section name="options" title="Advanced Options" expanded="False">
        <param argument="norm" type="select" optional="true" label="The norm to use to normalize non zero samples" help=" ">
          <option value="l1" selected="true">l1</option>
          <option value="l2">l2</option>
          <option value="max">max</option>
        </param>
        <param argument="copy" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="true"
            label="Use a copy of data for precomputing row normalization" help=" "/>
      </section>
    </when>
    <yield/>
  </xml>

  <xml name="sparse_preprocessor_options_ext">
    <expand macro="sparse_preprocessor_options">
      <when value="KernelCenterer">
        <section name="options" title="Advanced Options" expanded="False">
        </section>
      </when>
      <when value="MinMaxScaler">
          <section name="options" title="Advanced Options" expanded="False">
              <param argument="feature_range" type="text" value="(0, 1)" optional="true" help="Desired range of transformed data. None or tuple (min, max). None equals to (0, 1)"/>
              <param argument="copy" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolflase" checked="true"
                  label="Use a copy of data for precomputing normalization" help=" "/>
          </section>
      </when>
      <when value="PolynomialFeatures">
          <section name="options" title="Advanced Options" expanded="False">
              <param argument="degree" type="integer" optional="true" value="2" label="The degree of the polynomial features " help=""/>
              <param argument="interaction_only" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolflase" checked="false" label="Produce interaction features only" help="(Features that are products of at most degree distinct input features) "/>
              <param argument="include_bias" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolflase" checked="true" label="Include a bias column" help="Feature in which all polynomial powers are zero "/>
          </section>
      </when>
      <when value="RobustScaler">
          <section name="options" title="Advanced Options" expanded="False">
              <!--=True, =True, copy=True-->
              <param argument="with_centering" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolflase" checked="true"
                  label="Center the data before scaling" help=" "/>
              <param argument="with_scaling" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolflase" checked="true"
                  label="Scale the data to interquartile range" help=" "/>
              <param argument="copy" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolflase" checked="true"
                  label="Use a copy of data for inplace scaling" help=" "/>
          </section>
      </when>
      <when value="QuantileTransformer">
          <section name="options" title="Advanced Options" expanded="False">
              <param name="n_quantiles" type="integer" value="1000" min="0" label="Number of quantiles to be computed" />
              <param name="output_distribution" type="select" label="Marginal distribution for the transformed data">
                  <option value="uniform" selected="true">uniform</option>
                  <option value="normal">normal</option>
              </param>
              <param name="ignore_implicit_zeros" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="false" label="Whether to discard sparse entries" help="Only applies to sparse matrices. If False, sparse entries are treated as zeros"/>
              <param name="subsample" type="integer" value="100000" label="Maximum number of samples used to estimate the quantiles for computational efficiency" help="Note that the subsampling procedure may differ for value-identical sparse and dense matrices."/>
              <expand macro="random_state" help_text="This is used by subsampling and smoothing noise"/>
          </section>
      </when>
      <when value="PowerTransformer">
          <section name="options" title="Advanced Options" expanded="False">
              <param name="method" type="select" label="The power transform method">
                  <option value="yeo-johnson" selected="true">yeo-johnson (works with positive and negative values)</option>
                  <option value="box-cox">box-cox (might perform better, but only works with strictly positive values)</option>
              </param>
              <param name="standardize" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="true" label="Whether to apply zero-mean, unit-variance normalization to the transformed output."/>
          </section>
      </when>
      <when value="KBinsDiscretizer">
          <section name="options" title="Advanced Options" expanded="False">
              <param name="n_bins" type="integer" value="5" min="2" label="The number of bins to produce"/>
              <param name="encode" type="select" label="Method used to encode the transformed result">
                  <option value="onehot" selected="true">onehot (encode the transformed result with one-hot encoding and return a sparse matrix)</option>
                  <option value="onehot-dense">onehot-dense (encode the transformed result with one-hot encoding and return a dense array)</option>
                  <option value="ordinal">ordinal (return the bin identifier encoded as an integer value)</option>
              </param>
              <param name="strategy" type="select" label="Strategy used to define the widths of the bins">
                  <option value="uniform">uniform (all bins in each feature have identical widths)</option>
                  <option value="quantile" selected="true">quantile (all bins in each feature have the same number of points)</option>
                  <option value="kmeans">kmeans (values in each bin have the same nearest center of a 1D k-means cluster)</option>
              </param>
          </section>
      </when>
    </expand>
  </xml>

  <xml name="cv_splitter">
    <option value="default" selected="true">default splitter</option>
    <option value="KFold">KFold</option>
    <option value="StratifiedKFold">StratifiedKFold</option>
    <option value="LeaveOneOut">LeaveOneOut</option>
    <option value="LeavePOut">LeavePOut</option>
    <option value="RepeatedKFold">RepeatedKFold</option>
    <option value="RepeatedStratifiedKFold">RepeatedStratifiedKFold</option>
    <option value="ShuffleSplit">ShuffleSplit</option>
    <option value="StratifiedShuffleSplit">StratifiedShuffleSplit</option>
    <option value="TimeSeriesSplit">TimeSeriesSplit</option>
    <option value="PredefinedSplit">PredefinedSplit</option>
    <option value="OrderedKFold">OrderedKFold</option>
    <option value="RepeatedOrderedKFold">RepeatedOrderedKFold</option>
    <yield/>
  </xml>

  <xml name="cv_splitter_options">
    <when value="default">
      <expand macro="cv_n_splits"/>
    </when>
    <when value="KFold">
      <expand macro="cv_n_splits"/>
      <expand macro="cv_shuffle"/>
      <expand macro="random_state"/>
    </when>
    <when value="StratifiedKFold">
      <expand macro="cv_n_splits"/>
      <expand macro="cv_shuffle"/>
      <expand macro="random_state"/>
    </when>
    <when value="LeaveOneOut">
    </when>
    <when value="LeavePOut">
      <param argument="p" type="integer" value="" label="p" help="Integer. Size of the test sets."/>
    </when>
    <when value="RepeatedKFold">
      <expand macro="cv_n_splits" value="5"/>
      <param argument="n_repeats" type="integer" value="10" label="n_repeats" help="Number of times cross-validator needs to be repeated." />
      <expand macro="random_state" />
    </when>
    <when value="RepeatedStratifiedKFold">
      <expand macro="cv_n_splits" value="5"/>
      <param argument="n_repeats" type="integer" value="10" label="n_repeats" help="Number of times cross-validator needs to be repeated." />
      <expand macro="random_state" />
    </when>
    <when value="ShuffleSplit">
      <expand macro="cv_n_splits" value="10" help="Number of re-shuffling and splitting iterations."/>
      <expand macro="cv_test_size" value="0.1" />
      <expand macro="random_state"/>
    </when>
    <when value="StratifiedShuffleSplit">
      <expand macro="cv_n_splits" value="10" help="Number of re-shuffling and splitting iterations."/>
      <expand macro="cv_test_size" value="0.1" />
      <expand macro="random_state"/>
    </when>
    <when value="TimeSeriesSplit">
      <expand macro="cv_n_splits"/>
      <param argument="max_train_size" type="integer" value="" optional="true" label="Maximum size of the training set" help="Maximum size for a single training set." />
    </when>
    <when value="PredefinedSplit">
      <param argument="test_fold" type="text" value="" area="true" label="test_fold" help="List, e.g., [0, 1, -1, 1], represents two test sets, [X[0]] and [X[1], X[3]], X[2] is excluded from any test set due to '-1'."/>
    </when>
    <when value="OrderedKFold">
      <expand macro="cv_n_splits"/>
      <expand macro="cv_shuffle"/>
      <expand macro="random_state"/>
    </when>
    <when value="RepeatedOrderedKFold">
      <expand macro="cv_n_splits"/>
      <param argument="n_repeats" type="integer" value="5"/>
      <expand macro="random_state"/>
    </when>
    <yield/>
  </xml>

  <xml name="cv">
    <conditional name="cv_selector">
      <param name="selected_cv" type="select" label="Select the cv splitter:">
        <expand macro="cv_splitter">
          <option value="GroupKFold">GroupKFold</option>
          <option value="GroupShuffleSplit">GroupShuffleSplit</option>
          <option value="LeaveOneGroupOut">LeaveOneGroupOut</option>
          <option value="LeavePGroupsOut">LeavePGroupsOut</option>
        </expand>
      </param>
      <expand macro="cv_splitter_options">
        <when value="GroupKFold">
          <expand macro="cv_n_splits"/>
          <expand macro="cv_groups" />
        </when>
        <when value="GroupShuffleSplit">
          <expand macro="cv_n_splits" value="5"/>
          <expand macro="cv_test_size"/>
          <expand macro="random_state"/>
          <expand macro="cv_groups"/>
        </when>
        <when value="LeaveOneGroupOut">
          <expand macro="cv_groups"/>
        </when>
        <when value="LeavePGroupsOut">
          <param argument="n_groups" type="integer" value="" label="n_groups" help="Number of groups (p) to leave out in the test split." />
          <expand macro="cv_groups"/>
        </when>
      </expand>
    </conditional>
  </xml>

  <xml name="cv_reduced" token_label="Select the cv splitter">
    <conditional name="cv_selector">
      <param name="selected_cv" type="select" label="@LABEL@">
        <expand macro="cv_splitter"/>
      </param>
      <expand macro="cv_splitter_options"/>
    </conditional>
  </xml>

  <xml name="cv_n_splits" token_value="3" token_help="Number of folds. Must be at least 2.">
    <param argument="n_splits" type="integer" value="@VALUE@" min="1" label="n_splits" help="@HELP@"/>
  </xml>

  <xml name="cv_shuffle">
    <param argument="shuffle" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="false" label="Whether to shuffle data before splitting" />
  </xml>

  <xml name="cv_test_size" token_value="0.2">
    <param argument="test_size" type="float" value="@VALUE@" min="0.0" label="Portion or number of the test set" help="0.0-1.0, proportion of the dataset to include in the test split; >1, integer only, the absolute number of test samples "/>
  </xml>

  <xml name="cv_groups" >
    <section name="groups_selector" title="Groups column selector" expanded="true">
      <param name="infile_g" type="data" format="tabular" label="Choose dataset containing groups info:"/>
      <param name="header_g" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="False" label="Does the dataset contain header:" />
      <conditional name="column_selector_options_g">
        <expand macro="samples_column_selector_options" column_option="selected_column_selector_option_g" col_name="col_g" multiple="False" infile="infile_g"/>
      </conditional>
    </section>
  </xml>

  <xml name="train_test_split_params">
    <conditional name="split_algos">
      <param name="shuffle" type="select" label="Select the splitting method">
        <option value="None">No shuffle</option>
        <option value="simple" selected="true">ShuffleSplit</option>
        <option value="stratified">StratifiedShuffleSplit -- target values serve as class labels</option>
        <option value="group">GroupShuffleSplit or split by group names</option>
      </param>
      <when value="None">
        <expand macro="train_test_split_test_size"/>
      </when>
      <when value="simple">
        <expand macro="train_test_split_test_size"/>
        <expand macro="random_state"/>
      </when>
      <when value="stratified">
        <expand macro="train_test_split_test_size"/>
        <expand macro="random_state"/>
      </when>
      <when value="group">
        <expand macro="train_test_split_test_size" optional="true"/>
        <expand macro="random_state"/>
        <param argument="group_names" type="text" value="" optional="true" label="Type in group names instead"
        help="For example: chr6, chr7. This parameter is optional. If used, it will override the holdout size and random seed."/>
        <yield/>
      </when>
    </conditional>
    <!--param argument="train_size" type="float" optional="True" value="" label="Train size:"/>-->
  </xml>

  <xml name="train_test_split_test_size" token_optional="false">
    <param name="test_size" type="float" value="0.2" optional="@OPTIONAL@" label="Holdout size" help="Leass than 1, for preportion; greater than 1 (integer), for number of samples."/>
  </xml>

  <xml name="feature_selection_algorithms">
    <option value="SelectKBest" selected="true">SelectKBest - Select features according to the k highest scores</option>
    <option value="GenericUnivariateSelect">GenericUnivariateSelect - Univariate feature selector with configurable strategy</option>
    <option value="SelectPercentile">SelectPercentile - Select features according to a percentile of the highest scores</option>
    <option value="SelectFpr">SelectFpr - Filter: Select the p-values below alpha based on a FPR test</option>
    <option value="SelectFdr">SelectFdr - Filter: Select the p-values for an estimated false discovery rate</option>
    <option value="SelectFwe">SelectFwe - Filter: Select the p-values corresponding to Family-wise error rate</option>
    <option value="VarianceThreshold">VarianceThreshold - Feature selector that removes all low-variance features</option>
    <option value="SelectFromModel">SelectFromModel - Meta-transformer for selecting features based on importance weights</option>
    <option value="RFE">RFE - Feature ranking with recursive feature elimination</option>
    <option value="RFECV">RFECV - Feature ranking with recursive feature elimination and cross-validated selection of the best number of features</option>
    <yield/>
  </xml>

  <xml name="feature_selection_algorithm_details">
    <when value="GenericUnivariateSelect">
      <expand macro="feature_selection_score_function" />
      <section name="options" title="Advanced Options" expanded="False">
        <param argument="mode" type="select" label="Feature selection mode">
          <option value="percentile">percentile</option>
          <option value="k_best">k_best</option>
          <option value="fpr">fpr</option>
          <option value="fdr">fdr</option>
          <option value="fwe">fwe</option>
        </param>
        <param argument="param" type="float" value="" optional="true" label="Parameter of the corresponding mode" help="float or int depending on the feature selection mode" />
      </section>
    </when>
    <when value="SelectPercentile">
      <expand macro="feature_selection_score_function" />
      <section name="options" title="Advanced Options" expanded="False">
        <param argument="percentile" type="integer" value="10" optional="True" label="Percent of features to keep" />
      </section>
    </when>
    <when value="SelectKBest">
      <expand macro="feature_selection_score_function" />
      <section name="options" title="Advanced Options" expanded="False">
        <param argument="k" type="integer" value="10" optional="True" label="Number of top features to select" help="No 'all' option is supported." />
      </section>
    </when>
    <when value="SelectFpr">
      <expand macro="feature_selection_score_function" />
      <section name="options" title="Advanced Options" expanded="False">
        <param argument="alpha" type="float" value="" optional="True" label="Alpha" help="The highest p-value for features to be kept."/>
      </section>
    </when>
    <when value="SelectFdr">
      <expand macro="feature_selection_score_function" />
      <section name="options" title="Advanced Options" expanded="False">
        <param argument="alpha" type="float" value="" optional="True" label="Alpha" help="The highest uncorrected p-value for features to keep."/>
      </section>
    </when>
    <when value="SelectFwe">
      <expand macro="feature_selection_score_function" />
      <section name="options" title="Advanced Options" expanded="False">
        <param argument="alpha" type="float" value="" optional="True" label="Alpha" help="The highest uncorrected p-value for features to keep."/>
      </section>
    </when>
    <when value="VarianceThreshold">
      <section name="options" title="Options" expanded="False">
        <param argument="threshold" type="float" value="0.0" optional="True" label="Threshold" help="Features with a training-set variance lower than this threshold will be removed."/>
      </section>
    </when>
  </xml>

  <xml name="feature_selection_SelectFromModel">
    <when value="SelectFromModel">
      <conditional name="model_inputter">
        <param name="input_mode" type="select" label="Construct a new estimator from a selection list?" >
          <option value="new" selected="true">Yes</option>
          <option value="prefitted">No. Load a prefitted estimator</option>
        </param>
        <when value="new">
          <expand macro="estimator_selector_fs"/>
        </when>
        <when value="prefitted">
          <param name="fitted_estimator" type="data" format='zip' label="Load a prefitted estimator" />
        </when>
      </conditional>
      <expand macro="feature_selection_SelectFromModel_options"/>
    </when>
  </xml>

  <xml name="feature_selection_SelectFromModel_no_prefitted">
    <when value="SelectFromModel">
      <conditional name="model_inputter">
        <param name="input_mode" type="select" label="Construct a new estimator from a selection list?" >
          <option value="new" selected="true">Yes</option>
        </param>
        <when value="new">
          <expand macro="estimator_selector_all"/>
        </when>
      </conditional>
      <expand macro="feature_selection_SelectFromModel_options"/>
    </when>
  </xml>

  <xml name="feature_selection_SelectFromModel_options">
    <section name="options" title="Advanced Options" expanded="False">
      <param argument="threshold" type="text" value="" optional="true" label="threshold" help="The threshold value to use for feature selection. e.g. 'mean', 'median', '1.25*mean'." />
      <param argument="norm_order" type="integer" value="1" label="norm_order" help="Order of the norm used to filter the vectors of coefficients below threshold in the case where the coef_ attribute of the estimator is of dimension 2. " />
      <param argument="max_features" type="integer" value="" optional="true" label="The maximum number of features selected scoring above threshold" help="To disable threshold and only select based on max_features, set threshold=-np.inf."/>
    </section>
  </xml>

  <xml name="feature_selection_RFE">
    <when value="RFE">
      <yield/>
      <section name="options" title="Advanced Options" expanded="False">
        <param argument="n_features_to_select" type="integer" value="" optional="true" label="n_features_to_select" help="The number of features to select. If None, half of the features are selected." />
        <param argument="step" type="float" value="1" label="step" optional="true" help="Default = 1. " />
        <param argument="verbose" type="integer" value="0" label="verbose" help="Controls verbosity of output." />
      </section>
    </when>
  </xml>

  <xml name="feature_selection_RFECV_fs">
    <when value="RFECV">
      <yield/>
      <section name="options" title="Advanced Options" expanded="False">
        <param argument="step" type="float" value="1" label="step" optional="true" help="Default = 1. " />
        <param argument="min_features_to_select" type="integer" value="1" optional="true" label="The minimum number of features to be selected"/>
        <expand macro="cv"/>
        <expand macro="scoring_selection"/>
        <param argument="verbose" type="integer" value="0" label="verbose" help="Controls verbosity of output." />
      </section>
    </when>
  </xml>

  <xml name="feature_selection_RFECV_pipeline">
    <when value="RFECV">
      <yield/>
      <section name="options" title="Advanced Options" expanded="False">
        <param argument="step" type="float" value="1" label="step" optional="true" help="Default = 1. " />
        <param argument="min_features_to_select" type="integer" value="1" optional="true" label="The minimum number of features to be selected"/>
        <expand macro="cv_reduced"/>
        <!-- TODO: group splitter support-->
        <expand macro="scoring_selection"/>
        <param argument="verbose" type="integer" value="0" label="verbose" help="Controls verbosity of output." />
      </section>
    </when>
  </xml>

  <xml name="feature_selection_DyRFECV_fs">
    <when value="DyRFECV">
      <yield/>
      <section name="options" title="Advanced Options" expanded="False">
        <param argument="step" type="text" size="30" value="1" label="step" optional="true" help="Default = 1. Support float, int and list." >
          <sanitizer>
            <valid initial="default">
              <add value="["/>
              <add value="]"/>
            </valid>
          </sanitizer>
        </param>
        <param argument="min_features_to_select" type="integer" value="1" optional="true" label="The minimum number of features to be selected"/>
        <expand macro="cv"/>
        <expand macro="scoring_selection"/>
        <param argument="verbose" type="integer" value="0" label="verbose" help="Controls verbosity of output." />
      </section>
    </when>
  </xml>

  <xml name="feature_selection_pipeline">
    <!--compare to `feature_selection_fs`, no fitted estimator for SelectFromModel and no custom estimator for RFE and RFECV-->
    <conditional name="fs_algorithm_selector">
      <param name="selected_algorithm" type="select" label="Select a feature selection algorithm">
        <expand macro="feature_selection_algorithms"/>
      </param>
      <expand macro="feature_selection_algorithm_details"/>
      <expand macro="feature_selection_SelectFromModel_no_prefitted"/>
      <expand macro="feature_selection_RFE">
        <expand macro="estimator_selector_all"/>
      </expand>  
      <expand macro="feature_selection_RFECV_pipeline">
        <expand macro="estimator_selector_all"/>
      </expand>
      <!-- TODO: add DyRFECV to pipeline-->
    </conditional>
  </xml>

  <xml name="feature_selection_fs">
    <conditional name="fs_algorithm_selector">
      <param name="selected_algorithm" type="select" label="Select a feature selection algorithm">
        <expand macro="feature_selection_algorithms">
          <option value="DyRFECV">DyRFECV - Extended RFECV with changeable steps</option>
        </expand>
      </param>
      <expand macro="feature_selection_algorithm_details"/>
      <expand macro="feature_selection_SelectFromModel"/>
      <expand macro="feature_selection_RFE">
        <expand macro="estimator_selector_fs"/>
      </expand>  
      <expand macro="feature_selection_RFECV_fs">
        <expand macro="estimator_selector_fs"/>
      </expand>
      <expand macro="feature_selection_DyRFECV_fs">
        <expand macro="estimator_selector_fs"/>
      </expand>
    </conditional>
  </xml>

  <xml name="feature_selection_score_function">
    <param argument="score_func" type="select" label="Select a score function">
      <option value="chi2">chi2 - Compute chi-squared stats between each non-negative feature and class</option>
      <option value="f_classif">f_classif - Compute the ANOVA F-value for the provided sample</option>
      <option value="f_regression">f_regression - Univariate linear regression tests</option>
      <option value="mutual_info_classif">mutual_info_classif - Estimate mutual information for a discrete target variable</option>
      <option value="mutual_info_regression">mutual_info_regression - Estimate mutual information for a continuous target variable</option>
    </param>
  </xml>

  <xml name="model_validation_common_options">
    <expand macro="cv"/>
    <expand macro="verbose"/>
    <yield/>
  </xml>

  <xml name="scoring_selection">
    <conditional name="scoring">
      <param name="primary_scoring" type="select" multiple="false" label="Select the primary metric (scoring):" help="Metric to refit the best estimator.">
        <option value="default" selected="true">default with estimator</option>
        <option value="accuracy">Classification -- 'accuracy'</option>
        <option value="balanced_accuracy">Classification -- 'balanced_accuracy'</option>
        <option value="average_precision">Classification -- 'average_precision'</option>
        <option value="f1">Classification -- 'f1'</option>
        <option value="f1_micro">Classification -- 'f1_micro'</option>
        <option value="f1_macro">Classification -- 'f1_macro'</option>
        <option value="f1_weighted">Classification -- 'f1_weighted'</option>
        <option value="f1_samples">Classification -- 'f1_samples'</option>
        <option value="neg_log_loss">Classification -- 'neg_log_loss'</option>
        <option value="precision">Classification -- 'precision'</option>
        <option value="precision_micro">Classification -- 'precision_micro'</option>
        <option value="precision_macro">Classification -- 'precision_macro'</option>
        <option value="precision_wighted">Classification -- 'precision_wighted'</option>
        <option value="precision_samples">Classification -- 'precision_samples'</option>
        <option value="recall">Classification -- 'recall'</option>
        <option value="recall_micro">Classification -- 'recall_micro'</option>
        <option value="recall_macro">Classification -- 'recall_macro'</option>
        <option value="recall_wighted">Classification -- 'recall_wighted'</option>
        <option value="recall_samples">Classification -- 'recall_samples'</option>
        <option value="roc_auc">Classification -- 'roc_auc'</option>
        <option value="explained_variance">Regression -- 'explained_variance'</option>
        <option value="neg_mean_absolute_error">Regression -- 'neg_mean_absolute_error'</option>
        <option value="neg_mean_squared_error">Regression -- 'neg_mean_squared_error'</option>
        <option value="neg_mean_squared_log_error">Regression -- 'neg_mean_squared_log_error'</option>
        <option value="neg_median_absolute_error">Regression -- 'neg_median_absolute_error'</option>
        <option value="r2">Regression -- 'r2'</option>
        <option value="max_error">Regression -- 'max_error'</option>
        <option value="binarize_auc_scorer">anomaly detection -- binarize_auc_scorer</option>
        <option value="binarize_average_precision_scorer">anomaly detection -- binarize_average_precision_scorer</option>
      </param>
      <when value="default"/>
      <when value="accuracy"><expand macro="secondary_scoring_selection_classification"/></when>
      <when value="balanced_accuracy"><expand macro="secondary_scoring_selection_classification"/></when>
      <when value="average_precision"><expand macro="secondary_scoring_selection_classification"/></when>
      <when value="f1"><expand macro="secondary_scoring_selection_classification"/></when>
      <when value="f1_micro"><expand macro="secondary_scoring_selection_classification"/></when>
      <when value="f1_macro"><expand macro="secondary_scoring_selection_classification"/></when>
      <when value="f1_weighted"><expand macro="secondary_scoring_selection_classification"/></when>
      <when value="f1_samples"><expand macro="secondary_scoring_selection_classification"/></when>
      <when value="neg_log_loss"><expand macro="secondary_scoring_selection_classification"/></when>
      <when value="precision"><expand macro="secondary_scoring_selection_classification"/></when>
      <when value="precision_micro"><expand macro="secondary_scoring_selection_classification"/></when>
      <when value="precision_macro"><expand macro="secondary_scoring_selection_classification"/></when>
      <when value="precision_wighted"><expand macro="secondary_scoring_selection_classification"/></when>
      <when value="precision_samples"><expand macro="secondary_scoring_selection_classification"/></when>
      <when value="recall"><expand macro="secondary_scoring_selection_classification"/></when>
      <when value="recall_micro"><expand macro="secondary_scoring_selection_classification"/></when>
      <when value="recall_macro"><expand macro="secondary_scoring_selection_classification"/></when>
      <when value="recall_wighted"><expand macro="secondary_scoring_selection_classification"/></when>
      <when value="recall_samples"><expand macro="secondary_scoring_selection_classification"/></when>
      <when value="roc_auc"><expand macro="secondary_scoring_selection_classification"/></when>
      <when value="explained_variance"><expand macro="secondary_scoring_selection_regression"/></when>
      <when value="neg_mean_absolute_error"><expand macro="secondary_scoring_selection_regression"/></when>
      <when value="neg_mean_squared_error"><expand macro="secondary_scoring_selection_regression"/></when>
      <when value="neg_mean_squared_log_error"><expand macro="secondary_scoring_selection_regression"/></when>
      <when value="neg_median_absolute_error"><expand macro="secondary_scoring_selection_regression"/></when>
      <when value="r2"><expand macro="secondary_scoring_selection_regression"/></when>
      <when value="max_error"><expand macro="secondary_scoring_selection_regression"/></when>
      <when value="binarize_auc_scorer"><expand macro="secondary_scoring_selection_anormaly"/></when>
      <when value="binarize_average_precision_scorer"><expand macro="secondary_scoring_selection_anormaly"/></when>
    </conditional>
  </xml>

  <xml name="secondary_scoring_selection_classification">
    <param name="secondary_scoring" type="select" multiple="true" label="Additional scoring used in multi-metric mode:" help="If the same metric with the primary is chosen, the metric will be ignored.">
      <option value="accuracy">Classification -- 'accuracy'</option>
      <option value="balanced_accuracy">Classification -- 'balanced_accuracy'</option>
      <option value="average_precision">Classification -- 'average_precision'</option>
      <option value="f1">Classification -- 'f1'</option>
      <option value="f1_micro">Classification -- 'f1_micro'</option>
      <option value="f1_macro">Classification -- 'f1_macro'</option>
      <option value="f1_weighted">Classification -- 'f1_weighted'</option>
      <option value="f1_samples">Classification -- 'f1_samples'</option>
      <option value="neg_log_loss">Classification -- 'neg_log_loss'</option>
      <option value="precision">Classification -- 'precision'</option>
      <option value="precision_micro">Classification -- 'precision_micro'</option>
      <option value="precision_macro">Classification -- 'precision_macro'</option>
      <option value="precision_wighted">Classification -- 'precision_wighted'</option>
      <option value="precision_samples">Classification -- 'precision_samples'</option>
      <option value="recall">Classification -- 'recall'</option>
      <option value="recall_micro">Classification -- 'recall_micro'</option>
      <option value="recall_macro">Classification -- 'recall_macro'</option>
      <option value="recall_wighted">Classification -- 'recall_wighted'</option>
      <option value="recall_samples">Classification -- 'recall_samples'</option>
      <option value="roc_auc">Classification -- 'roc_auc'</option>
    </param>
  </xml>

  <xml name="secondary_scoring_selection_regression">
    <param name="secondary_scoring" type="select" multiple="true" label="Additional scoring used in multi-metric mode:" help="If the same metric with the primary is chosen, the metric will be ignored.">
      <option value="explained_variance">Regression -- 'explained_variance'</option>
      <option value="neg_mean_absolute_error">Regression -- 'neg_mean_absolute_error'</option>
      <option value="neg_mean_squared_error">Regression -- 'neg_mean_squared_error'</option>
      <option value="neg_mean_squared_log_error">Regression -- 'neg_mean_squared_log_error'</option>
      <option value="neg_median_absolute_error">Regression -- 'neg_median_absolute_error'</option>
      <option value="r2">Regression -- 'r2'</option>
      <option value="max_error">Regression -- 'max_error'</option>
    </param>
  </xml>

  <xml name="secondary_scoring_selection_anormaly">
    <param name="secondary_scoring" type="select" multiple="true" label="Additional scoring used in multi-metric mode:" help="If the same metric with the primary is chosen, the metric will be ignored.">
      <option value="binarize_auc_scorer">anomaly detection -- binarize_auc_scorer</option>
      <option value="binarize_average_precision_scorer">anomaly detection -- binarize_average_precision_scorer</option>
    </param>
  </xml>

  <xml name="pre_dispatch" token_type="hidden" token_default_value="all" token_help="Number of predispatched jobs for parallel execution">
    <param argument="pre_dispatch" type="@TYPE@" value="@DEFAULT_VALUE@" optional="true" label="pre_dispatch" help="@HELP@"/>
  </xml>

  <xml name="estimator_and_hyperparameter">
    <param name="infile_estimator" type="data" format="zip" label="Choose the dataset containing pipeline/estimator object"/>
    <section name="hyperparams_swapping" title="Hyperparameter Swapping" expanded="false">
      <param name="infile_params" type="data" format="tabular" optional="true" label="Choose the dataset containing hyperparameters for the pipeline/estimator above" help="This dataset could be the output of `get_params` in the `Estimator Attributes` tool."/>
      <repeat name="param_set" min="1" max="30" title="New hyperparameter setting">
          <param name="sp_name" type="select" optional="true" label="Choose a parameter name (with current value)">
            <options from_dataset="infile_params" startswith="@">
              <column name="name" index="2"/>
              <column name="value" index="1"/>
              <filter type="unique_value" name="unique_param" column="1"/>
            </options>
          </param>
          <param name="sp_value" type="text" value="" optional="true" label="New value" help="Supports int, float, boolean, single quoted string, and selected object constructor. Similar to the `Parameter settings for search` section in `searchcv` tool except that only single value is expected here.">
            <sanitizer>
              <valid initial="default">
                <add value="&apos;"/>
                <add value="&quot;"/>
              </valid>
            </sanitizer>
          </param>
      </repeat>
    </section>
  </xml>

  <xml name="search_cv_options">
      <expand macro="scoring_selection"/>
      <expand macro="model_validation_common_options"/>
      <!--expand macro="pre_dispatch" default_value="2*n_jobs" help="Controls the number of jobs that get dispatched during parallel execution"/-->
      <param argument="iid" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="true" label="iid" help="If True, data is identically distributed across the folds"/>
      <!--param argument="refit" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="true" label="refit" help="Refit an estimator using the best found parameters on the whole dataset. Be aware that `refit=True` invokes extra computation, but it's REQUIRED for outputting the best estimator!"/> -->
      <param argument="error_score" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="true" label="Raise fit error:" help="If false, the metric score is assigned to NaN if an error occurs in estimator fitting and FitFailedWarning is raised."/>
      <param argument="return_train_score" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="false" label="return_train_score" help=""/>
  </xml>

  <xml name="estimator_module_options">
      <option value="svm" selected="true">sklearn.svm</option>
      <option value="linear_model">sklearn.linear_model</option>
      <option value="ensemble">sklearn.ensemble</option>
      <option value="naive_bayes">sklearn.naive_bayes</option>
      <option value="tree">sklearn.tree</option>
      <option value="neighbors">sklearn.neighbors</option>
      <option value="xgboost">xgboost</option>
      <yield/>
  </xml>

  <xml name="estimator_suboptions">
      <when value="svm">
        <param name="selected_estimator" type="select" label="Choose estimator class:">
          <option value="LinearSVC" selected="true">LinearSVC</option>
          <option value="LinearSVR">LinearSVR</option>
          <option value="NuSVC">NuSVC</option>
          <option value="NuSVR">NuSVR</option>
          <option value="OneClassSVM">OneClassSVM</option>
          <option value="SVC">SVC</option>
          <option value="SVR">SVR</option>
        </param>
        <expand macro="estimator_params_text"/>
      </when>
      <when value="linear_model">
        <param name="selected_estimator" type="select" label="Choose estimator class:">
          <option value="ARDRegression" selected="true">ARDRegression</option>
          <option value="BayesianRidge">BayesianRidge</option>
          <option value="ElasticNet">ElasticNet</option>
          <option value="ElasticNetCV">ElasticNetCV</option>
          <option value="HuberRegressor">HuberRegressor</option>
          <option value="Lars">Lars</option>
          <option value="LarsCV">LarsCV</option>
          <option value="Lasso">Lasso</option>
          <option value="LassoCV">LassoCV</option>
          <option value="LassoLars">LassoLars</option>
          <option value="LassoLarsCV">LassoLarsCV</option>
          <option value="LassoLarsIC">LassoLarsIC</option>
          <option value="LinearRegression">LinearRegression</option>
          <option value="LogisticRegression">LogisticRegression</option>
          <option value="LogisticRegressionCV">LogisticRegressionCV</option>
          <option value="MultiTaskLasso">MultiTaskLasso</option>
          <option value="MultiTaskElasticNet">MultiTaskElasticNet</option>
          <option value="MultiTaskLassoCV">MultiTaskLassoCV</option>
          <option value="MultiTaskElasticNetCV">MultiTaskElasticNetCV</option>
          <option value="OrthogonalMatchingPursuit">OrthogonalMatchingPursuit</option>
          <option value="OrthogonalMatchingPursuitCV">OrthogonalMatchingPursuitCV</option>
          <option value="PassiveAggressiveClassifier">PassiveAggressiveClassifier</option>
          <option value="PassiveAggressiveRegressor">PassiveAggressiveRegressor</option>
          <option value="Perceptron">Perceptron</option>
          <option value="RANSACRegressor">RANSACRegressor</option>
          <option value="Ridge">Ridge</option>
          <option value="RidgeClassifier">RidgeClassifier</option>
          <option value="RidgeClassifierCV">RidgeClassifierCV</option>
          <option value="RidgeCV">RidgeCV</option>
          <option value="SGDClassifier">SGDClassifier</option>
          <option value="SGDRegressor">SGDRegressor</option>
          <option value="TheilSenRegressor">TheilSenRegressor</option>
        </param>
        <expand macro="estimator_params_text"/>
      </when>
      <when value="ensemble">
        <param name="selected_estimator" type="select" label="Choose estimator class:">
          <option value="AdaBoostClassifier" selected="true">AdaBoostClassifier</option>
          <option value="AdaBoostRegressor">AdaBoostRegressor</option>
          <option value="BaggingClassifier">BaggingClassifier</option>
          <option value="BaggingRegressor">BaggingRegressor</option>
          <option value="ExtraTreesClassifier">ExtraTreesClassifier</option>
          <option value="ExtraTreesRegressor">ExtraTreesRegressor</option>
          <option value="GradientBoostingClassifier">GradientBoostingClassifier</option>
          <option value="GradientBoostingRegressor">GradientBoostingRegressor</option>
          <option value="IsolationForest">IsolationForest</option>
          <option value="HistGradientBoostingClassifier">HistGradientBoostingClassifier</option>
          <option value="HistGradientBoostingRegressor">HistGradientBoostingRegressor</option>
          <option value="RandomForestClassifier">RandomForestClassifier</option>
          <option value="RandomForestRegressor">RandomForestRegressor</option>
          <option value="RandomTreesEmbedding">RandomTreesEmbedding</option>
          <!--option value="VotingClassifier">VotingClassifier</option-->
        </param>
        <expand macro="estimator_params_text"/>
      </when>
      <when value="naive_bayes">
        <param name="selected_estimator" type="select" label="Choose estimator class:">
          <option value="BernoulliNB" selected="true">BernoulliNB</option>
          <option value="GaussianNB">GaussianNB</option>
          <option value="MultinomialNB">MultinomialNB</option>
        </param>
        <expand macro="estimator_params_text"/>
      </when>
      <when value="tree">
        <param name="selected_estimator" type="select" label="Choose estimator class:">
          <option value="DecisionTreeClassifier" selected="true">DecisionTreeClassifier</option>
          <option value="DecisionTreeRegressor">DecisionTreeRegressor</option>
          <option value="ExtraTreeClassifier">ExtraTreeClassifier</option>
          <option value="ExtraTreeRegressor">ExtraTreeRegressor</option>
        </param>
        <expand macro="estimator_params_text"/>
      </when>
      <when value="neighbors">
        <param name="selected_estimator" type="select" label="Choose estimator class:">
          <option value="KNeighborsClassifier" selected="true">KNeighborsClassifier</option>
          <option value="KNeighborsRegressor">KNeighborsRegressor</option>
          <!--option value="BallTree">BallTree</option-->
          <!--option value="KDTree">KDTree</option-->
          <option value="KernelDensity">KernelDensity</option>
          <option value="LocalOutlierFactor">LocalOutlierFactor</option>
          <option value="RadiusNeighborsClassifier">RadiusNeighborsClassifier</option>
          <option value="RadiusNeighborsRegressor">RadiusNeighborsRegressor</option>
          <option value="NearestCentroid">NearestCentroid</option>
          <option value="NearestNeighbors">NearestNeighbors</option>
        </param>
        <expand macro="estimator_params_text"/>
      </when>
      <when value="xgboost">
        <param name="selected_estimator" type="select" label="Choose estimator class:">
          <option value="XGBRegressor" selected="true">XGBRegressor</option>
          <option value="XGBClassifier">XGBClassifier</option>
        </param>
        <expand macro="estimator_params_text"/>
      </when>
      <yield/>
  </xml>

  <xml name="estimator_selector_all">
    <conditional name="estimator_selector">
      <param name="selected_module" type="select" label="Choose the module that contains target estimator:" >
        <expand macro="estimator_module_options"/>
      </param>
      <expand macro="estimator_suboptions"/>
    </conditional>
  </xml>

  <xml name="estimator_selector_fs">
    <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="custom_estimator">Load a custom estimator</option>
        </expand>
      </param>
      <expand macro="estimator_suboptions">
        <when value="custom_estimator">
            <param name="c_estimator" type="data" format="zip" label="Choose the dataset containing the custom estimator or pipeline:"/>
        </when>
      </expand>
    </conditional>
  </xml>

  <xml name="estimator_params_text" token_label="Type in parameter settings if different from default:" token_default_value=''
        token_help="Dictionary-capable, e.g., C=1, kernel='linear'. No double quotes. Leave this box blank for default estimator.">
    <param name="text_params" type="text" value="@DEFAULT_VALUE@" optional="true" label="@LABEL@" help="@HELP@">
      <sanitizer>
        <valid initial="default">
          <add value="&apos;"/>
        </valid>
      </sanitizer>
    </param>
  </xml>

  <xml name="kernel_approximation_all">
    <conditional name="kernel_approximation_selector">
      <param name="select_algorithm" type="select" label="Choose a kernel approximation algorithm:">
        <option value="Nystroem" selected="true">Nystroem</option>
        <option value="RBFSampler">RBFSampler</option>
        <option value="AdditiveChi2Sampler">AdditiveChi2Sampler</option>
        <option value="SkewedChi2Sampler">SkewedChi2Sampler</option>
      </param>
      <when value="Nystroem">
        <expand macro="estimator_params_text"
              help="Default(=blank): coef0=None, degree=None, gamma=None, kernel='rbf', kernel_params=None, n_components=100, random_state=None. No double quotes"/>
      </when>
      <when value="RBFSampler">
        <expand macro="estimator_params_text"
              help="Default(=blank): gamma=1.0, n_components=100, random_state=None."/>
      </when>
      <when value="AdditiveChi2Sampler">
        <expand macro="estimator_params_text"
              help="Default(=blank): sample_interval=None, sample_steps=2."/>
      </when>
      <when value="SkewedChi2Sampler">
        <expand macro="estimator_params_text"
              help="Default(=blank): n_components=100, random_state=None, skewedness=1.0."/>
      </when>
    </conditional>
  </xml>

  <xml name="matrix_decomposition_all">
    <conditional name="matrix_decomposition_selector">
      <param name="select_algorithm" type="select" label="Choose a matrix decomposition algorithm:">
        <option value="DictionaryLearning" selected="true">DictionaryLearning</option>
        <option value="FactorAnalysis">FactorAnalysis</option>
        <option value="FastICA">FastICA</option>
        <option value="IncrementalPCA">IncrementalPCA</option>
        <option value="KernelPCA">KernelPCA</option>
        <option value="LatentDirichletAllocation">LatentDirichletAllocation</option>
        <option value="MiniBatchDictionaryLearning">MiniBatchDictionaryLearning</option>
        <option value="MiniBatchSparsePCA">MiniBatchSparsePCA</option>
        <option value="NMF">NMF</option>
        <option value="PCA">PCA</option>
        <option value="SparsePCA">SparsePCA</option>
        <!--option value="SparseCoder">SparseCoder</option-->
        <option value="TruncatedSVD">TruncatedSVD</option>
      </param>
      <when value="DictionaryLearning">
        <expand macro="estimator_params_text"
              help="Default(=blank): alpha=1, code_init=None, dict_init=None, fit_algorithm='lars', max_iter=1000, n_components=None, random_state=None, split_sign=False, tol=1e-08, transform_algorithm='omp', transform_alpha=None, transform_n_nonzero_coefs=None, verbose=False."/>
      </when>
      <when value="FactorAnalysis">
        <expand macro="estimator_params_text"
              help="Default(=blank): copy=True, iterated_power=3, max_iter=1000, n_components=None, noise_variance_init=None, random_state=0, svd_method='randomized', tol=0.01."/>
      </when>
      <when value="FastICA">
        <expand macro="estimator_params_text"
              help="Default(=blank): algorithm='parallel', fun='logcosh', fun_args=None, max_iter=200, n_components=None, random_state=None, tol=0.0001, w_init=None, whiten=True. No double quotes."/>
      </when>
      <when value="IncrementalPCA">
        <expand macro="estimator_params_text"
              help="Default(=blank): batch_size=None, copy=True, n_components=None, whiten=False."/>
      </when>
      <when value="KernelPCA">
        <expand macro="estimator_params_text"
              help="Default(=blank): 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=None, remove_zero_eig=False, tol=0. No double quotes."/>
      </when>
      <when value="LatentDirichletAllocation">
        <expand macro="estimator_params_text"
              help="Default(=blank): 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=None, topic_word_prior=None, total_samples=1000000.0, verbose=0."/>
      </when>
      <when value="MiniBatchDictionaryLearning">
        <expand macro="estimator_params_text"
              help="Default(=blank): alpha=1, batch_size=3, dict_init=None, fit_algorithm='lars', n_components=None, n_iter=1000, random_state=None, shuffle=True, split_sign=False, transform_algorithm='omp', transform_alpha=None, transform_n_nonzero_coefs=None, verbose=False."/>
      </when>
      <when value="MiniBatchSparsePCA">
        <expand macro="estimator_params_text"
              help="Default(=blank): alpha=1, batch_size=3, callback=None, method='lars', n_components=None, n_iter=100, random_state=None, ridge_alpha=0.01, shuffle=True, verbose=False."/>
      </when>
      <when value="NMF">
        <expand macro="estimator_params_text"
              help="Default(=blank): alpha=0.0, beta_loss='frobenius', init=None, l1_ratio=0.0, max_iter=200, n_components=None, random_state=None, shuffle=False, solver='cd', tol=0.0001, verbose=0."/>
      </when>
      <when value="PCA">
        <expand macro="estimator_params_text"
              help="Default(=blank): copy=True, iterated_power='auto', n_components=None, random_state=None, svd_solver='auto', tol=0.0, whiten=False."/>
      </when>
      <when value="SparsePCA">
        <expand macro="estimator_params_text"
              help="Default(=blank): U_init=None, V_init=None, alpha=1, max_iter=1000, method='lars', n_components=None, random_state=None, ridge_alpha=0.01, tol=1e-08, verbose=False."/>
      </when>
      <when value="TruncatedSVD">
        <expand macro="estimator_params_text"
              help="Default(=blank): algorithm='randomized', n_components=2, n_iter=5, random_state=None, tol=0.0."/>
      </when>
    </conditional>
  </xml>

  <xml name="FeatureAgglomeration">
    <conditional name="FeatureAgglomeration_selector">
      <param name="select_algorithm" type="select" label="Choose the algorithm:">
        <option value="FeatureAgglomeration" selected="true">FeatureAgglomeration</option>
      </param>
      <when value="FeatureAgglomeration">
        <expand macro="estimator_params_text"
              help="Default(=blank): affinity='euclidean', compute_full_tree='auto', connectivity=None, linkage='ward', memory=None, n_clusters=2, pooling_func=np.mean."/>
      </when>
    </conditional>
  </xml>

  <xml name="skrebate">
    <conditional name="skrebate_selector">
      <param name="select_algorithm" type="select" label="Choose the algorithm:">
        <option value="ReliefF">ReliefF</option>
        <option value="SURF">SURF</option>
        <option value="SURFstar">SURFstar</option>
        <option value="MultiSURF">MultiSURF</option>
        <option value="MultiSURFstar">MultiSURFstar</option>
        <!--option value="TuRF">TuRF</option> -->
      </param>
      <when value="ReliefF">
        <expand macro="estimator_params_text"
              help="Default(=blank): discrete_threshold=10, n_features_to_select=10, n_neighbors=100, verbose=False."/>
      </when>
      <when value="SURF">
        <expand macro="estimator_params_text"
              help="Default(=blank): discrete_threshold=10, n_features_to_select=10, verbose=False."/>
      </when>
      <when value="SURFstar">
        <expand macro="estimator_params_text"
              help="Default(=blank): discrete_threshold=10, n_features_to_select=10, verbose=False."/>
      </when>
      <when value="MultiSURF">
        <expand macro="estimator_params_text"
              help="Default(=blank): discrete_threshold=10, n_features_to_select=10, verbose=False."/>
      </when>
      <when value="MultiSURFstar">
        <expand macro="estimator_params_text"
              help="Default(=blank): discrete_threshold=10, n_features_to_select=10, verbose=False."/>
      </when>
      <!--when value="TuRF">
        <expand macro="estimator_params_text"
              help="Default(=blank): core_algorithm='ReliefF', discrete_threshold=10, n_features_to_select=10, n_neighbors=100, pct=0.5, verbose=False."/>
      </when> -->
    </conditional>
  </xml>

  <xml name="imbalanced_learn_sampling">
    <conditional name="imblearn_selector">
      <param name="select_algorithm" type="select" label="Choose the algorithm:">
        <option value="under_sampling.ClusterCentroids" selected="true">under_sampling.ClusterCentroids</option>
        <option value="under_sampling.CondensedNearestNeighbour">under_sampling.CondensedNearestNeighbour</option>
        <option value="under_sampling.EditedNearestNeighbours">under_sampling.EditedNearestNeighbours</option>
        <option value="under_sampling.RepeatedEditedNearestNeighbours">under_sampling.RepeatedEditedNearestNeighbours</option>
        <option value="under_sampling.AllKNN">under_sampling.AllKNN</option>
        <option value="under_sampling.InstanceHardnessThreshold">under_sampling.InstanceHardnessThreshold</option>
        <option value="under_sampling.NearMiss">under_sampling.NearMiss</option>
        <option value="under_sampling.NeighbourhoodCleaningRule">under_sampling.NeighbourhoodCleaningRule</option>
        <option value="under_sampling.OneSidedSelection">under_sampling.OneSidedSelection</option>
        <option value="under_sampling.RandomUnderSampler">under_sampling.RandomUnderSampler</option>
        <option value="under_sampling.TomekLinks">under_sampling.TomekLinks</option>
        <option value="over_sampling.ADASYN">over_sampling.ADASYN</option>
        <option value="over_sampling.RandomOverSampler">over_sampling.RandomOverSampler</option>
        <option value="over_sampling.SMOTE">over_sampling.SMOTE</option>
        <option value="over_sampling.SVMSMOTE">over_sampling.SVMSMOTE</option>
        <option value="over_sampling.BorderlineSMOTE">over_sampling.BorderlineSMOTE</option>
        <option value="over_sampling.SMOTENC">over_sampling.SMOTENC</option>
        <option value="combine.SMOTEENN">combine.SMOTEENN</option>
        <option value="combine.SMOTETomek">combine.SMOTETomek</option>
        <option value="Z_RandomOverSampler">Z_RandomOverSampler - for regression</option>
      </param>
      <when value="under_sampling.ClusterCentroids">
        <expand macro="estimator_params_text"
              help="Default(=blank): sampling_strategy='auto', random_state=None, estimator=None, voting='auto'."/>
      </when>
      <when value="under_sampling.CondensedNearestNeighbour">
        <expand macro="estimator_params_text"
              help="Default(=blank): sampling_strategy='auto', random_state=None, n_neighbors=None, n_seeds_S=1."/>
      </when>
      <when value="under_sampling.EditedNearestNeighbours">
        <expand macro="estimator_params_text"
              help="Default(=blank): sampling_strategy='auto', random_state=None, n_neighbors=3, max_iter=100, kind_sel='all'."/>
      </when>
      <when value="under_sampling.RepeatedEditedNearestNeighbours">
        <expand macro="estimator_params_text"
              help="Default(=blank): sampling_strategy='auto', random_state=None, n_neighbors=3, max_iter=100, kind_sel='all'."/>
      </when>
      <when value="under_sampling.AllKNN">
        <expand macro="estimator_params_text"
              help="Default(=blank): sampling_strategy='auto', random_state=None, n_neighbors=3, kind_sel='all', allow_minority=False."/>
      </when>
      <when value="under_sampling.InstanceHardnessThreshold">
        <expand macro="estimator_params_text"
              help="Default(=blank): estimator=None, sampling_strategy='auto', random_state=None, cv=5."/>
      </when>
      <when value="under_sampling.NearMiss">
        <expand macro="estimator_params_text"
              help="Default(=blank): sampling_strategy='auto', random_state=None, version=1, n_neighbors=3, n_neighbors_ver3=3."/>
      </when>
      <when value="under_sampling.NeighbourhoodCleaningRule">
        <expand macro="estimator_params_text"
              help="Default(=blank): sampling_strategy='auto', random_state=None, n_neighbors=3, kind_sel='all', threshold_cleaning=0.5."/>
      </when>
      <when value="under_sampling.OneSidedSelection">
        <expand macro="estimator_params_text"
              help="Default(=blank): sampling_strategy='auto', random_state=None, n_neighbors=None, n_seeds_S=1."/>
      </when>
      <when value="under_sampling.RandomUnderSampler">
        <expand macro="estimator_params_text"
              help="Default(=blank): sampling_strategy='auto', random_state=None, replacement=False."/>
      </when>
      <when value="under_sampling.TomekLinks">
        <expand macro="estimator_params_text"
              help="Default(=blank): sampling_strategy='auto', random_state=None."/>
      </when>
      <when value="over_sampling.ADASYN">
        <expand macro="estimator_params_text"
              help="Default(=blank): sampling_strategy='auto', random_state=None, n_neighbors=5."/>
      </when>
      <when value="over_sampling.RandomOverSampler">
        <expand macro="estimator_params_text"
              help="Default(=blank): sampling_strategy='auto', random_state=None."/>
      </when>
      <when value="over_sampling.SMOTE">
        <expand macro="estimator_params_text"
              help="Default(=blank): sampling_strategy='auto', random_state=None, k_neighbors=5."/>
      </when>
      <when value="over_sampling.SVMSMOTE">
        <expand macro="estimator_params_text"
              help="Default(=blank): sampling_strategy='auto', k_neighbors=5, m_neighbors=10, out_step=0.5, random_state=None, svm_estimator=None."/>
      </when>
      <when value="over_sampling.BorderlineSMOTE">
        <expand macro="estimator_params_text"
              help="Default(=blank): sampling_strategy='auto', k_neighbors=5, kind='borderline-1', m_neighbors=10, random_state=None."/>
      </when>
      <when value="over_sampling.SMOTENC">
        <expand macro="estimator_params_text"
              help="Default: categorical_features=[], sampling_strategy='auto', random_state=None, k_neighbors=5."/>
      </when>
      <when value="combine.SMOTEENN">
        <expand macro="estimator_params_text"
              help="Default(=blank): sampling_strategy='auto', random_state=None, smote=None, enn=None."/>
      </when>
      <when value="combine.SMOTETomek">
        <expand macro="estimator_params_text"
              help="Default(=blank): sampling_strategy='auto', random_state=None, smote=None, tomek=None."/>
      </when>
      <when value="Z_RandomOverSampler">
        <expand macro="estimator_params_text"
              help="Default(=blank): sampling_strategy='auto', random_state=None, negative_thres=0, positive_thres=-1."/>
      </when>
    </conditional>
  </xml>

  <xml name="stacking_ensemble_inputs">
    <section name="options" title="Advanced Options" expanded="false">
        <yield/>
        <param argument="use_features_in_secondary" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="false"/>
        <param argument="store_train_meta_features" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="false"/>
    </section>
  </xml>

  <xml name="stacking_base_estimator">
    <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="custom_estimator">Load a custom estimator</option>
            </expand>
        </param>
        <expand macro="estimator_suboptions">
            <when value="custom_estimator">
                <param name="c_estimator" type="data" format="zip" label="Choose the dataset containing the custom estimator or pipeline"/>
            </when>
        </expand>
    </conditional>
  </xml>

  <xml name="stacking_voting_weights">
    <section name="options" title="Advanced Options" expanded="false">
        <param argument="weights" type="text" value="[]" optional="true" help="Sequence of weights (float or int). Uses uniform weights if None (`[]`).">
          <sanitizer>
            <valid initial="default">
              <add value="["/>
              <add value="]"/>
            </valid>
          </sanitizer>
        </param>
        <yield/>
    </section>
  </xml>

  <xml name="preprocessors_sequence_encoders">
    <conditional name="encoder_selection">
        <param name="encoder_type" type="select" label="Choose the sequence encoder class">
            <option value="GenomeOneHotEncoder">GenomeOneHotEncoder</option>
            <option value="ProteinOneHotEncoder">ProteinOneHotEncoder</option>
        </param>
        <when value="GenomeOneHotEncoder">
            <expand macro="preprocessors_sequence_encoder_arguments"/>
        </when>
        <when value="ProteinOneHotEncoder">
            <expand macro="preprocessors_sequence_encoder_arguments"/>
        </when>
    </conditional>
  </xml>

  <xml name="preprocessors_sequence_encoder_arguments">
    <param argument="seq_length" type="integer" value="" min="0" optional="true" help="Integer. Sequence length"/>
    <param argument="padding" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="true" help="Whether to pad or truncate sequence to meet the sequence length."/>
  </xml>

  <!-- Outputs -->

  <xml name="output">
    <outputs>
      <data format="tabular" name="outfile_predict">
          <filter>selected_tasks['selected_task'] == 'load'</filter>
      </data>
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                    and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
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                    Jason H. Moore},
        title     = {Benchmarking Relief-Based Feature Selection Methods},
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        journal = {Journal of Machine Learning Research},
        year    = {2017},
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