comparison main_macros.xml @ 32:a36e1455971d draft

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

31:faa3d0b9d1a6

<macros>
  <token name="@VERSION@">1.0.0.4</token>

  <xml name="python_requirements">
      <requirements>
          <requirement type="package" version="3.6">python</requirement>
          <requirement type="package" version="0.20.3">scikit-learn</requirement>
          <requirement type="package" version="0.24.2">pandas</requirement>
          <requirement type="package" version="0.80">xgboost</requirement>
          <requirement type="package" version="0.9.13">asteval</requirement>
          <requirement type="package" version="0.6">skrebate</requirement>
          <requirement type="package" version="0.4.2">imbalanced-learn</requirement>
          <requirement type="package" version="0.16.0">mlxtend</requirement>
          <yield/>
      </requirements>
  </xml>

  <xml name="macro_stdio">

    </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@"/>
    <param name="infile2" type="data" format="@FORMAT2@" label="@LABEL2@" help="@HELP2@"/>
    <param name="col2" multiple="@MULTIPLE@" type="data_column" data_ref="infile2" label="Select target column(s):"/>
  </xml>

  <xml name="sl_mixed_input">
    <conditional name="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>
      </param>
      <when value="tabular">
          <expand macro="samples_tabular" multiple1="true" multiple2="false"/>
      </when>
      <when value="sparse">
          <expand macro="sparse_target"/>
      </when>
    </conditional>
  </xml>

  <!--Advanced options-->
  <xml name="nn_advanced_options">

  <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="Imputer">Imputer (Completes missing values)</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>

                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="Imputer">
      <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 imputation" help=" "/>
          <param argument="strategy" type="select" optional="true" label="Imputation strategy" help=" ">
              <option value="mean" selected="true">Replace missing values using the mean along the axis</option>
              <option value="median">Replace missing values using the median along the axis</option>
              <option value="most_frequent">Replace missing using the most frequent value along the axis</option>
          </param>
          <param argument="missing_values" type="text" optional="true" value="NaN"
                label="Placeholder for missing values" help="For missing values encoded as numpy.nan, use the string value “NaN”"/>
          <!--param argument="axis" type="boolean" optional="true" truevalue="1" falsevalue="0"
                label="Impute along axis = 1" help="If fasle, axis = 0 is selected for imputation. "/> -->
          <!--param argument="axis" type="select" optional="true" label="The axis along which to impute" help=" ">
              <option value="0" selected="true">Impute along columns</option>
              <option value="1">Impute along rows</option>
          </param-->
      </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=" "/>

        <section name="options" title="Advanced Options" expanded="False">
        </section>
      </when>
      <when value="MinMaxScaler">
          <section name="options" title="Advanced Options" expanded="False">
              <!--feature_range-->
              <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">

        </when>
      </expand>
    </conditional>
  </xml>

  <xml name="cv_reduced">
    <conditional name="cv_selector">
      <param name="selected_cv" type="select" label="Select the cv splitter:">
        <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="2" 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>

      <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="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>

    </param>
  </xml>

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

  <xml name="scoring_selection">
    <conditional name="scoring">

  </xml>

  <xml name="search_cv_estimator">
    <param name="infile_estimator" type="data" format="zip" label="Choose the dataset containing pipeline/estimator object"/>
    <section name="search_params_builder" title="Search parameters Builder" expanded="true">
      <param name="infile_params" type="data" format="tabular" label="Choose the dataset containing parameter names"/>
      <repeat name="param_set" min="1" max="30" title="Parameter settings for search:">
          <param name="sp_name" type="select" 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"/>
              <filter type="sort_by" name="sorted_param" column="2"/>
            </options>
          </param>
          <param name="sp_list" type="text" value="" optional="true" label="Search list" help="list or array-like, for example: [1, 10, 100, 1000], [True, False] and ['auto', 'sqrt', None]. See `help` section for more examples">
            <sanitizer>
              <valid initial="default">
                <add value="&apos;"/>
                <add value="&quot;"/>
                <add value="["/>
                <add value="]"/>
              </valid>
            </sanitizer>
          </param>
      </repeat>
    </section>

            </when>
        </expand>
    </conditional>
  </xml>

  <!-- Outputs -->

  <xml name="output">
    <outputs>
      <data format="tabular" name="outfile_predict">

        keywords = {large-scale machine learning},
      }
    </citation>
  </xml>

    <xml name="imblearn_citation">
    <citation type="bibtex">
      @article{JMLR:v18:16-365,
        author  = {Guillaume  Lema{{\^i}}tre and Fernando Nogueira and Christos K. Aridas},
        title   = {Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning},
        journal = {Journal of Machine Learning Research},

        url     = {http://jmlr.org/papers/v18/16-365.html}
      }
    </citation>
  </xml>

</macros>

32:a36e1455971d

<macros>
  <token name="@VERSION@">1.0.7.10</token>

  <token name="@ENSEMBLE_VERSION@">0.2.0</token>

  <xml name="python_requirements">
      <requirements>
          <requirement type="package" version="3.6">python</requirement>
          <requirement type="package" version="0.7.10">Galaxy-ML</requirement>
          <yield/>
      </requirements>
  </xml>

  <xml name="macro_stdio">

    </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">
      <param name="selected_input" type="select" label="Select input type:">
          <option value="tabular" selected="true">tabular data</option>
          <option value="sparse">sparse matrix</option>
          <option value="seq_fasta">sequnences in a fasta file</option>
          <option value="refseq_and_interval">reference genome and intervals</option>
      </param>
      <when value="tabular">
          <expand macro="samples_tabular" multiple1="true" multiple2="false"/>
      </when>
      <when value="sparse">
          <expand macro="sparse_target"/>
      </when>
      <when value="seq_fasta">
          <expand macro="inputs_seq_fasta"/>
      </when>
      <when value="refseq_and_interval">
          <expand macro="inputs_refseq_and_interval"/>
      </when>
    </conditional>
  </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">

  <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>

                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=" "/>

        <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">

        </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>

      <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>

    </param>
  </xml>

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

  <xml name="scoring_selection">
    <conditional name="scoring">

  </xml>

  <xml name="search_cv_estimator">
    <param name="infile_estimator" type="data" format="zip" label="Choose the dataset containing pipeline/estimator object"/>
    <section name="search_params_builder" title="Search parameters Builder" expanded="true">
      <param name="infile_params" type="data" format="tabular" optional="true" label="Choose the dataset containing parameter names" help="This dataset could be the output of `get_params` in the `Estimator Attributes` tool."/>
      <repeat name="param_set" min="1" max="30" title="Parameter settings for search:">
          <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_list" type="text" value="" optional="true" label="Search list" help="list or array-like, for example: [1, 10, 100, 1000], [True, False] and ['auto', 'sqrt', None]. See `help` section for more examples">
            <sanitizer>
              <valid initial="default">
                <add value="&apos;"/>
                <add value="&quot;"/>
                <add value="["/>
                <add value="]"/>
              </valid>
            </sanitizer>
          </param>
      </repeat>
    </section>
  </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>

            </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">

        keywords = {large-scale machine learning},
      }
    </citation>
  </xml>

  <xml name="imblearn_citation">
    <citation type="bibtex">
      @article{JMLR:v18:16-365,
        author  = {Guillaume  Lema{{\^i}}tre and Fernando Nogueira and Christos K. Aridas},
        title   = {Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning},
        journal = {Journal of Machine Learning Research},

        url     = {http://jmlr.org/papers/v18/16-365.html}
      }
    </citation>
  </xml>

  <xml name="selene_citation">
    <citation type="bibtex">
      @article{chen2019selene,
        title={Selene: a PyTorch-based deep learning library for sequence data},
        author={Chen, Kathleen M and Cofer, Evan M and Zhou, Jian and Troyanskaya, Olga G},
        journal={Nature methods},
        volume={16},
        number={4},
        pages={315},
        year={2019},
        publisher={Nature Publishing Group}
      }
    </citation>
  </xml>

</macros>