comparison main_macros.xml @ 1:d5c4fb103281 draft

planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 79fe42239dcf077b13f85cbcd6c6e30d7e1e4832

0:e1a494495d9f

<macros>
  <token name="@VERSION@">0.9</token>

  <token name="@COLUMNS_FUNCTION@">
def read_columns(f, c, **args):
  data = pandas.read_csv(f, **args)
  cols = c.split (',')
  cols = map(int, cols)
  cols = list(map(lambda x: x - 1, cols))
  y = data.iloc[:,cols].values
  return y
  </token>

  <xml name="python_requirements">
      <requirements>

            label="Use a copy of data for precomputing row normalization" help=" "/>
      </section>
    </when>
    <yield/>
  </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>

1:d5c4fb103281

<macros>
  <token name="@VERSION@">0.9</token>

  <token name="@COLUMNS_FUNCTION@">
def read_columns(f, c, return_df=False, **args):
  data = pandas.read_csv(f, **args)
  cols = c.split (',')
  cols = map(int, cols)
  cols = list(map(lambda x: x - 1, cols))
  data = data.iloc[:,cols]
  y = data.values
  if return_df:
    return y, data
  else:
    return y
  return y
  </token>

  <xml name="python_requirements">
      <requirements>

            label="Use a copy of data for precomputing row normalization" help=" "/>
      </section>
    </when>
    <yield/>
  </xml>
  <xml name="feature_selection_all">
    <conditional name="feature_selection_algorithms">
      <param name="selected_algorithm" type="select" label="Select a feature selection algorithm">
        <option value="SelectFromModel" selected="true">SelectFromModel - Meta-transformer for selecting features based on importance weights</option>
        <option value="GenericUnivariateSelect" selected="true">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="SelectKBest">SelectKBest - Select features according to the k 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="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>
        <option value="VarianceThreshold">VarianceThreshold - Feature selector that removes all low-variance features</option>
        <!--option value="chi2">Compute chi-squared stats between each non-negative feature and class</option-->
        <!--option value="f_classif">Compute the ANOVA F-value for the provided sample</option-->
        <!--option value="f_regression">Univariate linear regression tests</option-->
        <!--option value="mutual_info_classif">Estimate mutual information for a discrete target variable</option-->
        <!--option value="mutual_info_regression">Estimate mutual information for a continuous target variable</option-->
      </param>
      <when value="SelectFromModel">
        <expand macro="feature_selection_estimator" />
        <conditional name="extra_estimator">
          <expand macro="feature_selection_extra_estimator" >
            <option value="no_load">No, I will load a prefitted estimator</option>
          </expand>
          <expand macro="feature_selection_estimator_choices" >
            <when value="no_load">
              <param name="fitted_estimator" type="data" format='zip' label="Load a prefitted estimator" />
            </when>
          </expand>
        </conditional>
        <section name="options" title="Other Options" expanded="True">
          <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. " />
        </section>
      </when>
      <when value="GenericUnivariateSelect">
        <expand macro="feature_selection_score_function" />
        <section name="options" title="Other Options" expanded="True">
          <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="Other Options" expanded="True">
          <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="Other Options" expanded="True">
          <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="Other Options" expanded="True">
          <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="Other Options" expanded="True">
          <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="Other Options" expanded="True">
          <param argument="alpha" type="float" value="" optional="True" label="Alpha" help="The highest uncorrected p-value for features to keep."/>
        </section>
      </when>
      <when value="RFE">
        <expand macro="feature_selection_estimator" />
        <conditional name="extra_estimator">
          <expand macro="feature_selection_extra_estimator" />
          <expand macro="feature_selection_estimator_choices" />
        </conditional>
        <section name="options" title="Other Options" expanded="True">
          <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>
      <when value="RFECV">
        <expand macro="feature_selection_estimator" />
        <conditional name="extra_estimator">
          <expand macro="feature_selection_extra_estimator" />
          <expand macro="feature_selection_estimator_choices" />
        </conditional>
        <section name="options" title="Other Options" expanded="True">
          <param argument="step" type="float" value="1" label="step" optional="true" help="Default = 1. " />
          <param argument="cv" type="integer" value="" optional="true" label="cv" help="Determines the cross-validation splitting strategy" />
          <param argument="scoring" type="text" value="" optional="true" label="scoring" help="A string (see model evaluation documentation) or a scorer callable object / function with signature scorer(estimator, X, y)."/>
          <param argument="verbose" type="integer" value="0" label="verbose" help="Controls verbosity of output." />
          <param argument="n_jobs" type="integer" value="1" label="n_jobs" help="Number of cores to run in parallel while fitting across folds. Defaults to 1 core."/>
        </section>
      </when>
      <when value="VarianceThreshold">
        <section name="options" title="Options" expanded="True">
          <param argument="threshold" type="float" value="" optional="True" label="Threshold" help="Features with a training-set variance lower than this threshold will be removed."/>
        </section>
      </when>
      <!--when value="chi2">
      </when>
      <when value="f_classif">
      </when>
      <when value="f_regression">
      </when>
      <when value="mutual_info_classif">
      </when>
      <when value="mutual_info_regression">
      </when-->
    </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>