comparison main_macros.xml @ 3:0dc80ab8ec21 draft

planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 4ed8c4f6ef9ece81797a398b17a99bbaf49a6978

2:5a06c81f044d

  if return_df:
    return y, data
  else:
    return y
  return y
  </token>

  <xml name="python_requirements">
      <requirements>
          <requirement type="package" version="2.7">python</requirement>

            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>

  <xml name="scoring">
    <param argument="scoring" type="text" value="" optional="true" label="scoring" help="A metric used to evaluate the estimator"/>
  </xml>

  <xml name="pre_dispatch">
    <param argument="pre_dispatch" type="text" value="all" optional="true" label="pre_dispatch" help="Number of predispatched jobs for parallel execution"/>
  </xml>

  <!-- Outputs -->

  <xml name="output">

3:0dc80ab8ec21

  if return_df:
    return y, data
  else:
    return y
  return y
  </token>

## generate an instance for one of sklearn.feature_selection classes
## must call "@COLUMNS_FUNCTION@"
  <token name="@FEATURE_SELECTOR_FUNCTION@">
def feature_selector(inputs):
  selector = inputs["selected_algorithm"]
  selector = getattr(sklearn.feature_selection, selector)
  options = inputs["options"]

  if inputs['selected_algorithm'] == 'SelectFromModel':
    if not options['threshold'] or options['threshold'] == 'None':
      options['threshold'] = None
      if 'extra_estimator' in inputs and inputs['extra_estimator']['has_estimator'] == 'no_load':
        fitted_estimator = pickle.load(open("inputs['extra_estimator']['fitted_estimator']", 'r'))
        new_selector = selector(fitted_estimator, prefit=True, **options)
      else:
        estimator=inputs["estimator"]
        if inputs["extra_estimator"]["has_estimator"]=='no':
          estimator=inputs["extra_estimator"]["new_estimator"]
        estimator=eval(estimator.replace('__dq__', '"').replace("__sq__","'"))
        new_selector = selector(estimator, **options)

  elif inputs['selected_algorithm'] in ['RFE', 'RFECV']:
    if 'scoring' in options and (not options['scoring'] or options['scoring'] == 'None'):
      options['scoring'] = None
    estimator=inputs["estimator"]
    if inputs["extra_estimator"]["has_estimator"]=='no':
      estimator=inputs["extra_estimator"]["new_estimator"]
    estimator=eval(estimator.replace('__dq__', '"').replace("__sq__","'"))
    new_selector = selector(estimator, **options)

  elif inputs['selected_algorithm'] == "VarianceThreshold":
    new_selector = selector(**options)

  else:
    score_func = inputs["score_func"]
    score_func = getattr(sklearn.feature_selection, score_func)
    new_selector = selector(score_func, **options)

  return new_selector
  </token>

  <xml name="python_requirements">
      <requirements>
          <requirement type="package" version="2.7">python</requirement>

            label="Use a copy of data for precomputing row normalization" help=" "/>
      </section>
    </when>
    <yield/>
  </xml>
  <xml name="estimator_input_no_fit">
    <expand macro="feature_selection_estimator" />
    <conditional name="extra_estimator">
      <expand macro="feature_selection_extra_estimator" />
      <expand macro="feature_selection_estimator_choices" />
    </conditional>
  </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>

  <xml name="scoring">
    <param argument="scoring" type="text" value="" optional="true" label="scoring" help="A metric used to evaluate the estimator"/>
  </xml>

  <xml name="pre_dispatch" token_type="text" 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>

  <!-- Outputs -->

  <xml name="output">