comparison main_macros.xml @ 19:f196d4715cfb draft

planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d00173591e4a783a4c1cb2664e4bb192ab5414f7

18:a886cf4c8392

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

  <token name="@COLUMNS_FUNCTION@">
def read_columns(f, c=None, c_option='by_index_number', return_df=False, **args):
  data = pandas.read_csv(f, **args)
  if c_option == 'by_index_number':
    cols = list(map(lambda x: x - 1, c))
    data = data.iloc[:,cols]
  if c_option == 'all_but_by_index_number':
    cols = list(map(lambda x: x - 1, c))
    data.drop(data.columns[cols], axis=1, inplace=True)
  if c_option == 'by_header_name':
    cols = [e.strip() for e in c.split(',')]
    data = data[cols]
  if c_option == 'all_but_by_header_name':
    cols = [e.strip() for e in c.split(',')]
    data.drop(cols, axis=1, inplace=True)
  y = data.values
  if return_df:
    return y, data
  else:
    return y
  return y
  </token>

## generate an instance for one of sklearn.feature_selection classes
  <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 inputs['model_inputter']['input_mode'] == 'prefitted':
      model_file = inputs['model_inputter']['fitted_estimator']
      with open(model_file, 'rb') as model_handler:
        fitted_estimator = pickle.load(model_handler)
      new_selector = selector(fitted_estimator, prefit=True, **options)
    else:
      estimator_json = inputs['model_inputter']["estimator_selector"]
      estimator = get_estimator(estimator_json)
      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=get_estimator(inputs["estimator_selector"])
    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>

  <token name="@GET_X_y_FUNCTION@">
def get_X_y(params, file1, file2):
  input_type = params["selected_tasks"]["selected_algorithms"]["input_options"]["selected_input"]
  if input_type=="tabular":
    header = 'infer' if params["selected_tasks"]["selected_algorithms"]["input_options"]["header1"] else None
    column_option = params["selected_tasks"]["selected_algorithms"]["input_options"]["column_selector_options_1"]["selected_column_selector_option"]
    if column_option in ["by_index_number", "all_but_by_index_number", "by_header_name", "all_but_by_header_name"]:
      c = params["selected_tasks"]["selected_algorithms"]["input_options"]["column_selector_options_1"]["col1"]
    else:
      c = None
    X = read_columns(
      file1,
      c = c,
      c_option = column_option,
      sep='\t',
      header=header,
      parse_dates=True
    )
  else:
    X = mmread(file1)

  header = 'infer' if params["selected_tasks"]["selected_algorithms"]["input_options"]["header2"] else None
  column_option = params["selected_tasks"]["selected_algorithms"]["input_options"]["column_selector_options_2"]["selected_column_selector_option2"]
  if column_option in ["by_index_number", "all_but_by_index_number", "by_header_name", "all_but_by_header_name"]:
    c = params["selected_tasks"]["selected_algorithms"]["input_options"]["column_selector_options_2"]["col2"]
  else:
    c = None
  y = read_columns(
    file2,
    c = c,
    c_option = column_option,
    sep='\t',
    header=header,
    parse_dates=True
  )
  y=y.ravel()
  return X, y
  </token>

  <token name="@SAFE_EVAL_FUNCTION@">
def safe_eval(literal):

  FROM_SCIPY_STATS = [  'bernoulli', 'binom', 'boltzmann', 'dlaplace', 'geom', 'hypergeom',
                        'logser', 'nbinom', 'planck', 'poisson', 'randint', 'skellam', 'zipf' ]

  FROM_NUMPY_RANDOM = [ 'beta', 'binomial', 'bytes', 'chisquare', 'choice', 'dirichlet', 'division',
                        'exponential', 'f', 'gamma', 'geometric', 'gumbel', 'hypergeometric',
                        'laplace', 'logistic', 'lognormal', 'logseries', 'mtrand', 'multinomial',
                        'multivariate_normal', 'negative_binomial', 'noncentral_chisquare', 'noncentral_f',
                        'normal', 'pareto', 'permutation', 'poisson', 'power', 'rand', 'randint',
                        'randn', 'random', 'random_integers', 'random_sample', 'ranf', 'rayleigh',
                        'sample', 'seed', 'set_state', 'shuffle', 'standard_cauchy', 'standard_exponential',
                        'standard_gamma', 'standard_normal', 'standard_t', 'triangular', 'uniform',
                        'vonmises', 'wald', 'weibull', 'zipf' ]

  # File opening and other unneeded functions could be dropped
  UNWANTED = ['open', 'type', 'dir', 'id', 'str', 'repr']

  # Allowed symbol table. Add more if needed.
  new_syms = {
    'np_arange': getattr(np, 'arange'),
    'ensemble_ExtraTreesClassifier': getattr(ensemble, 'ExtraTreesClassifier')
  }

  syms = make_symbol_table(use_numpy=False, **new_syms)

  for method in FROM_SCIPY_STATS:
    syms['scipy_stats_' + method] = getattr(scipy.stats, method)

  for func in FROM_NUMPY_RANDOM:
    syms['np_random_' + func] = getattr(np.random, func)

  for key in UNWANTED:
    syms.pop(key, None)

  aeval = Interpreter(symtable=syms, use_numpy=False, minimal=False,
                    no_if=True, no_for=True, no_while=True, no_try=True,
                    no_functiondef=True, no_ifexp=True, no_listcomp=False,
                    no_augassign=False, no_assert=True, no_delete=True,
                    no_raise=True, no_print=True)

  return aeval(literal)
  </token>

  <token name="@GET_SEARCH_PARAMS_FUNCTION@">
def get_search_params(params_builder):
  search_params = {}

  for p in params_builder['param_set']:
    search_p = p['search_param_selector']['search_p']
    if search_p.strip() == '':
      continue
    param_type = p['search_param_selector']['selected_param_type']

    lst = search_p.split(":")
    assert (len(lst) == 2), "Error, make sure there is one and only one colon in search parameter input."
    literal = lst[1].strip()
    ev = safe_eval(literal)
    if param_type == "final_estimator_p":
      search_params["estimator__" + lst[0].strip()] = ev
    else:
      search_params["preprocessing_" + param_type[5:6] + "__" + lst[0].strip()] = ev

  return search_params
  </token>

  <token name="@GET_ESTIMATOR_FUNCTION@">
def get_estimator(estimator_json):
  estimator_module = estimator_json['selected_module']
  estimator_cls = estimator_json['selected_estimator']

  if estimator_module == "xgboost":
    cls = getattr(xgboost, estimator_cls)
  else:
    module = getattr(sklearn, estimator_module)
    cls = getattr(module, estimator_cls)

  estimator = cls()

  estimator_params = estimator_json['text_params'].strip()
  if estimator_params != "":
    try:
      params = ast.literal_eval('{' + estimator_params + '}')
    except ValueError:
      sys.exit("Unsupported parameter input: `%s`" %estimator_params)
    estimator.set_params(**params)

  return estimator
  </token>

  <token name="@GET_CV_FUNCTION@">
def get_cv(literal):
  if literal == "":
    return None
  if re.match(r'^\d+$', literal):
    return int(literal)
  m = re.match(r'^(?P&lt;method&gt;\w+)\((?P&lt;args&gt;.*)\)$', literal)
  if m:
    my_class = getattr( model_selection, m.group('method') )
    args = safe_eval( 'dict('+ m.group('args') + ')' )
    return my_class( **args )
  sys.exit("Unsupported CV input: %s" %literal)
  </token>

  <xml name="python_requirements">
      <requirements>
          <requirement type="package" version="2.7">python</requirement>
          <requirement type="package" version="0.19.1">scikit-learn</requirement>
          <requirement type="package" version="0.22.0">pandas</requirement>
          <requirement type="package" version="0.72.1">xgboost</requirement>
          <yield />
      </requirements>
  </xml>

  <xml name="macro_stdio">

  <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_jobs" token_default_value="1" token_label="The number of jobs to run in parallel for both fit and predict">
    <param argument="n_jobs" type="integer" value="@DEFAULT_VALUE@" optional="true" label="@LABEL@" help="If -1, then the number of jobs is set to the number of cores"/>
  </xml>

  <xml name="n_iter" token_default_value="5" token_help_text="The number of passes over the training data (aka epochs). ">
    <param argument="n_iter" 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 name="infile1" type="data" format="tabular" label="Training samples dataset:"/>
    <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:"/>
    <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/>

    <when value="new">
      <expand macro="estimator_selector_all"/>
    </when>
  </xml>

  <xml name="feature_selection_all">
    <conditional name="fs_algorithm_selector">
      <param name="selected_algorithm" type="select" label="Select a feature selection algorithm">
        <option value="SelectKBest" selected="true">SelectKBest - Select features according to the k highest scores</option>
        <option value="SelectFromModel">SelectFromModel - Meta-transformer for selecting features based on importance weights</option>

      </when>
      <when value="RFECV">
        <expand macro="estimator_selector_all"/>
        <section name="options" title="Advanced Options" expanded="False">
          <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="False">
          <param argument="threshold" type="float" value="" optional="True" label="Threshold" help="Features with a training-set variance lower than this threshold will be removed."/>

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

  <xml name="model_validation_common_options">
    <param argument="cv" type="text" value="" size="50" optional="true" label="cv" help="Optional. Integer or evalable splitter object, e.g., StratifiedKFold(n_splits=3, shuffle=True, random_state=10). Leave blank for default." />
    <expand macro="n_jobs"/>
    <expand macro="verbose"/>
    <yield/>
  </xml>

  <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="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>

      </repeat>
    </section>
  </xml>

  <xml name="search_param_input" token_label="Estimator parameter:" token_help="One parameter per box. For example: C: [1, 10, 100, 1000]. See bottom for more examples">
    <param name="search_p" type="text" value="" size="100" optional="true" label="@LABEL@" help="@HELP@">
      <sanitizer>
        <valid initial="default">
          <add value="&apos;"/>
          <add value="&quot;"/>
          <add value="["/>

      </sanitizer>
    </param>
  </xml>

  <xml name="search_cv_options">
      <expand macro="scoring"/>
      <expand macro="model_validation_common_options"/>
      <expand macro="pre_dispatch" 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."/>
      <!--error_score-->
      <param argument="return_train_score" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="false" label="return_train_score" help=""/>
  </xml>

  <xml name="estimator_selector_all">
    <conditional name="estimator_selector">

          <option value="GradientBoostingRegressor">GradientBoostingRegressor</option>
          <option value="IsolationForest">IsolationForest</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:">

        </param>
        <expand macro="estimator_params_text"/>
      </when>
      <when value="neighbors">
        <param name="selected_estimator" type="select" label="Choose estimator class:">
          <option value="BallTree" selected="true">BallTree</option>
          <option value="DistanceMetric">DistanceMetric</option>
          <option value="KDTree">KDTree</option>
          <option value="KernelDensity">KernelDensity</option>
          <option value="KNeighborsClassifier">KNeighborsClassifier</option>
          <option value="KNeighborsRegressor">KNeighborsRegressor</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>

        <expand macro="estimator_params_text"/>
      </when>
    </conditional>
  </xml>

  <xml name="estimator_params_text" token_label="Type in estimator parameters:"
        token_help="Parameters in dictionary without braces ('{}'), e.g., 'C': 1, 'kernel': 'linear'. No double quotes. Leave this box blank for default estimator.">
    <param name="text_params" type="text" value="" size="50" optional="true" label="@LABEL@" help="@HELP@">
      <sanitizer>
        <valid initial="default">
          <add value="&apos;"/>
        </valid>
      </sanitizer>

        <option value="RBFSampler">RBFSampler</option>
        <option value="AdditiveChi2Sampler">AdditiveChi2Sampler</option>
        <option value="SkewedChi2Sampler">SkewedChi2Sampler</option>
      </param>
      <when value="Nystroem">
        <expand macro="estimator_params_text" label="Type in kernel approximater parameters:"
              help="Parameters in dictionary without braces ('{}'), e.g., 'n_components': 100, 'kernel': 'rbf'. No double quotes. Leave this box blank for class default."/>
      </when>
      <when value="RBFSampler">
        <expand macro="estimator_params_text" label="Type in kernel approximater parameters:"
              help="Parameters in dictionary without braces ('{}'), e.g., 'n_components': 100, 'gamma': 1.0. No double quotes. Leave this box blank for class default."/>
      </when>
      <when value="AdditiveChi2Sampler">
        <expand macro="estimator_params_text" label="Type in kernel approximater parameters:"
              help="Parameters in dictionary without braces ('{}'), e.g., 'sample_steps': 2, 'sample_interval': None. No double quotes. Leave this box blank for class default."/>
      </when>
      <when value="SkewedChi2Sampler">
        <expand macro="estimator_params_text" label="Type in kernel approximater parameters:"
              help="Parameters in dictionary without braces ('{}'), e.g., 'n_components': 100, 'skewedness': 1.0. No double quotes. Leave this box blank for class default."/>
      </when>
    </conditional>
  </xml>

  <xml name="matrix_decomposition_all">

        <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" label="Type in maxtrix decomposition parameters:"
              help="Parameters in dictionary without braces ('{}'), e.g., 'n_components': None, 'alpha': 1.0. No double quotes. Leave this box blank for class default."/>
      </when>
      <when value="FactorAnalysis">
        <expand macro="estimator_params_text" label="Type in maxtrix decomposition parameters:"
              help="Parameters in dictionary without braces ('{}'), e.g., 'n_components': 100, 'random_state': 42. No double quotes. Leave this box blank for class default."/>
      </when>
      <when value="FastICA">
        <expand macro="estimator_params_text" label="Type in maxtrix decomposition parameters:"
              help="Parameters in dictionary without braces ('{}'), e.g., 'n_components': 100, 'random_state': 42. No double quotes. Leave this box blank for class default."/>
      </when>
      <when value="IncrementalPCA">
        <expand macro="estimator_params_text" label="Type in maxtrix decomposition parameters:"
              help="Parameters in dictionary without braces ('{}'), e.g., 'n_components': 100, 'whiten': False. No double quotes. Leave this box blank for class default."/>
      </when>
      <when value="KernelPCA">
        <expand macro="estimator_params_text" label="Type in maxtrix decomposition parameters:"
              help="Parameters in dictionary without braces ('{}'), e.g., 'n_components': 100, 'random_state': 42. No double quotes. Leave this box blank for class default."/>
      </when>
      <when value="LatentDirichletAllocation">
        <expand macro="estimator_params_text" label="Type in maxtrix decomposition parameters:"
              help="Parameters in dictionary without braces ('{}'), e.g., 'n_components': 100, 'random_state': 42. No double quotes. Leave this box blank for class default."/>
      </when>
      <when value="MiniBatchDictionaryLearning">
        <expand macro="estimator_params_text" label="Type in maxtrix decomposition parameters:"
              help="Parameters in dictionary without braces ('{}'), e.g., 'n_components': 100, 'random_state': 42. No double quotes. Leave this box blank for class default."/>
      </when>
      <when value="MiniBatchSparsePCA">
        <expand macro="estimator_params_text" label="Type in maxtrix decomposition parameters:"
              help="Parameters in dictionary without braces ('{}'), e.g., 'n_components': 100, 'random_state': 42. No double quotes. Leave this box blank for class default."/>
      </when>
      <when value="NMF">
        <expand macro="estimator_params_text" label="Type in maxtrix decomposition parameters:"
              help="Parameters in dictionary without braces ('{}'), e.g., 'n_components': 100, 'init': 'random'. No double quotes. Leave this box blank for class default."/>
      </when>
      <when value="PCA">
        <expand macro="estimator_params_text" label="Type in maxtrix decomposition parameters:"
              help="Parameters in dictionary without braces ('{}'), e.g., 'n_components': 100, 'random_state': 42. No double quotes. Leave this box blank for class default."/>
      </when>
      <when value="SparsePCA">
        <expand macro="estimator_params_text" label="Type in maxtrix decomposition parameters:"
              help="Parameters in dictionary without braces ('{}'), e.g., 'n_components': 100, 'random_state': 42. No double quotes. Leave this box blank for class default."/>
      </when>
      <when value="SparseCoder">
        <expand macro="estimator_params_text" label="Type in maxtrix decomposition parameters:"
              help="Parameters in dictionary without braces ('{}'), e.g., 'transform_algorithm': 'omp', 'transform_alpha': 1.0. No double quotes. Leave this box blank for class default."/>
      </when>
      <when value="TruncatedSVD">
        <expand macro="estimator_params_text" label="Type in maxtrix decomposition parameters:"
              help="Parameters in dictionary without braces ('{}'), e.g., 'n_components': 2, 'algorithm': 'randomized'. No double quotes. Leave this box blank for default estimator."/>
      </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" label="Type in parameters:"
              help="Parameters in dictionary without braces ('{}'), e.g., 'n_clusters': 2, 'affinity': 'euclidean'. No double quotes. Leave this box blank for class default."/>
      </when>
    </conditional>
  </xml>
  <!-- Outputs -->

19:f196d4715cfb

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

  <xml name="python_requirements">
      <requirements>
          <requirement type="package" version="2.7">python</requirement>
          <requirement type="package" version="0.19.1">scikit-learn</requirement>
          <requirement type="package" version="0.22.0">pandas</requirement>
          <requirement type="package" version="0.72.1">xgboost</requirement>
          <requirement type="package" version="0.9.12">asteval</requirement>
          <yield />
      </requirements>
  </xml>

  <xml name="macro_stdio">

  <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" token_default_value="5" token_help_text="The number of passes over the training data (aka epochs). ">
    <param argument="n_iter" 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 name="infile1" type="data" format="tabular" label="Training samples dataset:"/>
    <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/>

    <when value="new">
      <expand macro="estimator_selector_all"/>
    </when>
  </xml>

  <xml name="cv">
    <param argument="cv" type="text" value="" optional="true" label="cv" help="Optional. Integer or evalable splitter object, e.g., StratifiedKFold(n_splits=3, shuffle=True, random_state=10). Leave blank for default." >
      <sanitizer>
        <valid initial="default">
          <add value="&apos;"/>
        </valid>
      </sanitizer>
    </param>
  </xml>

  <xml name="feature_selection_all">
    <conditional name="fs_algorithm_selector">
      <param name="selected_algorithm" type="select" label="Select a feature selection algorithm">
        <option value="SelectKBest" selected="true">SelectKBest - Select features according to the k highest scores</option>
        <option value="SelectFromModel">SelectFromModel - Meta-transformer for selecting features based on importance weights</option>

      </when>
      <when value="RFECV">
        <expand macro="estimator_selector_all"/>
        <section name="options" title="Advanced Options" expanded="False">
          <param argument="step" type="float" value="1" label="step" optional="true" help="Default = 1. " />
          <expand macro="cv"/>
          <expand macro="scoring_selection"/>
          <param argument="verbose" type="integer" value="0" label="verbose" help="Controls verbosity of output." />
        </section>
      </when>
      <when value="VarianceThreshold">
        <section name="options" title="Options" expanded="False">
          <param argument="threshold" type="float" value="" optional="True" label="Threshold" help="Features with a training-set variance lower than this threshold will be removed."/>

      </when>
    </conditional>
  </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>
      </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>
    </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>
    </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>

      </repeat>
    </section>
  </xml>

  <xml name="search_param_input" token_label="Estimator parameter:" token_help="One parameter per box. For example: C: [1, 10, 100, 1000]. See bottom for more examples">
    <param name="search_p" type="text" value="" optional="true" label="@LABEL@" help="@HELP@">
      <sanitizer>
        <valid initial="default">
          <add value="&apos;"/>
          <add value="&quot;"/>
          <add value="["/>

      </sanitizer>
    </param>
  </xml>

  <xml name="search_cv_options">
      <expand macro="scoring_selection"/>
      <expand macro="model_validation_common_options"/>
      <expand macro="pre_dispatch" 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."/>
      <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 0 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_selector_all">
    <conditional name="estimator_selector">

          <option value="GradientBoostingRegressor">GradientBoostingRegressor</option>
          <option value="IsolationForest">IsolationForest</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:">

        </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>

        <expand macro="estimator_params_text"/>
      </when>
    </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>

        <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">

        <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>
  <!-- Outputs -->