sklearn_nn_classifier: main_macros.xml comparison

comparison main_macros.xml @ 0:b7d0fc23bfe4 draft

planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 8cbb681224f23fa95783514f949c97d6c2c60966

author	bgruening
date	Sat, 04 Aug 2018 12:49:47 -0400
parents
children	23fb598a09d7

comparison

equal deleted inserted replaced

--1:000000000000
+:b7d0fc23bfe4
+<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="@GET_SEARCH_PARAMS_FUNCTION@">
+def get_search_params(params_builder):
+search_params = {}
+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)
+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>
+<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">
+<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_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 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.">
+<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_multiple1="false" token_multiple2="false">
+<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/>
+</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="by_header_name">Select columns by column header name(s)</option>
+<option value="all_but_by_index_number">All columns but by column index number(s)</option>
+<option value="all_but_by_header_name">All columns but 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" 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_index_number">
+<param name="@COL_NAME@" multiple="@MULTIPLE@" type="data_column" data_ref="@INFILE@" label="Select target column(s):"/>
+</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@"/>
+<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"/>
+</when>
+<when value="sparse">
+<expand macro="sparse_target"/>
+</when>
+</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."/>
+</section>
+</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="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>
+<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>
+</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="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=" "/>
+<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">
+<!--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">
+<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>
+</expand>
+</xml>
+<xml name="fs_selectfrommodel_prefitted">
+<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_all"/>
+</when>
+<when value="prefitted">
+<param name="fitted_estimator" type="data" format='zip' label="Load a prefitted estimator" />
+</when>
+</xml>
+<xml name="fs_selectfrommodel_no_prefitted">
+<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>
+</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>
+<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="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>
+</param>
+<when value="SelectFromModel">
+<conditional name="model_inputter">
+<yield/>
+</conditional>
+<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. " />
+</section>
+</when>
+<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="RFE">
+<expand macro="estimator_selector_all"/>
+<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>
+<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."/>
+</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>
+<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="feature_selection_output_mothods">
+<conditional name="output_method_selector">
+<param name="selected_method" type="select" label="Select an output method:">
+<option value="fit_transform">fit_transform - Fit to data, then transform it</option>
+<option value="get_support">get_support - Get a mask, or integer index, of the features selected</option>
+</param>
+<when value="fit_transform">
+<!--**fit_params-->
+</when>
+<when value="get_support">
+<param name="indices" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="false" label="Indices" help="If True, the return value will be an array of integers, rather than a boolean mask."/>
+</when>
+</conditional>
+</xml>
+<xml name="model_validation_common_options">
+<param argument="cv" type="integer" value="" optional="true" label="cv" help="The number of folds in a (Stratified)KFold" />
+<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>
+<xml name="search_cv_estimator">
+<param name="infile_pipeline" type="data" format="zip" label="Choose the dataset containing pipeline object:"/>
+<section name="search_params_builder" title="Search parameters Builder" expanded="true">
+<repeat name="param_set" min="1" max="20" title="Parameter setting for search:">
+<conditional name="search_param_selector">
+<param name="selected_param_type" type="select" label="Choose the transformation the parameter belongs to">
+<option value="final_estimator_p" selected="true">Final estimator</option>
+<option value="prep_1_p">Pre-processing step #1</option>
+<option value="prep_2_p">Pre-processing step #2</option>
+<option value="prep_3_p">Pre-processing step #3</option>
+<option value="prep_4_p">Pre-processing step #4</option>
+<option value="prep_5_p">Pre-processing step #5</option>
+</param>
+<when value="final_estimator_p">
+<expand macro="search_param_input" />
+</when>
+<when value="prep_1_p">
+<expand macro="search_param_input" label="Pre_processing component #1  parameter:" help="One parameter per box. For example: with_centering: [True, False]."/>
+</when>
+<when value="prep_2_p">
+<expand macro="search_param_input" label="Pre_processing component #2 parameter:" help="One parameter per box. For example: k: [3, 5, 7, 9]. See bottom for more examples"/>
+</when>
+<when value="prep_3_p">
+<expand macro="search_param_input" label="Pre_processing component #3 parameter:" help="One parameter per box. For example: n_components: [1, 10, 100, 1000]. See bottom for more examples"/>
+</when>
+<when value="prep_4_p">
+<expand macro="search_param_input" label="Pre_processing component #4 parameter:" help="One parameter per box. For example: n_components: [1, 10, 100, 1000]. See bottom for more examples"/>
+</when>
+<when value="prep_5_p">
+<expand macro="search_param_input" label="Pre_processing component #5 parameter:" help="One parameter per box. For example: affinity: ['euclidean', 'l1', 'l2', 'manhattan']. See bottom for more examples"/>
+</when>
+</conditional>
+</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="["/>
+<add value="]"/>
+</valid>
+</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">
+<param name="selected_module" type="select" label="Choose the module that contains target estimator:" >
+<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>
+<!--more-->
+</param>
+<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="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="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>
+</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>
+</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>
+</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" 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">
+<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" 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 -->
+<xml name="output">
+<outputs>
+<data format="tabular" name="outfile_predict">
+<filter>selected_tasks['selected_task'] == 'load'</filter>
+</data>
+<data format="zip" name="outfile_fit">
+<filter>selected_tasks['selected_task'] == 'train'</filter>
+</data>
+</outputs>
+</xml>
+<!--Citations-->
+<xml name="eden_citation">
+<citations>
+<citation type="doi">10.5281/zenodo.15094</citation>
+</citations>
+</xml>
+<xml name="sklearn_citation">
+<citations>
+<citation type="bibtex">
+@article{scikit-learn,
+title={Scikit-learn: Machine Learning in {P}ython},
+author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
+and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
+and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
+Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
+journal={Journal of Machine Learning Research},
+volume={12},
+pages={2825--2830},
+year={2011}
+url = {https://github.com/scikit-learn/scikit-learn}
+}
+</citation>
+</citations>
+</xml>
+<xml name="scipy_citation">
+<citations>
+<citation type="bibtex">
+@Misc{,
+author =    {Eric Jones and Travis Oliphant and Pearu Peterson and others},
+title =     {{SciPy}: Open source scientific tools for {Python}},
+year =      {2001--},
+url = "http://www.scipy.org/",
+note = {[Online; accessed 2016-04-09]}
+}
+</citation>
+</citations>
+</xml>
+</macros>

Mercurial > repos > bgruening > sklearn_nn_classifier

comparison main_macros.xml @ 0:b7d0fc23bfe4 draft