Mercurial > repos > bgruening > stacking_ensemble_models
diff main_macros.xml @ 3:0a1812986bc3 draft
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 9981e25b00de29ed881b2229a173a8c812ded9bb
| author | bgruening |
|---|---|
| date | Wed, 09 Aug 2023 11:10:37 +0000 |
| parents | 38c4f8a98038 |
| children |
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--- a/main_macros.xml Mon Dec 16 10:07:37 2019 +0000 +++ b/main_macros.xml Wed Aug 09 11:10:37 2023 +0000 @@ -1,49 +1,51 @@ <macros> - <token name="@VERSION@">1.0.8.1</token> + <token name="@VERSION@">1.0.10.0</token> + <token name="@PROFILE@">21.05</token> - <xml name="python_requirements"> - <requirements> - <requirement type="package" version="3.6">python</requirement> - <requirement type="package" version="0.8.1">Galaxy-ML</requirement> - <yield/> - </requirements> - </xml> + <xml name="python_requirements"> + <requirements> + <requirement type="package" version="3.9">python</requirement> + <requirement type="package" version="0.10.0">galaxy-ml</requirement> + <yield /> + </requirements> + </xml> - <xml name="macro_stdio"> + <xml name="macro_stdio"> <stdio> - <exit_code range="1:" level="fatal" description="Error occurred. Please check Tool Standard Error"/> + <exit_code range=":-1" level="fatal" description="Error occurred. Please check Tool Standard Error" /> + <exit_code range="137" level="fatal_oom" description="Out of Memory" /> + <exit_code range="1:" level="fatal" description="Error occurred. Please check Tool Standard Error" /> </stdio> </xml> - - - <!--Generic interface--> + + <!--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> + <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> @@ -57,114 +59,114 @@ <!--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/> + <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/> + <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@"/> + <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@"/> + <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/> + <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@"/> + <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@"/> + <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=" "/> + <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. "/> + <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. "/> + <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"/> + <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> + <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"/> + <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@"/> + <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@"/> + <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@"/> + <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@"/> + <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@"/> + <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@"/> + <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@"/> + <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@"/> + <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/> + <option value="gini" selected="true">Gini impurity</option> + <option value="entropy">Information gain</option> + <yield /> </param> </xml> @@ -172,12 +174,12 @@ <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/> + <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@"/> + <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"> @@ -195,21 +197,21 @@ <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."/> + <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@"/> + <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@"/> + <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@"/> + <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"> @@ -219,93 +221,114 @@ <option value="false">false</option> </param> </xml> + + <!-- LightGBM --> + <xml name="feature_fraction" token_help="LightGBM will randomly select part of the features for each iteration (tree) if feature_fraction is smaller than 1.0. For example, if you set it to 0.8, LightGBM will select 80% of features before training each tree."> + <param argument="feature_fraction" type="float" value="1.0" label="Proportion of features to train each tree" help="@HELP@" /> + </xml> + + <xml name="lambda_l1" token_help=" "> + <param argument="lambda_l1" type="float" value="0.0" label="L1 regularization" help="@HELP@" /> + </xml> + + <xml name="lambda_l2" token_help=" "> + <param argument="lambda_l2" type="float" value="0.0" label="L1 regularization" help="@HELP@" /> + </xml> + + <xml name="min_gain_to_split" token_help=" "> + <param argument="min_gain_to_split" type="float" value="0.0" label="Minimal gain to perform split" help="@HELP@" /> + </xml> + + <xml name="min_child_weight" token_help="Minimal sum hessian in one leaf. It can be used to deal with over-fitting."> + <param argument="min_child_weight" type="float" value="0.0" label="Minimal sum hessian in one leaf" help="@HELP@" /> + </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@"/> + <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=" "/> + <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."/> + <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_no_change" token_default_value="5" token_help_text="Number of iterations with no improvement to wait before early stopping. "> - <param argument="n_iter_no_change" type="integer" optional="true" value="@DEFAULT_VALUE@" label="Number of iterations" help="@HELP_TEXT@"/> + <param argument="n_iter_no_change" 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@"/> + <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. default=None."> - <param argument="random_state" type="integer" optional="true" value="@DEFAULT_VALUE@" label="Random seed number" help="@HELP_TEXT@"/> + <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@"/> + <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@"/> + <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@"/> + <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@"/> + <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@"/> + <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@"/> + <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@"/> + <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@"/> + <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@"/> + <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=" "/> + <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> + <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@"/> + <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@"/> + <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@"/> + <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=" "/> + <param argument="pos_label" type="integer" optional="true" value="@DEFAULT_VALUE@" label="Label of the positive class" help=" " /> </xml> <xml name="average"> @@ -315,29 +338,29 @@ <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/> + <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=" "/> + <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_label1="Training samples dataset:" token_multiple1="false" token_multiple2="false"> - <param name="infile1" type="data" format="tabular" label="@LABEL1@"/> + <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@"/> + <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="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"/> + <expand macro="samples_column_selector_options" column_option="selected_column_selector_option2" col_name="col2" multiple="@MULTIPLE2@" infile="infile2" /> </conditional> - <yield/> + <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"> @@ -349,16 +372,16 @@ <option value="all_columns">All columns</option> </param> <when value="by_index_number"> - <param name="@COL_NAME@" multiple="@MULTIPLE@" type="data_column" use_header_names="true" data_ref="@INFILE@" label="Select target column(s):"/> + <param name="@COL_NAME@" multiple="@MULTIPLE@" type="data_column" use_header_names="true" data_ref="@INFILE@" label="Select target column(s):" /> </when> <when value="all_but_by_index_number"> - <param name="@COL_NAME@" multiple="@MULTIPLE@" type="data_column" use_header_names="true" data_ref="@INFILE@" label="Select target column(s):"/> + <param name="@COL_NAME@" multiple="@MULTIPLE@" type="data_column" use_header_names="true" 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"/> + <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_header_name"> - <param name="@COL_NAME@" type="text" value="" label="Type header name(s):" help="Comma-separated string. For example: target1,target2"/> + <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> @@ -367,137 +390,137 @@ <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> + <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:"/> + <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@"/> + <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> + <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):"/> + <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@"/> + <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="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@"/> + <expand macro="samples_column_selector_options" multiple="@MULTIPLE1@" /> </conditional> - <param name="infile2" type="data" format="tabular" label="@LABEL2@"/> + <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"/> + <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@"/> + <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@"/> - <expand macro="input_tabular_target"/> + <param name="infile1" type="data" format="@FORMAT1@" label="@LABEL1@" help="@HELP1@" /> + <expand macro="input_tabular_target" /> </xml> <xml name="sl_mixed_input"> <conditional name="input_options"> - <expand macro="data_input_options"/> - <expand macro="data_input_whens"/> + <expand macro="data_input_options" /> + <expand macro="data_input_whens" /> </conditional> </xml> <xml name="sl_mixed_input_plus_sequence"> <conditional name="input_options"> - <expand macro="data_input_options"> - <option value="seq_fasta">sequnences in a fasta file</option> - <option value="refseq_and_interval">reference genome and intervals</option> - </expand> - <expand macro="data_input_whens"> - <when value="seq_fasta"> - <expand macro="inputs_seq_fasta"/> - </when> - <when value="refseq_and_interval"> - <expand macro="inputs_refseq_and_interval"/> - </when> - </expand> + <expand macro="data_input_options"> + <option value="seq_fasta">sequnences in a fasta file</option> + <option value="refseq_and_interval">reference genome and intervals</option> + </expand> + <expand macro="data_input_whens"> + <when value="seq_fasta"> + <expand macro="inputs_seq_fasta" /> + </when> + <when value="refseq_and_interval"> + <expand macro="inputs_refseq_and_interval" /> + </when> + </expand> </conditional> </xml> <xml name="data_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> - <yield/> + <option value="tabular" selected="true">tabular data</option> + <option value="sparse">sparse matrix</option> + <yield /> </param> </xml> <xml name="data_input_whens"> <when value="tabular"> - <expand macro="samples_tabular" multiple1="true" multiple2="false"/> + <expand macro="samples_tabular" multiple1="true" multiple2="false" /> </when> <when value="sparse"> - <expand macro="sparse_target"/> + <expand macro="sparse_target" /> </when> - <yield/> + <yield /> </xml> <xml name="input_tabular_target"> - <param name="infile2" type="data" format="tabular" label="Dataset containing class labels or target values:"/> + <param name="infile2" type="data" format="tabular" label="Dataset containing class labels or target values:" /> <param name="header2" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="false" label="Does the dataset contain header:" /> <conditional name="column_selector_options_2"> - <expand macro="samples_column_selector_options" column_option="selected_column_selector_option2" col_name="col2" multiple="false" infile="infile2"/> + <expand macro="samples_column_selector_options" column_option="selected_column_selector_option2" col_name="col2" multiple="false" infile="infile2" /> </conditional> </xml> <xml name="inputs_seq_fasta"> - <param name="fasta_path" type="data" format="fasta" label="Dataset containing fasta genomic/protein sequences" help="Sequences will be one-hot encoded to arrays."/> - <expand macro="input_tabular_target"/> + <param name="fasta_path" type="data" format="fasta" label="Dataset containing fasta genomic/protein sequences" help="Sequences will be one-hot encoded to arrays." /> + <expand macro="input_tabular_target" /> </xml> <xml name="inputs_refseq_and_interval"> - <param name="ref_genome_file" type="data" format="fasta" label="Dataset containing reference genomic sequence"/> - <param name="interval_file" type="data" format="interval" label="Dataset containing sequence intervals for training" help="interval. Sequences will be retrieved from the reference genome and one-hot encoded to training arrays."/> - <param name="target_file" type="data" format="bed" label="Dataset containing positions and features for target values." help="bed. The file will be compressed with `bgzip` and then indexed using `tabix`."/> - <param name="infile2" type="data" format="tabular" label="Dataset containing the feature list for prediction"/> + <param name="ref_genome_file" type="data" format="fasta" label="Dataset containing reference genomic sequence" /> + <param name="interval_file" type="data" format="interval" label="Dataset containing sequence intervals for training" help="interval. Sequences will be retrieved from the reference genome and one-hot encoded to training arrays." /> + <param name="target_file" type="data" format="bed" label="Dataset containing positions and features for target values." help="bed. The file will be compressed with `bgzip` and then indexed using `tabix`." /> + <param name="infile2" type="data" format="tabular" label="Dataset containing the feature list for prediction" /> <param name="header2" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="false" label="Does the dataset contain header:" /> <conditional name="column_selector_options_2"> - <expand macro="samples_column_selector_options" column_option="selected_column_selector_option2" col_name="col2" multiple="true" infile="infile2"/> + <expand macro="samples_column_selector_options" column_option="selected_column_selector_option2" col_name="col2" multiple="true" infile="infile2" /> </conditional> </xml> <!--Advanced options--> <xml name="nn_advanced_options"> <section name="options" title="Advanced Options" expanded="False"> - <yield/> + <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> + <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> + <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 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"--> @@ -506,91 +529,91 @@ <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."/> + <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"--> + <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."/> + <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."/> + <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."/> - <expand macro="kmeans_algorithm"/> + <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." /> + <expand macro="kmeans_algorithm" /> </section> </xml> @@ -604,51 +627,51 @@ <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="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="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> + <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."/> + <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> + <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"/> + <expand macro="kmeans_advanced_options" /> </when> <when value="DBSCAN"> - <expand macro="dbscan_advanced_options"/> + <expand macro="dbscan_advanced_options" /> </when> <when value="Birch"> - <expand macro="birch_advanced_options"/> + <expand macro="birch_advanced_options" /> </when> <when value="SpectralClustering"> - <expand macro="spectral_clustering_advanced_options"/> + <expand macro="spectral_clustering_advanced_options" /> </when> <when value="MiniBatchKMeans"> - <expand macro="minibatch_kmeans_advanced_options"/> + <expand macro="minibatch_kmeans_advanced_options" /> </when> </conditional> </xml> @@ -661,7 +684,7 @@ <option value="l1">l1</option> <option value="l2">l2</option> <option value="manhattan">manhattan</option> - <yield/> + <yield /> </param> </xml> @@ -702,7 +725,7 @@ <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/> + <yield /> </param> </xml> @@ -720,15 +743,15 @@ <xml name="sparse_pairwise_condition"> <when value="pairwise_distances"> <section name="options" title="Advanced Options" expanded="False"> - <expand macro="distance_metrics"> - <yield/> - </expand> + <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=" "/> + <param argument="squared" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="false" + label="Return squared Euclidean distances" help=" " /> </section> </when> </xml> @@ -736,11 +759,11 @@ <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."/> + <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> @@ -751,7 +774,7 @@ <option value="Binarizer">Binarizer (Binarizes data)</option> <option value="MaxAbsScaler">Max Abs Scaler (Scales features by their maximum absolute value)</option> <option value="Normalizer">Normalizer (Normalizes samples individually to unit norm)</option> - <yield/> + <yield /> </param> </xml> @@ -769,28 +792,28 @@ <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> + <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="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=" "/> + 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=" "/> + 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=" "/> + 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=" "/> + label="Use a copy of data for precomputing scaling" help=" " /> </section> </when> <when value="Normalizer"> @@ -801,10 +824,10 @@ <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=" "/> + label="Use a copy of data for precomputing row normalization" help=" " /> </section> </when> - <yield/> + <yield /> </xml> <xml name="sparse_preprocessor_options_ext"> @@ -814,65 +837,65 @@ </section> </when> <when value="MinMaxScaler"> - <section name="options" title="Advanced Options" expanded="False"> - <param argument="feature_range" type="text" value="(0, 1)" optional="true" help="Desired range of transformed data. None or tuple (min, max). None equals to (0, 1)"/> - <param argument="copy" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolflase" checked="true" - label="Use a copy of data for precomputing normalization" help=" "/> - </section> + <section name="options" title="Advanced Options" expanded="False"> + <param argument="feature_range" type="text" value="(0, 1)" optional="true" help="Desired range of transformed data. None or tuple (min, max). None equals to (0, 1)" /> + <param argument="copy" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolflase" checked="true" + label="Use a copy of data for precomputing normalization" help=" " /> + </section> </when> <when value="PolynomialFeatures"> - <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> + <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> + <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> <when value="QuantileTransformer"> - <section name="options" title="Advanced Options" expanded="False"> - <param name="n_quantiles" type="integer" value="1000" min="0" label="Number of quantiles to be computed" /> - <param name="output_distribution" type="select" label="Marginal distribution for the transformed data"> - <option value="uniform" selected="true">uniform</option> - <option value="normal">normal</option> - </param> - <param name="ignore_implicit_zeros" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="false" label="Whether to discard sparse entries" help="Only applies to sparse matrices. If False, sparse entries are treated as zeros"/> - <param name="subsample" type="integer" value="100000" label="Maximum number of samples used to estimate the quantiles for computational efficiency" help="Note that the subsampling procedure may differ for value-identical sparse and dense matrices."/> - <expand macro="random_state" help_text="This is used by subsampling and smoothing noise"/> - </section> + <section name="options" title="Advanced Options" expanded="False"> + <param name="n_quantiles" type="integer" value="1000" min="0" label="Number of quantiles to be computed" /> + <param name="output_distribution" type="select" label="Marginal distribution for the transformed data"> + <option value="uniform" selected="true">uniform</option> + <option value="normal">normal</option> + </param> + <param name="ignore_implicit_zeros" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="false" label="Whether to discard sparse entries" help="Only applies to sparse matrices. If False, sparse entries are treated as zeros" /> + <param name="subsample" type="integer" value="100000" label="Maximum number of samples used to estimate the quantiles for computational efficiency" help="Note that the subsampling procedure may differ for value-identical sparse and dense matrices." /> + <expand macro="random_state" help_text="This is used by subsampling and smoothing noise" /> + </section> </when> <when value="PowerTransformer"> - <section name="options" title="Advanced Options" expanded="False"> - <param name="method" type="select" label="The power transform method"> - <option value="yeo-johnson" selected="true">yeo-johnson (works with positive and negative values)</option> - <option value="box-cox">box-cox (might perform better, but only works with strictly positive values)</option> - </param> - <param name="standardize" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="true" label="Whether to apply zero-mean, unit-variance normalization to the transformed output."/> - </section> + <section name="options" title="Advanced Options" expanded="False"> + <param name="method" type="select" label="The power transform method"> + <option value="yeo-johnson" selected="true">yeo-johnson (works with positive and negative values)</option> + <option value="box-cox">box-cox (might perform better, but only works with strictly positive values)</option> + </param> + <param name="standardize" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="true" label="Whether to apply zero-mean, unit-variance normalization to the transformed output." /> + </section> </when> <when value="KBinsDiscretizer"> - <section name="options" title="Advanced Options" expanded="False"> - <param name="n_bins" type="integer" value="5" min="2" label="The number of bins to produce"/> - <param name="encode" type="select" label="Method used to encode the transformed result"> - <option value="onehot" selected="true">onehot (encode the transformed result with one-hot encoding and return a sparse matrix)</option> - <option value="onehot-dense">onehot-dense (encode the transformed result with one-hot encoding and return a dense array)</option> - <option value="ordinal">ordinal (return the bin identifier encoded as an integer value)</option> - </param> - <param name="strategy" type="select" label="Strategy used to define the widths of the bins"> - <option value="uniform">uniform (all bins in each feature have identical widths)</option> - <option value="quantile" selected="true">quantile (all bins in each feature have the same number of points)</option> - <option value="kmeans">kmeans (values in each bin have the same nearest center of a 1D k-means cluster)</option> - </param> - </section> + <section name="options" title="Advanced Options" expanded="False"> + <param name="n_bins" type="integer" value="5" min="2" label="The number of bins to produce" /> + <param name="encode" type="select" label="Method used to encode the transformed result"> + <option value="onehot" selected="true">onehot (encode the transformed result with one-hot encoding and return a sparse matrix)</option> + <option value="onehot-dense">onehot-dense (encode the transformed result with one-hot encoding and return a dense array)</option> + <option value="ordinal">ordinal (return the bin identifier encoded as an integer value)</option> + </param> + <param name="strategy" type="select" label="Strategy used to define the widths of the bins"> + <option value="uniform">uniform (all bins in each feature have identical widths)</option> + <option value="quantile" selected="true">quantile (all bins in each feature have the same number of points)</option> + <option value="kmeans">kmeans (values in each bin have the same nearest center of a 1D k-means cluster)</option> + </param> + </section> </when> </expand> </xml> @@ -891,66 +914,68 @@ <option value="PredefinedSplit">PredefinedSplit</option> <option value="OrderedKFold">OrderedKFold</option> <option value="RepeatedOrderedKFold">RepeatedOrderedKFold</option> - <yield/> + <yield /> </xml> <xml name="cv_splitter_options"> <when value="default"> - <expand macro="cv_n_splits"/> + <expand macro="cv_n_splits" /> </when> <when value="KFold"> - <expand macro="cv_n_splits"/> - <expand macro="cv_shuffle"/> - <expand macro="random_state"/> + <expand macro="cv_n_splits" /> + <expand macro="cv_shuffle" /> + <expand macro="random_state" /> </when> <when value="StratifiedKFold"> - <expand macro="cv_n_splits"/> - <expand macro="cv_shuffle"/> - <expand macro="random_state"/> + <expand macro="cv_n_splits" /> + <expand macro="cv_shuffle" /> + <expand macro="random_state" /> </when> <when value="LeaveOneOut"> </when> <when value="LeavePOut"> - <param argument="p" type="integer" value="" label="p" help="Integer. Size of the test sets."/> + <param argument="p" type="integer" value="" label="p" help="Integer. Size of the test sets." /> </when> <when value="RepeatedKFold"> - <expand macro="cv_n_splits" value="5"/> + <expand macro="cv_n_splits" value="5" /> <param argument="n_repeats" type="integer" value="10" label="n_repeats" help="Number of times cross-validator needs to be repeated." /> <expand macro="random_state" /> </when> <when value="RepeatedStratifiedKFold"> - <expand macro="cv_n_splits" value="5"/> + <expand macro="cv_n_splits" value="5" /> <param argument="n_repeats" type="integer" value="10" label="n_repeats" help="Number of times cross-validator needs to be repeated." /> <expand macro="random_state" /> </when> <when value="ShuffleSplit"> - <expand macro="cv_n_splits" value="10" help="Number of re-shuffling and splitting iterations."/> + <expand macro="cv_n_splits" value="10" help="Number of re-shuffling and splitting iterations." /> <expand macro="cv_test_size" value="0.1" /> - <expand macro="random_state"/> + <expand macro="random_state" /> </when> <when value="StratifiedShuffleSplit"> - <expand macro="cv_n_splits" value="10" help="Number of re-shuffling and splitting iterations."/> + <expand macro="cv_n_splits" value="10" help="Number of re-shuffling and splitting iterations." /> <expand macro="cv_test_size" value="0.1" /> - <expand macro="random_state"/> + <expand macro="random_state" /> </when> <when value="TimeSeriesSplit"> - <expand macro="cv_n_splits"/> + <expand macro="cv_n_splits" /> <param argument="max_train_size" type="integer" value="" optional="true" label="Maximum size of the training set" help="Maximum size for a single training set." /> </when> <when value="PredefinedSplit"> - <param argument="test_fold" type="text" value="" area="true" label="test_fold" help="List, e.g., [0, 1, -1, 1], represents two test sets, [X[0]] and [X[1], X[3]], X[2] is excluded from any test set due to '-1'."/> + <param argument="test_fold" type="text" value="" area="true" label="test_fold" help="List, e.g., [0, 1, -1, 1], represents two test sets, [X[0]] and [X[1], X[3]], X[2] is excluded from any test set due to '-1'." /> </when> <when value="OrderedKFold"> - <expand macro="cv_n_splits"/> - <expand macro="cv_shuffle"/> - <expand macro="random_state"/> + <expand macro="cv_n_splits" /> + <expand macro="cv_shuffle" /> + <expand macro="random_state" /> + <expand macro="cv_n_stratification_bins" /> </when> <when value="RepeatedOrderedKFold"> - <expand macro="cv_n_splits"/> - <param argument="n_repeats" type="integer" value="5"/> - <expand macro="random_state"/> + <expand macro="cv_n_splits" /> + <param argument="n_repeats" type="integer" value="5" /> + <expand macro="random_state" /> + <expand macro="cv_n_stratification_bins" /> </when> - <yield/> + <yield /> </xml> <xml name="cv"> @@ -965,21 +990,21 @@ </param> <expand macro="cv_splitter_options"> <when value="GroupKFold"> - <expand macro="cv_n_splits"/> + <expand macro="cv_n_splits" /> <expand macro="cv_groups" /> </when> <when value="GroupShuffleSplit"> - <expand macro="cv_n_splits" value="5"/> - <expand macro="cv_test_size"/> - <expand macro="random_state"/> - <expand macro="cv_groups"/> + <expand macro="cv_n_splits" value="5" /> + <expand macro="cv_test_size" /> + <expand macro="random_state" /> + <expand macro="cv_groups" /> </when> <when value="LeaveOneGroupOut"> - <expand macro="cv_groups"/> + <expand macro="cv_groups" /> </when> <when value="LeavePGroupsOut"> <param argument="n_groups" type="integer" value="" label="n_groups" help="Number of groups (p) to leave out in the test split." /> - <expand macro="cv_groups"/> + <expand macro="cv_groups" /> </when> </expand> </conditional> @@ -988,30 +1013,35 @@ <xml name="cv_reduced" token_label="Select the cv splitter"> <conditional name="cv_selector"> <param name="selected_cv" type="select" label="@LABEL@"> - <expand macro="cv_splitter"/> + <expand macro="cv_splitter" /> </param> - <expand macro="cv_splitter_options"/> + <expand macro="cv_splitter_options" /> </conditional> </xml> - <xml name="cv_n_splits" token_value="3" token_help="Number of folds. Must be at least 2."> - <param argument="n_splits" type="integer" value="@VALUE@" min="1" label="n_splits" help="@HELP@"/> + <xml name="cv_n_splits" token_value="5" token_help="Number of folds. Must be at least 2."> + <!--why set min to 1?--> + <param argument="n_splits" type="integer" value="@VALUE@" min="1" label="n_splits" help="@HELP@" /> </xml> <xml name="cv_shuffle"> <param argument="shuffle" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="false" label="Whether to shuffle data before splitting" /> </xml> + <xml name="cv_n_stratification_bins"> + <param argument="n_stratification_bins" type="integer" value="" optional="true" help="Integer. The number of stratification bins. Only relevent when shuffle is True. Valid in [2, `n_samples // n_splits`]. Default value is None, which is same as `n_samples // n_splits`. The higher the value is, the distribution of target values is more approximately the ame across all split folds." /> + </xml> + <xml name="cv_test_size" token_value="0.2"> - <param argument="test_size" type="float" value="@VALUE@" min="0.0" label="Portion or number of the test set" help="0.0-1.0, proportion of the dataset to include in the test split; >1, integer only, the absolute number of test samples "/> + <param argument="test_size" type="float" value="@VALUE@" min="0.0" label="Portion or number of the test set" help="0.0-1.0, proportion of the dataset to include in the test split; >1, integer only, the absolute number of test samples " /> </xml> <xml name="cv_groups" > <section name="groups_selector" title="Groups column selector" expanded="true"> - <param name="infile_g" type="data" format="tabular" label="Choose dataset containing groups info:"/> + <param name="infile_g" type="data" format="tabular" label="Choose dataset containing groups info:" /> <param name="header_g" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="False" label="Does the dataset contain header:" /> <conditional name="column_selector_options_g"> - <expand macro="samples_column_selector_options" column_option="selected_column_selector_option_g" col_name="col_g" multiple="False" infile="infile_g"/> + <expand macro="samples_column_selector_options" column_option="selected_column_selector_option_g" col_name="col_g" multiple="False" infile="infile_g" /> </conditional> </section> </xml> @@ -1025,29 +1055,29 @@ <option value="group">GroupShuffleSplit or split by group names</option> </param> <when value="None"> - <expand macro="train_test_split_test_size"/> + <expand macro="train_test_split_test_size" /> </when> <when value="simple"> - <expand macro="train_test_split_test_size"/> - <expand macro="random_state"/> + <expand macro="train_test_split_test_size" /> + <expand macro="random_state" /> </when> <when value="stratified"> - <expand macro="train_test_split_test_size"/> - <expand macro="random_state"/> + <expand macro="train_test_split_test_size" /> + <expand macro="random_state" /> </when> <when value="group"> - <expand macro="train_test_split_test_size" optional="true"/> - <expand macro="random_state"/> + <expand macro="train_test_split_test_size" optional="true" /> + <expand macro="random_state" /> <param argument="group_names" type="text" value="" optional="true" label="Type in group names instead" - help="For example: chr6, chr7. This parameter is optional. If used, it will override the holdout size and random seed."/> - <yield/> + help="For example: chr6, chr7. This parameter is optional. If used, it will override the holdout size and random seed." /> + <yield /> </when> </conditional> - <!--param argument="train_size" type="float" optional="True" value="" label="Train size:"/>--> + <!--param argument="train_size" type="float" optional="True" value="" label="Train size:" />--> </xml> <xml name="train_test_split_test_size" token_optional="false"> - <param name="test_size" type="float" value="0.2" optional="@OPTIONAL@" label="Holdout size" help="Leass than 1, for preportion; greater than 1 (integer), for number of samples."/> + <param name="test_size" type="float" value="0.2" optional="@OPTIONAL@" label="Holdout size" help="Leass than 1, for preportion; greater than 1 (integer), for number of samples." /> </xml> <xml name="feature_selection_algorithms"> @@ -1061,7 +1091,7 @@ <option value="SelectFromModel">SelectFromModel - Meta-transformer for selecting features based on importance weights</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> - <yield/> + <yield /> </xml> <xml name="feature_selection_algorithm_details"> @@ -1093,24 +1123,24 @@ <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."/> + <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."/> + <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."/> + <param argument="alpha" type="float" value="" optional="True" label="Alpha" help="The highest uncorrected p-value for features to keep." /> </section> </when> <when value="VarianceThreshold"> <section name="options" title="Options" expanded="False"> - <param argument="threshold" type="float" value="0.0" optional="True" label="Threshold" help="Features with a training-set variance lower than this threshold will be removed."/> + <param argument="threshold" type="float" value="0.0" optional="True" label="Threshold" help="Features with a training-set variance lower than this threshold will be removed." /> </section> </when> </xml> @@ -1123,13 +1153,13 @@ <option value="prefitted">No. Load a prefitted estimator</option> </param> <when value="new"> - <expand macro="estimator_selector_fs"/> + <expand macro="estimator_selector_fs" /> </when> <when value="prefitted"> - <param name="fitted_estimator" type="data" format='zip' label="Load a prefitted estimator" /> + <param name="fitted_estimator" type="data" format='h5mlm' label="Load a prefitted estimator" /> </when> </conditional> - <expand macro="feature_selection_SelectFromModel_options"/> + <expand macro="feature_selection_SelectFromModel_options" /> </when> </xml> @@ -1140,10 +1170,10 @@ <option value="new" selected="true">Yes</option> </param> <when value="new"> - <expand macro="estimator_selector_all"/> + <expand macro="estimator_selector_all" /> </when> </conditional> - <expand macro="feature_selection_SelectFromModel_options"/> + <expand macro="feature_selection_SelectFromModel_options" /> </when> </xml> @@ -1151,13 +1181,13 @@ <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. " /> - <param argument="max_features" type="integer" value="" optional="true" label="The maximum number of features selected scoring above threshold" help="To disable threshold and only select based on max_features, set threshold=-np.inf."/> + <param argument="max_features" type="integer" value="" optional="true" label="The maximum number of features selected scoring above threshold" help="To disable threshold and only select based on max_features, set threshold=-np.inf." /> </section> </xml> <xml name="feature_selection_RFE"> <when value="RFE"> - <yield/> + <yield /> <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. " /> @@ -1168,12 +1198,12 @@ <xml name="feature_selection_RFECV_fs"> <when value="RFECV"> - <yield/> + <yield /> <section name="options" title="Advanced Options" expanded="False"> <param argument="step" type="float" value="1" label="step" optional="true" help="Default = 1. " /> - <param argument="min_features_to_select" type="integer" value="1" optional="true" label="The minimum number of features to be selected"/> - <expand macro="cv"/> - <expand macro="scoring_selection"/> + <param argument="min_features_to_select" type="integer" value="1" optional="true" label="The minimum number of features to be selected" /> + <expand macro="cv" /> + <expand macro="scoring_selection" /> <param argument="verbose" type="integer" value="0" label="verbose" help="Controls verbosity of output." /> </section> </when> @@ -1181,13 +1211,13 @@ <xml name="feature_selection_RFECV_pipeline"> <when value="RFECV"> - <yield/> + <yield /> <section name="options" title="Advanced Options" expanded="False"> <param argument="step" type="float" value="1" label="step" optional="true" help="Default = 1. " /> - <param argument="min_features_to_select" type="integer" value="1" optional="true" label="The minimum number of features to be selected"/> - <expand macro="cv_reduced"/> + <param argument="min_features_to_select" type="integer" value="1" optional="true" label="The minimum number of features to be selected" /> + <expand macro="cv_reduced" /> <!-- TODO: group splitter support--> - <expand macro="scoring_selection"/> + <expand macro="scoring_selection" /> <param argument="verbose" type="integer" value="0" label="verbose" help="Controls verbosity of output." /> </section> </when> @@ -1195,19 +1225,19 @@ <xml name="feature_selection_DyRFECV_fs"> <when value="DyRFECV"> - <yield/> + <yield /> <section name="options" title="Advanced Options" expanded="False"> <param argument="step" type="text" size="30" value="1" label="step" optional="true" help="Default = 1. Support float, int and list." > <sanitizer> <valid initial="default"> - <add value="["/> - <add value="]"/> + <add value="[" /> + <add value="]" /> </valid> </sanitizer> </param> - <param argument="min_features_to_select" type="integer" value="1" optional="true" label="The minimum number of features to be selected"/> - <expand macro="cv"/> - <expand macro="scoring_selection"/> + <param argument="min_features_to_select" type="integer" value="1" optional="true" label="The minimum number of features to be selected" /> + <expand macro="cv" /> + <expand macro="scoring_selection" /> <param argument="verbose" type="integer" value="0" label="verbose" help="Controls verbosity of output." /> </section> </when> @@ -1217,15 +1247,15 @@ <!--compare to `feature_selection_fs`, no fitted estimator for SelectFromModel and no custom estimator for RFE and RFECV--> <conditional name="fs_algorithm_selector"> <param name="selected_algorithm" type="select" label="Select a feature selection algorithm"> - <expand macro="feature_selection_algorithms"/> + <expand macro="feature_selection_algorithms" /> </param> - <expand macro="feature_selection_algorithm_details"/> - <expand macro="feature_selection_SelectFromModel_no_prefitted"/> + <expand macro="feature_selection_algorithm_details" /> + <expand macro="feature_selection_SelectFromModel_no_prefitted" /> <expand macro="feature_selection_RFE"> - <expand macro="estimator_selector_all"/> + <expand macro="estimator_selector_all" /> </expand> <expand macro="feature_selection_RFECV_pipeline"> - <expand macro="estimator_selector_all"/> + <expand macro="estimator_selector_all" /> </expand> <!-- TODO: add DyRFECV to pipeline--> </conditional> @@ -1238,16 +1268,16 @@ <option value="DyRFECV">DyRFECV - Extended RFECV with changeable steps</option> </expand> </param> - <expand macro="feature_selection_algorithm_details"/> - <expand macro="feature_selection_SelectFromModel"/> + <expand macro="feature_selection_algorithm_details" /> + <expand macro="feature_selection_SelectFromModel" /> <expand macro="feature_selection_RFE"> - <expand macro="estimator_selector_fs"/> + <expand macro="estimator_selector_fs" /> </expand> <expand macro="feature_selection_RFECV_fs"> - <expand macro="estimator_selector_fs"/> + <expand macro="estimator_selector_fs" /> </expand> <expand macro="feature_selection_DyRFECV_fs"> - <expand macro="estimator_selector_fs"/> + <expand macro="estimator_selector_fs" /> </expand> </conditional> </xml> @@ -1263,78 +1293,84 @@ </xml> <xml name="model_validation_common_options"> - <expand macro="cv"/> - <expand macro="verbose"/> - <yield/> + <expand macro="cv" /> + <expand macro="verbose" /> + <yield /> </xml> - <xml name="scoring_selection"> + <xml name="scoring_selection" token_help="Metric to refit the best estimator."> <conditional name="scoring"> - <param name="primary_scoring" type="select" multiple="false" label="Select the primary metric (scoring):" help="Metric to refit the best estimator."> + <param name="primary_scoring" type="select" multiple="false" label="Select the primary metric (scoring):" help="@HELP@"> <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> - <option value="max_error">Regression -- 'max_error'</option> - <option value="binarize_auc_scorer">anomaly detection -- binarize_auc_scorer</option> - <option value="binarize_average_precision_scorer">anomaly detection -- binarize_average_precision_scorer</option> + <expand macro="scoring_selection_options" /> </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> - <when value="max_error"><expand macro="secondary_scoring_selection_regression"/></when> - <when value="binarize_auc_scorer"><expand macro="secondary_scoring_selection_anormaly"/></when> - <when value="binarize_average_precision_scorer"><expand macro="secondary_scoring_selection_anormaly"/></when> + <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> + <when value="max_error"><expand macro="secondary_scoring_selection_regression" /></when> + <when value="spearman_correlation"><expand macro="secondary_scoring_selection_regression" /></when> + <when value="binarize_auc_scorer"><expand macro="secondary_scoring_selection_anormaly" /></when> + <when value="binarize_average_precision_scorer"><expand macro="secondary_scoring_selection_anormaly" /></when> </conditional> </xml> + <xml name="scoring_selection_options"> + <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> + <option value="max_error">Regression -- 'max_error'</option> + <option value="spearman_correlation">Regression -- Spearman's rank correlation coefficient</option> + <option value="binarize_auc_scorer">anomaly detection -- binarize_auc_scorer</option> + <option value="binarize_average_precision_scorer">anomaly detection -- binarize_average_precision_scorer</option> + </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> @@ -1369,37 +1405,36 @@ <option value="neg_median_absolute_error">Regression -- 'neg_median_absolute_error'</option> <option value="r2">Regression -- 'r2'</option> <option value="max_error">Regression -- 'max_error'</option> + <option value="spearman_correlation">Regression -- Spearman's rank correlation coefficient</option> </param> </xml> <xml name="secondary_scoring_selection_anormaly"> <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="binarize_auc_scorer">anomaly detection -- binarize_auc_scorer</option> - <option value="binarize_average_precision_scorer">anomaly detection -- binarize_average_precision_scorer</option> + <expand macro="scoring_selection_options" /> </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@"/> + <param argument="pre_dispatch" type="@TYPE@" value="@DEFAULT_VALUE@" optional="true" label="pre_dispatch" help="@HELP@" /> </xml> <xml name="estimator_and_hyperparameter"> - <param name="infile_estimator" type="data" format="zip" label="Choose the dataset containing pipeline/estimator object"/> + <param name="infile_estimator" type="data" format="h5mlm" label="Choose the dataset containing pipeline/estimator object" /> <section name="hyperparams_swapping" title="Hyperparameter Swapping" expanded="false"> - <param name="infile_params" type="data" format="tabular" optional="true" label="Choose the dataset containing hyperparameters for the pipeline/estimator above" help="This dataset could be the output of `get_params` in the `Estimator Attributes` tool."/> <repeat name="param_set" min="1" max="30" title="New hyperparameter setting"> <param name="sp_name" type="select" optional="true" label="Choose a parameter name (with current value)"> - <options from_dataset="infile_params" startswith="@"> - <column name="name" index="2"/> - <column name="value" index="1"/> - <filter type="unique_value" name="unique_param" column="1"/> + <options from_dataset="infile_estimator" meta_file_key="hyper_params" startswith="@"> + <column name="name" index="2" /> + <column name="value" index="1" /> + <filter type="unique_value" name="unique_param" column="1" /> </options> </param> <param name="sp_value" type="text" value="" optional="true" label="New value" help="Supports int, float, boolean, single quoted string, and selected object constructor. Similar to the `Parameter settings for search` section in `searchcv` tool except that only single value is expected here."> <sanitizer> <valid initial="default"> - <add value="'"/> - <add value="""/> + <add value="'" /> + <add value=""" /> </valid> </sanitizer> </param> @@ -1408,13 +1443,13 @@ </xml> <xml name="search_cv_options"> - <expand macro="scoring_selection"/> - <expand macro="model_validation_common_options"/> + <expand macro="scoring_selection" /> + <expand macro="model_validation_common_options" /> <!--expand macro="pre_dispatch" default_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. Be aware that `refit=True` invokes extra computation, but it's REQUIRED for outputting the best estimator!"/> --> - <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 NaN 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=""/> + <!--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. Be aware that `refit=True` invokes extra computation, but it's REQUIRED for outputting the best estimator!" /> --> + <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 NaN 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_module_options"> @@ -1425,7 +1460,7 @@ <option value="tree">sklearn.tree</option> <option value="neighbors">sklearn.neighbors</option> <option value="xgboost">xgboost</option> - <yield/> + <yield /> </xml> <xml name="estimator_suboptions"> @@ -1439,7 +1474,7 @@ <option value="SVC">SVC</option> <option value="SVR">SVR</option> </param> - <expand macro="estimator_params_text"/> + <expand macro="estimator_params_text" /> </when> <when value="linear_model"> <param name="selected_estimator" type="select" label="Choose estimator class:"> @@ -1476,7 +1511,7 @@ <option value="SGDRegressor">SGDRegressor</option> <option value="TheilSenRegressor">TheilSenRegressor</option> </param> - <expand macro="estimator_params_text"/> + <expand macro="estimator_params_text" /> </when> <when value="ensemble"> <param name="selected_estimator" type="select" label="Choose estimator class:"> @@ -1496,7 +1531,7 @@ <option value="RandomTreesEmbedding">RandomTreesEmbedding</option> <!--option value="VotingClassifier">VotingClassifier</option--> </param> - <expand macro="estimator_params_text"/> + <expand macro="estimator_params_text" /> </when> <when value="naive_bayes"> <param name="selected_estimator" type="select" label="Choose estimator class:"> @@ -1504,7 +1539,7 @@ <option value="GaussianNB">GaussianNB</option> <option value="MultinomialNB">MultinomialNB</option> </param> - <expand macro="estimator_params_text"/> + <expand macro="estimator_params_text" /> </when> <when value="tree"> <param name="selected_estimator" type="select" label="Choose estimator class:"> @@ -1513,7 +1548,7 @@ <option value="ExtraTreeClassifier">ExtraTreeClassifier</option> <option value="ExtraTreeRegressor">ExtraTreeRegressor</option> </param> - <expand macro="estimator_params_text"/> + <expand macro="estimator_params_text" /> </when> <when value="neighbors"> <param name="selected_estimator" type="select" label="Choose estimator class:"> @@ -1528,24 +1563,24 @@ <option value="NearestCentroid">NearestCentroid</option> <option value="NearestNeighbors">NearestNeighbors</option> </param> - <expand macro="estimator_params_text"/> + <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"/> + <expand macro="estimator_params_text" /> </when> - <yield/> + <yield /> </xml> <xml name="estimator_selector_all"> <conditional name="estimator_selector"> <param name="selected_module" type="select" label="Choose the module that contains target estimator:" > - <expand macro="estimator_module_options"/> + <expand macro="estimator_module_options" /> </param> - <expand macro="estimator_suboptions"/> + <expand macro="estimator_suboptions" /> </conditional> </xml> @@ -1553,12 +1588,12 @@ <conditional name="estimator_selector"> <param name="selected_module" type="select" label="Choose the module that contains target estimator:" > <expand macro="estimator_module_options"> - <option value="custom_estimator">Load a custom estimator</option> + <option value="custom_estimator">Load a custom estimator</option> </expand> </param> <expand macro="estimator_suboptions"> <when value="custom_estimator"> - <param name="c_estimator" type="data" format="zip" label="Choose the dataset containing the custom estimator or pipeline:"/> + <param name="c_estimator" type="data" format="h5mlm" label="Choose the dataset containing the custom estimator or pipeline:" /> </when> </expand> </conditional> @@ -1569,7 +1604,7 @@ <param name="text_params" type="text" value="@DEFAULT_VALUE@" optional="true" label="@LABEL@" help="@HELP@"> <sanitizer> <valid initial="default"> - <add value="'"/> + <add value="'" /> </valid> </sanitizer> </param> @@ -1585,19 +1620,19 @@ </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"/> + 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."/> + 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."/> + 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."/> + help="Default(=blank): n_components=100, random_state=None, skewedness=1.0." /> </when> </conditional> </xml> @@ -1621,51 +1656,51 @@ </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."/> + 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."/> + 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."/> + 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."/> + 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."/> + 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."/> + 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."/> + 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."/> + 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."/> + 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."/> + 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."/> + 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."/> + help="Default(=blank): algorithm='randomized', n_components=2, n_iter=5, random_state=None, tol=0.0." /> </when> </conditional> </xml> @@ -1677,7 +1712,7 @@ </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."/> + 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> @@ -1694,27 +1729,27 @@ </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."/> + 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."/> + 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."/> + 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."/> + 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."/> + 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."/> + 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> @@ -1745,142 +1780,105 @@ </param> <when value="under_sampling.ClusterCentroids"> <expand macro="estimator_params_text" - help="Default(=blank): sampling_strategy='auto', random_state=None, estimator=None, voting='auto'."/> + help="Default(=blank): sampling_strategy='auto', random_state=None, estimator=None, voting='auto'." /> </when> <when value="under_sampling.CondensedNearestNeighbour"> <expand macro="estimator_params_text" - help="Default(=blank): sampling_strategy='auto', random_state=None, n_neighbors=None, n_seeds_S=1."/> + help="Default(=blank): sampling_strategy='auto', random_state=None, n_neighbors=None, n_seeds_S=1." /> </when> <when value="under_sampling.EditedNearestNeighbours"> <expand macro="estimator_params_text" - help="Default(=blank): sampling_strategy='auto', random_state=None, n_neighbors=3, max_iter=100, kind_sel='all'."/> + help="Default(=blank): sampling_strategy='auto', random_state=None, n_neighbors=3, max_iter=100, kind_sel='all'." /> </when> <when value="under_sampling.RepeatedEditedNearestNeighbours"> <expand macro="estimator_params_text" - help="Default(=blank): sampling_strategy='auto', random_state=None, n_neighbors=3, max_iter=100, kind_sel='all'."/> + help="Default(=blank): sampling_strategy='auto', random_state=None, n_neighbors=3, max_iter=100, kind_sel='all'." /> </when> <when value="under_sampling.AllKNN"> <expand macro="estimator_params_text" - help="Default(=blank): sampling_strategy='auto', random_state=None, n_neighbors=3, kind_sel='all', allow_minority=False."/> + help="Default(=blank): sampling_strategy='auto', random_state=None, n_neighbors=3, kind_sel='all', allow_minority=False." /> </when> <when value="under_sampling.InstanceHardnessThreshold"> <expand macro="estimator_params_text" - help="Default(=blank): estimator=None, sampling_strategy='auto', random_state=None, cv=5."/> + help="Default(=blank): estimator=None, sampling_strategy='auto', random_state=None, cv=5." /> </when> <when value="under_sampling.NearMiss"> <expand macro="estimator_params_text" - help="Default(=blank): sampling_strategy='auto', random_state=None, version=1, n_neighbors=3, n_neighbors_ver3=3."/> + help="Default(=blank): sampling_strategy='auto', random_state=None, version=1, n_neighbors=3, n_neighbors_ver3=3." /> </when> <when value="under_sampling.NeighbourhoodCleaningRule"> <expand macro="estimator_params_text" - help="Default(=blank): sampling_strategy='auto', random_state=None, n_neighbors=3, kind_sel='all', threshold_cleaning=0.5."/> + help="Default(=blank): sampling_strategy='auto', random_state=None, n_neighbors=3, kind_sel='all', threshold_cleaning=0.5." /> </when> <when value="under_sampling.OneSidedSelection"> <expand macro="estimator_params_text" - help="Default(=blank): sampling_strategy='auto', random_state=None, n_neighbors=None, n_seeds_S=1."/> + help="Default(=blank): sampling_strategy='auto', random_state=None, n_neighbors=None, n_seeds_S=1." /> </when> <when value="under_sampling.RandomUnderSampler"> <expand macro="estimator_params_text" - help="Default(=blank): sampling_strategy='auto', random_state=None, replacement=False."/> + help="Default(=blank): sampling_strategy='auto', random_state=None, replacement=False." /> </when> <when value="under_sampling.TomekLinks"> <expand macro="estimator_params_text" - help="Default(=blank): sampling_strategy='auto', random_state=None."/> + help="Default(=blank): sampling_strategy='auto', random_state=None." /> </when> <when value="over_sampling.ADASYN"> <expand macro="estimator_params_text" - help="Default(=blank): sampling_strategy='auto', random_state=None, n_neighbors=5."/> + help="Default(=blank): sampling_strategy='auto', random_state=None, n_neighbors=5." /> </when> <when value="over_sampling.RandomOverSampler"> <expand macro="estimator_params_text" - help="Default(=blank): sampling_strategy='auto', random_state=None."/> + help="Default(=blank): sampling_strategy='auto', random_state=None." /> </when> <when value="over_sampling.SMOTE"> <expand macro="estimator_params_text" - help="Default(=blank): sampling_strategy='auto', random_state=None, k_neighbors=5."/> + help="Default(=blank): sampling_strategy='auto', random_state=None, k_neighbors=5." /> </when> <when value="over_sampling.SVMSMOTE"> <expand macro="estimator_params_text" - help="Default(=blank): sampling_strategy='auto', k_neighbors=5, m_neighbors=10, out_step=0.5, random_state=None, svm_estimator=None."/> + help="Default(=blank): sampling_strategy='auto', k_neighbors=5, m_neighbors=10, out_step=0.5, random_state=None, svm_estimator=None." /> </when> <when value="over_sampling.BorderlineSMOTE"> <expand macro="estimator_params_text" - help="Default(=blank): sampling_strategy='auto', k_neighbors=5, kind='borderline-1', m_neighbors=10, random_state=None."/> + help="Default(=blank): sampling_strategy='auto', k_neighbors=5, kind='borderline-1', m_neighbors=10, random_state=None." /> </when> <when value="over_sampling.SMOTENC"> <expand macro="estimator_params_text" - help="Default: categorical_features=[], sampling_strategy='auto', random_state=None, k_neighbors=5."/> + help="Default: categorical_features=[], sampling_strategy='auto', random_state=None, k_neighbors=5." /> </when> <when value="combine.SMOTEENN"> <expand macro="estimator_params_text" - help="Default(=blank): sampling_strategy='auto', random_state=None, smote=None, enn=None."/> + help="Default(=blank): sampling_strategy='auto', random_state=None, smote=None, enn=None." /> </when> <when value="combine.SMOTETomek"> <expand macro="estimator_params_text" - help="Default(=blank): sampling_strategy='auto', random_state=None, smote=None, tomek=None."/> + help="Default(=blank): sampling_strategy='auto', random_state=None, smote=None, tomek=None." /> </when> <when value="Z_RandomOverSampler"> <expand macro="estimator_params_text" - help="Default(=blank): sampling_strategy='auto', random_state=None, negative_thres=0, positive_thres=-1."/> + help="Default(=blank): sampling_strategy='auto', random_state=None, negative_thres=0, positive_thres=-1." /> </when> </conditional> </xml> - <xml name="stacking_ensemble_inputs"> - <section name="options" title="Advanced Options" expanded="false"> - <yield/> - <param argument="use_features_in_secondary" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="false"/> - <param argument="store_train_meta_features" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="false"/> - </section> - </xml> - - <xml name="stacking_base_estimator"> - <conditional name="estimator_selector"> - <param name="selected_module" type="select" label="Choose the module that contains target estimator:" > - <expand macro="estimator_module_options"> - <option value="custom_estimator">Load a custom estimator</option> - </expand> - </param> - <expand macro="estimator_suboptions"> - <when value="custom_estimator"> - <param name="c_estimator" type="data" format="zip" label="Choose the dataset containing the custom estimator or pipeline"/> - </when> - </expand> - </conditional> - </xml> - - <xml name="stacking_voting_weights"> - <section name="options" title="Advanced Options" expanded="false"> - <param argument="weights" type="text" value="[]" optional="true" help="Sequence of weights (float or int). Uses uniform weights if None (`[]`)."> - <sanitizer> - <valid initial="default"> - <add value="["/> - <add value="]"/> - </valid> - </sanitizer> - </param> - <yield/> - </section> - </xml> - <xml name="preprocessors_sequence_encoders"> <conditional name="encoder_selection"> - <param name="encoder_type" type="select" label="Choose the sequence encoder class"> - <option value="GenomeOneHotEncoder">GenomeOneHotEncoder</option> - <option value="ProteinOneHotEncoder">ProteinOneHotEncoder</option> - </param> - <when value="GenomeOneHotEncoder"> - <expand macro="preprocessors_sequence_encoder_arguments"/> - </when> - <when value="ProteinOneHotEncoder"> - <expand macro="preprocessors_sequence_encoder_arguments"/> - </when> + <param name="encoder_type" type="select" label="Choose the sequence encoder class"> + <option value="GenomeOneHotEncoder">GenomeOneHotEncoder</option> + <option value="ProteinOneHotEncoder">ProteinOneHotEncoder</option> + </param> + <when value="GenomeOneHotEncoder"> + <expand macro="preprocessors_sequence_encoder_arguments" /> + </when> + <when value="ProteinOneHotEncoder"> + <expand macro="preprocessors_sequence_encoder_arguments" /> + </when> </conditional> </xml> <xml name="preprocessors_sequence_encoder_arguments"> - <param argument="seq_length" type="integer" value="" min="0" optional="true" help="Integer. Sequence length"/> - <param argument="padding" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="true" help="Whether to pad or truncate sequence to meet the sequence length."/> + <param argument="seq_length" type="integer" value="" min="0" optional="true" help="Integer. Sequence length" /> + <param argument="padding" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="true" help="Whether to pad or truncate sequence to meet the sequence length." /> </xml> <!-- Outputs --> @@ -1888,10 +1886,10 @@ <xml name="output"> <outputs> <data format="tabular" name="outfile_predict"> - <filter>selected_tasks['selected_task'] == 'load'</filter> + <filter>selected_tasks['selected_task'] == 'load'</filter> </data> - <data format="zip" name="outfile_fit" label="${tool.name}.${selected_tasks.selected_algorithms.selected_algorithm}"> - <filter>selected_tasks['selected_task'] == 'train'</filter> + <data format="h5mlm" name="outfile_fit" label="${tool.name}.${selected_tasks.selected_algorithms.selected_algorithm}"> + <filter>selected_tasks['selected_task'] == 'train'</filter> </data> </outputs> </xml> @@ -1899,40 +1897,40 @@ <!--Citations--> <xml name="eden_citation"> <citations> - <citation type="doi">10.5281/zenodo.15094</citation> + <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} - } - </citation> - <yield/> + <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} + } + </citation> + <yield /> </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> + <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>