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1 <?xml version="1.0" ?>
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2 <tool id="qiime_sample-classifier_regress-samples-ncv" name="qiime sample-classifier regress-samples-ncv" version="2019.4">
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3 <description> - Nested cross-validated supervised learning regressor.</description>
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4 <requirements>
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5 <requirement type="package" version="2019.4">qiime2</requirement>
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6 </requirements>
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7 <command><![CDATA[
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8 qiime sample-classifier regress-samples-ncv
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9
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10 --i-table=$itable
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11 --m-metadata-column="$mmetadatacolumn"
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12
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13 #if $pcv:
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14 --p-cv=$pcv
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15 #end if
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16
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17 #if str($prandomstate):
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18 --p-random-state="$prandomstate"
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19 #end if
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20
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21 #set $pnjobs = '${GALAXY_SLOTS:-4}'
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22
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23 #if str($pnjobs):
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24 --p-n-jobs="$pnjobs"
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25 #end if
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26
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27
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28 #if $pnestimators:
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29 --p-n-estimators=$pnestimators
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30 #end if
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31
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32 #if str($pestimator) != 'None':
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33 --p-estimator=$pestimator
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34 #end if
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35
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36 #if $pstratify:
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37 --p-stratify
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38 #end if
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39
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40 #if $pparametertuning:
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41 --p-parameter-tuning
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42 #end if
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43
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44 #if str($pmissingsamples) != 'None':
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45 --p-missing-samples=$pmissingsamples
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46 #end if
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47
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48
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5
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49
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50 #if $metadatafile:
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51 --m-metadata-file=$metadatafile
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0
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52 #end if
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53
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54
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5
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55
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0
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56 --o-predictions=opredictions
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57 --o-feature-importance=ofeatureimportance
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58 ;
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59 cp opredictions.qza $opredictions;
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60 cp ofeatureimportance.qza $ofeatureimportance
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61 ]]></command>
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62 <inputs>
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63 <param format="qza,no_unzip.zip" label="--i-table: ARTIFACT FeatureTable[Frequency] Feature table containing all features that should be used for target prediction. [required]" name="itable" optional="False" type="data"/>
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64 <param label="--m-metadata-column: COLUMN MetadataColumn[Numeric] Numeric metadata column to use as prediction target. [required]" name="mmetadatacolumn" optional="False" type="text"/>
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65 <param label="--p-cv: INTEGER Number of k-fold cross-validations to perform. Range(1, None) [default: 5]" name="pcv" optional="True" type="integer" value="5" min="1"/>
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66 <param label="--p-random-state: INTEGER Seed used by random number generator. [optional]" name="prandomstate" optional="True" type="integer"/>
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67 <param label="--p-n-estimators: INTEGER Range(1, None) Number of trees to grow for estimation. More trees will improve predictive accuracy up to a threshold level, but will also increase time and memory requirements. This parameter only affects ensemble estimators, such as Random Forest, AdaBoost, ExtraTrees, and GradientBoosting. [default: 100]" name="pnestimators" optional="True" type="integer" value="100" min="1"/>
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68 <param label="--p-estimator: " name="pestimator" optional="True" type="select">
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69 <option selected="True" value="None">Selection is Optional</option>
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70 <option value="RandomForestRegressor">RandomForestRegressor</option>
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71 <option value="ExtraTreesRegressor">ExtraTreesRegressor</option>
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72 <option value="GradientBoostingRegressor">GradientBoostingRegressor</option>
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73 <option value="AdaBoostRegressor">AdaBoostRegressor</option>
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74 <option value="ElasticNet">ElasticNet</option>
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75 <option value="Ridge">Ridge</option>
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76 <option value="Lasso">Lasso</option>
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77 <option value="KNeighborsRegressor">KNeighborsRegressor</option>
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78 <option value="LinearSVR">LinearSVR</option>
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79 <option value="SVR">SVR</option>
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80 </param>
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81 <param label="--p-stratify: --p-no-stratify Evenly stratify training and test data among metadata categories. If True, all values in column must match at least two samples. [default: False]" name="pstratify" selected="False" type="boolean"/>
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82 <param label="--p-parameter-tuning: --p-no-parameter-tuning Automatically tune hyperparameters using random grid search. [default: False]" name="pparametertuning" selected="False" type="boolean"/>
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83 <param label="--p-missing-samples: " name="pmissingsamples" optional="True" type="select">
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84 <option selected="True" value="None">Selection is Optional</option>
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85 <option value="error">error</option>
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86 <option value="ignore">ignore</option>
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87 </param>
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88
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5
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89
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90 <param label="--m-metadata-file METADATA" name="metadatafile" type="data" format="tabular,qza,no_unzip.zip" />
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91
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0
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92 </inputs>
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93 <outputs>
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94 <data format="qza" label="${tool.name} on ${on_string}: predictions.qza" name="opredictions"/>
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95 <data format="qza" label="${tool.name} on ${on_string}: featureimportance.qza" name="ofeatureimportance"/>
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96 </outputs>
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97 <help><![CDATA[
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98 Nested cross-validated supervised learning classifier.
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99 ######################################################
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100
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101 Predicts a categorical sample metadata column using a supervised learning
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102 classifier. Uses nested stratified k-fold cross validation for automated
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103 hyperparameter optimization and sample prediction. Outputs predicted values
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104 for each input sample, and relative importance of each feature for model
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105 accuracy.
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106
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107 Parameters
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108 ----------
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109 table : FeatureTable[Frequency]
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110 Feature table containing all features that should be used for target
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111 prediction.
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112 metadata : MetadataColumn[Categorical]
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113 Categorical metadata column to use as prediction target.
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114 cv : Int % Range(1, None), optional
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115 Number of k-fold cross-validations to perform.
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116 random_state : Int, optional
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117 Seed used by random number generator.
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118 n_estimators : Int % Range(1, None), optional
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119 Number of trees to grow for estimation. More trees will improve
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120 predictive accuracy up to a threshold level, but will also increase
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121 time and memory requirements. This parameter only affects ensemble
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122 estimators, such as Random Forest, AdaBoost, ExtraTrees, and
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123 GradientBoosting.
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124 estimator : Str % Choices('RandomForestClassifier', 'ExtraTreesClassifier', 'GradientBoostingClassifier', 'AdaBoostClassifier', 'KNeighborsClassifier', 'LinearSVC', 'SVC'), optional
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125 Estimator method to use for sample prediction.
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126 parameter_tuning : Bool, optional
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127 Automatically tune hyperparameters using random grid search.
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128 missing_samples : Str % Choices('error', 'ignore'), optional
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129 How to handle missing samples in metadata. "error" will fail if missing
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130 samples are detected. "ignore" will cause the feature table and
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131 metadata to be filtered, so that only samples found in both files are
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132 retained.
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133
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134 Returns
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135 -------
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136 predictions : SampleData[ClassifierPredictions]
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137 Predicted target values for each input sample.
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138 feature_importance : FeatureData[Importance]
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139 Importance of each input feature to model accuracy.
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140 ]]></help>
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141 <macros>
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142 <import>qiime_citation.xml</import>
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143 </macros>
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144 <expand macro="qiime_citation"/>
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145 </tool>
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