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1 <?xml version="1.0" ?>
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2 <tool id="qiime_sample-classifier_fit-classifier" name="qiime sample-classifier fit-classifier" version="2019.4">
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3 <description> - Fit a supervised learning classifier.</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 fit-classifier
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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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5
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13 #if str($pstep):
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0
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14 --p-step=$pstep
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15 #end if
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16
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17 #if $pcv:
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18 --p-cv=$pcv
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19 #end if
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20
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21 #if str($prandomstate):
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22 --p-random-state="$prandomstate"
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23 #end if
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24
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25 #set $pnjobs = '${GALAXY_SLOTS:-4}'
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26
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27 #if str($pnjobs):
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28 --p-n-jobs="$pnjobs"
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29 #end if
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30
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31
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32 #if $pnestimators:
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33 --p-n-estimators=$pnestimators
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34 #end if
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35
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36 #if str($pestimator) != 'None':
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37 --p-estimator=$pestimator
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38 #end if
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39
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40 #if $poptimizefeatureselection:
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41 --p-optimize-feature-selection
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42 #end if
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43
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44 #if $pparametertuning:
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45 --p-parameter-tuning
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46 #end if
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47
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48 #if str($pmissingsamples) != 'None':
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49 --p-missing-samples=$pmissingsamples
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50 #end if
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51
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52
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5
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53
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54 #if $metadatafile:
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55 --m-metadata-file=$metadatafile
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0
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56 #end if
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57
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58
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5
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59
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0
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60 --o-sample-estimator=osampleestimator
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61 --o-feature-importance=ofeatureimportance
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62 ;
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63 cp osampleestimator.qza $osampleestimator;
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64 cp ofeatureimportance.qza $ofeatureimportance
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65 ]]></command>
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66 <inputs>
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67 <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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68 <param label="--m-metadata-column: COLUMN MetadataColumn[Categorical] Numeric metadata column to use as prediction target. [required]" name="mmetadatacolumn" optional="False" type="text"/>
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69 <param label="--p-step: PROPORTION Range(0.0, 1.0, inclusive_start=False) If optimize-feature-selection is True, step is the percentage of features to remove at each iteration. [default: 0.05]" name="pstep" optional="True" type="float" value="0.05" min="0" max="1" exclusive_start="True"/>
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70 <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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71 <param label="--p-random-state: INTEGER Seed used by random number generator. [optional]" name="prandomstate" optional="True" type="integer"/>
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72 <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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73 <param label="--p-estimator: " name="pestimator" optional="True" type="select">
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74 <option selected="True" value="None">Selection is Optional</option>
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75 <option value="RandomForestClassifier">RandomForestClassifier</option>
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76 <option value="ExtraTreesClassifier">ExtraTreesClassifier</option>
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77 <option value="GradientBoostingClassifier">GradientBoostingClassifier</option>
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78 <option value="AdaBoostClassifier">AdaBoostClassifier</option>
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79 <option value="KNeighborsClassifier">KNeighborsClassifier</option>
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80 <option value="LinearSVC">LinearSVC</option>
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81 <option value="SVC">SVC</option>
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82 </param>
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83 <param label="--p-optimize-feature-selection: --p-no-optimize-feature-selection Automatically optimize input feature selection using recursive feature elimination. [default: False]" name="poptimizefeatureselection" selected="False" type="boolean"/>
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84 <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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85 <param label="--p-missing-samples: " name="pmissingsamples" optional="True" type="select">
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86 <option selected="True" value="None">Selection is Optional</option>
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87 <option value="error">error</option>
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88 <option value="ignore">ignore</option>
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89 </param>
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90
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5
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91 <param label="--m-metadata-file METADATA" name="metadatafile" type="data" format="tabular,qza,no_unzip.zip" />
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0
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92
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93 </inputs>
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94 <outputs>
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95 <data format="qza" label="${tool.name} on ${on_string}: sampleestimator.qza" name="osampleestimator"/>
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96 <data format="qza" label="${tool.name} on ${on_string}: featureimportance.qza" name="ofeatureimportance"/>
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97 </outputs>
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98 <help><![CDATA[
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99 Fit a supervised learning classifier.
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100 #####################################
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101
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102 Fit a supervised learning classifier. Outputs the fit estimator (for
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103 prediction of test samples and/or unknown samples) and the relative
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104 importance of each feature for model accuracy. Optionally use k-fold cross-
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105 validation for automatic recursive feature elimination and hyperparameter
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106 tuning.
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107
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108 Parameters
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109 ----------
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110 table : FeatureTable[Frequency]
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111 Feature table containing all features that should be used for target
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112 prediction.
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113 metadata : MetadataColumn[Categorical]
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114 Numeric metadata column to use as prediction target.
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115 step : Float % Range(0.0, 1.0, inclusive_start=False), optional
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116 If optimize_feature_selection is True, step is the percentage of
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117 features to remove at each iteration.
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118 cv : Int % Range(1, None), optional
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119 Number of k-fold cross-validations to perform.
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120 random_state : Int, optional
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121 Seed used by random number generator.
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122 n_estimators : Int % Range(1, None), optional
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123 Number of trees to grow for estimation. More trees will improve
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124 predictive accuracy up to a threshold level, but will also increase
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125 time and memory requirements. This parameter only affects ensemble
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126 estimators, such as Random Forest, AdaBoost, ExtraTrees, and
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127 GradientBoosting.
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128 estimator : Str % Choices('RandomForestClassifier', 'ExtraTreesClassifier', 'GradientBoostingClassifier', 'AdaBoostClassifier', 'KNeighborsClassifier', 'LinearSVC', 'SVC'), optional
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129 Estimator method to use for sample prediction.
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130 optimize_feature_selection : Bool, optional
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131 Automatically optimize input feature selection using recursive feature
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132 elimination.
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133 parameter_tuning : Bool, optional
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134 Automatically tune hyperparameters using random grid search.
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135 missing_samples : Str % Choices('error', 'ignore'), optional
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136 How to handle missing samples in metadata. "error" will fail if missing
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137 samples are detected. "ignore" will cause the feature table and
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138 metadata to be filtered, so that only samples found in both files are
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139 retained.
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140
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141 Returns
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142 -------
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143 sample_estimator : SampleEstimator[Classifier]
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144 Trained sample classifier.
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145 feature_importance : FeatureData[Importance]
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146 Importance of each input feature to model accuracy.
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147 ]]></help>
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148 <macros>
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149 <import>qiime_citation.xml</import>
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150 </macros>
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151 <expand macro="qiime_citation"/>
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152 </tool>
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