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1 <?xml version="1.0" ?>
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2 <tool id="qiime_sample-classifier_fit-regressor" name="qiime sample-classifier fit-regressor" version="2019.4">
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3 <description> - Fit a 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 fit-regressor
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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 $pstep:
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14 --p-step=$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 #if str($pnjobs):
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27 --p-n-jobs="$pnjobs"
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28 #end if
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29
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30
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31 #if $pnestimators:
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32 --p-n-estimators=$pnestimators
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33 #end if
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34
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35 #if str($pestimator) != 'None':
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36 --p-estimator=$pestimator
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37 #end if
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38
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39 #if $poptimizefeatureselection:
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40 --p-optimize-feature-selection
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41 #end if
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42
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43 #if $pparametertuning:
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44 --p-parameter-tuning
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45 #end if
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46
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47 #if str($pmissingsamples) != 'None':
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48 --p-missing-samples=$pmissingsamples
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49 #end if
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50
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51
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52 #if $input_files_mmetadatafile:
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53 #def list_dict_to_string(list_dict):
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54 #set $file_list = list_dict[0]['additional_input'].__getattr__('file_name')
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55 #for d in list_dict[1:]:
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56 #set $file_list = $file_list + ' --m-metadata-file=' + d['additional_input'].__getattr__('file_name')
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57 #end for
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58 #return $file_list
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59 #end def
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60 --m-metadata-file=$list_dict_to_string($input_files_mmetadatafile)
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61 #end if
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62
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63
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64 --o-sample-estimator=osampleestimator
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65 --o-feature-importance=ofeatureimportance
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66 ;
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67 cp osampleestimator.qza $osampleestimator;
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68 cp ofeatureimportance.qza $ofeatureimportance
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69 ]]></command>
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70 <inputs>
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71 <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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72 <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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73 <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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74 <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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75 <param label="--p-random-state: INTEGER Seed used by random number generator. [optional]" name="prandomstate" optional="True" type="integer"/>
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76 <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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77 <param label="--p-estimator: " name="pestimator" optional="True" type="select">
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78 <option selected="True" value="None">Selection is Optional</option>
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79 <option value="RandomForestRegressor">RandomForestRegressor</option>
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80 <option value="ExtraTreesRegressor">ExtraTreesRegressor</option>
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81 <option value="GradientBoostingRegressor">GradientBoostingRegressor</option>
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82 <option value="AdaBoostRegressor">AdaBoostRegressor</option>
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83 <option value="ElasticNet">ElasticNet</option>
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84 <option value="Ridge">Ridge</option>
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85 <option value="Lasso">Lasso</option>
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86 <option value="KNeighborsRegressor">KNeighborsRegressor</option>
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87 <option value="LinearSVR">LinearSVR</option>
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88 <option value="SVR">SVR</option>
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89 </param>
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90 <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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91 <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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92 <param label="--p-missing-samples: " name="pmissingsamples" optional="True" type="select">
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93 <option selected="True" value="None">Selection is Optional</option>
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94 <option value="error">error</option>
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95 <option value="ignore">ignore</option>
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96 </param>
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97
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98 <repeat name="input_files_mmetadatafile" optional="True" title="--m-metadata-file">
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99 <param label="--m-metadata-file: Metadata file or artifact viewable as metadata. This option may be supplied multiple times to merge metadata. [optional]" name="additional_input" type="data" format="tabular,qza,no_unzip.zip" />
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100 </repeat>
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101
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102 </inputs>
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103 <outputs>
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104 <data format="qza" label="${tool.name} on ${on_string}: sampleestimator.qza" name="osampleestimator"/>
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105 <data format="qza" label="${tool.name} on ${on_string}: featureimportance.qza" name="ofeatureimportance"/>
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106 </outputs>
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107 <help><![CDATA[
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108 Fit a supervised learning regressor.
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109 ####################################
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110
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111 Fit a supervised learning regressor. Outputs the fit estimator (for
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112 prediction of test samples and/or unknown samples) and the relative
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113 importance of each feature for model accuracy. Optionally use k-fold cross-
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114 validation for automatic recursive feature elimination and hyperparameter
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115 tuning.
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116
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117 Parameters
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118 ----------
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119 table : FeatureTable[Frequency]
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120 Feature table containing all features that should be used for target
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121 prediction.
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122 metadata : MetadataColumn[Numeric]
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123 Numeric metadata column to use as prediction target.
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124 step : Float % Range(0.0, 1.0, inclusive_start=False), optional
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125 If optimize_feature_selection is True, step is the percentage of
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126 features to remove at each iteration.
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127 cv : Int % Range(1, None), optional
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128 Number of k-fold cross-validations to perform.
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129 random_state : Int, optional
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130 Seed used by random number generator.
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131 n_estimators : Int % Range(1, None), optional
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132 Number of trees to grow for estimation. More trees will improve
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133 predictive accuracy up to a threshold level, but will also increase
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134 time and memory requirements. This parameter only affects ensemble
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135 estimators, such as Random Forest, AdaBoost, ExtraTrees, and
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136 GradientBoosting.
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137 estimator : Str % Choices('RandomForestRegressor', 'ExtraTreesRegressor', 'GradientBoostingRegressor', 'AdaBoostRegressor', 'ElasticNet', 'Ridge', 'Lasso', 'KNeighborsRegressor', 'LinearSVR', 'SVR'), optional
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138 Estimator method to use for sample prediction.
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139 optimize_feature_selection : Bool, optional
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140 Automatically optimize input feature selection using recursive feature
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141 elimination.
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142 parameter_tuning : Bool, optional
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143 Automatically tune hyperparameters using random grid search.
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144 missing_samples : Str % Choices('error', 'ignore'), optional
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145 How to handle missing samples in metadata. "error" will fail if missing
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146 samples are detected. "ignore" will cause the feature table and
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147 metadata to be filtered, so that only samples found in both files are
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148 retained.
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149
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150 Returns
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151 -------
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152 sample_estimator : SampleEstimator[Regressor]
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153 \
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154 feature_importance : FeatureData[Importance]
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155 Importance of each input feature to model accuracy.
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156 ]]></help>
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157 <macros>
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158 <import>qiime_citation.xml</import>
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159 </macros>
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160 <expand macro="qiime_citation"/>
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161 </tool>
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