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1 <?xml version="1.0" ?>
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2 <tool id="qiime_sample-classifier_regress-samples" name="qiime sample-classifier regress-samples"
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3 version="2020.8">
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4 <description>Train and test a cross-validated supervised learning regressor.</description>
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5 <requirements>
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6 <requirement type="package" version="2020.8">qiime2</requirement>
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7 </requirements>
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8 <command><![CDATA[
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9 qiime sample-classifier regress-samples
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10
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11 --i-table=$itable
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12 # if $input_files_mmetadatafile:
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13 # def list_dict_to_string(list_dict):
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14 # set $file_list = list_dict[0]['additional_input'].__getattr__('file_name')
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15 # for d in list_dict[1:]:
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16 # set $file_list = $file_list + ' --m-metadata-file=' + d['additional_input'].__getattr__('file_name')
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17 # end for
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18 # return $file_list
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19 # end def
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20 --m-metadata-file=$list_dict_to_string($input_files_mmetadatafile)
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21 # end if
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22
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23 #if '__ob__' in str($mmetadatacolumn):
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24 #set $mmetadatacolumn_temp = $mmetadatacolumn.replace('__ob__', '[')
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25 #set $mmetadatacolumn = $mmetadatacolumn_temp
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26 #end if
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27 #if '__cb__' in str($mmetadatacolumn):
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28 #set $mmetadatacolumn_temp = $mmetadatacolumn.replace('__cb__', ']')
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29 #set $mmetadatacolumn = $mmetadatacolumn_temp
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30 #end if
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31 #if 'X' in str($mmetadatacolumn):
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32 #set $mmetadatacolumn_temp = $mmetadatacolumn.replace('X', '\\')
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33 #set $mmetadatacolumn = $mmetadatacolumn_temp
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34 #end if
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35 #if '__sq__' in str($mmetadatacolumn):
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36 #set $mmetadatacolumn_temp = $mmetadatacolumn.replace('__sq__', "'")
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37 #set $mmetadatacolumn = $mmetadatacolumn_temp
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38 #end if
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39 #if '__db__' in str($mmetadatacolumn):
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40 #set $mmetadatacolumn_temp = $mmetadatacolumn.replace('__db__', '"')
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41 #set $mmetadatacolumn = $mmetadatacolumn_temp
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42 #end if
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43
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44 --m-metadata-column=$mmetadatacolumn
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45
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46
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47 --p-test-size=$ptestsize
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48
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49 --p-step=$pstep
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50
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51 --p-cv=$pcv
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52
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53 #if str($prandomstate):
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54 --p-random-state=$prandomstate
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55 #end if
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56 --p-n-jobs=$pnjobs
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57
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58 --p-n-estimators=$pnestimators
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59
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60 #if str($pestimator) != 'None':
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61 --p-estimator=$pestimator
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62 #end if
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63
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64 #if $poptimizefeatureselection:
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65 --p-optimize-feature-selection
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66 #end if
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67
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68 #if $pstratify:
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69 --p-stratify
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70 #end if
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71
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72 #if $pparametertuning:
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73 --p-parameter-tuning
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74 #end if
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75
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76 #if str($pmissingsamples) != 'None':
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77 --p-missing-samples=$pmissingsamples
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78 #end if
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79
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80 --o-sample-estimator=osampleestimator
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81
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82 --o-feature-importance=ofeatureimportance
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83
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84 --o-predictions=opredictions
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85
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86 --o-model-summary=omodelsummary
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87
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88 --o-accuracy-results=oaccuracyresults
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89
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90 #if str($examples) != 'None':
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91 --examples=$examples
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92 #end if
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93
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94 ;
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95 cp opredictions.qza $opredictions
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96
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97 ;
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98 qiime tools export omodelsummary.qzv --output-path out
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99 && mkdir -p '$omodelsummary.files_path'
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100 && cp -r out/* '$omodelsummary.files_path'
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101 && mv '$omodelsummary.files_path/index.html' '$omodelsummary'
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102
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103 ;
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104 qiime tools export oaccuracyresults.qzv --output-path out
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105 && mkdir -p '$oaccuracyresults.files_path'
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106 && cp -r out/* '$oaccuracyresults.files_path'
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107 && mv '$oaccuracyresults.files_path/index.html' '$oaccuracyresults'
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108
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109 ]]></command>
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110 <inputs>
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111 <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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112 <repeat name="input_files_mmetadatafile" optional="True" title="--m-metadata-file">
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113 <param format="tabular,qza,no_unzip.zip" label="--m-metadata-file: METADATA" name="additional_input" optional="True" type="data" />
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114 </repeat>
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115 <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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116 <param exclude_min="True" label="--p-test-size: PROPORTION Range(0.0, 1.0, inclusive_start=False) Fraction of input samples to exclude from training set and use for classifier testing. [default: 0.2]" max="1.0" min="0.0" name="ptestsize" optional="True" type="float" value="0.2" />
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117 <param exclude_min="True" 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]" max="1.0" min="0.0" name="pstep" optional="True" type="float" value="0.05" />
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118 <param label="--p-cv: INTEGER Number of k-fold cross-validations to perform. Range(1, None) [default: 5]" min="1" name="pcv" optional="True" type="integer" value="5" />
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119 <param label="--p-random-state: INTEGER Seed used by random number generator. [optional]" name="prandomstate" optional="False" type="text" />
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120 <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]" min="1" name="pnestimators" optional="True" type="integer" value="100" />
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121 <param label="--p-estimator: " name="pestimator" optional="True" type="select">
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122 <option selected="True" value="None">Selection is Optional</option>
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123 <option value="RandomForestRegressor">RandomForestRegressor</option>
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124 <option value="ExtraTreesRegressor">ExtraTreesRegressor</option>
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125 <option value="GradientBoostingRegressor">GradientBoostingRegressor</option>
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126 <option value="AdaBoostRegressor">AdaBoostRegressor</option>
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127 <option value="ElasticNet">ElasticNet</option>
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128 <option value="Ridge">Ridge</option>
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129 <option value="Lasso">Lasso</option>
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130 <option value="KNeighborsRegressor">KNeighborsRegressor</option>
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131 <option value="LinearSVR">LinearSVR</option>
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132 <option value="SVR">SVR</option>
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133 </param>
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134 <param label="--p-optimize-feature-selection: --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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135 <param label="--p-stratify: --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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136 <param label="--p-parameter-tuning: --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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137 <param label="--p-missing-samples: " name="pmissingsamples" optional="True" type="select">
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138 <option selected="True" value="None">Selection is Optional</option>
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139 <option value="error">error</option>
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140 <option value="ignore">ignore</option>
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141 </param>
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142 <param label="--examples: Show usage examples and exit." name="examples" optional="False" type="data" />
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143
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144 </inputs>
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145
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146 <outputs>
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147 <data format="qza" label="${tool.name} on ${on_string}: sampleestimator.qza" name="osampleestimator" />
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148 <data format="qza" label="${tool.name} on ${on_string}: featureimportance.qza" name="ofeatureimportance" />
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149 <data format="qza" label="${tool.name} on ${on_string}: predictions.qza" name="opredictions" />
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150 <data format="html" label="${tool.name} on ${on_string}: modelsummary.html" name="omodelsummary" />
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151 <data format="html" label="${tool.name} on ${on_string}: accuracyresults.html" name="oaccuracyresults" />
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152
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153 </outputs>
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154
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155 <help><![CDATA[
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156 Train and test a cross-validated supervised learning regressor.
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157 ###############################################################
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158
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159 Predicts a continuous sample metadata column using a supervised learning
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160 regressor. Splits input data into training and test sets. The training set
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161 is used to train and test the estimator using a stratified k-fold cross-
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162 validation scheme. This includes optional steps for automated feature
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163 extraction and hyperparameter optimization. The test set validates
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164 classification accuracy of the optimized estimator. Outputs classification
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165 results for test set. For more details on the learning algorithm, see
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166 http://scikit-learn.org/stable/supervised_learning.html
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167
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168 Parameters
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169 ----------
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170 table : FeatureTable[Frequency]
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171 Feature table containing all features that should be used for target
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172 prediction.
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173 metadata : MetadataColumn[Numeric]
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174 Numeric metadata column to use as prediction target.
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175 test_size : Float % Range(0.0, 1.0, inclusive_start=False), optional
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176 Fraction of input samples to exclude from training set and use for
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177 classifier testing.
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178 step : Float % Range(0.0, 1.0, inclusive_start=False), optional
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179 If optimize_feature_selection is True, step is the percentage of
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180 features to remove at each iteration.
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181 cv : Int % Range(1, None), optional
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182 Number of k-fold cross-validations to perform.
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183 random_state : Int, optional
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184 Seed used by random number generator.
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185 n_jobs : Int, optional
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186 Number of jobs to run in parallel.
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187 n_estimators : Int % Range(1, None), optional
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188 Number of trees to grow for estimation. More trees will improve
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189 predictive accuracy up to a threshold level, but will also increase
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190 time and memory requirements. This parameter only affects ensemble
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191 estimators, such as Random Forest, AdaBoost, ExtraTrees, and
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192 GradientBoosting.
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193 estimator : Str % Choices('RandomForestRegressor', 'ExtraTreesRegressor', 'GradientBoostingRegressor', 'AdaBoostRegressor', 'ElasticNet', 'Ridge', 'Lasso', 'KNeighborsRegressor', 'LinearSVR', 'SVR'), optional
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194 Estimator method to use for sample prediction.
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195 optimize_feature_selection : Bool, optional
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196 Automatically optimize input feature selection using recursive feature
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197 elimination.
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198 stratify : Bool, optional
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199 Evenly stratify training and test data among metadata categories. If
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200 True, all values in column must match at least two samples.
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201 parameter_tuning : Bool, optional
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202 Automatically tune hyperparameters using random grid search.
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203 missing_samples : Str % Choices('error', 'ignore'), optional
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204 How to handle missing samples in metadata. "error" will fail if missing
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205 samples are detected. "ignore" will cause the feature table and
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206 metadata to be filtered, so that only samples found in both files are
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207 retained.
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208
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209 Returns
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210 -------
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211 sample_estimator : SampleEstimator[Regressor]
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212 Trained sample estimator.
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213 feature_importance : FeatureData[Importance]
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214 Importance of each input feature to model accuracy.
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215 predictions : SampleData[RegressorPredictions]
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216 Predicted target values for each input sample.
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217 model_summary : Visualization
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218 Summarized parameter and (if enabled) feature selection information for
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219 the trained estimator.
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220 accuracy_results : Visualization
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221 Accuracy results visualization.
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222 ]]></help>
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223 <macros>
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224 <import>qiime_citation.xml</import>
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225 </macros>
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226 <expand macro="qiime_citation"/>
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227 </tool> |
