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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" version="2018.4">
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3 <description> - Supervised learning regressor.</description>
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4 <requirements>
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5 <requirement type="package" version="2018.4">qiime2</requirement>
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6 </requirements>
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7 <command>
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8 <![CDATA[
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9 qiime sample-classifier regress-samples --i-table=$itable
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10
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11 #def list_dict_to_string(list_dict):
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12 #set $file_list = list_dict[0]['additional_input'].__getattr__('file_name')
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13 #for d in list_dict[1:]:
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14 #set $file_list = $file_list + ' --m-metadata-file=' + d['additional_input'].__getattr__('file_name')
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15 #end for
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16 #return $file_list
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17 #end def
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18
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19 --m-metadata-file=$list_dict_to_string($input_files_mmetadatafile) --m-metadata-column="$mmetadatacolumn"
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20 #set $pnjobs = '${GALAXY_SLOTS:-4}'
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21
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22 #if str($pnjobs):
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23 --p-n-jobs="$pnjobs"
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24 #end if
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25
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26
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27 #if $pstep:
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28 --p-step=$pstep
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29 #end if
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30
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31 #if $pstratify:
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32 --p-stratify
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33 #else
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34 --p-no-stratify
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35 #end if
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36
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37 #if $poptimizefeatureselection:
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38 --p-optimize-feature-selection
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39 #else
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40 --p-no-optimize-feature-selection
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41 #end if
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42
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43 #if $ptestsize:
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44 --p-test-size=$ptestsize
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45 #end if
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46 --o-visualization=ovisualization
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47 #if str($pestimator) != 'None':
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48 --p-estimator=$pestimator
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49 #end if
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50
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51 #if $pnestimators:
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52 --p-n-estimators=$pnestimators
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53 #end if
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54
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55 #if str($cmdconfig) != 'None':
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56 --cmd-config=$cmdconfig
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57 #end if
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58
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59 #if $pcv:
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60 --p-cv=$pcv
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61 #end if
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62
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63 #if $pparametertuning:
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64 --p-parameter-tuning
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65 #else
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66 --p-no-parameter-tuning
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67 #end if
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68
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69 #if str($prandomstate):
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70 --p-random-state="$prandomstate"
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71 #end if
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72 ;
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73 qiime tools export ovisualization.qzv --output-dir out && mkdir -p '$ovisualization.files_path'
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74 && cp -r out/* '$ovisualization.files_path'
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75 && mv '$ovisualization.files_path/index.html' '$ovisualization'
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76 ]]>
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77 </command>
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78 <inputs>
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79 <param format="qza,no_unzip.zip" label="--i-table: 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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80 <repeat name="input_files_mmetadatafile" optional="False" title="--m-metadata-file">
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81 <param label="--m-metadata-file: Metadata file or artifact viewable as metadata. This option may be supplied multiple times to merge metadata. [required]" name="additional_input" type="data" format="tabular,qza,no_unzip.zip" />
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82 </repeat>
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83 <param label="--m-metadata-column: MetadataColumn[Numeric] Column from metadata file or artifact viewable as metadata. Numeric metadata column to use as prediction target. [required]" name="mmetadatacolumn" optional="False" type="text"/>
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84
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85 <param label="--p-test-size: Fraction of input samples to exclude from training set and use for classifier testing. [default: 0.2]" name="ptestsize" optional="True" type="float" value="0.2"/>
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86
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87 <param label="--p-step: 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"/>
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88
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89 <param label="--p-cv: Number of k-fold cross-validations to perform. [default: 5]" name="pcv" optional="True" type="integer" value="5"/>
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90
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91 <param label="--p-random-state: Seed used by random number generator. [optional]" name="prandomstate" optional="True" type="text"/>
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92
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93 <param label="--p-n-estimators: 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"/>
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94 <param label="--p-estimator: Estimator method to use for sample
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95 prediction. [default:
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96 RandomForestRegressor]" name="pestimator" optional="True" type="select">
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97 <option selected="True" value="None">Selection is Optional</option>
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98 <option value="Ridge">Ridge</option>
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99 <option value="RandomForestRegressor">RandomForestRegressor</option>
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100 <option value="GradientBoostingRegressor">GradientBoostingRegressor</option>
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101 <option value="AdaBoostRegressor">AdaBoostRegressor</option>
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102 <option value="LinearSVR">LinearSVR</option>
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103 <option value="ExtraTreesRegressor">ExtraTreesRegressor</option>
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104 <option value="KNeighborsRegressor">KNeighborsRegressor</option>
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105 <option value="SVR">SVR</option>
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106 <option value="ElasticNet">ElasticNet</option>
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107 <option value="Lasso">Lasso</option>
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108 </param>
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109
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110 <param label="--p-optimize-feature-selection: --p-no-optimize-feature-selection Automatically optimize input feature selection using recursive feature elimination. [default: False]" name="poptimizefeatureselection" checked="False" type="boolean"/>
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111
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112 <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" checked="False" type="boolean"/>
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113
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114 <param label="--p-parameter-tuning: --p-no-parameter-tuning Automatically tune hyperparameters using random grid search. [default: False]" name="pparametertuning" checked="False" type="boolean"/>
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115
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116 <param label="--cmd-config: Use config file for command options" name="cmdconfig" optional="True" type="data"/>
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117 </inputs>
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118 <outputs>
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119 <data format="html" label="${tool.name} on ${on_string}: visualization.qzv" name="ovisualization"/>
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120 </outputs>
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121 <help>
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122 <![CDATA[
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123 Supervised learning regressor.
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124 -------------------------------
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125
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126 Predicts a continuous sample metadata column using a supervised learning
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127 regressor. Splits input data into training and test sets. The training set
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128 is used to train and test the estimator using a stratified k-fold cross-
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129 validation scheme. This includes optional steps for automated feature
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130 extraction and hyperparameter optimization. The test set validates
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131 classification accuracy of the optimized estimator. Outputs classification
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132 results for test set. For more details on the learning algorithm, see
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133 http://scikit-learn.org/stable/supervised_learning.html
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134
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135 Parameters
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136 ----------
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137 table : FeatureTable[Frequency]
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138 Feature table containing all features that should be used for target
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139 prediction.
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140 metadata : MetadataColumn[Numeric]
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141 Numeric metadata column to use as prediction target.
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142 test_size : Float % Range(0.0, 1.0, inclusive_start=False), optional
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143 Fraction of input samples to exclude from training set and use for
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144 classifier testing.
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145 step : Float % Range(0.0, 1.0, inclusive_start=False), optional
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146 If optimize_feature_selection is True, step is the percentage of
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147 features to remove at each iteration.
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148 cv : Int % Range(1, None), optional
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149 Number of k-fold cross-validations to perform.
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150 random_state : Int, optional
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151 Seed used by random number generator.
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152 n_estimators : Int % Range(1, None), optional
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153 Number of trees to grow for estimation. More trees will improve
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154 predictive accuracy up to a threshold level, but will also increase
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155 time and memory requirements. This parameter only affects ensemble
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156 estimators, such as Random Forest, AdaBoost, ExtraTrees, and
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157 GradientBoosting.
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158 estimator : Str % Choices({'AdaBoostRegressor', 'ElasticNet', 'ExtraTreesRegressor', 'GradientBoostingRegressor', 'KNeighborsRegressor', 'Lasso', 'LinearSVR', 'RandomForestRegressor', 'Ridge', 'SVR'}), optional
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159 Estimator method to use for sample prediction.
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160 optimize_feature_selection : Bool, optional
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161 Automatically optimize input feature selection using recursive feature
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162 elimination.
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163 stratify : Bool, optional
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164 Evenly stratify training and test data among metadata categories. If
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165 True, all values in column must match at least two samples.
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166 parameter_tuning : Bool, optional
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167 Automatically tune hyperparameters using random grid search.
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168
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169 Returns
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170 -------
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171 visualization : Visualization
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172 \
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173 ]]>
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174 </help>
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175 </tool>
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