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1 <tool id="secimtools_random_forest" name="Random Forest (RF)" version="@WRAPPER_VERSION@">
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2 <description>algorithm to select features.</description>
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3 <macros>
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4 <import>macros.xml</import>
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5 </macros>
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6 <expand macro="requirements" />
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7 <command detect_errors="exit_code"><![CDATA[
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8 random_forest.py
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9 --input $input
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10 --design $design
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11 --ID $uniqID
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12 --group $group
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13 --snum $number_of_estimators
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14 --num $number_of_factors
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15 --out $outfile1
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16 --out2 $outfile2
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17 --figure $figure
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18 ]]></command>
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19 <inputs>
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20 <param name="input" type="data" format="tabular" label="Wide Dataset" help="Input your tab-separated wide format dataset. If file is not tab separated see TIP below."/>
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21 <param name="design" type="data" format="tabular" label="Design File" help="Input your design file (tab-separated). Note you need a 'sampleID' column. If not tab separated see TIP below."/>
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22 <param name="uniqID" type="text" size="30" value="" label="Unique Feature ID" help="Name of the column in your wide dataset that has unique identifiers."/>
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23 <param name="group" type="text" size="30" value="" label="Group/Treatment" help="Name of the column in your design file that contains group classifications."/>
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24 <param name="number_of_estimators" type="integer" size="30" value="1000" label="Number of trees in the forest" help="Recommend a minimum of 1000 trees."/>
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25 <param name="number_of_factors" type="integer" size="30" value="20" label="Number of factors to plot" help="Plots the (Default = 20) most important factors."/>
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26 </inputs>
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27 <outputs>
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28 <data format="csv" name="outfile1" label="${tool.name} on ${on_string}: Transformed Data"/>
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29 <data format="csv" name="outfile2" label="${tool.name} on ${on_string}: Importance Factors"/>
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30 <data format="pdf" name="figure" label="${tool.name} on ${on_string}: Variable Importance Plot"/>
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31 </outputs>
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32 <tests>
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33 <test>
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34 <param name="input" value="ST000006_data.tsv"/>
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35 <param name="design" value="ST000006_design.tsv"/>
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36 <param name="uniqID" value="Retention_Index" />
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37 <param name="group" value="White_wine_type_and_source" />
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38 <output name="outfile1" file="ST000006_random_forest_out.tsv" compare="sim_size" delta="10000" />
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39 <output name="outfile2" file="ST000006_random_forest_out2.tsv" compare="sim_size" delta="10000" />
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40 <output name="figure" file="ST000006_random_forest_figure.pdf" compare="sim_size" delta="10000" />
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41 </test>
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42 </tests>
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43 <help><![CDATA[
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44
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45 @TIP_AND_WARNING@
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46
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47 **Tool Description**
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48
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49 The tool identifies features that are different between treatment groups based on the random forest algorithm.
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50 Based on Classification and Regression Trees (CART), random forests are an ensemble learning method for classification, regression and variable importance evaluation.
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51 More details about the algorithm can be found in the book:
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52
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53 Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32.
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54
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55 **NOTE: The use of machine learning algorithms (including random forest) on datasets with a small number of samples is ambiguous and should be executed with caution.**
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56
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57 --------------------------------------------------------------------------------------------------------------
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58
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59 **Input**
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60
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61 - Two input datasets are required.
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62
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63 @WIDE@
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64
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65 **NOTE:** The sample IDs must match the sample IDs in the Design File (below).
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66 Extra columns will automatically be ignored.
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67
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68
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69 @METADATA@
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70
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71 @UNIQID@
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72
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73 @GROUP@
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74
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75 **Number of Trees in the Forest**
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76
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77 - Run a minimum of 1000 trees.
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78
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79 **Number of factors to plot**
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80
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81 - Plots the 20 most important factors.
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82
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83 --------------------------------------------------------------------------------
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84
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85 **Output**
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86
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87 This tool will always output three different files:
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88
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89 (1) a TSV file with features ranked according to their relative importance
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90
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91 (2) a TSV file where ranked features from the wide format dataset are saved in columns in the order that corresponds to their relative importance
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92
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93 (3) and a PDF file a variable importance plot for the first 50 components. The variable importance plot displays the X (Default = 20) most important features based on the random forest algorithm. The color of each feature changes from the most important (dark blue) to the least important (light blue).
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94
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95 **NOTE:** The user can take the resulting TSV file and plot any two (or three) features using the Scatter Plot 2D or Scatter Plot 2D tools.
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96
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97 A plot of two (or three) most important features is recommended since they are probably the most meaningful, but other features can be also considered for plotting.
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98
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99 **To plot the 2 most important features**: use the SECIM Tools 'Scatter Plot 2D' tool on the transformed dataset to plot the features against each other and evaluate separation levels.
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100
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101 ]]></help>
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102 <expand macro="citations"/>
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103 </tool>
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