Mercurial > repos > bgruening > sklearn_discriminant_classifier
annotate discriminant.xml @ 16:0b57ef8831fb draft
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 76583c1fcd9d06a4679cc46ffaee44117b9e22cd
author | bgruening |
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date | Sat, 04 Aug 2018 12:29:43 -0400 |
parents | fb232caca397 |
children | 98b632c407ae |
rev | line source |
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e0067d9baffc
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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1 <tool id="sklearn_discriminant_classifier" name="Discriminant Analysis" version="@VERSION@"> |
e0067d9baffc
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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2 <description></description> |
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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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3 <macros> |
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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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4 <import>main_macros.xml</import> |
e0067d9baffc
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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5 <!--macro name="priors"--> |
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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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6 </macros> |
e0067d9baffc
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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7 <expand macro="python_requirements"/> |
e0067d9baffc
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
bgruening
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8 <expand macro="macro_stdio"/> |
e0067d9baffc
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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9 <version_command>echo "@VERSION@"</version_command> |
e0067d9baffc
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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10 <command><![CDATA[ |
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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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11 python "$discriminant_script" '$inputs' |
e0067d9baffc
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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12 ]]> |
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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
bgruening
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13 </command> |
e0067d9baffc
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
bgruening
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14 <configfiles> |
e0067d9baffc
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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15 <inputs name="inputs"/> |
e0067d9baffc
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
bgruening
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16 <configfile name="discriminant_script"> |
e0067d9baffc
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
bgruening
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17 <![CDATA[ |
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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
bgruening
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18 import sys |
e0067d9baffc
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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19 import json |
e0067d9baffc
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
bgruening
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20 import numpy as np |
e0067d9baffc
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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21 import sklearn.discriminant_analysis |
e0067d9baffc
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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22 import pandas |
e0067d9baffc
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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23 import pickle |
e0067d9baffc
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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24 |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5d71c93a3dd804b1469852240a86021ab9130364
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25 @COLUMNS_FUNCTION@ |
f46da2feb233
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5d71c93a3dd804b1469852240a86021ab9130364
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26 @GET_X_y_FUNCTION@ |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5d71c93a3dd804b1469852240a86021ab9130364
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27 |
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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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28 input_json_path = sys.argv[1] |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit f54ff2ba2f8e7542d68966ce5a6b17d7f624ac48
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29 with open(input_json_path, "r") as param_handler: |
fb232caca397
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit f54ff2ba2f8e7542d68966ce5a6b17d7f624ac48
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30 params = json.load(param_handler) |
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e0067d9baffc
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
bgruening
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31 |
e0067d9baffc
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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32 #if $selected_tasks.selected_task == "load": |
e0067d9baffc
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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33 |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit f54ff2ba2f8e7542d68966ce5a6b17d7f624ac48
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34 with open("$infile_model", 'rb') as model_handler: |
fb232caca397
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit f54ff2ba2f8e7542d68966ce5a6b17d7f624ac48
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35 classifier_object = pickle.load(model_handler) |
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e0067d9baffc
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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36 |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5d71c93a3dd804b1469852240a86021ab9130364
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37 header = 'infer' if params["selected_tasks"]["header"] else None |
f46da2feb233
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5d71c93a3dd804b1469852240a86021ab9130364
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38 data = pandas.read_csv("$selected_tasks.infile_data", sep='\t', header=header, index_col=None, parse_dates=True, encoding=None, tupleize_cols=False) |
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e0067d9baffc
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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39 prediction = classifier_object.predict(data) |
e0067d9baffc
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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40 prediction_df = pandas.DataFrame(prediction) |
e0067d9baffc
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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41 res = pandas.concat([data, prediction_df], axis=1) |
e0067d9baffc
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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42 res.to_csv(path_or_buf = "$outfile_predict", sep="\t", index=False) |
e0067d9baffc
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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43 |
e0067d9baffc
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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44 #else: |
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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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45 |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5d71c93a3dd804b1469852240a86021ab9130364
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46 X, y = get_X_y(params, "$selected_tasks.selected_algorithms.input_options.infile1" ,"$selected_tasks.selected_algorithms.input_options.infile2") |
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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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47 |
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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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48 options = params["selected_tasks"]["selected_algorithms"]["options"] |
e0067d9baffc
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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49 selected_algorithm = params["selected_tasks"]["selected_algorithms"]["selected_algorithm"] |
e0067d9baffc
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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50 |
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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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51 my_class = getattr(sklearn.discriminant_analysis, selected_algorithm) |
e0067d9baffc
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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52 classifier_object = my_class(**options) |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5d71c93a3dd804b1469852240a86021ab9130364
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53 classifier_object.fit(X, y) |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit f54ff2ba2f8e7542d68966ce5a6b17d7f624ac48
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54 with open("$outfile_fit", 'wb') as out_handler: |
fb232caca397
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit f54ff2ba2f8e7542d68966ce5a6b17d7f624ac48
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55 pickle.dump(classifier_object, out_handler, pickle.HIGHEST_PROTOCOL) |
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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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56 |
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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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57 #end if |
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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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58 ]]> |
e0067d9baffc
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
bgruening
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59 </configfile> |
e0067d9baffc
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
bgruening
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60 </configfiles> |
e0067d9baffc
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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61 <inputs> |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5d71c93a3dd804b1469852240a86021ab9130364
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62 <expand macro="sl_Conditional" model="zip"> |
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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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63 <param name="selected_algorithm" type="select" label="Classifier type"> |
e0067d9baffc
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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64 <option value="LinearDiscriminantAnalysis" selected="true">Linear Discriminant Classifier</option> |
e0067d9baffc
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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65 <option value="QuadraticDiscriminantAnalysis">Quadratic Discriminant Classifier</option> |
e0067d9baffc
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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66 </param> |
e0067d9baffc
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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67 <when value="LinearDiscriminantAnalysis"> |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5d71c93a3dd804b1469852240a86021ab9130364
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68 <expand macro="sl_mixed_input"/> |
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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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69 <section name="options" title="Advanced Options" expanded="False"> |
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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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70 <param argument="solver" type="select" optional="true" label="Solver" help=""> |
e0067d9baffc
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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71 <option value="svd" selected="true">Singular Value Decomposition</option> |
e0067d9baffc
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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72 <option value="lsqr">Least Squares Solution</option> |
e0067d9baffc
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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73 <option value="eigen">Eigenvalue Decomposition</option> |
e0067d9baffc
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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74 </param> |
e0067d9baffc
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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75 <!--param name="shrinkage"--> |
e0067d9baffc
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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76 <!--expand macro="priors"/--> |
e0067d9baffc
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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77 <param argument="n_components" type="integer" optional="true" value="" label="Number of components" |
e0067d9baffc
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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78 help="Number of components for dimensionality reduction. ( always less than n_classes - 1 )"/> |
e0067d9baffc
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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79 <expand macro="tol" default_value="0.0001" help_text="Rank estimation threshold used in SVD solver."/> |
e0067d9baffc
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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80 <param argument="store_covariance" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolflase" checked="false" |
e0067d9baffc
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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81 label="Store covariance" help="Compute class covariance matrix."/> |
e0067d9baffc
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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82 </section> |
e0067d9baffc
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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83 </when> |
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84 <when value="QuadraticDiscriminantAnalysis"> |
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85 <expand macro="sl_mixed_input"/> |
0
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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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86 <section name="options" title="Advanced Options" expanded="False"> |
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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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87 <!--expand macro="priors"/--> |
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88 <param argument="reg_param" type="float" optional="true" value="0.0" label="Regularization coefficient" help="Covariance estimate regularizer."/> |
e0067d9baffc
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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89 <expand macro="tol" default_value="0.00001" help_text="Rank estimation threshold used in SVD solver."/> |
e0067d9baffc
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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90 <param argument="store_covariances" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolflase" checked="false" |
e0067d9baffc
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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91 label="Store covariances" help="Compute class covariance matrixes."/> |
e0067d9baffc
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92 </section> |
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93 </when> |
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94 </expand> |
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95 </inputs> |
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96 <expand macro="output"/> |
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97 <tests> |
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98 <test> |
13
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99 <param name="infile1" value="train.tabular" ftype="tabular"/> |
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100 <param name="infile2" value="train.tabular" ftype="tabular"/> |
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101 <param name="header1" value="True"/> |
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102 <param name="header2" value="True"/> |
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103 <param name="col1" value="1,2,3,4"/> |
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104 <param name="col2" value="5"/> |
0
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105 <param name="selected_task" value="train"/> |
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106 <param name="selected_algorithm" value="LinearDiscriminantAnalysis"/> |
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107 <param name="solver" value="svd" /> |
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108 <param name="store_covariances" value="True"/> |
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109 <output name="outfile_fit" file="lda_model01" compare="sim_size" delta="500"/> |
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110 </test> |
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111 <test> |
13
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112 <param name="infile1" value="train.tabular" ftype="tabular"/> |
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113 <param name="infile2" value="train.tabular" ftype="tabular"/> |
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114 <param name="header1" value="True"/> |
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115 <param name="header2" value="True"/> |
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116 <param name="col1" value="1,2,3,4"/> |
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117 <param name="col2" value="5"/> |
0
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118 <param name="selected_task" value="train"/> |
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119 <param name="selected_algorithm" value="LinearDiscriminantAnalysis"/> |
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120 <param name="solver" value="lsqr"/> |
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121 <output name="outfile_fit" file="lda_model02" compare="sim_size" delta="500"/> |
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122 </test> |
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123 <test> |
13
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124 <param name="infile1" value="train.tabular" ftype="tabular"/> |
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125 <param name="infile2" value="train.tabular" ftype="tabular"/> |
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126 <param name="header1" value="True"/> |
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127 <param name="header2" value="True"/> |
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128 <param name="col1" value="1,2,3,4"/> |
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129 <param name="col2" value="5"/> |
0
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130 <param name="selected_task" value="train"/> |
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131 <param name="selected_algorithm" value="QuadraticDiscriminantAnalysis"/> |
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132 <output name="outfile_fit" file="qda_model01" compare="sim_size" delta="500"/> |
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133 </test> |
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134 <test> |
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135 <param name="infile_model" value="lda_model01" ftype="zip"/> |
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136 <param name="infile_data" value="test.tabular" ftype="tabular"/> |
13
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137 <param name="header" value="True"/> |
0
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138 <param name="selected_task" value="load"/> |
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139 <output name="outfile_predict" file="lda_prediction_result01.tabular"/> |
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140 </test> |
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141 <test> |
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142 <param name="infile_model" value="lda_model02" ftype="zip"/> |
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143 <param name="infile_data" value="test.tabular" ftype="tabular"/> |
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144 <param name="header" value="True"/> |
0
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145 <param name="selected_task" value="load"/> |
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146 <output name="outfile_predict" file="lda_prediction_result02.tabular"/> |
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147 </test> |
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148 <test> |
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149 <param name="infile_model" value="qda_model01" ftype="zip"/> |
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150 <param name="infile_data" value="test.tabular" ftype="tabular"/> |
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151 <param name="header" value="True"/> |
0
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152 <param name="selected_task" value="load"/> |
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153 <output name="outfile_predict" file="qda_prediction_result01.tabular"/> |
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154 </test> |
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155 </tests> |
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156 <help><![CDATA[ |
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157 ***What it does*** |
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158 Linear and Quadratic Discriminant Analysis are two classic classifiers with a linear and a quadratic decision surface respectively. These classifiers are fast and easy to interprete. |
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159 |
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160 |
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161 **1 - Training input** |
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162 |
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163 When you choose to train a model, discriminant analysis tool expects a tabular file with numeric values, the order of the columns being as follows: |
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164 |
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165 :: |
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166 |
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167 "feature_1" "feature_2" "..." "feature_n" "class_label" |
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168 |
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169 **Example for training data** |
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170 The following training dataset contains 3 feature columns and a column containing class labels: |
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171 |
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172 :: |
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173 |
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174 4.01163365529 -6.10797684314 8.29829894763 1 |
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175 10.0788438916 1.59539821454 10.0684278289 0 |
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176 -5.17607775503 -0.878286135332 6.92941850665 2 |
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177 4.00975406235 -7.11847496542 9.3802423585 1 |
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178 4.61204065139 -5.71217537352 9.12509610964 1 |
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179 |
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180 |
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181 **2 - Trainig output** |
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182 |
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183 Based on your choice, this tool fits a sklearn discriminant_analysis.LinearDiscriminantAnalysis or discriminant_analysis.QuadraticDiscriminantAnalysis on the traning data and outputs the trained model in the form of pickled object in a text file. |
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184 |
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185 |
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186 **3 - Prediction input** |
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187 |
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188 When you choose to load a model and do prediction, the tool expects an already trained Discriminant Analysis estimator and a tabular dataset as input. The dataset is a tabular file with new samples which you want to classify. It just contains feature columns. |
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189 |
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190 **Example for prediction data** |
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191 |
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193 |
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194 8.26530668997 2.96705005011 8.88881190248 |
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195 2.96366327113 -3.76295851562 11.7113372463 |
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196 8.13319631944 -0.223645298585 10.5820605308 |
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197 |
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198 .. class:: warningmark |
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199 |
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200 The number of feature columns must be the same in training and prediction datasets! |
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201 |
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202 |
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203 **3 - Prediction output** |
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204 The tool predicts the class labels for new samples and adds them as the last column to the prediction dataset. The new dataset then is output as a tabular file. The prediction output format should look like the training dataset. |
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205 |
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206 Discriminant Analysis is based on sklearn.discriminant_analysis library from Scikit-learn. |
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207 For more information please refer to `Scikit-learn site`_. |
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208 |
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209 .. _`Scikit-learn site`: http://scikit-learn.org/stable/modules/lda_qda.html |
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210 |
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211 ]]></help> |
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212 <expand macro="sklearn_citation"/> |
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213 </tool> |