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annotate ensemble.xml @ 46:0087d6db4290 draft

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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5eca9041ce0154eded5aec07195502d5eb3cdd4f
author bgruening
date Fri, 03 Nov 2023 23:03:23 +0000
parents 6546d7c9f08b
children
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6546d7c9f08b planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 9981e25b00de29ed881b2229a173a8c812ded9bb
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1 <tool id="sklearn_ensemble" name="Ensemble methods" version="@VERSION@" profile="@PROFILE@">
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2 <description>for classification and regression</description>
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3 <macros>
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4 <import>main_macros.xml</import>
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5 </macros>
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6 <expand macro="python_requirements" />
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7 <expand macro="macro_stdio" />
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8 <version_command>echo "@VERSION@"</version_command>
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9 <command><![CDATA[
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10 python "$ensemble_script" '$inputs'
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11 ]]>
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12 </command>
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13 <configfiles>
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14 <inputs name="inputs" />
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15 <configfile name="ensemble_script">
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16 <![CDATA[
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17 import json
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18 import numpy as np
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19 import pandas
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20 import sys
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22 from scipy.io import mmread
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23 import sklearn.ensemble
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24 from galaxy_ml.model_persist import dump_model_to_h5, load_model_from_h5
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25 from galaxy_ml.utils import clean_params, get_X_y
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28 N_JOBS = int(__import__('os').environ.get('GALAXY_SLOTS', 1))
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30 # Get inputs, outputs.
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31 input_json_path = sys.argv[1]
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32 with open(input_json_path, "r") as param_handler:
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33 params = json.load(param_handler)
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34 print(params)
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36 # Put all cheetah up here to avoid confusion.
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37 #if $selected_tasks.selected_task == "train":
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38 infile1 = "$selected_tasks.selected_algorithms.input_options.infile1"
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39 infile2 = "$selected_tasks.selected_algorithms.input_options.infile2"
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40 #else:
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41 infile_model = "$selected_tasks.infile_model"
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42 infile_data = "$selected_tasks.infile_data"
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43 #end if
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44 outfile_fit = "$outfile_fit"
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45 outfile_predict = "$outfile_predict"
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47 # All Python from here on out:
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48
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49 if params["selected_tasks"]["selected_task"] == "train":
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50 algorithm = params["selected_tasks"]["selected_algorithms"]["selected_algorithm"]
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51 options = params["selected_tasks"]["selected_algorithms"]["options"]
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52 if algorithm in ['RandomForestClassifier', 'RandomForestRegressor']:
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53 options['n_jobs'] = N_JOBS
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54 if "select_max_features" in options:
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55 if options["select_max_features"]["max_features"] == "number_input":
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56 options["select_max_features"]["max_features"] = options["select_max_features"]["num_max_features"]
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57 options["select_max_features"].pop("num_max_features")
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58 options["max_features"] = options["select_max_features"]["max_features"]
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59 options.pop("select_max_features")
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60 if "min_samples_leaf" in options and options["min_samples_leaf"] == 1.0:
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61 options["min_samples_leaf"] = 1
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62 if "min_samples_split" in options and options["min_samples_split"] > 1.0:
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63 options["min_samples_split"] = int(options["min_samples_split"])
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64
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65 X, y = get_X_y(params, infile1, infile2)
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66
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67 my_class = getattr(sklearn.ensemble, algorithm)
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68 estimator = my_class(**options)
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69 estimator.fit(X,y)
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70 dump_model_to_h5(estimator, outfile_fit)
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72 else:
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73 classifier_object = load_model_from_h5(infile_model)
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74 classifier_object = clean_params(classifier_object)
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75 header = 'infer' if params["selected_tasks"]["header"] else None
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76 data = pandas.read_csv(infile_data, sep='\t', header=header, index_col=None, parse_dates=True, encoding=None)
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77 prediction = classifier_object.predict(data)
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78 prediction_df = pandas.DataFrame(prediction, columns=["predicted"])
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79 res = pandas.concat([data, prediction_df], axis=1)
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80 res.to_csv(path_or_buf = outfile_predict, sep="\t", index=False)
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82 ]]>
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83 </configfile>
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84 </configfiles>
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85 <inputs>
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86 <expand macro="sl_Conditional" model="h5mlm">
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87 <param name="selected_algorithm" type="select" label="Select an ensemble method:">
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88 <option value="RandomForestClassifier" selected="true">Random forest classifier</option>
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89 <option value="AdaBoostClassifier">Ada boost classifier</option>
5
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90 <option value="GradientBoostingClassifier">Gradient Boosting Classifier</option>
0
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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91 <option value="RandomForestRegressor">Random forest regressor</option>
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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92 <option value="AdaBoostRegressor">Ada boost regressor</option>
5
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93 <option value="GradientBoostingRegressor">Gradient Boosting Regressor</option>
0
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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94 </param>
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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95 <when value="RandomForestClassifier">
35
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96 <expand macro="sl_mixed_input" />
0
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97 <section name="options" title="Advanced Options" expanded="False">
35
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98 <expand macro="n_estimators" default_value="100" />
19d6c2745d34 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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99 <expand macro="criterion" />
19d6c2745d34 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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100 <expand macro="max_features" />
19d6c2745d34 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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101 <expand macro="max_depth" />
19d6c2745d34 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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102 <expand macro="min_samples_split" />
19d6c2745d34 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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103 <expand macro="min_samples_leaf" />
19d6c2745d34 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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104 <expand macro="min_weight_fraction_leaf" />
19d6c2745d34 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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105 <expand macro="max_leaf_nodes" />
19d6c2745d34 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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106 <expand macro="bootstrap" />
19d6c2745d34 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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107 <expand macro="warm_start" checked="false" />
19d6c2745d34 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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108 <expand macro="random_state" />
19d6c2745d34 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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109 <expand macro="oob_score" />
0
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110 <!--class_weight=None-->
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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111 </section>
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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112 </when>
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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113 <when value="AdaBoostClassifier">
35
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114 <expand macro="sl_mixed_input" />
0
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115 <section name="options" title="Advanced Options" expanded="False">
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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116 <!--base_estimator=None-->
35
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117 <expand macro="n_estimators" default_value="50" />
19d6c2745d34 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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118 <expand macro="learning_rate" />
19d6c2745d34 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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119 <param argument="algorithm" type="select" label="Boosting algorithm" help=" ">
0
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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120 <option value="SAMME.R" selected="true">SAMME.R</option>
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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121 <option value="SAMME">SAMME</option>
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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122 </param>
35
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123 <expand macro="random_state" />
0
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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124 </section>
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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125 </when>
5
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126 <when value="GradientBoostingClassifier">
35
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127 <expand macro="sl_mixed_input" />
5
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128 <section name="options" title="Advanced Options" expanded="False">
f1761288587e planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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129 <!--base_estimator=None-->
f1761288587e planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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130 <param argument="loss" type="select" label="Loss function">
f1761288587e planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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131 <option value="deviance" selected="true">deviance - logistic regression with probabilistic outputs</option>
f1761288587e planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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132 <option value="exponential">exponential - gradient boosting recovers the AdaBoost algorithm</option>
f1761288587e planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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133 </param>
35
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134 <expand macro="learning_rate" default_value='0.1' />
19d6c2745d34 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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135 <expand macro="n_estimators" default_value="100" help="The number of boosting stages to perform" />
19d6c2745d34 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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136 <expand macro="max_depth" default_value="3" help="maximum depth of the individual regression estimators" />
5
f1761288587e planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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137 <expand macro="criterion2">
f1761288587e planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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138 <option value="friedman_mse" selected="true">friedman_mse - mean squared error with improvement score by Friedman</option>
f1761288587e planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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139 </expand>
35
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diff changeset
140 <expand macro="min_samples_split" type="float" />
19d6c2745d34 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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diff changeset
141 <expand macro="min_samples_leaf" type="float" label="The minimum number of samples required to be at a leaf node" />
19d6c2745d34 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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diff changeset
142 <expand macro="min_weight_fraction_leaf" />
19d6c2745d34 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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diff changeset
143 <expand macro="subsample" />
19d6c2745d34 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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diff changeset
144 <expand macro="max_features" />
19d6c2745d34 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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145 <expand macro="max_leaf_nodes" />
19d6c2745d34 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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diff changeset
146 <expand macro="min_impurity_decrease" />
19d6c2745d34 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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diff changeset
147 <expand macro="verbose" />
19d6c2745d34 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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148 <expand macro="warm_start" checked="false" />
19d6c2745d34 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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diff changeset
149 <expand macro="random_state" />
5
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diff changeset
150 </section>
f1761288587e planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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151 </when>
0
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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152 <when value="RandomForestRegressor">
35
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diff changeset
153 <expand macro="sl_mixed_input" />
0
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154 <section name="options" title="Advanced Options" expanded="False">
35
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155 <expand macro="n_estimators" default_value="100" />
19d6c2745d34 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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diff changeset
156 <expand macro="criterion2" />
19d6c2745d34 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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diff changeset
157 <expand macro="max_features" />
19d6c2745d34 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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diff changeset
158 <expand macro="max_depth" />
19d6c2745d34 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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diff changeset
159 <expand macro="min_samples_split" />
19d6c2745d34 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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diff changeset
160 <expand macro="min_samples_leaf" />
19d6c2745d34 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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diff changeset
161 <expand macro="min_weight_fraction_leaf" />
19d6c2745d34 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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diff changeset
162 <expand macro="max_leaf_nodes" />
19d6c2745d34 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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diff changeset
163 <expand macro="min_impurity_decrease" />
19d6c2745d34 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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diff changeset
164 <expand macro="bootstrap" />
19d6c2745d34 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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165 <expand macro="oob_score" />
19d6c2745d34 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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diff changeset
166 <expand macro="random_state" />
19d6c2745d34 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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diff changeset
167 <expand macro="verbose" />
19d6c2745d34 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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168 <expand macro="warm_start" checked="false" />
0
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169 </section>
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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170 </when>
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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171 <when value="AdaBoostRegressor">
35
19d6c2745d34 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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diff changeset
172 <expand macro="sl_mixed_input" />
0
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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173 <section name="options" title="Advanced Options" expanded="False">
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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174 <!--base_estimator=None-->
35
19d6c2745d34 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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175 <expand macro="n_estimators" default_value="50" />
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176 <expand macro="learning_rate" />
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177 <param argument="loss" type="select" label="Loss function" optional="true" help="Used when updating the weights after each boosting iteration. ">
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178 <option value="linear" selected="true">linear</option>
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179 <option value="square">square</option>
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180 <option value="exponential">exponential</option>
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181 </param>
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182 <expand macro="random_state" />
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183 </section>
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184 </when>
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185 <when value="GradientBoostingRegressor">
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186 <expand macro="sl_mixed_input" />
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187 <section name="options" title="Advanced Options" expanded="False">
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188 <param argument="loss" type="select" label="Loss function">
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189 <option value="ls" selected="true">ls - least squares regression</option>
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190 <option value="lad">lad - least absolute deviation</option>
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191 <option value="huber">huber - combination of least squares regression and least absolute deviation</option>
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192 <option value="quantile">quantile - use alpha to specify the quantile</option>
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193 </param>
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194 <expand macro="learning_rate" default_value="0.1" />
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195 <expand macro="n_estimators" default_value="100" help="The number of boosting stages to perform" />
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196 <expand macro="max_depth" default_value="3" help="maximum depth of the individual regression estimators" />
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197 <expand macro="criterion2">
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198 <option value="friedman_mse" selected="true">friedman_mse - mean squared error with improvement score by Friedman</option>
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199 </expand>
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200 <expand macro="min_samples_split" type="float" />
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201 <expand macro="min_samples_leaf" type="float" label="The minimum number of samples required to be at a leaf node" />
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202 <expand macro="min_weight_fraction_leaf" />
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203 <expand macro="subsample" />
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204 <expand macro="max_features" />
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205 <expand macro="max_leaf_nodes" />
19d6c2745d34 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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206 <expand macro="min_impurity_decrease" />
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207 <param argument="alpha" type="float" value="0.9" label="alpha" help="The alpha-quantile of the huber loss function and the quantile loss function" />
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208 <!--base_estimator=None-->
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209 <expand macro="verbose" />
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210 <expand macro="warm_start" checked="false" />
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211 <expand macro="random_state" />
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212 </section>
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213 </when>
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214 </expand>
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215 </inputs>
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216
35
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217 <expand macro="output" />
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218
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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219 <tests>
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220 <test>
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221 <param name="infile1" value="train.tabular" ftype="tabular" />
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222 <param name="infile2" value="train.tabular" ftype="tabular" />
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223 <param name="col1" value="1,2,3,4" />
19d6c2745d34 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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224 <param name="col2" value="5" />
19d6c2745d34 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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225 <param name="selected_task" value="train" />
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226 <param name="selected_algorithm" value="RandomForestClassifier" />
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227 <param name="random_state" value="10" />
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228 <output name="outfile_fit" file="rfc_model01" compare="sim_size" delta="5" />
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229 </test>
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230 <test>
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231 <param name="infile_model" value="rfc_model01" ftype="h5mlm" />
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232 <param name="infile_data" value="test.tabular" ftype="tabular" />
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233 <param name="selected_task" value="load" />
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234 <output name="outfile_predict" file="rfc_result01" />
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235 </test>
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236 <test>
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237 <param name="infile1" value="regression_train.tabular" ftype="tabular" />
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238 <param name="infile2" value="regression_train.tabular" ftype="tabular" />
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239 <param name="col1" value="1,2,3,4,5" />
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240 <param name="col2" value="6" />
19d6c2745d34 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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241 <param name="selected_task" value="train" />
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242 <param name="selected_algorithm" value="RandomForestRegressor" />
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243 <param name="random_state" value="10" />
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244 <output name="outfile_fit" file="rfr_model01" compare="sim_size" delta="5" />
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245 </test>
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246 <test>
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247 <param name="infile_model" value="rfr_model01" ftype="h5mlm" />
35
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248 <param name="infile_data" value="regression_test.tabular" ftype="tabular" />
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249 <param name="selected_task" value="load" />
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250 <output name="outfile_predict" file="rfr_result01" />
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251 </test>
5
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252 <test>
35
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253 <param name="infile1" value="regression_X.tabular" ftype="tabular" />
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254 <param name="infile2" value="regression_y.tabular" ftype="tabular" />
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255 <param name="header1" value="True" />
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256 <param name="selected_column_selector_option" value="all_columns" />
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257 <param name="header2" value="True" />
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258 <param name="col2" value="1" />
19d6c2745d34 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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259 <param name="selected_task" value="train" />
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260 <param name="selected_algorithm" value="GradientBoostingRegressor" />
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261 <param name="max_features" value="number_input" />
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262 <param name="num_max_features" value="0.5" />
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263 <param name="random_state" value="42" />
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264 <output name="outfile_fit" file="gbr_model01" compare="sim_size" delta="5" />
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265 </test>
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266 <test>
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267 <param name="infile_model" value="gbr_model01" ftype="h5mlm" />
35
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268 <param name="infile_data" value="regression_test_X.tabular" ftype="tabular" />
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269 <param name="selected_task" value="load" />
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270 <param name="header" value="True" />
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271 <output name="outfile_predict" file="gbr_prediction_result01.tabular" />
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272 </test>
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273 <test>
35
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274 <param name="infile1" value="train.tabular" ftype="tabular" />
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275 <param name="infile2" value="train.tabular" ftype="tabular" />
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276 <param name="col1" value="1,2,3,4" />
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277 <param name="col2" value="5" />
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278 <param name="selected_task" value="train" />
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279 <param name="selected_algorithm" value="GradientBoostingClassifier" />
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280 <output name="outfile_fit" file="gbc_model01" compare="sim_size" delta="5" />
5
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281 </test>
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282 <test>
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283 <param name="infile_model" value="gbc_model01" ftype="h5mlm" />
35
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284 <param name="infile_data" value="test.tabular" ftype="tabular" />
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285 <param name="selected_task" value="load" />
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286 <output name="outfile_predict" file="gbc_result01" />
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287 </test>
22
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288 <test>
35
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289 <param name="infile1" value="train.tabular" ftype="tabular" />
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290 <param name="infile2" value="train.tabular" ftype="tabular" />
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291 <param name="col1" value="1,2,3,4" />
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292 <param name="col2" value="5" />
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293 <param name="selected_task" value="train" />
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294 <param name="selected_algorithm" value="AdaBoostClassifier" />
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295 <param name="random_state" value="10" />
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296 <output name="outfile_fit" file="abc_model01" compare="sim_size" delta="5" />
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297 </test>
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298 <test>
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299 <param name="infile_model" value="abc_model01" ftype="h5mlm" />
35
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300 <param name="infile_data" value="test.tabular" ftype="tabular" />
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301 <param name="selected_task" value="load" />
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302 <output name="outfile_predict" file="abc_result01" />
22
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303 </test>
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304 <test>
35
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305 <param name="infile1" value="regression_train.tabular" ftype="tabular" />
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306 <param name="infile2" value="regression_train.tabular" ftype="tabular" />
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307 <param name="col1" value="1,2,3,4,5" />
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308 <param name="col2" value="6" />
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309 <param name="selected_task" value="train" />
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310 <param name="selected_algorithm" value="AdaBoostRegressor" />
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311 <param name="random_state" value="10" />
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312 <output name="outfile_fit" file="abr_model01" compare="sim_size" delta="5" />
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313 </test>
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314 <test>
41
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315 <param name="infile_model" value="abr_model01" ftype="h5mlm" />
35
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316 <param name="infile_data" value="regression_test.tabular" ftype="tabular" />
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317 <param name="selected_task" value="load" />
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318 <output name="outfile_predict" file="abr_result01" />
22
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319 </test>
0
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320 </tests>
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321 <help><![CDATA[
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322 ***What it does***
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323 The goal of ensemble methods is to combine the predictions of several base estimators built with a given learning algorithm in order to improve generalizability / robustness over a single estimator. This tool offers two sets of ensemble algorithms for classification and regression: random forests and ADA boosting which are based on sklearn.ensemble library from Scikit-learn. Here you can find out about the input, output and methods presented in the tools. For information about ensemble methods and parameters settings please refer to `Scikit-learn ensemble`_.
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324
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325 .. _`Scikit-learn ensemble`: http://scikit-learn.org/stable/modules/ensemble.html
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326
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327 **1 - Methods**
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328 There are two groups of operations available:
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329
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330 1 - Train a model : A training set containing samples and their respective labels (or predicted values) are input. Based on the selected algorithm and options, an estimator object is fit to the data and is returned.
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331
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332 2 - Load a model and predict : An existing model predicts the class labels (or regression values) for a new dataset.
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333
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334 **2 - Trainig input**
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335 When you choose to train a model, you need a features dataset X and a labels set y. This tool expects tabular or sparse data for X and a single column for y (tabular). You can select a subset of columns in a tabular dataset as your features dataset or labels column. Below you find some examples:
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336
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337 **Sample tabular features dataset**
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338 The following training dataset contains 3 feature columns and a column containing class labels. You can simply select the first 3 columns as features and the last column as labels:
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339
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340 ::
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341
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342 4.01163365529 -6.10797684314 8.29829894763 1
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343 10.0788438916 1.59539821454 10.0684278289 0
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344 -5.17607775503 -0.878286135332 6.92941850665 2
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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345 4.00975406235 -7.11847496542 9.3802423585 1
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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346 4.61204065139 -5.71217537352 9.12509610964 1
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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347
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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348
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349 **Sample sparse features dataset**
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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350 In this case you cannot specifiy a column range.
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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351
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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352 ::
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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353
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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354 4 1048577 8738
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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355 1 271 0.02083333333333341
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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356 1 1038 0.02461995616119806
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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357 2 829017 0.01629088031127686
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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358 2 829437 0.01209127083516686
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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359 2 830752 0.02535100632816968
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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360 3 1047487 0.01485722929945572
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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361 3 1047980 0.02640566620767753
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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362 3 1048475 0.01665869913262564
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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363 4 608 0.01662975263094352
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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364 4 1651 0.02519674277562741
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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365 4 4053 0.04223659971350601
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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366
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367
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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368 **2 - Trainig output**
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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369 The trained model is generated and output in the form of a binary file.
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370
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371
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372 **3 - Prediction input**
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373
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374 When you choose to load a model and do prediction, the tool expects an already trained estimator and a tabular dataset as input. The dataset contains new samples which you want to classify or predict regression values for.
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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375
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376
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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377 .. class:: warningmark
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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378
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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379 The number of feature columns must be the same in training and prediction datasets!
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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380
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381
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382 **3 - Prediction output**
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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383 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.
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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384
35
19d6c2745d34 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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385 ]]> </help>
19d6c2745d34 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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386 <expand macro="sklearn_citation" />
0
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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387 </tool>