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annotate ensemble.xml @ 26:8d0d31567045 draft

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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c64ccc5850c8e061a95fb64e07ed388384e82393
author bgruening
date Thu, 11 Oct 2018 03:22:26 -0400
parents 18e29854c803
children 5af731ab6995
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31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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1 <tool id="sklearn_ensemble" name="Ensemble methods" version="@VERSION@">
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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2 <description>for classification and regression</description>
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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3 <macros>
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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4 <import>main_macros.xml</import>
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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5 </macros>
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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6 <expand macro="python_requirements"/>
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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7 <expand macro="macro_stdio"/>
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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8 <version_command>echo "@VERSION@"</version_command>
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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9 <command><![CDATA[
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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10 python "$ensemble_script" '$inputs'
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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11 ]]>
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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12 </command>
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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13 <configfiles>
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14 <inputs name="inputs"/>
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15 <configfile name="ensemble_script">
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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16 <![CDATA[
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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17 import sys
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18 import os
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19 import json
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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20 import numpy as np
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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21 import sklearn.ensemble
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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22 import pandas
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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23 from scipy.io import mmread
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24
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25 with open("$__tool_directory__/sk_whitelist.json", "r") as f:
18e29854c803 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 2a058459e6daf0486871f93845f00fdb4a4eaca1
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26 sk_whitelist = json.load(f)
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27 exec(open("$__tool_directory__/utils.py").read(), globals())
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29 # Get inputs, outputs.
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30 input_json_path = sys.argv[1]
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31 with open(input_json_path, "r") as param_handler:
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32 params = json.load(param_handler)
128bd4ab2b82 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit f54ff2ba2f8e7542d68966ce5a6b17d7f624ac48
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33 print(params)
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34
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35 # Put all cheetah up here to avoid confusion.
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31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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36 #if $selected_tasks.selected_task == "train":
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37 infile1 = "$selected_tasks.selected_algorithms.input_options.infile1"
0070e491573f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 7c2fd140e89605fe689c39e21d70a400545e38cf
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38 infile2 = "$selected_tasks.selected_algorithms.input_options.infile2"
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39 #else:
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40 infile_model = "$selected_tasks.infile_model"
0070e491573f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 7c2fd140e89605fe689c39e21d70a400545e38cf
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41 infile_data = "$selected_tasks.infile_data"
0070e491573f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 7c2fd140e89605fe689c39e21d70a400545e38cf
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42 #end if
0070e491573f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 7c2fd140e89605fe689c39e21d70a400545e38cf
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43 outfile_fit = "$outfile_fit"
0070e491573f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 7c2fd140e89605fe689c39e21d70a400545e38cf
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44 outfile_predict = "$outfile_predict"
0070e491573f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 7c2fd140e89605fe689c39e21d70a400545e38cf
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45
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46 # All Python from here on out:
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31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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47
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48 if params["selected_tasks"]["selected_task"] == "train":
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49 algorithm = params["selected_tasks"]["selected_algorithms"]["selected_algorithm"]
0070e491573f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 7c2fd140e89605fe689c39e21d70a400545e38cf
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50 options = params["selected_tasks"]["selected_algorithms"]["options"]
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51 if algorithm in ['RandomForestClassifier', 'RandomForestRegressor']:
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52 options['n_jobs'] = N_JOBS
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53 if "select_max_features" in options:
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54 if options["select_max_features"]["max_features"] == "number_input":
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55 options["select_max_features"]["max_features"] = options["select_max_features"]["num_max_features"]
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56 options["select_max_features"].pop("num_max_features")
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57 options["max_features"] = options["select_max_features"]["max_features"]
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58 options.pop("select_max_features")
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59 if "presort" in options:
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60 if options["presort"] == "true":
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61 options["presort"] = True
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62 if options["presort"] == "false":
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63 options["presort"] = False
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64 if "min_samples_leaf" in options and options["min_samples_leaf"] == 1.0:
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65 options["min_samples_leaf"] = 1
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66 if "min_samples_split" in options and options["min_samples_split"] > 1.0:
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67 options["min_samples_split"] = int(options["min_samples_split"])
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68
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69 X, y = get_X_y(params, infile1, infile2)
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70
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71 my_class = getattr(sklearn.ensemble, algorithm)
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72 estimator = my_class(**options)
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73 estimator.fit(X,y)
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74 with open(outfile_fit, 'wb') as out_handler:
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75 pickle.dump(estimator, out_handler, pickle.HIGHEST_PROTOCOL)
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76
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77 else:
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78 with open(infile_model, 'rb') as model_handler:
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79 classifier_object = load_model(model_handler)
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80 header = 'infer' if params["selected_tasks"]["header"] else None
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81 data = pandas.read_csv(infile_data, sep='\t', header=header, index_col=None, parse_dates=True, encoding=None, tupleize_cols=False)
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82 prediction = classifier_object.predict(data)
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83 prediction_df = pandas.DataFrame(prediction, columns=["predicted"])
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84 res = pandas.concat([data, prediction_df], axis=1)
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85 res.to_csv(path_or_buf = outfile_predict, sep="\t", index=False)
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87 ]]>
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88 </configfile>
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89 </configfiles>
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90 <inputs>
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91 <expand macro="sl_Conditional" model="zip">
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92 <param name="selected_algorithm" type="select" label="Select an ensemble method:">
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93 <option value="RandomForestClassifier" selected="true">Random forest classifier</option>
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94 <option value="AdaBoostClassifier">Ada boost classifier</option>
9
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95 <option value="GradientBoostingClassifier">Gradient Boosting Classifier</option>
0
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96 <option value="RandomForestRegressor">Random forest regressor</option>
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97 <option value="AdaBoostRegressor">Ada boost regressor</option>
9
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98 <option value="GradientBoostingRegressor">Gradient Boosting Regressor</option>
0
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99 </param>
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100 <when value="RandomForestClassifier">
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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101 <expand macro="sl_mixed_input"/>
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102 <section name="options" title="Advanced Options" expanded="False">
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103 <expand macro="n_estimators"/>
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104 <expand macro="criterion"/>
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105 <expand macro="max_features"/>
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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106 <expand macro="max_depth"/>
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107 <expand macro="min_samples_split"/>
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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108 <expand macro="min_samples_leaf"/>
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109 <expand macro="min_weight_fraction_leaf"/>
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110 <expand macro="max_leaf_nodes"/>
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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111 <expand macro="bootstrap"/>
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112 <expand macro="warm_start" checked="false"/>
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113 <expand macro="random_state"/>
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114 <expand macro="oob_score"/>
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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115 <!--class_weight=None-->
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116 </section>
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117 </when>
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118 <when value="AdaBoostClassifier">
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119 <expand macro="sl_mixed_input"/>
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120 <section name="options" title="Advanced Options" expanded="False">
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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121 <!--base_estimator=None-->
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122 <expand macro="n_estimators" default_value="50"/>
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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123 <expand macro="learning_rate"/>
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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124 <param argument="algorithm" type="select" label="Boosting algorithm" help=" ">
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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125 <option value="SAMME.R" selected="true">SAMME.R</option>
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126 <option value="SAMME">SAMME</option>
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127 </param>
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128 <expand macro="random_state"/>
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129 </section>
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130 </when>
9
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131 <when value="GradientBoostingClassifier">
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132 <expand macro="sl_mixed_input"/>
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133 <section name="options" title="Advanced Options" expanded="False">
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134 <!--base_estimator=None-->
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135 <param argument="loss" type="select" label="Loss function">
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136 <option value="deviance" selected="true">deviance - logistic regression with probabilistic outputs</option>
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137 <option value="exponential">exponential - gradient boosting recovers the AdaBoost algorithm</option>
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138 </param>
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139 <expand macro="learning_rate" default_value='0.1'/>
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140 <expand macro="n_estimators" default_value="100" help="The number of boosting stages to perform"/>
e4fcbbc81083 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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141 <expand macro="max_depth" default_value="3" help="maximum depth of the individual regression estimators"/>
e4fcbbc81083 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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142 <expand macro="criterion2">
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143 <option value="friedman_mse" selected="true">friedman_mse - mean squared error with improvement score by Friedman</option>
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144 </expand>
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145 <expand macro="min_samples_split" type="float"/>
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146 <expand macro="min_samples_leaf" type="float" label="The minimum number of samples required to be at a leaf node"/>
e4fcbbc81083 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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147 <expand macro="min_weight_fraction_leaf"/>
e4fcbbc81083 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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148 <expand macro="subsample"/>
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149 <expand macro="max_features"/>
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150 <expand macro="max_leaf_nodes"/>
e4fcbbc81083 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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diff changeset
151 <expand macro="min_impurity_decrease"/>
e4fcbbc81083 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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152 <expand macro="verbose"/>
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153 <expand macro="warm_start" checked="false"/>
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154 <expand macro="random_state"/>
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155 <expand macro="presort"/>
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156 </section>
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157 </when>
0
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158 <when value="RandomForestRegressor">
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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159 <expand macro="sl_mixed_input"/>
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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160 <section name="options" title="Advanced Options" expanded="False">
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161 <expand macro="n_estimators"/>
9
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162 <expand macro="criterion2"/>
0
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163 <expand macro="max_features"/>
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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164 <expand macro="max_depth"/>
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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165 <expand macro="min_samples_split"/>
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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166 <expand macro="min_samples_leaf"/>
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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167 <expand macro="min_weight_fraction_leaf"/>
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168 <expand macro="max_leaf_nodes"/>
9
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169 <expand macro="min_impurity_decrease"/>
0
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170 <expand macro="bootstrap"/>
9
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171 <expand macro="oob_score"/>
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172 <expand macro="random_state"/>
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173 <expand macro="verbose"/>
0
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174 <expand macro="warm_start" checked="false"/>
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175 </section>
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176 </when>
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177 <when value="AdaBoostRegressor">
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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178 <expand macro="sl_mixed_input"/>
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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179 <section name="options" title="Advanced Options" expanded="False">
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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180 <!--base_estimator=None-->
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181 <expand macro="n_estimators" default_value="50"/>
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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182 <expand macro="learning_rate"/>
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183 <param argument="loss" type="select" label="Loss function" optional="true" help="Used when updating the weights after each boosting iteration. ">
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184 <option value="linear" selected="true">linear</option>
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185 <option value="square">square</option>
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186 <option value="exponential">exponential</option>
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187 </param>
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188 <expand macro="random_state"/>
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189 </section>
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190 </when>
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191 <when value="GradientBoostingRegressor">
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192 <expand macro="sl_mixed_input"/>
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193 <section name="options" title="Advanced Options" expanded="False">
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194 <param argument="loss" type="select" label="Loss function">
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195 <option value="ls" selected="true">ls - least squares regression</option>
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196 <option value="lad">lad - least absolute deviation</option>
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197 <option value="huber">huber - combination of least squares regression and least absolute deviation</option>
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198 <option value="quantile">quantile - use alpha to specify the quantile</option>
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199 </param>
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200 <expand macro="learning_rate" default_value="0.1"/>
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201 <expand macro="n_estimators" default_value="100" help="The number of boosting stages to perform"/>
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202 <expand macro="max_depth" default_value="3" help="maximum depth of the individual regression estimators"/>
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203 <expand macro="criterion2">
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204 <option value="friedman_mse" selected="true">friedman_mse - mean squared error with improvement score by Friedman</option>
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205 </expand>
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206 <expand macro="min_samples_split" type="float"/>
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207 <expand macro="min_samples_leaf" type="float" label="The minimum number of samples required to be at a leaf node"/>
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208 <expand macro="min_weight_fraction_leaf"/>
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209 <expand macro="subsample"/>
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210 <expand macro="max_features"/>
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211 <expand macro="max_leaf_nodes"/>
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212 <expand macro="min_impurity_decrease"/>
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213 <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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214 <!--base_estimator=None-->
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215 <expand macro="verbose"/>
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216 <expand macro="warm_start" checked="false"/>
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217 <expand macro="random_state"/>
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218 <expand macro="presort"/>
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219 </section>
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220 </when>
0
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221 </expand>
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222 </inputs>
4
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223
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224 <expand macro="output"/>
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225
0
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226 <tests>
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227 <test>
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228 <param name="infile1" value="train.tabular" ftype="tabular"/>
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229 <param name="infile2" value="train.tabular" ftype="tabular"/>
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230 <param name="col1" value="1,2,3,4"/>
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231 <param name="col2" value="5"/>
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232 <param name="selected_task" value="train"/>
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233 <param name="selected_algorithm" value="RandomForestClassifier"/>
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234 <param name="random_state" value="10"/>
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235 <output name="outfile_fit" file="rfc_model01" compare="sim_size" delta="500"/>
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236 </test>
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237 <test>
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238 <param name="infile_model" value="rfc_model01" ftype="zip"/>
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239 <param name="infile_data" value="test.tabular" ftype="tabular"/>
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240 <param name="selected_task" value="load"/>
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241 <output name="outfile_predict" file="rfc_result01" compare="sim_size" delta="500"/>
0
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242 </test>
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243 <test>
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244 <param name="infile1" value="regression_train.tabular" ftype="tabular"/>
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245 <param name="infile2" value="regression_train.tabular" ftype="tabular"/>
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246 <param name="col1" value="1,2,3,4,5"/>
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247 <param name="col2" value="6"/>
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248 <param name="selected_task" value="train"/>
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249 <param name="selected_algorithm" value="RandomForestRegressor"/>
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250 <param name="random_state" value="10"/>
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251 <output name="outfile_fit" file="rfr_model01" compare="sim_size" delta="500"/>
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252 </test>
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253 <test>
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254 <param name="infile_model" value="rfr_model01" ftype="zip"/>
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255 <param name="infile_data" value="regression_test.tabular" ftype="tabular"/>
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256 <param name="selected_task" value="load"/>
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257 <output name="outfile_predict" file="rfr_result01" compare="sim_size" delta="500"/>
0
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258 </test>
9
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259 <test>
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260 <param name="infile1" value="regression_X.tabular" ftype="tabular"/>
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261 <param name="infile2" value="regression_y.tabular" ftype="tabular"/>
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262 <param name="header1" value="True"/>
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263 <param name="selected_column_selector_option" value="all_columns"/>
9
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264 <param name="header2" value="True"/>
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265 <param name="col2" value="1"/>
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266 <param name="selected_task" value="train"/>
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267 <param name="selected_algorithm" value="GradientBoostingRegressor"/>
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268 <param name="max_features" value="number_input"/>
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269 <param name="num_max_features" value="0.5"/>
9
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270 <param name="random_state" value="42"/>
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271 <output name="outfile_fit" file="gbr_model01" compare="sim_size" delta="500"/>
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272 </test>
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273 <test>
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274 <param name="infile_model" value="gbr_model01" ftype="zip"/>
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275 <param name="infile_data" value="regression_test_X.tabular" ftype="tabular"/>
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276 <param name="selected_task" value="load"/>
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277 <param name="header" value="True"/>
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278 <output name="outfile_predict" file="gbr_prediction_result01.tabular" compare="sim_size" delta="500"/>
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279 </test>
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280 <test>
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281 <param name="infile1" value="train.tabular" ftype="tabular"/>
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282 <param name="infile2" value="train.tabular" ftype="tabular"/>
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283 <param name="col1" value="1,2,3,4"/>
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284 <param name="col2" value="5"/>
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285 <param name="selected_task" value="train"/>
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286 <param name="selected_algorithm" value="GradientBoostingClassifier"/>
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287 <output name="outfile_fit" file="gbc_model01" compare="sim_size" delta="500"/>
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288 </test>
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289 <test>
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290 <param name="infile_model" value="gbc_model01" ftype="zip"/>
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291 <param name="infile_data" value="test.tabular" ftype="tabular"/>
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292 <param name="selected_task" value="load"/>
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293 <output name="outfile_predict" file="gbc_result01" compare="sim_size" delta="500"/>
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294 </test>
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295 <test>
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296 <param name="infile1" value="train.tabular" ftype="tabular"/>
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297 <param name="infile2" value="train.tabular" ftype="tabular"/>
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298 <param name="col1" value="1,2,3,4"/>
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299 <param name="col2" value="5"/>
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300 <param name="selected_task" value="train"/>
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301 <param name="selected_algorithm" value="AdaBoostClassifier"/>
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302 <param name="random_state" value="10"/>
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303 <output name="outfile_fit" file="abc_model01" compare="sim_size" delta="500"/>
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304 </test>
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305 <test>
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306 <param name="infile_model" value="abc_model01" ftype="zip"/>
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307 <param name="infile_data" value="test.tabular" ftype="tabular"/>
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308 <param name="selected_task" value="load"/>
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309 <output name="outfile_predict" file="abc_result01" compare="sim_size" delta="500"/>
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310 </test>
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311 <test>
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312 <param name="infile1" value="regression_train.tabular" ftype="tabular"/>
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313 <param name="infile2" value="regression_train.tabular" ftype="tabular"/>
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314 <param name="col1" value="1,2,3,4,5"/>
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315 <param name="col2" value="6"/>
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316 <param name="selected_task" value="train"/>
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317 <param name="selected_algorithm" value="AdaBoostRegressor"/>
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318 <param name="random_state" value="10"/>
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319 <output name="outfile_fit" file="abr_model01" compare="sim_size" delta="500"/>
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320 </test>
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321 <test>
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322 <param name="infile_model" value="abr_model01" ftype="zip"/>
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323 <param name="infile_data" value="regression_test.tabular" ftype="tabular"/>
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324 <param name="selected_task" value="load"/>
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325 <output name="outfile_predict" file="abr_result01" compare="sim_size" delta="500"/>
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326 </test>
0
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327 </tests>
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328 <help><![CDATA[
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329 ***What it does***
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330 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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331
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332 .. _`Scikit-learn ensemble`: http://scikit-learn.org/stable/modules/ensemble.html
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333
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334 **1 - Methods**
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335 There are two groups of operations available:
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336
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337 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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338
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339 2 - Load a model and predict : An existing model predicts the class labels (or regression values) for a new dataset.
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340
0
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341 **2 - Trainig input**
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342 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:
4
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343
0
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344 **Sample tabular features dataset**
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345 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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346
0
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347 ::
4
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348
0
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349 4.01163365529 -6.10797684314 8.29829894763 1
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350 10.0788438916 1.59539821454 10.0684278289 0
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351 -5.17607775503 -0.878286135332 6.92941850665 2
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352 4.00975406235 -7.11847496542 9.3802423585 1
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353 4.61204065139 -5.71217537352 9.12509610964 1
4
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354
0
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355
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356 **Sample sparse features dataset**
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357 In this case you cannot specifiy a column range.
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358
0
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359 ::
4
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360
0
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361 4 1048577 8738
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362 1 271 0.02083333333333341
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363 1 1038 0.02461995616119806
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364 2 829017 0.01629088031127686
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365 2 829437 0.01209127083516686
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366 2 830752 0.02535100632816968
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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parents:
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367 3 1047487 0.01485722929945572
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
bgruening
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368 3 1047980 0.02640566620767753
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
bgruening
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369 3 1048475 0.01665869913262564
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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370 4 608 0.01662975263094352
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
bgruening
parents:
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371 4 1651 0.02519674277562741
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
bgruening
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372 4 4053 0.04223659971350601
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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373
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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374
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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375 **2 - Trainig output**
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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376 The trained model is generated and output in the form of a binary file.
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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377
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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378
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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379 **3 - Prediction input**
4
3bc536788043 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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380
0
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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381 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.
4
3bc536788043 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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382
0
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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383
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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384 .. class:: warningmark
4
3bc536788043 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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385
0
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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386 The number of feature columns must be the same in training and prediction datasets!
4
3bc536788043 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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387
3bc536788043 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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388
0
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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389 **3 - Prediction output**
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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390 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.
4
3bc536788043 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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391
0
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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392 ]]></help>
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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393 <expand macro="sklearn_citation"/>
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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394 </tool>