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annotate ensemble.xml @ 23:39ae276e75d9 draft

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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 57f4407e278a615f47a377a3328782b1d8e0b54d
author bgruening
date Sun, 30 Dec 2018 01:56:11 -0500
parents 2e69c6ca6e91
children e94395c672bd
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569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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1 <tool id="sklearn_ensemble" name="Ensemble methods" version="@VERSION@">
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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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[
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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17 import sys
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18 import os
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19 import json
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20 import numpy as np
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21 import sklearn.ensemble
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22 import pandas
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23 from scipy.io import mmread
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25 with open("$__tool_directory__/sk_whitelist.json", "r") as f:
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26 sk_whitelist = json.load(f)
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27 exec(open("$__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)
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33 print(params)
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35 # Put all cheetah up here to avoid confusion.
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36 #if $selected_tasks.selected_task == "train":
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37 infile1 = "$selected_tasks.selected_algorithms.input_options.infile1"
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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"
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41 infile_data = "$selected_tasks.infile_data"
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42 #end if
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43 outfile_fit = "$outfile_fit"
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44 outfile_predict = "$outfile_predict"
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46 # All Python from here on out:
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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"]
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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>
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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>
5
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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">
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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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" default_value="100"/>
0
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104 <expand macro="criterion"/>
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105 <expand macro="max_features"/>
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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106 <expand macro="max_depth"/>
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107 <expand macro="min_samples_split"/>
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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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"/>
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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"/>
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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">
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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121 <!--base_estimator=None-->
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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122 <expand macro="n_estimators" default_value="50"/>
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123 <expand macro="learning_rate"/>
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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124 <param argument="algorithm" type="select" label="Boosting algorithm" help=" ">
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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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>
5
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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>
f1761288587e planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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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"/>
f1761288587e 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"/>
f1761288587e planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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142 <expand macro="criterion2">
f1761288587e planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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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"/>
f1761288587e planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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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"/>
f1761288587e planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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diff changeset
147 <expand macro="min_weight_fraction_leaf"/>
f1761288587e planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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diff changeset
148 <expand macro="subsample"/>
f1761288587e planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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diff changeset
149 <expand macro="max_features"/>
f1761288587e planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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150 <expand macro="max_leaf_nodes"/>
f1761288587e 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"/>
f1761288587e planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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152 <expand macro="verbose"/>
f1761288587e planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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153 <expand macro="warm_start" checked="false"/>
f1761288587e planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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154 <expand macro="random_state"/>
f1761288587e planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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155 <expand macro="presort"/>
f1761288587e planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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156 </section>
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157 </when>
0
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158 <when value="RandomForestRegressor">
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159 <expand macro="sl_mixed_input"/>
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160 <section name="options" title="Advanced Options" expanded="False">
23
39ae276e75d9 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 57f4407e278a615f47a377a3328782b1d8e0b54d
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161 <expand macro="n_estimators" default_value="100"/>
5
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162 <expand macro="criterion2"/>
0
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163 <expand macro="max_features"/>
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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164 <expand macro="max_depth"/>
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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165 <expand macro="min_samples_split"/>
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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166 <expand macro="min_samples_leaf"/>
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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167 <expand macro="min_weight_fraction_leaf"/>
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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168 <expand macro="max_leaf_nodes"/>
5
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diff changeset
169 <expand macro="min_impurity_decrease"/>
0
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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170 <expand macro="bootstrap"/>
5
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diff changeset
171 <expand macro="oob_score"/>
f1761288587e planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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diff changeset
172 <expand macro="random_state"/>
f1761288587e planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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173 <expand macro="verbose"/>
0
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174 <expand macro="warm_start" checked="false"/>
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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175 </section>
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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176 </when>
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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177 <when value="AdaBoostRegressor">
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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178 <expand macro="sl_mixed_input"/>
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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179 <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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180 <!--base_estimator=None-->
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181 <expand macro="n_estimators" default_value="50"/>
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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>
5
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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>
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223
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224 <expand macro="output"/>
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225
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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"/>
23
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235 <output name="outfile_fit" file="rfc_model01" compare="sim_size" delta="5"/>
0
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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"/>
23
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241 <output name="outfile_predict" file="rfc_result01"/>
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"/>
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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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"/>
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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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"/>
23
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251 <output name="outfile_fit" file="rfr_model01" compare="sim_size" delta="5"/>
0
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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"/>
0
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258 </test>
5
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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"/>
10
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263 <param name="selected_column_selector_option" value="all_columns"/>
5
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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"/>
5
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270 <param name="random_state" value="42"/>
23
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271 <output name="outfile_fit" file="gbr_model01" compare="sim_size" delta="5"/>
5
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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"/>
5
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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="5"/>
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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"/>
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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="5"/>
22
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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"/>
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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="5"/>
22
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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"/>
22
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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
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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:
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343
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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
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347 ::
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348
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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
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354
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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
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359 ::
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360
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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
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
bgruening
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365 2 829437 0.01209127083516686
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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366 2 830752 0.02535100632816968
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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367 3 1047487 0.01485722929945572
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
bgruening
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368 3 1047980 0.02640566620767753
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
bgruening
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369 3 1048475 0.01665869913262564
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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370 4 608 0.01662975263094352
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
bgruening
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371 4 1651 0.02519674277562741
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
bgruening
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372 4 4053 0.04223659971350601
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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373
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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374
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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375 **2 - Trainig output**
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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376 The trained model is generated and output in the form of a binary file.
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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377
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 **3 - Prediction input**
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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380
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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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.
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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382
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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383
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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384 .. class:: warningmark
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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385
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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386 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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387
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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388
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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389 **3 - Prediction output**
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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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.
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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391
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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392 ]]></help>
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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393 <expand macro="sklearn_citation"/>
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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394 </tool>