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annotate ensemble.xml @ 3:968863bd9008 draft

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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 99231ce3ef238975a6614926f2fa6326b9454ee8
author bgruening
date Tue, 13 Mar 2018 06:30:55 -0400
parents 6e6726be0728
children 0431274c367d
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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@">
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2 <description>for classification and regression</description>
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3 <macros>
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4 <import>main_macros.xml</import>
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5 </macros>
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6 <expand macro="python_requirements"/>
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7 <expand macro="macro_stdio"/>
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8 <version_command>echo "@VERSION@"</version_command>
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9 <command><![CDATA[
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10 python "$ensemble_script" '$inputs'
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11 ]]>
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12 </command>
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13 <configfiles>
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14 <inputs name="inputs"/>
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15 <configfile name="ensemble_script">
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16 <![CDATA[
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17 import sys
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18 import json
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19 import numpy as np
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20 import sklearn.ensemble
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21 import pandas
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22 import pickle
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23 from scipy.io import mmread
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24
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25 input_json_path = sys.argv[1]
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26 params = json.load(open(input_json_path, "r"))
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27
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28 @COLUMNS_FUNCTION@
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30 #if $selected_tasks.selected_task == "train":
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31
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32 algorithm = params["selected_tasks"]["selected_algorithms"]["selected_algorithm"]
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33 options = params["selected_tasks"]["selected_algorithms"]["options"]
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34 input_type = params["selected_tasks"]["selected_algorithms"]["input_options"]["selected_input"]
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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35 if input_type=="tabular":
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36 X = read_columns(
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37 "$selected_tasks.selected_algorithms.input_options.infile1",
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38 "$selected_tasks.selected_algorithms.input_options.col1",
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39 sep='\t',
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40 header=None,
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41 parse_dates=True
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42 )
0
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43 else:
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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44 X = mmread(open("$selected_tasks.selected_algorithms.input_options.infile1", 'r'))
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45
2
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46 y = read_columns(
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47 "$selected_tasks.selected_algorithms.input_options.infile2",
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48 "$selected_tasks.selected_algorithms.input_options.col2",
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49 sep='\t',
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50 header=None,
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51 parse_dates=True
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52 )
0
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53
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54 my_class = getattr(sklearn.ensemble, algorithm)
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55 estimator = my_class(**options)
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56 estimator.fit(X,y)
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57 pickle.dump(estimator,open("$outfile_fit", 'w+'), pickle.HIGHEST_PROTOCOL)
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58
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59 #else:
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60 classifier_object = pickle.load(open("$selected_tasks.infile_model", 'r'))
2
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61 data = pandas.read_csv("$selected_tasks.infile_data", sep='\t', header=0, index_col=None, parse_dates=True, encoding=None, tupleize_cols=False)
0
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62 prediction = classifier_object.predict(data)
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63 prediction_df = pandas.DataFrame(prediction)
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64 res = pandas.concat([data, prediction_df], axis=1)
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65 res.to_csv(path_or_buf = "$outfile_predict", sep="\t", index=False)
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66 #end if
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67
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68 ]]>
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69 </configfile>
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70 </configfiles>
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71 <inputs>
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72 <expand macro="sl_Conditional" model="zip">
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73 <param name="selected_algorithm" type="select" label="Select an ensemble method:">
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74 <option value="RandomForestClassifier" selected="true">Random forest classifier</option>
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75 <option value="AdaBoostClassifier">Ada boost classifier</option>
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76 <option value="RandomForestRegressor">Random forest regressor</option>
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77 <option value="AdaBoostRegressor">Ada boost regressor</option>
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78 </param>
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79 <when value="RandomForestClassifier">
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80 <expand macro="sl_mixed_input"/>
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81 <section name="options" title="Advanced Options" expanded="False">
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82 <expand macro="n_estimators"/>
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83 <expand macro="criterion"/>
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84 <expand macro="max_features"/>
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85 <expand macro="max_depth"/>
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86 <expand macro="min_samples_split"/>
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87 <expand macro="min_samples_leaf"/>
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88 <expand macro="min_weight_fraction_leaf"/>
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89 <expand macro="max_leaf_nodes"/>
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90 <expand macro="bootstrap"/>
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91 <expand macro="warm_start" checked="false"/>
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92 <expand macro="random_state"/>
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93 <expand macro="oob_score"/>
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94 <!--class_weight=None-->
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95 </section>
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96 </when>
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97 <when value="AdaBoostClassifier">
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98 <expand macro="sl_mixed_input"/>
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99 <section name="options" title="Advanced Options" expanded="False">
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100 <!--base_estimator=None-->
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101 <expand macro="n_estimators" default_value="50"/>
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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102 <expand macro="learning_rate"/>
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103 <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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104 <option value="SAMME.R" selected="true">SAMME.R</option>
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105 <option value="SAMME">SAMME</option>
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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106 </param>
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107 <expand macro="random_state"/>
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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108 </section>
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109 </when>
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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110 <when value="RandomForestRegressor">
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111 <expand macro="sl_mixed_input"/>
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112 <section name="options" title="Advanced Options" expanded="False">
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113 <expand macro="n_estimators"/>
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114 <expand macro="max_features"/>
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115 <expand macro="max_depth"/>
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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116 <expand macro="min_samples_split"/>
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117 <expand macro="min_samples_leaf"/>
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118 <expand macro="min_weight_fraction_leaf"/>
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119 <expand macro="max_leaf_nodes"/>
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120 <expand macro="bootstrap"/>
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121 <expand macro="warm_start" checked="false"/>
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122 <expand macro="random_state"/>
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123 <expand macro="oob_score"/>
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124 </section>
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125 </when>
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126 <when value="AdaBoostRegressor">
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127 <expand macro="sl_mixed_input"/>
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128 <section name="options" title="Advanced Options" expanded="False">
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129 <!--base_estimator=None-->
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130 <expand macro="n_estimators" default_value="50"/>
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131 <expand macro="learning_rate"/>
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132 <param argument="loss" type="select" label="Loss function" optional="true" help="Used when updating the weights after each boosting iteration. ">
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133 <option value="linear" selected="true">linear</option>
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134 <option value="square">square</option>
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135 <option value="exponential">exponential</option>
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136 </param>
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137 <expand macro="random_state"/>
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138 </section>
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139 </when>
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140 </expand>
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141 </inputs>
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142
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143 <expand macro="output"/>
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144
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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145 <tests>
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146 <test>
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147 <param name="infile1" value="train.tabular" ftype="tabular"/>
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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148 <param name="infile2" value="train.tabular" ftype="tabular"/>
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149 <param name="col1" value="1,2,3,4"/>
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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150 <param name="col2" value="5"/>
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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151 <param name="selected_task" value="train"/>
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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152 <param name="selected_algorithm" value="RandomForestClassifier"/>
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153 <param name="random_state" value="10"/>
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154 <output name="outfile_fit" file="rfc_model01" compare="sim_size" delta="500"/>
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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155 </test>
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156 <test>
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157 <param name="infile_model" value="rfc_model01" ftype="zip"/>
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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158 <param name="infile_data" value="test.tabular" ftype="tabular"/>
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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159 <param name="selected_task" value="load"/>
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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160 <output name="outfile_predict" file="rfc_result01" compare="sim_size" delta="500"/>
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161 </test>
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162
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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163 <test>
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164 <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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165 <param name="infile2" value="regression_train.tabular" ftype="tabular"/>
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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166 <param name="col1" value="1,2,3,4,5"/>
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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167 <param name="col2" value="6"/>
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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168 <param name="selected_task" value="train"/>
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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169 <param name="selected_algorithm" value="RandomForestRegressor"/>
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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170 <param name="random_state" value="10"/>
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171 <output name="outfile_fit" file="rfr_model01" compare="sim_size" delta="500"/>
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172 </test>
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173 <test>
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174 <param name="infile_model" value="rfr_model01" ftype="zip"/>
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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175 <param name="infile_data" value="regression_test.tabular" ftype="tabular"/>
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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176 <param name="selected_task" value="load"/>
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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177 <output name="outfile_predict" file="rfr_result01" compare="sim_size" delta="500"/>
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178 </test>
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179 </tests>
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180 <help><![CDATA[
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181 ***What it does***
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182 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`_.
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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183
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184 .. _`Scikit-learn ensemble`: http://scikit-learn.org/stable/modules/ensemble.html
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185
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186 **1 - Methods**
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187 There are two groups of operations available:
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188
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189 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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190
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191 2 - Load a model and predict : An existing model predicts the class labels (or regression values) for a new dataset.
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192
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193 **2 - Trainig input**
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194 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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195
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196 **Sample tabular features dataset**
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197 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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198
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199 ::
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200
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201 4.01163365529 -6.10797684314 8.29829894763 1
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202 10.0788438916 1.59539821454 10.0684278289 0
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203 -5.17607775503 -0.878286135332 6.92941850665 2
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204 4.00975406235 -7.11847496542 9.3802423585 1
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205 4.61204065139 -5.71217537352 9.12509610964 1
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206
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207
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208 **Sample sparse features dataset**
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209 In this case you cannot specifiy a column range.
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210
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211 ::
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212
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213 4 1048577 8738
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214 1 271 0.02083333333333341
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215 1 1038 0.02461995616119806
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216 2 829017 0.01629088031127686
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217 2 829437 0.01209127083516686
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218 2 830752 0.02535100632816968
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219 3 1047487 0.01485722929945572
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220 3 1047980 0.02640566620767753
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221 3 1048475 0.01665869913262564
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222 4 608 0.01662975263094352
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223 4 1651 0.02519674277562741
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224 4 4053 0.04223659971350601
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225
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226
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227 **2 - Trainig output**
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228 The trained model is generated and output in the form of a binary file.
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229
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230
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231 **3 - Prediction input**
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232
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233 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.
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234
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235
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236 .. class:: warningmark
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237
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238 The number of feature columns must be the same in training and prediction datasets!
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239
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240
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241 **3 - Prediction output**
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242 The tool predicts the class labels for new samples and adds them as the last column to the prediction dataset. The new dataset then is output as a tabular file. The prediction output format should look like the training dataset.
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243
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244 ]]></help>
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245 <expand macro="sklearn_citation"/>
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246 </tool>