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annotate ensemble.xml @ 0:569eefee7ed8 draft

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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
author bgruening
date Fri, 16 Feb 2018 09:18:19 -0500
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children 6e6726be0728
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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>
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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3 <macros>
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4 <import>main_macros.xml</import>
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5 </macros>
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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6 <expand macro="python_requirements"/>
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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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[
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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10 python "$ensemble_script" '$inputs'
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11 ]]>
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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12 </command>
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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13 <configfiles>
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14 <inputs name="inputs"/>
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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15 <configfile name="ensemble_script">
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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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 json
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19 import numpy as np
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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20 import sklearn.ensemble
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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21 import pandas
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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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"))
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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27
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28 #if $selected_tasks.selected_task == "train":
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29
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30 algorithm = params["selected_tasks"]["selected_algorithms"]["selected_algorithm"]
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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31 options = params["selected_tasks"]["selected_algorithms"]["options"]
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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32 input_type = params["selected_tasks"]["selected_algorithms"]["input_options"]["selected_input"]
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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33 if input_type=="tabular":
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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34 col1 = params["selected_tasks"]["selected_algorithms"]["input_options"]["col1"]
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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35 col1 = list(map(lambda x: x - 1, col1))
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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36 f1 = pandas.read_csv("$selected_tasks.selected_algorithms.input_options.infile1", sep='\t', header=None, index_col=None, parse_dates=True, encoding=None, tupleize_cols=False )
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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37 X = f1.iloc[:,col1].values
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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38 else:
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39 X = mmread(open("$selected_tasks.selected_algorithms.input_options.infile1", 'r'))
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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40
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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41 col2 = params["selected_tasks"]["selected_algorithms"]["input_options"]["col2"]
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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42 col2 = list(map(lambda x: x - 1, col2))
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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43 f2 = pandas.read_csv("$selected_tasks.selected_algorithms.input_options.infile2", sep='\t', header=None, index_col=None, parse_dates=True, encoding=None, tupleize_cols=False )
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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44 y = f2.iloc[:,col2].values
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45
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46 my_class = getattr(sklearn.ensemble, algorithm)
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47 estimator = my_class(**options)
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48 estimator.fit(X,y)
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49 pickle.dump(estimator,open("$outfile_fit", 'w+'), pickle.HIGHEST_PROTOCOL)
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50
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51 #else:
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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52 classifier_object = pickle.load(open("$selected_tasks.infile_model", 'r'))
569eefee7ed8 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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53 data = pandas.read_csv("$selected_tasks.infile_data", sep='\t', header=0, index_col=None, parse_dates=True, encoding=None, tupleize_cols=False )
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54 prediction = classifier_object.predict(data)
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55 prediction_df = pandas.DataFrame(prediction)
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56 res = pandas.concat([data, prediction_df], axis=1)
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57 res.to_csv(path_or_buf = "$outfile_predict", sep="\t", index=False)
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58 #end if
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59
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60 ]]>
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61 </configfile>
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62 </configfiles>
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63 <inputs>
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64 <expand macro="sl_Conditional" model="zip">
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65 <param name="selected_algorithm" type="select" label="Select an ensemble method:">
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66 <option value="RandomForestClassifier" selected="true">Random forest classifier</option>
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67 <option value="AdaBoostClassifier">Ada boost classifier</option>
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68 <option value="RandomForestRegressor">Random forest regressor</option>
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69 <option value="AdaBoostRegressor">Ada boost regressor</option>
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70 </param>
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71 <when value="RandomForestClassifier">
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72 <expand macro="sl_mixed_input"/>
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73 <section name="options" title="Advanced Options" expanded="False">
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74 <expand macro="n_estimators"/>
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75 <expand macro="criterion"/>
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76 <expand macro="max_features"/>
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77 <expand macro="max_depth"/>
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78 <expand macro="min_samples_split"/>
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79 <expand macro="min_samples_leaf"/>
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80 <expand macro="min_weight_fraction_leaf"/>
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81 <expand macro="max_leaf_nodes"/>
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82 <expand macro="bootstrap"/>
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83 <expand macro="warm_start" checked="false"/>
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84 <expand macro="random_state"/>
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85 <expand macro="oob_score"/>
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86 <!--class_weight=None-->
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87 </section>
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88 </when>
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89 <when value="AdaBoostClassifier">
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90 <expand macro="sl_mixed_input"/>
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91 <section name="options" title="Advanced Options" expanded="False">
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92 <!--base_estimator=None-->
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93 <expand macro="n_estimators" default_value="50"/>
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94 <expand macro="learning_rate"/>
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95 <param argument="algorithm" type="select" label="Boosting algorithm" help=" ">
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96 <option value="SAMME.R" selected="true">SAMME.R</option>
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97 <option value="SAMME">SAMME</option>
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98 </param>
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99 <expand macro="random_state"/>
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100 </section>
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101 </when>
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102 <when value="RandomForestRegressor">
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103 <expand macro="sl_mixed_input"/>
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104 <section name="options" title="Advanced Options" expanded="False">
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105 <expand macro="n_estimators"/>
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106 <expand macro="max_features"/>
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107 <expand macro="max_depth"/>
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108 <expand macro="min_samples_split"/>
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109 <expand macro="min_samples_leaf"/>
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110 <expand macro="min_weight_fraction_leaf"/>
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111 <expand macro="max_leaf_nodes"/>
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112 <expand macro="bootstrap"/>
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113 <expand macro="warm_start" checked="false"/>
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114 <expand macro="random_state"/>
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115 <expand macro="oob_score"/>
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116 </section>
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117 </when>
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118 <when value="AdaBoostRegressor">
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119 <expand macro="sl_mixed_input"/>
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120 <section name="options" title="Advanced Options" expanded="False">
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121 <!--base_estimator=None-->
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122 <expand macro="n_estimators" default_value="50"/>
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123 <expand macro="learning_rate"/>
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124 <param argument="loss" type="select" label="Loss function" optional="true" help="Used when updating the weights after each boosting iteration. ">
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125 <option value="linear" selected="true">linear</option>
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126 <option value="square">square</option>
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127 <option value="exponential">exponential</option>
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128 </param>
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129 <expand macro="random_state"/>
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130 </section>
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131 </when>
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132 </expand>
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133 </inputs>
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134
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135 <expand macro="output"/>
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136
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137 <tests>
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138 <test>
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139 <param name="infile1" value="train.tabular" ftype="tabular"/>
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140 <param name="infile2" value="train.tabular" ftype="tabular"/>
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141 <param name="col1" value="1,2,3,4"/>
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142 <param name="col2" value="5"/>
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143 <param name="selected_task" value="train"/>
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144 <param name="selected_algorithm" value="RandomForestClassifier"/>
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145 <param name="random_state" value="10"/>
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146 <output name="outfile_fit" file="rfc_model01" compare="sim_size" delta="500"/>
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147 </test>
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148 <test>
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149 <param name="infile_model" value="rfc_model01" ftype="zip"/>
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150 <param name="infile_data" value="test.tabular" ftype="tabular"/>
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151 <param name="selected_task" value="load"/>
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152 <output name="outfile_predict" file="rfc_result01" compare="sim_size" delta="500"/>
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153 </test>
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154
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155 <test>
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156 <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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157 <param name="infile2" value="regression_train.tabular" ftype="tabular"/>
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158 <param name="col1" value="1,2,3,4,5"/>
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159 <param name="col2" value="6"/>
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160 <param name="selected_task" value="train"/>
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161 <param name="selected_algorithm" value="RandomForestRegressor"/>
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162 <param name="random_state" value="10"/>
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163 <output name="outfile_fit" file="rfr_model01" compare="sim_size" delta="500"/>
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164 </test>
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165 <test>
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166 <param name="infile_model" value="rfr_model01" ftype="zip"/>
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167 <param name="infile_data" value="regression_test.tabular" ftype="tabular"/>
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168 <param name="selected_task" value="load"/>
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169 <output name="outfile_predict" file="rfr_result01" compare="sim_size" delta="500"/>
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170 </test>
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171 </tests>
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172 <help><![CDATA[
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173 ***What it does***
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174 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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175
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176 .. _`Scikit-learn ensemble`: http://scikit-learn.org/stable/modules/ensemble.html
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177
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178 **1 - Methods**
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179 There are two groups of operations available:
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180
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181 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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182
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183 2 - Load a model and predict : An existing model predicts the class labels (or regression values) for a new dataset.
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184
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185 **2 - Trainig input**
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186 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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187
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188 **Sample tabular features dataset**
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189 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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190
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191 ::
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192
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193 4.01163365529 -6.10797684314 8.29829894763 1
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194 10.0788438916 1.59539821454 10.0684278289 0
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195 -5.17607775503 -0.878286135332 6.92941850665 2
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196 4.00975406235 -7.11847496542 9.3802423585 1
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197 4.61204065139 -5.71217537352 9.12509610964 1
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198
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199
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200 **Sample sparse features dataset**
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201 In this case you cannot specifiy a column range.
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202
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203 ::
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204
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205 4 1048577 8738
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206 1 271 0.02083333333333341
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207 1 1038 0.02461995616119806
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208 2 829017 0.01629088031127686
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209 2 829437 0.01209127083516686
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210 2 830752 0.02535100632816968
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211 3 1047487 0.01485722929945572
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212 3 1047980 0.02640566620767753
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213 3 1048475 0.01665869913262564
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214 4 608 0.01662975263094352
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215 4 1651 0.02519674277562741
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216 4 4053 0.04223659971350601
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217
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218
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219 **2 - Trainig output**
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220 The trained model is generated and output in the form of a binary file.
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221
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222
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223 **3 - Prediction input**
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224
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225 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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226
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227
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228 .. class:: warningmark
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229
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230 The number of feature columns must be the same in training and prediction datasets!
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231
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232
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233 **3 - Prediction output**
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234 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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235
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236 ]]></help>
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237 <expand macro="sklearn_citation"/>
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238 </tool>