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annotate ensemble.xml @ 23:090cb13556f1 draft

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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d00173591e4a783a4c1cb2664e4bb192ab5414f7
author bgruening
date Fri, 17 Aug 2018 12:21:35 -0400
parents 128bd4ab2b82
children 8ba06b67d65f
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31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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1 <tool id="sklearn_ensemble" name="Ensemble methods" version="@VERSION@">
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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2 <description>for classification and regression</description>
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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3 <macros>
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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4 <import>main_macros.xml</import>
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5 </macros>
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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6 <expand macro="python_requirements"/>
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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7 <expand macro="macro_stdio"/>
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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8 <version_command>echo "@VERSION@"</version_command>
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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9 <command><![CDATA[
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10 python "$ensemble_script" '$inputs'
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11 ]]>
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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12 </command>
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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13 <configfiles>
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14 <inputs name="inputs"/>
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15 <configfile name="ensemble_script">
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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16 <![CDATA[
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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17 import sys
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18 import os
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19 import json
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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20 import numpy as np
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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21 import sklearn.ensemble
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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22 import pandas
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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23 import pickle
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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24 from scipy.io import mmread
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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26 execfile("$__tool_directory__/utils.py")
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28 # Get inputs, outputs.
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29 input_json_path = sys.argv[1]
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30 with open(input_json_path, "r") as param_handler:
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31 params = json.load(param_handler)
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32 print(params)
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33
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34 # Put all cheetah up here to avoid confusion.
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31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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35 #if $selected_tasks.selected_task == "train":
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36 infile1 = "$selected_tasks.selected_algorithms.input_options.infile1"
0070e491573f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 7c2fd140e89605fe689c39e21d70a400545e38cf
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37 infile2 = "$selected_tasks.selected_algorithms.input_options.infile2"
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38 #else:
0070e491573f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 7c2fd140e89605fe689c39e21d70a400545e38cf
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39 infile_model = "$selected_tasks.infile_model"
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40 infile_data = "$selected_tasks.infile_data"
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41 #end if
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42 outfile_fit = "$outfile_fit"
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43 outfile_predict = "$outfile_predict"
0070e491573f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 7c2fd140e89605fe689c39e21d70a400545e38cf
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44
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45 # All Python from here on out:
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46
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47 if params["selected_tasks"]["selected_task"] == "train":
0070e491573f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 7c2fd140e89605fe689c39e21d70a400545e38cf
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48 algorithm = params["selected_tasks"]["selected_algorithms"]["selected_algorithm"]
0070e491573f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 7c2fd140e89605fe689c39e21d70a400545e38cf
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49 options = params["selected_tasks"]["selected_algorithms"]["options"]
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50 if algorithm in ['RandomForestClassifier', 'RandomForestRegressor']:
090cb13556f1 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d00173591e4a783a4c1cb2664e4bb192ab5414f7
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51 options['n_jobs'] = N_JOBS
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52 if "select_max_features" in options:
0070e491573f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 7c2fd140e89605fe689c39e21d70a400545e38cf
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53 if options["select_max_features"]["max_features"] == "number_input":
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54 options["select_max_features"]["max_features"] = options["select_max_features"]["num_max_features"]
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55 options["select_max_features"].pop("num_max_features")
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56 options["max_features"] = options["select_max_features"]["max_features"]
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57 options.pop("select_max_features")
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58 if "presort" in options:
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59 if options["presort"] == "true":
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60 options["presort"] = True
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61 if options["presort"] == "false":
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62 options["presort"] = False
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63 if "min_samples_leaf" in options and options["min_samples_leaf"] == 1.0:
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64 options["min_samples_leaf"] = 1
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65 if "min_samples_split" in options and options["min_samples_split"] > 1.0:
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66 options["min_samples_split"] = int(options["min_samples_split"])
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67
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68 X, y = get_X_y(params, infile1, infile2)
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69
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70 my_class = getattr(sklearn.ensemble, algorithm)
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71 estimator = my_class(**options)
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72 estimator.fit(X,y)
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73 with open(outfile_fit, 'wb') as out_handler:
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74 pickle.dump(estimator, out_handler, pickle.HIGHEST_PROTOCOL)
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75
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76 else:
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77 with open(infile_model, 'rb') as model_handler:
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78 classifier_object = pickle.load(model_handler)
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79 header = 'infer' if params["selected_tasks"]["header"] else None
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80 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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81 prediction = classifier_object.predict(data)
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82 prediction_df = pandas.DataFrame(prediction, columns=["predicted"])
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83 res = pandas.concat([data, prediction_df], axis=1)
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84 res.to_csv(path_or_buf = outfile_predict, sep="\t", index=False)
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85
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86 ]]>
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87 </configfile>
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88 </configfiles>
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89 <inputs>
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90 <expand macro="sl_Conditional" model="zip">
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91 <param name="selected_algorithm" type="select" label="Select an ensemble method:">
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92 <option value="RandomForestClassifier" selected="true">Random forest classifier</option>
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93 <option value="AdaBoostClassifier">Ada boost classifier</option>
9
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94 <option value="GradientBoostingClassifier">Gradient Boosting Classifier</option>
0
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95 <option value="RandomForestRegressor">Random forest regressor</option>
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96 <option value="AdaBoostRegressor">Ada boost regressor</option>
9
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97 <option value="GradientBoostingRegressor">Gradient Boosting Regressor</option>
0
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98 </param>
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99 <when value="RandomForestClassifier">
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100 <expand macro="sl_mixed_input"/>
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101 <section name="options" title="Advanced Options" expanded="False">
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102 <expand macro="n_estimators"/>
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103 <expand macro="criterion"/>
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104 <expand macro="max_features"/>
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105 <expand macro="max_depth"/>
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106 <expand macro="min_samples_split"/>
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107 <expand macro="min_samples_leaf"/>
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108 <expand macro="min_weight_fraction_leaf"/>
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109 <expand macro="max_leaf_nodes"/>
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110 <expand macro="bootstrap"/>
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111 <expand macro="warm_start" checked="false"/>
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112 <expand macro="random_state"/>
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113 <expand macro="oob_score"/>
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114 <!--class_weight=None-->
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115 </section>
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116 </when>
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117 <when value="AdaBoostClassifier">
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118 <expand macro="sl_mixed_input"/>
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119 <section name="options" title="Advanced Options" expanded="False">
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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120 <!--base_estimator=None-->
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121 <expand macro="n_estimators" default_value="50"/>
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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122 <expand macro="learning_rate"/>
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123 <param argument="algorithm" type="select" label="Boosting algorithm" help=" ">
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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124 <option value="SAMME.R" selected="true">SAMME.R</option>
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125 <option value="SAMME">SAMME</option>
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126 </param>
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127 <expand macro="random_state"/>
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128 </section>
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129 </when>
9
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130 <when value="GradientBoostingClassifier">
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131 <expand macro="sl_mixed_input"/>
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132 <section name="options" title="Advanced Options" expanded="False">
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133 <!--base_estimator=None-->
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134 <param argument="loss" type="select" label="Loss function">
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135 <option value="deviance" selected="true">deviance - logistic regression with probabilistic outputs</option>
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136 <option value="exponential">exponential - gradient boosting recovers the AdaBoost algorithm</option>
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137 </param>
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138 <expand macro="learning_rate" default_value='0.1'/>
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139 <expand macro="n_estimators" default_value="100" help="The number of boosting stages to perform"/>
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140 <expand macro="max_depth" default_value="3" help="maximum depth of the individual regression estimators"/>
e4fcbbc81083 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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141 <expand macro="criterion2">
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142 <option value="friedman_mse" selected="true">friedman_mse - mean squared error with improvement score by Friedman</option>
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143 </expand>
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144 <expand macro="min_samples_split" type="float"/>
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145 <expand macro="min_samples_leaf" type="float" label="The minimum number of samples required to be at a leaf node"/>
e4fcbbc81083 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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diff changeset
146 <expand macro="min_weight_fraction_leaf"/>
e4fcbbc81083 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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147 <expand macro="subsample"/>
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148 <expand macro="max_features"/>
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149 <expand macro="max_leaf_nodes"/>
e4fcbbc81083 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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diff changeset
150 <expand macro="min_impurity_decrease"/>
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151 <expand macro="verbose"/>
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152 <expand macro="warm_start" checked="false"/>
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153 <expand macro="random_state"/>
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154 <expand macro="presort"/>
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155 </section>
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156 </when>
0
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157 <when value="RandomForestRegressor">
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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158 <expand macro="sl_mixed_input"/>
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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159 <section name="options" title="Advanced Options" expanded="False">
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160 <expand macro="n_estimators"/>
9
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161 <expand macro="criterion2"/>
0
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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162 <expand macro="max_features"/>
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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163 <expand macro="max_depth"/>
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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164 <expand macro="min_samples_split"/>
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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165 <expand macro="min_samples_leaf"/>
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166 <expand macro="min_weight_fraction_leaf"/>
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167 <expand macro="max_leaf_nodes"/>
9
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168 <expand macro="min_impurity_decrease"/>
0
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169 <expand macro="bootstrap"/>
9
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170 <expand macro="oob_score"/>
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171 <expand macro="random_state"/>
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172 <expand macro="verbose"/>
0
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173 <expand macro="warm_start" checked="false"/>
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174 </section>
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175 </when>
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176 <when value="AdaBoostRegressor">
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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177 <expand macro="sl_mixed_input"/>
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178 <section name="options" title="Advanced Options" expanded="False">
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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179 <!--base_estimator=None-->
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180 <expand macro="n_estimators" default_value="50"/>
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181 <expand macro="learning_rate"/>
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182 <param argument="loss" type="select" label="Loss function" optional="true" help="Used when updating the weights after each boosting iteration. ">
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183 <option value="linear" selected="true">linear</option>
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184 <option value="square">square</option>
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185 <option value="exponential">exponential</option>
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186 </param>
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187 <expand macro="random_state"/>
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188 </section>
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189 </when>
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190 <when value="GradientBoostingRegressor">
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191 <expand macro="sl_mixed_input"/>
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192 <section name="options" title="Advanced Options" expanded="False">
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193 <param argument="loss" type="select" label="Loss function">
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194 <option value="ls" selected="true">ls - least squares regression</option>
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195 <option value="lad">lad - least absolute deviation</option>
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196 <option value="huber">huber - combination of least squares regression and least absolute deviation</option>
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197 <option value="quantile">quantile - use alpha to specify the quantile</option>
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198 </param>
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199 <expand macro="learning_rate" default_value="0.1"/>
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200 <expand macro="n_estimators" default_value="100" help="The number of boosting stages to perform"/>
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201 <expand macro="max_depth" default_value="3" help="maximum depth of the individual regression estimators"/>
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202 <expand macro="criterion2">
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203 <option value="friedman_mse" selected="true">friedman_mse - mean squared error with improvement score by Friedman</option>
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204 </expand>
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205 <expand macro="min_samples_split" type="float"/>
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206 <expand macro="min_samples_leaf" type="float" label="The minimum number of samples required to be at a leaf node"/>
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207 <expand macro="min_weight_fraction_leaf"/>
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208 <expand macro="subsample"/>
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209 <expand macro="max_features"/>
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210 <expand macro="max_leaf_nodes"/>
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211 <expand macro="min_impurity_decrease"/>
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212 <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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213 <!--base_estimator=None-->
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214 <expand macro="verbose"/>
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215 <expand macro="warm_start" checked="false"/>
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216 <expand macro="random_state"/>
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217 <expand macro="presort"/>
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218 </section>
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219 </when>
0
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220 </expand>
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221 </inputs>
4
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222
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223 <expand macro="output"/>
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224
0
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225 <tests>
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226 <test>
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227 <param name="infile1" value="train.tabular" ftype="tabular"/>
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228 <param name="infile2" value="train.tabular" ftype="tabular"/>
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229 <param name="col1" value="1,2,3,4"/>
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230 <param name="col2" value="5"/>
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231 <param name="selected_task" value="train"/>
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232 <param name="selected_algorithm" value="RandomForestClassifier"/>
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233 <param name="random_state" value="10"/>
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234 <output name="outfile_fit" file="rfc_model01" compare="sim_size" delta="500"/>
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235 </test>
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236 <test>
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237 <param name="infile_model" value="rfc_model01" ftype="zip"/>
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238 <param name="infile_data" value="test.tabular" ftype="tabular"/>
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239 <param name="selected_task" value="load"/>
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240 <output name="outfile_predict" file="rfc_result01" compare="sim_size" delta="500"/>
0
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241 </test>
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242 <test>
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243 <param name="infile1" value="regression_train.tabular" ftype="tabular"/>
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244 <param name="infile2" value="regression_train.tabular" ftype="tabular"/>
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245 <param name="col1" value="1,2,3,4,5"/>
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246 <param name="col2" value="6"/>
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247 <param name="selected_task" value="train"/>
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248 <param name="selected_algorithm" value="RandomForestRegressor"/>
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249 <param name="random_state" value="10"/>
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250 <output name="outfile_fit" file="rfr_model01" compare="sim_size" delta="500"/>
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251 </test>
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252 <test>
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253 <param name="infile_model" value="rfr_model01" ftype="zip"/>
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254 <param name="infile_data" value="regression_test.tabular" ftype="tabular"/>
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255 <param name="selected_task" value="load"/>
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256 <output name="outfile_predict" file="rfr_result01" compare="sim_size" delta="500"/>
0
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257 </test>
9
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258 <test>
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259 <param name="infile1" value="regression_X.tabular" ftype="tabular"/>
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260 <param name="infile2" value="regression_y.tabular" ftype="tabular"/>
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261 <param name="header1" value="True"/>
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262 <param name="selected_column_selector_option" value="all_columns"/>
9
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263 <param name="header2" value="True"/>
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264 <param name="col2" value="1"/>
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265 <param name="selected_task" value="train"/>
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266 <param name="selected_algorithm" value="GradientBoostingRegressor"/>
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267 <param name="max_features" value="number_input"/>
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268 <param name="num_max_features" value=""/>
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269 <param name="random_state" value="42"/>
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270 <output name="outfile_fit" file="gbr_model01" compare="sim_size" delta="500"/>
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271 </test>
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272 <test>
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273 <param name="infile_model" value="gbr_model01" ftype="zip"/>
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274 <param name="infile_data" value="regression_test_X.tabular" ftype="tabular"/>
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275 <param name="selected_task" value="load"/>
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276 <param name="header" value="True"/>
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277 <output name="outfile_predict" file="gbr_prediction_result01.tabular" compare="sim_size" delta="500"/>
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278 </test>
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279 <test>
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280 <param name="infile1" value="train.tabular" ftype="tabular"/>
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281 <param name="infile2" value="train.tabular" ftype="tabular"/>
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282 <param name="col1" value="1,2,3,4"/>
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283 <param name="col2" value="5"/>
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284 <param name="selected_task" value="train"/>
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285 <param name="selected_algorithm" value="GradientBoostingClassifier"/>
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286 <output name="outfile_fit" file="gbc_model01" compare="sim_size" delta="500"/>
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287 </test>
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288 <test>
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289 <param name="infile_model" value="gbc_model01" ftype="zip"/>
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290 <param name="infile_data" value="test.tabular" ftype="tabular"/>
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291 <param name="selected_task" value="load"/>
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292 <output name="outfile_predict" file="gbc_result01" compare="sim_size" delta="500"/>
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293 </test>
0
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294 </tests>
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295 <help><![CDATA[
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296 ***What it does***
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297 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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298
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299 .. _`Scikit-learn ensemble`: http://scikit-learn.org/stable/modules/ensemble.html
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300
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301 **1 - Methods**
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302 There are two groups of operations available:
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303
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304 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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305
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306 2 - Load a model and predict : An existing model predicts the class labels (or regression values) for a new dataset.
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307
0
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308 **2 - Trainig input**
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309 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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310
0
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311 **Sample tabular features dataset**
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312 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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313
0
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314 ::
4
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315
0
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316 4.01163365529 -6.10797684314 8.29829894763 1
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317 10.0788438916 1.59539821454 10.0684278289 0
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318 -5.17607775503 -0.878286135332 6.92941850665 2
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319 4.00975406235 -7.11847496542 9.3802423585 1
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320 4.61204065139 -5.71217537352 9.12509610964 1
4
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321
0
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322
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323 **Sample sparse features dataset**
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324 In this case you cannot specifiy a column range.
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325
0
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326 ::
4
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327
0
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328 4 1048577 8738
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329 1 271 0.02083333333333341
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330 1 1038 0.02461995616119806
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331 2 829017 0.01629088031127686
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332 2 829437 0.01209127083516686
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333 2 830752 0.02535100632816968
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334 3 1047487 0.01485722929945572
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335 3 1047980 0.02640566620767753
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336 3 1048475 0.01665869913262564
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337 4 608 0.01662975263094352
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338 4 1651 0.02519674277562741
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339 4 4053 0.04223659971350601
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340
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341
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342 **2 - Trainig output**
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343 The trained model is generated and output in the form of a binary file.
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344
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345
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346 **3 - Prediction input**
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347
0
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348 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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349
0
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350
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351 .. class:: warningmark
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352
0
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353 The number of feature columns must be the same in training and prediction datasets!
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354
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355
0
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356 **3 - Prediction output**
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357 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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358
0
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359 ]]></help>
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360 <expand macro="sklearn_citation"/>
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361 </tool>