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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 80417bf0158a9b596e485dd66408f738f405145a
author bgruening
date Mon, 02 Oct 2023 08:05:39 +0000
parents fce065687d98
children
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fce065687d98 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 9981e25b00de29ed881b2229a173a8c812ded9bb
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1 <tool id="sklearn_ensemble" name="Ensemble methods" version="@VERSION@" profile="@PROFILE@">
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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 json
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18 import numpy as np
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19 import pandas
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20 import sys
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22 from scipy.io import mmread
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23 import sklearn.ensemble
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24 from galaxy_ml.model_persist import dump_model_to_h5, load_model_from_h5
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25 from galaxy_ml.utils import clean_params, get_X_y
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28 N_JOBS = int(__import__('os').environ.get('GALAXY_SLOTS', 1))
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30 # Get inputs, outputs.
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31 input_json_path = sys.argv[1]
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32 with open(input_json_path, "r") as param_handler:
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33 params = json.load(param_handler)
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34 print(params)
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36 # Put all cheetah up here to avoid confusion.
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37 #if $selected_tasks.selected_task == "train":
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38 infile1 = "$selected_tasks.selected_algorithms.input_options.infile1"
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39 infile2 = "$selected_tasks.selected_algorithms.input_options.infile2"
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40 #else:
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41 infile_model = "$selected_tasks.infile_model"
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42 infile_data = "$selected_tasks.infile_data"
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43 #end if
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44 outfile_fit = "$outfile_fit"
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45 outfile_predict = "$outfile_predict"
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47 # All Python from here on out:
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49 if params["selected_tasks"]["selected_task"] == "train":
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50 algorithm = params["selected_tasks"]["selected_algorithms"]["selected_algorithm"]
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51 options = params["selected_tasks"]["selected_algorithms"]["options"]
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52 if algorithm in ['RandomForestClassifier', 'RandomForestRegressor']:
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53 options['n_jobs'] = N_JOBS
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54 if "select_max_features" in options:
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55 if options["select_max_features"]["max_features"] == "number_input":
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56 options["select_max_features"]["max_features"] = options["select_max_features"]["num_max_features"]
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57 options["select_max_features"].pop("num_max_features")
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58 options["max_features"] = options["select_max_features"]["max_features"]
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59 options.pop("select_max_features")
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60 if "min_samples_leaf" in options and options["min_samples_leaf"] == 1.0:
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61 options["min_samples_leaf"] = 1
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62 if "min_samples_split" in options and options["min_samples_split"] > 1.0:
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63 options["min_samples_split"] = int(options["min_samples_split"])
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64
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65 X, y = get_X_y(params, infile1, infile2)
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66
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67 my_class = getattr(sklearn.ensemble, algorithm)
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68 estimator = my_class(**options)
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69 estimator.fit(X,y)
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70 dump_model_to_h5(estimator, outfile_fit)
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71
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72 else:
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73 classifier_object = load_model_from_h5(infile_model)
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74 classifier_object = clean_params(classifier_object)
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75 header = 'infer' if params["selected_tasks"]["header"] else None
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76 data = pandas.read_csv(infile_data, sep='\t', header=header, index_col=None, parse_dates=True, encoding=None)
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77 prediction = classifier_object.predict(data)
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78 prediction_df = pandas.DataFrame(prediction, columns=["predicted"])
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79 res = pandas.concat([data, prediction_df], axis=1)
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80 res.to_csv(path_or_buf = outfile_predict, sep="\t", index=False)
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82 ]]>
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83 </configfile>
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84 </configfiles>
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85 <inputs>
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86 <expand macro="sl_Conditional" model="h5mlm">
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87 <param name="selected_algorithm" type="select" label="Select an ensemble method:">
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88 <option value="RandomForestClassifier" selected="true">Random forest classifier</option>
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89 <option value="AdaBoostClassifier">Ada boost classifier</option>
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90 <option value="GradientBoostingClassifier">Gradient Boosting Classifier</option>
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91 <option value="RandomForestRegressor">Random forest regressor</option>
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92 <option value="AdaBoostRegressor">Ada boost regressor</option>
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93 <option value="GradientBoostingRegressor">Gradient Boosting Regressor</option>
0
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94 </param>
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95 <when value="RandomForestClassifier">
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96 <expand macro="sl_mixed_input" />
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97 <section name="options" title="Advanced Options" expanded="False">
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98 <expand macro="n_estimators" default_value="100" />
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99 <expand macro="criterion" />
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100 <expand macro="max_features" />
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101 <expand macro="max_depth" />
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102 <expand macro="min_samples_split" />
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103 <expand macro="min_samples_leaf" />
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104 <expand macro="min_weight_fraction_leaf" />
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105 <expand macro="max_leaf_nodes" />
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106 <expand macro="bootstrap" />
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107 <expand macro="warm_start" checked="false" />
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108 <expand macro="random_state" />
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109 <expand macro="oob_score" />
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110 <!--class_weight=None-->
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111 </section>
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112 </when>
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113 <when value="AdaBoostClassifier">
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114 <expand macro="sl_mixed_input" />
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115 <section name="options" title="Advanced Options" expanded="False">
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116 <!--base_estimator=None-->
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117 <expand macro="n_estimators" default_value="50" />
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118 <expand macro="learning_rate" />
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119 <param argument="algorithm" type="select" label="Boosting algorithm" help=" ">
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120 <option value="SAMME.R" selected="true">SAMME.R</option>
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121 <option value="SAMME">SAMME</option>
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122 </param>
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123 <expand macro="random_state" />
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124 </section>
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125 </when>
9
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126 <when value="GradientBoostingClassifier">
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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 <param argument="loss" type="select" label="Loss function">
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131 <option value="deviance" selected="true">deviance - logistic regression with probabilistic outputs</option>
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132 <option value="exponential">exponential - gradient boosting recovers the AdaBoost algorithm</option>
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133 </param>
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134 <expand macro="learning_rate" default_value='0.1' />
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135 <expand macro="n_estimators" default_value="100" help="The number of boosting stages to perform" />
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136 <expand macro="max_depth" default_value="3" help="maximum depth of the individual regression estimators" />
9
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137 <expand macro="criterion2">
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138 <option value="friedman_mse" selected="true">friedman_mse - mean squared error with improvement score by Friedman</option>
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139 </expand>
38
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140 <expand macro="min_samples_split" type="float" />
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141 <expand macro="min_samples_leaf" type="float" label="The minimum number of samples required to be at a leaf node" />
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142 <expand macro="min_weight_fraction_leaf" />
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143 <expand macro="subsample" />
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144 <expand macro="max_features" />
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145 <expand macro="max_leaf_nodes" />
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146 <expand macro="min_impurity_decrease" />
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147 <expand macro="verbose" />
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148 <expand macro="warm_start" checked="false" />
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149 <expand macro="random_state" />
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150 </section>
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151 </when>
0
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152 <when value="RandomForestRegressor">
38
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153 <expand macro="sl_mixed_input" />
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154 <section name="options" title="Advanced Options" expanded="False">
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155 <expand macro="n_estimators" default_value="100" />
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156 <expand macro="criterion2" />
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157 <expand macro="max_features" />
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158 <expand macro="max_depth" />
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159 <expand macro="min_samples_split" />
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160 <expand macro="min_samples_leaf" />
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161 <expand macro="min_weight_fraction_leaf" />
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162 <expand macro="max_leaf_nodes" />
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163 <expand macro="min_impurity_decrease" />
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164 <expand macro="bootstrap" />
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165 <expand macro="oob_score" />
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166 <expand macro="random_state" />
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167 <expand macro="verbose" />
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168 <expand macro="warm_start" checked="false" />
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169 </section>
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170 </when>
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171 <when value="AdaBoostRegressor">
38
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172 <expand macro="sl_mixed_input" />
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173 <section name="options" title="Advanced Options" expanded="False">
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174 <!--base_estimator=None-->
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175 <expand macro="n_estimators" default_value="50" />
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176 <expand macro="learning_rate" />
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177 <param argument="loss" type="select" label="Loss function" optional="true" help="Used when updating the weights after each boosting iteration. ">
0
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178 <option value="linear" selected="true">linear</option>
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179 <option value="square">square</option>
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180 <option value="exponential">exponential</option>
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181 </param>
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182 <expand macro="random_state" />
0
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183 </section>
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184 </when>
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185 <when value="GradientBoostingRegressor">
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186 <expand macro="sl_mixed_input" />
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187 <section name="options" title="Advanced Options" expanded="False">
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188 <param argument="loss" type="select" label="Loss function">
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189 <option value="ls" selected="true">ls - least squares regression</option>
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190 <option value="lad">lad - least absolute deviation</option>
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191 <option value="huber">huber - combination of least squares regression and least absolute deviation</option>
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192 <option value="quantile">quantile - use alpha to specify the quantile</option>
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193 </param>
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194 <expand macro="learning_rate" default_value="0.1" />
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195 <expand macro="n_estimators" default_value="100" help="The number of boosting stages to perform" />
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196 <expand macro="max_depth" default_value="3" help="maximum depth of the individual regression estimators" />
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197 <expand macro="criterion2">
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198 <option value="friedman_mse" selected="true">friedman_mse - mean squared error with improvement score by Friedman</option>
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199 </expand>
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200 <expand macro="min_samples_split" type="float" />
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201 <expand macro="min_samples_leaf" type="float" label="The minimum number of samples required to be at a leaf node" />
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202 <expand macro="min_weight_fraction_leaf" />
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203 <expand macro="subsample" />
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204 <expand macro="max_features" />
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205 <expand macro="max_leaf_nodes" />
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206 <expand macro="min_impurity_decrease" />
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207 <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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208 <!--base_estimator=None-->
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209 <expand macro="verbose" />
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210 <expand macro="warm_start" checked="false" />
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211 <expand macro="random_state" />
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212 </section>
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213 </when>
0
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214 </expand>
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215 </inputs>
4
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216
38
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217 <expand macro="output" />
4
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218
0
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219 <tests>
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220 <test>
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221 <param name="infile1" value="train.tabular" ftype="tabular" />
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222 <param name="infile2" value="train.tabular" ftype="tabular" />
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223 <param name="col1" value="1,2,3,4" />
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224 <param name="col2" value="5" />
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225 <param name="selected_task" value="train" />
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226 <param name="selected_algorithm" value="RandomForestClassifier" />
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227 <param name="random_state" value="10" />
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228 <output name="outfile_fit" file="rfc_model01" compare="sim_size" delta="5" />
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229 </test>
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230 <test>
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231 <param name="infile_model" value="rfc_model01" ftype="h5mlm" />
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232 <param name="infile_data" value="test.tabular" ftype="tabular" />
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233 <param name="selected_task" value="load" />
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234 <output name="outfile_predict" file="rfc_result01" />
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235 </test>
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236 <test>
38
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237 <param name="infile1" value="regression_train.tabular" ftype="tabular" />
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238 <param name="infile2" value="regression_train.tabular" ftype="tabular" />
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239 <param name="col1" value="1,2,3,4,5" />
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240 <param name="col2" value="6" />
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241 <param name="selected_task" value="train" />
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242 <param name="selected_algorithm" value="RandomForestRegressor" />
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243 <param name="random_state" value="10" />
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244 <output name="outfile_fit" file="rfr_model01" compare="sim_size" delta="5" />
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245 </test>
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246 <test>
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247 <param name="infile_model" value="rfr_model01" ftype="h5mlm" />
38
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248 <param name="infile_data" value="regression_test.tabular" ftype="tabular" />
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249 <param name="selected_task" value="load" />
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250 <output name="outfile_predict" file="rfr_result01" />
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251 </test>
9
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252 <test>
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253 <param name="infile1" value="regression_X.tabular" ftype="tabular" />
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254 <param name="infile2" value="regression_y.tabular" ftype="tabular" />
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255 <param name="header1" value="True" />
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256 <param name="selected_column_selector_option" value="all_columns" />
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257 <param name="header2" value="True" />
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258 <param name="col2" value="1" />
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259 <param name="selected_task" value="train" />
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260 <param name="selected_algorithm" value="GradientBoostingRegressor" />
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261 <param name="max_features" value="number_input" />
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262 <param name="num_max_features" value="0.5" />
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263 <param name="random_state" value="42" />
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264 <output name="outfile_fit" file="gbr_model01" compare="sim_size" delta="5" />
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265 </test>
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266 <test>
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267 <param name="infile_model" value="gbr_model01" ftype="h5mlm" />
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268 <param name="infile_data" value="regression_test_X.tabular" ftype="tabular" />
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269 <param name="selected_task" value="load" />
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270 <param name="header" value="True" />
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271 <output name="outfile_predict" file="gbr_prediction_result01.tabular" />
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272 </test>
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273 <test>
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274 <param name="infile1" value="train.tabular" ftype="tabular" />
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275 <param name="infile2" value="train.tabular" ftype="tabular" />
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276 <param name="col1" value="1,2,3,4" />
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277 <param name="col2" value="5" />
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278 <param name="selected_task" value="train" />
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279 <param name="selected_algorithm" value="GradientBoostingClassifier" />
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280 <output name="outfile_fit" file="gbc_model01" compare="sim_size" delta="5" />
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281 </test>
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282 <test>
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283 <param name="infile_model" value="gbc_model01" ftype="h5mlm" />
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284 <param name="infile_data" value="test.tabular" ftype="tabular" />
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285 <param name="selected_task" value="load" />
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286 <output name="outfile_predict" file="gbc_result01" />
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287 </test>
26
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288 <test>
38
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289 <param name="infile1" value="train.tabular" ftype="tabular" />
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290 <param name="infile2" value="train.tabular" ftype="tabular" />
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291 <param name="col1" value="1,2,3,4" />
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292 <param name="col2" value="5" />
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293 <param name="selected_task" value="train" />
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294 <param name="selected_algorithm" value="AdaBoostClassifier" />
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295 <param name="random_state" value="10" />
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296 <output name="outfile_fit" file="abc_model01" compare="sim_size" delta="5" />
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297 </test>
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298 <test>
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299 <param name="infile_model" value="abc_model01" ftype="h5mlm" />
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300 <param name="infile_data" value="test.tabular" ftype="tabular" />
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301 <param name="selected_task" value="load" />
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302 <output name="outfile_predict" file="abc_result01" />
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303 </test>
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304 <test>
38
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305 <param name="infile1" value="regression_train.tabular" ftype="tabular" />
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306 <param name="infile2" value="regression_train.tabular" ftype="tabular" />
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307 <param name="col1" value="1,2,3,4,5" />
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308 <param name="col2" value="6" />
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309 <param name="selected_task" value="train" />
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310 <param name="selected_algorithm" value="AdaBoostRegressor" />
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311 <param name="random_state" value="10" />
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312 <output name="outfile_fit" file="abr_model01" compare="sim_size" delta="5" />
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313 </test>
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314 <test>
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315 <param name="infile_model" value="abr_model01" ftype="h5mlm" />
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316 <param name="infile_data" value="regression_test.tabular" ftype="tabular" />
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317 <param name="selected_task" value="load" />
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318 <output name="outfile_predict" file="abr_result01" />
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319 </test>
0
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320 </tests>
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321 <help><![CDATA[
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322 ***What it does***
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323 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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324
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325 .. _`Scikit-learn ensemble`: http://scikit-learn.org/stable/modules/ensemble.html
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326
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327 **1 - Methods**
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328 There are two groups of operations available:
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329
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330 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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331
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332 2 - Load a model and predict : An existing model predicts the class labels (or regression values) for a new dataset.
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333
0
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334 **2 - Trainig input**
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335 When you choose to train a model, you need a features dataset X and a labels set y. This tool expects tabular or sparse data for X and a single column for y (tabular). You can select a subset of columns in a tabular dataset as your features dataset or labels column. Below you find some examples:
4
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336
0
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337 **Sample tabular features dataset**
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338 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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339
0
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340 ::
4
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341
0
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342 4.01163365529 -6.10797684314 8.29829894763 1
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343 10.0788438916 1.59539821454 10.0684278289 0
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344 -5.17607775503 -0.878286135332 6.92941850665 2
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345 4.00975406235 -7.11847496542 9.3802423585 1
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346 4.61204065139 -5.71217537352 9.12509610964 1
4
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347
0
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348
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349 **Sample sparse features dataset**
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350 In this case you cannot specifiy a column range.
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351
0
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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352 ::
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3bc536788043 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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353
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31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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354 4 1048577 8738
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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355 1 271 0.02083333333333341
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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356 1 1038 0.02461995616119806
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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357 2 829017 0.01629088031127686
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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358 2 829437 0.01209127083516686
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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359 2 830752 0.02535100632816968
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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360 3 1047487 0.01485722929945572
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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361 3 1047980 0.02640566620767753
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
bgruening
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362 3 1048475 0.01665869913262564
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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363 4 608 0.01662975263094352
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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364 4 1651 0.02519674277562741
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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365 4 4053 0.04223659971350601
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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366
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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367
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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368 **2 - Trainig output**
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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369 The trained model is generated and output in the form of a binary file.
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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370
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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371
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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372 **3 - Prediction input**
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3bc536788043 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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373
0
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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374 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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3bc536788043 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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375
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31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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377 .. class:: warningmark
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3bc536788043 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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378
0
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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379 The number of feature columns must be the same in training and prediction datasets!
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3bc536788043 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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380
3bc536788043 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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381
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31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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382 **3 - Prediction output**
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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383 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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3bc536788043 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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384
38
142f27ae0806 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 208a8d348e7c7a182cfbe1b6f17868146428a7e2"
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385 ]]> </help>
142f27ae0806 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 208a8d348e7c7a182cfbe1b6f17868146428a7e2"
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386 <expand macro="sklearn_citation" />
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31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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387 </tool>