Mercurial > repos > bgruening > sklearn_nn_classifier

annotate nn_classifier.xml @ 14:9871a634540f draft

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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 02087ce2966cf8b4aac9197a41171e7f986c11d1-dirty"
author bgruening
date Wed, 02 Oct 2019 03:58:00 -0400
parents d0efc68a3ddb
children 699024d5c451
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1 <tool id="sklearn_nn_classifier" name="Nearest Neighbors Classification" version="@VERSION@">
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2 <description></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 '$nnc_script' '$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="nnc_script">
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16 <![CDATA[
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17 import sys
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18 import json
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19 import numpy as np
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20 import sklearn.neighbors
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21 import pandas
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22 import pickle
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24 from galaxy_ml.utils import load_model, get_X_y
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27 input_json_path = sys.argv[1]
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28 with open(input_json_path, "r") as param_handler:
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29 params = json.load(param_handler)
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31 #if $selected_tasks.selected_task == "load":
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33 with open("$infile_model", 'rb') as model_handler:
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34 classifier_object = load_model(model_handler)
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36 header = 'infer' if params["selected_tasks"]["header"] else None
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37 data = pandas.read_csv("$selected_tasks.infile_data", sep='\t', header=header, index_col=None, parse_dates=True, encoding=None, tupleize_cols=False)
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38 prediction = classifier_object.predict(data)
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39 prediction_df = pandas.DataFrame(prediction)
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40 res = pandas.concat([data, prediction_df], axis=1)
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41 res.to_csv(path_or_buf = "$outfile_predict", sep="\t", index=False)
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43 #else:
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45 X, y = get_X_y(params, "$selected_tasks.selected_algorithms.input_options.infile1" ,"$selected_tasks.selected_algorithms.input_options.infile2")
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47 selected_algorithm = params["selected_tasks"]["selected_algorithms"]["selected_algorithm"]
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49 if selected_algorithm == "nneighbors":
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50 classifier = params["selected_tasks"]["selected_algorithms"]["sampling_methods"]["sampling_method"]
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51 sys.stdout.write(classifier)
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52 options = params["selected_tasks"]["selected_algorithms"]["sampling_methods"]["options"]
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53 sys.stdout.write(str(options))
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54 elif selected_algorithm == "ncentroid":
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55 options = params["selected_tasks"]["selected_algorithms"]["options"]
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56 classifier = "NearestCentroid"
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57
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58 my_class = getattr(sklearn.neighbors, classifier)
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59 classifier_object = my_class(**options)
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60 classifier_object.fit(X, y)
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61
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62 with open("$outfile_fit", 'wb') as out_handler:
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63 pickle.dump(classifier_object, out_handler)
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64
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65 #end if
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66
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67 ]]>
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68 </configfile>
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69 </configfiles>
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70 <inputs>
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71 <expand macro="sl_Conditional" model="zip"><!--Todo: add sparse to targets-->
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72 <param name="selected_algorithm" type="select" label="Classifier type">
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73 <option value="nneighbors">Nearest Neighbors</option>
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74 <option value="ncentroid">Nearest Centroid</option>
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75 </param>
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76 <when value="nneighbors">
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77 <expand macro="sl_mixed_input"/>
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78 <conditional name="sampling_methods">
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79 <param name="sampling_method" type="select" label="Neighbor selection method">
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80 <option value="KNeighborsClassifier" selected="true">K-nearest neighbors</option>
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81 <option value="RadiusNeighborsClassifier">Radius-based</option>
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82 </param>
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83 <when value="KNeighborsClassifier">
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84 <expand macro="nn_advanced_options">
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85 <param argument="n_neighbors" type="integer" optional="true" value="5" label="Number of neighbors" help=" "/>
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86 </expand>
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87 </when>
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88 <when value="RadiusNeighborsClassifier">
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89 <expand macro="nn_advanced_options">
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90 <param argument="radius" type="float" optional="true" value="1.0" label="Radius"
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91 help="Range of parameter space to use by default for :meth ''radius_neighbors'' queries."/>
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92 </expand>
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93 </when>
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94 </conditional>
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95 </when>
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96 <when value="ncentroid">
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97 <expand macro="sl_mixed_input"/>
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98 <section name="options" title="Advanced Options" expanded="False">
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99 <param argument="metric" type="text" optional="true" value="euclidean" label="Metric"
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100 help="The metric to use when calculating distance between instances in a feature array."/>
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101 <param argument="shrink_threshold" type="float" optional="true" value="" label="Shrink threshold"
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102 help="Floating point number for shrinking centroids to remove features."/>
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103 </section>
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104 </when>
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105 </expand>
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106 </inputs>
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107
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108 <expand macro="output"/>
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109
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110 <tests>
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111 <test>
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112 <param name="infile1" value="train_set.tabular" ftype="tabular"/>
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113 <param name="infile2" value="train_set.tabular" ftype="tabular"/>
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114 <param name="header1" value="True"/>
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115 <param name="header2" value="True"/>
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116 <param name="col1" value="1,2,3,4"/>
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117 <param name="col2" value="5"/>
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118 <param name="selected_task" value="train"/>
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119 <param name="selected_algorithm" value="nneighbors"/>
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120 <param name="sampling_method" value="KNeighborsClassifier" />
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121 <param name="algorithm" value="brute" />
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122 <output name="outfile_fit" file="nn_model01" compare="sim_size" delta="5"/>
0
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123 </test>
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124 <test>
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125 <param name="infile1" value="train_set.tabular" ftype="tabular"/>
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126 <param name="infile2" value="train_set.tabular" ftype="tabular"/>
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127 <param name="header1" value="True"/>
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128 <param name="header2" value="True"/>
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129 <param name="col1" value="1,2,3,4"/>
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130 <param name="col2" value="5"/>
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131 <param name="selected_task" value="train"/>
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132 <param name="selected_algorithm" value="nneighbors"/>
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133 <param name="sampling_method" value="RadiusNeighborsClassifier" />
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134 <output name="outfile_fit" file="nn_model02" compare="sim_size" delta="5"/>
0
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135 </test>
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136 <test>
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137 <param name="infile1" value="train_set.tabular" ftype="tabular"/>
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138 <param name="infile2" value="train_set.tabular" ftype="tabular"/>
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139 <param name="header1" value="True"/>
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140 <param name="header2" value="True"/>
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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="ncentroid"/>
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145 <output name="outfile_fit" file="nn_model03" compare="sim_size" delta="5"/>
0
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146 </test>
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147 <test>
7
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148 <param name="infile_model" value="nn_model01" ftype="zip"/>
0
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149 <param name="infile_data" value="test_set.tabular" ftype="tabular"/>
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150 <param name="header" value="True"/>
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151 <param name="selected_task" value="load"/>
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152 <output name="outfile_predict" file="nn_prediction_result01.tabular"/>
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153 </test>
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154 <test>
7
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155 <param name="infile_model" value="nn_model02" ftype="zip"/>
0
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156 <param name="infile_data" value="test_set.tabular" ftype="tabular"/>
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157 <param name="header" value="True"/>
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158 <param name="selected_task" value="load"/>
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159 <output name="outfile_predict" file="nn_prediction_result02.tabular"/>
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160 </test>
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161 <test>
7
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162 <param name="infile_model" value="nn_model03" ftype="zip"/>
0
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163 <param name="infile_data" value="test_set.tabular" ftype="tabular"/>
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164 <param name="header" value="True"/>
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165 <param name="selected_task" value="load"/>
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166 <output name="outfile_predict" file="nn_prediction_result03.tabular"/>
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167 </test>
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168 </tests>
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169 <help><![CDATA[
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170 **What it does**
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171 This module implements the k-nearest neighbors classification algorithms.
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172 For more information check http://scikit-learn.org/stable/modules/neighbors.html
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173 ]]></help>
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174 <expand macro="sklearn_citation"/>
b7d0fc23bfe4 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 8cbb681224f23fa95783514f949c97d6c2c60966
bgruening
parents:
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175 </tool>