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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
author bgruening
date Tue, 10 Apr 2018 15:14:41 -0400
parents 25a68adb2ade
children c64f57fe1b97
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1 <tool id="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 input_json_path = sys.argv[1]
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25 params = json.load(open(input_json_path, "r"))
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28 #if $selected_tasks.selected_task == "load":
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30 classifier_object = pickle.load(open("$infile_model", 'r'))
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32 data = pandas.read_csv("$selected_tasks.infile_data", sep='\t', header=0, index_col=None, parse_dates=True, encoding=None, tupleize_cols=False )
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33 prediction = classifier_object.predict(data)
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34 prediction_df = pandas.DataFrame(prediction)
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35 res = pandas.concat([data, prediction_df], axis=1)
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36 res.to_csv(path_or_buf = "$outfile_predict", sep="\t", index=False)
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38 #else:
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40 data_train = pandas.read_csv("$selected_tasks.infile_train", sep='\t', header=0, index_col=None, parse_dates=True, encoding=None, tupleize_cols=False )
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42 data = data_train.ix[:,0:len(data_train.columns)-1]
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43 labels = np.array(data_train[data_train.columns[len(data_train.columns)-1]])
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45 selected_algorithm = params["selected_tasks"]["selected_algorithms"]["selected_algorithm"]
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47 if selected_algorithm == "nneighbors":
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48 classifier = params["selected_tasks"]["selected_algorithms"]["sampling_methods"]["sampling_method"]
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49 sys.stdout.write(classifier)
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50 options = params["selected_tasks"]["selected_algorithms"]["sampling_methods"]["options"]
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51 sys.stdout.write(str(options))
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52 elif selected_algorithm == "ncentroid":
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53 options = params["selected_tasks"]["selected_algorithms"]["options"]
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54 classifier = "NearestCentroid"
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55
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56 my_class = getattr(sklearn.neighbors, classifier)
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57 classifier_object = my_class(**options)
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58 classifier_object.fit(data,labels)
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59
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60 pickle.dump(classifier_object,open("$outfile_fit", 'w+'))
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62 #end if
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64 ]]>
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65 </configfile>
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66 </configfiles>
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67 <inputs>
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68 <expand macro="train_loadConditional" model="zip"><!--Todo: add sparse to targets-->
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69 <param name="selected_algorithm" type="select" label="Classifier type">
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70 <option value="nneighbors">Nearest Neighbors</option>
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71 <option value="ncentroid">Nearest Centroid</option>
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72 </param>
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73 <when value="nneighbors">
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74 <conditional name="sampling_methods">
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75 <param name="sampling_method" type="select" label="Neighbor selection method">
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76 <option value="KNeighborsClassifier" selected="true">K-nearest neighbors</option>
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77 <option value="RadiusNeighborsClassifier">Radius-based</option>
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78 </param>
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79 <when value="KNeighborsClassifier">
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80 <expand macro="nn_advanced_options">
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81 <param argument="n_neighbors" type="integer" optional="true" value="5" label="Number of neighbors" help=" "/>
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82 </expand>
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83 </when>
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84 <when value="RadiusNeighborsClassifier">
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85 <expand macro="nn_advanced_options">
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86 <param argument="radius" type="float" optional="true" value="1.0" label="Radius"
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87 help="Range of parameter space to use by default for :meth ''radius_neighbors'' queries."/>
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88 </expand>
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89 </when>
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90 </conditional>
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91 </when>
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92 <when value="ncentroid">
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93 <section name="options" title="Advanced Options" expanded="False">
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94 <param argument="metric" type="text" optional="true" value="euclidean" label="Metric"
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95 help="The metric to use when calculating distance between instances in a feature array."/>
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96 <param argument="shrink_threshold" type="float" optional="true" value="" label="Shrink threshold"
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97 help="Floating point number for shrinking centroids to remove features."/>
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98 </section>
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99 </when>
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100 </expand>
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101 </inputs>
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102
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103 <expand macro="output"/>
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104
0
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105 <tests>
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106 <test>
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107 <param name="infile_train" value="train_set.tabular" ftype="tabular"/>
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108 <param name="selected_task" value="train"/>
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109 <param name="selected_algorithm" value="nneighbors"/>
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110 <param name="sampling_method" value="KNeighborsClassifier" />
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111 <param name="algorithm" value="brute" />
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112 <output name="outfile_fit" file="nn_model01.txt"/>
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113 </test>
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114 <test>
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115 <param name="infile_train" value="train_set.tabular" ftype="tabular"/>
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116 <param name="selected_task" value="train"/>
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117 <param name="selected_algorithm" value=""/>
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118 <param name="selected_algorithm" value="nneighbors"/>
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119 <param name="sampling_method" value="RadiusNeighborsClassifier" />
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120 <output name="outfile_fit" file="nn_model02.txt"/>
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121 </test>
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122 <test>
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123 <param name="infile_train" value="train_set.tabular" ftype="tabular"/>
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124 <param name="selected_task" value="train"/>
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125 <param name="selected_algorithm" value="ncentroid"/>
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126 <output name="outfile_fit" file="nn_model03.txt"/>
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127 </test>
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128 <test>
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129 <param name="infile_model" value="nn_model01.txt" ftype="txt"/>
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130 <param name="infile_data" value="test_set.tabular" ftype="tabular"/>
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131 <param name="selected_task" value="load"/>
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132 <output name="outfile_predict" file="nn_prediction_result01.tabular"/>
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133 </test>
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134 <test>
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135 <param name="infile_model" value="nn_model02.txt" ftype="txt"/>
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136 <param name="infile_data" value="test_set.tabular" ftype="tabular"/>
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137 <param name="selected_task" value="load"/>
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138 <output name="outfile_predict" file="nn_prediction_result02.tabular"/>
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139 </test>
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140 <test>
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141 <param name="infile_model" value="nn_model03.txt" ftype="txt"/>
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142 <param name="infile_data" value="test_set.tabular" ftype="tabular"/>
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143 <param name="selected_task" value="load"/>
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144 <output name="outfile_predict" file="nn_prediction_result03.tabular"/>
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145 </test>
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146 </tests>
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147 <help><![CDATA[
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148 **What it does**
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149 This module implements the k-nearest neighbors classification algorithms.
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150 For more information check http://scikit-learn.org/stable/modules/neighbors.html
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151 ]]></help>
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152 <expand macro="sklearn_citation"/>
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153 </tool>