Mercurial > repos > bgruening > sklearn_discriminant_classifier

annotate discriminant.xml @ 5:8e489f4ff47c draft

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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
author bgruening
date Tue, 10 Apr 2018 15:19:43 -0400
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children f46da2feb233
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1 <tool id="sklearn_discriminant_classifier" name="Discriminant Analysis" 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 <!--macro name="priors"-->
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6 </macros>
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7 <expand macro="python_requirements"/>
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8 <expand macro="macro_stdio"/>
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9 <version_command>echo "@VERSION@"</version_command>
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10 <command><![CDATA[
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11 python "$discriminant_script" '$inputs'
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12 ]]>
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13 </command>
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14 <configfiles>
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15 <inputs name="inputs"/>
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16 <configfile name="discriminant_script">
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17 <![CDATA[
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18 import sys
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19 import json
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20 import numpy as np
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21 import sklearn.discriminant_analysis
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22 import pandas
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23 import pickle
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24
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25 input_json_path = sys.argv[1]
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26 params = json.load(open(input_json_path, "r"))
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27
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28
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29 #if $selected_tasks.selected_task == "load":
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30
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31 classifier_object = pickle.load(open("$infile_model", 'r'))
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32
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33 data = pandas.read_csv("$selected_tasks.infile_data", sep='\t', header=0, index_col=None, parse_dates=True, encoding=None, tupleize_cols=False )
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34 prediction = classifier_object.predict(data)
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35 prediction_df = pandas.DataFrame(prediction)
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36 res = pandas.concat([data, prediction_df], axis=1)
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37 res.to_csv(path_or_buf = "$outfile_predict", sep="\t", index=False)
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38
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39 #else:
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40
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41 data_train = pandas.read_csv("$selected_tasks.infile_train", sep='\t', header=0, index_col=None, parse_dates=True, encoding=None, tupleize_cols=False )
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42
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43 data = data_train.ix[:,0:len(data_train.columns)-1]
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44 labels = np.array(data_train[data_train.columns[len(data_train.columns)-1]])
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45
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46 options = params["selected_tasks"]["selected_algorithms"]["options"]
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47 selected_algorithm = params["selected_tasks"]["selected_algorithms"]["selected_algorithm"]
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48
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49 my_class = getattr(sklearn.discriminant_analysis, selected_algorithm)
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50 classifier_object = my_class(**options)
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51 classifier_object.fit(data,labels)
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52 pickle.dump(classifier_object,open("$outfile_fit", 'w+'), pickle.HIGHEST_PROTOCOL)
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53
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54 #end if
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55 ]]>
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56 </configfile>
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57 </configfiles>
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58 <inputs>
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59 <expand macro="train_loadConditional" model="zip">
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60 <param name="selected_algorithm" type="select" label="Classifier type">
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61 <option value="LinearDiscriminantAnalysis" selected="true">Linear Discriminant Classifier</option>
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62 <option value="QuadraticDiscriminantAnalysis">Quadratic Discriminant Classifier</option>
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63 </param>
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64 <when value="LinearDiscriminantAnalysis">
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65 <section name="options" title="Advanced Options" expanded="False">
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66 <param argument="solver" type="select" optional="true" label="Solver" help="">
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67 <option value="svd" selected="true">Singular Value Decomposition</option>
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68 <option value="lsqr">Least Squares Solution</option>
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69 <option value="eigen">Eigenvalue Decomposition</option>
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70 </param>
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71 <!--param name="shrinkage"-->
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72 <!--expand macro="priors"/-->
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73 <param argument="n_components" type="integer" optional="true" value="" label="Number of components"
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74 help="Number of components for dimensionality reduction. ( always less than n_classes - 1 )"/>
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75 <expand macro="tol" default_value="0.0001" help_text="Rank estimation threshold used in SVD solver."/>
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76 <param argument="store_covariance" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolflase" checked="false"
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77 label="Store covariance" help="Compute class covariance matrix."/>
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78 </section>
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79 </when>
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80 <when value="QuadraticDiscriminantAnalysis">
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81 <section name="options" title="Advanced Options" expanded="False">
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82 <!--expand macro="priors"/-->
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83 <param argument="reg_param" type="float" optional="true" value="0.0" label="Regularization coefficient" help="Covariance estimate regularizer."/>
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84 <expand macro="tol" default_value="0.00001" help_text="Rank estimation threshold used in SVD solver."/>
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85 <param argument="store_covariances" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolflase" checked="false"
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86 label="Store covariances" help="Compute class covariance matrixes."/>
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87 </section>
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88 </when>
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89 </expand>
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90 </inputs>
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91 <expand macro="output"/>
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92 <tests>
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93 <test>
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94 <param name="infile_train" value="train.tabular" ftype="tabular"/>
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95 <param name="selected_task" value="train"/>
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96 <param name="selected_algorithm" value="LinearDiscriminantAnalysis"/>
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97 <param name="solver" value="svd" />
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98 <param name="store_covariances" value="True"/>
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99 <output name="outfile_fit" file="lda_model01" compare="sim_size" delta="500"/>
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100 </test>
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101 <test>
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102 <param name="infile_train" value="train.tabular" ftype="tabular"/>
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103 <param name="selected_task" value="train"/>
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104 <param name="selected_algorithm" value="LinearDiscriminantAnalysis"/>
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105 <param name="solver" value="lsqr"/>
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106 <output name="outfile_fit" file="lda_model02" compare="sim_size" delta="500"/>
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107 </test>
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108 <test>
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109 <param name="infile_train" value="train.tabular" ftype="tabular"/>
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110 <param name="selected_task" value="train"/>
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111 <param name="selected_algorithm" value="QuadraticAnalysis"/>
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112 <output name="outfile_fit" file="qda_model01" compare="sim_size" delta="500"/>
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113 </test>
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114 <test>
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115 <param name="infile_model" value="lda_model01" ftype="zip"/>
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116 <param name="infile_data" value="test.tabular" ftype="tabular"/>
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117 <param name="selected_task" value="load"/>
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118 <output name="outfile_predict" file="lda_prediction_result01.tabular"/>
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119 </test>
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120 <test>
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121 <param name="infile_model" value="lda_model02" ftype="zip"/>
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122 <param name="infile_data" value="test.tabular" ftype="tabular"/>
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123 <param name="selected_task" value="load"/>
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124 <output name="outfile_predict" file="lda_prediction_result02.tabular"/>
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125 </test>
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126 <test>
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127 <param name="infile_model" value="qda_model01" ftype="zip"/>
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128 <param name="infile_data" value="test.tabular" ftype="tabular"/>
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129 <param name="selected_task" value="load"/>
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130 <output name="outfile_predict" file="qda_prediction_result01.tabular"/>
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131 </test>
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132 </tests>
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133 <help><![CDATA[
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134 ***What it does***
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135 Linear and Quadratic Discriminant Analysis are two classic classifiers with a linear and a quadratic decision surface respectively. These classifiers are fast and easy to interprete.
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136
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137
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138 **1 - Training input**
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139
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140 When you choose to train a model, discriminant analysis tool expects a tabular file with numeric values, the order of the columns being as follows:
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141
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142 ::
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143
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144 "feature_1" "feature_2" "..." "feature_n" "class_label"
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145
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146 **Example for training data**
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147 The following training dataset contains 3 feature columns and a column containing class labels:
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148
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149 ::
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150
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151 4.01163365529 -6.10797684314 8.29829894763 1
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152 10.0788438916 1.59539821454 10.0684278289 0
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153 -5.17607775503 -0.878286135332 6.92941850665 2
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154 4.00975406235 -7.11847496542 9.3802423585 1
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155 4.61204065139 -5.71217537352 9.12509610964 1
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156
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157
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158 **2 - Trainig output**
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159
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160 Based on your choice, this tool fits a sklearn discriminant_analysis.LinearDiscriminantAnalysis or discriminant_analysis.QuadraticDiscriminantAnalysis on the traning data and outputs the trained model in the form of pickled object in a text file.
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161
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162
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163 **3 - Prediction input**
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164
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165 When you choose to load a model and do prediction, the tool expects an already trained Discriminant Analysis estimator and a tabular dataset as input. The dataset is a tabular file with new samples which you want to classify. It just contains feature columns.
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166
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167 **Example for prediction data**
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168
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169 ::
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170
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171 8.26530668997 2.96705005011 8.88881190248
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172 2.96366327113 -3.76295851562 11.7113372463
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173 8.13319631944 -0.223645298585 10.5820605308
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174
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175 .. class:: warningmark
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176
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177 The number of feature columns must be the same in training and prediction datasets!
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178
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179
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180 **3 - Prediction output**
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181 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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182
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183 Discriminant Analysis is based on sklearn.discriminant_analysis library from Scikit-learn.
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184 For more information please refer to `Scikit-learn site`_.
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186 .. _`Scikit-learn site`: http://scikit-learn.org/stable/modules/lda_qda.html
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188 ]]></help>
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189 <expand macro="sklearn_citation"/>
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190 </tool>