Mercurial > repos > bgruening > sklearn_discriminant_classifier

annotate discriminant.xml @ 27:8e49f26b14d3 draft

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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ba6a47bdf76bbf4cb276206ac1a8cbf61332fd16"
author bgruening
date Fri, 13 Sep 2019 12:10:41 -0400
parents 9bb505eafac9
children 64b771b1471a
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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 json
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19 import numpy as np
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20 import pandas
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21 import pickle
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22 import sklearn.discriminant_analysis
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23 import sys
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25 from galaxy_ml.utils import load_model, get_X_y
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28 input_json_path = sys.argv[1]
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29 with open(input_json_path, "r") as param_handler:
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30 params = json.load(param_handler)
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32 #if $selected_tasks.selected_task == "load":
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34 with open("$infile_model", 'rb') as model_handler:
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35 classifier_object = load_model(model_handler)
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37 header = 'infer' if params["selected_tasks"]["header"] else None
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38 data = pandas.read_csv("$selected_tasks.infile_data", sep='\t', header=header, index_col=None, parse_dates=True, encoding=None, tupleize_cols=False)
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39 prediction = classifier_object.predict(data)
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40 prediction_df = pandas.DataFrame(prediction)
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41 res = pandas.concat([data, prediction_df], axis=1)
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42 res.to_csv(path_or_buf = "$outfile_predict", sep="\t", index=False)
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44 #else:
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46 X, y = get_X_y(params, "$selected_tasks.selected_algorithms.input_options.infile1" ,"$selected_tasks.selected_algorithms.input_options.infile2")
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48 options = params["selected_tasks"]["selected_algorithms"]["options"]
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49 selected_algorithm = params["selected_tasks"]["selected_algorithms"]["selected_algorithm"]
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51 my_class = getattr(sklearn.discriminant_analysis, selected_algorithm)
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52 classifier_object = my_class(**options)
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53 classifier_object.fit(X, y)
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54 with open("$outfile_fit", 'wb') as out_handler:
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55 pickle.dump(classifier_object, out_handler, pickle.HIGHEST_PROTOCOL)
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57 #end if
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58 ]]>
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59 </configfile>
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60 </configfiles>
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61 <inputs>
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62 <expand macro="sl_Conditional" model="zip">
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63 <param name="selected_algorithm" type="select" label="Classifier type">
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64 <option value="LinearDiscriminantAnalysis" selected="true">Linear Discriminant Classifier</option>
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65 <option value="QuadraticDiscriminantAnalysis">Quadratic Discriminant Classifier</option>
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66 </param>
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67 <when value="LinearDiscriminantAnalysis">
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68 <expand macro="sl_mixed_input"/>
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69 <section name="options" title="Advanced Options" expanded="False">
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70 <param argument="solver" type="select" optional="true" label="Solver" help="">
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71 <option value="svd" selected="true">Singular Value Decomposition</option>
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72 <option value="lsqr">Least Squares Solution</option>
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73 <option value="eigen">Eigenvalue Decomposition</option>
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74 </param>
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75 <!--param name="shrinkage"-->
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76 <!--expand macro="priors"/-->
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77 <param argument="n_components" type="integer" optional="true" value="" label="Number of components"
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78 help="Number of components for dimensionality reduction. ( always less than n_classes - 1 )"/>
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79 <expand macro="tol" default_value="0.0001" help_text="Rank estimation threshold used in SVD solver."/>
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80 <param argument="store_covariance" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolflase" checked="false"
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81 label="Store covariance" help="Compute class covariance matrix."/>
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82 </section>
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83 </when>
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84 <when value="QuadraticDiscriminantAnalysis">
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85 <expand macro="sl_mixed_input"/>
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86 <section name="options" title="Advanced Options" expanded="False">
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87 <!--expand macro="priors"/-->
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88 <param argument="reg_param" type="float" optional="true" value="0.0" label="Regularization coefficient" help="Covariance estimate regularizer."/>
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89 <expand macro="tol" default_value="0.00001" help_text="Rank estimation threshold used in SVD solver."/>
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90 <param argument="store_covariance" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolflase" checked="false"
0
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91 label="Store covariances" help="Compute class covariance matrixes."/>
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92 </section>
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93 </when>
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94 </expand>
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95 </inputs>
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96 <expand macro="output"/>
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97 <tests>
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98 <test>
13
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99 <param name="infile1" value="train.tabular" ftype="tabular"/>
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100 <param name="infile2" value="train.tabular" ftype="tabular"/>
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101 <param name="header1" value="True"/>
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102 <param name="header2" value="True"/>
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103 <param name="col1" value="1,2,3,4"/>
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104 <param name="col2" value="5"/>
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105 <param name="selected_task" value="train"/>
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106 <param name="selected_algorithm" value="LinearDiscriminantAnalysis"/>
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107 <param name="solver" value="svd" />
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108 <param name="store_covariance" value="True"/>
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109 <output name="outfile_fit" file="lda_model01" compare="sim_size" delta="1"/>
0
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110 </test>
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111 <test>
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112 <param name="infile1" value="train.tabular" ftype="tabular"/>
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113 <param name="infile2" value="train.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"/>
0
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118 <param name="selected_task" value="train"/>
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119 <param name="selected_algorithm" value="LinearDiscriminantAnalysis"/>
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120 <param name="solver" value="lsqr"/>
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121 <output name="outfile_fit" file="lda_model02" compare="sim_size" delta="1"/>
0
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122 </test>
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123 <test>
13
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124 <param name="infile1" value="train.tabular" ftype="tabular"/>
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125 <param name="infile2" value="train.tabular" ftype="tabular"/>
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126 <param name="header1" value="True"/>
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127 <param name="header2" value="True"/>
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128 <param name="col1" value="1,2,3,4"/>
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129 <param name="col2" value="5"/>
0
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130 <param name="selected_task" value="train"/>
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131 <param name="selected_algorithm" value="QuadraticDiscriminantAnalysis"/>
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132 <output name="outfile_fit" file="qda_model01" compare="sim_size" delta="1"/>
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133 </test>
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134 <test>
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135 <param name="infile_model" value="lda_model01" ftype="zip"/>
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136 <param name="infile_data" value="test.tabular" ftype="tabular"/>
13
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137 <param name="header" value="True"/>
0
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138 <param name="selected_task" value="load"/>
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139 <output name="outfile_predict" file="lda_prediction_result01.tabular"/>
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140 </test>
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141 <test>
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142 <param name="infile_model" value="lda_model02" ftype="zip"/>
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143 <param name="infile_data" value="test.tabular" ftype="tabular"/>
13
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144 <param name="header" value="True"/>
0
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145 <param name="selected_task" value="load"/>
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146 <output name="outfile_predict" file="lda_prediction_result02.tabular"/>
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147 </test>
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148 <test>
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149 <param name="infile_model" value="qda_model01" ftype="zip"/>
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150 <param name="infile_data" value="test.tabular" ftype="tabular"/>
13
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151 <param name="header" value="True"/>
0
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152 <param name="selected_task" value="load"/>
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153 <output name="outfile_predict" file="qda_prediction_result01.tabular"/>
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154 </test>
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155 </tests>
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156 <help><![CDATA[
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157 ***What it does***
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158 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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159
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160
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161 **1 - Training input**
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162
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163 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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164
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165 ::
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166
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167 "feature_1" "feature_2" "..." "feature_n" "class_label"
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168
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169 **Example for training data**
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170 The following training dataset contains 3 feature columns and a column containing class labels:
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171
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172 ::
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173
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174 4.01163365529 -6.10797684314 8.29829894763 1
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175 10.0788438916 1.59539821454 10.0684278289 0
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176 -5.17607775503 -0.878286135332 6.92941850665 2
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177 4.00975406235 -7.11847496542 9.3802423585 1
e0067d9baffc planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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178 4.61204065139 -5.71217537352 9.12509610964 1
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179
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180
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181 **2 - Trainig output**
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182
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183 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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184
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185
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186 **3 - Prediction input**
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187
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188 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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189
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190 **Example for prediction data**
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191
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192 ::
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193
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194 8.26530668997 2.96705005011 8.88881190248
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195 2.96366327113 -3.76295851562 11.7113372463
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196 8.13319631944 -0.223645298585 10.5820605308
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197
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198 .. class:: warningmark
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199
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200 The number of feature columns must be the same in training and prediction datasets!
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201
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202
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203 **3 - Prediction output**
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204 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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205
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206 Discriminant Analysis is based on sklearn.discriminant_analysis library from Scikit-learn.
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207 For more information please refer to `Scikit-learn site`_.
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208
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209 .. _`Scikit-learn site`: http://scikit-learn.org/stable/modules/lda_qda.html
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211 ]]></help>
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212 <expand macro="sklearn_citation"/>
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213 </tool>