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1 <tool id="hgv_lps" name="LPS" version="1.0.0">
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2 <description>LASSO-Patternsearch algorithm</description>
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3
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4 <command interpreter="bash">
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5 lps_tool_wrapper.sh $lambda_fac $input_file $label_column $output_file $log_file
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6 Initialization 0
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7 #if $advanced.options == "true":
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8 Sample $advanced.sample
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9 Verbosity $advanced.verbosity
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10 Standardize $advanced.standardize
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11 initialLambda $advanced.initialLambda
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12 #if $advanced.continuation.continuation == "1":
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13 Continuation $advanced.continuation.continuation
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14 continuationSteps $advanced.continuation.continuationSteps
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15 accurateIntermediates $advanced.continuation.accurateIntermediates
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16 #end if
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17 printFreq $advanced.printFreq
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18 #if $advanced.newton.newton == "1":
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19 Newton $advanced.newton.newton
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20 NewtonThreshold $advanced.newton.newtonThreshold
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21 #end if
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22 HessianSampleFraction $advanced.hessianSampleFraction
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23 BB 0
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24 Monotone 0
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25 FullGradient $advanced.fullGradient
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26 GradientFraction $advanced.gradientFraction
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27 InitialAlpha $advanced.initialAlpha
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28 AlphaIncrease $advanced.alphaIncrease
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29 AlphaDecrease $advanced.alphaDecrease
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30 AlphaMax $advanced.alphaMax
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31 c1 $advanced.c1
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32 MaxIter $advanced.maxIter
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33 StopTol $advanced.stopTol
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34 IntermediateTol $advanced.intermediateTol
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35 FinalOnly $advanced.finalOnly
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36 #end if
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37 </command>
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38
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39 <inputs>
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40 <param name="input_file" type="data" format="tabular" label="Dataset"/>
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41 <param name="label_column" type="data_column" data_ref="input_file" numerical="true" label="Label column" help="Column containing outcome labels: +1 or -1."/>
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42 <param name="lambda_fac" label="Lambda_fac" type="float" value="0.03" help="Target value of the regularization parameter, expressed as a fraction of the calculated lambda_max.">
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43 <validator type="in_range" message="0.00 < lambda_fac <= 1.00" min="0.00" max="1.00"/>
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44 </param>
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45 <conditional name="advanced">
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46 <param name="options" type="select" label="Advanced Options">
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47 <option value="false" selected="true">Hide advanced options</option>
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48 <option value="true">Show advanced options</option>
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49 </param>
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50 <when value="false">
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51 <!-- no options -->
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52 </when>
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53 <when value="true">
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54 <!-- HARDCODED: 'Sample' we don't support passing an array -->
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55 <param name="sample" type="float" value="1.0" label="Sample fraction" help="Sample this fraction of the data set.">
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56 <validator type="in_range" message="0.0 <= sample <= 1.0" min="0.0" max="1.0"/>
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57 </param>
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58 <!-- HARDCODED: 'Initialization' = 0 :: Initialize at beta=0 -->
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59 <param name="verbosity" type="select" format="integer" label="Verbosity">
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60 <option value="0" selected="true">Little output</option>
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61 <option value="1">More output</option>
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62 <option value="2">Still more output</option>
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63 </param>
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64 <param name="standardize" type="select" format="integer" label="Standardize" help="Scales and shifts each column so that it has mean zero and variance 1.">
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65 <option value="0" selected="true">Don't standardize</option>
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66 <option value="1">Standardize</option>
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67 </param>
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68 <param name="initialLambda" type="float" value="0.8" label="Initial lambda" help="First value of lambda to be used in the continuation scheme, expressed as a fraction of lambda_max.">
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69 <validator type="in_range" message="0.0 < initialLambda < 1.0" min="0.0" max="1.0"/>
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70 </param>
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71 <conditional name="continuation">
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72 <param name="continuation" type="select" format="integer" label="Continuation" help="Use continuation strategy to start with a larger value of lambda, decreasing it successively to lambda_fac.">
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73 <option value="0" selected="true">Don't use continuation</option>
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74 <option value="1">Use continuation</option>
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75 </param>
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76 <when value="0">
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77 <!-- no options -->
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78 </when>
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79 <when value="1">
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80 <param name="continuationSteps" type="integer" value="5" label="Continuation steps" help="Number of lambda values to use in continuation <em>prior</em> to target value lambda_fac."/>
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81
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82 <param name="accurateIntermediates" type="select" format="integer" label="Accurate intermediates" help="Indicates whether accurate solutions are required for lambda values other than the target value lambda_fac.">
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83 <option value="0" selected="true">Don't need accurate intemediates</option>
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84 <option value="1">Calculate accurate intermediates</option>
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85 </param>
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86 </when>
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87 </conditional> <!-- name="continuation" -->
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88 <param name="printFreq" type="integer" value="1" label="Print frequency" help="Print a progress report every NI iterations, where NI is the supplied value of this parameter.">
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89 <validator type="in_range" message="printFreq >= 1" min="1"/>
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90 </param>
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91 <conditional name="newton">
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92 <param name="newton" type="select" format="integer" label="Projected Newton steps">
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93 <option value="0" selected="true">No Newton steps</option>
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94 <option value="1">Try projected Newton steps</option>
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95 </param>
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96 <when value="0">
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97 <!-- no options -->
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98 </when>
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99 <when value="1">
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100 <param name="newtonThreshold" type="integer" value="500" label="Newton threshold" help="Maximum size of free variable subvector for Newton."/>
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101 </when>
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102 </conditional>
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103 <param name="hessianSampleFraction" type="float" value="1.0" label="Hessian sample fraction" help="Fraction of terms to use in approximate Hessian calculation.">
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104 <validator type="in_range" message="0.01 < hessianSampleFraction <= 1.00" min="0.01" max="1.00"/>
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105 </param>
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106 <!-- HARDCODED: 'BB' = 0 :: don't use Barzilai-Borwein steps -->
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107 <!-- HARDCODED: 'Monotone' = 0 :: don't force monotonicity -->
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108 <param name="fullGradient" type="select" format="integer" label="Partial gradient vector selection">
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109 <option value="0">Use randomly selected partial gradient, including current active components ("biased")</option>
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110 <option value="1">Use full gradient vector at every step</option>
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111 <option value="2">Randomly selected partial gradient, without regard to current active set ("unbiased")</option>
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112 </param>
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113 <param name="gradientFraction" type="float" value="0.1" label="Gradient fraction" help="Fraction of inactive gradient vector to evaluate.">
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114 <validator type="in_range" message="0.0 < gradientFraction <= 1" min="0.0" max="1.0"/>
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115 </param>
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116 <param name="initialAlpha" type="float" value="1.0" label="Initial value of alpha"/>
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117 <param name="alphaIncrease" type="float" value="2.0" label="Alpha increase" help="Factor by which to increase alpha after descent not obtained."/>
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118 <param name="alphaDecrease" type="float" value="0.8" label="Alpha decrease" help="Factor by which to decrease alpha after successful first-order step."/>
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119 <param name="alphaMax" type="float" value="1e12" label="Alpha max" help="Maximum value of alpha; terminate with error if we exceed this."/>
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120 <param name="c1" type="float" value="1e-3" help="Parameter defining the margin by which the first-order step is required to decrease before being taken.">
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121 <validator type="in_range" message="0.0 < c1 < 1.0" min="0.0" max="1.0"/>
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122 </param>
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123 <param name="maxIter" type="integer" value="10000" label="Maximum number of iterations" help="Terminate with error if we exceed this."/>
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124 <param name="stopTol" type="float" value="1e-6" label="Stop tolerance" help="Convergence tolerance for target value of lambda."/>
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125 <param name="intermediateTol" type="float" value="1e-4" label="Intermediate tolerance" help="Convergence tolerance for intermediate values of lambda."/>
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126 <param name="finalOnly" type="select" format="integer" label="Final only">
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127 <option value="0" selected="true">Return information for all intermediate values</option>
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128 <option value="1">Just return information at the last lambda</option>
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129 </param>
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130 </when> <!-- value="advanced" -->
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131 </conditional> <!-- name="advanced" -->
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132 </inputs>
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133
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134 <outputs>
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135 <data name="output_file" format="tabular" label="${tool.name} on ${on_string}: results"/>
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136 <data name="log_file" format="txt" label="${tool.name} on ${on_string}: log"/>
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137 </outputs>
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138
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139 <requirements>
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140 <requirement type="package">lps_tool</requirement>
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141 </requirements>
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142
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143 <tests>
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144 <test>
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145 <param name="input_file" value="lps_arrhythmia.tabular"/>
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146 <param name="label_column" value="280"/>
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147 <param name="lambda_fac" value="0.03"/>
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148 <param name="options" value="true"/>
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149 <param name="sample" value="1.0"/>
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150 <param name="verbosity" value="1"/>
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151 <param name="standardize" value="0"/>
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152 <param name="initialLambda" value="0.9"/>
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153 <param name="continuation" value="1"/>
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154 <param name="continuationSteps" value="10"/>
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155 <param name="accurateIntermediates" value="0"/>
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156 <param name="printFreq" value="1"/>
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157 <param name="newton" value="1"/>
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158 <param name="newtonThreshold" value="500"/>
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159 <param name="hessianSampleFraction" value="1.0"/>
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160 <param name="fullGradient" value="1"/>
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161 <param name="gradientFraction" value="0.5"/>
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162 <param name="initialAlpha" value="1.0"/>
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163 <param name="alphaIncrease" value="2.0"/>
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164 <param name="alphaDecrease" value="0.8"/>
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165 <param name="alphaMax" value="1e12"/>
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166 <param name="c1" value="1e-3"/>
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167 <param name="maxIter" value="2500"/>
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168 <param name="stopTol" value="1e-6"/>
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169 <param name="intermediateTol" value="1e-6"/>
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170 <param name="finalOnly" value="0"/>
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171 <output name="ouput_file" file="lps_arrhythmia_beta.tabular"/>
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172 <output name="log_file" file="lps_arrhythmia_log.txt"/>
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173 </test>
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174 </tests>
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175
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176 <help>
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177 **Dataset formats**
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178
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179 The input and output datasets are tabular_. The columns are described below.
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180 There is a second output dataset (a log) that is in text_ format.
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181 (`Dataset missing?`_)
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182
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183 .. _tabular: ./static/formatHelp.html#tab
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184 .. _text: ./static/formatHelp.html#text
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185 .. _Dataset missing?: ./static/formatHelp.html
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186
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187 -----
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188
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189 **What it does**
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190
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191 The LASSO-Patternsearch algorithm fits your dataset to an L1-regularized
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192 logistic regression model. A benefit of using L1-regularization is
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193 that it typically yields a weight vector with relatively few non-zero
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194 coefficients.
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195
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196 For example, say you have a dataset containing M rows (subjects)
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197 and N columns (attributes) where one of these N attributes is binary,
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198 indicating whether or not the subject has some property of interest P.
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199 In simple terms, LPS calculates a weight for each of the other attributes
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200 in your dataset. This weight indicates how "relevant" that attribute
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201 is for predicting whether or not a given subject has property P.
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202 The L1-regularization causes most of these weights to be equal to zero,
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203 which means LPS will find a "small" subset of the remaining N-1 attributes
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204 in your dataset that can be used to predict P.
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205
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206 In other words, LPS can be used for feature selection.
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207
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208 The input dataset is tabular, and must contain a label column which
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209 indicates whether or not a given row has property P. In the current
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210 version of this tool, P must be encoded using +1 and -1. The Lambda_fac
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211 parameter ranges from 0 to 1, and controls how sparse the weight
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212 vector will be. At the low end, when Lambda_fac = 0, there will be
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213 no regularization. At the high end, when Lambda_fac = 1, there will be
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214 "too much" regularization, and all of the weights will equal zero.
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215
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216 The LPS tool creates two output datasets. The first, called the results
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217 file, is a tabular dataset containing one column of weights for each
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218 value of the regularization parameter lambda that was tried. The weight
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219 columns are in order from left to right by decreasing values of lambda.
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220 The first N-1 rows in each column are the weights for the N-1 attributes
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221 in your input dataset. The final row is a constant, the intercept.
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222
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223 Let **x** be a row from your input dataset and let **b** be a column
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224 from the results file. To compute the probability that row **x** has
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225 a label value of +1:
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226
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227 Probability(row **x** has label value = +1) = 1 / [1 + exp{**x** \* **b**\[1..N-1\] + **b**\[N\]}]
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228
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229 where **x** \* **b**\[1..N-1\] represents matrix multiplication.
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230
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231 The second output dataset, called the log file, is a text file which
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232 contains additional data about the fitted L1-regularized logistic
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233 regression model. These data include the number of features, the
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234 computed value of lambda_max, the actual values of lambda used, the
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235 optimal values of the log-likelihood and regularized log-likelihood
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236 functions, the number of non-zeros, and the number of iterations.
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237
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238 Website: http://pages.cs.wisc.edu/~swright/LPS/
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239
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240 -----
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241
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242 **Example**
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243
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244 - input file::
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245
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246 +1 1 0 0 0 0 1 0 1 1 ...
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247 +1 1 1 1 0 0 1 0 1 1 ...
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248 +1 1 0 1 0 1 0 1 0 1 ...
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249 etc.
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250
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251 - output results file::
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252
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253 0
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254 0
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255 0
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256 0
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257 0.025541
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258 etc.
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259
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260 - output log file::
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261
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262 Data set has 100 vectors with 50 features.
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263 calculateLambdaMax: n=50, m=100, m+=50, m-=50
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264 computed value of lambda_max: 5.0000e-01
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265
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266 lambda=2.96e-02 solution:
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267 optimal log-likelihood function value: 6.46e-01
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268 optimal *regularized* log-likelihood function value: 6.79e-01
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269 number of nonzeros at the optimum: 5
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270 number of iterations required: 43
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271 etc.
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272
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273 -----
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274
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275 **References**
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276
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277 Koh K, Kim S-J, Boyd S. (2007)
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278 An interior-point method for large-scale l1-regularized logistic regression.
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279 Journal of Machine Learning Research. 8:1519-1555.
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280
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281 Shi W, Wahba G, Wright S, Lee K, Klein R, Klein B. (2008)
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282 LASSO-Patternsearch algorithm with application to ophthalmology and genomic data.
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283 Stat Interface. 1(1):137-153.
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284
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285 <!--
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286 Wright S, Novak R, Figueiredo M. (2009)
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287 Sparse reconstruction via separable approximation.
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288 IEEE Transactions on Signal Processing. 57:2479-2403.
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289
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290 Shi J, Yin W, Osher S, Sajda P. (2010)
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291 A fast hybrid algorithm for large scale l1-regularized logistic regression.
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292 Journal of Machine Learning Research. 11:713-741.
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293
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294 Byrd R, Chin G, Neveitt W, Nocedal J. (2010)
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295 On the use of stochastic Hessian information in unconstrained optimization.
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296 Technical Report. Northwestern University. June 16, 2010.
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297
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298 Wright S. (2010)
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299 Accelerated block-coordinate relaxation for regularized optimization.
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300 Technical Report. University of Wisconsin. August 10, 2010.
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301 -->
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302
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303 </help>
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304 </tool>
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