couldn't get top p% attributes
DavidRaju
New Altair Community Member
Could you please clarify me - to get top p% of attributes from the obtained weighting attributes and then the relevant accuracy.
code attached
Further whatever the value entered in the p parameter, after its deselection( inactive mode)-it is set to default value 1.0. To fix it, moved to XML code to change the value from default 1.0 to the desired p value. So how to enter the desired p value in the p parameter in graph layout?
<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<process version="5.3.013">
<context>
<input/>
<output/>
<macros/>
</context>
<operator activated="true" class="process" compatibility="5.3.013" expanded="true" name="Process">
<process expanded="true">
<operator activated="true" class="retrieve" compatibility="5.3.013" expanded="true" height="60" name="Retrieve Sonar" width="90" x="45" y="30">
<parameter key="repository_entry" value="//Samples/data/Sonar"/>
</operator>
<operator activated="true" class="weight_by_information_gain_ratio" compatibility="5.3.013" expanded="true" height="76" name="Weight by Information Gain Ratio" width="90" x="179" y="30"/>
<operator activated="true" class="select_by_weights" compatibility="5.3.013" expanded="true" height="94" name="Select by Weights" width="90" x="380" y="30">
<parameter key="weight_relation" value="top p%"/>
<parameter key="weight" value="0.0"/>
<parameter key="p" value="10"/>
</operator>
<operator activated="true" class="x_validation" compatibility="5.3.013" expanded="true" height="112" name="Validation" width="90" x="581" y="30">
<parameter key="use_local_random_seed" value="true"/>
<process expanded="true">
<operator activated="true" class="k_nn" compatibility="5.3.013" expanded="true" height="76" name="k-NN" width="90" x="112" y="30"/>
<operator activated="false" class="naive_bayes" compatibility="5.3.013" expanded="true" height="76" name="Naive Bayes" width="90" x="112" y="300"/>
<connect from_port="training" to_op="k-NN" to_port="training set"/>
<connect from_op="k-NN" from_port="model" to_port="model"/>
<portSpacing port="source_training" spacing="0"/>
<portSpacing port="sink_model" spacing="0"/>
<portSpacing port="sink_through 1" spacing="0"/>
</process>
<process expanded="true">
<operator activated="true" class="apply_model" compatibility="5.3.013" expanded="true" height="76" name="Apply Model" width="90" x="45" y="30">
<list key="application_parameters"/>
</operator>
<operator activated="true" class="performance_classification" compatibility="5.3.013" expanded="true" height="76" name="Performance" width="90" x="179" y="30">
<list key="class_weights"/>
</operator>
<connect from_port="model" to_op="Apply Model" to_port="model"/>
<connect from_port="test set" to_op="Apply Model" to_port="unlabelled data"/>
<connect from_op="Apply Model" from_port="labelled data" to_op="Performance" to_port="labelled data"/>
<connect from_op="Performance" from_port="performance" to_port="averagable 1"/>
<portSpacing port="source_model" spacing="0"/>
<portSpacing port="source_test set" spacing="0"/>
<portSpacing port="source_through 1" spacing="0"/>
<portSpacing port="sink_averagable 1" spacing="0"/>
<portSpacing port="sink_averagable 2" spacing="0"/>
</process>
</operator>
<operator activated="false" class="weight_by_information_gain_ratio" compatibility="5.3.013" expanded="true" height="76" name="Weight by Information Gain Ratio (9)" width="90" x="179" y="615"/>
<operator activated="false" class="select_by_weights" compatibility="5.3.013" expanded="true" height="94" name="Select by Weights (9)" width="90" x="313" y="615">
<parameter key="weight_relation" value="top p%"/>
<parameter key="weight" value="0.126"/>
<parameter key="k" value="18"/>
<parameter key="p" value="10"/>
</operator>
<operator activated="false" class="multiply" compatibility="5.3.013" expanded="true" height="60" name="Multiply (10)" width="90" x="447" y="615"/>
<operator activated="false" class="x_validation" compatibility="5.3.013" expanded="true" height="112" name="Validation (9)" width="90" x="581" y="615">
<parameter key="use_local_random_seed" value="true"/>
<process expanded="true">
<operator activated="true" class="k_nn" compatibility="5.3.013" expanded="true" name="k-NN (9)"/>
<operator activated="false" class="naive_bayes" compatibility="5.3.013" expanded="true" name="Naive Bayes (9)"/>
<connect from_port="training" to_op="k-NN (9)" to_port="training set"/>
<connect from_op="k-NN (9)" from_port="model" to_port="model"/>
<portSpacing port="source_training" spacing="0"/>
<portSpacing port="sink_model" spacing="0"/>
<portSpacing port="sink_through 1" spacing="0"/>
</process>
<process expanded="true">
<operator activated="true" class="apply_model" compatibility="5.3.013" expanded="true" name="Apply Model (9)">
<list key="application_parameters"/>
</operator>
<operator activated="true" class="performance_classification" compatibility="5.3.013" expanded="true" name="Performance (9)">
<list key="class_weights"/>
</operator>
<connect from_port="model" to_op="Apply Model (9)" to_port="model"/>
<connect from_port="test set" to_op="Apply Model (9)" to_port="unlabelled data"/>
<connect from_op="Apply Model (9)" from_port="labelled data" to_op="Performance (9)" to_port="labelled data"/>
<connect from_op="Performance (9)" from_port="performance" to_port="averagable 1"/>
<portSpacing port="source_model" spacing="0"/>
<portSpacing port="source_test set" spacing="0"/>
<portSpacing port="source_through 1" spacing="0"/>
<portSpacing port="sink_averagable 1" spacing="0"/>
<portSpacing port="sink_averagable 2" spacing="0"/>
</process>
</operator>
<operator activated="false" class="weight_by_information_gain_ratio" compatibility="5.3.013" expanded="true" height="76" name="Weight by Information Gain Ratio (10)" width="90" x="179" y="705"/>
<operator activated="false" class="select_by_weights" compatibility="5.3.013" expanded="true" height="94" name="Select by Weights (10)" width="90" x="313" y="705">
<parameter key="weight_relation" value="top p%"/>
<parameter key="weight" value="0.164"/>
<parameter key="k" value="15"/>
<parameter key="p" value="100"/>
</operator>
<operator activated="false" class="x_validation" compatibility="5.3.013" expanded="true" height="112" name="Validation (10)" width="90" x="581" y="705">
<parameter key="use_local_random_seed" value="true"/>
<process expanded="true">
<operator activated="true" class="k_nn" compatibility="5.3.013" expanded="true" height="76" name="k-NN (10)" width="90" x="106" y="30"/>
<operator activated="false" class="naive_bayes" compatibility="5.3.013" expanded="true" height="76" name="Naive Bayes (10)" width="90" x="179" y="300"/>
<connect from_port="training" to_op="k-NN (10)" to_port="training set"/>
<connect from_op="k-NN (10)" from_port="model" to_port="model"/>
<portSpacing port="source_training" spacing="0"/>
<portSpacing port="sink_model" spacing="0"/>
<portSpacing port="sink_through 1" spacing="0"/>
</process>
<process expanded="true">
<operator activated="true" class="apply_model" compatibility="5.3.013" expanded="true" height="76" name="Apply Model (10)" width="90" x="45" y="30">
<list key="application_parameters"/>
</operator>
<operator activated="true" class="performance_classification" compatibility="5.3.013" expanded="true" height="76" name="Performance (10)" width="90" x="175" y="30">
<list key="class_weights"/>
</operator>
<connect from_port="model" to_op="Apply Model (10)" to_port="model"/>
<connect from_port="test set" to_op="Apply Model (10)" to_port="unlabelled data"/>
<connect from_op="Apply Model (10)" from_port="labelled data" to_op="Performance (10)" to_port="labelled data"/>
<connect from_op="Performance (10)" from_port="performance" to_port="averagable 1"/>
<portSpacing port="source_model" spacing="0"/>
<portSpacing port="source_test set" spacing="0"/>
<portSpacing port="source_through 1" spacing="0"/>
<portSpacing port="sink_averagable 1" spacing="0"/>
<portSpacing port="sink_averagable 2" spacing="0"/>
</process>
</operator>
<connect from_op="Retrieve Sonar" from_port="output" to_op="Weight by Information Gain Ratio" to_port="example set"/>
<connect from_op="Weight by Information Gain Ratio" from_port="weights" to_op="Select by Weights" to_port="weights"/>
<connect from_op="Weight by Information Gain Ratio" from_port="example set" to_op="Select by Weights" to_port="example set input"/>
<connect from_op="Select by Weights" from_port="example set output" to_op="Validation" to_port="training"/>
<connect from_op="Select by Weights" from_port="original" to_port="result 1"/>
<connect from_op="Select by Weights" from_port="weights" to_port="result 2"/>
<connect from_op="Validation" from_port="averagable 1" to_port="result 3"/>
<portSpacing port="source_input 1" spacing="0"/>
<portSpacing port="sink_result 1" spacing="0"/>
<portSpacing port="sink_result 2" spacing="0"/>
<portSpacing port="sink_result 3" spacing="0"/>
<portSpacing port="sink_result 4" spacing="0"/>
</process>
</operator>
</process>
Tagged:
0
Answers
-
Hi,
actually the label of the parameter is wrong: it does not take the top p%, but you have to specify a fraction, i.e. if you want the top 10% of the attributes the value should be 0.1. That's probably also the reason for your second issue.
Anyway, I have created an internal ticket requesting to either fix the label or the behavior of the parameter.
Best regards,
Marius0 -
Thank you, I try it0