Collect each performance of a Backward elimination
JohnQuest
New Altair Community Member
I am using rapid miner for my data mining research, I used backward elimination for my feature (attribute) selections. I was wondering how to set up the process in order to gather each performance for the backward elimination. For example: feature set one (A, B, C , D, E, F), performance one(…); feature set two(A, B, C, D, E), performance two(…); ….
I am currently processing a data table with 21 features and 157000 items. A brute force feature selection simply overload my computer memory. I was wonder how to find the best combination as well as plot a graph that shows which combination of features performance low, and which combination performance high.
Thanks in advance for your kindly support.
I am currently processing a data table with 21 features and 157000 items. A brute force feature selection simply overload my computer memory. I was wonder how to find the best combination as well as plot a graph that shows which combination of features performance low, and which combination performance high.
Thanks in advance for your kindly support.
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Answers
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This script reads the iris dataset, splits it in a test and training set and then logs performance for each subset on the test set.
I had the same problem, hope this helps.
If you need further refinement let me know.<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<process version="5.0">
<context>
<input>
<location/>
</input>
<output>
<location/>
<location/>
<location/>
</output>
<macros/>
</context>
<operator activated="true" class="process" expanded="true" name="Root">
<process expanded="true" height="638" width="573">
<operator activated="true" class="retrieve" expanded="true" height="60" name="Retrieve" width="90" x="45" y="30">
<parameter key="repository_entry" value="//Samples/data/Iris"/>
</operator>
<operator activated="true" class="rename_by_replacing" expanded="true" height="76" name="rename" width="90" x="45" y="120">
<parameter key="replace_what" value="attribute_"/>
<parameter key="replace_by" value="a"/>
</operator>
<operator activated="true" class="split_data" expanded="true" height="94" name="split" width="90" x="179" y="30">
<enumeration key="partitions">
<parameter key="ratio" value="0.5"/>
<parameter key="ratio" value="0.5"/>
</enumeration>
</operator>
<operator activated="true" class="optimize_selection" expanded="true" height="94" name="FS" width="90" x="313" y="30">
<process expanded="true" height="638" width="573">
<operator activated="true" class="select_attributes" expanded="true" height="76" name="no special" width="90" x="45" y="30">
<parameter key="attribute_filter_type" value="regular_expression"/>
<parameter key="regular_expression" value="class"/>
<parameter key="invert_selection" value="true"/>
<parameter key="include_special_attributes" value="true"/>
</operator>
<operator activated="true" class="execute_script" expanded="true" height="76" name="Execute Script" width="90" x="179" y="30">
<parameter key="script" value="ExampleSet exampleSet = operator.getInput(ExampleSet.class); String atts = ""; for (Attribute attribute : exampleSet.getAttributes()) { String name = attribute.getName(); 	atts = atts + " " + name; } operator.getProcess().getMacroHandler().addMacro("atts", atts)"/>
</operator>
<operator activated="true" class="weka:W-J48" expanded="true" height="76" name="W-J48" width="90" x="179" y="165"/>
<operator activated="true" class="apply_model" expanded="true" height="76" name="Applier" width="90" x="112" y="300">
<list key="application_parameters"/>
</operator>
<operator activated="true" class="performance" expanded="true" height="76" name="Performance" width="90" x="246" y="300"/>
<operator activated="true" class="provide_macro_as_log_value" expanded="true" height="76" name="Provide Macro as Log Value" width="90" x="313" y="30">
<parameter key="macro_name" value="atts"/>
</operator>
<operator activated="true" class="log" expanded="true" height="94" name="MyLog" width="90" x="380" y="120">
<list key="log">
<parameter key="generation" value="operator.FS.value.generation"/>
<parameter key="performance" value="operator.Performance.value.performance"/>
<parameter key="atts" value="operator.Provide Macro as Log Value.value.macro_value"/>
</list>
</operator>
<connect from_port="example set" to_op="no special" to_port="example set input"/>
<connect from_port="through 1" to_op="Applier" to_port="unlabelled data"/>
<connect from_op="no special" from_port="example set output" to_op="Execute Script" to_port="input 1"/>
<connect from_op="no special" from_port="original" to_op="W-J48" to_port="training set"/>
<connect from_op="Execute Script" from_port="output 1" to_op="Provide Macro as Log Value" to_port="through 1"/>
<connect from_op="W-J48" from_port="model" to_op="Applier" to_port="model"/>
<connect from_op="Applier" from_port="labelled data" to_op="Performance" to_port="labelled data"/>
<connect from_op="Performance" from_port="performance" to_op="MyLog" to_port="through 1"/>
<connect from_op="Provide Macro as Log Value" from_port="through 1" to_op="MyLog" to_port="through 2"/>
<connect from_op="MyLog" from_port="through 1" to_port="performance"/>
<portSpacing port="source_example set" spacing="0"/>
<portSpacing port="source_through 1" spacing="0"/>
<portSpacing port="source_through 2" spacing="0"/>
<portSpacing port="sink_performance" spacing="0"/>
</process>
</operator>
<operator activated="true" class="log_to_data" expanded="true" height="94" name="Log to Data" width="90" x="447" y="120">
<parameter key="log_name" value="MyLog"/>
</operator>
<connect from_op="Retrieve" from_port="output" to_op="rename" to_port="example set input"/>
<connect from_op="rename" from_port="example set output" to_op="split" to_port="example set"/>
<connect from_op="split" from_port="partition 1" to_op="FS" to_port="example set in"/>
<connect from_op="split" from_port="partition 2" to_op="FS" to_port="through 1"/>
<connect from_op="FS" from_port="weights" to_port="result 1"/>
<connect from_op="FS" from_port="performance" to_op="Log to Data" to_port="through 1"/>
<connect from_op="Log to Data" from_port="exampleSet" to_port="result 2"/>
<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"/>
</process>
</operator>
</process>0 -
Is this quirky script still needed?
Or can you now log the performance of a sub process directly?
And more importantly log the "attributes used" at each iteration of attribute selection.
0 -
Hi,
actually this isn't needed. Was it ever? Don't know exactly...Take a look at the following process:<?xml version="1.0" encoding="UTF-8" standalone="no"?>
Greetings,
<process version="5.0">
<context>
<input/>
<output/>
<macros/>
</context>
<operator activated="true" class="process" compatibility="5.0.8" expanded="true" name="Process">
<process expanded="true" height="338" width="616">
<operator activated="true" class="retrieve" compatibility="5.0.8" expanded="true" height="60" name="Retrieve" width="90" x="45" y="30">
<parameter key="repository_entry" value="//Samples/data/Iris"/>
</operator>
<operator activated="true" class="optimize_selection_forward" compatibility="5.0.10" expanded="true" height="94" name="First Round" width="90" x="179" y="30">
<parameter key="maximal_number_of_attributes" value="25"/>
<parameter key="speculative_rounds" value="3"/>
<process expanded="true" height="597" width="804">
<operator activated="true" class="materialize_data" compatibility="5.0.10" expanded="true" height="76" name="Materialize Data" width="90" x="45" y="30"/>
<operator activated="true" class="x_validation" compatibility="5.0.10" expanded="true" height="112" name="Validation" width="90" x="179" y="30">
<process expanded="true" height="615" width="377">
<operator activated="true" class="normalize" compatibility="5.0.10" expanded="true" height="94" name="Normalize" width="90" x="45" y="30">
<parameter key="create_view" value="true"/>
</operator>
<operator activated="true" class="linear_regression" compatibility="5.0.10" expanded="true" height="94" name="Linear Regression" width="90" x="179" y="30">
<parameter key="feature_selection" value="none"/>
<parameter key="eliminate_colinear_features" value="false"/>
</operator>
<connect from_port="training" to_op="Normalize" to_port="example set input"/>
<connect from_op="Normalize" from_port="example set output" to_op="Linear Regression" to_port="training set"/>
<connect from_op="Normalize" from_port="preprocessing model" to_port="through 1"/>
<connect from_op="Linear Regression" 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"/>
<portSpacing port="sink_through 2" spacing="0"/>
</process>
<process expanded="true" height="615" width="413">
<operator activated="true" class="apply_model" compatibility="5.0.10" expanded="true" height="76" name="Apply Model (2)" width="90" x="66" y="104">
<list key="application_parameters"/>
<parameter key="create_view" value="true"/>
</operator>
<operator activated="true" class="apply_model" compatibility="5.0.10" expanded="true" height="76" name="Apply Model" width="90" x="179" y="30">
<list key="application_parameters"/>
</operator>
<operator activated="true" class="performance_regression" compatibility="5.0.10" expanded="true" height="76" name="Performance" width="90" x="313" y="30">
<parameter key="absolute_error" value="true"/>
<parameter key="squared_correlation" value="true"/>
</operator>
<connect from_port="model" to_op="Apply Model" to_port="model"/>
<connect from_port="test set" to_op="Apply Model (2)" to_port="unlabelled data"/>
<connect from_port="through 1" to_op="Apply Model (2)" to_port="model"/>
<connect from_op="Apply Model (2)" from_port="labelled data" 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="source_through 2" spacing="0"/>
<portSpacing port="sink_averagable 1" spacing="0"/>
<portSpacing port="sink_averagable 2" spacing="0"/>
</process>
</operator>
<operator activated="true" class="log" compatibility="5.0.10" expanded="true" height="76" name="Log" width="90" x="380" y="30">
<list key="log">
<parameter key="numberOfAttributes" value="operator.First Round.value.number of attributes"/>
<parameter key="attributes" value="operator.First Round.value.feature_names"/>
<parameter key="absolute error" value="operator.Validation.value.performance2"/>
<parameter key="std" value="operator.Validation.value.deviation"/>
<parameter key="sqrd cor" value="operator.Validation.value.performance3"/>
</list>
</operator>
<connect from_port="example set" to_op="Materialize Data" to_port="example set input"/>
<connect from_op="Materialize Data" from_port="example set output" to_op="Validation" to_port="training"/>
<connect from_op="Validation" from_port="averagable 1" to_op="Log" to_port="through 1"/>
<connect from_op="Log" from_port="through 1" to_port="performance"/>
<portSpacing port="source_example set" spacing="0"/>
<portSpacing port="sink_performance" spacing="0"/>
</process>
</operator>
<connect from_op="Retrieve" from_port="output" to_op="First Round" to_port="example set"/>
<portSpacing port="source_input 1" spacing="0"/>
<portSpacing port="sink_result 1" spacing="0"/>
</process>
</operator>
</process>
Sebastian0
