The Altair Community is migrating to a new platform to provide a better experience for you. In preparation for the migration, the Altair Community is on read-only mode from October 28 - November 6, 2024. Technical support via cases will continue to work as is. For any urgent requests from Students/Faculty members, please submit the form linked here
"[Solved] Set Macro dynamically based on Dataset"
aryan_hosseinza
Member Posts: 74 
Contributor II
Hi ,
I am doing a down sampling by use of clustering , it's an imbalanced dataset which the number of example with 'f' label is about 6 times more than the number of examples with 't' label,
I want to set the K in clustering module equals to number of 't' examples in the dataset ,
How can I do that ?
Thanks
I am doing a down sampling by use of clustering , it's an imbalanced dataset which the number of example with 'f' label is about 6 times more than the number of examples with 't' label,
I want to set the K in clustering module equals to number of 't' examples in the dataset ,
How can I do that ?
Thanks
<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<process version="5.2.008">
<context>
<input/>
<output/>
<macros/>
</context>
<operator activated="true" class="process" compatibility="5.2.008" expanded="true" name="Process">
<process expanded="true" height="539" width="2225">
<operator activated="true" class="retrieve" compatibility="5.2.008" expanded="true" height="60" name="Retrieve" width="90" x="45" y="75">
<parameter key="repository_entry" value="//NewLocalRepository/temp_5000sampled_MI4"/>
</operator>
<operator activated="true" class="sample_stratified" compatibility="5.2.008" expanded="true" height="76" name="Sample (Stratified)" width="90" x="112" y="210">
<parameter key="sample" value="relative"/>
</operator>
<operator activated="true" class="nominal_to_numerical" compatibility="5.2.008" expanded="true" height="94" name="Nominal to Numerical" width="90" x="246" y="75">
<parameter key="attribute_filter_type" value="single"/>
<parameter key="attribute" value="sex"/>
<list key="comparison_groups"/>
</operator>
<operator activated="true" class="normalize" compatibility="5.2.008" expanded="true" height="94" name="Normalize" width="90" x="380" y="75"/>
<operator activated="false" class="select_attributes" compatibility="5.2.008" expanded="true" height="76" name="Select Attributes" width="90" x="179" y="345">
<parameter key="attribute_filter_type" value="single"/>
<parameter key="attribute" value="event"/>
<parameter key="invert_selection" value="true"/>
</operator>
<operator activated="true" class="multiply" compatibility="5.2.008" expanded="true" height="94" name="Multiply" width="90" x="581" y="75"/>
<operator activated="true" class="filter_examples" compatibility="5.2.008" expanded="true" height="76" name="Filter Examples (2)" width="90" x="782" y="255">
<parameter key="condition_class" value="attribute_value_filter"/>
<parameter key="parameter_string" value="event=t"/>
</operator>
<operator activated="true" class="filter_examples" compatibility="5.2.008" expanded="true" height="76" name="Filter Examples" width="90" x="782" y="30">
<parameter key="condition_class" value="attribute_value_filter"/>
<parameter key="parameter_string" value="event=f"/>
</operator>
<operator activated="true" class="multiply" compatibility="5.2.008" expanded="true" height="94" name="Multiply (2)" width="90" x="916" y="30"/>
<operator activated="true" class="k_means" compatibility="5.2.008" expanded="true" height="76" name="Clustering" width="90" x="1117" y="30">
<parameter key="k" value="4"/>
<parameter key="max_runs" value="100"/>
<parameter key="measure_types" value="MixedMeasures"/>
</operator>
<operator activated="true" class="apply_model" compatibility="5.2.008" expanded="true" height="76" name="Apply Model" width="90" x="1117" y="165">
<list key="application_parameters"/>
</operator>
<operator activated="true" class="remove_duplicates" compatibility="5.2.008" expanded="true" height="76" name="Remove Duplicates" width="90" x="1452" y="165">
<parameter key="attribute_filter_type" value="single"/>
<parameter key="attribute" value="cluster"/>
<parameter key="include_special_attributes" value="true"/>
</operator>
<operator activated="true" class="union" compatibility="5.2.008" expanded="true" height="76" name="Union" width="90" x="1720" y="210"/>
<operator activated="true" class="select_attributes" compatibility="5.2.008" expanded="true" height="76" name="Select Attributes (3)" width="90" x="1921" y="210">
<parameter key="attribute_filter_type" value="single"/>
<parameter key="attribute" value="cluster"/>
<parameter key="invert_selection" value="true"/>
<parameter key="include_special_attributes" value="true"/>
</operator>
<operator activated="true" class="shuffle" compatibility="5.2.008" expanded="true" height="76" name="Shuffle" width="90" x="2055" y="210"/>
<connect from_op="Retrieve" from_port="output" to_op="Sample (Stratified)" to_port="example set input"/>
<connect from_op="Sample (Stratified)" from_port="example set output" to_op="Nominal to Numerical" to_port="example set input"/>
<connect from_op="Nominal to Numerical" from_port="example set output" to_op="Normalize" to_port="example set input"/>
<connect from_op="Normalize" from_port="example set output" to_op="Multiply" to_port="input"/>
<connect from_op="Multiply" from_port="output 1" to_op="Filter Examples" to_port="example set input"/>
<connect from_op="Multiply" from_port="output 2" to_op="Filter Examples (2)" to_port="example set input"/>
<connect from_op="Filter Examples (2)" from_port="example set output" to_op="Union" to_port="example set 2"/>
<connect from_op="Filter Examples" from_port="example set output" to_op="Multiply (2)" to_port="input"/>
<connect from_op="Multiply (2)" from_port="output 1" to_op="Clustering" to_port="example set"/>
<connect from_op="Multiply (2)" from_port="output 2" to_op="Apply Model" to_port="unlabelled data"/>
<connect from_op="Clustering" from_port="cluster model" to_op="Apply Model" to_port="model"/>
<connect from_op="Apply Model" from_port="labelled data" to_op="Remove Duplicates" to_port="example set input"/>
<connect from_op="Remove Duplicates" from_port="example set output" to_op="Union" to_port="example set 1"/>
<connect from_op="Union" from_port="union" to_op="Select Attributes (3)" to_port="example set input"/>
<connect from_op="Select Attributes (3)" from_port="example set output" to_op="Shuffle" to_port="example set input"/>
<connect from_op="Shuffle" from_port="example set output" to_port="result 1"/>
<portSpacing port="source_input 1" spacing="0"/>
<portSpacing port="sink_result 1" spacing="0"/>
<portSpacing port="sink_result 2" spacing="0"/>
</process>
</operator>
</process>
0
Answers
One approach is to set a macro equal to the number of rows where the label is 't'. This can be done using the "Extract Macro" operator.
Then you need to use this macro as a parameter to the k-means operator.
It's very important to make sure the extraction happens before the k-means otherwise you will get an error.
I don't have your data so I can't test it but here's an example regards
Andrew
The calculation of the macro is happening after it is being used. Change the ordering from the GUI using Process->Operator Execution Order->Order Execution.
regards
Andrew