"GridParameterOptimization and performances"
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New Altair Community Member
Hi,
how can I use the GridParameterOptimizationOperator to save the PerformanceVectors after each iteration? The result should be the currently selected parameters and the resulting performances, e.g.:
k Sim | Accuracy
1 cos 0.3
2 cos 0.5
3 cos 0.4
1 Euc 0.2
2 Euc 0.4
3 Euc 0.3
P.S.: Didn't older RM versions have more similarity measures for the kNN learner?
Thanks
how can I use the GridParameterOptimizationOperator to save the PerformanceVectors after each iteration? The result should be the currently selected parameters and the resulting performances, e.g.:
k Sim | Accuracy
1 cos 0.3
2 cos 0.5
3 cos 0.4
1 Euc 0.2
2 Euc 0.4
3 Euc 0.3
P.S.: Didn't older RM versions have more similarity measures for the kNN learner?
Thanks
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Answers
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Hello,
you can use the ProcessLog operator for this (please refer to the sample process "...Meta/01_ParameterOptimization.xml"). Here is the basic setup:
You can add a filename for the ProcessLog operator into which the results should be written. The results are of course also available in the Log tab of the Results View.
<operator name="Root" class="Process" expanded="yes">
<operator name="ExampleSetGenerator" class="ExampleSetGenerator">
<parameter key="number_examples" value="200"/>
<parameter key="number_of_attributes" value="2"/>
<parameter key="target_function" value="checkerboard classification"/>
</operator>
<operator name="ParameterOptimization" class="GridParameterOptimization" expanded="yes">
<list key="parameters">
<parameter key="NearestNeighbors.k" value="[1.0;3.0;2;linear]"/>
</list>
<operator name="Validation" class="XValidation" expanded="yes">
<parameter key="sampling_type" value="shuffled sampling"/>
<operator name="NearestNeighbors" class="NearestNeighbors">
<parameter key="k" value="3"/>
</operator>
<operator name="ApplierChain" class="OperatorChain" expanded="yes">
<operator name="Test" class="ModelApplier">
<list key="application_parameters">
</list>
</operator>
<operator name="ClassificationPerformance" class="ClassificationPerformance">
<parameter key="accuracy" value="true"/>
<list key="class_weights">
</list>
</operator>
</operator>
</operator>
<operator name="Log" class="ProcessLog">
<parameter key="filename" value="paraopt.log"/>
<list key="log">
<parameter key="k" value="operator.NearestNeighbors.parameter.k"/>
<parameter key="Accuracy" value="operator.Validation.value.performance"/>
</list>
</operator>
</operator>
</operator>
Yes, and they will again be integrated. We just completely revised the KNN learner which led to a speed-up of factor 10. However, we had to remove the usual similarity measure in order to get this speed-up and did not manage to re-implement all measures for KNN yet. But (at least most of them) will be available again in future releases.
P.S.: Didn't older RM versions have more similarity measures for the kNN learner?
Cheers,
Ingo0 -
Thanks for your answer. I hope the cosine similarity will be back soon.0