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Performance Average (Robust) - Process failed

svjosvjo Member Posts: 4 Newbie
edited April 2020 in Product Feedback
Hi there,

I am trying to calculate the average performance for multiple hold-out sets via the Performance Average (Robust) operator. Unfortunately I get a process failed message with a java.lang.RuntimeException: Clone of average vector is not supported: cannot clone averagable: … .performance.SquaredCorrelationCriterion.

Why is the Squared Correlation Criterion not accepted in this operator?


Cheers,
Sven
0
0 votes

Sent to Engineering · Last Updated

[Update 9 Mar 2020: Engineering has a fix and will complete soon.] RM-4200

Comments

  • varunm1varunm1 Moderator, Member Posts: 1,207   Unicorn
    edited September 2019
    Looks like the issue arises when there are two performances that need to be averaged, especially for squared correlation. Here is the XML code I used to reproduce the error. @svjo how many performances are you trying to average? Cause I am unable to reproduce with more than 2 performances. Just asking to know.

    <?xml version="1.0" encoding="UTF-8"?><process version="9.3.001">
    <context>
    <input/>
    <output/>
    <macros/>
    </context>
    <operator activated="true" class="process" compatibility="9.3.001" expanded="true" name="Process">
    <parameter key="logverbosity" value="init"/>
    <parameter key="random_seed" value="2001"/>
    <parameter key="send_mail" value="never"/>
    <parameter key="notification_email" value=""/>
    <parameter key="process_duration_for_mail" value="30"/>
    <parameter key="encoding" value="SYSTEM"/>
    <process expanded="true">
    <operator activated="true" class="retrieve" compatibility="9.3.001" expanded="true" height="68" name="Retrieve Titanic Training" width="90" x="45" y="85">
    <parameter key="repository_entry" value="//Samples/data/Titanic Training"/>
    </operator>
    <operator activated="true" class="multiply" compatibility="9.3.001" expanded="true" height="124" name="Multiply" width="90" x="179" y="85"/>
    <operator activated="true" class="concurrency:cross_validation" compatibility="9.3.001" expanded="true" height="145" name="Cross Validation (2)" width="90" x="179" y="289">
    <parameter key="split_on_batch_attribute" value="false"/>
    <parameter key="leave_one_out" value="false"/>
    <parameter key="number_of_folds" value="10"/>
    <parameter key="sampling_type" value="automatic"/>
    <parameter key="use_local_random_seed" value="false"/>
    <parameter key="local_random_seed" value="1992"/>
    <parameter key="enable_parallel_execution" value="true"/>
    <process expanded="true">
    <operator activated="true" class="k_nn" compatibility="9.3.001" expanded="true" height="82" name="k-NN (2)" width="90" x="112" y="34">
    <parameter key="k" value="7"/>
    <parameter key="weighted_vote" value="true"/>
    <parameter key="measure_types" value="MixedMeasures"/>
    <parameter key="mixed_measure" value="MixedEuclideanDistance"/>
    <parameter key="nominal_measure" value="NominalDistance"/>
    <parameter key="numerical_measure" value="EuclideanDistance"/>
    <parameter key="divergence" value="GeneralizedIDivergence"/>
    <parameter key="kernel_type" value="radial"/>
    <parameter key="kernel_gamma" value="1.0"/>
    <parameter key="kernel_sigma1" value="1.0"/>
    <parameter key="kernel_sigma2" value="0.0"/>
    <parameter key="kernel_sigma3" value="2.0"/>
    <parameter key="kernel_degree" value="3.0"/>
    <parameter key="kernel_shift" value="1.0"/>
    <parameter key="kernel_a" value="1.0"/>
    <parameter key="kernel_b" value="0.0"/>
    </operator>
    <connect from_port="training set" to_op="k-NN (2)" to_port="training set"/>
    <connect from_op="k-NN (2)" from_port="model" to_port="model"/>
    <portSpacing port="source_training set" 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="9.3.001" expanded="true" height="82" name="Apply Model (2)" width="90" x="45" y="34">
    <list key="application_parameters"/>
    <parameter key="create_view" value="false"/>
    </operator>
    <operator activated="true" class="performance_classification" compatibility="9.3.001" expanded="true" height="82" name="Performance (2)" width="90" x="179" y="34">
    <parameter key="main_criterion" value="first"/>
    <parameter key="accuracy" value="true"/>
    <parameter key="classification_error" value="false"/>
    <parameter key="kappa" value="false"/>
    <parameter key="weighted_mean_recall" value="false"/>
    <parameter key="weighted_mean_precision" value="false"/>
    <parameter key="spearman_rho" value="false"/>
    <parameter key="kendall_tau" value="false"/>
    <parameter key="absolute_error" value="false"/>
    <parameter key="relative_error" value="false"/>
    <parameter key="relative_error_lenient" value="false"/>
    <parameter key="relative_error_strict" value="false"/>
    <parameter key="normalized_absolute_error" value="false"/>
    <parameter key="root_mean_squared_error" value="false"/>
    <parameter key="root_relative_squared_error" value="false"/>
    <parameter key="squared_error" value="false"/>
    <parameter key="correlation" value="false"/>
    <parameter key="squared_correlation" value="true"/>
    <parameter key="cross-entropy" value="false"/>
    <parameter key="margin" value="false"/>
    <parameter key="soft_margin_loss" value="false"/>
    <parameter key="logistic_loss" value="false"/>
    <parameter key="skip_undefined_labels" value="true"/>
    <parameter key="use_example_weights" value="true"/>
    <list key="class_weights"/>
    </operator>
    <connect from_port="model" to_op="Apply Model (2)" to_port="model"/>
    <connect from_port="test set" to_op="Apply Model (2)" to_port="unlabelled data"/>
    <connect from_op="Apply Model (2)" from_port="labelled data" to_op="Performance (2)" to_port="labelled data"/>
    <connect from_op="Performance (2)" from_port="performance" to_port="performance 1"/>
    <portSpacing port="source_model" spacing="0"/>
    <portSpacing port="source_test set" spacing="0"/>
    <portSpacing port="source_through 1" spacing="0"/>
    <portSpacing port="sink_test set results" spacing="0"/>
    <portSpacing port="sink_performance 1" spacing="0"/>
    <portSpacing port="sink_performance 2" spacing="0"/>
    </process>
    </operator>
    <operator activated="true" class="concurrency:cross_validation" compatibility="9.3.001" expanded="true" height="145" name="Cross Validation" width="90" x="313" y="85">
    <parameter key="split_on_batch_attribute" value="false"/>
    <parameter key="leave_one_out" value="false"/>
    <parameter key="number_of_folds" value="10"/>
    <parameter key="sampling_type" value="automatic"/>
    <parameter key="use_local_random_seed" value="false"/>
    <parameter key="local_random_seed" value="1992"/>
    <parameter key="enable_parallel_execution" value="true"/>
    <process expanded="true">
    <operator activated="true" class="k_nn" compatibility="9.3.001" expanded="true" height="82" name="k-NN" width="90" x="112" y="34">
    <parameter key="k" value="5"/>
    <parameter key="weighted_vote" value="true"/>
    <parameter key="measure_types" value="MixedMeasures"/>
    <parameter key="mixed_measure" value="MixedEuclideanDistance"/>
    <parameter key="nominal_measure" value="NominalDistance"/>
    <parameter key="numerical_measure" value="EuclideanDistance"/>
    <parameter key="divergence" value="GeneralizedIDivergence"/>
    <parameter key="kernel_type" value="radial"/>
    <parameter key="kernel_gamma" value="1.0"/>
    <parameter key="kernel_sigma1" value="1.0"/>
    <parameter key="kernel_sigma2" value="0.0"/>
    <parameter key="kernel_sigma3" value="2.0"/>
    <parameter key="kernel_degree" value="3.0"/>
    <parameter key="kernel_shift" value="1.0"/>
    <parameter key="kernel_a" value="1.0"/>
    <parameter key="kernel_b" value="0.0"/>
    </operator>
    <connect from_port="training set" to_op="k-NN" to_port="training set"/>
    <connect from_op="k-NN" from_port="model" to_port="model"/>
    <portSpacing port="source_training set" 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="9.3.001" expanded="true" height="82" name="Apply Model" width="90" x="45" y="34">
    <list key="application_parameters"/>
    <parameter key="create_view" value="false"/>
    </operator>
    <operator activated="true" class="performance_classification" compatibility="9.3.001" expanded="true" height="82" name="Performance" width="90" x="179" y="34">
    <parameter key="main_criterion" value="first"/>
    <parameter key="accuracy" value="true"/>
    <parameter key="classification_error" value="false"/>
    <parameter key="kappa" value="false"/>
    <parameter key="weighted_mean_recall" value="false"/>
    <parameter key="weighted_mean_precision" value="false"/>
    <parameter key="spearman_rho" value="false"/>
    <parameter key="kendall_tau" value="false"/>
    <parameter key="absolute_error" value="false"/>
    <parameter key="relative_error" value="false"/>
    <parameter key="relative_error_lenient" value="false"/>
    <parameter key="relative_error_strict" value="false"/>
    <parameter key="normalized_absolute_error" value="false"/>
    <parameter key="root_mean_squared_error" value="false"/>
    <parameter key="root_relative_squared_error" value="false"/>
    <parameter key="squared_error" value="false"/>
    <parameter key="correlation" value="false"/>
    <parameter key="squared_correlation" value="true"/>
    <parameter key="cross-entropy" value="false"/>
    <parameter key="margin" value="false"/>
    <parameter key="soft_margin_loss" value="false"/>
    <parameter key="logistic_loss" value="false"/>
    <parameter key="skip_undefined_labels" value="true"/>
    <parameter key="use_example_weights" value="true"/>
    <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="performance 1"/>
    <portSpacing port="source_model" spacing="0"/>
    <portSpacing port="source_test set" spacing="0"/>
    <portSpacing port="source_through 1" spacing="0"/>
    <portSpacing port="sink_test set results" spacing="0"/>
    <portSpacing port="sink_performance 1" spacing="0"/>
    <portSpacing port="sink_performance 2" spacing="0"/>
    </process>
    </operator>
    <operator activated="true" class="model_simulator:robust_performance_average" compatibility="9.3.001" expanded="true" height="124" name="Performance Average (Robust)" width="90" x="514" y="85"/>
    <connect from_op="Retrieve Titanic Training" from_port="output" to_op="Multiply" to_port="input"/>
    <connect from_op="Multiply" from_port="output 1" to_op="Cross Validation" to_port="example set"/>
    <connect from_op="Multiply" from_port="output 2" to_op="Cross Validation (2)" to_port="example set"/>
    <connect from_op="Cross Validation (2)" from_port="performance 1" to_op="Performance Average (Robust)" to_port="performance vectors 2"/>
    <connect from_op="Cross Validation" from_port="performance 1" to_op="Performance Average (Robust)" to_port="performance vectors 1"/>
    <connect from_op="Performance Average (Robust)" from_port="average" 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>


    Regards,
    Varun
    https://www.varunmandalapu.com/

    Be Safe. Follow precautions and Maintain Social Distancing

  • svjosvjo Member Posts: 4 Newbie
    I used a saved auto model process and copied the Performance for Hold-out sets and the performance average into my own process. So the standard criteria are used (RMSE, absolute error, relative error lenient, squared error and correlation). I added squared correlation to the list. I works if I leave squared correlation out.
  • sgenzersgenzer 12Administrator, Moderator, Employee, RapidMiner Certified Analyst, Community Manager, Member, University Professor, PM Moderator Posts: 2,959  Community Manager
    thank you @varunm1!
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