re-use of previous calculation (intermediate result)

Legacy User

Hello,

in my model I would like to iterate through the maximum number of generations. Does rapidminer re-use the  results from the previous step in order to save calculation time? I mean after finishing the calculation with only one feature (and saving this result to file, or something similar) it can directly continue to add the next attributes to the best n-features and continue instead of starting again from 1 for the next iteration ...


<operator name="Root" class="Process" expanded="yes">
    <parameter key="logfile" value="logfile.log"/>
    <parameter key="resultfile" value="result.res"/>
    <operator name="ExcelExampleSource" class="ExcelExampleSource">
        <parameter key="excel_file" value="Data.xls"/>
        <parameter key="first_row_as_names" value="true"/>
        <parameter key="create_label" value="true"/>
    </operator>
    <operator name="ParameterIteration" class="ParameterIteration" expanded="yes">
        <list key="parameters">
          <parameter key="FeatureSelection.maximum_number_of_generations" value="1,2,3,4"/>
        </list>
        <operator name="FeatureSelection" class="FeatureSelection" expanded="yes">
            <parameter key="show_population_plotter" value="true"/>
            <parameter key="plot_generations" value="1"/>
            <parameter key="keep_best" value="10"/>
            <parameter key="maximum_number_of_generations" value="3"/>
            <operator name="LinearRegression" class="LinearRegression">
                <parameter key="keep_example_set" value="true"/>
                <parameter key="feature_selection" value="none"/>
            </operator>
            <operator name="ModelApplier" class="ModelApplier">
                <parameter key="keep_model" value="true"/>
                <list key="application_parameters">
                </list>
                <parameter key="create_view" value="true"/>
            </operator>
            <operator name="RegressionPerformance" class="RegressionPerformance">
                <parameter key="keep_example_set" value="true"/>
                <parameter key="main_criterion" value="squared_correlation"/>
                <parameter key="correlation" value="true"/>
                <parameter key="squared_correlation" value="true"/>
                <parameter key="skip_undefined_labels" value="false"/>
                <parameter key="use_example_weights" value="false"/>
            </operator>
        </operator>
    </operator>
</operator>

Thanks and Best Regards

Volker

Legacy User

Hello,

... or is it possible / how is it possible to save the best feature combination (for each generation) in a separate file ?

Best Regards

Volker

IngoRM

Hi Volker,

both requests are only possible for the evolutionary feature selection and weighting schemes. There are parameters for "intermediate" weights savings as well as initilizing the operator by input weights. But this is not possible for the greedy feature selection heuristics.

Cheers,
Ingo