RapidMiner

Optimizing Set Macro on 7.5

RM Certified Expert
RM Certified Expert

Optimizing Set Macro on 7.5

Is anyone else finding problems optimizing Set Macro in version 7.5 of RapidMiner?  

Was trying to optimize a python model & found that the value parametrer of Set Macro doesn't appear in Optimize Evolutionary.  

 

 

<?xml version="1.0" encoding="UTF-8"?><process version="7.5.001">
  <context>
    <input/>
    <output/>
    <macros/>
  </context>
  <operator activated="true" class="process" compatibility="7.5.001" expanded="true" name="Process">
    <process expanded="true">
      <operator activated="true" class="retrieve" compatibility="7.5.001" expanded="true" height="68" name="Retrieve Iris" width="90" x="45" y="34">
        <parameter key="repository_entry" value="//Samples/data/Iris"/>
      </operator>
      <operator activated="true" class="optimize_parameters_evolutionary" compatibility="6.0.003" expanded="true" height="103" name="Optimize Parameters (Evolutionary)" width="90" x="313" y="34">
        <list key="parameters">
          <parameter key="nTree.value" value="[1.0;100.0]"/>
        </list>
        <process expanded="true">
          <operator activated="true" class="subprocess" compatibility="7.5.001" expanded="true" height="82" name="Hyperparameters" width="90" x="112" y="34">
            <process expanded="true">
              <operator activated="true" class="set_macro" compatibility="7.5.001" expanded="true" height="82" name="nTree" width="90" x="45" y="34">
                <parameter key="macro" value="nTree"/>
                <parameter key="value" value="200"/>
              </operator>
              <operator activated="true" class="set_macro" compatibility="7.5.001" expanded="true" height="82" name="minSizeSplit" width="90" x="246" y="34">
                <parameter key="macro" value="minSizeSplit"/>
                <parameter key="value" value="4"/>
              </operator>
              <operator activated="true" class="set_macro" compatibility="7.5.001" expanded="true" height="82" name="minLeafSize" width="90" x="45" y="289">
                <parameter key="macro" value="minLeafSize"/>
                <parameter key="value" value="2"/>
              </operator>
              <operator activated="true" class="set_macro" compatibility="7.5.001" expanded="true" height="82" name="maxDepth" width="90" x="45" y="391">
                <parameter key="macro" value="maxDepth"/>
                <parameter key="value" value="20"/>
              </operator>
              <connect from_port="in 1" to_op="nTree" to_port="through 1"/>
              <connect from_op="nTree" from_port="through 1" to_op="minSizeSplit" to_port="through 1"/>
              <connect from_op="minSizeSplit" from_port="through 1" to_op="minLeafSize" to_port="through 1"/>
              <connect from_op="minLeafSize" from_port="through 1" to_op="maxDepth" to_port="through 1"/>
              <connect from_op="maxDepth" from_port="through 1" to_port="out 1"/>
              <portSpacing port="source_in 1" spacing="0"/>
              <portSpacing port="source_in 2" spacing="0"/>
              <portSpacing port="sink_out 1" spacing="0"/>
              <portSpacing port="sink_out 2" spacing="0"/>
            </process>
          </operator>
          <operator activated="true" class="select_attributes" compatibility="7.5.001" expanded="true" height="82" name="Select Attributes" width="90" x="246" y="34">
            <parameter key="attribute_filter_type" value="single"/>
            <parameter key="attribute" value="id"/>
            <parameter key="invert_selection" value="true"/>
            <parameter key="include_special_attributes" value="true"/>
          </operator>
          <operator activated="true" class="concurrency:cross_validation" compatibility="7.5.001" expanded="true" height="145" name="Cross Validation 2" width="90" x="447" y="34">
            <parameter key="use_local_random_seed" value="true"/>
            <process expanded="true">
              <operator activated="true" class="python_scripting:execute_python" compatibility="7.4.000" expanded="true" height="82" name="BDT (sklearn)" width="90" x="112" y="34">
                <parameter key="script" value="&#10;import pandas as pd&#10;from sklearn.ensemble import GradientBoostingClassifier&#10;from sklearn.ensemble import RandomForestClassifier #use RandomForestRegressor for regression problem&#10;&#10;# This script creates a RandomForestClassifier from SKLearn on RM data&#10;# It can be used as a generic template for other sklearn classifiers or regressors&#10;&#10;def rm_main(data):&#10;    metadata =  data.rm_metadata&#10;&#10;    # Get the list of regular attributes and the label&#10;    df = pd.DataFrame(metadata).T&#10;    label = df[df[1]==&quot;label&quot;].index.values&#10;    regular = df[df[1] != df[1]].index.values&#10;&#10;    # === RandomForest === #&#10;    # Assumed you have, X (predictor) and Y (target) for training data set and x_test(predictor) of test_dataset&#10;    # Create Random Forest object&#10;    model= RandomForestClassifier(n_estimators = %{nTree}&#10;                                , max_depth = %{maxDepth}&#10;                                , min_samples_split = %{minSizeSplit} # The minimum number of samples required to split an internal node&#10;                                , min_samples_leaf = %{minLeafSize} # The minimum number of samples required to be at a leaf node&#10;                                &#10;                                 )&#10;    # Train the model using the training sets and check score&#10;    # model.fit(X, y)&#10;    model.fit(data[regular], data[label])&#10;    # Predict Output&#10;    # predicted = model.predict(x_test)&#10;    return (model,regular,label[0]), data"/>
              </operator>
              <connect from_port="training set" to_op="BDT (sklearn)" to_port="input 1"/>
              <connect from_op="BDT (sklearn)" from_port="output 1" 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="python_scripting:execute_python" compatibility="7.4.000" expanded="true" height="103" name="Apply Model (2)" width="90" x="112" y="34">
                <parameter key="script" value="import pandas as pd&#10;&#10;# rm_main is a mandatory function,&#10;# the number of arguments has to be the number of input ports (can be none)&#10;&#10;def rm_main(rfinfo, data):&#10;    rf = rfinfo[0]&#10;    regular = rfinfo[1]&#10;    label = rfinfo[2]&#10;    meta = data.rm_metadata&#10;    predictions = rf.predict(data[regular])&#10;    confidences = rf.predict_proba(data[regular])&#10;&#10;&#10;    predictions = pd.DataFrame(predictions, columns=[&quot;prediction(&quot;+label+&quot;)&quot;])&#10;    confidences = pd.DataFrame(confidences,&#10;                               columns=[&quot;confidence(&quot; + str(c) + &quot;)&quot; for c in rf.classes_])&#10;&#10;    data = data.join(predictions)&#10;    data = data.join(confidences)&#10;    data.rm_metadata = meta&#10;    data.rm_metadata[&quot;prediction(&quot;+label+&quot;)&quot;] = (&quot;nominal&quot;,&quot;prediction&quot;)&#10;&#10;    for c in rf.classes_:&#10;        data.rm_metadata[&quot;confidence(&quot;+str(c)+&quot;)&quot;] = (&quot;numerical&quot;,&quot;confidence_&quot;+str(c))&#10;&#10;    return data, rf"/>
              </operator>
              <operator activated="true" class="performance_classification" compatibility="7.5.001" expanded="true" height="82" name="Python" width="90" x="246" y="34">
                <list key="class_weights"/>
              </operator>
              <connect from_port="model" to_op="Apply Model (2)" to_port="input 1"/>
              <connect from_port="test set" to_op="Apply Model (2)" to_port="input 2"/>
              <connect from_op="Apply Model (2)" from_port="output 1" to_op="Python" to_port="labelled data"/>
              <connect from_op="Python" 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>
            <description align="center" color="transparent" colored="false" width="126">Python</description>
          </operator>
          <operator activated="true" class="log" compatibility="7.5.001" expanded="true" height="82" name="Log" width="90" x="715" y="30">
            <list key="log">
              <parameter key="Count" value="operator.Apply Model (2).value.applycount"/>
              <parameter key=" Testing Error" value="operator.Cross Validation 2.value.performance 1"/>
              <parameter key="Training StdDev" value="operator.Cross Validation 2.value.std deviation 1"/>
              <parameter key="nTree" value="operator.nTree.parameter.value"/>
              <parameter key="maxDepth" value="operator.maxDepth.parameter.value"/>
              <parameter key="minLeafSize" value="operator.minLeafSize.parameter.value"/>
              <parameter key="minSizeSplit" value="operator.minSizeSplit.parameter.value"/>
            </list>
          </operator>
          <connect from_port="input 1" to_op="Hyperparameters" to_port="in 1"/>
          <connect from_op="Hyperparameters" from_port="out 1" to_op="Select Attributes" to_port="example set input"/>
          <connect from_op="Select Attributes" from_port="example set output" to_op="Cross Validation 2" to_port="example set"/>
          <connect from_op="Cross Validation 2" from_port="performance 1" to_op="Log" to_port="through 1"/>
          <connect from_op="Log" from_port="through 1" to_port="performance"/>
          <portSpacing port="source_input 1" spacing="0"/>
          <portSpacing port="source_input 2" spacing="0"/>
          <portSpacing port="sink_performance" spacing="0"/>
          <portSpacing port="sink_result 1" spacing="0"/>
        </process>
      </operator>
      <connect from_op="Retrieve Iris" from_port="output" to_op="Optimize Parameters (Evolutionary)" to_port="input 1"/>
      <connect from_op="Optimize Parameters (Evolutionary)" from_port="performance" to_port="result 1"/>
      <connect from_op="Optimize Parameters (Evolutionary)" from_port="parameter" 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>
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2 REPLIES
RM Certified Expert
RM Certified Expert

Re: Optimizing Set Macro on 7.5

I think this isn't a bug because the Evolutionary optimzer uses the genetic parameters to 'randomly' assign values, so you can't take a Grid approach this. Did you try this in a regular Grid optmizer?

Highlighted
RM Certified Expert
RM Certified Expert

Re: Optimizing Set Macro on 7.5

If not a bug then it's a missing feature. I've managed to create a workaround which works, but is clearly not the most efficient. Let's move this thread into feature requests. 

 

Edit: realise my process didn't display properly.  

 

As you can see, the workaround uses RM modelling operators to represent the values that I want to change in the Python code.  So the feature I'd like is an operator which Optimize Parameters Evolutionary can access allowing values to be set and used by macros.  

 

<?xml version="1.0" encoding="UTF-8"?><process version="7.5.001">
  <context>
    <input/>
    <output/>
    <macros/>
  </context>
  <operator activated="true" class="process" compatibility="7.5.001" expanded="true" name="Process">
    <process expanded="true">
      <operator activated="true" class="retrieve" compatibility="7.5.001" expanded="true" height="68" name="Retrieve Iris" width="90" x="45" y="34">
        <parameter key="repository_entry" value="//Samples/data/Iris"/>
      </operator>
      <operator activated="true" class="optimize_parameters_evolutionary" compatibility="6.0.003" expanded="true" height="124" name="Optimize Parameters (Evolutionary)" width="90" x="313" y="34">
        <list key="parameters">
          <parameter key="parameterSet1.number_of_trees" value="[1.0;100.0]"/>
          <parameter key="parameterSet1.maximal_depth" value="[-1.0;100.0]"/>
          <parameter key="parameterSet1.minimal_leaf_size" value="[1.0;100.0]"/>
          <parameter key="parameterSet1.minimal_size_for_split" value="[1.0;100.0]"/>
        </list>
        <parameter key="use_early_stopping" value="true"/>
        <parameter key="population_size" value="3"/>
        <process expanded="true">
          <operator activated="true" class="subprocess" compatibility="7.5.001" expanded="true" height="103" name="Hyperparameters" width="90" x="45" y="34">
            <process expanded="true">
              <operator activated="true" class="generate_data" compatibility="7.5.001" expanded="true" height="68" name="Generate Data" width="90" x="45" y="34">
                <parameter key="target_function" value="random classification"/>
              </operator>
              <operator activated="true" class="concurrency:parallel_random_forest" compatibility="7.5.001" expanded="true" height="82" name="parameterSet1" width="90" x="179" y="136">
                <parameter key="number_of_trees" value="57"/>
                <parameter key="maximal_depth" value="94"/>
                <parameter key="minimal_leaf_size" value="7"/>
                <parameter key="minimal_size_for_split" value="70"/>
              </operator>
              <operator activated="true" class="operator_toolbox:get_parameters" compatibility="0.3.000" expanded="true" height="103" name="Get Parameters" width="90" x="313" y="85">
                <parameter key="Operator name" value="parameterSet1"/>
              </operator>
              <operator activated="false" class="set_macro" compatibility="7.5.001" expanded="true" height="82" name="nTree" width="90" x="447" y="493">
                <parameter key="macro" value="nTree"/>
                <parameter key="value" value="200"/>
              </operator>
              <operator activated="false" class="set_macro" compatibility="7.5.001" expanded="true" height="82" name="minSizeSplit" width="90" x="581" y="493">
                <parameter key="macro" value="minSizeSplit"/>
                <parameter key="value" value="4"/>
              </operator>
              <operator activated="false" class="set_macro" compatibility="7.5.001" expanded="true" height="82" name="minLeafSize" width="90" x="715" y="493">
                <parameter key="macro" value="minLeafSize"/>
                <parameter key="value" value="2"/>
              </operator>
              <operator activated="false" class="set_macro" compatibility="7.5.001" expanded="true" height="103" name="maxDepth" width="90" x="849" y="493">
                <parameter key="macro" value="maxDepth"/>
                <parameter key="value" value="20"/>
              </operator>
              <operator activated="true" class="converters:parameter_set_2_example_set" compatibility="0.3.000" expanded="true" height="103" name="Parameter Set to ExampleSet" width="90" x="447" y="85"/>
              <operator activated="true" class="extract_macro" compatibility="7.5.001" expanded="true" height="68" name="Extract Macro" width="90" x="648" y="85">
                <parameter key="macro" value="nTree"/>
                <parameter key="macro_type" value="data_value"/>
                <parameter key="attribute_name" value="parameterSet1.number_of_trees"/>
                <parameter key="example_index" value="1"/>
                <list key="additional_macros">
                  <parameter key="minSizeSplit" value="parameterSet1.minimal_size_for_split"/>
                  <parameter key="minLeafSize" value="parameterSet1.minimal_leaf_size"/>
                  <parameter key="maxDepth" value="parameterSet1.maximal_depth"/>
                </list>
                <description align="center" color="transparent" colored="false" width="126">Extracts the parameters to macro values</description>
              </operator>
              <connect from_port="in 1" to_port="out 1"/>
              <connect from_op="Generate Data" from_port="output" to_op="parameterSet1" to_port="training set"/>
              <connect from_op="parameterSet1" from_port="model" to_op="Get Parameters" to_port="through 1"/>
              <connect from_op="Get Parameters" from_port="parameters" to_op="Parameter Set to ExampleSet" to_port="parameters"/>
              <connect from_op="nTree" from_port="through 1" to_op="minSizeSplit" to_port="through 1"/>
              <connect from_op="minSizeSplit" from_port="through 1" to_op="minLeafSize" to_port="through 1"/>
              <connect from_op="minLeafSize" from_port="through 1" to_op="maxDepth" to_port="through 1"/>
              <connect from_op="maxDepth" from_port="through 1" to_op="maxDepth" to_port="through 2"/>
              <connect from_op="Parameter Set to ExampleSet" from_port="exampleSet" to_op="Extract Macro" to_port="example set"/>
              <connect from_op="Extract Macro" from_port="example set" to_port="out 2"/>
              <portSpacing port="source_in 1" spacing="0"/>
              <portSpacing port="source_in 2" spacing="0"/>
              <portSpacing port="sink_out 1" spacing="0"/>
              <portSpacing port="sink_out 2" spacing="0"/>
              <portSpacing port="sink_out 3" spacing="0"/>
            </process>
          </operator>
          <operator activated="true" class="select_attributes" compatibility="7.5.001" expanded="true" height="82" name="Select Attributes" width="90" x="246" y="34">
            <parameter key="attribute_filter_type" value="single"/>
            <parameter key="attribute" value="id"/>
            <parameter key="invert_selection" value="true"/>
            <parameter key="include_special_attributes" value="true"/>
          </operator>
          <operator activated="true" class="concurrency:cross_validation" compatibility="7.5.001" expanded="true" height="145" name="Cross Validation 2" width="90" x="447" y="34">
            <parameter key="use_local_random_seed" value="true"/>
            <process expanded="true">
              <operator activated="true" class="python_scripting:execute_python" compatibility="7.4.000" expanded="true" height="82" name="Random Forest" width="90" x="112" y="34">
                <parameter key="script" value="&#10;import pandas as pd&#10;from sklearn.ensemble import GradientBoostingClassifier&#10;from sklearn.ensemble import RandomForestClassifier #use RandomForestRegressor for regression problem&#10;&#10;# This script creates a RandomForestClassifier from SKLearn on RM data&#10;# It can be used as a generic template for other sklearn classifiers or regressors&#10;&#10;def rm_main(data):&#10;    metadata =  data.rm_metadata&#10;&#10;    # Get the list of regular attributes and the label&#10;    df = pd.DataFrame(metadata).T&#10;    label = df[df[1]==&quot;label&quot;].index.values&#10;    regular = df[df[1] != df[1]].index.values&#10;&#10;    # === RandomForest === #&#10;    # Assumed you have, X (predictor) and Y (target) for training data set and x_test(predictor) of test_dataset&#10;    # Create Random Forest object&#10;    model= RandomForestClassifier(n_estimators = %{nTree}&#10;                                , max_depth = %{maxDepth}&#10;                                , min_samples_split = %{minSizeSplit} # The minimum number of samples required to split an internal node&#10;                                , min_samples_leaf = %{minLeafSize} # The minimum number of samples required to be at a leaf node&#10;                                , random_state = 1992&#10;                                 )&#10;    # Train the model using the training sets and check score&#10;    # model.fit(X, y)&#10;    model.fit(data[regular], data[label])&#10;    # Predict Output&#10;    # predicted = model.predict(x_test)&#10;    return (model,regular,label[0]), data"/>
              </operator>
              <connect from_port="training set" to_op="Random Forest" to_port="input 1"/>
              <connect from_op="Random Forest" from_port="output 1" 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="python_scripting:execute_python" compatibility="7.4.000" expanded="true" height="103" name="Apply Model (2)" width="90" x="112" y="34">
                <parameter key="script" value="import pandas as pd&#10;&#10;# rm_main is a mandatory function,&#10;# the number of arguments has to be the number of input ports (can be none)&#10;&#10;def rm_main(rfinfo, data):&#10;    rf = rfinfo[0]&#10;    regular = rfinfo[1]&#10;    label = rfinfo[2]&#10;    meta = data.rm_metadata&#10;    predictions = rf.predict(data[regular])&#10;    confidences = rf.predict_proba(data[regular])&#10;&#10;&#10;    predictions = pd.DataFrame(predictions, columns=[&quot;prediction(&quot;+label+&quot;)&quot;])&#10;    confidences = pd.DataFrame(confidences,&#10;                               columns=[&quot;confidence(&quot; + str(c) + &quot;)&quot; for c in rf.classes_])&#10;&#10;    data = data.join(predictions)&#10;    data = data.join(confidences)&#10;    data.rm_metadata = meta&#10;    data.rm_metadata[&quot;prediction(&quot;+label+&quot;)&quot;] = (&quot;nominal&quot;,&quot;prediction&quot;)&#10;&#10;    for c in rf.classes_:&#10;        data.rm_metadata[&quot;confidence(&quot;+str(c)+&quot;)&quot;] = (&quot;numerical&quot;,&quot;confidence_&quot;+str(c))&#10;&#10;    return data, rf"/>
              </operator>
              <operator activated="true" class="performance_classification" compatibility="7.5.001" expanded="true" height="82" name="Python" width="90" x="246" y="34">
                <list key="class_weights"/>
              </operator>
              <connect from_port="model" to_op="Apply Model (2)" to_port="input 1"/>
              <connect from_port="test set" to_op="Apply Model (2)" to_port="input 2"/>
              <connect from_op="Apply Model (2)" from_port="output 1" to_op="Python" to_port="labelled data"/>
              <connect from_op="Python" 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>
            <description align="center" color="transparent" colored="false" width="126">Python</description>
          </operator>
          <operator activated="true" class="subprocess" compatibility="7.5.001" expanded="true" height="82" name="Extract Performance Log" width="90" x="648" y="187">
            <process expanded="true">
              <operator activated="true" class="provide_macro_as_log_value" compatibility="7.5.001" expanded="true" height="82" name="LognTree" width="90" x="45" y="34">
                <parameter key="macro_name" value="nTree"/>
              </operator>
              <operator activated="true" class="provide_macro_as_log_value" compatibility="7.5.001" expanded="true" height="82" name="Log maxDepth" width="90" x="179" y="34">
                <parameter key="macro_name" value="maxDepth"/>
              </operator>
              <operator activated="true" class="provide_macro_as_log_value" compatibility="7.5.001" expanded="true" height="82" name="Log minLeafSize" width="90" x="313" y="34">
                <parameter key="macro_name" value="minLeafSize"/>
              </operator>
              <operator activated="true" class="provide_macro_as_log_value" compatibility="7.5.001" expanded="true" height="82" name="Log minSizeSplit" width="90" x="447" y="34">
                <parameter key="macro_name" value="minSizeSplit"/>
              </operator>
              <operator activated="true" class="log" compatibility="7.5.001" expanded="true" height="82" name="Log" width="90" x="581" y="34">
                <list key="log">
                  <parameter key="Count" value="operator.Apply Model (2).value.applycount"/>
                  <parameter key=" Testing Error" value="operator.Cross Validation 2.value.performance 1"/>
                  <parameter key="Training StdDev" value="operator.Cross Validation 2.value.std deviation 1"/>
                  <parameter key="maxDepth" value="operator.Log maxDepth.value.macro_value"/>
                  <parameter key="minLeafSize" value="operator.Log minLeafSize.value.macro_value"/>
                  <parameter key="minSizeSplit" value="operator.Log minSizeSplit.value.macro_value"/>
                  <parameter key="Number of Trees" value="operator.LognTree.value.macro_value"/>
                </list>
              </operator>
              <connect from_port="in 1" to_op="LognTree" to_port="through 1"/>
              <connect from_op="LognTree" from_port="through 1" to_op="Log maxDepth" to_port="through 1"/>
              <connect from_op="Log maxDepth" from_port="through 1" to_op="Log minLeafSize" to_port="through 1"/>
              <connect from_op="Log minLeafSize" from_port="through 1" to_op="Log minSizeSplit" to_port="through 1"/>
              <connect from_op="Log minSizeSplit" from_port="through 1" to_op="Log" to_port="through 1"/>
              <connect from_op="Log" from_port="through 1" to_port="out 1"/>
              <portSpacing port="source_in 1" spacing="0"/>
              <portSpacing port="source_in 2" spacing="0"/>
              <portSpacing port="sink_out 1" spacing="0"/>
              <portSpacing port="sink_out 2" spacing="0"/>
            </process>
          </operator>
          <connect from_port="input 1" to_op="Hyperparameters" to_port="in 1"/>
          <connect from_op="Hyperparameters" from_port="out 1" to_op="Select Attributes" to_port="example set input"/>
          <connect from_op="Select Attributes" from_port="example set output" to_op="Cross Validation 2" to_port="example set"/>
          <connect from_op="Cross Validation 2" from_port="model" to_port="result 1"/>
          <connect from_op="Cross Validation 2" from_port="performance 1" to_op="Extract Performance Log" to_port="in 1"/>
          <connect from_op="Extract Performance Log" from_port="out 1" to_port="performance"/>
          <portSpacing port="source_input 1" spacing="0"/>
          <portSpacing port="source_input 2" spacing="0"/>
          <portSpacing port="sink_performance" spacing="0"/>
          <portSpacing port="sink_result 1" spacing="0"/>
          <portSpacing port="sink_result 2" spacing="0"/>
        </process>
      </operator>
      <connect from_op="Retrieve Iris" from_port="output" to_op="Optimize Parameters (Evolutionary)" to_port="input 1"/>
      <connect from_op="Optimize Parameters (Evolutionary)" from_port="performance" to_port="result 1"/>
      <connect from_op="Optimize Parameters (Evolutionary)" from_port="parameter" 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>

 

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