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Can I use the 'Model Simulator' operator using a model from the 'Multi Label Modeling' operator?

User: "kimjk3559"
kimjk3559
Altair Community Member
Updated by Jocelyn
We were able to successfully obtain predicted values for data with two labels (labels independent of each other) using the 'Multi Label Modeling' operator.
However, when I connect to ‘mod’ of ‘Model Simulator’, a ‘Wrong Model Type’ error message appears.
Ultimately, I wanted to set specific target values for the two labels and check the values of the remaining variables. Is there another way?

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    User: "MartinLiebig"
    MartinLiebig
    Altair Employee
    Accepted Answer
    Hi,
    the model simulator was build to only show one model, it does not work with a multi-label one.

    There is actualy no operator to extract a single model from the multi-label one which you can then use. I've created a small script which does it for you. I hope that helps!

    Best,
    Martin 

    <?xml version="1.0" encoding="UTF-8"?><process version="10.2.000">

      <context>

        <input/>

        <output/>

        <macros/>

      </context>

      <operator activated="true" class="process" compatibility="10.2.000" expanded="true" name="Process" origin="GENERATED_TUTORIAL">

        <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="10.2.000" expanded="true" height="68" name="Retrieve Titanic" origin="GENERATED_TUTORIAL" width="90" x="45" y="187">

            <parameter key="repository_entry" value="//Samples/data/Titanic"/>

          </operator>

          <operator activated="true" class="replace_missing_values" compatibility="10.2.000" expanded="true" height="103" name="Replace Missing Values" width="90" x="179" y="187">

            <parameter key="return_preprocessing_model" value="false"/>

            <parameter key="attribute_filter_type" value="all"/>

            <parameter key="attribute" value=""/>

            <parameter key="attributes" value=""/>

            <parameter key="use_except_expression" value="false"/>

            <parameter key="value_type" value="attribute_value"/>

            <parameter key="use_value_type_exception" value="false"/>

            <parameter key="except_value_type" value="time"/>

            <parameter key="block_type" value="attribute_block"/>

            <parameter key="use_block_type_exception" value="false"/>

            <parameter key="except_block_type" value="value_matrix_row_start"/>

            <parameter key="invert_selection" value="false"/>

            <parameter key="include_special_attributes" value="false"/>

            <parameter key="default" value="average"/>

            <list key="columns"/>

          </operator>

          <operator activated="true" class="blending:set_role" compatibility="10.2.000" expanded="true" height="82" name="Set Role" origin="GENERATED_TUTORIAL" width="90" x="313" y="187">

            <list key="set_roles">

              <parameter key="Port of Embarkation" value="metadata"/>

              <parameter key="Survived" value="metadata"/>

            </list>

          </operator>

          <operator activated="true" class="split_data" compatibility="10.1.003" expanded="true" height="103" name="Split Data" origin="GENERATED_TUTORIAL" width="90" x="447" y="187">

            <enumeration key="partitions">

              <parameter key="ratio" value="0.7"/>

              <parameter key="ratio" value="0.3"/>

            </enumeration>

            <parameter key="sampling_type" value="automatic"/>

            <parameter key="use_local_random_seed" value="false"/>

            <parameter key="local_random_seed" value="1992"/>

          </operator>

          <operator activated="true" class="time_series:multi_label_model_learner" compatibility="10.1.000" expanded="true" height="82" name="Multi Label Modeling" origin="GENERATED_TUTORIAL" width="90" x="648" y="34">

            <parameter key="attribute_filter_type" value="subset"/>

            <parameter key="attribute" value=""/>

            <parameter key="attributes" value="Port of Embarkation|Survived|Age"/>

            <parameter key="use_except_expression" value="false"/>

            <parameter key="value_type" value="attribute_value"/>

            <parameter key="use_value_type_exception" value="false"/>

            <parameter key="except_value_type" value="time"/>

            <parameter key="block_type" value="attribute_block"/>

            <parameter key="use_block_type_exception" value="false"/>

            <parameter key="except_block_type" value="value_matrix_row_start"/>

            <parameter key="invert_selection" value="false"/>

            <parameter key="include_special_attributes" value="true"/>

            <parameter key="add_macros" value="true"/>

            <parameter key="current_label_name_macro" value="current_label_attribute"/>

            <parameter key="current_label_type_macro" value="current_label_type"/>

            <parameter key="enable_parallel_execution" value="true"/>

            <process expanded="true">

              <operator activated="true" class="h2o:gradient_boosted_trees" compatibility="10.2.000" expanded="true" height="103" name="Gradient Boosted Trees" width="90" x="246" y="34">

                <parameter key="number_of_trees" value="3"/>

                <parameter key="reproducible" value="false"/>

                <parameter key="maximum_number_of_threads" value="4"/>

                <parameter key="use_local_random_seed" value="false"/>

                <parameter key="local_random_seed" value="1992"/>

                <parameter key="maximal_depth" value="5"/>

                <parameter key="min_rows" value="10.0"/>

                <parameter key="min_split_improvement" value="1.0E-5"/>

                <parameter key="number_of_bins" value="20"/>

                <parameter key="learning_rate" value="0.01"/>

                <parameter key="sample_rate" value="1.0"/>

                <parameter key="distribution" value="AUTO"/>

                <parameter key="early_stopping" value="false"/>

                <parameter key="stopping_rounds" value="1"/>

                <parameter key="stopping_metric" value="AUTO"/>

                <parameter key="stopping_tolerance" value="0.001"/>

                <list key="monotone_constraints"/>

                <parameter key="max_runtime_seconds" value="0"/>

                <list key="expert_parameters"/>

              </operator>

              <connect from_port="training set" to_op="Gradient Boosted Trees" to_port="training set"/>

              <connect from_op="Gradient Boosted Trees" from_port="model" to_port="model"/>

              <portSpacing port="source_training set" spacing="0"/>

              <portSpacing port="source_input 1" spacing="0"/>

              <portSpacing port="sink_model" spacing="0"/>

              <portSpacing port="sink_output 1" spacing="0"/>

            </process>

          </operator>

          <operator activated="true" class="execute_script" compatibility="10.2.000" expanded="true" height="82" name="Execute Script" width="90" x="782" y="34">

            <parameter key="script" value="&#10;import java.util.HashMap;&#10;&#10;import com.rapidminer.operator.IOObjectCollection;&#10;import com.rapidminer.operator.learner.PredictionModel;&#10;import com.rapidminer.extension.timeseries.operator.multi_label_model.MultiLabelModel;&#10;&#10;MultiLabelModel model = input[0];&#10;HashMap&lt;String,PredictionModel&gt; map = model.labelModelMap;&#10;IOObjectCollection&lt;PredictionModel&gt; col = new IOObjectCollection&lt;&gt;();&#10;for(String key : map.keySet()){&#10;&#9;&#9;col.add(map.get(key));&#10;}&#10;&#9;&#9;&#10;&#10;return col;"/>

            <parameter key="standard_imports" value="true"/>

          </operator>

          <operator activated="true" class="select" compatibility="10.2.000" expanded="true" height="68" name="Select" width="90" x="916" y="34">

            <parameter key="index" value="1"/>

            <parameter key="unfold" value="false"/>

          </operator>

          <operator activated="true" class="model_simulator:model_simulator" compatibility="10.2.000" expanded="true" height="103" name="Model Simulator" width="90" x="1117" y="187"/>

          <connect from_op="Retrieve Titanic" from_port="output" to_op="Replace Missing Values" to_port="example set input"/>

          <connect from_op="Replace Missing Values" from_port="example set output" to_op="Set Role" to_port="example set input"/>

          <connect from_op="Set Role" from_port="example set output" to_op="Split Data" to_port="example set"/>

          <connect from_op="Split Data" from_port="partition 1" to_op="Multi Label Modeling" to_port="training set"/>

          <connect from_op="Split Data" from_port="partition 2" to_op="Model Simulator" to_port="training data"/>

          <connect from_op="Multi Label Modeling" from_port="model" to_op="Execute Script" to_port="input 1"/>

          <connect from_op="Execute Script" from_port="output 1" to_op="Select" to_port="collection"/>

          <connect from_op="Select" from_port="selected" to_op="Model Simulator" to_port="model"/>

          <connect from_op="Model Simulator" from_port="simulator 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>



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