Due to recent updates, all users are required to create an Altair One account to login to the RapidMiner community. Click the Register button to create your account using the same email that you have previously used to login to the RapidMiner community. This will ensure that any previously created content will be synced to your Altair One account. Once you login, you will be asked to provide a username that identifies you to other Community users. Email us at Community with questions.

MetaCost and Stacking - no obvious errors

mcomsamcomsa Member Posts: 3 Contributor I
edited December 2018 in Help

Hi,

I tried to run the code below, but the process did not worked and no errors were mentioned. Please, any help/ideas? Thank you.

<?xml version="1.0" encoding="UTF-8"?><process version="7.5.003">
  <context>
    <input/>
    <output/>
    <macros/>
  </context>
  <operator activated="true" class="process" compatibility="7.5.003" expanded="true" name="Process">
    <process expanded="true">
      <operator activated="true" class="retrieve" compatibility="7.5.003" expanded="true" height="68" name="Retrieve Titanic Training" width="90" x="112" y="34">
        <parameter key="repository_entry" value="//Samples/data/Titanic Training"/>
      </operator>
      <operator activated="true" class="split_data" compatibility="7.5.003" expanded="true" height="103" name="Split Data" width="90" x="313" y="34">
        <enumeration key="partitions">
          <parameter key="ratio" value="0.5"/>
          <parameter key="ratio" value="0.5"/>
        </enumeration>
        <parameter key="sampling_type" value="stratified sampling"/>
      </operator>
      <operator activated="true" class="concurrency:cross_validation" compatibility="7.5.003" expanded="true" height="145" name="Validation" width="90" x="514" y="34">
        <parameter key="sampling_type" value="stratified sampling"/>
        <process expanded="true">
          <operator activated="true" class="metacost" compatibility="7.5.003" expanded="true" height="82" name="MetaCost" width="90" x="246" y="34">
            <parameter key="cost_matrix" value="[0.0 8.0;1.0 0.0]"/>
            <process expanded="true">
              <operator activated="true" class="stacking" compatibility="7.5.003" expanded="true" height="68" name="Stacking" width="90" x="246" y="34">
                <process expanded="true">
                  <operator activated="true" class="concurrency:parallel_decision_tree" compatibility="7.5.003" expanded="true" height="82" name="Decision Tree" width="90" x="112" y="34"/>
                  <operator activated="true" class="concurrency:parallel_random_forest" compatibility="7.5.003" expanded="true" height="82" name="Random Forest" width="90" x="313" y="85"/>
                  <operator activated="true" class="h2o:gradient_boosted_trees" compatibility="7.5.000" expanded="true" height="103" name="Gradient Boosted Trees" width="90" x="112" y="136">
                    <list key="expert_parameters"/>
                  </operator>
                  <operator activated="true" class="k_nn" compatibility="7.5.003" expanded="true" height="82" name="k-NN (1)" width="90" x="313" y="187">
                    <parameter key="k" value="2"/>
                  </operator>
                  <operator activated="true" class="k_nn" compatibility="7.5.003" expanded="true" height="82" name="k-NN (2)" width="90" x="112" y="238">
                    <parameter key="k" value="5"/>
                  </operator>
                  <operator activated="true" class="naive_bayes" compatibility="7.5.003" expanded="true" height="82" name="Naive Bayes" width="90" x="313" y="289"/>
                  <operator activated="true" class="naive_bayes_kernel" compatibility="7.5.003" expanded="true" height="82" name="Naive Bayes (Kernel1)" width="90" x="112" y="340"/>
                  <operator activated="true" class="naive_bayes_kernel" compatibility="7.5.003" expanded="true" height="82" name="Naive Bayes (Kernel2)" width="90" x="313" y="391"/>
                  <operator activated="true" class="h2o:deep_learning" compatibility="7.5.000" expanded="true" height="82" name="Deep Learning (1)" width="90" x="112" y="442">
                    <enumeration key="hidden_layer_sizes">
                      <parameter key="hidden_layer_sizes" value="50"/>
                      <parameter key="hidden_layer_sizes" value="50"/>
                    </enumeration>
                    <enumeration key="hidden_dropout_ratios"/>
                    <list key="expert_parameters"/>
                    <list key="expert_parameters_"/>
                  </operator>
                  <operator activated="true" class="h2o:deep_learning" compatibility="7.5.000" expanded="true" height="82" name="Deep Learning (2)" width="90" x="313" y="493">
                    <parameter key="activation" value="RectifierWithDropout"/>
                    <enumeration key="hidden_layer_sizes">
                      <parameter key="hidden_layer_sizes" value="50"/>
                      <parameter key="hidden_layer_sizes" value="50"/>
                    </enumeration>
                    <enumeration key="hidden_dropout_ratios">
                      <parameter key="hidden_dropout_ratio" value="0.5"/>
                      <parameter key="hidden_dropout_ratio" value="0.5"/>
                    </enumeration>
                    <list key="expert_parameters"/>
                    <list key="expert_parameters_"/>
                  </operator>
                  <connect from_port="training set 1" to_op="Decision Tree" to_port="training set"/>
                  <connect from_port="training set 2" to_op="Random Forest" to_port="training set"/>
                  <connect from_port="training set 3" to_op="Gradient Boosted Trees" to_port="training set"/>
                  <connect from_port="training set 4" to_op="k-NN (1)" to_port="training set"/>
                  <connect from_port="training set 5" to_op="k-NN (2)" to_port="training set"/>
                  <connect from_port="training set 6" to_op="Naive Bayes" to_port="training set"/>
                  <connect from_port="training set 7" to_op="Naive Bayes (Kernel1)" to_port="training set"/>
                  <connect from_port="training set 8" to_op="Naive Bayes (Kernel2)" to_port="training set"/>
                  <connect from_port="training set 9" to_op="Deep Learning (1)" to_port="training set"/>
                  <connect from_port="training set 10" to_op="Deep Learning (2)" to_port="training set"/>
                  <connect from_op="Decision Tree" from_port="model" to_port="base model 1"/>
                  <connect from_op="Random Forest" from_port="model" to_port="base model 2"/>
                  <connect from_op="Gradient Boosted Trees" from_port="model" to_port="base model 3"/>
                  <connect from_op="k-NN (1)" from_port="model" to_port="base model 4"/>
                  <connect from_op="k-NN (2)" from_port="model" to_port="base model 5"/>
                  <connect from_op="Naive Bayes" from_port="model" to_port="base model 6"/>
                  <connect from_op="Naive Bayes (Kernel1)" from_port="model" to_port="base model 7"/>
                  <connect from_op="Naive Bayes (Kernel2)" from_port="model" to_port="base model 8"/>
                  <connect from_op="Deep Learning (1)" from_port="model" to_port="base model 9"/>
                  <connect from_op="Deep Learning (2)" from_port="model" to_port="base model 10"/>
                  <portSpacing port="source_training set 1" spacing="0"/>
                  <portSpacing port="source_training set 2" spacing="0"/>
                  <portSpacing port="source_training set 3" spacing="0"/>
                  <portSpacing port="source_training set 4" spacing="0"/>
                  <portSpacing port="source_training set 5" spacing="0"/>
                  <portSpacing port="source_training set 6" spacing="0"/>
                  <portSpacing port="source_training set 7" spacing="0"/>
                  <portSpacing port="source_training set 8" spacing="0"/>
                  <portSpacing port="source_training set 9" spacing="0"/>
                  <portSpacing port="source_training set 10" spacing="0"/>
                  <portSpacing port="source_training set 11" spacing="0"/>
                  <portSpacing port="sink_base model 1" spacing="0"/>
                  <portSpacing port="sink_base model 2" spacing="0"/>
                  <portSpacing port="sink_base model 3" spacing="0"/>
                  <portSpacing port="sink_base model 4" spacing="0"/>
                  <portSpacing port="sink_base model 5" spacing="0"/>
                  <portSpacing port="sink_base model 6" spacing="0"/>
                  <portSpacing port="sink_base model 7" spacing="0"/>
                  <portSpacing port="sink_base model 8" spacing="0"/>
                  <portSpacing port="sink_base model 9" spacing="0"/>
                  <portSpacing port="sink_base model 10" spacing="0"/>
                  <portSpacing port="sink_base model 11" spacing="0"/>
                </process>
                <process expanded="true">
                  <operator activated="true" class="h2o:deep_learning" compatibility="7.5.000" expanded="true" height="82" name="Deep Learning (meta)" width="90" x="179" y="34">
                    <enumeration key="hidden_layer_sizes">
                      <parameter key="hidden_layer_sizes" value="50"/>
                      <parameter key="hidden_layer_sizes" value="50"/>
                    </enumeration>
                    <enumeration key="hidden_dropout_ratios"/>
                    <list key="expert_parameters"/>
                    <list key="expert_parameters_"/>
                  </operator>
                  <connect from_port="stacking examples" to_op="Deep Learning (meta)" to_port="training set"/>
                  <connect from_op="Deep Learning (meta)" from_port="model" to_port="stacking model"/>
                  <portSpacing port="source_stacking examples" spacing="0"/>
                  <portSpacing port="sink_stacking model" spacing="0"/>
                </process>
              </operator>
              <connect from_port="training set" to_op="Stacking" to_port="training set"/>
              <connect from_op="Stacking" from_port="model" to_port="model"/>
              <portSpacing port="source_training set" spacing="0"/>
              <portSpacing port="sink_model" spacing="0"/>
            </process>
          </operator>
          <connect from_port="training set" to_op="MetaCost" to_port="training set"/>
          <connect from_op="MetaCost" 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="7.5.003" expanded="true" height="82" name="Apply Model" width="90" x="45" y="34">
            <list key="application_parameters"/>
          </operator>
          <operator activated="true" class="performance" compatibility="7.5.003" expanded="true" height="82" name="Performance" width="90" x="179" y="34"/>
          <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"/>
          <connect from_op="Performance" from_port="example set" to_port="test set results"/>
          <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="apply_model" compatibility="7.5.003" expanded="true" height="82" name="Apply Model (real)" width="90" x="648" y="187">
        <list key="application_parameters"/>
      </operator>
      <operator activated="true" class="performance_classification" compatibility="7.5.003" expanded="true" height="82" name="Performance (real)" width="90" x="782" y="136">
        <parameter key="classification_error" value="true"/>
        <parameter key="kappa" value="true"/>
        <parameter key="weighted_mean_recall" value="true"/>
        <parameter key="weighted_mean_precision" value="true"/>
        <parameter key="spearman_rho" value="true"/>
        <parameter key="kendall_tau" value="true"/>
        <parameter key="absolute_error" value="true"/>
        <parameter key="relative_error" value="true"/>
        <parameter key="relative_error_lenient" value="true"/>
        <parameter key="relative_error_strict" value="true"/>
        <parameter key="normalized_absolute_error" value="true"/>
        <parameter key="root_mean_squared_error" value="true"/>
        <parameter key="root_relative_squared_error" value="true"/>
        <parameter key="squared_error" value="true"/>
        <parameter key="correlation" value="true"/>
        <parameter key="squared_correlation" value="true"/>
        <parameter key="cross-entropy" value="true"/>
        <parameter key="margin" value="true"/>
        <parameter key="soft_margin_loss" value="true"/>
        <parameter key="logistic_loss" value="true"/>
        <list key="class_weights"/>
      </operator>
      <connect from_op="Retrieve Titanic Training" from_port="output" to_op="Split Data" to_port="example set"/>
      <connect from_op="Split Data" from_port="partition 1" to_op="Validation" to_port="example set"/>
      <connect from_op="Split Data" from_port="partition 2" to_op="Apply Model (real)" to_port="unlabelled data"/>
      <connect from_op="Validation" from_port="model" to_op="Apply Model (real)" to_port="model"/>
      <connect from_op="Validation" from_port="performance 1" to_port="result 3"/>
      <connect from_op="Apply Model (real)" from_port="labelled data" to_op="Performance (real)" to_port="labelled data"/>
      <connect from_op="Apply Model (real)" from_port="model" to_port="result 4"/>
      <connect from_op="Performance (real)" from_port="performance" to_port="result 1"/>
      <connect from_op="Performance (real)" from_port="example set" 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"/>
      <portSpacing port="sink_result 4" spacing="0"/>
      <portSpacing port="sink_result 5" spacing="0"/>
    </process>
  </operator>
</process>
Tagged:

Best Answer

  • mcomsamcomsa Member Posts: 3 Contributor I
    Solution Accepted

    If I swith the operators (metacost inside stacking) is working. Strange. I have done that innitially, but did not worked.

Sign In or Register to comment.