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MetaCost and Stacking - no obvious errors

mcomsamcomsa Member Posts: 3 Contributor I
edited November 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>
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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.

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