Best way to combine multiple Featureset Objects into one?

pblack476
pblack476 New Altair Community Member
edited November 2024 in Community Q&A
I would like to combine 5 FeatureSets into a single one. Ideally I would like all "5 factorial (!)" combinations of sets possible. But for now a single bundle will do.

How does one would go about this on RM?
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Best Answer

  • varunm1
    varunm1 New Altair Community Member
    Answer ✓
    Here is the working sample of how to apply the model using loop attribute subset. "Log" has performance.

    <?xml version="1.0" encoding="UTF-8"?><process version="9.5.001">
    <context>
    <input/>
    <output/>
    <macros/>
    </context>
    <operator activated="true" class="process" compatibility="6.0.002" 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="9.5.001" expanded="true" height="68" name="Retrieve Titanic Training" width="90" x="313" y="136">
    <parameter key="repository_entry" value="//Samples/data/Titanic Training"/>
    </operator>
    <operator activated="true" class="select_attributes" compatibility="9.5.001" expanded="true" height="82" name="Select Attributes" width="90" x="447" y="136">
    <parameter key="attribute_filter_type" value="single"/>
    <parameter key="attribute" value="Age"/>
    <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="true"/>
    <parameter key="include_special_attributes" value="false"/>
    </operator>
    <operator activated="true" class="loop_attribute_subsets" compatibility="9.5.001" expanded="true" height="68" name="Loop Subsets" origin="GENERATED_TUTORIAL" width="90" x="581" y="136">
    <parameter key="use_exact_number" value="false"/>
    <parameter key="exact_number_of_attributes" value="-1"/>
    <parameter key="min_number_of_attributes" value="1"/>
    <parameter key="limit_max_number" value="false"/>
    <parameter key="max_number_of_attributes" value="-1"/>
    <process expanded="true">
    <operator activated="true" class="concurrency:cross_validation" compatibility="9.5.001" expanded="true" height="145" name="Cross Validation" width="90" x="112" y="85">
    <parameter key="split_on_batch_attribute" value="false"/>
    <parameter key="leave_one_out" value="false"/>
    <parameter key="number_of_folds" value="5"/>
    <parameter key="sampling_type" value="automatic"/>
    <parameter key="use_local_random_seed" value="false"/>
    <parameter key="local_random_seed" value="1992"/>
    <parameter key="enable_parallel_execution" value="true"/>
    <process expanded="true">
    <operator activated="true" class="concurrency:parallel_decision_tree" compatibility="9.5.001" expanded="true" height="103" name="Decision Tree" width="90" x="112" y="34">
    <parameter key="criterion" value="gain_ratio"/>
    <parameter key="maximal_depth" value="10"/>
    <parameter key="apply_pruning" value="true"/>
    <parameter key="confidence" value="0.1"/>
    <parameter key="apply_prepruning" value="true"/>
    <parameter key="minimal_gain" value="0.01"/>
    <parameter key="minimal_leaf_size" value="2"/>
    <parameter key="minimal_size_for_split" value="4"/>
    <parameter key="number_of_prepruning_alternatives" value="3"/>
    </operator>
    <connect from_port="training set" to_op="Decision Tree" to_port="training set"/>
    <connect from_op="Decision Tree" 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="9.5.001" expanded="true" height="82" name="Apply Model" width="90" x="45" y="34">
    <list key="application_parameters"/>
    <parameter key="create_view" value="false"/>
    </operator>
    <operator activated="true" class="performance_classification" compatibility="9.5.001" expanded="true" height="82" name="Performance" width="90" x="179" y="34">
    <parameter key="main_criterion" value="first"/>
    <parameter key="accuracy" value="true"/>
    <parameter key="classification_error" value="false"/>
    <parameter key="kappa" value="true"/>
    <parameter key="weighted_mean_recall" value="false"/>
    <parameter key="weighted_mean_precision" value="false"/>
    <parameter key="spearman_rho" value="false"/>
    <parameter key="kendall_tau" value="false"/>
    <parameter key="absolute_error" value="false"/>
    <parameter key="relative_error" value="false"/>
    <parameter key="relative_error_lenient" value="false"/>
    <parameter key="relative_error_strict" value="false"/>
    <parameter key="normalized_absolute_error" value="false"/>
    <parameter key="root_mean_squared_error" value="false"/>
    <parameter key="root_relative_squared_error" value="false"/>
    <parameter key="squared_error" value="false"/>
    <parameter key="correlation" value="false"/>
    <parameter key="squared_correlation" value="false"/>
    <parameter key="cross-entropy" value="false"/>
    <parameter key="margin" value="false"/>
    <parameter key="soft_margin_loss" value="false"/>
    <parameter key="logistic_loss" value="false"/>
    <parameter key="skip_undefined_labels" value="true"/>
    <parameter key="use_example_weights" value="true"/>
    <list key="class_weights"/>
    </operator>
    <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"/>
    <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="log" compatibility="9.5.001" expanded="true" height="82" name="Log" origin="GENERATED_TUTORIAL" width="90" x="447" y="85">
    <list key="log">
    <parameter key="Attributes" value="operator.Loop Subsets.value.feature_names"/>
    <parameter key="Performance_Accuracy" value="operator.Cross Validation.value.performance 1"/>
    <parameter key="Performance_Kappa" value="operator.Cross Validation.value.performance 2"/>
    </list>
    <parameter key="sorting_type" value="none"/>
    <parameter key="sorting_k" value="100"/>
    <parameter key="persistent" value="false"/>
    </operator>
    <connect from_port="example set" to_op="Cross Validation" to_port="example set"/>
    <connect from_op="Cross Validation" from_port="performance 1" to_op="Log" to_port="through 1"/>
    <portSpacing port="source_example set" spacing="0"/>
    </process>
    </operator>
    <connect from_op="Retrieve Titanic Training" from_port="output" to_op="Select Attributes" to_port="example set input"/>
    <connect from_op="Select Attributes" from_port="example set output" to_op="Loop Subsets" to_port="example set"/>
    <connect from_op="Loop Subsets" from_port="example set" to_port="result 1"/>
    <portSpacing port="source_input 1" spacing="0"/>
    <portSpacing port="sink_result 1" spacing="90"/>
    <portSpacing port="sink_result 2" spacing="0"/>
    </process>
    </operator>
    </process>

    Hope this helps

Answers

  • lionelderkrikor
    lionelderkrikor New Altair Community Member
    Hi @pblack476,

    I don't know how to create the  "5 factorial" combinaisons  of sets possible automatically. But to combine 5 featureSets , have you tried to use the Apply Feature Set operator .. ??

    Regards,

    Lionel
  • varunm1
    varunm1 New Altair Community Member
    I would like all "5 factorial (!)" combinations of sets possible.
    Can you explain this requirement? I am a bit confused about  "5 factorial" are you looking for 120 combinations?

  • pblack476
    pblack476 New Altair Community Member
    edited November 2019
    @varunm1 Yes. that is what  I would like. All 120 possible combinations of 5 featuresets without repetition. (I dont need all possible combinations of attributes, Just featuresets)

    @lionelderkrikor The problem with that approach is that it cannot add multiple featuresets at once. I have to loop through them somehow (probably a collection since there is no Loop FeatureSet operator) and use 'keep originals' option. But I am running into trouble there as well because 'keep original' brings back every attribute. Even the ones I had previously filtered with select attributes before, and I don't want that. I want to have an exampleset with the combination of 5 featuresets and just those 5. This really seems like a bug to me but for now that is where I am at.

    The best I can do so far is use 'keep originals' with 'originals special role' to make RM ignore them on further processing. And that bugs if I use inside a loop collection operator.
  • varunm1
    varunm1 New Altair Community Member
    Yes. that is what  I would like. All 120 possible combinations of 5 feature sets without repetition.
    So you are looking for 31 combinations as you said they shouldn't repeat. This is a tricky and nice challenge.

    120 combinations will have repeats like column A, Column B in one set and Column B, Column A in another (order changes).
  • varunm1
    varunm1 New Altair Community Member
    @pblack476

    Did you try "Loop Attribute Subsets". This is doing what you are asking I guess
  • pblack476
    pblack476 New Altair Community Member
    I got a part of it!

    I Merge Attributes after the loop. No need to keep orignals. It merges all ExampleSets in the collection into one. And My collection is nothing more than 5 ExampleSets of the applied featureSets one by one.

    Now I will try to figure out how to get all 120 combinations....
  • pblack476
    pblack476 New Altair Community Member
    @varunm1 True. I had my math wrong there. I will try to use loop attribute subsets and check back.
  • varunm1
    varunm1 New Altair Community Member
    Answer ✓
    Here is the working sample of how to apply the model using loop attribute subset. "Log" has performance.

    <?xml version="1.0" encoding="UTF-8"?><process version="9.5.001">
    <context>
    <input/>
    <output/>
    <macros/>
    </context>
    <operator activated="true" class="process" compatibility="6.0.002" 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="9.5.001" expanded="true" height="68" name="Retrieve Titanic Training" width="90" x="313" y="136">
    <parameter key="repository_entry" value="//Samples/data/Titanic Training"/>
    </operator>
    <operator activated="true" class="select_attributes" compatibility="9.5.001" expanded="true" height="82" name="Select Attributes" width="90" x="447" y="136">
    <parameter key="attribute_filter_type" value="single"/>
    <parameter key="attribute" value="Age"/>
    <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="true"/>
    <parameter key="include_special_attributes" value="false"/>
    </operator>
    <operator activated="true" class="loop_attribute_subsets" compatibility="9.5.001" expanded="true" height="68" name="Loop Subsets" origin="GENERATED_TUTORIAL" width="90" x="581" y="136">
    <parameter key="use_exact_number" value="false"/>
    <parameter key="exact_number_of_attributes" value="-1"/>
    <parameter key="min_number_of_attributes" value="1"/>
    <parameter key="limit_max_number" value="false"/>
    <parameter key="max_number_of_attributes" value="-1"/>
    <process expanded="true">
    <operator activated="true" class="concurrency:cross_validation" compatibility="9.5.001" expanded="true" height="145" name="Cross Validation" width="90" x="112" y="85">
    <parameter key="split_on_batch_attribute" value="false"/>
    <parameter key="leave_one_out" value="false"/>
    <parameter key="number_of_folds" value="5"/>
    <parameter key="sampling_type" value="automatic"/>
    <parameter key="use_local_random_seed" value="false"/>
    <parameter key="local_random_seed" value="1992"/>
    <parameter key="enable_parallel_execution" value="true"/>
    <process expanded="true">
    <operator activated="true" class="concurrency:parallel_decision_tree" compatibility="9.5.001" expanded="true" height="103" name="Decision Tree" width="90" x="112" y="34">
    <parameter key="criterion" value="gain_ratio"/>
    <parameter key="maximal_depth" value="10"/>
    <parameter key="apply_pruning" value="true"/>
    <parameter key="confidence" value="0.1"/>
    <parameter key="apply_prepruning" value="true"/>
    <parameter key="minimal_gain" value="0.01"/>
    <parameter key="minimal_leaf_size" value="2"/>
    <parameter key="minimal_size_for_split" value="4"/>
    <parameter key="number_of_prepruning_alternatives" value="3"/>
    </operator>
    <connect from_port="training set" to_op="Decision Tree" to_port="training set"/>
    <connect from_op="Decision Tree" 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="9.5.001" expanded="true" height="82" name="Apply Model" width="90" x="45" y="34">
    <list key="application_parameters"/>
    <parameter key="create_view" value="false"/>
    </operator>
    <operator activated="true" class="performance_classification" compatibility="9.5.001" expanded="true" height="82" name="Performance" width="90" x="179" y="34">
    <parameter key="main_criterion" value="first"/>
    <parameter key="accuracy" value="true"/>
    <parameter key="classification_error" value="false"/>
    <parameter key="kappa" value="true"/>
    <parameter key="weighted_mean_recall" value="false"/>
    <parameter key="weighted_mean_precision" value="false"/>
    <parameter key="spearman_rho" value="false"/>
    <parameter key="kendall_tau" value="false"/>
    <parameter key="absolute_error" value="false"/>
    <parameter key="relative_error" value="false"/>
    <parameter key="relative_error_lenient" value="false"/>
    <parameter key="relative_error_strict" value="false"/>
    <parameter key="normalized_absolute_error" value="false"/>
    <parameter key="root_mean_squared_error" value="false"/>
    <parameter key="root_relative_squared_error" value="false"/>
    <parameter key="squared_error" value="false"/>
    <parameter key="correlation" value="false"/>
    <parameter key="squared_correlation" value="false"/>
    <parameter key="cross-entropy" value="false"/>
    <parameter key="margin" value="false"/>
    <parameter key="soft_margin_loss" value="false"/>
    <parameter key="logistic_loss" value="false"/>
    <parameter key="skip_undefined_labels" value="true"/>
    <parameter key="use_example_weights" value="true"/>
    <list key="class_weights"/>
    </operator>
    <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"/>
    <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="log" compatibility="9.5.001" expanded="true" height="82" name="Log" origin="GENERATED_TUTORIAL" width="90" x="447" y="85">
    <list key="log">
    <parameter key="Attributes" value="operator.Loop Subsets.value.feature_names"/>
    <parameter key="Performance_Accuracy" value="operator.Cross Validation.value.performance 1"/>
    <parameter key="Performance_Kappa" value="operator.Cross Validation.value.performance 2"/>
    </list>
    <parameter key="sorting_type" value="none"/>
    <parameter key="sorting_k" value="100"/>
    <parameter key="persistent" value="false"/>
    </operator>
    <connect from_port="example set" to_op="Cross Validation" to_port="example set"/>
    <connect from_op="Cross Validation" from_port="performance 1" to_op="Log" to_port="through 1"/>
    <portSpacing port="source_example set" spacing="0"/>
    </process>
    </operator>
    <connect from_op="Retrieve Titanic Training" from_port="output" to_op="Select Attributes" to_port="example set input"/>
    <connect from_op="Select Attributes" from_port="example set output" to_op="Loop Subsets" to_port="example set"/>
    <connect from_op="Loop Subsets" from_port="example set" to_port="result 1"/>
    <portSpacing port="source_input 1" spacing="0"/>
    <portSpacing port="sink_result 1" spacing="90"/>
    <portSpacing port="sink_result 2" spacing="0"/>
    </process>
    </operator>
    </process>

    Hope this helps

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