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Hybrid models

Hybrid models are an ensemble model type containing the routing logic. They serve the following two purposes:

  • Support classification cascades, enabling engineers to handle multiple classes with a single model.

  • Support combined classification+information extraction (IE) models, for instance, using a classification model for selecting an IE model.

note

Current limitations are as follows:

Build hybrid model

To build a hybrid model from a list of already trained models, follow the instruction below:

  1. Create an aggregation root model. Use your custom hyper model to extend com.workfusion.automl.hypermodel.hybrid.AggregationHybridModel as shown below.

    @ModelDescription(
      code = "my_hybrid",
      title = "hybrid_model",
      description = "custom hybrid model",
      version = "1.0"
)
@HypermodelConfiguration(HybridExampleModelConfiguration.class)
public class HybridExampleModel extends AggregationHybridModel

    Note that the aggregation model type makes no predictions. Its purpose is to aggregate the results of executed models.

  1. Add com.workfusion.automl.hypermodel.hybrid.AggregationHybridModelExtractionDocumentProcessor to your configuration. By default, the Processor only returns the results of sub-models as an ordered set of modelId, modelResult pairs.

    However, you can implement any other aggregation logic. For that, use the above Processor with ExtractionProcessorConfiguration as shown below:

    @Named("extractionProcessorConfiguration")
public ExtractionProcessorConfiguration
extractionProcessorConfiguration() {
return new ExtractionProcessorConfiguration.Builder()
            .extractionProcessor(AggregationHybridModelExtractionDocumentProcessor.class)
            .build();
}

  2. Build a model artifact.

  3. Create a directory with the trained_model_id hybrid model. The directory must have with following structure:

    trained-hybrid-model
├── output
       ├── model-info.json
       ├── hybrid.yml

    where:

    • model-info.json is a standard AutoML file with model information. Below is an example of its content.
    {
  "modelId": "%your_trained_model_id%", // trained-hybrid-model
  "code": "%your_hypermodel_code%", // my_hybrid
  "version": ""%your_hypermodel_version%" // 1.0
}
    • hybrid.yml defines the routing logic within the hybrid model. It is similar to ensemble.yml, but has extensions. AutoML services analyze the file to route requests.

Explore examples

Classification cascade example

In this example, the document is classified by the document_classification_udfr-1234-6789 model. If the resulting class is another document, it is classified by the id_classification_1403b4f0-cd3b-4e1a-8934-c2ac264f4053 model. The results are processed by the trained-hybrid-model hybrid model and returned as a response to the execution request.

id: "root"
code: "my_hybrid"
version: "1.0"
trainedId: "trained-hybrid-model"
models:
    - id: "class1"
      code: "custom_classification"
      version: "2.0"
      trainedId: "document_classification_udfr-1234-6789"
      connections:
        - condition: '${outcome.class} == 'other''
          destination: class2
        - condition: '${outcome.class} != 'other'',
          destination: root
    - id: "class2"
      code: "multi-class-classification-generic-se-20"
      version: "10.1.1.36"
      trainedId: "id_classification_1403b4f0-cd3b-4e1a-8934-c2ac264f4053"
      connections:
        - destination: root

Pay attention to the following:

  • The cascade's root model must be a hybrid one. For the model data, specify:

    • id—identifier for the hybrid code or version of the hyper model
    • trainedId—the trained model ID

  • In the models section, specify the models to be executed in terms of the hybrid execution with routing connections (.yaml list). Each connection must have a destination. They define the ID of the model to be executed next.

  • Specifying conditions is optional. Conditions support the standard Spring Expression Language. The ${outcome.class} part is a reserved word for the document label after classification.

Hybrid classification+IE model example

In this example, an IE model is executed based on the classification results.

id: "root"
code: "hybrid-model-code"
version: "1.0"
trainedId: "trained-hybrid-model"
models: 
    - id: "class1"
      code: "classification-se-20"
      version: "10.1.1.36"
      trainingId: "document_classification_101136"
      connections:
        - condition: '${outcome.class} == 'drivers_license''
          destination: ie1
        - condition: '${outcome.class} == 'passport'',
          destination: ie2
    - id: "ie1"
      code: "generic-ie-se-20"
      version: "10.1.1.13"
      trainedId: "document_link_c2ac264f4053-cd3b-4e1a-8934-1403b4f0"
      connections:
        - destination : root
    - id: "ie2"
      code: "custom-passport-ie"
      version: "2.3"
      trainedId: "custom_ie_passport_v_2_3_trained"
      connections:
        - destination : root
note

The models mentioned in the trainedId section must already be present on your environment or must be part of an imported Asset Bundle. AutoML services fully support import and export of hybrid models, as well as validation of their structure.

Train OOTB classification cascade

In certain use cases, labels are too numerous for a single AutoML model to handle them all. For instance, email classification use cases can include 50+ labels. To address the problem, you can use the out-of-the-box (OOTB) classification cascade SplittingHybridClassificationHypermodel(code: splitting-hybrid-classification).

The main idea is that the model splits a data set by labels and trains a cascade of models for a subset of labels.

Splitting algorithm

In its current implementation, the OOTB model sorts all documents by the label frequency and splits them by the batch size + 1.

The default settings for the splitting model are:

  • 15—the threshold size of the label batch
  • MultiClassClassificationGenericSe20Hypermodel—the default classification model for sub-models

Advanced tuning

There are some extension points that you can use for tuning SplittingHybridClassificationHypermodel.

  • You can extend SplittingHybridClassificationHypermodel with a custom hyper model and change the batch size parameter.

  • You can select a different default classification model by changing the model configuration as illustrated below:

    @Named("pipelineConfiguration")
public PipelineConfiguration pipelineConfiguration() {
    return new PipelineConfiguration.Builder()
            .parameter(ConfigurationConstants.DEFAULT_HYBRID_SUB_MODEL_CLASS, MultiClassClassificationGenericSe20Hypermodel.class)
            .parameter(ConfigurationConstants.DEFAULT_HYBRID_BUCKET_SIZE, 15)
            .build();
}
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