Ensemble model#1535
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| @Inject | ||
| lateinit var config: EMConfig | ||
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| @Inject |
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why are you using @Inject in this class? this class is not instantiated with an injector, isn't it?
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Yes, but I think we need it because the injected fields are populated afterward viatest.injector.injectMembers(test)
| * among all the other used models based on the average of key metrics. | ||
| * For the single-model scenario, the function simply returns the single model. | ||
| */ | ||
| private fun selectBestModel(endpoint: Endpoint): AIModel? { |
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should return AIModel not AIModel?
| // Return the single model if there is only one delegate. | ||
| if (delegates.size == 1) return delegates.first() | ||
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| return delegates.maxByOrNull { model -> |
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if there was no delegate, ie delegates.isEmpty, then should had crashed at init time
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| val bestModel = selectBestModel(input.endpoint) | ||
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| val result = bestModel?.classify(input) ?: AIResponseClassification() |
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bestModel should never be null
| "Ensemble model is a combination of a comma-separated list, e.g., GLM,NN,KDE.") | ||
| var aiModelForResponseClassification: String = "NONE" | ||
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| fun setAIModels(vararg models: AIResponseClassifierModel) { |
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these functions shouldn't mixed up in the list of Cfg declaration. move for example to bottom of file with the other support functions
| @Test | ||
| fun testRunDeterministic(){ | ||
| testRunEM(AIResponseClassifierModel.DETERMINISTIC) | ||
| testRunEM("DETERMINISTIC") |
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sorry, but don't like this. should avoid magic constant, and rather use enums. you can also have an overriden version of testRunEM where you take as input a list of enums, which then can convert internally to a join string with ,.
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| @Test | ||
| fun testRunEnsemble(){ | ||
| testRunEM("GAUSSIAN,GLM,KDE,KNN,NN") |
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testRunEM(listOf(AIResponseClassifierModel.GAUSSIAN,AIResponseClassifierModel.GLM,AIResponseClassifierModel.KDE,AIResponseClassifierModel.KNN,AIResponseClassifierModel.NN))
can also statically import AIResponseClassifierModel.* to avoid repeating it
| @Cfg("Models used to learn input constraints and predict the response status before issuing a request. " + | ||
| "Supports both single-model and ensemble configurations. " + | ||
| "Ensemble model is a combination of a comma-separated list, e.g., GLM,NN,KDE.") | ||
| var aiModelForResponseClassification: String = "NONE" |
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hmmm... converting to string lose all information on type checking, and automated documentation of available entries in the generated docs. need to support list of enums. but maybe easier if i implement it
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Implementation of the ensemble model