Refactor DatasetRNN to be subclassable for different target types.#126
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Refactor DatasetRNN to be subclassable for different target types.#126copybara-service[bot] merged 1 commit intomainfrom
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Initial classes are categorical, continuous, and mixed. These reproduce the functionality of using the y_type argument to the previous version. DatasetRNNMixed keeps the logic of the current "hybrid" loss -- the intention is to refactor this in a future CL to have separate fields for the continuous and categorical portions of the loss. This also required a refactor of dataset_list_to_multisubject, to be more careful about merging only lists where the datasets are compatible with each other. PiperOrigin-RevId: 869193985
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Refactor DatasetRNN to be subclassable for different target types.
Initial classes are categorical, continuous, and mixed. These reproduce the functionality of using the y_type argument to the previous version. DatasetRNNMixed keeps the logic of the current "hybrid" loss -- the intention is to refactor this in a future CL to have separate fields for the continuous and categorical portions of the loss.
This also required a refactor of dataset_list_to_multisubject, to be more careful about merging only lists where the datasets are compatible with each other.