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9666a895f7
This is a major refactor of the underlying inference logic. The main refactor is now we are separating the model configuration and the inference interfaces. This has the following benefits: - we can store extra things with the model that are not necessary for inference (i.e. treenode split information gain) - we can optimize inference separate from model serialization and storage. - The user is oblivious to the optimizations (other than seeing the benefits). A major part of this commit is removing all inference related methods from the trained model configurations (ensemble, tree, etc.) and moving them to a new class. This new class satisfies a new interface that is ONLY for inference. The optimizations applied currently are: - feature maps are flattened once - feature extraction only happens once at the highest level (improves inference + feature importance through put) - Only storing what we need for inference + feature importance on heap