Source code for ax.models.base

#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.

from typing import Any, Dict

[docs]class Model: """Base class for an Ax model. Note: the core methods each model has: `fit`, `predict`, `gen`, `cross_validate`, and `best_point` are not present in this base class, because the signatures for those methods vary based on the type of the model. This class only contains the methods that all models have in common and for which they all share the signature. """
[docs] @classmethod def serialize_state(cls, raw_state: Dict[str, Any]) -> Dict[str, Any]: """Serialized output of `self._get_state` to a JSON-ready dict. This may involve storing part of state in files / external storage and saving handles for that storage in the resulting serialized state. """ return raw_state # pragma: no cover
[docs] @classmethod def deserialize_state(cls, serialized_state: Dict[str, Any]) -> Dict[str, Any]: """Restores model's state from its serialized form, to the format it expects to receive as kwargs. """ return serialized_state # pragma: no cover
def _get_state(self) -> Dict[str, Any]: """Obtain the state of this model, in order to be able to serialize it and restore it from the serialized version. While most models in Ax aren't stateful, some models, like `SobolGenerator`, are. For Sobol, the value of the `init_position` changes throughout the generation process as more arms are generated, and restoring the Sobol generator with all the same settings as it was initialized with, will not result in the same model, because the `init_position` setting changed throughout optimization. Stateful settings like that are returned from this method, so that a model can be reinstantiated and 'pick up where it left off' –– more arms can be generated as if the model just continued generation and was never interrupted and serialized. NOTE: In most cases, `state` is passed into the model's initialization as kwargs, so keys in the state dict should correspond to model's kwargs. """ return {} # pragma: no cover
[docs] def feature_importances(self) -> Any: raise NotImplementedError( "Feature importance not available for this Model type" )