#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# pyre-strict
from logging import Logger
from typing import Any, Callable, Dict, Optional, Tuple, Type
from ax.core.map_metric import MapMetric
from ax.core.metric import Metric
from ax.metrics.branin import BraninMetric
from ax.metrics.branin_map import BraninTimestampMapMetric
from ax.metrics.chemistry import ChemistryMetric
from ax.metrics.factorial import FactorialMetric
from ax.metrics.hartmann6 import Hartmann6Metric
from ax.metrics.noisy_function import NoisyFunctionMetric
from ax.metrics.sklearn import SklearnMetric
from ax.storage.json_store.encoders import metric_to_dict
from ax.storage.json_store.registry import (
CORE_DECODER_REGISTRY,
CORE_ENCODER_REGISTRY,
TDecoderRegistry,
)
from ax.storage.utils import stable_hash
from ax.utils.common.logger import get_logger
logger: Logger = get_logger(__name__)
"""
Mapping of Metric classes to ints.
All metrics will be stored in the same table in the database. When
saving, we look up the metric subclass in METRIC_REGISTRY, and store
the corresponding type field in the database.
"""
CORE_METRIC_REGISTRY: Dict[Type[Metric], int] = {
Metric: 0,
FactorialMetric: 1,
BraninMetric: 2,
NoisyFunctionMetric: 3,
Hartmann6Metric: 4,
SklearnMetric: 5,
ChemistryMetric: 7,
MapMetric: 8,
BraninTimestampMapMetric: 9,
}
# pyre-fixme[3]: Return annotation cannot contain `Any`.
[docs]def register_metrics(
metric_clss: Dict[Type[Metric], Optional[int]],
# pyre-fixme[2]: Parameter annotation cannot contain `Any`.
# pyre-fixme[24]: Generic type `type` expects 1 type parameter, use
# `typing.Type` to avoid runtime subscripting errors.
encoder_registry: Dict[
Type, Callable[[Any], Dict[str, Any]]
] = CORE_ENCODER_REGISTRY,
decoder_registry: TDecoderRegistry = CORE_DECODER_REGISTRY,
# pyre-fixme[24]: Generic type `type` expects 1 type parameter, use `typing.Type` to
# avoid runtime subscripting errors.
) -> Tuple[
Dict[Type[Metric], int],
Dict[Type, Callable[[Any], Dict[str, Any]]],
TDecoderRegistry,
]:
"""Add custom metric classes to the SQA and JSON registries.
For the SQA registry, if no int is specified, use a hash of the class name.
"""
new_metric_registry = {
metric_cls: (
val if val is not None else abs(stable_hash(metric_cls.__name__)) % (10**5)
)
for metric_cls, val in metric_clss.items()
}
new_encoder_registry = {
**{metric_cls: metric_to_dict for metric_cls in metric_clss},
**encoder_registry,
}
new_decoder_registry = {
**{metric_cls.__name__: metric_cls for metric_cls in metric_clss},
**decoder_registry,
}
return new_metric_registry, new_encoder_registry, new_decoder_registry
