Source code for ax.modelbridge.base

#!/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.

import time
import warnings
from abc import ABC
from collections import OrderedDict
from copy import deepcopy
from dataclasses import dataclass, field

from logging import Logger
from typing import Any, cast, Dict, List, MutableMapping, Optional, Set, Tuple, Type

import numpy as np
from ax.core.arm import Arm
from import Data
from ax.core.experiment import Experiment
from ax.core.generator_run import extract_arm_predictions, GeneratorRun
from ax.core.observation import (
from ax.core.optimization_config import OptimizationConfig
from ax.core.parameter import ParameterType, RangeParameter
from ax.core.search_space import SearchSpace
from ax.core.types import TCandidateMetadata, TModelCov, TModelMean, TModelPredict
from ax.exceptions.core import UnsupportedError, UserInputError
from ax.modelbridge.transforms.base import Transform
from ax.modelbridge.transforms.cast import Cast
from ax.models.types import TConfig
from ax.utils.common.logger import get_logger
from ax.utils.common.typeutils import checked_cast, not_none
from ax.utils.stats.model_fit_stats import (
from botorch.exceptions.warnings import InputDataWarning

logger: Logger = get_logger(__name__)

[docs]@dataclass(frozen=True) class BaseGenArgs: search_space: SearchSpace optimization_config: Optional[OptimizationConfig] pending_observations: Dict[str, List[ObservationFeatures]] fixed_features: Optional[ObservationFeatures]
[docs]@dataclass(frozen=True) class GenResults: observation_features: List[ObservationFeatures] weights: List[float] best_observation_features: Optional[ObservationFeatures] = None gen_metadata: Dict[str, Any] = field(default_factory=dict)
[docs]class ModelBridge(ABC): """The main object for using models in Ax. ModelBridge specifies 3 methods for using models: - predict: Make model predictions. This method is not optimized for speed and so should be used primarily for plotting or similar tasks and not inside an optimization loop. - gen: Use the model to generate new candidates. - cross_validate: Do cross validation to assess model predictions. ModelBridge converts Ax types like Data and Arm to types that are meant to be consumed by the models. The data sent to the model will depend on the implementation of the subclass, which will specify the actual API for external model. This class also applies a sequence of transforms to the input data and problem specification which can be used to ensure that the external model receives appropriate inputs. Subclasses will implement what is here referred to as the "terminal transform," which is a transform that changes types of the data and problem specification. """ def __init__( self, search_space: SearchSpace, # pyre-fixme[2]: Parameter annotation cannot be `Any`. model: Any, transforms: Optional[List[Type[Transform]]] = None, experiment: Optional[Experiment] = None, data: Optional[Data] = None, transform_configs: Optional[Dict[str, TConfig]] = None, status_quo_name: Optional[str] = None, status_quo_features: Optional[ObservationFeatures] = None, optimization_config: Optional[OptimizationConfig] = None, fit_out_of_design: bool = False, fit_abandoned: bool = False, fit_tracking_metrics: bool = True, fit_on_init: bool = True, ) -> None: """ Applies transforms and fits model. Args: experiment: Is used to get arm parameters. Is not mutated. search_space: Search space for fitting the model. Constraints need not be the same ones used in gen. data: Ax Data. model: Interface will be specified in subclass. If model requires initialization, that should be done prior to its use here. transforms: List of uninitialized transform classes. Forward transforms will be applied in this order, and untransforms in the reverse order. transform_configs: A dictionary from transform name to the transform config dictionary. status_quo_name: Name of the status quo arm. Can only be used if Data has a single set of ObservationFeatures corresponding to that arm. status_quo_features: ObservationFeatures to use as status quo. Either this or status_quo_name should be specified, not both. optimization_config: Optimization config defining how to optimize the model. fit_out_of_design: If specified, all training data is returned. Otherwise, only in design points are returned. fit_abandoned: Whether data for abandoned arms or trials should be included in model training data. If ``False``, only non-abandoned points are returned. fit_tracking_metrics: Whether to fit a model for tracking metrics. Setting this to False will improve runtime at the expense of models not being available for predicting tracking metrics. NOTE: This can only be set to False when the optimization config is provided. fit_on_init: Whether to fit the model on initialization. This can be used to skip model fitting when a fitted model is not needed. To fit the model afterwards, use `_process_and_transform_data` to get the transformed inputs and call `_fit_if_implemented` with the transformed inputs. """ t_fit_start = time.monotonic() transforms = transforms or [] # pyre-ignore: Cast is a Tranform transforms: List[Type[Transform]] = [Cast] + transforms self.fit_time: float = 0.0 self.fit_time_since_gen: float = 0.0 self._metric_names: Set[str] = set() self._training_data: List[Observation] = [] self._optimization_config: Optional[OptimizationConfig] = optimization_config self._training_in_design: List[bool] = [] self._status_quo: Optional[Observation] = None self._arms_by_signature: Optional[Dict[str, Arm]] = None self.transforms: MutableMapping[str, Transform] = OrderedDict() self._model_key: Optional[str] = None self._model_kwargs: Optional[Dict[str, Any]] = None self._bridge_kwargs: Optional[Dict[str, Any]] = None self._model_space: SearchSpace = search_space.clone() self._raw_transforms = transforms self._transform_configs: Optional[Dict[str, TConfig]] = transform_configs self._fit_out_of_design = fit_out_of_design self._fit_abandoned = fit_abandoned self._fit_tracking_metrics = fit_tracking_metrics self.outcomes: List[str] = [] self._experiment_has_immutable_search_space_and_opt_config: bool = ( experiment is not None and experiment.immutable_search_space_and_opt_config ) if experiment is not None: if self._optimization_config is None: self._optimization_config = experiment.optimization_config self._arms_by_signature = experiment.arms_by_signature if self._fit_tracking_metrics is False: if self._optimization_config is None: raise UserInputError( "Optimization config is required when " "`fit_tracking_metrics` is False." ) self.outcomes = sorted(self._optimization_config.metrics.keys()) # Set training data (in the raw / untransformed space). This also omits # out-of-design and abandoned observations depending on the corresponding flags. observations_raw = self._prepare_observations(experiment=experiment, data=data) observations_raw = self._set_training_data( observations=observations_raw, search_space=self._model_space ) # Set model status quo. # NOTE: training data must be set before setting the status quo. self._set_status_quo( experiment=experiment, status_quo_name=status_quo_name, status_quo_features=status_quo_features, ) # Save model, apply terminal transform, and fit. self.model = model if fit_on_init: observations, search_space = self._transform_data( observations=observations_raw, search_space=self._model_space, transforms=self._raw_transforms, transform_configs=self._transform_configs, ) self._fit_if_implemented( search_space=search_space, observations=observations, time_so_far=time.monotonic() - t_fit_start, ) def _fit_if_implemented( self, search_space: SearchSpace, observations: List[Observation], time_so_far: float, ) -> None: r"""Fits the model if `_fit` is implemented and stores fit time. Args: search_space: A transformed search space for fitting the model. observations: The observations to fit the model with. These should also be transformed. time_so_far: Time spent in initializing the model up to `_fit_if_implemented` call. """ try: t_fit_start = time.monotonic() self._fit( model=self.model, search_space=search_space, observations=observations, ) increment = time.monotonic() - t_fit_start + time_so_far self.fit_time += increment self.fit_time_since_gen += increment except NotImplementedError: pass def _process_and_transform_data( self, experiment: Optional[Experiment] = None, data: Optional[Data] = None, ) -> Tuple[List[Observation], SearchSpace]: r"""Processes the data into observations and returns transformed observations and the search space. This packages the following methods: * self._prepare_observations * self._set_training_data * self._transform_data """ observations = self._prepare_observations(experiment=experiment, data=data) observations_raw = self._set_training_data( observations=observations, search_space=self._model_space ) return self._transform_data( observations=observations_raw, search_space=self._model_space, transforms=self._raw_transforms, transform_configs=self._transform_configs, ) def _prepare_observations( self, experiment: Optional[Experiment], data: Optional[Data] ) -> List[Observation]: if experiment is None or data is None: return [] return observations_from_data( experiment=experiment, data=data, include_abandoned=self._fit_abandoned ) def _transform_data( self, observations: List[Observation], search_space: SearchSpace, transforms: Optional[List[Type[Transform]]], transform_configs: Optional[Dict[str, TConfig]], assign_transforms: bool = True, ) -> Tuple[List[Observation], SearchSpace]: """Initialize transforms and apply them to provided data.""" # Initialize transforms search_space = search_space.clone() if transforms is not None: if transform_configs is None: transform_configs = {} for t in transforms: t_instance = t( search_space=search_space, observations=observations, modelbridge=self, config=transform_configs.get(t.__name__, None), ) search_space = t_instance.transform_search_space(search_space) observations = t_instance.transform_observations(observations) if assign_transforms: self.transforms[t.__name__] = t_instance return observations, search_space def _prepare_training_data( self, observations: List[Observation] ) -> List[Observation]: observation_features, observation_data = separate_observations(observations) if len(observation_features) != len(set(observation_features)): raise ValueError( "Observation features are not unique. " "Something went wrong constructing training data..." ) return observations def _set_training_data( self, observations: List[Observation], search_space: SearchSpace ) -> List[Observation]: """Store training data, not-transformed. If the modelbridge specifies _fit_out_of_design, all training data is returned. Otherwise, only in design points are returned. """ observations = self._prepare_training_data(observations=observations) self._training_data = deepcopy(observations) self._metric_names: Set[str] = set() for obs in observations: self._metric_names.update( return self._process_in_design( search_space=search_space, observations=observations, ) def _extend_training_data( self, observations: List[Observation] ) -> List[Observation]: """Extend and return training data, not-transformed. If the modelbridge specifies _fit_out_of_design, all training data is returned. Otherwise, only in design points are returned. Args: observations: New observations. Returns: New + old observations. """ observations = self._prepare_training_data(observations=observations) for obs in observations: for metric_name in if metric_name not in self._metric_names: raise ValueError( f"Unrecognised metric {metric_name}; cannot update " "training data with metrics that were not in the original " "training data." ) # Initialize with all points in design. self._training_data.extend(deepcopy(observations)) all_observations = self.get_training_data() return self._process_in_design( search_space=self._model_space, observations=all_observations, ) def _process_in_design( self, search_space: SearchSpace, observations: List[Observation], ) -> List[Observation]: """Set training_in_design, and decide whether to filter out of design points.""" # Don't filter points. if self._fit_out_of_design: # Use all data for training # Set training_in_design to True for all observations so that # all observations are used in CV and plotting self.training_in_design = [True] * len(observations) return observations in_design = self._compute_in_design( search_space=search_space, observations=observations ) self.training_in_design = in_design in_design_obs = [ observations[i] for i, is_in_design in enumerate(in_design) if is_in_design ] return in_design_obs def _compute_in_design( self, search_space: SearchSpace, observations: List[Observation], ) -> List[bool]: return [ search_space.check_membership(obs.features.parameters) for obs in observations ] def _set_status_quo( self, experiment: Optional[Experiment], status_quo_name: Optional[str], status_quo_features: Optional[ObservationFeatures], ) -> None: """Set model status quo. First checks for status quo in inputs status_quo_name and status_quo_features. If neither of these is provided, checks the experiment for a status quo. If that is set, it is handled by name in the same way as input status_quo_name. Args: experiment: Experiment that will be checked for status quo. status_quo_name: Name of status quo arm. status_quo_features: Features for status quo. """ self._status_quo: Optional[Observation] = None if ( status_quo_name is None and status_quo_features is None and experiment is not None and experiment.status_quo is not None ): status_quo_name = if status_quo_name is not None: if status_quo_features is not None: raise ValueError( "Specify either status_quo_name or status_quo_features, not both." ) sq_obs = [ obs for obs in self._training_data if obs.arm_name == status_quo_name ] if len(sq_obs) == 0: logger.warning(f"Status quo {status_quo_name} not present in data") elif len(sq_obs) > 1: logger.warning( f"Status quo {status_quo_name} found in data with multiple " "features. Use status_quo_features to specify which to use." ) else: self._status_quo = sq_obs[0] elif status_quo_features is not None: sq_obs = [ obs for obs in self._training_data if (obs.features.parameters == status_quo_features.parameters) and (obs.features.trial_index == status_quo_features.trial_index) ] if len(sq_obs) == 0: logger.warning( f"Status quo features {status_quo_features} not found in data." ) else: # len(sq_obs) will not be > 1, # unique features verified in _set_training_data. self._status_quo = sq_obs[0] @property def status_quo(self) -> Optional[Observation]: """Observation corresponding to status quo, if any.""" return self._status_quo @property def metric_names(self) -> Set[str]: """Metric names present in training data.""" return self._metric_names @property def model_space(self) -> SearchSpace: """SearchSpace used to fit model.""" return self._model_space
[docs] def get_training_data(self) -> List[Observation]: """A copy of the (untransformed) data with which the model was fit.""" return deepcopy(self._training_data)
@property def training_in_design(self) -> List[bool]: """For each observation in the training data, a bool indicating if it is in-design for the model. """ return self._training_in_design @training_in_design.setter def training_in_design(self, training_in_design: List[bool]) -> None: if len(training_in_design) != len(self._training_data): raise ValueError( f"In-design indicators not same length ({len(training_in_design)})" f" as training data ({len(self._training_data)})." ) # Identify out-of-design arms if sum(training_in_design) < len(training_in_design): ood_names = [] for i, obs in enumerate(self._training_data): if not training_in_design[i] and obs.arm_name is not None: ood_names.append(obs.arm_name) ood_str = ", ".join(set(ood_names))"Leaving out out-of-design observations for arms: {ood_str}") self._training_in_design = training_in_design def _fit( self, # pyre-fixme[2]: Parameter annotation cannot be `Any`. model: Any, search_space: SearchSpace, observations: List[Observation], ) -> None: """Apply terminal transform and fit model.""" raise NotImplementedError def _batch_predict( self, observation_features: List[ObservationFeatures] ) -> List[ObservationData]: """Predict a list of ObservationFeatures together.""" # Get modifiable version observation_features = deepcopy(observation_features) # Transform for t in self.transforms.values(): observation_features = t.transform_observation_features( observation_features ) # Apply terminal transform and predict observation_data = self._predict(observation_features) # Apply reverse transforms, in reverse order pred_observations = recombine_observations( observation_features=observation_features, observation_data=observation_data ) for t in reversed(list(self.transforms.values())): pred_observations = t.untransform_observations(pred_observations) return [ for obs in pred_observations] def _single_predict( self, observation_features: List[ObservationFeatures] ) -> List[ObservationData]: """Predict one ObservationFeature at a time.""" observation_data = [] for obsf in observation_features: try: obsd = self._batch_predict([obsf]) observation_data += obsd except (TypeError, ValueError) as e: # If the prediction is not out of design, this is a real error. # Let's re-raise. if self.model_space.check_membership(obsf.parameters): logger.debug(obsf.parameters) logger.debug(self.model_space) raise e from None # Prediction is out of design. # Training data is untranformed already. observation = next( ( data for data in self.get_training_data() if obsf.parameters == data.features.parameters and obsf.trial_index == data.features.trial_index ), None, ) if not observation: raise ValueError( "Out-of-design point could not be transformed, and was " "not found in the training data." ) observation_data.append( return observation_data def _predict_observation_data( self, observation_features: List[ObservationFeatures] ) -> List[ObservationData]: """ Like 'predict' method, but returns results as a list of ObservationData Predictions are made for all outcomes. If an out-of-design observation can successfully be transformed, the predicted value will be returned. Othwerise, we will attempt to find that observation in the training data and return the raw value. Args: observation_features: observation features Returns: List of `ObservationData` """ # Predict in single batch. try: observation_data = self._batch_predict(observation_features) # Predict one by one. except (TypeError, ValueError): observation_data = self._single_predict(observation_features) return observation_data
[docs] def predict(self, observation_features: List[ObservationFeatures]) -> TModelPredict: """Make model predictions (mean and covariance) for the given observation features. Predictions are made for all outcomes. If an out-of-design observation can successfully be transformed, the predicted value will be returned. Othwerise, we will attempt to find that observation in the training data and return the raw value. Args: observation_features: observation features Returns: 2-element tuple containing - Dictionary from metric name to list of mean estimates, in same order as observation_features. - Nested dictionary with cov['metric1']['metric2'] a list of cov(metric1@x, metric2@x) for x in observation_features. """ observation_data = self._predict_observation_data( observation_features=observation_features ) f, cov = unwrap_observation_data(observation_data) return f, cov
def _predict( self, observation_features: List[ObservationFeatures] ) -> List[ObservationData]: """Apply terminal transform, predict, and reverse terminal transform on output. """ raise NotImplementedError
[docs] def update(self, new_data: Data, experiment: Experiment) -> None: """Update the model bridge and the underlying model with new data. This method should be used instead of `fit`, in cases where the underlying model does not need to be re-fit from scratch, but rather updated. Note: `update` expects only new data (obtained since the model initialization or last update) to be passed in, not all data in the experiment. Args: new_data: Data from the experiment obtained since the last call to `update`. experiment: Experiment, in which this data was obtained. """ t_update_start = time.monotonic() observations = self._prepare_observations(experiment=experiment, data=new_data) obs_raw = self._extend_training_data(observations=observations) observations, search_space = self._transform_data( observations=obs_raw, search_space=self._model_space, transforms=self._raw_transforms, transform_configs=self._transform_configs, ) self._update( search_space=search_space, observations=observations, ) self.fit_time += time.monotonic() - t_update_start self.fit_time_since_gen += time.monotonic() - t_update_start
def _update( self, search_space: SearchSpace, observations: List[Observation], ) -> None: """Apply terminal transform and update model. Note: This function requires ALL observation_features and observation_data observed thus far, not just the new data to update with. Args: observation_features: All observation features observed so far. observation_data: All observation data observed so far. """ raise NotImplementedError def _get_transformed_gen_args( self, search_space: SearchSpace, optimization_config: Optional[OptimizationConfig] = None, pending_observations: Optional[Dict[str, List[ObservationFeatures]]] = None, fixed_features: Optional[ObservationFeatures] = None, ) -> BaseGenArgs: if pending_observations is None: pending_observations = {} if optimization_config is None: optimization_config = ( self._optimization_config.clone() if self._optimization_config is not None else None ) else: if not self._fit_tracking_metrics: # Check that the optimization config has the same metrics as # the original one. Otherwise, we may attempt to optimize over # metrics that do not have a fitted model. outcomes = set(optimization_config.metrics.keys()) if not outcomes.issubset(self.outcomes): raise UnsupportedError( "When fit_tracking_metrics is False, the optimization config " "can only include metrics that were included in the " "optimization config used while initializing the ModelBridge. " f"Metrics {outcomes} is not a subset of {self.outcomes}." ) optimization_config = optimization_config.clone() # TODO(T34225037): replace deepcopy with native clone() in Ax pending_observations = deepcopy(pending_observations) fixed_features = deepcopy(fixed_features) # Transform for t in self.transforms.values(): search_space = t.transform_search_space(search_space) if optimization_config is not None: optimization_config = t.transform_optimization_config( optimization_config=optimization_config, modelbridge=self, fixed_features=fixed_features, ) for metric, po in pending_observations.items(): pending_observations[metric] = t.transform_observation_features(po) fixed_features = ( t.transform_observation_features([fixed_features])[0] if fixed_features is not None else None ) return BaseGenArgs( search_space=search_space, optimization_config=optimization_config, pending_observations=pending_observations, fixed_features=fixed_features, ) def _validate_gen_inputs( self, n: int, search_space: Optional[SearchSpace] = None, optimization_config: Optional[OptimizationConfig] = None, pending_observations: Optional[Dict[str, List[ObservationFeatures]]] = None, fixed_features: Optional[ObservationFeatures] = None, model_gen_options: Optional[TConfig] = None, ) -> None: """Validate inputs to `ModelBridge.gen`. Currently, this is only used to ensure that `n` is a positive integer. """ if n < 1: raise UserInputError( f"Attempted to generate n={n} points. Number of points to generate " "must be a positive integer." )
[docs] def gen( self, n: int, search_space: Optional[SearchSpace] = None, optimization_config: Optional[OptimizationConfig] = None, pending_observations: Optional[Dict[str, List[ObservationFeatures]]] = None, fixed_features: Optional[ObservationFeatures] = None, model_gen_options: Optional[TConfig] = None, ) -> GeneratorRun: """ Generate new points from the underlying model according to search_space, optimization_config and other parameters. Args: n: Number of points to generate search_space: Search space optimization_config: Optimization config pending_observations: A map from metric name to pending observations for that metric. fixed_features: An ObservationFeatures object containing any features that should be fixed at specified values during generation. model_gen_options: A config dictionary that is passed along to the model. See `TorchOptConfig` for details. Returns: A GeneratorRun object that contains the generated points and other metadata. """ self._validate_gen_inputs( n=n, search_space=search_space, optimization_config=optimization_config, pending_observations=pending_observations, fixed_features=fixed_features, model_gen_options=model_gen_options, ) t_gen_start = time.monotonic() # Get modifiable versions if search_space is None: search_space = self._model_space orig_search_space = search_space search_space = search_space.clone() base_gen_args = self._get_transformed_gen_args( search_space=search_space, optimization_config=optimization_config, pending_observations=pending_observations, fixed_features=fixed_features, ) # Apply terminal transform and gen gen_results = self._gen( n=n, search_space=base_gen_args.search_space, optimization_config=base_gen_args.optimization_config, pending_observations=base_gen_args.pending_observations, fixed_features=base_gen_args.fixed_features, model_gen_options=model_gen_options, ) observation_features = gen_results.observation_features best_obsf = gen_results.best_observation_features # Apply reverse transforms for t in reversed(list(self.transforms.values())): observation_features = t.untransform_observation_features( observation_features ) if best_obsf is not None: best_obsf = t.untransform_observation_features([best_obsf])[0] # Clamp the untransformed data to the original search space if # we don't fit/gen OOD points if not self._fit_out_of_design: observation_features = clamp_observation_features( observation_features, orig_search_space ) if best_obsf is not None: best_obsf = clamp_observation_features([best_obsf], orig_search_space)[ 0 ] best_point_predictions = None try: model_predictions = self.predict(observation_features) if best_obsf is not None: best_point_predictions = extract_arm_predictions( model_predictions=self.predict([best_obsf]), arm_idx=0 ) except NotImplementedError: model_predictions = None if best_obsf is None: best_arm = None else: best_arms, _ = gen_arms( observation_features=[best_obsf], arms_by_signature=self._arms_by_signature, ) best_arm = best_arms[0] arms, candidate_metadata = gen_arms( observation_features=observation_features, arms_by_signature=self._arms_by_signature, ) # If experiment has immutable search space and metrics, no need to # save them on generator runs. immutable = getattr( self, "_experiment_has_immutable_search_space_and_opt_config", False ) optimization_config = None if immutable else base_gen_args.optimization_config gr = GeneratorRun( arms=arms, weights=gen_results.weights, optimization_config=optimization_config, search_space=None if immutable else base_gen_args.search_space, model_predictions=model_predictions, best_arm_predictions=None if best_arm is None else (best_arm, best_point_predictions), fit_time=self.fit_time_since_gen, gen_time=time.monotonic() - t_gen_start, model_key=self._model_key, model_kwargs=self._model_kwargs, bridge_kwargs=self._bridge_kwargs, gen_metadata=gen_results.gen_metadata, model_state_after_gen=self._get_serialized_model_state(), candidate_metadata_by_arm_signature=candidate_metadata, ) self.fit_time_since_gen = 0.0 return gr
def _gen( self, n: int, search_space: SearchSpace, optimization_config: Optional[OptimizationConfig], pending_observations: Dict[str, List[ObservationFeatures]], fixed_features: Optional[ObservationFeatures], model_gen_options: Optional[TConfig], ) -> GenResults: """Apply terminal transform, gen, and reverse terminal transform on output. """ raise NotImplementedError
[docs] def cross_validate( self, cv_training_data: List[Observation], cv_test_points: List[ObservationFeatures], ) -> List[ObservationData]: """Make a set of cross-validation predictions. Args: cv_training_data: The training data to use for cross validation. cv_test_points: The test points at which predictions will be made. Returns: A list of predictions at the test points. """ # Apply transforms to cv_training_data and cv_test_points cv_test_points = deepcopy(cv_test_points) cv_training_data = deepcopy(cv_training_data) search_space = self._model_space.clone() for t in self.transforms.values(): cv_training_data = t.transform_observations(cv_training_data) cv_test_points = t.transform_observation_features(cv_test_points) search_space = t.transform_search_space(search_space) # Apply terminal transform, and get predictions. with warnings.catch_warnings(): # Since each CV fold removes points from the training data, the remaining # observations will not pass the standardization test. To avoid confusing # users with this warning, we filter it out. warnings.filterwarnings( "ignore", message="Input data is not standardized.", category=InputDataWarning, ) cv_predictions = self._cross_validate( search_space=search_space, cv_training_data=cv_training_data, cv_test_points=cv_test_points, ) # Apply reverse transforms, in reverse order cv_test_observations = [ Observation(features=obsf, data=cv_predictions[i]) for i, obsf in enumerate(cv_test_points) ] for t in reversed(list(self.transforms.values())): cv_test_observations = t.untransform_observations(cv_test_observations) return [ for obs in cv_test_observations]
def _cross_validate( self, search_space: SearchSpace, cv_training_data: List[Observation], cv_test_points: List[ObservationFeatures], ) -> List[ObservationData]: """Apply the terminal transform, make predictions on the test points, and reverse terminal transform on the results. """ raise NotImplementedError
[docs] def compute_model_fit_metrics( self, experiment: Experiment, fit_metrics_dict: Optional[Dict[str, ModelFitMetricProtocol]] = None, ) -> Dict[str, Dict[str, float]]: """Computes the model fit metrics from the scheduler state. Args: experiment: The experiment with whose data to compute the model fit metrics. fit_metrics_dict: An optional dictionary with model fit metric functions, i.e. a ModelFitMetricProtocol, as values and their names as keys. Returns: A nested dictionary mapping from the *model fit* metric names and the *experimental metric* names to the values of the model fit metrics. Example for an imaginary AutoML experiment that seeks to minimize the test error after training an expensive model, with respect to hyper-parameters: ``` model_fit_dict = model_fit_metrics_from_scheduler(scheduler) model_fit_dict["coefficient_of_determination"]["test error"] = `coefficient of determination of the test error predictions` ``` """ # TODO: cross_validate_by_trial-based generalization quality # IDEA: store y_obs, y_pred, se_pred as well y_obs, y_pred, se_pred = _predict_on_training_data( model_bridge=self, experiment=experiment ) if fit_metrics_dict is None: fit_metrics_dict = { "coefficient_of_determination": coefficient_of_determination, "mean_of_the_standardized_error": mean_of_the_standardized_error, "std_of_the_standardized_error": std_of_the_standardized_error, } fit_metrics_dict = cast(Dict[str, ModelFitMetricProtocol], fit_metrics_dict) return compute_model_fit_metrics( y_obs=y_obs, y_pred=y_pred, se_pred=se_pred, fit_metrics_dict=fit_metrics_dict, )
def _set_kwargs_to_save( self, model_key: str, model_kwargs: Dict[str, Any], bridge_kwargs: Dict[str, Any], ) -> None: """Set properties used to save the model that created a given generator run, on the `GeneratorRun` object. Each generator run produced by the `gen` method of this model bridge will have the model key and kwargs fields set as provided in arguments to this function. """ self._model_key = model_key self._model_kwargs = model_kwargs self._bridge_kwargs = bridge_kwargs def _get_serialized_model_state(self) -> Dict[str, Any]: """Obtains the state of the underlying model (if using a stateful one) in a readily JSON-serializable form. """ model = not_none(self.model) return model.serialize_state(raw_state=model._get_state()) def _deserialize_model_state( self, serialized_state: Dict[str, Any] ) -> Dict[str, Any]: model = not_none(self.model) return model.deserialize_state(serialized_state=serialized_state)
[docs] def feature_importances(self, metric_name: str) -> Dict[str, float]: """Computes feature importances for a single metric. Depending on the type of the model, this method will approach sensitivity analysis (calculating the sensitivity of the metric to changes in the search space's parameters, a.k.a. features) differently. For Bayesian optimization models (BoTorch models), this method uses parameter inverse lengthscales to compute normalized feature importances. NOTE: Currently, this is only implemented for GP models. Args: metric_name: Name of metric to compute feature importances for. Returns: A dictionary mapping parameter names to their corresponding feature importances. """ raise NotImplementedError( "Feature importance not available for this model type" )
# pyre-fixme[3]: Return annotation cannot be `Any`.
[docs] def transform_observations(self, observations: List[Observation]) -> Any: """Applies transforms to given observation features and returns them in the model space. Args: observation_features: ObservationFeatures to be transformed. Returns: Transformed values. This could be e.g. a torch Tensor, depending on the ModelBridge subclass. """ observations = deepcopy(observations) for t in self.transforms.values(): observations = t.transform_observations(observations) # Apply terminal transform and return return self._transform_observations(observations)
# pyre-fixme[3]: Return annotation cannot be `Any`. def _transform_observations(self, observations: List[Observation]) -> Any: """Apply terminal transform to given observations and return result.""" raise NotImplementedError # pyre-fixme[3]: Return annotation cannot be `Any`.
[docs] def transform_observation_features( self, observation_features: List[ObservationFeatures] ) -> Any: """Applies transforms to given observation features and returns them in the model space. Args: observation_features: ObservationFeatures to be transformed. Returns: Transformed values. This could be e.g. a torch Tensor, depending on the ModelBridge subclass. """ obsf = deepcopy(observation_features) for t in self.transforms.values(): obsf = t.transform_observation_features(obsf) # Apply terminal transform and return return self._transform_observation_features(obsf)
# pyre-fixme[3]: Return annotation cannot be `Any`. def _transform_observation_features( self, observation_features: List[ObservationFeatures] ) -> Any: """Apply terminal transform to given observation features and return result.""" raise NotImplementedError
[docs]def unwrap_observation_data(observation_data: List[ObservationData]) -> TModelPredict: """Converts observation data to the format for model prediction outputs. That format assumes each observation data has the same set of metrics. """ metrics = set(observation_data[0].metric_names) f: TModelMean = {metric: [] for metric in metrics} cov: TModelCov = {m1: {m2: [] for m2 in metrics} for m1 in metrics} for od in observation_data: if set(od.metric_names) != metrics: raise ValueError( "Each ObservationData should use same set of metrics. " "Expected {exp}, got {got}.".format( exp=metrics, got=set(od.metric_names) ) ) for i, m1 in enumerate(od.metric_names): f[m1].append(od.means[i]) for j, m2 in enumerate(od.metric_names): cov[m1][m2].append(od.covariance[i, j]) return f, cov
[docs]def gen_arms( observation_features: List[ObservationFeatures], arms_by_signature: Optional[Dict[str, Arm]] = None, ) -> Tuple[List[Arm], Optional[Dict[str, TCandidateMetadata]]]: """Converts observation features to a tuple of arms list and candidate metadata dict, where arm signatures are mapped to their respective candidate metadata. """ # TODO(T34225939): handle static context (which is stored on observation_features) arms = [] candidate_metadata = {} for of in observation_features: arm = Arm(parameters=of.parameters) if arms_by_signature is not None and arm.signature in arms_by_signature: existing_arm = arms_by_signature[arm.signature] arm = Arm(, parameters=existing_arm.parameters) arms.append(arm) if of.metadata: candidate_metadata[arm.signature] = of.metadata return arms, candidate_metadata or None # None if empty cand. metadata.
[docs]def clamp_observation_features( observation_features: List[ObservationFeatures], search_space: SearchSpace ) -> List[ObservationFeatures]: range_parameters = [ p for p in search_space.parameters.values() if isinstance(p, RangeParameter) ] for obsf in observation_features: for p in range_parameters: if not in obsf.parameters: continue if p.parameter_type == ParameterType.FLOAT: val = checked_cast(float, obsf.parameters[]) else: val = checked_cast(int, obsf.parameters[]) if val < p.lower: f"Untransformed parameter {val} " f"less than lower bound {p.lower}, clamping" ) obsf.parameters[] = p.lower elif val > p.upper: f"Untransformed parameter {val} " f"greater than upper bound {p.upper}, clamping" ) obsf.parameters[] = p.upper return observation_features
""" ############################## Model Fit Metrics Utils ############################## """ def _predict_on_training_data( model_bridge: ModelBridge, experiment: Experiment, ) -> Tuple[Dict[str, np.ndarray], Dict[str, np.ndarray], Dict[str, np.ndarray],]: """Makes predictions on the training data of a given experiment using a ModelBridge and returning the observed values, and the corresponding predictive means and predictive standard deviations of the model. NOTE: This is a helper function for `ModelBridge.compute_model_fit_metrics` and could be attached to the class. Args: model_bridge: A ModelBridge object with which to make predictions. experiment: The experiment with whose data to compute the model fit metrics. Returns: A tuple containing three dictionaries for 1) observed metric values, and the model's associated 2) predictive means and 3) predictive standard deviations. """ data = experiment.fetch_data() observations = observations_from_data( experiment=experiment, data=data ) # List[Observation] observation_features = [obs.features for obs in observations] mean_predicted, cov_predicted = model_bridge.predict( observation_features=observation_features ) # Dict[str, List[float]] mean_observed = [ for obs in observations ] # List[Dict[str, float]] metric_names = list(data.metric_names) mean_observed = _list_of_dicts_to_dict_of_lists( list_of_dicts=mean_observed, keys=metric_names ) # converting dictionary values to arrays mean_observed = {k: np.array(v) for k, v in mean_observed.items()} mean_predicted = {k: np.array(v) for k, v in mean_predicted.items()} std_predicted = {m: np.sqrt(np.array(cov_predicted[m][m])) for m in cov_predicted} return mean_observed, mean_predicted, std_predicted def _list_of_dicts_to_dict_of_lists( list_of_dicts: List[Dict[str, float]], keys: List[str] ) -> Dict[str, List[float]]: """Converts a list of dicts indexed by a string to a dict of lists.""" return {key: [d[key] for d in list_of_dicts] for key in keys}