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BQ stopping criteria that depend on model statistics #463
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0f44c1d
add BQLoopState BQOuterLoop and COV stopping condition
mmahsereci 695fa22
some simplifications
mmahsereci e24fb06
adding changes to docs
mmahsereci 10c130d
Merge branch 'main' into mm-bq-stopping-conditions
mmahsereci be501a2
Merge branch 'main' into mm-bq-stopping-conditions
mmahsereci addb62d
docstring updates
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Original file line number | Diff line number | Diff line change |
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# Copyright 2020-2024 The Emukit Authors. All Rights Reserved. | ||
# SPDX-License-Identifier: Apache-2.0 | ||
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from typing import List, Optional | ||
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import numpy as np | ||
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from ...core.loop.loop_state import LoopState, create_loop_state | ||
from ...core.loop.user_function_result import UserFunctionResult | ||
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class QuadratureLoopState(LoopState): | ||
"""Contains the state of the BQ loop, which includes a history of all integrand evaluations and integral mean and | ||
variance estimates. | ||
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:param initial_results: The results from previous integrand evaluations. | ||
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""" | ||
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def __init__(self, initial_results: List[UserFunctionResult]) -> None: | ||
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super().__init__(initial_results) | ||
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self.integral_means = [] | ||
self.integral_vars = [] | ||
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def update_integral_stats(self, integral_mean: float, integral_var: float) -> None: | ||
"""Adds the latest integral mean and variance to the loop state. | ||
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:param integral_mean: The latest integral mean estimate. | ||
:param integral_var: The latest integral variance. | ||
""" | ||
self.integral_means.append(integral_mean) | ||
self.integral_vars.append(integral_var) | ||
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def create_bq_loop_state(x_init: np.ndarray, y_init: np.ndarray, **kwargs) -> QuadratureLoopState: | ||
"""Creates a BQ loop state object using the provided data. | ||
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:param x_init: x values for initial function evaluations. Shape: (n_initial_points x n_input_dims) | ||
:param y_init: y values for initial function evaluations. Shape: (n_initial_points x n_output_dims) | ||
:param kwargs: extra outputs observed from a function evaluation. Shape: (n_initial_points x n_dims) | ||
:return: The BQ loop state. | ||
""" | ||
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loop_state = create_loop_state(x_init, y_init, **kwargs) | ||
return QuadratureLoopState(loop_state.results) |
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# Copyright 2020-2024 The Emukit Authors. All Rights Reserved. | ||
# SPDX-License-Identifier: Apache-2.0 | ||
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from typing import List, Union | ||
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from ...core.loop import OuterLoop | ||
from ...core.loop.candidate_point_calculators import CandidatePointCalculator | ||
from ...core.loop.model_updaters import ModelUpdater | ||
from .bq_loop_state import QuadratureLoopState | ||
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class QuadratureOuterLoop(OuterLoop): | ||
"""Base class for a Bayesian quadrature loop. | ||
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:param candidate_point_calculator: Finds next point(s) to evaluate. | ||
:param model_updaters: Updates the model with the new data and fits the model hyper-parameters. | ||
:param loop_state: Object that keeps track of the history of the BQ loop. Default is None, resulting in empty | ||
initial state. | ||
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:raises ValueError: If more than one model updater is provided. | ||
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""" | ||
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def __init__( | ||
self, | ||
candidate_point_calculator: CandidatePointCalculator, | ||
model_updaters: Union[ModelUpdater, List[ModelUpdater]], | ||
loop_state: QuadratureLoopState = None, | ||
): | ||
if isinstance(model_updaters, list): | ||
raise ValueError("The BQ loop only supports a single model.") | ||
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super().__init__(candidate_point_calculator, model_updaters, loop_state) | ||
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def _update_loop_state(self) -> None: | ||
model = self.model_updaters[0].model # only works if there is one model, but for BQ nothing else makes sense | ||
integral_mean, integral_var = model.integrate() | ||
self.loop_state.update_integral_stats(integral_mean, integral_var) |
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Original file line number | Diff line number | Diff line change |
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# Copyright 2020-2024 The Emukit Authors. All Rights Reserved. | ||
# SPDX-License-Identifier: Apache-2.0 | ||
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import logging | ||
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import numpy as np | ||
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from ...core.loop.stopping_conditions import StoppingCondition | ||
from .bq_loop_state import QuadratureLoopState | ||
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_log = logging.getLogger(__name__) | ||
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class CoefficientOfVariationStoppingCondition(StoppingCondition): | ||
r"""Stops once the coefficient of variation (COV) falls below a threshold. | ||
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The COV is given by | ||
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.. math:: | ||
COV = \frac{\sigma}{\mu} | ||
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where :math:`\mu` and :math:`\sigma^2` are the current mean and variance respectively of the integral according to | ||
the BQ posterior model. | ||
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:param eps: Threshold under which the COV must fall. | ||
:param delay: Number of times the stopping condition needs to be true in a row in order to stop. Defaults to 1. | ||
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:raises ValueError: If `delay` is smaller than 1. | ||
:raises ValueError: If `eps` is non-negative. | ||
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""" | ||
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def __init__(self, eps: float, delay: int = 1) -> None: | ||
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if delay < 1: | ||
raise ValueError(f"delay ({delay}) must be and integer greater than zero.") | ||
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if eps <= 0.0: | ||
raise ValueError(f"eps ({eps}) must be positive.") | ||
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self.eps = eps | ||
self.delay = delay | ||
self.times_true = 0 # counts how many times stopping has been triggered in a row | ||
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def should_stop(self, loop_state: QuadratureLoopState) -> bool: | ||
if len(loop_state.integral_means) < 1: | ||
return False | ||
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m = loop_state.integral_means[-1] | ||
v = loop_state.integral_vars[-1] | ||
should_stop = (np.sqrt(v) / m) < self.eps | ||
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if should_stop: | ||
self.times_true += 1 | ||
else: | ||
self.times_true = 0 | ||
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should_stop = should_stop and (self.times_true >= self.delay) | ||
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if should_stop: | ||
_log.info(f"Stopped as coefficient of variation is below threshold of {self.eps}.") | ||
return should_stop |
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because if you pass float('inf'), then you will have an infinite loop right?