#!/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
import hashlib
import json
from typing import Dict, List, Optional, Tuple
import numpy as np
from ax.core.types import TGenMetadata, TParamValue, TParamValueList
from ax.exceptions.constants import TS_MIN_WEIGHT_ERROR, TS_NO_FEASIBLE_ARMS_ERROR
from ax.exceptions.model import ModelError
from ax.models.discrete_base import DiscreteModel
from ax.models.types import TConfig
from ax.utils.common.docutils import copy_doc
[docs]class ThompsonSampler(DiscreteModel):
"""Generator for Thompson sampling.
The generator performs Thompson sampling on the data passed in via `fit`.
Arms are given weight proportional to the probability that they are
winners, according to Monte Carlo simulations.
"""
def __init__(
self,
num_samples: int = 10000,
min_weight: Optional[float] = None,
uniform_weights: bool = False,
) -> None:
"""
Args:
num_samples: The number of samples to draw from the posterior.
min_weight: The minimum weight a arm must be
given in order for it to be returned from the gernerator. If not
specified, will be set to 2 / (number of arms).
uniform_weights: If True, the arms returned from the
generator will each be given a weight of 1 / (number of arms).
"""
self.num_samples = num_samples
self.min_weight = min_weight
self.uniform_weights = uniform_weights
# pyre-fixme[4]: Attribute must be annotated.
self.X = None
# pyre-fixme[4]: Attribute must be annotated.
self.Ys = None
# pyre-fixme[4]: Attribute must be annotated.
self.Yvars = None
# pyre-fixme[4]: Attribute must be annotated.
self.X_to_Ys_and_Yvars = None
[docs] @copy_doc(DiscreteModel.fit)
def fit(
self,
Xs: List[List[TParamValueList]],
Ys: List[List[float]],
Yvars: List[List[float]],
parameter_values: List[TParamValueList],
outcome_names: List[str],
) -> None:
self.X = self._fit_X(Xs=Xs)
self.Ys, self.Yvars = self._fit_Ys_and_Yvars(
Ys=Ys, Yvars=Yvars, outcome_names=outcome_names
)
self.X_to_Ys_and_Yvars = self._fit_X_to_Ys_and_Yvars(
X=self.X, Ys=self.Ys, Yvars=self.Yvars
)
[docs] @copy_doc(DiscreteModel.gen)
def gen(
self,
n: int,
parameter_values: List[TParamValueList],
objective_weights: Optional[np.ndarray],
outcome_constraints: Optional[Tuple[np.ndarray, np.ndarray]] = None,
fixed_features: Optional[Dict[int, TParamValue]] = None,
pending_observations: Optional[List[List[TParamValueList]]] = None,
model_gen_options: Optional[TConfig] = None,
) -> Tuple[List[TParamValueList], List[float], TGenMetadata]:
if objective_weights is None:
raise ValueError("ThompsonSampler requires objective weights.")
arms = self.X
k = len(arms)
weights = self._generate_weights(
objective_weights=objective_weights, outcome_constraints=outcome_constraints
)
min_weight = self.min_weight if self.min_weight is not None else 2.0 / k
# Second entry is used for tie-breaking
weighted_arms = [
(weights[i], np.random.random(), arms[i])
for i in range(k)
if weights[i] > min_weight
]
if len(weighted_arms) == 0:
raise ModelError(
TS_MIN_WEIGHT_ERROR.format(
min_weight=min_weight, max_weight=max(weights)
)
)
weighted_arms.sort(reverse=True)
top_weighted_arms = weighted_arms[:n] if n > 0 else weighted_arms
top_arms = [arm for _, _, arm in top_weighted_arms]
top_weights = [weight for weight, _, _ in top_weighted_arms]
# N TS arms should have total weight N
if self.uniform_weights:
top_weights = [1.0 for _ in top_weights]
else:
top_weights = [
(x * len(top_weights)) / sum(top_weights) for x in top_weights
]
return top_arms, top_weights, {"arms_to_weights": list(zip(arms, weights))}
[docs] @copy_doc(DiscreteModel.predict)
def predict(self, X: List[TParamValueList]) -> Tuple[np.ndarray, np.ndarray]:
n = len(X) # number of parameterizations at which to make predictions
m = len(self.Ys) # number of outcomes
f = np.zeros((n, m)) # array of outcome predictions
cov = np.zeros((n, m, m)) # array of predictive covariances
predictX = [self._hash_TParamValueList(x) for x in X]
for i, X_to_Y_and_Yvar in enumerate(self.X_to_Ys_and_Yvars):
# iterate through outcomes
for j, x in enumerate(predictX):
# iterate through parameterizations at which to make predictions
if x not in X_to_Y_and_Yvar:
raise ValueError(
"ThompsonSampler does not support out-of-sample prediction."
)
f[j, i], cov[j, i, i] = X_to_Y_and_Yvar[x]
return f, cov
def _generate_weights(
self,
objective_weights: np.ndarray,
outcome_constraints: Optional[Tuple[np.ndarray, np.ndarray]] = None,
) -> List[float]:
samples, fraction_all_infeasible = self._produce_samples(
num_samples=self.num_samples,
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
)
if fraction_all_infeasible > 0.99:
raise ModelError(TS_NO_FEASIBLE_ARMS_ERROR)
num_valid_samples = samples.shape[1]
while num_valid_samples < self.num_samples:
num_additional_samples = (self.num_samples - num_valid_samples) / (
1 - fraction_all_infeasible
)
num_additional_samples = int(np.maximum(num_additional_samples, 100))
new_samples, _ = self._produce_samples(
num_samples=num_additional_samples,
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
)
samples = np.concatenate([samples, new_samples], axis=1)
num_valid_samples = samples.shape[1]
winner_indices = np.argmax(samples, axis=0) # (num_samples,)
winner_counts = np.zeros(len(self.X)) # (k,)
for index in winner_indices:
winner_counts[index] += 1
weights = winner_counts / winner_counts.sum()
return weights.tolist()
def _generate_samples_per_metric(self, num_samples: int) -> np.ndarray:
k = len(self.X)
samples_per_metric = np.zeros(
(k, num_samples, len(self.Ys))
) # k x num_samples x m
for i, Y in enumerate(self.Ys): # (k x 1)
Yvar = self.Yvars[i] # (k x 1)
cov = np.diag(Yvar) # (k x k)
samples = np.random.multivariate_normal(
Y, cov, num_samples
).T # (k x num_samples)
samples_per_metric[:, :, i] = samples
return samples_per_metric
def _produce_samples(
self,
num_samples: int,
objective_weights: np.ndarray,
outcome_constraints: Optional[Tuple[np.ndarray, np.ndarray]],
) -> Tuple[np.ndarray, float]:
k = len(self.X)
samples_per_metric = self._generate_samples_per_metric(num_samples=num_samples)
any_violation = np.zeros((k, num_samples), dtype=bool) # (k x num_samples)
if outcome_constraints:
# A is (num_constraints x m)
# b is (num_constraints x 1)
A, b = outcome_constraints
# (k x num_samples x m) dot (num_constraints x m)^T
# = (k x num_samples x m) dot (m x num_constraints)
# ==> (k x num_samples x num_constraints)
constraint_values = np.dot(samples_per_metric, A.T)
violations = constraint_values > b.T
any_violation = np.max(violations, axis=2) # (k x num_samples)
objective_values = np.dot(
samples_per_metric, objective_weights
) # (k x num_samples)
objective_values[any_violation] = -np.inf
best_arm = objective_values.max(axis=0) # (num_samples,)
all_arms_infeasible = best_arm == -np.inf # (num_samples,)
fraction_all_infeasible = all_arms_infeasible.mean()
filtered_objective = objective_values[:, ~all_arms_infeasible] # (k x ?)
return filtered_objective, fraction_all_infeasible
def _validate_Xs(self, Xs: List[List[TParamValueList]]) -> None:
"""
1. Require that all Xs have the same arms, i.e. we have observed
all arms for all metrics. If so, we can safely use Xs[0] exclusively.
2. Require that all rows of X are unique, i.e. only one observation
per parameterization.
"""
if not all(x == Xs[0] for x in Xs[1:]):
raise ValueError(
"ThompsonSampler requires that all elements of Xs are identical; "
"i.e. that we have observed all arms for all metrics."
)
X = Xs[0]
uniqueX = {self._hash_TParamValueList(x) for x in X}
if len(uniqueX) != len(X):
raise ValueError(
"ThompsonSampler requires all rows of X to be unique; "
"i.e. that there is only one observation per parameterization."
)
def _fit_X(self, Xs: List[List[TParamValueList]]) -> List[TParamValueList]:
"""After validation has been performed, it's safe to use Xs[0]."""
self._validate_Xs(Xs=Xs)
return Xs[0]
def _fit_Ys_and_Yvars(
self, Ys: List[List[float]], Yvars: List[List[float]], outcome_names: List[str]
) -> Tuple[List[List[float]], List[List[float]]]:
"""For plain Thompson Sampling, there's nothing to be done here.
EmpiricalBayesThompsonSampler will overwrite this method to perform
shrinkage.
"""
return Ys, Yvars
def _fit_X_to_Ys_and_Yvars(
self, X: List[TParamValueList], Ys: List[List[float]], Yvars: List[List[float]]
) -> List[Dict[TParamValueList, Tuple[float, float]]]:
"""Construct lists of mappings, one per outcome, of parameterizations
to the a tuple of their mean and variance.
"""
X_to_Ys_and_Yvars = []
hashableX = [self._hash_TParamValueList(x) for x in X]
for Y, Yvar in zip(Ys, Yvars):
X_to_Ys_and_Yvars.append(dict(zip(hashableX, zip(Y, Yvar))))
return X_to_Ys_and_Yvars
def _hash_TParamValueList(self, x: TParamValueList) -> str:
"""Hash a list of parameter values. This is safer than converting the
list to a tuple because of int/floats.
"""
param_values_str = json.dumps(x)
return hashlib.md5(param_values_str.encode("utf-8")).hexdigest()
