#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from typing import Dict, List, Optional, Tuple
import numpy as np
from ax.core.types import TConfig, TParamValue, TParamValueList
[docs]class DiscreteModel:
"""This class specifies the interface for a model based on discrete parameters.
These methods should be implemented to have access to all of the features
of Ax.
"""
[docs] def fit(
self,
Xs: List[List[TParamValueList]],
Ys: List[List[float]],
Yvars: List[List[float]],
parameter_values: List[TParamValueList],
outcome_names: List[str],
) -> None:
"""Fit model to m outcomes.
Args:
Xs: A list of m lists X of parameterizations (each parameterization
is a list of parameter values of length d), each of length k_i,
for each outcome.
Ys: The corresponding list of m lists Y, each of length k_i, for
each outcome.
Yvars: The variances of each entry in Ys, same shape.
parameter_values: A list of possible values for each parameter.
outcome_names: A list of m outcome names.
"""
pass
[docs] def predict(self, X: List[TParamValueList]) -> Tuple[np.ndarray, np.ndarray]:
"""Predict
Args:
X: List of the j parameterizations at which to make predictions.
Returns:
2-element tuple containing
- (j x m) array of outcome predictions at X.
- (j x m x m) array of predictive covariances at X.
cov[j, m1, m2] is Cov[m1@j, m2@j].
"""
raise NotImplementedError
[docs] 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]]:
"""
Generate new candidates.
Args:
n: Number of candidates to generate.
parameter_values: A list of possible values for each parameter.
objective_weights: The objective is to maximize a weighted sum of
the columns of f(x). These are the weights.
outcome_constraints: A tuple of (A, b). For k outcome constraints
and m outputs at f(x), A is (k x m) and b is (k x 1) such that
A f(x) <= b.
fixed_features: A map {feature_index: value} for features that
should be fixed to a particular value during generation.
pending_observations: A list of m lists of parameterizations
(each parameterization is a list of parameter values of length d),
each of length k_i, for each outcome i.
model_gen_options: A config dictionary that can contain
model-specific options.
Returns:
2-element tuple containing
- List of n generated points, where each point is represented
by a list of parameter values.
- List of weights for each of the n points.
"""
raise NotImplementedError
[docs] def cross_validate(
self,
Xs_train: List[List[TParamValueList]],
Ys_train: List[List[float]],
Yvars_train: List[List[float]],
X_test: List[TParamValueList],
) -> Tuple[np.ndarray, np.ndarray]:
"""Do cross validation with the given training and test sets.
Training set is given in the same format as to fit. Test set is given
in the same format as to predict.
Args:
Xs_train: A list of m lists X of parameterizations (each parameterization
is a list of parameter values of length d), each of length k_i,
for each outcome.
Ys_train: The corresponding list of m lists Y, each of length k_i, for
each outcome.
Yvars_train: The variances of each entry in Ys, same shape.
X_test: List of the j parameterizations at which to make predictions.
Returns:
2-element tuple containing
- (j x m) array of outcome predictions at X.
- (j x m x m) array of predictive covariances at X.
`cov[j, m1, m2]` is `Cov[m1@j, m2@j]`.
"""
raise NotImplementedError
[docs] def best_point(
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,
) -> Optional[TParamValueList]:
"""Obtains the point that has the best value according to the model
prediction and its model predictions.
Returns:
(1 x d) parameter value list representing the point with the best
value according to the model prediction. None if this function
is not implemented for the given model.
"""
return None # pragma: no cover TODO[bletham, drfreund]
