#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from typing import List, Optional, Tuple
import numpy as np
from ax.models.numpy_base import NumpyModel
from ax.utils.common.docutils import copy_doc
from sklearn.ensemble import RandomForestRegressor
from sklearn.tree import DecisionTreeRegressor
[docs]class RandomForest(NumpyModel):
"""A Random Forest model.
Uses a parametric bootstrap to handle uncertainty in Y.
Can be used to fit data, make predictions, and do cross validation; however
gen is not implemented and so this model cannot generate new points.
Args:
max_features: Maximum number of features at each split. With one-hot
encoding, this should be set to None. Defaults to "sqrt", which is
Breiman's version of Random Forest.
num_trees: Number of trees.
"""
def __init__(
self, max_features: Optional[str] = "sqrt", num_trees: int = 500
) -> None:
self.max_features = max_features
self.num_trees = num_trees
self.models: List[RandomForestRegressor] = []
[docs] @copy_doc(NumpyModel.fit)
def fit(
self,
Xs: List[np.ndarray],
Ys: List[np.ndarray],
Yvars: List[np.ndarray],
bounds: List[Tuple[float, float]],
task_features: List[int],
feature_names: List[str],
metric_names: List[str],
fidelity_features: List[int],
) -> None:
for i, X in enumerate(Xs):
self.models.append(
_get_rf(
X=X,
Y=Ys[i],
Yvar=Yvars[i],
num_trees=self.num_trees,
max_features=self.max_features,
)
)
[docs] @copy_doc(NumpyModel.predict)
def predict(self, X: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
return _rf_predict(self.models, X)
[docs] @copy_doc(NumpyModel.cross_validate)
def cross_validate(
self,
Xs_train: List[np.ndarray],
Ys_train: List[np.ndarray],
Yvars_train: List[np.ndarray],
X_test: np.ndarray,
) -> Tuple[np.ndarray, np.ndarray]:
cv_models: List[RandomForestRegressor] = []
for i, X in enumerate(Xs_train):
cv_models.append(
_get_rf(
X=X,
Y=Ys_train[i],
Yvar=Yvars_train[i],
num_trees=self.num_trees,
max_features=self.max_features,
)
)
return _rf_predict(cv_models, X_test)
def _get_rf(
X: np.ndarray,
Y: np.ndarray,
Yvar: np.ndarray,
num_trees: int,
max_features: Optional[str],
) -> RandomForestRegressor:
"""Fit a Random Forest model.
Args:
X: X
Y: Y
Yvar: Variance for Y
num_trees: Number of trees
max_features: Max features specifier
Returns: Fitted Random Forest.
"""
r = RandomForestRegressor(
n_estimators=num_trees, max_features=max_features, bootstrap=True
)
# pyre-fixme[16]: `RandomForestRegressor` has no attribute `estimators_`.
r.estimators_ = [
DecisionTreeRegressor() for i in range(r.n_estimators) # pyre-ignore
]
for estimator in r.estimators_:
# Parametric bootstrap
y = np.random.normal(loc=Y[:, 0], scale=np.sqrt(Yvar[:, 0]))
estimator.fit(X, y)
return r
def _rf_predict(
models: List[RandomForestRegressor], X: np.ndarray
) -> Tuple[np.ndarray, np.ndarray]:
"""Make predictions with Random Forest models.
Args:
models: List of models for each outcome
X: X to predict
Returns: mean and covariance estimates
"""
f = np.zeros((X.shape[0], len(models)))
cov = np.zeros((X.shape[0], len(models), len(models)))
for i, m in enumerate(models):
preds = np.vstack([tree.predict(X) for tree in m.estimators_]) # pyre-ignore
f[:, i] = preds.mean(0)
cov[:, i, i] = preds.var(0)
return f, cov
