Source code for ax.models.torch.randomforest

#!/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.

from __future__ import annotations

from typing import Any, List, Optional, Tuple

import numpy as np
import torch
from ax.core.search_space import SearchSpaceDigest
from ax.core.types import TCandidateMetadata
from ax.models.torch.utils import _datasets_to_legacy_inputs
from ax.models.torch_base import TorchModel
from ax.utils.common.docutils import copy_doc
from botorch.utils.datasets import SupervisedDataset
from sklearn.ensemble import RandomForestRegressor
from sklearn.tree import DecisionTreeRegressor
from torch import Tensor

[docs]class RandomForest(TorchModel): """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( def fit( self, datasets: List[SupervisedDataset], metric_names: List[str], search_space_digest: SearchSpaceDigest, candidate_metadata: Optional[List[List[TCandidateMetadata]]] = None, ) -> None: Xs, Ys, Yvars = _datasets_to_legacy_inputs(datasets=datasets) for X, Y, Yvar in zip(Xs, Ys, Yvars): self.models.append( _get_rf( X=X.numpy(), Y=Y.numpy(), Yvar=Yvar.numpy(), num_trees=self.num_trees, max_features=self.max_features, ) )
[docs] @copy_doc(TorchModel.predict) def predict(self, X: Tensor) -> Tuple[Tensor, Tensor]: return _rf_predict(self.models, X)
[docs] @copy_doc(TorchModel.cross_validate) def cross_validate( # pyre-ignore [14]: not using metric_names or ssd self, datasets: List[SupervisedDataset], X_test: Tensor, **kwargs: Any, ) -> Tuple[Tensor, Tensor]: Xs, Ys, Yvars = _datasets_to_legacy_inputs(datasets=datasets) cv_models: List[RandomForestRegressor] = [] for X, Y, Yvar in zip(Xs, Ys, Yvars): cv_models.append( _get_rf( X=X.numpy(), Y=Y.numpy(), Yvar=Yvar.numpy(), 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)] for estimator in r.estimators_: # Parametric bootstrap y = np.random.normal(loc=Y[:, 0], scale=np.sqrt(Yvar[:, 0])), y) return r def _rf_predict( models: List[RandomForestRegressor], X: Tensor ) -> Tuple[Tensor, Tensor]: """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): # pyre-fixme[16]: `RandomForestRegressor` has no attribute `estimators_`. preds = np.vstack([tree.predict(X.numpy()) for tree in m.estimators_]) f[:, i] = preds.mean(0) cov[:, i, i] = preds.var(0) return torch.from_numpy(f), torch.from_numpy(cov)