import torch
from collections import Counter
from ._RegressionTree import RegressionTree
from ....Exceptions import NotFittedError
[docs]
class RandomForestRegressor:
"""
RandomForestRegressor implements a regression algorithm fitting many :class:`RegressionTrees <DLL.MachineLearning.SupervisedLearning.Trees.RegressionTree>` to bootstrapped data.
Args:
n_trees (int, optional): The number of trees used for predictiong. Defaults to 10. Must be a positive integer.
max_depth (int, optional): The maximum depth of the tree. Defaults to 10. Must be a positive integer.
min_samples_split (int, optional): The minimum required samples in a leaf to make a split. Defaults to 2. Must be a positive integer.
"""
def __init__(self, n_trees=10, max_depth=25, min_samples_split=2):
if not isinstance(n_trees, int) or n_trees < 1:
raise ValueError("n_trees must be a positive integer.")
if not isinstance(max_depth, int) or max_depth < 1:
raise ValueError("max_depth must be a positive integer.")
if not isinstance(min_samples_split, int) or min_samples_split < 1:
raise ValueError("min_samples_split must be a positive integer.")
self.n_trees = n_trees
self.max_depth = max_depth
self.min_samples_split = min_samples_split
self.trees = [RegressionTree(max_depth=max_depth, min_samples_split=min_samples_split) for _ in range(n_trees)]
[docs]
def fit(self, X, y):
"""
Fits the RandomForestRegressor model to the input data by generating trees, which split the data appropriately.
Args:
X (torch.Tensor of shape (n_samples, n_features)): The input data, where each row is a sample and each column is a feature.
y (torch.Tensor of shape (n_samples,)): The target values corresponding to each sample.
Returns:
None
Raises:
TypeError: If the input matrix or the target matrix is not a PyTorch tensor.
ValueError: If the input matrix or the target matrix is not the correct shape.
"""
if not isinstance(X, torch.Tensor) or not isinstance(y, torch.Tensor):
raise TypeError("The input matrix and the target matrix must be a PyTorch tensor.")
if X.ndim != 2:
raise ValueError("The input matrix must be a 2 dimensional tensor.")
if y.ndim != 1 or y.shape[0] != X.shape[0]:
raise ValueError("The target must be 1 dimensional with the same number of samples as the input data")
for tree in self.trees:
tree.fit(*self._bootstrap_sample(X, y))
[docs]
def predict(self, X):
"""
Applies the fitted RandomForestRegressor model to the input data, predicting the correct values.
Args:
X (torch.Tensor of shape (n_samples, n_features)): The input data to be regressed.
Returns:
labels (torch.Tensor of shape (n_samples,)): The predicted target values corresponding to each sample.
Raises:
NotFittedError: If the RandomForestRegressor model has not been fitted before predicting.
TypeError: If the input matrix is not a PyTorch tensor.
ValueError: If the input matrix is not the correct shape.
"""
if self.trees is None:
raise NotFittedError("RandomForestRegressor.fit() must be called before predicting.")
if not isinstance(X, torch.Tensor):
raise TypeError("The input matrix must be a PyTorch tensor.")
if X.ndim != 2 or X.shape[1] != self.trees[0].n_features:
raise ValueError("The input matrix must be a 2 dimensional tensor with the same number of features as the fitted tensor.")
predictions = torch.stack([tree.predict(X) for tree in self.trees]).T
return torch.tensor([Counter(sample_prediction).most_common(1)[0][0] for sample_prediction in predictions])
def _bootstrap_sample(self, x, y):
indices = torch.randint(high=len(y), size=(len(y), 1)).flatten()
return x[indices], y[indices]