import torch
from ._XGBoostTree import _XGBoostTree
from ....DeepLearning.Losses import MSE, MAE, Huber
from ....Exceptions import NotFittedError
from ....Data.Metrics import calculate_metrics
[docs]
class XGBoostingRegressor:
"""
XGBoostingRegressor implements a regression algorithm fitting many consecutive trees to gradients and hessians of the loss function.
Args:
n_trees (int, optional): The number of trees used for predicting. Defaults to 50. Must be a positive integer.
learning_rate (float, optional): The number multiplied to each additional trees residuals. Must be a real number in range (0, 1). Defaults to 0.5.
max_depth (int, optional): The maximum depth of the tree. Defaults to 3. 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.
reg_lambda (float | int, optional): The regularisation parameter used in fitting the trees. The larger the parameter, the smaller the trees. Must be a positive real number. Defaults to 1.
gamma (float | int, optional): The minimum gain to make a split. Must be a non-negative real number. Defaults to 0.
loss (string, optional): The loss function used in calculations of the gradients and hessians. Must be one of "squared", "absolute" or "huber". Defaults to "squared".
huber_delta (float | int, optional): The delta parameter for the possibly used huber loss. If loss is not "huber", this parameter is ignored.
Attributes:
n_features (int): The number of features. Available after fitting.
"""
def __init__(self, n_trees=50, learning_rate=0.5, max_depth=3, min_samples_split=2, reg_lambda=1, gamma=0, loss="squared", huber_delta=1):
if not isinstance(n_trees, int) or n_trees < 1:
raise ValueError("n_trees must be a positive integer.")
if not isinstance(learning_rate, float) or learning_rate <= 0 or learning_rate >= 1:
raise ValueError("learning_rate must be a float in range (0, 1).")
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.")
if not isinstance(reg_lambda, int | float) or reg_lambda <= 0:
raise ValueError("reg_lambda must be a positive real number.")
if not isinstance(gamma, int | float) or gamma < 0:
raise ValueError("gamma must be a non-negative real number.")
if loss not in ["squared", "absolute", "huber"]:
raise ValueError('loss must be one in ["squared", "absolute", "huber"].')
if not isinstance(huber_delta, int | float) or huber_delta <= 0:
raise ValueError("huber_delta must be a positive real number.")
self.n_trees = n_trees
self.learning_rate = learning_rate
self.max_depth = max_depth
self.min_samples_split = min_samples_split
self.reg_lambda = reg_lambda
self.gamma = gamma
self.trees = None
self.loss = self._choose_loss(loss, huber_delta)
def _choose_loss(self, loss, huber_delta):
if loss == "squared":
return MSE(reduction="sum")
elif loss == "absolute":
return MAE(reduction="sum")
elif loss == "huber":
return Huber(reduction="sum", delta=huber_delta)
[docs]
def fit(self, X, y, metrics=["loss"]):
"""
Fits the XGBoostingRegressor model to the input data by fitting trees to the errors made by previous trees.
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.
metrics (dict[str, torch.Tensor]): Contains the metrics that will be calculated between fitting each tree and returned.
Returns:
metrics (dict[str, torch.Tensor]): The calculated metrics.
Raises:
TypeError: If the input matrix or the target vector is not a PyTorch tensor.
ValueError: If the input matrix or the target vector 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")
if not isinstance(metrics, list | tuple):
raise ValueError("metrics must be a list or tuple containing the shorthand names of each wanted metric.")
self.n_features = X.shape[1]
self.initial_pred = y.mean()
pred = torch.full(y.shape, self.initial_pred)
trees = []
history = {metric: torch.zeros(self.n_trees) for metric in metrics}
for i in range(self.n_trees):
gradient = self.loss.gradient(pred, y)
hessian = self.loss.hessian(pred, y)
tree = _XGBoostTree(max_depth=self.max_depth, min_samples_split=self.min_samples_split, reg_lambda=self.reg_lambda, gamma=self.gamma)
tree.fit(X, gradient, hessian)
prediction = tree.predict(X)
pred += self.learning_rate * prediction
trees.append(tree)
values = calculate_metrics(data=(pred, y), metrics=metrics, loss=self.loss.loss)
for metric, value in values.items():
history[metric][i] = value
self.trees = trees
return history
[docs]
def predict(self, X):
"""
Applies the fitted XGBoostingRegressor model to the input data, predicting the target values.
Args:
X (torch.Tensor of shape (n_samples, n_features)): The input data to be classified.
Returns:
targets (torch.Tensor of shape (n_samples,)): The predicted target values corresponding to each sample.
Raises:
NotFittedError: If the XGBoostingRegressor 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 not hasattr(self, "initial_pred"):
raise NotFittedError("XGBoostingRegressor.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.n_features:
raise ValueError("The input matrix must be a 2 dimensional tensor with the same number of features as the fitted tensor.")
pred = torch.full((X.shape[0],), self.initial_pred)
for tree in self.trees:
pred += self.learning_rate * tree.predict(X)
return pred