forecaster.wrappers.xgb

forecaster.wrappers.xgb

Recursive forecaster model wrappers for different estimators.

Classes

Name	Description
ForecasterRecursiveXGB	ForecasterRecursive specialization using XGBoost.

ForecasterRecursiveXGB

forecaster.wrappers.xgb.ForecasterRecursiveXGB(iteration, lags=12, **kwargs)

ForecasterRecursive specialization using XGBoost.

Attributes

Name	Type	Description
forecaster		The XGBoost forecaster.
name		The name of the forecaster.

Examples

import numpy as np
import pandas as pd
from sklearn.linear_model import Ridge

from spotforecast2_safe.forecaster.recursive import ForecasterRecursive
from spotforecast2_safe.forecaster.wrappers import (
    ForecasterRecursiveModel,
    ForecasterRecursiveXGB,
)

model = ForecasterRecursiveXGB(iteration=0, lags=3)
assert model.name == "xgb"
assert isinstance(model, ForecasterRecursiveModel)
print(f"name: {model.name}")
print(f"random_state: {model.random_state}")

# When xgboost is not installed, substitute a Ridge estimator so the
# fit/predict lifecycle can still be demonstrated end-to-end.
if model.forecaster is None:
    model.forecaster = ForecasterRecursive(
        estimator=Ridge(random_state=1234), lags=3
    )

rng = np.random.default_rng(0)
y = pd.Series(
    rng.random(20),
    index=pd.date_range("2023-01-01", periods=20, freq="h"),
)
model.fit(y=y)
assert model.forecaster.is_fitted
pred = model.forecaster.predict(steps=2)
assert len(pred) == 2
print(f"is_fitted: {model.forecaster.is_fitted}")
print(f"forecast horizon: {len(pred)} steps")

XGBoost not installed. This model will fail during fit/predict.

name: xgb
random_state: 123456789
is_fitted: True
forecast horizon: 2 steps