from __future__ import annotations
from typing import Any, Optional, Union
import numpy as np
import pandas as pd
import scipy as sp
from scipy.stats import t
from sklearn.linear_model import LinearRegression as LinearRegressionSK
[docs]class LinearRegression(LinearRegressionSK):
"""Class to build linear regression models."""
def __init__(self, *args: Any, **kwargs: Any) -> None:
super().__init__(*args, **kwargs)
self._pvalues: Optional[np.ndarray] = None
self._tvalues: Optional[np.ndarray] = None
[docs] def fit(
self, X: np.ndarray, y: Union[pd.Series, np.ndarray],
sample_weight: Optional[float] = None,
) -> LinearRegression:
super().fit(X, y, sample_weight)
n = len(y) # pylint: disable=invalid-name
x_consts = np.column_stack([np.ones(X.shape[0]), X])
pinv_x, rank = sp.linalg.pinv(x_consts, return_rank=True)
df_resid = x_consts.shape[0] - np.linalg.matrix_rank(x_consts)
resid = y - self.predict(X)
scale = np.dot(resid, resid) / df_resid
cov_params = np.dot(pinv_x, pinv_x.T) * scale
beta = np.dot(pinv_x, y)
bse = np.sqrt(np.diag(cov_params))
tvalues = beta / bse
pvalues = t.sf(np.abs(tvalues), n - rank) * 2
self._tvalues = tvalues
self._pvalues = pvalues
return self
@property
def tvalues(self) -> Optional[np.ndarray]:
return self._tvalues
@property
def pvalues(self) -> Optional[np.ndarray]:
return self._pvalues