calculate t statistics and p-values for coefficients in Linear Model in python, using scikit-learn framework.

linear_model.py

from sklearn import linear_model
from scipy import stats
import numpy as np


class LinearRegression(linear_model.LinearRegression):
    """
    LinearRegression class after sklearn's, but calculate t-statistics
    and p-values for model coefficients (betas).
    Additional attributes available after .fit()
    are `t` and `p` which are of the shape (y.shape[1], X.shape[1])
    which is (n_features, n_coefs)
    This class sets the intercept to 0 by default, since usually we include it
    in X.
    """

    def __init__(self, *args, **kwargs):
        if not "fit_intercept" in kwargs:
            kwargs['fit_intercept'] = False
        super(LinearRegression, self)\
                .__init__(*args, **kwargs)

    def fit(self, X, y, n_jobs=1):
        self = super(LinearRegression, self).fit(X, y, n_jobs)

        sse = np.sum((self.predict(X) - y) ** 2, axis=0) / float(X.shape[0] - X.shape[1])
        se = np.array([
            np.sqrt(np.diagonal(sse[i] * np.linalg.inv(np.dot(X.T, X))))
                                                    for i in range(sse.shape[0])
                    ])

        self.t = self.coef_ / se
        self.p = 2 * (1 - stats.t.cdf(np.abs(self.t), y.shape[0] - X.shape[1]))
        return self

I'm stuck using this because it fails on line 29 for i in range(sse.shape[0]) with
IndexError: tuple index out of range

Problem seems to be that for me, sse has shape (), whereas it seems to be expecting a dimension. Perhaps related, I started with a one-dimension ndarray for my X (when I was using the base class LinearRegression) and had to do
X=district_data["my field name"][:,numpy.newaxis]
to get LinearRegression.fit() to work.

or do this : se = np.array([np.sqrt(np.diagonal(sse * np.linalg.inv(np.dot(X.T, X))))])

Would this manipulation also work for extending scikit's Lasso?

Sorry I know it's really stupid, but I try to run the following code in my notebook and it didn't work

from sklearn import linear_model
from scipy import stats
import numpy as np

def __init__(self, *args, **kwargs):
    if not "fit_intercept" in kwargs:
        kwargs['fit_intercept'] = False
    super(LinearRegression, self)\
            .__init__(*args, **kwargs)

def fit(self, X, y, n_jobs=1):
    self = super(LinearRegression, self).fit(X, y, n_jobs)

    sse = np.sum((self.predict(X) - y) ** 2, axis=0) / float(X.shape[0] - X.shape[1])
    se = np.array([
        np.sqrt(np.diagonal(sse[i] * np.linalg.inv(np.dot(X.T, X))))
                                                for i in range(sse.shape[0])
                ])

    self.t = self.coef_ / se
    self.p = 2 * (1 - stats.t.cdf(np.abs(self.t), y.shape[0] - X.shape[1]))
    return self

regr = LinearRegression()
regr.fit(X_train, y_train.to_frame())

It returns an error

RuntimeError: scikit-learn estimators should always specify their parameters in the signature of their __init__ (no varargs). <class '__main__.LinearRegression'> with constructor (self, *args, **kwargs) doesn't  follow this convention.
> 

I've tried to google the baseEstimator of sklearn but I couldn't understand at all. Can anyone point out what mistake I have made?

maybe you can change the init method into

def __init__(self, fit_intercept=True, normalize=False, copy_X=True,
                 n_jobs=1):
        self.fit_intercept = fit_intercept
        self.normalize = normalize
        self.copy_X = copy_X
        self.n_jobs = n_jobs

it seems this code doesn't work, neighter original, nor with __init__method from above

In the code below I am running time-series regression for the last 36 periods. I can find coefficients through 'model.coef_' but how do I find p-value for each of these coefficients?

for c in range(36,167):
Port = df2.iloc[range(c-36,c),3]
Var_x = df2.iloc[range(c-36,c),[0,4,5]]
y = Port.to_numpy()
x = Var_x.to_numpy().reshape(-1,3)
model = LinearRegression()
model.fit(x, y)
model = LinearRegression().fit(x, y)
r_sq = model.score(x, y)
print(model.coef_)

In the code below I am running time-series regression for the last 36 periods. I can find coefficients through 'model.coef_' but how do I find p-value for each of these coefficients?

for c in range(36,167): Port = df2.iloc[range(c-36,c),3] Var_x = df2.iloc[range(c-36,c),[0,4,5]] y = Port.to_numpy() x = Var_x.to_numpy().reshape(-1,3) model = LinearRegression() model.fit(x, y) model = LinearRegression().fit(x, y) r_sq = model.score(x, y) print(model.coef_)

You can try the following:

from sklearn.feature_selection import f_regression

f_statistic, p_value = f_regression(X, y)