Python Function to Compute a Beta Matrix

I am not aware of any package that already does this. But you can create all those combinations (2^n-1), where n is the number of columns in X (independent variables), and fit a linear regression model for each combination and then get coefficients/betas for each model.

Here is how I would do it, hope this helps

from sklearn import datasets, linear_model
import numpy as np
from itertools import combinations

#test dataset
X, y = datasets.load_boston(return_X_y=True)

X = X[:,:3] # Orginal X has 13 columns, only taking n=3 instead of 13 columns

#create all 2^n-1 (here 7 because n=3) combinations of columns, where n is the number of features/indepdent variables

all_combs = [] 
for i in range(X.shape[1]):
    all_combs.extend(combinations(range(X.shape[1]),i+1))

# print 2^n-1 combinations
print('2^n-1 combinations are:')
print(all_combs) 

 ## Create a betas/coefficients as zero matrix with rows (2^n-1) and columns equal to X
betas = np.zeros([len(all_combs), X.shape[1]])+np.NaN

## Fit a model for each combination of columns and add the coefficients into betas matrix
lr = linear_model.LinearRegression()
for regression_no, comb in enumerate(all_combs):
    lr.fit(X[:,comb], y)
    betas[regression_no, comb] = lr.coef_

## Print Coefficients of each model
print('Regression No'.center(15)+" ".join(['column {}'.format(i).center(10) for i in range(X.shape[1])]))  
print('_'*50)
for index, beta in enumerate(betas):
    print('{}'.format(index + 1).center(15), " ".join(['{:.4f}'.format(beta[i]).center(10) for i in range(X.shape[1])]))

results in

2^n-1 combinations are:
[(0,), (1,), (2,), (0, 1), (0, 2), (1, 2), (0, 1, 2)]


    Regression No  column 0   column 1   column 2 
__________________________________________________
       1         -0.4152      nan        nan    
       2           nan       0.1421      nan    
       3           nan        nan      -0.6485  
       4         -0.3521     0.1161      nan    
       5         -0.2455      nan      -0.5234  
       6           nan       0.0564    -0.5462  
       7         -0.2486     0.0585    -0.4156