# How do I get confidence intervals for an ElasticNet in sklearn?

I need to produce a row for the confidence interval for every field that I am calculating coefficients and scores off of. So here is my code so far-

import pandas as pd
import numpy as np
from sklearn.linear_model import ElasticNet
from sklearn.model_selection import GridSearchCV

lr = ElasticNet(max_iter=50000)
parameters = {'fit_intercept': [False],
'l1_ratio': [x/10 for x in range(2, 10)],
'selection': ['random', 'cyclic'],
'alpha': [x/10 for x in range(5, 10)],
'normalize':[False]}

df = {
'Field':[], 'A': [],
'b': [], 'Score': [],
'Row_Counts': [], 'Average_Spend': []
}
dupes = []
for field in all_df['Field']:
if field not in dupes:
filt = all_df[all_df['Field'] == field]
filt = filt[filt['LFA\'s'] != 0]
filt['root_Spend'] = np.sqrt(filt['Spend'])
filt.replace(-np.inf, 0, inplace=True)
X = filt[['Spend', 'root_Spend', 'CL', 'V']]
y = filt['LFA\'s']
if len(X) >= 5:
gs = GridSearchCV(lr, parameters, cv=5, scoring='neg_mean_squared_error', n_jobs=10, error_score='ignore', verbose=1)
gs.fit(X, y)
df['Field'].append(field)
df['A'].append(list(gs.best_estimator_.coef_))
df['b'].append(list(gs.best_estimator_.coef_)[-1])
df['Score'].append(gs.best_estimator_.score(X, y))
df['Row_Counts'].append(len(X))
df['Average_Spend'].append(np.mean(X['Spend']))
dupes.append(field)


I need to make df have a column for confidence intervals as well. Any ideas/answers?