Why can't I get the result I got with the sklearn LogisticRegression with the coefficients_sgd method?

from math import exp
import numpy as np
from sklearn.linear_model import LogisticRegression

I used code below from How To Implement Logistic Regression From Scratch in Python

def predict(row, coefficients):
    yhat = coefficients[0]
    for i in range(len(row)-1):
        yhat += coefficients[i + 1] * row[i]
    return 1.0 / (1.0 + exp(-yhat))

def coefficients_sgd(train, l_rate, n_epoch):
    coef = [0.0 for i in range(len(train[0]))]
    for epoch in range(n_epoch):
        sum_error = 0
        for row in train:
            yhat = predict(row, coef)
            error = row[-1] - yhat
            sum_error += error**2
            coef[0] = coef[0] + l_rate * error * yhat * (1.0 - yhat)
            for i in range(len(row)-1):
                coef[i + 1] = coef[i + 1] + l_rate * error * yhat * (1.0 - yhat) * row[i]
    return coef

dataset = [[2.7810836,2.550537003,0],
[1.465489372,2.362125076,0],
[3.396561688,4.400293529,0],
[1.38807019,1.850220317,0],
[3.06407232,3.005305973,0],
[7.627531214,2.759262235,1],
[5.332441248,2.088626775,1],
[6.922596716,1.77106367,1],
[8.675418651,-0.242068655,1],
[7.673756466,3.508563011,1]]

l_rate = 0.3
n_epoch = 100
coef = coefficients_sgd(dataset, l_rate, n_epoch)
print(coef)

[-0.39233141593823756, 1.4791536027917747, -2.316697087065274]

x = np.array(dataset)[:,:2]
y = np.array(dataset)[:,2]
model = LogisticRegression(penalty="none")
model.fit(x,y)
print(model.intercept_.tolist() + model.coef_.ravel().tolist())

[-3.233238244349982, 6.374828107647225, -9.631487530388092]

What should I change to get the same or closer coefficients ? How can I establish initial coefficients , learning rate , n_epoch ?

from Why can't I get the result I got with the sklearn LogisticRegression with the coefficients_sgd method?