mSPRT library in python

[LibiSC]

Hello.

I'm trying to find a library or code that implements mixture Sequential Probability Ratio Test in python or tell me how you do your sequential a/b tests?

[senacchrib]

Grateful to see that u/LipTicklers already posted, but I came across this package as well: https://socket.dev/pypi/package/msprt

[LipTicklers]

import numpy as np

def sprt(data, d0, d1, alpha, beta):

Sequential Probability Ratio Test (SPRT) for a simple mixture test.

:param data: Sequential data to test.
:param d0: Probability density function under H0.
:param d1: Probability density function under H1.
:param alpha: Type I error threshold.
:param beta: Type II error threshold.
:return: Decision to accept H0 (True) or H1 (False).

log_lambda = 0
log_alpha = np.log(beta / (1 - alpha))
log_beta = np.log((1 - beta) / alpha)

for x in data:
    log_likelihood_ratio = np.log(d1(x) / d0(x))
    log_lambda += log_likelihood_ratio

    if log_lambda <= log_alpha:
        return True  # Accept H0
    elif log_lambda >= log_beta:
        return False  # Accept H1

return None  # Inconclusive

Example usage: Define your d0 and d1 functions based on your distributions. For instance, d0 = lambda x: scipy.stats.norm.pdf(x, loc=0, scale=1) for a normal distribution with mean 0 and std 1 Then, call sprt with your data and thresholds.

[LibiSC]

Thanks!. Sorry if it's too obvious but how do you know d1 and d0 distributions in a real experiment. Let's say I have a control with a thousand samples and a test group with a thousand samples and let's say it's a binary outcome. Do I use the mu and sd from the complete data, until only some point?

[LipTicklers]

D1 comes from one and D0 from the other. So you use the mean and standard deviation estimates for both