Reference library

A/B testing & experimentation

User bucketing, experiment metrics, statistical comparison, and rollout guardrails.

4 matches

How to Create an Interrupted Time Series Mock in Python

Generate simulated interrupted time series data with a pre/post-intervention trend, level shift, and noise to test segmented regression models.

interrupted-time-series simulation numpy

Python

import numpy as np

# Mock interrupted time series data
np.random.seed(42)
n_pre = 50
n_post = 50
time = np.arange(0, n_pre + n_post)

# Pre-intervention: linear trend + noise
pre_trend = 0.05 * time[:n_pre] + np.random.normal(0, 0.5, n_pre)

# Post-intervention: new slope + level shift + noise
post_trend = 0.05 * tim…

15 0 Open

A/B testing & experimentation medium

How to Mock Mutual Exclusion for A/B Experiment Groups in Python

Simulate mutual exclusion for experiment groups using a thread-safe lock, ensuring only one member updates the shared counter at a time.

threading mutual-exclusion ab-testing

Python

import threading
import time
import random


class CountingGate:
    """A mock mutual exclusion gate using a lock."""
    def __init__(self):
        self.counter = 0
        self.lock = threading.Lock()

    def enter(self, group_id, member_id):
        with self.lock:
            current = self.counter
            t…

13 0 Open

A/B testing & experimentation medium

How to Mock Time for Cache TTL Testing in Python

This code demonstrates how to test a cache's TTL expiration logic by mocking time.time with unittest.mock to control the passage of time.

caching ttl unit-testing

Python

import time
from unittest.mock import patch

class ConfigCache:
    def __init__(self, ttl=60):
        self.ttl = ttl
        self._store = {}
        self._timestamps = {}

    def get(self, key):
        if key not in self._store:
            return None
        if time.time() - self._timestamps[key] > self.ttl:
  …

17 0 Open

A/B testing & experimentation medium

Thompson Sampling Mock Bandit in Python

Implement a Thompson sampling multi-armed bandit to explore and exploit reward probabilities across multiple options, updating Beta distributions over time.

thompson-sampling bandit-algorithms exploration-exploitation

Python

import random

class ThompsonSamplingBandit:
    def __init__(self, num_arms, alpha=1.0, beta=1.0):
        self.num_arms = num_arms
        self.alpha = [alpha] * num_arms
        self.beta = [beta] * num_arms

    def select_arm(self):
        samples = [random.betavariate(a, b) for a, b in zip(self.alpha, self.beta…

12 0 Open

Browse by section

Each section groups closely related Python snippets.