Reference library

A/B testing & experimentation

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

4 matches

How to Create a Mock That Returns Inverse Counter Values in Python

Builds a Mock whose side_effect returns the inverse (1/count) of each Counter value, defaulting to 0.0 for unseen keys.

from collections import Counter
from unittest.mock import Mock

def inverse_mock(counter: Counter) -> Mock:
    """
    Return a Mock that mimics the inverse of a Counter:
    each key returns a value representing the inverse of its count.
    The Mock's side_effect maps keys to their inverse counts.
    """
    mock …

13 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 easy

Simulate a Ramp Rollout Percentage in Python

Simulates a percentage-based ramp rollout with deterministic seeding, returning success/failure/in-progress counts for a mock user population.

rollout simulation random

Python

import random
from enum import Enum

class RolloutStatus(Enum):
    SUCCESS = "success"
    FAILED = "failed"
    IN_PROGRESS = "in_progress"

def simulate_ramp_rollout(total_users: int, percentage: int, seed: int = 42) -> dict:
    """
    Simulates a mock ramp rollout for a given percentage of users.
    Returns sta…

14 0 Open

A/B testing & experimentation medium

Synthetic Control in Python: Mock Example

Implements synthetic control from scratch: learns donor weights via ridge regression on pre-period data, then predicts a counterfactual for the treated unit.

synthetic-control causal-inference numpy

Python

import numpy as np

class SyntheticControl:
    def __init__(self, data, treated_index, pre_periods, post_periods):
        self.data = np.array(data, dtype=float)
        self.treated_index = treated_index
        self.pre_periods = pre_periods
        self.post_periods = post_periods
        
    def fit_weights(sel…

15 0 Open

Browse by section

Each section groups closely related Python snippets.