Reference library

A/B testing & experimentation

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

28 matches

How to Simulate Geo Experiments in Python

Build a mock geo experiment simulator with ramp-up/down periods, measuring weekly lift between treatment and control markets.

geo-experiment ab-testing simulation

Python

import random
import math
from dataclasses import dataclass

@dataclass
class GeoMarket:
    name: str
    base_demand: float
    geo_coefficient: float

def simulate_geo_experiment(markets, weeks=12, control_weeks=6):
    """
    Simulates a geo experiment with ramp-up and ramp-down periods.
    Returns weekly lift p…

18 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

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

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