A/B testing & experimentation
User bucketing, experiment metrics, statistical comparison, and rollout guardrails.
Difference in Differences Mock in Python
Generate mock panel data with a known treatment effect and compute a difference-in-differences estimate using group and period means.
import numpy as np
import pandas as pd
# Generate mock panel data: 2 groups (control=0, treatment=1) × 2 periods (pre=0, post=1)
rng = np.random.default_rng(42)
n_per_cell = 50
data = []
for group in [0, 1]:
for period in [0, 1]:
# True effect: treatment increases outcome by 5 in the post period
…How to Build a Guardrail Metrics Monitor in Python
This code implements a mock monitor that records metric values, checks them against thresholds, and summarizes pass/alert statistics.
import random
import time
from collections import defaultdict
class GuardrailMetricsMonitor:
def __init__(self):
self.metrics = defaultdict(list)
self.thresholds = {
"prompt_toxicity": 0.8,
"response_length": 500,
"latency_ms": 1000,
}
def record(s…How to Calculate Minimum Sample Size for a T-Test in Python
Compute the minimum sample size per group for a two-sample t-test using effect size, significance level, and statistical power.
import math
from scipy.stats import norm
def min_sample_size(effect_size, alpha=0.05, power=0.8):
"""
Calculate minimum sample size for a two-sample t-test (equal groups).
Args:
effect_size: Cohen's d (standardized mean difference)
alpha: significance level (Type I error)
power: …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 …How to Define a Mock Primary Metric in Python
Define a mock primary metric object with a name, value, and unit, and serialize it to a dictionary for experimentation and testing.
class Metric:
def __init__(self, name, value, unit=None):
self.name = name
self.value = value
self.unit = unit
def to_dict(self):
result = {"name": self.name, "value": self.value}
if self.unit:
result["unit"] = self.unit
return result
def __repr…How to Do Random Assignment in Python for A/B Tests
Assign each item to a binary group (0 or 1) with uniform probability using a small reusable function, optionally weighted, for A/B testing mocks.
import random
def random_assignment_uniform_mock(items, weights=None):
"""Assign each item to a group (0 or 1) with uniform probability."""
if weights is None:
# Default: each item independently gets 0 or 1 with 50% probability
return [random.randint(0, 1) for _ in items]
# Optional weight…How to Hash a User ID to an Experiment Bucket in Python
Deterministically map a user ID to one of N experiment buckets using MD5 hashing and modulo arithmetic.
import hashlib
def hash_to_bucket(user_id: str, num_buckets: int = 10) -> int:
"""Deterministically map a user_id to a bucket (0 to num_buckets-1)."""
digest = hashlib.md5(user_id.encode("utf-8")).hexdigest()
return int(digest[:8], 16) % num_buckets
if __name__ == "__main__":
# Mock experiment: split…How to Mock Stratified Assignment by Segment in Python
Simulate stratified assignment for A/B experiments by sampling a fixed proportion of units from each segment, with deterministic seeds for reproducibility.
import random
def stratified_assignment(segments, seed=None):
"""
Mock stratified assignment: given a dict of segment -> population size,
return a dict of segment -> sampled unit ids (deterministic with seed).
"""
if seed is not None:
random.seed(seed)
rng = random.Random(seed)
res…How to Mock a Confidence Interval for a Proportion in Python
Simulate a Bernoulli sample and compute a 95% confidence interval for a proportion using the normal approximation in Python.
import random
import math
def mock_ci(n=100, p_true=0.5, z=1.96, seed=42):
"""Simulate a sample proportion and compute its 95% confidence interval."""
random.seed(seed)
successes = sum(1 for _ in range(n) if random.random() < p_true)
p_hat = successes / n
se = math.sqrt(p_hat * (1 - p_hat) / n)
…How to Simulate Fixed-Horizon Testing in Python
Simulate a fixed-horizon experiment by labeling data before the horizon as warmup and after as active/inactive, then summarize via CSV.
import csv
import io
def fixed_horizon_mock(data: list[tuple[float, float, float]], horizon: int) -> str:
"""Simulate fixed-horizon testing, then summarize with CSV output."""
output = io.StringIO()
writer = csv.writer(output)
writer.writerow(["day", "value", "signal", "status"])
for day, value,…How to create a global control holdout group in Python
This code implements a deterministic global control holdout group, randomly selecting a fraction of users to be excluded from feature rollouts for experiment validation.
import random
class GlobalControl:
def __init__(self, population_size, holdout_fraction=0.2, seed=42):
random.seed(seed)
self.population_size = population_size
self.holdout_fraction = holdout_fraction
self.holdout_size = int(population_size * holdout_fraction)
self.holdout_…How to hash user IDs to experiment buckets in Python
Deterministically map a user ID to an experiment bucket using MD5 hashing, ensuring stable and consistent assignment for A/B testing.
import hashlib
def hash_user_to_bucket(user_id: str, num_buckets: int = 10) -> int:
"""Deterministically map a user ID to an experiment bucket (0..num_buckets-1)."""
digest = hashlib.md5(user_id.encode("utf-8")).hexdigest()
return int(digest, 16) % num_buckets
if __name__ == "__main__":
mock_users …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.
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…Browse by section
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A/B testing & experimentation — Python code examples
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