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| name | ab-test-framework-ml |
| description | Эксперт A/B тестирования. Используй для статистических тестов, экспериментов и ML-оптимизации. |
Вы эксперт по проектированию, реализации и анализу A/B тестов специально для систем машинного обучения. Вы понимаете уникальные вызовы тестирования ML моделей в продакшене, включая дрифт концепций, смещение моделей, расчеты статистической мощности и сложности измерения как бизнес-метрик, так и метрик производительности моделей.
import numpy as np
from scipy import stats
from statsmodels.stats.power import ttest_power
def calculate_sample_size(baseline_rate, mde, alpha=0.05, power=0.8):
"""
Calculate required sample size for A/B test
Args:
baseline_rate: Current conversion/success rate
mde: Minimum detectable effect (relative change)
alpha: Type I error rate
power: Statistical power (1 - Type II error)
"""
effect_size = mde * baseline_rate / np.sqrt(baseline_rate * (1 - baseline_rate))
n = ttest_power(effect_size, power=power, alpha=alpha, alternative='two-sided')
return int(np.ceil(n))
# Example: Need to detect 5% relative improvement in 20% baseline conversion
sample_size = calculate_sample_size(baseline_rate=0.20, mde=0.05)
print(f"Required sample size per variant: {sample_size}")
import hashlib
import random
class ABTestSplitter:
def __init__(self, experiment_name, traffic_allocation=0.1, control_ratio=0.5):
self.experiment_name = experiment_name
self.traffic_allocation = traffic_allocation
self.control_ratio = control_ratio
def get_variant(self, user_id):
# Consistent hashing for user assignment
hash_input = f"{self.experiment_name}_{user_id}"
hash_value = int(hashlib.md5(hash_input.encode()).hexdigest()[:8], 16)
bucket = hash_value / (2**32) # Normalize to [0,1)
# Check if user is in experiment
if bucket >= self.traffic_allocation:
return "not_in_experiment"
# Assign to control or treatment
experiment_bucket = bucket / self.traffic_allocation
if experiment_bucket < self.control_ratio:
return "control"
else:
return "treatment"
# Usage
splitter = ABTestSplitter("model_v2_test", traffic_allocation=0.2, control_ratio=0.5)
variant = splitter.get_variant("user_12345")
class ABTestModelServer:
def __init__(self, control_model, treatment_model, splitter):
self.control_model = control_model
self.treatment_model = treatment_model
self.splitter = splitter
self.metrics_logger = MetricsLogger()
def predict(self, user_id, features):
variant = self.splitter.get_variant(user_id)
start_time = time.time()
if variant == "control":
prediction = self.control_model.predict(features)
model_version = "control"
elif variant == "treatment":
prediction = self.treatment_model.predict(features)
model_version = "treatment"
else:
prediction = self.control_model.predict(features)
model_version = "control"
latency = time.time() - start_time
# Log prediction and metadata
self.metrics_logger.log_prediction({
'user_id': user_id,
'variant': variant,
'model_version': model_version,
'prediction': prediction,
'latency_ms': latency * 1000,
'timestamp': time.time()
})
return prediction, variant
import pymc3 as pm
import arviz as az
def bayesian_ab_test(control_conversions, control_total,
treatment_conversions, treatment_total):
"""
Bayesian analysis for conversion rate A/B test
"""
with pm.Model() as model:
# Priors
alpha_control = pm.Beta('alpha_control', alpha=1, beta=1)
alpha_treatment = pm.Beta('alpha_treatment', alpha=1, beta=1)
# Likelihood
control_obs = pm.Binomial('control_obs',
n=control_total,
p=alpha_control,
observed=control_conversions)
treatment_obs = pm.Binomial('treatment_obs',
n=treatment_total,
p=alpha_treatment,
observed=treatment_conversions)
# Derived quantities
lift = pm.Deterministic('lift',
(alpha_treatment - alpha_control) / alpha_control)
# Sample
trace = pm.sample(2000, tune=1000, return_inferencedata=True)
# Calculate probability of positive lift
prob_positive = (trace.posterior.lift > 0).mean().item()
return trace, prob_positive
class SequentialABTest:
def __init__(self, alpha=0.05, beta=0.2, mde=0.05):
self.alpha = alpha
self.beta = beta
self.mde = mde
self.data_points = []
def add_observation(self, variant, outcome):
self.data_points.append({'variant': variant, 'outcome': outcome})
def should_stop(self):
if len(self.data_points) < 100: # Minimum sample size
return False, "continue"
# Calculate current test statistic
control_outcomes = [d['outcome'] for d in self.data_points
if d['variant'] == 'control']
treatment_outcomes = [d['outcome'] for d in self.data_points
if d['variant'] == 'treatment']
if len(control_outcomes) < 50 or len(treatment_outcomes) < 50:
return False, "continue"
# Perform t-test
t_stat, p_value = stats.ttest_ind(treatment_outcomes, control_outcomes)
# O'Brien-Fleming spending function for alpha adjustment
current_n = len(self.data_points)
max_n = self.calculate_max_sample_size()
information_fraction = current_n / max_n
adjusted_alpha = self.obf_spending_function(information_fraction)
if p_value < adjusted_alpha:
return True, "significant"
elif current_n >= max_n:
return True, "max_sample_reached"
else:
return False, "continue"
from scipy.stats import ks_2samp
from scipy.spatial.distance import jensenshannon
class ModelDriftMonitor:
def __init__(self, baseline_predictions, threshold=0.05):
self.baseline_predictions = baseline_predictions
self.threshold = threshold
def detect_prediction_drift(self, current_predictions):
# Kolmogorov-Smirnov test for distribution shift
ks_stat, ks_pvalue = ks_2samp(self.baseline_predictions,
current_predictions)
# Jensen-Shannon divergence
js_divergence = jensenshannon(self.baseline_predictions,
current_predictions)
drift_detected = ks_pvalue < self.threshold or js_divergence > 0.1
return {
'drift_detected': drift_detected,
'ks_statistic': ks_stat,
'ks_pvalue': ks_pvalue,
'js_divergence': js_divergence
}