Enterprise A/B Testing Framework

System Architecture

A comprehensive A/B testing framework that supports advanced experimentation techniques, statistical analysis, and automated decision-making.

Core Components

1. Experiment Manager

class ExperimentManager:
    def __init__(self, config: Dict[str, Any]):
        self.store = ExperimentStore(config['store_config'])
        self.validator = ExperimentValidator(config['validation_rules'])
        self.sampler = TrafficSampler(config['sampling_config'])
        self.monitor = ExperimentMonitor(config['monitoring_config'])
        
    async def create_experiment(
        self,
        experiment_config: ExperimentConfig
    ) -> str:
        # Validate experiment configuration
        await self.validator.validate(experiment_config)
        
        # Generate experiment ID and store metadata
        experiment_id = await self.store.create_experiment(
            experiment_config
        )
        
        # Initialize monitoring
        await self.monitor.initialize_experiment(
            experiment_id,
            experiment_config
        )
        
        return experiment_id
        
    async def get_variant(
        self,
        experiment_id: str,
        user_id: str,
        context: Dict[str, Any]
    ) -> str:
        experiment = await self.store.get_experiment(experiment_id)
        
        if not experiment.is_active:
            return experiment.default_variant
            
        return await self.sampler.get_variant(
            experiment,
            user_id,
            context
        )

class ExperimentConfig:
    def __init__(
        self,
        name: str,
        variants: List[str],
        metrics: List[Metric],
        population: PopulationSpec,
        hypothesis: Hypothesis,
        min_sample_size: int,
        max_duration: timedelta
    ):
        self.name = name
        self.variants = variants
        self.metrics = metrics
        self.population = population
        self.hypothesis = hypothesis
        self.min_sample_size = min_sample_size
        self.max_duration = max_duration

2. Statistical Analysis Engine

class StatisticalAnalyzer:
    def __init__(self, config: Dict[str, Any]):
        self.analyzers = {
            'frequentist': FrequentistAnalyzer(),
            'bayesian': BayesianAnalyzer(),
            'sequential': SequentialAnalyzer()
        }
        self.effect_size_calculator = EffectSizeCalculator()
        
    async def analyze_experiment(
        self,
        experiment_id: str,
        data: pd.DataFrame
    ) -> AnalysisResults:
        experiment = await self.store.get_experiment(experiment_id)
        
        # Run all relevant analyses
        results = {}
        for analyzer_name, analyzer in self.analyzers.items():
            if analyzer_name in experiment.analysis_methods:
                results[analyzer_name] = await analyzer.analyze(
                    data,
                    experiment.hypothesis
                )
                
        # Calculate effect sizes
        effect_sizes = await self.effect_size_calculator.calculate(
            data,
            experiment.metrics
        )
        
        return AnalysisResults(
            statistical_results=results,
            effect_sizes=effect_sizes
        )

class BayesianAnalyzer:
    def __init__(self):
        self.prior_generator = PriorGenerator()
        self.mcmc_sampler = MCMCSampler()
        
    async def analyze(
        self,
        data: pd.DataFrame,
        hypothesis: Hypothesis
    ) -> BayesianResults:
        # Generate priors
        priors = self.prior_generator.generate_priors(data)
        
        # Run MCMC sampling
        posterior_samples = await self.mcmc_sampler.sample(
            data,
            priors,
            num_samples=10000
        )
        
        # Calculate probabilities
        prob_superior = self._calculate_superiority_probability(
            posterior_samples
        )
        
        return BayesianResults(
            posterior_samples=posterior_samples,
            prob_superior=prob_superior
        )

3. Multi-Armed Bandit System

class BanditOptimizer:
    def __init__(self, config: Dict[str, Any]):
        self.algorithms = {
            'thompson': ThompsonSampling(),
            'ucb': UpperConfidenceBound(),
            'eps_greedy': EpsilonGreedy(config['epsilon'])
        }
        self.reward_calculator = RewardCalculator()
        
    async def get_variant(
        self,
        experiment_id: str,
        context: Dict[str, Any]
    ) -> str:
        experiment = await self.store.get_experiment(experiment_id)
        algorithm = self.algorithms[experiment.bandit_algorithm]
        
        # Get current statistics
        stats = await self.store.get_experiment_stats(experiment_id)
        
        # Select variant using bandit algorithm
        selected_variant = algorithm.select_arm(
            stats,
            context
        )
        
        return selected_variant
        
    async def update_statistics(
        self,
        experiment_id: str,
        variant: str,
        outcome: float
    ) -> None:
        await self.store.update_variant_stats(
            experiment_id,
            variant,
            outcome
        )

class ThompsonSampling:
    def select_arm(
        self,
        stats: Dict[str, VariantStats],
        context: Dict[str, Any]
    ) -> str:
        samples = {}
        for variant, stat in stats.items():
            samples[variant] = np.random.beta(
                stat.successes + 1,
                stat.failures + 1
            )
            
        return max(samples.items(), key=lambda x: x[1])[0]

4. Automated Decision Making

class DecisionMaker:
    def __init__(self, config: Dict[str, Any]):
        self.decision_rules = DecisionRules(config['rules'])
        self.notifier = DecisionNotifier(config['notification'])
        
    async def make_decision(
        self,
        experiment_id: str,
        analysis_results: AnalysisResults
    ) -> ExperimentDecision:
        # Apply decision rules
        decision = await self.decision_rules.evaluate(
            analysis_results
        )
        
        if decision.is_conclusive:
            # Notify stakeholders
            await self.notifier.notify_decision(
                experiment_id,
                decision
            )
            
            # Update experiment status
            await self.store.update_experiment_status(
                experiment_id,
                decision.status
            )
            
        return decision

Usage Example

# Initialize A/B testing framework
config = {
    'store_config': {
        'database_url': 'postgresql://localhost:5432/ab_testing'
    },
    'sampling_config': {
        'strategy': 'deterministic',
        'seed': 42
    },
    'monitoring_config': {
        'metrics_store': 'prometheus',
        'alert_threshold': 0.1
    }
}

ab_testing = ABTestingFramework(config)

# Create new experiment
experiment_config = ExperimentConfig(
    name='new_recommendation_algorithm',
    variants=['control', 'treatment'],
    metrics=[
        Metric('click_through_rate', 'ratio'),
        Metric('conversion_rate', 'ratio')
    ],
    population=PopulationSpec(
        target_size=10000,
        segments=['mobile_users', 'desktop_users']
    ),
    hypothesis=Hypothesis(
        metric='click_through_rate',
        min_detectable_effect=0.1,
        confidence_level=0.95
    ),
    min_sample_size=5000,
    max_duration=timedelta(days=14)
)

experiment_id = await ab_testing.create_experiment(experiment_config)

# Get variant for user
variant = await ab_testing.get_variant(
    experiment_id,
    user_id='user_123',
    context={'platform': 'mobile'}
)

# Record outcome
await ab_testing.record_outcome(
    experiment_id,
    user_id='user_123',
    variant=variant,
    metrics={
        'click_through_rate': 1.0,
        'conversion_rate': 0.5
    }
)

# Analyze results
results = await ab_testing.analyze_experiment(experiment_id)

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