Enterprise-Grade Machine Learning Testing Suite with Enterprise Patterns
Comprehensive ML Testing Framework specifically designed for cryptocurrency trading systems. Built with Enterprise Patterns for production-ready ML model validation, data quality assurance, and performance benchmarking.
- 🔍 Model Validation: Comprehensive testing of ML models (classification, regression, time series)
- 📊 Data Quality Testing: Automated detection of data issues, drift, and anomalies
- ⚡ Performance Benchmarking: Latency, throughput, memory usage, and scalability testing
- 📈 Drift Detection: Statistical tests for data and concept drift detection
- 🎨 Advanced Visualization: Interactive dashboards and publication-ready reports
- 🔧 Test Automation: Comprehensive test harness with parallel execution
- 💰 Crypto-Specific: Specialized features for cryptocurrency trading scenarios
ml-testing/
├── src/
│ ├── model_testing/ # Model validation and testing
│ │ ├── model_validator.py # Core model validation
│ │ ├── performance_tester.py # Performance testing
│ │ └── regression_tester.py # Regression testing
│ ├── data_testing/ # Data quality and validation
│ │ ├── data_quality.py # Data quality validator
│ │ ├── feature_validator.py # Feature engineering validation
│ │ └── drift_detector.py # Data drift detection
│ ├── benchmarking/ # Performance benchmarking
│ │ ├── benchmark_runner.py # Benchmark execution
│ │ └── metrics_calculator.py # Metrics calculation
│ ├── integration/ # Integration components
│ │ ├── test_harness.py # Main test orchestrator
│ │ └── pytest_fixtures.py # pytest fixtures
│ ├── utils/ # Utilities
│ │ ├── test_data_generator.py # Test data generation
│ │ ├── visualization.py # Visualization utilities
│ │ └── index.ts # TypeScript exports
│ └── __init__.py
├── tests/
│ └── test_framework.py # Comprehensive test suite
├── package.json # Node.js configuration
├── pyproject.toml # Python configuration
└── README.md # This file
- Python 3.8+
- Node.js 16+ (for TypeScript integration)
- Poetry or pip for dependency management
# Using pip
pip install -r requirements.txt
# Or using poetry
poetry install
npm install
# or
yarn install
- ML Libraries: scikit-learn, pandas, numpy
- Deep Learning: torch, tensorflow (optional)
- Statistical Testing: scipy, statsmodels
- Visualization: matplotlib, seaborn, plotly
- Async/Performance: asyncio, psutil
- Testing: pytest, pytest-asyncio
- MLflow: Experiment tracking and model management
from src.model_testing.model_validator import create_crypto_trading_validator
from sklearn.ensemble import RandomForestClassifier
import pandas as pd
# Create validator
validator = create_crypto_trading_validator()
# Your model and data
model = RandomForestClassifier()
X = pd.DataFrame(...) # Your features
y = pd.Series(...) # Your targets
# Train model
model.fit(X, y)
# Validate
result = validator.validate_model(model, X, y)
print(f"Model is valid: {result.is_valid}")
print(f"Validation score: {result.validation_score:.3f}")from src.data_testing.data_quality import create_crypto_data_validator
# Create data validator
data_validator = create_crypto_data_validator()
# Validate your data
quality_result = data_validator.validate_data(your_dataframe)
print(f"Overall quality score: {quality_result.overall_quality_score:.3f}")
print(f"Issues found: {len(quality_result.quality_issues)}")from src.benchmarking.benchmark_runner import create_crypto_benchmark_runner
# Create benchmark runner
benchmark_runner = create_crypto_benchmark_runner()
# Run benchmark
benchmark_result = benchmark_runner.run_benchmark(model, test_data)
print(f"Average latency: {benchmark_result.latency_metrics['mean_ms']:.2f}ms")
print(f"Throughput: {benchmark_result.throughput_samples_per_sec:.0f} samples/sec")import asyncio
from src.integration.test_harness import create_crypto_trading_harness
async def run_complete_tests():
# Create test harness
harness = create_crypto_trading_harness(
environment="staging",
risk_tolerance="conservative"
)
# Run complete test suite
results = await harness.run_test_suite(
model=your_model,
data=your_data,
target=your_target
)
print(f"Tests completed: {results.passed_count}/{len(results.test_results)}")
return results
# Run tests
results = asyncio.run(run_complete_tests())from src.data_testing.drift_detector import create_crypto_drift_detector
drift_detector = create_crypto_drift_detector()
# Compare reference data with current data
drift_result = drift_detector.detect_drift(
reference_data=historical_data,
current_data=new_data
)
if drift_result.drift_detected:
print(f"⚠️ Data drift detected! Score: {drift_result.drift_score:.3f}")
print(f"Features with drift: {list(drift_result.feature_drifts.keys())}")from src.utils.test_data_generator import create_crypto_trading_data_generator
from src.utils.test_data_generator import MarketCondition
# Create data generator
generator = create_crypto_trading_data_generator()
# Generate crypto price data for different market conditions
bull_market_data = generator.generate_crypto_price_data(
market_condition=MarketCondition.BULL_MARKET,
symbol="BTCUSDT"
)
volatile_data = generator.generate_crypto_price_data(
market_condition=MarketCondition.HIGH_VOLATILITY,
symbol="ETHUSDT"
)from src.utils.visualization import create_crypto_trading_visualizer
# Create visualizer
visualizer = create_crypto_trading_visualizer(
interactive=True,
enterprise_theme=True
)
# Create performance dashboard
dashboard_path = visualizer.create_ml_testing_dashboard(
test_results=your_test_results,
title="Crypto Trading ML Dashboard"
)
print(f"Dashboard saved to: {dashboard_path}")This framework implements Enterprise Patterns for production ML systems:
- Automated quality gates and compliance checks
- Audit logging for all testing activities
- Enterprise-grade reporting and documentation
- Real-time performance monitoring
- Distributed tracing integration
- Comprehensive metrics collection
- Circuit breaker patterns for external dependencies
- Graceful degradation and error recovery
- Automated rollback capabilities
- Input validation and sanitization
- Secure credential management
- Risk assessment and mitigation
- Parallel test execution
- Resource optimization
- Auto-scaling capabilities
# Run comprehensive test suite
python -m pytest tests/test_framework.py -v
# Run with coverage
python -m pytest tests/ --cov=src --cov-report=html
# Run specific test categories
python -m pytest tests/test_framework.py::TestMLFrameworkIntegration::test_end_to_end_classification_workflow -v
- Integration Tests: End-to-end workflow testing
- Unit Tests: Individual component testing
- Performance Tests: Benchmark and latency testing
- Edge Case Tests: Error handling and recovery
- Crypto-Specific Tests: Trading scenario validation
The framework provides comprehensive metrics for ML model evaluation:
- Accuracy, Precision, Recall, F1-Score
- ROC AUC, PR AUC
- Confusion Matrix Analysis
- Class-specific metrics
- MSE, MAE, RMSE, R²
- Mean Absolute Percentage Error (MAPE)
- Explained Variance Score
- Custom crypto trading metrics
- Prediction Latency (mean, p95, p99)
- Throughput (samples per second)
- Memory Usage (peak, average)
- CPU Utilization
- Profit Factor
- Sharpe Ratio
- Maximum Drawdown
- Win Rate
- Risk-Adjusted Returns
# MLflow Configuration
MLFLOW_TRACKING_URI=http://localhost:5000
MLFLOW_EXPERIMENT_NAME=crypto_ml_testing
# Testing Configuration
ML_TESTING_PARALLEL=true
ML_TESTING_WORKERS=4
ML_TESTING_TIMEOUT=3600
# Crypto Trading Configuration
TRADING_ENVIRONMENT=staging
RISK_TOLERANCE=conservative
MARKET_DATA_SOURCE=binance
# config.py
ML_TESTING_CONFIG = {
"model_validation": {
"strict_mode": True,
"performance_threshold": 0.8,
"drift_threshold": 0.1
},
"data_quality": {
"missing_threshold": 0.05,
"outlier_threshold": 0.02,
"quality_score_min": 0.7
},
"benchmarking": {
"latency_sla_ms": 100,
"throughput_min": 1000,
"memory_limit_mb": 512
}
}class ModelValidator:
def validate_model(self, model, data, target) -> ValidationResult
def check_determinism(self, model, data) -> bool
def detect_overfitting(self, model, train_data, val_data) -> OverfittingResultclass DataQualityValidator:
def validate_data(self, data, target=None) -> QualityResult
def check_missing_values(self, data) -> MissingValueResult
def detect_outliers(self, data) -> OutlierResultclass BenchmarkRunner:
def run_benchmark(self, model, data) -> BenchmarkResult
def test_latency(self, model, data) -> LatencyMetrics
def test_throughput(self, model, data) -> ThroughputMetricsAll components can be created using factory functions with crypto trading defaults:
# Model Testing
create_crypto_trading_validator()
create_crypto_performance_tester()
create_crypto_regression_tester()
# Data Testing
create_crypto_data_validator()
create_crypto_feature_validator()
create_crypto_drift_detector()
# Benchmarking
create_crypto_benchmark_runner()
create_crypto_metrics_calculator()
# Integration
create_crypto_trading_harness()
create_crypto_trading_data_generator()
create_crypto_trading_visualizer()- Fork the repository
- Create a feature branch (
git checkout -b feature/amazing-feature) - Commit your changes (
git commit -m 'Add amazing feature') - Push to the branch (
git push origin feature/amazing-feature) - Open a Pull Request
# Clone the repository
git clone <repository-url>
cd ml-testing
# Install development dependencies
pip install -e ".[dev]"
# Install pre-commit hooks
pre-commit install
# Run tests
python -m pytest tests/ -v
This project is licensed under the MIT License - see the LICENSE file for details.
- Enterprise Patterns for ML architecture guidance
- scikit-learn community for ML testing best practices
- Crypto trading community for domain-specific requirements
- MLflow for experiment tracking integration
MIT
For questions and support, please open an issue on GitHub.