Build Physics-Informed AI Surrogate Models for Industrial Equipment with PhysicsNeMo and MLflow

"""
Production implementation for Building Physics-Informed AI Surrogate Models for Industrial Equipment using PhysicsNeMo and MLflow.
Provides secure, scalable operations.
"""

from typing import Dict, Any, Tuple
import os
import logging
import time
import numpy as np
import pandas as pd
from physics_nemo import PhysicsNeMoModel
from mlflow import log_metric, log_param, start_run, end_run

# Setup logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """
    Configuration class to load environment variables.
    """
    database_url: str = os.getenv('DATABASE_URL')
    mlflow_uri: str = os.getenv('MLFLOW_URI')

async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate incoming data for model training.
    
    Args:
        data: Input data to validate
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'features' not in data or 'labels' not in data:
        raise ValueError('Missing features or labels in input data')
    return True

async def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input data fields.
    
    Args:
        data: Raw data to sanitize
    Returns:
        Sanitized data
    """
    # Example sanitization logic
    return {k: v.strip() if isinstance(v, str) else v for k, v in data.items()}

async def normalize_data(data: pd.DataFrame) -> pd.DataFrame:
    """Normalize data using min-max scaling.
    
    Args:
        data: Input DataFrame to normalize
    Returns:
        Normalized DataFrame
    """
    return (data - data.min()) / (data.max() - data.min())

async def transform_records(records: pd.DataFrame) -> pd.DataFrame:
    """Transform records for model training.
    
    Args:
        records: Raw records to transform
    Returns:
        Transformed records
    """
    # Placeholder for transformations
    return records.apply(np.log1p)

async def process_batch(batch: pd.DataFrame) -> np.ndarray:
    """Process a batch of data for the model.
    
    Args:
        batch: Input batch of data
    Returns:
        Processed batch as numpy array
    """
    # Example processing logic
    return batch.values

async def aggregate_metrics(metrics: Dict[str, float]) -> None:
    """Aggregate metrics for logging.
    
    Args:
        metrics: Dictionary of metrics to log
    """
    for key, value in metrics.items():
        log_metric(key, value)

async def fetch_data(query: str) -> pd.DataFrame:
    """Fetch data from the database.
    
    Args:
        query: SQL query to execute
    Returns:
        DataFrame containing fetched data
    """
    # Simulated fetch
    logger.info('Fetching data with query: %s', query)
    return pd.DataFrame()  # Placeholder for actual DB fetch

async def save_to_db(data: pd.DataFrame) -> None:
    """Save processed data back to the database.
    
    Args:
        data: DataFrame to save
    """
    logger.info('Saving processed data to database')

class SurrogateModel:
    """Main class to orchestrate the model training and evaluation.
    """
    def __init__(self, config: Config) -> None:
        self.config = config
        self.model = PhysicsNeMoModel()

    async def train(self, input_data: Dict[str, Any]) -> None:
        """Train the surrogate model.
        
        Args:
            input_data: Data to train the model
        """
        try:
            valid = await validate_input(input_data)  # Validate input data
            if valid:
                data = await sanitize_fields(input_data)  # Sanitize fields
                data_df = pd.DataFrame(data)  # Convert to DataFrame
                normalized_data = await normalize_data(data_df)  # Normalize data
                # Log parameters and start MLflow run
                with start_run():
                    log_param('model_type', 'Physics-Informed')
                    processed_data = await process_batch(normalized_data)  # Process batch
                    # Train model
                    self.model.train(processed_data)
                    logger.info('Model training completed successfully.')
        except ValueError as e:
            logger.error('Validation error: %s', e)
        except Exception as e:
            logger.error('An error occurred during training: %s', e)

    async def evaluate(self, input_data: Dict[str, Any]) -> None:
        """Evaluate the surrogate model.
        
        Args:
            input_data: Data to evaluate the model
        """
        try:
            valid = await validate_input(input_data)  # Validate input data
            if valid:
                data = await sanitize_fields(input_data)  # Sanitize fields
                # Simulate evaluation logic
                logger.info('Evaluating input data...')
        except ValueError as e:
            logger.error('Validation error: %s', e)
        except Exception as e:
            logger.error('An error occurred during evaluation: %s', e)

if __name__ == '__main__':
    # Example usage of the SurrogateModel class
    config = Config()
    model = SurrogateModel(config)
    sample_data = {'features': [[1.0, 2.0]], 'labels': [[0.5]]}
    # Perform training
    import asyncio
    asyncio.run(model.train(sample_data))
    # Perform evaluation
    asyncio.run(model.evaluate(sample_data))