Building Weave: Intelligent Dataset Management (Part 3)

# weave/datasets/base.py
class DatasetManager:
    """Core class for dataset management."""
    
    def __init__(self, config: Dict[str, Any]):
        self.config = config
        self.validators = self._initialize_validators()
        self.transformers = self._initialize_transformers()
        self.storage = self._initialize_storage()
        
    def load(self, source: Union[str, Path]) -> Dataset:
        """Load dataset from various sources."""
        if isinstance(source, str) and source.startswith("http"):
            return self._load_remote(source)
        return self._load_local(source)
        
    def merge(self, datasets: List[Dataset]) -> Dataset:
        """Merge multiple datasets with conflict resolution."""
        return self._smart_merge(datasets)
        
    def validate(self, dataset: Dataset) -> ValidationReport:
        """Run quality checks on dataset."""
        return self._run_validation_pipeline(dataset)

# weave/datasets/loaders.py
class SmartLoader:
    """Intelligent data loading with format detection."""
    
    SUPPORTED_FORMATS = {
        '.json': JsonLoader,
        '.csv': CsvLoader,
        '.parquet': ParquetLoader,
        '.jsonl': JsonLinesLoader
    }
    
    def load(self, path: Union[str, Path]) -> Dataset:
        # Detect format
        format = self._detect_format(path)
        
        # Get appropriate loader
        loader = self.SUPPORTED_FORMATS[format]()
        
        # Load with schema inference
        data = loader.load(path)
        
        # Apply automatic cleaning
        return self._clean_dataset(data)
        
    def _detect_format(self, path: Union[str, Path]) -> str:
        """Detect file format from content and extension."""
        if isinstance(path, str) and path.startswith("http"):
            return self._detect_remote_format(path)
        return self._detect_local_format(path)

# weave/datasets/validation.py
class ValidationPipeline:
    """Multi-stage validation pipeline."""
    
    def __init__(self, config: Dict[str, Any]):
        self.schema_validator = SchemaValidator(config)
        self.quality_validator = QualityValidator(config)
        self.semantic_validator = SemanticValidator(config)
        
    def validate(self, dataset: Dataset) -> ValidationReport:
        report = ValidationReport()
        
        # Schema validation
        schema_results = self.schema_validator.validate(dataset)
        report.add_results("schema", schema_results)
        
        # Quality checks
        quality_results = self.quality_validator.validate(dataset)
        report.add_results("quality", quality_results)
        
        # Semantic validation
        semantic_results = self.semantic_validator.validate(dataset)
        report.add_results("semantic", semantic_results)
        
        return report

# weave/datasets/metrics.py
class QualityMetrics:
    """Dataset quality metrics calculator."""
    
    def calculate_metrics(self, dataset: Dataset) -> Dict[str, float]:
        return {
            "completeness": self._calc_completeness(dataset),
            "consistency": self._calc_consistency(dataset),
            "uniqueness": self._calc_uniqueness(dataset),
            "semantic_coherence": self._calc_semantic_score(dataset)
        }
        
    def _calc_semantic_score(self, dataset: Dataset) -> float:
        """Calculate semantic coherence using embeddings."""
        embeddings = self.model.embed_batch(dataset.texts)
        return self._compute_coherence_score(embeddings)

# weave/datasets/processing.py
class StreamingProcessor:
    """Process large datasets efficiently."""
    
    def __init__(self, chunk_size: int = 1000):
        self.chunk_size = chunk_size
        
    def process(self, dataset: Dataset, transform_fn: Callable) -> Dataset:
        # Process in chunks to manage memory
        for chunk in dataset.iter_chunks(self.chunk_size):
            # Transform chunk
            transformed = transform_fn(chunk)
            
            # Validate transformation
            if not self._is_valid(transformed):
                transformed = self._repair_chunk(transformed)
                
            yield transformed

# weave/datasets/memory.py
class MemoryManager:
    """Manage memory usage for large datasets."""
    
    def __init__(self, max_memory_gb: float = 4.0):
        self.max_memory = max_memory_gb * 1024 * 1024 * 1024
        self.current_usage = 0
        
    def can_load(self, size_bytes: int) -> bool:
        """Check if loading data would exceed memory limit."""
        return (self.current_usage + size_bytes) <= self.max_memory
        
    def optimize_storage(self, dataset: Dataset) -> Dataset:
        """Optimize dataset storage."""
        if self._needs_optimization(dataset):
            return self._compress_dataset(dataset)
        return dataset

# weave/datasets/conversion.py
class FormatConverter:
    """Convert between different data formats."""
    
    def convert(self, dataset: Dataset, target_format: str) -> Dataset:
        if target_format not in self.SUPPORTED_FORMATS:
            raise ValueError(f"Unsupported format: {target_format}")
            
        # Get converter for target format
        converter = self._get_converter(target_format)
        
        # Convert while preserving metadata
        converted = converter.convert(dataset)
        
        # Validate conversion
        self._validate_conversion(dataset, converted)
        
        return converted