Python library designed provide pipelining tools dqm-ml library to compute data quality metrics for Machine Learning
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Project description
DQM-ML Pipeline
This package provides the orchestration engine for DQM-ML V2. It handles the lifecycle of data processing, from loading to metric computation and output writing.
Key Components
DatasetPipeline
The main orchestrator that:
- Loads the configuration.
- Discovers plugins via entry points.
- Executes the streaming loop.
- Manages memory and I/O efficiency.
Protocols
DataLoader: A factory for creating data selections (e.g., Parquet, CSV loaders).DataSelection: Represents a specific subset of data and provides an iterator over batches.OutputWriter: Persists computed features or metrics to disk.
Built-in Loaders
parquet: Optimized loading using PyArrow.csv: Flexible loading using Pandas.
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