Python library designed provide core dqml metrics without huge dependencies, as well as common API shared by metrics
Project description
DQM-ML Core
This package defines the foundational API and core metrics for the DQM-ML V2 framework.
Key Concepts
DatametricProcessor
The base class for all metrics and feature extractors. It supports a streaming architecture by splitting computation into two phases:
- Batch Level:
compute_batch_metric()updates intermediate statistics for a single chunk of data. - Dataset Level:
compute()aggregates these statistics into final scores.
Included Metrics
- Completeness: Analyzes null/missing values.
- Representativeness: Statistical distribution analysis (Chi-Square, KS, etc.).
For Developers
To create a new metric:
- Subclass
dqm_ml_core.api.data_processor.DatametricProcessor. - Define
needed_columns(),generated_features(), andgenerated_metrics(). - Implement the streaming logic in
compute_batch_metric()andcompute().
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