These components are fundamental because they represent a clear separation of concerns in the evaluation toolkit: What to evaluate (Metric Set Definitions), How to evaluate (Metric Implementations), and Orchestrating the evaluation (Evaluation Orchestration). This modular design aligns perfectly with the "Machine Learning/Data Science Utility Library" pattern, emphasizing reusability, extensibility, and a clear, intuitive API for users.
Components
Evaluation Orchestration (ScoreCard)
This is the core component responsible for driving the evaluation process. It takes input data and a defined set of metrics, computes the scores, and aggregates them into a structured scorecard. It acts as a facade, simplifying the interaction with various metric implementations and metric sets. `CoverageScoreCard` extends `ScoreCard` to specifically handle coverage-related evaluations.
Metric Set Definitions
This component defines collections of metrics that can be applied together for a specific evaluation context (e.g., classification, ranking, coverage, rating). Classes like `rexmex.metricset.ClassificationMetricSet`, `CoverageMetricSet`, `RankingMetricSet`, and `RatingMetricSet` (all inheriting from `rexmex.metricset.MetricSet`) encapsulate these predefined sets.
Metric Implementations (Performance & Coverage)
These components provide the concrete algorithms and functions for calculating individual performance and coverage metrics. Examples include metrics found within `rexmex.metrics.performance` (e.g., precision, recall) and `rexmex.metrics.coverage` (e.g., catalog coverage, diversity).