tamarix-analytics 0.0.8

A Python package for row matching and F1 score calculations.

Tamarix Analytics

A Python package for row matching and F1 score calculations using the Hungarian algorithm.

Installation

pip install tamarix-analytics

Usage

from tamarix_analytics import (
    match_rows,
    f1_score_unordered,
    f1_score_ordered,
    get_row_score,
    f1_score_ordered_by_field,
    f1_score_ordered_by_field_with_nested,
    extract_nested_rows,
    detect_nested_list_fields,
)

Methods

Document-level scores

• match_rows(tentative_data, baseline_data) — Hungarian row alignment.
• f1_score_unordered(tentative_data, baseline_data) — bag-of-values F1.
• f1_score_ordered(tentative_data, baseline_data) — structure-aware F1 after matching.
• get_row_score(tentative_data, baseline_data) — len(tentative) / len(baseline).

Field-level scores

• f1_score_ordered_by_field(tentative_data, baseline_data) -> dict[str, float] — per-field ordered F1. Returns {} when both sides have at most one row. Skips list-of-dict container fields. Omits fields with no evidence (both-None).
• f1_score_ordered_by_field_with_nested(...) — same as above, plus nested list-of-dict fields scored after concatenating all nested objects across main rows. Nested keys are namespaced (sub_amounts.amount, sub_amounts.type, …).
• extract_nested_rows(rows, nested_field) / detect_nested_list_fields(rows) — helpers for nested tables.

Both sides must be list[BaseModel] with a shared field schema (except nested union keys, which are aligned automatically by f1_score_ordered_by_field_with_nested).