pedigree-graph

Sparse-matrix pedigree relationship extraction and kinship computation.

Builds parent→child CSR adjacency matrices and extracts relationship categories using sparse matrix algebra (A @ A.T for siblings, A² @ A²ᵀ for cousins, etc.). Each relationship type is parameterised by (up, down, n_ancestors):

• up: meioses from the first member (the junior role: offspring, descendant, niece_nephew, junior_cousin) up to the common ancestor(s); collateral categories are stored up ≥ down
• down: meioses from common ancestor(s) down to individual B
• n_ancestors: 1 (half/lineal) or 2 (full, i.e. mated pair)
• kinship = n_ancestors × (1/2)^(up + down + 1)

Install

pip install pedigree-graph

Binary wheels cover manylinux x86-64 and AArch64, macOS x86-64 and Apple Silicon, and Windows x86-64 (one abi3 wheel per platform for CPython >= 3.13). Elsewhere pip builds the sdist, which needs a Rust toolchain (rustc >= 1.85).

For development the locked pixi environment carries Python, Rust, and maturin and installs the package editable (the extension module is rebuilt with pixi run maturin develop --release after a Rust change):

git clone https://github.com/rwaples/pedigree-graph.git
cd pedigree-graph
pixi install --locked
pixi run pytest -m "not slow"   # inner loop, about 2.5 minutes
pixi run pytest                 # before a commit, about 13 minutes
pixi run cargo test --release   # the Rust core

Five of the nine slow tests are the random_30k integration gates. Each runs the pair-kinship kernel over the whole 30,300-row pedigree, and a single kernel call there costs 30 to 250 seconds regardless of how many pairs it is asked for, so the full suite is dominated by a handful of tests. The other four are the N=2000 effective-size scaling tests in tests/test_effective_size_scaling.py, which touch neither random_30k nor the pair-kinship kernel.

Requires Python ≥ 3.13. Runtime deps: numpy, scipy, numba; the relationship, topology, and error kernels are compiled Rust (pedigree_graph._native, sources under crates/). Pandas is optional and only needed if you pass DataFrames to the constructors.

Usage

import numpy as np
from pedigree_graph import RELATIONSHIPS, PedigreeGraph

# Construct from arrays (no pandas needed)
pg = PedigreeGraph.from_arrays(
    ids=np.array([0, 1, 2, 3, 4]),
    mother_ids=np.array([-1, -1, 0, 0, 0]),
    father_ids=np.array([-1, -1, 1, 1, 1]),
)

# Or from a table: a dict of columns (pandas-free), or any pandas/polars frame
pg = PedigreeGraph.from_frame(
    {
        "id": np.array([0, 1, 2, 3]),
        "mother": np.array([-1, -1, 0, 0]),
        "father": np.array([-1, -1, 1, 1]),
        "twin": np.array([-1, -1, -1, -1]),
        "sex": np.array([0, 1, 0, 1], dtype=np.int8),
        "generation": np.array([0, 0, 1, 1], dtype=np.int32),
    }
)
# pg = PedigreeGraph.from_frame(df)

# Pairs by relationship category, up to a given degree; each block holds
# read-only int32 first_rows / second_rows in the category's role orientation
pairs = pg.relationship_pairs(max_degree=3)
first, second = pairs["FS"]  # full sibs
print(len(pairs["1C"]))  # 1st cousins (degree 3)
print(RELATIONSHIPS["FS"].nominal_kinship)  # 0.25

# Pedigree-expected kinship for those pairs, or for any two row arrays
kin = pg.pair_kinship(pairs)  # {code: float32 array}
kin_fs = pg.pair_kinship(first, second)

# Counts without materialising pairs, and the three kinship-matrix families
counts = pg.relationship_counts(max_degree=3)
K = pg.kinship_matrix()  # complete, CSC float32

Absent optional columns read as absent: there is no sex default and no generation fallback. Effective-size estimators live in pedigree_graph.effective_size; the FrameLike protocol in pedigree_graph.typing. Migrating from 0.7.1: see the old-to-new table in CHANGELOG.md.

Relationship registry

Codes follow the convention up_down_n_anc:

See RELATIONSHIPS for the complete list; each RelationshipCategory carries code, label, degree, nominal_kinship, up, down, ancestor_count, and the two positional roles.

Experimental engines

The package ships an alternate relationship-counting engine in pedigree_graph.experimental for exploring large-pedigree scaling:

from pedigree_graph import PedigreeGraph
from pedigree_graph.experimental import count_pairs_bfs

pg = PedigreeGraph.from_frame(df)
counts = count_pairs_bfs(pg)  # dict[str, int] over 23 codes

count_pairs_bfs uses boolean sparse matmul (set-union semantics) plus a parallel numba kernel for cousin-style codes. It is counts-only; there is no pair-array equivalent of relationship_pairs.

The submodule is not re-exported at the top level — callers must import explicitly via pedigree_graph.experimental. First call emits a FutureWarning.

Caveats — read before using

1. Experimental contract. API, signature, and semantics may change or the function may be removed in any minor release. No deprecation cycle is owed.

2. Counts are unfolded, and inbred-pedigree counting differs from the matrix engine. BFS counts a pair under every category it satisfies, where PedigreeGraph.relationship_counts keeps only the closest. On non-inbred pedigrees the BFS counts equal the matrix engine's unfolded blocks exactly. On inbred pedigrees, BFS counts distinct shared ancestors at depth ≥ 2 while the matrix engine counts paths (multiplicity); the four cousin-style codes (1C1R, H1C1R, 1C2R, 2C) may diverge. See tests/test_experimental.py::test_inbred_with_cousins_cousin_codes_diverge for a hand-built fixture pinning the exact divergence.

3. max_degree=5 only. Lower values raise NotImplementedError — use PedigreeGraph.relationship_counts(max_degree=k) for partial extractions.

4. No view support. Pass a full graph; PedigreeView has no BFS counterpart.

5. Threading. The numba kernel uses prange for cousin-style enumeration. Numba reads NUMBA_NUM_THREADS at first JIT compilation; the optional n_threads kwarg only takes effect on the first call in a process. Set NUMBA_NUM_THREADS=N in the environment to control threading on all calls.

6. Performance. Scaling claims (BFS faster than matrix above ~5M individuals, where the matrix engine OOMs) are unverified at the time of v0.2.0. The matrix engine is faster at n=2M in the only head-to-head we have run. See open issues #2 (numba kernel parallelisation) and #3 (10M+ scaling). Treat this engine as an experimental scalability spike, not a tuned alternative.

Architecture

For contributors: docs/architecture.md maps the module layout and the hidden contracts (coordinate space, exact vs approximate counts, path-count vs distinct-ancestor semantics, sparse-ID handling, default sex), each with its source of truth and regression test. The relationship/coordinate vocabulary is in CONTEXT.md; design decisions are in docs/adr/.

License

MIT — see LICENSE.