cohera
Hierarchical forecast reconciliation (OLS, WLS, MinTrace), conformal prediction intervals, community detection (Leiden, Louvain, Label Propagation), and clustering evaluation metrics. Backed by a Rust implementation.
Python bindings for the sheaf Rust crate.
Install
pip install cohera
Quick start
import cohera
# Reconcile incoherent forecasts to satisfy a hierarchy
s = cohera.simple_star_matrix(3) # 1 root + 3 leaves
base = [12.0, 3.0, 4.0, 5.0]
reconciled = cohera.reconcile(s, base, method="ols")
# reconciled[0] == reconciled[1] + reconciled[2] + reconciled[3]
# Community detection on a weighted graph
edges = [(0, 1, 1.0), (1, 2, 1.0), (0, 2, 1.0)]
communities = cohera.leiden(edges, n_nodes=3, resolution=1.0)
# Dendrogram: build from merge history, cut to k clusters
merges = [(0, 1, 0.5, 2), (2, 3, 0.7, 2), (4, 5, 1.0, 4)]
dendro = cohera.Dendrogram(merges, n_items=4)
labels = dendro.cut_to_k(k=2) # array([0, 0, 1, 1])
# kNN graph from embeddings -> community detection
import numpy as np
embeddings = np.random.randn(100, 64).astype(np.float32)
embeddings /= np.linalg.norm(embeddings, axis=1, keepdims=True)
edges = cohera.knn_graph(embeddings, k=10)
communities = cohera.leiden(edges, n_nodes=100)
# Clustering evaluation
nmi_score = cohera.nmi([0, 0, 1, 1], [0, 0, 1, 1]) # 1.0
ari_score = cohera.ari([0, 0, 1, 1], [0, 0, 1, 1]) # 1.0
API
| Name | Description |
|---|---|
reconcile |
Reconcile base forecasts via OLS, WLS, or MinTrace |
simple_star_matrix |
Build summing matrix for a 2-level hierarchy |
summing_matrix_from_tree |
Build summing matrix from dendrogram merge history |
Dendrogram |
Hierarchical clustering dendrogram with cut operations |
Dendrogram.cut_at_distance |
Cut dendrogram at a distance threshold |
Dendrogram.cut_to_k |
Cut dendrogram to exactly k clusters |
knn_graph |
Build k-nearest-neighbor graph from embeddings (HNSW) |
HierarchicalConformal |
Conformal predictor with hierarchical reconciliation |
HierarchicalConformal.calibrate |
Calibrate intervals from held-out data |
HierarchicalConformal.predict |
Produce reconciled (lower, upper) prediction intervals |
leiden |
Leiden community detection |
louvain |
Louvain community detection |
label_propagation |
Label propagation community detection |
nmi |
Normalized Mutual Information |
ari |
Adjusted Rand Index |
v_measure |
V-Measure (harmonic mean of homogeneity and completeness) |
purity |
Clustering purity |
homogeneity |
Each cluster contains only one class |
completeness |
All members of a class are in the same cluster |
fowlkes_mallows |
Fowlkes-Mallows Index |
numpy support
All functions accept numpy arrays or Python lists. Reconciled forecasts and community labels are returned as numpy arrays.
License
MIT OR Apache-2.0
1 maintainer
Release files for cohera 0.1.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Built distributions (wheels)
Total release size: 21.7 MB
Release files / cohera-0.1.1-pp311-pypy311_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
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