yobx.sklearn.cluster.bisecting_kmeans#

yobx.sklearn.cluster.bisecting_kmeans.sklearn_bisecting_kmeans(g: GraphBuilderExtendedProtocol, sts: Dict, outputs: List[str], estimator: BisectingKMeans, X: str, name: str = 'bisecting_kmeans') → str | Tuple[str, str][source]#

Converts a sklearn.cluster.BisectingKMeans into ONNX.

The converter produces two outputs:

labels — predicted cluster index for each sample, replicating predict() by traversing the internal bisection tree.
distances — Euclidean distances from each sample to every cluster centre, replicating transform().

Label assignment via tree traversal

BisectingKMeans assigns labels by walking down the hierarchical bisection tree rather than finding the globally nearest centre. At each internal node the sample is routed to the child whose (X_mean-adjusted) centre is closest. Reached at a leaf, the leaf’s label is assigned.

Each internal node defines a linear decision boundary:

go_left  iff  X · (right_eff - left_eff) ≤ (||right_eff||² - ||left_eff||²) / 2

which requires only one MatMul per internal node. A sample reaches a leaf when all conditions on the root-to-leaf path are satisfied. Since leaves are mutually exclusive and exhaustive, the final label is computed as:

label = Σ_leaf  leaf_label × Cast(leaf_mask, int64)

Distance computation (transform output):

When the com.microsoft opset is available the CDist operator is used directly (metric="euclidean"). Otherwise the distances are computed manually:

||x - c||² = ||x||² - 2·x·cᵀ + ||c||²  →  Sqrt

Full graph structure (three-cluster example):

X (N,F)
  │
  ├──Mul──ReduceSum(axis=1,keepdims=1)──────────────────────► x_sq (N,1)
  │                                                                │
  └──MatMul(centersᵀ)──────────────────────────────────────► cross (N,K)
                                                                   │
c_sq (1,K) ──────────────── Add(x_sq) ── Sub(2·cross) ──► sq_dists (N,K)
                                                                   │
                                    Sqrt ──────────────────► distances (N,K)
  │
  ├──MatMul(dir_0)──LessOrEqual(thr_0) ──► go_left_0 (N,)
  │     …
  └── label accumulation via And / Cast / Mul / Add ──► labels (N,)

Parameters:

g – the graph builder to add nodes to
sts – shapes defined by scikit-learn
estimator – a fitted BisectingKMeans
outputs – desired output names; outputs[0] receives the cluster labels and outputs[1] (if present) receives the distances matrix
X – input tensor name
name – prefix names for the added nodes

Returns:

tuple (labels, distances) when two outputs are requested, otherwise just labels

yobx.sklearn.cluster.bisecting_kmeans#

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