ONNX Export¶

Prosemble can export fitted models to ONNX format for cross-platform deployment. An exported ONNX model reproduces the predict() output of the original model and runs anywhere ONNX Runtime is available — no JAX or prosemble dependency needed at inference time.

73 of 87 models are supported.

Installation¶

ONNX export requires the onnx package. For inference, install onnxruntime as well:

pip install onnx onnxruntime

Basic Usage¶

from prosemble.models import GLVQ
from prosemble.core.onnx_export import export_onnx

model = GLVQ(n_prototypes_per_class=2, max_iter=100, lr=0.01)
model.fit(X_train, y_train)

# Export to ONNX
onnx_model = export_onnx(model, path='glvq_model.onnx')

The export_onnx function accepts:

model — a fitted prosemble model
batch_size — fixed batch dimension (default 1; use -1 for dynamic batch size)
opset_version — ONNX opset version (default 17)
path — optional file path to save the ONNX model

Running with ONNX Runtime¶

import numpy as np
import onnxruntime as ort

session = ort.InferenceSession('glvq_model.onnx')
X_test_np = np.asarray(X_test, dtype=np.float32)

onnx_preds = session.run(None, {'X': X_test_np})[0]

The ONNX model takes a single input X of shape (batch_size, n_features) and returns an integer array of predicted labels.

Full Workflow Example¶

import numpy as np
from prosemble.models import GMLVQ
from prosemble.datasets import load_iris_jax
from prosemble.core.onnx_export import export_onnx
import onnxruntime as ort

# Train
dataset = load_iris_jax()
X, y = dataset.input_data, dataset.target_data
model = GMLVQ(
    n_prototypes_per_class=1, max_iter=100,
    lr=0.01, latent_dim=2,
)
model.fit(X, y)

# Export
onnx_model = export_onnx(model, batch_size=-1, path='gmlvq.onnx')

# Run with ONNX Runtime
session = ort.InferenceSession('gmlvq.onnx')
X_np = np.asarray(X, dtype=np.float32)
onnx_preds = session.run(None, {'X': X_np})[0]

# Verify
jax_preds = model.predict(X)
assert np.array_equal(jax_preds, onnx_preds)

Supported Models¶

Family	Count	Models
Supervised LVQ (squared Euclidean)	9	GLVQ, GLVQ1, GLVQ21, LVQ1, LVQ21, MedianLVQ, CELVQ, SLVQ, RSLVQ
Supervised LVQ (global omega)	2	GMLVQ, MRSLVQ
Supervised LVQ (local omega)	2	LGMLVQ, LMRSLVQ
Supervised LVQ (relevance-weighted)	1	GRLVQ
Supervised LVQ (tangent)	1	GTLVQ
Supervised NG (squared Euclidean)	3	SRNG, CELVQ_NG, RSLVQ_NG
Supervised NG (global omega)	3	SMNG, MCELVQ_NG, MRSLVQ_NG
Supervised NG (local omega)	3	SLNG, LCELVQ_NG, LMRSLVQ_NG
Supervised NG (tangent)	2	STNG, TCELVQ_NG
Unsupervised	4	NeuralGas, GrowingNeuralGas, KohonenSOM, HeskesSOM
Fuzzy clustering	8	FCM, PCM, FPCM, PFCM, AFCM, HCM, IPCM, IPCM2
One-class GLVQ	10	OCGLVQ, OCGLVQ_NG, OCGRLVQ, OCGRLVQ_NG, OCGMLVQ, OCGMLVQ_NG, OCLGMLVQ, OCLGMLVQ_NG, OCGTLVQ, OCGTLVQ_NG
One-class RSLVQ	6	OCRSLVQ, OCRSLVQ_NG, OCMRSLVQ, OCMRSLVQ_NG, OCLMRSLVQ, OCLMRSLVQ_NG
SVQ-OCC	5	SVQOCC, SVQOCC_R, SVQOCC_M, SVQOCC_LM, SVQOCC_T
MLP encoder + WTAC	4	SiameseGLVQ, SiameseGMLVQ, SiameseGTLVQ, LVQMLN
CNN encoder + WTAC	3	ImageGLVQ, ImageGMLVQ, ImageGTLVQ
PLVQ (Gaussian mixture)	1	PLVQ
CBC (reasoning matrices)	2	CBC, ImageCBC
Riemannian SO(n) (chordal)	1	RiemannianSRNG
Riemannian SO(n) (tangent-space metric)	3	RiemannianSMNG, RiemannianSLNG, RiemannianSTNG
Riemannian Grassmannian (tangent-space metric)	(same 3)	RiemannianSMNG, RiemannianSLNG, RiemannianSTNG (alternate manifold config)

Encoder Models¶

Models with MLP or CNN backbones (Siamese, Image, LVQMLN, PLVQ, ImageCBC) are fully supported. The ONNX graph encodes the backbone as standard ops:

MLP: MatMul \(\rightarrow\) Add \(\rightarrow\) Activation per layer
CNN: Conv (SAME padding) \(\rightarrow\) Activation per layer \(\rightarrow\) GlobalAveragePool \(\rightarrow\) Linear

Prototypes are pre-computed in the latent space at export time, so only the input needs to be encoded at runtime. Supported activations: Sigmoid, ReLU, Tanh, LeakyReLU, SELU.

Distance Functions in ONNX¶

Distance	ONNX Implementation	Models
Squared Euclidean	Expansion trick	GLVQ family, NG, NeuralGas, FCM, OC, SVQ-OCC, Encoders, CBC
Global Omega	Project then squared Euclidean	GMLVQ, MRSLVQ, SMNG, OCGMLVQ, SVQOCC_M
Local Omega	Batched MatMul per prototype	LGMLVQ, SLNG, OCLGMLVQ, SVQOCC_LM
Tangent	Batched project-reconstruct	GTLVQ, STNG, OCGTLVQ, SVQOCC_T
Relevance-weighted	Element-wise weighted squared diff	GRLVQ, OCGRLVQ, SVQOCC_R
SO(n) Chordal	Broadcast subtract + Frobenius norm	RiemannianSRNG
SO(n) Tangent	Skew-symmetric log map + metric adaptation	RiemannianSMNG/SLNG/STNG (SO)
Grassmannian Tangent	Projection log map + metric adaptation	RiemannianSMNG/SLNG/STNG (Gr)

Not Supported¶

Models	Count	Reason
Kernel fuzzy clustering (KFCM, KPCM, KFPCM, KPFCM, KAFCM, KIPCM, KIPCM2)	7	Gaussian kernel distance has no standard ONNX operator
RiemannianNeuralGas	1	Matrix logarithm via Schur decomposition has no ONNX operator
Riemannian models + SPD(n) manifold	(config)	Eigendecomposition (eigh) has no ONNX operator
RiemannianSRNG + Grassmannian manifold	(config)	SVD-based geodesic distance has no ONNX operator
KNN	1	k-nearest-neighbor logic, not prototype-based
NPC	1	Different predict pattern
Utility (KMeansPlusPlus, Kmeans, SOM, BGPC)	4	Not prototype model base classes