yobx.sklearn.mixture.gaussian_mixture#

yobx.sklearn.mixture.gaussian_mixture.sklearn_gaussian_mixture(g: GraphBuilderExtendedProtocol, sts: Dict, outputs: List[str], estimator: GaussianMixture, X: str, name: str = 'gaussian_mixture') → Tuple[str, str][source]#

Converts a sklearn.mixture.GaussianMixture into ONNX.

The converter supports all four covariance types supported by GaussianMixture: 'full', 'tied', 'diag', and 'spherical'.

In each case the weighted log-probability for sample n under component k is computed as:

log_p[n, k] = log(weight_k) + log_det_k
              - 0.5 * n_features * log(2π)
              - 0.5 * quad[n, k]

where quad[n, k] is the Mahalanobis distance squared, computed differently depending on covariance_type. label is the ArgMax of log_p and proba is its Softmax.

‘full’ — per-component Cholesky of the precision matrix L_k (shape (K, F, F)):

L_2d  = L.transpose(1,0,2).reshape(F, K*F)          # (F, K*F) constant
b     = einsum('ki,kij->kj', means_, L)              # (K, F)   constant
XL    = MatMul(X, L_2d)                              # (N, K*F)
Y     = Reshape(XL - b, [-1, K, F])                  # (N, K, F)
quad  = ReduceSum(Y * Y, axis=2)                     # (N, K)

‘tied’ — single shared Cholesky L (shape (F, F)):

means_L = means_ @ L                                 # (K, F)  constant
XL      = MatMul(X, L)                               # (N, F)
Y       = Reshape(XL, [-1, 1, F]) - means_L          # (N, K, F)
quad    = ReduceSum(Y * Y, axis=2)                   # (N, K)

‘diag’ — per-component diagonal precision A = prec_chol**2 (shape (K, F)):

B     = means_ * A                                   # (K, F)  constant
log_p = -0.5 * MatMul(X², Aᵀ) + MatMul(X, Bᵀ) + c  # (N, K)

‘spherical’ — scalar precision prec = prec_chol**2 per component (shape (K,)):

x_sq  = ReduceSum(X * X, axis=1, keepdims=1)        # (N, 1)
cross = MatMul(X, means_ᵀ)                          # (N, K)
log_p = prec * cross - 0.5 * prec * x_sq + c        # (N, K)

Parameters:

g – the graph builder to add nodes to
sts – shapes defined by scikit-learn
outputs – desired output names; outputs[0] receives the predicted component labels and outputs[1] receives the posterior probabilities
estimator – a fitted GaussianMixture
X – input tensor name
name – prefix for added node names

Returns:

tuple (label_result_name, proba_result_name)

Raises:

NotImplementedError – for unsupported covariance_type values

yobx.sklearn.mixture.gaussian_mixture#

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