"""
Relevance-Weighted SVQ-OCC (SVQ-OCC-R).
Extends SVQ-OCC with per-feature adaptive relevance weighting,
following the GRLVQ pattern. The distance becomes:
.. math::
d_{\\lambda}(x, w_k) = \\sum_j \\lambda_j (x_j - w_{k,j})^2
where :math:`\\lambda = \\mathrm{softmax}(\\text{relevances})` are learned
per-feature weights. This enables the model to identify which features
are most discriminative for one-class classification.
References
----------
.. [1] Staps et al. (2022). Prototype-based One-Class-Classification
Learning Using Local Representations. IEEE WSOM+ 2022.
.. [2] Hammer, Villmann (2002). Generalized Relevance Learning Vector
Quantization. Neural Networks, 15(8-9), 1059-1068.
"""
import jax
import jax.numpy as jnp
import numpy as np
from prosemble.models.svq_occ import SVQOCC
[docs]
class SVQOCC_R(SVQOCC):
"""Relevance-Weighted SVQ-OCC.
Extends SVQ-OCC with per-feature relevance weighting (like GRLVQ).
Learns which features are most important for distinguishing
target from non-target data.
Parameters
----------
n_prototypes : int
Number of prototypes for the target class.
target_label : int, optional
Which label is the target (normal) class. Default: auto-detect
as the most frequent class.
alpha : float
Balance between representation (R) and classification (C) cost.
E = alpha * R + (1 - alpha) * C. Default: 0.5.
cost_function : str
Classification cost variant: 'contrastive', 'brier', 'cross_entropy'.
Default: 'contrastive'.
response_type : str
Response probability model: 'gaussian', 'student_t', 'uniform'.
Default: 'gaussian'.
sigma : float
Sigmoid sharpness for differentiable Heaviside approximation.
Smaller = sharper boundary. Default: 0.1.
gamma_resp : float
Response bandwidth for Gaussian probabilistic assignment. Default: 1.0.
nu : float
Degrees of freedom for Student-t response. Default: 1.0.
lambda_init : float, optional
Initial NG neighborhood range. Default: n_prototypes / 2.
lambda_final : float
Final NG neighborhood range. Default: 0.01.
lambda_decay : float, optional
Per-step multiplicative decay for lambda. Default: computed from
max_iter.
max_iter : int
Maximum training iterations.
lr : float
Learning rate.
epsilon : float
Convergence threshold on loss change.
random_seed : int
Random seed for reproducibility.
distance_fn : callable, optional
Distance function (default: squared Euclidean).
optimizer : str or optax optimizer, optional
Optimizer name ('adam', 'sgd') or optax GradientTransformation.
Default: 'adam'.
transfer_fn : callable, optional
Transfer function for loss shaping (default: identity).
margin : float
Margin for loss computation.
callbacks : list, optional
List of Callback objects.
use_scan : bool
If True (default), use jax.lax.scan for training (faster,
JIT-compiled, but runs all max_iter iterations even after
convergence). If False, use a Python for-loop with true early
stopping (no wasted compute after convergence, but slower per
iteration).
batch_size : int, optional
Mini-batch size. If None (default), use full-batch training.
When set, each epoch iterates over shuffled mini-batches of this
size.
lr_scheduler : str or optax.Schedule, optional
Learning rate schedule. Supported strings: 'exponential_decay',
'cosine_decay', 'warmup_cosine_decay', 'warmup_exponential_decay',
'warmup_constant', 'polynomial', 'linear', 'piecewise_constant',
'sgdr'. Or pass a custom optax.Schedule. Default: None.
lr_scheduler_kwargs : dict, optional
Keyword arguments passed to the learning rate scheduler
(e.g. ``decay_rate``, ``transition_steps``). Default: None.
prototypes_initializer : str or callable, optional
How to initialize prototypes. Supported strings:
'stratified_random' (default), 'class_mean',
'class_conditional_mean', 'stratified_noise', 'random_normal',
'uniform', 'zeros', 'ones', 'fill_value'. Or pass a callable
``(X, y, n_per_class, key) -> (protos, labels)``.
patience : int, optional
Number of consecutive epochs with no improvement before stopping.
If None (default), stops after a single non-improving step
(epsilon check). Requires use_scan=False for true early stopping.
restore_best : bool
If True, restore the parameters that achieved the lowest loss
(or validation loss if validation data is provided). Default:
False.
class_weight : dict or 'balanced', optional
Weights for each class. Dict maps class label to weight, e.g.
{0: 1.0, 1: 2.0, 2: 1.5}. 'balanced' auto-computes weights
inversely proportional to class frequencies. Default: None
(uniform).
gradient_accumulation_steps : int, optional
Accumulate gradients over this many steps before applying an
update. Effective batch size = batch_size *
gradient_accumulation_steps. Default: None (no accumulation).
ema_decay : float, optional
Exponential moving average decay for parameters
(0 < ema_decay < 1). After training, model parameters are
replaced with EMA-smoothed values. Typical values: 0.999,
0.9999. Default: None (no EMA).
freeze_params : list of str, optional
List of parameter group names to freeze (zero gradients).
E.g. ['backbone'] to freeze the backbone and only train
prototypes. Default: None (all parameters trainable).
lookahead : dict, optional
Enable lookahead optimizer wrapper. Dict with keys:
- 'sync_period': int (default 6) -- sync every k steps
- 'slow_step_size': float (default 0.5) -- interpolation factor
Default: None (no lookahead).
mixed_precision : str or None, optional
Compute dtype for mixed precision training. 'float16' or
'bfloat16'. Master weights stay in float32; forward/backward
pass runs in lower precision for ~2x speed and ~half memory on
GPU. Float16 uses static loss scaling to prevent gradient
underflow. Default: None (disabled).
Attributes
----------
relevances_ : array of shape (n_features,)
Learned per-feature relevance weights (softmax-normalized).
See Also
--------
SVQOCC : Base SVQ-OCC model.
"""
def __init__(self, n_prototypes=3, target_label=None, alpha=0.5,
cost_function='contrastive', response_type='gaussian',
sigma=0.1, gamma_resp=1.0, nu=1.0,
lambda_init=None, lambda_final=0.01, lambda_decay=None,
max_iter=100, lr=0.01, epsilon=1e-6, random_seed=42,
distance_fn=None, optimizer='adam', transfer_fn=None,
margin=0.0, callbacks=None, use_scan=True, batch_size=None,
lr_scheduler=None, lr_scheduler_kwargs=None,
prototypes_initializer=None, patience=None,
restore_best=False, class_weight=None,
gradient_accumulation_steps=None, ema_decay=None,
freeze_params=None, lookahead=None, mixed_precision=None):
super().__init__(
n_prototypes=n_prototypes, target_label=target_label,
alpha=alpha, cost_function=cost_function,
response_type=response_type, sigma=sigma,
gamma_resp=gamma_resp, nu=nu, lambda_init=lambda_init,
lambda_final=lambda_final, lambda_decay=lambda_decay,
max_iter=max_iter, lr=lr, epsilon=epsilon,
random_seed=random_seed, distance_fn=distance_fn,
optimizer=optimizer, transfer_fn=transfer_fn, margin=margin,
callbacks=callbacks, use_scan=use_scan, batch_size=batch_size,
lr_scheduler=lr_scheduler,
lr_scheduler_kwargs=lr_scheduler_kwargs,
prototypes_initializer=prototypes_initializer,
patience=patience, restore_best=restore_best,
class_weight=class_weight,
gradient_accumulation_steps=gradient_accumulation_steps,
ema_decay=ema_decay, freeze_params=freeze_params,
lookahead=lookahead, mixed_precision=mixed_precision,
)
self.relevances_ = None
def _get_resume_params(self, params):
params = super()._get_resume_params(params)
params['relevances'] = self._raw_relevances
return params
def _init_state(self, X, y, key):
state, params, proto_labels = super()._init_state(X, y, key)
n_features = X.shape[1]
# Initialize uniform relevances (softmax of zeros = uniform)
params['relevances'] = jnp.ones(n_features) / n_features
# Reinitialize optimizer with the added parameter
opt_state = self._optimizer.init(params)
from prosemble.models.prototype_base import SupervisedState
state = SupervisedState(
prototypes=params['prototypes'],
opt_state=opt_state,
loss=jnp.array(float('inf')),
iteration=0,
converged=False,
)
return state, params, proto_labels
def _compute_loss(self, params, X, y, proto_labels):
prototypes = params['prototypes']
thetas = params['thetas']
lambda_ng = params['lambda_ng']
relevances = params['relevances']
n_protos = prototypes.shape[0]
# Relevance-weighted squared Euclidean distances
lam = jax.nn.softmax(relevances) # (d,)
diff = X[:, None, :] - prototypes[None, :, :] # (n, K, d)
sq_distances = jnp.sum(lam[None, None, :] * diff ** 2, axis=2) # (n, K)
# Target / non-target masks
target_mask = (y == self._target_label)
# ===== Representation cost R (target data only) =====
order = jnp.argsort(sq_distances, axis=1)
ranks = jnp.argsort(order, axis=1).astype(jnp.float32)
h_ng = jnp.exp(-ranks / (lambda_ng + 1e-10))
R_per_sample = jnp.sum(h_ng * sq_distances, axis=1)
R_per_sample = jnp.where(target_mask, R_per_sample, 0.0)
n_target = jnp.sum(target_mask) + 1e-10
R = jnp.sum(R_per_sample) / n_target
# ===== Classification cost C =====
if self.response_type == 'gaussian':
logits = -self.gamma_resp * sq_distances
p_k = jax.nn.softmax(logits, axis=1)
elif self.response_type == 'student_t':
p_unnorm = (1.0 + sq_distances / self.nu) ** (-(self.nu + 1) / 2)
p_k = p_unnorm / (jnp.sum(p_unnorm, axis=1, keepdims=True) + 1e-10)
else: # uniform
p_k = jnp.ones_like(sq_distances) / n_protos
thetas_pos = jnp.maximum(thetas, 1e-6)
heaviside = jax.nn.sigmoid(
(thetas_pos[None, :] - sq_distances) / (self.sigma + 1e-10)
)
responsibility = p_k * heaviside
total_resp = jnp.sum(responsibility, axis=1)
total_resp = jnp.clip(total_resp, 1e-10, 1.0 - 1e-10)
y_binary = target_mask.astype(jnp.float32)
if self.cost_function == 'contrastive':
TP = jnp.sum(y_binary * total_resp)
FN = jnp.sum(y_binary) - TP
FP = jnp.sum((1.0 - y_binary) * total_resp)
TN = jnp.sum(1.0 - y_binary) - FP
numerator = TP * TN - FP * FN
denominator = (TP + FP + 1e-10) * (TN + FN + 1e-10)
C = 1.0 - numerator / denominator
elif self.cost_function == 'brier':
C = jnp.mean((y_binary - total_resp) ** 2)
else: # cross_entropy
C = -jnp.mean(
y_binary * jnp.log(total_resp) +
(1.0 - y_binary) * jnp.log(1.0 - total_resp)
)
return self.alpha * R + (1.0 - self.alpha) * C
def _extract_results(self, params, proto_labels, loss_history, n_iter,
**kwargs):
super()._extract_results(
params, proto_labels, loss_history, n_iter, **kwargs
)
self.relevances_ = jax.nn.softmax(params['relevances'])
self._raw_relevances = params['relevances']
[docs]
def decision_function(self, X):
"""Compute scores using relevance-weighted distances."""
self._check_fitted()
X = jnp.asarray(X, dtype=jnp.float32)
diff = X[:, None, :] - self.prototypes_[None, :, :]
sq_distances = jnp.sum(
self.relevances_[None, None, :] * diff ** 2, axis=2
)
n_protos = self.prototypes_.shape[0]
if self.response_type == 'gaussian':
logits = -self.gamma_resp * sq_distances
p_k = jax.nn.softmax(logits, axis=1)
elif self.response_type == 'student_t':
p_unnorm = (
(1.0 + sq_distances / self.nu) ** (-(self.nu + 1) / 2)
)
p_k = p_unnorm / (
jnp.sum(p_unnorm, axis=1, keepdims=True) + 1e-10
)
else:
p_k = jnp.ones_like(sq_distances) / n_protos
heaviside = jax.nn.sigmoid(
(self.thetas_[None, :] - sq_distances) / (self.sigma + 1e-10)
)
responsibility = p_k * heaviside
return jnp.clip(jnp.sum(responsibility, axis=1), 0.0, 1.0)
@property
def relevance_profile(self):
"""Return the learned per-feature relevance weights (normalized)."""
if self.relevances_ is None:
raise ValueError("Model not fitted. Call fit() first.")
return self.relevances_
def _get_quantizable_attrs(self):
attrs = super()._get_quantizable_attrs()
if self.relevances_ is not None:
attrs.append('relevances_')
return attrs
def _get_fitted_arrays(self):
arrays = super()._get_fitted_arrays()
if self.relevances_ is not None:
arrays['relevances_'] = np.asarray(self.relevances_)
if hasattr(self, '_raw_relevances') and self._raw_relevances is not None:
arrays['_raw_relevances'] = np.asarray(self._raw_relevances)
return arrays
def _set_fitted_arrays(self, arrays):
super()._set_fitted_arrays(arrays)
if 'relevances_' in arrays:
self.relevances_ = jnp.asarray(arrays['relevances_'])
if '_raw_relevances' in arrays:
self._raw_relevances = jnp.asarray(arrays['_raw_relevances'])