"""
RSLVQ with Neural Gas Cooperation (RSLVQ_NG).
Combines RSLVQ's probabilistic soft-assignment with Neural Gas
neighborhood cooperation. All prototypes contribute to the loss
via Gaussian mixture probabilities, modulated by NG rank-based
neighborhood weights.
When :math:`\\gamma \\to 0`, only the nearest prototype matters, recovering
standard RSLVQ behavior.
References
----------
.. [1] Seo, S., & Obermayer, K. (2007). Soft Nearest Prototype
Classification. IEEE Trans. Neural Networks.
.. [2] Hammer, B., Strickert, M., & Villmann, T. (2003). Supervised
Neural Gas with General Similarity Measure. Neural Processing
Letters.
"""
import jax
import jax.numpy as jnp
import numpy as np
from prosemble.models.prototype_base import SupervisedPrototypeModel
from prosemble.core.losses import ng_rslvq_loss
[docs]
class RSLVQ_NG(SupervisedPrototypeModel):
"""Robust Soft LVQ with Neural Gas Cooperation.
Combines:
- RSLVQ probabilistic loss: :math:`-\\log(P(\\text{correct}|x))`
- Neural Gas cooperation: all prototypes weighted by rank via
:math:`\\exp(-\\text{rank} / \\gamma)`
- Euclidean distance
The NG neighborhood modulates RSLVQ's Gaussian probabilities,
emphasizing nearby prototypes. :math:`\\gamma` decays during training from
:math:`\\gamma_{\\text{init}}` to :math:`\\gamma_{\\text{final}}`.
Parameters
----------
sigma : float
Bandwidth for RSLVQ Gaussian mixture probability computation.
gamma_init : float, optional
Initial neighborhood range for NG cooperation.
Default: max prototypes per class / 2.
gamma_final : float
Final neighborhood range. Default: 0.01.
gamma_decay : float, optional
Per-step multiplicative decay factor for gamma.
Default: computed from max_iter so gamma reaches gamma_final.
rejection_confidence : float, optional
Minimum class probability for confident prediction (0 to 1).
Samples below this threshold are rejected (label -1).
n_prototypes_per_class : int
Number of prototypes per class.
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).
"""
def __init__(self, sigma=1.0, gamma_init=None, gamma_final=0.01,
gamma_decay=None, rejection_confidence=None,
n_prototypes_per_class=1, 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_per_class=n_prototypes_per_class,
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.sigma = sigma
self.gamma_init = gamma_init
self.gamma_final = gamma_final
self.gamma_decay = gamma_decay
self.rejection_confidence = rejection_confidence
self.gamma_ = None
# Freeze gamma from optimizer
if self.freeze_params is None:
self.freeze_params = ['gamma']
elif 'gamma' not in self.freeze_params:
self.freeze_params = list(self.freeze_params) + ['gamma']
def _get_resume_params(self, params):
gamma = self.gamma_ if self.gamma_ is not None else (
self._gamma_init_actual if hasattr(self, '_gamma_init_actual') else 1.0
)
params['gamma'] = jnp.array(gamma, dtype=jnp.float32)
return params
def _init_state(self, X, y, key):
key1, key2 = jax.random.split(key)
prototypes, proto_labels = self._init_prototypes(
X, y, self.n_prototypes_per_class, key1
)
# Compute gamma_init from prototype count
if isinstance(self.n_prototypes_per_class, int):
max_per_class = self.n_prototypes_per_class
elif isinstance(self.n_prototypes_per_class, dict):
max_per_class = max(self.n_prototypes_per_class.values())
else:
max_per_class = max(self.n_prototypes_per_class)
gamma_init = (self.gamma_init if self.gamma_init is not None
else max_per_class / 2.0)
gamma_init = max(gamma_init, self.gamma_final + 1e-6)
self._gamma_init_actual = gamma_init
if self.gamma_decay is not None:
self._gamma_decay = self.gamma_decay
else:
self._gamma_decay = (
self.gamma_final / gamma_init
) ** (1.0 / self.max_iter)
params = {
'prototypes': prototypes,
'gamma': jnp.array(gamma_init, dtype=jnp.float32),
}
opt_state = self._optimizer.init(params)
from prosemble.models.prototype_base import SupervisedState
state = SupervisedState(
prototypes=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']
gamma = params['gamma']
distances = self.distance_fn(X, prototypes)
return ng_rslvq_loss(distances, y, proto_labels,
sigma=self.sigma, gamma=gamma)
def _post_update(self, params):
new_gamma = params['gamma'] * self._gamma_decay
new_gamma = jnp.maximum(new_gamma, self.gamma_final)
return {**params, 'gamma': new_gamma}
def _extract_results(self, params, proto_labels, loss_history, n_iter,
**kwargs):
super()._extract_results(
params, proto_labels, loss_history, n_iter, **kwargs
)
self.gamma_ = float(params['gamma'])
def predict_with_rejection(self, X, confidence=None):
"""Predict with rejection option.
Samples whose maximum class probability is below the confidence
threshold are assigned label -1 (rejected).
Parameters
----------
X : array-like of shape (n_samples, n_features)
confidence : float, optional
Override the model's rejection_confidence for this call.
Returns
-------
labels : array of shape (n_samples,)
"""
self._check_fitted()
threshold = (confidence if confidence is not None
else self.rejection_confidence)
if threshold is None:
return self.predict(X)
X = jnp.asarray(X, dtype=jnp.float32)
proba = self.predict_proba(X)
max_proba = jnp.max(proba, axis=1)
preds = jnp.argmax(proba, axis=1)
return jnp.where(max_proba >= threshold, preds, -1)
def _get_fitted_arrays(self):
arrays = super()._get_fitted_arrays()
if self.gamma_ is not None:
arrays['gamma_'] = np.asarray(self.gamma_)
return arrays
def _set_fitted_arrays(self, arrays):
super()._set_fitted_arrays(arrays)
if 'gamma_' in arrays:
self.gamma_ = float(arrays['gamma_'])
def _get_hyperparams(self):
hp = super()._get_hyperparams()
hp['sigma'] = self.sigma
hp['gamma_init'] = self.gamma_init
hp['gamma_final'] = self.gamma_final
hp['gamma_decay'] = self.gamma_decay
hp['rejection_confidence'] = self.rejection_confidence
return hp