pyrregular.models.ncde

NCDE. Neural CDE for time series classification.

Module Attributes

ncde_pipeline

This pipeline applies NeuralCDE.

Classes

NeuralCDEClassifier([hidden_size, ...])

A minimal scikit-learn API wrapper for a Neural CDE multiclass classifier.
class pyrregular.models.ncde.NeuralCDEClassifier(hidden_size=8, width_size=32, depth=1, max_iter=50, lr=0.001, seed=0, print_step=10, validate=True, patience_lr=10, patience_es=20, lr_reduce_factor=0.5, solver=Euler(), max_steps=4096, reset_loss_after_lr_reduction=False)[source]

Bases: BaseEstimator, ClassifierMixin

A minimal scikit-learn API wrapper for a Neural CDE multiclass classifier.

X.shape = (n_instances, n_signals, n_timesteps)

  • X[:, :-1, :] => data channels

  • X[:, -1, :] => the time array for each instance

y.shape = (n_instances,) with integer labels in {0, 1, …, n_classes-1}.

Parameters:

  • hidden_size (int) – Number of hidden units in the Neural CDE.

  • width_size (int) – Width of the hidden layers.

  • depth (int) – Number of layers in the Neural CDE.

  • max_iter (int) – Maximum number of training iterations.

  • lr (float) – Learning rate for the optimizer.

  • seed (int) – Random seed for reproducibility.

  • print_step (int) – Frequency of printing training progress.

  • validate (bool) – Whether to use validation during training.

  • patience_lr (int) – Number of iterations to wait before reducing the learning rate if no improvement.

  • patience_es (int) – Number of iterations to wait before early stopping if no improvement.

  • lr_reduce_factor (float) – Factor by which to reduce the learning rate.

  • solver (diffrax.AbstractSolver) – Numerical solver for the ODE.

  • max_steps (int) – Maximum number of steps for the ODE solver.

  • reset_loss_after_lr_reduction (bool) – Whether to reset loss tracking after learning rate reduction.

fit(X, y)[source]
predict(X)[source]

Predict the most likely class for each instance in X. Return shape: (n_instances,).

predict_proba(X)[source]

Predict class probabilities for each instance in X. Return shape: (n_instances, n_classes).

score(X, y, sample_weight=None)[source]

Return the mean accuracy on the given test data and labels.

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → NeuralCDEClassifier

Configure whether metadata should be requested to be passed to the score method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to score.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Parameters:

sample_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in score.

Returns:

self – The updated object.

Return type:

object

pyrregular.models.ncde.ncde_pipeline = NeuralCDEClassifier(lr=0.01, max_iter=1000, max_steps=100, patience_es=200,                     patience_lr=50)

This pipeline applies NeuralCDE.