GRUClassifier

class GRUClassifier(hidden_dim: int = 256, n_layers: int = 4, batch_first: bool = False, bias: bool = True, init_weights: bool = True, dropout: float = 0.0, fc_dropout: float = 0.0, bidirectional: bool = False, num_epochs: int = 10, batch_size: int = 8, optimizer: str = None, criterion: str = None, criterion_kwargs: dict = None, optimizer_kwargs: dict = None, lr: float = 0.001, verbose: bool = False, random_state: int = None)[source]

Gated Recurrent Unit (GRU) for time series classification.

This classifier has been wrapped around implementations from [1], [2] and [3].

Parameters:

hidden_dimint: Number of features in the hidden state.
n_layersint: Number of recurrent layers.
batch_firstbool: If True, then the input and output tensors are provided as (batch, seq, feature), default is False.
biasbool: If False, then the layer does not use bias weights, default is True.
init_weightsbool: If True, then the weights are initialized, default is True.
dropoutfloat: Dropout rate to apply. default is 0.0
fc_dropoutfloat: Dropout rate to apply to the fully connected layer. default is 0.0
bidirectionalbool: If True, then the GRU is bidirectional, default is False.
num_epochsint, optional (default=10): The number of epochs to train the model.
optimizerstr, optional (default=None): The optimizer to use. If None, Adam will be used.
activationstr, optional (default=”relu”): The activation function to use. Options: [“relu”, “softmax”].
batch_sizeint, optional (default=8): The size of each mini-batch during training.
criterioncallable, optional (default=None): The loss function to use. If None, CrossEntropyLoss will be used.
criterion_kwargsdict, optional (default=None): Additional keyword arguments to pass to the loss function.
optimizer_kwargsdict, optional (default=None): Additional keyword arguments to pass to the optimizer.
lrfloat, optional (default=0.001): The learning rate to use for the optimizer.
verbosebool, optional (default=False): Whether to print progress information during training.
random_stateint, optional (default=None): Seed to ensure reproducibility.

Attributes:

is_fitted: Whether fit has been called.

References

[1]

Cho, Kyunghyun, et al. “Learning phrase representations using RNN encoder-decoder for statistical machine translation.” arXiv preprint arXiv:1406.1078 (2014).

[2]

Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, Yoshua Bengio. Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. arXiv preprint arXiv:1412.3555 (2014).

[3]

https://pytorch.org/docs/stable/generated/torch.nn.GRU.html

Methods

`check_is_fitted`([method_name])	Check if the estimator has been fitted.
`clone`()	Obtain a clone of the object with same hyper-parameters and config.
`clone_tags`(estimator[, tag_names])	Clone tags from another object as dynamic override.
`create_test_instance`([parameter_set])	Construct an instance of the class, using first test parameter set.
`create_test_instances_and_names`([parameter_set])	Create list of all test instances and a list of names for them.
`fit`(X, y)	Fit time series classifier to training data.
`fit_predict`(X, y[, cv, change_state])	Fit and predict labels for sequences in X.
`fit_predict_proba`(X, y[, cv, change_state])	Fit and predict labels probabilities for sequences in X.
`get_class_tag`(tag_name[, tag_value_default])	Get class tag value from class, with tag level inheritance from parents.
`get_class_tags`()	Get class tags from class, with tag level inheritance from parent classes.
`get_config`()	Get config flags for self.
`get_fitted_params`([deep])	Get fitted parameters.
`get_param_defaults`()	Get object's parameter defaults.
`get_param_names`([sort])	Get object's parameter names.
`get_params`([deep])	Get a dict of parameters values for this object.
`get_tag`(tag_name[, tag_value_default, ...])	Get tag value from instance, with tag level inheritance and overrides.
`get_tags`()	Get tags from instance, with tag level inheritance and overrides.
`get_test_params`([parameter_set])	Return testing parameter settings for the estimator.
`is_composite`()	Check if the object is composed of other BaseObjects.
`load_from_path`(serial)	Load object from file location.
`load_from_serial`(serial)	Load object from serialized memory container.
`predict`(X)	Predicts labels for sequences in X.
`predict_proba`(X)	Predicts labels probabilities for sequences in X.
`reset`()	Reset the object to a clean post-init state.
`save`([path, serialization_format])	Save serialized self to bytes-like object or to (.zip) file.
`score`(X, y)	Scores predicted labels against ground truth labels on X.
`set_config`(**config_dict)	Set config flags to given values.
`set_params`(**params)	Set the parameters of this object.
`set_random_state`([random_state, deep, ...])	Set random_state pseudo-random seed parameters for self.
`set_tags`(**tag_dict)	Set instance level tag overrides to given values.