CNTCClassifier

CNTCClassifier

class CNTCClassifier(n_epochs=2000, batch_size=16, filter_sizes=(16, 8), kernel_sizes=(1, 1), rnn_size=64, lstm_size=8, dense_size=64, dropout=(0.2, 0.2, 0.1, 0.1, 0.1, 0.1, 0.1), callbacks=None, verbose=False, loss='categorical_crossentropy', metrics=None, random_state=0, activation='softmax', activation_attention='sigmoid', activation_hidden='relu')[source]

Contextual Time-series Neural Classifier (CNTC), as described in [1].

Adapted from the implementation from Fullah et. al https://github.com/AmaduFullah/CNTC_MODEL/blob/master/cntc.ipynb

Parameters:

activationstring or a tf callable, default=”softmax”

Activation function used in the output layer. List of available activation functions: https://keras.io/api/layers/activations/

activation_attentionstring, default = “sigmoid”

Activation function inside the self attention module; List of available keras activation functions: https://keras.io/api/layers/activations/

activation_hiddenstring or a tf callable, default=”relu”

Activation function used in the hidden layers. List of available activation functions: https://keras.io/api/layers/activations/

n_epochsint, default = 2000

the number of epochs to train the model

batch_sizeint, default = 16

the number of samples per gradient update.

filter_sizestuple of shape (2), default = (16, 8)

filter sizes for CNNs in CCNN arm.

kernel_sizestwo-tuple, default = (1, 1)

the length of the 1D convolution window for CNNs in CCNN arm.

rnn_sizeint, default = 64

number of rnn units in the CCNN arm.

lstm_sizeint, default = 8

number of lstm units in the CLSTM arm.

dense_sizeint, default = 64

dimension of dense layer in CNTC.

dropoutfloat or tuple of floats, default = (0.2, 0.2, 0.1, 0.1, 0.1, 0.1, 0.1)

dropout rate(s), in the range [0, 1). If a single float is provided, the same dropout rate is applied to all layers. If a tuple is provided, it should have 7 values corresponding to: (conv1_dropout, rnn1_dropout, conv2_dropout, lstm_dropout,

avg_dropout, att_dropout, mlp_dropout)

where mlp_dropout is applied to both MLP layers.

random_stateint or None, default=None

Seed for random number generation.

verboseboolean, default = False

whether to output extra information

lossstring, default=”mean_squared_error”

fit parameter for the keras model

optimizerkeras.optimizer, default=keras.optimizers.Adam(),

metricslist of strings, default=[“accuracy”],

callbackslist of keras.callbacks, default = None,

Attributes:

is_fitted

Whether fit has been called.

References

[1]

Network originally defined in:

@article{FULLAHKAMARA202057, title = {Combining contextual neural networks for time series classification}, journal = {Neurocomputing}, volume = {384}, pages = {57-66}, year = {2020}, issn = {0925-2312}, doi = {https://doi.org/10.1016/j.neucom.2019.10.113}, url = {https://www.sciencedirect.com/science/article/pii/S0925231219316364}, author = {Amadu {Fullah Kamara} and Enhong Chen and Qi Liu and Zhen Pan}, keywords = {Time series classification, Contextual convolutional neural

networks, Contextual long short-term memory, Attention, Multilayer perceptron},

}

Examples

>>> from sktime.classification.deep_learning.cntc import CNTCClassifier
>>> from sktime.datasets import load_unit_test
>>> X_train, y_train = load_unit_test(split="train", return_X_y=True)
>>> X_test, y_test = load_unit_test(split="test", return_X_y=True)
>>> cntc = CNTCClassifier()
>>> cntc.fit(X_train, y_train)
CNTCClassifier(...)

Methods

build_model(input_shape, n_classes, **kwargs)	Construct a compiled, un-trained, keras model that is ready for training.
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.
prepare_input(X)	Prepare input for the CLSTM arm of the model.
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.
summary()	Summary function to return the losses/metrics for model fit.