MCDCNNClassifier

class MCDCNNClassifier(n_epochs=120, batch_size=16, kernel_size=5, pool_size=2, filter_sizes=(8, 8), dense_units=732, conv_padding='same', pool_padding='same', loss='categorical_crossentropy', activation='sigmoid', activation_hidden='relu', use_bias=True, callbacks=None, metrics=None, optimizer=None, verbose=False, random_state=0)[source]

Multi Channel Deep Convolutional Neural Classifier, as described in [1].

Adapted from the implementation of Fawaz et. al https://github.com/hfawaz/dl-4-tsc/blob/master/classifiers/mcdcnn.py

Parameters:

n_epochsint, optional (default=120): The number of epochs to train the model.
batch_sizeint, optional (default=16): The number of samples per gradient update.
kernel_sizeint, optional (default=5): The size of kernel in Conv1D layer.
pool_sizeint, optional (default=2): The size of kernel in (Max) Pool layer.
filter_sizestuple, optional (default=(8, 8)): The sizes of filter for Conv1D layer corresponding to each Conv1D in the block.
dense_unitsint, optional (default=732): The number of output units of the final Dense layer of this Network. This is NOT the final layer but the penultimate layer.
conv_paddingstr or None, optional (default=”same”): The type of padding to be applied to convolutional layers.
pool_paddingstr or None, optional (default=”same”): The type of padding to be applied to pooling layers.
lossstr, optional (default=”categorical_crossentropy”): The name of the loss function to be used during training, should be supported by keras.
activationstr, optional (default=”sigmoid”): The activation function to apply at the output. List of available 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/
use_biasbool, optional (default=True): Whether bias should be included in the output layer.
metricsNone or string, optional (default=None): The string which will be used during model compilation. If left as None, then “accuracy” is passed to model.compile().
optimizer: None or keras.optimizers.Optimizer instance, optional (default=None): The optimizer that is used for model compiltation. If left as None, then keras.optimizers.SGD is used with the following parameters - learning_rate=0.01, momentum=0.9, weight_decay=0.0005.
callbacksNone or list of keras.callbacks.Callback, optional (default=None): The callback(s) to use during training.
random_stateint, optional (default=0): The seed to any random action.

Attributes:

is_fitted: Whether fit has been called.

References

[1]

Zheng et. al, Time series classification using multi-channels deep convolutional neural networks, International Conference on Web-Age Information Management, Pages 298-310, year 2014, organization: Springer.

Examples

>>> from sktime.classification.deep_learning.mcdcnn import MCDCNNClassifier
>>> from sktime.datasets import load_unit_test
>>> X_train, y_tain = load_unit_test(split="train")
>>> mcdcnn = MCDCNNClassifier()
>>> mcdcnn.fit(X_train, y_train)
MCDCNNClassifier(...)

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.
`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.