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| # -*- coding: utf-8 -*- | ||
| """Mutli-scale Attention Convolutional Neural Classifier.""" | ||
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| __author__ = ["jnrusson1"] | ||
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| from copy import deepcopy | ||
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| from sklearn.utils import check_random_state | ||
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| from sktime.classification.deep_learning.base import BaseDeepClassifier | ||
| from sktime.networks.macnn import MACNNNetwork | ||
| from sktime.utils.validation._dependencies import _check_dl_dependencies | ||
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| class MACNNClassifier(BaseDeepClassifier): | ||
| """Multi-Scale Attention Convolutional Neural Classifier, as described in [1]_. | ||
| Parameters | ||
| ---------- | ||
| n_epochs : int, optional (default=1500) | ||
| The number of epochs to train the model. | ||
| batch_size : int, optional (default=4) | ||
| The number of sample per gradient update. | ||
| padding : str, optional (default="same") | ||
| The type of padding to be provided in MACNN Blocks. Accepts | ||
| all the string values that keras.layers supports. | ||
| pool_size : int, optional (default=3) | ||
| A single value representing pooling windows which are applied | ||
| between two MACNN Blocks. | ||
| strides : int, optional (default=2) | ||
| A single value representing strides to be taken during the | ||
| pooling operation. | ||
| repeats : int, optional (default=2) | ||
| The number of MACNN Blocks to be stacked. | ||
| filter_sizes : tuple, optional (default=(64, 128, 256)) | ||
| The input size of Conv1D layers within each MACNN Block. | ||
| kernel_size : tuple, optional (default=(3, 6, 12)) | ||
| The output size of Conv1D layers within each MACNN Block. | ||
| reduction : int, optional (default = 16) | ||
| The factor by which the first dense layer of a MACNN Block will be divided by. | ||
| loss : str, optional (default="categorical_crossentropy") | ||
| The name of the loss function to be used during training, | ||
| should be supported by keras. | ||
| activation : str, optional (default="sigmoid") | ||
| The activation function to apply at the output. It should be | ||
| "software" if response variable has more than two types. | ||
| use_bias : bool, optional (default=True) | ||
| Whether bias should be included in the output layer. | ||
| metrics : None 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.Adam(learning_rate=0.0001)` is used. | ||
| callbacks : None or list of keras.callbacks.Callback, optinal (default=None) | ||
| The callback(s) to use during training. | ||
| random_state : int, optional (default=0) | ||
| The seed to any random action. | ||
| verbose : bool, optional (default=False) | ||
| Verbosity during model training, making it `True` will | ||
| print model summary, training information etc. | ||
| References | ||
| ---------- | ||
| .. [1] Wei Chen et. al, Multi-scale Attention Convolutional | ||
| Neural Network for time series classification, | ||
| Neural Networks, Volume 136, 2021, Pages 126-140, ISSN 0893-6080, | ||
| https://doi.org/10.1016/j.neunet.2021.01.001. | ||
| Examples | ||
| -------- | ||
| >>> from sktime.classification.deep_learning.macnn import MACNNClassifier | ||
| >>> from sktime.datasets import load_unit_test | ||
| >>> X_train, y_train = load_unit_test(split="train") | ||
| >>> X_test, y_test = load_unit_test(split="test") | ||
| >>> macnn = MACNNClassifier(n_epochs=3) # doctest: +SKIP | ||
| >>> macnn.fit(X_train, y_train) # doctest: +SKIP | ||
| MACNNClassifier(...) | ||
| """ | ||
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| _tags = {"python_dependencies": "tensorflow"} | ||
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| def __init__( | ||
| self, | ||
| n_epochs=1500, | ||
| batch_size=4, | ||
| padding="same", | ||
| pool_size=3, | ||
| strides=2, | ||
| repeats=2, | ||
| filter_sizes=(64, 128, 256), | ||
| kernel_size=(3, 6, 12), | ||
| reduction=16, | ||
| loss="categorical_crossentropy", | ||
| activation="sigmoid", | ||
| use_bias=True, | ||
| metrics=None, | ||
| optimizer=None, | ||
| callbacks=None, | ||
| random_state=0, | ||
| verbose=False, | ||
| ): | ||
| _check_dl_dependencies(severity="error") | ||
| super(MACNNClassifier, self).__init__() | ||
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| self.n_epochs = n_epochs | ||
| self.batch_size = batch_size | ||
| self.padding = padding | ||
| self.pool_size = pool_size | ||
| self.strides = strides | ||
| self.repeats = repeats | ||
| self.filter_sizes = filter_sizes | ||
| self.kernel_size = kernel_size | ||
| self.reduction = reduction | ||
| self.loss = loss | ||
| self.activation = activation | ||
| self.use_bias = use_bias | ||
| self.metrics = metrics | ||
| self.optimizer = optimizer | ||
| self.callbacks = callbacks | ||
| self.random_state = random_state | ||
| self.verbose = verbose | ||
| self.history = None | ||
| self._network = MACNNNetwork( | ||
| padding=self.padding, | ||
| pool_size=self.pool_size, | ||
| strides=self.strides, | ||
| repeats=self.repeats, | ||
| filter_sizes=self.filter_sizes, | ||
| kernel_size=self.kernel_size, | ||
| reduction=self.reduction, | ||
| random_state=self.random_state, | ||
| ) | ||
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| def build_model(self, input_shape, n_classes, **kwargs): | ||
| """ | ||
| Construct a compiled, un-trained, keras model that is ready for training. | ||
| In sktime, time series are stored in numpy arrays of shape (d,m), where d | ||
| is the number of dimensions, m is the series length. Keras/tensorflow assume | ||
| data is in shape (m,d). This method also assumes (m,d). Transpose should | ||
| happen in fit. | ||
| Parameters | ||
| ---------- | ||
| input_shape : tuple | ||
| The shape of the data fed into the input layer, should be (m,d) | ||
| n_classes: int | ||
| The number of classes, which becomes the size of the output layer | ||
| Returns | ||
| ------- | ||
| output : a compiled Keras Model | ||
| """ | ||
| import tensorflow as tf | ||
| from tensorflow import keras | ||
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| tf.random.set_seed(self.random_state) | ||
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| metrics = ["accuracy"] if self.metrics is None else self.metrics | ||
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| input_layer, output_layer = self._network.build_network(input_shape, **kwargs) | ||
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| output_layer = keras.layers.Dense( | ||
| units=n_classes, activation=self.activation, use_bias=self.use_bias | ||
| )(output_layer) | ||
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| self.optimizer_ = ( | ||
| keras.optimizers.Adam(learning_rate=0.0001) | ||
| if self.optimizer is None | ||
| else self.optimizer | ||
| ) | ||
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| model = keras.models.Model(inputs=input_layer, outputs=output_layer) | ||
| model.compile( | ||
| loss=self.loss, | ||
| optimizer=self.optimizer_, | ||
| metrics=metrics, | ||
| ) | ||
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| return model | ||
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| def _fit(self, X, y): | ||
| """ | ||
| Fit the classifier on the training set (X, y). | ||
| Parameters | ||
| ---------- | ||
| X : np.ndarray of shape = (n_instances (n), n_dimensions (d), series_length (m)) | ||
| The training input samples. | ||
| y : np.ndarray of shape n | ||
| The training data class labels. | ||
| Returns | ||
| ------- | ||
| self : object | ||
| """ | ||
| y_onehot = self.convert_y_to_keras(y) | ||
| X = X.transpose(0, 2, 1) | ||
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| check_random_state(self.random_state) | ||
| self.input_shape = X.shape[1:] | ||
| self.model_ = self.build_model(self.input_shape, self.n_classes_) | ||
| self.callbacks_ = deepcopy(self.callbacks) | ||
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| if self.verbose: | ||
| self.model_.summary() | ||
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| self.history = self.model_.fit( | ||
| X, | ||
| y_onehot, | ||
| batch_size=self.batch_size, | ||
| epochs=self.n_epochs, | ||
| verbose=self.verbose, | ||
| callbacks=self.callbacks_, | ||
| ) | ||
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| return self | ||
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| @classmethod | ||
| def get_test_params(cls, parameter_set="default"): | ||
| """Return testing parameter settings for the estimator. | ||
| Parameters | ||
| ---------- | ||
| parameter_set : str, optional (default="default") | ||
| Name of the set of test parameters to return, for use in tests. If no | ||
| special parameters are defined for a value, will return `"default"` set. | ||
| For classifiers, a "default" set of parameters should be provided for | ||
| general testing, and a "results_comparison" set for comparing against | ||
| previously recorded results if the general set does not produce suitable | ||
| probabilities to compare against. | ||
| Returns | ||
| ------- | ||
| params : dict or list of dict | ||
| Parameters to create testing instances of the class. | ||
| Each dict are parameters to construct an "interesting" test instance, i.e., | ||
| `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance. | ||
| `create_test_instance` uses the first (or only) dictionary in `params`. | ||
| """ | ||
| params1 = { | ||
| "n_epochs": 5, | ||
| "batch_size": 3, | ||
| "filter_sizes": (2, 4, 8), | ||
| "repeats": 1, | ||
| } | ||
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| params2 = { | ||
| "n_epochs": 1, | ||
| "filter_sizes": (1, 2, 4), | ||
| "reduction": 8, | ||
| "repeats": 1, | ||
| "random_state": 1, | ||
| } | ||
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| return [params1, params2] |
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