_lite_time.py

"""LITETime classifier."""

__author__ = ["hadifawaz1999"]
__all__ = ["LITETimeClassifier"]

import gc
import os
import time
from copy import deepcopy

import numpy as np
from sklearn.utils import check_random_state

from aeon.classification.base import BaseClassifier
from aeon.classification.deep_learning.base import BaseDeepClassifier
from aeon.networks import LITENetwork


class LITETimeClassifier(BaseClassifier):
    """LITETime ensemble classifier.

    Ensemble of IndividualLITETimeClassifier objects, as described in [1]_.

    Parameters
    ----------
    n_classifiers : int, default = 5,
        the number of LITE models used for the
        Ensemble in order to create
        LITETime.
    nb_filters : int or list of int32, default = 32
        The number of filters used in one lite layer, if not a list, the same
        number of filters is used in all lite layers.
    kernel_size : int or list of int, default = 40
        The head kernel size used for each lite layer, if not a list, the same
        is used in all lite module.
    strides : int or list of int, default = 1
        The strides of kernels in convolution layers for each lite layer,
        if not a list, the same is used in all lite layers.
    activation : str or list of str, default = 'relu'
        The activation function used in each lite layer, if not a list,
        the same is used in all lite layers.
    batch_size : int, default = 64
        the number of samples per gradient update.
    use_mini_batch_size : bool, default = False
        condition on using the mini batch size
        formula Wang et al.
    n_epochs : int, default = 1500
        the number of epochs to train the model.
    callbacks : callable or None, default = ReduceOnPlateau and ModelCheckpoint
        list of tf.keras.callbacks.Callback objects.
    file_path : str, default = "./"
        file_path when saving model_Checkpoint callback
    save_best_model : bool, default = False
        Whether or not to save the best model, if the
        model checkpoint callback is used by default,
        this condition, if True, will prevent the
        automatic deletion of the best saved model from
        file and the user can choose the file name
    save_last_model : bool, default = False
        Whether or not to save the last model, last
        epoch trained, using the base class method
        save_last_model_to_file
    best_file_name : str, default = "best_model"
        The name of the file of the best model, if
        save_best_model is set to False, this parameter
        is discarded
    last_file_name : str, default = "last_model"
        The name of the file of the last model, if
        save_last_model is set to False, this parameter
        is discarded
    random_state : int, default = 0
        seed to any needed random actions.
    verbose : boolean, default = False
        whether to output extra information
    optimizer : keras optimizer, default = Adam
    loss : keras loss, default = categorical_crossentropy
    metrics : keras metrics, default = None,
        will be set to accuracy as default if None

    Notes
    -----
    ..[1] Ismail-Fawaz et al. LITE: Light Inception with boosTing
    tEchniques for Time Series Classificaion, IEEE International
    Conference on Data Science and Advanced Analytics, 2023.

    Adapted from the implementation from Ismail-Fawaz et. al
    https://github.com/MSD-IRIMAS/LITE

    Examples
    --------
    >>> from aeon.classification.deep_learning import LITETimeClassifier
    >>> from aeon.datasets import load_unit_test
    >>> X_train, y_train = load_unit_test(split="train")
    >>> X_test, y_test = load_unit_test(split="test")
    >>> ltime = LITETimeClassifier(n_epochs=20,batch_size=4)  # doctest: +SKIP
    >>> ltime.fit(X_train, y_train)  # doctest: +SKIP
    LITETimeClassifier(...)
    """

    _tags = {
        "python_dependencies": "tensorflow",
        "capability:multivariate": True,
        "non-deterministic": True,
        "cant-pickle": True,
        "algorithm_type": "deeplearning",
    }

    def __init__(
        self,
        n_classifiers=5,
        nb_filters=32,
        kernel_size=40,
        strides=1,
        activation="relu",
        file_path="./",
        save_last_model=False,
        save_best_model=False,
        best_file_name="best_model",
        last_file_name="last_model",
        batch_size=64,
        use_mini_batch_size=False,
        n_epochs=1500,
        callbacks=None,
        random_state=None,
        verbose=False,
        loss="categorical_crossentropy",
        metrics=None,
        optimizer=None,
    ):
        self.n_classifiers = n_classifiers

        self.strides = strides
        self.activation = activation
        self.nb_filters = nb_filters

        self.kernel_size = kernel_size
        self.batch_size = batch_size
        self.n_epochs = n_epochs

        self.file_path = file_path

        self.save_last_model = save_last_model
        self.save_best_model = save_best_model
        self.best_file_name = best_file_name
        self.last_file_name = last_file_name

        self.callbacks = callbacks
        self.random_state = random_state
        self.verbose = verbose
        self.use_mini_batch_size = use_mini_batch_size
        self.loss = loss
        self.metrics = metrics
        self.optimizer = optimizer

        self.classifers_ = []

        super().__init__()

    def _fit(self, X, y):
        """Fit the ensemble of IndividualLITEClassifier models.

        Parameters
        ----------
        X : np.ndarray of shape = (n_instances (n), n_channels (c), n_timepoints (m))
            The training input samples.
        y : np.ndarray of shape n
            The training data class labels.

        Returns
        -------
        self : object
        """
        self.classifers_ = []
        rng = check_random_state(self.random_state)

        for n in range(0, self.n_classifiers):
            cls = IndividualLITEClassifier(
                nb_filters=self.nb_filters,
                kernel_size=self.kernel_size,
                file_path=self.file_path,
                save_best_model=self.save_best_model,
                save_last_model=self.save_last_model,
                best_file_name=self.best_file_name + str(n),
                last_file_name=self.last_file_name + str(n),
                batch_size=self.batch_size,
                use_mini_batch_size=self.use_mini_batch_size,
                n_epochs=self.n_epochs,
                callbacks=self.callbacks,
                loss=self.loss,
                metrics=self.metrics,
                optimizer=self.optimizer,
                random_state=rng.randint(0, np.iinfo(np.int32).max),
                verbose=self.verbose,
            )
            cls.fit(X, y)
            self.classifers_.append(cls)
            gc.collect()

        return self

    def _predict(self, X) -> np.ndarray:
        """Predict the labels of the test set using LITETime.

        Parameters
        ----------
        X : np.ndarray of shape = (n_instances (n), n_channels (c), n_timepoints (m))
            The testing input samples.

        Returns
        -------
        Y : np.ndarray of shape = (n_instances (n)), the predicted labels

        """
        rng = check_random_state(self.random_state)
        return np.array(
            [
                self.classes_[int(rng.choice(np.flatnonzero(prob == prob.max())))]
                for prob in self.predict_proba(X)
            ]
        )

    def _predict_proba(self, X) -> np.ndarray:
        """Predict the proba of labels of the test set using LITETime.

        Parameters
        ----------
        X : np.ndarray of shape = (n_instances (n), n_channels (c), n_timepoints (m))
            The testing input samples.

        Returns
        -------
        Y : np.ndarray of shape = (n_instances (n), n_classes (c)), the predicted probs

        """
        probs = np.zeros((X.shape[0], self.n_classes_))

        for cls in self.classifers_:
            probs += cls._predict_proba(X)

        probs = probs / self.n_classifiers

        return probs

    @classmethod
    def get_test_params(cls, parameter_set="default"):
        """Return testing parameter settings for the estimator.

        Parameters
        ----------
        parameter_set : str, 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, default={}
            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`.
        """
        param1 = {
            "n_classifiers": 1,
            "n_epochs": 10,
            "batch_size": 4,
            "kernel_size": 4,
        }

        return [param1]


class IndividualLITEClassifier(BaseDeepClassifier):
    """Single LITETime classifier.

    One LITE deep model, as described in [1]_.

    Parameters
    ----------
        nb_filters : int or list of int32, default = 32
        The number of filters used in one lite layer, if not a list, the same
        number of filters is used in all lite layers.
    kernel_size : int or list of int, default = 40
        The head kernel size used for each lite layer, if not a list, the same
        is used in all lite layers.
    strides : int or list of int, default = 1
        The strides of kernels in convolution layers for each lite layer,
        if not a list, the same is used in all lite layers.
    activation : str or list of str, default = 'relu'
        The activation function used in each lite layer, if not a list,
        the same is used in all lite layers.
    batch_size : int, default = 64
        the number of samples per gradient update.
    use_mini_batch_size : bool, default = False
        condition on using the mini batch size
        formula Wang et al.
    n_epochs : int, default = 1500
        the number of epochs to train the model.
    callbacks : callable or None, default = ReduceOnPlateau and ModelCheckpoint
        list of tf.keras.callbacks.Callback objects.
    file_path : str, default = "./"
        file_path when saving model_Checkpoint callback
    save_best_model : bool, default = False
        Whether or not to save the best model, if the
        model checkpoint callback is used by default,
        this condition, if True, will prevent the
        automatic deletion of the best saved model from
        file and the user can choose the file name
    save_last_model     : bool, default = False
        Whether or not to save the last model, last
        epoch trained, using the base class method
        save_last_model_to_file
    best_file_name      : str, default = "best_model"
        The name of the file of the best model, if
        save_best_model is set to False, this parameter
        is discarded
    last_file_name      : str, default = "last_model"
        The name of the file of the last model, if
        save_last_model is set to False, this parameter
        is discarded
    random_state : int, default = 0
        seed to any needed random actions.
    verbose : boolean, default = False
        whether to output extra information
    optimizer : keras optimizer, default = Adam
    loss : keras loss, default = categorical_crossentropy
    metrics : keras metrics, default = None,
        will be set to accuracy as default if None

    Notes
    -----
    ..[1] Ismail-Fawaz et al. LITE: Light Inception with boosTing
    tEchniques for Time Series Classificaion, IEEE International
    Conference on Data Science and Advanced Analytics, 2023.

    Adapted from the implementation from Ismail-Fawaz et. al
    https://github.com/MSD-IRIMAS/LITE

    Examples
    --------
    >>> from aeon.classification.deep_learning import IndividualLITEClassifier
    >>> from aeon.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)
    >>> lite = IndividualLITEClassifier(n_epochs=20,batch_size=4)  # doctest: +SKIP
    >>> lite.fit(X_train, y_train)  # doctest: +SKIP
    IndividualLITEClassifier(...)
    """

    def __init__(
        self,
        nb_filters=32,
        kernel_size=40,
        strides=1,
        activation="relu",
        file_path="./",
        save_best_model=False,
        save_last_model=False,
        best_file_name="best_model",
        last_file_name="last_model",
        batch_size=64,
        use_mini_batch_size=False,
        n_epochs=1500,
        callbacks=None,
        random_state=None,
        verbose=False,
        loss="categorical_crossentropy",
        metrics=None,
        optimizer=None,
    ):
        # predefined
        self.nb_filters = nb_filters
        self.strides = strides
        self.activation = activation

        self.kernel_size = kernel_size
        self.n_epochs = n_epochs

        self.file_path = file_path

        self.save_best_model = save_best_model
        self.save_last_model = save_last_model
        self.best_file_name = best_file_name

        self.callbacks = callbacks
        self.verbose = verbose
        self.use_mini_batch_size = use_mini_batch_size
        self.loss = loss
        self.metrics = metrics
        self.optimizer = optimizer

        super().__init__(
            batch_size=batch_size,
            random_state=random_state,
            last_file_name=last_file_name,
        )

        self._network = LITENetwork(
            nb_filters=self.nb_filters,
            kernel_size=self.kernel_size,
            strides=self.strides,
            activation=self.activation,
        )

    def build_model(self, input_shape, n_classes, **kwargs):
        """
        Construct a compiled, un-trained, keras model that is ready for training.

        Parameters
        ----------
        input_shape : tuple
            The shape of the data fed into the input layer
        n_classes: int
            The number of classes, which shall become the size of the output
             layer

        Returns
        -------
        output : a compiled Keras Model
        """
        import tensorflow as tf

        input_layer, output_layer = self._network.build_network(input_shape, **kwargs)

        output_layer = tf.keras.layers.Dense(n_classes, activation="softmax")(
            output_layer
        )

        model = tf.keras.models.Model(inputs=input_layer, outputs=output_layer)

        tf.random.set_seed(self.random_state)

        if self.metrics is None:
            metrics = ["accuracy"]
        else:
            metrics = self.metrics

        self.optimizer_ = (
            tf.keras.optimizers.Adam() if self.optimizer is None else self.optimizer
        )

        model.compile(
            loss=self.loss,
            optimizer=self.optimizer_,
            metrics=metrics,
        )

        return model

    def _fit(self, X, y):
        """
        Fit the classifier on the training set (X, y).

        Parameters
        ----------
        X : array-like of shape = (n_instances, n_channels, n_timepoints)
            The training input samples. If a 2D array-like is passed,
            n_channels is assumed to be 1.
        y : array-like, shape = (n_instances)
            The training data class labels.

        Returns
        -------
        self : object
        """
        import tensorflow as tf

        y_onehot = self.convert_y_to_keras(y)
        # Transpose to conform to Keras input style.
        X = X.transpose(0, 2, 1)

        # ignore the number of instances, X.shape[0],
        # just want the shape of each instance
        self.input_shape = X.shape[1:]

        if self.use_mini_batch_size:
            mini_batch_size = int(min(X.shape[0] // 10, self.batch_size))
        else:
            mini_batch_size = self.batch_size
        self.training_model_ = self.build_model(self.input_shape, self.n_classes_)

        if self.verbose:
            self.training_model_.summary()

        self.file_name_ = (
            self.best_file_name if self.save_best_model else str(time.time_ns())
        )

        self.callbacks_ = (
            [
                tf.keras.callbacks.ReduceLROnPlateau(
                    monitor="loss", factor=0.5, patience=50, min_lr=0.0001
                ),
                tf.keras.callbacks.ModelCheckpoint(
                    filepath=self.file_path + self.file_name_ + ".hdf5",
                    monitor="loss",
                    save_best_only=True,
                ),
            ]
            if self.callbacks is None
            else self.callbacks
        )

        self.history = self.training_model_.fit(
            X,
            y_onehot,
            batch_size=mini_batch_size,
            epochs=self.n_epochs,
            verbose=self.verbose,
            callbacks=self.callbacks_,
        )

        try:
            self.model_ = tf.keras.models.load_model(
                self.file_path + self.file_name_ + ".hdf5", compile=False
            )
            if not self.save_best_model:
                os.remove(self.file_path + self.file_name_ + ".hdf5")
        except FileNotFoundError:
            self.model_ = deepcopy(self.training_model_)

        if self.save_last_model:
            self.save_last_model_to_file(file_path=self.file_path)

        gc.collect()
        return self

    @classmethod
    def get_test_params(cls, parameter_set="default"):
        """Return testing parameter settings for the estimator.

        Parameters
        ----------
        parameter_set : str, 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, default={}
            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`.
        """
        param1 = {
            "n_epochs": 10,
            "batch_size": 4,
            "kernel_size": 4,
        }

        return [param1]