Merge branch 'main' into ajb/normalise

# Conflicts:
#	examples/transformations/preprocessing.ipynb

Author: TonyBagnall

.pre-commit-config.yaml

@@ -29,7 +29,7 @@ repos:
         args: [ "--create", "--python-folders", "aeon" ]
 
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.7.1
+    rev: v0.7.2
     hooks:
       - id: ruff
         args: [ "--fix"]

aeon/anomaly_detection/_stray.py

@@ -66,13 +66,10 @@ class STRAY(BaseAnomalyDetector):
     --------
     >>> from aeon.anomaly_detection import STRAY
     >>> from aeon.datasets import load_airline
-    >>> from sklearn.preprocessing import MinMaxScaler
     >>> import numpy as np
-    >>> X = load_airline().to_frame().to_numpy()
-    >>> scaler = MinMaxScaler()
-    >>> X = scaler.fit_transform(X)
+    >>> X = load_airline()
     >>> detector = STRAY(k=3)
-    >>> y = detector.fit_predict(X, axis=0)
+    >>> y = detector.fit_predict(X)
     >>> y[:5]
     array([False, False, False, False, False])
     """

aeon/clustering/deep_learning/__init__.py

@@ -1,6 +1,12 @@
 """Deep learning based clusterers."""
 
-__all__ = ["BaseDeepClusterer", "AEFCNClusterer", "AEResNetClusterer"]
+__all__ = [
+    "BaseDeepClusterer",
+    "AEBiGRUClusterer",
+    "AEFCNClusterer",
+    "AEResNetClusterer",
+]
+from aeon.clustering.deep_learning._ae_bgru import AEBiGRUClusterer
 from aeon.clustering.deep_learning._ae_fcn import AEFCNClusterer
 from aeon.clustering.deep_learning._ae_resnet import AEResNetClusterer
 from aeon.clustering.deep_learning.base import BaseDeepClusterer

aeon/clustering/deep_learning/_ae_bgru.py

@@ -0,0 +1,322 @@
+"""Deep Learning Auto-Encoder using Bidirectional GRU Network."""
+
+__maintainer__ = []
+__all__ = ["AEBiGRUClusterer"]
+
+import gc
+import os
+import time
+from copy import deepcopy
+
+from sklearn.utils import check_random_state
+
+from aeon.clustering import DummyClusterer
+from aeon.clustering.deep_learning.base import BaseDeepClusterer
+from aeon.networks import AEBiGRUNetwork
+
+
+class AEBiGRUClusterer(BaseDeepClusterer):
+    """Auto-Encoder based Bidirectional GRU Network.
+
+    Parameters
+    ----------
+    n_clusters : int, default=None
+        Number of clusters for the deep learnign model.
+    clustering_algorithm : str, default="deprecated"
+        Use 'estimator' parameter instead.
+    clustering_params : dict, default=None
+        Use 'estimator' parameter instead.
+    estimator : aeon clusterer, default=None
+        An aeon estimator to be built using the transformed data.
+        Defaults to aeon TimeSeriesKMeans() with euclidean distance
+        and mean averaging method and n_clusters set to 2.
+    latent_space_dim : int, default=128
+        Dimension of the latent space of the auto-encoder.
+    temporal_latent_space : bool, default = False
+        Flag to choose whether the latent space is an MTS or Euclidean space.
+    n_layers : int, default = 2
+        Number of Bidirectional GRU Layers.
+    activation : str or list of str, default = "relu"
+        Activation used after the Bidirectional GRU Layer.
+    n_epochs : int, default = 2000
+        The number of epochs to train the model.
+    batch_size : int, default = 16
+        The number of samples per gradient update.
+    use_mini_batch_size : bool, default = True,
+        Whether or not to use the mini batch size formula.
+    random_state : int, RandomState instance or None, default=None
+        If `int`, random_state is the seed used by the random number generator;
+        If `RandomState` instance, random_state is the random number generator;
+        If `None`, the random number generator is the `RandomState` instance used
+        by `np.random`.
+        Seeded random number generation can only be guaranteed on CPU processing,
+        GPU processing will be non-deterministic.
+    verbose : boolean, default = False
+        Whether to output extra information.
+    loss : str, default="mean_squared_error"
+        Fit parameter for the keras model.
+    metrics : str, default=["mean_squared_error"]
+        Metrics to evaluate model predictions.
+    optimizer : keras.optimizers object, default = Adam(lr=0.01)
+        Specify the optimizer and the learning rate to be used.
+    file_path : str, default = "./"
+        File path to save best model.
+    save_best_model : bool, default = False
+        Whether or not to save the best model, if the
+        modelcheckpoint 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.
+    callbacks : keras.callbacks, default = None
+        List of keras callbacks.
+
+
+    Examples
+    --------
+    >>> from aeon.clustering.deep_learning import AEBiGRUClusterer
+    >>> from aeon.clustering import DummyClusterer
+    >>> 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")
+    >>> _clst = DummyClusterer(n_clusters=2)
+    >>> aebgru=AEBiGRUClusterer( estimator=_clst, n_epochs=20,
+    ... batch_size=4 )  # doctest: +SKIP
+    >>> aebgru.fit(X_train)  # doctest: +SKIP
+    AEBiGRUClusterer(...)
+    """
+
+    def __init__(
+        self,
+        n_clusters=None,
+        clustering_algorithm="deprecated",
+        estimator=None,
+        clustering_params=None,
+        latent_space_dim=128,
+        temporal_latent_space=False,
+        n_layers=2,
+        n_units=None,
+        activation="relu",
+        n_epochs=2000,
+        batch_size=32,
+        use_mini_batch_size=False,
+        random_state=None,
+        verbose=False,
+        loss="mse",
+        metrics=None,
+        optimizer="Adam",
+        file_path="./",
+        save_best_model=False,
+        save_last_model=False,
+        best_file_name="best_model",
+        last_file_name="last_file",
+        callbacks=None,
+    ):
+        self.latent_space_dim = latent_space_dim
+        self.temporal_latent_space = temporal_latent_space
+        self.n_layers = n_layers
+        self.n_units = n_units
+        self.activation = activation
+        self.optimizer = optimizer
+        self.loss = loss
+        self.metrics = metrics
+        self.verbose = verbose
+        self.use_mini_batch_size = use_mini_batch_size
+        self.callbacks = callbacks
+        self.file_path = file_path
+        self.n_epochs = n_epochs
+        self.save_best_model = save_best_model
+        self.save_last_model = save_last_model
+        self.best_file_name = best_file_name
+        self.random_state = random_state
+        self.estimator = estimator
+
+        super().__init__(
+            n_clusters=n_clusters,
+            estimator=estimator,
+            batch_size=batch_size,
+            last_file_name=last_file_name,
+        )
+
+        self._network = AEBiGRUNetwork(
+            latent_space_dim=self.latent_space_dim,
+            n_layers=self.n_layers,
+            n_units=self.n_units,
+            activation=self.activation,
+            temporal_latent_space=self.temporal_latent_space,
+        )
+
+    def build_model(self, input_shape, **kwargs):
+        """Construct a compiled, un-trained, keras model that is ready for training.
+
+        In aeon, time series are stored in numpy arrays of shape
+        (n_channels,n_timepoints). Keras/tensorflow assume
+        data is in shape (n_timepoints,n_channels). This method also assumes
+        (n_timepoints,n_channels). Transpose should happen in fit.
+
+        Parameters
+        ----------
+        input_shape : tuple
+            The shape of the data fed into the input layer, should be
+            (n_timepoints,n_channels).
+
+        Returns
+        -------
+        output : a compiled Keras Model.
+        """
+        import numpy as np
+        import tensorflow as tf
+
+        rng = check_random_state(self.random_state)
+        self.random_state_ = rng.randint(0, np.iinfo(np.int32).max)
+        tf.keras.utils.set_random_seed(self.random_state_)
+        encoder, decoder = self._network.build_network(input_shape, **kwargs)
+
+        input_layer = tf.keras.layers.Input(input_shape, name="input layer")
+        encoder_output = encoder(input_layer)
+        decoder_output = decoder(encoder_output)
+        output_layer = tf.keras.layers.Reshape(
+            target_shape=input_shape, name="outputlayer"
+        )(decoder_output)
+
+        model = tf.keras.models.Model(inputs=input_layer, outputs=output_layer)
+
+        self.optimizer_ = (
+            tf.keras.optimizers.Adam() if self.optimizer is None else self.optimizer
+        )
+
+        if self.metrics is None:
+            self._metrics = ["mean_squared_error"]
+        elif isinstance(self.metrics, list):
+            self._metrics = self.metrics
+        elif isinstance(self.metrics, str):
+            self._metrics = [self.metrics]
+        else:
+            raise ValueError("Metrics should be a list, string, or None.")
+
+        model.compile(optimizer=self.optimizer_, loss=self.loss, metrics=self._metrics)
+
+        return model
+
+    def _fit(self, X):
+        """Fit the classifier on the training set (X, y).
+
+        Parameters
+        ----------
+        X : np.ndarray of shape = (n_cases (n), n_channels (d), n_timepoints (m))
+            The training input samples.
+
+        Returns
+        -------
+        self : object
+        """
+        import tensorflow as tf
+
+        # Transpose to conform to Keras input style.
+        X = X.transpose(0, 2, 1)
+
+        self.input_shape = X.shape[1:]
+        self.training_model_ = self.build_model(self.input_shape)
+
+        if self.verbose:
+            self.training_model_.summary()
+
+        if self.use_mini_batch_size:
+            mini_batch_size = min(self.batch_size, X.shape[0] // 10)
+        else:
+            mini_batch_size = self.batch_size
+
+        self.file_name_ = (
+            self.best_file_name if self.save_best_model else str(time.time_ns())
+        )
+
+        if self.callbacks is None:
+            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_ + ".keras",
+                    monitor="loss",
+                    save_best_only=True,
+                ),
+            ]
+        else:
+            self.callbacks_ = self._get_model_checkpoint_callback(
+                callbacks=self.callbacks,
+                file_path=self.file_path,
+                file_name=self.file_name_,
+            )
+
+        self.history = self.training_model_.fit(
+            X,
+            X,
+            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_ + ".keras", compile=False
+            )
+            if not self.save_best_model:
+                os.remove(self.file_path + self.file_name_ + ".keras")
+        except FileNotFoundError:
+            self.model_ = deepcopy(self.training_model_)
+
+        self._fit_clustering(X=X)
+
+        gc.collect()
+
+        return self
+
+    def _score(self, X, y=None):
+        # Transpose to conform to Keras input style.
+        X = X.transpose(0, 2, 1)
+        latent_space = self.model_.layers[1].predict(X)
+        return self._estimator.score(latent_space)
+
+    @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 = {
+            "estimator": DummyClusterer(n_clusters=2),
+            "n_epochs": 1,
+            "batch_size": 4,
+            "n_layers": 1,
+            "n_units": 2,
+        }
+
+        return [param1]