[ENH] Implement load_model function for ensemble classifiers

aeon/classification/deep_learning/_inception_time.py

@@ -350,6 +350,46 @@ def _predict_proba(self, X) -> np.ndarray:
 
         return probs
 
+    @classmethod
+    def load_model(self, model_path, classes):
+        """Load pre-trained classifiers instead of fitting.
+
+        When calling this function, all funcationalities can be used
+        such as predict, predict_proba, etc. with the loaded models.
+
+        Parameters
+        ----------
+        model_path : list of str (list of paths including the model names and extension)
+            The directory where the models will be saved including the model
+            names with a ".keras" extension.
+        classes : np.ndarray
+            The set of unique classes the pre-trained loaded model is trained
+            to predict during the classification task.
+
+        Returns
+        -------
+        None
+        """
+        assert (
+            type(model_path) is list
+        ), "model_path should be a list of paths to the models"
+
+        classifier = self()
+        classifier.classifiers_ = []
+
+        for i in range(len(model_path)):
+            clf = IndividualInceptionClassifier()
+            clf.load_model(model_path[i], classes)
+            classifier.classifiers_.append(clf)
+
+        classifier.n_classifiers = len(classifier.classifiers_)
+
+        classifier.classes_ = classes
+        classifier.n_classes_ = len(classes)
+        classifier.is_fitted = True
+
+        return classifier
+
     @classmethod
     def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.

aeon/classification/deep_learning/_lite_time.py

@@ -282,6 +282,46 @@ def _predict_proba(self, X) -> np.ndarray:
 
         return probs
 
+    @classmethod
+    def load_model(self, model_path, classes):
+        """Load pre-trained classifiers instead of fitting.
+
+        When calling this function, all funcationalities can be used
+        such as predict, predict_proba, etc. with the loaded models.
+
+        Parameters
+        ----------
+        model_path : list of str (list of paths including the model names and extension)
+            The director where the models will be saved including the model
+            names with a ".keras" extension.
+        classes : np.ndarray
+            The set of unique classes the pre-trained loaded model is trained
+            to predict during the classification task.
+
+        Returns
+        -------
+        None
+        """
+        assert (
+            type(model_path) is list
+        ), "model_path should be a list of paths to the models"
+
+        classifier = self()
+        classifier.classifiers_ = []
+
+        for i in range(len(model_path)):
+            clf = IndividualLITEClassifier()
+            clf.load_model(model_path=model_path[i], classes=classes)
+            classifier.classifiers_.append(clf)
+
+        classifier.n_classifiers = len(classifier.classifiers_)
+
+        classifier.classes_ = classes
+        classifier.n_classes_ = len(classes)
+        classifier.is_fitted = True
+
+        return classifier
+
     @classmethod
     def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.

aeon/classification/deep_learning/tests/test_inception_time.py

@@ -0,0 +1,47 @@
+"""Tests for save/load functionality of InceptionTimeClassifier."""
+
+import glob
+import os
+import tempfile
+
+import numpy as np
+import pytest
+
+from aeon.classification.deep_learning import InceptionTimeClassifier
+from aeon.testing.data_generation import make_example_3d_numpy
+from aeon.utils.validation._dependencies import _check_soft_dependencies
+
+
+@pytest.mark.skipif(
+    not _check_soft_dependencies("tensorflow", severity="none"),
+    reason="skip test if required soft dependency not available",
+)
+def test_save_load_inceptiontime():
+    """Test saving and loading for InceptionTimeClassifier."""
+    with tempfile.TemporaryDirectory() as temp:
+        temp_dir = os.path.join(temp, "")
+
+        X, y = make_example_3d_numpy(
+            n_cases=10, n_channels=1, n_timepoints=12, return_y=True
+        )
+
+        model = InceptionTimeClassifier(
+            n_epochs=1, random_state=42, save_best_model=True, file_path=temp_dir
+        )
+        model.fit(X, y)
+
+        y_pred_orig = model.predict(X)
+
+        model_file = glob.glob(os.path.join(temp_dir, f"{model.best_file_name}*.keras"))
+
+        loaded_model = InceptionTimeClassifier.load_model(
+            model_path=model_file, classes=model.classes_
+        )
+
+        assert isinstance(loaded_model, InceptionTimeClassifier)
+
+        preds = loaded_model.predict(X)
+        assert isinstance(preds, np.ndarray)
+
+        assert len(preds) == len(y)
+        np.testing.assert_array_equal(preds, y_pred_orig)

aeon/classification/deep_learning/tests/test_lite_time.py

@@ -0,0 +1,49 @@
+"""Tests for save/load functionality of LiteTimeClassifier."""
+
+import glob
+import os
+import tempfile
+
+import numpy as np
+import pytest
+
+from aeon.classification.deep_learning import LITETimeClassifier
+from aeon.testing.data_generation import make_example_3d_numpy
+from aeon.utils.validation._dependencies import _check_soft_dependencies
+
+
+@pytest.mark.skipif(
+    not _check_soft_dependencies("tensorflow", severity="none"),
+    reason="skip test if required soft dependency not available",
+)
+def test_save_load_litetim():
+    """Test saving and loading for LiteTimeClassifier."""
+    with tempfile.TemporaryDirectory() as temp:
+        temp_dir = os.path.join(temp, "")
+
+        X, y = make_example_3d_numpy(
+            n_cases=10, n_channels=1, n_timepoints=12, return_y=True
+        )
+
+        model = LITETimeClassifier(
+            n_epochs=1, random_state=42, save_best_model=True, file_path=temp_dir
+        )
+        model.fit(X, y)
+
+        y_pred_orig = model.predict(X)
+
+        model_files = glob.glob(
+            os.path.join(temp_dir, f"{model.best_file_name}*.keras")
+        )
+
+        loaded_model = LITETimeClassifier.load_model(
+            model_path=model_files, classes=model.classes_
+        )
+
+        assert isinstance(loaded_model, LITETimeClassifier)
+
+        preds = loaded_model.predict(X)
+        assert isinstance(preds, np.ndarray)
+
+        assert len(preds) == len(y)
+        np.testing.assert_array_equal(preds, y_pred_orig)