added more test cases and updated fit_predict method to set fitted to…

… true

aeon/clustering/feature_based/_r_cluster.py

@@ -448,15 +448,17 @@ def _fit_predict(self, X, y=None) -> np.ndarray:
         pca = PCA().fit(X_std)
         optimal_dimensions = np.argmax(pca.explained_variance_ratio_ < 0.01)
 
-        pca = PCA(n_components=optimal_dimensions, random_state=self.random_state)
-        transformed_data_pca = pca.fit_transform(X_std)
+        self.pca = PCA(n_components=optimal_dimensions, random_state=self.random_state)
+        self.pca.fit(X_std)
+        transformed_data_pca = self.pca.transform(X_std)
         self.estimator = KMeans(
             n_clusters=self.n_clusters,
             random_state=self.random_state,
             n_init=self.n_init,
         )
         Y = self.estimator.fit_predict(transformed_data_pca)
         self.labels_ = self.estimator.labels_
+        self.is_fitted = True
         return Y
 
     @classmethod
@@ -479,4 +481,4 @@ def _get_test_params(cls, parameter_set="default") -> dict:
         return {
             "n_clusters": 2,
             "random_state": 1,
-        }
+        }

aeon/clustering/feature_based/tests/test_r_cluster.py

@@ -1,11 +1,9 @@
 """Test For RCluster."""
 
 import numpy as np
-import pytest
 from sklearn import metrics
-
+from aeon.datasets import load_gunpoint
 from aeon.clustering.feature_based._r_cluster import RClusterer
-from aeon.utils.validation._dependencies import _check_estimator_deps
 
 X_ = [
     [
@@ -132,15 +130,47 @@
 Y = ["22", "28", "21", "15", "2", "18", "21", "36", "11", "21"]
 
 
-@pytest.mark.skipif(
-    not _check_estimator_deps(RClusterer, severity="none"),
-    reason="skip test if required soft dependencies not available",
-)
-def test_r_cluster():
+def test_r_cluster_custom_dataset():
     """Test implementation of RCluster."""
     X_train = np.array(X_)
     X = np.expand_dims(X_train, axis=1)
     Rcluster = RClusterer(n_clusters=8, n_init=10, random_state=1)
     labels_pred1 = Rcluster.fit_predict(X)
     score = metrics.adjusted_rand_score(labels_true=Y, labels_pred=labels_pred1)
     assert score > 0.36
+
+def test_r_cluster_dataset():
+    """Test implementation of RCluster using aeon dataset."""
+
+    X_train, y_train = load_gunpoint(split="train")
+    X_test, y_test = load_gunpoint(split="test")
+    num_points = 20
+
+    X_train = X_train[:num_points]
+    y_train = y_train[:num_points]
+    X_test = X_test[:num_points]
+    y_test = y_test[:num_points]
+
+    rcluster = RClusterer(
+        random_state=1,
+        n_init=2,
+        n_clusters=2,
+    )
+    train_result = rcluster.fit_predict(X_train)
+    train_score = metrics.rand_score(y_train, train_result)
+    test_result = rcluster.predict(X_test)
+    test_score = metrics.rand_score(y_test, test_result)
+    assert np.array_equal(
+        test_result,
+        [1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+    )
+    assert np.array_equal(
+        train_result,
+        [1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],
+    )
+    assert test_score == 0.5210526315789473
+    assert train_score == 0.5210526315789473
+    assert rcluster.estimator.n_iter_ == 3
+    assert np.array_equal(
+        rcluster.labels_, [1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0]
+    )