Adapted tests to be semi-supervised

Author: pattplatt

aeon/anomaly_detection/series/distance_based/tests/test_rockad.py

@@ -10,8 +10,9 @@
 def test_rockad_univariate():
     """Test ROCKAD univariate output."""
     rng = check_random_state(seed=2)
-    series = rng.normal(size=(100,))
-    series[50:58] -= 5
+    train_series = rng.normal(size=(100,))
+    test_series = rng.normal(size=(100,))
+    test_series[50:58] -= 5
 
     ad = ROCKAD(
         n_estimators=100,
@@ -22,7 +23,8 @@ def test_rockad_univariate():
         stride=1,
     )
 
-    pred = ad.fit_predict(series, axis=0)
+    ad.fit(train_series, axis=0)
+    pred = ad.predict(test_series, axis=0)
 
     assert pred.shape == (100,)
     assert pred.dtype == np.float64
@@ -32,9 +34,10 @@ def test_rockad_univariate():
 def test_rockad_multivariate():
     """Test ROCKAD multivariate output."""
     rng = check_random_state(seed=2)
-    series = rng.normal(size=(100, 3))
-    series[50:58, 0] -= 5
-    series[87:90, 1] += 0.1
+    train_series = rng.normal(size=(100, 3))
+    test_series = rng.normal(size=(100, 3))
+    test_series[50:58, 0] -= 5
+    test_series[87:90, 1] += 0.1
 
     ad = ROCKAD(
         n_estimators=1000,
@@ -45,7 +48,8 @@ def test_rockad_multivariate():
         stride=1,
     )
 
-    pred = ad.fit_predict(series, axis=0)
+    ad.fit(train_series, axis=0)
+    pred = ad.predict(test_series, axis=0)
 
     assert pred.shape == (100,)
     assert pred.dtype == np.float64
@@ -55,21 +59,28 @@ def test_rockad_multivariate():
 def test_rockad_incorrect_input():
     """Test ROCKAD incorrect input."""
     rng = check_random_state(seed=2)
-    series = rng.normal(size=(100,))
+    train_series = rng.normal(size=(100,))
+    test_series = rng.normal(size=(5,))
 
     with pytest.raises(ValueError, match="The window size must be at least 1"):
         ad = ROCKAD(window_size=0)
-        ad.fit_predict(series)
+        ad.fit(train_series)
     with pytest.raises(ValueError, match="The stride must be at least 1"):
         ad = ROCKAD(stride=0)
-        ad.fit_predict(series)
+        ad.fit(train_series)
     with pytest.raises(
         ValueError, match=r"Window count .* has to be larger than n_neighbors .*"
     ):
         ad = ROCKAD(stride=1, window_size=100)
-        ad.fit_predict(series)
+        ad.fit(train_series)
     with pytest.warns(
         UserWarning, match=r"Power Transform failed and thus has been disabled."
     ):
         ad = ROCKAD(stride=1, window_size=5)
-        ad.fit_predict(series)
+        ad.fit(train_series)
+    with pytest.raises(
+        ValueError, match=r"window shape cannot be larger than input array shape"
+    ):
+        ad = ROCKAD(stride=1, window_size=10)
+        ad.fit(train_series)
+        ad.predict(test_series)