[ENH] forecasting coverage

aeon/forecasting/tests/test_base.py

@@ -37,9 +37,13 @@ def test_convert_y():
     f.set_tags(**{"y_inner_type": "pd.DataFrame"})
     y2 = f._convert_y(y, axis=0)
     assert isinstance(y2, pd.DataFrame)
+    y2 = f._convert_y(y, axis=1)
+    assert isinstance(y2, pd.DataFrame)
     f.set_tags(**{"y_inner_type": "pd.Series"})
     with pytest.raises(ValueError, match="Unsupported inner type"):
         f._convert_y(y, axis=1)
+    with pytest.raises(ValueError, match="must be greater than or equal to 1"):
+        f.iterative_forecast(y, prediction_horizon=0)
 
 
 def test_direct_forecast():

aeon/forecasting/tests/test_naive.py

@@ -1,6 +1,7 @@
 """Test Naive Forecaster."""
 
 import numpy as np
+import pytest
 
 from aeon.forecasting import NaiveForecaster
 
@@ -62,3 +63,19 @@ def test_naive_forecaster_seasonal_last_strategy():
     pred3 = forecaster.predict(data)
     expected = 6  # predicts the 2nd element of the new last season.
     np.testing.assert_allclose(pred, pred2, pred3, expected)
+
+
+def test_predict():
+    """Test different input for private predict."""
+    forecaster = NaiveForecaster(strategy="mean")
+    y = np.array([1, 2, 3, 4, 5, 6, 7, 8])
+    x = forecaster._predict(y)
+    assert isinstance(x, float)
+    forecaster = NaiveForecaster(strategy="seasonal_last", seasonal_period=2)
+    forecaster._fit(y)
+    assert forecaster.forecast_ == 7.0
+    forecaster = NaiveForecaster(strategy="FOOBAR")
+    with pytest.raises(ValueError, match="Unknown strategy"):
+        x = forecaster._fit(y)
+    with pytest.raises(ValueError, match="Unknown strategy"):
+        x = forecaster._predict(y)

aeon/forecasting/tests/test_regressor.py

@@ -76,6 +76,9 @@ def test_regression_forecaster_with_exog():
     f.fit(y, exog_m)
     p2 = f.predict(y, exog_m)
     assert p1 == p2
+    y = np.random.random((1, 100))
+    exog = np.random.random((1, 100))
+    f._fit(y, exog)
 
 
 def test_regression_forecaster_with_exog_errors():