[ENH] remove horizon from ETS constructor, parametrize ETS tests (#2898)

* remove horizon from ETS argument, parametrize tests

* remove horizon from example

* docstring

Author: TonyBagnall

aeon/forecasting/_ets.py

@@ -44,8 +44,6 @@ class ETSForecaster(BaseForecaster):
         Seasonal smoothing parameter.
     phi : float, default=0.99
         Trend damping parameter (used only for damped trend models).
-    horizon : int, default=1
-        Forecasting horizon (number of time steps ahead to predict).
 
     Attributes
     ----------
@@ -82,7 +80,7 @@ class ETSForecaster(BaseForecaster):
     >>> from aeon.datasets import load_airline
     >>> y = load_airline()
     >>> forecaster = ETSForecaster(
-    ...     alpha=0.4, beta=0.2, gamma=0.5, phi=0.8, horizon=1,
+    ...     alpha=0.4, beta=0.2, gamma=0.5, phi=0.8,
     ...     error_type='additive', trend_type='multiplicative',
     ...     seasonality_type='multiplicative', seasonal_period=4
     ... )
@@ -106,7 +104,6 @@ def __init__(
         beta: float = 0.01,
         gamma: float = 0.01,
         phi: float = 0.99,
-        horizon: int = 1,
     ):
         self.alpha = alpha
         self.beta = beta
@@ -130,7 +127,7 @@ def __init__(
         self.aic_ = 0
         self.residuals_ = []
         self.fitted_values_ = []
-        super().__init__(horizon=horizon, axis=1)
+        super().__init__(horizon=1, axis=1)
 
     def _fit(self, y, exog=None):
         """Fit Exponential Smoothing forecaster to series y.

aeon/forecasting/tests/test_ets.py

@@ -10,88 +10,70 @@
 from aeon.forecasting._ets import _validate_parameter
 
 
-def test_ets_forecaster_additive():
-    """TestETSForecaster."""
-    data = np.array(
-        [3, 10, 12, 13, 12, 10, 12, 3, 10, 12, 13, 12, 10, 12]
-    )  # Sample seasonal data
-    forecaster = ETSForecaster(
-        alpha=0.5,
-        beta=0.3,
-        gamma=0.4,
-        phi=1,
-        horizon=1,
-        error_type="additive",
-        trend_type="additive",
-        seasonality_type="additive",
-        seasonal_period=4,
-    )
+@pytest.mark.parametrize(
+    "params, expected",
+    [
+        (
+            dict(
+                alpha=0.5,
+                beta=0.3,
+                gamma=0.4,
+                phi=1,
+                error_type="additive",
+                trend_type="additive",
+                seasonality_type="additive",
+                seasonal_period=4,
+            ),
+            9.191190608800001,
+        ),
+        (
+            dict(
+                alpha=0.7,
+                beta=0.6,
+                gamma=0.1,
+                phi=0.97,
+                error_type="multiplicative",
+                trend_type="additive",
+                seasonality_type="additive",
+                seasonal_period=4,
+            ),
+            16.20176819429869,
+        ),
+        (
+            dict(
+                alpha=0.4,
+                beta=0.2,
+                gamma=0.5,
+                phi=0.8,
+                error_type="additive",
+                trend_type="multiplicative",
+                seasonality_type="multiplicative",
+                seasonal_period=4,
+            ),
+            12.301259229712382,
+        ),
+        (
+            dict(
+                alpha=0.7,
+                beta=0.5,
+                gamma=0.2,
+                phi=0.85,
+                error_type="multiplicative",
+                trend_type="multiplicative",
+                seasonality_type="multiplicative",
+                seasonal_period=4,
+            ),
+            16.811888294476528,
+        ),
+    ],
+)
+def test_ets_forecaster(params, expected):
+    """Test ETSForecaster for multiple parameter combinations."""
+    data = np.array([3, 10, 12, 13, 12, 10, 12, 3, 10, 12, 13, 12, 10, 12])
+    forecaster = ETSForecaster(**params)
     forecaster.fit(data)
     p = forecaster.predict()
-    assert np.isclose(p, 9.191190608800001)
-
-
-def test_ets_forecaster_mult_error():
-    """TestETSForecaster."""
-    data = np.array(
-        [3, 10, 12, 13, 12, 10, 12, 3, 10, 12, 13, 12, 10, 12]
-    )  # Sample seasonal data
-    forecaster = ETSForecaster(
-        alpha=0.7,
-        beta=0.6,
-        gamma=0.1,
-        phi=0.97,
-        horizon=1,
-        error_type="multiplicative",
-        trend_type="additive",
-        seasonality_type="additive",
-        seasonal_period=4,
-    )
-    forecaster.fit(data)
-    p = forecaster.predict()
-    assert np.isclose(p, 16.20176819429869)
-
-
-def test_ets_forecaster_mult_compnents():
-    """TestETSForecaster."""
-    data = np.array(
-        [3, 10, 12, 13, 12, 10, 12, 3, 10, 12, 13, 12, 10, 12]
-    )  # Sample seasonal data
-    forecaster = ETSForecaster(
-        alpha=0.4,
-        beta=0.2,
-        gamma=0.5,
-        phi=0.8,
-        horizon=1,
-        error_type="additive",
-        trend_type="multiplicative",
-        seasonality_type="multiplicative",
-        seasonal_period=4,
-    )
-    forecaster.fit(data)
-    p = forecaster.predict()
-    assert np.isclose(p, 12.301259229712382)
-
-
-def test_ets_forecaster_multiplicative():
-    """TestETSForecaster."""
-    data = np.array(
-        [3, 10, 12, 13, 12, 10, 12, 3, 10, 12, 13, 12, 10, 12]
-    )  # Sample seasonal data
-    forecaster = ETSForecaster(
-        alpha=0.7,
-        beta=0.5,
-        gamma=0.2,
-        phi=0.85,
-        horizon=1,
-        error_type="multiplicative",
-        trend_type="multiplicative",
-        seasonality_type="multiplicative",
-        seasonal_period=4,
-    )
-    forecaster.fit(data)
-    p = forecaster.predict()
-    assert np.isclose(p, 16.811888294476528)
+    assert np.isclose(p, expected)
 
 
 def test_incorrect_parameters():