Update get_test_params

sktime · Apr 13, 2024 · 74e3ff8 · 74e3ff8
1 parent 757b9c7
commit 74e3ff8
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Showing 3 changed files with 13 additions and 59 deletions.
diff --git a/sktime/forecasting/base/adapters/_neuralforecast.py b/sktime/forecasting/base/adapters/_neuralforecast.py
@@ -177,7 +177,9 @@ def _get_valid_parameters(self: "_NeuralForecastAdapter") -> dict:
                 f" not found in the __init__ method "
                 f"from {Trainer}. "
                 f"Check your pytorch_lightning version "
-                f"to find out the right API parameters."
+                f"to find out the right API parameters.",
+                obj=self,
+                stacklevel=2,
             )
             filter_params["trainer_kwargs"].pop(invalid_param)
 
@@ -188,7 +190,9 @@ def _get_valid_parameters(self: "_NeuralForecastAdapter") -> dict:
                     f" not found in the __init__ method "
                     f"from {self.algorithm_class}. "
                     f"Check your neuralforecast version "
-                    f"to find out the right API parameters."
+                    f"to find out the right API parameters.",
+                    obj=self,
+                    stacklevel=2,
                 )
             filter_params.pop(unsupported_param)
 

diff --git a/sktime/forecasting/neuralforecast.py b/sktime/forecasting/neuralforecast.py
@@ -358,6 +358,7 @@ def get_test_params(cls, parameter_set="default"):
             ]
         else:
             from neuralforecast.losses.pytorch import SMAPE, QuantileLoss
+            from torch.optim import Adam
 
             params = [
                 {
@@ -378,6 +379,8 @@ def get_test_params(cls, parameter_set="default"):
                     "max_steps": 4,
                     "val_check_steps": 2,
                     "trainer_kwargs": {"logger": False},
+                    "optimizer": Adam,
+                    "optimizer_kwargs": {"lr": 0.001},
                 },
             ]
 
@@ -696,6 +699,7 @@ def get_test_params(cls, parameter_set="default"):
 
         try:
             _check_soft_dependencies("neuralforecast", severity="error")
+            _check_soft_dependencies("torch", severity="error")
         except ModuleNotFoundError:
             params = [
                 {
@@ -718,6 +722,7 @@ def get_test_params(cls, parameter_set="default"):
             ]
         else:
             from neuralforecast.losses.pytorch import SMAPE, QuantileLoss
+            from torch.optim import Adam
 
             params = [
                 {
@@ -738,6 +743,8 @@ def get_test_params(cls, parameter_set="default"):
                     "max_steps": 4,
                     "val_check_steps": 2,
                     "trainer_kwargs": {"logger": False},
+                    "optimizer": Adam,
+                    "optimizer_kwargs": {"lr": 0.001},
                 },
             ]
 

diff --git a/sktime/forecasting/tests/test_neuralforecast.py b/sktime/forecasting/tests/test_neuralforecast.py
@@ -322,60 +322,3 @@ def test_neural_forecast_with_auto_freq_on_missing_date_like(
         ValueError, match="(could not interpret freq).*(use a valid offset in index)"
     ):
         model.fit(y, fh=[1, 2, 3])
-
-
-@pytest.mark.parametrize("model_class", [NeuralForecastLSTM, NeuralForecastRNN])
-@pytest.mark.skipif(
-    not run_test_for_class([NeuralForecastLSTM, NeuralForecastRNN]),
-    reason="run test only if softdeps are present and incrementally (if requested)",
-)
-def test_neural_forecast_with_non_default_optimizer(model_class) -> None:
-    """Test with user defined optimizer."""
-    # import non-default pytorch optimizer
-    from torch.optim import Adam
-
-    # define model
-    model = model_class(
-        freq="A-DEC",
-        max_steps=5,
-        optimizer=Adam,
-        trainer_kwargs={"logger": False},
-    )
-
-    # train model
-    model.fit(X_train, fh=[1, 2, 3, 4])
-
-    # predict with trained model
-    X_pred = model.predict()
-
-    # check prediction index
-    pandas.testing.assert_index_equal(X_pred.index, X_test.index, check_names=False)
-
-
-@pytest.mark.parametrize("model_class", [NeuralForecastLSTM, NeuralForecastRNN])
-@pytest.mark.skipif(
-    not run_test_for_class([NeuralForecastLSTM, NeuralForecastRNN]),
-    reason="run test only if softdeps are present and incrementally (if requested)",
-)
-def test_neural_forecast_with_non_default_optimizer_with_kwargs(model_class) -> None:
-    """Test with user defined optimizer and optimizer_kwargs."""
-    # import non-default pytorch optimizer
-    from torch.optim import Adagrad
-
-    # define model
-    model = model_class(
-        freq="A-DEC",
-        optimizer=Adagrad,
-        optimizer_kwargs={"lr": 0.1},
-        max_steps=5,
-        trainer_kwargs={"logger": False},
-    )
-
-    # train model
-    model.fit(X_train, fh=[1, 2, 3, 4])
-
-    # predict with trained model
-    X_pred = model.predict()
-
-    # check prediction index
-    pandas.testing.assert_index_equal(X_pred.index, X_test.index, check_names=False)