Add params_to_tune for DeepARModel and TFTModel

CHANGELOG.md

@@ -48,6 +48,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Add default `params_to_tune` for `SARIMAXModel`, change default parameters for the model ([#1206](https://github.com/tinkoff-ai/etna/pull/1206))
 - Add default `params_to_tune` for linear models ([#1204](https://github.com/tinkoff-ai/etna/pull/1204))
 - Add default `params_to_tune` for `SeasonalMovingAverageModel`, `MovingAverageModel`, `NaiveModel` and `DeadlineMovingAverageModel` ([#1208](https://github.com/tinkoff-ai/etna/pull/1208))
+- Add default `params_to_tune` for `DeepARModel` and `TFTModel` ([#1210](https://github.com/tinkoff-ai/etna/pull/1210))
 ### Fixed
 - Fix bug in `GaleShapleyFeatureSelectionTransform` with wrong number of remaining features ([#1110](https://github.com/tinkoff-ai/etna/pull/1110))
 - `ProphetModel` fails with additional seasonality set ([#1157](https://github.com/tinkoff-ai/etna/pull/1157))

etna/models/nn/deepar.py

@@ -23,6 +23,12 @@
     from pytorch_forecasting.models import DeepAR
     from pytorch_lightning import LightningModule
 
+if SETTINGS.auto_required:
+    from optuna.distributions import BaseDistribution
+    from optuna.distributions import IntUniformDistribution
+    from optuna.distributions import LogUniformDistribution
+    from optuna.distributions import UniformDistribution
+
 
 class DeepARModel(_DeepCopyMixin, PytorchForecastingMixin, SaveNNMixin, PredictionIntervalContextRequiredAbstractModel):
     """Wrapper for :py:class:`pytorch_forecasting.models.deepar.DeepAR`.
@@ -240,12 +246,26 @@ def predict(
     def get_model(self) -> Any:
         """Get internal model that is used inside etna class.
 
-        Internal model is a model that is used inside etna to forecast segments,
-        e.g. :py:class:`catboost.CatBoostRegressor` or :py:class:`sklearn.linear_model.Ridge`.
+        Model is the instance of :py:class:`pytorch_forecasting.models.deepar.DeepAR`.
 
         Returns
         -------
         :
            Internal model
         """
         return self.model
+
+    def params_to_tune(self) -> Dict[str, "BaseDistribution"]:
+        """Get default grid for tuning hyperparameters.
+
+        Returns
+        -------
+        :
+            Grid to tune.
+        """
+        return {
+            "hidden_size": IntUniformDistribution(low=4, high=64, step=4),
+            "rnn_layers": IntUniformDistribution(low=1, high=3, step=1),
+            "dropout": UniformDistribution(low=0, high=0.5),
+            "lr": LogUniformDistribution(low=1e-5, high=1e-2),
+        }

etna/models/nn/tft.py

@@ -23,6 +23,12 @@
     from pytorch_forecasting.models import TemporalFusionTransformer
     from pytorch_lightning import LightningModule
 
+if SETTINGS.auto_required:
+    from optuna.distributions import BaseDistribution
+    from optuna.distributions import IntUniformDistribution
+    from optuna.distributions import LogUniformDistribution
+    from optuna.distributions import UniformDistribution
+
 
 class TFTModel(_DeepCopyMixin, PytorchForecastingMixin, SaveNNMixin, PredictionIntervalContextRequiredAbstractModel):
     """Wrapper for :py:class:`pytorch_forecasting.models.temporal_fusion_transformer.TemporalFusionTransformer`.
@@ -269,12 +275,27 @@ def predict(
     def get_model(self) -> Any:
         """Get internal model that is used inside etna class.
 
-        Internal model is a model that is used inside etna to forecast segments,
-        e.g. :py:class:`catboost.CatBoostRegressor` or :py:class:`sklearn.linear_model.Ridge`.
+        Model is the instance of :py:class:`pytorch_forecasting.models.temporal_fusion_transformer.TemporalFusionTransformer`.
 
         Returns
         -------
         :
            Internal model
         """
         return self.model
+
+    def params_to_tune(self) -> Dict[str, "BaseDistribution"]:
+        """Get default grid for tuning hyperparameters.
+
+        Returns
+        -------
+        :
+            Grid to tune.
+        """
+        return {
+            "hidden_size": IntUniformDistribution(low=4, high=64, step=4),
+            "lstm_layers": IntUniformDistribution(low=1, high=3, step=1),
+            "dropout": UniformDistribution(low=0, high=0.5),
+            "attention_head_size": IntUniformDistribution(low=2, high=8, step=2),
+            "lr": LogUniformDistribution(low=1e-5, high=1e-2),
+        }

tests/test_models/nn/test_deepar.py

@@ -2,6 +2,7 @@
 
 import pandas as pd
 import pytest
+from optuna.samplers import RandomSampler
 from pytorch_forecasting.data import GroupNormalizer
 
 from etna.datasets.tsdataset import TSDataset
@@ -191,3 +192,15 @@ def test_repr():
 def test_deepar_forecast_throw_error_on_return_components():
     with pytest.raises(NotImplementedError, match="This mode isn't currently implemented!"):
         DeepARModel.forecast(self=Mock(), ts=Mock(), prediction_size=Mock(), return_components=True)
+
+
+def test_params_to_tune():
+    model = DeepARModel(decoder_length=3, encoder_length=4)
+    grid = model.params_to_tune()
+    # we need sampler to get a value from distribution
+    sampler = RandomSampler()
+
+    assert len(grid) > 0
+    for name, distribution in grid.items():
+        value = sampler.sample_independent(study=None, trial=None, param_name=name, param_distribution=distribution)
+        _ = model.set_params(**{name: value})

tests/test_models/nn/test_tft.py

@@ -2,6 +2,7 @@
 
 import pandas as pd
 import pytest
+from optuna.samplers import RandomSampler
 
 from etna.metrics import MAE
 from etna.models.nn import TFTModel
@@ -196,3 +197,15 @@ def test_repr():
 def test_tft_forecast_throw_error_on_return_components():
     with pytest.raises(NotImplementedError, match="This mode isn't currently implemented!"):
         TFTModel.forecast(self=Mock(), ts=Mock(), prediction_size=Mock(), return_components=True)
+
+
+def test_params_to_tune():
+    model = TFTModel(decoder_length=3, encoder_length=4)
+    grid = model.params_to_tune()
+    # we need sampler to get a value from distribution
+    sampler = RandomSampler()
+
+    assert len(grid) > 0
+    for name, distribution in grid.items():
+        value = sampler.sample_independent(study=None, trial=None, param_name=name, param_distribution=distribution)
+        _ = model.set_params(**{name: value})