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Fix/bug in ensemble models to support underlying stochastic models #1423

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merged 5 commits into from
Dec 11, 2022
Merged

Fix/bug in ensemble models to support underlying stochastic models #1423

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08cc7e0
- added _is_probabilistic to baseline models
eliane-maalouf Dec 10, 2022
aa3b7aa
formatting
eliane-maalouf Dec 10, 2022
ffb1583
corrected as per PR review
eliane-maalouf Dec 11, 2022
a02e709
Merge branch 'master' into fix/1390-naiveensemblemodel-nor-regression…
eliane-maalouf Dec 11, 2022
0adeab2
Update theta.py
eliane-maalouf Dec 11, 2022
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3 changes: 3 additions & 0 deletions darts/models/forecasting/ensemble_model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -196,3 +196,6 @@ def ensemble(
@property
def min_train_series_length(self) -> int:
return max(model.min_train_series_length for model in self.models)

def _is_probabilistic(self) -> bool:
return all([model._is_probabilistic() for model in self.models])
3 changes: 3 additions & 0 deletions darts/models/forecasting/theta.py
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Expand Up @@ -182,6 +182,9 @@ def min_train_series_length(self) -> int:
else:
return 3

def _is_probabilistic(self) -> bool:
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this one can be removed as well :) after that all looks good!

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;) missed this one, thanks

return False


class FourTheta(LocalForecastingModel):
def __init__(
Expand Down
13 changes: 13 additions & 0 deletions darts/tests/models/forecasting/test_ensemble_models.py
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Expand Up @@ -11,6 +11,7 @@
NaiveDrift,
NaiveEnsembleModel,
NaiveSeasonal,
RegressionEnsembleModel,
Theta,
)
from darts.tests.base_test_class import DartsBaseTestClass
Expand Down Expand Up @@ -83,6 +84,18 @@ def test_predict_ensemble_local_models(self):
np.array_equal(forecast_naive_ensemble.values(), forecast_mean.values())
)

def test_stochastic_ensemble(self):
model1 = LinearRegressionModel(lags=1, likelihood="quantile")
model2 = LinearRegressionModel(lags=2, likelihood="quantile")

naive_ensemble = NaiveEnsembleModel([model1, model2])
self.assertTrue(naive_ensemble._is_probabilistic())

regression_ensemble = RegressionEnsembleModel(
[model1, model2], regression_train_n_points=1
)
self.assertTrue(regression_ensemble._is_probabilistic())

@unittest.skipUnless(TORCH_AVAILABLE, "requires torch")
def test_input_models_global_models(self):
NaiveEnsembleModel([RNNModel(12), TCNModel(10, 2), NBEATSModel(10, 2)])
Expand Down