[BUG] RegressionEnsembleModel predict error

[GeorgeXiaojie]

Describe the bug A clear and concise description of what the bug is.

scene1：as below, RegressionEnsembleModel train and predict works

train:

RegressionEnsembleModel(
    forecasting_models = [
        LightGBMModel(
            lags=args.input_length,
            lags_future_covariates=list(range(args.forecast_horizon)),
            # add_encoders={'datetime_attribute': {'future': ['hour', 'minute']}},
            output_chunk_length=args.forecast_horizon,
            random_state=2022,
            force_col_wise=True,
            device='gpu' if args.device != 'cpu' else args.device,
            verbose=1
        ),
        XGBModel(
            lags=args.input_length,
            lags_future_covariates=list(range(args.forecast_horizon)),
            # add_encoders={'datetime_attribute': {'future': ['hour', 'minute']}},
            output_chunk_length=args.forecast_horizon,
            random_state=2022
        )],
    regression_train_n_points =args.forecast_horizon
)

model.fit(
    train_series,
    future_covariates=cov_train_series
)

model.save("model.pt"))

predict:

model = RegressionEnsembleModel.load("model.pt"))

model.predict(
    n=args.forecast_horizon,
    series=series,
    future_covariates=future_covariates,
    num_samples=1,
    verbose=False
)

scene2：as bellow, RegressionEnsembleModel train works, but predict doesn't work, and raises an exception: AttributeError: 'NoneType' object has no attribute 'set_predict_parameters'

train:

model = RegressionEnsembleModel(
    forecasting_models = [
        TiDEModel(
            input_chunk_length=args.input_length,
            output_chunk_length=args.forecast_horizon,
            n_epochs=args.epochs,
            loss_fn=torch.nn.MSELoss(),
            model_name=model_name,
            force_reset=True,
            save_checkpoints=False,
            random_state=42,
            pl_trainer_kwargs=pl_trainer_kwargs,
            use_static_covariates=False
        ),
        NLinearModel(
            input_chunk_length=args.input_length,
            output_chunk_length=args.forecast_horizon,
            model_name=model_name,
            force_reset=True,
            save_checkpoints=False,
            random_state=42,
            pl_trainer_kwargs=pl_trainer_kwargs,
            use_static_covariates=False
        )],
    regression_train_n_points = args.input_length
)

model.fit(
    train_series,
    future_covariates=cov_train_series
)

model.save("model.pt"))

predict:

model = RegressionEnsembleModel.load("model.pt"))

model.predict(
    n=args.forecast_horizon,
    series=series,
    future_covariates=future_covariates,
    num_samples=1,
    verbose=False
)

I debugged into the code below and found that self.model is indeed None, I'm not sure if it's because of a bug?

self.model.set_predict_parameters(
    n=n,
    num_samples=num_samples,
    roll_size=roll_size,
    batch_size=batch_size,
    n_jobs=n_jobs,
    predict_likelihood_parameters=predict_likelihood_parameters,
    mc_dropout=mc_dropout,
)

To Reproduce Steps to reproduce the behavior, preferably code snippet.

Expected behavior A clear and concise description of what you expected to happen.

System (please complete the following information):

• Python version: [e.g. 3.7]
• darts version [e.g. 0.30.0]
• torch[2.0.1]
• pytorch-lightning[1.9.5]

Additional context Add any other context about the problem here.

[madtoinou]

Hi @GeorgeXiaojie,

I don't see fit() being called in the code you shared, if it isn't, the model does not exist and there is not much to actually save. Can you try the following:

from darts.models import RegressionEnsembleModel, LightGBMModel, XGBModel
from darts.datasets import AirPassengersDataset

ts = AirPassengersDataset().load()

model = RegressionEnsembleModel(
    forecasting_models = [
        LightGBMModel(
            lags=3,
            output_chunk_length=1,
            random_state=2022,
        ),
        XGBModel(
            lags=3,
            output_chunk_length=1,
            random_state=2022
        )],
    regression_train_n_points = 10
)
model.fit(ts)
# works
model.predict(3)
model.save("model.pt")

model_loaded = RegressionEnsembleModel.load("model.pt")
# works as well
model_loaded.predict(3)

[GeorgeXiaojie]

Hi @GeorgeXiaojie,

I don't see fit() being called in the code you shared, if it isn't, the model does not exist and there is not much to actually save. Can you try the following:

from darts.models import RegressionEnsembleModel, LightGBMModel, XGBModel
from darts.datasets import AirPassengersDataset

ts = AirPassengersDataset().load()

model = RegressionEnsembleModel(
  forecasting_models = [
      LightGBMModel(
          lags=3,
          output_chunk_length=1,
          random_state=2022,
      ),
      XGBModel(
          lags=3,
          output_chunk_length=1,
          random_state=2022
      )],
  regression_train_n_points = 10
)
model.fit(ts)
# works
model.predict(3)
model.save("model.pt")

model_loaded = RegressionEnsembleModel.load("model.pt")
# works as well
model_loaded.predict(3)

Thank you for your reply. There is a model.fit and I've followed up on the issue.

RegressionEnsembleModel is OK using the predictive models LightGBMModel and XGBModel.

However, RegressionEnsembleModel using TiDEModel and NLinearModel is not working.

[madtoinou]

Oh thanks for the clarification, managed to reproduce the problem.

It seems like the torch models weights are not being loaded properly, we will investigate this further.