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Description
I am trying to implement grid search for 3 parameters in the elasticnet regression model from sklearn and wrapping the darts RegressionModel around that. Based on the documentation of grid search, this is how I initialised the grid search:
grid_params = {
'max_iter': [1, 5, 10, 50, 100, 200],
'alpha': [1e-5, 1e-4, 1e-3, 1e-2, 1e-1, 0.0, 1.0, 10.0, 100.0],
'l1_ratio': [0.01, 0.1, 0.3, 0.6, 0.9, 1]
}
sklearn_elasticnet_model = make_pipeline(
ElasticNet(random_state = 42)
)
elasticnet_model = RegressionModel(
lags=target_lags,
lags_past_covariates=past_cov_lags,
lags_future_covariates=future_cov_lags,
output_chunk_length=forecast_horizon,
multi_models=True,
model=sklearn_elasticnet_model
)
elasticnet_model.gridsearch(grid_params,
series=target_series_sample,
future_covariates=future_cov_sample,
forecast_horizon=24,
stride=24)
However, I am getting this error: TypeError: RegressionModel.__init__() got an unexpected keyword argument 'max_iter'. Am I implementing it wrongly?