def test_accepts_one_model(self):
regr1 = LinearRegression()
regr2 = RandomForest(lags_future_covariates=[0])
model0 = RegressionEnsembleModel([self.get_local_models()[0]], 10)
model1 = RegressionEnsembleModel([self.get_local_models()[0]], 10,
regr1)
model2 = RegressionEnsembleModel([self.get_local_models()[0]], 10,
regr2)
models = [model0, model1, model2]
for model in models:
model.fit(series=self.combined)
model.predict(10)
def test_accepts_different_regression_models(self):
regr1 = LinearRegression()
regr2 = RandomForestRegressor()
regr3 = RandomForest(lags_future_covariates=[0])
model0 = RegressionEnsembleModel(self.get_local_models(), 10)
model1 = RegressionEnsembleModel(self.get_local_models(), 10, regr1)
model2 = RegressionEnsembleModel(self.get_local_models(), 10, regr2)
model3 = RegressionEnsembleModel(self.get_local_models(), 10, regr3)
models = [model0, model1, model2, model3]
for model in models:
model.fit(series=self.combined)
model.predict(10)
def test_ensemble_models_denoising_multi_input(self):
# for every model, test whether it correctly denoises ts_sum_2 using ts_random_multi and ts_sum_2 as inputs
# WARNING: this test isn't numerically stable, changing self.RANDOM_SEED can lead to exploding coefficients
horizon = 10
_, _, ts_sum2, ts_cov2 = self.denoising_input()
torch.manual_seed(self.RANDOM_SEED)
ensemble_models = [
RNNModel(
input_chunk_length=20,
output_chunk_length=horizon,
n_epochs=1,
random_state=self.RANDOM_SEED,
),
BlockRNNModel(
input_chunk_length=20,
output_chunk_length=horizon,
n_epochs=1,
random_state=self.RANDOM_SEED,
),
RegressionModel(lags_past_covariates=[-1]),
RegressionModel(lags_past_covariates=[-1]),
]
ensemble = RegressionEnsembleModel(ensemble_models, horizon)
self.helper_test_models_accuracy(ensemble, horizon, ts_sum2, ts_cov2,
3)
def test_train_predict_global_models_multivar_with_covariates(self):
ensemble_models = self.get_global_models(output_chunk_length=10)
ensemble_models.append(
RegressionModel(lags=1, lags_past_covariates=[-1]))
ensemble = RegressionEnsembleModel(ensemble_models, 10)
ensemble.fit(self.seq1, self.cov1)
ensemble.predict(10, self.seq2, self.cov2)
def test_torch_models_retrain(self):
model1 = BlockRNNModel(input_chunk_length=12,
output_chunk_length=1,
random_state=0,
n_epochs=2)
model2 = BlockRNNModel(input_chunk_length=12,
output_chunk_length=1,
random_state=0,
n_epochs=2)
ensemble = RegressionEnsembleModel([model1], 5)
ensemble.fit(self.combined)
model1_fitted = ensemble.models[0]
forecast1 = model1_fitted.predict(10)
model2.fit(self.combined)
forecast2 = model2.predict(10)
self.assertAlmostEqual(sum(forecast1.values() - forecast2.values())[0],
0.0,
places=2)
def test_train_predict_global_models_univar(self):
ensemble_models = self.get_global_models(output_chunk_length=10)
ensemble_models.append(RegressionModel(lags=1))
ensemble = RegressionEnsembleModel(ensemble_models, 10)
ensemble.fit(series=self.combined)
ensemble.predict(10)
def test_train_n_points(self):
regr = LinearRegressionModel(lags_future_covariates=[0])
# same values
ensemble = RegressionEnsembleModel(self.get_local_models(), 5, regr)
# too big value to perform the split
ensemble = RegressionEnsembleModel(self.get_local_models(), 100)
with self.assertRaises(ValueError):
ensemble.fit(self.combined)
ensemble = RegressionEnsembleModel(self.get_local_models(), 50)
with self.assertRaises(ValueError):
ensemble.fit(self.combined)
# too big value considering min_train_series_length
ensemble = RegressionEnsembleModel(self.get_local_models(), 45)
with self.assertRaises(ValueError):
ensemble.fit(self.combined)