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Fix/dlinear and nlinear use_static_cov. with multivariate series #2070

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merged 7 commits into from
Nov 18, 2023
Merged

Fix/dlinear and nlinear use_static_cov. with multivariate series #2070

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64164a2
feat: added test to check that use_static_covariates covers all possi…
madtoinou Nov 16, 2023
2b94f4e
fix: properly account for the two possible static covariates represen…
madtoinou Nov 16, 2023
63bac6f
fix: typo in the warning message
madtoinou Nov 16, 2023
07ca413
feat: added type hint, reordered docstring to match argument order
madtoinou Nov 16, 2023
a6d850d
feat: added type hint, reordered docstring to match argument order (d…
madtoinou Nov 16, 2023
03c3dac
feat: updated changelog
madtoinou Nov 17, 2023
01fbe3d
Merge branch 'master' into fix/xlinear_use_static_cov
dennisbader Nov 18, 2023
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feat: added test to check that use_static_covariates covers all possi… 
…ble static covariates representations
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@madtoinou
madtoinou committed Nov 16, 2023
commit 64164a2049ef54c31ded087bc3b79b338ae1ae45
41 changes: 41 additions & 0 deletions darts/tests/models/forecasting/test_global_forecasting_models.py
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Original file line number Diff line number Diff line change
@@ -1,5 +1,6 @@
import os
from copy import deepcopy
from itertools import product
from unittest.mock import ANY, patch

import numpy as np
Expand Down Expand Up @@ -206,6 +207,15 @@ class TestGlobalForecastingModels:
target = sine_1_ts + sine_2_ts + linear_ts + sine_3_ts
target_past, target_future = target.split_after(split_ratio)

# various ts with different static covariates representations
ts_w_static_cov = tg.linear_timeseries(length=80).with_static_covariates(
pd.Series([1, 2])
)
ts_shared_static_cov = ts_w_static_cov.stack(tg.sine_timeseries(length=80))
ts_comps_static_cov = ts_shared_static_cov.with_static_covariates(
pd.DataFrame([[0, 1], [2, 3]], columns=["st1", "st2"])
)

@pytest.mark.parametrize("config", models_cls_kwargs_errs)
def test_save_model_parameters(self, config):
# model creation parameters were saved before. check if re-created model has same params as original
Expand Down Expand Up @@ -450,6 +460,37 @@ def test_future_covariates(self):
with pytest.raises(ValueError):
model.predict(n=161, future_covariates=self.covariates)

@pytest.mark.parametrize(
"model_cls,ts",
product(
[TFTModel, DLinearModel, NLinearModel, TiDEModel],
[ts_w_static_cov, ts_shared_static_cov, ts_comps_static_cov],
),
)
def test_use_static_covariates(self, model_cls, ts):
"""
Check that both static covariates representations are supported (component-specific and shared)
for both uni- and multivariate series when fitting the model.
Also check that the static covariates are present in the forecasted series
"""
model = model_cls(
input_chunk_length=IN_LEN,
output_chunk_length=OUT_LEN,
random_state=0,
use_static_covariates=True,
n_epochs=1,
**tfm_kwargs,
)
# must provide mandatory future_covariates to TFTModel
model.fit(
series=ts,
future_covariates=self.sine_1_ts
if model.supports_future_covariates
else None,
)
pred = model.predict(OUT_LEN)
assert pred.static_covariates.equals(ts.static_covariates)

def test_batch_predictions(self):
# predicting multiple time series at once needs to work for arbitrary batch sizes
# univariate case
Expand Down