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HNiTS rename to match model naming convection #1000

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merged 5 commits into from
Jun 11, 2022
Merged

HNiTS rename to match model naming convection #1000

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368fdbc
renamed nhits to match naming convention of other models
gdevos010 Jun 9, 2022
dda3e21
renamed nhits to match naming convention of other models
gdevos010 Jun 9, 2022
d12cd95
Merge branch 'master' into HNiTs-rename
gdevos010 Jun 9, 2022
9c1efe6
renamed nhits to match naming convention of other models
gdevos010 Jun 10, 2022
0e94504
Merge remote-tracking branch 'origin/HNiTs-rename' into HNiTs-rename
gdevos010 Jun 10, 2022
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2 changes: 1 addition & 1 deletion README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -141,7 +141,7 @@ Model | Univariate | Multivariate | Probabilistic | Multiple-series training | P
`RNNModel` (incl. LSTM and GRU); equivalent to DeepAR in its probabilistic version | ✅ | ✅ | ✅ | ✅ | | ✅ | [DeepAR paper](https://arxiv.org/abs/1704.04110)
`BlockRNNModel` (incl. LSTM and GRU) | ✅ | ✅ | ✅ | ✅ | ✅ | |
`NBEATSModel` | ✅ | ✅ | ✅ | ✅ | ✅ | | [N-BEATS paper](https://arxiv.org/abs/1905.10437)
`NHiTS` | ✅ | ✅ | ✅ | ✅ | ✅ | | [N-HiTS paper](https://arxiv.org/abs/2201.12886)
`NHiTSModel` | ✅ | ✅ | ✅ | ✅ | ✅ | | [N-HiTS paper](https://arxiv.org/abs/2201.12886)
`TCNModel` | ✅ | ✅ | ✅ | ✅ | ✅ | | [TCN paper](https://arxiv.org/abs/1803.01271), [DeepTCN paper](https://arxiv.org/abs/1906.04397), [blog post](https://medium.com/unit8-machine-learning-publication/temporal-convolutional-networks-and-forecasting-5ce1b6e97ce4)
`TransformerModel` | ✅ | ✅ | ✅ | ✅ | ✅ | |
`TFTModel` (Temporal Fusion Transformer) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | [TFT paper](https://arxiv.org/pdf/1912.09363.pdf), [PyTorch Forecasting](https://pytorch-forecasting.readthedocs.io/en/latest/models.html)
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2 changes: 1 addition & 1 deletion darts/models/__init__.py
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Expand Up @@ -43,7 +43,7 @@
try:
from darts.models.forecasting.block_rnn_model import BlockRNNModel
from darts.models.forecasting.nbeats import NBEATSModel
from darts.models.forecasting.nhits import NHiTS
from darts.models.forecasting.nhits import NHiTSModel
from darts.models.forecasting.rnn_model import RNNModel
from darts.models.forecasting.tcn_model import TCNModel
from darts.models.forecasting.tft_model import TFTModel
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2 changes: 1 addition & 1 deletion darts/models/forecasting/nhits.py
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Expand Up @@ -456,7 +456,7 @@ def forward(self, x_in: Tuple):
return y


class NHiTS(PastCovariatesTorchModel):
class NHiTSModel(PastCovariatesTorchModel):
def __init__(
self,
input_chunk_length: int,
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18 changes: 9 additions & 9 deletions darts/tests/models/forecasting/test_nbeats_nhits.py
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Expand Up @@ -11,7 +11,7 @@

try:
from darts.models.forecasting.nbeats import NBEATSModel
from darts.models.forecasting.nhits import NHiTS
from darts.models.forecasting.nhits import NHiTSModel

TORCH_AVAILABLE = True
except ImportError:
Expand Down Expand Up @@ -39,7 +39,7 @@ def test_creation(self):
)

with self.assertRaises(ValueError):
NHiTS(
NHiTSModel(
input_chunk_length=1,
output_chunk_length=1,
num_stacks=3,
Expand All @@ -50,7 +50,7 @@ def test_fit(self):
large_ts = tg.constant_timeseries(length=100, value=1000)
small_ts = tg.constant_timeseries(length=100, value=10)

for model_cls in [NBEATSModel, NHiTS]:
for model_cls in [NBEATSModel, NHiTSModel]:
# Test basic fit and predict
model = model_cls(
input_chunk_length=1,
Expand Down Expand Up @@ -88,7 +88,7 @@ def test_multivariate(self):
tg.linear_timeseries(length=100, start_value=0, end_value=0.5)
)

for model_cls in [NBEATSModel, NHiTS]:
for model_cls in [NBEATSModel, NHiTSModel]:
model = model_cls(
input_chunk_length=3,
output_chunk_length=1,
Expand Down Expand Up @@ -131,7 +131,7 @@ def test_nhits_sampling_sizes(self):
with self.assertRaises(ValueError):

# wrong number of coeffs for stacks and blocks
NHiTS(
NHiTSModel(
input_chunk_length=1,
output_chunk_length=1,
num_stacks=1,
Expand All @@ -140,7 +140,7 @@ def test_nhits_sampling_sizes(self):
n_freq_downsample=((1,), (1,)),
)
with self.assertRaises(ValueError):
NHiTS(
NHiTSModel(
input_chunk_length=1,
output_chunk_length=1,
num_stacks=2,
Expand All @@ -150,7 +150,7 @@ def test_nhits_sampling_sizes(self):
)

# it shouldn't fail with the right number of coeffs
_ = NHiTS(
_ = NHiTSModel(
input_chunk_length=1,
output_chunk_length=1,
num_stacks=2,
Expand All @@ -160,7 +160,7 @@ def test_nhits_sampling_sizes(self):
)

# default freqs should be such that last one is 1
model = NHiTS(
model = NHiTSModel(
input_chunk_length=1,
output_chunk_length=1,
num_stacks=2,
Expand Down Expand Up @@ -189,7 +189,7 @@ def test_logtensorboard(self):
def test_activation_fns(self):
ts = tg.constant_timeseries(length=50, value=10)

for model_cls in [NBEATSModel, NHiTS]:
for model_cls in [NBEATSModel, NHiTSModel]:
model = model_cls(
input_chunk_length=1,
output_chunk_length=1,
Expand Down