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Feature add activation to BlockRNN (#2492) #2504

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merged 11 commits into from
Sep 13, 2024
Merged

Feature add activation to BlockRNN (#2492) #2504

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cbdf275
Feature add activation to BlockRNN (#2492)
SzymonCogiel Aug 17, 2024
6286b0d
Update darts/models/forecasting/block_rnn_model.py
SzymonCogiel Aug 28, 2024
0973fee
Update darts/models/forecasting/block_rnn_model.py
SzymonCogiel Aug 28, 2024
c834a4a
Merge branch 'unit8co:master' into feature/add-activation-to-blockrnn
SzymonCogiel Aug 29, 2024
5625896
Feature add activation to BlockRNN (#2492)
SzymonCogiel Aug 29, 2024
e2114be
Update darts/models/forecasting/block_rnn_model.py
SzymonCogiel Aug 31, 2024
4f9ccf9
Merge branch 'unit8co:master' into feature/add-activation-to-blockrnn
SzymonCogiel Aug 31, 2024
70e1bcc
Feature add activation to BlockRNN (#2492)
SzymonCogiel Aug 31, 2024
7db6794
Merge branch 'master' into feature/add-activation-to-blockrnn
dennisbader Sep 5, 2024
92a6c48
Update darts/models/forecasting/block_rnn_model.py
SzymonCogiel Sep 5, 2024
e94b3c7
Feature add activation to BlockRNN (#2492)
SzymonCogiel Sep 5, 2024
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Feature add activation to BlockRNN (#2492) 
* Add a check that raise an error when activation is None and hidden_fc_sizes is greater than 0
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@SzymonCogiel
SzymonCogiel committed Aug 29, 2024
commit 56258964ff14c3233439a7152c70dad5dece05ed
12 changes: 11 additions & 1 deletion darts/models/forecasting/block_rnn_model.py
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Expand Up @@ -260,7 +260,8 @@ def __init__(
dropout
Fraction of neurons afected by Dropout.
activation
The name of a torch.nn activation function to be applied between the layers of the fully connected network. Default: None.
The name of a torch.nn activation function to be applied between the layers of the fully connected network.
Default: None.
**kwargs
Optional arguments to initialize the pytorch_lightning.Module, pytorch_lightning.Trainer, and
Darts' :class:`TorchForecastingModel`.
Expand Down Expand Up @@ -448,6 +449,15 @@ def encode_year(idx):
logger=logger,
)

raise_if(
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With the comment from above, we could remove this check

activation is None
and hidden_fc_sizes is not None
and len(hidden_fc_sizes) > 0,
"The model contains hidden fully connected layers, but the activation function is not set; "
"please specify a valid activation function or remove the hidden layers.",
logger=logger,
)

# check we got right activation function specified:
if activation is not None:
try:
Expand Down
13 changes: 13 additions & 0 deletions darts/tests/models/forecasting/test_block_RNN.py
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Expand Up @@ -153,6 +153,19 @@ def test_raise_if_activation_with_single_linear_layer(self):
**tfm_kwargs,
)

def test_raise_if_no_activation_with_hidden_fc_layers(self):
with pytest.raises(ValueError):
BlockRNNModel(
input_chunk_length=1,
output_chunk_length=1,
model="RNN",
activation=None,
hidden_fc_sizes=[10],
n_epochs=1,
random_state=42,
**tfm_kwargs,
)

def test_fit(self, tmpdir_module):
# Test basic fit()
model = BlockRNNModel(
Expand Down