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[TO_REVIEW] fix param for Deepjdot #234

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merged 2 commits into from
Sep 12, 2024
Merged

[TO_REVIEW] fix param for Deepjdot #234

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4ff7d62
fix param for jdot
tgnassou Sep 12, 2024
b913839
fix example
tgnassou Sep 12, 2024
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2 changes: 1 addition & 1 deletion examples/deep/plot_optimal_transport.py
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Original file line number Diff line number Diff line change
Expand Up @@ -49,7 +49,7 @@
batch_size=128,
max_epochs=5,
train_split=False,
reg=0.1,
reg_dist=0.1,
reg_cl=0.01,
lr=1e-2,
)
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12 changes: 8 additions & 4 deletions skada/deep/_optimal_transport.py
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Expand Up @@ -24,6 +24,8 @@ class DeepJDOTLoss(BaseDALoss):

Parameters
----------
reg_dist : float, default=1
Divergence regularization parameter.
reg_cl : float, default=1
Class distance term regularization parameter.
target_criterion : torch criterion (class)
Expand All @@ -40,8 +42,9 @@ class DeepJDOTLoss(BaseDALoss):
September 2018. Springer.
"""

def __init__(self, reg_cl=1, target_criterion=None):
def __init__(self, reg_dist=1, reg_cl=1, target_criterion=None):
super().__init__()
self.reg_dist = reg_dist
self.reg_cl = reg_cl
self.criterion_ = target_criterion

Expand All @@ -61,6 +64,7 @@ def forward(
y_pred_t,
features_s,
features_t,
self.reg_dist,
self.reg_cl,
criterion=self.criterion_,
)
Expand All @@ -70,7 +74,7 @@ def forward(
def DeepJDOT(
module,
layer_name,
reg=1,
reg_dist=1,
reg_cl=1,
base_criterion=None,
target_criterion=None,
Expand Down Expand Up @@ -117,8 +121,8 @@ def DeepJDOT(
iterator_train=DomainBalancedDataLoader,
criterion=DomainAwareCriterion,
criterion__base_criterion=base_criterion,
criterion__adapt_criterion=DeepJDOTLoss(reg_cl, target_criterion),
criterion__reg=reg,
criterion__adapt_criterion=DeepJDOTLoss(reg_dist, reg_cl, target_criterion),
criterion__reg=1,
**kwargs,
)
return net
5 changes: 4 additions & 1 deletion skada/deep/losses.py
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Expand Up @@ -47,6 +47,7 @@ def deepjdot_loss(
y_pred_t,
features_s,
features_t,
reg_dist,
reg_cl,
sample_weights=None,
target_sample_weights=None,
Expand All @@ -64,6 +65,8 @@ def deepjdot_loss(
features of the source data used to perform the distance matrix.
features_t : tensor
features of the target data used to perform the distance matrix.
reg_dist : float
Divergence term regularization parameter.
reg_cl : float, default=1
Class distance term regularization parameter.
sample_weights : tensor
Expand Down Expand Up @@ -98,7 +101,7 @@ def deepjdot_loss(
criterion = torch.nn.CrossEntropyLoss(reduction="none")

loss_target = criterion(y_target_matrix, y_s.repeat(len(y_s), 1)).T
M = dist + reg_cl * loss_target
M = reg_dist * dist + reg_cl * loss_target

# Compute the loss
if sample_weights is None:
Expand Down
2 changes: 1 addition & 1 deletion skada/deep/tests/test_deep_optimal_transport.py
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Expand Up @@ -29,7 +29,7 @@ def test_deepjdot():

method = DeepJDOT(
ToyModule2D(),
reg=1,
reg_dist=1,
reg_cl=1,
layer_name="dropout",
batch_size=10,
Expand Down
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