Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

fix: feature importance not dependent from dataloader #372

Merged
merged 1 commit into from
Mar 23, 2022
Merged

fix: feature importance not dependent from dataloader #372

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Jump to
Jump to file
Failed to load files.
Diff view
Diff view
32 changes: 15 additions & 17 deletions pytorch_tabnet/abstract_model.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -251,7 +251,7 @@ def fit(
self.network.eval()

# compute feature importance once the best model is defined
self._compute_feature_importances(train_dataloader)
self.feature_importances_ = self._compute_feature_importances(X_train)

def predict(self, X):
"""
Expand Down Expand Up @@ -283,14 +283,16 @@ def predict(self, X):
res = np.vstack(results)
return self.predict_func(res)

def explain(self, X):
def explain(self, X, normalize=False):
"""
Return local explanation

Parameters
----------
X : tensor: `torch.Tensor`
Input data
normalize : bool (default False)
Wheter to normalize so that sum of features are equal to 1

Returns
-------
Expand Down Expand Up @@ -318,9 +320,9 @@ def explain(self, X):
value.cpu().detach().numpy(), self.reducing_matrix
)

res_explain.append(
csc_matrix.dot(M_explain.cpu().detach().numpy(), self.reducing_matrix)
)
original_feat_explain = csc_matrix.dot(M_explain.cpu().detach().numpy(),
self.reducing_matrix)
res_explain.append(original_feat_explain)

if batch_nb == 0:
res_masks = masks
Expand All @@ -330,6 +332,9 @@ def explain(self, X):

res_explain = np.vstack(res_explain)

if normalize:
res_explain /= np.sum(res_explain, axis=1)[:, None]

return res_explain, res_masks

def load_weights_from_unsupervised(self, unsupervised_model):
Expand Down Expand Up @@ -697,7 +702,7 @@ def _construct_loaders(self, X_train, y_train, eval_set):
)
return train_dataloader, valid_dataloaders

def _compute_feature_importances(self, loader):
def _compute_feature_importances(self, X):
"""Compute global feature importance.

Parameters
Expand All @@ -706,17 +711,10 @@ def _compute_feature_importances(self, loader):
Pytorch dataloader.

"""
self.network.eval()
feature_importances_ = np.zeros((self.network.post_embed_dim))
for data, targets in loader:
data = data.to(self.device).float()
M_explain, masks = self.network.forward_masks(data)
feature_importances_ += M_explain.sum(dim=0).cpu().detach().numpy()

feature_importances_ = csc_matrix.dot(
feature_importances_, self.reducing_matrix
)
self.feature_importances_ = feature_importances_ / np.sum(feature_importances_)
M_explain, _ = self.explain(X, normalize=False)
sum_explain = M_explain.sum(axis=0)
feature_importances_ = sum_explain / np.sum(sum_explain)
return feature_importances_

def _update_network_params(self):
self.network.virtual_batch_size = self.virtual_batch_size
Expand Down