I am confused, that I am getting a message -
Automatically created module for IPython interactive environment
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-7-5ffbb87ce56b> in <module>()
17 roc_auc = dict()
18 for i in range(nb_classes):
---> 19 fpr[i], tpr[i], _ = roc_curve(labels[i], prediction[i]) # here change y_test to labels
20 roc_auc[i] = auc(fpr[i], tpr[i])
21 # Compute micro-average ROC curve and ROC area
/usr/local/lib/python3.6/dist-packages/sklearn/metrics/ranking.py in roc_curve(y_true, y_score, pos_label, sample_weight, drop_intermediate)
616 """
617 fps, tps, thresholds = _binary_clf_curve(
--> 618 y_true, y_score, pos_label=pos_label, sample_weight=sample_weight)
619
620 # Attempt to drop thresholds corresponding to points in between and
/usr/local/lib/python3.6/dist-packages/sklearn/metrics/ranking.py in _binary_clf_curve(y_true, y_score, pos_label, sample_weight)
392 """
393 # Check to make sure y_true is valid
--> 394 y_type = type_of_target(y_true)
395 if not (y_type == "binary" or
396 (y_type == "multiclass" and pos_label is not None)):
/usr/local/lib/python3.6/dist-packages/sklearn/utils/multiclass.py in type_of_target(y)
247 raise ValueError("y cannot be class 'SparseSeries'.")
248
--> 249 if is_multilabel(y):
250 return 'multilabel-indicator'
251
/usr/local/lib/python3.6/dist-packages/sklearn/utils/multiclass.py in is_multilabel(y)
138 """
139 if hasattr(y, '__array__'):
--> 140 y = np.asarray(y)
141 if not (hasattr(y, "shape") and y.ndim == 2 and y.shape[1] > 1):
142 return False
/usr/local/lib/python3.6/dist-packages/numpy/core/numeric.py in asarray(a, dtype, order)
490
491 """
--> 492 return array(a, dtype, copy=False, order=order)
493
494
/usr/local/lib/python3.6/dist-packages/torch/tensor.py in __array__(self, dtype)
448 def __array__(self, dtype=None):
449 if dtype is None:
--> 450 return self.numpy()
451 else:
452 return self.numpy().astype(dtype, copy=False)
TypeError: can't convert CUDA tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first.
I am trying to make a multi-class ROC curve from my dataset. Some friend told me that, in the documentation for sklearn.metrics.roc_curve says:
“Note: this implementation is restricted to the binary classification task.”
that is -
# Binarize the output
y = label_binarize(y, classes=[0, 1, 2])
n_classes = y.shape[1]
In my code I haven’t binarized it, how can I do it? I have no clue about it, I have followed some post about it and found out that, it can cause due to argmax function, So here is the use of it -
for epoch in range(epochs):
running_loss = 0
model.train()
for images, labels in dataloader_train:
#steps += 1
images, labels = images.to(device), labels.to(device)
optimizer.zero_grad()
output = model.forward(images)
p = torch.nn.functional.softmax(output, dim=1)
prediction = torch.argmax(p, dim=1)
#loss = torch.nn.functional.nll_loss(torch.log(p), y)
loss = criterion(output, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
#if steps % print_every == 0:
valid_loss = 0
accuracy = 0
model.eval()
for images, labels in dataloader_test:
optimizer.zero_grad()
with torch.no_grad():
images, labels = images.to(device), labels.to(device)
output = model.forward(images)
p = torch.nn.functional.softmax(output, dim=1)
prediction = torch.argmax(p, dim=1)
loss = criterion(output, labels)
valid_loss += loss.item()
ps = torch.exp(output)
top_p, top_class = ps.topk(1, dim = 1)
equals = top_class == labels.view(*top_class.shape)
accuracy += torch.mean(equals.type(torch.FloatTensor))
print("Epoch: {}/{} " .format(epoch+1, epochs))
print("Train loss: {:.4f}.. " .format(running_loss/len(dataloader_train)))
print("Valid loss: {:.4f}.. " .format(valid_loss/len(dataloader_test)))
print("Accuracy: {:.4f}.. " .format(accuracy/len(dataloader_test)))
model.train()
Can any one of you guys please tell me how to print ROC curve from it? Thanks a lot.