Related
I am using torchmetrics to calculate metrics such as F1 score, Recall, Precision and Accuracy in multilabel classification setting. With random initiliazed weights the softmax output (i.e. prediction) might look like this with a batch size of 8:
import torch
y_pred = torch.tensor([[0.1944, 0.1931, 0.2184, 0.1968, 0.1973],
[0.2182, 0.1932, 0.1945, 0.1973, 0.1968],
[0.2182, 0.1932, 0.1944, 0.1973, 0.1969],
[0.2182, 0.1931, 0.1945, 0.1973, 0.1968],
[0.2184, 0.1931, 0.1944, 0.1973, 0.1968],
[0.2181, 0.1932, 0.1941, 0.1970, 0.1976],
[0.2183, 0.1932, 0.1944, 0.1974, 0.1967],
[0.2182, 0.1931, 0.1945, 0.1973, 0.1968]])
With the correct labels (one-hot encoded):
y_true = torch.tensor([[0, 0, 1, 0, 1],
[0, 1, 0, 0, 1],
[0, 1, 0, 0, 1],
[0, 0, 1, 1, 0],
[0, 0, 1, 1, 0],
[0, 1, 0, 1, 0],
[0, 1, 0, 1, 0],
[0, 0, 1, 0, 1]])
And I can calculate the metrics by taking argmax:
import torchmetrics
torchmetrics.functional.f1_score(y_pred.argmax(-1), y_true.argmax(-1))
output:
tensor(0.1250)
The first prediction happens to be correct while the rest are wrong. However, none of the predictive probabilities are above 0.3, which means that the model is generally uncertain about the predictions. I would like to encode this and say that the f1 score should be 0.0 because none of the predictive probabilities are above a 0.3 threshold.
Is this possible with torchmetrics or sklearn library?
Is this common practice?
You need to threshold you predictions before passing them to your torchmetrics
t0, t1, mask_gt = batch
mask_pred = self.forward(t0, t1)
loss = self.criterion(mask_pred.squeeze().float(), mask_gt.squeeze().float())
mask_pred = torch.sigmoid(mask_pred).squeeze()
mask_pred = torch.where(mask_pred > 0.5, 1, 0)
# integers to comply with metrics input type
mask_pred = mask_pred.long()
mask_gt = mask_gt.long()
f1_score = self.f1(mask_pred, mask_gt)
precision = self.precision_(mask_pred, mask_gt)
recall = self.recall(mask_pred, mask_gt)
jaccard = self.jaccard(mask_pred, mask_gt)
The defined torchmetrics
self.f1 = F1Score(num_classes=2, average='macro', mdmc_average='samplewise')
self.recall = Recall(num_classes=2, average='macro', mdmc_average='samplewise')
self.precision_ = Precision(num_classes=2, average='macro', mdmc_average='samplewise') # self.precision exists in torch.nn.Module. Hence '_' symbol
self.jaccard = JaccardIndex(num_classes=2)
I want to make an auto calibration system using PyTorch.
I try to deal with a homogeneous transform matrix as weights of neural networks.
I write a code referring to PyTorch tutorials, but my custom parameters are not updated after backward method is called.
When I print a 'grad' attribute of each parameter, it is a None.
My code is below. Is there anything wrong?
Please give any advise to me. Thank you.
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import numpy as np
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.params = nn.Parameter(torch.rand(6))
self.rx, self.ry, self.rz = self.params[0], self.params[1], self.params[2]
self.tx, self.ty, self.tz = self.params[3], self.params[4], self.params[5]
def forward(self, x):
tr_mat = torch.tensor([[1, 0, 0, self.params[3]],
[0, 1, 0, self.params[4]],
[0, 0, 1, self.params[5]],
[0, 0, 0, 1]], requires_grad=True)
rz_mat = torch.tensor([[torch.cos(self.params[2]), -torch.sin(self.params[2]), 0, 0],
[torch.sin(self.params[2]), torch.cos(self.params[2]), 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1]], requires_grad=True)
ry_mat = torch.tensor([[torch.cos(self.params[1]), 0, torch.sin(self.params[1]), 0],
[0, 1, 0, 0],
[-torch.sin(self.params[1]), 0, torch.cos(self.params[1]), 0],
[0, 0, 0, 1]], requires_grad=True)
rx_mat = torch.tensor([[1, 0, 0, 0],
[0, torch.cos(self.params[0]), -torch.sin(self.params[0]), 0],
[0, torch.sin(self.params[0]), torch.cos(self.params[0]), 0],
[0, 0, 0, 1]], requires_grad=True)
tf1 = torch.matmul(tr_mat, rz_mat)
tf2 = torch.matmul(tf1, ry_mat)
tf3 = torch.matmul(tf2, rx_mat)
tr_local = torch.tensor([[1, 0, 0, x[0]],
[0, 1, 0, x[1]],
[0, 0, 1, x[2]],
[0, 0, 0, 1]])
tf_output = torch.matmul(tf3, tr_local)
output = tf_output[:3, 3]
return output
def get_loss(self, output):
pass
model = Net()
input_ex = np.array([[-0.01, 0.05, 0.92],
[-0.06, 0.03, 0.94]])
output_ex = np.array([[-0.3, 0.4, 0.09],
[-0.5, 0.2, 0.07]])
print(list(model.parameters()))
optimizer = optim.Adam(model.parameters(), 0.001)
criterion = nn.MSELoss()
for input_np, label_np in zip(input_ex, output_ex):
input_tensor = torch.from_numpy(input_np).float()
label_tensor = torch.from_numpy(label_np).float()
output = model(input_tensor)
optimizer.zero_grad()
loss = criterion(output, label_tensor)
loss.backward()
optimizer.step()
print(list(model.parameters()))
What happens
Your problem is related to PyTorch's implicit conversion of torch.tensor to float. Let's say you have this:
tr_mat = torch.tensor(
[
[1, 0, 0, self.params[3]],
[0, 1, 0, self.params[4]],
[0, 0, 1, self.params[5]],
[0, 0, 0, 1],
],
requires_grad=True,
)
torch.tensor can only be constructed from list which has Python like values, it cannot have torch.tensor inside it. What happens under the hood (let's say) is each element of self.params which can be converted to float is (in this case all of them can, e.g. self.params[3], self.params[4], self.params[5]).
When tensor's value is casted to float it's value is copied into Python counterpart hence it is not part of computational graph anymore, it's a new pure Python variable (which cannot be backpropagated obviously).
Solution
What you can do is choose elements of your self.params and insert them into eye matrices so the gradient flows. You can see a rewrite of your forward method taking this into account:
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.params = nn.Parameter(torch.randn(6))
def forward(self, x):
sinus = torch.cos(self.params)
cosinus = torch.cos(self.params)
tr_mat = torch.eye(4)
tr_mat[:-1, -1] = self.params[3:]
rz_mat = torch.eye(4)
rz_mat[0, 0] = cosinus[2]
rz_mat[0, 1] = -sinus[2]
rz_mat[1, 0] = sinus[2]
rz_mat[1, 1] = cosinus[2]
ry_mat = torch.eye(4)
ry_mat[0, 0] = cosinus[1]
ry_mat[0, 2] = sinus[1]
ry_mat[2, 0] = -sinus[1]
ry_mat[2, 2] = cosinus[1]
rx_mat = torch.eye(4)
rx_mat[1, 1] = cosinus[0]
rx_mat[1, 2] = -sinus[0]
rx_mat[2, 1] = sinus[0]
rx_mat[2, 2] = cosinus[0]
tf1 = torch.matmul(tr_mat, rz_mat)
tf2 = torch.matmul(tf1, ry_mat)
tf3 = torch.matmul(tf2, rx_mat)
tr_local = torch.tensor(
[[1, 0, 0, x[0]], [0, 1, 0, x[1]], [0, 0, 1, x[2]], [0, 0, 0, 1]],
)
tf_output = torch.matmul(tf3, tr_local)
output = tf_output[:3, 3]
return output
(you may want to double check this rewrite but the idea holds).
Also notice tr_local can be done "your way" as we don't need any values to keep gradient.
requires_grad
You can see requires_grad wasn't used anywhere in the code. It's because what requires gradient is not the whole eye matrix (we will not optimize 0 and 1), but parameters which are inserted into it. Usually you don't need requires_grad at all in your neural network code because:
input tensors are not optimized (usually, those could be when you are doing adversarial attacks or such)
nn.Parameter requires gradient by default (unless frozen)
layers and other neural network specific stuff requires gradient by default (unless frozen)
values which don't need gradient (input tensors) going through layers which do require it (or parameters or w/e) can be backpropagated
I have actual values and predicted values.
Actual:
33.3663, 38.2561, 28.6362, 35.6252
Predicted:
28.9721, 35.6161, 27.9561, 22.6272
I want to apply confusion matrix to find the accuracy.
Solution
First thing, confusion matrix is not for continuous values. AND you can also use it by converting continuous values to classes. check https://datascience.stackexchange.com/questions/46019/continuous-variable-not-supported-in-confusion-matrix
from sklearn.metrics import confusion_matrix
expected = [1, 1, 0, 1, 0, 0, 1, 0, 0, 0]
predicted = [1, 0, 0, 1, 0, 0, 1, 1, 1, 0]
results = confusion_matrix(expected, predicted)
print(results)
Output
[[4 2]
[1 3]]
Reference
https://machinelearningmastery.com/confusion-matrix-machine-learning/
I am facing an issue in a multi label, multi class classification task. I have a dataset of size 33000, each samples containing 104 classes. I split the dataset in 16500 samples with labels such as [1, 0, 1, 0, 0, …], [0, 1, 1, 0, 1, …], [1, 0, 0, 0] (each label has at least one element 1 in it) and 16500 labels such as [0, 0, 0, …], [0, 0, 0, …] (all elements in all labels are 0). When calculating the pos_count for each class, the number pos_count_0 for class 0 is how many of 1 appear in the first position of each label in my dataset. For class 1, pos_count_1 the number of 1 in the second position and so on. And after that, the pos_weight of class 0 is (33000-pos_count_0)/pos_count_0, pos_weight of class 1 is (33000-pos_count_1)/pos_count_1 ? I am a little bit confused how neg_count and pos_count for a class are calculated.
The following code is used to do KFold Validation but I am to train the model as it is throwing the error
ValueError: Error when checking target: expected dense_14 to have shape (7,) but got array with shape (1,)
My target Variable has 7 classes. I am using LabelEncoder to encode the classes into numbers.
By seeing this error, If I am changing the into MultiLabelBinarizer to encode the classes. I am getting the following error
ValueError: Supported target types are: ('binary', 'multiclass'). Got 'multilabel-indicator' instead.
The following is the code for KFold validation
skf = StratifiedKFold(n_splits=10, shuffle=True)
scores = np.zeros(10)
idx = 0
for index, (train_indices, val_indices) in enumerate(skf.split(X, y)):
print("Training on fold " + str(index+1) + "/10...")
# Generate batches from indices
xtrain, xval = X[train_indices], X[val_indices]
ytrain, yval = y[train_indices], y[val_indices]
model = None
model = load_model() //defined above
scores[idx] = train_model(model, xtrain, ytrain, xval, yval)
idx+=1
print(scores)
print(scores.mean())
I don't know what to do. I want to use Stratified K Fold on my model. Please help me.
MultiLabelBinarizer returns a vector which is of the length of your number of classes.
If you look at how StratifiedKFold splits your dataset, you will see that it only accepts a one-dimensional target variable, whereas you are trying to pass a target variable with dimensions [n_samples, n_classes]
Stratefied split basically preserves your class distribution. And if you think about it, it does not make a lot of sense if you have a multi-label classification problem.
If you want to preserve the distribution in terms of the different combinations of classes in your target variable, then the answer here explains two ways in which you can define your own stratefied split function.
UPDATE:
The logic is something like this:
Assuming you have n classes and your target variable is a combination of these n classes. You will have (2^n) - 1 combinations (Not including all 0s). You can now create a new target variable considering each combination as a new label.
For example, if n=3, you will have 7 unique combinations:
1. [1, 0, 0]
2. [0, 1, 0]
3. [0, 0, 1]
4. [1, 1, 0]
5. [1, 0, 1]
6. [0, 1, 1]
7. [1, 1, 1]
Map all your labels to this new target variable. You can now look at your problem as simple multi-class classification, instead of multi-label classification.
Now you can directly use StartefiedKFold using y_new as your target. Once the splits are done, you can map your labels back.
Code sample:
import numpy as np
np.random.seed(1)
y = np.random.randint(0, 2, (10, 7))
y = y[np.where(y.sum(axis=1) != 0)[0]]
OUTPUT:
array([[1, 1, 0, 0, 1, 1, 1],
[1, 1, 0, 0, 1, 0, 1],
[1, 0, 0, 1, 0, 0, 0],
[1, 0, 0, 1, 0, 0, 0],
[1, 0, 0, 0, 1, 1, 1],
[1, 1, 0, 0, 0, 1, 1],
[1, 1, 1, 1, 0, 1, 1],
[0, 0, 1, 0, 0, 1, 1],
[1, 0, 1, 0, 0, 1, 1],
[0, 1, 1, 1, 1, 0, 0]])
Label encode your class vectors:
from sklearn.preprocessing import LabelEncoder
def get_new_labels(y):
y_new = LabelEncoder().fit_transform([''.join(str(l)) for l in y])
return y_new
y_new = get_new_labels(y)
OUTPUT:
array([7, 6, 3, 3, 2, 5, 8, 0, 4, 1])