import torch

# import our library
import torchmetrics

# initialize metric
metric = torchmetrics.classification.Accuracy(task="multiclass", num_classes=5)

# move the metric to device you want computations to take place
device = "cuda" if torch.cuda.is_available() else "cpu"
metric.to(device)

n_batches = 10
for i in range(n_batches):
    # simulate a classification problem
    preds = torch.randn(10, 5).softmax(dim=-1).to(device)
    target = torch.randint(5, (10,)).to(device)

    # metric on current batch
    acc = metric(preds, target)
    print(f"Accuracy on batch {i}: {acc}")

# metric on all batches using custom accumulation
acc = metric.compute()
print(f"Accuracy on all data: {acc}")

import torch
from torchmetrics import Metric


class MyAccuracy(Metric):
    def __init__(self):
        # remember to call super
        super().__init__()
        # call `self.add_state`for every internal state that is needed for the metrics computations
        # dist_reduce_fx indicates the function that should be used to reduce
        # state from multiple processes
        self.add_state("correct", default=torch.tensor(0), dist_reduce_fx="sum")
        self.add_state("total", default=torch.tensor(0), dist_reduce_fx="sum")

    def update(self, preds: torch.Tensor, target: torch.Tensor) -> None:
        # extract predicted class index for computing accuracy
        preds = preds.argmax(dim=-1)
        assert preds.shape == target.shape
        # update metric states
        self.correct += torch.sum(preds == target)
        self.total += target.numel()

    def compute(self) -> torch.Tensor:
        # compute final result
        return self.correct.float() / self.total


my_metric = MyAccuracy()
preds = torch.randn(10, 5).softmax(dim=-1)
target = torch.randint(5, (10,))

print(my_metric(preds, target))

import torch

# import our library
import torchmetrics

# simulate a classification problem
preds = torch.randn(10, 5).softmax(dim=-1)
target = torch.randint(5, (10,))

acc = torchmetrics.functional.classification.multiclass_accuracy(
    preds, target, num_classes=5
)

import torch
from torchmetrics.classification import MulticlassAccuracy, MulticlassConfusionMatrix

num_classes = 3

# this will generate two distributions that comes more similar as iterations increase
w = torch.randn(num_classes)
target = lambda it: torch.multinomial((it * w).softmax(dim=-1), 100, replacement=True)
preds = lambda it: torch.multinomial((it * w).softmax(dim=-1), 100, replacement=True)

acc = MulticlassAccuracy(num_classes=num_classes, average="micro")
acc_per_class = MulticlassAccuracy(num_classes=num_classes, average=None)
confmat = MulticlassConfusionMatrix(num_classes=num_classes)

# plot single value
for i in range(5):
    acc_per_class.update(preds(i), target(i))
    confmat.update(preds(i), target(i))
fig1, ax1 = acc_per_class.plot()
fig2, ax2 = confmat.plot()

# plot multiple values
values = []
for i in range(10):
    values.append(acc(preds(i), target(i)))
fig3, ax3 = acc.plot(values)