Accuracy

class ignite.metrics.Accuracy(output_transform=<function Accuracy.<lambda>>, is_multilabel=False, device=device(type='cpu'), skip_unrolling=False)

Calculates the accuracy for binary, multiclass and multilabel data.

$$\text{Accuracy} = \frac{ TP + TN }{ TP + TN + FP + FN }$$

where $\text{TP}$ is true positives, $\text{TN}$ is true negatives, $\text{FP}$ is false positives and $\text{FN}$ is false negatives.

• update must receive output of the form (y_pred, y).

• y_pred must be in the following shape (batch_size, num_categories, …) or (batch_size, …).

• y must be in the following shape (batch_size, …).

• y and y_pred must be in the following shape of (batch_size, num_categories, …) and num_categories must be greater than 1 for multilabel cases.

Parameters

• output_transform (Callable) – a callable that is used to transform the Engine’s process_function’s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs.

• is_multilabel (bool) – flag to use in multilabel case. By default, False.

• device (Union[str, device]) – specifies which device updates are accumulated on. Setting the metric’s device to be the same as your update arguments ensures the update method is non-blocking. By default, CPU.

• skip_unrolling (bool) – specifies whether output should be unrolled before being fed to update method. Should be true for multi-output model, for example, if y_pred contains multi-ouput as (y_pred_a, y_pred_b) Alternatively, output_transform can be used to handle this.

Examples

For more information on how metric works with Engine, visit Attach Engine API.

from collections import OrderedDict

import torch
from torch import nn, optim

from ignite.engine import *
from ignite.handlers import *
from ignite.metrics import *
from ignite.metrics.clustering import *
from ignite.metrics.regression import *
from ignite.utils import *

# create default evaluator for doctests

def eval_step(engine, batch):
    return batch

default_evaluator = Engine(eval_step)

# create default optimizer for doctests

param_tensor = torch.zeros([1], requires_grad=True)
default_optimizer = torch.optim.SGD([param_tensor], lr=0.1)

# create default trainer for doctests
# as handlers could be attached to the trainer,
# each test must define his own trainer using `.. testsetup:`

def get_default_trainer():

    def train_step(engine, batch):
        return batch

    return Engine(train_step)

# create default model for doctests

default_model = nn.Sequential(OrderedDict([
    ('base', nn.Linear(4, 2)),
    ('fc', nn.Linear(2, 1))
]))

manual_seed(666)

Binary case

metric = Accuracy()
metric.attach(default_evaluator, "accuracy")
y_true = torch.tensor([1, 0, 1, 1, 0, 1])
y_pred = torch.tensor([1, 0, 1, 0, 1, 1])
state = default_evaluator.run([[y_pred, y_true]])
print(state.metrics["accuracy"])

0.6666...

Multiclass case

metric = Accuracy()
metric.attach(default_evaluator, "accuracy")
y_true = torch.tensor([2, 0, 2, 1, 0, 1])
y_pred = torch.tensor([
    [0.0266, 0.1719, 0.3055],
    [0.6886, 0.3978, 0.8176],
    [0.9230, 0.0197, 0.8395],
    [0.1785, 0.2670, 0.6084],
    [0.8448, 0.7177, 0.7288],
    [0.7748, 0.9542, 0.8573],
])
state = default_evaluator.run([[y_pred, y_true]])
print(state.metrics["accuracy"])

0.5

Multilabel case

metric = Accuracy(is_multilabel=True)
metric.attach(default_evaluator, "accuracy")
y_true = torch.tensor([
    [0, 0, 1, 0, 1],
    [1, 0, 1, 0, 0],
    [0, 0, 0, 0, 1],
    [1, 0, 0, 0, 1],
    [0, 1, 1, 0, 1],
])
y_pred = torch.tensor([
    [1, 1, 0, 0, 0],
    [1, 0, 1, 0, 0],
    [1, 0, 0, 0, 0],
    [1, 0, 1, 1, 1],
    [1, 1, 0, 0, 1],
])
state = default_evaluator.run([[y_pred, y_true]])
print(state.metrics["accuracy"])

0.2

In binary and multilabel cases, the elements of y and y_pred should have 0 or 1 values. Thresholding of predictions can be done as below:

def thresholded_output_transform(output):
    y_pred, y = output
    y_pred = torch.round(y_pred)
    return y_pred, y

metric = Accuracy(output_transform=thresholded_output_transform)
metric.attach(default_evaluator, "accuracy")
y_true = torch.tensor([1, 0, 1, 1, 0, 1])
y_pred = torch.tensor([0.6, 0.2, 0.9, 0.4, 0.7, 0.65])
state = default_evaluator.run([[y_pred, y_true]])
print(state.metrics["accuracy"])

0.6666...

Changed in version 0.5.1: skip_unrolling argument is added.

Methods

compute	Computes the metric based on its accumulated state.
reset	Resets the metric to its initial state.
update	Updates the metric's state using the passed batch output.

compute()

Computes the metric based on its accumulated state.

By default, this is called at the end of each epoch.

Returns

the actual quantity of interest. However, if a Mapping is returned, it will be (shallow) flattened into engine.state.metrics when completed() is called.

Return type

Any

Raises

NotComputableError – raised when the metric cannot be computed.

reset()

Resets the metric to its initial state.

By default, this is called at the start of each epoch.

Return type

None

update(output)

Updates the metric’s state using the passed batch output.

By default, this is called once for each batch.

Parameters

output (Sequence[Tensor]) – the is the output from the engine’s process function.

Return type

None