SelectionRate

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

Calculates the selection rate (rate of positive predictions).

• 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, …).

Parameters:

• output_transform (Callable) – a callable that is used to transform the engine output.

• is_multilabel (bool) – if True, multilabel selection rate is calculated. By default, False.

• device (str | device) – specifies the computation device.

• skip_unrolling (bool) – specifies whether output should be unrolled before being fed to update method.

Examples

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.fairness import *
from ignite.metrics.rec_sys 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)

metric = SelectionRate()
metric.attach(default_evaluator, 'selection_rate')
y_pred = torch.tensor([[0.1, 0.9], [0.2, 0.8], [0.9, 0.1], [0.9, 0.1]])
y_true = torch.tensor([1, 1, 0, 0])  # ignored
state = default_evaluator.run([[y_pred, y_true]])
print(state.metrics['selection_rate'])

tensor([0.5000, 0.5000])

New in version 0.5.4.

Methods

compute	Computes the selection rate.
reset	Resets the metric to its initial state.
update	Updates the metric's state using the passed batch output.

compute()

Computes the selection rate.

Returns:

The selection rate for each category/label.

Return type:

Tensor

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