AveragePrecision#

class ignite.metrics.AveragePrecision(output_transform=<function AveragePrecision.<lambda>>, check_compute_fn=False, device=device(type='cpu'), skip_unrolling=False)[source]#

Computes Average Precision accumulating predictions and the ground-truth during an epoch and applying sklearn.metrics.average_precision_score .

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.
check_compute_fn (bool) – Default False. If True, average_precision_score is run on the first batch of data to ensure there are no issues. User will be warned in case there are any issues computing the function.
device (Union[str, device]) – optional device specification for internal storage.
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.

Note

AveragePrecision expects y to be comprised of 0’s and 1’s. y_pred must either be probability estimates or confidence values. To apply an activation to y_pred, use output_transform as shown below:

def activated_output_transform(output):
    y_pred, y = output
    y_pred = torch.softmax(y_pred, dim=1)
    return y_pred, y
avg_precision = AveragePrecision(activated_output_transform)

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.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)

y_pred = torch.tensor([[0.79, 0.21], [0.30, 0.70], [0.46, 0.54], [0.16, 0.84]])
y_true = torch.tensor([[1, 1], [1, 1], [0, 1], [0, 1]])

avg_precision = AveragePrecision()
avg_precision.attach(default_evaluator, 'average_precision')
state = default_evaluator.run([[y_pred, y_true]])
print(state.metrics['average_precision'])

0.9166...

Changed in version 0.5.1: skip_unrolling argument is added.

Methods