# RocCurve#

class ignite.contrib.metrics.RocCurve(output_transform=<function RocCurve.<lambda>>, check_compute_fn=False)[source]#

Compute Receiver operating characteristic (ROC) for binary classification task by accumulating predictions and the ground-truth during an epoch and applying sklearn.metrics.roc_curve .

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, sklearn.metrics.roc_curve 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.

Return type

None

Note

RocCurve 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 sigmoid_output_transform(output):
y_pred, y = output
y_pred = torch.sigmoid(y_pred)
return y_pred, y
avg_precision = RocCurve(sigmoid_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.utils import *
from ignite.contrib.metrics.regression import *
from ignite.contrib.metrics import *

# create default evaluator for doctests

def eval_step(engine, batch):
return batch

default_evaluator = Engine(eval_step)

# create default optimizer for doctests

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)

roc_auc = RocCurve()
#The output_transform arg of the metric can be used to perform a sigmoid on the y_pred.
roc_auc.attach(default_evaluator, 'roc_auc')
y_pred = torch.tensor([0.0474, 0.5987, 0.7109, 0.9997])
y_true = torch.tensor([0, 0, 1, 0])
state = default_evaluator.run([[y_pred, y_true]])
print("FPR", [round(i, 3) for i in state.metrics['roc_auc'][0].tolist()])
print("TPR", [round(i, 3) for i in state.metrics['roc_auc'][1].tolist()])
print("Thresholds", [round(i, 3) for i in state.metrics['roc_auc'][2].tolist()])

FPR [0.0, 0.333, 0.333, 1.0]
TPR [0.0, 0.0, 1.0, 1.0]
Thresholds [2.0, 1.0, 0.711, 0.047]


Methods