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RocCurve#

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

  • 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

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.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)
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 [inf, 1.0, 0.711, 0.047]

Changed in version 0.4.11: added device argument

Changed in version 0.5.1: skip_unrolling argument is added.

Methods

compute

Computes the metric based on its accumulated state.

compute()[source]#

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.