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
’sprocess_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
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 whencompleted()
is called. - Return type
Any
- Raises
NotComputableError – raised when the metric cannot be computed.