torcheval.metrics.ClickThroughRate

class torcheval.metrics.ClickThroughRate(*, num_tasks: int = 1, device: device | None = None)

Compute the click through rate given click events. Its functional version is torcheval.metrics.functional.click_through_rate.

Parameters:

num_tasks (int) – Number of tasks that need weighted_calibration calculation. Default value is 1.

Examples:

>>> import torch
>>> from torcheval.metrics.ranking import ClickThroughRate
>>> metric = ClickThroughRate()
>>> input = torch.tensor([0, 1, 0, 1, 1, 0, 0, 1])
>>> metric.update(input)
>>> metric.compute()
tensor([0.5])
>>> metric = ClickThroughRate()
>>> weights = torch.tensor([1.0, 2.0, 1.0, 2.0, 1.0, 2.0, 1.0, 2.0])
>>> metric.update(input, weights)
>>> metric.compute()
tensor([0.58333])
>>> metric = ClickThroughRate(num_tasks=2)
>>> input = torch.tensor([[0, 1, 0, 1], [1, 0, 0, 1]])
>>> weights = torch.tensor([[1.0, 2.0, 1.0, 2.0],[1.0, 2.0, 1.0, 1.0]])
>>> metric.update(input, weights)
>>> metric.compute()
tensor([0.6667, 0.4])

__init__(*, num_tasks: int = 1, device: device | None = None) → None

Initialize a metric object and its internal states.

Use self._add_state() to initialize state variables of your metric class. The state variables should be either torch.Tensor, a list of torch.Tensor, a dictionary with torch.Tensor as values, or a deque of torch.Tensor.

Methods

__init__(*[, num_tasks, device])	Initialize a metric object and its internal states.
compute()	Return the stacked click through rank scores.
load_state_dict(state_dict[, strict])	Loads metric state variables from state_dict.
merge_state(metrics)	Merge the metric state with its counterparts from other metric instances.
reset()	Reset the metric state variables to their default value.
state_dict()	Save metric state variables in state_dict.
to(device, *args, **kwargs)	Move tensors in metric state variables to device.
update(input[, weights])	Update the metric state with new inputs.

Attributes

device

The last input device of Metric.to().