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afe.apis.statistic

Source: afe/apis/statistic.py

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Analysis of statistics on tensors.

Imports​

  • afe.apis.compilation_job_base.Tensor
  • afe.core.evaluate_networks.checked_zip
  • afe.driver.statistic.Statistic
  • dataclasses
  • numpy as np
  • typing.Any
  • typing.Callable
  • typing.Generic
  • typing.List
  • typing.Tuple
  • typing.Type
  • typing.TypeVar

Constants​

  • Metric (line 28) [default/value Callable[[Tensor, Tensor], float]]

Functions​

  • equality(x: Tensor, y: Tensor) -> float (line 31): Equality as a distance metric.

    Parameters:

    • x: type Tensor
    • y: type Tensor

    Returns: float

  • mean_float(x: Tensor, y: Tensor) -> float (line 40): Mean value of difference between input values and ground truth values.

    Parameters:

    • x: type Tensor
    • y: type Tensor

    Returns: float

  • threshold_test_counter(metric: Metric, threshold: float) -> Statistic[Tuple[Tensor, Tensor], str] (line 58): Create a Statistic over a stream of (x, y) pairs that counts the number of times metric(x, y) < threshold is satisfied.

    Parameters:

    • metric: Distance metric
    • threshold: Threshold to compare against

    Returns: Statistic that captures the data

  • tensor_set_statistics(statistics: List[Statistic[Tuple[Any, Any], str]]) -> Statistic[Tuple[List[Any], List[Any]], str] (line 82): Apply an independent Statistic to each tensor in a stream of pairs of fixed-length lists.

    Parameters:

    • statistics: Statistic to apply to each pair of values

    Returns: Composed statistic that applies the statistics to list items

  • mean(metric: Metric) -> Statistic[Tuple[List[Tensor], Tensor], float] (line 114): Create a statistic that takes input pairs (i, g) and computes the arithmetic mean of metric(i, g) over all given inputs.

    Parameters:

    • metric: Mean metric.

    Returns: Statistic that captures the data

  • mean_text(metric: Metric) -> Statistic[Tuple[List[Tensor], Tensor], str] (line 136): Create a statistic that takes input pairs (i, g) and computes the arithmetic mean of metric(i, g) over all given inputs and formats the results as text message.

    Parameters:

    • metric: Mean metric.

    Returns: Statistic that captures the data