fix comments in metric

This commit is contained in:
liutongtong 2021-11-24 17:00:43 +08:00
parent 43fabaeef8
commit d3d5439c70
5 changed files with 9 additions and 9 deletions

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@ -162,15 +162,15 @@ class ConfusionMatrixMetric(Metric):
skip_channel (bool): Whether to skip the measurement calculation on the first channel of the predicted output.
Default: True.
metric_name (str): Names of supported metrics , users can also set the industry common aliases for them. Choose
from: ["sensitivity", "specificity", "precision", "negative predictive value", "miss rate",
from: ["sensitivity", "specificity", "precision", "negative predictive value", "miss rate",
"fall out", "false discovery rate", "false omission rate", "prevalence threshold",
"threat score", "accuracy", "balanced accuracy", "f1 score",
"matthews correlation coefficient", "fowlkes mallows index", "informedness", "markedness"].
calculation_method (bool): If true, the measurement for each sample will be calculated first.
If not, the confusion matrix of all samples will be accumulated first.
As for classification task, 'calculation_method' should be False. Default: False.
If not, the confusion matrix of all samples will be accumulated first.
As for classification task, 'calculation_method' should be False. Default: False.
decrease (str): The reduction method on data batch. `decrease` takes effect only when calculation_method
is True. Default: "mean". Choose from:
is True. Default: "mean". Choose from:
["none", "mean", "sum", "mean_batch", "sum_batch", "mean_channel", "sum_channel"].
Supported Platforms:

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@ -25,7 +25,7 @@ class MeanSurfaceDistance(Metric):
on average, the surface varies between the segmentation and the GT (ground truth).
Given two sets A and B, S(A) denotes the set of surface voxels of A. The shortest distance of an arbitrary voxel v
to S(A) is defined as:
to S(A) is defined as:
.. math::
{\text{dis}}\left (v, S(A)\right ) = \underset{s_{A} \in S(A)}{\text{min }}\rVert v - s_{A} \rVert \
@ -36,7 +36,7 @@ class MeanSurfaceDistance(Metric):
AvgSurDis(B\rightarrow A) = \frac{\sum_{s_{B} \in S(B)}^{} {\text{dis} \
left ( s_{B}, S(A) \right )} } {\left | S(B) \right |}
Where the ||*|| denotes a distance measure. |*| denotes the number of elements.
Where the \|\|\*\|\| denotes a distance measure. \|\*\| denotes the number of elements.
The mean of surface distance form set(B) to set(A) and from set(A) to set(B) is:

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@ -38,7 +38,7 @@ class Recall(EvaluationBase):
Args:
eval_type (str): 'classification' or 'multilabel' are supported. Default: 'classification'.
Default: 'classification'.
Default: 'classification'.
Examples:
>>> import numpy as np

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@ -35,7 +35,7 @@ class RootMeanSquareDistance(Metric):
RmsSurDis(B \rightarrow A) = \sqrt{\frac{\sum_{s_{B} \in S(B)}^{} {\text{dis}^2 \left ( s_{B}, S(A)
\right )} }{\left | S(B) \right |}}
Where the ||\*|| denotes a distance measure. |\*| denotes the number of elements.
Where the \|\|\*\|\| denotes a distance measure.\ |\*\| denotes the number of elements.
The Root Mean Square Surface Distance form set(B) to set(A) and from set(A) to set(B) is:

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@ -66,7 +66,7 @@ class TopKCategoricalAccuracy(Metric):
Updates the internal evaluation result `y_pred` and `y`.
Args:
inputs: Input `y_pred` and `y`. ` y_pred` and `y` are Tensor, list or numpy.ndarray.
inputs: Input `y_pred` and `y`. `y_pred` and `y` are Tensor, list or numpy.ndarray.
`y_pred` is in most cases (not strictly) a list of floating numbers in range :math:`[0, 1]`
and the shape is :math:`(N, C)`, where :math:`N` is the number of cases and :math:`C`
is the number of categories. `y` contains values of integers. The shape is :math:`(N, C)`