forked from mindspore-Ecosystem/mindspore
fix comments in metric
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@ -162,15 +162,15 @@ class ConfusionMatrixMetric(Metric):
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skip_channel (bool): Whether to skip the measurement calculation on the first channel of the predicted output.
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Default: True.
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metric_name (str): Names of supported metrics , users can also set the industry common aliases for them. Choose
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from: ["sensitivity", "specificity", "precision", "negative predictive value", "miss rate",
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from: ["sensitivity", "specificity", "precision", "negative predictive value", "miss rate",
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"fall out", "false discovery rate", "false omission rate", "prevalence threshold",
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"threat score", "accuracy", "balanced accuracy", "f1 score",
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"matthews correlation coefficient", "fowlkes mallows index", "informedness", "markedness"].
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calculation_method (bool): If true, the measurement for each sample will be calculated first.
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If not, the confusion matrix of all samples will be accumulated first.
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As for classification task, 'calculation_method' should be False. Default: False.
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If not, the confusion matrix of all samples will be accumulated first.
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As for classification task, 'calculation_method' should be False. Default: False.
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decrease (str): The reduction method on data batch. `decrease` takes effect only when calculation_method
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is True. Default: "mean". Choose from:
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is True. Default: "mean". Choose from:
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["none", "mean", "sum", "mean_batch", "sum_batch", "mean_channel", "sum_channel"].
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Supported Platforms:
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@ -25,7 +25,7 @@ class MeanSurfaceDistance(Metric):
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on average, the surface varies between the segmentation and the GT (ground truth).
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Given two sets A and B, S(A) denotes the set of surface voxels of A. The shortest distance of an arbitrary voxel v
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to S(A) is defined as:
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to S(A) is defined as:
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.. math::
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{\text{dis}}\left (v, S(A)\right ) = \underset{s_{A} \in S(A)}{\text{min }}\rVert v - s_{A} \rVert \
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@ -36,7 +36,7 @@ class MeanSurfaceDistance(Metric):
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AvgSurDis(B\rightarrow A) = \frac{\sum_{s_{B} \in S(B)}^{} {\text{dis} \
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left ( s_{B}, S(A) \right )} } {\left | S(B) \right |}
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Where the ||*|| denotes a distance measure. |*| denotes the number of elements.
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Where the \|\|\*\|\| denotes a distance measure. \|\*\| denotes the number of elements.
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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):
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Args:
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eval_type (str): 'classification' or 'multilabel' are supported. Default: 'classification'.
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Default: 'classification'.
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Default: 'classification'.
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Examples:
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>>> import numpy as np
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@ -35,7 +35,7 @@ class RootMeanSquareDistance(Metric):
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RmsSurDis(B \rightarrow A) = \sqrt{\frac{\sum_{s_{B} \in S(B)}^{} {\text{dis}^2 \left ( s_{B}, S(A)
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\right )} }{\left | S(B) \right |}}
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Where the ||\*|| denotes a distance measure. |\*| denotes the number of elements.
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Where the \|\|\*\|\| denotes a distance measure.\ |\*\| denotes the number of elements.
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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):
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Updates the internal evaluation result `y_pred` and `y`.
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Args:
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inputs: Input `y_pred` and `y`. ` y_pred` and `y` are Tensor, list or numpy.ndarray.
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inputs: Input `y_pred` and `y`. `y_pred` and `y` are Tensor, list or numpy.ndarray.
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`y_pred` is in most cases (not strictly) a list of floating numbers in range :math:`[0, 1]`
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and the shape is :math:`(N, C)`, where :math:`N` is the number of cases and :math:`C`
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is the number of categories. `y` contains values of integers. The shape is :math:`(N, C)`
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