ignite.contrib.metrics#
Contrib module metrics#
Computes Average Precision accumulating predictions and the ground-truth during an epoch and applying sklearn.metrics.average_precision_score . |
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Compute different types of Cohen's Kappa: Non-Wieghted, Linear, Quadratic. |
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Provides GPU information: a) used memory percentage, b) gpu utilization percentage values as Metric on each iterations. |
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Compute precision-recall pairs for different probability thresholds for binary classification task by accumulating predictions and the ground-truth during an epoch and applying sklearn.metrics.precision_recall_curve . |
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Computes Area Under the Receiver Operating Characteristic Curve (ROC AUC) accumulating predictions and the ground-truth during an epoch and applying sklearn.metrics.roc_auc_score . |
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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 . |
Regression metrics#
Module ignite.contrib.metrics.regression
provides implementations of
metrics useful for regression tasks. Definitions of metrics are based on Botchkarev 2018, page 30 “Appendix 2. Metrics mathematical definitions”.
Complete list of metrics:
Calculates the Canberra Metric. |
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Calculates the Fractional Absolute Error. |
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Calculates the Fractional Bias. |
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Calculates the Geometric Mean Absolute Error. |
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Calculates the Geometric Mean Relative Absolute Error. |
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Calculates the Manhattan Distance. |
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Calculates the Maximum Absolute Error. |
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Calculate Mean Absolute Relative Error. |
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Calculates the Mean Error. |
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Calculates the Mean Normalized Bias. |
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Calculates the Median Absolute Error. |
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Calculates the Median Absolute Percentage Error. |
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Calculates the Median Relative Absolute Error. |
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Calculates the R-Squared, the coefficient of determination. |
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Calculates the Wave Hedges Distance. |