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. |