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--- |
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title: ECE |
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datasets: |
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- "null" |
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tags: |
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- evaluate |
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- metric |
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description: "Expected calibration error (ECE)" |
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sdk: gradio |
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sdk_version: 3.19.1 |
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app_file: app.py |
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pinned: false |
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--- |
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# Metric Card for ECE |
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## Metric Description |
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This metrics computes the expected calibration error (ECE). |
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It directly calls the torchmetrics package: |
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https://torchmetrics.readthedocs.io/en/stable/classification/calibration_error.html |
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## How to Use |
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### Inputs |
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*List all input arguments in the format below* |
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- **predictions** *(float32): predictions (after softmax). They must have a shape (N,C,...) if multiclass, or (N,...) if binary.* |
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- **references** *(int64): reference for each prediction, with a shape (N,...).* |
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### Output Values |
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ECE as float. |
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### Examples |
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```Python |
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ce = evaluate.load("Natooz/ece") |
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results = ece.compute( |
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references=np.array([[0.25, 0.20, 0.55], |
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[0.55, 0.05, 0.40], |
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[0.10, 0.30, 0.60], |
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[0.90, 0.05, 0.05]]), |
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predictions=np.array(), |
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num_classes=3, |
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n_bins=3, |
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norm="l1", |
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) |
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print(results) |
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``` |
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## Citation |
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```bibtex |
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@inproceedings{NEURIPS2019_f8c0c968, |
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author = {Kumar, Ananya and Liang, Percy S and Ma, Tengyu}, |
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booktitle = {Advances in Neural Information Processing Systems}, |
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editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett}, |
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publisher = {Curran Associates, Inc.}, |
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title = {Verified Uncertainty Calibration}, |
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url = {https://papers.nips.cc/paper_files/paper/2019/hash/f8c0c968632845cd133308b1a494967f-Abstract.html}, |
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volume = {32}, |
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year = {2019} |
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} |
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``` |
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