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Kaleidophon
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96b3437
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Parent(s):
ab7e385
Fix path to comparison module in Huggingface hub
Browse files- README.md +1 -1
- almost_stochastic_order.py +1 -1
README.md
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@@ -64,7 +64,7 @@ The Almost Stochastic Order comparison output a single scalar:
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Example comparison:
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```python
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aso = evaluate.load("almost_stochastic_order")
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results = aso.compute(predictions1=[-7, 123.45, 43, 4.91, 5], predictions2=[1337.12, -9.74, 1, 2, 3.21])
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print(results)
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{'violation_ratio': 1.0}
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Example comparison:
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```python
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aso = evaluate.load("kaleidophon/almost_stochastic_order")
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results = aso.compute(predictions1=[-7, 123.45, 43, 4.91, 5], predictions2=[1337.12, -9.74, 1, 2, 3.21])
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print(results)
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{'violation_ratio': 1.0}
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almost_stochastic_order.py
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@@ -40,7 +40,7 @@ Kwargs:
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Returns:
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violation_ratio (`float`): (Frequentist upper bound to) Degree of violation of the stochastic order. When it is smaller than 0.5, the model producing predictions1 performs better than the other model at a confidence level specified by confidence_level argument (default is 0.95). Ulmer et al. (2022) recommend to reject the null hypothesis when violation_ratio is under 0.2.
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Examples:
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>>> aso = evaluate.load("almost_stochastic_order")
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>>> results = aso.compute(predictions1=[-7, 123.45, 43, 4.91, 5], predictions2=[1337.12, -9.74, 1, 2, 3.21])
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>>> print(results)
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{'violation_ratio': 1.0}
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Returns:
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violation_ratio (`float`): (Frequentist upper bound to) Degree of violation of the stochastic order. When it is smaller than 0.5, the model producing predictions1 performs better than the other model at a confidence level specified by confidence_level argument (default is 0.95). Ulmer et al. (2022) recommend to reject the null hypothesis when violation_ratio is under 0.2.
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Examples:
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>>> aso = evaluate.load("kaleidophon/almost_stochastic_order")
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>>> results = aso.compute(predictions1=[-7, 123.45, 43, 4.91, 5], predictions2=[1337.12, -9.74, 1, 2, 3.21])
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>>> print(results)
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{'violation_ratio': 1.0}
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