Encoder improvement #1332
Encoder improvement #1332
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| val modelMetricsFullHistory: ModelMetricsFullHistory = ModelMetricsFullHistory() | ||
| val modelMetrics: ModelMetrics = ModelMetricsWithTimeWindow(20) | ||
| val modelMetricsWithTimeWindow: ModelMetrics = ModelMetricsWithTimeWindow(1000) |
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is this wise? as this is per endpoint, and there might be 100s in an API, this might endup being the whole history. maybe something like 100 could be more reasonable, unless there was some specific reason for such a value. note that, in typical experiments with 1 hour budget, total number of calls is typically in the range 100k to 300k
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| companion object { | ||
| private const val NOT_400 = 200 | ||
| private const val MAX_SAMPLES = 10_000 // Fixed-size buffer for samples to avoid memory overload |
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why in KNN this is a constant, but a variable (having same 10_000 value) in KDE?
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Basically, it must be a constant. I'll fix it.
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| /** Cap on stored samples based on the reservoir-style uniform downsampling to avoid memory overload.*/ | ||
| companion object { | ||
| private const val MAX_SAMPLES = 20_000 |
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why some have 10_000 and others 20_000? is bit confusing. also it might open to critiscims from reviewers, unless we can document good reasons for this (and, in such case, those good reeasons should be briefly mentioned here)
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Agree, I'll add it to the Config with a clear explanation
| //TODO any better way to use this accuracy? | ||
| if(!randomness.nextBoolean(accuracy)){ | ||
| /** | ||
| * Skips repair if the classifier is still weak, as indicated by low accuracy and F1-score |
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I think AND, because the classifier needs to perform well in both accuracy and precision–recall, otherwise, its knowledge of true positives is no better than a random guess.
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