feat: Add classification functions #11792
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Summary: Add the classification functions from presto into velox: https://prestodb.io/docs/current/functions/aggregate.html#classification-metrics-aggregate-functions Classification functions all use `FixedDoubleHistogram`, which is a data structure to represent the bucket of weights. The index of the bucket for the histogram is evenly distributed between the min and value values. For all of the classification functions, the only difference is the extraction phase. All other steps will be the same. At a high level: - addRawInput will add a value into either the true or false weight bucket. The bucket to add the value to will depend on the prediction value. The prediction value is linearly mapped into a bucket based on (min, max and bucketCount) by normalizing the prediction between min and max. - The schema of the intermediate states is [version header][bucket count][min][max][weights] Differential Revision: D66684198
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| in.copyTo(&min, 1); | ||
| in.copyTo(&max, 1); | ||
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| auto ret = FixedDoubleHistogram(bucketCount, min, max, allocator); |
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Can we avoid memory allocation for the buckets? Just merge with a view on the deserialized bytes (be careful about the alignment though).
| /// std::vector<double>::max_size(), which may be less than 2^63 depending. To | ||
| /// account for this, we have two buckets which may be used to store the | ||
| /// weights with each bucket being at most kMaxBucketCount in size. | ||
| static constexpr int64_t kMaxBucketCount = |
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In practice max_size is at least 2^60 on 64 bits system, I don't think any system can give that large contiguous memory in one go. So you don't need to split the weights into 2 arrays for the discrepancy between 2^60 vs 2^64 (you can put a VELOX_CHECK_LT(bucketCount, weights_.max_size()) in validateParameters if you are really concerned about this).
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Agreed, I added this because it was something the fuzzer caught
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Summary: Add the classification functions from presto into velox: https://prestodb.io/docs/current/functions/aggregate.html#classification-metrics-aggregate-functions Classification functions all use `FixedDoubleHistogram`, which is a data structure to represent the bucket of weights. The index of the bucket for the histogram is evenly distributed between the min and value values. For all of the classification functions, the only difference is the extraction phase. All other steps will be the same. At a high level: - addRawInput will add a value into either the true or false weight bucket. The bucket to add the value to will depend on the prediction value. The prediction value is linearly mapped into a bucket based on (min, max and bucketCount) by normalizing the prediction between min and max. - The schema of the intermediate states is [version header][bucket count][min][max][weights] Differential Revision: D66684198
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Summary: Add the classification functions from presto into velox: https://prestodb.io/docs/current/functions/aggregate.html#classification-metrics-aggregate-functions Classification functions all use `FixedDoubleHistogram`, which is a data structure to represent the bucket of weights. The index of the bucket for the histogram is evenly distributed between the min and value values. For all of the classification functions, the only difference is the extraction phase. All other steps will be the same. At a high level: - addRawInput will add a value into either the true or false weight bucket. The bucket to add the value to will depend on the prediction value. The prediction value is linearly mapped into a bucket based on (min, max and bucketCount) by normalizing the prediction between min and max. - The schema of the intermediate states is [version header][bucket count][min][max][weights] Differential Revision: D66684198
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Summary: Add the classification functions from presto into velox: https://prestodb.io/docs/current/functions/aggregate.html#classification-metrics-aggregate-functions Classification functions all use `FixedDoubleHistogram`, which is a data structure to represent the bucket of weights. The index of the bucket for the histogram is evenly distributed between the min and value values. For all of the classification functions, the only difference is the extraction phase. All other steps will be the same. At a high level: - addRawInput will add a value into either the true or false weight bucket. The bucket to add the value to will depend on the prediction value. The prediction value is linearly mapped into a bucket based on (min, max and bucketCount) by normalizing the prediction between min and max. - The schema of the intermediate states is [version header][bucket count][min][max][weights] Differential Revision: D66684198
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Summary: Add the classification functions from presto into velox: https://prestodb.io/docs/current/functions/aggregate.html#classification-metrics-aggregate-functions Classification functions all use `FixedDoubleHistogram`, which is a data structure to represent the bucket of weights. The index of the bucket for the histogram is evenly distributed between the min and value values. For all of the classification functions, the only difference is the extraction phase. All other steps will be the same. At a high level: - addRawInput will add a value into either the true or false weight bucket. The bucket to add the value to will depend on the prediction value. The prediction value is linearly mapped into a bucket based on (min, max and bucketCount) by normalizing the prediction between min and max. - The schema of the intermediate states is [version header][bucket count][min][max][weights] Reviewed By: Yuhta Differential Revision: D66684198
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Summary: Add the classification functions from presto into velox: https://prestodb.io/docs/current/functions/aggregate.html#classification-metrics-aggregate-functions Classification functions all use `FixedDoubleHistogram`, which is a data structure to represent the bucket of weights. The index of the bucket for the histogram is evenly distributed between the min and value values. For all of the classification functions, the only difference is the extraction phase. All other steps will be the same. At a high level: - addRawInput will add a value into either the true or false weight bucket. The bucket to add the value to will depend on the prediction value. The prediction value is linearly mapped into a bucket based on (min, max and bucketCount) by normalizing the prediction between min and max. - The schema of the intermediate states is [version header][bucket count][min][max][weights] Reviewed By: Yuhta Differential Revision: D66684198
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Summary: Add the classification functions from presto into velox: https://prestodb.io/docs/current/functions/aggregate.html#classification-metrics-aggregate-functions Classification functions all use `FixedDoubleHistogram`, which is a data structure to represent the bucket of weights. The index of the bucket for the histogram is evenly distributed between the min and value values. For all of the classification functions, the only difference is the extraction phase. All other steps will be the same. At a high level: - addRawInput will add a value into either the true or false weight bucket. The bucket to add the value to will depend on the prediction value. The prediction value is linearly mapped into a bucket based on (min, max and bucketCount) by normalizing the prediction between min and max. - The schema of the intermediate states is [version header][bucket count][min][max][weights] Reviewed By: Yuhta Differential Revision: D66684198
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Summary:
Add the classification functions from presto into velox: https://prestodb.io/docs/current/functions/aggregate.html#classification-metrics-aggregate-functions
Classification functions all use
FixedDoubleHistogram, which is a data structure to represent the bucket of weights. The index of the bucket for the histogram is evenly distributed between the min and value values.For all of the classification functions, the only difference is the extraction phase. All other steps will be the same.
At a high level:
addRawInput will add a value into either the true or false weight bucket. The bucket to add the value to will depend on the prediction value. The prediction value is linearly mapped into a bucket based on (min, max and bucketCount) by normalizing the prediction between min and max.
The schema of the intermediate states is [version header][bucket count][min][max][weights]
Differential Revision: D66684198