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com.spotify.featran.transformers

HashNHotWeightedEncoder

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object HashNHotWeightedEncoder extends Serializable

Transform a collection of weighted categorical features to columns of weight sums, with at most N values. Similar to NHotWeightedEncoder but uses MurmursHash3 to hash features into buckets to reduce CPU and memory overhead.

Weights of the same labels in a row are summed instead of 1.0 as is the case with the normal NHotEncoder.

If hashBucketSize is inferred with HLL, the estimate is scaled by sizeScalingFactor to reduce the number of collisions.

Rough table of relationship of scaling factor to % collisions, measured from a corpus of 466544 English words:

sizeScalingFactor     % Collisions
-----------------     ------------
                2     17.9934%
                4     10.5686%
                8      5.7236%
               16      3.0019%
               32      1.5313%
               64      0.7864%
              128      0.3920%
              256      0.1998%
              512      0.0975%
             1024      0.0478%
             2048      0.0236%
             4096      0.0071%
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  4. def apply(name: String, hashBucketSize: Int = 0, sizeScalingFactor: Double = 8.0): Transformer[Seq[WeightedLabel], HLL, Int]

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    Create a new HashNHotWeightedEncoder instance.

    Create a new HashNHotWeightedEncoder instance.

    hashBucketSize

    number of buckets, or 0 to infer from data with HyperLogLog

    sizeScalingFactor

    when hashBucketSize is 0, scale HLL estimate by this amount

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