[jira] [Created] (SPARK-28062) HuberAggregator copies coefficients vector every time an instance is added

["Andrew Crosby (JIRA)" <ji...@apache.org>]

Andrew Crosby created SPARK-28062:
-------------------------------------

             Summary: HuberAggregator copies coefficients vector every time an instance is added
                 Key: SPARK-28062
                 URL: https://issues.apache.org/jira/browse/SPARK-28062
             Project: Spark
          Issue Type: Bug
          Components: ML
    Affects Versions: 3.0.0
            Reporter: Andrew Crosby


Every time an instance is added to the HuberAggregator, a copy of the coefficients vector is created (see code snippet below). This causes a performance degradation, which is particularly severe when the instances have long sparse feature vectors.

{code:scala}
def add(instance: Instance): HuberAggregator = {
    instance match { case Instance(label, weight, features) =>
      require(numFeatures == features.size, s"Dimensions mismatch when adding new sample." +
        s" Expecting $numFeatures but got ${features.size}.")
      require(weight >= 0.0, s"instance weight, $weight has to be >= 0.0")

      if (weight == 0.0) return this
      val localFeaturesStd = bcFeaturesStd.value
      val localCoefficients = bcParameters.value.toArray.slice(0, numFeatures)
val localGradientSumArray = gradientSumArray

// Snip

}
{code}

The LeastSquaresAggregator class avoids this performance issue via the use of transient lazy class variables to store such reused values. Applying a similar approach to HuberAggregator gives a significant speed boost. Running the script below locally on my machine gives the following timing results:

{noformat}
Current implementation: 
    Time(s): 540.1439919471741
    Iterations: 26
    Intercept: 0.518109382890512
    Coefficients: [0.0, -0.2516936902000245, 0.0, 0.0, -0.19633887469839809, 0.0, -0.39565545053893925, 0.0, -0.18617574426698882, 0.0478922416670529]

Modified implementation to match LeastSquaresAggregator:
    Time(s): 46.82946586608887
    Iterations: 26
    Intercept: 0.5181093828893774
    Coefficients: [0.0, -0.25169369020031357, 0.0, 0.0, -0.1963388746927919, 0.0, -0.3956554505389966, 0.0, -0.18617574426702874, 0.04789224166878518]
{noformat}




{code:python}
from random import random, randint, seed
import time

from pyspark.ml.feature import OneHotEncoder
from pyspark.ml.regression import LinearRegression
from pyspark.sql import SparkSession

seed(0)

spark = SparkSession.builder.appName('huber-speed-test').getOrCreate()
df = spark.createDataFrame([[randint(0, 100000), random()] for i in range(100000)],  ["category", "target"])
ohe = OneHotEncoder(inputCols=["category"], outputCols=["encoded_category"]).fit(df)
lr = LinearRegression(featuresCol="encoded_category", labelCol="target", loss="huber", regParam=1.0)

start = time.time()
model = lr.fit(ohe.transform(df))
end = time.time()

print("Time(s): " + str(end - start))
print("Iterations: " + str(model.summary.totalIterations))
print("Intercept: " + str(model.intercept))
print("Coefficients: " + str(list(model.coefficients)[0:10]))
{code}





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