Spark: Normalising/Stantardizing test-set using training set statistics - apache-spark

Question

This is a very common process in Machine Learning.
I have a dataset and I split it into training set and test set.
Since I apply some normalizing and standardization to the training set,
I would like to use the same info of the training set (mean/std/min/max
values of each feature), to apply the normalizing and standardization
to the test set too. Do you know any optimal way to do that?
I am aware of the functions of MinMaxScaler, StandardScaler etc..

Answer 1

You can achieve this via a few lines of code on both the training and test set.
On the training side there are two approaches:
MultivariateStatisticalSummary
http://spark.apache.org/docs/latest/mllib-statistics.html
val summary: MultivariateStatisticalSummary = Statistics.colStats(observations)
println(summary.mean) // a dense vector containing the mean value for each column
println(summary.variance) // column-wise variance
println(summary.numNonzeros) // number of nonzeros in each
Using SQL
from pyspark.sql.functions import mean, min, max
In [6]: df.select([mean('uniform'), min('uniform'), max('uniform')]).show()
+------------------+-------------------+------------------+
| AVG(uniform)| MIN(uniform)| MAX(uniform)|
+------------------+-------------------+------------------+
|0.5215336029384192|0.19657711634539565|0.9970412477032209|
+------------------+-------------------+------------------+
On the testing data - you can then manually "normalize the data using the statistics obtained above from the training data. You can decide in which sense you wish to normalize: e.g.
Student's T
val normalized = testData.map{ m =>
(m - trainMean) / trainingSampleStddev
}
Feature Scaling
val normalized = testData.map{ m =>
(m - trainMean) / (trainMax - trainMin)
}
There are others: take a look at https://en.wikipedia.org/wiki/Normalization_(statistics)