Train GAM Model in Sparkling Water¶
Note: GAM models are currently experimental.
Introduction¶
A generalized additive model (GAM) is a Generalized Linear Model (GLM) in which the linear predictor depends linearly on predictor variables and smooth functions of predictor variables. For more more comprehensive description see H2O-3 GAM documentation.
Example¶
The following section describes how to train the GAM model in Sparkling Water in Scala & Python following the same example as H2O-3 documentation mentioned above. See also Parameters of H2OGAM and Details of H2OGAMMOJOModel.
- Scala
- Python
First, let’s start Sparkling Shell as
./bin/sparkling-shell
Start H2O cluster inside the Spark environment
import ai.h2o.sparkling._
val hc = H2OContext.getOrCreate()
Create the frame knots
val knots1 = Seq(-1.99905699, -0.98143075, 0.02599159, 1.00770987, 1.99942290).toDF()
val frameKnots1 = hc.asH2OFrame(knots1)
val knots2 = Seq(-1.999821861, -1.005257990, -0.006716042, 1.002197392, 1.999073589).toDF()
val frameKnots2 = hc.asH2OFrame(knots2)
val knots3 = Seq(-1.999675688, -0.979893796, 0.007573327, 1.011437347, 1.999611676).toDF()
val frameKnots3 = hc.asH2OFrame(knots3)
Import the dataset and split into train and validation sets
import org.apache.spark.SparkFiles
val datasetUrl = "https://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/multinomial_10_classes_10_cols_10000_Rows_train.csv"
spark.sparkContext.addFile(datasetUrl) //for example purposes, on a real cluster it's better to load directly from distributed storage
val sparkDF =
spark
.read
.option("header", "true")
.option("inferSchema", "true").csv(SparkFiles.get("multinomial_10_classes_10_cols_10000_Rows_train.csv"))
val Array(trainingDF, testingDF) = sparkDF.randomSplit(Array(0.8, 0.2))
Set the predictor and response columns, specify the knots array
val y = "C11"
val x = Array("C1", "C2")
val numKnots = Array(5, 5, 5)
Train the model. You can configure all the available GAM arguments using provided setters, such as the label column and gam columns, which are mandatory.
val gam = new H2OGAM()
.setFeaturesCols(x)
.setLabelCol(y)
.setFamily("multinomial")
.setGamCols(Array("C6", "C7", "C8"))
.setColumnsToCategorical("C1", "C2", "C11")
.setScale(Array(1.0, 1.0, 1.0))
.setNumKnots(numKnots)
.setKnotIds(Array(frameKnots1.frameId, frameKnots2.frameId, frameKnots3.frameId))
val gamModel = gam.fit(trainingDF)
By default, the H2OGAM
algorithm distinguishes between a classification and regression problem based on the type of
the label column of the training dataset. If the label column is a string column, a classification model will be trained.
If the label column is a numeric column, a regression model will be trained. If you don’t want to be worried about
column data types, you can explicitly identify the problem by using ai.h2o.sparkling.ml.algos.classification.H2OGAMClassifier
or ai.h2o.sparkling.ml.algos.regression.H2OGAMRegressor
instead.
Run Predictions
val predictions = gamModel.transform(testingDF)
predictions.show(truncate = false)
You can also get model details via calling methods listed in Details of H2OGAMMOJOModel.
Clean up
frameKnots1.delete()
frameKnots2.delete()
frameKnots3.delete()