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
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()
Python
First, let’s start PySparkling Shell as
./bin/pysparkling
Start H2O cluster inside the Spark environment
from pysparkling import *
hc = H2OContext.getOrCreate()
Create the frame knots
knots1 = [-1.99905699, -0.98143075, 0.02599159, 1.00770987, 1.99942290]
frameKnots1 = h2o.H2OFrame(python_obj=knots1)
knots2 = [-1.999821861, -1.005257990, -0.006716042, 1.002197392, 1.999073589]
frameKnots2 = h2o.H2OFrame(python_obj=knots2)
knots3 = [-1.999675688, -0.979893796, 0.007573327,1.011437347, 1.999611676]
frameKnots3 = h2o.H2OFrame(python_obj=knots3)
Import the dataset and split into train and validation sets
import h2o
frame = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/multinomial_10_classes_10_cols_10000_Rows_train.csv")
sparkDF = hc.asSparkFrame(frame)
[trainingDF, testingDF] = sparkDF.randomSplit([0.8, 0.2])
Set the predictor and response columns, specify the knots array
y = "C11"
x = ["C1","C2"]
numKnots = [5,5,5]
Train the model. You can configure all the available GAM arguments using provided setters or constructor parameters, such as the label column and gam columns, which are mandatory.
from pysparkling.ml import H2OGAM
estimator = H2OGAM(
featuresCols = x,
labelCol = y,
family = "multinomial",
gamCols = ["C6", "C7", "C8"],
columnsToCategorical = ["C1", "C2", "C11"],
scale = [1.0, 1.0, 1.0],
numKnots = numKnots,
knotIds = [frameKnots1.key, frameKnots2.key, frameKnots3.key])
model = estimator.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 H2OGAMClassifier
or H2OGAMRegressor
instead.
Run Predictions
model.transform(testingDF).show(truncate = False)
You can also get model details via calling methods listed in Details of H2OGAMMOJOModel.
Clean up
h2o.remove(frameKnots1)
h2o.remove(frameKnots2)
h2o.remove(frameKnots3)
h2o.remove(frame)