`plug_values`

• Available in: GLM, GAM, HGLM, ANOVAGLM, ModelSelection
• Hyperparameter: yes

Description

When missing_values_handling="PlugValues", this option is used to specify a frame containing values that will be used to impute missing values. Whereas other options mean-impute rows or skip them entirely, plug values allow you to specify values of your own choosing in the form of a single row frame that contains the desired value.

Related parameters

• missing_values_handling

Example

R

library(h2o)
h2o.init()

# import the cars dataset:
cars <- h2o.importFile("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
cars$name <- NULL

# create an H2O frame using the mean of the cars dataset
means <- h2o.mean(cars, na.rm = TRUE, return_frame = TRUE)

# train GLM models, configuring plug_values in the second
glm1 <- h2o.glm(training_frame = cars, y = "cylinders")
glm2 <- h2o.glm(training_frame = cars, 
                y = "cylinders", 
                missing_values_handling = "PlugValues", 
                plug_values = means)

# determine if the coefficients are equal
h2o.coef(glm1)
    Intercept       economy  displacement         power        weight 
 2.8316269982  0.0043748133  0.0141242460 -0.0030047140  0.0001410077 
 acceleration          year economy_20mpg 
-0.0146035179  0.0017987846 -0.3754994243

h2o.coef(glm2)
    Intercept       economy  displacement         power        weight 
 2.8316269982  0.0043748133  0.0141242460 -0.0030047140  0.0001410077 
 acceleration          year economy_20mpg 
-0.0146035179  0.0017987846 -0.3754994243

Python

import h2o
from h2o.estimators.glm import H2OGeneralizedLinearEstimator
from h2o import H2OFrame
from h2o.expr import ExprNode
h2o.init()

# import the cars dataset:
cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
cars = cars.drop(0)

# create an H2O frame using the mean of the cars dataset
means = cars.mean()
means = H2OFrame._expr(ExprNode("mean", cars, True, 0))

# train a GLM
glm_means = H2OGeneralizedLinearEstimator(seed=42)
glm_means.train(training_frame=cars, y="cylinders")

# configure plug_values in a second model
glm_plugs1 = H2OGeneralizedLinearEstimator(seed=42,
                                           missing_values_handling="PlugValues",
                                           plug_values=means)
glm_plugs1.train(training_frame=cars, y="cylinders")

# check that the GLM coefficients are equal
glm_means.coef() == glm_plugs1.coef()

# modify the means to use with another GLM
not_means = 0.1 + (means * 0.5)

# configure plug values for the second model
glm_plugs2 = H2OGeneralizedLinearEstimator(seed=42,
                                           missing_values_handling="PlugValues",
                                           plug_values=not_means)
glm_plugs2.train(training_frame=cars, y="cylinders")

# confirm that plug values are not being ignored
glm_means.coef() != glm_plugs2.coef()

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