interaction_pairs

  • Available in: GLM, GAM, CoxPH

  • Hyperparameter: no

Description

By default, interactions between predictor columns are expanded and computed on the fly as GLM iterates over dataset. The interaction_pairs parameter allows you to define a list of specific interactions to include instead of all interactions.

Note that adding a list of interactions to a model changes the interpretation of all of the coefficients. For example, a typical predictor has the form ‘response ~ terms’ where ‘response’ is the (numeric) response vector, and ‘terms’ is a series of terms that specify a linear predictor for ‘response’. For ‘binomial’ and ‘quasibinomial’ families in GLM, the response can also be specified as a ‘factor’ (when the first level denotes failure and all other levels denote success) or as a two-column matrix with the columns giving the numbers of successes and failures.

When using this parameter, specify a list of pairwise columns that should interact. When specified, GLM will compute interactions between

Note that this option is mutually exclusive with interactions.

Example

R

Python

library(h2o)
h2o.init()
# import the airlines dataset
df <-  h2o.importFile("http://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")

# specify the columns to include
XY <- names(df)[c(1, 2, 3, 4, 6, 8, 9, 13, 17, 18, 19, 31)

# specify the predictor column indices to interact
interactions <- XY[c(5, 7, 9)]

# train the model and build the coefficients table
m1 <- h2o.glm(x = XY[-length(XY)],
             y = XY[length(XY)],
             training_frame = df,
             interactions = interactions,
             lambda_search = TRUE,
             family = "binomial")
m1_coefs <- m1@model$coefficients_table

# train the model with the interaction pairs
m2 <- h2o.glm(x = XY[-length(XY)],
              y = XY[length(XY)],
              training_frame = df,
              interaction_pairs = list(
               c("CRSDepTime", "UniqueCarrier"),
               c("CRSDepTime", "Origin"),
               c("UniqueCarrier", "Origin")
               ),
              lambda_search = TRUE,
              family = "binomial")
m2_coefs <- m2@model$coefficients_table

import(h2o)
h2o.init()
from h2o.estimators.glm import H2OGeneralizedLinearEstimator

# import the airlines dataset
df = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")

# specify the columns to include
XY = [df.names[i-1] for i in [1,2,3,4,6,8,9,13,17,18,19,31]]

# specify the predictor column indices to interact
interactions = [XY[i-1] for i in [5,7,9]]

# train the model and build the coefficients table
m = H2OGeneralizedLinearEstimator(lambda_search=True,
                                  family="binomial",
                                  interactions=interactions)
m.train(x=XY[:len(XY)], y=XY[-1],training_frame=df)
coef_m = m._model_json['output']['coefficients_table']

# define specific interaction pairs
interaction_pairs = [("CRSDepTime", "UniqueCarrier"),
                     ("CRSDepTime", "Origin"),
                     ("UniqueCarrier", "Origin")]

# train the model with the interaction pairs
mexp = H2OGeneralizedLinearEstimator(lambda_search=True,
                                     family="binomial",
                                     interaction_pairs=interaction_pairs)
mexp.train(x=XY[:len(XY)], y=XY[-1],training_frame=df)
coef_mexp = mexp._model_json['output']['coefficients_table']