`single_node_mode`

• Available in: CoxPH, Deep Learning
• Hyperparameter: no

Description

Specify whether to run on a single node for fine-tuning of model parameters. Running on a single node reduces the effect of network overhead for smaller datasets.

Example

R

library(h2o)
h2o.init()

# Import the heart dataset:
heart <- h2o.importFile("http://s3.amazonaws.com/h2o-public-test-data/smalldata/coxph_test/heart.csv")

# Build and train the model:
coxph_model <- h2o.coxph(x = "age", 
                         event_column = "event", 
                         start_column = "start", 
                         stop_column = "stop",
                         training_frame = heart, 
                         single_node_mode = TRUE)

# Generate predictions: 
pred <- h2o.predict(object = coxph_model, newdata = heart)

Python

import h2o
from h2o.estimators import H2OCoxProportionalHazardsEstimator
h2o.init()

# Import the heart dataset:
heart = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/coxph_test/heart.csv")

# Build and train the model:
coxph = H2OCoxProportionalHazardsEstimator(start_column="start", 
                                           stop_column="stop", 
                                           single_node_mode=True)
coxph.train(x="age", y="event", training_frame=heart)

# Generate predictions:
pred = coxph.predict(test_data=heart)

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