Why is H2O autoencoder so slow for one data set but not the other?

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When I run H2O autoencoder on two different data sets of about the same size (see below), I can finish one data set (A) within 5 minutes, but the other data set (B) is really slow. It takes >30 minutes to complete only 1% for data set B. I tried restarting R session and H2O a couple of times, but that didn’t help. There are about the same number of parameters (or coefficients) in the model for both data sets.

Data set A: 4 * 1,000,000 in size (<5 minutes)

Data set B: 8 * 477,613 in size (very slow)

The model below is used for both data sets:

model.dl = h2o.deeplearning(x = x, training_frame = data.hex, autoencoder = TRUE, activation = "Tanh", hidden = c(25,25,25), variable_importances = TRUE)

The memory of the H2O cluster is 15GB for both data sets. The same computer is used (OS X 10.14.6, 16 GB memory). Below is some information about the versions of H2O and R.

H2O cluster version:        3.30.0.1
H2O cluster total nodes:    1
H2O cluster total memory:   15.00 GB
H2O cluster total cores:    16
H2O cluster allowed cores:  16
H2O cluster healthy:        TRUE
R Version:                  R version 3.6.3 (2020-02-29)

Please let me know if there is any other information I can provide to get this issue resolved.

h2o autoencoder deep-learning machine-learning r
1 Answers

This problem has been resolved.

The problem is that there are a lot more columns for data set B after one-hot-encoding during the model run. Please see below.

Data set A:

There are 4 categorical features. The number of unique values for these categorical features is 12, 14, 25, and 10, respectively.

Data set B:

There are 7 categorical features and 1 numerical feature. The number of unique values for the categorical features is 17, 49, 52, 85, 5032 (!), 18445 (!!) and 392124 (!!!), respectively. This explains why it's so slow.

user_137564650
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