nbins_top_level

  • Available in: GBM, DRF, Uplift DRF

  • Hyperparameter: yes

Description

For numerical columns (real/int), the nbins_top_level option is the number of bins to use at the top of each tree. It then divides by 2 at each ensuing level to find a new number. This option defaults to 1024 and is used with nbins, which controls when the algorithm stops dividing by 2.

To make a model more general, decrease nbins_top_level and nbins_cats. To make a model more specific, increase nbins and/or nbins_top_level and nbins_cats. Keep in mind that increasing nbins_cats can lead to in overfitting on the training set.

Example

R

Python

library(h2o)
h2o.init()
# import the EEG dataset:
# All data is from one continuous EEG measurement with the Emotiv EEG Neuroheadset.
# The duration of the measurement was 117 seconds. The eye state was detected via a camera during the
# EEG measurement and added later manually to the file after analysing the video frames.
# '1' indicates the eye-closed and '0' the eye-open state. All values are in chronological
# order with the first measured value at the top of the data.
# original dataset can be found at the UCI Machine Learning Repository http://archive.ics.uci.edu/ml/datasets/EEG+Eye+State
eeg <-  h2o.importFile("https://h2o-public-test-data.s3.amazonaws.com/smalldata/eeg/eeg_eyestate.csv")

# convert response column to a factor
eeg['eyeDetection'] <- as.factor(eeg['eyeDetection'])

# set the predictor names and the response column name
predictors <- colnames(eeg)[1:(length(eeg)-1)]
response <- "eyeDetection"

# split into train and validation
eeg_splits <- h2o.splitFrame(data =  eeg, ratios = 0.8, seed = 1234)
train <- eeg_splits[[1]]
valid <- eeg_splits[[2]]

# try a range of nbins_top_level:
bin_num = c(32, 64, 128, 256, 512, 1024, 2048, 4096)
label = c("32", "64", "128", "256", "512", "1024", "2048", "4096")
lapply(seq_along(1:length(bin_num)),function(num) {
  eeg_gbm <- h2o.gbm(x = predictors, y = response, training_frame = train, validation_frame = valid,
                          nbins_top_level = bin_num[num], nfolds = 5, seed = 1234)
  # print the value used and AUC score for train and valid
  print(paste(label[num], 'training score',  h2o.auc(eeg_gbm, train = TRUE)))
  print(paste(label[num], 'validation score',  h2o.auc(eeg_gbm, valid = TRUE)))
})


# Example of values to grid over for `nbins_top_level`
hyper_params <- list( nbins_top_level = c(32, 64, 128, 256, 512, 1024, 2048, 4096) )

# this example uses cartesian grid search because the search space is small
# and we want to see the performance of all models. For a larger search space use
# random grid search instead: list(strategy = "RandomDiscrete")
# this GBM uses early stopping once the validation AUC doesn't improve by at least 0.01% for
# 5 consecutive scoring events
grid <- h2o.grid(x = predictors, y = response, training_frame = train, validation_frame = valid,
                 algorithm = "gbm", grid_id = "eeg_grid", hyper_params = hyper_params,
                 stopping_rounds = 5, stopping_tolerance = 1e-4, stopping_metric = "AUC",
                 search_criteria = list(strategy = "Cartesian"), seed = 1234)

## Sort the grid models by AUC
sorted_grid <- h2o.getGrid("eeg_grid", sort_by = "auc", decreasing = TRUE)
sorted_grid

import h2o
from h2o.estimators.gbm import H2OGradientBoostingEstimator
h2o.init()
h2o.cluster().show_status()

# import the EEG dataset:
# All data is from one continuous EEG measurement with the Emotiv EEG Neuroheadset.
# The duration of the measurement was 117 seconds. The eye state was detected via a camera during the
# EEG measurement and added later manually to the file after analysing the video frames.
# '1' indicates the eye-closed and '0' the eye-open state. All values are in chronological
# order with the first measured value at the top of the data.
# original dataset can be found at the UCI Machine Learning Repository http://archive.ics.uci.edu/ml/datasets/EEG+Eye+State
eeg = h2o.import_file("https://h2o-public-test-data.s3.amazonaws.com/smalldata/eeg/eeg_eyestate.csv")

# convert response column to a factor
eeg['eyeDetection'] = eeg['eyeDetection'].asfactor()

# set the predictor names and the response column name
predictors = eeg.columns[:-1]
response = 'eyeDetection'

# split into train and validation sets
train, valid = eeg.split_frame(ratios = [.8], seed = 1234)

# try a range of values for `nbins_top_level`
# we start at 32 because the default for nbins is 20, and nbins_top_level
# must be greater than nbins
bin_num = [32, 64, 128, 256, 512, 1024, 2048, 4096]
label = ["32", "64", "128", "256", "512", "1024", "2048", "4096"]
for key, num in enumerate(bin_num):
    # initialize the GBM estimator and set a seed for reproducibility
    eeg_gbm = H2OGradientBoostingEstimator(nbins_top_level = num, seed = 1234)
    eeg_gbm.train(x = predictors, y = response, training_frame = train, validation_frame = valid)
    # print the value used and AUC score for train and validation sets
    print(label[key], 'training score', eeg_gbm.auc(train = True))
    print(label[key], 'validation score', eeg_gbm.auc(valid = True))


# Example of values to grid over for `nbins_top_level`
# import Grid Search
from h2o.grid.grid_search import H2OGridSearch

# select the values for `nbins_top_level` to grid over
hyper_params = {'nbins_top_level': [32, 64, 128, 256, 512, 1024, 2048, 4096]}

# this example uses cartesian grid search because the search space is small
# and we want to see the performance of all models. For a larger search space use
# random grid search instead: {'strategy': "RandomDiscrete"}
# initialize the GBM estimator
# use early stopping once the validation AUC doesn't improve by at least 0.01% for
# 5 consecutive scoring events
eeg_gbm_2 = H2OGradientBoostingEstimator(stopping_rounds = 5, stopping_metric = "AUC",
                                         stopping_tolerance = 1e-4, seed = 1234)

# build grid search with previously made GBM and hyper parameters
grid = H2OGridSearch(model = eeg_gbm_2, hyper_params = hyper_params,
                     search_criteria = {'strategy': "Cartesian"})

# train using the grid
grid.train(x = predictors, y = response, training_frame = train, validation_frame = valid)

# sort the grid models by decreasing AUC
sorted_grid = grid.get_grid(sort_by='auc', decreasing=True)
print(sorted_grid)