max_after_balance_size

  • Available in: GBM, DRF, Deep Learning, GLM, Naïve-Bayes, AutoML

  • Hyperparameter: yes

Description

When your datasest includes imbalanced data, you may find it necessary to balance the data using the balance_classes option. When specified, the algorithm will either undersample the majority classes or oversampling the minority classes. In most cases, though, enabling the balance_classes option will increase the data frame size. To reduce the data frame size, you can use the max_after_balance_size option. This specifies the maximum relative size of the training data after balancing class counts and defaults to 5.0.

Example

R

Python

library(h2o)
h2o.init()

# import the covtype dataset:
# this dataset is used to classify the correct forest cover type
# original dataset can be found at https://archive.ics.uci.edu/ml/datasets/Covertype
covtype <- h2o.importFile("https://s3.amazonaws.com/h2o-public-test-data/smalldata/covtype/covtype.20k.data")

# convert response column to a factor
covtype[,55] <- as.factor(covtype[,55])

# set the predictor names and the response column name
predictors <- colnames(covtype[1:54])
response <- 'C55'

# split into train and validation sets
covtype.splits <- h2o.splitFrame(data =  covtype, ratios = .8, seed = 1234)
train <- covtype.splits[[1]]
valid <- covtype.splits[[2]]

# look at the frequencies of each class
print(h2o.table(covtype['C55']))

# try using the max_after_balance_size parameter:
max <- .85
cov_gbm <- h2o.gbm(x = predictors, y = response, training_frame = train,
                   validation_frame = valid, balance_classes = TRUE,
                   max_after_balance_size = max, seed = 1234)

# print the logloss for your model
print(h2o.logloss(cov_gbm, valid = TRUE))

# grid over `max_after_balance_size`
# select the values for `max_after_balance_size` to grid over
# the first and last max_after_balance_sizes reduce the size of the
# original dataset, the second increases the dataset by 1.7
hyper_params <- list( max_after_balance_size = c(.85, 1.7, .5) )

# 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"}

# build grid search with previously made GBM and hyperparameters
grid <- h2o.grid(x = predictors, y = response, training_frame = train, validation_frame = valid,
                 algorithm = "gbm", grid_id = "covtype_grid", balance_classes =  TRUE, hyper_params = hyper_params,
                 search_criteria = list(strategy = "Cartesian"), seed = 1234)

# Sort the grid models by logloss
sortedGrid <- h2o.getGrid("covtype_grid", sort_by = "logloss", decreasing = FALSE)
sortedGrid

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

# import the covtype dataset:
# this dataset is used to classify the correct forest cover type
# original dataset can be found at https://archive.ics.uci.edu/ml/datasets/Covertype
covtype = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/covtype/covtype.20k.data")

# convert response column to a factor
covtype[54] = covtype[54].asfactor()

# set the predictor names and the response column name
predictors = covtype.columns[0:54]
response = 'C55'

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

# look at the frequencies of each class
print(covtype[54].table())

# try using the max_after_balance_size parameter:
max = .85
cov_gbm = H2OGradientBoostingEstimator(balance_classes = True,
                                       max_after_balance_size = max,
                                       seed = 1234)

cov_gbm.train(x = predictors, y = response, training_frame = train, validation_frame = valid)

# print the logloss for your model
print('logloss', cov_gbm.logloss(valid = True))

# grid over `max_after_balance_size`
# import Grid Search
from h2o.grid.grid_search import H2OGridSearch

# select the values for `max_after_balance_size` to grid over
# the first and last max_after_balance_sizes reduce the size of the
# original dataset, the second increases the dataset by 1.7
hyper_params = {'max_after_balance_size': [.85, 1.7,.5]}

# 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
cov_gbm_2 = H2OGradientBoostingEstimator(balance_classes = True, seed = 1234)

# build grid search with previously made GBM and hyperparameters
grid = H2OGridSearch(model = cov_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 logloss
sorted_grid = grid.get_grid(sort_by='logloss', decreasing=False)
print(sorted_grid)