sample_rate

  • Available in: GBM, DRF
  • Hyperparameter: yes

Description

This option is used to specify the row sampling rate (x-axis). The range is 0.0 to 1.0. Row and column sampling (sample_rate and col_sample_rate) can improve generalization and lead to lower validation and test set errors. Good general values for large datasets are around 0.7 to 0.8 (sampling 70-80 percent of the data) for both parameters, as higher values generally improve training accuracy.

For highly imbalanced classification datasets, stratified row sampling based on response class membership can help improve predictive accuracy. This is configured with sample_rate_per_class (array of ratios, one per response class in lexicographic order).

Note: If sample_rate_per_class is specified, then sample_rate will be ignored.

Example

library(h2o)
h2o.init()
# import the airlines dataset:
# This dataset is used to classify whether a flight will be delayed 'YES' or not "NO"
# original data can be found at http://www.transtats.bts.gov/
airlines <-  h2o.importFile("http://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")

# convert columns to factors
airlines["Year"] <- as.factor(airlines["Year"])
airlines["Month"] <- as.factor(airlines["Month"])
airlines["DayOfWeek"] <- as.factor(airlines["DayOfWeek"])
airlines["Cancelled"] <- as.factor(airlines["Cancelled"])
airlines['FlightNum'] <- as.factor(airlines['FlightNum'])

# set the predictor names and the response column name
predictors <- c("Origin", "Dest", "Year", "UniqueCarrier", "DayOfWeek", "Month", "Distance", "FlightNum")
response <- "IsDepDelayed"

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

# try using the `sample_rate` parameter:
airlines.gbm <- h2o.gbm(x = predictors, y = response, training_frame = train,
                        validation_frame = valid, sample_rate =.7 ,
                        seed = 1234)

# print the AUC for the validation data
print(h2o.auc(airlines.gbm, valid = TRUE))


# Example of values to grid over for `sample_rate`
hyper_params <- list( sample_rate = c(.7, .8, 1) )

# 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 = "air_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
sortedGrid <- h2o.getGrid("air_grid", sort_by = "auc", decreasing = TRUE)
sortedGrid

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

# import the airlines dataset:
# This dataset is used to classify whether a flight will be delayed 'YES' or not "NO"
# original data can be found at http://www.transtats.bts.gov/
airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")

# convert columns to factors
airlines["Year"]= airlines["Year"].asfactor()
airlines["Month"]= airlines["Month"].asfactor()
airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor()
airlines["Cancelled"] = airlines["Cancelled"].asfactor()
airlines['FlightNum'] = airlines['FlightNum'].asfactor()

# set the predictor names and the response column name
predictors = ["Origin", "Dest", "Year", "UniqueCarrier", "DayOfWeek", "Month", "Distance", "FlightNum"]
response = "IsDepDelayed"

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

# try using the `sample_rate` parameter:
# initialize your estimator
airlines_gbm = H2OGradientBoostingEstimator(sample_rate = .7, seed =1234)

# then train your model
airlines_gbm.train(x = predictors, y = response, training_frame = train, validation_frame = valid)

# print the auc for the validation data
print(airlines_gbm.auc(valid=True))


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

# select the values for sample_rate to grid over
hyper_params = {'sample_rate': [.7, .8, 1]}

# 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
airlines_gbm_2 = H2OGradientBoostingEstimator(seed = 1234,
                                              stopping_rounds = 5,
                                              stopping_metric = "AUC", stopping_tolerance = 1e-4)

# build grid search with previously made GBM and hyper parameters
grid = H2OGridSearch(model = airlines_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)