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parallel_gs.R
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142 lines (114 loc) · 11 KB
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library(h2o)
run_grid_search_bench <- function(training_data, features, response, parallelism, grid_name){
# Constants
ntrees = c(1000)
algorithms_used = "gbm" # GBM is used as a single point of reference
# Space to search
max_depth_opts <- c(3, 9,17)
min_rows_opts <- c(30, 100)
learn_rate_opts <- c(0.1, 0.5, 0.8)
sample_rate_opts <- c(0.50, 0.80, 1.00)
col_sample_rate_opts <- c(0.4, 1.0)
col_sample_rate_per_tree_opts <- c(0.4, 1.0)
min_split_improvement_opts <- c(1e-5)
# Everything else is left to the default settings
hyper_parameters = list(ntrees = ntrees,
learn_rate = learn_rate_opts,
max_depth = max_depth_opts,
min_rows = min_rows_opts,
sample_rate = sample_rate_opts,
col_sample_rate = col_sample_rate_opts,
col_sample_rate_per_tree = col_sample_rate_per_tree_opts,
min_split_improvement = min_split_improvement_opts)
start_time = Sys.time()
grid <- h2o.grid(algorithm = algorithms_used,
grid_id=grid_name,
x=features,
y=response,
seed=42,
training_frame= training_data,
hyper_params = hyper_parameters,
parallelism = parallelism)
end_time = Sys.time()
return(end_time - start_time)
}
# Connects to a local H2O cluster by default, change if required
h2o.init(ip = "localhost", strict_version_check = FALSE)
#█████╗ ██╗██████╗ ██╗ ██╗███╗ ██╗███████╗███████╗ ███████╗███╗ ███╗ █████╗ ██╗ ██╗
#██╔══██╗██║██╔══██╗██║ ██║████╗ ██║██╔════╝██╔════╝ ██╔════╝████╗ ████║██╔══██╗██║ ██║
#███████║██║██████╔╝██║ ██║██╔██╗ ██║█████╗ ███████╗ ███████╗██╔████╔██║███████║██║ ██║
#██╔══██║██║██╔══██╗██║ ██║██║╚██╗██║██╔══╝ ╚════██║ ╚════██║██║╚██╔╝██║██╔══██║██║ ██║
#██║ ██║██║██║ ██║███████╗██║██║ ╚████║███████╗███████║ ███████║██║ ╚═╝ ██║██║ ██║███████╗███████╗
#╚═╝ ╚═╝╚═╝╚═╝ ╚═╝╚══════╝╚═╝╚═╝ ╚═══╝╚══════╝╚══════╝ ╚══════╝╚═╝ ╚═╝╚═╝ ╚═╝╚══════╝╚══════╝
h2o.removeAll() # Ensure the whole cluster is empty both in terms of models and frames
airlines_small <- h2o.importFile("https://0xdata-public.s3.amazonaws.com/parallel-gs-benchmark/airlines_small.csv")
airlines_small_features <- c("Origin", "Dest", "Distance")
airlines_small_response <- c("IsDepDelayed")
# Airlines small - sequential grid search
airlines_small_sequential_duration <- run_grid_search_bench(training_data = airlines_small,
features = airlines_small_features,
response = airlines_small_response,
parallelism = 1, # 1 implicates sequential grid search - one model at a time
grid_name = "airlines_small_sequential")
print("Airlines Small (Sequential) duration:")
print(airlines_small_sequential_duration)
# Airlines small - parallel grid search
airlines_small_parallel_duration <- run_grid_search_bench(training_data = airlines_small,
features = airlines_small_features,
response = airlines_small_response,
parallelism = 0, # 0 implicates automatic level of parallelism determined by H2O
grid_name = "airlines_small_parallel")
print("Airlines Small (Parallel) duration:")
print(airlines_small_parallel_duration)
#█████╗ ██╗██████╗ ██╗ ██╗███╗ ██╗███████╗███████╗ ███╗ ███╗███████╗██████╗ ██╗██╗ ██╗███╗ ███╗
#██╔══██╗██║██╔══██╗██║ ██║████╗ ██║██╔════╝██╔════╝ ████╗ ████║██╔════╝██╔══██╗██║██║ ██║████╗ ████║
#███████║██║██████╔╝██║ ██║██╔██╗ ██║█████╗ ███████╗ ██╔████╔██║█████╗ ██║ ██║██║██║ ██║██╔████╔██║
#██╔══██║██║██╔══██╗██║ ██║██║╚██╗██║██╔══╝ ╚════██║ ██║╚██╔╝██║██╔══╝ ██║ ██║██║██║ ██║██║╚██╔╝██║
#██║ ██║██║██║ ██║███████╗██║██║ ╚████║███████╗███████║ ██║ ╚═╝ ██║███████╗██████╔╝██║╚██████╔╝██║ ╚═╝ ██║
#╚═╝ ╚═╝╚═╝╚═╝ ╚═╝╚══════╝╚═╝╚═╝ ╚═══╝╚══════╝╚══════╝ ╚═╝ ╚═╝╚══════╝╚═════╝ ╚═╝ ╚═════╝ ╚═╝ ╚═╝
h2o.removeAll() # Ensure the whole cluster is empty both in terms of models and frames
airlines_medium <- h2o.importFile("https://0xdata-public.s3.amazonaws.com/parallel-gs-benchmark/airlines_medium.csv")
airlines_medium_features <- c("Origin", "Dest", "Distance", "FlightNum", "Diverted")
airlines_medium_response <- c("IsDepDelayed")
# Airlines medium - sequential grid search
airlines_medium_sequential_duration <- run_grid_search_bench(training_data = airlines_medium,
features = airlines_medium_features,
response = airlines_medium_response,
parallelism = 1, # 1 implicates sequential grid search - one model at a time
grid_name = "airlines_medium_sequential")
print("Airlines Medium (Sequential) duration:")
print(airlines_medium_sequential_duration)
# Airlines medium - parallel grid search
airlines_medium_parallel_duration <- run_grid_search_bench(training_data = airlines_medium,
features = airlines_medium_features,
response = airlines_medium_response,
parallelism = 0, # 0 implicates automatic level of parallelism determined by H2O
grid_name = "airlines_medium_parallel")
print("Airlines Medium (Parallel) duration:")
print(airlines_medium_parallel_duration)
#█████╗ ██╗██████╗ ██╗ ██╗███╗ ██╗███████╗███████╗ ██╗ █████╗ ██████╗ ██████╗ ███████╗
#██╔══██╗██║██╔══██╗██║ ██║████╗ ██║██╔════╝██╔════╝ ██║ ██╔══██╗██╔══██╗██╔════╝ ██╔════╝
#███████║██║██████╔╝██║ ██║██╔██╗ ██║█████╗ ███████╗ ██║ ███████║██████╔╝██║ ███╗█████╗
#██╔══██║██║██╔══██╗██║ ██║██║╚██╗██║██╔══╝ ╚════██║ ██║ ██╔══██║██╔══██╗██║ ██║██╔══╝
#██║ ██║██║██║ ██║███████╗██║██║ ╚████║███████╗███████║ ███████╗██║ ██║██║ ██║╚██████╔╝███████╗
#╚═╝ ╚═╝╚═╝╚═╝ ╚═╝╚══════╝╚═╝╚═╝ ╚═══╝╚══════╝╚══════╝ ╚══════╝╚═╝ ╚═╝╚═╝ ╚═╝ ╚═════╝ ╚══════╝
h2o.removeAll() # Ensure the whole cluster is empty both in terms of models and frames
airlines_large <- h2o.importFile("https://0xdata-public.s3.amazonaws.com/parallel-gs-benchmark/airlines_large.csv")
airlines_large_features <- c("Origin", "Dest", "Distance", "FlightNum", "Diverted")
airlines_large_response <- c("IsDepDelayed")
# Airlines large - sequential grid search
airlines_large_sequential_duration <- run_grid_search_bench(training_data = airlines_large,
features = airlines_large_features,
response = airlines_large_response,
parallelism = 1, # 1 implicates sequential grid search - one model at a time
grid_name = "airlines_large_sequential")
print("Airlines Large (Sequential) duration:")
print(airlines_large_sequential_duration)
# Airlines large - parallel grid search
airlines_large_parallel_duration <- run_grid_search_bench(training_data = airlines_large,
features = airlines_large_features,
response = airlines_large_response,
parallelism = 0, # 0 implicates automatic level of parallelism determined by H2O
grid_name = "airlines_large_parallel")
print("Airlines Large (Parallel) duration:")
print(airlines_large_parallel_duration)