h2o官网哪个（h2o是什么品牌）
时间：2022-10-22
官网：http://www.h2o.ai/
H2o开源的机器学习框架，支持glm，rf，gbm，深度学习等算法，借助hadoop spark计算平台，实现large scale 机器学习
H2o 机器学习包
R版本 https://cran.r-project.org/web/packages/h2o/index.html
Python版本 https://pypi.python.org/pypi/h2o/
文档 http://h2o-release.s3.amazonaws.com/h2o/rel-turchin/9/docs-website/h2o-docs/index.html
基于h2o的gbm参数调整实验
https://github.com/h2oai/h2o-3/blob/master/h2o-docs/src/product/tutorials/gbm/gbmTuning.Rmd
1 下载安装 R h2o
install.packages('h2o')
2 启动h2o
>; library(h2o)
>; h2o.init(nthreads = 3)
R is connected to the H2O cluster:
3 导入数据
## 'path' can point to a local file, hdfs, s3, nfs, Hive, directories, etc.
df <;- h2o.importFile(path = 'http://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv')
dim(df)
head(df)
tail(df)
summary(df,exact_quantiles=TRUE)
## pick a response for the supervised problem
response <;- 'survived'
## the response variable is an integer, we will turn it into a categorical/factor for binary classification
df[[response]] <;- as.factor(df[[response]])
## use all other columns (except for the name) as predictors
predictors <;- setdiff(names(df), c(response, 'name'))
4 数据切分
splits <;- h2o.splitFrame(
)
train <;- splits[[1]]
valid <;- splits[[2]]
test
5 基础模型
## We only provide the required parameters, everything else is default
gbm <;- h2o.gbm(x = predictors, y = response, training_frame = train)
## Show a detailed model summary
gbm
## Get the AUC on the validation set
h2o.auc(h2o.performance(gbm, newdata = valid))
6 模型调参优化
## Depth 10 is usually plenty of depth for most datasets, but you never know
hyper_params = list( max_depth = seq(1,29,2) )
#hyper_params = list( max_depth = c(4,6,8,12,16,20) ) ##faster for larger datasets
grid <;- h2o.grid(
## hyper parameters
hyper_params = hyper_params,
## full Cartesian hyper-parameter search
search_criteria = list(strategy = 'Cartesian'),
## which algorithm to run
algorithm='gbm',
## identifier for the grid, to later retrieve it
grid_id='depth_grid',
## standard model parameters
x = predictors,
y = response,
training_frame = train,
validation_frame = valid,
## more trees is better if the learning rate is small enough
## here, use 'more than enough' trees - we have early stopping
ntrees = 10000,
## smaller learning rate is better
## since we have learning_rate_annealing, we can afford to start with a bigger learning rate
learn_rate = 0.05,
## learning rate annealing: learning_rate shrinks by 1% after every tree
## (use 1.00 to disable, but then lower the learning_rate)
learn_rate_annealing = 0.99,
## sample 80% of rows per tree
sample_rate = 0.8,
## sample 80% of columns per split
col_sample_rate = 0.8,
## fix a random number generator seed for reproducibility
seed = 1234,
## early stopping once the validation AUC doesn't improve by at least 0.01% for 5 consecutive scoring events
stopping_rounds = 5,
stopping_tolerance = 1e-4,
stopping_metric = 'AUC',
## score every 10 trees to make early stopping reproducible (it depends on the scoring interval)
score_tree_interval = 10
)
## by default, display the grid search results sorted by increasing logloss (since this is a classification task)
grid
## sort the grid models by decreasing AUC
sortedGrid <;- h2o.getGrid('depth_grid', sort_by='auc', decreasing = TRUE)
sortedGrid
## find the range of max_depth for the top 5 models
topDepths = sortedGrid@summary_table$max_depth[1:5]
minDepth = min(as.numeric(topDepths))
maxDepth = max(as.numeric(topDepths))
hyper_params = list(
## restrict the search to the range of max_depth established above
max_depth = seq(minDepth,maxDepth,1),
## search a large space of row sampling rates per tree
sample_rate = seq(0.2,1,0.01),
## search a large space of column sampling rates per split
col_sample_rate = seq(0.2,1,0.01),
## search a large space of column sampling rates per tree
col_sample_rate_per_tree = seq(0.2,1,0.01),
## search a large space of how column sampling per split should change as a function of the depth of the split
col_sample_rate_change_per_level = seq(0.9,1.1,0.01),
## search a large space of the number of min rows in a terminal node
min_rows = 2^seq(0,log2(nrow(train))-1,1),
## search a large space of the number of bins for split-finding for continuous and integer columns
nbins = 2^seq(4,10,1),
## search a large space of the number of bins for split-finding for categorical columns
nbins_cats = 2^seq(4,12,1),
## search a few minimum required relative error improvement thresholds for a split to happen
min_split_improvement = c(0,1e-8,1e-6,1e-4),
## try all histogram types (QuantilesGlobal and RoundRobin are good for numeric columns with outliers)
histogram_type = c('UniformAdaptive','QuantilesGlobal','RoundRobin')
)
search_criteria = list(
## Random grid search
strategy = 'RandomDiscrete',
## limit the runtime to 60 minutes
max_runtime_secs = 3600,
## build no more than 100 models
max_models = 100,
## random number generator seed to make sampling of parameter combinations reproducible
seed = 1234,
## early stopping once the leaderboard of the top 5 models is converged to 0.1% relative difference
stopping_rounds = 5,
stopping_metric = 'AUC',
stopping_tolerance = 1e-3
)
grid <;- h2o.grid(
## hyper parameters
hyper_params = hyper_params,
## hyper-parameter search configuration (see above)
search_criteria = search_criteria,
## which algorithm to run
algorithm = 'gbm',
## identifier for the grid, to later retrieve it
grid_id = 'final_grid',
## standard model parameters
x = predictors,
y = response,
training_frame = train,
validation_frame = valid,
## more trees is better if the learning rate is small enough
## use 'more than enough' trees - we have early stopping
ntrees = 10000,
## smaller learning rate is better
## since we have learning_rate_annealing, we can afford to start with a bigger learning rate
learn_rate = 0.05,
## learning rate annealing: learning_rate shrinks by 1% after every tree
## (use 1.00 to disable, but then lower the learning_rate)
learn_rate_annealing = 0.99,
## early stopping based on timeout (no model should take more than 1 hour - modify as needed)
max_runtime_secs = 3600,
## early stopping once the validation AUC doesn't improve by at least 0.01% for 5 consecutive scoring events
stopping_rounds = 5, stopping_tolerance = 1e-4, stopping_metric = 'AUC',
## score every 10 trees to make early stopping reproducible (it depends on the scoring interval)
score_tree_interval = 10,
## base random number generator seed for each model (automatically gets incremented internally for each model)
seed = 1234
