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)
Connection successful!
R is connected to the H2O cluster:
H2O cluster uptime: 5 hours 9 minutes
H2O cluster version: 3.8.2.6
H2O cluster name: H2O_started_from_R_xxx_phg216
H2O cluster total nodes: 1
H2O cluster total memory: 1.42 GB
H2O cluster total cores: 4
H2O cluster allowed cores: 3
H2O cluster healthy: TRUE
H2O Connection ip: localhost
H2O Connection port: 54321
H2O Connection proxy: NA
R Version: R version 3.2.3 (2015-12-10)
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(
data = df,
ratios = c(0.6,0.2), ## only need to specify 2 fractions, the 3rd is implied
destination_frames = c("train.hex", "valid.hex", "test.hex"), seed = 1234
)
train <- splits[[1]]
valid <- splits[[2]]
test <- splits[[3]]
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 ) ## Sort the grid models by AUC sortedGrid <- h2o.getGrid("final_grid", sort_by = "auc", decreasing = TRUE) sortedGrid
7 模型验证和测试
gbm <- h2o.getModel(sortedGrid@model_ids[[1]])
print(h2o.auc(h2o.performance(gbm, newdata = test)))
交叉验证
for (i in 1:5) { gbm <- h2o.getModel(sortedGrid@model_ids[[i]]) cvgbm <- do.call(h2o.gbm, ## update parameters in place { p <- gbm@parameters p$model_id = NULL ## do not overwrite the original grid model p$training_frame = df ## use the full dataset p$validation_frame = NULL ## no validation frame p$nfolds = 5 ## cross-validation p } ) print(gbm@model_id) print(cvgbm@model$cross_validation_metrics_summary[5,]) ## Pick out the "AUC" row }gbm <- h2o.getModel(sortedGrid@model_ids[[1]]) preds <- h2o.predict(gbm, test) head(preds) gbm@model$validation_metrics@metrics$max_criteria_and_metric_scores
web ui查看各种结果，模型，评估等等
http://localhost:54321/flow/index.html
结果保存
h2o.saveModel(gbm, "/tmp/bestModel.csv", force=TRUE) h2o.exportFile(preds, "/tmp/bestPreds.csv", force=TRUE)
8 模型部署
h2o.download_pojo(gbm)得到pojo代码
通过java 打分服务，可以将模型部署到实际工业应用场景
（1）下载pojo代码 javabean
$ mkdir experiment
$ cd experiment
$ mv ~/Downloads/gbm_pojo_test.java .
$ curl http://localhost:54321/3/h2o-genmodel.jar > h2o-genmodel.jar
（2）编写 打分程序
import java.io.*;
import hex.genmodel.easy.RowData;
import hex.genmodel.easy.EasyPredictModelWrapper;
import hex.genmodel.easy.prediction.*;
public class main {
private static String modelClassName = "gbm_pojo_test";
public static void main(String[] args) throws Exception {
hex.genmodel.GenModel rawModel;
rawModel = (hex.genmodel.GenModel) Class.forName(modelClassName).newInstance();
EasyPredictModelWrapper model = new EasyPredictModelWrapper(rawModel);
RowData row = new RowData();
row.put("Year", "1987");
row.put("Month", "10");
row.put("DayofMonth", "14");
row.put("DayOfWeek", "3");
row.put("CRSDepTime", "730");
row.put("UniqueCarrier", "PS");
row.put("Origin", "SAN");
row.put("Dest", "SFO");
BinomialModelPrediction p = model.predictBinomial(row);
System.out.println("Label (aka prediction) is flight departure delayed: " + p.label);
System.out.print("Class probabilities: ");
for (int i = 0; i < p.classProbabilities.length; i++) {
if (i > 0) {
System.out.print(",");
}
System.out.print(p.classProbabilities[i]);
}
System.out.println("");
}
}
（3）编译，输出打分结果
$ javac -cp h2o-genmodel.jar -J-Xmx2g -J-XX:MaxPermSize=128m gbm_pojo_test.java main.java
$ java -cp .:h2o-genmodel.jar main
The following output displays:
Label (aka prediction) is flight departure delayed: YES
Class probabilities: 0.4790490513429604,0.5209509486570396
9 关闭h2o 集群
> h2o.shutdown() Are you sure you want to shutdown the H2O instance running at http://localhost:54321/ (Y/N)? n [1] TRUE
参考：
[1] http://blog.h2o.ai/
[2] https://github.com/h2oai
总结，相对于其他开源的机器学习算法包，h2o是一个机器学习产品，更加好用适用，从实际问题出发，结合茶品的思维，开发实现的机器学习框架，适合工业应用。
