library(h2o)
##
## ----------------------------------------------------------------------
##
## Your next step is to start H2O:
## > h2o.init()
##
## For H2O package documentation, ask for help:
## > ??h2o
##
## After starting H2O, you can use the Web UI at http://localhost:54321
## For more information visit http://docs.h2o.ai
##
## ----------------------------------------------------------------------
##
## Attaching package: 'h2o'
## The following objects are masked from 'package:stats':
##
## cor, sd, var
## The following objects are masked from 'package:base':
##
## %*%, %in%, &&, ||, apply, as.factor, as.numeric, colnames,
## colnames<-, ifelse, is.character, is.factor, is.numeric, log,
## log10, log1p, log2, round, signif, trunc
h2o.init()
##
## H2O is not running yet, starting it now...
##
## Note: In case of errors look at the following log files:
## C:\Users\somy\AppData\Local\Temp\RtmpiAVn1B/h2o_somy_started_from_r.out
## C:\Users\somy\AppData\Local\Temp\RtmpiAVn1B/h2o_somy_started_from_r.err
##
##
## Starting H2O JVM and connecting: .. Connection successful!
##
## R is connected to the H2O cluster:
## H2O cluster uptime: 21 seconds 483 milliseconds
## H2O cluster timezone: Asia/Kolkata
## H2O data parsing timezone: UTC
## H2O cluster version: 3.26.0.4804
## H2O cluster version age: 11 days
## H2O cluster name: H2O_started_from_R_somy_kxv057
## H2O cluster total nodes: 1
## H2O cluster total memory: 1.75 GB
## H2O cluster total cores: 8
## H2O cluster allowed cores: 8
## H2O cluster healthy: TRUE
## H2O Connection ip: localhost
## H2O Connection port: 54321
## H2O Connection proxy: NA
## H2O Internal Security: FALSE
## H2O API Extensions: Amazon S3, Algos, AutoML, Core V3, TargetEncoder, Core V4
## R Version: R version 3.6.1 (2019-07-05)
#to see the h2o flow in your localhost type : http://127.0.0.1:54321
library(MASS)
Datafram<-Boston
scale.dat<-scale(Datafram)
colMeans(scale.dat)
## crim zn indus chas nox
## -6.899468e-18 2.298337e-17 1.516683e-17 -3.510587e-18 -2.149412e-16
## rm age dis rad tax
## -1.058524e-16 -1.645039e-16 1.144506e-16 4.651527e-17 1.906139e-17
## ptratio black lstat medv
## -3.931034e-16 -1.155991e-16 -7.012260e-17 -1.379311e-16
apply(scale.dat,2,sd)
## crim zn indus chas nox rm age dis rad
## 1 1 1 1 1 1 1 1 1
## tax ptratio black lstat medv
## 1 1 1 1 1
scale.dat<-as.data.frame(scale.dat)
y<-"medv"
x<-setdiff(colnames(scale.dat),y)
ind<-sample(1:nrow(Datafram),400)
trainDF<-Datafram[ind,]
testDF<-Datafram[-ind,]
?h2o.deeplearning
## starting httpd help server ...
## done
model<-h2o.deeplearning(x=x,y=y,seed = 1234,training_frame = as.h2o(trainDF),nfolds = 3,stopping_rounds = 7,epochs = 500,overwrite_with_best_model = T
,activation = 'Tanh',input_dropout_ratio = 0.1,hidden = c(10,10,10),l1 = 6e-4,loss = 'Automatic',distribution = "AUTO",
stopping_metric = 'MSE')
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plot(model)
pred<-as.data.frame(predict(model,as.h2o(testDF)))
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str(pred)
## 'data.frame': 106 obs. of 1 variable:
## $ predict: num 30.6 19.7 18.1 17.9 18.5 ...
plot(testDF$medv,pred$predict)
abline(0,1,col='black')
