Complete Machine Learning and Deep Learning With H2O in R
Complete Machine Learning and Deep Learning With H2O in R
Source file ⇒ CompleteMLDLWith-H2OinR.rmd
Read in Data From Different Sources
## [1] "C:/Users/HP/Desktop/CompleteMDLWithH2OinR_MinervaSingh/rfiles"setwd("C:/Users/HP/Desktop/CompleteMDLWithH2OinR_MinervaSingh/rfiles")
#read in the CSV data UCL website:
#https://archive.ics.uci.edu/ml/datasets/Wine+Quality
winer1=read.csv("winequality-red.csv",header=T)
#header= T will read in column names as well
head(winer1)## fixed.acidity.volatile.acidity.citric.acid.residual.sugar.chlorides.free.sulfur.dioxide.total.sulfur.dioxide.density.pH.sulphates.alcohol.quality
## 1 7.4;0.7;0;1.9;0.076;11;34;0.9978;3.51;0.56;9.4;5
## 2 7.8;0.88;0;2.6;0.098;25;67;0.9968;3.2;0.68;9.8;5
## 3 7.8;0.76;0.04;2.3;0.092;15;54;0.997;3.26;0.65;9.8;5
## 4 11.2;0.28;0.56;1.9;0.075;17;60;0.998;3.16;0.58;9.8;6
## 5 7.4;0.7;0;1.9;0.076;11;34;0.9978;3.51;0.56;9.4;5
## 6 7.4;0.66;0;1.8;0.075;13;40;0.9978;3.51;0.56;9.4;5## fixed.acidity.volatile.acidity.citric.acid.residual.sugar.chlorides.free.sulfur.dioxide.total.sulfur.dioxide.density.pH.sulphates.alcohol.quality
## 6.7;0.46;0.24;1.7;0.077;18;34;0.9948;3.39;0.6;10.6;6 : 4
## 7.2;0.36;0.46;2.1;0.074;24;44;0.99534;3.4;0.85;11;7 : 4
## 7.2;0.695;0.13;2;0.076;12;20;0.99546;3.29;0.54;10.1;5 : 4
## 7.5;0.51;0.02;1.7;0.084;13;31;0.99538;3.36;0.54;10.5;6: 4
## 11.5;0.18;0.51;4;0.104;4;23;0.9996;3.28;0.97;10.1;6 : 3
## 6.4;0.64;0.21;1.8;0.081;14;31;0.99689;3.59;0.66;9.8;5 : 3
## (Other) :1577winer1=read.csv("winequality-red.csv",header=T,sep=",")
#header= T will read in column names as well
head(winer1)## fixed.acidity.volatile.acidity.citric.acid.residual.sugar.chlorides.free.sulfur.dioxide.total.sulfur.dioxide.density.pH.sulphates.alcohol.quality
## 1 7.4;0.7;0;1.9;0.076;11;34;0.9978;3.51;0.56;9.4;5
## 2 7.8;0.88;0;2.6;0.098;25;67;0.9968;3.2;0.68;9.8;5
## 3 7.8;0.76;0.04;2.3;0.092;15;54;0.997;3.26;0.65;9.8;5
## 4 11.2;0.28;0.56;1.9;0.075;17;60;0.998;3.16;0.58;9.8;6
## 5 7.4;0.7;0;1.9;0.076;11;34;0.9978;3.51;0.56;9.4;5
## 6 7.4;0.66;0;1.8;0.075;13;40;0.9978;3.51;0.56;9.4;5## fixed.acidity.volatile.acidity.citric.acid.residual.sugar.chlorides.free.sulfur.dioxide.total.sulfur.dioxide.density.pH.sulphates.alcohol.quality
## 6.7;0.46;0.24;1.7;0.077;18;34;0.9948;3.39;0.6;10.6;6 : 4
## 7.2;0.36;0.46;2.1;0.074;24;44;0.99534;3.4;0.85;11;7 : 4
## 7.2;0.695;0.13;2;0.076;12;20;0.99546;3.29;0.54;10.1;5 : 4
## 7.5;0.51;0.02;1.7;0.084;13;31;0.99538;3.36;0.54;10.5;6: 4
## 11.5;0.18;0.51;4;0.104;4;23;0.9996;3.28;0.97;10.1;6 : 3
## 6.4;0.64;0.21;1.8;0.081;14;31;0.99689;3.59;0.66;9.8;5 : 3
## (Other) :1577#specify the correct seperator
winer=read.table("winequality-red.csv",header=T,sep=";")
#header= T will read in column names as well
head(winer)## fixed.acidity volatile.acidity citric.acid residual.sugar chlorides
## 1 7.4 0.70 0.00 1.9 0.076
## 2 7.8 0.88 0.00 2.6 0.098
## 3 7.8 0.76 0.04 2.3 0.092
## 4 11.2 0.28 0.56 1.9 0.075
## 5 7.4 0.70 0.00 1.9 0.076
## 6 7.4 0.66 0.00 1.8 0.075
## free.sulfur.dioxide total.sulfur.dioxide density pH sulphates alcohol
## 1 11 34 1 3.5 0.56 9.4
## 2 25 67 1 3.2 0.68 9.8
## 3 15 54 1 3.3 0.65 9.8
## 4 17 60 1 3.2 0.58 9.8
## 5 11 34 1 3.5 0.56 9.4
## 6 13 40 1 3.5 0.56 9.4
## quality
## 1 5
## 2 5
## 3 5
## 4 6
## 5 5
## 6 5## fixed.acidity volatile.acidity citric.acid residual.sugar chlorides
## Min. : 4.6 Min. :0.12 Min. :0.00 Min. : 0.9 Min. :0.01
## 1st Qu.: 7.1 1st Qu.:0.39 1st Qu.:0.09 1st Qu.: 1.9 1st Qu.:0.07
## Median : 7.9 Median :0.52 Median :0.26 Median : 2.2 Median :0.08
## Mean : 8.3 Mean :0.53 Mean :0.27 Mean : 2.5 Mean :0.09
## 3rd Qu.: 9.2 3rd Qu.:0.64 3rd Qu.:0.42 3rd Qu.: 2.6 3rd Qu.:0.09
## Max. :15.9 Max. :1.58 Max. :1.00 Max. :15.5 Max. :0.61
## free.sulfur.dioxide total.sulfur.dioxide density pH
## Min. : 1 Min. : 6 Min. :0.99 Min. :2.7
## 1st Qu.: 7 1st Qu.: 22 1st Qu.:1.00 1st Qu.:3.2
## Median :14 Median : 38 Median :1.00 Median :3.3
## Mean :16 Mean : 46 Mean :1.00 Mean :3.3
## 3rd Qu.:21 3rd Qu.: 62 3rd Qu.:1.00 3rd Qu.:3.4
## Max. :72 Max. :289 Max. :1.00 Max. :4.0
## sulphates alcohol quality
## Min. :0.33 Min. : 8.4 Min. :3.0
## 1st Qu.:0.55 1st Qu.: 9.5 1st Qu.:5.0
## Median :0.62 Median :10.2 Median :6.0
## Mean :0.66 Mean :10.4 Mean :5.6
## 3rd Qu.:0.73 3rd Qu.:11.1 3rd Qu.:6.0
## Max. :2.00 Max. :14.9 Max. :8.0##Read in excel data
#excel
#summary(boston1)
library(readxl)
dfb <- read_excel("boston1.xls")
head(dfb)## # A tibble: 6 x 10
## MV INDUS NOX RM TAX PT LSTAT X__1 X__2 X__3
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <lgl> <chr>
## 1 24 2.31 53.8 6.58 296 15.3 4.98 NA NA Subset of Boston housin~
## 2 21.6 7.07 46.9 6.42 242 17.8 9.14 NA NA data of Harrison and Ru~
## 3 34.7 7.07 46.9 7.18 242 17.8 4.03 NA NA (1978). Each case is o~
## 4 33.4 2.18 45.8 7.00 222 18.7 2.94 NA NA Census tract in the Bos~
## 5 36.2 2.18 45.8 7.15 222 18.7 5.33 NA NA <NA>
## 6 28.7 2.18 45.8 6.43 222 18.7 5.21 NA NA <NA>## MV INDUS NOX RM TAX
## Min. : 5 Min. : 0.5 Min. :38 Min. :3.6 Min. :187
## 1st Qu.:17 1st Qu.: 5.2 1st Qu.:45 1st Qu.:5.9 1st Qu.:279
## Median :21 Median : 9.7 Median :54 Median :6.2 Median :330
## Mean :23 Mean :11.1 Mean :55 Mean :6.3 Mean :408
## 3rd Qu.:25 3rd Qu.:18.1 3rd Qu.:62 3rd Qu.:6.6 3rd Qu.:666
## Max. :50 Max. :27.7 Max. :87 Max. :8.8 Max. :711
## PT LSTAT X__1 X__2 X__3
## Min. :12.6 Min. : 2 Mode:logical Mode:logical Length:506
## 1st Qu.:17.4 1st Qu.: 7 NA's:506 NA's:506 Class :character
## Median :19.1 Median :11 Mode :character
## Mean :18.5 Mean :13
## 3rd Qu.:20.2 3rd Qu.:17
## Max. :22.0 Max. :38##################################################################
### Read in data from Wikipedia HTML tables
library(rvest)
#Summer olympics medal tally
url <- "https://en.wikipedia.org/wiki/2016_Summer_Olympics_medal_table"
medal_tally <- url %>% read_html() %>%
html_nodes(xpath='//*[@id="mw-content-text"]/div/table[2]') %>% html_table(fill=TRUE)
## copy xpath
## //*[@id="mw-content-text"]/div/table[2]
# //*[@id="mw-content-text"]/div/table[2]
medal_tally <- medal_tally[[1]]
head(medal_tally)## Rank NOC Gold Silver Bronze Total
## 1 1 United States (USA) 46 37 38 121
## 2 2 Great Britain (GBR) 27 23 17 67
## 3 3 China (CHN) 26 18 26 70
## 4 4 Russia (RUS) 19 17 20 56
## 5 5 Germany (GER) 17 10 15 42
## 6 6 Japan (JPN) 12 8 21 41#WHS Sites in the UK
url2="https://en.wikipedia.org/wiki/List_of_World_Heritage_Sites_in_the_United_Kingdom_and_the_British_Overseas_Territories"
whsuk <- url2 %>% read_html() %>%
html_nodes(xpath='//*[@id="mw-content-text"]/div/table[3]') %>% html_table(fill=TRUE)
whsuk <- whsuk[[1]]
head(whsuk)## Name Image
## 1 Blaenavon Industrial Landscape NA
## 2 Blenheim Palace NA
## 3 Canterbury Cathedral, St Augustine's Abbey, and St Martin's Church NA
## 4 Castles and Town Walls of King Edward in Gwynedd NA
## 5 City of Bath NA
## 6 Cornwall and West Devon Mining Landscape NA
## Location
## 1 Blaenavon, Wales51°47'N 3°05'W<U+FEFF> / <U+FEFF>51.78°N 3.08°W<U+FEFF> / 51.78; -3.08<U+FEFF> (Blaenavon Industrial Landscape)[14]
## 2 Woodstock, Oxfordshire, England51°50'28<U+2033>N 1°21'40<U+2033>W<U+FEFF> / <U+FEFF>51.841°N 1.361°W<U+FEFF> / 51.841; -1.361<U+FEFF> (Blenheim Palace)[15]
## 3 Canterbury, Kent, England51°17'N 1°05'E<U+FEFF> / <U+FEFF>51.28°N 1.08°E<U+FEFF> / 51.28; 1.08<U+FEFF> (Canterbury Cathedral, St Augustine's Abbey, and St Martin's Church)[16]
## 4 Conwy, Isle of Anglesey and Gwynedd, Wales53°08'20<U+2033>N 4°16'34<U+2033>W<U+FEFF> / <U+FEFF>53.139°N 4.276°W<U+FEFF> / 53.139; -4.276<U+FEFF> (Castles and Town Walls of King Edward in Gwynedd)[19]
## 5 Bath, Somerset, England51°22'48<U+2033>N 2°21'36<U+2033>W<U+FEFF> / <U+FEFF>51.380°N 2.360°W<U+FEFF> / 51.380; -2.360<U+FEFF> (City of Bath)[21]
## 6 Cornwall and Devon, England50°08'N 5°23'W<U+FEFF> / <U+FEFF>50.13°N 5.38°W<U+FEFF> / 50.13; -5.38<U+FEFF> (Cornwall and West Devon Mining Landscape)[22]
## Date UNESCO data
## 1 19th century[14] 984; 2000;iii, iv[14]
## 2 1705–1722[15] 425; 1987;ii, iv[15]
## 3 11th century[16] 496; 1988;i, ii, vi[16]
## 4 13th–14th centuries[19] 374; 1986;i, iii, iv[19]
## 5 1st–19th centuries[21] 428; 1987;i, ii, iv[21]
## 6 18th and 19th centuries[22] 1,215; 2006;ii, iii, iv[22]
## Description
## 1 In the 19th century, Wales was the world's foremost producer of iron and coal. Blaenavon is an example of the landscape created by the industrial processes associated with the production of these materials. The site includes quarries, public buildings, workers' housing, and a railway.[14]
## 2 Blenheim Palace, the residence of John Churchill, 1st Duke of Marlborough, was designed by architects John Vanbrugh and Nicholas Hawksmoor. The associated park was landscaped by Capability Brown. The palace celebrated victory over the French and is significant for establishing English Romantic Architecture as a separate entity from French Classical Architecture.[15]
## 3 St Martin's Church is the oldest church in England. The church and St Augustine's Abbey were founded during the early stages of the introduction of Christianity to the Anglo-Saxons. The cathedral exhibits Romanesque and Gothic architecture, and is the seat of the Church of England.[16][17][18]
## 4 During the reign of Edward I of England (1272–1307), a series of castles was constructed in Wales with the purpose of subduing the population and establishing English colonies in Wales. The World Heritage Site covers many castles including Beaumaris, Caernarfon, Conwy, and Harlech. The castles of Edward I are considered the pinnacle of military architecture by military historians.[19][20]
## 5 Founded by the Romans as a spa, an important centre of the wool industry in the medieval period, and a spa town in the 18th century, Bath has a varied history. The city is preserved for its Roman remains and Palladian architecture.[21]
## 6 Tin and copper mining in Devon and Cornwall boomed in the 18th and 19th centuries, and at its peak the area produced two-thirds of the world's copper. The techniques and technology involved in deep mining developed in Devon and Cornwall were used around the world.[22]#################################################################
### JSON-->Javascript Object Notation
library(rjson)
#name/url of json file
json_file <- "http://api.worldbank.org/country?per_page=10®ion=OED&lendingtype=LNX&format=json"
#json data is stored in json_data
json_data <- fromJSON(file=json_file)
#you can see that this json file has two objects in the outer most list
json_data[[1]]## $page
## [1] 1
##
## $pages
## [1] 4
##
## $per_page
## [1] "10"
##
## $total
## [1] 32## [[1]]
## [[1]]$id
## [1] "AUS"
##
## [[1]]$iso2Code
## [1] "AU"
##
## [[1]]$name
## [1] "Australia"
##
## [[1]]$region
## [[1]]$region$id
## [1] "EAS"
##
## [[1]]$region$value
## [1] "East Asia & Pacific"
##
##
## [[1]]$adminregion
## [[1]]$adminregion$id
## [1] ""
##
## [[1]]$adminregion$value
## [1] ""
##
##
## [[1]]$incomeLevel
## [[1]]$incomeLevel$id
## [1] "HIC"
##
## [[1]]$incomeLevel$value
## [1] "High income"
##
##
## [[1]]$lendingType
## [[1]]$lendingType$id
## [1] "LNX"
##
## [[1]]$lendingType$value
## [1] "Not classified"
##
##
## [[1]]$capitalCity
## [1] "Canberra"
##
## [[1]]$longitude
## [1] "149.129"
##
## [[1]]$latitude
## [1] "-35.282"
##
##
## [[2]]
## [[2]]$id
## [1] "AUT"
##
## [[2]]$iso2Code
## [1] "AT"
##
## [[2]]$name
## [1] "Austria"
##
## [[2]]$region
## [[2]]$region$id
## [1] "ECS"
##
## [[2]]$region$value
## [1] "Europe & Central Asia"
##
##
## [[2]]$adminregion
## [[2]]$adminregion$id
## [1] ""
##
## [[2]]$adminregion$value
## [1] ""
##
##
## [[2]]$incomeLevel
## [[2]]$incomeLevel$id
## [1] "HIC"
##
## [[2]]$incomeLevel$value
## [1] "High income"
##
##
## [[2]]$lendingType
## [[2]]$lendingType$id
## [1] "LNX"
##
## [[2]]$lendingType$value
## [1] "Not classified"
##
##
## [[2]]$capitalCity
## [1] "Vienna"
##
## [[2]]$longitude
## [1] "16.3798"
##
## [[2]]$latitude
## [1] "48.2201"
##
##
## [[3]]
## [[3]]$id
## [1] "BEL"
##
## [[3]]$iso2Code
## [1] "BE"
##
## [[3]]$name
## [1] "Belgium"
##
## [[3]]$region
## [[3]]$region$id
## [1] "ECS"
##
## [[3]]$region$value
## [1] "Europe & Central Asia"
##
##
## [[3]]$adminregion
## [[3]]$adminregion$id
## [1] ""
##
## [[3]]$adminregion$value
## [1] ""
##
##
## [[3]]$incomeLevel
## [[3]]$incomeLevel$id
## [1] "HIC"
##
## [[3]]$incomeLevel$value
## [1] "High income"
##
##
## [[3]]$lendingType
## [[3]]$lendingType$id
## [1] "LNX"
##
## [[3]]$lendingType$value
## [1] "Not classified"
##
##
## [[3]]$capitalCity
## [1] "Brussels"
##
## [[3]]$longitude
## [1] "4.36761"
##
## [[3]]$latitude
## [1] "50.8371"
##
##
## [[4]]
## [[4]]$id
## [1] "CAN"
##
## [[4]]$iso2Code
## [1] "CA"
##
## [[4]]$name
## [1] "Canada"
##
## [[4]]$region
## [[4]]$region$id
## [1] "NAC"
##
## [[4]]$region$value
## [1] "North America"
##
##
## [[4]]$adminregion
## [[4]]$adminregion$id
## [1] ""
##
## [[4]]$adminregion$value
## [1] ""
##
##
## [[4]]$incomeLevel
## [[4]]$incomeLevel$id
## [1] "HIC"
##
## [[4]]$incomeLevel$value
## [1] "High income"
##
##
## [[4]]$lendingType
## [[4]]$lendingType$id
## [1] "LNX"
##
## [[4]]$lendingType$value
## [1] "Not classified"
##
##
## [[4]]$capitalCity
## [1] "Ottawa"
##
## [[4]]$longitude
## [1] "-75.6919"
##
## [[4]]$latitude
## [1] "45.4215"
##
##
## [[5]]
## [[5]]$id
## [1] "CHE"
##
## [[5]]$iso2Code
## [1] "CH"
##
## [[5]]$name
## [1] "Switzerland"
##
## [[5]]$region
## [[5]]$region$id
## [1] "ECS"
##
## [[5]]$region$value
## [1] "Europe & Central Asia"
##
##
## [[5]]$adminregion
## [[5]]$adminregion$id
## [1] ""
##
## [[5]]$adminregion$value
## [1] ""
##
##
## [[5]]$incomeLevel
## [[5]]$incomeLevel$id
## [1] "HIC"
##
## [[5]]$incomeLevel$value
## [1] "High income"
##
##
## [[5]]$lendingType
## [[5]]$lendingType$id
## [1] "LNX"
##
## [[5]]$lendingType$value
## [1] "Not classified"
##
##
## [[5]]$capitalCity
## [1] "Bern"
##
## [[5]]$longitude
## [1] "7.44821"
##
## [[5]]$latitude
## [1] "46.948"
##
##
## [[6]]
## [[6]]$id
## [1] "CZE"
##
## [[6]]$iso2Code
## [1] "CZ"
##
## [[6]]$name
## [1] "Czech Republic"
##
## [[6]]$region
## [[6]]$region$id
## [1] "ECS"
##
## [[6]]$region$value
## [1] "Europe & Central Asia"
##
##
## [[6]]$adminregion
## [[6]]$adminregion$id
## [1] ""
##
## [[6]]$adminregion$value
## [1] ""
##
##
## [[6]]$incomeLevel
## [[6]]$incomeLevel$id
## [1] "HIC"
##
## [[6]]$incomeLevel$value
## [1] "High income"
##
##
## [[6]]$lendingType
## [[6]]$lendingType$id
## [1] "LNX"
##
## [[6]]$lendingType$value
## [1] "Not classified"
##
##
## [[6]]$capitalCity
## [1] "Prague"
##
## [[6]]$longitude
## [1] "14.4205"
##
## [[6]]$latitude
## [1] "50.0878"
##
##
## [[7]]
## [[7]]$id
## [1] "DEU"
##
## [[7]]$iso2Code
## [1] "DE"
##
## [[7]]$name
## [1] "Germany"
##
## [[7]]$region
## [[7]]$region$id
## [1] "ECS"
##
## [[7]]$region$value
## [1] "Europe & Central Asia"
##
##
## [[7]]$adminregion
## [[7]]$adminregion$id
## [1] ""
##
## [[7]]$adminregion$value
## [1] ""
##
##
## [[7]]$incomeLevel
## [[7]]$incomeLevel$id
## [1] "HIC"
##
## [[7]]$incomeLevel$value
## [1] "High income"
##
##
## [[7]]$lendingType
## [[7]]$lendingType$id
## [1] "LNX"
##
## [[7]]$lendingType$value
## [1] "Not classified"
##
##
## [[7]]$capitalCity
## [1] "Berlin"
##
## [[7]]$longitude
## [1] "13.4115"
##
## [[7]]$latitude
## [1] "52.5235"
##
##
## [[8]]
## [[8]]$id
## [1] "DNK"
##
## [[8]]$iso2Code
## [1] "DK"
##
## [[8]]$name
## [1] "Denmark"
##
## [[8]]$region
## [[8]]$region$id
## [1] "ECS"
##
## [[8]]$region$value
## [1] "Europe & Central Asia"
##
##
## [[8]]$adminregion
## [[8]]$adminregion$id
## [1] ""
##
## [[8]]$adminregion$value
## [1] ""
##
##
## [[8]]$incomeLevel
## [[8]]$incomeLevel$id
## [1] "HIC"
##
## [[8]]$incomeLevel$value
## [1] "High income"
##
##
## [[8]]$lendingType
## [[8]]$lendingType$id
## [1] "LNX"
##
## [[8]]$lendingType$value
## [1] "Not classified"
##
##
## [[8]]$capitalCity
## [1] "Copenhagen"
##
## [[8]]$longitude
## [1] "12.5681"
##
## [[8]]$latitude
## [1] "55.6763"
##
##
## [[9]]
## [[9]]$id
## [1] "ESP"
##
## [[9]]$iso2Code
## [1] "ES"
##
## [[9]]$name
## [1] "Spain"
##
## [[9]]$region
## [[9]]$region$id
## [1] "ECS"
##
## [[9]]$region$value
## [1] "Europe & Central Asia"
##
##
## [[9]]$adminregion
## [[9]]$adminregion$id
## [1] ""
##
## [[9]]$adminregion$value
## [1] ""
##
##
## [[9]]$incomeLevel
## [[9]]$incomeLevel$id
## [1] "HIC"
##
## [[9]]$incomeLevel$value
## [1] "High income"
##
##
## [[9]]$lendingType
## [[9]]$lendingType$id
## [1] "LNX"
##
## [[9]]$lendingType$value
## [1] "Not classified"
##
##
## [[9]]$capitalCity
## [1] "Madrid"
##
## [[9]]$longitude
## [1] "-3.70327"
##
## [[9]]$latitude
## [1] "40.4167"
##
##
## [[10]]
## [[10]]$id
## [1] "EST"
##
## [[10]]$iso2Code
## [1] "EE"
##
## [[10]]$name
## [1] "Estonia"
##
## [[10]]$region
## [[10]]$region$id
## [1] "ECS"
##
## [[10]]$region$value
## [1] "Europe & Central Asia"
##
##
## [[10]]$adminregion
## [[10]]$adminregion$id
## [1] ""
##
## [[10]]$adminregion$value
## [1] ""
##
##
## [[10]]$incomeLevel
## [[10]]$incomeLevel$id
## [1] "HIC"
##
## [[10]]$incomeLevel$value
## [1] "High income"
##
##
## [[10]]$lendingType
## [[10]]$lendingType$id
## [1] "LNX"
##
## [[10]]$lendingType$value
## [1] "Not classified"
##
##
## [[10]]$capitalCity
## [1] "Tallinn"
##
## [[10]]$longitude
## [1] "24.7586"
##
## [[10]]$latitude
## [1] "59.4392"#you can access any particular object from the json data as shown below
d3 <- lapply(json_data[[2]], function(x) c(x["id"], x["iso2Code"]))
d3 <- do.call(rbind, d3)
d3## id iso2Code
## [1,] "AUS" "AU"
## [2,] "AUT" "AT"
## [3,] "BEL" "BE"
## [4,] "CAN" "CA"
## [5,] "CHE" "CH"
## [6,] "CZE" "CZ"
## [7,] "DEU" "DE"
## [8,] "DNK" "DK"
## [9,] "ESP" "ES"
## [10,] "EST" "EE"d4 <- lapply(json_data[[2]], function(x) c(x["id"], x["iso2Code"], x$region["id"], x$region["value"], x["capitalCity"]))
d4 <- do.call(rbind, d4)
d4## id iso2Code id value capitalCity
## [1,] "AUS" "AU" "EAS" "East Asia & Pacific" "Canberra"
## [2,] "AUT" "AT" "ECS" "Europe & Central Asia" "Vienna"
## [3,] "BEL" "BE" "ECS" "Europe & Central Asia" "Brussels"
## [4,] "CAN" "CA" "NAC" "North America" "Ottawa"
## [5,] "CHE" "CH" "ECS" "Europe & Central Asia" "Bern"
## [6,] "CZE" "CZ" "ECS" "Europe & Central Asia" "Prague"
## [7,] "DEU" "DE" "ECS" "Europe & Central Asia" "Berlin"
## [8,] "DNK" "DK" "ECS" "Europe & Central Asia" "Copenhagen"
## [9,] "ESP" "ES" "ECS" "Europe & Central Asia" "Madrid"
## [10,] "EST" "EE" "ECS" "Europe & Central Asia" "Tallinn"#other example
json_file <- "skorea.json"
#json data is stored in json_data
json_data <- fromJSON(file=json_file)
#you can access your data as simply shown below
json_data[[1]]## $Description
## [1] ""
##
## $Image
## [1] "/wiki/File:MuryeongsTomb.jpg"
##
## $Criteria
## [1] "Cultural: (ii)(iii)"
##
## $Site
## [1] "Baekje Historic Areas"
##
## $`Area ha (acre)`
## [1] "135 (330)"
##
## $Location
## [1] "South Chungcheong, North Jeolla"
##
## $Year
## [1] "2015"## $Description
## [1] ""
##
## $Image
## [1] "/wiki/File:Korea-Gwangju-Gochang_Dolmens_5350-06.JPG"
##
## $Criteria
## [1] "Cultural: (iii)"
##
## $Site
## [1] "Gochang, Hwasun and Ganghwa Dolmen Sites"
##
## $`Area ha (acre)`
## [1] ""
##
## $Location
## [1] "Incheon, North Jeolla, South Jeolla"
##
## $Year
## [1] "2000"## $Description
## [1] ""
##
## $Image
## [1] "/wiki/File:Haeinsa_Temple_(6222053899).jpg"
##
## $Criteria
## [1] "Cultural: (iv)(vi)"
##
## $Site
## [1] "Haeinsa Temple Janggyeong Panjeon, the Depositories for the Tripitaka Koreana Woodblocks"
##
## $`Area ha (acre)`
## [1] ""
##
## $Location
## [1] "South Gyeongsang"
##
## $Year
## [1] "1995"#or you can extract some usefull data
d <- lapply(json_data, function(x) c(x['Image'], x['Criteria'], x['Site'], x['Area ha (acre)']))
d <- do.call(rbind, d)
d## Image
## [1,] "/wiki/File:MuryeongsTomb.jpg"
## [2,] "/wiki/File:Korea-Gwangju-Gochang_Dolmens_5350-06.JPG"
## [3,] "/wiki/File:Juhamnu,_Changdeokgung_-_Seoul,_Korea.JPG"
## [4,] "/wiki/File:Korea-Gyeongju-Bunhwangsa-Lanterns-03.jpg"
## [5,] "/wiki/File:Haeinsa_Temple_(6222053899).jpg"
## [6,] "/wiki/File:Hahoe_8784.jpg"
## [7,] "/wiki/File:Hwaseong2.jpg"
## [8,] "/wiki/File:KOCIS_Halla_Mountain_in_Jeju-do_(6387785543).jpg"
## [9,] "/wiki/File:Chongmyo_repository_(1509268349).jpg"
## [10,] "/wiki/File:Khitai5.jpg"
## [11,] "/wiki/File:Sejong_tomb_1.jpg"
## [12,] "/wiki/File:Bulguk_Tempel.jpg"
## Criteria
## [1,] "Cultural: (ii)(iii)"
## [2,] "Cultural: (iii)"
## [3,] "Cultural: (ii)(iii)(iv)"
## [4,] "Cultural: (ii)(iii)"
## [5,] "Cultural: (iv)(vi)"
## [6,] "Cultural: (iii)(iv)"
## [7,] "Cultural: (ii)(iii)"
## [8,] "Natural: (vii)(viii)"
## [9,] "Cultural: (iv)"
## [10,] "Cultural: (ii)(iv)"
## [11,] "Cultural: (iii)(iv)(vi)"
## [12,] "Cultural: (i)(iv)"
## Site
## [1,] "Baekje Historic Areas"
## [2,] "Gochang, Hwasun and Ganghwa Dolmen Sites"
## [3,] "Changdeokgung Palace Complex"
## [4,] "Gyeongju Historic Areas"
## [5,] "Haeinsa Temple Janggyeong Panjeon, the Depositories for the Tripitaka Koreana Woodblocks"
## [6,] "Historic Villages of Korea: Hahoe and Yangdong"
## [7,] "Hwaseong Fortress"
## [8,] "Jeju Volcanic Island and Lava Tubes"
## [9,] "Jongmyo Shrine"
## [10,] "Namhansanseong"
## [11,] "Royal Tombs of the Joseon Dynasty"
## [12,] "Seokguram Grotto and Bulguksa Temple"
## Area ha (acre)
## [1,] "135 (330)"
## [2,] ""
## [3,] ""
## [4,] "2,880 (7,100)"
## [5,] ""
## [6,] "600 (1,500)"
## [7,] ""
## [8,] "9,475 (23,410)"
## [9,] "19 (47)"
## [10,] "409 (1,010)"
## [11,] "1,891 (4,670)"
## [12,] ""## [,1] [,2] [,3] [,4]
## [1,] 1 2 3 4
## [2,] 1 2 3 4
## [3,] 1 2 3 4
## [4,] 1 2 3 4
## [5,] 1 2 3 4
## [6,] 1 2 3 4
## [7,] 1 2 3 4
## [8,] 1 2 3 4
## [9,] 1 2 3 4
## [10,] 1 2 3 4
## [11,] 1 2 3 4
## [12,] 1 2 3 4## [[1]]
## [1] "/wiki/File:MuryeongsTomb.jpg"
##
## [[2]]
## [1] "/wiki/File:Korea-Gwangju-Gochang_Dolmens_5350-06.JPG"
##
## [[3]]
## [1] "/wiki/File:Juhamnu,_Changdeokgung_-_Seoul,_Korea.JPG"
##
## [[4]]
## [1] "/wiki/File:Korea-Gyeongju-Bunhwangsa-Lanterns-03.jpg"
##
## [[5]]
## [1] "/wiki/File:Haeinsa_Temple_(6222053899).jpg"
##
## [[6]]
## [1] "/wiki/File:Hahoe_8784.jpg"
##
## [[7]]
## [1] "/wiki/File:Hwaseong2.jpg"
##
## [[8]]
## [1] "/wiki/File:KOCIS_Halla_Mountain_in_Jeju-do_(6387785543).jpg"
##
## [[9]]
## [1] "/wiki/File:Chongmyo_repository_(1509268349).jpg"
##
## [[10]]
## [1] "/wiki/File:Khitai5.jpg"
##
## [[11]]
## [1] "/wiki/File:Sejong_tomb_1.jpg"
##
## [[12]]
## [1] "/wiki/File:Bulguk_Tempel.jpg"## $Image
## [1] "/wiki/File:MuryeongsTomb.jpg"
##
## $Criteria
## [1] "Cultural: (ii)(iii)"
##
## $Site
## [1] "Baekje Historic Areas"
##
## $`Area ha (acre)`
## [1] "135 (330)"######################################################################
library(h2o)
#Start up a 1-node H2O server on your local machine, and allow it to use all CPU cores and up to 2GB of memory:
#
h2o.init(nthreads=-1, max_mem_size="2G")## Connection successful!
##
## R is connected to the H2O cluster:
## H2O cluster uptime: 6 minutes 40 seconds
## H2O cluster timezone: Asia/Kolkata
## H2O data parsing timezone: UTC
## H2O cluster version: 3.30.0.1
## H2O cluster version age: 1 month and 21 days
## H2O cluster name: H2O_started_from_R_HP_fjx183
## H2O cluster total nodes: 1
## H2O cluster total memory: 1.31 GB
## H2O cluster total cores: 4
## H2O cluster allowed cores: 4
## 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 3.5.0 (2018-04-23)h2o.removeAll() ## clean slate - just in case the cluster was already running
glass = h2o.importFile("glassClass.csv")##
|
| | 0%
|
|======================================================================| 100%## RI Na Mg Al Si K Ca Ba Fe Type
## 1 1.5 14 4.5 1.1 72 0.06 8.8 0 0.00 1
## 2 1.5 14 3.6 1.4 73 0.48 7.8 0 0.00 1
## 3 1.5 14 3.5 1.5 73 0.39 7.8 0 0.00 1
## 4 1.5 13 3.7 1.3 73 0.57 8.2 0 0.00 1
## 5 1.5 13 3.6 1.2 73 0.55 8.1 0 0.00 1
## 6 1.5 13 3.6 1.6 73 0.64 8.1 0 0.26 1## RI Na Mg Al
## Min. :1.511 Min. :10.73 Min. :0.000 Min. :0.290
## 1st Qu.:1.517 1st Qu.:12.91 1st Qu.:2.115 1st Qu.:1.190
## Median :1.518 Median :13.30 Median :3.480 Median :1.360
## Mean :1.518 Mean :13.41 Mean :2.685 Mean :1.445
## 3rd Qu.:1.519 3rd Qu.:13.82 3rd Qu.:3.600 3rd Qu.:1.630
## Max. :1.534 Max. :17.38 Max. :4.490 Max. :3.500
## Si K Ca Ba
## Min. :69.81 Min. :0.0000 Min. : 5.430 Min. :0.000
## 1st Qu.:72.28 1st Qu.:0.1225 1st Qu.: 8.240 1st Qu.:0.000
## Median :72.79 Median :0.5550 Median : 8.600 Median :0.000
## Mean :72.65 Mean :0.4971 Mean : 8.957 Mean :0.175
## 3rd Qu.:73.09 3rd Qu.:0.6100 3rd Qu.: 9.172 3rd Qu.:0.000
## Max. :75.41 Max. :6.2100 Max. :16.190 Max. :3.150
## Fe Type
## Min. :0.00000 Min. :1.00
## 1st Qu.:0.00000 1st Qu.:1.00
## Median :0.00000 Median :2.00
## Mean :0.05701 Mean :2.78
## 3rd Qu.:0.10000 3rd Qu.:3.00
## Max. :0.51000 Max. :7.00## K Counts
## 1 0.00 30
## 2 0.02 1
## 3 0.03 1
## 4 0.04 2
## 5 0.05 1
## 6 0.06 4
##
## [65 rows x 2 columns]Data Preprocessing
Removing NAs
## 'data.frame': 153 obs. of 6 variables:
## $ Ozone : int 41 36 12 18 NA 28 23 19 8 NA ...
## $ Solar.R: int 190 118 149 313 NA NA 299 99 19 194 ...
## $ Wind : num 7.4 8 12.6 11.5 14.3 14.9 8.6 13.8 20.1 8.6 ...
## $ Temp : int 67 72 74 62 56 66 65 59 61 69 ...
## $ Month : int 5 5 5 5 5 5 5 5 5 5 ...
## $ Day : int 1 2 3 4 5 6 7 8 9 10 ...## Ozone Solar.R Wind Temp Month Day
## 1 41 190 7.4 67 5 1
## 2 36 118 8.0 72 5 2
## 3 12 149 12.6 74 5 3
## 4 18 313 11.5 62 5 4
## 5 NA NA 14.3 56 5 5
## 6 28 NA 14.9 66 5 6## Ozone Solar.R Wind Temp Month
## Min. : 1 Min. : 7 Min. : 1.7 Min. :56 Min. :5
## 1st Qu.: 18 1st Qu.:116 1st Qu.: 7.4 1st Qu.:72 1st Qu.:6
## Median : 32 Median :205 Median : 9.7 Median :79 Median :7
## Mean : 42 Mean :186 Mean :10.0 Mean :78 Mean :7
## 3rd Qu.: 63 3rd Qu.:259 3rd Qu.:11.5 3rd Qu.:85 3rd Qu.:8
## Max. :168 Max. :334 Max. :20.7 Max. :97 Max. :9
## NA's :37 NA's :7
## Day
## Min. : 1.0
## 1st Qu.: 8.0
## Median :16.0
## Mean :15.8
## 3rd Qu.:23.0
## Max. :31.0
## ## Ozone Solar.R Wind Temp Month Day
## 1 41 190 7.4 67 5 1
## 2 36 118 8.0 72 5 2
## 3 12 149 12.6 74 5 3
## 4 18 313 11.5 62 5 4
## 7 23 299 8.6 65 5 7
## 8 19 99 13.8 59 5 8## Ozone Solar.R Wind Temp Month
## Min. : 1 Min. : 7 Min. : 2.3 Min. :57 Min. :5.0
## 1st Qu.: 18 1st Qu.:114 1st Qu.: 7.4 1st Qu.:71 1st Qu.:6.0
## Median : 31 Median :207 Median : 9.7 Median :79 Median :7.0
## Mean : 42 Mean :185 Mean : 9.9 Mean :78 Mean :7.2
## 3rd Qu.: 62 3rd Qu.:256 3rd Qu.:11.5 3rd Qu.:84 3rd Qu.:9.0
## Max. :168 Max. :334 Max. :20.7 Max. :97 Max. :9.0
## Day
## Min. : 1.0
## 1st Qu.: 9.0
## Median :16.0
## Mean :15.9
## 3rd Qu.:22.5
## Max. :31.0## Ozone Solar.R Wind Temp Month Day
## 1 41 190 7.4 67 5 1
## 2 36 118 8.0 72 5 2
## 3 12 149 12.6 74 5 3
## 4 18 313 11.5 62 5 4
## 7 23 299 8.6 65 5 7
## 8 19 99 13.8 59 5 8## Ozone Solar.R Wind Temp Month
## Min. : 1 Min. : 7 Min. : 2.3 Min. :57 Min. :5.0
## 1st Qu.: 18 1st Qu.:114 1st Qu.: 7.4 1st Qu.:71 1st Qu.:6.0
## Median : 31 Median :207 Median : 9.7 Median :79 Median :7.0
## Mean : 42 Mean :185 Mean : 9.9 Mean :78 Mean :7.2
## 3rd Qu.: 62 3rd Qu.:256 3rd Qu.:11.5 3rd Qu.:84 3rd Qu.:9.0
## Max. :168 Max. :334 Max. :20.7 Max. :97 Max. :9.0
## Day
## Min. : 1.0
## 1st Qu.: 9.0
## Median :16.0
## Mean :15.9
## 3rd Qu.:22.5
## Max. :31.0## Ozone Solar.R Wind Temp Month Day
## 1 41 190 7.4 67 5 1
## 2 36 118 8.0 72 5 2
## 3 12 149 12.6 74 5 3
## 4 18 313 11.5 62 5 4
## 5 0 0 14.3 56 5 5
## 6 28 0 14.9 66 5 6## Ozone Solar.R Wind Temp Month
## Min. : 0 Min. : 0 Min. : 1.7 Min. :56 Min. :5
## 1st Qu.: 4 1st Qu.: 95 1st Qu.: 7.4 1st Qu.:72 1st Qu.:6
## Median : 21 Median :194 Median : 9.7 Median :79 Median :7
## Mean : 32 Mean :177 Mean :10.0 Mean :78 Mean :7
## 3rd Qu.: 46 3rd Qu.:256 3rd Qu.:11.5 3rd Qu.:85 3rd Qu.:8
## Max. :168 Max. :334 Max. :20.7 Max. :97 Max. :9
## Day
## Min. : 1.0
## 1st Qu.: 8.0
## Median :16.0
## Mean :15.8
## 3rd Qu.:23.0
## Max. :31.0## replcae missing values with average values
meanOzone=mean(airquality$Ozone,na.rm=T)
# remove NAs while computing mean of Ozone
#with na mean value will be na
aqty.fix=ifelse(is.na(airquality$Ozone),meanOzone,airquality$Ozone)
summary(aqty.fix)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1 21 42 42 46 168
## Wind Temp Month Day Solar.R Ozone
## 111 1 1 1 1 1 1 0
## 35 1 1 1 1 1 0 1
## 5 1 1 1 1 0 1 1
## 2 1 1 1 1 0 0 2
## 0 0 0 0 7 37 44#111 observations with no values
library(VIM) #visualize the pattern of NAs
mp <- aggr(aqty2, col=c('navyblue','yellow'),
numbers=TRUE, sortVars=TRUE,
labels=names(aqty2), cex.axis=.7,
gap=3, ylab=c("Missing data","Pattern"))
##
## Variables sorted by number of missings:
## Variable Count
## Ozone 0.242
## Solar.R 0.046
## Wind 0.000
## Temp 0.000
## Month 0.000
## Day 0.000#72.5% observations in the entire data have no missing values
#22.9% missing values in Ozone
#impute
#500 iterataions of predictive mean mapping for imputing
#5 datasets
im_aqty<- mice(aqty2, m=5, maxit = 50, method = 'pmm', seed = 500)##
## iter imp variable
## 1 1 Ozone Solar.R
## 1 2 Ozone Solar.R
## 1 3 Ozone Solar.R
## 1 4 Ozone Solar.R
## 1 5 Ozone Solar.R
## 2 1 Ozone Solar.R
## 2 2 Ozone Solar.R
## 2 3 Ozone Solar.R
## 2 4 Ozone Solar.R
## 2 5 Ozone Solar.R
## 3 1 Ozone Solar.R
## 3 2 Ozone Solar.R
## 3 3 Ozone Solar.R
## 3 4 Ozone Solar.R
## 3 5 Ozone Solar.R
## 4 1 Ozone Solar.R
## 4 2 Ozone Solar.R
## 4 3 Ozone Solar.R
## 4 4 Ozone Solar.R
## 4 5 Ozone Solar.R
## 5 1 Ozone Solar.R
## 5 2 Ozone Solar.R
## 5 3 Ozone Solar.R
## 5 4 Ozone Solar.R
## 5 5 Ozone Solar.R
## 6 1 Ozone Solar.R
## 6 2 Ozone Solar.R
## 6 3 Ozone Solar.R
## 6 4 Ozone Solar.R
## 6 5 Ozone Solar.R
## 7 1 Ozone Solar.R
## 7 2 Ozone Solar.R
## 7 3 Ozone Solar.R
## 7 4 Ozone Solar.R
## 7 5 Ozone Solar.R
## 8 1 Ozone Solar.R
## 8 2 Ozone Solar.R
## 8 3 Ozone Solar.R
## 8 4 Ozone Solar.R
## 8 5 Ozone Solar.R
## 9 1 Ozone Solar.R
## 9 2 Ozone Solar.R
## 9 3 Ozone Solar.R
## 9 4 Ozone Solar.R
## 9 5 Ozone Solar.R
## 10 1 Ozone Solar.R
## 10 2 Ozone Solar.R
## 10 3 Ozone Solar.R
## 10 4 Ozone Solar.R
## 10 5 Ozone Solar.R
## 11 1 Ozone Solar.R
## 11 2 Ozone Solar.R
## 11 3 Ozone Solar.R
## 11 4 Ozone Solar.R
## 11 5 Ozone Solar.R
## 12 1 Ozone Solar.R
## 12 2 Ozone Solar.R
## 12 3 Ozone Solar.R
## 12 4 Ozone Solar.R
## 12 5 Ozone Solar.R
## 13 1 Ozone Solar.R
## 13 2 Ozone Solar.R
## 13 3 Ozone Solar.R
## 13 4 Ozone Solar.R
## 13 5 Ozone Solar.R
## 14 1 Ozone Solar.R
## 14 2 Ozone Solar.R
## 14 3 Ozone Solar.R
## 14 4 Ozone Solar.R
## 14 5 Ozone Solar.R
## 15 1 Ozone Solar.R
## 15 2 Ozone Solar.R
## 15 3 Ozone Solar.R
## 15 4 Ozone Solar.R
## 15 5 Ozone Solar.R
## 16 1 Ozone Solar.R
## 16 2 Ozone Solar.R
## 16 3 Ozone Solar.R
## 16 4 Ozone Solar.R
## 16 5 Ozone Solar.R
## 17 1 Ozone Solar.R
## 17 2 Ozone Solar.R
## 17 3 Ozone Solar.R
## 17 4 Ozone Solar.R
## 17 5 Ozone Solar.R
## 18 1 Ozone Solar.R
## 18 2 Ozone Solar.R
## 18 3 Ozone Solar.R
## 18 4 Ozone Solar.R
## 18 5 Ozone Solar.R
## 19 1 Ozone Solar.R
## 19 2 Ozone Solar.R
## 19 3 Ozone Solar.R
## 19 4 Ozone Solar.R
## 19 5 Ozone Solar.R
## 20 1 Ozone Solar.R
## 20 2 Ozone Solar.R
## 20 3 Ozone Solar.R
## 20 4 Ozone Solar.R
## 20 5 Ozone Solar.R
## 21 1 Ozone Solar.R
## 21 2 Ozone Solar.R
## 21 3 Ozone Solar.R
## 21 4 Ozone Solar.R
## 21 5 Ozone Solar.R
## 22 1 Ozone Solar.R
## 22 2 Ozone Solar.R
## 22 3 Ozone Solar.R
## 22 4 Ozone Solar.R
## 22 5 Ozone Solar.R
## 23 1 Ozone Solar.R
## 23 2 Ozone Solar.R
## 23 3 Ozone Solar.R
## 23 4 Ozone Solar.R
## 23 5 Ozone Solar.R
## 24 1 Ozone Solar.R
## 24 2 Ozone Solar.R
## 24 3 Ozone Solar.R
## 24 4 Ozone Solar.R
## 24 5 Ozone Solar.R
## 25 1 Ozone Solar.R
## 25 2 Ozone Solar.R
## 25 3 Ozone Solar.R
## 25 4 Ozone Solar.R
## 25 5 Ozone Solar.R
## 26 1 Ozone Solar.R
## 26 2 Ozone Solar.R
## 26 3 Ozone Solar.R
## 26 4 Ozone Solar.R
## 26 5 Ozone Solar.R
## 27 1 Ozone Solar.R
## 27 2 Ozone Solar.R
## 27 3 Ozone Solar.R
## 27 4 Ozone Solar.R
## 27 5 Ozone Solar.R
## 28 1 Ozone Solar.R
## 28 2 Ozone Solar.R
## 28 3 Ozone Solar.R
## 28 4 Ozone Solar.R
## 28 5 Ozone Solar.R
## 29 1 Ozone Solar.R
## 29 2 Ozone Solar.R
## 29 3 Ozone Solar.R
## 29 4 Ozone Solar.R
## 29 5 Ozone Solar.R
## 30 1 Ozone Solar.R
## 30 2 Ozone Solar.R
## 30 3 Ozone Solar.R
## 30 4 Ozone Solar.R
## 30 5 Ozone Solar.R
## 31 1 Ozone Solar.R
## 31 2 Ozone Solar.R
## 31 3 Ozone Solar.R
## 31 4 Ozone Solar.R
## 31 5 Ozone Solar.R
## 32 1 Ozone Solar.R
## 32 2 Ozone Solar.R
## 32 3 Ozone Solar.R
## 32 4 Ozone Solar.R
## 32 5 Ozone Solar.R
## 33 1 Ozone Solar.R
## 33 2 Ozone Solar.R
## 33 3 Ozone Solar.R
## 33 4 Ozone Solar.R
## 33 5 Ozone Solar.R
## 34 1 Ozone Solar.R
## 34 2 Ozone Solar.R
## 34 3 Ozone Solar.R
## 34 4 Ozone Solar.R
## 34 5 Ozone Solar.R
## 35 1 Ozone Solar.R
## 35 2 Ozone Solar.R
## 35 3 Ozone Solar.R
## 35 4 Ozone Solar.R
## 35 5 Ozone Solar.R
## 36 1 Ozone Solar.R
## 36 2 Ozone Solar.R
## 36 3 Ozone Solar.R
## 36 4 Ozone Solar.R
## 36 5 Ozone Solar.R
## 37 1 Ozone Solar.R
## 37 2 Ozone Solar.R
## 37 3 Ozone Solar.R
## 37 4 Ozone Solar.R
## 37 5 Ozone Solar.R
## 38 1 Ozone Solar.R
## 38 2 Ozone Solar.R
## 38 3 Ozone Solar.R
## 38 4 Ozone Solar.R
## 38 5 Ozone Solar.R
## 39 1 Ozone Solar.R
## 39 2 Ozone Solar.R
## 39 3 Ozone Solar.R
## 39 4 Ozone Solar.R
## 39 5 Ozone Solar.R
## 40 1 Ozone Solar.R
## 40 2 Ozone Solar.R
## 40 3 Ozone Solar.R
## 40 4 Ozone Solar.R
## 40 5 Ozone Solar.R
## 41 1 Ozone Solar.R
## 41 2 Ozone Solar.R
## 41 3 Ozone Solar.R
## 41 4 Ozone Solar.R
## 41 5 Ozone Solar.R
## 42 1 Ozone Solar.R
## 42 2 Ozone Solar.R
## 42 3 Ozone Solar.R
## 42 4 Ozone Solar.R
## 42 5 Ozone Solar.R
## 43 1 Ozone Solar.R
## 43 2 Ozone Solar.R
## 43 3 Ozone Solar.R
## 43 4 Ozone Solar.R
## 43 5 Ozone Solar.R
## 44 1 Ozone Solar.R
## 44 2 Ozone Solar.R
## 44 3 Ozone Solar.R
## 44 4 Ozone Solar.R
## 44 5 Ozone Solar.R
## 45 1 Ozone Solar.R
## 45 2 Ozone Solar.R
## 45 3 Ozone Solar.R
## 45 4 Ozone Solar.R
## 45 5 Ozone Solar.R
## 46 1 Ozone Solar.R
## 46 2 Ozone Solar.R
## 46 3 Ozone Solar.R
## 46 4 Ozone Solar.R
## 46 5 Ozone Solar.R
## 47 1 Ozone Solar.R
## 47 2 Ozone Solar.R
## 47 3 Ozone Solar.R
## 47 4 Ozone Solar.R
## 47 5 Ozone Solar.R
## 48 1 Ozone Solar.R
## 48 2 Ozone Solar.R
## 48 3 Ozone Solar.R
## 48 4 Ozone Solar.R
## 48 5 Ozone Solar.R
## 49 1 Ozone Solar.R
## 49 2 Ozone Solar.R
## 49 3 Ozone Solar.R
## 49 4 Ozone Solar.R
## 49 5 Ozone Solar.R
## 50 1 Ozone Solar.R
## 50 2 Ozone Solar.R
## 50 3 Ozone Solar.R
## 50 4 Ozone Solar.R
## 50 5 Ozone Solar.R## Class: mids
## Number of multiple imputations: 5
## Imputation methods:
## Ozone Solar.R Wind Temp Month Day
## "pmm" "pmm" "" "" "" ""
## PredictorMatrix:
## Ozone Solar.R Wind Temp Month Day
## Ozone 0 1 1 1 1 1
## Solar.R 1 0 1 1 1 1
## Wind 1 1 0 1 1 1
## Temp 1 1 1 0 1 1
## Month 1 1 1 1 0 1
## Day 1 1 1 1 1 0## 1 2 3 4 5
## 5 6 32 14 18 6
## 10 12 23 27 21 41
## 25 8 19 6 14 19
## 26 32 9 28 19 28
## 27 18 22 37 18 9
## 32 59 47 44 45 52
## 33 16 16 20 11 18
## 34 1 13 13 37 13
## 35 44 71 40 40 71
## 36 35 64 89 35 39
## 37 14 13 30 30 44
## 39 115 91 135 168 82
## 42 64 77 168 66 76
## 43 61 91 135 82 91
## 45 23 29 45 44 59
## 46 45 63 29 45 32
## 52 16 71 47 52 52
## 53 20 64 35 23 49
## 54 45 37 40 52 35
## 55 20 39 23 20 7
## 56 13 40 45 36 45
## 57 36 35 52 46 44
## 58 32 16 21 23 23
## 59 16 52 31 39 28
## 60 23 14 13 44 24
## 61 40 85 48 71 48
## 65 23 16 23 28 59
## 72 59 47 29 52 35
## 75 35 89 59 59 108
## 83 32 23 23 44 35
## 84 28 47 35 7 35
## 102 115 85 80 168 91
## 103 16 39 16 32 47
## 107 12 22 41 22 23
## 115 24 22 16 21 36
## 119 64 78 82 61 78
## 150 12 32 12 21 16## Ozone Solar.R Wind Temp Month Day
## 1 41 190 7.4 67 5 1
## 2 36 118 8.0 72 5 2
## 3 12 149 12.6 74 5 3
## 4 18 313 11.5 62 5 4
## 5 6 115 14.3 56 5 5
## 6 28 274 14.9 66 5 6Using pipe operator %>%
################################################################
library(magrittr)
library(dplyr)
head(iris)## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa## tells R to take the value of that which is to the left
## and pass it to the right as an argument
## Select Columns
iris %>% select(Species,Petal.Width)## Species Petal.Width
## 1 setosa 0.2
## 2 setosa 0.2
## 3 setosa 0.2
## 4 setosa 0.2
## 5 setosa 0.2
## 6 setosa 0.4
## 7 setosa 0.3
## 8 setosa 0.2
## 9 setosa 0.2
## 10 setosa 0.1
## 11 setosa 0.2
## 12 setosa 0.2
## 13 setosa 0.1
## 14 setosa 0.1
## 15 setosa 0.2
## 16 setosa 0.4
## 17 setosa 0.4
## 18 setosa 0.3
## 19 setosa 0.3
## 20 setosa 0.3
## 21 setosa 0.2
## 22 setosa 0.4
## 23 setosa 0.2
## 24 setosa 0.5
## 25 setosa 0.2
## 26 setosa 0.2
## 27 setosa 0.4
## 28 setosa 0.2
## 29 setosa 0.2
## 30 setosa 0.2
## 31 setosa 0.2
## 32 setosa 0.4
## 33 setosa 0.1
## 34 setosa 0.2
## 35 setosa 0.2
## 36 setosa 0.2
## 37 setosa 0.2
## 38 setosa 0.1
## 39 setosa 0.2
## 40 setosa 0.2
## 41 setosa 0.3
## 42 setosa 0.3
## 43 setosa 0.2
## 44 setosa 0.6
## 45 setosa 0.4
## 46 setosa 0.3
## 47 setosa 0.2
## 48 setosa 0.2
## 49 setosa 0.2
## 50 setosa 0.2
## 51 versicolor 1.4
## 52 versicolor 1.5
## 53 versicolor 1.5
## 54 versicolor 1.3
## 55 versicolor 1.5
## 56 versicolor 1.3
## 57 versicolor 1.6
## 58 versicolor 1.0
## 59 versicolor 1.3
## 60 versicolor 1.4
## 61 versicolor 1.0
## 62 versicolor 1.5
## 63 versicolor 1.0
## 64 versicolor 1.4
## 65 versicolor 1.3
## 66 versicolor 1.4
## 67 versicolor 1.5
## 68 versicolor 1.0
## 69 versicolor 1.5
## 70 versicolor 1.1
## 71 versicolor 1.8
## 72 versicolor 1.3
## 73 versicolor 1.5
## 74 versicolor 1.2
## 75 versicolor 1.3
## 76 versicolor 1.4
## 77 versicolor 1.4
## 78 versicolor 1.7
## 79 versicolor 1.5
## 80 versicolor 1.0
## 81 versicolor 1.1
## 82 versicolor 1.0
## 83 versicolor 1.2
## 84 versicolor 1.6
## 85 versicolor 1.5
## 86 versicolor 1.6
## 87 versicolor 1.5
## 88 versicolor 1.3
## 89 versicolor 1.3
## 90 versicolor 1.3
## 91 versicolor 1.2
## 92 versicolor 1.4
## 93 versicolor 1.2
## 94 versicolor 1.0
## 95 versicolor 1.3
## 96 versicolor 1.2
## 97 versicolor 1.3
## 98 versicolor 1.3
## 99 versicolor 1.1
## 100 versicolor 1.3
## 101 virginica 2.5
## 102 virginica 1.9
## 103 virginica 2.1
## 104 virginica 1.8
## 105 virginica 2.2
## 106 virginica 2.1
## 107 virginica 1.7
## 108 virginica 1.8
## 109 virginica 1.8
## 110 virginica 2.5
## 111 virginica 2.0
## 112 virginica 1.9
## 113 virginica 2.1
## 114 virginica 2.0
## 115 virginica 2.4
## 116 virginica 2.3
## 117 virginica 1.8
## 118 virginica 2.2
## 119 virginica 2.3
## 120 virginica 1.5
## 121 virginica 2.3
## 122 virginica 2.0
## 123 virginica 2.0
## 124 virginica 1.8
## 125 virginica 2.1
## 126 virginica 1.8
## 127 virginica 1.8
## 128 virginica 1.8
## 129 virginica 2.1
## 130 virginica 1.6
## 131 virginica 1.9
## 132 virginica 2.0
## 133 virginica 2.2
## 134 virginica 1.5
## 135 virginica 1.4
## 136 virginica 2.3
## 137 virginica 2.4
## 138 virginica 1.8
## 139 virginica 1.8
## 140 virginica 2.1
## 141 virginica 2.4
## 142 virginica 2.3
## 143 virginica 1.9
## 144 virginica 2.3
## 145 virginica 2.5
## 146 virginica 2.3
## 147 virginica 1.9
## 148 virginica 2.0
## 149 virginica 2.3
## 150 virginica 1.8## Species Petal.Width
## 1 setosa 0.2
## 2 setosa 0.2
## 3 setosa 0.2
## 4 setosa 0.2
## 5 setosa 0.2
## 6 setosa 0.4## Sepal.Length Sepal.Width Petal.Length Petal.Width
## 1 5.1 3.5 1.4 0.2
## 2 4.9 3.0 1.4 0.2
## 3 4.7 3.2 1.3 0.2
## 4 4.6 3.1 1.5 0.2
## 5 5.0 3.6 1.4 0.2
## 6 5.4 3.9 1.7 0.4
## 7 4.6 3.4 1.4 0.3
## 8 5.0 3.4 1.5 0.2
## 9 4.4 2.9 1.4 0.2
## 10 4.9 3.1 1.5 0.1
## 11 5.4 3.7 1.5 0.2
## 12 4.8 3.4 1.6 0.2
## 13 4.8 3.0 1.4 0.1
## 14 4.3 3.0 1.1 0.1
## 15 5.8 4.0 1.2 0.2
## 16 5.7 4.4 1.5 0.4
## 17 5.4 3.9 1.3 0.4
## 18 5.1 3.5 1.4 0.3
## 19 5.7 3.8 1.7 0.3
## 20 5.1 3.8 1.5 0.3
## 21 5.4 3.4 1.7 0.2
## 22 5.1 3.7 1.5 0.4
## 23 4.6 3.6 1.0 0.2
## 24 5.1 3.3 1.7 0.5
## 25 4.8 3.4 1.9 0.2
## 26 5.0 3.0 1.6 0.2
## 27 5.0 3.4 1.6 0.4
## 28 5.2 3.5 1.5 0.2
## 29 5.2 3.4 1.4 0.2
## 30 4.7 3.2 1.6 0.2
## 31 4.8 3.1 1.6 0.2
## 32 5.4 3.4 1.5 0.4
## 33 5.2 4.1 1.5 0.1
## 34 5.5 4.2 1.4 0.2
## 35 4.9 3.1 1.5 0.2
## 36 5.0 3.2 1.2 0.2
## 37 5.5 3.5 1.3 0.2
## 38 4.9 3.6 1.4 0.1
## 39 4.4 3.0 1.3 0.2
## 40 5.1 3.4 1.5 0.2
## 41 5.0 3.5 1.3 0.3
## 42 4.5 2.3 1.3 0.3
## 43 4.4 3.2 1.3 0.2
## 44 5.0 3.5 1.6 0.6
## 45 5.1 3.8 1.9 0.4
## 46 4.8 3.0 1.4 0.3
## 47 5.1 3.8 1.6 0.2
## 48 4.6 3.2 1.4 0.2
## 49 5.3 3.7 1.5 0.2
## 50 5.0 3.3 1.4 0.2
## 51 7.0 3.2 4.7 1.4
## 52 6.4 3.2 4.5 1.5
## 53 6.9 3.1 4.9 1.5
## 54 5.5 2.3 4.0 1.3
## 55 6.5 2.8 4.6 1.5
## 56 5.7 2.8 4.5 1.3
## 57 6.3 3.3 4.7 1.6
## 58 4.9 2.4 3.3 1.0
## 59 6.6 2.9 4.6 1.3
## 60 5.2 2.7 3.9 1.4
## 61 5.0 2.0 3.5 1.0
## 62 5.9 3.0 4.2 1.5
## 63 6.0 2.2 4.0 1.0
## 64 6.1 2.9 4.7 1.4
## 65 5.6 2.9 3.6 1.3
## 66 6.7 3.1 4.4 1.4
## 67 5.6 3.0 4.5 1.5
## 68 5.8 2.7 4.1 1.0
## 69 6.2 2.2 4.5 1.5
## 70 5.6 2.5 3.9 1.1
## 71 5.9 3.2 4.8 1.8
## 72 6.1 2.8 4.0 1.3
## 73 6.3 2.5 4.9 1.5
## 74 6.1 2.8 4.7 1.2
## 75 6.4 2.9 4.3 1.3
## 76 6.6 3.0 4.4 1.4
## 77 6.8 2.8 4.8 1.4
## 78 6.7 3.0 5.0 1.7
## 79 6.0 2.9 4.5 1.5
## 80 5.7 2.6 3.5 1.0
## 81 5.5 2.4 3.8 1.1
## 82 5.5 2.4 3.7 1.0
## 83 5.8 2.7 3.9 1.2
## 84 6.0 2.7 5.1 1.6
## 85 5.4 3.0 4.5 1.5
## 86 6.0 3.4 4.5 1.6
## 87 6.7 3.1 4.7 1.5
## 88 6.3 2.3 4.4 1.3
## 89 5.6 3.0 4.1 1.3
## 90 5.5 2.5 4.0 1.3
## 91 5.5 2.6 4.4 1.2
## 92 6.1 3.0 4.6 1.4
## 93 5.8 2.6 4.0 1.2
## 94 5.0 2.3 3.3 1.0
## 95 5.6 2.7 4.2 1.3
## 96 5.7 3.0 4.2 1.2
## 97 5.7 2.9 4.2 1.3
## 98 6.2 2.9 4.3 1.3
## 99 5.1 2.5 3.0 1.1
## 100 5.7 2.8 4.1 1.3
## 101 6.3 3.3 6.0 2.5
## 102 5.8 2.7 5.1 1.9
## 103 7.1 3.0 5.9 2.1
## 104 6.3 2.9 5.6 1.8
## 105 6.5 3.0 5.8 2.2
## 106 7.6 3.0 6.6 2.1
## 107 4.9 2.5 4.5 1.7
## 108 7.3 2.9 6.3 1.8
## 109 6.7 2.5 5.8 1.8
## 110 7.2 3.6 6.1 2.5
## 111 6.5 3.2 5.1 2.0
## 112 6.4 2.7 5.3 1.9
## 113 6.8 3.0 5.5 2.1
## 114 5.7 2.5 5.0 2.0
## 115 5.8 2.8 5.1 2.4
## 116 6.4 3.2 5.3 2.3
## 117 6.5 3.0 5.5 1.8
## 118 7.7 3.8 6.7 2.2
## 119 7.7 2.6 6.9 2.3
## 120 6.0 2.2 5.0 1.5
## 121 6.9 3.2 5.7 2.3
## 122 5.6 2.8 4.9 2.0
## 123 7.7 2.8 6.7 2.0
## 124 6.3 2.7 4.9 1.8
## 125 6.7 3.3 5.7 2.1
## 126 7.2 3.2 6.0 1.8
## 127 6.2 2.8 4.8 1.8
## 128 6.1 3.0 4.9 1.8
## 129 6.4 2.8 5.6 2.1
## 130 7.2 3.0 5.8 1.6
## 131 7.4 2.8 6.1 1.9
## 132 7.9 3.8 6.4 2.0
## 133 6.4 2.8 5.6 2.2
## 134 6.3 2.8 5.1 1.5
## 135 6.1 2.6 5.6 1.4
## 136 7.7 3.0 6.1 2.3
## 137 6.3 3.4 5.6 2.4
## 138 6.4 3.1 5.5 1.8
## 139 6.0 3.0 4.8 1.8
## 140 6.9 3.1 5.4 2.1
## 141 6.7 3.1 5.6 2.4
## 142 6.9 3.1 5.1 2.3
## 143 5.8 2.7 5.1 1.9
## 144 6.8 3.2 5.9 2.3
## 145 6.7 3.3 5.7 2.5
## 146 6.7 3.0 5.2 2.3
## 147 6.3 2.5 5.0 1.9
## 148 6.5 3.0 5.2 2.0
## 149 6.2 3.4 5.4 2.3
## 150 5.9 3.0 5.1 1.8## Sepal.Length Sepal.Width Petal.Length Petal.Width
## 1 5.1 3.5 1.4 0.2
## 2 4.9 3.0 1.4 0.2
## 3 4.7 3.2 1.3 0.2
## 4 4.6 3.1 1.5 0.2
## 5 5.0 3.6 1.4 0.2
## 6 5.4 3.9 1.7 0.4## Sepal.Length Sepal.Width Petal.Length
## 1 5.1 3.5 1.4
## 2 4.9 3.0 1.4
## 3 4.7 3.2 1.3
## 4 4.6 3.1 1.5
## 5 5.0 3.6 1.4
## 6 5.4 3.9 1.7## Sepal.Length Sepal.Width Petal.Length Petal.Width
## 1 5.1 3.5 1.4 0.2
## 2 4.9 3.0 1.4 0.2
## 3 4.7 3.2 1.3 0.2
## 4 4.6 3.1 1.5 0.2
## 5 5.0 3.6 1.4 0.2
## 6 5.4 3.9 1.7 0.4## Petal.Width Species
## 1 0.2 setosa
## 2 0.2 setosa
## 3 0.2 setosa
## 4 0.2 setosa
## 5 0.2 setosa
## 6 0.4 setosa## Sepal.Length Sepal.Width Species
## 1 5.1 3.5 setosa
## 2 4.9 3.0 setosa
## 3 4.7 3.2 setosa
## 4 4.6 3.1 setosa
## 5 5.0 3.6 setosa
## 6 5.4 3.9 setosa## Species
## 1 setosa
## 2 setosa
## 3 setosa
## 4 setosa
## 5 setosa
## 6 setosa
## 7 setosa
## 8 setosa
## 9 setosa
## 10 setosa
## 11 setosa
## 12 setosa
## 13 setosa
## 14 setosa
## 15 setosa
## 16 setosa
## 17 setosa
## 18 setosa
## 19 setosa
## 20 setosa
## 21 setosa
## 22 setosa
## 23 setosa
## 24 setosa
## 25 setosa
## 26 setosa
## 27 setosa
## 28 setosa
## 29 setosa
## 30 setosa
## 31 setosa
## 32 setosa
## 33 setosa
## 34 setosa
## 35 setosa
## 36 setosa
## 37 setosa
## 38 setosa
## 39 setosa
## 40 setosa
## 41 setosa
## 42 setosa
## 43 setosa
## 44 setosa
## 45 setosa
## 46 setosa
## 47 setosa
## 48 setosa
## 49 setosa
## 50 setosa
## 51 versicolor
## 52 versicolor
## 53 versicolor
## 54 versicolor
## 55 versicolor
## 56 versicolor
## 57 versicolor
## 58 versicolor
## 59 versicolor
## 60 versicolor
## 61 versicolor
## 62 versicolor
## 63 versicolor
## 64 versicolor
## 65 versicolor
## 66 versicolor
## 67 versicolor
## 68 versicolor
## 69 versicolor
## 70 versicolor
## 71 versicolor
## 72 versicolor
## 73 versicolor
## 74 versicolor
## 75 versicolor
## 76 versicolor
## 77 versicolor
## 78 versicolor
## 79 versicolor
## 80 versicolor
## 81 versicolor
## 82 versicolor
## 83 versicolor
## 84 versicolor
## 85 versicolor
## 86 versicolor
## 87 versicolor
## 88 versicolor
## 89 versicolor
## 90 versicolor
## 91 versicolor
## 92 versicolor
## 93 versicolor
## 94 versicolor
## 95 versicolor
## 96 versicolor
## 97 versicolor
## 98 versicolor
## 99 versicolor
## 100 versicolor
## 101 virginica
## 102 virginica
## 103 virginica
## 104 virginica
## 105 virginica
## 106 virginica
## 107 virginica
## 108 virginica
## 109 virginica
## 110 virginica
## 111 virginica
## 112 virginica
## 113 virginica
## 114 virginica
## 115 virginica
## 116 virginica
## 117 virginica
## 118 virginica
## 119 virginica
## 120 virginica
## 121 virginica
## 122 virginica
## 123 virginica
## 124 virginica
## 125 virginica
## 126 virginica
## 127 virginica
## 128 virginica
## 129 virginica
## 130 virginica
## 131 virginica
## 132 virginica
## 133 virginica
## 134 virginica
## 135 virginica
## 136 virginica
## 137 virginica
## 138 virginica
## 139 virginica
## 140 virginica
## 141 virginica
## 142 virginica
## 143 virginica
## 144 virginica
## 145 virginica
## 146 virginica
## 147 virginica
## 148 virginica
## 149 virginica
## 150 virginica## Sepal.Width Petal.Width
## 1 3.5 0.2
## 2 3.0 0.2
## 3 3.2 0.2
## 4 3.1 0.2
## 5 3.6 0.2
## 6 3.9 0.4## Sepal.Length Petal.Length
## 1 5.1 1.4
## 2 4.9 1.4
## 3 4.7 1.3
## 4 4.6 1.5
## 5 5.0 1.4
## 6 5.4 1.7Using tidyverse library
##############################################################
library(tidyverse)
##tidyverse is a coherent system of packages for
##data manipulation, exploration and visualization that share a common design philosophy.
### underpinned by tibbles
#vignette("tibble")
class(iris)## [1] "data.frame"## # A tibble: 6 x 5
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## <dbl> <dbl> <dbl> <dbl> <fct>
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa## # A tibble: 6 x 19
## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## <int> <int> <int> <int> <int> <dbl> <int> <int>
## 1 2013 1 1 517 515 2 830 819
## 2 2013 1 1 533 529 4 850 830
## 3 2013 1 1 542 540 2 923 850
## 4 2013 1 1 544 545 -1 1004 1022
## 5 2013 1 1 554 600 -6 812 837
## 6 2013 1 1 554 558 -4 740 728
## # ... with 11 more variables: arr_delay <dbl>, carrier <chr>, flight <int>,
## # tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,
## # hour <dbl>, minute <dbl>, time_hour <dttm>Basic EDA
## [1] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width" "Species"
##
## setosa versicolor virginica
## 50 50 50
## Improved data viz
library(ggplot2)
# relation bw Sepal length and width of 3 different species
qplot(Sepal.Length, Petal.Length, data = iris, color = Species)
# We see that Iris setosa flowers have the narrowest petals.
qplot(Sepal.Length, Petal.Length, data = iris, color = Species, size = Petal.Width)
##Add labels to the plot
qplot(Sepal.Length, Petal.Length, data = iris, color = Species,
xlab = "Sepal Length", ylab = "Petal Length",
main = "Sepal vs. Petal Length in Iris data")
# Plot the number of movies each director has.
qplot(Species, data = iris, geom = "bar", ylab = "Samples/species")
Unsupervised Classification with H2o
Implement k-Means Classification using H2o
#############
library(caret)
#library(mxnet)
# loading data
df=read.csv("covtype.csv") #predict if the tumor is
#malignant (M) or benign (B)
head(df)## Elevation Aspect Slope Horizontal_Distance_To_Hydrology
## 1 2596 51 3 258
## 2 2590 56 2 212
## 3 2804 139 9 268
## 4 2785 155 18 242
## 5 2595 45 2 153
## 6 2579 132 6 300
## Vertical_Distance_To_Hydrology Horizontal_Distance_To_Roadways Hillshade_9am
## 1 0 510 221
## 2 -6 390 220
## 3 65 3180 234
## 4 118 3090 238
## 5 -1 391 220
## 6 -15 67 230
## Hillshade_Noon Hillshade_3pm Horizontal_Distance_To_Fire_Points
## 1 232 148 6279
## 2 235 151 6225
## 3 238 135 6121
## 4 238 122 6211
## 5 234 150 6172
## 6 237 140 6031
## Wilderness_Area1 Wilderness_Area2 Wilderness_Area3 Wilderness_Area4
## 1 1 0 0 0
## 2 1 0 0 0
## 3 1 0 0 0
## 4 1 0 0 0
## 5 1 0 0 0
## 6 1 0 0 0
## Soil_Type1 Soil_Type2 Soil_Type3 Soil_Type4 Soil_Type5 Soil_Type6 Soil_Type7
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0
## Soil_Type8 Soil_Type9 Soil_Type10 Soil_Type11 Soil_Type12 Soil_Type13
## 1 0 0 0 0 0 0
## 2 0 0 0 0 0 0
## 3 0 0 0 0 1 0
## 4 0 0 0 0 0 0
## 5 0 0 0 0 0 0
## 6 0 0 0 0 0 0
## Soil_Type14 Soil_Type15 Soil_Type16 Soil_Type17 Soil_Type18 Soil_Type19
## 1 0 0 0 0 0 0
## 2 0 0 0 0 0 0
## 3 0 0 0 0 0 0
## 4 0 0 0 0 0 0
## 5 0 0 0 0 0 0
## 6 0 0 0 0 0 0
## Soil_Type20 Soil_Type21 Soil_Type22 Soil_Type23 Soil_Type24 Soil_Type25
## 1 0 0 0 0 0 0
## 2 0 0 0 0 0 0
## 3 0 0 0 0 0 0
## 4 0 0 0 0 0 0
## 5 0 0 0 0 0 0
## 6 0 0 0 0 0 0
## Soil_Type26 Soil_Type27 Soil_Type28 Soil_Type29 Soil_Type30 Soil_Type31
## 1 0 0 0 1 0 0
## 2 0 0 0 1 0 0
## 3 0 0 0 0 0 0
## 4 0 0 0 0 1 0
## 5 0 0 0 1 0 0
## 6 0 0 0 1 0 0
## Soil_Type32 Soil_Type33 Soil_Type34 Soil_Type35 Soil_Type36 Soil_Type37
## 1 0 0 0 0 0 0
## 2 0 0 0 0 0 0
## 3 0 0 0 0 0 0
## 4 0 0 0 0 0 0
## 5 0 0 0 0 0 0
## 6 0 0 0 0 0 0
## Soil_Type38 Soil_Type39 Soil_Type40 Cover_Type
## 1 0 0 0 5
## 2 0 0 0 5
## 3 0 0 0 2
## 4 0 0 0 2
## 5 0 0 0 5
## 6 0 0 0 2## [1] 55## Connection successful!
##
## R is connected to the H2O cluster:
## H2O cluster uptime: 7 minutes 17 seconds
## H2O cluster timezone: Asia/Kolkata
## H2O data parsing timezone: UTC
## H2O cluster version: 3.30.0.1
## H2O cluster version age: 1 month and 21 days
## H2O cluster name: H2O_started_from_R_HP_fjx183
## H2O cluster total nodes: 1
## H2O cluster total memory: 1.38 GB
## H2O cluster total cores: 4
## H2O cluster allowed cores: 4
## 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 3.5.0 (2018-04-23)##
|
| | 0%
|
|======================================================================| 100%## Elevation Aspect Slope Horizontal_Distance_To_Hydrology
## 1 2596 51 3 258
## 2 2590 56 2 212
## 3 2804 139 9 268
## 4 2785 155 18 242
## 5 2595 45 2 153
## 6 2579 132 6 300
## Vertical_Distance_To_Hydrology Horizontal_Distance_To_Roadways Hillshade_9am
## 1 0 510 221
## 2 -6 390 220
## 3 65 3180 234
## 4 118 3090 238
## 5 -1 391 220
## 6 -15 67 230
## Hillshade_Noon Hillshade_3pm Horizontal_Distance_To_Fire_Points
## 1 232 148 6279
## 2 235 151 6225
## 3 238 135 6121
## 4 238 122 6211
## 5 234 150 6172
## 6 237 140 6031
## Wilderness_Area1 Wilderness_Area2 Wilderness_Area3 Wilderness_Area4
## 1 1 0 0 0
## 2 1 0 0 0
## 3 1 0 0 0
## 4 1 0 0 0
## 5 1 0 0 0
## 6 1 0 0 0
## Soil_Type1 Soil_Type2 Soil_Type3 Soil_Type4 Soil_Type5 Soil_Type6 Soil_Type7
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0
## Soil_Type8 Soil_Type9 Soil_Type10 Soil_Type11 Soil_Type12 Soil_Type13
## 1 0 0 0 0 0 0
## 2 0 0 0 0 0 0
## 3 0 0 0 0 1 0
## 4 0 0 0 0 0 0
## 5 0 0 0 0 0 0
## 6 0 0 0 0 0 0
## Soil_Type14 Soil_Type15 Soil_Type16 Soil_Type17 Soil_Type18 Soil_Type19
## 1 0 0 0 0 0 0
## 2 0 0 0 0 0 0
## 3 0 0 0 0 0 0
## 4 0 0 0 0 0 0
## 5 0 0 0 0 0 0
## 6 0 0 0 0 0 0
## Soil_Type20 Soil_Type21 Soil_Type22 Soil_Type23 Soil_Type24 Soil_Type25
## 1 0 0 0 0 0 0
## 2 0 0 0 0 0 0
## 3 0 0 0 0 0 0
## 4 0 0 0 0 0 0
## 5 0 0 0 0 0 0
## 6 0 0 0 0 0 0
## Soil_Type26 Soil_Type27 Soil_Type28 Soil_Type29 Soil_Type30 Soil_Type31
## 1 0 0 0 1 0 0
## 2 0 0 0 1 0 0
## 3 0 0 0 0 0 0
## 4 0 0 0 0 1 0
## 5 0 0 0 1 0 0
## 6 0 0 0 1 0 0
## Soil_Type32 Soil_Type33 Soil_Type34 Soil_Type35 Soil_Type36 Soil_Type37
## 1 0 0 0 0 0 0
## 2 0 0 0 0 0 0
## 3 0 0 0 0 0 0
## 4 0 0 0 0 0 0
## 5 0 0 0 0 0 0
## 6 0 0 0 0 0 0
## Soil_Type38 Soil_Type39 Soil_Type40 Cover_Type
## 1 0 0 0 5
## 2 0 0 0 5
## 3 0 0 0 2
## 4 0 0 0 2
## 5 0 0 0 5
## 6 0 0 0 2## Class 'H2OFrame' <environment: 0x000000003d89b4f8>
## - attr(*, "op")= chr "Parse"
## - attr(*, "id")= chr "d.hex"
## - attr(*, "eval")= logi FALSE
## - attr(*, "nrow")= int 581012
## - attr(*, "ncol")= int 55
## - attr(*, "types")=List of 55
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## ..$ : chr "int"
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## ..$ : chr "int"
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## ..$ : chr "int"
## ..$ : chr "int"
## ..$ : chr "int"
## ..$ : chr "int"
## ..$ : chr "int"
## ..$ : chr "int"
## ..$ : chr "int"
## ..$ : chr "int"
## ..$ : chr "int"
## ..$ : chr "int"
## ..$ : chr "int"
## ..$ : chr "int"
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## ..$ : chr "int"
## ..$ : chr "int"
## ..$ : chr "int"
## ..$ : chr "int"
## - attr(*, "data")='data.frame': 10 obs. of 55 variables:
## ..$ Elevation : num 2596 2590 2804 2785 2595 ...
## ..$ Aspect : num 51 56 139 155 45 132 45 49 45 59
## ..$ Slope : num 3 2 9 18 2 6 7 4 9 10
## ..$ Horizontal_Distance_To_Hydrology : num 258 212 268 242 153 300 270 234 240 247
## ..$ Vertical_Distance_To_Hydrology : num 0 -6 65 118 -1 -15 5 7 56 11
## ..$ Horizontal_Distance_To_Roadways : num 510 390 3180 3090 391 67 633 573 666 636
## ..$ Hillshade_9am : num 221 220 234 238 220 230 222 222 223 228
## ..$ Hillshade_Noon : num 232 235 238 238 234 237 225 230 221 219
## ..$ Hillshade_3pm : num 148 151 135 122 150 140 138 144 133 124
## ..$ Horizontal_Distance_To_Fire_Points: num 6279 6225 6121 6211 6172 ...
## ..$ Wilderness_Area1 : num 1 1 1 1 1 1 1 1 1 1
## ..$ Wilderness_Area2 : num 0 0 0 0 0 0 0 0 0 0
## ..$ Wilderness_Area3 : num 0 0 0 0 0 0 0 0 0 0
## ..$ Wilderness_Area4 : num 0 0 0 0 0 0 0 0 0 0
## ..$ Soil_Type1 : num 0 0 0 0 0 0 0 0 0 0
## ..$ Soil_Type2 : num 0 0 0 0 0 0 0 0 0 0
## ..$ Soil_Type3 : num 0 0 0 0 0 0 0 0 0 0
## ..$ Soil_Type4 : num 0 0 0 0 0 0 0 0 0 0
## ..$ Soil_Type5 : num 0 0 0 0 0 0 0 0 0 0
## ..$ Soil_Type6 : num 0 0 0 0 0 0 0 0 0 0
## ..$ Soil_Type7 : num 0 0 0 0 0 0 0 0 0 0
## ..$ Soil_Type8 : num 0 0 0 0 0 0 0 0 0 0
## ..$ Soil_Type9 : num 0 0 0 0 0 0 0 0 0 0
## ..$ Soil_Type10 : num 0 0 0 0 0 0 0 0 0 0
## ..$ Soil_Type11 : num 0 0 0 0 0 0 0 0 0 0
## ..$ Soil_Type12 : num 0 0 1 0 0 0 0 0 0 0
## ..$ Soil_Type13 : num 0 0 0 0 0 0 0 0 0 0
## ..$ Soil_Type14 : num 0 0 0 0 0 0 0 0 0 0
## ..$ Soil_Type15 : num 0 0 0 0 0 0 0 0 0 0
## ..$ Soil_Type16 : num 0 0 0 0 0 0 0 0 0 0
## ..$ Soil_Type17 : num 0 0 0 0 0 0 0 0 0 0
## ..$ Soil_Type18 : num 0 0 0 0 0 0 0 0 0 0
## ..$ Soil_Type19 : num 0 0 0 0 0 0 0 0 0 0
## ..$ Soil_Type20 : num 0 0 0 0 0 0 0 0 0 0
## ..$ Soil_Type21 : num 0 0 0 0 0 0 0 0 0 0
## ..$ Soil_Type22 : num 0 0 0 0 0 0 0 0 0 0
## ..$ Soil_Type23 : num 0 0 0 0 0 0 0 0 0 0
## ..$ Soil_Type24 : num 0 0 0 0 0 0 0 0 0 0
## ..$ Soil_Type25 : num 0 0 0 0 0 0 0 0 0 0
## ..$ Soil_Type26 : num 0 0 0 0 0 0 0 0 0 0
## ..$ Soil_Type27 : num 0 0 0 0 0 0 0 0 0 0
## ..$ Soil_Type28 : num 0 0 0 0 0 0 0 0 0 0
## ..$ Soil_Type29 : num 1 1 0 0 1 1 1 1 1 1
## ..$ Soil_Type30 : num 0 0 0 1 0 0 0 0 0 0
## ..$ Soil_Type31 : num 0 0 0 0 0 0 0 0 0 0
## ..$ Soil_Type32 : num 0 0 0 0 0 0 0 0 0 0
## ..$ Soil_Type33 : num 0 0 0 0 0 0 0 0 0 0
## ..$ Soil_Type34 : num 0 0 0 0 0 0 0 0 0 0
## ..$ Soil_Type35 : num 0 0 0 0 0 0 0 0 0 0
## ..$ Soil_Type36 : num 0 0 0 0 0 0 0 0 0 0
## ..$ Soil_Type37 : num 0 0 0 0 0 0 0 0 0 0
## ..$ Soil_Type38 : num 0 0 0 0 0 0 0 0 0 0
## ..$ Soil_Type39 : num 0 0 0 0 0 0 0 0 0 0
## ..$ Soil_Type40 : num 0 0 0 0 0 0 0 0 0 0
## ..$ Cover_Type : num 5 5 2 2 5 2 5 5 5 5##
|
| | 0%
|
|======= | 10%
|
|============== | 20%
|
|======================================================================| 100%par(mfrow = c(1,2))
d.ctrs = as.data.frame(d.km@model$centers)
plot(d.ctrs[,1:2])
plot(d.ctrs[,13:14])
title("K-Means Centers", outer = TRUE, line = -2.0)
Implement PCA With H2O
library(caret)
#library(mxnet)
# loading data
df=read.csv("Seabmass_typ.csv") #predict if the tumor is
#malignant (M) or benign (B)
head(df)## Country AGB bio01 bio02 bio03 bio04 bio05 bio06 bio07 bio08 bio09 bio10 bio11
## 1 Vietnam 127 27 6.6 79 64 31 23 8.0 27 27 28 26
## 2 Vietnam 103 27 5.1 64 90 31 23 8.0 27 26 28 26
## 3 Vietnam 101 27 5.0 64 83 31 24 7.9 28 27 29 26
## 4 Vietnam 50 27 5.0 64 83 31 24 7.9 28 27 29 26
## 5 Vietnam 236 27 5.2 64 96 31 23 8.2 27 26 28 26
## 6 Vietnam 119 27 6.8 79 63 31 23 8.1 27 27 28 26
## bio12 bio13 bio14 bio15 bio16 bio17 bio18 bio19 CanopyHt_1 cwd1km DEM_1km
## 1 2497 357 30 67 1044 156 565 591 3.3 61 3.4
## 2 1460 238 1 85 663 8 449 31 4.3 62 2.2
## 3 2290 345 7 70 1003 54 635 122 0.0 36 2.0
## 4 2290 345 7 70 1003 54 635 122 0.0 36 2.0
## 5 1421 237 1 84 645 10 434 31 8.5 60 4.0
## 6 2517 358 31 68 1045 159 563 567 0.0 59 3.9
## Slope1km soil1 soil2 soil4 SoilMois_1 Class
## 1 0.00048 1 1 1 132 Delta
## 2 0.00035 1 1 1 127 Delta
## 3 0.00086 1 1 1 90 Delta
## 4 0.00113 1 1 1 90 Delta
## 5 0.00023 1 1 1 132 Delta
## 6 0.00039 1 1 1 143 Delta## [1] 30## Connection successful!
##
## R is connected to the H2O cluster:
## H2O cluster uptime: 7 minutes 55 seconds
## H2O cluster timezone: Asia/Kolkata
## H2O data parsing timezone: UTC
## H2O cluster version: 3.30.0.1
## H2O cluster version age: 1 month and 21 days
## H2O cluster name: H2O_started_from_R_HP_fjx183
## H2O cluster total nodes: 1
## H2O cluster total memory: 1.37 GB
## H2O cluster total cores: 4
## H2O cluster allowed cores: 4
## 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 3.5.0 (2018-04-23)##
|
| | 0%
|
|======================================================================| 100%## Country AGB bio01 bio02 bio03 bio04 bio05 bio06 bio07 bio08 bio09 bio10 bio11
## 1 Vietnam 127 27 6.6 79 64 31 23 8.0 27 27 28 26
## 2 Vietnam 103 27 5.1 64 90 31 23 8.0 27 26 28 26
## 3 Vietnam 101 27 5.0 64 83 31 24 7.9 28 27 29 26
## 4 Vietnam 50 27 5.0 64 83 31 24 7.9 28 27 29 26
## 5 Vietnam 236 27 5.2 64 96 31 23 8.2 27 26 28 26
## 6 Vietnam 119 27 6.8 79 63 31 23 8.1 27 27 28 26
## bio12 bio13 bio14 bio15 bio16 bio17 bio18 bio19 CanopyHt_1 cwd1km DEM_1km
## 1 2497 357 30 67 1044 156 565 591 3.3 61 3.4
## 2 1460 238 1 85 663 8 449 31 4.3 62 2.2
## 3 2290 345 7 70 1003 54 635 122 0.0 36 2.0
## 4 2290 345 7 70 1003 54 635 122 0.0 36 2.0
## 5 1421 237 1 84 645 10 434 31 8.5 60 4.0
## 6 2517 358 31 68 1045 159 563 567 0.0 59 3.9
## Slope1km soil1 soil2 soil4 SoilMois_1 Class
## 1 0.00048 1 1 1 132 Delta
## 2 0.00035 1 1 1 127 Delta
## 3 0.00086 1 1 1 90 Delta
## 4 0.00113 1 1 1 90 Delta
## 5 0.00023 1 1 1 132 Delta
## 6 0.00039 1 1 1 143 Delta## Class 'H2OFrame' <environment: 0x000000003daedb00>
## - attr(*, "op")= chr "Parse"
## - attr(*, "id")= chr "d.hex"
## - attr(*, "eval")= logi FALSE
## - attr(*, "nrow")= int 120
## - attr(*, "ncol")= int 30
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## ..$ : chr "enum"
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## ..$ : chr "real"
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## ..$ : chr "real"
## ..$ : chr "real"
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## ..$ : chr "real"
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## ..$ : chr "real"
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## ..$ : chr "real"
## ..$ : chr "int"
## ..$ : chr "int"
## ..$ : chr "int"
## ..$ : chr "real"
## ..$ : chr "enum"
## - attr(*, "data")='data.frame': 10 obs. of 30 variables:
## ..$ Country : Factor w/ 5 levels "Burma","Indonesia",..: 5 5 5 5 5 5 5 5 5 5
## ..$ AGB : num 127.3 103.1 101 50.1 236.1 ...
## ..$ bio01 : num 27.2 27 27.5 27.5 27.1 ...
## ..$ bio02 : num 6.65 5.11 5.03 5.03 5.24 ...
## ..$ bio03 : num 79.2 63.9 63.7 63.7 63.9 ...
## ..$ bio04 : num 64 89.8 83.1 83.1 95.9 ...
## ..$ bio05 : num 31.3 31 31.4 31.4 31.2 ...
## ..$ bio06 : num 23 23 23.5 23.5 23 ...
## ..$ bio07 : num 7.98 8 7.9 7.9 8.2 ...
## ..$ bio08 : num 27.1 27.2 27.6 27.6 27.2 ...
## ..$ bio09 : num 26.8 26.3 26.8 26.8 26.3 ...
## ..$ bio10 : num 28.1 28.1 28.5 28.5 28.3 ...
## ..$ bio11 : num 26.4 25.8 26.4 26.4 25.8 ...
## ..$ bio12 : num 2497 1460 2290 2290 1421 ...
## ..$ bio13 : num 357 238 345 345 237 ...
## ..$ bio14 : num 30.4 1 7 7 1 ...
## ..$ bio15 : num 67.1 84.8 69.7 69.7 83.9 ...
## ..$ bio16 : num 1044 663 1003 1003 645 ...
## ..$ bio17 : num 156 8 54 54 10 ...
## ..$ bio18 : num 565 449 635 635 434 ...
## ..$ bio19 : num 591 31 122 122 31 ...
## ..$ CanopyHt_1: num 3.32 4.34 0 0 8.45 ...
## ..$ cwd1km : num 61.2 62.4 36 36 60 ...
## ..$ DEM_1km : num 3.4 2.2 2 2 4 ...
## ..$ Slope1km : num 0.000478 0.000351 0.000864 0.001125 0.000231 ...
## ..$ soil1 : num 1 1 1 1 1 1 1 1 1 1
## ..$ soil2 : num 1 1 1 1 1 1 1 1 1 1
## ..$ soil4 : num 1 1 1 1 1 1 1 1 1 1
## ..$ SoilMois_1: num 132.4 127.2 90.2 90.2 132 ...
## ..$ Class : Factor w/ 4 levels "","Delta","Estuary",..: 2 2 2 2 2 2 2 2 2 2##
|
| | 0%
|
|======================================================================| 100%## Model Details:
## ==============
##
## H2ODimReductionModel: pca
## Model ID: PCA_model_R_1590343748665_1605
## Importance of components:
## pc1 pc2 pc3 pc4 pc5
## Standard deviation 4269.490516 609.799088 237.352230 86.149300 62.943873
## Proportion of Variance 0.976294 0.019916 0.003017 0.000397 0.000212
## Cumulative Proportion 0.976294 0.996210 0.999227 0.999625 0.999837
## pc6 pc7 pc8 pc9 pc10
## Standard deviation 42.976617 24.090042 18.304677 9.715752 8.551729
## Proportion of Variance 0.000099 0.000031 0.000018 0.000005 0.000004
## Cumulative Proportion 0.999936 0.999967 0.999985 0.999990 0.999994
##
##
## H2ODimReductionMetrics: pca
##
## No model metrics available for PCA##
|
| | 0%
|
|======================================================================| 100%
## Model Details:
## ==============
##
## H2ODimReductionModel: pca
## Model ID: PCA_model_R_1590343748665_1606
## Importance of components:
## pc1 pc2 pc3 pc4 pc5 pc6
## Standard deviation 1.175571 0.494469 0.410985 0.276184 0.253576 0.218807
## Proportion of Variance 0.649576 0.114924 0.079393 0.035853 0.030224 0.022504
## Cumulative Proportion 0.649576 0.764500 0.843893 0.879746 0.909970 0.932474
## pc7 pc8 pc9 pc10
## Standard deviation 0.184299 0.162108 0.140943 0.136902
## Proportion of Variance 0.015965 0.012352 0.009337 0.008809
## Cumulative Proportion 0.948439 0.960791 0.970128 0.978938
##
##
## H2ODimReductionMetrics: pca
##
## No model metrics available for PCA######Generalized Low Rank Decomposition
model=h2o.glrm(training_frame = d.hex, k = 5, loss = "Quadratic", regularization_x = "L1",
gamma_x = 0.5, gamma_y = 0, max_iterations = 1000)##
|
| | 0%
|
|= | 1%
|
|======================================================================| 100%## Model Details:
## ==============
##
## H2ODimReductionModel: glrm
## Model ID: GLRM_model_R_1590343748665_1607
## Model Summary:
## number_of_iterations final_step_size final_objective_value
## 1 8 0.00009 318441551.15294
##
##
## H2ODimReductionMetrics: glrm
## ** Reported on training data. **
##
## Sum of Squared Error (Numeric): 318440336
## Misclassification Error (Categorical): 118
## Number of Numeric Entries: 3360
## Number of Categorical Entries: 240# Decompose training frame into XY
X <- h2o.getFrame(model@model$representation_name)
Y <- model@model$archetypes
library(plotly)
# Visualize first two archetypes of Y
archetypes_y <- as.data.frame(t(Y))
archetypes_y$attribute <- rownames(archetypes_y)
m <- archetypes_y[archetypes_y$attribute %in% c("bio1", "bio2", "soil1", "soil4", "price", "Class", "bio15"), ]
a <- list(
x = m$Arch1,
y = m$Arch2,
text = m$attribute,
xref = "x",
yref = "y"
)
p<-plot_ly(data = archetypes_y, x = ~Arch1, y = ~Arch2, mode = "markers", text = ~attribute, type = "scatter") %>% layout(annotations = a)
p### Variables close to each other are similar
# Visualize first two archetypes of X
archetypes_x <- as.data.frame(X)
archetypes_x$id <- as.character(as.matrix(df$Country))
set.seed(1234)
sample_indices <- sample(c(1:nrow(archetypes_x)), 100)
archetypes_x <- archetypes_x[sample_indices, ]
# Plot
p<-plot_ly(data = archetypes_x, x = ~Arch1, y = ~Arch2, mode = "markers",
text = ~paste0("Country: ", id),type = "scatter")
pSupervised Classification with H2O
Generalized Linear Models with H2O
## REPAY LOAN MORTDUE VALUE REASON JOB YOJ DEROG DELINQ CLAGE NINQ CLNO
## 1 BAD 1100 25860 39025 HomeImp Other 10.5 0.00 0.00 94 1.0 9
## 2 BAD 1300 70053 68400 HomeImp Other 7.0 0.00 2.00 122 0.0 14
## 3 BAD 1500 13500 16700 HomeImp Other 4.0 0.00 0.00 149 1.0 10
## 4 BAD 1500 73761 101776 DebtCon Other 8.9 0.25 0.45 180 1.2 21
## 5 GOOD 1700 97800 112000 HomeImp Office 3.0 0.00 0.00 93 0.0 14
## 6 BAD 1700 30548 40320 HomeImp Other 9.0 0.00 0.00 101 1.0 8
## DEBTINC
## 1 34
## 2 34
## 3 34
## 4 34
## 5 34
## 6 37## REPAY LOAN MORTDUE VALUE REASON
## BAD :1189 Min. : 1100 Min. : 2063 Min. : 8000 DebtCon:4111
## GOOD:4771 1st Qu.:11100 1st Qu.: 48139 1st Qu.: 66490 HomeImp:1849
## Median :16300 Median : 69529 Median : 90000
## Mean :18608 Mean : 73761 Mean :101776
## 3rd Qu.:23300 3rd Qu.: 88200 3rd Qu.:119005
## Max. :89900 Max. :399550 Max. :855909
## JOB YOJ DEROG DELINQ CLAGE
## Mgr : 808 Min. : 0 Min. : 0.0 Min. : 0.0 Min. : 0
## Office : 987 1st Qu.: 3 1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 117
## Other :2504 Median : 8 Median : 0.0 Median : 0.0 Median : 178
## ProfExe:1343 Mean : 9 Mean : 0.3 Mean : 0.4 Mean : 180
## Sales : 115 3rd Qu.:12 3rd Qu.: 0.0 3rd Qu.: 0.4 3rd Qu.: 227
## Self : 203 Max. :41 Max. :10.0 Max. :15.0 Max. :1168
## NINQ CLNO DEBTINC
## Min. : 0.0 Min. : 0 Min. : 1
## 1st Qu.: 0.0 1st Qu.:15 1st Qu.: 31
## Median : 1.0 Median :21 Median : 34
## Mean : 1.2 Mean :21 Mean : 34
## 3rd Qu.: 2.0 3rd Qu.:26 3rd Qu.: 38
## Max. :17.0 Max. :71 Max. :203library(caret)
trainIndex <- createDataPartition(df$REPAY, p = .75,
list = FALSE,
times = 1)
dfTrain <- df[ trainIndex,] #75% data training
dfTest <- df[-trainIndex,] #25% testing
Yte <- df[-trainIndex,1] #test Y
library(h2o)
h2o.init(nthreads = 20, max_mem_size = "16g")## Connection successful!
##
## R is connected to the H2O cluster:
## H2O cluster uptime: 8 minutes 738 milliseconds
## H2O cluster timezone: Asia/Kolkata
## H2O data parsing timezone: UTC
## H2O cluster version: 3.30.0.1
## H2O cluster version age: 1 month and 21 days
## H2O cluster name: H2O_started_from_R_HP_fjx183
## H2O cluster total nodes: 1
## H2O cluster total memory: 1.38 GB
## H2O cluster total cores: 4
## H2O cluster allowed cores: 4
## 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 3.5.0 (2018-04-23)##
|
| | 0%
|
|======================================================================| 100%##
|
| | 0%
|
|======================================================================| 100%y <- "REPAY" #name of the response variable column
X <- setdiff(names(train), y) # names of the predictors
# Train the GLM model
train.glm <- h2o.glm(x = X, # Vector of predictor variable names
y = y, # Name of response/dependent variable
training_frame = train, # Training data
seed = 1234567, # Seed for random numbers
family = "binomial", # Outcome variable
lambda_search = TRUE, # Optimum regularisation lambda
alpha = 0.5, # Elastic net regularisation
nfolds = 5 # N-fold cross validation
)##
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|==================================== | 51%
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|======================================================================| 100%## [1] 0.79## [1] 0.79# Predict on test data
library(tidyverse)
pred_class = h2o.predict(train.glm, test) %>% as.data.frame() %>% pull(predict)##
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|======================================================================| 100%## Confusion Matrix and Statistics
##
## Reference
## Prediction BAD GOOD
## BAD 89 24
## GOOD 208 1168
##
## Accuracy : 0.844
## 95% CI : (0.825, 0.862)
## No Information Rate : 0.801
## P-Value [Acc > NIR] : 0.00000842
##
## Kappa : 0.364
##
## Mcnemar's Test P-Value : < 0.0000000000000002
##
## Sensitivity : 0.2997
## Specificity : 0.9799
## Pos Pred Value : 0.7876
## Neg Pred Value : 0.8488
## Prevalence : 0.1995
## Detection Rate : 0.0598
## Detection Prevalence : 0.0759
## Balanced Accuracy : 0.6398
##
## 'Positive' Class : BAD
## 
Implement Random Forest For Binary Classification Problem with H2O
## REPAY LOAN MORTDUE VALUE REASON JOB YOJ DEROG DELINQ CLAGE NINQ CLNO
## 1 BAD 1100 25860 39025 HomeImp Other 10.5 0.00 0.00 94 1.0 9
## 2 BAD 1300 70053 68400 HomeImp Other 7.0 0.00 2.00 122 0.0 14
## 3 BAD 1500 13500 16700 HomeImp Other 4.0 0.00 0.00 149 1.0 10
## 4 BAD 1500 73761 101776 DebtCon Other 8.9 0.25 0.45 180 1.2 21
## 5 GOOD 1700 97800 112000 HomeImp Office 3.0 0.00 0.00 93 0.0 14
## 6 BAD 1700 30548 40320 HomeImp Other 9.0 0.00 0.00 101 1.0 8
## DEBTINC
## 1 34
## 2 34
## 3 34
## 4 34
## 5 34
## 6 37## REPAY LOAN MORTDUE VALUE REASON
## BAD :1189 Min. : 1100 Min. : 2063 Min. : 8000 DebtCon:4111
## GOOD:4771 1st Qu.:11100 1st Qu.: 48139 1st Qu.: 66490 HomeImp:1849
## Median :16300 Median : 69529 Median : 90000
## Mean :18608 Mean : 73761 Mean :101776
## 3rd Qu.:23300 3rd Qu.: 88200 3rd Qu.:119005
## Max. :89900 Max. :399550 Max. :855909
## JOB YOJ DEROG DELINQ CLAGE
## Mgr : 808 Min. : 0 Min. : 0.0 Min. : 0.0 Min. : 0
## Office : 987 1st Qu.: 3 1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 117
## Other :2504 Median : 8 Median : 0.0 Median : 0.0 Median : 178
## ProfExe:1343 Mean : 9 Mean : 0.3 Mean : 0.4 Mean : 180
## Sales : 115 3rd Qu.:12 3rd Qu.: 0.0 3rd Qu.: 0.4 3rd Qu.: 227
## Self : 203 Max. :41 Max. :10.0 Max. :15.0 Max. :1168
## NINQ CLNO DEBTINC
## Min. : 0.0 Min. : 0 Min. : 1
## 1st Qu.: 0.0 1st Qu.:15 1st Qu.: 31
## Median : 1.0 Median :21 Median : 34
## Mean : 1.2 Mean :21 Mean : 34
## 3rd Qu.: 2.0 3rd Qu.:26 3rd Qu.: 38
## Max. :17.0 Max. :71 Max. :203library(caret)
trainIndex <- createDataPartition(df$REPAY, p = .75,
list = FALSE,
times = 1)
fitControl <- trainControl(## 10-fold CV
method = "repeatedcv",
number = 10,
## repeated ten times
repeats = 10)
dfTrain <- df[ trainIndex,] #75% data training
dfTest <- df[-trainIndex,] #25% testing
Yte <- df[-trainIndex,1] #test Y
library(h2o)
h2o.init(nthreads = 20, max_mem_size = "16g")## Connection successful!
##
## R is connected to the H2O cluster:
## H2O cluster uptime: 8 minutes 5 seconds
## H2O cluster timezone: Asia/Kolkata
## H2O data parsing timezone: UTC
## H2O cluster version: 3.30.0.1
## H2O cluster version age: 1 month and 21 days
## H2O cluster name: H2O_started_from_R_HP_fjx183
## H2O cluster total nodes: 1
## H2O cluster total memory: 1.38 GB
## H2O cluster total cores: 4
## H2O cluster allowed cores: 4
## 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 3.5.0 (2018-04-23)##
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|======================================================================| 100%##
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|======================================================================| 100%y <- "REPAY"
x <- setdiff(names(train), y) #predictors
# Train default Random Forest:
default_rf <- h2o.randomForest(x = x, y = y,
training_frame = train,
stopping_rounds = 5,
stopping_tolerance = 0.001,
stopping_metric = "AUC",
seed = 29,
balance_classes = FALSE,
nfolds = 10)##
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|============================================= | 64%
|
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|
|============================================================= | 87%
|
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|======================================================================| 100%# Model performance based on test data:
library(tidyverse)
pred_class <- h2o.predict(default_rf, test) %>% as.data.frame() %>% pull(predict)##
|
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|======================================================================| 100%## Confusion Matrix and Statistics
##
## Reference
## Prediction BAD GOOD
## BAD 217 30
## GOOD 80 1162
##
## Accuracy : 0.926
## 95% CI : (0.912, 0.939)
## No Information Rate : 0.801
## P-Value [Acc > NIR] : < 0.0000000000000002
##
## Kappa : 0.753
##
## Mcnemar's Test P-Value : 0.00000298
##
## Sensitivity : 0.731
## Specificity : 0.975
## Pos Pred Value : 0.879
## Neg Pred Value : 0.936
## Prevalence : 0.199
## Detection Rate : 0.146
## Detection Prevalence : 0.166
## Balanced Accuracy : 0.853
##
## 'Positive' Class : BAD
## ## [1] 0.96## [1] 0.96
Implement Random Forest For Multiple Classification Problem with H2o
## Connection successful!
##
## R is connected to the H2O cluster:
## H2O cluster uptime: 8 minutes 18 seconds
## H2O cluster timezone: Asia/Kolkata
## H2O data parsing timezone: UTC
## H2O cluster version: 3.30.0.1
## H2O cluster version age: 1 month and 21 days
## H2O cluster name: H2O_started_from_R_HP_fjx183
## H2O cluster total nodes: 1
## H2O cluster total memory: 1.36 GB
## H2O cluster total cores: 4
## H2O cluster allowed cores: 4
## 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 3.5.0 (2018-04-23)##
|
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|
|================================================================== | 94%
|
|======================================================================| 100%## Elevation Aspect Slope Horizontal_Distance_To_Hydrology
## 1 2596 51 3 258
## 2 2590 56 2 212
## 3 2804 139 9 268
## 4 2785 155 18 242
## 5 2595 45 2 153
## 6 2579 132 6 300
## Vertical_Distance_To_Hydrology Horizontal_Distance_To_Roadways Hillshade_9am
## 1 0 510 221
## 2 -6 390 220
## 3 65 3180 234
## 4 118 3090 238
## 5 -1 391 220
## 6 -15 67 230
## Hillshade_Noon Hillshade_3pm Horizontal_Distance_To_Fire_Points
## 1 232 148 6279
## 2 235 151 6225
## 3 238 135 6121
## 4 238 122 6211
## 5 234 150 6172
## 6 237 140 6031
## Wilderness_Area1 Wilderness_Area2 Wilderness_Area3 Wilderness_Area4
## 1 1 0 0 0
## 2 1 0 0 0
## 3 1 0 0 0
## 4 1 0 0 0
## 5 1 0 0 0
## 6 1 0 0 0
## Soil_Type1 Soil_Type2 Soil_Type3 Soil_Type4 Soil_Type5 Soil_Type6 Soil_Type7
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0
## Soil_Type8 Soil_Type9 Soil_Type10 Soil_Type11 Soil_Type12 Soil_Type13
## 1 0 0 0 0 0 0
## 2 0 0 0 0 0 0
## 3 0 0 0 0 1 0
## 4 0 0 0 0 0 0
## 5 0 0 0 0 0 0
## 6 0 0 0 0 0 0
## Soil_Type14 Soil_Type15 Soil_Type16 Soil_Type17 Soil_Type18 Soil_Type19
## 1 0 0 0 0 0 0
## 2 0 0 0 0 0 0
## 3 0 0 0 0 0 0
## 4 0 0 0 0 0 0
## 5 0 0 0 0 0 0
## 6 0 0 0 0 0 0
## Soil_Type20 Soil_Type21 Soil_Type22 Soil_Type23 Soil_Type24 Soil_Type25
## 1 0 0 0 0 0 0
## 2 0 0 0 0 0 0
## 3 0 0 0 0 0 0
## 4 0 0 0 0 0 0
## 5 0 0 0 0 0 0
## 6 0 0 0 0 0 0
## Soil_Type26 Soil_Type27 Soil_Type28 Soil_Type29 Soil_Type30 Soil_Type31
## 1 0 0 0 1 0 0
## 2 0 0 0 1 0 0
## 3 0 0 0 0 0 0
## 4 0 0 0 0 1 0
## 5 0 0 0 1 0 0
## 6 0 0 0 1 0 0
## Soil_Type32 Soil_Type33 Soil_Type34 Soil_Type35 Soil_Type36 Soil_Type37
## 1 0 0 0 0 0 0
## 2 0 0 0 0 0 0
## 3 0 0 0 0 0 0
## 4 0 0 0 0 0 0
## 5 0 0 0 0 0 0
## 6 0 0 0 0 0 0
## Soil_Type38 Soil_Type39 Soil_Type40 Cover_Type
## 1 0 0 0 5
## 2 0 0 0 5
## 3 0 0 0 2
## 4 0 0 0 2
## 5 0 0 0 5
## 6 0 0 0 2## Elevation Aspect Slope Horizontal_Distance_To_Hydrology
## Min. :1859 Min. : 0.0 Min. : 0.0 Min. : 0.0
## 1st Qu.:2809 1st Qu.: 58.0 1st Qu.: 9.0 1st Qu.: 107.6
## Median :2995 Median :127.0 Median :13.0 Median : 216.7
## Mean :2959 Mean :155.7 Mean :14.1 Mean : 269.4
## 3rd Qu.:3163 3rd Qu.:260.0 3rd Qu.:18.0 3rd Qu.: 383.1
## Max. :3858 Max. :360.0 Max. :66.0 Max. :1397.0
## Vertical_Distance_To_Hydrology Horizontal_Distance_To_Roadways Hillshade_9am
## Min. :-173.00 Min. : 0 Min. : 0.0
## 1st Qu.: 7.00 1st Qu.:1103 1st Qu.:198.0
## Median : 30.00 Median :1993 Median :218.0
## Mean : 46.42 Mean :2350 Mean :212.1
## 3rd Qu.: 69.00 3rd Qu.:3324 3rd Qu.:231.0
## Max. : 601.00 Max. :7117 Max. :254.0
## Hillshade_Noon Hillshade_3pm Horizontal_Distance_To_Fire_Points
## Min. : 0.0 Min. : 0.0 Min. : 0
## 1st Qu.:213.0 1st Qu.:119.0 1st Qu.:1019
## Median :226.0 Median :143.0 Median :1707
## Mean :223.3 Mean :142.5 Mean :1980
## 3rd Qu.:237.0 3rd Qu.:168.0 3rd Qu.:2547
## Max. :254.0 Max. :254.0 Max. :7173
## Wilderness_Area1 Wilderness_Area2 Wilderness_Area3 Wilderness_Area4
## Min. :0.0000 Min. :0.00000 Min. :0.0000 Min. :0.00000
## 1st Qu.:0.0000 1st Qu.:0.00000 1st Qu.:0.0000 1st Qu.:0.00000
## Median :0.0000 Median :0.00000 Median :0.0000 Median :0.00000
## Mean :0.4489 Mean :0.05143 Mean :0.4361 Mean :0.06363
## 3rd Qu.:1.0000 3rd Qu.:0.00000 3rd Qu.:1.0000 3rd Qu.:0.00000
## Max. :1.0000 Max. :1.00000 Max. :1.0000 Max. :1.00000
## Soil_Type1 Soil_Type2 Soil_Type3 Soil_Type4
## Min. :0.000000 Min. :0.00000 Min. :0.000000 Min. :0.00000
## 1st Qu.:0.000000 1st Qu.:0.00000 1st Qu.:0.000000 1st Qu.:0.00000
## Median :0.000000 Median :0.00000 Median :0.000000 Median :0.00000
## Mean :0.005217 Mean :0.01295 Mean :0.008301 Mean :0.02134
## 3rd Qu.:0.000000 3rd Qu.:0.00000 3rd Qu.:0.000000 3rd Qu.:0.00000
## Max. :1.000000 Max. :1.00000 Max. :1.000000 Max. :1.00000
## Soil_Type5 Soil_Type6 Soil_Type7 Soil_Type8
## Min. :0.000000 Min. :0.00000 Min. :0.0000000 Min. :0.0000000
## 1st Qu.:0.000000 1st Qu.:0.00000 1st Qu.:0.0000000 1st Qu.:0.0000000
## Median :0.000000 Median :0.00000 Median :0.0000000 Median :0.0000000
## Mean :0.002749 Mean :0.01132 Mean :0.0001807 Mean :0.0003081
## 3rd Qu.:0.000000 3rd Qu.:0.00000 3rd Qu.:0.0000000 3rd Qu.:0.0000000
## Max. :1.000000 Max. :1.00000 Max. :1.0000000 Max. :1.0000000
## Soil_Type9 Soil_Type10 Soil_Type11 Soil_Type12
## Min. :0.000000 Min. :0.00000 Min. :0.00000 Min. :0.00000
## 1st Qu.:0.000000 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000
## Median :0.000000 Median :0.00000 Median :0.00000 Median :0.00000
## Mean :0.001974 Mean :0.05617 Mean :0.02136 Mean :0.05158
## 3rd Qu.:0.000000 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000
## Max. :1.000000 Max. :1.00000 Max. :1.00000 Max. :1.00000
## Soil_Type13 Soil_Type14 Soil_Type15 Soil_Type16
## Min. :0.00 Min. :0.000000 Min. :0.000000000 Min. :0.000000
## 1st Qu.:0.00 1st Qu.:0.000000 1st Qu.:0.000000000 1st Qu.:0.000000
## Median :0.00 Median :0.000000 Median :0.000000000 Median :0.000000
## Mean :0.03 Mean :0.001031 Mean :0.000005163 Mean :0.004897
## 3rd Qu.:0.00 3rd Qu.:0.000000 3rd Qu.:0.000000000 3rd Qu.:0.000000
## Max. :1.00 Max. :1.000000 Max. :1.000000000 Max. :1.000000
## Soil_Type17 Soil_Type18 Soil_Type19 Soil_Type20
## Min. :0.00000 Min. :0.000000 Min. :0.000000 Min. :0.00000
## 1st Qu.:0.00000 1st Qu.:0.000000 1st Qu.:0.000000 1st Qu.:0.00000
## Median :0.00000 Median :0.000000 Median :0.000000 Median :0.00000
## Mean :0.00589 Mean :0.003268 Mean :0.006921 Mean :0.01594
## 3rd Qu.:0.00000 3rd Qu.:0.000000 3rd Qu.:0.000000 3rd Qu.:0.00000
## Max. :1.00000 Max. :1.000000 Max. :1.000000 Max. :1.00000
## Soil_Type21 Soil_Type22 Soil_Type23 Soil_Type24
## Min. :0.000000 Min. :0.00000 Min. :0.0000 Min. :0.00000
## 1st Qu.:0.000000 1st Qu.:0.00000 1st Qu.:0.0000 1st Qu.:0.00000
## Median :0.000000 Median :0.00000 Median :0.0000 Median :0.00000
## Mean :0.001442 Mean :0.05744 Mean :0.0994 Mean :0.03662
## 3rd Qu.:0.000000 3rd Qu.:0.00000 3rd Qu.:0.0000 3rd Qu.:0.00000
## Max. :1.000000 Max. :1.00000 Max. :1.0000 Max. :1.00000
## Soil_Type25 Soil_Type26 Soil_Type27 Soil_Type28
## Min. :0.0000000 Min. :0.000000 Min. :0.000000 Min. :0.000000
## 1st Qu.:0.0000000 1st Qu.:0.000000 1st Qu.:0.000000 1st Qu.:0.000000
## Median :0.0000000 Median :0.000000 Median :0.000000 Median :0.000000
## Mean :0.0008158 Mean :0.004456 Mean :0.001869 Mean :0.001628
## 3rd Qu.:0.0000000 3rd Qu.:0.000000 3rd Qu.:0.000000 3rd Qu.:0.000000
## Max. :1.0000000 Max. :1.000000 Max. :1.000000 Max. :1.000000
## Soil_Type29 Soil_Type30 Soil_Type31 Soil_Type32
## Min. :0.0000 Min. :0.00000 Min. :0.00000 Min. :0.00000
## 1st Qu.:0.0000 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000
## Median :0.0000 Median :0.00000 Median :0.00000 Median :0.00000
## Mean :0.1984 Mean :0.05193 Mean :0.04417 Mean :0.09039
## 3rd Qu.:0.0000 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000
## Max. :1.0000 Max. :1.00000 Max. :1.00000 Max. :1.00000
## Soil_Type33 Soil_Type34 Soil_Type35 Soil_Type36
## Min. :0.00000 Min. :0.000000 Min. :0.000000 Min. :0.0000000
## 1st Qu.:0.00000 1st Qu.:0.000000 1st Qu.:0.000000 1st Qu.:0.0000000
## Median :0.00000 Median :0.000000 Median :0.000000 Median :0.0000000
## Mean :0.07772 Mean :0.002773 Mean :0.003255 Mean :0.0002048
## 3rd Qu.:0.00000 3rd Qu.:0.000000 3rd Qu.:0.000000 3rd Qu.:0.0000000
## Max. :1.00000 Max. :1.000000 Max. :1.000000 Max. :1.0000000
## Soil_Type37 Soil_Type38 Soil_Type39 Soil_Type40
## Min. :0.0000000 Min. :0.0000 Min. :0.00000 Min. :0.00000
## 1st Qu.:0.0000000 1st Qu.:0.0000 1st Qu.:0.00000 1st Qu.:0.00000
## Median :0.0000000 Median :0.0000 Median :0.00000 Median :0.00000
## Mean :0.0005129 Mean :0.0268 Mean :0.02376 Mean :0.01506
## 3rd Qu.:0.0000000 3rd Qu.:0.0000 3rd Qu.:0.00000 3rd Qu.:0.00000
## Max. :1.0000000 Max. :1.0000 Max. :1.00000 Max. :1.00000
## Cover_Type
## 2:283301
## 1:211840
## 3: 35754
## 7: 20510
## 6: 17367
## 5: 9493### split data for training, validation and testing
splits <- h2o.splitFrame(df,c(0.7,0.1),seed=1234)
train <- h2o.assign(splits[[1]], "train.hex") #70%
## assign the first result the R variable train
## and the H2O name train.hex
valid <- h2o.assign(splits[[2]], "valid.hex") ## R valid, H2O valid.hex
test <- h2o.assign(splits[[3]], "test.hex") ## R test, H2O test.hex
rf1=h2o.randomForest(training_frame = train, ## the H2O frame for training
validation_frame = valid, ## the H2O frame for validation (not required)
x=1:12, ## the predictor columns, by column index
y=13, ## the target index (what we are predicting)
model_id = "rf_covType_v1", ## name the model in H2O
## not required, but helps use Flow
ntrees = 150, ## use a maximum of 200 trees to create the
## random forest model. The default is 50.
## I have increased it because I will let
## the early stopping criteria decide when
## the random forest is sufficiently accurate
stopping_rounds = 2, ## Stop fitting new trees when the 2-tree
## average is within 0.001 (default) of
## the prior two 2-tree averages.
## Can be thought of as a convergence setting
score_each_iteration = T, ## Predict against training and validation for
## each tree. Default will skip several.
seed = 1000000) ## Set the random seed so that this can be##
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|======================================================================| 100%## Model Details:
## ==============
##
## H2ORegressionModel: drf
## Model Key: rf_covType_v1
## Model Summary:
## number_of_trees number_of_internal_trees model_size_in_bytes min_depth
## 1 20 20 306131 20
## max_depth mean_depth min_leaves max_leaves mean_leaves
## 1 20 20.00000 992 1555 1212.85000
##
## H2ORegressionMetrics: drf
## ** Reported on training data. **
## ** Metrics reported on Out-Of-Bag training samples **
##
## MSE: 0.0018
## RMSE: 0.043
## MAE: 0.0051
## RMSLE: 0.031
## Mean Residual Deviance : 0.0018
##
##
## H2ORegressionMetrics: drf
## ** Reported on validation data. **
##
## MSE: 0.0014
## RMSE: 0.037
## MAE: 0.005
## RMSLE: 0.027
## Mean Residual Deviance : 0.0014
##
##
##
##
## Scoring History:
## timestamp duration number_of_trees training_rmse training_mae
## 1 2020-05-24 23:47:33 0.001 sec 0 NA NA
## 2 2020-05-24 23:47:34 0.348 sec 1 0.06631 0.00475
## 3 2020-05-24 23:47:34 0.662 sec 2 0.06426 0.00490
## 4 2020-05-24 23:47:34 0.964 sec 3 0.06143 0.00486
## 5 2020-05-24 23:47:35 1.269 sec 4 0.05858 0.00474
## training_deviance validation_rmse validation_mae validation_deviance
## 1 NA NA NA NA
## 2 0.00440 0.06592 0.00476 0.00435
## 3 0.00413 0.05138 0.00461 0.00264
## 4 0.00377 0.04616 0.00451 0.00213
## 5 0.00343 0.04422 0.00459 0.00196
##
## ---
## timestamp duration number_of_trees training_rmse training_mae
## 16 2020-05-24 23:47:40 6.487 sec 15 0.04471 0.00503
## 17 2020-05-24 23:47:40 6.908 sec 16 0.04421 0.00505
## 18 2020-05-24 23:47:41 7.324 sec 17 0.04393 0.00507
## 19 2020-05-24 23:47:41 7.952 sec 18 0.04351 0.00508
## 20 2020-05-24 23:47:42 8.563 sec 19 0.04336 0.00515
## 21 2020-05-24 23:47:42 9.118 sec 20 0.04291 0.00514
## training_deviance validation_rmse validation_mae validation_deviance
## 16 0.00200 0.03764 0.00483 0.00142
## 17 0.00195 0.03758 0.00486 0.00141
## 18 0.00193 0.03754 0.00487 0.00141
## 19 0.00189 0.03721 0.00487 0.00138
## 20 0.00188 0.03755 0.00497 0.00141
## 21 0.00184 0.03723 0.00495 0.00139
##
## Variable Importances: (Extract with `h2o.varimp`)
## =================================================
##
## Variable Importances:
## variable relative_importance scaled_importance
## 1 Wilderness_Area1 863430.062500 1.000000
## 2 Elevation 179718.796875 0.208145
## 3 Wilderness_Area2 151199.687500 0.175115
## 4 Horizontal_Distance_To_Roadways 127215.937500 0.147338
## 5 Horizontal_Distance_To_Fire_Points 81181.640625 0.094022
## 6 Slope 8564.956055 0.009920
## 7 Aspect 8198.708984 0.009496
## 8 Hillshade_Noon 7518.732910 0.008708
## 9 Vertical_Distance_To_Hydrology 6939.757324 0.008037
## 10 Horizontal_Distance_To_Hydrology 6224.021973 0.007208
## 11 Hillshade_9am 3533.488281 0.004092
## 12 Hillshade_3pm 2926.276611 0.003389
## percentage
## 1 0.596847
## 2 0.124231
## 3 0.104517
## 4 0.087938
## 5 0.056117
## 6 0.005921
## 7 0.005667
## 8 0.005197
## 9 0.004797
## 10 0.004302
## 11 0.002443
## 12 0.002023## Variable Importances:
## variable relative_importance scaled_importance
## 1 Wilderness_Area1 863430.062500 1.000000
## 2 Elevation 179718.796875 0.208145
## 3 Wilderness_Area2 151199.687500 0.175115
## 4 Horizontal_Distance_To_Roadways 127215.937500 0.147338
## 5 Horizontal_Distance_To_Fire_Points 81181.640625 0.094022
## 6 Slope 8564.956055 0.009920
## 7 Aspect 8198.708984 0.009496
## 8 Hillshade_Noon 7518.732910 0.008708
## 9 Vertical_Distance_To_Hydrology 6939.757324 0.008037
## 10 Horizontal_Distance_To_Hydrology 6224.021973 0.007208
## 11 Hillshade_9am 3533.488281 0.004092
## 12 Hillshade_3pm 2926.276611 0.003389
## percentage
## 1 0.596847
## 2 0.124231
## 3 0.104517
## 4 0.087938
## 5 0.056117
## 6 0.005921
## 7 0.005667
## 8 0.005197
## 9 0.004797
## 10 0.004302
## 11 0.002443
## 12 0.002023## H2ORegressionMetrics: drf
## ** Reported on validation data. **
##
## MSE: 0.0014
## RMSE: 0.037
## MAE: 0.005
## RMSLE: 0.027
## Mean Residual Deviance : 0.0014## Variable Importances:
## variable relative_importance scaled_importance
## 1 Wilderness_Area1 863430.062500 1.000000
## 2 Elevation 179718.796875 0.208145
## 3 Wilderness_Area2 151199.687500 0.175115
## 4 Horizontal_Distance_To_Roadways 127215.937500 0.147338
## 5 Horizontal_Distance_To_Fire_Points 81181.640625 0.094022
## 6 Slope 8564.956055 0.009920
## 7 Aspect 8198.708984 0.009496
## 8 Hillshade_Noon 7518.732910 0.008708
## 9 Vertical_Distance_To_Hydrology 6939.757324 0.008037
## 10 Horizontal_Distance_To_Hydrology 6224.021973 0.007208
## 11 Hillshade_9am 3533.488281 0.004092
## 12 Hillshade_3pm 2926.276611 0.003389
## percentage
## 1 0.596847
## 2 0.124231
## 3 0.104517
## 4 0.087938
## 5 0.056117
## 6 0.005921
## 7 0.005667
## 8 0.005197
## 9 0.004797
## 10 0.004302
## 11 0.002443
## 12 0.002023## [1] "Variable importance from Random Forest without normalization"## [,1] [,2] [,3]
## variable "Wilderness_Area1" "Elevation" "Wilderness_Area2"
## relative_importance "863430" "179719" "151200"
## [,4]
## variable "Horizontal_Distance_To_Roadways"
## relative_importance "127216"
## [,5] [,6] [,7]
## variable "Horizontal_Distance_To_Fire_Points" "Slope" "Aspect"
## relative_importance " 81182" " 8565" " 8199"
## [,8] [,9]
## variable "Hillshade_Noon" "Vertical_Distance_To_Hydrology"
## relative_importance " 7519" " 6940"
## [,10] [,11]
## variable "Horizontal_Distance_To_Hydrology" "Hillshade_9am"
## relative_importance " 6224" " 3533"
## [,12]
## variable "Hillshade_3pm"
## relative_importance " 2926"# RF variable importance With normalization, i.e scale =T (divide mean decrease accuracy by standard deviation)
norm_rf.VI <- rf.VI$relative_importance/max(rf.VI$relative_importance)
print("Variable importance from Random Forest with normalization")## [1] "Variable importance from Random Forest with normalization"## [,1] [,2] [,3]
## variable "Wilderness_Area1" "Elevation" "Wilderness_Area2"
## scaled_importance "1.0000" "0.2081" "0.1751"
## [,4]
## variable "Horizontal_Distance_To_Roadways"
## scaled_importance "0.1473"
## [,5] [,6] [,7]
## variable "Horizontal_Distance_To_Fire_Points" "Slope" "Aspect"
## scaled_importance "0.0940" "0.0099" "0.0095"
## [,8] [,9]
## variable "Hillshade_Noon" "Vertical_Distance_To_Hydrology"
## scaled_importance "0.0087" "0.0080"
## [,10] [,11]
## variable "Horizontal_Distance_To_Hydrology" "Hillshade_9am"
## scaled_importance "0.0072" "0.0041"
## [,12]
## variable "Hillshade_3pm"
## scaled_importance "0.0034"# Plot variable importance from Random Forest
barplot(rf.VI$scaled_importance,beside=T,names.arg=rf.VI$variable,las=2,main="VI from RF")
Gradient Boosting Machines (GBM) for Binary Classification using H2o
## REPAY LOAN MORTDUE VALUE REASON JOB YOJ DEROG DELINQ CLAGE NINQ CLNO
## 1 BAD 1100 25860 39025 HomeImp Other 10.5 0.00 0.00 94 1.0 9
## 2 BAD 1300 70053 68400 HomeImp Other 7.0 0.00 2.00 122 0.0 14
## 3 BAD 1500 13500 16700 HomeImp Other 4.0 0.00 0.00 149 1.0 10
## 4 BAD 1500 73761 101776 DebtCon Other 8.9 0.25 0.45 180 1.2 21
## 5 GOOD 1700 97800 112000 HomeImp Office 3.0 0.00 0.00 93 0.0 14
## 6 BAD 1700 30548 40320 HomeImp Other 9.0 0.00 0.00 101 1.0 8
## DEBTINC
## 1 34
## 2 34
## 3 34
## 4 34
## 5 34
## 6 37## REPAY LOAN MORTDUE VALUE REASON
## BAD :1189 Min. : 1100 Min. : 2063 Min. : 8000 DebtCon:4111
## GOOD:4771 1st Qu.:11100 1st Qu.: 48139 1st Qu.: 66490 HomeImp:1849
## Median :16300 Median : 69529 Median : 90000
## Mean :18608 Mean : 73761 Mean :101776
## 3rd Qu.:23300 3rd Qu.: 88200 3rd Qu.:119005
## Max. :89900 Max. :399550 Max. :855909
## JOB YOJ DEROG DELINQ CLAGE
## Mgr : 808 Min. : 0 Min. : 0.0 Min. : 0.0 Min. : 0
## Office : 987 1st Qu.: 3 1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 117
## Other :2504 Median : 8 Median : 0.0 Median : 0.0 Median : 178
## ProfExe:1343 Mean : 9 Mean : 0.3 Mean : 0.4 Mean : 180
## Sales : 115 3rd Qu.:12 3rd Qu.: 0.0 3rd Qu.: 0.4 3rd Qu.: 227
## Self : 203 Max. :41 Max. :10.0 Max. :15.0 Max. :1168
## NINQ CLNO DEBTINC
## Min. : 0.0 Min. : 0 Min. : 1
## 1st Qu.: 0.0 1st Qu.:15 1st Qu.: 31
## Median : 1.0 Median :21 Median : 34
## Mean : 1.2 Mean :21 Mean : 34
## 3rd Qu.: 2.0 3rd Qu.:26 3rd Qu.: 38
## Max. :17.0 Max. :71 Max. :203library(caret)
trainIndex <- createDataPartition(df$REPAY, p = .75,
list = FALSE,
times = 1)
fitControl <- trainControl(## 10-fold CV
method = "repeatedcv",
number = 10,
## repeated ten times
repeats = 10)
dfTrain <- df[ trainIndex,] #75% data training
dfTest <- df[-trainIndex,] #25% testing
Yte <- df[-trainIndex,1] #test Y
library(h2o)
h2o.init(nthreads = 20, max_mem_size = "16g")## Connection successful!
##
## R is connected to the H2O cluster:
## H2O cluster uptime: 8 minutes 34 seconds
## H2O cluster timezone: Asia/Kolkata
## H2O data parsing timezone: UTC
## H2O cluster version: 3.30.0.1
## H2O cluster version age: 1 month and 21 days
## H2O cluster name: H2O_started_from_R_HP_fjx183
## H2O cluster total nodes: 1
## H2O cluster total memory: 1.69 GB
## H2O cluster total cores: 4
## H2O cluster allowed cores: 4
## 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 3.5.0 (2018-04-23)##
|
| | 0%
|
|======================================================================| 100%##
|
| | 0%
|
|======================================================================| 100%y <- "REPAY"
x <- setdiff(names(train), y) #predictors
# Train default Random Forest:
default_gbm <- h2o.gbm(x = x, y = y,
training_frame = train,
stopping_rounds = 5,
stopping_tolerance = 0.001,
stopping_metric = "AUC",
seed = 29,
balance_classes = FALSE,
nfolds = 10)##
|
| | 0%
|
|==================== | 28%
|
|=================================================== | 72%
|
|============================================================= | 87%
|
|=================================================================== | 96%
|
|======================================================================| 100%# Model performance based on test data:
library(tidyverse)
pred_class <- h2o.predict(default_gbm, test) %>% as.data.frame() %>% pull(predict)##
|
| | 0%
|
|======================================================================| 100%## Confusion Matrix and Statistics
##
## Reference
## Prediction BAD GOOD
## BAD 196 51
## GOOD 101 1141
##
## Accuracy : 0.898
## 95% CI : (0.881, 0.913)
## No Information Rate : 0.801
## P-Value [Acc > NIR] : < 0.0000000000000002
##
## Kappa : 0.659
##
## Mcnemar's Test P-Value : 0.0000705
##
## Sensitivity : 0.660
## Specificity : 0.957
## Pos Pred Value : 0.794
## Neg Pred Value : 0.919
## Prevalence : 0.199
## Detection Rate : 0.132
## Detection Prevalence : 0.166
## Balanced Accuracy : 0.809
##
## 'Positive' Class : BAD
## ## [1] 0.97## [1] 0.93
## H2OBinomialMetrics: gbm
## ** Reported on training data. **
##
## MSE: 0.055
## RMSE: 0.24
## LogLoss: 0.2
## Mean Per-Class Error: 0.14
## AUC: 0.97
## AUCPR: 0.99
## Gini: 0.93
## R^2: 0.65
##
## Confusion Matrix (vertical: actual; across: predicted) for F1-optimal threshold:
## BAD GOOD Error Rate
## BAD 671 221 0.247758 =221/892
## GOOD 111 3468 0.031014 =111/3579
## Totals 782 3689 0.074256 =332/4471
##
## Maximum Metrics: Maximum metrics at their respective thresholds
## metric threshold value idx
## 1 max f1 0.533824 0.954320 218
## 2 max f2 0.351409 0.976892 281
## 3 max f0point5 0.824648 0.961664 128
## 4 max accuracy 0.537417 0.925744 217
## 5 max precision 0.980550 1.000000 0
## 6 max recall 0.188421 1.000000 328
## 7 max specificity 0.980550 1.000000 0
## 8 max absolute_mcc 0.734786 0.771097 162
## 9 max min_per_class_accuracy 0.820411 0.908072 130
## 10 max mean_per_class_accuracy 0.824648 0.909619 128
## 11 max tns 0.980550 892.000000 0
## 12 max fns 0.980550 3574.000000 0
## 13 max fps 0.009547 892.000000 399
## 14 max tps 0.188421 3579.000000 328
## 15 max tnr 0.980550 1.000000 0
## 16 max fnr 0.980550 0.998603 0
## 17 max fpr 0.009547 1.000000 399
## 18 max tpr 0.188421 1.000000 328
##
## Gains/Lift Table: Extract with `h2o.gainsLift(<model>, <data>)` or `h2o.gainsLift(<model>, valid=<T/F>, xval=<T/F>)`## [1] 0.97# retrieve the AUC for both the training and validation data:
h2o.auc(default_gbm, train=TRUE, valid=FALSE, xval=FALSE)## [1] 0.97
Artificial Neural Networks (ANN) and Deep Neural Networks with H2o
Implement an ANN with H2o For Multi-Class Supervised Classification using H2o
############################################################
library(nnet)
library(caret)
set.seed(1234)
# loading data
f.data = read.csv("dataset.csv")
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## [list output truncated]## Connection successful!
##
## R is connected to the H2O cluster:
## H2O cluster uptime: 9 minutes 3 seconds
## H2O cluster timezone: Asia/Kolkata
## H2O data parsing timezone: UTC
## H2O cluster version: 3.30.0.1
## H2O cluster version age: 1 month and 21 days
## H2O cluster name: H2O_started_from_R_HP_fjx183
## H2O cluster total nodes: 1
## H2O cluster total memory: 1.70 GB
## H2O cluster total cores: 4
## H2O cluster allowed cores: 4
## 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 3.5.0 (2018-04-23)##
|
| | 0%
|
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## 6 0 0 0g.split = h2o.splitFrame(data = d.hex,ratios = 0.75)
train = g.split[[1]]#75% training data
test = g.split[[2]]
### 1 hidden layer with 10 neurons
##ann
fmnist_nn = h2o.deeplearning(x = 2:785,
y = "label",
training_frame = train,
distribution = "multinomial",
model_id = "fmnist_nn",
l2 = 0.4,
ignore_const_cols = FALSE,
hidden = 15,
export_weights_and_biases = TRUE)##
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|======================================================================| 100%## Confusion Matrix: Row labels: Actual class; Column labels: Predicted class
## 0 1 2 3 4 5 6 7 8 9 Error Rate
## 0 1191 3 12 164 12 26 40 0 18 1 0.1881 = 276 / 1,467
## 1 7 1443 24 83 5 0 7 0 2 0 0.0815 = 128 / 1,571
## 2 30 3 848 15 402 36 118 0 16 0 0.4223 = 620 / 1,468
## 3 53 9 4 1372 27 15 32 0 1 1 0.0938 = 142 / 1,514
## 4 5 8 98 126 1134 24 54 0 12 0 0.2238 = 327 / 1,461
## 5 0 0 0 1 0 1345 0 99 3 64 0.1104 = 167 / 1,512
## 6 343 3 174 95 501 57 260 2 34 1 0.8231 = 1,210 / 1,470
## 7 0 0 0 0 0 217 0 1206 2 79 0.1981 = 298 / 1,504
## 8 11 0 19 34 9 65 7 29 1330 4 0.1180 = 178 / 1,508
## 9 0 1 0 1 0 63 0 117 0 1393 0.1156 = 182 / 1,575
## Totals 1640 1470 1179 1891 2090 1848 518 1453 1418 1543 0.2344 = 3,528 / 15,050Implement a DNN with H2o ForMulti-Class Supervised Classification using H20
set.seed(1234)
# loading data
f.data = read.csv("dataset.csv")
##1)T-shirt/top (2) Trouser (3) Pullover (4)Dress (5)Coat
## 6)Sandal (7)Shirt (8) Sneaker (9) Bag (10) Ankle boot
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## 6 5 0 0 0## [1] "factor"## 0 1 2 3 4 5 6 7 8 9
## 6000 6000 6000 6000 6000 6000 6000 6000 6000 6000## Connection successful!
##
## R is connected to the H2O cluster:
## H2O cluster uptime: 10 minutes 17 seconds
## H2O cluster timezone: Asia/Kolkata
## H2O data parsing timezone: UTC
## H2O cluster version: 3.30.0.1
## H2O cluster version age: 1 month and 21 days
## H2O cluster name: H2O_started_from_R_HP_fjx183
## H2O cluster total nodes: 1
## H2O cluster total memory: 1.32 GB
## H2O cluster total cores: 4
## H2O cluster allowed cores: 4
## 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 3.5.0 (2018-04-23)##
|
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## 6 0 0 0g.split = h2o.splitFrame(data = d.hex,ratios = 0.75)
train = g.split[[1]]#75% training data
test = g.split[[2]]
### DNN with 3 hidden layers (1000 neurons each)
### activation function: Rectifier with droput (improve generalization.)
### epochs:The number of iterations to be carried out. More epochs=more accuracy
### Adaptive learning rates self adjust to avoid local minima or slow convergence.
### learning rate annealing gradually reduces learning
### Momentum modifies back-propagation by allowing prior iterations to influence
##the current update.
#This takes lot of time
#fmnist_nn = h2o.deeplearning(x = 2:785,
# y = "label",
# training_frame = train,
# distribution = "multinomial",
# model_id = "fmnist_nn",
# activation = "RectifierWithDropout",
# hidden=c(1000, 1000, 2000),
# epochs = 180,
# adaptive_rate = FALSE,
# rate=0.01,
# rate_annealing = 1.0e-6,
# rate_decay = 1.0,
# momentum_start = 0.4,
# momentum_ramp = 384000,
# momentum_stable = 0.98,
# input_dropout_ratio = 0.22,
# l1 = 1.0e-5,
# max_w2 = 15.0,
# initial_weight_distribution = "Normal",
# initial_weight_scale = 0.01,
# nesterov_accelerated_gradient = TRUE,
# loss = "CrossEntropy",
# fast_mode = TRUE,
# diagnostics = TRUE,
# ignore_const_cols = TRUE,
# force_load_balance = TRUE,
# seed = 3.656455e+18)
#h2o.confusionMatrix(fmnist_nn, test)
## DNN with 2 hidden layers
## default- 2 hidden layers (200 neurons each)
m = h2o.deeplearning(x = 2:785,
y = "label",
training_frame = train,
distribution = "multinomial",
model_id = "m",
activation = "Tanh",
l2 = 0.00001,
hidden = c(50,50),
loss = "CrossEntropy")##
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|======================================================================| 100%## Confusion Matrix: Row labels: Actual class; Column labels: Predicted class
## 0 1 2 3 4 5 6 7 8 9 Error
## 0 1237 4 25 37 5 1 149 0 9 0 0.1568
## 1 7 1527 1 21 5 2 8 0 0 0 0.0280
## 2 18 1 1230 17 109 3 86 0 4 0 0.1621
## 3 49 20 15 1333 64 2 25 0 6 0 0.1196
## 4 4 2 176 45 1147 0 83 0 4 0 0.2149
## 5 0 0 0 0 0 1442 0 26 8 36 0.0463
## 6 174 5 164 35 100 1 977 1 11 2 0.3354
## 7 0 0 0 0 0 48 0 1378 8 70 0.0838
## 8 14 1 11 8 7 5 20 4 1437 1 0.0471
## 9 0 2 0 0 0 24 0 42 0 1507 0.0432
## Totals 1503 1562 1622 1496 1437 1528 1348 1451 1487 1616 0.1219
## Rate
## 0 = 230 / 1,467
## 1 = 44 / 1,571
## 2 = 238 / 1,468
## 3 = 181 / 1,514
## 4 = 314 / 1,461
## 5 = 70 / 1,512
## 6 = 493 / 1,470
## 7 = 126 / 1,504
## 8 = 71 / 1,508
## 9 = 68 / 1,575
## Totals = 1,835 / 15,050## [1] 0.88variable importance
library(caret)
#library(mxnet)
# loading data
tumor=read.csv("cancer_tumor.csv") #predict if the tumor is
#malignant (M) or benign (B)
head(tumor)## id diagnosis radius_mean texture_mean perimeter_mean area_mean
## 1 842302 M 18 10 123 1001
## 2 842517 M 21 18 133 1326
## 3 84300903 M 20 21 130 1203
## 4 84348301 M 11 20 78 386
## 5 84358402 M 20 14 135 1297
## 6 843786 M 12 16 83 477
## smoothness_mean compactness_mean concavity_mean concave.points_mean
## 1 0.118 0.278 0.300 0.147
## 2 0.085 0.079 0.087 0.070
## 3 0.110 0.160 0.197 0.128
## 4 0.142 0.284 0.241 0.105
## 5 0.100 0.133 0.198 0.104
## 6 0.128 0.170 0.158 0.081
## symmetry_mean fractal_dimension_mean radius_se texture_se perimeter_se
## 1 0.24 0.079 1.09 0.91 8.6
## 2 0.18 0.057 0.54 0.73 3.4
## 3 0.21 0.060 0.75 0.79 4.6
## 4 0.26 0.097 0.50 1.16 3.4
## 5 0.18 0.059 0.76 0.78 5.4
## 6 0.21 0.076 0.33 0.89 2.2
## area_se smoothness_se compactness_se concavity_se concave.points_se
## 1 153 0.0064 0.049 0.054 0.016
## 2 74 0.0052 0.013 0.019 0.013
## 3 94 0.0062 0.040 0.038 0.021
## 4 27 0.0091 0.075 0.057 0.019
## 5 94 0.0115 0.025 0.057 0.019
## 6 27 0.0075 0.033 0.037 0.011
## symmetry_se fractal_dimension_se radius_worst texture_worst perimeter_worst
## 1 0.030 0.0062 25 17 185
## 2 0.014 0.0035 25 23 159
## 3 0.022 0.0046 24 26 152
## 4 0.060 0.0092 15 26 99
## 5 0.018 0.0051 23 17 152
## 6 0.022 0.0051 15 24 103
## area_worst smoothness_worst compactness_worst concavity_worst
## 1 2019 0.16 0.67 0.71
## 2 1956 0.12 0.19 0.24
## 3 1709 0.14 0.42 0.45
## 4 568 0.21 0.87 0.69
## 5 1575 0.14 0.20 0.40
## 6 742 0.18 0.52 0.54
## concave.points_worst symmetry_worst fractal_dimension_worst X
## 1 0.27 0.46 0.119 NA
## 2 0.19 0.28 0.089 NA
## 3 0.24 0.36 0.088 NA
## 4 0.26 0.66 0.173 NA
## 5 0.16 0.24 0.077 NA
## 6 0.17 0.40 0.124 NA## diagnosis radius_mean texture_mean perimeter_mean area_mean smoothness_mean
## 1 M 18 10 123 1001 0.118
## 2 M 21 18 133 1326 0.085
## 3 M 20 21 130 1203 0.110
## 4 M 11 20 78 386 0.142
## 5 M 20 14 135 1297 0.100
## 6 M 12 16 83 477 0.128
## compactness_mean concavity_mean concave.points_mean symmetry_mean
## 1 0.278 0.300 0.147 0.24
## 2 0.079 0.087 0.070 0.18
## 3 0.160 0.197 0.128 0.21
## 4 0.284 0.241 0.105 0.26
## 5 0.133 0.198 0.104 0.18
## 6 0.170 0.158 0.081 0.21
## fractal_dimension_mean radius_se texture_se perimeter_se area_se
## 1 0.079 1.09 0.91 8.6 153
## 2 0.057 0.54 0.73 3.4 74
## 3 0.060 0.75 0.79 4.6 94
## 4 0.097 0.50 1.16 3.4 27
## 5 0.059 0.76 0.78 5.4 94
## 6 0.076 0.33 0.89 2.2 27
## smoothness_se compactness_se concavity_se concave.points_se symmetry_se
## 1 0.0064 0.049 0.054 0.016 0.030
## 2 0.0052 0.013 0.019 0.013 0.014
## 3 0.0062 0.040 0.038 0.021 0.022
## 4 0.0091 0.075 0.057 0.019 0.060
## 5 0.0115 0.025 0.057 0.019 0.018
## 6 0.0075 0.033 0.037 0.011 0.022
## fractal_dimension_se radius_worst texture_worst perimeter_worst area_worst
## 1 0.0062 25 17 185 2019
## 2 0.0035 25 23 159 1956
## 3 0.0046 24 26 152 1709
## 4 0.0092 15 26 99 568
## 5 0.0051 23 17 152 1575
## 6 0.0051 15 24 103 742
## smoothness_worst compactness_worst concavity_worst concave.points_worst
## 1 0.16 0.67 0.71 0.27
## 2 0.12 0.19 0.24 0.19
## 3 0.14 0.42 0.45 0.24
## 4 0.21 0.87 0.69 0.26
## 5 0.14 0.20 0.40 0.16
## 6 0.18 0.52 0.54 0.17
## symmetry_worst fractal_dimension_worst
## 1 0.46 0.119
## 2 0.28 0.089
## 3 0.36 0.088
## 4 0.66 0.173
## 5 0.24 0.077
## 6 0.40 0.124df=na.omit(df)
library(h2o)
h2o.init(max_mem_size = "2G",
nthreads = 2,
ip = "localhost",
port = 54321)## Connection successful!
##
## R is connected to the H2O cluster:
## H2O cluster uptime: 13 minutes 54 seconds
## H2O cluster timezone: Asia/Kolkata
## H2O data parsing timezone: UTC
## H2O cluster version: 3.30.0.1
## H2O cluster version age: 1 month and 21 days
## H2O cluster name: H2O_started_from_R_HP_fjx183
## H2O cluster total nodes: 1
## H2O cluster total memory: 1.35 GB
## H2O cluster total cores: 4
## H2O cluster allowed cores: 4
## 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 3.5.0 (2018-04-23)##
|
| | 0%
|
|======================================================================| 100%## diagnosis radius_mean texture_mean perimeter_mean area_mean smoothness_mean
## 1 M 18 10 123 1001 0.118
## 2 M 21 18 133 1326 0.085
## 3 M 20 21 130 1203 0.110
## 4 M 11 20 78 386 0.142
## 5 M 20 14 135 1297 0.100
## 6 M 12 16 83 477 0.128
## compactness_mean concavity_mean concave.points_mean symmetry_mean
## 1 0.278 0.300 0.147 0.24
## 2 0.079 0.087 0.070 0.18
## 3 0.160 0.197 0.128 0.21
## 4 0.284 0.241 0.105 0.26
## 5 0.133 0.198 0.104 0.18
## 6 0.170 0.158 0.081 0.21
## fractal_dimension_mean radius_se texture_se perimeter_se area_se
## 1 0.079 1.09 0.91 8.6 153
## 2 0.057 0.54 0.73 3.4 74
## 3 0.060 0.75 0.79 4.6 94
## 4 0.097 0.50 1.16 3.4 27
## 5 0.059 0.76 0.78 5.4 94
## 6 0.076 0.33 0.89 2.2 27
## smoothness_se compactness_se concavity_se concave.points_se symmetry_se
## 1 0.0064 0.049 0.054 0.016 0.030
## 2 0.0052 0.013 0.019 0.013 0.014
## 3 0.0062 0.040 0.038 0.021 0.022
## 4 0.0091 0.075 0.057 0.019 0.060
## 5 0.0115 0.025 0.057 0.019 0.018
## 6 0.0075 0.033 0.037 0.011 0.022
## fractal_dimension_se radius_worst texture_worst perimeter_worst area_worst
## 1 0.0062 25 17 185 2019
## 2 0.0035 25 23 159 1956
## 3 0.0046 24 26 152 1709
## 4 0.0092 15 26 99 568
## 5 0.0051 23 17 152 1575
## 6 0.0051 15 24 103 742
## smoothness_worst compactness_worst concavity_worst concave.points_worst
## 1 0.16 0.67 0.71 0.27
## 2 0.12 0.19 0.24 0.19
## 3 0.14 0.42 0.45 0.24
## 4 0.21 0.87 0.69 0.26
## 5 0.14 0.20 0.40 0.16
## 6 0.18 0.52 0.54 0.17
## symmetry_worst fractal_dimension_worst
## 1 0.46 0.119
## 2 0.28 0.089
## 3 0.36 0.088
## 4 0.66 0.173
## 5 0.24 0.077
## 6 0.40 0.124## Class 'H2OFrame' <environment: 0x000000003e2546c0>
## - attr(*, "op")= chr "Parse"
## - attr(*, "id")= chr "d.hex"
## - attr(*, "eval")= logi FALSE
## - attr(*, "nrow")= int 569
## - attr(*, "ncol")= int 31
## - attr(*, "types")=List of 31
## ..$ : chr "enum"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## - attr(*, "data")='data.frame': 10 obs. of 31 variables:
## ..$ diagnosis : Factor w/ 2 levels "B","M": 2 2 2 2 2 2 2 2 2 2
## ..$ radius_mean : num 18 20.6 19.7 11.4 20.3 ...
## ..$ texture_mean : num 10.4 17.8 21.2 20.4 14.3 ...
## ..$ perimeter_mean : num 122.8 132.9 130 77.6 135.1 ...
## ..$ area_mean : num 1001 1326 1203 386 1297 ...
## ..$ smoothness_mean : num 0.1184 0.0847 0.1096 0.1425 0.1003 ...
## ..$ compactness_mean : num 0.2776 0.0786 0.1599 0.2839 0.1328 ...
## ..$ concavity_mean : num 0.3001 0.0869 0.1974 0.2414 0.198 ...
## ..$ concave.points_mean : num 0.1471 0.0702 0.1279 0.1052 0.1043 ...
## ..$ symmetry_mean : num 0.242 0.181 0.207 0.26 0.181 ...
## ..$ fractal_dimension_mean : num 0.0787 0.0567 0.06 0.0974 0.0588 ...
## ..$ radius_se : num 1.095 0.543 0.746 0.496 0.757 ...
## ..$ texture_se : num 0.905 0.734 0.787 1.156 0.781 ...
## ..$ perimeter_se : num 8.59 3.4 4.58 3.44 5.44 ...
## ..$ area_se : num 153.4 74.1 94 27.2 94.4 ...
## ..$ smoothness_se : num 0.0064 0.00522 0.00615 0.00911 0.01149 ...
## ..$ compactness_se : num 0.049 0.0131 0.0401 0.0746 0.0246 ...
## ..$ concavity_se : num 0.0537 0.0186 0.0383 0.0566 0.0569 ...
## ..$ concave.points_se : num 0.0159 0.0134 0.0206 0.0187 0.0188 ...
## ..$ symmetry_se : num 0.03 0.0139 0.0225 0.0596 0.0176 ...
## ..$ fractal_dimension_se : num 0.00619 0.00353 0.00457 0.00921 0.00511 ...
## ..$ radius_worst : num 25.4 25 23.6 14.9 22.5 ...
## ..$ texture_worst : num 17.3 23.4 25.5 26.5 16.7 ...
## ..$ perimeter_worst : num 184.6 158.8 152.5 98.9 152.2 ...
## ..$ area_worst : num 2019 1956 1709 568 1575 ...
## ..$ smoothness_worst : num 0.162 0.124 0.144 0.21 0.137 ...
## ..$ compactness_worst : num 0.666 0.187 0.424 0.866 0.205 ...
## ..$ concavity_worst : num 0.712 0.242 0.45 0.687 0.4 ...
## ..$ concave.points_worst : num 0.265 0.186 0.243 0.258 0.163 ...
## ..$ symmetry_worst : num 0.46 0.275 0.361 0.664 0.236 ...
## ..$ fractal_dimension_worst: num 0.1189 0.089 0.0876 0.173 0.0768 ...#set variables
y <- "diagnosis"
x <- setdiff(colnames(d.hex),y)
#train the model - without hidden layer
deepmodel = h2o.deeplearning(x = x
,y = y
,training_frame = d.hex
,standardize = T
,model_id = "deep_model"
,activation = "Rectifier"
,epochs = 100
,seed = 1
,nfolds = 5
,variable_importances = T)##
|
| | 0%
|
|======================================================== | 79%
|
|================================================================== | 94%
|
|==================================================================== | 97%
|
|======================================================================| 100%
## H2OBinomialMetrics: deeplearning
## ** Reported on cross-validation data. **
## ** 5-fold cross-validation on training data (Metrics computed for combined holdout predictions) **
##
## MSE: 0.038
## RMSE: 0.2
## LogLoss: 0.27
## Mean Per-Class Error: 0.04
## AUC: 0.99
## AUCPR: 0.99
## Gini: 0.98
##
## Confusion Matrix (vertical: actual; across: predicted) for F1-optimal threshold:
## B M Error Rate
## B 354 3 0.008403 =3/357
## M 15 197 0.070755 =15/212
## Totals 369 200 0.031634 =18/569
##
## Maximum Metrics: Maximum metrics at their respective thresholds
## metric threshold value idx
## 1 max f1 0.974770 0.956311 26
## 2 max f2 0.221098 0.947712 49
## 3 max f0point5 0.997343 0.978916 22
## 4 max accuracy 0.997343 0.968366 22
## 5 max precision 1.000000 1.000000 0
## 6 max recall 0.000000 1.000000 291
## 7 max specificity 1.000000 1.000000 0
## 8 max absolute_mcc 0.997343 0.933026 22
## 9 max min_per_class_accuracy 0.245683 0.943978 47
## 10 max mean_per_class_accuracy 0.974770 0.960421 26
## 11 max tns 1.000000 357.000000 0
## 12 max fns 1.000000 43.000000 0
## 13 max fps 0.000000 357.000000 395
## 14 max tps 0.000000 212.000000 291
## 15 max tnr 1.000000 1.000000 0
## 16 max fnr 1.000000 0.202830 0
## 17 max fpr 0.000000 1.000000 395
## 18 max tpr 0.000000 1.000000 291
##
## Gains/Lift Table: Extract with `h2o.gainsLift(<model>, <data>)` or `h2o.gainsLift(<model>, valid=<T/F>, xval=<T/F>)`DNN For Regression using H2o
##### regression
library(tidyverse)
library(ggplot2)
library(mlbench)
data(BostonHousing)
head(BostonHousing)## crim zn indus chas nox rm age dis rad tax ptratio b lstat medv
## 1 0.0063 18 2.3 0 0.54 6.6 65 4.1 1 296 15 397 5.0 24
## 2 0.0273 0 7.1 0 0.47 6.4 79 5.0 2 242 18 397 9.1 22
## 3 0.0273 0 7.1 0 0.47 7.2 61 5.0 2 242 18 393 4.0 35
## 4 0.0324 0 2.2 0 0.46 7.0 46 6.1 3 222 19 395 2.9 33
## 5 0.0690 0 2.2 0 0.46 7.1 54 6.1 3 222 19 397 5.3 36
## 6 0.0299 0 2.2 0 0.46 6.4 59 6.1 3 222 19 394 5.2 29## 'data.frame': 506 obs. of 14 variables:
## $ crim : num 0.00632 0.02731 0.02729 0.03237 0.06905 ...
## $ zn : num 18 0 0 0 0 0 12.5 12.5 12.5 12.5 ...
## $ indus : num 2.31 7.07 7.07 2.18 2.18 2.18 7.87 7.87 7.87 7.87 ...
## $ chas : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ nox : num 0.538 0.469 0.469 0.458 0.458 0.458 0.524 0.524 0.524 0.524 ...
## $ rm : num 6.58 6.42 7.18 7 7.15 ...
## $ age : num 65.2 78.9 61.1 45.8 54.2 58.7 66.6 96.1 100 85.9 ...
## $ dis : num 4.09 4.97 4.97 6.06 6.06 ...
## $ rad : num 1 2 2 3 3 3 5 5 5 5 ...
## $ tax : num 296 242 242 222 222 222 311 311 311 311 ...
## $ ptratio: num 15.3 17.8 17.8 18.7 18.7 18.7 15.2 15.2 15.2 15.2 ...
## $ b : num 397 397 393 395 397 ...
## $ lstat : num 4.98 9.14 4.03 2.94 5.33 ...
## $ medv : num 24 21.6 34.7 33.4 36.2 28.7 22.9 27.1 16.5 18.9 ...## Connection successful!
##
## R is connected to the H2O cluster:
## H2O cluster uptime: 15 minutes 34 seconds
## H2O cluster timezone: Asia/Kolkata
## H2O data parsing timezone: UTC
## H2O cluster version: 3.30.0.1
## H2O cluster version age: 1 month and 21 days
## H2O cluster name: H2O_started_from_R_HP_fjx183
## H2O cluster total nodes: 1
## H2O cluster total memory: 1.41 GB
## H2O cluster total cores: 4
## H2O cluster allowed cores: 4
## 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 3.5.0 (2018-04-23)##
|
| | 0%
|
|======================================================================| 100%## crim zn indus chas nox rm age dis rad tax ptratio b lstat medv
## 1 0.0063 18 2.3 0 0.54 6.6 65 4.1 1 296 15 397 5.0 24
## 2 0.0273 0 7.1 0 0.47 6.4 79 5.0 2 242 18 397 9.1 22
## 3 0.0273 0 7.1 0 0.47 7.2 61 5.0 2 242 18 393 4.0 35
## 4 0.0324 0 2.2 0 0.46 7.0 46 6.1 3 222 19 395 2.9 33
## 5 0.0690 0 2.2 0 0.46 7.1 54 6.1 3 222 19 397 5.3 36
## 6 0.0299 0 2.2 0 0.46 6.4 59 6.1 3 222 19 394 5.2 29## Class 'H2OFrame' <environment: 0x0000000046499ba8>
## - attr(*, "op")= chr "Parse"
## - attr(*, "id")= chr "d.hex"
## - attr(*, "eval")= logi FALSE
## - attr(*, "nrow")= int 506
## - attr(*, "ncol")= int 14
## - attr(*, "types")=List of 14
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "enum"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "int"
## ..$ : chr "int"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## - attr(*, "data")='data.frame': 10 obs. of 14 variables:
## ..$ crim : num 0.00632 0.02731 0.02729 0.03237 0.06905 ...
## ..$ zn : num 18 0 0 0 0 0 12.5 12.5 12.5 12.5
## ..$ indus : num 2.31 7.07 7.07 2.18 2.18 2.18 7.87 7.87 7.87 7.87
## ..$ chas : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1
## ..$ nox : num 0.538 0.469 0.469 0.458 0.458 0.458 0.524 0.524 0.524 0.524
## ..$ rm : num 6.58 6.42 7.18 7 7.15 ...
## ..$ age : num 65.2 78.9 61.1 45.8 54.2 58.7 66.6 96.1 100 85.9
## ..$ dis : num 4.09 4.97 4.97 6.06 6.06 ...
## ..$ rad : num 1 2 2 3 3 3 5 5 5 5
## ..$ tax : num 296 242 242 222 222 222 311 311 311 311
## ..$ ptratio: num 15.3 17.8 17.8 18.7 18.7 18.7 15.2 15.2 15.2 15.2
## ..$ b : num 397 397 393 395 397 ...
## ..$ lstat : num 4.98 9.14 4.03 2.94 5.33 ...
## ..$ medv : num 24 21.6 34.7 33.4 36.2 28.7 22.9 27.1 16.5 18.9## crim zn indus chas nox
## Min. : 0.00632 Min. : 0.00 Min. : 0.460 0:471 Min. :0.3850
## 1st Qu.: 0.00632 1st Qu.: 0.00 1st Qu.: 5.179 1: 35 1st Qu.:0.4487
## Median : 0.18426 Median : 0.00 Median : 9.681 Median :0.5376
## Mean : 3.61352 Mean : 11.36 Mean :11.137 Mean :0.5547
## 3rd Qu.: 3.65409 3rd Qu.: 12.50 3rd Qu.:18.083 3rd Qu.:0.6236
## Max. :88.97620 Max. :100.00 Max. :27.740 Max. :0.8710
## rm age dis rad
## Min. :3.561 Min. : 2.90 Min. : 1.130 Min. : 1.000
## 1st Qu.:5.883 1st Qu.: 44.97 1st Qu.: 2.097 1st Qu.: 4.000
## Median :6.207 Median : 77.47 Median : 3.203 Median : 5.000
## Mean :6.285 Mean : 68.57 Mean : 3.795 Mean : 9.549
## 3rd Qu.:6.622 3rd Qu.: 94.05 3rd Qu.: 5.185 3rd Qu.:24.000
## Max. :8.780 Max. :100.00 Max. :12.127 Max. :24.000
## tax ptratio b lstat
## Min. :187.0 Min. :12.60 Min. : 0.32 Min. : 1.73
## 1st Qu.:279.0 1st Qu.:17.39 1st Qu.:375.19 1st Qu.: 6.93
## Median :330.0 Median :19.04 Median :391.35 Median :11.35
## Mean :408.2 Mean :18.46 Mean :356.67 Mean :12.65
## 3rd Qu.:666.0 3rd Qu.:20.20 3rd Qu.:396.11 3rd Qu.:16.94
## Max. :711.0 Max. :22.00 Max. :396.90 Max. :37.97
## medv
## Min. : 5.00
## 1st Qu.:16.99
## Median :21.20
## Mean :22.53
## 3rd Qu.:24.98
## Max. :50.00## [1] 14g.split = h2o.splitFrame(data = d.hex,ratios = 0.75)
train = g.split[[1]]#75% training data
test = g.split[[2]]
##2 layer dnn
m = h2o.deeplearning(x = 1:13,
y = "medv",
training_frame = train,
#distribution = "multinomial",
model_id = "m",
activation = "Tanh",
l2 = 0.00001,
hidden = c(162,162,100),
)##
|
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|
|================================================= | 70%
|
|======================================================================| 100%##
|
| | 0%
|
|======================================================================| 100%## [1] 2.6Deep Learning Based Unsupervised Classification using H2o
Autoencoders for Unsupervised Learning using H2o
library(ggplot2)
set.seed(1234)
# loading data
tumor=read.csv("cancer_tumor.csv") #predict if the tumor is
#malignant (M) or benign (B)
head(tumor)## id diagnosis radius_mean texture_mean perimeter_mean area_mean
## 1 842302 M 18 10 123 1001
## 2 842517 M 21 18 133 1326
## 3 84300903 M 20 21 130 1203
## 4 84348301 M 11 20 78 386
## 5 84358402 M 20 14 135 1297
## 6 843786 M 12 16 83 477
## smoothness_mean compactness_mean concavity_mean concave.points_mean
## 1 0.118 0.278 0.300 0.147
## 2 0.085 0.079 0.087 0.070
## 3 0.110 0.160 0.197 0.128
## 4 0.142 0.284 0.241 0.105
## 5 0.100 0.133 0.198 0.104
## 6 0.128 0.170 0.158 0.081
## symmetry_mean fractal_dimension_mean radius_se texture_se perimeter_se
## 1 0.24 0.079 1.09 0.91 8.6
## 2 0.18 0.057 0.54 0.73 3.4
## 3 0.21 0.060 0.75 0.79 4.6
## 4 0.26 0.097 0.50 1.16 3.4
## 5 0.18 0.059 0.76 0.78 5.4
## 6 0.21 0.076 0.33 0.89 2.2
## area_se smoothness_se compactness_se concavity_se concave.points_se
## 1 153 0.0064 0.049 0.054 0.016
## 2 74 0.0052 0.013 0.019 0.013
## 3 94 0.0062 0.040 0.038 0.021
## 4 27 0.0091 0.075 0.057 0.019
## 5 94 0.0115 0.025 0.057 0.019
## 6 27 0.0075 0.033 0.037 0.011
## symmetry_se fractal_dimension_se radius_worst texture_worst perimeter_worst
## 1 0.030 0.0062 25 17 185
## 2 0.014 0.0035 25 23 159
## 3 0.022 0.0046 24 26 152
## 4 0.060 0.0092 15 26 99
## 5 0.018 0.0051 23 17 152
## 6 0.022 0.0051 15 24 103
## area_worst smoothness_worst compactness_worst concavity_worst
## 1 2019 0.16 0.67 0.71
## 2 1956 0.12 0.19 0.24
## 3 1709 0.14 0.42 0.45
## 4 568 0.21 0.87 0.69
## 5 1575 0.14 0.20 0.40
## 6 742 0.18 0.52 0.54
## concave.points_worst symmetry_worst fractal_dimension_worst X
## 1 0.27 0.46 0.119 NA
## 2 0.19 0.28 0.089 NA
## 3 0.24 0.36 0.088 NA
## 4 0.26 0.66 0.173 NA
## 5 0.16 0.24 0.077 NA
## 6 0.17 0.40 0.124 NA## diagnosis radius_mean texture_mean perimeter_mean area_mean smoothness_mean
## 1 M 18 10 123 1001 0.118
## 2 M 21 18 133 1326 0.085
## 3 M 20 21 130 1203 0.110
## 4 M 11 20 78 386 0.142
## 5 M 20 14 135 1297 0.100
## 6 M 12 16 83 477 0.128
## compactness_mean concavity_mean concave.points_mean symmetry_mean
## 1 0.278 0.300 0.147 0.24
## 2 0.079 0.087 0.070 0.18
## 3 0.160 0.197 0.128 0.21
## 4 0.284 0.241 0.105 0.26
## 5 0.133 0.198 0.104 0.18
## 6 0.170 0.158 0.081 0.21
## fractal_dimension_mean radius_se texture_se perimeter_se area_se
## 1 0.079 1.09 0.91 8.6 153
## 2 0.057 0.54 0.73 3.4 74
## 3 0.060 0.75 0.79 4.6 94
## 4 0.097 0.50 1.16 3.4 27
## 5 0.059 0.76 0.78 5.4 94
## 6 0.076 0.33 0.89 2.2 27
## smoothness_se compactness_se concavity_se concave.points_se symmetry_se
## 1 0.0064 0.049 0.054 0.016 0.030
## 2 0.0052 0.013 0.019 0.013 0.014
## 3 0.0062 0.040 0.038 0.021 0.022
## 4 0.0091 0.075 0.057 0.019 0.060
## 5 0.0115 0.025 0.057 0.019 0.018
## 6 0.0075 0.033 0.037 0.011 0.022
## fractal_dimension_se radius_worst texture_worst perimeter_worst area_worst
## 1 0.0062 25 17 185 2019
## 2 0.0035 25 23 159 1956
## 3 0.0046 24 26 152 1709
## 4 0.0092 15 26 99 568
## 5 0.0051 23 17 152 1575
## 6 0.0051 15 24 103 742
## smoothness_worst compactness_worst concavity_worst concave.points_worst
## 1 0.16 0.67 0.71 0.27
## 2 0.12 0.19 0.24 0.19
## 3 0.14 0.42 0.45 0.24
## 4 0.21 0.87 0.69 0.26
## 5 0.14 0.20 0.40 0.16
## 6 0.18 0.52 0.54 0.17
## symmetry_worst fractal_dimension_worst
## 1 0.46 0.119
## 2 0.28 0.089
## 3 0.36 0.088
## 4 0.66 0.173
## 5 0.24 0.077
## 6 0.40 0.124##
## B M
## 357 212##
## B M
## 0.63 0.37## Connection successful!
##
## R is connected to the H2O cluster:
## H2O cluster uptime: 15 minutes 38 seconds
## H2O cluster timezone: Asia/Kolkata
## H2O data parsing timezone: UTC
## H2O cluster version: 3.30.0.1
## H2O cluster version age: 1 month and 21 days
## H2O cluster name: H2O_started_from_R_HP_fjx183
## H2O cluster total nodes: 1
## H2O cluster total memory: 1.42 GB
## H2O cluster total cores: 4
## H2O cluster allowed cores: 4
## 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 3.5.0 (2018-04-23)##
|
| | 0%
|
|======================================================================| 100%## diagnosis radius_mean texture_mean perimeter_mean area_mean smoothness_mean
## 1 M 18 10 123 1001 0.118
## 2 M 21 18 133 1326 0.085
## 3 M 20 21 130 1203 0.110
## 4 M 11 20 78 386 0.142
## 5 M 20 14 135 1297 0.100
## 6 M 12 16 83 477 0.128
## compactness_mean concavity_mean concave.points_mean symmetry_mean
## 1 0.278 0.300 0.147 0.24
## 2 0.079 0.087 0.070 0.18
## 3 0.160 0.197 0.128 0.21
## 4 0.284 0.241 0.105 0.26
## 5 0.133 0.198 0.104 0.18
## 6 0.170 0.158 0.081 0.21
## fractal_dimension_mean radius_se texture_se perimeter_se area_se
## 1 0.079 1.09 0.91 8.6 153
## 2 0.057 0.54 0.73 3.4 74
## 3 0.060 0.75 0.79 4.6 94
## 4 0.097 0.50 1.16 3.4 27
## 5 0.059 0.76 0.78 5.4 94
## 6 0.076 0.33 0.89 2.2 27
## smoothness_se compactness_se concavity_se concave.points_se symmetry_se
## 1 0.0064 0.049 0.054 0.016 0.030
## 2 0.0052 0.013 0.019 0.013 0.014
## 3 0.0062 0.040 0.038 0.021 0.022
## 4 0.0091 0.075 0.057 0.019 0.060
## 5 0.0115 0.025 0.057 0.019 0.018
## 6 0.0075 0.033 0.037 0.011 0.022
## fractal_dimension_se radius_worst texture_worst perimeter_worst area_worst
## 1 0.0062 25 17 185 2019
## 2 0.0035 25 23 159 1956
## 3 0.0046 24 26 152 1709
## 4 0.0092 15 26 99 568
## 5 0.0051 23 17 152 1575
## 6 0.0051 15 24 103 742
## smoothness_worst compactness_worst concavity_worst concave.points_worst
## 1 0.16 0.67 0.71 0.27
## 2 0.12 0.19 0.24 0.19
## 3 0.14 0.42 0.45 0.24
## 4 0.21 0.87 0.69 0.26
## 5 0.14 0.20 0.40 0.16
## 6 0.18 0.52 0.54 0.17
## symmetry_worst fractal_dimension_worst
## 1 0.46 0.119
## 2 0.28 0.089
## 3 0.36 0.088
## 4 0.66 0.173
## 5 0.24 0.077
## 6 0.40 0.124## Class 'H2OFrame' <environment: 0x000000003d73a7f8>
## - attr(*, "op")= chr "Parse"
## - attr(*, "id")= chr "d.hex"
## - attr(*, "eval")= logi FALSE
## - attr(*, "nrow")= int 569
## - attr(*, "ncol")= int 31
## - attr(*, "types")=List of 31
## ..$ : chr "enum"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## - attr(*, "data")='data.frame': 10 obs. of 31 variables:
## ..$ diagnosis : Factor w/ 2 levels "B","M": 2 2 2 2 2 2 2 2 2 2
## ..$ radius_mean : num 18 20.6 19.7 11.4 20.3 ...
## ..$ texture_mean : num 10.4 17.8 21.2 20.4 14.3 ...
## ..$ perimeter_mean : num 122.8 132.9 130 77.6 135.1 ...
## ..$ area_mean : num 1001 1326 1203 386 1297 ...
## ..$ smoothness_mean : num 0.1184 0.0847 0.1096 0.1425 0.1003 ...
## ..$ compactness_mean : num 0.2776 0.0786 0.1599 0.2839 0.1328 ...
## ..$ concavity_mean : num 0.3001 0.0869 0.1974 0.2414 0.198 ...
## ..$ concave.points_mean : num 0.1471 0.0702 0.1279 0.1052 0.1043 ...
## ..$ symmetry_mean : num 0.242 0.181 0.207 0.26 0.181 ...
## ..$ fractal_dimension_mean : num 0.0787 0.0567 0.06 0.0974 0.0588 ...
## ..$ radius_se : num 1.095 0.543 0.746 0.496 0.757 ...
## ..$ texture_se : num 0.905 0.734 0.787 1.156 0.781 ...
## ..$ perimeter_se : num 8.59 3.4 4.58 3.44 5.44 ...
## ..$ area_se : num 153.4 74.1 94 27.2 94.4 ...
## ..$ smoothness_se : num 0.0064 0.00522 0.00615 0.00911 0.01149 ...
## ..$ compactness_se : num 0.049 0.0131 0.0401 0.0746 0.0246 ...
## ..$ concavity_se : num 0.0537 0.0186 0.0383 0.0566 0.0569 ...
## ..$ concave.points_se : num 0.0159 0.0134 0.0206 0.0187 0.0188 ...
## ..$ symmetry_se : num 0.03 0.0139 0.0225 0.0596 0.0176 ...
## ..$ fractal_dimension_se : num 0.00619 0.00353 0.00457 0.00921 0.00511 ...
## ..$ radius_worst : num 25.4 25 23.6 14.9 22.5 ...
## ..$ texture_worst : num 17.3 23.4 25.5 26.5 16.7 ...
## ..$ perimeter_worst : num 184.6 158.8 152.5 98.9 152.2 ...
## ..$ area_worst : num 2019 1956 1709 568 1575 ...
## ..$ smoothness_worst : num 0.162 0.124 0.144 0.21 0.137 ...
## ..$ compactness_worst : num 0.666 0.187 0.424 0.866 0.205 ...
## ..$ concavity_worst : num 0.712 0.242 0.45 0.687 0.4 ...
## ..$ concave.points_worst : num 0.265 0.186 0.243 0.258 0.163 ...
## ..$ symmetry_worst : num 0.46 0.275 0.361 0.664 0.236 ...
## ..$ fractal_dimension_worst: num 0.1189 0.089 0.0876 0.173 0.0768 ...NN_model = h2o.deeplearning(
x = 2:31,
training_frame = d.hex,
hidden = c(100, 50, 2, 50, 100 ),
epochs = 100,
activation = "Tanh",
autoencoder = TRUE
)##
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|======================================================================| 100%plotdata2 = as.data.frame(train_supervised_features2)
plotdata2$label = as.character(as.vector(d.hex[,1]))
qplot(DF.L3.C1, DF.L3.C2, data = plotdata2, color = label, main = "Neural network: 100 - 50 - 2 - 50 - 100 ")
More Autoencoders : Credit Card Fraud Detection using H2o
library(tidyverse)
library(ggplot2)
creditcard = read.csv("creditcard.csv")
head(creditcard) #v1-v28 are numerical perdictors## Time V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11
## 1 0 -1.36 -0.073 2.54 1.38 -0.34 0.462 0.240 0.099 0.36 0.091 -0.55
## 2 0 1.19 0.266 0.17 0.45 0.06 -0.082 -0.079 0.085 -0.26 -0.167 1.61
## 3 1 -1.36 -1.340 1.77 0.38 -0.50 1.800 0.791 0.248 -1.51 0.208 0.62
## 4 1 -0.97 -0.185 1.79 -0.86 -0.01 1.247 0.238 0.377 -1.39 -0.055 -0.23
## 5 2 -1.16 0.878 1.55 0.40 -0.41 0.096 0.593 -0.271 0.82 0.753 -0.82
## 6 2 -0.43 0.961 1.14 -0.17 0.42 -0.030 0.476 0.260 -0.57 -0.371 1.34
## V12 V13 V14 V15 V16 V17 V18 V19 V20 V21 V22
## 1 -0.618 -0.99 -0.31 1.47 -0.47 0.208 0.026 0.404 0.251 -0.0183 0.2778
## 2 1.065 0.49 -0.14 0.64 0.46 -0.115 -0.183 -0.146 -0.069 -0.2258 -0.6387
## 3 0.066 0.72 -0.17 2.35 -2.89 1.110 -0.121 -2.262 0.525 0.2480 0.7717
## 4 0.178 0.51 -0.29 -0.63 -1.06 -0.684 1.966 -1.233 -0.208 -0.1083 0.0053
## 5 0.538 1.35 -1.12 0.18 -0.45 -0.237 -0.038 0.803 0.409 -0.0094 0.7983
## 6 0.360 -0.36 -0.14 0.52 0.40 -0.058 0.069 -0.033 0.085 -0.2083 -0.5598
## V23 V24 V25 V26 V27 V28 Amount Class
## 1 -0.110 0.067 0.13 -0.19 0.134 -0.021 149.6 0
## 2 0.101 -0.340 0.17 0.13 -0.009 0.015 2.7 0
## 3 0.909 -0.689 -0.33 -0.14 -0.055 -0.060 378.7 0
## 4 -0.190 -1.176 0.65 -0.22 0.063 0.061 123.5 0
## 5 -0.137 0.141 -0.21 0.50 0.219 0.215 70.0 0
## 6 -0.026 -0.371 -0.23 0.11 0.254 0.081 3.7 0## Connection successful!
##
## R is connected to the H2O cluster:
## H2O cluster uptime: 16 minutes 15 seconds
## H2O cluster timezone: Asia/Kolkata
## H2O data parsing timezone: UTC
## H2O cluster version: 3.30.0.1
## H2O cluster version age: 1 month and 21 days
## H2O cluster name: H2O_started_from_R_HP_fjx183
## H2O cluster total nodes: 1
## H2O cluster total memory: 1.46 GB
## H2O cluster total cores: 4
## H2O cluster allowed cores: 4
## 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 3.5.0 (2018-04-23)##
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|======================================================================| 100%## Time V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11
## 1 0 -1.36 -0.073 2.54 1.38 -0.34 0.462 0.240 0.099 0.36 0.091 -0.55
## 2 0 1.19 0.266 0.17 0.45 0.06 -0.082 -0.079 0.085 -0.26 -0.167 1.61
## 3 1 -1.36 -1.340 1.77 0.38 -0.50 1.800 0.791 0.248 -1.51 0.208 0.62
## 4 1 -0.97 -0.185 1.79 -0.86 -0.01 1.247 0.238 0.377 -1.39 -0.055 -0.23
## 5 2 -1.16 0.878 1.55 0.40 -0.41 0.096 0.593 -0.271 0.82 0.753 -0.82
## 6 2 -0.43 0.961 1.14 -0.17 0.42 -0.030 0.476 0.260 -0.57 -0.371 1.34
## V12 V13 V14 V15 V16 V17 V18 V19 V20 V21 V22
## 1 -0.618 -0.99 -0.31 1.47 -0.47 0.208 0.026 0.404 0.251 -0.0183 0.2778
## 2 1.065 0.49 -0.14 0.64 0.46 -0.115 -0.183 -0.146 -0.069 -0.2258 -0.6387
## 3 0.066 0.72 -0.17 2.35 -2.89 1.110 -0.121 -2.262 0.525 0.2480 0.7717
## 4 0.178 0.51 -0.29 -0.63 -1.06 -0.684 1.966 -1.233 -0.208 -0.1083 0.0053
## 5 0.538 1.35 -1.12 0.18 -0.45 -0.237 -0.038 0.803 0.409 -0.0094 0.7983
## 6 0.360 -0.36 -0.14 0.52 0.40 -0.058 0.069 -0.033 0.085 -0.2083 -0.5598
## V23 V24 V25 V26 V27 V28 Amount Class
## 1 -0.110 0.067 0.13 -0.19 0.134 -0.021 149.6 0
## 2 0.101 -0.340 0.17 0.13 -0.009 0.015 2.7 0
## 3 0.909 -0.689 -0.33 -0.14 -0.055 -0.060 378.7 0
## 4 -0.190 -1.176 0.65 -0.22 0.063 0.061 123.5 0
## 5 -0.137 0.141 -0.21 0.50 0.219 0.215 70.0 0
## 6 -0.026 -0.371 -0.23 0.11 0.254 0.081 3.7 0## Class 'H2OFrame' <environment: 0x000000002771f740>
## - attr(*, "op")= chr "Parse"
## - attr(*, "id")= chr "d.hex"
## - attr(*, "eval")= logi FALSE
## - attr(*, "nrow")= int 284807
## - attr(*, "ncol")= int 31
## - attr(*, "types")=List of 31
## ..$ : chr "int"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "real"
## ..$ : chr "int"
## - attr(*, "data")='data.frame': 10 obs. of 31 variables:
## ..$ Time : num 0 0 1 1 2 2 4 7 7 9
## ..$ V1 : num -1.36 1.192 -1.358 -0.966 -1.158 ...
## ..$ V2 : num -0.0728 0.2662 -1.3402 -0.1852 0.8777 ...
## ..$ V3 : num 2.536 0.166 1.773 1.793 1.549 ...
## ..$ V4 : num 1.378 0.448 0.38 -0.863 0.403 ...
## ..$ V5 : num -0.3383 0.06 -0.5032 -0.0103 -0.4072 ...
## ..$ V6 : num 0.4624 -0.0824 1.8005 1.2472 0.0959 ...
## ..$ V7 : num 0.2396 -0.0788 0.7915 0.2376 0.5929 ...
## ..$ V8 : num 0.0987 0.0851 0.2477 0.3774 -0.2705 ...
## ..$ V9 : num 0.364 -0.255 -1.515 -1.387 0.818 ...
## ..$ V10 : num 0.0908 -0.167 0.2076 -0.055 0.7531 ...
## ..$ V11 : num -0.552 1.613 0.625 -0.226 -0.823 ...
## ..$ V12 : num -0.6178 1.0652 0.0661 0.1782 0.5382 ...
## ..$ V13 : num -0.991 0.489 0.717 0.508 1.346 ...
## ..$ V14 : num -0.311 -0.144 -0.166 -0.288 -1.12 ...
## ..$ V15 : num 1.468 0.636 2.346 -0.631 0.175 ...
## ..$ V16 : num -0.47 0.464 -2.89 -1.06 -0.451 ...
## ..$ V17 : num 0.208 -0.115 1.11 -0.684 -0.237 ...
## ..$ V18 : num 0.0258 -0.1834 -0.1214 1.9658 -0.0382 ...
## ..$ V19 : num 0.404 -0.146 -2.262 -1.233 0.803 ...
## ..$ V20 : num 0.2514 -0.0691 0.525 -0.208 0.4085 ...
## ..$ V21 : num -0.01831 -0.22578 0.248 -0.1083 -0.00943 ...
## ..$ V22 : num 0.27784 -0.63867 0.77168 0.00527 0.79828 ...
## ..$ V23 : num -0.11 0.101 0.909 -0.19 -0.137 ...
## ..$ V24 : num 0.0669 -0.3398 -0.6893 -1.1756 0.1413 ...
## ..$ V25 : num 0.129 0.167 -0.328 0.647 -0.206 ...
## ..$ V26 : num -0.189 0.126 -0.139 -0.222 0.502 ...
## ..$ V27 : num 0.13356 -0.00898 -0.05535 0.06272 0.21942 ...
## ..$ V28 : num -0.0211 0.0147 -0.0598 0.0615 0.2152 ...
## ..$ Amount: num 149.62 2.69 378.66 123.5 69.99 ...
## ..$ Class : num 0 0 0 0 0 0 0 0 0 0## Time V1
## Min. : 0 Min. :-56.40750963130000173
## 1st Qu.: 54084 1st Qu.: -0.95909150689599998
## Median : 84669 Median : -0.01729247293550000
## Mean : 94814 Mean : 0.00000000000000115
## 3rd Qu.:139271 3rd Qu.: 1.27768119876000008
## Max. :172792 Max. : 2.45492999121000022
## V2 V3
## Min. :-72.7157275628999997252 Min. :-48.32558936240000236
## 1st Qu.: -0.6879005823299999545 1st Qu.: -0.94720002252299995
## Median : -0.0244863864562000003 Median : 0.14925478558600000
## Mean : 0.0000000000000003624 Mean : -0.00000000000000189
## 3rd Qu.: 0.7337012659710000495 3rd Qu.: 1.01487700251000001
## Max. : 22.0577289904999993553 Max. : 9.38255843281999979
## V4 V5
## Min. :-5.683171198170000160 Min. :-113.743306711000002451
## 1st Qu.:-0.855648938571999973 1st Qu.: -0.700582571818999966
## Median :-0.020983874996300000 Median : -0.106402681467999999
## Mean : 0.000000000000002082 Mean : 0.000000000000001175
## 3rd Qu.: 0.723447127652000033 3rd Qu.: 0.487777208883000024
## Max. :16.875344033600001126 Max. : 34.801665876699999558
## V6 V7
## Min. :-26.160505935799999833 Min. :-43.5572415712000022836
## 1st Qu.: -0.797662407982000055 1st Qu.: -0.7149436014429999542
## Median : -0.300351750573000009 Median : -0.0583566593771000022
## Mean : 0.000000000000001526 Mean : -0.0000000000000006131
## 3rd Qu.: 0.395883169799000012 3rd Qu.: 0.4340835471719999838
## Max. : 73.301625545999996802 Max. :120.5894939450000009629
## V8 V9
## Min. :-73.2167184552999970037 Min. :-13.434066318199999301
## 1st Qu.: -0.2223837549029999971 1st Qu.: -0.661279511072999981
## Median : -0.0359359012623000004 Median : -0.051669231640099997
## Mean : 0.0000000000000001533 Mean : -0.000000000000002453
## 3rd Qu.: 0.2437358791989999995 3rd Qu.: 0.586970108718000039
## Max. : 20.0072083650999985593 Max. : 15.594994607100000295
## V10 V11
## Min. :-24.588262437200000932 Min. :-4.797473464800000365
## 1st Qu.: -0.566563353970000017 1st Qu.:-0.778357056303999983
## Median : -0.131562766948999987 Median :-0.038436043861400003
## Mean : 0.000000000000002264 Mean : 0.000000000000001622
## 3rd Qu.: 0.448438015746000007 3rd Qu.: 0.735117741874000030
## Max. : 23.745136120699999793 Max. :12.018913181600000328
## V12 V13
## Min. :-18.683714633299999264 Min. :-5.7918812063200002527
## 1st Qu.: -0.429625217559999995 1st Qu.:-0.6502130686849999508
## Median : 0.127549023329000005 Median :-0.0171936246040000004
## Mean : -0.000000000000001194 Mean : 0.0000000000000008399
## 3rd Qu.: 0.605126944090999985 3rd Qu.: 0.6545821119720000025
## Max. : 7.848392075639999632 Max. : 7.1268829585900004275
## V14 V15
## Min. :-19.21432549029999848 Min. :-4.498944676769999873
## 1st Qu.: -0.44769672722999998 1st Qu.:-0.592952284610000047
## Median : 0.02816073744240000 Median : 0.035751970292299998
## Mean : 0.00000000000000126 Mean : 0.000000000000004803
## 3rd Qu.: 0.47427711057299998 3rd Qu.: 0.637702852645000040
## Max. : 10.52676605179999925 Max. : 8.877741597740000046
## V16 V17
## Min. :-14.129854517500000100 Min. :-25.1627993693000000519
## 1st Qu.: -0.482739258251000003 1st Qu.: -0.4862938999299999954
## Median : 0.051825164346399999 Median : -0.0732979924922999987
## Mean : 0.000000000000001404 Mean : -0.0000000000000003992
## 3rd Qu.: 0.492054688838000021 3rd Qu.: 0.3741142405649999780
## Max. : 17.315111517599998336 Max. : 9.2535262504699993258
## V18 V19
## Min. :-9.4987459210500002627 Min. :-7.213527430180000088
## 1st Qu.:-0.5131401852550000475 1st Qu.:-0.465029532267999979
## Median :-0.0042466565293000002 Median :-0.004031573397670000
## Mean : 0.0000000000000009836 Mean : 0.000000000000001028
## 3rd Qu.: 0.4901070570900000201 3rd Qu.: 0.456966385473000003
## Max. : 5.0410691854099995979 Max. : 5.591971427339999856
## V20 V21
## Min. :-54.4977204946000028940 Min. :-34.8303821447999979455
## 1st Qu.: -0.2127553932360000122 1st Qu.: -0.2778778795280000269
## Median : -0.1188367684929999973 Median : -0.0297449943196999994
## Mean : 0.0000000000000006451 Mean : 0.0000000000000001916
## 3rd Qu.: 0.0690004809926000051 3rd Qu.: 0.1563546695870000058
## Max. : 39.4209042482000029395 Max. : 27.2028391573000014603
## V22 V23
## Min. :-10.9331436977000002742 Min. :-44.8077352038000000789
## 1st Qu.: -0.5580065444559999532 1st Qu.: -0.1638698133569999882
## Median : -0.0006644659787960000 Median : -0.0291975195698000005
## Mean : -0.0000000000000002938 Mean : 0.0000000000000002682
## 3rd Qu.: 0.5138051449239999791 3rd Qu.: 0.1054747742169999947
## Max. : 10.5030900899000005921 Max. : 22.5284116897999986406
## V24 V25
## Min. :-2.836626918699999944 Min. :-10.2953970750000003420
## 1st Qu.:-0.357954116132000011 1st Qu.: -0.3190050529160000092
## Median : 0.035368214814400001 Median : 0.0016646906501800000
## Mean : 0.000000000000004468 Mean : 0.0000000000000005109
## 3rd Qu.: 0.436111721816999975 3rd Qu.: 0.3401494199699999776
## Max. : 4.584549136899999766 Max. : 7.5195886787099999182
## V26 V27
## Min. :-2.604550552810000141 Min. :-22.5656793208000010509
## 1st Qu.:-0.327205179638999977 1st Qu.: -0.0818601886112999971
## Median :-0.057841748404500001 Median : -0.0276823111843999999
## Mean : 0.000000000000001685 Mean : -0.0000000000000003674
## 3rd Qu.: 0.236009267487999996 3rd Qu.: 0.0806734436694000051
## Max. : 3.517345611620000145 Max. : 31.6121981060999992508
## V28 Amount Class
## Min. :-15.4300839055000000855 Min. : 0.00 Min. :0.000000
## 1st Qu.: -0.0553816875165000014 1st Qu.: 0.00 1st Qu.:0.000000
## Median : -0.0061037957920700003 Median : 0.00 Median :0.000000
## Mean : -0.0000000000000001221 Mean : 88.35 Mean :0.001727
## 3rd Qu.: 0.0431740959324000001 3rd Qu.: 77.07 3rd Qu.:0.000000
## Max. : 33.8478078189000015641 Max. :25691.16 Max. :1.000000## [1] 31g.split = h2o.splitFrame(data = d.hex,ratios = 0.75)
train = g.split[[1]]#75% training data
test = g.split[[2]]
##3 hidden layers
model_unsup = h2o.deeplearning(x = 1:30,
training_frame = train,
model_id = "model_unsup",
autoencoder = TRUE,
reproducible = TRUE, #slow - turn off for real problems
ignore_const_cols = FALSE,
seed = 42,
hidden = c(10, 10, 10),
epochs = 100,
activation = "Tanh")##
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