Complete all Exercises, and submit answers to VtopBeta
### load packages
library(caret)## Loading required package: lattice## Loading required package: ggplot2library(knitr)| Sepal.Length | Sepal.Width | Petal.Length | Petal.Width | Species |
|---|---|---|---|---|
| 5.1 | 3.5 | 1.4 | 0.2 | setosa |
| 4.9 | 3.0 | 1.4 | 0.2 | setosa |
| 4.7 | 3.2 | 1.3 | 0.2 | setosa |
| 4.6 | 3.1 | 1.5 | 0.2 | setosa |
| 5.0 | 3.6 | 1.4 | 0.2 | setosa |
ir_data=iris
set.seed(100)
head(ir_data)## 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 setosaintrain <- createDataPartition(y = ir_data$Species, p= 0.7, list = FALSE)
training<-iris[intrain,]
testing<-ir_data[-intrain,]
dim(training);dim(testing)## [1] 105 5## [1] 45 5summary(ir_data)## Sepal.Length Sepal.Width Petal.Length Petal.Width
## Min. :4.300 Min. :2.000 Min. :1.000 Min. :0.100
## 1st Qu.:5.100 1st Qu.:2.800 1st Qu.:1.600 1st Qu.:0.300
## Median :5.800 Median :3.000 Median :4.350 Median :1.300
## Mean :5.843 Mean :3.057 Mean :3.758 Mean :1.199
## 3rd Qu.:6.400 3rd Qu.:3.300 3rd Qu.:5.100 3rd Qu.:1.800
## Max. :7.900 Max. :4.400 Max. :6.900 Max. :2.500
## Species
## setosa :50
## versicolor:50
## virginica :50
##
##
## training[["Species"]] = factor(training[["Species"]])
trctrl <- trainControl(method = "repeatedcv", number = 10, repeats = 3)The results of confusion matrix show that this time the accuracy on the test set is 95.56%.
library(e1071)
model <- naiveBayes(Species ~., data = training)
class(model)## [1] "naiveBayes"summary(model)## Length Class Mode
## apriori 3 table numeric
## tables 4 -none- list
## levels 3 -none- character
## call 4 -none- callprint(model)##
## Naive Bayes Classifier for Discrete Predictors
##
## Call:
## naiveBayes.default(x = X, y = Y, laplace = laplace)
##
## A-priori probabilities:
## Y
## setosa versicolor virginica
## 0.3333333 0.3333333 0.3333333
##
## Conditional probabilities:
## Sepal.Length
## Y [,1] [,2]
## setosa 5.071429 0.3409083
## versicolor 5.825714 0.4667427
## virginica 6.540000 0.6611932
##
## Sepal.Width
## Y [,1] [,2]
## setosa 3.517143 0.3416962
## versicolor 2.748571 0.2974118
## virginica 2.962857 0.3263756
##
## Petal.Length
## Y [,1] [,2]
## setosa 1.471429 0.1856173
## versicolor 4.182857 0.4712223
## virginica 5.525714 0.5653437
##
## Petal.Width
## Y [,1] [,2]
## setosa 0.2514286 0.1039554
## versicolor 1.3114286 0.1794951
## virginica 1.9885714 0.2857101preds <- predict(model, newdata = training)
table(preds,training$Species)##
## preds setosa versicolor virginica
## setosa 35 0 0
## versicolor 0 33 3
## virginica 0 2 32(35+33+32)/(35+33+2+32+3)#change this according to the diagonal element of the previous statement result ## [1] 0.952381Accuracy is 95.2381%.
library(mlbench)
data("HouseVotes84")
data(HouseVotes84, package = "mlbench")
model <- naiveBayes(Class ~ ., data = HouseVotes84)
predict(model, HouseVotes84[1:10,])## [1] republican republican republican democrat democrat democrat
## [7] republican republican republican democrat
## Levels: democrat republicanpredict(model, HouseVotes84[1:10,], type = "raw")## democrat republican
## [1,] 1.029209e-07 9.999999e-01
## [2,] 5.820415e-08 9.999999e-01
## [3,] 5.684937e-03 9.943151e-01
## [4,] 9.985798e-01 1.420152e-03
## [5,] 9.666720e-01 3.332802e-02
## [6,] 8.121430e-01 1.878570e-01
## [7,] 1.751512e-04 9.998248e-01
## [8,] 8.300100e-06 9.999917e-01
## [9,] 8.277705e-08 9.999999e-01
## [10,] 1.000000e+00 5.029425e-11pred <- predict(model, HouseVotes84)
table(pred, HouseVotes84$Class)##
## pred democrat republican
## democrat 238 13
## republican 29 155(238+155)/(238+155+29+13)## [1] 0.9034483Accuracy is 90.34483%.
## using laplace smoothing:
model <- naiveBayes(Class ~ ., data = HouseVotes84, laplace = 3)
pred <- predict(model, HouseVotes84[,-1])
table(pred, HouseVotes84$Class)##
## pred democrat republican
## democrat 237 12
## republican 30 156(237+156)/(237+156+12+30)## [1] 0.9034483Accuracy is still 90.34483%.
data(Titanic)
m <- naiveBayes(Survived ~ ., data = Titanic)
m##
## Naive Bayes Classifier for Discrete Predictors
##
## Call:
## naiveBayes.formula(formula = Survived ~ ., data = Titanic)
##
## A-priori probabilities:
## Survived
## No Yes
## 0.676965 0.323035
##
## Conditional probabilities:
## Class
## Survived 1st 2nd 3rd Crew
## No 0.08187919 0.11208054 0.35436242 0.45167785
## Yes 0.28551336 0.16596343 0.25035162 0.29817159
##
## Sex
## Survived Male Female
## No 0.91543624 0.08456376
## Yes 0.51617440 0.48382560
##
## Age
## Survived Child Adult
## No 0.03489933 0.96510067
## Yes 0.08016878 0.91983122predict(m, as.data.frame(Titanic))## [1] Yes No No No Yes Yes Yes Yes No No No No Yes Yes Yes Yes Yes
## [18] No No No Yes Yes Yes Yes No No No No Yes Yes Yes Yes
## Levels: No Yes# Load additional libraries
library(tm)## Loading required package: NLP##
## Attaching package: 'NLP'## The following object is masked from 'package:ggplot2':
##
## annotatelibrary(RTextTools)## Loading required package: SparseM##
## Attaching package: 'SparseM'## The following object is masked from 'package:base':
##
## backsolvelibrary(dplyr)## Warning: package 'dplyr' was built under R version 3.5.1##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union# Library for parallel processing
library(doMC)## Loading required package: foreach## Loading required package: iterators## Loading required package: parallelregisterDoMC(cores=detectCores()) # Use all available coresdf<- read.csv("movie-pang02.csv", stringsAsFactors = FALSE)
glimpse(df)## Observations: 2,000
## Variables: 2
## $ class <chr> "Pos", "Pos", "Pos", "Pos", "Pos", "Pos", "Pos", "Pos", ...
## $ text <chr> " films adapted from comic books have had plenty of succ...# Randomize the dataset
set.seed(1)
df <- df[sample(nrow(df)), ]
df <- df[sample(nrow(df)), ]
glimpse(df)## Observations: 2,000
## Variables: 2
## $ class <chr> "Neg", "Pos", "Neg", "Neg", "Neg", "Neg", "Neg", "Neg", ...
## $ text <chr> " frank detorri s bill murray a single dad who lives...# Convert the 'class' variable from character to factor.
df$class <- as.factor(df$class)corpus <- Corpus(VectorSource(df$text))
corpus## <<SimpleCorpus>>
## Metadata: corpus specific: 1, document level (indexed): 0
## Content: documents: 2000inspect(corpus[1:3])## <<SimpleCorpus>>
## Metadata: corpus specific: 1, document level (indexed): 0
## Content: documents: 3
##
## [1] frank detorri s bill murray a single dad who lives on beer and junk food with no apparent understanding of sanitation or hygiene much to the dismay of his preteen daughter shane elena franklin when he uses the 10 second rule to retrieve a hard boiled egg from a chimp s cage at the zoo and downs it he introduces a lethal bacteria into his system inside his skin the city of frank is in turmoil thanks to the vote pandering of mayor phlegmming voice of william shatner so it s up to one frank pd white blood cell voice of chris rock to save the day in peter and bobby farrelly s osmosis jones the city of frank is a brightly animated animation directed by piet kroon and tom sito cellular municipality where osmosis jones is a typical rogue cop looking for another chance he s inadvertently teamed up with drix voice of david hyde pierce tv s frasier a cold capsule with 12 hours worth of painkillers to dispense this quarrelling duo are about to go on a fantastic voyage in order to hunt down thrax voice of laurence fishburne the virus intent on shutting down frank while the animation is certainly colorful to look at osmosis jones story is a hackneyed one the story cries out for puny puns but we only get occasional sprinklings of wit or bodily humor drix graduated phi beta capsule he departs on a bus headed for bladder neither the hero or villain is particularly interesting thrax looks like an animated predator although hyde pierce is a delightful sidekick adults can desperately keep their eyes peeled for small amusements the animators dot along the landscape meanwhile back in live action land bill murray is reduced to nothing more than a walking gross out joke there s no particular enjoyment to be found watching him vomit on molly shannon she plays shane s teacher mrs boyd or hoisting his ingrown toenail onto a restaurant table one must wonder how the climatic flatlining of a child s father will play to the family audience as well rest assured the whole enchilada is wrapped up with a fart joke while far less offensive than the farrelly s last effort me myself and irene that film at least spiked some comic highs with jim carrey s hijinx osmosis jones will probably be ok for the kids but the farrellys playing for the family audience is like watching marilyn manson croon a phil collins tune
## [2] synopsis in phantom menace the galaxy is divided into power groups whose interests will inevitably collide in later sequels there is an overarching galactic united nations type organization called the senate presided by a weak chancellor within the senate two camps are at odds a bickering isolationist alliance called the republic and their aggressive rival the trade federation preserving law and order are a council of jedi knights who are meanwhile searching for a prophesied chosen one of virgin birth manipulating events behind the scenes is a dangerous reemerging clan called the dark lords of sith so shadowy and secretive that they comprise a phantom menace jedi knight qui gon jinn liam neeson and his apprentice obi wan kenobi ewan mcgregor witness an invasion of teenage queen amidala s home planet naboo and befriend a gungan named jar jar ahmed best on the desert planet of tatooine the two jedi jar jar and amidala natalie portman attend a lengthy drag race involving the young boy anakin skywalker jake lloyd the five protagonists try to solicit help for freeing naboo by visiting the city planet of coruscant where a lot of debate and political maneuvering takes place can they free amidala s helpless planet opinion on tv last night i watched young wannabe celebs pay $400 a ticket and come running out of theaters to bask in front of news cameras gushing with testimonials of the phantom menace s greatness in exchange for a few seconds of being on national television given this kind of media mania i wondered if phantom menace the most anticipated movie of 1999 could possibly live up to the extraordinary hype that preceded it does phantom menace match the exaggerated hype director george lucas answers it s only a movie to me any movie with russian sounding accents for bad guys jamaican accents for good guys and middle eastern sounding accents for seedy gamblers accents can be expected to be more tongue in cheek than profound visually star wars episode i the phantom menace 1999 is a kid show where parents can take their young ones to marvel at child friendly cgi characters and wondrous backdrops even if the character dialogue mostly geopolitics is beyond the level of children it is left to parents to patiently explain the conversation droid origins family lineage the definitions of terms like blockade appeasement federation alliance symbiosis satellite controlled robots et cetera at least this much is clear there s plenty of eye candy and in the last few minutes it s good guys and joe camel lookalikes versus a caped horned red devil character and his mechanical hordes weaknesses weaknesses lie in the writing and in the performance at first it seems like the film is to be an invasion story but then phantom takes an hour long detour to cover one chariot race before returning to the invasion theme this dilutes the central story additionally smaller scenes seem written self consciously as if they were added more to fill us in on extraneous background information for other movies rather than form an integral part of the present movie veteran actors liam neeson and ewan mcgregor noticeably outperform the other acting leads better ensemble chemistry between the five leads and background information that is central to a tight story line could have made have given phantom stronger performances and storytelling punch strengths on the bright side phantom menace as a big budget production is far ahead of the competition in terms of making whimsical creatures worlds and vehicles appear real the film boasts sophisticated top of the line visuals and quality exotic costumes a musical score entertaining enough to stand alone and three worthwhile sequences in the second half bottom line seeing the film is entertaining and informative like a visual theme park with star wars filler information serving as dialogue between impressive money shots we are bound to be completely inundated by star wars publicity music and tie ins for the next few months
## [3] terrence malick made an excellent 90 minute film adaptation of james jones world war ii novel unfortunately he buried it within an overlong and overreaching 3 hour long pseudo epic this is a shame because the film features an outstanding performance by nick nolte the best scene is when nick nolte s character lt col tall is forced to deal with the direct refusal by capt staros elias koteas to execute an order nolte s reaction and transformation may be the best work of his career had terrence malick concentrated on the great performances of nolte and koteas as well as those by sean penn woody harrelson and john cusack he could have made a truly great film instead malick saddled the film with plodding pacing unnecessary flashbacks and a voice over narration all designed to telegraph the great philosophical underpinnings of the story the narration was especially annoying as much of it sounded like very bad high school poetry with a lot of editing the core story could be transformed into a truly classic war film hopefully the dvd version of this film will feature options to suppress the narration and perhaps will even provide for an alternate shorter version of the film i give this film# Use dplyr's %>% (pipe) utility to do this neatly.
corpus.clean <- corpus %>%
tm_map(content_transformer(tolower)) %>%
tm_map(removePunctuation) %>%
tm_map(removeNumbers) %>%
tm_map(removeWords, stopwords(kind="en")) %>%
tm_map(stripWhitespace)## Warning in tm_map.SimpleCorpus(., content_transformer(tolower)):
## transformation drops documents## Warning in tm_map.SimpleCorpus(., removePunctuation): transformation drops
## documents## Warning in tm_map.SimpleCorpus(., removeNumbers): transformation drops
## documents## Warning in tm_map.SimpleCorpus(., removeWords, stopwords(kind = "en")):
## transformation drops documents## Warning in tm_map.SimpleCorpus(., stripWhitespace): transformation drops
## documentsdtm <- DocumentTermMatrix(corpus.clean)
inspect(dtm[40:50, 10:15])## <<DocumentTermMatrix (documents: 11, terms: 6)>>
## Non-/sparse entries: 6/60
## Sparsity : 91%
## Maximal term length: 8
## Weighting : term frequency (tf)
## Sample :
## Terms
## Docs apparent assured audience back bacteria beer
## 40 0 0 1 1 0 0
## 41 0 0 1 0 0 0
## 42 0 0 0 0 0 0
## 43 0 0 0 0 0 0
## 44 0 0 0 1 0 0
## 45 0 0 0 0 0 0
## 46 0 0 2 0 0 0
## 47 0 0 0 0 0 0
## 48 0 0 0 0 0 0
## 50 0 0 2 0 0 0df.train <- df[1:1500,]
df.test <- df[1501:2000,]
dtm.train <- dtm[1:1500,]
dtm.test <- dtm[1501:2000,]
corpus.clean.train <- corpus.clean[1:1500]
corpus.clean.test <- corpus.clean[1501:2000]dim(dtm.train)## [1] 1500 38957fivefreq <- findFreqTerms(dtm.train, 5)
length((fivefreq))## [1] 12144# Use only 5 most frequent words (fivefreq) to build the DTM
dtm.train.nb <- DocumentTermMatrix(corpus.clean.train, control=list(dictionary = fivefreq))
dim(dtm.train.nb)## [1] 1500 12144dtm.test.nb <- DocumentTermMatrix(corpus.clean.test, control=list(dictionary = fivefreq))
dim(dtm.train.nb)## [1] 1500 12144# Function to convert the word frequencies to yes (presence) and no (absence) labels
convert_count <- function(x) {
y <- ifelse(x > 0, 1,0)
y <- factor(y, levels=c(0,1), labels=c("No", "Yes"))
y
}
# Apply the convert_count function to get final training and testing DTMs
trainNB <- apply(dtm.train.nb, 2, convert_count)
testNB <- apply(dtm.test.nb, 2, convert_count)
# Train the classifier
system.time( classifier <- naiveBayes(trainNB, df.train$class, laplace = 1) )## user system elapsed
## 9.563 0.804 11.693# Use the NB classifier we built to make predictions on the test set.
system.time( pred <- predict(classifier, newdata=testNB) )## user system elapsed
## 287.448 8.662 365.658# Create a truth table by tabulating the predicted class labels with the actual class labels
table("Predictions"= pred, "Actual" = df.test$class )## Actual
## Predictions Neg Pos
## Neg 224 54
## Pos 41 181conf.mat <- confusionMatrix(pred, df.test$class)
conf.mat## Confusion Matrix and Statistics
##
## Reference
## Prediction Neg Pos
## Neg 224 54
## Pos 41 181
##
## Accuracy : 0.81
## 95% CI : (0.7728, 0.8435)
## No Information Rate : 0.53
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.6174
## Mcnemar's Test P-Value : 0.2183
##
## Sensitivity : 0.8453
## Specificity : 0.7702
## Pos Pred Value : 0.8058
## Neg Pred Value : 0.8153
## Prevalence : 0.5300
## Detection Rate : 0.4480
## Detection Prevalence : 0.5560
## Balanced Accuracy : 0.8077
##
## 'Positive' Class : Neg
## conf.mat$byClass## Sensitivity Specificity Pos Pred Value
## 0.8452830 0.7702128 0.8057554
## Neg Pred Value Precision Recall
## 0.8153153 0.8057554 0.8452830
## F1 Prevalence Detection Rate
## 0.8250460 0.5300000 0.4480000
## Detection Prevalence Balanced Accuracy
## 0.5560000 0.8077479conf.mat$overall## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.100000e-01 6.174291e-01 7.728180e-01 8.434678e-01 5.300000e-01
## AccuracyPValue McnemarPValue
## 3.570547e-39 2.182578e-01# Prediction Accuracy
conf.mat$overall['Accuracy']## Accuracy
## 0.81