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Yelp_business_PCA_EE1027.R
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### YELP PCA MODEL ON BUSINESS DATA
### PCA ANALYSIS AND PRINCIPAL COMPONENTS FED INTO LOGISTIC REGRESSION
### Results:
# 31 principal components = 95% of the variance in the data
# Logistic Regression with 31 principal components run across 3 trials to create results means.
# Mean accuracy: 0.3323
# Mean F1 score (only classes 1.5 - 4.5): 0.2031
# Mean Precision (all classes):0.1610
# Mean Recall: (only classes 1.5-4.5): 0.3035
# ROC Curves produced for all 3 models
data.path <- 'C:\\Users\\Elaine\\Documents\\Desk R\\YelpR\\';
data.file <- paste(data.path,'yelp_business_clean_version6.csv',sep='');
df = read.csv(data.file,header=TRUE);
options(max.print = 100000)
# drop rows to match linear regression 1
df2 = subset(df, select = -c(stars,business_id,name,address,city,state,postal_code,latitude,
longitude,attributes,categories,BusinessParking,market,
Alcohol_None, BYOBCorkage_yes_free, NoiseLevel_average,
NoiseLevel_quiet, Smoking_yes, WiFi_no, Nightlife,
Bars, Sandwiches) )
#scale the review_count and restaurantpricerange 2 before PCA
library(caret) #apply scaling between 0-1
#trainTransformed <- predict(preProcValues, training)
#create df for just scaled columns
df_scale = subset(df2, select = c(review_count,RestaurantsPriceRange2))
#pp <- preProcess(df2, method = list("range", scale = names(df2)['review_count']))
preprocvalues <- preProcess(df_scale, method = c("range"))
df_scale2 <- predict(preprocvalues, df_scale)
#add scaled columns to df2
df2$review_count_scale = df_scale2$review_count
df2$RestaurantsPriceRange2_scale = df_scale2$RestaurantsPriceRange2
#create new df to drop unscaled columns
df3 <- subset(df2, select= -c(review_count,RestaurantsPriceRange2))
### PCA
library(psych)
library(DataExplorer)
plot_correlation(df3)
#parking NA columns have a high correlation to each other
pc <- prcomp(df3, scale = FALSE)
summary(pc)
#Top 31 PCs = 95% of the variance
# look at pc loadings since there are too many variables to plot
pc$rotation
#export loadings to csv
pc_loadings <- as.data.frame(pc$rotation)
library(readr)
coln <- colnames(df3)
pc_loadings$coln <-coln
write_csv(pc_loadings, "~/Desk R/YelpR/pcloadings.csv")
### pass pcs to logistic regression
# isolate the principal components that explain 95% of the variation
yelp.pcs <- pc$x[,1:31]
yelp.pcsdf <- as.data.frame(yelp.pcs)
yelp.pcsdf$stars <- df$stars
#yelp_modelpc = "stars ~ PC1 + PC2 + PC3 + PC4 + PC5 + PC6 + PC7 + PC8 +
# PC9 + PC10 + PC11 + PC12+ PC13 + PC14 + PC15 + PC16 + PC17 + PC18 + PC19 +
# PC20 + PC21+ PC22 + PC23 + PC24 + PC25+ PC26 + PC27 + PC28 + PC29+ PC30 + PC31"
### LOGISTIC REGRESSION
library(nnet) #for multinomial logistic regression
library(tidyverse)
# Split the data into training and test set
set.seed(123)
training.samples <- yelp.pcsdf$stars %>%
createDataPartition(p = 0.8, list = FALSE)
train.data <- yelp.pcsdf[training.samples, ]
test.data <- yelp.pcsdf[-training.samples, ]
# Fit the model
pcalogregmodel <- nnet::multinom(stars ~., data = train.data)
# Summarize the model
summary(pcalogregmodel)
# Make predictions
predicted.classes <- pcalogregmodel %>% predict(test.data)
head(predicted.classes)
# Model accuracy
mean_accuracy <- mean(predicted.classes == test.data$stars)
# Building classification table
ctable <- table(test.data$stars, predicted.classes)
#use caret to get performance metrics
cm <- confusionMatrix(ctable)
print(cm)
# extract F1 score for all classes
f1pca = cm[["byClass"]][ , "F1"] #for multiclass classification problems
#print(mean(f1pca))
#extract precision
precisionpca = cm[["byClass"]][ , "Precision"]
#print(precisionpca)
#print(mean(precisionpca))
#extract RECALL
recallpca = cm[["byClass"]][ , "Recall"]
#print(recallpca)
#print(mean(recallpca))
logisticresults = data.frame(mean_accuracy, f1pca, precisionpca, recallpca )
cat("\n\nLogistic Model with PCA Results:\n")
print(round(logisticresults, digits = 3))
write_csv(logisticresults, "~/Desk R/YelpR/logisticpca1.csv")
# create AUC-ROC curve for model 1 & 3 for comparison
library(pROC)
library(multiROC)
roc1 <- pROC::multiclass.roc(test.data$stars, as.numeric(predicted.classes))
plot.roc(smooth(roc1$rocs[[1]]),
print.auc=T,
legacy.axes = T,
print.auc.adj = c(-1,-5))
plot.roc(smooth(roc1$rocs[[2]]),
add=T, col = 'red',
print.auc = T,
legacy.axes = T,
print.auc.adj = c(-1,-3))
plot.roc(smooth(roc1$rocs[[3]]),add=T, col = 'blue',
print.auc=T,
legacy.axes = T,
print.auc.adj = c(-1,-1))
plot.roc(smooth(roc1$rocs[[4]]),add=T, col = 'green',
print.auc=T,
legacy.axes = T,
print.auc.adj = c(-1,1))
plot.roc(smooth(roc1$rocs[[5]]),add=T, col = 'orange',
print.auc=T,
legacy.axes = T,
print.auc.adj = c(-1,3))
plot.roc(smooth(roc1$rocs[[6]]),add=T, col = 'purple',
print.auc=T,
legacy.axes = T,
print.auc.adj = c(-1,5))
plot.roc(smooth(roc1$rocs[[7]]),add=T, col = 'pink',
print.auc=T,
legacy.axes = T,
print.auc.adj = c(-1,7))
plot.roc(smooth(roc1$rocs[[8]]),add=T, col = 'brown',
print.auc=T,
legacy.axes = T,
print.auc.adj = c(-1,9))
plot.roc(smooth(roc1$rocs[[9]]),add=T, col = 'cadetblue1',
print.auc=T,
legacy.axes = T,
print.auc.adj = c(-1,11))
legend('bottomright',
legend = c('1',
'1.5',
'2',
'2.5',
'3',
'3.5',
'4',
'4.5',
'5'),
col=c('black','red','blue', 'green', 'orange', 'purple', 'pink', 'brown',
'cadetblue1'),lwd=2)
title("ROC-AUC Logistic Regression with PCA 1", line = 2.5)
### PCA LOGISTIC REGRESSION MODEL2
# Split the data into training and test set
set.seed(789)
training.samples <- yelp.pcsdf$stars %>%
createDataPartition(p = 0.8, list = FALSE)
train.data <- yelp.pcsdf[training.samples, ]
test.data <- yelp.pcsdf[-training.samples, ]
# Fit the model
pcalogregmodel <- nnet::multinom(stars ~., data = train.data)
# Summarize the model
summary(pcalogregmodel)
# Make predictions
predicted.classes <- pcalogregmodel %>% predict(test.data)
head(predicted.classes)
# Model accuracy
mean_accuracy <- mean(predicted.classes == test.data$stars)
# Building classification table
ctable <- table(test.data$stars, predicted.classes)
#use caret to get performance metrics
cm <- confusionMatrix(ctable)
print(cm)
# extract F1 score for all classes
f1pca = cm[["byClass"]][ , "F1"] #for multiclass classification problems
#print(mean(f1pca))
#extract precision
precisionpca = cm[["byClass"]][ , "Precision"]
#print(precisionpca)
#print(mean(precisionpca))
#extract RECALL
recallpca = cm[["byClass"]][ , "Recall"]
#print(recallpca)
#print(mean(recallpca))
logisticresults = data.frame(mean_accuracy, f1pca, precisionpca, recallpca )
cat("\n\nLogistic Model with PCA Results:\n")
print(round(logisticresults, digits = 3))
write_csv(logisticresults, "~/Desk R/YelpR/logisticpca2.csv")
# ROC-AUC CURVE FOR PCA LOGISTIC MODEL 2
roc1 <- pROC::multiclass.roc(test.data$stars, as.numeric(predicted.classes))
plot.roc(smooth(roc1$rocs[[1]]),
print.auc=T,
legacy.axes = T,
print.auc.adj = c(-1,-5))
plot.roc(smooth(roc1$rocs[[2]]),
add=T, col = 'red',
print.auc = T,
legacy.axes = T,
print.auc.adj = c(-1,-3))
plot.roc(smooth(roc1$rocs[[3]]),add=T, col = 'blue',
print.auc=T,
legacy.axes = T,
print.auc.adj = c(-1,-1))
plot.roc(smooth(roc1$rocs[[4]]),add=T, col = 'green',
print.auc=T,
legacy.axes = T,
print.auc.adj = c(-1,1))
plot.roc(smooth(roc1$rocs[[5]]),add=T, col = 'orange',
print.auc=T,
legacy.axes = T,
print.auc.adj = c(-1,3))
plot.roc(smooth(roc1$rocs[[6]]),add=T, col = 'purple',
print.auc=T,
legacy.axes = T,
print.auc.adj = c(-1,5))
plot.roc(smooth(roc1$rocs[[7]]),add=T, col = 'pink',
print.auc=T,
legacy.axes = T,
print.auc.adj = c(-1,7))
plot.roc(smooth(roc1$rocs[[8]]),add=T, col = 'brown',
print.auc=T,
legacy.axes = T,
print.auc.adj = c(-1,9))
plot.roc(smooth(roc1$rocs[[9]]),add=T, col = 'cadetblue1',
print.auc=T,
legacy.axes = T,
print.auc.adj = c(-1,11))
legend('bottomright',
legend = c('1',
'1.5',
'2',
'2.5',
'3',
'3.5',
'4',
'4.5',
'5'),
col=c('black','red','blue', 'green', 'orange', 'purple', 'pink', 'brown',
'cadetblue1'),lwd=2)
title("ROC-AUC Logistic Regression with PCA 2", line = 2.5)
### PCA LOGISTIC REGRESSION MODEL3
# Split the data into training and test set
set.seed(2003)
training.samples <- yelp.pcsdf$stars %>%
createDataPartition(p = 0.8, list = FALSE)
train.data <- yelp.pcsdf[training.samples, ]
test.data <- yelp.pcsdf[-training.samples, ]
# Fit the model
pcalogregmodel <- nnet::multinom(stars ~., data = train.data)
# Summarize the model
summary(pcalogregmodel)
# Make predictions
predicted.classes <- pcalogregmodel %>% predict(test.data)
head(predicted.classes)
# Model accuracy
mean_accuracy <-mean(predicted.classes == test.data$stars)
# Building classification table
ctable <- table(test.data$stars, predicted.classes)
#use caret to get performance metrics
cm <- confusionMatrix(ctable)
#print(cm)
# extract F1 score for all classes
f1pca = cm[["byClass"]][ , "F1"] #for multiclass classification problems
#print(f1pca)
#extract precision
precisionpca = cm[["byClass"]][ , "Precision"]
#print(precisionpca)
#print(mean(precisionpca))
#extract RECALL
recallpca = cm[["byClass"]][ , "Recall"]
#print(recallpca)
logisticresults = data.frame(mean_accuracy, f1pca, precisionpca, recallpca )
cat("\n\nLogistic Model with PCA Results:\n")
print(round(logisticresults, digits = 3))
write_csv(logisticresults, "~/Desk R/YelpR/logisticpca3.csv")
# ROC-AUC CURVE FOR PCA LOGISTIC MODEL 3
roc1 <- pROC::multiclass.roc(test.data$stars, as.numeric(predicted.classes))
plot.roc(smooth(roc1$rocs[[1]]),
print.auc=T,
legacy.axes = T,
print.auc.adj = c(-1,-5))
plot.roc(smooth(roc1$rocs[[2]]),
add=T, col = 'red',
print.auc = T,
legacy.axes = T,
print.auc.adj = c(-1,-3))
plot.roc(smooth(roc1$rocs[[3]]),add=T, col = 'blue',
print.auc=T,
legacy.axes = T,
print.auc.adj = c(-1,-1))
plot.roc(smooth(roc1$rocs[[4]]),add=T, col = 'green',
print.auc=T,
legacy.axes = T,
print.auc.adj = c(-1,1))
plot.roc(smooth(roc1$rocs[[5]]),add=T, col = 'orange',
print.auc=T,
legacy.axes = T,
print.auc.adj = c(-1,3))
plot.roc(smooth(roc1$rocs[[6]]),add=T, col = 'purple',
print.auc=T,
legacy.axes = T,
print.auc.adj = c(-1,5))
plot.roc(smooth(roc1$rocs[[7]]),add=T, col = 'pink',
print.auc=T,
legacy.axes = T,
print.auc.adj = c(-1,7))
plot.roc(smooth(roc1$rocs[[8]]),add=T, col = 'brown',
print.auc=T,
legacy.axes = T,
print.auc.adj = c(-1,9))
plot.roc(smooth(roc1$rocs[[9]]),add=T, col = 'cadetblue1',
print.auc=T,
legacy.axes = T,
print.auc.adj = c(-1,11))
legend('bottomright',
legend = c('1',
'1.5',
'2',
'2.5',
'3',
'3.5',
'4',
'4.5',
'5'),
col=c('black','red','blue', 'green', 'orange', 'purple', 'pink', 'brown',
'cadetblue1'),lwd=2)
title("ROC-AUC Logistic Regression with PCA 3", line = 2.5)