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assignment2 initial commit

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rm(list=ls())
require(lubridate)
suppressWarnings(require(data.table))
require(stringr)
require(geohash)
library(raster)
require(dplyr)
library(sp)
library(broom)
library(httr)
library(rgdal)
require(sparklyr)
require(rJava)
require(readr)
first = TRUE
n = 0
for(i in 1:12){
if(first == TRUE){
taxi_jul <- fread("~/yellow_tripdata_2015-07.csv",
nrows=1000000)
taxi_jul <- taxi_jul %>% filter(weekdays(ymd_hms(tpep_pickup_datetime, tz = "America/New_York")) == "Wednesday")
n1 = nrow(taxi_jul)
first <- FALSE
}else{
taxi_bind1 <- fread("~/yellow_tripdata_2015-07.csv",
nrows=1000000, skip=n1+1)
names(taxi_bind1) <- names(taxi_jul)
taxi_bind1 <- taxi_bind1 %>% filter(weekdays(ymd_hms(tpep_pickup_datetime, tz = "America/New_York")) == "Wednesday")
taxi_jul <- bind_rows(taxi_jul, taxi_bind1)
n1 = nrow(taxi_jul)
}
}
taxi <- taxi_jul
rm(taxi_jul)
hm_pickup <- (lubridate::hour(taxi$tpep_pickup_datetime)*60)+as.numeric(lubridate::minute(taxi$tpep_pickup_datetime))
interval_5min <- vector()
for(j in seq_len(nrow(taxi))){
for(i in seq(0, 2350, by = 5)){
if(between(hm_pickup[j], i, i+5)){
interval_5min[j] = paste0("(", i, " - ", i+5, "]")
}
}
}
taxi$interval_5min <- interval_5min
#convert dropoff times to ordered factor with monday as 1, sunday as 7
dropoff_weekday_num <- weekdays(ymd_hms(taxi$tpep_dropoff_datetime, tz = "America/New_York"))
dropoff_weekday_num <- factor(dropoff_weekday_num,
levels = c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday"),
ordered = TRUE)
pickup_weekday_num <- weekdays(ymd_hms(taxi$tpep_pickup_datetime, tz = "America/New_York"))
pickup_weekday_num <- factor(pickup_weekday_num,
levels = c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday"),
ordered = TRUE)
#convert time to seconds and then make cosine and sine versions for smooth transitions between time intervals
taxi <- taxi %>% dplyr::mutate(time_hour = lubridate::hour(taxi$tpep_pickup_datetime),
time_num = (((lubridate::hour(taxi$tpep_pickup_datetime)*60*60)+
(lubridate::minute(taxi$tpep_pickup_datetime)*60)+
lubridate::second(taxi$tpep_pickup_datetime))/(60*60*24)),
time_num_cosine = cos(time_num*2*pi),
time_num_sine = sin(time_num*2*pi))
#add a feature for weekday as a numeric value - 1 for monday, 7 for sunday
taxi <- taxi %>% dplyr::mutate(weekday = pickup_weekday_num,
weekday_num = (as.numeric(weekday)+time_num)/7,
weekday_num_cosine = cos(weekday_num*2*pi),
weekday_num_sine = sin(weekday_num*2*pi))
#time difference between pickup and dropoff in seconds
taxi <- taxi %>% dplyr::mutate(time_diff_secs =
((lubridate::hour(taxi$tpep_dropoff_datetime)*60*60)+
(lubridate::minute(taxi$tpep_dropoff_datetime)*60)+
lubridate::second(taxi$tpep_dropoff_datetime)) -
((lubridate::hour(taxi$tpep_pickup_datetime)*60*60)+
(lubridate::minute(taxi$tpep_pickup_datetime)*60)+
lubridate::second(taxi$tpep_pickup_datetime)))
#create week as numeric value
taxi <- taxi %>% dplyr::mutate(weekofyear = lubridate::week(tpep_pickup_datetime),
weekofyear_num = (weekofyear+weekday_num)/53,
weekofyear_num_cosine = cos(weekofyear_num*2*pi),
weekofyear_num_sine = sin(weekofyear_num*2*pi))
#remove impossible and unlikely values
taxi <- taxi %>% dplyr::filter(time_diff_secs > 0 & time_diff_secs/60 < 300)
#convert time difference feature to be between 0 and 1 (scaled by max)
taxi <- taxi %>% dplyr::mutate(timediffsec_num = time_diff_secs/max(time_diff_secs))
#miles to kms
taxi$trip_distance <- taxi$trip_distance*1.609344
#outlier for trip distance, just remove as don't know how else to deal with it
taxi <- taxi %>% dplyr::filter(trip_distance > 0 & trip_distance < 150)
#remove longitude and latitude equal to 0
taxi <- taxi %>% dplyr::filter(pickup_longitude != 0 | pickup_latitude != 0)
taxi <- taxi %>% dplyr::filter(trip_distance > 0)
taxi <- taxi %>% dplyr::filter(total_amount < 200)
#data features inspired by https://sdaulton.github.io/TaxiPrediction/
#specifically geohashing, find out this is best in analysis below comparing to boroughs and neighbourhoods
#Also numeric values between 0 and 1 for time of day, day of week, week of year
#And extra features converting these scaled numeric values functions of sine and cosine
#to make for a more smooth transition between time periods
taxi$pickup_geohash <-gh_encode(taxi$pickup_latitude, taxi$pickup_longitude, 5)
#scale trip distance to be between 0 and 1
taxi$trip_distance_num <- taxi$trip_distance/max(taxi$trip_distance)
taxi$RatecodeID <- as.factor(taxi$RatecodeID)
taxi$passenger_count <- as.factor(taxi$passenger_count)
#scale total amount to be between 0 and 1
taxi$total_amount_num <- taxi$total_amount/max(taxi$total_amount)
#59 unique geohashes for pickups
taxi %>% count(pickup_geohash)
r <- GET('http://data.beta.nyc//dataset/0ff93d2d-90ba-457c-9f7e-39e47bf2ac5f/resource/35dd04fb-81b3-479b-a074-a27a37888ce7/download/d085e2f8d0b54d4590b1e7d1f35594c1pediacitiesnycneighborhoods.geojson')
nyc_neighborhoods <- readOGR(content(r,'text'), 'OGRGeoJSON', verbose = F)
taxi <- as.data.frame(taxi)
taxi_spdf <- SpatialPointsDataFrame(taxi[,c('pickup_longitude', 'pickup_latitude')],
proj4string = CRS("+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0"),
data=taxi)
taxi_spdf <- over(taxi_spdf, nyc_neighborhoods[,c('neighborhood', 'borough')])
taxi$neighbourhood <- taxi_spdf$neighborhood
taxi$borough <- taxi_spdf$borough
glimpse(taxi)
taxi %>% group_by(neighbourhood, interval_5min) %>% count() %>% arrange(desc(n)) %>%
group_by(neighbourhood) %>% summarise(n = sum(n)) %>% arrange(desc(n))
#incredible that there is upward of 500 pickups in 5 minute intervals on a wednesday in Midtown in NYC
taxi %>% filter(neighbourhood %in% c("Midtown", "Upper East Side", "Chelsea", "Upper West Side",
"Hell's Kitchen")) %>% count(interval_5min) %>%
ggplot() + geom_col(aes(x=interval_5min, y=n)) +
theme(axis.text.x = element_text(angle = 55, hjust = 1)) + ggtitle("Pickup density for Midtown on Wednesday's in July of 2015")
tt <- taxi %>% filter(neighbourhood %in% c("Midtown", "Upper East Side")) %>% group_by(neighbourhood, interval_5min) %>%
count() %>% ungroup(taxi)
taxi2 <- taxi %>% filter(neighbourhood %in% c("Midtown", "Upper East Side"))
taxi3 <- taxi %>% filter(neighbourhood %in% c("Midtown", "Upper East Side"))
dim(taxi2)
sort(tt$n, decreasing = T)
pickup_n <- vector()
tt$neighbourhood <- as.character(tt$neighbourhood)
tt_mid <- tt %>% filter(neighbourhood == "Midtown")
names(tt_mid)[3] <- "n1"
tt_ues <- tt %>% filter(neighbourhood == "Upper East Side")
names(tt_ues)[2:3] <- c("neighbourhood2", "n2")
tt_ues <- tt_ues %>% dplyr::select(neighbourhood2, n2)
tt <- cbind(tt_mid, tt_ues)
head(tt)
for(i in 1:nrow(taxi2)){
for(j in 1:length(unique(tt$interval_5min))){
if(taxi2$interval_5min[i] == tt$interval_5min[j]){
if(taxi2$neighbourhood[i] == tt$neighbourhood[j]){
pickup_n[i] = tt$n1[j]
}else{
pickup_n[i] = tt$n2[j]
}
}
}
}
taxi2$pickup_n <- pickup_n
glimpse(taxi2)
taxi2 %>% filter(neighbourhood == "Midtown") %>% count(interval_5min) %>%
ggplot() + geom_col(aes(x=reorder(interval_5min, n), y=n)) +
theme(axis.text.x=element_blank(), axis.ticks.x=element_blank()) + xlab("Time")+ggtitle("Midtown")
taxi2 %>% filter(neighbourhood == "Upper East Side") %>% count(interval_5min) %>%
ggplot() + geom_col(aes(x=reorder(interval_5min, n), y=n)) + theme(axis.text.x=element_blank(),
axis.ticks.x=element_blank()) +ggtitle("Upper East Side") + xlab("Time")
taxi2 %>% filter(neighbourhood == "Midtown") %>% count(interval_5min) %>% mutate(sd = sd(n))
taxi2 %>% filter(neighbourhood == "Upper East Side") %>% count(interval_5min) %>% mutate(sd = sd(n))
glimpse(taxi2)
taxi2$VendorID <- as.factor(taxi2$VendorID)
taxi2$payment_type <- as.factor(taxi2$payment_type)
taxi2 <- taxi2 %>% dplyr::select(VendorID, passenger_count, RatecodeID, payment_type, time_num, time_num_cosine, time_num_sine,
weekday_num, weekday_num_cosine, weekday_num_sine, timediffsec_num, trip_distance_num,
total_amount_num, neighbourhood, interval_5min, pickup_n)
taxi2 %>% filter(neighbourhood == "Midtown") %>% group_by(interval_5min) %>%
summarise(tan = median(total_amount_num), tdn = median(trip_distance_num), tdsn = median(timediffsec_num),
woyns = mean(weekofyear_num_sine), woync = mean(weekofyear_num_cosine), woyn = median(weekofyear_num),
tns = mean(time_num_sine), tnc = mean(time_num_cosine), tn = median(time_num))
Y <- taxi %>% filter(neighbourhood == "Midtown") %>% count(pickup_halfhr_cat) %>% dplyr::select(n)
taxi <- taxi %>% dplyr::mutate(Y = Y$n)
taxi2 %>% filter(neighbourhood == "Midtown") %>%
ggplot() + geom_col(aes(x=interval_5min, y=pickup_n))
write.csv(taxi2, "D:/R/TaxiAssignment/taxi_ml_format.csv")
#######SPARK
cluster_url <- paste0("spark://", system("hostname -i", intern = TRUE), ":7077")
library(sparklyr)
library(readr)
library(dplyr)
sc <- spark_connect(master = cluster_url)
spark_read_csv(sc, "taxi_ml_format.csv", path = "")
taxi_ml_format <- read_csv("taxi_ml_format.csv")
taxi_ml_format <- taxi_ml_format %>% select(-X1)
X_data <- taxi_ml_format
X_data$VendorID <- as.factor(X_data$VendorID)
X_data$RatecodeID <- as.factor(X_data$RatecodeID)
X_data$payment_type <- as.factor(X_data$payment_type)
X_data$passenger_count <- as.factor(X_data$passenger_count)
X_tbl <- copy_to(sc, X_data, "x_data", overwrite=T)
partitions <- X_tbl %>%
sdf_partition(training = 0.75, test = 0.25, seed = 1099)
taxi_training <- partitions$training
taxi_test <- partitions$test
#baseline
lm <- ml_linear_regression(taxi_training, pickup_n~.)
rf_model <- taxi_training %>%
ml_random_forest(pickup_n ~ ., type = "regression")
pred_lm <- sdf_predict(taxi_test, lm)
pred_rf <- sdf_predict(taxi_test, rf_model)
ml_regression_evaluator(pred_lm, label_col = "pickup_n", prediction_col = "prediction", metric_name="r2")
ml_regression_evaluator(pred_rf, label_col = "pickup_n", prediction_col = "prediction", metric_name="r2")
ml_regression_evaluator(pred_lm, label_col = "pickup_n", prediction_col = "prediction", metric_name="rmse")
ml_regression_evaluator(pred_rf, label_col = "pickup_n", prediction_col = "prediction", metric_name="rmse")
imp <- sparklyr::ml_feature_importances(rf_model)
spark_disconnect(sc)
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