Initial commit

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(taxitpep_pickup_datetime)*60)+as.numeric(lubridate::minute(taxitpep_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)6060)+ (lubridate::minute(taxi$tpep_pickup_datetime)60)+ lubridate::second(taxi$tpep_pickup_datetime))/(606024)), time_num_cosine = cos(time_num2pi), time_num_sine = sin(time_num2*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_num2pi), weekday_num_sine = sin(weekday_num2pi))

#time difference between pickup and dropoff in seconds taxi <- taxi %>% dplyr::mutate(time_diff_secs = ((lubridate::hour(taxi$tpep_dropoff_datetime)6060)+ (lubridate::minute(taxi$tpep_dropoff_datetime)*60)+ lubridate::second(taxi$tpep_dropoff_datetime)) - ((lubridate::hour(taxi$tpep_pickup_datetime)6060)+ (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_num2pi), weekofyear_num_sine = sin(weekofyear_num2pi))

#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(taxi2interval_5min[i] == ttinterval_5min[j]){ if(taxi2neighbourhood[i] == ttneighbourhood[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)

		# Assignment2