airlines demo R template

avniwadhwa · avniwadhwa · commit fcd0f987649c · 2016-12-13T17:27:12.000-08:00
diff --git a/tutorials/training/airlines_demo_template.R b/tutorials/training/airlines_demo_template.R
@@ -0,0 +1,87 @@
+## Code for scatterplots
+scatter_plot <- function(data, x, y, max_points = 1000, fit = F) {
+  if (fit) {
+    lr <- h2o.glm(x = x, y = y, training_frame = data, family = "gaussian")
+    coeff <- lr@model$coefficients_table$standardized_coefficients    
+  }
+  
+  df <- data[,c(x, y)]
+  
+  
+  runif <- h2o.runif(df)
+  df.subset <- df[runif < max_points/nrow(data),]
+  df.R <- as.data.frame(df.subset)
+  
+  if (fit) h2o.rm(lr@model_id)
+  
+  plot(x = df.R[,x], y = df.R[,y], col = "blue", xlab = x, 
+       ylab = y, ylim = c(0, 550))
+  if (fit) abline(coef = coeff, col = "black")
+}
+
+## Load library and initialize h2o
+library(h2o)
+h2o.init(nthreads = -1)
+
+
+## Set file path and import data. Drop constant column (23).
+pathToAirlines <- "https://s3.amazonaws.com/h2o-airlines-unpacked/allyears2k.csv"
+
+airlines.hex <- h2o.importFile(path = pathToAirlines, destination_frame = "airlines.hex")
+
+airlines.hex <- airlines.hex[-23]
+dim(airlines.hex)
+
+
+## Get a summary of the data. Build a histogram examining the "Year" column using h2o.hist()
+summary(airlines.hex)
+
+h2o.hist(airlines.hex$Year)
+
+
+## Scatter plot of airlines dataset examining the relationship between the "Distance" and "AirTime" columns
+scatter_plot(data = airlines.hex, x = "Distance", y = "AirTime", max_points = 10000)
+
+
+## Use h2o.group_by to calcualte the flights in a given month
+
+
+## Use as.factor to change the "Year," "Month," "DayOfWeek," and "Cancelled" columns to factors
+airlines.hex$Year      <- as.factor(airlines.hex$Year)
+airlines.hex$Month     <- as.factor(airlines.hex$Month)
+airlines.hex$DayOfWeek <- as.factor(airlines.hex$DayOfWeek)
+airlines.hex$Cancelled <- as.factor(airlines.hex$Cancelled)
+
+## Calculate and plot travel timef
+hour1 <- airlines.hex$CRSArrTime %/% 100
+mins1 <- airlines.hex$CRSArrTime %% 100
+arrTime <- hour1*60+mins1
+
+hour2 <- airlines.hex$CRSDepTime %/% 100
+mins2 <- airlines.hex$CRSDepTime %% 100
+depTime <- hour2*60+mins2
+
+
+
+
+## Impute missing travel times by the "Origin" and "Dest" columns and re-plot. 
+
+
+## Create test/train split
+
+
+## Set predictor and response variables
+myY <- "IsDepDelayed"
+
+
+
+## Simple GLM and GBM models - Predict Delays
+
+
+
+## Get summary of models
+
+
+## Get variable importances for both models
+
+
