Minor test tweaks to make it easier to demo offline.

R/tests/testdir_demos/runit_demo_VI_all_algos.R

@@ -13,7 +13,13 @@ test <- function(h) {
 # Parse data into H2O
 print("Parsing data into H2O")
 # From an h2o git workspace.
-data.hex = h2o.importFile(h, normalizePath(locate("smalldata/bank-additional-full.csv")), key="data.hex")
+if (FALSE) {
+  h = h2o.init()
+  data.hex = h2o.importFile(h, "/Users/tomk/0xdata/ws/h2o/smalldata/bank-additional-full.csv", key="data.hex")
+}
+else {
+  data.hex = h2o.importFile(h, normalizePath(locate("smalldata/bank-additional-full.csv")), key="data.hex")
+}
 # Or directly from github.
 # data.hex = h2o.importFile(h, path = "https://raw.github.com/0xdata/h2o/master/smalldata/bank-additional-full.csv", key="data.hex")
 
@@ -31,7 +37,7 @@ myY="y"
 
 # Run GBM with variable importance
 my.gbm <- h2o.gbm(x = myX, y = myY, distribution = "bernoulli", data = data.hex, n.trees =100,
-                  interaction.depth = 2, shrinkage = 0.01, importance = T) 
+                  interaction.depth = 2, shrinkage = 0.01, importance = T)
 
 # Access Variable Importance from the built model
 gbm.VI = my.gbm@model$varimp
@@ -73,18 +79,18 @@ my.glm = h2o.glm(x=myX, y=myY, data=data.hex, family="binomial",standardize=T,us
 # Select the best model picked by glm
 best_model = my.glm@best_model
 
-# Get the normalized coefficients of the best model 
-n_coeff = abs(my.glm@models[[best_model]]@model$normalized_coefficients)  
+# Get the normalized coefficients of the best model
+n_coeff = abs(my.glm@models[[best_model]]@model$normalized_coefficients)
 
 # Access Variable Importance by removing the intercept term
-VI = abs(n_coeff[-length(n_coeff)])                                     
+VI = abs(n_coeff[-length(n_coeff)])
 
 glm.VI = VI[order(VI,decreasing=T)]
 print("Variable importance from GLM")
 print(glm.VI)
 
 # Plot variable importance from glm
-barplot(glm.VI[1:20],las=2,main="VI from GLM") 
+barplot(glm.VI[1:20],las=2,main="VI from GLM")
 
 #--------------------------------------------------
 # Run deeplearning with variable importance

R/tests/testdir_demos/runit_demo_tk_cm_roc.R

@@ -15,6 +15,8 @@ if (TRUE) {
 
   if (FALSE) {
       setwd("/Users/tomk/0xdata/ws/h2o/R/tests/testdir_demos")
+      filePath <- "/Users/tomk/0xdata/ws/h2o/smalldata/airlines/AirlinesTrain.csv.zip"
+      testFilePath <- "/Users/tomk/0xdata/ws/h2o/smalldata/airlines/AirlinesTest.csv.zip"
   }
 
   source('../findNSourceUtils.R')
@@ -23,8 +25,8 @@ if (TRUE) {
   testFilePath <- normalizePath(locate("smalldata/airlines/AirlinesTest.csv.zip"))
 } else {
   stop("need to hardcode ip and port")
-  # myIP = "127.0.0.1"
-  # myPort = 54321
+  myIP = "127.0.0.1"
+  myPort = 54321
 
   library(h2o)
   PASS_BANNER <- function() { cat("\nPASS\n\n") }
@@ -48,7 +50,7 @@ myX = c("Origin", "Dest", "Distance", "UniqueCarrier", "fMonth", "fDayofMonth",
 myY="IsDepDelayed"
 
 #gbm
-air.gbm = h2o.gbm(x = myX, y = myY, distribution = "multinomial", data = air.train, n.trees = 10, 
+air.gbm = h2o.gbm(x = myX, y = myY, distribution = "multinomial", data = air.train, n.trees = 10,
                   interaction.depth = 3, shrinkage = 0.01, n.bins = 100, validation = air.valid, importance = T)
 print(air.gbm@model)
 air.gbm@model$auc
@@ -62,7 +64,7 @@ air.test=h2o.importFile(conn,testFilePath,key="air.test")
 
 model_object=air.rf #air.glm air.rf air.dl
 
-#predicting on test file 
+#predicting on test file
 pred = h2o.predict(model_object,air.test)
 head(pred)
 
@@ -80,14 +82,27 @@ plot(perf,type="roc")
 
 PASS_BANNER()
 
-if (FALSE) {
-    h = h2o.init(ip="mr-0xb1", port=60024)
+BIGDATA = FALSE
+if (BIGDATA) {
+    h = h2o.init(ip="172.16.2.190", port=60024)
     df = h2o.importFile(h, "/home/tomk/airlines_all.csv")
     nrow(df)
     ncol(df)
     head(df)
+
+    s = h2o.runif(df)    # Useful when number of rows too large for R to handle
+    air.train = df[s <= 0.8,]
+    air.test = df[s > 0.8,]
+
     myX = c("Origin", "Dest", "Distance", "UniqueCarrier", "Month", "DayofMonth", "DayOfWeek")
     myY = "IsDepDelayed"
-    air.glm = h2o.glm(x = myX, y = myY, data = df, family = "binomial", nfolds = 10, alpha = 0.25, lambda = 0.001)
-    air.glm@model$confusion
+    air.glm = h2o.glm(x = myX, y = myY, data = air.train,
+                      family = "binomial", nfolds = 1, alpha = 0.25, lambda = 0.001)
+
+    pred = h2o.predict(air.glm, air.test)
+    dim(pred)
+    head(pred)
+    perf = h2o.performance(pred$YES,air.test$IsDepDelayed)
+    perf
+    plot(perf,type="roc")
 }

R/tests/testdir_demos/runit_demo_tk_steam.R

@@ -11,19 +11,20 @@
 if (TRUE) {
   # Set working directory so that the source() below works.
   setwd(normalizePath(dirname(R.utils::commandArgs(asValues=TRUE)$"f")))
-  
+
   if (FALSE) {
     setwd("/Users/tomk/0xdata/ws/h2o/R/tests/testdir_demos")
+    filePath <- "/Users/tomk/0xdata/ws/h2o/smalldata/airlines/allyears2k_headers.zip"
   }
-  
+
   source('../findNSourceUtils.R')
   options(echo=TRUE)
   filePath <- normalizePath(locate("smalldata/airlines/allyears2k_headers.zip"))
 } else {
   stop("need to hardcode ip and port")
-  # myIP = "127.0.0.1"
-  # myPort = 54321
-  
+  myIP = "127.0.0.1"
+  myPort = 54321
+
   library(h2o)
   PASS_BANNER <- function() { cat("\nPASS\n\n") }
   filePath <- "https://raw.github.com/0xdata/h2o/master/smalldata/airlines/allyears2k_headers.zip"