updating the way xl tests print time

R/tests/testdir_hdfs_xlarge/runit_DL_186KRows_3.2KCols_xlarge.R

@@ -24,7 +24,8 @@ conn <- h2o.init(ip=myIP, port=myPort, startH2O = FALSE)
 # Parameters for the test.
 #----------------------------------------------------------------------
 parse_time <- system.time(data.hex <- h2o.importFile(conn, "/mnt/0xcustomer-datasets/c25/df_h2o.csv", header = T))
-paste("Time it took to parse", parse_time[[1]])
+print("Time it took to parse")
+print(parse_time)
 
 colNames = {}
 for(col in names(data.hex)) {
@@ -42,7 +43,8 @@ myX = setdiff(names(data.hex), myY)
 #Deep Learning
 dl_time <- system.time(data1.dl <- h2o.deeplearning(x=myX, y=myY, data=data.hex,
   epochs=.1, hidden=c(5,5)))
-paste("Time it took to build DL ", dl_time[[1]])
+print("Time it took to build DL ")
+print(dl_time)
 data1.dl
 
 PASS_BANNER()

R/tests/testdir_hdfs_xlarge/runit_DL_1MRows_2.2KCols_xlarge.R

@@ -26,7 +26,8 @@ hdfs_data_file = "/datasets/1Mx2.2k.csv"
 
 url <- sprintf("hdfs://%s%s", hdfs_name_node, hdfs_data_file)
 parse_time <- system.time(data.hex <- h2o.importFile(conn, url))
-paste("Time it took to parse", parse_time[[1]])
+print("Time it took to parse")
+print(parse_time)
 
 response=1 #1:1000 imbalance
 predictors=c(3:ncol(data.hex))
@@ -36,7 +37,8 @@ predictors=c(3:ncol(data.hex))
 dl_time <- system.time(mdl.dl <- h2o.deeplearning(x=predictors, y=response,
   data=data.hex, replicate_training_data=FALSE, epochs=.1, hidden=c(5,5)))
 mdl.dl
-paste("Time it took to build DL ", dl_time[[1]])
+print("Time it took to build DL ")
+print(dl_time)
 
 PASS_BANNER()
 

R/tests/testdir_hdfs_xlarge/runit_DL_airlines_billion_xlarge.R

@@ -27,7 +27,8 @@ hdfs_data_file = "/datasets/airlinesbillion.csv"
 heading("Testing single file importHDFS")
 url <- sprintf("hdfs://%s%s", hdfs_name_node, hdfs_data_file)
 parse_time <- system.time(data.hex <- h2o.importFile(conn, url))
-paste("Time it took to parse", parse_time[[1]])
+print("Time it took to parse")
+print(parse_time)
 
 data1.hex <- data.hex
 
@@ -52,6 +53,7 @@ dl_time <- system.time(data1.dl <- h2o.deeplearning(x=myX, y=myY,
   data=data.train, validation=data.valid, replicate_training_data=FALSE,
   epochs=.1, hidden=c(5,5)))
 data1.dl
-paste("Time it took to build DL ", dl_time[[1]])
+print("Time it took to build DL ")
+print(dl_time)
 
 PASS_BANNER()

R/tests/testdir_hdfs_xlarge/runit_GBM_15MRows_2.2KCols_xlarge.R

@@ -26,7 +26,8 @@ hdfs_data_file = "/datasets/15Mx2.2k.csv"
 
 url <- sprintf("hdfs://%s%s", hdfs_name_node, hdfs_data_file)
 parse_time <- system.time(data.hex <- h2o.importFile(conn, url))
-paste("Time it took to parse", parse_time[[1]])
+print("Time it took to parse")
+print(parse_time)
 
 response=1 #1:1000 imbalance
 predictors=c(3:ncol(data.hex))
@@ -35,7 +36,8 @@ predictors=c(3:ncol(data.hex))
 # Gradient Boosted Trees
 gbm_time <- system.time(mdl.gbm <- h2o.gbm(x=predictors, y=response, data=data.hex, distribution = "bernoulli"))
 mdl.gbm
-paste("Time it took to build GBM ", gbm_time[[1]])
+print("Time it took to build DL ")
+print(gbm_time)
 
 PASS_BANNER()
 

R/tests/testdir_hdfs_xlarge/runit_GBM_186KRows_3.2KCols_xlarge.R

@@ -24,7 +24,8 @@ conn <- h2o.init(ip=myIP, port=myPort, startH2O = FALSE)
 # Parameters for the test.
 #----------------------------------------------------------------------
 parse_time <- system.time(data.hex <- h2o.importFile(conn, "/mnt/0xcustomer-datasets/c25/df_h2o.csv", header = T))
-paste("Time it took to parse", parse_time[[1]])
+print("Time it took to parse")
+print(parse_time)
 
 colNames = {}
 for(col in names(data.hex)) {
@@ -42,7 +43,8 @@ myX = setdiff(names(data.hex), myY)
 #GBM on original dataset
 gbm_time <- system.time(data1.gbm <-  h2o.gbm(x = myX, y = myY, data = data.hex,
   n.trees = 10, interaction.depth = 5, distribution = "multinomial"))
-paste("Time it took to build GBM ", gbm_time[[1]])
+print("Time it took to build DL ")
+print(gbm_time)
 data1.gbm
 
 PASS_BANNER()

R/tests/testdir_hdfs_xlarge/runit_GBM_1MRows_2.2KCols_xlarge.R

@@ -26,7 +26,8 @@ hdfs_data_file = "/datasets/1Mx2.2k.csv"
 
 url <- sprintf("hdfs://%s%s", hdfs_name_node, hdfs_data_file)
 parse_time <- system.time(data.hex <- h2o.importFile(conn, url))
-paste("Time it took to parse", parse_time[[1]])
+print("Time it took to parse")
+print(parse_time)
 
 response=1 #1:1000 imbalance
 predictors=c(3:ncol(data.hex))
@@ -36,7 +37,8 @@ predictors=c(3:ncol(data.hex))
 gbm_time <- system.time(mdl.gbm <- h2o.gbm(x=predictors, y=response,
   data=data.hex, distribution = "bernoulli"))
 mdl.gbm
-paste("Time it took to build GBM ", gbm_time[[1]])
+print("Time it took to build DL ")
+print(gbm_time)
 
 PASS_BANNER()
 

R/tests/testdir_hdfs_xlarge/runit_GBM_376KRows_6KCols_xlarge.R

@@ -25,7 +25,8 @@ h2o.ls(conn)
 # Parameters for the test.
 #----------------------------------------------------------------------
 parse_time <- system.time(data.hex <- h2o.importFile(conn, "/mnt/0xcustomer-datasets/c28/mr_output.tsv.sorted.gz"))
-paste("Time it took to parse", parse_time[[1]])
+print("Time it took to parse")
+print(parse_time)
 
 dim(data.hex)
 
@@ -36,7 +37,8 @@ valid = data.hex[s > 0.8,]
 #GBM model
 gbm_time <- system.time(model.gbm <- h2o.gbm(x = 3:(ncol(train)), y = 2,
   data = train, validation=valid, n.trees=10, interaction.depth=5))
-paste("Time it took to build GBM ", gbm_time[[1]])
+print("Time it took to build DL ")
+print(gbm_time)
 model.gbm
 
 pred = h2o.predict(model.gbm, valid)

R/tests/testdir_hdfs_xlarge/runit_GBM_AUTO_airlines_billion_xlarge.R

@@ -27,7 +27,8 @@ hdfs_data_file = "/datasets/airlinesbillion.csv"
 heading("Testing single file importHDFS")
 url <- sprintf("hdfs://%s%s", hdfs_name_node, hdfs_data_file)
 parse_time <- system.time(data.hex <- h2o.importFile(conn, url))
-paste("Time it took to parse", parse_time[[1]])
+print("Time it took to parse")
+print(parse_time)
 
 data1.hex <- data.hex
 
@@ -51,12 +52,14 @@ gbm_10tree_time <- system.time(data1.gbm <- h2o.gbm(x = myX, y = myY,
   data = data.train, validation=data.valid, n.trees = 10, interaction.depth = 5,
   distribution = "AUTO"))
 data1.gbm
-paste("Time it took to build GBM ", gbm_10tree_time[[1]])
+print("Time it took to build DL ")
+print(gbm_10tree_time)
 
 gbm_50tree_time <- system.time(data2.gbm <- h2o.gbm(x = myX, y = myY,
   data = data.train, validation=data.valid, n.trees = 50, interaction.depth = 5,
   distribution = "AUTO"))
 data2.gbm
-paste("Time it took to build GBM ", gbm_50tree_time[[1]])
+print("Time it took to build DL ")
+print(gbm_50tree_time)
 
 PASS_BANNER()

R/tests/testdir_hdfs_xlarge/runit_GBM_Bernoulli_airlines_billion_xlarge.R

@@ -27,7 +27,8 @@ hdfs_data_file = "/datasets/airlinesbillion.csv"
 heading("Testing single file importHDFS")
 url <- sprintf("hdfs://%s%s", hdfs_name_node, hdfs_data_file)
 parse_time <- system.time(data.hex <- h2o.importFile(conn, url))
-paste("Time it took to parse", parse_time[[1]])
+print("Time it took to parse")
+print(parse_time)
 
 data1.hex <- data.hex
 
@@ -51,6 +52,7 @@ gbm_10tree_time <- system.time(data1.gbm <- h2o.gbm(x = myX, y = myY,
   data = data.train, validation=data.valid, n.trees = 10, interaction.depth = 5,
   distribution = "bernoulli"))
 data1.gbm
-paste("Time it took to build GBM ", gbm_10tree_time[[1]])
+print("Time it took to build DL ")
+print(gbm_10tree_time)
 
 PASS_BANNER()

R/tests/testdir_hdfs_xlarge/runit_GBM_Multinomial_airlines_billion_xlarge.R

@@ -27,7 +27,8 @@ hdfs_data_file = "/datasets/airlinesbillion.csv"
 heading("Testing single file importHDFS")
 url <- sprintf("hdfs://%s%s", hdfs_name_node, hdfs_data_file)
 parse_time <- system.time(data.hex <- h2o.importFile(conn, url))
-paste("Time it took to parse", parse_time[[1]])
+print("Time it took to parse")
+print(parse_time)
 
 data1.hex <- data.hex
 
@@ -51,6 +52,7 @@ gbm_10tree_time <- system.time(data1.gbm <- h2o.gbm(x = myX, y = myY,
   data = data.train, validation=data.valid, n.trees = 10, interaction.depth = 5,
   distribution = "multinomial"))
 data1.gbm
-paste("Time it took to build GBM ", gbm_10tree_time[[1]])
+print("Time it took to build DL ")
+print(gbm_10tree_time)
 
 PASS_BANNER()

R/tests/testdir_hdfs_xlarge/runit_GLM_15MRows_2.2KCols_xlarge.R

@@ -26,7 +26,8 @@ hdfs_data_file = "/datasets/15Mx2.2k.csv"
 
 url <- sprintf("hdfs://%s%s", hdfs_name_node, hdfs_data_file)
 parse_time <- system.time(data.hex <- h2o.importFile(conn, url))
-paste("Time it took to parse", parse_time[[1]])
+print("Time it took to parse")
+print(parse_time)
 
 response=1 #1:1000 imbalance
 predictors=c(3:ncol(data.hex))
@@ -36,7 +37,8 @@ predictors=c(3:ncol(data.hex))
 glm_time <- system.time(mdl.glm <- h2o.glm(x=predictors, y=response,
   data=data.hex, family = "binomial"))
 mdl.glm
-paste("Time it took to build GLM ", glm_time[[1]])
+print("Time it took to build DL ")
+print(glm_time)
 
 PASS_BANNER()
 

R/tests/testdir_hdfs_xlarge/runit_GLM_186KRows_3.2KCols_xlarge.R

@@ -24,7 +24,8 @@ conn <- h2o.init(ip=myIP, port=myPort, startH2O = FALSE)
 # Parameters for the test.
 #----------------------------------------------------------------------
 parse_time <- system.time(data.hex <- h2o.importFile(conn, "/mnt/0xcustomer-datasets/c25/df_h2o.csv", header = T))
-paste("Time it took to parse", parse_time[[1]])
+print("Time it took to parse")
+print(parse_time)
 
 colNames = {}
 for(col in names(data.hex)) {
@@ -43,6 +44,7 @@ myX = setdiff(names(data.hex), myY)
 glm_time <- system.time(data1.glm <- h2o.glm(x=myX, y=myY, data = data.hex,
   family="gaussian"))
 data1.glm
-paste("Time it took to build GLM ", glm_time[[1]])
+print("Time it took to build DL ")
+print(glm_time)
 
 PASS_BANNER()

R/tests/testdir_hdfs_xlarge/runit_GLM_1MRows_2.2KCols_xlarge.R

@@ -26,7 +26,8 @@ hdfs_data_file = "/datasets/1Mx2.2k.csv"
 
 url <- sprintf("hdfs://%s%s", hdfs_name_node, hdfs_data_file)
 parse_time <- system.time(data.hex <- h2o.importFile(conn, url))
-paste("Time it took to parse", parse_time[[1]])
+print("Time it took to parse")
+print(parse_time)
 
 response=1 #1:1000 imbalance
 predictors=c(3:ncol(data.hex))
@@ -36,7 +37,8 @@ predictors=c(3:ncol(data.hex))
 glm_time <- system.time(mdl.glm <- h2o.glm(x=predictors, y=response,
   data=data.hex, family = "binomial"))
 mdl.glm
-paste("Time it took to build GLM ", glm_time[[1]])
+print("Time it took to build DL ")
+print(glm_time)
 
 PASS_BANNER()
 

R/tests/testdir_hdfs_xlarge/runit_GLM_376KRows_6KCols_xlarge.R

@@ -25,7 +25,8 @@ h2o.ls(conn)
 # Parameters for the test.
 #----------------------------------------------------------------------
 parse_time <- system.time(data.hex <- h2o.importFile(conn, "/mnt/0xcustomer-datasets/c28/mr_output.tsv.sorted.gz"))
-paste("Time it took to parse", parse_time[[1]])
+print("Time it took to parse")
+print(parse_time)
 
 dim(data.hex)
 
@@ -36,7 +37,8 @@ valid = data.hex[s > 0.8,]
 #GLM Model
 glm_time <- system.time(model.glm <- h2o.glm(x = 3:(ncol(train)), y = 6,
   data = train, validation=valid, family = "binomial"))
-paste("Time it took to build GLM ", glm_time[[1]])
+print("Time it took to build DL ")
+print(glm_time)
 model.glm
 
 pred = h2o.predict(model.glm, valid)

R/tests/testdir_hdfs_xlarge/runit_GLM_airlines_billion_xlarge.R

@@ -27,7 +27,8 @@ hdfs_data_file = "/datasets/airlinesbillion.csv"
 heading("Testing single file importHDFS")
 url <- sprintf("hdfs://%s%s", hdfs_name_node, hdfs_data_file)
 parse_time <- system.time(data.hex <- h2o.importFile(conn, url))
-paste("Time it took to parse", parse_time[[1]])
+print("Time it took to parse")
+print(parse_time)
 
 data1.hex <- data.hex
 
@@ -53,6 +54,7 @@ myX = c("C20", "C21", "C22", "C23", "C24", "C25", "C26", "C27", "C28", "C29")
 #glm_irlsm_time <- system.time(data_irlsm.glm <- h2o.glm(x = myX, y = myY, data = data.train, validation=data.valid, family = "gaussian", solver = "IRLSM"))
 glm_time <- system.time(data.glm <- h2o.glm(x = myX, y = myY, data = data.train, family = "gaussian"))
 data.glm
-paste("Time it took to build GLM ", glm_time[[1]])
+print("Time it took to build DL ")
+print(glm_time)
 
 PASS_BANNER()

R/tests/testdir_hdfs_xlarge/runit_RF_15MRows_2.2KCols_xlarge.R

@@ -26,7 +26,8 @@ hdfs_data_file = "/datasets/15Mx2.2k.csv"
 
 url <- sprintf("hdfs://%s%s", hdfs_name_node, hdfs_data_file)
 parse_time <- system.time(data.hex <- h2o.importFile(conn, url))
-paste("Time it took to parse", parse_time[[1]])
+print("Time it took to parse")
+print(parse_time)
 
 response=1 #1:1000 imbalance
 predictors=c(3:ncol(data.hex))
@@ -36,7 +37,8 @@ predictors=c(3:ncol(data.hex))
 rf_time <- system.time(mdl.rf <- h2o.randomForest(x=predictors, y=response,
   data=data.hex, ntree=10, depth=5))
 mdl.rf
-paste("Time it took to build RF ", rf_time[[1]])
+print("Time it took to build DL ")
+print(rf_time)
 
 PASS_BANNER()
 

R/tests/testdir_hdfs_xlarge/runit_RF_1MRows_2.2KCols_xlarge.R

@@ -26,7 +26,8 @@ hdfs_data_file = "/datasets/1Mx2.2k.csv"
 
 url <- sprintf("hdfs://%s%s", hdfs_name_node, hdfs_data_file)
 parse_time <- system.time(data.hex <- h2o.importFile(conn, url))
-paste("Time it took to parse", parse_time[[1]])
+print("Time it took to parse")
+print(parse_time)
 
 response=1 #1:1000 imbalance
 predictors=c(3:ncol(data.hex))
@@ -36,7 +37,8 @@ predictors=c(3:ncol(data.hex))
 rf_time <- system.time(mdl.rf <- h2o.randomForest(x=predictors, y=response,
   data=data.hex, n.tree=10, interaction.depth=5))
 mdl.rf
-paste("Time it took to build RF ", rf_time[[1]])
+print("Time it took to build DL ")
+print(rf_time)
 
 PASS_BANNER()
 

R/tests/testdir_hdfs_xlarge/runit_RF_airlines_billion_xlarge.R

@@ -27,7 +27,8 @@ hdfs_data_file = "/datasets/airlinesbillion.csv"
 heading("Testing single file importHDFS")
 url <- sprintf("hdfs://%s%s", hdfs_name_node, hdfs_data_file)
 parse_time <- system.time(data.hex <- h2o.importFile(conn, url))
-paste("Time it took to parse", parse_time[[1]])
+print("Time it took to parse")
+print(parse_time)
 
 data1.hex <- data.hex
 
@@ -53,6 +54,7 @@ rf_time <- system.time(data1.rf <- h2o.randomForest(x = myX, y = myY,
   data = data.train, validation=data.valid, ntree = 10, depth = 5,
   type = "BigData"))
 data1.rf
-paste("Time it took to build RF ", rf_time[[1]])
+print("Time it took to build DL ")
+print(rf_time)
 
 PASS_BANNER()