New demos for OML4R (from AskTOM Session from October 28, 2020) (#131)

marancibia · web-flow · commit 8e8dc2f28c0f · 2020-10-30T17:12:49.000-07:00
* New OML4Py Notebook demos and updates to OML4SQL Notebooks

New OML4Py Notebook demos and updates to OML4SQL Notebooks

* New Demos for OML4R

New Demos for Oracle Machine Learning for R
diff --git a/machine-learning/r/oml4r/oml4r-tour-0-introduction.r b/machine-learning/r/oml4r/oml4r-tour-0-introduction.r
@@ -0,0 +1,226 @@
+#########################################################################
+##
+## Oracle Machine Learning for R Tour
+## 
+## Copyright (c) 2020 Oracle Corporation 
+##
+## The Universal Permissive License (UPL), Version 1.0
+## 
+## https://oss.oracle.com/licenses/upl/
+##
+###########################################################################
+# In this Tour, we explore the different features of OML4R
+
+rm(list=ls())
+
+#-----------------------
+# TRANSPARENCY LAYER
+#-----------------------
+
+library(ORE)
+options(ore.warn.order=FALSE)
+ore.connect(user="rquser",
+            conn_string="ORCLPDB",
+            host="localhost",
+            password="rquser",
+            all=TRUE)
+
+#-- What tables are in the database schema we connected to?
+
+ore.ls()
+
+class(NARROW)
+colnames(NARROW)
+dim(NARROW)
+
+summary(NARROW[,1:5])
+
+#-- retrieve data from the database
+
+narrow <- ore.pull(NARROW)
+class(narrow)
+
+str(narrow)     # data.frame
+
+str(NARROW)     # ore.frame proxy object
+
+NARROW@dataQry  # underlying data query for proxy object
+
+#-- Column selection using standard R syntax
+
+colnames(ONTIME_S)
+dim(ONTIME_S)
+
+df <- ONTIME_S[,c("YEAR","DEST","ARRDELAY")]
+class(df)  # an ore.frame proxy object
+dim(df)
+
+head(df)
+head(ONTIME_S[,c(1,4,23)])  # project columns using column indexes
+head(ONTIME_S[,-(5:26)])    # exlcude columns using column indexes
+
+#-- Row and column filtering
+
+df1 <- df[df$ARRDELAY>20 | df$DEST=="BOS",1:3]
+head(df1,6)
+
+#-- Aggregation
+
+# How many flights per destination?
+
+aggdata <- aggregate(ONTIME_S$DEST, 
+                     by = list(ONTIME_S$DEST), 
+                     FUN = length)
+names(aggdata) <- c("Destination","FlightCnt")
+class(aggdata)
+head(aggdata)
+
+
+#-- Overloaded dplyr using OREdplyr on ore.frames
+
+library(OREdplyr)   # load OREdplyr explicitly to use
+
+select(ONTIME_S, YEAR, DEST, ARRDELAY, DEPDELAY) %>% head()   # select columns
+
+colnames(ONTIME_S)
+res <- select(ONTIME_S, -CANCELLED,-CANCELLATIONCODE, -DIVERTED) %>% head() # exclude columns
+colnames(res)
+
+dim(ONTIME_S)
+filter(ONTIME_S, MONTH == 1, DAYOFMONTH == 1) %>% dim()     # filter rows
+filter(ONTIME_S, DEPDELAY > 240) %>% dim()
+
+# Group mean arrival delay by airline
+tbl_avg <- ONTIME_S %>%
+  group_by(UNIQUECARRIER) %>%
+  summarise(avgArrDelay = round(mean(ARRDELAY, na.rm = TRUE), digits=2)) %>%
+  arrange(.$avgArrDelay)
+head(tbl_avg,10)
+tail(tbl_avg)
+
+#-- Join / merge data
+
+df1 <- data.frame(x1=1:5, y1=letters[1:5])  # create two data.frames
+df2 <- data.frame(x2=5:1, y2=letters[11:15])
+merge (df1, df2, by.x="x1", by.y="x2")      # merge the data.frames
+
+ore.drop(table="TEST_DF1")
+ore.drop(table="TEST_DF2")
+ore.create(df1, table="TEST_DF1")           # create tables from the same data.frames
+ore.create(df2, table="TEST_DF2")
+merge (TEST_DF1, TEST_DF2, by.x="x1", by.y="x2")  # merge the ore.frames
+
+# using OREdplyr
+res <- TEST_DF1 %>% left_join(TEST_DF2, by=c("x1"="x2"))   
+res
+
+#-- Overloaded graphics functions
+
+# Generate boxplot of airline flight delay by day of week
+delay <- ONTIME_S$ARRDELAY
+dayofweek <- ONTIME_S$DAYOFWEEK
+bd <- split(delay, dayofweek)
+boxplot(bd, notch = TRUE, col = "red", cex = 0.5,  # statistics computed in-database
+        outline = FALSE, axes = FALSE,
+        main = "Airline Flight Delay by Day of Week",
+        ylab = "Delay (minutes)", xlab = "Day of Week")
+axis(1, at=1:7, labels=c("Monday", "Tuesday", "Wednesday", "Thursday",
+                         "Friday", "Saturday", "Sunday"))
+axis(2)
+
+#---------------------------------------------
+# Machine Learning 
+#---------------------------------------------
+
+#-- Classification using ore.odmNB
+
+data(titanic3,package="PASWR")
+
+t3 <- ore.push(titanic3)  # create ore.frame proxy object as temporary table
+class(t3)   
+
+t3$survived <- ifelse(t3$survived == 1, "Yes", "No")  # recoding
+
+n.rows <- nrow(t3)
+set.seed(seed=6218945)
+random.sample <- sample(1:n.rows, ceiling(n.rows/2)) # generate sample indexes
+
+t3.train <- t3[random.sample,]                       # train/test sampling using row indexing
+t3.test  <- t3[setdiff(1:n.rows,random.sample),]
+
+class(t3.train)  # ore.frame proxy object
+
+priors <- c(0.4, 0.6)
+names(priors) <- c("Yes", "No")
+
+nb  <- ore.odmNB(survived ~ pclass+sex+age+fare+embarked, t3.train, class.priors=priors)
+
+nb.res  <- predict (nb, t3.test,"survived")
+
+head(nb.res,10)
+
+# Compute the confusion matrix in-database
+with(nb.res, table(survived,PREDICTION, dnn = c("Actual","Predicted")))
+
+#-----------------------
+# EMBEDDED R EXECUTION
+#-----------------------
+
+# Random Red Dots
+
+RandomRedDots <- function(numDots=100){
+  id <- 1:10
+  print(plot( 1:numDots, rnorm(numDots), pch = 21, 
+              bg = "red", cex = 2 ))
+  data.frame(id=id, val=id / 100)
+}
+
+RandomRedDots(100)
+
+dev.off()
+res <- NULL
+res <- ore.doEval(RandomRedDots, numDots=200)
+res
+
+ore.scriptDrop("RandomRedDots") 
+ore.scriptCreate("RandomRedDots",RandomRedDots)
+dev.off()
+ore.doEval(FUN.NAME="RandomRedDots")
+
+
+#-- Go to SQL Developer in script '~/OML4R/OML4R Vignettes.sql' and invoke function from SQL
+
+
+#-- Group Apply
+
+# Build one linear model per destination to predict arrival delay
+
+ONTIME_S$DEST <- substr(as.character(ONTIME_S$DEST),1,3)
+DAT <-   ONTIME_S[ONTIME_S$DEST %in% c("BOS","SFO","LAX","ORD","ATL","PHX","DEN"),]
+dim(DAT)
+
+modList <- ore.groupApply(X=DAT,
+                          INDEX=DAT$DEST,
+                          function(dat) {
+                            lm(ARRDELAY ~ DISTANCE + DEPDELAY, dat)
+                          })
+length(modList)
+summary(modList$BOS) # return model for BOS
+summary(modList$SFO) # return model for SFO
+
+# housekeeping
+
+rm(list=ls())
+dev.off()
+
+ore.drop(table="TEST_DF1")
+ore.drop(table="TEST_DF2")
+
+ore.scriptDrop("RandomRedDots") 
+
+ore.disconnect()
+
+################################################
+## End of Script
+################################################
+
diff --git a/machine-learning/r/oml4r/oml4r-tour-0-introduction.sql b/machine-learning/r/oml4r/oml4r-tour-0-introduction.sql
@@ -0,0 +1,54 @@
+--###########################################################################
+--##
+--## Oracle Machine Learning for R
+--##
+--## Demo Script for OML4R Tour
+--##
+--## Copyright (c) 2020 Oracle Corporation
+--##
+--## The Universal Permissive License (UPL), Version 1.0
+--## 
+--## https://oss.oracle.com/licenses/upl/
+--##
+--###########################################################################
+
+--# OML4R Tour
+
+-- Random Red Dots
+-- Generate images of random red dots and a simple data.frame
+begin
+  sys.rqScriptDrop('RandomRedDots2');
+  sys.rqScriptCreate('RandomRedDots2',
+ 'function(num_dots_1=100, num_dots_2=10){
+            id <- 1:10
+            plot( 1:num_dots_1, rnorm(num_dots_1), pch = 21, 
+                  bg = "red", cex = 2, main="Random Red Dots" )
+            plot( 1:num_dots_2, rnorm(num_dots_2), pch = 21, 
+                  bg = "red", cex = 2, main="Random Red Dots" )
+            data.frame(id=id, val=id / 100)
+            }');
+end;
+/
+
+-- Return image only as PNG BLOB, one per image per row
+-- Structured content not returned with PNG option
+select    ID, IMAGE
+from      table(rqEval( NULL,'PNG','RandomRedDots2'));
+
+-- Return structured data only by specifying table definition
+select    id, val
+from      table(rqEval( NULL,'select 1 id, 1 val from dual','RandomRedDots2'));
+
+-- Return structured and image content within XML string
+select    dbms_lob.substr( value, 4000, 1 ) 
+from      table(rqEval(NULL, 'XML', 'RandomRedDots2'));
+
+-- Pass arguments to change number of dots
+select    ID, IMAGE
+from      table(rqEval(cursor(select 500 "num_dots_1", 800 "num_dots_2" from dual),
+                       'PNG', 'RandomRedDots2'));
+  
+--#####################################################
+--##  End of Script
+--#####################################################
+
diff --git a/machine-learning/r/oml4r/oml4r-vignette-04-clustering.r b/machine-learning/r/oml4r/oml4r-vignette-04-clustering.r
@@ -93,7 +93,7 @@ tail(predict(km.mod1,X,type=c("class","raw"),supplemental.cols=c("x","y")),3)  #
 tail(predict(km.mod1,X,type="raw",supplemental.cols=c("x","y")),3)             # ask for only raw probabilities with supp data
 
 ###################################################
-# Use Oracle Orthoginal Partitioning Clustering 
+# Use Oracle Orthogonal Partitioning Clustering 
 # density-based algorithm
 ###################################################
 
