4.2.7 Extensible R Algorithm Model
The ore.odmRAlg function creates an Extensible R algorithm model.
The Extensible R algorithm builds, scores, and views an R model using registered R scripts. It supports classification, regression, clustering, feature extraction, attribute importance, and association machine learning functions.
For information on the
ore.odmRAlg function arguments and for an example of using the function, call help(ore.odmRAlg).
Note:
The R extensibility feature is available for on-premises only.Settings for an Extensible R Algorithm Model
The following table lists settings that apply to Extensible R Algorithm models.
Table 4-7 Extensible R Algorithm Model Settings
| Setting Name | Setting Value | Description |
|---|---|---|
|
|
|
Specifies the name of an existing registered R script for R algorithm mining model build function. The R script defines an R function for the first input argument for training data and returns an R model object. For Clustering and Feature Extraction mining function model build, the R attributes dm$nclus and dm$nfeat must be set on the R model to indicate the number of clusters and features respectively.
The |
|
|
SELECT value param_name, ...FROM DUAL |
Specifies a list of numeric and string scalar for optional input parameters of the model build function. |
|
|
|
Specifies the name of an existing registered R script to score data. The script returns a |
|
|
|
Specifies the name of an existing registered R script for R algorithm that computes the weight (contribution) for each attribute in scoring. The script returns a |
RALG_DETAILS_FUNCTION |
R_DETAILS_FUNCTION_SCRIPT_NAME |
Specifies the name of an existing registered R script for R algorithm that produces the model information. This setting is required to generate a model view. |
RALG_DETAILS_FORMAT |
SELECT type_value column_name,FROM DUAL |
Specifies the SELECT query for the list of numeric and string scalars for the output column type and the column name of the generated model view. This setting is required to generate a model view.
|
Example 4-14 Using the ore.odmRAlg Function
library(OREembed)
digits <- getOption("digits")
options(digits = 5L)
IRIS <- ore.push(iris)
# Regression with glm
ore.scriptCreate("glm_build",
function(data, form, family)
glm(formula = form, data = data, family = family))
ore.scriptCreate("glm_score",
function(mod, data)
{ res <- predict(mod, newdata = data);
data.frame(res) })
ore.scriptCreate("glm_detail", function(mod)
data.frame(name=names(mod$coefficients),
coef=mod$coefficients))
ore.scriptList(name = "glm_build")
ore.scriptList(name = "glm_score")
ore.scriptList(name = "glm_detail")
ralg.glm <- ore.odmRAlg(IRIS, mining.function = "regression",
formula = c(form="Sepal.Length ~ ."),
build.function = "glm_build",
build.parameter = list(family="gaussian"),
score.function = "glm_score",
detail.function = "glm_detail",
detail.value = data.frame(name="a", coef=1))
summary(ralg.glm)
predict(ralg.glm, newdata = head(IRIS), supplemental.cols = "Sepal.Length")
ore.scriptDrop(name = "glm_build")
ore.scriptDrop(name = "glm_score")
ore.scriptDrop(name = "glm_detail")
# Classification with nnet
ore.scriptCreate("nnet_build",
function(dat, form, sz){
require(nnet);
set.seed(1234);
nnet(formula = formula(form), data = dat,
size = sz, linout = TRUE, trace = FALSE);
},
overwrite = TRUE)
ore.scriptCreate("nnet_detail", function(mod)
data.frame(conn = mod$conn, wts = mod$wts),
overwrite = TRUE)
ore.scriptCreate("nnet_score",
function(mod, data) {
require(nnet);
res <- data.frame(predict(mod, newdata = data));
names(res) <- sort(mod$lev); res
})
ralg.nnet <- ore.odmRAlg(IRIS, mining.function = "classification",
formula = c(form="Species ~ ."),
build.function = "nnet_build",
build.parameter = list(sz=2),
score.function = "nnet_score",
detail.function = "nnet_detail",
detail.value = data.frame(conn=1, wts =1))
summary(ralg.nnet)
predict(ralg.nnet, newdata = head(IRIS), supplemental.cols = "Species")
ore.scriptDrop(name = "nnet_build")
ore.scriptDrop(name = "nnet_score")
ore.scriptDrop(name = "nnet_detail")
# Feature extraction with pca
# Feature extraction with pca
ore.scriptCreate("pca_build",
function(dat){
mod <- prcomp(dat, retx = FALSE)
attr(mod, "dm$nfeat") <- ncol(mod$rotation)
mod},
overwrite = TRUE)
ore.scriptCreate("pca_score",
function(mod, data) {
res <- predict(mod, data)
as.data.frame(res)},
overwrite=TRUE)
ore.scriptCreate("pca_detail",
function(mod) {
rotation_t <- t(mod$rotation)
data.frame(id = seq_along(rownames(rotation_t)),
rotation_t)},
overwrite = TRUE)
X <- IRIS[, -5L]
ralg.pca <- ore.odmRAlg(X,
mining.function = "feature_extraction",
formula = NULL,
build.function = "pca_build",
score.function = "pca_score",
detail.function = "pca_detail",
detail.value = data.frame(Feature.ID=1,
ore.pull(head(X,1L))))
summary(ralg.pca)
head(cbind(X, Pred = predict(ralg.pca, newdata = X)))
ore.scriptDrop(name = "pca_build")
ore.scriptDrop(name = "pca_score")
ore.scriptDrop(name = "pca_detail")
options(digits = digits)Listing for This Example
R> library(OREembed)
R>
R> digits <- getOption("digits")
R> options(digits = 5L)
R>
R> IRIS <- ore.push(iris)
R>
R> # Regression with glm
R> ore.scriptCreate("glm_build",
+ function(data, form, family)
+ glm(formula = form, data = data, family = family))
R>
R> ore.scriptCreate("glm_score",
+ function(mod, data)
+ { res <- predict(mod, newdata = data);
+ data.frame(res) })
R>
R> ore.scriptCreate("glm_detail", function(mod)
+ data.frame(name=names(mod$coefficients),
+ coef=mod$coefficients))
R>
R> ore.scriptList(name = "glm_build")
NAME SCRIPT
1 glm_build function (data, form, family) \nglm(formula = form, data = data, family = family)
R> ore.scriptList(name = "glm_score")
NAME SCRIPT
1 glm_score function (mod, data) \n{\n res <- predict(mod, newdata = data)\n data.frame(res)\n}
R> ore.scriptList(name = "glm_detail")
NAME SCRIPT
1 glm_detail function (mod) \ndata.frame(name = names(mod$coefficients), coef = mod$coefficients)
R>
R> ralg.glm <- ore.odmRAlg(IRIS, mining.function = "regression",
+ formula = c(form="Sepal.Length ~ ."),
+ build.function = "glm_build",
+ build.parameter = list(family="gaussian"),
+ score.function = "glm_score",
+ detail.function = "glm_detail",
+ detail.value = data.frame(name="a", coef=1))
R>
R> summary(ralg.glm)
Call:
ore.odmRAlg(data = IRIS, mining.function = "regression", formula = c(form = "Sepal.Length ~ ."),
build.function = "glm_build", build.parameter = list(family = "gaussian"),
score.function = "glm_score", detail.function = "glm_detail",
detail.value = data.frame(name = "a", coef = 1))
Settings:
value
odms.missing.value.treatment odms.missing.value.auto
odms.sampling odms.sampling.disable
prep.auto OFF
build.function OML_USER.glm_build
build.parameter select 'Sepal.Length ~ .' "form", 'gaussian' "family" from dual
details.format select cast('a' as varchar2(4000)) "name", 1 "coef" from dual
details.function OML_USER.glm_detail
score.function OML_USER.glm_score
name coef
1 (Intercept) 2.17127
2 Petal.Length 0.82924
3 Petal.Width -0.31516
4 Sepal.Width 0.49589
5 Speciesversicolor -0.72356
6 Speciesvirginica -1.02350
R> predict(ralg.glm, newdata = head(IRIS), supplemental.cols = "Sepal.Length")
Sepal.Length PREDICTION
1 5.1 5.0048
2 4.9 4.7568
3 4.7 4.7731
4 4.6 4.8894
5 5.0 5.0544
6 5.4 5.3889
R>
R> ore.scriptDrop(name = "glm_build")
R> ore.scriptDrop(name = "glm_score")
R> ore.scriptDrop(name = "glm_detail")
R>
R> # Classification with nnet
R> ore.scriptCreate("nnet_build",
+ function(dat, form, sz){
+ require(nnet);
+ set.seed(1234);
+ nnet(formula = formula(form), data = dat,
+ size = sz, linout = TRUE, trace = FALSE);
+ },
+ overwrite = TRUE)
R>
R> ore.scriptCreate("nnet_detail", function(mod)
+ data.frame(conn = mod$conn, wts = mod$wts),
+ overwrite = TRUE)
R>
R> ore.scriptCreate("nnet_score",
+ function(mod, data) {
+ require(nnet);
+ res <- data.frame(predict(mod, newdata = data));
+ names(res) <- sort(mod$lev); res
+ })
R>
R> ralg.nnet <- ore.odmRAlg(IRIS, mining.function = "classification",
+ formula = c(form="Species ~ ."),
+ build.function = "nnet_build",
+ build.parameter = list(sz=2),
+ score.function = "nnet_score",
+ detail.function = "nnet_detail",
+ detail.value = data.frame(conn=1, wts =1))
R>
R> summary(ralg.nnet)
Call:
ore.odmRAlg(data = IRIS, mining.function = "classification",
formula = c(form = "Species ~ ."), build.function = "nnet_build",
build.parameter = list(sz = 2), score.function = "nnet_score",
detail.function = "nnet_detail", detail.value = data.frame(conn = 1,
wts = 1))
Settings:
value
clas.weights.balanced OFF
odms.missing.value.treatment odms.missing.value.auto
odms.sampling odms.sampling.disable
prep.auto OFF
build.function OML_USER.nnet_build
build.parameter select 'Species ~ .' "form", 2 "sz" from dual
details.format select 1 "conn", 1 "wts" from dual
details.function OML_USER.nnet_detail
score.function OML_USER.nnet_score
conn wts
1 0 1.46775
2 1 -12.88542
3 2 -4.38886
4 3 9.98648
5 4 16.57056
6 0 0.97809
7 1 -0.51626
8 2 -0.94815
9 3 0.13692
10 4 0.35104
11 0 37.22475
12 5 -66.49123
13 6 70.81160
14 0 -4.50893
15 5 7.01611
16 6 20.88774
17 0 -32.15127
18 5 58.92088
19 6 -91.96989
R> predict(ralg.nnet, newdata = head(IRIS), supplemental.cols = "Species")
Species PREDICTION PROBABILITY
1 setosa setosa 0.99999
2 setosa setosa 0.99998
3 setosa setosa 0.99999
4 setosa setosa 0.99998
5 setosa setosa 1.00000
6 setosa setosa 0.99999
R>
R> ore.scriptDrop(name = "nnet_build")
R> ore.scriptDrop(name = "nnet_score")
R> ore.scriptDrop(name = "nnet_detail")
R>
R> ore.scriptCreate("pca_build",
+ function(dat){
+ mod <- prcomp(dat, retx = FALSE)
+ attr(mod, "dm$nfeat") <- ncol(mod$rotation)
+ mod},
+ overwrite = TRUE)
R>
R> ore.scriptCreate("pca_score",
+ function(mod, data) {
+ res <- predict(mod, data)
+ as.data.frame(res)},
+ overwrite=TRUE)
R>
R> ore.scriptCreate("pca_detail",
+ function(mod) {
+ rotation_t <- t(mod$rotation)
+ data.frame(id = seq_along(rownames(rotation_t)),
+ rotation_t)},
+ overwrite = TRUE)
R>
R> X <- IRIS[, -5L]
R> ralg.pca <- ore.odmRAlg(X,
+ mining.function = "feature_extraction",
+ formula = NULL,
+ build.function = "pca_build",
+ score.function = "pca_score",
+ detail.function = "pca_detail",
+ detail.value = data.frame(Feature.ID=1,
+ ore.pull(head(X,1L))))
R>
R> summary(ralg.pca)
Call:
ore.odmRAlg(data = X, mining.function = "feature_extraction",
formula = NULL, build.function = "pca_build", score.function = "pca_score",
detail.function = "pca_detail", detail.value = data.frame(Feature.ID = 1,
ore.pull(head(X, 1L))))
Settings:
value
odms.missing.value.treatment odms.missing.value.auto
odms.sampling odms.sampling.disable
prep.auto OFF
build.function OML_USER.pca_build
details.format select 1 "Feature.ID", 5.1 "Sepal.Length", 3.5 "Sepal.Width", 1.4 "Petal.Length", 0.2 "Petal.Width" from dual
details.function OML_USER.pca_detail
score.function OML_USER.pca_score
Feature.ID Sepal.Length Sepal.Width Petal.Length Petal.Width
1 1 0.856671 0.358289 0.36139 -0.084523
2 2 -0.173373 -0.075481 0.65659 0.730161
3 3 0.076236 0.545831 -0.58203 0.597911
4 4 0.479839 -0.753657 -0.31549 0.319723
R> head(cbind(X, Pred = predict(ralg.pca, newdata = X)))
Sepal.Length Sepal.Width Petal.Length Petal.Width FEATURE_ID
1 5.1 3.5 1.4 0.2 2
2 4.9 3.0 1.4 0.2 4
3 4.7 3.2 1.3 0.2 3
4 4.6 3.1 1.5 0.2 4
5 5.0 3.6 1.4 0.2 2
6 5.4 3.9 1.7 0.4 2
R>
R> ore.scriptDrop(name = "pca_build")
R> ore.scriptDrop(name = "pca_score")
R> ore.scriptDrop(name = "pca_detail")
R>
R> options(digits = digits)Parent topic: Build Oracle Machine Learning for SQL Models