RODM_list_dbms_models: List Oracle Data Mining models

In RODM: R interface to Oracle Data Mining

Description

This function list all of the Oracle Data Mining models in the user's schema in the Oracle database.

Usage

RODM_list_dbms_models(database) 

Arguments

database

Database ODBC channel identifier returned from a call to RODM_open_dbms_connection

Details

This function list all of the Oracle Data Mining models in the user's schema in the database. For each model, this function returns the model name, mining function (type of operation), algorithm, date the model was created, time it took to build the model (in seconds), size of the model (in megabytes), and comments associated with the model (if any).

Value

List of the following information:

MODEL_NAME	Name of the model.
MINING_FUNCTION	Mining function used when building the model.
ALGORITHM	Algorithm used when building the model.
CREATION_DATE	Date the model was created.
BUILD_DURATION	Duration to build the model in seconds.
MODEL_SIZE	Size of the model in MB.
COMMENTS	Comments associated with the model, if any.

Author(s)

Pablo Tamayo [e-mail]

Ari Mozes [e-mail]

References

Oracle Data Mining Concepts 11g Release 1 (11.1) http://download.oracle.com/docs/cd/B28359_01/datamine.111/b28129/toc.htm

Oracle Data Mining Application Developer's Guide 11g Release 1 (11.1) http://download.oracle.com/docs/cd/B28359_01/datamine.111/b28131/toc.htm

Oracle Data Mining Administrator's Guide 11g Release 1 (11.1) http://download.oracle.com/docs/cd/B28359_01/datamine.111/b28130/toc.htm

Oracle Database PL/SQL Packages and Types Reference 11g Release 1 (11.1) http://download.oracle.com/docs/cd/B28359_01/appdev.111/b28419/d_datmin.htm#ARPLS192

Examples

## Not run: 
DB <- RODM_open_dbms_connection(dsn="orcl11g", uid= "rodm", pwd = "rodm")

data(titanic3, package="PASWR")                                             # Load survival data from Titanic
ds <- titanic3[,c("pclass", "survived", "sex", "age", "fare", "embarked")]  # Select subset of attributes
ds[,"survived"] <- ifelse(ds[,"survived"] == 1, "Yes", "No")                # Rename target values
n.rows <- length(ds[,1])                                                    # Number of rows
set.seed(seed=6218945)
random_sample <- sample(1:n.rows, ceiling(n.rows/2))   # Split dataset randomly in train/test subsets
titanic_train <- ds[random_sample,]                         # Training set
titanic_test <-  ds[setdiff(1:n.rows, random_sample),]      # Test set
RODM_create_dbms_table(DB, "titanic_train")   # Push the training table to the database
RODM_create_dbms_table(DB, "titanic_test")    # Push the testing table to the database

# Create an ODM Naive Bayes model
nb <- RODM_create_nb_model(
   database = DB,                     # Database ODBC channel identifier
   model_name = "titanic_nb_model",   # ODM model name
   data_table_name = "titanic_train", # (in quotes) Data frame or database table containing the input dataset
   target_column_name = "survived")   # Target column name in data_table_name		

# Create an ODM Attribute Importance model
ai <- RODM_create_ai_model(
   database = DB,                     # Database ODBC channel identifier
   model_name = "titanic_ai_model",   # ODM model name
   data_table_name = "titanic_train", # (in quotes) Data frame or database table containing the input dataset
   target_column_name = "survived")   # Target column name in data_table_name		

# List the models
mlist <- RODM_list_dbms_models(DB)
mlist

RODM_drop_model(DB, "titanic_nb_model")
RODM_drop_model(DB, "titanic_ai_model")
RODM_drop_dbms_table(DB, "titanic_train")
RODM_drop_dbms_table(DB, "titanic_test")

RODM_close_dbms_connection(DB)

## End(Not run)

RODM documentation built on May 2, 2019, 7:03 a.m.