These release notes contain important information about Release 1.5.1 of OML4R.
Oracle is rebranding the suite of products and components that support machine learning with Oracle Database and Big Data. This technology is now known as Oracle Machine Learning (OML).
The OML application programming interface (API) for R, previously under the name Oracle R Enterprise, is now named Oracle Machine Learning for R (OML4R). The package, class, and function names are not rebranded. They remain
ore.connect, and so on.
The OML application programming interfaces (APIs) for SQL include PL/SQL packages, SQL functions, and data dictionary views. Using these APIs is described in publications, previously under the name Oracle Data Mining, that are now named Oracle Machine Learning for SQL (OML4SQL). The PL/SQL package and database view names are not rebranded. They remain
ALL_MINING_MODELS, and so on.
1.1 New Features in Oracle Machine Learning for R 1.5.1
OML4R 1.5.1 has some new features that are compatible with Oracle Database Release 220.127.116.11 and earlier, and other new features compatible with Oracle Database Release 18.104.22.168.
1.1.1 New Features for Oracle Database Release 18c and Later
Beginning with Oracle Database 18c, the Oracle Machine Learning for R Server installation script
rqcfg.sql replaces the
server installation script.
rqcfg.sql script is part of Oracle Database.
1.1.2 New Features for Oracle Database Release 22.214.171.124
Oracle Machine Learning for R 1.5.1 has the new graph analytics package
OAAgraph and has new functions in the OML4R package
126.96.36.199 OAAgraph Package
OAAgraph package provides an R interface to the powerful Oracle Spatial and Graph Property Graph In-Memory Analyst (PGX) for use in combination with Oracle Machine Learning for R and database tables.
The package provides a single, unified interface supporting the complementary use of machine learning and graph analytics technologies.
Graph analytics use graph representations of data, in which data entities are nodes and relationships are edges. Machine learning produces models that identify patterns in data for both descriptive and predictive analytics. Together, these technologies complement and augment one another.
188.8.131.52 New Features of the OREdm Package
OREdm package has some new functions that use in-database Oracle Machine Learning for SQL algorithms to create models in the database and new arguments for some functions.
New Functions in the OREdm Package
New functions in the
OREdm OML4SQL package that use in-database algorithms are the following:
ore.odmEM, Expectation Maximization models
ore.odmESA, Explicit Semantic Analysis models
ore.odmRAlg, Extensible R Algorithm models
ore.odmSVD, Singular Value Decomposition models
ore.odmRAlg enables users to use registered R scripts to create models that use the OML4SQL in-database model framework.
Other new functions are the following:
partitions, which returns partition names from a partitioned model
settings, which returns the OML4SQL parameter settings used to build the model.
New Arguments to Some Functions for OML4SQL Model Build Configuration and Text Processing
The new arguments for some of the machine learning model functions are:
odm.setting value is a list that specifies OML4SQL parameter settings. Both OML4SQL global and algorithm-specific parameters can be specified to configure the model build. Some new features are enabled through the parameter settings. For example, you can use this argument to specify the creation of a partitioned model, which is an ensemble model that consists of multiple sub-models. When you specify the parameter
ODMS_PARTITION_COLUMNS and the names of the columns by which to partition the input data, the function returns a model with a sub-model for each partition. The partitions are based on the unique values found in the columns.
Partitioned models can automate scoring by allowing you to reference the top-level model only, which causes the proper sub-model to be chosen based on the values of the partitioned column or columns for each row of data to be scored.
With this argument, you can specify Oracle Text attribute-specific settings. You specify the columns that should be treated as text and the type of text transformation to apply.
This argument applies to the following functions:
ore.odmESA, Explicit Semantic Analysis
ore.odmGLM, Generalized Linear Models
ore.odmNMF, Non-Negative Matrix Factorization
ore.odmSVD, Singular Value Decomposition
ore.odmSVM, Support Vector Machines
Note:To create an Oracle Text policy, the user must have the
1.1.3 New Features for Oracle Database Release 184.108.40.206 and Earlier
Oracle Machine Learning for R 1.5.1 has the new
OREdplyr package, improved performance of row ordering in
ore.frame objects, and faster loading of the OML4R packages.
OREdplyr Package for Data Manipulation
dplyr package provides a grammar of data manipulation functions for
data.frame objects and
numeric objects. The new
OREdplyr package implements much of this functionality for
ore.numeric objects. This enables in-database execution of
dplyr functionality such as selecting, filtering, ordering, and grouping columns and rows, and joining, summarizing, sampling, and ranking rows.
1.1.4 Other Changes
Oracle Machine Learning for R Release 1.5.1 has the following other changes, which are in effect for Oracle Database 12c Release 220.127.116.11 and earlier releases.
Updated supporting packages
Requirement for R 3.3.0; as with earlier releases of OML4R, Oracle recommends that you use Oracle R Distribution
A new RPM for Oracle R Distribution, R-core-extra-3.3.0-1.el6.x86_64.rpm
R-3.3.0 depends on newer versions of several third party compression libraries and no longer contains bundled copies of them. This means that R 3.3.0 won't build against Linux 6 as is, because the native versions of these libraries are older than those that R-3.3.0 requires.
The R-core-extra RPM contains the required versions of these libraries and is provided as a convenience for users of Oracle Linux 6. Adding the location of the libraries in R-core-extra to
LD_LIBRARY_PATH removes the need to built these libraries separately. Oracle Linux 7 introduces the required versions of these libraries, but the R-core-extra RPM is provided as a convenience if needed.
See Also:For information on installing Oracle R Distribution using RPMs, see Installing Oracle R Distribution on Linux in Oracle R Enterprise Installation and Administration Guide
1.2 Oracle Machine Learning for R 1.5.1 Platform and Configuration Requirements
OML4R runs on 64-bit platforms only.
Both client and server components are supported on each of the platforms described in this topic.
Table 1-1 Oracle Machine Learning for R Platform Requirements
|Operating System||Hardware Platform||Description|
Intel and AMD
Oracle Linux may be running on Oracle Exadata Database Machine.
Oracle Solaris on x86-64 (64-Bit)
Oracle Solaris on SPARC-64 (64-Bit)
Intel and SPARC
Oracle Solaris may be running on Oracle Exadata Database Machine.
IBM AIX on POWER Systems (64-Bit)
64-bit IBM AIX 5.3 or higher
Microsoft Windows x64 (64-Bit)
64-bit Microsoft Windows Professional
Table 1-2 Oracle Machine Learning for R Configuration Requirements and Server Support Matrix
|OML4R Version||Open Source R or Oracle R Distribution||Oracle Database Release|
|1.5.1||3.3.0||18.104.22.168, 22.214.171.124, 126.96.36.199, 188.8.131.52, 18c, 19c|
|1.5||3.2.0||184.108.40.206, 220.127.116.11, 18.104.22.168|
|1.4.1||3.0.1, 3.1.1||22.214.171.124, 126.96.36.199, 188.8.131.52, 184.108.40.206|
|1.4||2.15.2, 2.15.3, 3.0.1||220.127.116.11, 18.104.22.168, 22.214.171.124|
|1.3.1||2.15.1, 2.15.2, 2.15.3||126.96.36.199, 188.8.131.52, 184.108.40.206|
|1.3||2.15.1||220.127.116.11, 18.104.22.168, 22.214.171.124|
|1.2||2.15.1||126.96.36.199, 188.8.131.52, 184.108.40.206|
|1.1||2.13.2||220.127.116.11, 18.104.22.168, 22.214.171.124|
|1.0||2.13.2||126.96.36.199, 188.8.131.52, 184.108.40.206|
Note:In Oracle Database Release 220.127.116.11, for some embedded R operations to be successful, Oracle R Enterprise releases 1.4.1 and later require the database patch -- 20173897 WRONG RESULT OF GROUP BY FROM A TABLE RETURNED BY EXTPROC (Patch).
1.3 Bugs Fixed in Oracle Machine Learning for R 1.5.1
OML4R 1.5.1 fixes the problems listed in this topic.
Table 1-3 Bugs Fixed in OML4R 1.5.1
|18561846||ORE.PUSH: MIXING R AND ORE OBJECT NAMES CAN RESULT IN REMOVAL OF TEMP TABLE|
|21901178||VIEW CREATED BY ORE.CREATE ON ORE.FRAME DOES NOT PRESERVE IN MULTIPLE SESSION|
|22198902||ORE.STEPWISE RETURNS RESIDUALS AS 0 AND NO P-VALUES|
|22283078||ORE.DROP INCORRECTLY HANDLES VIEWS|
|22607954||DB TABLES WITH SPECIAL CHARACTER IS NOT ACCESSIBLE IN ORE|
|23512913||ORE.RANDOMFOREST DOES NOT ACCEPT SINGLE INDEPENDENT VARIABLE|
|25417402||STEPWISE DEMO FAILS INTERMITTENTLY|
1.4 About Upgrading to Oracle Machine Learning for R 1.5.1
Upgrading to OML4R Release 1.5.1 from Release 1.5 or earlier version.
OML4R 1.5.1 requires open source R or Oracle R Distribution 3.3.0 or later.
1.5 Documentation Accessibility
For information about Oracle's commitment to accessibility, visit the Oracle Accessibility Program website at http://www.oracle.com/pls/topic/lookup?ctx=acc&id=docacc.
Access to Oracle Support
Oracle customers that have purchased support have access to electronic support through My Oracle Support. For information, visit http://www.oracle.com/pls/topic/lookup?ctx=acc&id=info or visit http://www.oracle.com/pls/topic/lookup?ctx=acc&id=trs if you are hearing impaired.
Oracle Machine Learning for R Release Notes, Release 1.5.1
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Primary Author: David McDermid
Contributor: Mark Hornick, Sherry Lamonica, Qin Wang
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