Specifying Model Settings

5.3 Specifying Model Settings

Understand how to configure data mining models at build time.

Numerous configuration settings are available for configuring data mining models at build time. To specify settings, create a settings table with the columns shown in the following table and pass the table to CREATE_MODEL.

Table 5-4 Settings Table Required Columns

Column Name	Data Type
`setting_name`	`VARCHAR2(30)`
`setting_value`	`VARCHAR2(4000)`

Example 5-1 creates a settings table for an Support Vector Machine (SVM) Classification model. Since SVM is not the default classifier, the ALGO_NAME setting is used to specify the algorithm. Setting the SVMS_KERNEL_FUNCTION to SVMS_LINEAR causes the model to be built with a linear kernel. If you do not specify the kernel function, the algorithm chooses the kernel based on the number of attributes in the data.

Some settings apply generally to the model, others are specific to an algorithm. Model settings are referenced in Table 5-5 and Table 5-6.

Table 5-5 General Model Settings

Settings	Description
Mining function settings	See "Mining Function Settings" in Oracle Database PL/SQL Packages and Types Reference
Algorithm names	See "Algorithm Names" in Oracle Database PL/SQL Packages and Types Reference
Global model characteristics	See "Global Settings" in Oracle Database PL/SQL Packages and Types Reference
Automatic Data Preparation	See "Automatic Data Preparation" in Oracle Database PL/SQL Packages and Types Reference

Table 5-6 Algorithm-Specific Model Settings

Algorithm	Description
CUR Matrix Decomposition	See "DBMS_DATA_MINING —Algorithm Settings: CUR Matrix Decomposition"in Oracle Database PL/SQL Packages and Types Reference
Decision Tree	See "DBMS_DATA_MINING —Algorithm Settings: Decision Tree" in Oracle Database PL/SQL Packages and Types Reference
Expectation Maximization	See "DBMS_DATA_MINING —Algorithm Settings: Expectation Maximization" in Oracle Database PL/SQL Packages and Types Reference
Explicit Semantic Analysis	See “DBMS_DATA_MINING —Algorithm Settings: Explicit Semantic Analysis” in Oracle Database PL/SQL Packages and Types Reference
Exponential Smoothing	See "DBMS_DATA_MINING —Algorithm Settings: Exponential Smoothing Models" in Oracle Database PL/SQL Packages and Types Reference
Generalized Linear Models	See "DBMS_DATA_MINING —Algorithm Settings: Generalized Linear Models" in Oracle Database PL/SQL Packages and Types Reference
k-Means	See "DBMS_DATA_MINING —Algorithm Settings: k-Means" in Oracle Database PL/SQL Packages and Types Reference
Naive Bayes	See "Algorithm Settings: Naive Bayes" in Oracle Database PL/SQL Packages and Types Reference
Neural Network	See "DBMS_DATA_MINING —Algorithm Settings: Neural Network" in Oracle Database PL/SQL Packages and Types Reference
Non-Negative Matrix Factorization	See "DBMS_DATA_MINING —Algorithm Settings: Non-Negative Matrix Factorization" in Oracle Database PL/SQL Packages and Types Reference
O-Cluster	See "Algorithm Settings: O-Cluster" in Oracle Database PL/SQL Packages and Types Reference
Random Forest	See "DBMS_DATA_MINING — Algorithm Settings: Random Forest" in Oracle Database PL/SQL Packages and Types Reference
Singular Value Decomposition	See "DBMS_DATA_MINING —Algorithm Settings: Singular Value Decomposition" in Oracle Database PL/SQL Packages and Types Reference
Support Vector Machine	See "DBMS_DATA_MINING —Algorithm Settings: Support Vector Machine" in Oracle Database PL/SQL Packages and Types Reference

Example 5-1 Creating a Settings Table for an SVM Classification Model

CREATE TABLE svmc_sh_sample_settings (
  setting_name VARCHAR2(30),
  setting_value VARCHAR2(4000));

BEGIN 
  INSERT INTO svmc_sh_sample_settings (setting_name, setting_value) VALUES
    (dbms_data_mining.algo_name, dbms_data_mining.algo_support_vector_machines);
  INSERT INTO svmc_sh_sample_settings (setting_name, setting_value) VALUES
    (dbms_data_mining.svms_kernel_function, dbms_data_mining.svms_linear);
  COMMIT;
END;
/

Related Topics

Oracle Database PL/SQL Packages and Types Reference

5.3.1 Specifying Costs

Specify a cost matrix table to build a Decision Tree model.

The CLAS_COST_TABLE_NAME setting specifies the name of a cost matrix table to be used in building a Decision Tree model. A cost matrix biases a classification model to minimize costly misclassifications. The cost matrix table must have the columns shown in the following table:

Table 5-7 Cost Matrix Table Required Columns

Column Name	Data Type
`actual_target_value`	valid target data type
`predicted_target_value`	valid target data type
`cost`	`NUMBER`

Decision Tree is the only algorithm that supports a cost matrix at build time. However, you can create a cost matrix and associate it with any classification model for scoring.

If you want to use costs for scoring, create a table with the columns shown in Table 5-7, and use the DBMS_DATA_MINING.ADD_COST_MATRIX procedure to add the cost matrix table to the model. You can also specify a cost matrix inline when invoking a PREDICTION function. Table 3-1 has details for valid target data types.

Related Topics

Oracle Data Mining Concepts

5.3.2 Specifying Prior Probabilities

Prior probabilities can be used to offset differences in distribution between the build data and the actual population.

The CLAS_PRIORS_TABLE_NAME setting specifies the name of a table of prior probabilities to be used in building a Naive Bayes model. The priors table must have the columns shown in the following table.

Table 5-8 Priors Table Required Columns

Column Name	Data Type
`target_value`	valid target data type
`prior_probability`	`NUMBER`

Related Topics

5.3.3 Specifying Class Weights

Specify class weights table settings in Logistic Regression or Support Vector Machine (SVM) Classification to favour higher weighted classes.

The CLAS_WEIGHTS_TABLE_NAME setting specifies the name of a table of class weights to be used to bias a logistic regression (Generalized Linear Model Classification) or SVM Classification model to favor higher weighted classes. The weights table must have the columns shown in the following table.

Table 5-9 Class Weights Table Required Columns

Column Name	Data Type
`target_value`	valid target data type
`class_weight`	`NUMBER`

Related Topics

5.3.4 Model Settings in the Data Dictionary

Explains about ALL/USER/DBA_MINING_MODEL_SETTINGS in data dictionary view.

Information about mining model settings can be obtained from the data dictionary view ALL/USER/DBA_MINING_MODEL_SETTINGS. When used with the ALL prefix, this view returns information about the settings for the models accessible to the current user. When used with the USER prefix, it returns information about the settings for the models in the user's schema. The DBA prefix is only available for DBAs.

The columns of ALL_MINING_MODEL_SETTINGS are described as follows and explained in the following table.

SQL> describe all_mining_model_settings
 Name                                      Null?    Type
 ----------------------------------------- -------- ----------------------------
 OWNER                                     NOT NULL VARCHAR2(30)
 MODEL_NAME                                NOT NULL VARCHAR2(30)
 SETTING_NAME                              NOT NULL VARCHAR2(30)
 SETTING_VALUE                                      VARCHAR2(4000)
 SETTING_TYPE                                       VARCHAR2(7)

Table 5-10 ALL_MINING_MODEL_SETTINGS

Column	Description
`owner`	Owner of the mining model.
`model_name`	Name of the mining model.
`setting_name`	Name of the setting.
`setting_value`	Value of the setting.
`setting_type`	`INPUT` if the value is specified by a user. `DEFAULT` if the value is system-generated.

The following query lists the settings for the Support Vector Machine (SVM) Classification model SVMC_SH_CLAS_SAMPLE. The ALGO_NAME, CLAS_WEIGHTS_TABLE_NAME, and SVMS_KERNEL_FUNCTION settings are user-specified. These settings have been specified in a settings table for the model.

Example 5-2 ALL_MINING_MODEL_SETTINGS

SQL> COLUMN setting_value FORMAT A35
SQL> SELECT setting_name, setting_value, setting_type
            FROM all_mining_model_settings
            WHERE model_name in 'SVMC_SH_CLAS_SAMPLE';
 
SETTING_NAME                   SETTING_VALUE                       SETTING
------------------------------ ----------------------------------- -------
SVMS_ACTIVE_LEARNING           SVMS_AL_ENABLE                      DEFAULT
PREP_AUTO                      OFF                                 DEFAULT
SVMS_COMPLEXITY_FACTOR         0.244212                            DEFAULT
SVMS_KERNEL_FUNCTION           SVMS_LINEAR                         INPUT
CLAS_WEIGHTS_TABLE_NAME        svmc_sh_sample_class_wt             INPUT
SVMS_CONV_TOLERANCE            .001                                DEFAULT
ALGO_NAME                      ALGO_SUPPORT_VECTOR_MACHINES        INPUT

Related Topics

Oracle Database PL/SQL Packages and Types Reference

5.3.5 Specifying Mining Model Settings for R Model

The mining model settings for R model determine the characteristics of the model. You can specify the mining model settings in the mining_model_table.

You can build R models with the mining model settings by combining together generic settings that do not require an algorithm, such as ODMS_PARTITION_COLUMNS and ODMS_SAMPLING. The following settings are exclusive to R mining model, and they allow you to specify the R Mining model:

Related Topics

Registered R Scripts

5.3.5.1 ALGO_EXTENSIBLE_LANG

Use the ALGO_EXTENSIBLE_LANG setting to specify the Oracle Data Mining framework with extensible algorithms.

Currently, R is the only valid value for ALGO_EXTENSIBLE_LANG. When the value for ALGO_EXTENSIBLE_LANG is set to R, the mining models are built using the R language. You can use the following settings in the model_setting_table to specify the build, score, and view of the R model.

Related Topics

Registered R Scripts

5.3.5.2 RALG_BUILD_FUNCTION

Use the RALG_BUILD_FUNCTION to specify the name of an existing registered R script for R algorithm mining model build.

You must specify both RALG_BUILD_FUNCTION and ALGO_EXTENSIBLE_LANG in the model_setting_table. The R script defines an R function that has the first input argument of data.frame for training data, and it returns an R model object. The first data argument is mandatory. The RALG_BUILD_FUNCTION can accept additional model build parameters.

Note:

The valid inputs for input parameters are numeric and string scalar data types.

Example 5-3 Example of RALG_BUILD_FUNCTION

This example shows how to specify the name of the R script MY_LM_BUILD_SCRIPT that is used to build the model in the model_setting_table.

Begin
insert into model_setting_table values
(dbms_data_mining.ralg_build_function,'MY_LM_BUILD_SCRIPT');
End;
/

The R script MY_LM_BUILD_SCRIPT defines an R function that builds the LM model. You must register the script MY_LM_BUILD_SCRIPT in the R script repository which uses the existing ORE security restrictions. You can use Oracle R Enterprise API sys.rqScriptCreate to register the script. Oracle R Enterprise requires the RQADMIN role to register R scripts.

For example:

Begin
sys.rqScriptCreate('MY_LM_BUILD_SCRIPT', 'function(data, formula, model.frame) {lm(formula = formula, data=data, model = as.logical(model.frame)}');
End;
/

For Clustering and Feature Extraction mining function model build, the R attributes dm$nclus and dm$nfeat must be set on the return R model to indicate the number of clusters and features respectively.

The R script MY_KM_BUILD_SCRIPT defines an R function that builds the k-Means model for Clustering. R attribute dm$nclus is set with the number of clusters for the return Clustering model.

'function(dat) {dat.scaled <- scale(dat)
     set.seed(6543); mod <- list()
     fit <- kmeans(dat.scaled, centers = 3L)
     mod[[1L]] <- fit
     mod[[2L]] <- attr(dat.scaled, "scaled:center")
     mod[[3L]] <- attr(dat.scaled, "scaled:scale")
     attr(mod, "dm$nclus") <- nrow(fit$centers)
     mod}'

The R script MY_PCA_BUILD_SCRIPT defines an R function that builds the PCA model. R attribute dm$nfeat is set with the number of features for the return feature extraction model.

'function(dat) {
     mod <- prcomp(dat, retx = FALSE)
     attr(mod, "dm$nfeat") <- ncol(mod$rotation)
     mod}'

Related Topics

5.3.5.2.1 RALG_BUILD_PARAMETER

The RALG_BUILD_FUNCTION input parameter specifies a list of numeric and string scalar values in SQL SELECT query statement format.

Example 5-4 Example of RALG_BUILD_PARAMETER

The RALG_BUILD_FUNCTION input parameters must be a list of numeric and string scalar values. The input parameters are optional.

The syntax of the parameter is:

'SELECT value parameter name ...FROM dual'

This example shows how to specify a formula for the input argument 'formula' and a numeric value zero for input argument 'model.frame' using the RALG_BUILD_PARAMETER. These input arguments must match with the function signature of the R script used in RALG_BUILD_FUNCTION Parameter.

Begin
insert into model_setting_table values
(dbms_data_mining.ralg_build_parameter, 'select ''AGE ~ .'' as "formula", 0 as "model.frame" from dual');
End;
/

Related Topics

RALG_BUILD_FUNCTION

5.3.5.3 RALG_DETAILS_FUNCTION

The RALG_DETAILS_FUNCTION specifies the R model metadata that is returned in the data.frame.

Use the RALG_DETAILS_FUNCTION to specify an existing registered R script that generates model information. The specified R script defines an R function that contains the first input argument for the R model object. The output of the R function must be a data.frame. The columns of the data.frame are defined by RALG_DETAILS_FORMAT, and can contain only numeric or string scalar types.

Example 5-5 Example of RALG_DETAILS_FUNCTION

This example shows how to specify the name of the R script MY_LM_DETAILS_SCRIPT in the model_setting_table. This script defines the R function that is used to provide the model information.

Begin
insert into model_setting_table values
(dbms_data_mining.ralg_details_function, 'MY_LM_DETAILS_SCRIPT');
End;
/

In the R script repository, the script MY_LM_DETAILS_SCRIPT is registered as:

 'function(mod) data.frame(name=names(mod$coefficients),
    coef=mod$coefficients)'

Related Topics

5.3.5.3.1 RALG_DETAILS_FORMAT

Use the RALG_DETAILS_FORMAT parameter to specify the names and column types in the model view. It is a string that contains a SELECT query to specify a list of numeric and string scalar data types for the name and type of the model view columns.

When RALG_DETAILS_FORMAT and RALG_DETAILS_FUNCTION are both specified, a model view by the name DM$VD <model_name> is created along with an R model in the current schema. The first column of the model view is PARTITION_NAME. It has NULL value for non-partitioned models. The other columns of the model view are defined by RALG_DETATLS_FORMAT.

Example 5-6 Example of RALG_DETAILS_FORMAT

This example shows how to specify the name and type of the columns for the generated model view. The model view contains varchar2 column attr_name and number column coef_value after the first column partition_name.

Begin
insert into model_setting_table values
(dbms_data_mining.ralg_details_format, 'select cast(''a'' as varchar2(20)) as attr_name, 0 as coef_value from dual');
End;
/

Related Topics

RALG_DETAILS_FUNCTION

5.3.5.4 RALG_SCORE_FUNCTION

Use the RALG_SCORE_FUNCTION to specify an existing registered R script for R algorithm mining model score in the mining_model_table.

The specified R script defines an R function. The first input argument defines the model object. The second input argument defines the data.frame that is used for scoring data.

Example 5-7 Example of RALG_SCORE_FUNCTION

This example shows how the function takes the R model and scores the data in the data.frame. The argument object is the R Linear Model. The argument newdata contains scoring data in the data.frame.

function(object, newdata) {res <- predict.lm(object, newdata = newdata, se.fit = TRUE); data.frame(fit=res$fit, se=res$se.fit, df=summary(object)$df[1L])}

In this example,

object indicates the LM model
newdata indicates the scoring data.frame

The output of the specified R function must be a data.frame. Each row represents the prediction for the corresponding scoring data from the input data.frame. The columns of the data.frame are specific to mining functions, such as:

Regression: A single numeric column for predicted target value, with two optional columns containing standard error of model fit, and the degrees of freedom number. The optional columns are needed for query function PREDICTION_BOUNDS to work.

Example 5-8 Example of RALG_SCORE_FUNCTION for Regression

This example shows how to specify the name of the R script MY_LM_PREDICT_SCRIPT that is used to score the model in the model_setting_table.

Begin
insert into model_setting_table values
(dbms_data_mining.ralg_score_function, 'MY_LM_PREDICT_SCRIPT');
End;
/

In the R script repository, the script MY_LM_PREDICT_SCRIPT is registered as:

function(object, newdata) {data.frame(pre = predict(object, newdata = newdata))}

Classification: Each column represents the predicted probability of one target class. The column name is the target class name.

Example 5-9 Example of RALG_SCORE_FUNCTION for Classification

This example shows how to specify the name of the R script MY_LOGITGLM_PREDICT_SCRIPT that is used to score the logit Classification model in the model_setting_table.

Begin
insert into model_setting_table values
(dbms_data_mining.ralg_score_function, 'MY_LOGITGLM_PREDICT_SCRIPT');
End;
/

In the R script repository, MY_LOGITGLM_PREDICT_SCRIPT is registered as follows. It is a logit Classification with two target class "0" and "1".

'function(object, newdata) {
   pred <- predict(object, newdata = newdata, type="response");
   res <- data.frame(1-pred, pred);
   names(res) <- c("0", "1");
   res}'

Clustering: Each column represents the predicted probability of one cluster. The columns are arranged in order of cluster ID. Each cluster is assigned a cluster ID, and they are consecutive values starting from 1. To support CLUSTER_DISTANCE in the R model, the output of R score function returns extra column containing the value of the distance to each cluster in order of cluster ID after the columns for the predicted probability.

Example 5-10 Example of RALG_SCORE_FUNCTION for Clustering

This example shows how to specify the name of the R script MY_CLUSTER_PREDICT_SCRIPT that is used to score the model in the model_setting_table.

Begin
insert into model_setting_table values
(dbms_data_mining.ralg_score_function, 'MY_CLUSTER_PREDICT_SCRIPT');
End;
/

In the R script repository, the script MY_CLUSTER_PREDICT_SCRIPT is registered as:

'function(object, dat){
     mod <- object[[1L]]; ce <- object[[2L]]; sc <- object[[3L]];
     newdata = scale(dat, center = ce, scale = sc);
     centers <- mod$centers;
     ss <- sapply(as.data.frame(t(centers)),
     function(v) rowSums(scale(newdata, center=v, scale=FALSE)^2));
     if (!is.matrix(ss)) ss <- matrix(ss, ncol=length(ss));
     disp <- -1 / (2* mod$tot.withinss/length(mod$cluster));
     distr <- exp(disp*ss);
     prob <- distr / rowSums(distr);
     as.data.frame(cbind(prob, sqrt(ss)))}'

This function fetches the centers of the clusters from the R model, and computes the probabilities for belonging to each cluster based on the distance of the score data to the corresponding cluster center.

Feature Extraction: Each column represents the coefficient value of one feature. The columns are arranged in order of feature ID. Each feature is assigned a feature ID, and they are consecutive values starting from 1.

Example 5-11 Example of RALG_SCORE_FUNCTION for Feature Extraction

This example shows how to specify the name of the R script MY_FEATURE_EXTRACTION_SCRIPT that is used to score the model in the model_setting_table.

Begin
insert into model_setting_table values
(dbms_data_mining.ralg_score_function, 'MY_FEATURE_EXTRACTION_SCRIPT');
End;
/

In the R script repository, the script MY_FEATURE_EXTRACTION_SCRIPT is registered as:

 'function(object, dat) { as.data.frame(predict(object, dat)) }'

The function fetches the centers of the features from the R model, and computes the feature coefficient based on the distance of the score data to the corresponding feature center.

Related Topics

Registered R Scripts

5.3.5.5 RALG_WEIGHT_FUNCTION

Use the RALG_WEIGHT_FUNCTION to specify the name of an existing registered R script that computes weight or contribution for each attribute in scoring. The specified R script is used in the query function PREDICTION_DETAILS to evaluate attribute contribution.

The specified R script defines an R function containing the first input argument for model object, and the second input argument of data.frame for scoring data. When the mining function is Classification, Clustering, or Feature Extraction, the target class name or cluster ID or feature ID is passed by the third input argument to compute the weight for that particular class or cluster or feature. The script returns a data.frame containing the contributing weight for each attribute in a row. Each row corresponds to that input scoring data.frame.

Example 5-12 Example of RALG_WEIGHT_FUNCTION

This example shows how to specify the name of the R script MY_PREDICT_WEIGHT_SCRIPT that computes weight or contribution of R model attributes in the model_setting_table.

Begin
insert into model_setting_table values
(dbms_data_mining.ralg_weight_function, 'MY_PREDICT_WEIGHT_SCRIPT');
End;
/

In the R script repository, the script MY_PREDICT_WEIGHT_SCRIPT for Regression is registered as:

'function(mod, data) { coef(mod)[-1L]*data }'

In the R script repository, the script MY_PREDICT_WEIGHT_SCRIPT for logit Classification is registered as:

'function(mod, dat, clas) {
   v <- predict(mod, newdata=dat, type = "response");
   v0 <- data.frame(v, 1-v); names(v0) <- c("0", "1");
   res <- data.frame(lapply(seq_along(dat),
   function(x, dat) {
   if(is.numeric(dat[[x]])) dat[,x] <- as.numeric(0)
   else dat[,x] <- as.factor(NA);
   vv <- predict(mod, newdata = dat, type = "response");
   vv = data.frame(vv, 1-vv); names(vv) <- c("0", "1");
   v0[[clas]] / vv[[clas]]}, dat = dat));
   names(res) <- names(dat);
   res}'

Related Topics

Registered R Scripts

5.3.5.6 Registered R Scripts

The RALG_*_FUNCTION must specify R scripts that exist in the R script repository. You can register the R scripts using Oracle R Enterprise.

The RALG_*_FUNCTION includes the following functions:

RALG_BUILD_FUNCTION
RALG_DETAILS_FUNCTION
RALG_SCORE_FUNCTION
RALG_WEIGHT_FUNCTION

Note:

The R scripts must exist in the R script repository for an R model to function.

You can register the R scripts through Oracle Enterprise R (ORE). To register R scripts, you must have the RQADMIN role. After an R model is built, the names of these specified R scripts become model settings. These R scripts must exist in the R script repository for an R model to remain functional.

You can manage the R memory that is used to build, score, and view the R models through Oracle Enterprise R as well.

5.3.5.7 R Model Demonstration Scripts

You can access R model demonstration scripts under rdbms/demo

dmraidemo.sql  dmrglmdemo.sql dmrpcademo.sql
dmrardemo.sql  dmrkmdemo.sql  dmrrfdemo.sql
dmrdtdemo.sql  dmrnndemo.sql

5.3.5.8 Algorithm Meta Data Registration

Algorithm Meta Data Registration allows for a uniform and consistent approach of registering new algorithm functions and their settings.

User have the ability to add new algorithms through the registration process. The new algorithms can appear as available within Oracle Data Mining R within their appropriate mining functions. Based on the registration meta data, the settings page is dynamically rendered. Algorithm meta data registration extends the mining model capability of Oracle Data Mining.

Related Topics