About Transformations

Understand how you can transform data by using Automatic Data Preparation (ADP) and embedded data transformation.

A transformation is a SQL expression that modifies the data in one or more columns. Data must typically undergo certain transformations before it can be used to build a model. Many Oracle Machine Learning algorithms have specific transformation requirements. Before data can be scored, it must be transformed in the same way that the training data was transformed.

Oracle Machine Learning for SQL supports ADP, which automatically implements the transformations required by the algorithm. The transformations are embedded in the model and automatically run whenever the model is applied.

If additional transformations are required, you can specify them as SQL expressions and supply them as input when you create the model. These transformations are embedded in the model as they are with ADP.

With automatic and embedded data transformation, most of the work of data preparation is handled for you. You can create a model and score multiple data sets in a few steps:

  1. Identify the columns to include in the case table.

  2. Create nested columns if you want to include transactional data.

  3. Write SQL expressions for any transformations not handled by ADP.

  4. Create the model, supplying the SQL expressions (if specified) and identifying any columns that contain text data.

  5. Ensure that some or all of the columns in the scoring data have the same name and type as the columns used to train the model.

See Also:

OML provides algorithm-specific automatic data preparation and other model building-related features

Related Topics

Embed Transformations in a Model

You can specify your own transformations and embed them in a model by creating a transformation list and passing it to DBMS_DATA_MINING.CREATE_MODEL2 or DBMS_DATA_MINING.CREATE_MODEL.

The transformation instructions are embedded in the model and reapplied whenever the model is applied to new data.

The schema of how you can use xform_list to embed your transformations is shown here with CREATE_MODEL procedure. 


DBMS_DATA_MINING.CREATE_MODEL2 (
model_name 		IN VARCHAR2,
mining_function 	  IN VARCHAR2,
data_query 		IN CLOB,
set_list 		  IN SETTING_LIST,
case_id_column_name      IN VARCHAR2 DEFAULT NULL,
target_column_name       IN VARCHAR2 DEFAULT NULL,
xform_list 		IN TRANSFORM_LIST DEFAULT NULL);

DBMS_DATA_MINING.CREATE_MODEL(
                  model_name           IN VARCHAR2,
                  mining_function      IN VARCHAR2,
                  data_table_name      IN VARCHAR2,
                  case_id_column_name  IN VARCHAR2,
                  target_column_name   IN VARCHAR2 DEFAULT NULL,
                  settings_table_name  IN VARCHAR2 DEFAULT NULL,
                  data_schema_name     IN VARCHAR2 DEFAULT NULL,
                  settings_schema_name IN VARCHAR2 DEFAULT NULL,
                  xform_list           IN TRANSFORM_LIST DEFAULT NULL);

The following examples show how to create an embedded transform list with CREATE_MODEL and CREATE_MODEL2 procedures.

Here is an example with DBMS_DATA_MINING.CREATE_MODEL procedure: 


BEGIN 
DBMS_DATA_MINING.DROP_MODEL('model_sample2'); 
EXCEPTION WHEN OTHERS THEN NULL; 
END;
/
CREATE TABLE sett_table (SETTING_NAME  VARCHAR2(30),
                                    SETTING_VALUE VARCHAR2(4000));
 
BEGIN       
   INSERT INTO sett_table (SETTING_NAME, SETTING_VALUE) VALUES ('KMNS_DISTANCE','KMNS_EUCLIDEAN');
   INSERT INTO sett_table (SETTING_NAME, SETTING_VALUE) VALUES ('PREP_AUTO','ON');
   INSERT INTO sett_table (SETTING_NAME, SETTING_VALUE) VALUES ('KMNS_DETAILS', 'KMNS_DETAILS_ALL');
END;
DECLARE
  xformlist dbms_data_mining_transform.TRANSFORM_LIST;
BEGIN
  dbms_data_mining_transform.SET_TRANSFORM(xformlist, 'N_TRANS_ATM', null, 'TO_CHAR(N_TRANS_ATM)', null);
  dbms_data_mining_transform.SET_TRANSFORM(xformlist, 'BANK_FUNDS', null, 'BANK_FUNDS+BANK_FUNDS+BANK_FUNDS', null);
  dbms_data_mining_transform.SET_TRANSFORM(xformlist, 'AGE', null, 'log(10,AGE+1)', 'power(10, AGE)-1');

 DBMS_DATA_MINING.CREATE_MODEL(
    model_name          => 'model_sample2',
    mining_function     => dbms_data_mining.clustering,
    data_table_name     => 'INSUR_CUST_LTV',
    case_id_column_name => 'customer_id',
    settings_table_name => 'sett_table',
    xform_list          => xformlist);
END;
The following example shows how to create an embedded transformation using the DBMS_DATA_MINING.CREATE_MODEL2 procedure:
DECLARE 
  xformlist dbms_data_mining_transform.TRANSFORM_LIST;
  v_setlst DBMS_DATA_MINING.SETTING_LIST;
BEGIN
  dbms_data_mining_transform.SET_TRANSFORM(xformlist, 'N_TRANS_ATM', null, 'TO_CHAR(N_TRANS_ATM)', null);
  dbms_data_mining_transform.SET_TRANSFORM(xformlist, 'BANK_FUNDS', null, 'BANK_FUNDS+BANK_FUNDS+BANK_FUNDS', null);
  dbms_data_mining_transform.SET_TRANSFORM(xformlist, 'AGE', null, 'log(10,AGE+1)', 'power(10, AGE)-1');
  
  v_setlst('ALGO_NAME') := 'ALGO_KMEANS';

 DBMS_DATA_MINING.CREATE_MODEL2(
    model_name          => 'model_sample3',
    mining_function     => 'CLUSTERING',
    data_query          => 'select * from INSUR_CUST_LTV',
    set_list            => v_setlst,
    case_id_column_name => 'customer_id',
    xform_list          => xformlist);
END;

Build a Transformation List

You can build transformation list by SET_TRANSFORM, STACK, and GET_* methods. These methods are listed here.

A transformation list is a collection of transformation records. When a new transformation record is added, it is appended to the top of the transformation list. You can use any of the following methods to build a transformation list:

  • The SET_TRANFORM procedure in DBMS_DATA_MINING_TRANSFORM

  • The STACK interface in DBMS_DATA_MINING_TRANSFORM

  • The GET_MODEL_TRANSFORMATIONS and GET_TRANSFORM_LIST functions in DBMS_DATA_MINING

SET_TRANSFORM

The SET_TRANSFORM procedure applies a specified SQL expression to a specified attribute.

The SET_TRANSFORM procedure adds a single transformation record to a transformation list. 

DBMS_DATA_MINING_TRANSFORM.SET_TRANSFORM (
          xform_list               IN OUT NOCOPY TRANSFORM_LIST,
          attribute_name           VARCHAR2,
          attribute_subname        VARCHAR2,
          expression               VARCHAR2,
          reverse_expression       VARCHAR2,
          attribute_spec           VARCHAR2 DEFAULT NULL);

SQL expressions that you specify with SET_TRANSFORM must fit within a VARCHAR2. To specify a longer expression, you can use the SET_EXPRESSION procedure, which builds an expression by appending rows to a VARCHAR2 array. For example, the following statement appends a transformation instruction for country_id to a list of transformations called my_xforms. The transformation instruction divides country_id by 10 before algorithmic processing begins. The reverse transformation multiplies country_id by 10. 

  dbms_data_mining_transform.SET_TRANSFORM (my_xforms,
     'country_id', NULL, 'country_id/10', 'country_id*10');

The reverse transformation is applied in the model details. If country_id is the target of a supervised model, the reverse transformation is also applied to the scored target.

The STACK Interface

The STACK interface creates transformation records from a table of transformation instructions and adds them to a transformation list.

The STACK interface offers a set of pre-defined transformations that you can apply to an attribute or to a group of attributes. For example, you can specify supervised binning for all categorical attributes.

The STACK interface specifies that all or some of the attributes of a given type must be transformed in the same way. For example, STACK_BIN_CAT appends binning instructions for categorical attributes to a transformation list. The STACK interface consists of three steps:

  1. A CREATE procedure creates a transformation definition table. For example, CREATE_BIN_CAT creates a table to hold categorical binning instructions. The table has columns for storing the name of the attribute, the value of the attribute, and the bin assignment for the value.

  2. An INSERT procedure computes the bin boundaries for one or more attributes and populates the definition table. For example, INSERT_BIN_CAT_FREQ performs frequency-based binning on some or all of the categorical attributes in the data source and populates a table created by CREATE_BIN_CAT.

  3. A STACK procedure creates transformation records from the information in the definition table and appends the transformation records to a transformation list. For example, STACK_BIN_CAT creates transformation records for the information stored in a categorical binning definition table and appends the transformation records to a transformation list.

GET_MODEL_TRANSFORMATIONS and GET_TRANSFORM_LIST

Use the functions to create a new transformation list.

These two functions can be used to create a new transformation list from the transformations embedded in an existing model.

The GET_MODEL_TRANSFORMATIONS function returns a list of embedded transformations. 

DBMS_DATA_MINING.GET_MODEL_TRANSFORMATIONS (
      model_name     IN VARCHAR2)
RETURN DM_TRANSFORMS PIPELINED;

GET_MODEL_TRANSFORMATIONS returns a table of dm_transform objects. Each dm_transform has these fields 

attribute_name       VARCHAR2(4000)
attribute_subname    VARCHAR2(4000)
expression           CLOB
reverse_expression   CLOB

The components of a transformation list are transform_rec, not dm_transform. The fields of a transform_rec are described in Table 3-7. You can call GET_MODEL_TRANSFORMATIONS to convert a list of dm_transform objects to transform_rec objects and append each transform_rec to a transformation list. 

DBMS_DATA_MINING.GET_TRANSFORM_LIST (
      xform_list           OUT NOCOPY TRANSFORM_LIST,
      model_xforms         IN  DM_TRANSFORMS);

See Also:

"DBMS_DATA_MINING_TRANSFORM Operational Notes", "SET_TRANSFORM Procedure", "CREATE_MODEL Procedure", and "GET_MODEL_TRANSFORMATIONS Function" in Oracle AI Database PL/SQL Packages and Types Reference

Transformation List and Automatic Data Preparation

You can provide transformation list and Automatic Data Preparation (ADP) to customize the data transformation.

The transformation list argument to CREATE_MODEL2 and CREATE_MODEL interacts with the PREP_AUTO setting, which controls ADP:

  • When ADP is on and you specify a transformation list, your transformations are applied with the automatic transformations and embedded in the model. The transformations that you specify are processed before the automatic transformations.

  • When ADP is off and you specify a transformation list, your transformations are applied and embedded in the model, but no system-generated transformations are performed.

  • When ADP is on and you do not specify a transformation list, the system-generated transformations are applied and embedded in the model.

  • When ADP is off and you do not specify a transformation list, no transformations are embedded in the model; you must separately prepare the data sets you use for building, testing, and scoring the model.

Related Topics

Specify Transformation Instructions for an Attribute

You can pass transformation instructions for an attribute by defining a transformation list.

A transformation list is defined as a table of transformation records. Each record (transform_rec) specifies the transformation instructions for an attribute. 

TYPE transform_rec IS RECORD (
    attribute_name      VARCHAR2(30),
    attribute_subname   VARCHAR2(4000),
    expression          EXPRESSION_REC,
    reverse_expression  EXPRESSION_REC,
    attribute_spec      VARCHAR2(4000));

The fields in a transformation record are described in this table.

Table 3-7 Fields in a Transformation Record for an Attribute

Field Description

attribute_name and attribute_subname

These fields identify the attribute, as described in "Scoping of Model Attribute Name"

expression

A SQL expression for transforming the attribute. For example, this expression transforms the age attribute into two categories: child and adult:[0,19) for 'child' and [19,) for adult 

CASE WHEN age < 19 THEN 'child' ELSE 'adult'

Expression and reverse expressions are stored in expression_rec objects. See "Expression Records" for details.

reverse_expression

A SQL expression for reversing the transformation. For example, this expression reverses the transformation of the age attribute:

DECODE(age,'child','(-Inf,19)','[19,Inf)')

attribute_spec

Specifies special treatment for the attribute. The attribute_spec field can be null or it can have one or more of these values:

  • FORCE_IN — For GLM, forces the inclusion of the attribute in the model build when the ftr_selection_enable setting is enabled. (ftr_selection_enable is disabled by default.) If the model is not using GLM, this value has no effect. FORCE_IN cannot be specified for nested attributes or text.

  • NOPREP — When ADP is on, prevents automatic transformation of the attribute. If ADP is not on, this value has no effect. You can specify NOPREP for a nested attribute, but not for an individual subname (row) in the nested attribute.

  • TEXT — Indicates that the attribute contains unstructured text. ADP has no effect on this setting. TEXT may optionally include subsettings POLICY_NAME, TOKEN_TYPE, and MAX_FEATURES.

See Example 3-5 and Example 3-6.

Related Topics

Expression Records

Example of a transformation record.

The transformation expressions in a transformation record are expression_rec objects. 

TYPE expression_rec IS RECORD (
     lstmt       DBMS_SQL.VARCHAR2A,
     lb          BINARY_INTEGER DEFAULT 1,
     ub          BINARY_INTEGER DEFAULT 0);

TYPE varchar2a IS TABLE OF VARCHAR2(32767)
INDEX BY BINARY_INTEGER;

The lstmt field stores a VARCHAR2A, which allows transformation expressions to be very long, as they can be broken up across multiple rows of VARCHAR2. Use the DBMS_DATA_MINING_TRANSFORM.SET_EXPRESSION procedure to create an expression_rec.

Attribute Specifications

Learn how to define the characteristics specific to an attribute through attribute specification.

The attribute specification in a transformation record defines characteristics that are specific to this attribute. If not null, the attribute specification can include values FORCE_IN, NOPREP, or TEXT, as described in Table 3-7.

Example 3-5 An Attribute Specification with Multiple Keywords

If more than one attribute specification keyword is applicable, you can provide them in a comma-delimited list. The following expression is the specification for an attribute in a GLM model. Assuming that the ftr_selection_enable setting is enabled, this expression forces the attribute to be included in the model. If ADP is on, automatic transformation of the attribute is not performed. 

"FORCE_IN,NOPREP"

Example 3-6 A Text Attribute Specification

For text attributes, you can optionally specify subsettings POLICY_NAME, TOKEN_TYPE, and MAX_FEATURES. The subsettings provide configuration information that is specific to text transformation. In this example, the transformation instructions for the text content are defined in a text policy named my_policy with token type is THEME. The maximum number of extracted features is 3000. 

"TEXT(POLICY_NAME:my_policy)(TOKEN_TYPE:THEME)(MAX_FEATURES:3000)"

Oracle Machine Learning for SQL Transformation Routines

Learn about transformation routines.

OML4SQL provides routines that implement various transformation techniques in the DBMS_DATA_MINING_TRANSFORM package.

Related Topics

Binning Routines

Explains binning techniques in Oracle Machine Learning for SQL.

A number of factors go into deciding a binning strategy. Having fewer values typically leads to a more compact model and one that builds faster, but it can also lead to some loss in accuracy.

Model quality can improve significantly with well-chosen bin boundaries. For example, an appropriate way to bin ages is to separate them into groups of interest, such as children 0-13, teenagers 13-19, youth 19-24, working adults 24-35, and so on.

The following table lists the binning techniques provided by Oracle Machine Learning for SQL:

Table 3-8 Binning Methods in DBMS_DATA_MINING_TRANSFORM

Binning Method Description

Top-N Most Frequent Items

You can use this technique to bin categorical attributes. You specify the number of bins. The value that occurs most frequently is labeled as the first bin, the value that appears with the next frequency is labeled as the second bin, and so on. All remaining values are in an additional bin.

Supervised Binning

Supervised binning is a form of intelligent binning, where bin boundaries are derived from important characteristics of the data. Supervised binning builds a single-predictor decision tree to find the interesting bin boundaries with respect to a target. It can be used for numerical or categorical attributes.

Equi-Width Binning

You can use equi-width binning for numerical attributes. The range of values is computed by subtracting the minimum value from the maximum value, then the range of values is divided into equal intervals. You can specify the number of bins or it can be calculated automatically. Equi-width binning must usually be used with outlier treatment.

Quantile Binning

Quantile binning is a numerical binning technique. Quantiles are computed using the SQL analytic function NTILE. The bin boundaries are based on the minimum values for each quantile. Bins with equal left and right boundaries are collapsed, possibly resulting in fewer bins than requested.

Normalization Routines

Learn about normalization routines in Oracle Machine Learning for SQL.

Most normalization methods map the range of a single attribute to another range, typically 0 to 1 or -1 to +1.

Normalization is very sensitive to outliers. Without outlier treatment, most values are mapped to a tiny range, resulting in a significant loss of information.

Table 3-9 Normalization Methods in DBMS_DATA_MINING_TRANSFORM

Transformation Description

Min-Max Normalization

This technique computes the normalization of an attribute using the minimum and maximum values. The shift is the minimum value, and the scale is the difference between the maximum and minimum values.

Scale Normalization

This normalization technique also uses the minimum and maximum values. For scale normalization, shift = 0, and scale = max{abs(max), abs(min)}.

Z-Score Normalization

This technique computes the normalization of an attribute using the mean and the standard deviation. Shift is the mean, and scale is the standard deviation.
Outlier Treatment

Understand what you must do to treat outliers.

A value is considered an outlier if it deviates significantly from most other values in the column. The presence of outliers can have a skewing effect on the data and can interfere with the effectiveness of transformations such as normalization or binning.

Outlier treatment methods such as trimming or clipping can be implemented to minimize the effect of outliers.

Outliers represent problematic data, for example, a bad reading due to the unusual condition of an instrument. However, in some cases, especially in the business arena, outliers are perfectly valid. For example, in census data, the earnings for some of the richest individuals can vary significantly from the general population. Do not treat this information as an outlier, since it is an important part of the data. You need domain knowledge to determine outlier handling.

Routines for Outlier Treatment

Understand the transformations used for outlier treatment.

Outliers are extreme values, typically several standard deviations from the mean. To minimize the effect of outliers, you can Winsorize or trim the data.

Winsorizing involves setting the tail values of an attribute to some specified value. For example, for a 90% Winsorization, the bottom 5% of values are set equal to the minimum value in the 5th percentile, while the upper 5% of values are set equal to the maximum value in the 95th percentile.

Trimming sets the tail values to NULL. The algorithm treats them as missing values.

Outliers affect the different algorithms in different ways. In general, outliers cause distortion with equi-width binning and min-max normalization.

Table 3-10 Outlier Treatment Methods in DBMS_DATA_MINING_TRANSFORM

Transformation Description

Trimming

This technique trims the outliers in numeric columns by sorting the non-null values, computing the tail values based on some fraction, and replacing the tail values with nulls.

Windsorizing

This technique trims the outliers in numeric columns by sorting the non-null values, computing the tail values based on some fraction, and replacing the tail values with some specified value.

Understand Reverse Transformations

Reverse transformations ensure that information returned by the model is expressed in a format that is similar to or the same as the format of the data that was used to train the model. Internal transformation are reversed in the model details and in the results of scoring.

Some of the attributes used by the model correspond to columns in the build data. However, because of logic specific to the algorithm, nested data, and transformations, some attributes do not correspond to columns.

For example, a nested column in the training data is not interpreted as an attribute by the model. During the model build,Oracle Machine Learning for SQL explodes nested columns, and each row (an attribute name/value pair) becomes an attribute.

Some algorithms, for example Support Vector Machine (SVM) and Generalized Linear Model (GLM), only operate on numeric attributes. Any non-numeric column in the build data is exploded into binary attributes, one for each distinct value in the column (SVM). GLM does not generate a new attribute for the most frequent value in the original column. These binary attributes are set to one only if the column value for the case is equal to the value associated with the binary attribute.

Algorithms that generate coefficients present challenges in interpreting the results. Examples are SVM and Non-Negative Matrix Factorization (NMF). These algorithms produce coefficients that are used in combination with the transformed attributes. The coefficients are relevant to the data on the transformed scale, not the original data scale.

For all these reasons, the attributes listed in the model details do not resemble the columns of data used to train the model. However, attributes that undergo embedded transformations, whether initiated by Automatic Data Preparation (ADP) or by a user-specified transformation list, appear in the model details in their pre-transformed state, as close as possible to the original column values. Although the attributes are transformed when they are used by the model, they are visible in the model details in a form that can be interpreted by a user.