FEATURE_SET

Syntax

feature_set::=

Analytic Syntax

feature_set_analytic::=

mining_attribute_clause::=

mining_analytic_clause::=

Purpose

FEATURE_SET returns a set of feature ID and feature value pairs for each row in the selection. The return value is a varray of objects with field names FEATURE_ID and VALUE. The data type of both fields is NUMBER.

topN and cutoff

You can specify topN and cutoff to limit the number of features returned by the function. By default, both topN and cutoff are null and all features are returned.

• topN is the N highest value features. If multiple features have the Nth value, then the function chooses one of them.

• cutoff is a value threshold. Only features that are greater than or equal to cutoff are returned. To filter by cutoff only, specify NULL for topN.

To return up to N features that are greater than or equal to cutoff, specify both topN and cutoff.

Syntax Choice

FEATURE_SET can score the data in one of two ways: It can apply a mining model object to the data, or it can dynamically mine the data by executing an analytic clause that builds and applies one or more transient mining models. Choose Syntax or Analytic Syntax:

• Syntax — Use the first syntax to score the data with a pre-defined model. Supply the name of a feature extraction model.

• Analytic Syntax — Use the analytic syntax to score the data without a pre-defined model. Include INTO n, where n is the number of features to extract, and mining_analytic_clause, which specifies if the data should be partitioned for multiple model builds. The mining_analytic_clause supports a query_partition_clause and an order_by_clause. (See "analytic_clause::=".)

mining_attribute_clause

mining_attribute_clause identifies the column attributes to use as predictors for scoring. When the function is invoked with the analytic syntax, these predictors are also used for building the transient models. The mining_attribute_clause behaves as described for the PREDICTION function. (See "mining_attribute_clause::=".)

Example

This example lists the top features corresponding to a given customer record and determines the top attributes for each feature (based on coefficient > 0.25).

WITH
feat_tab AS (
SELECT F.feature_id fid,
       A.attribute_name attr,
       TO_CHAR(A.attribute_value) val,
       A.coefficient coeff
  FROM TABLE(DBMS_DATA_MINING.GET_MODEL_DETAILS_NMF('nmf_sh_sample')) F,
       TABLE(F.attribute_set) A
 WHERE A.coefficient > 0.25
),
feat AS (
SELECT fid,
       CAST(COLLECT(Featattr(attr, val, coeff))
         AS Featattrs) f_attrs
  FROM feat_tab
GROUP BY fid
),
cust_10_features AS (
SELECT T.cust_id, S.feature_id, S.value
  FROM (SELECT cust_id, FEATURE_SET(nmf_sh_sample, 10 USING *) pset
          FROM nmf_sh_sample_apply_prepared
         WHERE cust_id = 100002) T,
       TABLE(T.pset) S
)
SELECT A.value, A.feature_id fid,
       B.attr, B.val, B.coeff
  FROM cust_10_features A,
       (SELECT T.fid, F.*
          FROM feat T,
               TABLE(T.f_attrs) F) B
 WHERE A.feature_id = B.fid
ORDER BY A.value DESC, A.feature_id ASC, coeff DESC, attr ASC, val ASC;

   VALUE  FID ATTR                      VAL                        COEFF
-------- ---- ------------------------- ------------------------ -------
  6.8409    7 YRS_RESIDENCE                                       1.3879
  6.8409    7 BOOKKEEPING_APPLICATION                              .4388
  6.8409    7 CUST_GENDER               M                          .2956
  6.8409    7 COUNTRY_NAME              United States of America   .2848
  6.4975    3 YRS_RESIDENCE                                       1.2668
  6.4975    3 BOOKKEEPING_APPLICATION                              .3465
  6.4975    3 COUNTRY_NAME              United States of America   .2927
  6.4886    2 YRS_RESIDENCE                                       1.3285
  6.4886    2 CUST_GENDER               M                          .2819
  6.4886    2 PRINTER_SUPPLIES                                     .2704
  6.3953    4 YRS_RESIDENCE                                       1.2931
  5.9640    6 YRS_RESIDENCE                                       1.1585
  5.9640    6 HOME_THEATER_PACKAGE                                 .2576
  5.2424    5 YRS_RESIDENCE                                       1.0067
  2.4714    8 YRS_RESIDENCE                                        .3297
  2.3559    1 YRS_RESIDENCE                                        .2768
  2.3559    1 FLAT_PANEL_MONITOR                                   .2593