FEATURE_SET
Syntax
feature_set::=
Analytic Syntax
feature_set_analytic::=
mining_attribute_clause::=
mining_analytic_clause::=
See Also:
"Analytic Functions" for information on the syntax, semantics, and restrictions of 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 theN
highest value features. If multiple features have theN
th value, then the function chooses one of them. -
cutoff
is a value threshold. Only features that are greater than or equal tocutoff
are returned. To filter bycutoff
only, specifyNULL
fortopN
.
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
, wheren
is the number of features to extract, andmining_analytic_clause
, which specifies if the data should be partitioned for multiple model builds. Themining_analytic_clause
supports aquery_partition_clause
and anorder_by_clause
. (See "analytic_clause::=".)
The syntax of the FEATURE_SET
function can use an optional GROUPING
hint when scoring a partitioned model. See GROUPING Hint.
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::=".)
See Also:
-
Oracle Machine Learning for SQL User’s Guide for information about scoring.
-
Oracle Machine Learning for SQL Concepts for information about feature extraction.
Note:
The following example is excerpted from the Oracle Machine Learning for SQL sample programs. For more information about the sample programs, see Appendix A in Oracle Machine Learning for SQL User’s Guide.
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