MINI MINI MANI MO
Rem Copyright (c) 2002, 2017, Oracle and/or its affiliates.
Rem All rights reserved.
Rem
Rem NAME
Rem dbmsdm.sql - dbms Data Mining
Rem
Rem DESCRIPTION
Rem This package provides routines for Data Mining operations
Rem in an Oracle Server.
Rem
Rem NOTES
Rem The procedural option is needed to use this package. This package
Rem must be created under SYS. Operations provided by this package
Rem are performed under the current calling user, not under the package
Rem owner SYS.
Rem
Rem
Rem BEGIN SQL_FILE_METADATA
Rem SQL_SOURCE_FILE: rdbms/admin/dbmsdm.sql
Rem SQL_SHIPPED_FILE: rdbms/admin/dbmsdm.sql
Rem SQL_PHASE: DBMSDM
Rem SQL_STARTUP_MODE: NORMAL
Rem SQL_IGNORABLE_ERRORS: NONE
Rem SQL_CALLING_FILE: rdbms/admin/dbmsodm.sql
Rem END SQL_FILE_METADATA
Rem
Rem MODIFIED (MM/DD/YY)
REM amozes 05/03/17 - #(25992244): enable tablespace override
REM bmilenov 04/21/17 - fix a typo in NNET_ACTIVATIONS_LINEAR
REM amozes 04/13/17 - #(25889903): support model without details
REM yacheche 03/27/17 - bug-25772511: bad sample size in association rule
REM nihao 03/23/17 - add nnet_regularizer_none
REM amozes 02/14/17 - serialized export/import
REM gayyappa 12/28/16 - add random forest
REM ffeli 12/12/16 - Add algorithm registration functions
REM jyarmus 10/20/16 - add exponential smoothing optimization criteria
REM bmilenov 11/16/16 - bug-25107216: ADMM with LBFGS solver
REM nihao 09/21/16 - add neural network
REM yacheche 09/08/16 - add CUR settings
REM mmcracke 08/01/16 - #(24354682) Remove default_setting_type reference
REM nihao 03/28/16 - add CUR
REM gayyappa 11/16/15 - add ODMS_PARTITION_BUILD_TYPE
REM mmcracke 11/06/15 - #(21918919) Bug fix
REM nihao 10/15/15 - add glm sparse solver
REM gayyappa 08/31/15 - add CLAS_MAX_SUP_BINS
REM nihao 08/19/15 - bug-21393905: glm solver setting.
REM mmcampos 08/15/15 - Bug 21459062 - Add new SVD solver settings
REM qinwan 08/09/15 - add R model parameter name constant
REM bmilenov 07/06/15 - Add prep shift and scale constants
REM bmilenov 06/19/15 - bug-18328797: missing value delete row
REM bmilenov 04/20/15 - bug-20877814: SVM solver setting
REM bmilenov 03/09/15 - bug-20671066: sampling settings
REM nihao 02/20/15 - bug 20471864: put GLMS_DIAGNOSTICS_TABLE_NAME back
REM dbai 02/19/15 - bug-20145900: Add new AR settings
REM bmilenov 01/12/15 - bug-20201889: Add regularizer setting for SVM
REM mmcampos 01/02/15 - Add extra SVD settings
REM bmilenov 11/14/14 - Add ESA settings
REM bmilenov 09/22/14 - GLM SGD settings
REM qinwan 09/17/14 - move rq types to separate file
REM qinwan 08/18/14 - add R model dm$rqMod_DetailImpl type declaration
REM jyarmus 07/10/14 - change GLM row diagnostics setting
REM surman 12/29/13 - 13922626: Update SQL metadata
REM mmcracke 07/29/13 - bug 17221590 rename model versioning
REM jyarmus 03/25/13 - bug 16528667
REM xbarr 10/09/12 - bug 14737891
REM jyarmus 07/19/12 - bug 14250749
REM jyarmus 05/24/12 - fix bug 14112868
REM surman 03/27/12 - 13615447: Add SQL patching tags
REM pstengar 11/08/11 - bug 13071710: remove MAX_DOCTERMS
REM bmilenov 10/12/11 - bug-13083060: introduce approximate computation
REM in EM
REM amozes 10/13/11 - revert default topn for clustering
REM bmilenov 10/03/11 - Bug-10354925: make approximate computation an ODM
REM level setting
Rem xbarr 09/22/11 - add tablespace_remap
REM bmilenov 08/18/11 - bug-12878833: fix EM setting inconsistency
REM xbarr 07/20/11 - remove ABN
REM mmcracke 07/12/11 - #(11666638) topn attributes added to
REM get_model_details_km/oc/em
REM bmilenov 04/28/11 - Add Expectation Maximization
REM pstengar 12/10/10 - add settings for text support
REM jyarmus 08/24/10 - Change name of model pruning setting to
REM glms_prune_model and add setting
REM glms_feature_acceptance
REM pstengar 07/28/10 - Bug 9948278: PMML import, add parameter for
REM "strict" syntax check
REM jyarmus 12/24/09 - add setting to enable/disable final model pruning
REM pstengar 07/31/09 - add PMML import
REM bmilenov 06/15/09 - Bug-8661316: Add a new scoring setting to NMF
REM amozes 05/06/08 - #(6868134): add force to drop_model
REM bmilenov 10/04/07 - Add get_model_details_svd
REM bmilenov 09/24/07 - Add SVD constants
REM ramkrish 04/17/07 - add xnal input to create_model
REM jyarmus 03/20/08 - add GLM feature identification setting
REM dmukhin 03/04/08 - bug 6620177: ADP coefficients reversal
REM pstengar 10/23/07 - bug 6439266: add score_criterion_type parameter
REM jyarmus 07/09/07 - add feature selection and feature generation to
REM glm
REM dmukhin 02/09/07 - bug 5854733: remove coefficient reverse xform
REM jyarmus 01/30/07 - add glm setting VIF for ridge
REM dmukhin 12/13/06 - bug 5557333: AR scoping
REM dmukhin 11/13/06 - lob performance
REM dmukhin 10/06/06 - bug 5462460: alter reverse expression
REM bmilenov 08/16/06 - Change missing value treatment constants
REM bmilenov 08/07/06 - Bug #5447741 - GLM setting cleanup
REM amozes 06/08/06 - add transform_coeff to NMF and SVM
REM amozes 05/23/06 - add get_model_transformations
REM bmilenov 05/25/06 - Add get_model_details_global
REM dmukhin 05/11/06 - prj 18876: scoring cost matrix
REM dmukhin 03/27/06 - ADP: stack interface
REM ramkrish 03/24/06 - add GLM
REM mmcracke 03/31/05 - Change public synonyms from DMSYS to SYS
REM mmcracke 02/07/05 - Add max_rule_length filter to
REM get_association_rules.
REM gtang 02/04/05 - Fix bug #4107224
REM mmcracke 01/13/05 - Remove obsolete SVM delete_class API call
REM mmcracke 12/15/04 - Remove reference to DMSYS.
REM mmcracke 11/03/04 - Add filtering items to get_assocation_rules.
REM mmcracke 09/03/04 - Change name of topn parameter.
REM mmcracke 08/05/04 - Add top-N parameter to get_association_rules.
REM amozes 08/04/04 - make TREES singular
REM bmilenov 08/03/04 - Introduce SVM outlier rate setting
REM xbarr 06/25/04 - xbarr_dm_rdbms_migration
REM gtang 05/19/04 - add get_model_details_oc
REM cbhagwat 05/19/04 - Remove ref to predictor variance
REM jyarmus 05/14/04 - fix active learning parameter values
REM bmilenov 05/10/04 - create constant for default kernel
REM jyarmus 05/10/04 - add active learning
REM amozes 04/21/04 - add support for decision tree builds
REM mmcracke 03/12/04 - Add delete_class API call for SVM
REM gtang 02/18/04 - Adding O-Cluster model
REM pstengar 10/31/03 - fixed order of parameters in create_model
REM ramkrish 10/23/03 - replace predictor_variance w/ ai_mdl
REM hyoon 10/22/03 - to add MDL for AI
REM cbhagwat 10/17/03 - feature select renamed to feature_extract
REM cbhagwat 09/23/03 - svm comments
REM cbhagwat 09/15/03 - Remove NMFS_STOP_CRITERIA
REM ramkrish 09/08/03 - Add get_model_details_svm
REM cbhagwat 09/05/03 - KMN setting fixes
REM pstengar 08/29/03 - Removed exposure of get_model_details
REM cbhagwat 07/29/03 - Add settings (Attr Imp)
REM cbhagwat 07/21/03 - Fix 3058974
REM ramkrish 07/15/03 - fix rules for KM
REM ramkrish 07/11/03 - remove compute_rules/histograms settings
REM ramkrish 06/22/03 - chg BUILD to CREATE_MODEL
REM gtang 06/16/03 - Change import_model() signature
REM ramkrish 06/13/03 - remove get_target_values
REM pstengar 06/02/03 - Added get_model_details returning XMLType
REM gtang 06/04/03 - Fix tabulation in one line
REM gtang 05/30/03 - change type of modelnames to varchar2
REM in import_model()
REM ramkrish 05/30/03 - add get_frequent_itemsets
REM ramkrish 05/29/03 - get_model_details_ar to get_association_rules
REM cbhagwat 05/21/03 - kmns settings changes
REM pstengar 05/19/03 - removed precesion_recall since
REM multi target is not supported
REM pstengar 05/15/03 - Added get_default_settings table FUNCTION
REM and moved defaults to dmp_sec
REM ramkrish 05/09/03 - code review changes
REM cbhagwat 04/28/03 - renaming svms_tolerance
REM pstengar 04/22/03 - Made dm_kmn_conv_tolerance NUMBER type
REM cbhagwat 04/18/03 - approx => regression
REM pstengar 04/17/03 - Removed "p_" from parameter names
REM cbhagwat 04/16/03 - Package name change
REM cbhagwat 04/08/03 - new input params in rank_apply
REM pstengar 04/07/03 - Added parameters to compute specifications
REM pstengar 04/03/03 - Made get_model_signature pipelined
REM gtang 04/02/03 - Add model export/import
REM pstengar 03/31/03 - Added get_model_settings
REM cbhagwat 03/31/03 - Add nmf stop criteria enum
REM ramkrish 03/27/03 - add get_model_details_abn
REM cbhagwat 03/26/03 - remove named exceptions
REM bbloom 03/24/03 - Fix constants for ABNS model types to be
REM strings rather than numbers
REM pstengar 03/20/03 - Added cost matrix parameter to compute functions
REM cbhagwat 03/25/03 - Desupport CLAS_COST_MATRIX setting
REM bbloom 03/20/03 - Change "abns_nb_predictors" TO
REM "abns_max_nb_predictors"
REM cbhagwat 03/20/03 - Adding rank_apply
REM cbhagwat 03/14/03 - change complexity and std dev default for svm
REM bbloom 03/04/03 - Fix algo_adaptive_bayes_network
REM pstengar 03/03/03 - Added "DM_" prefix to public types
REM mmcracke 03/03/03 - implement get_model_details_nmf
REM bbloom 02/24/03 - Add default values for abn_param
REM bbloom 02/20/03 - Add constants for ABN
REM cbhagwat 02/20/03 - kmn-build
REM cbhagwat 02/18/03 - add get_model_details_nb
REM cbhagwat 02/13/03 - removing DATA_ settings
REM mmcracke 02/12/03 - Add additional nmf default params
REM pstengar 02/10/03 - Modified compute_confusion_matrix AND
REM compute lift signatures
REM ramkrish 02/10/03 - add named exceptions
REM cbhagwat 02/12/03 - km => cl
REM cbhagwat 02/10/03 - Adding k-means get_model_details code
REM cbhagwat 02/06/03 - change max ar rule length to 20
REM cbhagwat 02/03/03 - change order
REM ramkrish 01/30/03 - cleanup API signatures - add eval templates
REM cbhagwat 01/29/03 - take data prep out
REM cbhagwat 01/17/03 - implement get_model_details_ar
REM cbhagwat 01/14/03 - Adding nmf constants
REM ramkrish 01/10/03 - add get_model_details_ar
REM cbhagwat 01/10/03 - continue svm
REM cbhagwat 01/07/03 - supporting svm
REM ramkrish 12/28/02 - fix comments on settings table
REM cbhagwat 12/24/02 - code AR
REM cbhagwat 12/17/02 - fix errors
REM cbhagwat 12/16/02 - adding svm stubs
REM pstengar 12/11/02 - Added get_target_values function
REM cbhagwat 12/09/02 - case-id compulsory
REM cbhagwat 12/03/02 - Changing lift signature
REM cbhagwat 11/05/02 - name changes
REM ramkrish 11/04/02 - fix signatures
REM ramkrish 11/01/02 - reflect review comments
REM cbhagwat 09/19/02 - defining constants etc
REM cbhagwat 09/16/02 - Skeleton for pl/sql api
REM mmcampos 04/15/02 - Add header and settings and enums constants
REM dmukhin 02/15/02 - add more prototypes
REM ramkrish 01/11/02 - Creation
Rem
@@?/rdbms/admin/sqlsessstart.sql
REM ********************************************************************
REM THE FUNCTIONS SUPPLIED BY THIS PACKAGE AND ITS EXTERNAL INTERFACE
REM ARE RESERVED BY ORACLE AND ARE SUBJECT TO CHANGE IN FUTURE RELEASES.
REM ********************************************************************
REM ********************************************************************
REM THIS PACKAGE MUST NOT BE MODIFIED BY THE CUSTOMER. DOING SO COULD
REM CAUSE INTERNAL ERRORS AND SECURITY VIOLATIONS IN THE RDBMS.
REM ********************************************************************
REM ********************************************************************
REM THIS PACKAGE MUST BE CREATED UNDER DMSYS.
REM ********************************************************************
CREATE OR REPLACE PACKAGE dbms_data_mining AUTHID CURRENT_USER AS
------------
-- OVERVIEW
--
-- This package provides general purpose routines for Data Mining
-- operations viz.
-- . CREATE a MODEL against build data.
-- . DROP an existing MODEL.
-- . RENAME an existing MODEL.
-- . COMPUTE various metrics to test a model against the APPLY
-- results on test data, with cost inputs
-- . APPLY a model to (production) mining data
-- . RANK the APPLY results based on cost and other factors
-- . GET the MODEL SIGNATURE - i.e. retrieve the attributes
-- that constitute the model and their relevant characteristics.
-- . GET the MODEL DETAILS - i.e. retrieve the contents of
-- the model - the specific patterns and rules that were used
-- in making the prediction (in the case of predictive models),
-- and/or the declarative rules (in the case of declarative models).
--
------------------------
-- RULES AND LIMITATIONS
--
-- The following rules apply in the specification of functions and
-- procedures in this package.
--
-- A function/procedure will raise an INVALID_ARGVAL exception if the
-- the following restrictions are not followed in specifying values
-- for parameters (unless otherwise specified):
--
-- 1. Every BUILD operation MUST have the mining function
-- name specified at the minimum.
-- 2. All schema object names, except models, should be maximum
-- 30 bytes in size.
-- 3. All model names should be maximum 25 bytes in size.
-- 4. The SETTINGS discussed below under CONSTANTS represent the name
-- tags and values that act as column values in a user-created
-- Settings Table, with a fixed schema and column types:
--
-- SETTING_NAME SETTING_VALUE
-- varchar2(30) varchar2(30)
--
-- 5. For numerical settings, use TO_CHAR() to store them in the
-- SETTING_VALUE column - the API will interpret the values.
--
--
-----------
-- SECURITY
--
-- Privileges are associated with the the caller of the procedures/
-- functions in this package as follows:
-- If the caller is an anonymous PL/SQL block, the procedures/functions
-- are run with the privilege of the current user.
-- If the caller is a stored procedure, the procedures/functions are run
-- using the privileges of the owner of the stored procedure.
--
------------
-- CONSTANTS
--
-- General Settings - Begin ------------------------------------------------
-- Data Prep: Setting Names
prep_auto CONSTANT VARCHAR2(30) := 'PREP_AUTO';
-- Data Prep: Setting Values for prep_auto
prep_auto_off CONSTANT VARCHAR2(30) := 'OFF';
prep_auto_on CONSTANT VARCHAR2(30) := 'ON';
-- normalization settings
-- 2D numeric columns scale
prep_scale_2dnum CONSTANT VARCHAR2(30) := 'PREP_SCALE_2DNUM';
-- values for prep_scale_2dnum
prep_scale_stddev CONSTANT VARCHAR2(30) := 'PREP_SCALE_STDDEV';
prep_scale_range CONSTANT VARCHAR2(30) := 'PREP_SCALE_RANGE';
-- nested numeric columns scale
prep_scale_nnum CONSTANT VARCHAR2(30) := 'PREP_SCALE_NNUM';
-- value for prep_scale_nnum
prep_scale_maxabs CONSTANT VARCHAR2(30) := 'PREP_SCALE_MAXABS';
-- 2D numeric shift
prep_shift_2dnum CONSTANT VARCHAR2(30) := 'PREP_SHIFT_2DNUM';
-- values for prep_shift_2dnum
prep_shift_mean CONSTANT VARCHAR2(30) := 'PREP_SHIFT_MEAN';
prep_shift_min CONSTANT VARCHAR2(30) := 'PREP_SHIFT_MIN';
-- Score Criterion Type: Setting Values for score_criterion_type
score_criterion_probability CONSTANT VARCHAR2(30) := 'PROBABILITY';
score_criterion_cost CONSTANT VARCHAR2(30) := 'COST';
-- Row Weights - Setting Name
odms_row_weight_column_name CONSTANT VARCHAR2(30) :=
'ODMS_ROW_WEIGHT_COLUMN_NAME';
-- Cost Matrix
cost_matrix_type_score CONSTANT VARCHAR2(30) := 'SCORE';
cost_matrix_type_create CONSTANT VARCHAR2(30) := 'CREATE';
-- Missing Value Treatment - Setting Name
odms_missing_value_treatment CONSTANT VARCHAR2(30) :=
'ODMS_MISSING_VALUE_TREATMENT';
-- Missing Value Treatment: Setting Values for ODMS_MISSING_VALUE_TREATMENT
odms_missing_value_mean_mode CONSTANT VARCHAR2(30) :=
'ODMS_MISSING_VALUE_MEAN_MODE';
odms_missing_value_delete_row CONSTANT VARCHAR2(30) :=
'ODMS_MISSING_VALUE_DELETE_ROW';
odms_missing_value_auto CONSTANT VARCHAR2(30) :=
'ODMS_MISSING_VALUE_AUTO';
-- Transactional training data format: Setting Names
odms_item_id_column_name CONSTANT VARCHAR2(30) :=
'ODMS_ITEM_ID_COLUMN_NAME';
odms_item_value_column_name CONSTANT VARCHAR2(30) :=
'ODMS_ITEM_VALUE_COLUMN_NAME';
-- Unstructured Text Setting Names
odms_text_policy_name CONSTANT VARCHAR2(30) :=
'ODMS_TEXT_POLICY_NAME';
odms_text_max_features CONSTANT VARCHAR2(30) :=
'ODMS_TEXT_MAX_FEATURES';
odms_text_min_documents CONSTANT VARCHAR2(30) :=
'ODMS_TEXT_MIN_DOCUMENTS';
-- Approximate computation
odms_approximate_computation CONSTANT VARCHAR2(30) :=
'ODMS_APPROXIMATE_COMPUTATION';
-- Setting values for odms_approximate_computation
odms_appr_comp_enable CONSTANT VARCHAR2(30) := 'ODMS_APPR_COMP_ENABLE';
odms_appr_comp_disable CONSTANT VARCHAR2(30) := 'ODMS_APPR_COMP_DISABLE';
-- Sampling
odms_sampling CONSTANT VARCHAR2(30) := 'ODMS_SAMPLING';
-- Setting values for odms_sampling
odms_sampling_enable CONSTANT VARCHAR2(30) := 'ODMS_SAMPLING_ENABLE';
odms_sampling_disable CONSTANT VARCHAR2(30) := 'ODMS_SAMPLING_DISABLE';
-- Sample size
odms_sample_size CONSTANT VARCHAR2(30) := 'ODMS_SAMPLE_SIZE';
-- Partitioning
odms_partition_columns CONSTANT VARCHAR2(30) := 'ODMS_PARTITION_COLUMNS';
-- Max partition columns
odms_max_partitions CONSTANT VARCHAR2(30) := 'ODMS_MAX_PARTITIONS';
-- Max sup bins ---
clas_max_sup_bins CONSTANT VARCHAR2(30) := 'CLAS_MAX_SUP_BINS';
--Partition build type (inter/intra/hybrid)
odms_partition_build_type CONSTANT VARCHAR2(30) :=
'ODMS_PARTITION_BUILD_TYPE';
odms_partition_build_inter CONSTANT VARCHAR2(30) :=
'ODMS_PARTITION_BUILD_INTER';
odms_partition_build_intra CONSTANT VARCHAR2(30) :=
'ODMS_PARTITION_BUILD_INTRA';
odms_partition_build_hybrid CONSTANT VARCHAR2(30) :=
'ODMS_PARTITION_BUILD_HYBRID';
-- random seed
odms_random_seed CONSTANT VARCHAR2(30):= 'ODMS_RANDOM_SEED';
-- retain information for details (default is enable)
odms_details CONSTANT VARCHAR2(30):= 'ODMS_DETAILS';
odms_enable CONSTANT VARCHAR2(30):= 'ODMS_ENABLE';
odms_disable CONSTANT VARCHAR2(30):= 'ODMS_DISABLE';
-- override default tablespace
odms_tablespace_name CONSTANT VARCHAR2(30):= 'ODMS_TABLESPACE_NAME';
-- General Settings - End -------------------------------------------------
----------- Function and Algorithm Settings - Begin ---------------------
-- FUNCTION NAME (input as CREATE_MODEL parameter)
--
classification CONSTANT VARCHAR2(30) := 'CLASSIFICATION';
regression CONSTANT VARCHAR2(30) := 'REGRESSION';
clustering CONSTANT VARCHAR2(30) := 'CLUSTERING';
association CONSTANT VARCHAR2(30) := 'ASSOCIATION';
feature_extraction CONSTANT VARCHAR2(30) := 'FEATURE_EXTRACTION';
attribute_importance CONSTANT VARCHAR2(30) := 'ATTRIBUTE_IMPORTANCE';
time_series CONSTANT VARCHAR2(30) := 'TIME_SERIES';
-- FUNCTION: Setting Names (input to settings_name column in settings table)
clas_priors_table_name CONSTANT VARCHAR2(30) := 'CLAS_PRIORS_TABLE_NAME';
clas_weights_table_name CONSTANT VARCHAR2(30) := 'CLAS_WEIGHTS_TABLE_NAME';
clas_cost_table_name CONSTANT VARCHAR2(30) := 'CLAS_COST_TABLE_NAME';
-- Balanced weights (boolean: on/off) */
clas_weights_balanced CONSTANT VARCHAR2(30) := 'CLAS_WEIGHTS_BALANCED';
clas_weights_bal_off CONSTANT VARCHAR2(30) := 'OFF';
clas_weights_bal_on CONSTANT VARCHAR2(30) := 'ON';
-- AR: Setting Names
asso_max_rule_length CONSTANT VARCHAR2(30) := 'ASSO_MAX_RULE_LENGTH';
asso_min_confidence CONSTANT VARCHAR2(30) := 'ASSO_MIN_CONFIDENCE';
asso_min_support CONSTANT VARCHAR2(30) := 'ASSO_MIN_SUPPORT';
asso_min_support_int CONSTANT VARCHAR2(30) := 'ASSO_MIN_SUPPORT_INT';
asso_min_rev_confidence CONSTANT VARCHAR2(30) := 'ASSO_MIN_REV_CONFIDENCE';
asso_in_rules CONSTANT VARCHAR2(30) := 'ASSO_IN_RULES';
asso_ex_rules CONSTANT VARCHAR2(30) := 'ASSO_EX_RULES';
asso_ant_in_rules CONSTANT VARCHAR2(30) := 'ASSO_ANT_IN_RULES';
asso_ant_ex_rules CONSTANT VARCHAR2(30) := 'ASSO_ANT_EX_RULES';
asso_cons_in_rules CONSTANT VARCHAR2(30) := 'ASSO_CONS_IN_RULES';
asso_cons_ex_rules CONSTANT VARCHAR2(30) := 'ASSO_CONS_EX_RULES';
asso_aggregates CONSTANT VARCHAR2(30) := 'ASSO_AGGREGATES';
asso_abs_error CONSTANT VARCHAR2(30) := 'ASSO_ABS_ERROR';
asso_conf_level CONSTANT VARCHAR2(30) := 'ASSO_CONF_LEVEL';
feat_num_features CONSTANT VARCHAR2(30) := 'FEAT_NUM_FEATURES';
clus_num_clusters CONSTANT VARCHAR2(30) := 'CLUS_NUM_CLUSTERS';
-- ALGORITHM Setting Name (input to settings_name column in settings table)
--
algo_name CONSTANT VARCHAR2(30) := 'ALGO_NAME';
-- ALGORITHM: Setting Values for algo_name
algo_naive_bayes CONSTANT VARCHAR2(30) :=
'ALGO_NAIVE_BAYES';
algo_adaptive_bayes_network CONSTANT VARCHAR2(30) :=
'ALGO_ADAPTIVE_BAYES_NETWORK';
algo_support_vector_machines CONSTANT VARCHAR2(30) :=
'ALGO_SUPPORT_VECTOR_MACHINES';
algo_nonnegative_matrix_factor CONSTANT VARCHAR2(30) :=
'ALGO_NONNEGATIVE_MATRIX_FACTOR';
algo_apriori_association_rules CONSTANT VARCHAR2(30) :=
'ALGO_APRIORI_ASSOCIATION_RULES';
algo_kmeans CONSTANT VARCHAR2(30) :=
'ALGO_KMEANS';
algo_ocluster CONSTANT VARCHAR2(30) :=
'ALGO_O_CLUSTER';
algo_ai_mdl CONSTANT VARCHAR2(30) :=
'ALGO_AI_MDL';
algo_decision_tree CONSTANT VARCHAR2(30) :=
'ALGO_DECISION_TREE';
algo_random_forest CONSTANT VARCHAR2(30) :=
'ALGO_RANDOM_FOREST';
algo_generalized_linear_model CONSTANT VARCHAR2(30) :=
'ALGO_GENERALIZED_LINEAR_MODEL';
algo_singular_value_decomp CONSTANT VARCHAR2(30) :=
'ALGO_SINGULAR_VALUE_DECOMP';
algo_expectation_maximization CONSTANT VARCHAR2(30) :=
'ALGO_EXPECTATION_MAXIMIZATION';
algo_explicit_semantic_analys CONSTANT VARCHAR2(30) :=
'ALGO_EXPLICIT_SEMANTIC_ANALYS';
algo_neural_network CONSTANT VARCHAR2(30) :=
'ALGO_NEURAL_NETWORK';
algo_cur_decomposition CONSTANT VARCHAR2(30) :=
'ALGO_CUR_DECOMPOSITION';
algo_exponential_smoothing CONSTANT VARCHAR2(30) :=
'ALGO_EXPONENTIAL_SMOOTHING';
-- ALGORITHM SETTINGS AND VALUES
--
-- ABN: Setting Names
abns_model_type CONSTANT VARCHAR2(30) := 'ABNS_MODEL_TYPE';
abns_max_build_minutes CONSTANT VARCHAR2(30) := 'ABNS_MAX_BUILD_MINUTES';
abns_max_predictors CONSTANT VARCHAR2(30) := 'ABNS_MAX_PREDICTORS';
abns_max_nb_predictors CONSTANT VARCHAR2(30) := 'ABNS_MAX_NB_PREDICTORS';
-- ABN: Setting Values for abns_model_type
abns_multi_feature CONSTANT VARCHAR2(30) := 'ABNS_MULTI_FEATURE';
abns_single_feature CONSTANT VARCHAR2(30) := 'ABNS_SINGLE_FEATURE';
abns_naive_bayes CONSTANT VARCHAR2(30) := 'ABNS_NAIVE_BAYES';
-- NB: Setting Names
nabs_pairwise_threshold CONSTANT VARCHAR2(30) := 'NABS_PAIRWISE_THRESHOLD';
nabs_singleton_threshold CONSTANT VARCHAR2(30) := 'NABS_SINGLETON_THRESHOLD';
-- SVM: Setting Names
-- NOTE: svms_epsilon applies only for SVM Regression
-- svms_complexity_factor applies to both
-- svms_std_dev applies only for Gaussian Kernels
-- kernel_cache_size to Gaussian kernels only
svms_conv_tolerance CONSTANT VARCHAR2(30) := 'SVMS_CONV_TOLERANCE';
svms_std_dev CONSTANT VARCHAR2(30) := 'SVMS_STD_DEV';
svms_complexity_factor CONSTANT VARCHAR2(30) := 'SVMS_COMPLEXITY_FACTOR';
svms_kernel_cache_size CONSTANT VARCHAR2(30) := 'SVMS_KERNEL_CACHE_SIZE';
svms_epsilon CONSTANT VARCHAR2(30) := 'SVMS_EPSILON';
svms_kernel_function CONSTANT VARCHAR2(30) := 'SVMS_KERNEL_FUNCTION';
svms_active_learning CONSTANT VARCHAR2(30) := 'SVMS_ACTIVE_LEARNING';
svms_outlier_rate CONSTANT VARCHAR2(30) := 'SVMS_OUTLIER_RATE';
svms_num_iterations CONSTANT VARCHAR2(30) := 'SVMS_NUM_ITERATIONS';
svms_num_pivots CONSTANT VARCHAR2(30) := 'SVMS_NUM_PIVOTS';
svms_batch_rows CONSTANT VARCHAR2(30) := 'SVMS_BATCH_ROWS';
svms_regularizer CONSTANT VARCHAR2(30) := 'SVMS_REGULARIZER';
svms_solver CONSTANT VARCHAR2(30) := 'SVMS_SOLVER';
-- SVM: Setting Values for svms_kernel_function
svms_linear CONSTANT VARCHAR2(30) := 'SVMS_LINEAR';
svms_gaussian CONSTANT VARCHAR2(30) := 'SVMS_GAUSSIAN';
-- SVM: Setting Values for svms_active_learning
svms_al_enable CONSTANT VARCHAR2(30) := 'SVMS_AL_ENABLE';
svms_al_disable CONSTANT VARCHAR2(30) := 'SVMS_AL_DISABLE';
-- SVM: Setting Values for svms_regularizer
svms_regularizer_l1 CONSTANT VARCHAR2(30) := 'SVMS_REGULARIZER_L1';
svms_regularizer_l2 CONSTANT VARCHAR2(30) := 'SVMS_REGULARIZER_L2';
-- SVM: Setting Values for svms_solver
svms_solver_sgd CONSTANT VARCHAR2(30) := 'SVMS_SOLVER_SGD';
svms_solver_ipm CONSTANT VARCHAR2(30) := 'SVMS_SOLVER_IPM';
-- KMNS: Setting Names
kmns_distance CONSTANT VARCHAR2(30) := 'KMNS_DISTANCE';
kmns_iterations CONSTANT VARCHAR2(30) := 'KMNS_ITERATIONS';
kmns_conv_tolerance CONSTANT VARCHAR2(30) := 'KMNS_CONV_TOLERANCE';
kmns_split_criterion CONSTANT VARCHAR2(30) := 'KMNS_SPLIT_CRITERION';
kmns_min_pct_attr_support CONSTANT VARCHAR2(30):= 'KMNS_MIN_PCT_ATTR_SUPPORT';
kmns_block_growth CONSTANT VARCHAR2(30) := 'KMNS_BLOCK_GROWTH';
kmns_num_bins CONSTANT VARCHAR2(30) := 'KMNS_NUM_BINS';
kmns_details CONSTANT VARCHAR2(30) := 'KMNS_DETAILS';
kmns_random_seed CONSTANT VARCHAR2(30) := 'KMNS_RANDOM_SEED';
-- KMNS: Setting Values for kmns_distance
kmns_euclidean CONSTANT VARCHAR2(30) := 'KMNS_EUCLIDEAN';
kmns_cosine CONSTANT VARCHAR2(30) := 'KMNS_COSINE';
kmns_fast_cosine CONSTANT VARCHAR2(30) := 'KMNS_FAST_COSINE';
-- KMNS: Setting Values for kmns_split_criterion
kmns_size CONSTANT VARCHAR2(30) := 'KMNS_SIZE';
kmns_variance CONSTANT VARCHAR2(30) := 'KMNS_VARIANCE';
-- KMNS: Setting Values for kmns_details
kmns_details_none CONSTANT VARCHAR2(30) := 'KMNS_DETAILS_NONE';
kmns_details_hierarchy CONSTANT VARCHAR2(30) := 'KMNS_DETAILS_HIERARCHY';
kmns_details_all CONSTANT VARCHAR2(30) := 'KMNS_DETAILS_ALL';
-- NMF: Setting Names
nmfs_num_iterations CONSTANT VARCHAR2(30) := 'NMFS_NUM_ITERATIONS';
nmfs_conv_tolerance CONSTANT VARCHAR2(30) := 'NMFS_CONV_TOLERANCE';
nmfs_random_seed CONSTANT VARCHAR2(30) := 'NMFS_RANDOM_SEED';
nmfs_nonnegative_scoring CONSTANT VARCHAR2(30) :=
'NMFS_NONNEGATIVE_SCORING';
-- Setting values for NMFS_NONNEGATIVE_SCORING
nmfs_nonneg_scoring_enable CONSTANT VARCHAR2(30) :=
'NMFS_NONNEG_SCORING_ENABLE';
nmfs_nonneg_scoring_disable CONSTANT VARCHAR2(30) :=
'NMFS_NONNEG_SCORING_DISABLE';
-- OCLT: Setting Names for O-Cluster
oclt_sensitivity CONSTANT VARCHAR2(30) := 'OCLT_SENSITIVITY';
oclt_max_buffer CONSTANT VARCHAR2(30) := 'OCLT_MAX_BUFFER';
-- TREE: Setting Names
tree_impurity_metric CONSTANT VARCHAR2(30) := 'TREE_IMPURITY_METRIC';
tree_term_max_depth CONSTANT VARCHAR2(30) := 'TREE_TERM_MAX_DEPTH';
tree_term_minrec_split CONSTANT VARCHAR2(30) := 'TREE_TERM_MINREC_SPLIT';
tree_term_minpct_split CONSTANT VARCHAR2(30) := 'TREE_TERM_MINPCT_SPLIT';
tree_term_minrec_node CONSTANT VARCHAR2(30) := 'TREE_TERM_MINREC_NODE';
tree_term_minpct_node CONSTANT VARCHAR2(30) := 'TREE_TERM_MINPCT_NODE';
-- TREE: Setting Values for tree_impurity_metric
tree_impurity_gini CONSTANT VARCHAR2(30) := 'TREE_IMPURITY_GINI';
tree_impurity_entropy CONSTANT VARCHAR2(30) := 'TREE_IMPURITY_ENTROPY';
-- RANDOM FOREST: Setting Names
rfor_mtry CONSTANT VARCHAR2(30) := 'RFOR_MTRY';
rfor_num_trees CONSTANT VARCHAR2(30) := 'RFOR_NUM_TREES';
rfor_sampling_ratio CONSTANT VARCHAR2(30) := 'RFOR_SAMPLING_RATIO';
-- GLM: Setting Names
glms_ridge_regression CONSTANT VARCHAR2(30) := 'GLMS_RIDGE_REGRESSION';
glms_row_diagnostics CONSTANT VARCHAR2(30) := 'GLMS_ROW_DIAGNOSTICS';
glms_diagnostics_table_name CONSTANT VARCHAR2(30) :=
'GLMS_DIAGNOSTICS_TABLE_NAME';
glms_reference_class_name CONSTANT VARCHAR2(30) :=
'GLMS_REFERENCE_CLASS_NAME';
glms_ridge_value CONSTANT VARCHAR2(30) := 'GLMS_RIDGE_VALUE';
glms_conf_level CONSTANT VARCHAR2(30) := 'GLMS_CONF_LEVEL';
glms_vif_for_ridge CONSTANT VARCHAR2(30) := 'GLMS_VIF_FOR_RIDGE';
glms_solver CONSTANT VARCHAR2(30) := 'GLMS_SOLVER';
glms_sparse_solver CONSTANT VARCHAR2(30) := 'GLMS_SPARSE_SOLVER';
-- GLM: Setting Values for glms_ridge_regression
glms_ridge_reg_enable CONSTANT VARCHAR2(30) := 'GLMS_RIDGE_REG_ENABLE';
glms_ridge_reg_disable CONSTANT VARCHAR2(30) := 'GLMS_RIDGE_REG_DISABLE';
-- GLM: Setting Values for glms_row_diagnostics
glms_row_diag_enable CONSTANT VARCHAR2(30) := 'GLMS_ROW_DIAG_ENABLE';
glms_row_diag_disable CONSTANT VARCHAR2(30) := 'GLMS_ROW_DIAG_DISABLE';
-- GLM: Setting Values for glms_vif_for_ridge
glms_vif_ridge_enable CONSTANT VARCHAR2(30) := 'GLMS_VIF_RIDGE_ENABLE';
glms_vif_ridge_disable CONSTANT VARCHAR2(30) := 'GLMS_VIF_RIDGE_DISABLE';
-- GLM: Setting Values for glms_ftr_selection
glms_ftr_selection CONSTANT VARCHAR2(30) := 'GLMS_FTR_SELECTION';
glms_ftr_selection_enable CONSTANT VARCHAR2(30) :=
'GLMS_FTR_SELECTION_ENABLE';
glms_ftr_selection_disable CONSTANT VARCHAR2(30) :=
'GLMS_FTR_SELECTION_DISABLE';
-- GLM: Setting Values for glms_ftr_sel_crit
glms_ftr_sel_crit CONSTANT VARCHAR2(30) := 'GLMS_FTR_SEL_CRIT';
glms_ftr_sel_aic CONSTANT VARCHAR2(30) := 'GLMS_FTR_SEL_AIC';
glms_ftr_sel_sbic CONSTANT VARCHAR2(30) := 'GLMS_FTR_SEL_SBIC';
glms_ftr_sel_ric CONSTANT VARCHAR2(30) := 'GLMS_FTR_SEL_RIC';
glms_ftr_sel_alpha_inv CONSTANT VARCHAR2(30) := 'GLMS_FTR_SEL_ALPHA_INV';
-- GLM: Setting Values for glms_feature_generation
glms_ftr_generation CONSTANT VARCHAR2(30) := 'GLMS_FTR_GENERATION';
glms_ftr_generation_enable CONSTANT VARCHAR2(30) :=
'GLMS_FTR_GENERATION_ENABLE';
glms_ftr_generation_disable CONSTANT VARCHAR2(30) :=
'GLMS_FTR_GENERATION_DISABLE';
-- GLM: Setting Values for glms_feature_gen
glms_ftr_gen_method CONSTANT VARCHAR2(30) := 'GLMS_FTR_GEN_METHOD';
glms_ftr_gen_quadratic CONSTANT VARCHAR2(30) := 'GLMS_FTR_GEN_QUADRATIC';
glms_ftr_gen_cubic CONSTANT VARCHAR2(30) := 'GLMS_FTR_GEN_CUBIC';
-- GLM: feature selection categorical value handling
glms_select_block CONSTANT VARCHAR2(30) := 'GLMS_SELECT_BLOCK';
glms_select_block_disable CONSTANT VARCHAR2(30) := 'GLMS_SELECT_BLOCK_DISABLE';
glms_select_block_enable CONSTANT VARCHAR2(30) := 'GLMS_SELECT_BLOCK_ENABLE';
-- GLM: feature selection - max features selected
glms_max_features CONSTANT VARCHAR2(30) := 'GLMS_MAX_FEATURES';
-- GLM: feature identification - whether row sampling is used in the
-- selection of feature
glms_ftr_identification CONSTANT VARCHAR2(30) := 'GLMS_FTR_IDENTIFICATION';
glms_ftr_ident_quick CONSTANT VARCHAR2(30) :=
'GLMS_FTR_IDENT_QUICK';
glms_ftr_ident_complete CONSTANT VARCHAR2(30) :=
'GLMS_FTR_IDENT_COMPLETE';
-- GLM: model pruning - whether the final model features will be
-- pruned using t-statistics
glms_prune_model CONSTANT VARCHAR2(30) := 'GLMS_PRUNE_MODEL';
glms_prune_model_enable CONSTANT VARCHAR2(30) := 'GLMS_PRUNE_MODEL_ENABLE';
glms_prune_model_disable CONSTANT VARCHAR2(30) := 'GLMS_PRUNE_MODEL_DISABLE';
-- GLM: feature acceptance - whether partitioning the data into feature
-- ordering and feature selection sets will be used (strict) or
-- not (relaxed
glms_ftr_acceptance CONSTANT VARCHAR2(30) := 'GLMS_FTR_ACCEPTANCE';
glms_ftr_acceptance_strict CONSTANT VARCHAR2(30) :=
'GLMS_FTR_ACCEPTANCE_STRICT';
glms_ftr_acceptance_relaxed CONSTANT VARCHAR2(30) :=
'GLMS_FTR_ACCEPTANCE_RELAXED';
-- GLM: convergence tolerance
glms_conv_tolerance CONSTANT VARCHAR2(30) := 'GLMS_CONV_TOLERANCE';
-- GLM: number of iterations
glms_num_iterations CONSTANT VARCHAR2(30) := 'GLMS_NUM_ITERATIONS';
-- GLM: number of rows in a batch
glms_batch_rows CONSTANT VARCHAR2(30) := 'GLMS_BATCH_ROWS';
-- GLM: Setting Values for glms_solver
glms_solver_sgd CONSTANT VARCHAR2(30) := 'GLMS_SOLVER_SGD';
glms_solver_chol CONSTANT VARCHAR2(30) := 'GLMS_SOLVER_CHOL';
glms_solver_qr CONSTANT VARCHAR2(30) := 'GLMS_SOLVER_QR';
glms_solver_lbfgs_admm CONSTANT VARCHAR2(30) := 'GLMS_SOLVER_LBFGS_ADMM';
-- GLM: Setting Values for glms_sparse_solver
glms_sparse_solver_enable CONSTANT VARCHAR2(30) := 'GLMS_SPARSE_SOLVER_ENABLE';
glms_sparse_solver_disable CONSTANT VARCHAR2(30) := 'GLMS_SPARSE_SOLVER_DISABLE';
-- SVD
-- max number of features allowed
svds_max_num_features CONSTANT NUMBER := 2500;
svds_scoring_mode CONSTANT VARCHAR2(30) := 'SVDS_SCORING_MODE';
-- SVD: Setting values for svds_scoring_mode
svds_scoring_svd CONSTANT VARCHAR2(30) := 'SVDS_SCORING_SVD';
svds_scoring_pca CONSTANT VARCHAR2(30) := 'SVDS_SCORING_PCA';
svds_u_matrix_output CONSTANT VARCHAR2(30) := 'SVDS_U_MATRIX_OUTPUT';
-- SVD: Setting values for svds_u_matrix_output
svds_u_matrix_enable CONSTANT VARCHAR2(30) := 'SVDS_U_MATRIX_ENABLE';
svds_u_matrix_disable CONSTANT VARCHAR2(30) := 'SVDS_U_MATRIX_DISABLE';
-- SVD: tolerance
svds_tolerance CONSTANT VARCHAR2(30) := 'SVDS_TOLERANCE';
-- SVD: Random seed
svds_random_seed CONSTANT VARCHAR2(30) := 'SVDS_RANDOM_SEED';
-- SVD: Oversampling
svds_over_sampling CONSTANT VARCHAR2(30) := 'SVDS_OVER_SAMPLING';
-- SVD: Power iterations
svds_power_iterations CONSTANT VARCHAR2(30) := 'SVDS_POWER_ITERATIONS';
-- SVD: Solver
svds_solver CONSTANT VARCHAR2(30) := 'SVDS_SOLVER';
svds_solver_data_driven CONSTANT VARCHAR2(30) := 'SVDS_SOLVER_DATA_DRIVEN';
svds_solver_tssvd CONSTANT VARCHAR2(30) := 'SVDS_SOLVER_TSSVD';
svds_solver_ssvd CONSTANT VARCHAR2(30) := 'SVDS_SOLVER_SSVD';
svds_solver_tseigen CONSTANT VARCHAR2(30) := 'SVDS_SOLVER_TSEIGEN';
svds_solver_steigen CONSTANT VARCHAR2(30) := 'SVDS_SOLVER_STEIGEN';
-- EM
-- number of components
emcs_num_components CONSTANT VARCHAR2(30) := 'EMCS_NUM_COMPONENTS';
-- high-level component clustering
emcs_cluster_components CONSTANT VARCHAR2(30) :=
'EMCS_CLUSTER_COMPONENTS';
-- values for emcs_cluster_components
emcs_cluster_comp_enable CONSTANT VARCHAR2(30) :=
'EMCS_CLUSTER_COMP_ENABLE';
emcs_cluster_comp_disable CONSTANT VARCHAR2(30) :=
'EMCS_CLUSTER_COMP_DISABLE';
-- high-level cluster threshold
emcs_cluster_thresh CONSTANT VARCHAR2(30) := 'EMCS_CLUSTER_THRESH';
-- max number of 2D attributes
emcs_max_num_attr_2d CONSTANT VARCHAR2(30) := 'EMCS_MAX_NUM_ATTR_2D';
-- number of projections
emcs_num_projections CONSTANT VARCHAR2(30) := 'EMCS_NUM_PROJECTIONS';
-- number of quantile bins
emcs_num_quantile_bins CONSTANT VARCHAR2(30) := 'EMCS_NUM_QUANTILE_BINS';
-- number of topN bins
emcs_num_topn_bins CONSTANT VARCHAR2(30) := 'EMCS_NUM_TOPN_BINS';
-- number of equi-width bins
emcs_num_equiwidth_bins CONSTANT VARCHAR2(30) :=
'EMCS_NUM_EQUIWIDTH_BINS';
-- minimum percentage attribute support
emcs_min_pct_attr_support CONSTANT VARCHAR2(30) :=
'EMCS_MIN_PCT_ATTR_SUPPORT';
-- full covariance (next release)
-- emcs_full_covariance CONSTANT VARCHAR2(30) := 'EMCS_FULL_COVARIANCE';
-- values for emcs_full_covariance
-- emcs_full_cov_enable CONSTANT VARCHAR2(30) := 'EMCS_FULL_COV_ENABLE';
-- emcs_full_cov_disable CONSTANT VARCHAR2(30) := 'EMCS_FULL_COV_DISABLE';
-- cluster statistics
emcs_cluster_statistics CONSTANT VARCHAR2(30) := 'EMCS_CLUSTER_STATISTICS';
-- values for emcs_cluster_statistics
emcs_clus_stats_enable CONSTANT VARCHAR2(30) := 'EMCS_CLUS_STATS_ENABLE';
emcs_clus_stats_disable CONSTANT VARCHAR2(30) := 'EMCS_CLUS_STATS_DISABLE';
-- distribution for modeling numerical attributes
emcs_num_distribution CONSTANT VARCHAR2(30) := 'EMCS_NUM_DISTRIBUTION';
-- values for emcs_num_distribution
emcs_num_distr_bernoulli CONSTANT VARCHAR2(30) :=
'EMCS_NUM_DISTR_BERNOULLI';
emcs_num_distr_gaussian CONSTANT VARCHAR2(30) :=
'EMCS_NUM_DISTR_GAUSSIAN';
emcs_num_distr_system CONSTANT VARCHAR2(30) := 'EMCS_NUM_DISTR_SYSTEM';
-- number of iterations
emcs_num_iterations CONSTANT VARCHAR2(30) := 'EMCS_NUM_ITERATIONS';
-- required log likelihood improvement
emcs_loglike_improvement CONSTANT VARCHAR2(30) :=
'EMCS_LOGLIKE_IMPROVEMENT';
-- linkage function
emcs_linkage_function CONSTANT VARCHAR2(30) := 'EMCS_LINKAGE_FUNCTION';
-- values for linkage function
emcs_linkage_single CONSTANT VARCHAR2(30) := 'EMCS_LINKAGE_SINGLE';
emcs_linkage_average CONSTANT VARCHAR2(30) := 'EMCS_LINKAGE_AVERAGE';
emcs_linkage_complete CONSTANT VARCHAR2(30) := 'EMCS_LINKAGE_COMPLETE';
-- attribute filtering
emcs_attribute_filter CONSTANT VARCHAR2(30) := 'EMCS_ATTRIBUTE_FILTER';
-- values for attribute filtering
emcs_attr_filter_enable CONSTANT VARCHAR2(30) :=
'EMCS_ATTR_FILTER_ENABLE';
emcs_attr_filter_disable CONSTANT VARCHAR2(30) :=
'EMCS_ATTR_FILTER_DISABLE';
-- convergence criterion
emcs_convergence_criterion CONSTANT VARCHAR2(30) :=
'EMCS_CONVERGENCE_CRITERION';
-- values for convergence criterion
emcs_conv_crit_heldaside CONSTANT VARCHAR2(30) :=
'EMCS_CONV_CRIT_HELDASIDE';
emcs_conv_crit_bic CONSTANT VARCHAR2(30) :=
'EMCS_CONV_CRIT_BIC';
-- random seed
emcs_random_seed CONSTANT VARCHAR2(30) := 'EMCS_RANDOM_SEED';
-- model search
emcs_model_search CONSTANT VARCHAR2(30) := 'EMCS_MODEL_SEARCH';
-- values for model search
emcs_model_search_enable CONSTANT VARCHAR2(30) :=
'EMCS_MODEL_SEARCH_ENABLE';
emcs_model_search_disable CONSTANT VARCHAR2(30) :=
'EMCS_MODEL_SEARCH_DISABLE';
-- remove components
emcs_remove_components CONSTANT VARCHAR2(30) := 'EMCS_REMOVE_COMPONENTS';
-- values for remove components
emcs_remove_comps_enable CONSTANT VARCHAR2(30) :=
'EMCS_REMOVE_COMPS_ENABLE';
emcs_remove_comps_disable CONSTANT VARCHAR2(30) :=
'EMCS_REMOVE_COMPS_DISABLE';
-- ESA
esas_value_threshold CONSTANT VARCHAR2(30) := 'ESAS_VALUE_THRESHOLD';
esas_min_items CONSTANT VARCHAR2(30) := 'ESAS_MIN_ITEMS';
esas_topn_features CONSTANT VARCHAR2(30) := 'ESAS_TOPN_FEATURES';
-- ADMM
admm_iterations CONSTANT VARCHAR2(30) := 'ADMM_ITERATIONS';
admm_consensus CONSTANT VARCHAR2(30) := 'ADMM_CONSENSUS';
admm_tolerance CONSTANT VARCHAR2(30) := 'ADMM_TOLERANCE';
-- LBFGS
lbfgs_history_depth CONSTANT VARCHAR2(30) := 'LBFGS_HISTORY_DEPTH';
lbfgs_scale_hessian CONSTANT VARCHAR2(30) := 'LBFGS_SCALE_HESSIAN';
lbfgs_scale_hessian_enable CONSTANT VARCHAR2(30) :=
'LBFGS_SCALE_HESSIAN_ENABLE';
lbfgs_scale_hessian_disable CONSTANT VARCHAR2(30) :=
'LBFGS_SCALE_HESSIAN_DISABLE';
lbfgs_gradient_tolerance CONSTANT VARCHAR2(30) :=
'LBFGS_GRADIENT_TOLERANCE';
-- RGLU: Setting Values
ralg_build_function CONSTANT VARCHAR2(30) := 'RALG_BUILD_FUNCTION';
ralg_build_parameter CONSTANT VARCHAR2(30) := 'RALG_BUILD_PARAMETER';
ralg_score_function CONSTANT VARCHAR2(30) := 'RALG_SCORE_FUNCTION';
ralg_details_function CONSTANT VARCHAR2(30) := 'RALG_DETAILS_FUNCTION';
ralg_details_format CONSTANT VARCHAR2(30) := 'RALG_DETAILS_FORMAT';
ralg_weight_function CONSTANT VARCHAR2(30) := 'RALG_WEIGHT_FUNCTION';
ralg_featurematrix_function CONSTANT VARCHAR2(30)
:= 'RALG_FEATUREMATRIX_FUNCTION';
ralg_clustercenter_function CONSTANT VARCHAR2(30)
:= 'RALG_CLUSTERCENTER_FUNCTION';
r_formula CONSTANT VARCHAR2(30)
:= 'R_FORMULA';
-- NNET
nnet_hidden_layers CONSTANT VARCHAR2(30) := 'NNET_HIDDEN_LAYERS';
nnet_nodes_per_layer CONSTANT VARCHAR2(30) := 'NNET_NODES_PER_LAYER';
nnet_iterations CONSTANT VARCHAR2(30) := 'NNET_ITERATIONS';
nnet_tolerance CONSTANT VARCHAR2(30) := 'NNET_TOLERANCE';
nnet_activations CONSTANT VARCHAR2(30) := 'NNET_ACTIVATIONS';
nnet_activations_log_sig CONSTANT VARCHAR2(30) :=
'NNET_ACTIVATIONS_LOG_SIG';
nnet_activations_linear CONSTANT VARCHAR2(30) := 'NNET_ACTIVATIONS_LINEAR';
nnet_activations_tanh CONSTANT VARCHAR2(30) := 'NNET_ACTIVATIONS_TANH';
nnet_activations_arctan CONSTANT VARCHAR2(30) := 'NNET_ACTIVATIONS_ARCTAN';
nnet_activations_bipolar_sig CONSTANT VARCHAR2(30) :=
'NNET_ACTIVATIONS_BIPOLAR_SIG';
nnet_regularizer CONSTANT VARCHAR2(30) := 'NNET_REGULARIZER';
nnet_regularizer_heldaside CONSTANT VARCHAR2(30) :=
'NNET_REGULARIZER_HELDASIDE';
nnet_regularizer_l2 CONSTANT VARCHAR2(30) := 'NNET_REGULARIZER_L2';
nnet_regularizer_none CONSTANT VARCHAR2(30) := 'NNET_REGULARIZER_NONE';
nnet_heldaside_ratio CONSTANT VARCHAR2(30) := 'NNET_HELDASIDE_RATIO';
nnet_heldaside_max_fail CONSTANT VARCHAR2(30) := 'NNET_HELDASIDE_MAX_FAIL';
nnet_reg_lambda CONSTANT VARCHAR2(30) := 'NNET_REG_LAMBDA';
nnet_weight_lower_bound CONSTANT VARCHAR2(30) := 'NNET_WEIGHT_LOWER_BOUND';
nnet_weight_upper_bound CONSTANT VARCHAR2(30) := 'NNET_WEIGHT_UPPER_BOUND';
-- CUR
-- approximated number of selected attributes
curs_approx_attr_num CONSTANT VARCHAR2(30) := 'CURS_APPROX_ATTR_NUM';
-- row importance
curs_row_importance CONSTANT VARCHAR2(30) := 'CURS_ROW_IMPORTANCE';
-- row importance values
curs_row_imp_enable CONSTANT VARCHAR2(30) := 'CURS_ROW_IMP_ENABLE';
curs_row_imp_disable CONSTANT VARCHAR2(30) := 'CURS_ROW_IMP_DISABLE';
-- approximated number of selected rows
curs_approx_row_num CONSTANT VARCHAR2(30) := 'CURS_APPROX_ROW_NUM';
-- SVD rank
curs_svd_rank CONSTANT VARCHAR2(30) := 'CURS_SVD_RANK';
-- EXSM
exsm_model CONSTANT VARCHAR2(30) := 'EXSM_MODEL';
exsm_simple CONSTANT VARCHAR2(30) := 'EXSM_SIMPLE';
exsm_simple_mult CONSTANT VARCHAR2(30) := 'EXSM_SIMPLE_MULT_ERR';
exsm_holt CONSTANT VARCHAR2(30) := 'EXSM_HOLT';
exsm_holt_dmp CONSTANT VARCHAR2(30) := 'EXSM_HOLT_DAMPED';
exsm_mul_trnd CONSTANT VARCHAR2(30) := 'EXSM_MULT_TREND';
exsm_multrd_dmp CONSTANT VARCHAR2(30) := 'EXSM_MULT_TREND_DAMPED';
exsm_seas_add CONSTANT VARCHAR2(30) := 'EXSM_SEASON_ADD';
exsm_seas_mul CONSTANT VARCHAR2(30) := 'EXSM_SEASON_MUL';
exsm_hw CONSTANT VARCHAR2(30) := 'EXSM_WINTERS';
exsm_hw_dmp CONSTANT VARCHAR2(30) := 'EXSM_WINTERS_DAMPED';
exsm_hw_addsea CONSTANT VARCHAR2(30) := 'EXSM_ADDWINTERS';
exsm_dhw_addsea CONSTANT VARCHAR2(30) := 'EXSM_ADDWINTERS_DAMPED';
exsm_hwmt CONSTANT VARCHAR2(30) := 'EXSM_WINTERS_MUL_TREND';
exsm_hwmt_dmp CONSTANT VARCHAR2(30) := 'EXSM_WINTERS_MUL_TREND_DMP';
exsm_seasonality CONSTANT VARCHAR2(30) := 'EXSM_SEASONALITY';
exsm_interval CONSTANT VARCHAR2(30) := 'EXSM_INTERVAL';
exsm_interval_year CONSTANT VARCHAR2(30) := 'EXSM_INTERVAL_YEAR';
exsm_interval_qtr CONSTANT VARCHAR2(30) := 'EXSM_INTERVAL_QTR';
exsm_interval_month CONSTANT VARCHAR2(30) := 'EXSM_INTERVAL_MONTH';
exsm_interval_week CONSTANT VARCHAR2(30) := 'EXSM_INTERVAL_WEEK';
exsm_interval_day CONSTANT VARCHAR2(30) := 'EXSM_INTERVAL_DAY';
exsm_interval_hour CONSTANT VARCHAR2(30) := 'EXSM_INTERVAL_HOUR';
exsm_interval_min CONSTANT VARCHAR2(30) := 'EXSM_INTERVAL_MINUTE';
exsm_interval_sec CONSTANT VARCHAR2(30) := 'EXSM_INTERVAL_SECOND';
exsm_accumulate CONSTANT VARCHAR2(30) := 'EXSM_ACCUMULATE';
exsm_accu_total CONSTANT VARCHAR2(30) := 'EXSM_ACCU_TOTAL';
exsm_accu_std CONSTANT VARCHAR2(30) := 'EXSM_ACCU_STD';
exsm_accu_max CONSTANT VARCHAR2(30) := 'EXSM_ACCU_MAX';
exsm_accu_min CONSTANT VARCHAR2(30) := 'EXSM_ACCU_MIN';
exsm_accu_avg CONSTANT VARCHAR2(30) := 'EXSM_ACCU_AVG';
exsm_accu_median CONSTANT VARCHAR2(30) := 'EXSM_ACCU_MEDIAN';
exsm_accu_count CONSTANT VARCHAR2(30) := 'EXSM_ACCU_COUNT';
exsm_setmissing CONSTANT VARCHAR2(30) := 'EXSM_SETMISSING';
exsm_miss_min CONSTANT VARCHAR2(30) := 'EXSM_MISS_MIN';
exsm_miss_max CONSTANT VARCHAR2(30) := 'EXSM_MISS_MAX';
exsm_miss_avg CONSTANT VARCHAR2(30) := 'EXSM_MISS_AVG';
exsm_miss_median CONSTANT VARCHAR2(30) := 'EXSM_MISS_MEDIAN';
exsm_miss_last CONSTANT VARCHAR2(30) := 'EXSM_MISS_LAST';
exsm_miss_first CONSTANT VARCHAR2(30) := 'EXSM_MISS_FIRST';
exsm_miss_prev CONSTANT VARCHAR2(30) := 'EXSM_MISS_PREV';
exsm_miss_next CONSTANT VARCHAR2(30) := 'EXSM_MISS_NEXT';
exsm_miss_auto CONSTANT VARCHAR2(30) := 'EXSM_MISS_AUTO';
exsm_prediction_step CONSTANT VARCHAR2(30) := 'EXSM_PREDICTION_STEP';
exsm_opt_criterion CONSTANT VARCHAR2(30) := 'EXSM_OPTIMIZATION_CRIT';
exsm_opt_crit_lik CONSTANT VARCHAR2(30) := 'EXSM_OPT_CRIT_LIK';
exsm_opt_crit_mse CONSTANT VARCHAR2(30) := 'EXSM_OPT_CRIT_MSE';
exsm_opt_crit_amse CONSTANT VARCHAR2(30) := 'EXSM_OPT_CRIT_AMSE';
exsm_opt_crit_sig CONSTANT VARCHAR2(30) := 'EXSM_OPT_CRIT_SIG';
exsm_opt_crit_mae CONSTANT VARCHAR2(30) := 'EXSM_OPT_CRIT_MAE';
exsm_nmse CONSTANT VARCHAR2(30) := 'EXSM_NMSE';
exsm_confidence_level CONSTANT VARCHAR2(30) := 'EXSM_CONFIDENCE_LEVEL';
----------- Function and Algorithm Settings - End ------------------------
TYPE SETTING_LIST IS TABLE OF CLOB INDEX BY VARCHAR2(30);
--------------
-- LOCAL TYPES
--
SUBTYPE TRANSFORM_LIST IS dbms_data_mining_transform.TRANSFORM_LIST;
---------------------------
-- PROCEDURES AND FUNCTIONS
--
PROCEDURE apply(model_name IN VARCHAR2,
data_table_name IN VARCHAR2,
case_id_column_name IN VARCHAR2,
result_table_name IN VARCHAR2,
data_schema_name IN VARCHAR2 DEFAULT NULL);
PROCEDURE compute_confusion_matrix(
accuracy OUT NUMBER,
apply_result_table_name IN VARCHAR2,
target_table_name IN VARCHAR2,
case_id_column_name IN VARCHAR2,
target_column_name IN VARCHAR2,
confusion_matrix_table_name IN VARCHAR2,
score_column_name IN VARCHAR2 DEFAULT
'PREDICTION',
score_criterion_column_name IN VARCHAR2 DEFAULT
'PROBABILITY',
cost_matrix_table_name IN VARCHAR2 DEFAULT NULL,
apply_result_schema_name IN VARCHAR2 DEFAULT NULL,
target_schema_name IN VARCHAR2 DEFAULT NULL,
cost_matrix_schema_name IN VARCHAR2 DEFAULT NULL,
score_criterion_type IN VARCHAR2 DEFAULT NULL);
PROCEDURE compute_confusion_matrix_part(
accuracy OUT DM_NESTED_NUMERICALS,
apply_result_table_name IN VARCHAR2,
target_table_name IN VARCHAR2,
case_id_column_name IN VARCHAR2,
target_column_name IN VARCHAR2,
confusion_matrix_table_name IN VARCHAR2,
score_column_name IN VARCHAR2 DEFAULT
'PREDICTION',
score_criterion_column_name IN VARCHAR2 DEFAULT
'PROBABILITY',
score_partition_column_name IN VARCHAR2 DEFAULT
'PARTITION_NAME',
cost_matrix_table_name IN VARCHAR2 DEFAULT NULL,
apply_result_schema_name IN VARCHAR2 DEFAULT NULL,
target_schema_name IN VARCHAR2 DEFAULT NULL,
cost_matrix_schema_name IN VARCHAR2 DEFAULT NULL,
score_criterion_type IN VARCHAR2 DEFAULT NULL);
PROCEDURE compute_lift(
apply_result_table_name IN VARCHAR2,
target_table_name IN VARCHAR2,
case_id_column_name IN VARCHAR2,
target_column_name IN VARCHAR2,
lift_table_name IN VARCHAR2,
positive_target_value IN VARCHAR2,
score_column_name IN VARCHAR2 DEFAULT
'PREDICTION',
score_criterion_column_name IN VARCHAR2 DEFAULT
'PROBABILITY',
num_quantiles IN NUMBER DEFAULT 10,
cost_matrix_table_name IN VARCHAR2 DEFAULT NULL,
apply_result_schema_name IN VARCHAR2 DEFAULT NULL,
target_schema_name IN VARCHAR2 DEFAULT NULL,
cost_matrix_schema_name IN VARCHAR2 DEFAULT NULL,
score_criterion_type IN VARCHAR2 DEFAULT NULL);
PROCEDURE compute_lift_part(
apply_result_table_name IN VARCHAR2,
target_table_name IN VARCHAR2,
case_id_column_name IN VARCHAR2,
target_column_name IN VARCHAR2,
lift_table_name IN VARCHAR2,
positive_target_value IN VARCHAR2,
score_column_name IN VARCHAR2 DEFAULT 'PREDICTION',
score_criterion_column_name IN VARCHAR2 DEFAULT 'PROBABILITY',
score_partition_column_name IN VARCHAR2 DEFAULT 'PARTITION_NAME',
num_quantiles IN NUMBER DEFAULT 10,
cost_matrix_table_name IN VARCHAR2 DEFAULT NULL,
apply_result_schema_name IN VARCHAR2 DEFAULT NULL,
target_schema_name IN VARCHAR2 DEFAULT NULL,
cost_matrix_schema_name IN VARCHAR2 DEFAULT NULL,
score_criterion_type IN VARCHAR2 DEFAULT NULL);
PROCEDURE compute_roc(
roc_area_under_curve OUT NUMBER,
apply_result_table_name IN VARCHAR2,
target_table_name IN VARCHAR2,
case_id_column_name IN VARCHAR2,
target_column_name IN VARCHAR2,
roc_table_name IN VARCHAR2,
positive_target_value IN VARCHAR2,
score_column_name IN VARCHAR2 DEFAULT
'PREDICTION',
score_criterion_column_name IN VARCHAR2 DEFAULT
'PROBABILITY',
apply_result_schema_name IN VARCHAR2 DEFAULT NULL,
target_schema_name IN VARCHAR2 DEFAULT NULL);
PROCEDURE compute_roc_part(
roc_area_under_curve OUT DM_NESTED_NUMERICALS,
apply_result_table_name IN VARCHAR2,
target_table_name IN VARCHAR2,
case_id_column_name IN VARCHAR2,
target_column_name IN VARCHAR2,
roc_table_name IN VARCHAR2,
positive_target_value IN VARCHAR2,
score_column_name IN VARCHAR2 DEFAULT 'PREDICTION',
score_criterion_column_name IN VARCHAR2 DEFAULT 'PROBABILITY',
score_partition_column_name IN VARCHAR2 DEFAULT 'PARTITION_NAME',
apply_result_schema_name IN VARCHAR2 DEFAULT NULL,
target_schema_name IN VARCHAR2 DEFAULT NULL);
PROCEDURE register_algorithm (
algorithm_name IN VARCHAR2,
algorithm_metadata IN CLOB,
algorithm_description IN VARCHAR2 DEFAULT NULL);
PROCEDURE drop_algorithm ( algorithm_name IN VARCHAR2,
cascade IN BOOLEAN DEFAULT FALSE);
PROCEDURE 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);
PROCEDURE 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);
PROCEDURE drop_model(model_name IN VARCHAR2,
force IN BOOLEAN DEFAULT FALSE);
PROCEDURE export_model (filename IN VARCHAR2,
directory IN VARCHAR2,
model_filter IN VARCHAR2 DEFAULT NULL,
filesize IN VARCHAR2 DEFAULT NULL,
operation IN VARCHAR2 DEFAULT NULL,
remote_link IN VARCHAR2 DEFAULT NULL,
jobname IN VARCHAR2 DEFAULT NULL);
PROCEDURE export_sermodel (model_data IN OUT NOCOPY BLOB,
model_name IN VARCHAR2,
partition_name IN VARCHAR2 DEFAULT NULL);
-- XML (PMML) versions of get model details
FUNCTION get_model_details_xml(model_name IN VARCHAR2,
partition_name IN VARCHAR2 DEFAULT NULL)
RETURN XMLType;
-- Specifying topn orders by confidence DESC, support DESC
-- otherwise by rule_id
FUNCTION get_association_rules(model_name IN VARCHAR2,
topn IN NUMBER DEFAULT NULL,
rule_id IN INTEGER DEFAULT NULL,
min_confidence IN NUMBER DEFAULT NULL,
min_support IN NUMBER DEFAULT NULL,
max_rule_length IN INTEGER DEFAULT NULL,
min_rule_length IN INTEGER DEFAULT NULL,
sort_order IN ORA_MINING_VARCHAR2_NT DEFAULT NULL,
antecedent_items IN DM_ITEMS DEFAULT NULL,
consequent_items IN DM_ITEMS DEFAULT NULL,
min_lift IN NUMBER DEFAULT NULL,
partition_name IN VARCHAR2 DEFAULT NULL)
RETURN DM_Rules PIPELINED;
-- Specifying topn orders by support DESC otherwise there
-- is no ordering
FUNCTION get_frequent_itemsets(model_name IN VARCHAR2,
topn IN NUMBER DEFAULT NULL,
max_itemset_length IN NUMBER DEFAULT NULL,
partition_name IN VARCHAR2 DEFAULT NULL)
RETURN DM_ItemSets PIPELINED;
FUNCTION get_model_details_ai(model_name IN VARCHAR2,
partition_name IN VARCHAR2 DEFAULT NULL)
RETURN dm_ranked_attributes pipelined;
FUNCTION get_model_details_glm(model_name IN VARCHAR2,
partition_name IN VARCHAR2 DEFAULT NULL)
RETURN DM_GLM_Coeff_Set PIPELINED;
FUNCTION get_model_details_svd(model_name IN VARCHAR2,
matrix_type IN VARCHAR2 DEFAULT NULL,
partition_name VARCHAR2 DEFAULT NULL)
RETURN DM_SVD_MATRIX_Set PIPELINED;
FUNCTION get_model_details_km(model_name VARCHAR2,
cluster_id NUMBER DEFAULT NULL,
attribute VARCHAR2 DEFAULT NULL,
centroid NUMBER DEFAULT 1,
histogram NUMBER DEFAULT 1,
rules NUMBER DEFAULT 2,
attribute_subname VARCHAR2 DEFAULT NULL,
topn_attributes NUMBER DEFAULT NULL,
partition_name VARCHAR2 DEFAULT NULL)
RETURN dm_clusters PIPELINED;
FUNCTION get_model_details_nb(model_name IN VARCHAR2,
partition_name IN VARCHAR2 DEFAULT NULL)
RETURN DM_NB_Details PIPELINED;
FUNCTION get_model_details_nmf(model_name IN VARCHAR2,
partition_name VARCHAR2 DEFAULT NULL)
RETURN DM_NMF_Feature_Set PIPELINED;
FUNCTION get_model_details_oc(model_name VARCHAR2,
cluster_id NUMBER DEFAULT NULL,
attribute VARCHAR2 DEFAULT NULL,
centroid NUMBER DEFAULT 1,
histogram NUMBER DEFAULT 1,
rules NUMBER DEFAULT 2,
topn_attributes NUMBER DEFAULT NULL,
partition_name VARCHAR2 DEFAULT NULL)
RETURN dm_clusters PIPELINED;
FUNCTION get_model_details_svm(model_name VARCHAR2,
reverse_coef NUMBER DEFAULT 0,
partition_name VARCHAR2 DEFAULT NULL)
RETURN DM_SVM_Linear_Coeff_Set PIPELINED;
FUNCTION get_model_details_em(model_name VARCHAR2,
cluster_id NUMBER DEFAULT NULL,
attribute VARCHAR2 DEFAULT NULL,
centroid NUMBER DEFAULT 1,
histogram NUMBER DEFAULT 1,
rules NUMBER DEFAULT 2,
attribute_subname VARCHAR2 DEFAULT NULL,
topn_attributes NUMBER DEFAULT NULL,
partition_name IN VARCHAR2 DEFAULT NULL)
RETURN dm_clusters PIPELINED;
FUNCTION get_model_details_em_comp(model_name IN VARCHAR2,
partition_name IN VARCHAR2 DEFAULT NULL)
RETURN DM_EM_COMPONENT_SET PIPELINED;
FUNCTION get_model_details_em_proj(model_name IN VARCHAR2,
partition_name IN VARCHAR2 DEFAULT NULL)
RETURN DM_EM_PROJECTION_SET PIPELINED;
FUNCTION get_model_details_global(model_name IN VARCHAR2,
partition_name IN VARCHAR2 DEFAULT NULL)
RETURN DM_model_global_details PIPELINED;
FUNCTION get_model_settings(model_name IN VARCHAR2)
RETURN DM_Model_Settings PIPELINED;
FUNCTION get_default_settings
RETURN DM_Model_Settings PIPELINED;
FUNCTION get_model_signature(model_name IN VARCHAR2)
RETURN DM_Model_Signature PIPELINED;
FUNCTION get_model_transformations(model_name IN VARCHAR2,
partition_name IN VARCHAR2 DEFAULT NULL)
RETURN DM_Transforms PIPELINED;
PROCEDURE get_transform_list(xform_list OUT NOCOPY TRANSFORM_LIST,
model_xforms IN DM_TRANSFORMS);
PROCEDURE import_model (filename IN VARCHAR2,
directory IN VARCHAR2,
model_filter IN VARCHAR2 DEFAULT NULL,
operation IN VARCHAR2 DEFAULT NULL,
remote_link IN VARCHAR2 DEFAULT NULL,
jobname IN VARCHAR2 DEFAULT NULL,
schema_remap IN VARCHAR2 DEFAULT NULL,
tablespace_remap IN VARCHAR2 DEFAULT NULL);
PROCEDURE import_model (model_name IN VARCHAR2,
pmmldoc IN XMLTYPE,
strict_check IN BOOLEAN DEFAULT FALSE);
PROCEDURE import_sermodel (model_data IN BLOB,
model_name IN VARCHAR2);
PROCEDURE rank_apply(apply_result_table_name IN VARCHAR2,
case_id_column_name IN VARCHAR2,
score_column_name IN VARCHAR2,
score_criterion_column_name IN VARCHAR2,
ranked_apply_table_name IN VARCHAR2,
top_n IN INTEGER DEFAULT 1,
cost_matrix_table_name IN VARCHAR2 DEFAULT NULL,
apply_result_schema_name IN VARCHAR2 DEFAULT NULL,
cost_matrix_schema_name IN VARCHAR2 DEFAULT NULL);
PROCEDURE rename_model(model_name IN VARCHAR2,
new_model_name IN VARCHAR2,
versioned_model_name IN VARCHAR2 DEFAULT NULL);
PROCEDURE add_cost_matrix(model_name IN VARCHAR2,
cost_matrix_table_name IN VARCHAR2,
cost_matrix_schema_name IN VARCHAR2 DEFAULT NULL,
partition_name IN VARCHAR2 DEFAULT NULL);
PROCEDURE remove_cost_matrix(model_name IN VARCHAR2,
partition_name IN VARCHAR2 DEFAULT NULL);
FUNCTION get_model_cost_matrix(model_name IN VARCHAR2,
matrix_type IN VARCHAR2
DEFAULT cost_matrix_type_score,
partition_name IN VARCHAR2 DEFAULT NULL)
RETURN DM_COST_MATRIX PIPELINED;
PROCEDURE alter_reverse_expression(
model_name VARCHAR2,
expression CLOB,
attribute_name VARCHAR2 DEFAULT NULL,
attribute_subname VARCHAR2 DEFAULT NULL);
PROCEDURE drop_partition(
model_name VARCHAR2,
partition_name VARCHAR2);
PROCEDURE add_partition(
model_name VARCHAR2,
data_query CLOB,
add_options VARCHAR2 DEFAULT 'ERROR');
FUNCTION get_model_r_function(model_name IN VARCHAR2,
r_function_type IN VARCHAR2)
RETURN VARCHAR2;
FUNCTION get_model_details_ra(model_name IN VARCHAR2,
par_cur IN SYS_REFCURSOR,
out_qry IN VARCHAR2,
view_num IN NUMBER DEFAULT -1)
RETURN SYS.AnyDataSet
PIPELINED USING SYS.dm$rqMod_DetailImpl;
FUNCTION fetch_alg_schema
RETURN CLOB;
END dbms_data_mining;
/
CREATE OR REPLACE PUBLIC SYNONYM dbms_data_mining FOR sys.dbms_data_mining
/
GRANT EXECUTE ON dbms_data_mining TO PUBLIC
/
SHOW ERRORS
@?/rdbms/admin/sqlsessend.sql
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