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oml4sql-survival-analysis-xgboost.sql
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oml4sql-survival-analysis-xgboost.sql
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-----------------------------------------------------------------------------
-- Oracle Machine Learning for SQL (OML4SQL) 23ai
--
-- Survival Analysis Modeling using XGBoost
--
-- Copyright (c) 2024 Oracle Corporation and/or its affiliates.
-- The Universal Permissive License (UPL), Version 1.0
--
-- https://oss.oracle.com/licenses/upl/
-----------------------------------------------------------------------------
-- For more information...
-- Oracle ADW Documentation:
-- https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html
-- OML Folder on Github:
-- https://github.com/oracle/oracle-db-examples/tree/master/machine-learning
-- OML Web Page:
-- https://www.oracle.com/machine-learning
-- OML Regression:
-- https://www.oracle.com/goto/ml-regression
-- OML XGBoost:
-- https://http://www.oracle.com/goto/ml-xgboost
-----------------------------------------------------------------------------
-- EXAMPLES IN THIS SCRIPT
-----------------------------------------------------------------------------
-- Create a Survival Analysis XGBoost Model using CREATE_MODEL2
-- Walk through XGB algorithm settings with the model
-- Survival analysis with AFT model
-----------------------------------------------------------------------------
-- Examples of Setting Overrides for XGBoost
-----------------------------------------------------------------------------
-- If the user does not override the default settings, relevant settings
-- are determined by the algorithm.
-- A complete list of settings can be found in the documentation link:
-- https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/
-- DBMS_DATA_MINING.html#GUID-443B3D58-8B74-422E-8E51-C8F609249B2C
-- Set evaluation metric:
-- v_setlst('eval_metric') := 'cox-nloglik';
-- v_setlst('eval_metric') := 'aft-nloglik';
-- v_setlst('eval_metric') := 'c-index';
-- v_setlst('eval_metric') := 'ndcg';
-- Regularization parameters for all boosters:
-- v_setlst('alpha') := '0.1';
-- v_setlst('eta') := '0.3';
-- v_setlst('lambda') := '0.3';
-- Subsampling methods:
-- v_setlst('max_depth') := '6';
-- v_setlst('min_child_weight') := '1';
-- v_setlst('colsample_bytree') := '0.9';
-- Set a tree method:
-- v_setlst('tree_method') := 'hist';
-- v_setlst('tree_method') := 'exact';
-- v_setlst('tree_method') := 'approx';
-- v_setlst('tree_method') := 'gpu_exact';
-- v_setlst('tree_method') := 'gpu_hist';
-- Number of iteration rounds:
-- v_setlst('num_round') := '100';
-- For XGBoost survival tasks, the objective is:
-- v_setlst('objective') := 'survival:aft';
-- v_setlst('objective') := 'survival:cox';
-- v_setlst('objective') := 'survival:cox-nloglik';
-- v_setlst('objective') := 'survival:cox-gamma';
-- Specify loss distribution:
-- v_setlst('aft_loss_distribution') := 'normal';
-- v_setlst('aft_loss_distribution') := 'logistic';
-- v_setlst('aft_loss_distribution') := 'extreme';
-----------------------------------------------------------------------------
-- Create a data table with left and right bound columns
-----------------------------------------------------------------------------
-- The data table 'SURVIVAL_DATA' contains both exact data point and
-- right-censored data point. The left bound column is set by
-- parameter target_column_name. The right bound column is set
-- by setting aft_right_bound_column_name.
-- For right censored data point, the right bound is infinity,
-- which is represented as NULL in the right bound column.
BEGIN EXECUTE IMMEDIATE 'DROP TABLE SURVIVAL_DATA';
EXCEPTION WHEN OTHERS THEN NULL; END;
/
CREATE TABLE SURVIVAL_DATA (INST NUMBER, LBOUND NUMBER, AGE NUMBER,
SEX NUMBER, PHECOG NUMBER, PHKARNO NUMBER,
PATKARNO NUMBER, MEALCAL NUMBER, WTLOSS NUMBER,
RBOUND NUMBER);
INSERT INTO SURVIVAL_DATA VALUES(26, 235, 63, 2, 0, 100, 90, 413, 0, NULL);
INSERT INTO SURVIVAL_DATA VALUES(22, 444, 75, 2, 2, 70, 70, 438, 8, 444);
INSERT INTO SURVIVAL_DATA VALUES(16, 806, 44, 1, 1, 80, 80, 1025, 1, NULL);
INSERT INTO SURVIVAL_DATA VALUES(16, 551, 77, 2, 2, 80, 60, 750, 28, NULL);
INSERT INTO SURVIVAL_DATA VALUES(3, 202, 50, 2, 0, 100, 100, 635, 1, NULL);
INSERT INTO SURVIVAL_DATA VALUES(7, 583, 68, 1, 1, 60, 70, 1025, 7, 583);
INSERT INTO SURVIVAL_DATA VALUES(32, 135, 60, 1, 1, 90, 70, 1275, 0, 135);
INSERT INTO SURVIVAL_DATA VALUES(21, 237, 69, 1, 1, 80, 70, NULL, NULL, NULL);
INSERT INTO SURVIVAL_DATA VALUES(26, 356, 53, 2, 1, 90, 90, NULL, 2, NULL);
INSERT INTO SURVIVAL_DATA VALUES(13, 387, 56, 1, 2, 80, 60, 1075, NULL, 387);
-----------------------------------------------------------------------------
-- Build an XGBoost survival model with survival:aft
-----------------------------------------------------------------------------
BEGIN DBMS_DATA_MINING.DROP_MODEL('XGB_SURVIVAL_MODEL');
EXCEPTION WHEN OTHERS THEN NULL; END;
/
DECLARE
v_setlst DBMS_DATA_MINING.SETTING_LIST;
BEGIN
v_setlst('ALGO_NAME') := 'ALGO_XGBOOST';
v_setlst('max_depth') := '6';
v_setlst('eval_metric') := 'aft-nloglik';
v_setlst('num_round') := '100';
v_setlst('objective') := 'survival:aft';
v_setlst('aft_right_bound_column_name') := 'rbound';
v_setlst('aft_loss_distribution') := 'normal';
v_setlst('aft_loss_distribution_scale') := '1.20';
v_setlst('eta') := '0.05';
v_setlst('lambda') := '0.01';
v_setlst('alpha') := '0.02';
v_setlst('tree_method') := 'hist';
DBMS_DATA_MINING.CREATE_MODEL2(
MODEL_NAME => 'XGB_SURVIVAL_MODEL',
MINING_FUNCTION => 'REGRESSION',
DATA_QUERY => 'SELECT * FROM SURVIVAL_DATA',
TARGET_COLUMN_NAME => 'LBOUND',
CASE_ID_COLUMN_NAME => NULL,
SET_LIST => v_setlst);
END;
/
-----------------------------------------------------------------------------
-- Get Prediction Details
-----------------------------------------------------------------------------
-- NULL value in rbound (aft_right_bound_column_name) column
-- is intepreted as infinity.
COLUMN PRED FORMAT 99999
SET LONG 20000
SET LINES 100
SELECT LBOUND, RBOUND,
ROUND(PREDICTION(XGB_SURVIVAL_MODEL USING *),3) PRED
FROM SURVIVAL_DATA;
-----------------------------------------------------------------------------
-- Nest data into numerical values
-----------------------------------------------------------------------------
CREATE OR REPLACE VIEW SURVIVAL_NUMERIC AS
SELECT LBOUND, RBOUND, WTLOSS,
DM_NESTED_NUMERICALS(
DM_NESTED_NUMERICAL('INST', INST),
DM_NESTED_NUMERICAL('AGE', AGE),
DM_NESTED_NUMERICAL('SEX', SEX),
DM_NESTED_NUMERICAL('PHECOG', PHECOG),
DM_NESTED_NUMERICAL('PHKARNO', PHKARNO),
DM_NESTED_NUMERICAL('PATKARNO', PATKARNO),
DM_NESTED_NUMERICAL('MEALCAL', MEALCAL)) NNUM
FROM SURVIVAL_DATA;
-----------------------------------------------------------------------------
-- Build an XGBoost model using nested numeric data
-----------------------------------------------------------------------------
BEGIN DBMS_DATA_MINING.DROP_MODEL('XGB_SURVIVAL_MODEL');
EXCEPTION WHEN OTHERS THEN NULL; END;
/
DECLARE
v_setlst DBMS_DATA_MINING.SETTING_LIST;
BEGIN
v_setlst('ALGO_NAME') := 'ALGO_XGBOOST';
v_setlst('max_depth') := '6';
v_setlst('eval_metric') := 'aft-nloglik';
v_setlst('num_round') := '100';
v_setlst('objective') := 'survival:aft';
v_setlst('aft_right_bound_column_name') := 'rbound';
v_setlst('aft_loss_distribution') := 'normal';
v_setlst('aft_loss_distribution_scale') := '1.20';
v_setlst('eta') := '0.05';
v_setlst('lambda') := '0.01';
v_setlst('alpha') := '0.02';
v_setlst('tree_method') := 'hist';
DBMS_DATA_MINING.CREATE_MODEL2(
MODEL_NAME => 'XGB_SURVIVAL_MODEL',
MINING_FUNCTION => 'REGRESSION',
DATA_QUERY => 'SELECT * FROM SURVIVAL_NUMERIC',
TARGET_COLUMN_NAME => 'LBOUND',
CASE_ID_COLUMN_NAME => NULL,
SET_LIST => v_setlst);
END;
/
-----------------------------------------------------------------------------
-- Get Prediction Details
-----------------------------------------------------------------------------
-- NULL value in rbound (aft_right_bound_column_name) column
-- is intepreted as infinity.
COLUMN PRED FORMAT 99999
SET LONG 20000
SET LINES 100
SELECT LBOUND, RBOUND,
ROUND(PREDICTION(XGB_SURVIVAL_MODEL USING *),3) PRED
FROM SURVIVAL_NUMERIC;
-----------------------------------------------------------------------------
-- Build an XGBoost model with no eval_metric specified
-----------------------------------------------------------------------------
BEGIN DBMS_DATA_MINING.DROP_MODEL('XGB_SURVIVAL_MODEL');
EXCEPTION WHEN OTHERS THEN NULL; END;
/
DECLARE
v_setlst DBMS_DATA_MINING.SETTING_LIST;
begin
v_setlst('ALGO_NAME') := 'ALGO_XGBOOST';
v_setlst('max_depth') := '6';
v_setlst('num_round') := '100';
v_setlst('objective') := 'survival:aft';
v_setlst('aft_right_bound_column_name') := 'rbound';
v_setlst('aft_loss_distribution') := 'normal';
v_setlst('aft_loss_distribution_scale') := '1.20';
v_setlst('eta') := '0.05';
v_setlst('lambda') := '0.01';
v_setlst('alpha') := '0.02';
v_setlst('tree_method') := 'hist';
DBMS_DATA_MINING.CREATE_MODEL2(
MODEL_NAME => 'XGB_SURVIVAL_MODEL',
MINING_FUNCTION => 'REGRESSION',
DATA_QUERY => 'SELECT * FROM SURVIVAL_DATA',
TARGET_COLUMN_NAME => 'LBOUND',
CASE_ID_COLUMN_NAME => NULL,
SET_LIST => v_setlst);
END;
/
-----------------------------------------------------------------------------
-- Get Prediction Details
-----------------------------------------------------------------------------
SELECT LBOUND, RBOUND,
ROUND(PREDICTION(XGB_SURVIVAL_MODEL USING *),3) PRED
FROM SURVIVAL_DATA;
-----------------------------------------------------------------------------
-- Build an XGBoost model with aft_loss_distribution = logistic
-----------------------------------------------------------------------------
BEGIN DBMS_DATA_MINING.DROP_MODEL('XGB_SURVIVAL_MODEL');
EXCEPTION WHEN OTHERS THEN NULL; END;
/
DECLARE
v_setlst DBMS_DATA_MINING.SETTING_LIST;
BEGIN
v_setlst('ALGO_NAME') := 'ALGO_XGBOOST';
v_setlst('max_depth') := '6';
v_setlst('eval_metric') := 'aft-nloglik';
v_setlst('num_round') := '100';
v_setlst('objective') := 'survival:aft';
v_setlst('aft_right_bound_column_name') := 'rbound';
v_setlst('aft_loss_distribution') := 'logistic';
v_setlst('aft_loss_distribution_scale') := '1.20';
v_setlst('eta') := '0.05';
v_setlst('lambda') := '0.01';
v_setlst('alpha') := '0.02';
v_setlst('tree_method') := 'hist';
DBMS_DATA_MINING.CREATE_MODEL2(
MODEL_NAME => 'XGB_SURVIVAL_MODEL',
MINING_FUNCTION => 'REGRESSION',
DATA_QUERY => 'SELECT * FROM SURVIVAL_DATA',
TARGET_COLUMN_NAME => 'LBOUND',
CASE_ID_COLUMN_NAME => NULL,
SET_LIST => v_setlst);
END;
/
-----------------------------------------------------------------------------
-- Get Prediction Details
-----------------------------------------------------------------------------
SELECT LBOUND, RBOUND,
ROUND(PREDICTION(XGB_SURVIVAL_MODEL USING *),3) PRED
FROM SURVIVAL_DATA;
-----------------------------------------------------------------------------
-- Build an XGBoost model with aft_loss_distribution = extreme
-----------------------------------------------------------------------------
BEGIN DBMS_DATA_MINING.DROP_MODEL('XGB_SURVIVAL_MODEL');
EXCEPTION WHEN OTHERS THEN NULL; END;
/
DECLARE
v_setlst DBMS_DATA_MINING.SETTING_LIST;
BEGIN
v_setlst('ALGO_NAME') := 'ALGO_XGBOOST';
v_setlst('max_depth') := '6';
v_setlst('eval_metric') := 'aft-nloglik';
v_setlst('num_round') := '100';
v_setlst('objective') := 'survival:aft';
v_setlst('aft_right_bound_column_name') := 'rbound';
v_setlst('aft_loss_distribution') := 'extreme';
v_setlst('aft_loss_distribution_scale') := '1.20';
v_setlst('eta') := '0.05';
v_setlst('lambda') := '0.01';
v_setlst('alpha') := '0.02';
v_setlst('tree_method') := 'hist';
DBMS_DATA_MINING.CREATE_MODEL2(
MODEL_NAME => 'XGB_SURVIVAL_MODEL',
MINING_FUNCTION => 'REGRESSION',
DATA_QUERY => 'SELECT * FROM SURVIVAL_DATA',
TARGET_COLUMN_NAME => 'LBOUND',
CASE_ID_COLUMN_NAME => NULL,
SET_LIST => v_setlst);
END;
/
-----------------------------------------------------------------------------
-- Get Prediction Details
-----------------------------------------------------------------------------
SELECT LBOUND, RBOUND,
ROUND(PREDICTION(XGB_SURVIVAL_MODEL USING *),3) PRED
FROM SURVIVAL_DATA;
-----------------------------------------------------------------------------
-- Build an XGBoost model with aft_loss_distribution_scale = 0
-----------------------------------------------------------------------------
BEGIN DBMS_DATA_MINING.DROP_MODEL('XGB_SURVIVAL_MODEL');
EXCEPTION WHEN OTHERS THEN NULL; END;
/
DECLARE
v_setlst DBMS_DATA_MINING.SETTING_LIST;
BEGIN
v_setlst('ALGO_NAME') := 'ALGO_XGBOOST';
v_setlst('max_depth') := '6';
v_setlst('eval_metric') := 'aft-nloglik';
v_setlst('num_round') := '100';
v_setlst('objective') := 'survival:aft';
v_setlst('aft_right_bound_column_name') := 'rbound';
v_setlst('aft_loss_distribution') := 'extreme';
v_setlst('aft_loss_distribution_scale') := '0';
v_setlst('eta') := '0.05';
v_setlst('lambda') := '0.01';
v_setlst('alpha') := '0.02';
v_setlst('tree_method') := 'hist';
DBMS_DATA_MINING.CREATE_MODEL2(
MODEL_NAME => 'XGB_SURVIVAL_MODEL',
MINING_FUNCTION => 'REGRESSION',
DATA_QUERY => 'SELECT * FROM SURVIVAL_DATA',
TARGET_COLUMN_NAME => 'LBOUND',
CASE_ID_COLUMN_NAME => NULL,
SET_LIST => v_setlst);
END;
/
-----------------------------------------------------------------------------
-- Get Prediction Details
-----------------------------------------------------------------------------
SELECT LBOUND, RBOUND,
ROUND(PREDICTION(XGB_SURVIVAL_MODEL USING *),3) PRED
From SURVIVAL_DATA;
-----------------------------------------------------------------------------
-- Create a table with only one numerical column
-----------------------------------------------------------------------------
DROP TABLE SURVIVAL_DATA;
CREATE TABLE SURVIVAL_DATA (LBOUND NUMBER, RBOUND NUMBER);
INSERT INTO SURVIVAL_DATA VALUES(235, NULL);
INSERT INTO SURVIVAL_DATA VALUES(444, 444);
INSERT INTO SURVIVAL_DATA VALUES(806, NULL);
INSERT INTO SURVIVAL_DATA VALUES(551, NULL);
INSERT INTO SURVIVAL_DATA VALUES(202, NULL);
INSERT INTO SURVIVAL_DATA VALUES(583, 583);
INSERT INTO SURVIVAL_DATA VALUES(135, 135);
INSERT INTO SURVIVAL_DATA VALUES(237, NULL);
INSERT INTO SURVIVAL_DATA VALUES(356, NULL);
INSERT INTO SURVIVAL_DATA VALUES(387, 387);
-----------------------------------------------------------------------------
-- Build an XGBoost model using numerical table
-----------------------------------------------------------------------------
BEGIN DBMS_DATA_MINING.DROP_MODEL('XGB_SURVIVAL_MODEL');
EXCEPTION WHEN OTHERS THEN NULL; END;
/
DECLARE
v_setlst DBMS_DATA_MINING.SETTING_LIST;
BEGIN
v_setlst('ALGO_NAME') := 'ALGO_XGBOOST';
v_setlst('max_depth') := '6';
v_setlst('eval_metric') := 'aft-nloglik';
v_setlst('num_round') := '100';
v_setlst('objective') := 'survival:aft';
v_setlst('aft_right_bound_column_name') := 'rbound';
v_setlst('aft_loss_distribution') := 'normal';
v_setlst('aft_loss_distribution_scale') := '1.20';
v_setlst('eta') := '0.05';
v_setlst('lambda') := '0.01';
v_setlst('alpha') := '0.02';
v_setlst('tree_method') := 'hist';
DBMS_DATA_MINING.CREATE_MODEL2(
MODEL_NAME => 'XGB_SURVIVAL_MODEL',
MINING_FUNCTION => 'REGRESSION',
DATA_QUERY => 'SELECT * FROM SURVIVAL_DATA',
TARGET_COLUMN_NAME => 'LBOUND',
CASE_ID_COLUMN_NAME => NULL,
SET_LIST => v_setlst);
END;
/
-----------------------------------------------------------------------------
-- Get Prediction Details
-----------------------------------------------------------------------------
SELECT LBOUND, RBOUND,
ROUND(PREDICTION(XGB_SURVIVAL_MODEL USING *),3) PRED
FROM SURVIVAL_DATA;
-----------------------------------------------------------------------
-- End of script
-----------------------------------------------------------------------