7.00.02 - Input - Aster Analytics

Teradata Aster® Analytics Foundation User GuideUpdate 2

Product
Aster Analytics
Release Number
7.00.02
Release Date
September 2017
Content Type
Programming Reference
User Guide
Publication ID
B700-1022-700K
Language
English (United States)

Forest_Drive Example Input Data Descriptions describes the real estate sales data contained in the input table. There are six numerical predictors and six categorical predictors. The response variable is homestyle.

The table of raw training data, housing_train, is described by the following two tables.

AdaBoostDrive Example Raw Input Table housing_train (Columns 1-9)
sn price lotsize bedrooms bathrms stories driveway recroom fullbase
1 42000 5850 3 1 2 yes no yes
2 38500 4000 2 2 1 yes no no
3 49500 3060 3 3 1 yes no no
4 60500 6650 3 1 2 yes yes no
5 61000 6360 2 1 1 yes no no
6 66000 4160 3 1 1 yes yes yes
7 66000 3880 3 2 2 yes no yes
8 69000 4160 3 1 1 yes no no
9 83800 4800 3 1 1 yes yes yes
10 88500 5500 3 2 4 yes yes no
11 90000 7200 3 2 1 yes no yes
12 30500 3000 2 1 1 yes no no
14 36000 2880 3 1 1 no no no
15 37000 3600 2 1 1 yes no no
... ... ... ... ... ... ... ... ...
AdaBoostDrive Example Input Table housing_train (Columns 10-14)
gashw airco prefarea garagepl homestyle
no no 1 no Classic
no no 0 no Classic
no no 0 no Classic
no no 0 no Eclectic
no no 0 no Eclectic
no yes 0 no Eclectic
no no 2 no Eclectic
no no 0 no Eclectic
no no 0 no Eclectic
no yes 1 no Eclectic
no yes 3 no Eclectic
no no 0 no Classic
no no 0 no Classic
no no 0 no Classic
... ... ... ... ...

Create the input table for the AdaBoostDrive function, housing_train_att, by using the Unpivot function on the table of raw data, housing_train:

CREATE TABLE housing_train_att distribute BY HASH(sn) AS (
  SELECT * FROM unpivot (
    ON housing_train
    ColsToUnpivot ('price', 'lotsize', 'bedrooms', 'bathrms',
      'stories', 'driveway', 'recroom', 'fullbase', 'gashw', 'airco',
      'garagepl', 'prefarea')
    ColsToAccumulate ('sn')
  )
);

This query returns the following table:

SELECT * FROM housing_train_att ORDER BY 1, 2;
AdaBoost Functions Example Input Table housing_train_att
sn attribute value
1 airco no
1 bathrms 1
1 bedrooms 3
1 driveway yes
1 fullbase yes
1 garagepl 1
1 gashw no
1 lotsize 5850.0
1 prefarea no
1 price 42000.0
1 recroom no
1 stories 2
2 airco no
2 bathrms 1
2 bedrooms 2
2 fullbase no
2 garagepl 0
2 gashw no
2 lotsize 4000.0
2 prefarea no
2 price 38500.0
2 recroom no
2 stories 1
... ... ...

Create the response table for the AdaBoostDrive function, housing_train_response, by selecting the columns sn and homestyle from the table of raw data, housing_train:

CREATE TABLE housing_train_response DISTRIBUTE BY HASH(sn) AS
  (SELECT sn, homestyle AS response FROM housing_train);

This query returns the following table:

SELECT * FROM housing_train_response ORDER BY 1;
AdaBoost Functions Example Input Table housing_train_response
sn response
1 Classic
2 Classic
3 Classic
4 Eclectic
5 Eclectic
6 Eclectic
7 Eclectic
8 Eclectic
9 Eclectic
10 Eclectic
... ...

Create and populate the categorical attribute table, housing_cat, for the AdaBoostDrive function:

CREATE TABLE housing_cat (attribute VARCHAR)
  DISTRIBUTE BY HASH(attribute);
INSERT INTO housing_cat VALUES ('driveway');
INSERT INTO housing_cat VALUES ('recroom');
INSERT INTO housing_cat VALUES ('fullbase');
INSERT INTO housing_cat VALUES ( 'gashw');
INSERT INTO housing_cat VALUES ('airco');
INSERT INTO housing_cat VALUES ('prefarea');

This query returns the following table:

SELECT * FROM housing_cat ORDER BY 1;
AdaBoost Functions Example Input Table housing_cat
attribute
airco
driveway
fullbase
gashw
prefarea
recroom