1.1 - 8.10 - SVMSparse Example - Teradata Vantage

Teradata Vantage™ - Machine Learning Engine Analytic Function Reference

Product
Teradata Vantage
Release Number
1.1
8.10
Published
October 2019
Content Type
Programming Reference
Publication ID
B700-4003-079K
Language
English (United States)

Input

  • InputTable: svm_iris_input_train
    The example creates svm_iris_input_train in two steps:
    1. Create table svm_iris_input in sparse format by inputting table svm_iris to the Unpivoting function:
      CREATE MULTISET TABLE svm_iris_input AS (
        SELECT id, species, attribute, value_col
        FROM Unpivoting (
          ON svm_iris
          USING
          TargetColumns ('Sepal_Length', 'Sepal_Width', 'Petal_Length', 'Petal_Width')
          Accumulate ('ID', 'Species')
        ) AS dt
      ) WITH DATA;
    2. From svm_iris_input, create training table svm_iris_input_train (with 80% of the rows) and testing table svm_iris_input_test (with 20% of the rows):
      CREATE MULTISET TABLE svm_iris_input_train AS (
        SELECT * FROM svm_iris_input WHERE id MOD 5 <> 0
      ) WITH DATA;
      
      CREATE MULTISET TABLE svm_iris_input_test AS (
        SELECT * FROM svm_iris_input WHERE id MOD 5 = 0
      ) WITH DATA;

      The testing table is input to the SVMSparsePredict_MLE function.

The table svm_iris has four iris attributes (sepal length, sepal width, petal length, and petal width), grouped into three categories (setosa, versicolor, and virginica).

svm_iris
id sepal_length sepal_width petal_length petal_width species
1 5.1 3.5 1.4 0.2 setosa
2 4.9 3 1.4 0.2 setosa
3 4.7 3.2 1.3 0.2 setosa
4 4.6 3.1 1.5 0.2 setosa
5 5 3.6 1.4 0.2 setosa
6 5.4 3.9 1.7 0.4 setosa
7 4.6 3.4 1.4 0.3 setosa
8 5 3.4 1.5 0.2 setosa
9 4.4 2.9 1.4 0.2 setosa
10 4.9 3.1 1.5 0.1 setosa
11 5.4 3.7 1.5 0.2 setosa
12 4.8 3.4 1.6 0.2 setosa
13 4.8 3 1.4 0.1 setosa
14 4.3 3 1.1 0.1 setosa
... ... ... ... ... ...
svm_iris_input
id species attribute value_col
1 setosa sepal_length 5.1
1 setosa sepal_width 3.5
1 setosa petal_length 1.4
1 setosa petal_width 0.2
2 setosa sepal_length 4.9
2 setosa sepal_width 3.0
2 setosa petal_length 1.4
2 setosa petal_width 0.2
3 setosa sepal_length 4.7
3 setosa sepal_width 3.2
3 setosa petal_length 1.3
3 setosa petal_width 0.2
... ... ... ...
InputTable: svm_iris_input_train
id species attribute value_col
1 setosa sepal_length 5.1
1 setosa sepal_width 3.5
1 setosa petal_length 1.4
1 setosa petal_width 0.2
2 setosa sepal_length 4.9
2 setosa sepal_width 3.0
2 setosa petal_length 1.4
2 setosa petal_width 0.2
3 setosa sepal_length 4.7
3 setosa sepal_width 3.2
... ... ... ...

SQL Call

SELECT * FROM SVMSparse (
  ON svm_iris_input_train AS InputTable
  OUT TABLE ModelTable (svm_iris_model)
  USING
  IDColumn ('id')
  AttributeNameColumn ('attribute')
  ResponseColumn ('species')
  AttributeValueColumn ('value1')
  MaxIterNum (150)
  Seed (0)
)as dt ;

Output

 message                                                                                                                                        
 ---------------------------------------------------------------------------------------------------------------------------------------------- 
 Model table is created successfully                                                                                                           
 The model is trained with 120 samples and 4 unique attributes                                                                                 
 There are 3 different classes in the training set                                                                                             
 The model is not converged after 150 steps with epsilon 0.01, the average value of the loss function for the training set is 36.44926590032744
 The corresponding training parameters are cost:1.0 bias:0.0

The model table, svm_iris_model, is in binary format.

Download a zip file of all examples and a SQL script file that creates their input tables from the attachment in the left sidebar.