Input
- Input table: svm_iris_input_test
- Model table: svm_iris_model, the SVMSparse Example output table
svm_iris_input_test
id |
species |
attribute |
value1 |
5 |
setosa |
sepal_length |
5.0 |
5 |
setosa |
sepal_width |
3.6 |
5 |
setosa |
petal_length |
1.4 |
5 |
setosa |
petal_width |
0.2 |
10 |
setosa |
sepal_length |
4.9 |
10 |
setosa |
sepal_width |
3.1 |
10 |
setosa |
petal_length |
1.5 |
10 |
setosa |
petal_width |
0.1 |
15 |
setosa |
sepal_length |
5.8 |
15 |
setosa |
sepal_width |
4.0 |
15 |
setosa |
petal_length |
1.2 |
15 |
setosa |
petal_width |
0.2 |
... |
... |
... |
... |
SQL Call
CREATE MULTISET TABLE svm_iris_predict_out AS (
SELECT * FROM SparseSVMPredictor@coprocessor (
ON svm_iris_input_test AS input PARTITION BY id
ON svm_iris_model AS model DIMENSION
USING
SampleIDColumn ('id')
AttributeColumn ('attribute')
ValueColumn ('value1')
AccumulateLabel ('species')
) AS dt
) WITH DATA;
Output
This query returns the following table:
SELECT * FROM svm_iris_predict_out;
id |
predict_value |
predict_confidence |
species |
5 |
setosa |
0.867398140899245 |
setosa |
10 |
setosa |
0.819661754676374 |
setosa |
15 |
setosa |
0.927883208386689 |
setosa |
20 |
setosa |
0.864916306271814 |
setosa |
25 |
setosa |
0.762567726157518 |
setosa |
30 |
setosa |
0.793833738774273 |
setosa |
35 |
setosa |
0.81125364470445 |
setosa |
40 |
setosa |
0.843753709530057 |
setosa |
45 |
setosa |
0.797242598289854 |
setosa |
50 |
setosa |
0.846642017701705 |
setosa |
55 |
versicolor |
0.504706688492186 |
versicolor |
60 |
versicolor |
0.271953255632149 |
versicolor |
65 |
versicolor |
0.261217569263106 |
versicolor |
70 |
versicolor |
0.571486181065923 |
versicolor |
75 |
versicolor |
0.508780607775202 |
versicolor |
80 |
versicolor |
0.528403904925176 |
versicolor |
85 |
virginica |
0.357815100538804 |
versicolor |
90 |
versicolor |
0.457613698073775 |
versicolor |
95 |
versicolor |
0.437749794631343 |
versicolor |
100 |
versicolor |
0.396542514919166 |
versicolor |
105 |
virginica |
0.871426508573154 |
virginica |
110 |
virginica |
0.785585474251325 |
virginica |
115 |
virginica |
0.929674476937472 |
virginica |
120 |
versicolor |
0.718867788404435 |
virginica |
125 |
virginica |
0.669828467529083 |
virginica |
130 |
versicolor |
0.708950643747213 |
virginica |
135 |
versicolor |
0.752700333623089 |
virginica |
140 |
virginica |
0.513515135953709 |
virginica |
145 |
virginica |
0.856957955346407 |
virginica |
150 |
virginica |
0.626924808331081 |
virginica |
Prediction Accuracy
This query returns the prediction accuracy:
SELECT (SELECT count(id)
FROM svm_iris_predict_out
WHERE predict_value = species)/(
SELECT count(id) FROM svm_iris_predict_out) AS prediction_accuracy;
prediction_accuracy |
0.86666666666666666667 |