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Posted to user@flink.apache.org by Kürşat Kurt <ku...@kursatkurt.com> on 2016/10/13 18:52:20 UTC

SVM Multiclass classification

Hi;

 

I am trying to classify documents. 

When i try to predict (same of training set) there is only 1 and -1
predictions.

Accuracy is 0%.

 

 

Can you help me please?

 

val env = ExecutionEnvironment.getExecutionEnvironment

    val training = Seq(

      new LabeledVector(1.0, new SparseVector(10, Array(0, 2, 3), Array(1.0,
1.0, 1.0))),

      new LabeledVector(1.0, new SparseVector(10, Array(0, 1, 5, 9),
Array(1.0, 1.0, 1.0, 1.0))),

      new LabeledVector(0.0, new SparseVector(10, Array(0, 2), Array(0.0,
1.0))),

      new LabeledVector(0.0, new SparseVector(10, Array(0), Array(0.0))),

      new LabeledVector(2.0, new SparseVector(10, Array(0, 2), Array(0.0,
1.0))),

      new LabeledVector(2.0, new SparseVector(10, Array(0), Array(0.0))),

      new LabeledVector(1.0, new SparseVector(10, Array(0, 3), Array(1.0,
1.0))),

      new LabeledVector(0.0, new SparseVector(10, Array(0, 2, 3), Array(0.0,
1.0, 1.0))),

      new LabeledVector(2.0, new SparseVector(10, Array(0, 7, 9), Array(0.0,
1.0))),

      new LabeledVector(2.0, new SparseVector(10, Array(2,3,4),
Array(0.0,1.0,1.0))),

      new LabeledVector(2.0, new SparseVector(10, Array(0, 3), Array(1.0,
1.0))),

      new LabeledVector(0.0, new SparseVector(10, Array(2, 3,9), Array(1.0,
0.0, 1.0)))

    

    );

    val trainingDS = env.fromCollection(training)

    val testingDS = env.fromCollection(training)

    val svm = new SVM().setBlocks(env.getParallelism)

    svm.fit(trainingDS)    

    val predictions = svm.evaluate(testingDS.map(x => (x.vector, x.label)))

    predictions.print();

 

Sample output:

 

(1.0,1.0)

(1.0,1.0)

(0.0,1.0)

(0.0,-1.0)

(2.0,1.0)

(2.0,-1.0)

(1.0,1.0)

(0.0,1.0)

(2.0,1.0)

(2.0,1.0)

(2.0,1.0)

(0.0,1.0)

Re: SVM Multiclass classification

Posted by Theodore Vasiloudis <th...@gmail.com>.

Hello Kursat,

As noted in the documentation, the SVM implementation is for binary
classification only for the time being.

Regards,
Theodore

-- 
Sent from a mobile device. May contain autocorrect errors.

On Oct 13, 2016 8:53 PM, "Kürşat Kurt" <ku...@kursatkurt.com> wrote:

> Hi;
>
>
>
> I am trying to classify documents.
>
> When i try to predict (same of training set) there is only 1 and -1
> predictions.
>
> Accuracy is 0%.
>
>
>
>
>
> Can you help me please?
>
>
>
> *val* env = ExecutionEnvironment.getExecutionEnvironment
>
>     *val* training = Seq(
>
>       *new* LabeledVector(1.0, *new* SparseVector(10, Array(0, 2, 3),
> Array(1.0, 1.0, 1.0))),
>
>       *new* LabeledVector(1.0, *new* SparseVector(10, Array(0, 1, 5, 9),
> Array(1.0, 1.0, 1.0, 1.0))),
>
>       *new* LabeledVector(0.0, *new* SparseVector(10, Array(0, 2), Array(
> 0.0, 1.0))),
>
>       *new* LabeledVector(0.0, *new* SparseVector(10, Array(0), Array(0.0
> ))),
>
>       *new* LabeledVector(2.0, *new* SparseVector(10, Array(0, 2), Array(
> 0.0, 1.0))),
>
>       *new* LabeledVector(2.0, *new* SparseVector(10, Array(0), Array(0.0
> ))),
>
>       *new* LabeledVector(1.0, *new* SparseVector(10, Array(0, 3), Array(
> 1.0, 1.0))),
>
>       *new* LabeledVector(0.0, *new* SparseVector(10, Array(0, 2, 3),
> Array(0.0, 1.0, 1.0))),
>
>       *new* LabeledVector(2.0, *new* SparseVector(10, Array(0, 7, 9),
> Array(0.0, 1.0))),
>
>       *new* LabeledVector(2.0, *new* SparseVector(10, Array(2,3,4), Array(
> 0.0,1.0,1.0))),
>
>       *new* LabeledVector(2.0, *new* SparseVector(10, Array(0, 3), Array(
> 1.0, 1.0))),
>
>       *new* LabeledVector(0.0, *new* SparseVector(10, Array(2, 3,9),
> Array(1.0, 0.0, 1.0)))
>
>
>
>     );
>
>     *val* trainingDS = env.fromCollection(training)
>
>     *val* testingDS = env.fromCollection(training)
>
>     *val* svm = *new* SVM().setBlocks(env.getParallelism)
>
>     svm.fit(trainingDS)
>
>     *val* predictions = *svm*.evaluate(testingDS.map(x => (x.vector, x.
> label)))
>
>     predictions.print();
>
>
>
> Sample output:
>
>
>
> (1.0,1.0)
>
> (1.0,1.0)
>
> (0.0,1.0)
>
> (0.0,-1.0)
>
> (2.0,1.0)
>
> (2.0,-1.0)
>
> (1.0,1.0)
>
> (0.0,1.0)
>
> (2.0,1.0)
>
> (2.0,1.0)
>
> (2.0,1.0)
>
> (0.0,1.0)
>