Re: ML RandomForest - creating Mdl.predict(Vector) from One_hot dataset

[Alexey Zinoviev]

Hi, Ken.
Currently, the Preprocessors are not independent operations yet (to train
OHE separately and re-use the OHE for prediction).

But if you have trained preprocessor (encoder) filled with encoding values
after training process you could re-use it in prediction phase like in
example below.

The sequence of calls during the training is next:
vectorizer->encoder->trainer


The  *encoderPreprocessor *contains statistics learnt during training phase
and has special method *apply *that could take the key and value from
CacheEntry
Here, in loop, I call the apply method and it applies vectorizer and
encoder (encoder contains link to vectorizer internally) to the Object[] in
each CacheEntry value.

If you run this example, you get the same accuracy in both evaluation
sections.

The initial example is located here:
https://github.com/apache/ignite/blob/master/examples/src/main/java/org/apache/ignite/examples/ml/preprocessing/encoding/EncoderExample.java

Also the trainer could be changed on Random Forest, for example.

Hope it solves your problem.


public class EncoderExample {
    /**
     * Run example.
     */
    public static void main(String[] args) throws Exception {
        System.out.println();
        System.out.println(">>> Train Decision Tree model on
mushrooms.csv dataset.");

        try (Ignite ignite =
Ignition.start("examples/config/example-ignite.xml")) {
            try {
                IgniteCache<Integer, Object[]> dataCache = new
SandboxMLCache(ignite)

.fillObjectCacheWithDoubleLabels(MLSandboxDatasets.MUSHROOMS);

                final Vectorizer<Integer, Object[], Integer, Object>
vectorizer = new ObjectArrayVectorizer<Integer>(1, 2, 3).labeled(0);

                Preprocessor<Integer, Object[]> encoderPreprocessor =
new EncoderTrainer<Integer, Object[]>()
                    .withEncoderType(EncoderType.STRING_ENCODER)
                    .withEncodedFeature(0)
                    .withEncodedFeature(1)
                    .withEncodedFeature(2)
                    .fit(ignite,
                        dataCache,
                        vectorizer
                    );

                DecisionTreeClassificationTrainer trainer = new
DecisionTreeClassificationTrainer(5, 0);

                // Train decision tree model.
                DecisionTreeNode mdl = trainer.fit(
                    ignite,
                    dataCache,
                    encoderPreprocessor
                );

                System.out.println("\n>>> Trained model: " + mdl);

                int tp = 0;
                int total = 0;

                try (QueryCursor<Cache.Entry<Integer, Object[]>>
observations = dataCache.query(new ScanQuery<>())) {
                    for (Cache.Entry<Integer, Object[]> observation :
observations) {
                        Object[] val = observation.getValue();
                        double groundTruth = (double) val[0];

                        LabeledVector apply =
encoderPreprocessor.apply(observation.getKey(), val);
                        double prediction = mdl.predict(apply.features());

                        total++;
                        if (prediction == groundTruth)
                            tp++;


                        System.out.printf(">>> | %.4f\t\t\t|
%.4f\t\t|\n", prediction, groundTruth);
                    }


                    System.out.println(">>> ---------------------------------");
                    System.out.println("Manual accuracy is: " +
((double)tp)/total);
                    System.out.println(">>> Decision Tree algorithm
over cached dataset usage example completed.");
                }


                double accuracy = Evaluator.evaluate(
                    dataCache,
                    mdl,
                    encoderPreprocessor,
                    new Accuracy<>()
                );

                System.out.println("\n>>> Accuracy " + accuracy);
                System.out.println("\n>>> Test Error " + (1 - accuracy));

                System.out.println(">>> Train Decision Tree model on
mushrooms.csv dataset.");

            }
            catch (FileNotFoundException e) {
                e.printStackTrace();
            }
        }
        finally {
            System.out.flush();
        }
    }
}