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From "Alexey Zinoviev (Jira)" <j...@apache.org>
Subject [jira] [Created] (IGNITE-12257) [ML] Add Feature Filter for ML Partitioned Dataset
Date Thu, 03 Oct 2019 11:23:00 GMT
Alexey Zinoviev created IGNITE-12257:
----------------------------------------

             Summary: [ML] Add Feature Filter for ML Partitioned Dataset
                 Key: IGNITE-12257
                 URL: https://issues.apache.org/jira/browse/IGNITE-12257
             Project: Ignite
          Issue Type: Improvement
    Affects Versions: 2.9
            Reporter: Alexey Zinoviev
            Assignee: Alexey Zinoviev
             Fix For: 2.9


The behavior of this method ignores possible feature choosing on the previous levels and we
have no ability to make feature engineering during the preprocessing like simple sql: filter,
exclude, produce new features and so on

 

 

public SimpleDatasetData build(
 LearningEnvironment env,
 Iterator<UpstreamEntry<K, V>> upstreamData, long upstreamDataSize, C ctx) {
 // Prepares the matrix of features in flat column-major format.
 int cols = -1;
 double[] features = null;

 int ptr = 0;
 while (upstreamData.hasNext()) {
 UpstreamEntry<K, V> entry = upstreamData.next();
 Vector row = preprocessor.apply(entry.getKey(), entry.getValue()).features();

 if (cols < 0) {
 cols = row.size();
 features = new double[Math.toIntExact(upstreamDataSize * cols)];
 }
 else
 assert row.size() == cols : "Feature extractor must return exactly " + cols + " features";

 for (int i = 0; i < cols; i++)
 features[Math.toIntExact(i * upstreamDataSize + ptr)] = row.get(i);

 ptr++;
 }

 return new SimpleDatasetData(features, Math.toIntExact(upstreamDataSize));
}



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