Created
January 23, 2018 13:47
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Using index, term, doc frequencies to teach a neural network to rank docs
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package com.github.tteofili.looseen.dl4j; | |
import org.apache.lucene.index.IndexReader; | |
import org.apache.lucene.search.similarities.BasicStats; | |
import org.apache.lucene.search.similarities.SimilarityBase; | |
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork; | |
import org.nd4j.linalg.api.buffer.FloatBuffer; | |
import org.nd4j.linalg.api.ndarray.INDArray; | |
import org.nd4j.linalg.factory.Nd4j; | |
/** | |
* A simple {@link Similarity} based on a neural network which takes index, term and frequency stats as inputs, | |
* generating the score as output. | |
*/ | |
public class NNFreqScoringSimilarity extends SimilarityBase { | |
private final MultiLayerNetwork network; | |
public NNFreqScoringSimilarity(MultiLayerNetwork network) { | |
this.network = network; | |
} | |
@Override | |
protected float score(BasicStats stats, float freq, float docLen) { | |
int inputSize = 8; | |
float[] doubles = new float[inputSize]; | |
doubles[0] = stats.getAvgFieldLength(); | |
doubles[1] = stats.getBoost(); | |
doubles[2] = (float) stats.getDocFreq(); | |
doubles[3] = (float) stats.getNumberOfDocuments(); | |
doubles[4] = (float) stats.getNumberOfFieldTokens(); | |
doubles[5] = (float) stats.getTotalTermFreq(); | |
doubles[6] = freq; | |
doubles[7] = docLen; | |
INDArray input = Nd4j.create(new FloatBuffer(doubles), new int[] {1, inputSize}); | |
input.divi(input.ameanNumber()); | |
float v = network.feedForward(input, true).get(network.getnLayers()).maxNumber().floatValue(); | |
return Float.isFinite(v) ? v : 0; | |
} | |
@Override | |
public String toString() { | |
return network.toString(); | |
} | |
} |
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