hex.kmeans.KMeansModel Maven / Gradle / Ivy
package hex.kmeans;
import hex.*;
import hex.genmodel.IClusteringModel;
import hex.util.EffectiveParametersUtils;
import hex.util.LinearAlgebraUtils;
import water.DKV;
import water.Job;
import water.Key;
import water.MRTask;
import water.codegen.CodeGenerator;
import water.codegen.CodeGeneratorPipeline;
import water.exceptions.JCodeSB;
import water.fvec.Chunk;
import water.fvec.Frame;
import water.udf.CFuncRef;
import water.util.ArrayUtils;
import water.util.JCodeGen;
import water.util.SBPrintStream;
import java.util.Arrays;
import static hex.genmodel.GenModel.Kmeans_preprocessData;
public class KMeansModel extends ClusteringModel {
@Override public ToEigenVec getToEigenVec() { return LinearAlgebraUtils.toEigen; }
public static class KMeansParameters extends ClusteringModel.ClusteringParameters {
public String algoName() { return "KMeans"; }
public String fullName() { return "K-means"; }
public String javaName() { return KMeansModel.class.getName(); }
@Override public long progressUnits() { return _estimate_k ? _k : _max_iterations; }
public int _max_iterations = 10; // Max iterations for Lloyds
public boolean _standardize = true; // Standardize columns
public KMeans.Initialization _init = KMeans.Initialization.Furthest;
public Key _user_points;
public boolean _pred_indicator = false; // For internal use only: generate indicator cols during prediction
// Ex: k = 4, cluster = 3 -> [0, 0, 1, 0]
public boolean _estimate_k = false; // If enabled, iteratively find up to _k clusters
public int[] _cluster_size_constraints = null;
}
public static class KMeansOutput extends ClusteringModel.ClusteringOutput {
// Iterations executed
public int _iterations;
// Sum squared distance between each point and its cluster center.
public double[/*k*/] _withinss; // Within-cluster sum of square error
// Sum squared distance between each point and its cluster center.
public double _tot_withinss; // Within-cluster sum-of-square error
public double[/*iterations*/] _history_withinss = new double[]{Double.NaN};
// Sum squared distance between each point and grand mean.
public double _totss; // Total sum-of-square error to grand mean centroid
// Sum squared distance between each cluster center and grand mean, divided by total number of observations.
public double _betweenss; // Total between-cluster sum-of-square error (totss - tot_withinss)
// Number of categorical columns trained on
public int _categorical_column_count;
// Training time
public long[/*iterations*/] _training_time_ms = new long[]{System.currentTimeMillis()};
public double[/*iterations*/] _reassigned_count = new double[]{Double.NaN};
public int[/*iterations*/] _k = new int[]{0};
public KMeansOutput( KMeans b ) { super(b); }
}
public KMeansModel(Key selfKey, KMeansParameters parms, KMeansOutput output) {
super(selfKey,parms,output);
}
@Override
public void initActualParamValues() {
super.initActualParamValues();
EffectiveParametersUtils.initFoldAssignment(_parms);
EffectiveParametersUtils.initCategoricalEncoding(_parms, Model.Parameters.CategoricalEncodingScheme.Enum);
}
@Override public ModelMetrics.MetricBuilder makeMetricBuilder(String[] domain) {
assert domain == null;
return new ModelMetricsClustering.MetricBuilderClustering(_output.nfeatures(),_output._k[_output._k.length-1]);
}
@Override protected PredictScoreResult predictScoreImpl(Frame orig, Frame adaptedFr, String destination_key, final Job j, boolean computeMetrics, CFuncRef customMetricFunc) {
if (!_parms._pred_indicator) {
return super.predictScoreImpl(orig, adaptedFr, destination_key, j, computeMetrics, customMetricFunc);
} else {
final int len = _output._k[_output._k.length-1];
String prefix = "cluster_";
Frame adaptFrm = new Frame(adaptedFr);
for(int c = 0; c < len; c++)
adaptFrm.add(prefix + Double.toString(c+1), adaptFrm.anyVec().makeZero());
new MRTask() {
@Override public void map( Chunk chks[] ) {
if (isCancelled() || j != null && j.stop_requested()) return;
double tmp [] = new double[_output._names.length];
double preds[] = new double[len];
for(int row = 0; row < chks[0]._len; row++) {
Arrays.fill(preds,0);
double p[] = score_indicator(chks, row, tmp, preds);
for(int c = 0; c < preds.length; c++)
chks[_output._names.length + c].set(row, p[c]);
}
if (j != null) j.update(1);
}
}.doAll(adaptFrm);
// Return the predicted columns
int x = _output._names.length, y = adaptFrm.numCols();
Frame f = adaptFrm.extractFrame(x, y); // this will call vec_impl() and we cannot call the delete() below just yet
f = new Frame(Key.make(destination_key), f.names(), f.vecs());
DKV.put(f);
ModelMetrics.MetricBuilder mb = makeMetricBuilder(null);
return new PredictScoreResult(mb, f, f);
}
}
public double[] score_indicator(Chunk[] chks, int row_in_chunk, double[] tmp, double[] preds) {
assert _parms._pred_indicator;
assert tmp.length == _output._names.length && preds.length == _output._centers_raw.length;
for(int i = 0; i < tmp.length; i++)
tmp[i] = chks[i].atd(row_in_chunk);
double[] clus = new double[1];
score0(tmp, clus); // this saves cluster number into clus[0]
assert preds != null && ArrayUtils.l2norm2(preds) == 0 : "preds must be a vector of all zeros, got " + Arrays.toString(preds);
assert clus[0] >= 0 && clus[0] < preds.length : "Cluster number must be an integer in [0," + String.valueOf(preds.length) + ")";
preds[(int)clus[0]] = 1;
return preds;
}
public double[] score_ratio(Chunk[] chks, int row_in_chunk, double[] tmp) {
assert _parms._pred_indicator;
assert tmp.length == _output._names.length;
for(int i = 0; i < tmp.length; i++)
tmp[i] = chks[i].atd(row_in_chunk);
double[][] centers = _parms._standardize ? _output._centers_std_raw : _output._centers_raw;
double[] preds = hex.genmodel.GenModel.KMeans_simplex(centers,tmp,_output._domains);
assert preds.length == _output._k[_output._k.length-1];
assert Math.abs(ArrayUtils.sum(preds) - 1) < 1e-6 : "Sum of k-means distance ratios should equal 1";
return preds;
}
@Override
protected double[] score0(double[] data, double[] preds, double offset) {
return score0(data, preds);
}
@Override protected double[] score0(double data[/*ncols*/], double preds[/*nclasses+1*/]) {
double[][] centers = _parms._standardize ? _output._centers_std_raw : _output._centers_raw;
Kmeans_preprocessData(data, _output._normSub, _output._normMul, _output._mode);
preds[0] = hex.genmodel.GenModel.KMeans_closest(centers,data,_output._domains);
return preds;
}
@Override protected double data(Chunk[] chks, int row, int col){
return Kmeans_preprocessData(chks[col].atd(row),col,_output._normSub,_output._normMul,_output._mode);
}
@Override
protected Class[] getPojoInterfaces() {
return new Class[]{IClusteringModel.class};
}
// Override in subclasses to provide some top-level model-specific goodness
@Override protected void toJavaPredictBody(SBPrintStream body,
CodeGeneratorPipeline classCtx,
CodeGeneratorPipeline fileCtx,
final boolean verboseCode) {
// This is model name
final String mname = JCodeGen.toJavaId(_key.toString());
if(_parms._standardize) {
fileCtx.add(new CodeGenerator() {
@Override
public void generate(JCodeSB out) {
JCodeGen.toClassWithArray(out, null, mname + "_MEANS", _output._normSub,
"Column means of training data");
JCodeGen.toClassWithArray(out, null, mname + "_MULTS", _output._normMul,
"Reciprocal of column standard deviations of training data");
JCodeGen.toClassWithArray(out, null, mname + "_MODES", _output._mode,
"Mode for categorical columns");
JCodeGen.toClassWithArray(out, null, mname + "_CENTERS", _output._centers_std_raw,
"Normalized cluster centers[K][features]");
}
});
// Predict function body: Standardize data first
body.ip("Kmeans_preprocessData(data,")
.pj(mname + "_MEANS", "VALUES,")
.pj(mname + "_MULTS", "VALUES,")
.pj(mname + "_MODES", "VALUES")
.p(");").nl();
// Predict function body: main work function is a utility in GenModel class.
body.ip("preds[0] = KMeans_closest(")
.pj(mname + "_CENTERS", "VALUES")
.p(", data, DOMAINS); ").nl(); // at function level
} else {
fileCtx.add(new CodeGenerator() {
@Override
public void generate(JCodeSB out) {
JCodeGen.toClassWithArray(out, null, mname + "_CENTERS", _output._centers_raw,
"Denormalized cluster centers[K][features]");
}
});
// Predict function body: main work function is a utility in GenModel class.
body.ip("preds[0] = KMeans_closest(")
.pj(mname + "_CENTERS", "VALUES")
.p(",data, DOMAINS);").nl(); // at function level
}
}
@Override
protected SBPrintStream toJavaTransform(SBPrintStream ccsb,
CodeGeneratorPipeline fileCtx,
boolean verboseCode) { // ccsb = classContext
ccsb.nl();
ccsb.ip("// Pass in data in a double[], in a same way as to the score0 function.").nl();
ccsb.ip("// Cluster distances will be stored into the distances[] array. Function").nl();
ccsb.ip("// will return the closest cluster. This way the caller can avoid to call").nl();
ccsb.ip("// score0(..) to retrieve the cluster where the data point belongs.").nl();
ccsb.ip("public final int distances( double[] data, double[] distances ) {").nl();
toJavaDistancesBody(ccsb.ii(1));
ccsb.ip("return cluster;").nl();
ccsb.di(1).ip("}").nl();
ccsb.nl();
ccsb.ip("// Returns number of cluster used by this model.").nl();
ccsb.ip("public final int getNumClusters() {").nl();
toJavaGetNumClustersBody(ccsb.ii(1));
ccsb.ip("return nclusters;").nl();
ccsb.di(1).ip("}").nl();
// Output class context
CodeGeneratorPipeline classCtx = new CodeGeneratorPipeline(); //new SB().ii(1);
classCtx.generate(ccsb.ii(1));
ccsb.di(1);
return ccsb;
}
private void toJavaDistancesBody(SBPrintStream body) {
// This is model name
final String mname = JCodeGen.toJavaId(_key.toString());
if(_parms._standardize) {
// Distances function body: Standardize data first
body.ip("Kmeans_preprocessData(data,")
.pj(mname + "_MEANS", "VALUES,")
.pj(mname + "_MULTS", "VALUES,")
.pj(mname + "_MODES", "VALUES")
.p(");").nl();
// Distances function body: main work function is a utility in GenModel class.
body.ip("int cluster = KMeans_distances(")
.pj(mname + "_CENTERS", "VALUES")
.p(", data, DOMAINS, distances); ").nl(); // at function level
} else {
// Distances function body: main work function is a utility in GenModel class.
body.ip("int cluster = KMeans_distances(")
.pj(mname + "_CENTERS", "VALUES")
.p(",data, DOMAINS, distances);").nl(); // at function level
}
}
private void toJavaGetNumClustersBody(SBPrintStream body) {
// This is model name
final String mname = JCodeGen.toJavaId(_key.toString());
body.ip("int nclusters = ").pj(mname + "_CENTERS", "VALUES").p(".length;").nl();
}
@Override
protected boolean toJavaCheckTooBig() {
return _parms._standardize ?
_output._centers_std_raw.length * _output._centers_std_raw[0].length > 1e6 :
_output._centers_raw.length * _output._centers_raw[0].length > 1e6;
}
@Override
public KMeansMojoWriter getMojo() {
return new KMeansMojoWriter(this);
}
}
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