hex.pca.PCAModel Maven / Gradle / Ivy
package hex.pca;
import hex.DataInfo;
import hex.Model;
import hex.ModelCategory;
import hex.ModelMetrics;
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.JCodeGen;
import water.util.SBPrintStream;
import water.util.TwoDimTable;
import java.util.ArrayList;
public class PCAModel extends Model {
public static class PCAParameters extends Model.Parameters {
public String algoName() { return "PCA"; }
public String fullName() { return "Principal Components Analysis"; }
public String javaName() { return PCAModel.class.getName(); }
@Override public long progressUnits() { return _pca_method == PCAParameters.Method.GramSVD ? 5 : 3; }
public DataInfo.TransformType _transform = DataInfo.TransformType.NONE; // Data transformation
public Method _pca_method = Method.GramSVD; // Method for computing PCA
public PCAImplementation _pca_implementation = PCAImplementation.getFastestImplementation(); // PCA implementation
public int _k = 1; // Number of principal components
public int _max_iterations = 1000; // Max iterations
public boolean _use_all_factor_levels = false; // When expanding categoricals, should first level be kept or dropped?
public boolean _compute_metrics = true; // Should a second pass be made through data to compute metrics?
public boolean _impute_missing = false; // Should missing numeric values be imputed with the column mean?
public enum Method {
GramSVD, Power, Randomized, GLRM
}
}
public static class PCAOutput extends Model.Output {
// GLRM final value of loss function
public double _objective;
// Principal components (eigenvectors)
public double[/*feature*/][/*k*/] _eigenvectors_raw;
public TwoDimTable _eigenvectors;
// Standard deviation of each principal component
public double[] _std_deviation;
// Importance of principal components
// Standard deviation, proportion of variance explained, and cumulative proportion of variance explained
public TwoDimTable _importance;
// Number of categorical and numeric columns
public int _ncats;
public int _nnums;
// Number of good rows in training frame (not skipped)
public long _nobs;
// Total column variance for expanded and transformed data
public double _total_variance;
// Categorical offset vector
public int[] _catOffsets;
// If standardized, mean of each numeric data column
public double[] _normSub;
// If standardized, one over standard deviation of each numeric data column
public double[] _normMul;
// Permutation matrix mapping training col indices to adaptedFrame
public int[] _permutation;
// the following fields are added for scoring history which can different fields depending on the PCA Method
// here are the common fields for all PCA methods
public ArrayList _training_time_ms = new ArrayList<>();
public PCAOutput(PCA b) { super(b); }
/** Override because base class implements ncols-1 for features with the
* last column as a response variable; for PCA all the columns are
* features. */
@Override public int nfeatures() { return _names.length; }
@Override public ModelCategory getModelCategory() {
return ModelCategory.DimReduction;
}
}
public PCAModel(Key selfKey, PCAParameters parms, PCAOutput output) { super(selfKey,parms,output); }
@Override
public ModelMetrics.MetricBuilder makeMetricBuilder(String[] domain) {
return new ModelMetricsPCA.PCAModelMetrics(_parms._k);
}
@Override
protected PredictScoreResult predictScoreImpl(Frame origFr, Frame adaptedFr, String destination_key, final Job j, boolean computeMetrics, CFuncRef customMetricFunc) {
Frame adaptFrm = new Frame(adaptedFr);
for(int i = 0; i < _parms._k; i++)
adaptFrm.add("PC"+String.valueOf(i+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[_parms._k];
for( int row = 0; row < chks[0]._len; row++) {
double p[] = score0(chks, row, tmp, preds);
for( int c=0; cmake(destination_key), f.names(), f.vecs());
DKV.put(f);
ModelMetrics.MetricBuilder mb = makeMetricBuilder(null);
return new PredictScoreResult(mb, f, f);
}
@Override
protected double[] score0(double data[/*ncols*/], double preds[/*k*/]) {
int numStart = _output._catOffsets[_output._catOffsets.length-1];
assert data.length == _output._nnums + _output._ncats;
for(int i = 0; i < _parms._k; i++) {
preds[i] = 0;
for (int j = 0; j < _output._ncats; j++) {
double tmp = data[_output._permutation[j]];
if (Double.isNaN(tmp)) continue; // Missing categorical values are skipped
int last_cat = _output._catOffsets[j+1]-_output._catOffsets[j]-1;
int level = (int)tmp - (_parms._use_all_factor_levels ? 0:1); // Reduce index by 1 if first factor level dropped during training
if (level < 0 || level > last_cat) continue; // Skip categorical level in test set but not in train
preds[i] += _output._eigenvectors_raw[_output._catOffsets[j]+level][i];
}
int dcol = _output._ncats;
int vcol = numStart;
for (int j = 0; j < _output._nnums; j++) {
preds[i] += (data[_output._permutation[dcol]] - _output._normSub[j]) * _output._normMul[j] * _output._eigenvectors_raw[vcol][i];
dcol++; vcol++;
}
}
return preds;
}
@Override protected SBPrintStream toJavaInit(SBPrintStream sb, CodeGeneratorPipeline fileCtx) {
sb = super.toJavaInit(sb, fileCtx);
sb.ip("public boolean isSupervised() { return " + isSupervised() + "; }").nl();
sb.ip("public int nfeatures() { return "+_output.nfeatures()+"; }").nl();
sb.ip("public int nclasses() { return "+_parms._k+"; }").nl();
// This is model name
final String mname = JCodeGen.toJavaId(_key.toString());
fileCtx.add(new CodeGenerator() {
@Override
public void generate(JCodeSB out) {
if (_output._nnums > 0) {
JCodeGen.toClassWithArray(out, null, mname + "_NORMMUL", _output._normMul,
"Standardization/Normalization scaling factor for numerical variables.");
JCodeGen.toClassWithArray(out, null, mname + "_NORMSUB", _output._normSub,
"Standardization/Normalization offset for numerical variables.");
}
JCodeGen.toClassWithArray(out, null, mname + "_CATOFFS", _output._catOffsets,
"Categorical column offsets.");
JCodeGen.toClassWithArray(out, null, mname + "_PERMUTE", _output._permutation,
"Permutation index vector.");
JCodeGen.toClassWithArray(out, null, mname + "_EIGVECS", _output._eigenvectors_raw,
"Eigenvector matrix.");
}
});
return sb;
}
@Override protected void toJavaPredictBody(SBPrintStream bodySb,
CodeGeneratorPipeline classCtx,
CodeGeneratorPipeline fileCtx,
final boolean verboseCode) {
// This is model name
final String mname = JCodeGen.toJavaId(_key.toString());
bodySb.i().p("java.util.Arrays.fill(preds,0);").nl();
final int cats = _output._ncats;
final int nums = _output._nnums;
bodySb.i().p("final int nstart = ").pj(mname+"_CATOFFS", "VALUES").p("[").pj(mname+"_CATOFFS", "VALUES").p(".length-1];").nl();
bodySb.i().p("for(int i = 0; i < ").p(_parms._k).p("; i++) {").nl();
// Categorical columns
bodySb.i(1).p("for(int j = 0; j < ").p(cats).p("; j++) {").nl();
bodySb.i(2).p("double d = data[").pj(mname+"_PERMUTE", "VALUES").p("[j]];").nl();
bodySb.i(2).p("if(Double.isNaN(d)) continue;").nl();
bodySb.i(2).p("int last = ").pj(mname+"_CATOFFS", "VALUES").p("[j+1]-").pj(mname+"_CATOFFS", "VALUES").p("[j]-1;").nl();
bodySb.i(2).p("int c = (int)d").p(_parms._use_all_factor_levels ? ";":"-1;").nl();
bodySb.i(2).p("if(c < 0 || c > last) continue;").nl();
bodySb.i(2).p("preds[i] += ").pj(mname+"_EIGVECS", "VALUES").p("[").pj(mname+"_CATOFFS", "VALUES").p("[j]+c][i];").nl();
bodySb.i(1).p("}").nl();
// Numeric columns
if (_output._nnums > 0) {
bodySb.i(1).p("for(int j = 0; j < ").p(nums).p("; j++) {").nl();
bodySb.i(2).p("preds[i] += (data[").pj(mname + "_PERMUTE", "VALUES").p("[j" + (cats > 0 ? "+" + cats : "") + "]]-").pj(mname + "_NORMSUB", "VALUES").p("[j])*").pj(mname + "_NORMMUL", "VALUES").p("[j]*").pj(mname + "_EIGVECS", "VALUES").p("[j" + (cats > 0 ? "+ nstart" : "") + "][i];").nl();
bodySb.i(1).p("}").nl();
}
bodySb.i().p("}").nl();
}
@Override
public PCAMojoWriter getMojo() {
return new PCAMojoWriter(this);
}
}
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