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hex.genmodel.tools.PredictCsv Maven / Gradle / Ivy
package hex.genmodel.tools;
import au.com.bytecode.opencsv.CSVReader;
import hex.ModelCategory;
import hex.genmodel.GenModel;
import hex.genmodel.MojoModel;
import hex.genmodel.algos.glrm.GlrmMojoModel;
import hex.genmodel.algos.tree.SharedTreeMojoModel;
import hex.genmodel.easy.EasyPredictModelWrapper;
import hex.genmodel.easy.RowData;
import hex.genmodel.easy.prediction.*;
import hex.genmodel.utils.ArrayUtils;
import java.io.BufferedWriter;
import java.io.FileReader;
import java.io.FileWriter;
import java.io.IOException;
import java.util.*;
import java.util.concurrent.Callable;
import java.util.concurrent.ExecutorService;
import java.util.concurrent.Executors;
import java.util.concurrent.Future;
/**
* Simple driver program for reading a CSV file and making predictions. Added support for separators that are
* not commas. User needs to add the --separator separator_string to the input call. Do not escape
* the special Java characters, I will do it for you.
*
* This driver program is used as a test harness by several tests in the testdir_javapredict directory.
*
* See the top-of-tree master version of this file here on github .
*/
public class PredictCsv {
private final String inputCSVFileName;
private final String outputCSVFileName;
private final boolean useDecimalOutput;
private final char separator;
private final boolean setInvNumNA;
private final boolean getTreePath;
private final boolean predictContributions;
private final boolean predictCalibrated;
private final boolean returnGLRMReconstruct;
private final int glrmIterNumber;
private final boolean outputHeader;
// Model instance
private EasyPredictModelWrapper modelWrapper;
private PredictCsv(
String inputCSVFileName, String outputCSVFileName,
boolean useDecimalOutput, char separator, boolean setInvNumNA,
boolean getTreePath, boolean predictContributions, boolean predictCalibrated,
boolean returnGLRMReconstruct, int glrmIterNumber,
boolean outputHeader) {
this.inputCSVFileName = inputCSVFileName;
this.outputCSVFileName = outputCSVFileName;
this.useDecimalOutput = useDecimalOutput;
this.separator = separator;
this.setInvNumNA = setInvNumNA;
this.getTreePath = getTreePath;
this.predictContributions = predictContributions;
this.predictCalibrated = predictCalibrated;
this.returnGLRMReconstruct = returnGLRMReconstruct;
this.glrmIterNumber = glrmIterNumber;
this.outputHeader = outputHeader;
}
public static void main(String[] args) {
PredictCsvCollection predictors = buildPredictCsv(args);
PredictCsv main = predictors.main;
// Run the main program
try {
main.run();
} catch (Exception e) {
System.out.println("Predict error: " + e.getMessage());
System.out.println();
e.printStackTrace();
System.exit(1);
}
if (predictors.concurrent.length > 0) {
try {
ExecutorService executor = Executors.newFixedThreadPool(predictors.concurrent.length);
List callables = new ArrayList<>(predictors.concurrent.length);
for (int i = 0; i < predictors.concurrent.length; i++) {
callables.add(new PredictCsvCallable(predictors.concurrent[i]));
}
int numExceptions = 0;
for (Future future : executor.invokeAll(callables)) {
Exception e = future.get();
if (e != null) {
e.printStackTrace();
numExceptions++;
}
}
if (numExceptions > 0) {
throw new Exception("Some predictors failed (#failed=" + numExceptions + ")");
}
} catch (Exception e) {
System.out.println("Concurrent predict error: " + e.getMessage());
System.out.println();
e.printStackTrace();
System.exit(1);
}
}
// Predictions were successfully generated.
System.exit(0);
}
// Only meant to be used in tests
public static PredictCsv make(String[] args, GenModel model) {
final PredictCsvCollection predictorCollection = buildPredictCsv(args);
if (predictorCollection.concurrent.length != 0) {
throw new UnsupportedOperationException("Predicting with concurrent predictors is not supported in programmatic mode.");
}
final PredictCsv predictor = predictorCollection.main;
if (model != null) {
try {
predictor.setModelWrapper(model);
} catch (IOException e) {
throw new RuntimeException(e);
}
}
return predictor;
}
private static RowData formatDataRow(String[] splitLine, String[] inputColumnNames) {
// Assemble the input values for the row.
RowData row = new RowData();
int maxI = Math.min(inputColumnNames.length, splitLine.length);
for (int i = 0; i < maxI; i++) {
String columnName = inputColumnNames[i];
String cellData = splitLine[i];
switch (cellData) {
case "":
case "NA":
case "N/A":
case "-":
continue;
default:
row.put(columnName, cellData);
}
}
return row;
}
private String myDoubleToString(double d) {
if (Double.isNaN(d)) {
return "NA";
}
return useDecimalOutput? Double.toString(d) : Double.toHexString(d);
}
private void writeTreePathNames(BufferedWriter output) throws Exception {
String[] columnNames = ((SharedTreeMojoModel) modelWrapper.m).getDecisionPathNames();
writeColumnNames(output, columnNames);
}
private void writeCalibratedOutputNames(BufferedWriter output) throws Exception {
String[] outputNames = modelWrapper.m.getOutputNames();
String[] calibOutputNames = new String[outputNames.length - 1];
for (int i = 0; i < calibOutputNames.length; i++) {
calibOutputNames[i] = "cal_" + outputNames[i + 1];
}
writeColumnNames(output, ArrayUtils.append(outputNames, calibOutputNames));
}
private void writeContributionNames(BufferedWriter output) throws Exception {
writeColumnNames(output, modelWrapper.getContributionNames());
}
private void writeColumnNames(BufferedWriter output, String[] columnNames) throws Exception {
int lastIndex = columnNames.length-1;
for (int index = 0; index < lastIndex; index++) {
output.write(columnNames[index]);
output.write(",");
}
output.write(columnNames[lastIndex]);
}
public void run() throws Exception {
ModelCategory category = modelWrapper.getModelCategory();
CSVReader reader = new CSVReader(new FileReader(inputCSVFileName), separator);
BufferedWriter output = new BufferedWriter(new FileWriter(outputCSVFileName));
// Emit outputCSV column names.
if (outputHeader) {
switch (category) {
case Binomial:
case Multinomial:
case Regression:
if (getTreePath) {
writeTreePathNames(output);
} else if (predictContributions) {
writeContributionNames(output);
} else if (predictCalibrated) {
writeCalibratedOutputNames(output);
} else
writeHeader(modelWrapper.m.getOutputNames(), output);
break;
case DimReduction: // will write factor or the predicted value depending on what the user wants
if (returnGLRMReconstruct) {
int datawidth;
String[] colnames = this.modelWrapper.m.getNames();
datawidth = ((GlrmMojoModel) modelWrapper.m)._permutation.length;
int lastData = datawidth - 1;
for (int index = 0; index < datawidth; index++) { // add the numerical column names
output.write("reconstr_" + colnames[index]);
if (index < lastData)
output.write(',');
}
} else
writeHeader(modelWrapper.m.getOutputNames(), output);
break;
default:
writeHeader(modelWrapper.m.getOutputNames(), output);
}
output.write("\n");
}
// Loop over inputCSV one row at a time.
//
int lineNum=1; // count number of lines of input dataset file parsed
try {
String[] inputColumnNames;
String[] splitLine;
//Reader in the column names here.
if ((splitLine = reader.readNext()) != null) {
inputColumnNames = splitLine;
checkMissingColumns(inputColumnNames);
}
else // file empty, throw an error
throw new Exception("Input dataset file is empty!");
while ((splitLine = reader.readNext()) != null) {
// Parse the CSV line. Don't handle quoted commas. This isn't a parser test.
RowData row = formatDataRow(splitLine, inputColumnNames);
// Do the prediction.
// Emit the result to the output file.
String offsetColumn = modelWrapper.m.getOffsetName();
double offset = offsetColumn==null ? 0 : Double.parseDouble((String) row.get(offsetColumn));
switch (category) {
case AutoEncoder: { // write the expanded predictions out
AutoEncoderModelPrediction p = modelWrapper.predictAutoEncoder(row);
for (int i=0; i < p.reconstructed.length; i++) {
output.write(myDoubleToString(p.reconstructed[i]));
if (i < p.reconstructed.length-1)
output.write(',');
}
break;
}
case Binomial: {
BinomialModelPrediction p = modelWrapper.predictBinomial(row, offset);
if (getTreePath) {
writeTreePaths(p.leafNodeAssignments, output);
} else if (predictContributions) {
writeContributions(p.contributions, output);
} else {
output.write(p.label);
output.write(",");
for (int i = 0; i < p.classProbabilities.length; i++) {
if (i > 0) {
output.write(",");
}
output.write(myDoubleToString(p.classProbabilities[i]));
}
if (predictCalibrated) {
for (int i = 0; i < p.classProbabilities.length; i++) {
output.write(",");
double calibProb = p.calibratedClassProbabilities != null ?
p.calibratedClassProbabilities[i] : Double.NaN;
output.write(myDoubleToString(calibProb));
}
}
}
break;
}
case Multinomial: {
MultinomialModelPrediction p = modelWrapper.predictMultinomial(row);
if (getTreePath) {
writeTreePaths(p.leafNodeAssignments, output);
} else {
output.write(p.label);
output.write(",");
for (int i = 0; i < p.classProbabilities.length; i++) {
if (i > 0) {
output.write(",");
}
output.write(myDoubleToString(p.classProbabilities[i]));
}
}
break;
}
case Ordinal: {
OrdinalModelPrediction p = modelWrapper.predictOrdinal(row, offset);
output.write(p.label);
output.write(",");
for (int i = 0; i < p.classProbabilities.length; i++) {
if (i > 0) {
output.write(",");
}
output.write(myDoubleToString(p.classProbabilities[i]));
}
break;
}
case Clustering: {
ClusteringModelPrediction p = modelWrapper.predictClustering(row);
output.write(myDoubleToString(p.cluster));
break;
}
case Regression: {
RegressionModelPrediction p = modelWrapper.predictRegression(row, offset);
if (getTreePath) {
writeTreePaths(p.leafNodeAssignments, output);
} else if (predictContributions) {
writeContributions(p.contributions, output);
} else
output.write(myDoubleToString(p.value));
break;
}
case CoxPH: {
CoxPHModelPrediction p = modelWrapper.predictCoxPH(row, offset);
output.write(myDoubleToString(p.value));
break;
}
case DimReduction: {
DimReductionModelPrediction p = modelWrapper.predictDimReduction(row);
double[] out;
if (returnGLRMReconstruct) {
out = p.reconstructed; // reconstructed A
} else {
out = p.dimensions; // x factors
}
int lastOne = out.length-1;
for (int i=0; i < out.length; i++) {
output.write(myDoubleToString(out[i]));
if (i < lastOne)
output.write(',');
}
break;
}
case AnomalyDetection: {
AnomalyDetectionPrediction p = modelWrapper.predictAnomalyDetection(row);
double[] rawPreds = p.toPreds();
for (int i = 0; i < rawPreds.length - 1; i++) {
output.write(myDoubleToString(rawPreds[i]));
output.write(',');
}
output.write(myDoubleToString(rawPreds[rawPreds.length - 1]));
break;
}
default:
throw new Exception("Unknown model category " + category);
}
output.write("\n");
lineNum++;
}
}
catch (Exception e) {
throw new Exception("Prediction failed on line " + lineNum, e);
} finally {
// Clean up.
output.close();
reader.close();
}
}
private void writeHeader(String[] colNames, BufferedWriter output) throws Exception {
output.write(colNames[0]);
for (int i = 1; i < colNames.length; i++) {
output.write(",");
output.write(colNames[i]);
}
}
private void writeTreePaths(String[] treePaths, BufferedWriter output) throws Exception {
int len = treePaths.length-1;
for (int index=0; index 0) {
output.write(",");
}
output.write(myDoubleToString(contributions[i]));
}
}
private void setModelWrapper(GenModel genModel) throws IOException {
EasyPredictModelWrapper.Config config = new EasyPredictModelWrapper.Config()
.setModel(genModel)
.setConvertUnknownCategoricalLevelsToNa(true)
.setConvertInvalidNumbersToNa(setInvNumNA);
if (getTreePath)
config.setEnableLeafAssignment(true);
if (predictContributions)
config.setEnableContributions(true);
if (returnGLRMReconstruct)
config.setEnableGLRMReconstrut(true);
if (glrmIterNumber > 0) // set GLRM Mojo iteration number
config.setGLRMIterNumber(glrmIterNumber);
setModelWrapper(new EasyPredictModelWrapper(config));
}
private void setModelWrapper(EasyPredictModelWrapper modelWrapper) {
this.modelWrapper = modelWrapper;
}
private static void usage() {
System.out.println();
System.out.println("Usage: java [...java args...] hex.genmodel.tools.PredictCsv --mojo mojoName");
System.out.println(" --pojo pojoName --input inputFile --output outputFile --separator sepStr --decimal --setConvertInvalidNum");
System.out.println();
System.out.println(" --mojo Name of the zip file containing model's MOJO.");
System.out.println(" --pojo Name of the java class containing the model's POJO. Either this ");
System.out.println(" parameter or --model must be specified.");
System.out.println(" --input text file containing the test data set to score.");
System.out.println(" --output Name of the output CSV file with computed predictions.");
System.out.println(" --separator Separator to be used in input file containing test data set.");
System.out.println(" --decimal Use decimal numbers in the output (default is to use hexademical).");
System.out.println(" --setConvertInvalidNum Will call .setConvertInvalidNumbersToNa(true) when loading models.");
System.out.println(" --leafNodeAssignment will show the leaf node assignment for tree based models instead of" +
" prediction results");
System.out.println(" --predictContributions will output prediction contributions (Shapley values) for tree based" +
" models instead of regular model predictions");
System.out.println(" --glrmReconstruct will return the reconstructed dataset for GLRM mojo instead of X factor derived from the dataset.");
System.out.println(" --glrmIterNumber integer indicating number of iterations to go through when constructing X factor derived from the dataset.");
System.out.println(" --testConcurrent integer (for testing) number of concurrent threads that will be making predictions.");
System.out.println();
System.exit(1);
}
private void checkMissingColumns(final String[] parsedColumnNamesArr) {
final String[] modelColumnNames = modelWrapper.m._names;
final Set parsedColumnNames = new HashSet<>(parsedColumnNamesArr.length);
Collections.addAll(parsedColumnNames, parsedColumnNamesArr);
List missingColumns = new ArrayList<>();
for (String columnName : modelColumnNames) {
if (!parsedColumnNames.contains(columnName) && !columnName.equals(modelWrapper.m._responseColumn)) {
missingColumns.add(columnName);
} else {
parsedColumnNames.remove(columnName);
}
}
if(missingColumns.size() > 0){
final StringBuilder stringBuilder = new StringBuilder("There were ");
stringBuilder.append(missingColumns.size());
stringBuilder.append(" missing columns found in the input data set: {");
for (int i = 0; i < missingColumns.size(); i++) {
stringBuilder.append(missingColumns.get(i));
if(i != missingColumns.size() - 1) stringBuilder.append(",");
}
stringBuilder.append('}');
System.out.println(stringBuilder);
}
if(parsedColumnNames.size() > 0){
final StringBuilder stringBuilder = new StringBuilder("Detected ");
stringBuilder.append(parsedColumnNames.size());
stringBuilder.append(" unused columns in the input data set: {");
final Iterator iterator = parsedColumnNames.iterator();
while (iterator.hasNext()){
stringBuilder.append(iterator.next());
if(iterator.hasNext()) stringBuilder.append(",");
}
stringBuilder.append('}');
System.out.println(stringBuilder);
}
}
private static class PredictCsvCollection {
private final PredictCsv main;
private final PredictCsv[] concurrent;
private PredictCsvCollection(PredictCsv main, PredictCsv[] concurrent) {
this.main = main;
this.concurrent = concurrent;
}
}
private static PredictCsvCollection buildPredictCsv(String[] args) {
try {
PredictCsvBuilder builder = new PredictCsvBuilder();
builder.parseArgs(args);
final GenModel genModel;
switch (builder.loadType) {
case -1:
genModel = null;
break;
case 0:
genModel = loadPojo(builder.pojoMojoModelNames);
break;
case 1:
genModel = loadMojo(builder.pojoMojoModelNames);
break;
case 2:
genModel = loadModel(builder.pojoMojoModelNames);
break;
default:
throw new IllegalStateException("Unexpected value of loadType = " + builder.loadType);
}
PredictCsv mainPredictCsv = builder.newPredictCsv();
if (genModel != null) {
mainPredictCsv.setModelWrapper(genModel);
}
PredictCsv[] concurrentPredictCsvs = new PredictCsv[builder.testConcurrent];
for (int id = 0; id < concurrentPredictCsvs.length; id++) {
PredictCsv concurrentPredictCsv = builder.newConcurrentPredictCsv(id);
concurrentPredictCsv.setModelWrapper(mainPredictCsv.modelWrapper); // re-use both the wrapper and the MOJO
concurrentPredictCsvs[id] = concurrentPredictCsv;
}
return new PredictCsvCollection(mainPredictCsv, concurrentPredictCsvs);
} catch (Exception e) {
e.printStackTrace();
usage();
throw new IllegalStateException("Should not be reachable");
}
}
private static GenModel loadPojo(String className) throws Exception {
return (GenModel) Class.forName(className).newInstance();
}
private static GenModel loadMojo(String modelName) throws IOException {
return MojoModel.load(modelName);
}
private static GenModel loadModel(String modelName) throws Exception {
try {
return loadMojo(modelName);
} catch (IOException e) {
return loadPojo(modelName); // may throw an exception too
}
}
private static class PredictCsvBuilder {
// For PredictCsv
private String inputCSVFileName;
private String outputCSVFileName;
private boolean useDecimalOutput;
private char separator = ','; // separator used to delimite input datasets
private boolean setInvNumNA; // enable .setConvertInvalidNumbersToNa(true)
private boolean getTreePath; // enable tree models to obtain the leaf-assignment information
private boolean predictContributions; // enable tree models to predict contributions instead of regular predictions
private boolean predictCalibrated; // output also calibrated probabilities
private boolean returnGLRMReconstruct; // for GLRM, return x factor by default unless set this to true
private int glrmIterNumber = -1; // for GLRM, default to 100.
private boolean outputHeader = true; // should we write-out header to output files?
// For Model Loading
private int loadType = 0; // 0: load pojo, 1: load mojo, 2: load model, -1: special value when PredictCsv is used embedded and instance of Model is passed directly
private String pojoMojoModelNames = ""; // store Pojo/Mojo/Model names
private int testConcurrent = 0;
private PredictCsv newPredictCsv() {
return new PredictCsv(inputCSVFileName, outputCSVFileName, useDecimalOutput, separator, setInvNumNA,
getTreePath, predictContributions, predictCalibrated, returnGLRMReconstruct, glrmIterNumber, outputHeader);
}
private PredictCsv newConcurrentPredictCsv(int id) {
return new PredictCsv(inputCSVFileName, outputCSVFileName + "." + id, useDecimalOutput, separator, setInvNumNA,
getTreePath, predictContributions, predictCalibrated, returnGLRMReconstruct, glrmIterNumber, outputHeader);
}
private void parseArgs(String[] args) {
for (int i = 0; i < args.length; i++) {
String s = args[i];
if (s.equals("--header"))
continue;
if (s.equals("--decimal"))
useDecimalOutput = true;
else if (s.equals("--glrmReconstruct"))
returnGLRMReconstruct = true;
else if (s.equals("--setConvertInvalidNum"))
setInvNumNA = true;
else if (s.equals("--leafNodeAssignment"))
getTreePath = true;
else if (s.equals("--predictContributions")) {
predictContributions = true;
} else if (s.equals("--predictCalibrated")) {
predictCalibrated = true;
} else if (s.equals("--embedded")) {
loadType = -1;
} else {
i++;
if (i >= args.length) usage();
String sarg = args[i];
switch (s) {
case "--model":
pojoMojoModelNames = sarg;
loadType = 2;
break;
case "--mojo":
pojoMojoModelNames = sarg;
loadType = 1;
break;
case "--pojo":
pojoMojoModelNames = sarg;
loadType = 0;
break;
case "--input":
inputCSVFileName = sarg;
break;
case "--output":
outputCSVFileName = sarg;
break;
case "--separator":
separator = sarg.charAt(sarg.length() - 1);
break;
case "--glrmIterNumber":
glrmIterNumber = Integer.parseInt(sarg);
break;
case "--testConcurrent":
testConcurrent = Integer.parseInt(sarg);
break;
case "--outputHeader":
outputHeader = Boolean.parseBoolean(sarg);
break;
default:
System.out.println("ERROR: Unknown command line argument: " + s);
usage();
}
}
}
}
}
private static class PredictCsvCallable implements Callable {
private final PredictCsv predictCsv;
private PredictCsvCallable(PredictCsv predictCsv) {
this.predictCsv = predictCsv;
}
@Override
public Exception call() throws Exception {
try {
predictCsv.run();
} catch (Exception e) {
return e;
}
return null;
}
}
}
