use of edu.neu.ccs.pyramid.multilabel_classification.crf.CMLCRF in project pyramid by cheng-li.
the class CMLCRFTest method test5.
private static void test5() throws Exception {
MultiLabelClfDataSet dataSet = TRECFormat.loadMultiLabelClfDataSet(new File(DATASETS, "ohsumed/3/train.trec"), DataSetType.ML_CLF_SPARSE, true);
MultiLabelClfDataSet testSet = TRECFormat.loadMultiLabelClfDataSet(new File(DATASETS, "ohsumed/3/test.trec"), DataSetType.ML_CLF_SPARSE, true);
CMLCRF cmlcrf = new CMLCRF(dataSet);
CRFLoss crfLoss = new CRFLoss(cmlcrf, dataSet, 1);
cmlcrf.setConsiderPair(false);
MultiLabel[] predTrain;
MultiLabel[] predTest;
LBFGS optimizer = new LBFGS(crfLoss);
for (int i = 0; i < 5; i++) {
// System.out.print("Obj: " + optimizer.getTerminator().getLastValue());
System.out.println("iter: " + i);
optimizer.iterate();
System.out.println(crfLoss.getValue());
predTrain = cmlcrf.predict(dataSet);
predTest = cmlcrf.predict(testSet);
System.out.print("\tTrain acc: " + Accuracy.accuracy(dataSet.getMultiLabels(), predTrain));
System.out.print("\tTrain overlap " + Overlap.overlap(dataSet.getMultiLabels(), predTrain));
System.out.print("\tTest acc: " + Accuracy.accuracy(testSet.getMultiLabels(), predTest));
System.out.println("\tTest overlap " + Overlap.overlap(testSet.getMultiLabels(), predTest));
// System.out.println("crf = "+cmlcrf.getWeights());
// System.out.println(Arrays.toString(predTrain));
}
CRFLoss crfLoss2 = new CRFLoss(cmlcrf, dataSet, 1);
cmlcrf.setConsiderPair(true);
LBFGS optimizer2 = new LBFGS(crfLoss2);
for (int i = 0; i < 50; i++) {
System.out.println("consider pairs");
// System.out.print("Obj: " + optimizer.getTerminator().getLastValue());
System.out.println("iter: " + i);
optimizer2.iterate();
System.out.println(crfLoss2.getValue());
predTrain = cmlcrf.predict(dataSet);
predTest = cmlcrf.predict(testSet);
System.out.print("\tTrain acc: " + Accuracy.accuracy(dataSet.getMultiLabels(), predTrain));
System.out.print("\tTrain overlap " + Overlap.overlap(dataSet.getMultiLabels(), predTrain));
System.out.print("\tTest acc: " + Accuracy.accuracy(testSet.getMultiLabels(), predTest));
System.out.println("\tTest overlap " + Overlap.overlap(testSet.getMultiLabels(), predTest));
// System.out.println("crf = "+cmlcrf.getWeights());
// System.out.println(Arrays.toString(predTrain));
}
}
use of edu.neu.ccs.pyramid.multilabel_classification.crf.CMLCRF in project pyramid by cheng-li.
the class CMLCRFTest method test3.
private static void test3() throws Exception {
MultiLabelClfDataSet dataSet = TRECFormat.loadMultiLabelClfDataSet(new File(DATASETS, "/imdb/3/train.trec"), DataSetType.ML_CLF_SPARSE, true);
MultiLabelClfDataSet testSet = TRECFormat.loadMultiLabelClfDataSet(new File(DATASETS, "/imdb/3/test.trec"), DataSetType.ML_CLF_SPARSE, true);
CMLCRF cmlcrf = new CMLCRF(dataSet);
CRFLoss crfLoss = new CRFLoss(cmlcrf, dataSet, 1);
MultiLabel[] predTrain;
MultiLabel[] predTest;
LBFGS optimizer = new LBFGS(crfLoss);
for (int i = 0; i < 50; i++) {
// System.out.print("Obj: " + optimizer.getTerminator().getLastValue());
System.out.println("iter: " + i);
optimizer.iterate();
System.out.println(crfLoss.getValue());
predTrain = cmlcrf.predict(dataSet);
predTest = cmlcrf.predict(testSet);
System.out.print("\tTrain acc: " + Accuracy.accuracy(dataSet.getMultiLabels(), predTrain));
System.out.print("\tTrain overlap " + Overlap.overlap(dataSet.getMultiLabels(), predTrain));
System.out.print("\tTest acc: " + Accuracy.accuracy(testSet.getMultiLabels(), predTest));
System.out.println("\tTest overlap " + Overlap.overlap(testSet.getMultiLabels(), predTest));
// System.out.println("crf = "+cmlcrf.getWeights());
// System.out.println(Arrays.toString(predTrain));
}
}
use of edu.neu.ccs.pyramid.multilabel_classification.crf.CMLCRF in project pyramid by cheng-li.
the class CMLCRFTest method test7.
private static void test7() throws Exception {
System.out.println(config);
MultiLabelClfDataSet trainSet = TRECFormat.loadMultiLabelClfDataSet(config.getString("input.trainData"), DataSetType.ML_CLF_SEQ_SPARSE, true);
MultiLabelClfDataSet testSet = TRECFormat.loadMultiLabelClfDataSet(config.getString("input.testData"), DataSetType.ML_CLF_SEQ_SPARSE, true);
// loading or save model infos.
String output = config.getString("output");
String modelName = config.getString("modelName");
CMLCRF cmlcrf = null;
if (config.getString("train.warmStart").equals("true")) {
cmlcrf = CMLCRF.deserialize(new File(output, modelName));
System.out.println("loading model:");
System.out.println(cmlcrf);
} else if (config.getString("train.warmStart").equals("auto")) {
cmlcrf = CMLCRF.deserialize(new File(output, modelName));
System.out.println("retrain model:");
CMLCRFElasticNet cmlcrfElasticNet = new CMLCRFElasticNet(cmlcrf, trainSet, config.getDouble("l1Ratio"), config.getDouble("regularization"));
train(cmlcrfElasticNet, cmlcrf, trainSet, testSet, config);
} else if (config.getString("train.warmStart").equals("false")) {
cmlcrf = new CMLCRF(trainSet);
cmlcrf.setConsiderPair(config.getBoolean("considerLabelPair"));
CMLCRFElasticNet cmlcrfElasticNet = new CMLCRFElasticNet(cmlcrf, trainSet, config.getDouble("l1Ratio"), config.getDouble("regularization"));
train(cmlcrfElasticNet, cmlcrf, trainSet, testSet, config);
}
System.out.println();
System.out.println();
System.out.println("--------------------------------Results-----------------------------\n");
MLMeasures measures = new MLMeasures(cmlcrf, trainSet);
System.out.println("========== Train ==========\n");
System.out.println(measures);
System.out.println("========== Test ==========\n");
long startTimePred = System.nanoTime();
MultiLabel[] preds = cmlcrf.predict(testSet);
long stopTimePred = System.nanoTime();
long predTime = stopTimePred - startTimePred;
System.out.println("\nprediction time: " + TimeUnit.NANOSECONDS.toSeconds(predTime) + " sec.");
System.out.println(new MLMeasures(cmlcrf, testSet));
System.out.println("\n\n");
InstanceF1Predictor pluginF1 = new InstanceF1Predictor(cmlcrf);
System.out.println("Plugin F1");
System.out.println(new MLMeasures(pluginF1, testSet));
if (config.getBoolean("saveModel")) {
(new File(output)).mkdirs();
File serializeModel = new File(output, modelName);
cmlcrf.serialize(serializeModel);
}
}
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