"evaluationclassifierserver", "default-topic-model", "default-topic-model");
}
classifier.configure(getCanonicalConfiguration(__evaluationServer,solrCoreConfig));
}
} catch (Exception e) {
throw new ClassifierException(e);
}
// clean all previous concepts from the evaluation classifier in case we are reusing an existing solr
// index from OSGi.
classifier.removeAllConcepts();
// iterate over all the topics to register them in the evaluation classifier
batchOverTopics(new BatchProcessor<SolrDocument>() {
@Override
public int process(List<SolrDocument> batch) throws ClassifierException {
for (SolrDocument topicEntry : batch) {
String conceptId = topicEntry.getFirstValue(conceptUriField).toString();
Collection<Object> broader = topicEntry.getFieldValues(broaderField);
if (broader == null) {
classifier.addConcept(conceptId, null, null);
} else {
List<String> broaderConcepts = new ArrayList<String>();
for (Object broaderConcept : broader) {
broaderConcepts.add(broaderConcept.toString());
}
classifier.addConcept(conceptId, null, broaderConcepts);
}
}
return batch.size();
}
});
// build the model on the for the current train CV folds
classifier.setCrossValidationInfo(cvFoldIndex, cvFoldCount);
// bind our new classifier to the same training set at the parent
classifier.setTrainingSet(getTrainingSet());
classifier.updateModel(false);
final int foldCount = cvFoldCount;
final int foldIndex = cvFoldIndex;
// iterate over the topics again to compute scores on the test fold
int updatedTopics = batchOverTopics(new BatchProcessor<SolrDocument>() {
@Override
public int process(List<SolrDocument> batch) throws TrainingSetException, ClassifierException {
int offset;
int updated = 0;
for (SolrDocument topicMetadata : batch) {
String topic = topicMetadata.getFirstValue(conceptUriField).toString();
List<String> topics = Arrays.asList(topic);
List<String> falseNegativeExamples = new ArrayList<String>();
int truePositives = 0;
int falseNegatives = 0;
int positiveSupport = 0;
offset = 0;
Batch<Example> examples = Batch.emtpyBatch(Example.class);
boolean skipTopic = false;
do {
examples = getTrainingSet().getPositiveExamples(topics, examples.nextOffset);
if (offset == 0 && examples.items.size() < MIN_EVALUATION_SAMPLES) {
// we need a minimum about of examples otherwise it's really not
// worth computing statistics
skipTopic = true;
break;
}
for (Example example : examples.items) {
if (!(offset % foldCount == foldIndex)) {
// this example is not part of the test fold, skip it
offset++;
continue;
}
positiveSupport++;
offset++;
List<TopicSuggestion> suggestedTopics = classifier
.suggestTopics(example.contents);
boolean match = false;
for (TopicSuggestion suggestedTopic : suggestedTopics) {
if (topic.equals(suggestedTopic.conceptUri)) {
match = true;
truePositives++;
break;
}
}
if (!match) {
falseNegatives++;
if (falseNegativeExamples.size() < MAX_COLLECTED_EXAMPLES / foldCount) {
falseNegativeExamples.add(example.id);
}
}
}
} while (!skipTopic && examples.hasMore && offset < MAX_EVALUATION_SAMPLES);
List<String> falsePositiveExamples = new ArrayList<String>();
int falsePositives = 0;
int negativeSupport = 0;
offset = 0;
examples = Batch.emtpyBatch(Example.class);
do {
if (skipTopic) {
break;
}
examples = getTrainingSet().getNegativeExamples(topics, examples.nextOffset);
for (Example example : examples.items) {
if (!(offset % foldCount == foldIndex)) {
// this example is not part of the test fold, skip it
offset++;
continue;
}
negativeSupport++;
offset++;
List<TopicSuggestion> suggestedTopics = classifier
.suggestTopics(example.contents);
for (TopicSuggestion suggestedTopic : suggestedTopics) {
if (topic.equals(suggestedTopic.conceptUri)) {
falsePositives++;
if (falsePositiveExamples.size() < MAX_COLLECTED_EXAMPLES / foldCount) {
falsePositiveExamples.add(example.id);
}
break;
}
}
// we don't need to collect true negatives
}
} while (examples.hasMore && offset < MAX_EVALUATION_SAMPLES);
if (skipTopic) {
log.debug("Skipping evaluation of {} because too few positive examples.", topic);
} else {
// compute precision, recall and f1 score for the current test fold and topic
float precision = 0;
if (truePositives != 0 || falsePositives != 0) {
precision = truePositives / (float) (truePositives + falsePositives);
}
float recall = 0;
if (truePositives != 0 || falseNegatives != 0) {
recall = truePositives / (float) (truePositives + falseNegatives);
}
updatePerformanceMetadata(topic, precision, recall, positiveSupport, negativeSupport,
falsePositiveExamples, falseNegativeExamples);
updated += 1;
}
}
try {
getActiveSolrServer().commit();
} catch (Exception e) {
throw new ClassifierException(e);
}
return updated;
}
});