def run():
connection = PgSQL.connect(user = "******", database = DatabaseName);
memDb = redis.Redis( host='localhost', port=6379 );
TrainDbConfig = DbBuildConfig['train'];
TestDbConfig = DbBuildConfig['test'];
trainDocDb = DocumentsDatabase(connection,
TrainDbConfig['DocTagsTable'],
TrainDbConfig['RawDocTable'],
TrainDbConfig['TagsTable'],
TrainDbConfig['DocumentsTable'] );
trainFeatureDb = FeatureDatabase(connection,
memDb,
trainDocDb,
TrainDbConfig['FeaturesTable'],
TrainDbConfig['DocFeaturesTable'],
TrainDbConfig['TagSpecificFeatureTable']);
testFeatureDb = FeatureDatabase(connection,
memDb,
None,
TestDbConfig['FeaturesTable'],
TestDbConfig['DocFeaturesTable'],
TestDbConfig['TagSpecificFeatureTable']);
classifier = Classifier(connection, trainFeatureDb, testFeatureDb,
ClassifierTableConfig['predictedTrain'],
ClassifierTableConfig['predictedTest'], trainDocDb);
# classifier.createTables();
classifier.createTagPredictTables();
classifier.cleanClassificationTables();
tags = trainDocDb.getTagsList();
s1 = 0;
s2 = 0;
for tag in tags:
features = trainFeatureDb.getTagSpecificFeatures( tag );
testTag = tag;
hashes = trainFeatureDb.getTagSpecificFeatures(testTag);
if not hashes:
continue;
c1 = trainDocDb.getTagCount(testTag);
if c1 <= 25:
continue;
s1 += c1;
print classifier.predictForTag( tag );
classifier.saveClassificationResults();
