def runExperiment(args):
if not os.path.exists(SAVE_PATH):
os.makedirs(SAVE_PATH)
(trainingDataDup, labelRefs, documentCategoryMap,
documentTextMap) = readDataAndReshuffle(args)
# remove duplicates from training data
includedDocIds = set()
trainingData = []
for record in trainingDataDup:
if record[2] not in includedDocIds:
includedDocIds.add(record[2])
trainingData.append(record)
args.networkConfig = getNetworkConfig(args.networkConfigPath)
model = createModel(numLabels=1, **vars(args))
model = trainModel(args, model, trainingData, labelRefs)
numDocs = model.getClassifier()._numPatterns
print "Model trained with %d documents" % (numDocs,)
knn = model.getClassifier()
hc = HierarchicalClustering(knn)
hc.cluster("complete")
protos, clusterSizes = hc.getClusterPrototypes(args.numClusters,
numDocs)
# Run test to ensure consistency with KNN
if args.knnTest:
knnTest(protos, knn)
return
# Summary statistics
# bucketCounts[i, j] is the number of occurrances of bucket j in cluster i
bucketCounts = numpy.zeros((args.numClusters, len(labelRefs)))
for clusterId in xrange(len(clusterSizes)):
print
print "Cluster %d with %d documents" % (clusterId, clusterSizes[clusterId])
print "==============="
prototypeNum = 0
for index in protos[clusterId]:
if index != -1:
docId = trainingData[index][2]
prototypeNum += 1
display = prototypeNum <= args.numPrototypes
if display:
print "(%d) %s" % (docId, trainingData[index][0])
print "Buckets:"
# The docId keys in documentCategoryMap are strings rather than ints
if docId in documentCategoryMap:
for bucketId in documentCategoryMap[docId]:
bucketCounts[clusterId, bucketId] += 1
if display:
print " ", labelRefs[bucketId]
elif display:
print " <None>"
if display:
print "\n\n"
createBucketClusterPlot(args, bucketCounts)
create2DSVDProjection(args, protos, trainingData, documentCategoryMap, knn)
def runExperiment(args):
if not os.path.exists(SAVE_PATH):
os.makedirs(SAVE_PATH)
(trainingDataDup, labelRefs, documentCategoryMap,
documentTextMap) = readDataAndReshuffle(args)
# remove duplicates from training data
includedDocIds = set()
trainingData = []
for record in trainingDataDup:
if record[2] not in includedDocIds:
includedDocIds.add(record[2])
trainingData.append(record)
args.networkConfig = getNetworkConfig(args.networkConfigPath)
model = createModel(numLabels=1, **vars(args))
model = trainModel(args, model, trainingData, labelRefs)
numDocs = model.getClassifier()._numPatterns
print "Model trained with %d documents" % (numDocs, )
knn = model.getClassifier()
hc = HierarchicalClustering(knn)
hc.cluster("complete")
protos, clusterSizes = hc.getClusterPrototypes(args.numClusters, numDocs)
# Run test to ensure consistency with KNN
if args.knnTest:
knnTest(protos, knn)
return
# Summary statistics
# bucketCounts[i, j] is the number of occurrances of bucket j in cluster i
bucketCounts = numpy.zeros((args.numClusters, len(labelRefs)))
for clusterId in xrange(len(clusterSizes)):
print
print "Cluster %d with %d documents" % (clusterId,
clusterSizes[clusterId])
print "==============="
prototypeNum = 0
for index in protos[clusterId]:
if index != -1:
docId = trainingData[index][2]
prototypeNum += 1
display = prototypeNum <= args.numPrototypes
if display:
print "(%d) %s" % (docId, trainingData[index][0])
print "Buckets:"
# The docId keys in documentCategoryMap are strings rather than ints
if docId in documentCategoryMap:
for bucketId in documentCategoryMap[docId]:
bucketCounts[clusterId, bucketId] += 1
if display:
print " ", labelRefs[bucketId]
elif display:
print " <None>"
if display:
print "\n\n"
createBucketClusterPlot(args, bucketCounts)
create2DSVDProjection(args, protos, trainingData, documentCategoryMap, knn)
