class SimplePropertyRecommenderTestCase(unittest.TestCase):
def setUp(self):
sampler = T2DSampler()
self.testTables = sampler.getTablesPropertyAnnotation()
self.spr = SimplePropertyRecommender()
def testRecommendPropertiesForTable(self):
for num, table in enumerate(self.testTables):
print "table %s" %(num,)
properties = self.spr.recommendPropertiesForTable(table)
import ipdb; ipdb.set_trace()
def testLookupPropertiesLOV(self):
columnHeader = "city"
properties = self.spr.lookupPropertiesLOV(columnHeader)
def mapProperties(self, table):
properties = []
header = table.getHeader()
propertyRecommender = SimplePropertyRecommender()
for columnIndex, headerItem in enumerate(header):
lovProperties = propertyRecommender.lookupPropertiesLOV(headerItem)
if lovProperties == None:
continue
for _property in lovProperties:
if _property["score"] > self.scoreThreshold:
properties.append({"columnIndex": columnIndex, "uri": _property["uri"]})
break
return properties
def mapProperties(self, table):
properties = []
scores = self.getScores(table)
header = table.getHeader()
propertyRecommender = SimplePropertyRecommender()
for columnIndex, headerItem in enumerate(header):
lovProperties = propertyRecommender.lookupPropertiesLOV(headerItem)
if lovProperties == None:
continue
for _property in lovProperties:
if len(scores) > 0:
taipanScore = scores[columnIndex].get(_property['uri'], 0)*10
else:
taipanScore = 0
lovScore = _property['score']
score = float(taipanScore) + lovScore
if _property['score'] > self.scoreThreshold:
properties.append({"columnIndex": columnIndex, "uri": _property["uri"], "lovScore": lovScore, "taipanScore": taipanScore, "score": score})
#filter properties with maximum scores
propertiesRanked = []
for columnIndex, headerItem in enumerate(header):
#get all properties with the same columnIndex
columnIndexProperties = []
for _property in properties:
if _property['columnIndex'] == columnIndex:
columnIndexProperties.append(_property)
if len(columnIndexProperties) == 0:
continue
propertiesRanked.append(max(columnIndexProperties, key=lambda x: x['score']))
return propertiesRanked
def setUp(self):
sampler = T2DSampler()
self.testTables = sampler.getTablesPropertyAnnotation()
self.spr = SimplePropertyRecommender()
