# ====================================================================
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ====================================================================
from math import pi,sqrt,acos
from lia.common.LiaTestCase import LiaTestCase
from lucene import Document,IndexReader
class CategorizerTest(LiaTestCase):
def setUp(self):
super(CategorizerTest, self).setUp()
self.categoryMap = {}
self.buildCategoryVectors()
self.dumpCategoryVectors()
def testCategorization(self):
self.assertEqual("/technology/computers/programming/methodology",
self.getCategory("extreme agile methodology"))
self.assertEqual("/education/pedagogy",
self.getCategory("montessori education philosophy"))
def dumpCategoryVectors(self):
for category, vectorMap in self.categoryMap.iteritems():
print "Category", category
for term, freq in vectorMap.iteritems():
print " ", term, "=", freq
def buildCategoryVectors(self):
reader = IndexReader.open(self.directory, True)
for id in xrange(reader.maxDoc()):
doc = reader.document(id)
category = doc.get("category")
vectorMap = self.categoryMap.get(category, None)
if vectorMap is None:
vectorMap = self.categoryMap[category] = {}
termFreqVector = reader.getTermFreqVector(id, "subject")
self.addTermFreqToMap(vectorMap, termFreqVector)
def addTermFreqToMap(self, vectorMap, termFreqVector):
terms = termFreqVector.getTerms()
freqs = termFreqVector.getTermFrequencies()
i = 0
for term in terms:
if term in vectorMap:
vectorMap[term] += freqs[i]
else:
vectorMap[term] = freqs[i]
i += 1
def getCategory(self, subject):
words = subject.split(' ')
bestAngle = 2 * pi
bestCategory = None
for category, vectorMap in self.categoryMap.iteritems():
angle = self.computeAngle(words, category, vectorMap)
if angle != 'nan' and angle < bestAngle:
bestAngle = angle
bestCategory = category
return bestCategory
def computeAngle(self, words, category, vectorMap):
# assume words are unique and only occur once
dotProduct = 0
sumOfSquares = 0
for word in words:
categoryWordFreq = 0
if word in vectorMap:
categoryWordFreq = vectorMap[word]
# optimized because we assume frequency in words is 1
dotProduct += categoryWordFreq
sumOfSquares += categoryWordFreq ** 2
if sumOfSquares == 0:
return 'nan'
if sumOfSquares == len(words):
# avoid precision issues for special case
# sqrt x * sqrt x = x
denominator = sumOfSquares
else:
denominator = sqrt(sumOfSquares) * sqrt(len(words))
return acos(dotProduct / denominator)
|