class ComputeSimilarity( object ):
"""
Similarity measures.
Compute term similarity based on co-occurrence and
collocation likelihoods.
"""
DEFAULT_SLIDING_WINDOW_SIZE = 10
MAX_FREQ = 100.0
def __init__( self, logging_level ):
self.logger = logging.getLogger( 'ComputeSimilarity' )
self.logger.setLevel( logging_level )
handler = logging.StreamHandler( sys.stderr )
handler.setLevel( logging_level )
self.logger.addHandler( handler )
def execute( self, data_path, sliding_window_size = None ):
assert data_path is not None
if sliding_window_size is None:
sliding_window_size = ComputeSimilarity.DEFAULT_SLIDING_WINDOW_SIZE
self.logger.info( '--------------------------------------------------------------------------------' )
self.logger.info( 'Computing term similarity...' )
self.logger.info( ' data_path = %s', data_path )
self.logger.info( ' sliding_window_size = %d', sliding_window_size )
self.logger.info( 'Connecting to data...' )
self.tokens = TokensAPI( data_path )
self.similarity = SimilarityAPI( data_path )
self.logger.info( 'Reading data from disk...' )
self.tokens.read()
self.logger.info( 'Computing document co-occurrence...' )
self.computeDocumentCooccurrence()
self.logger.info( 'Computing sliding-window co-occurrence...' )
self.computeSlidingWindowCooccurrence( sliding_window_size )
self.logger.info( 'Counting total number of tokens, unigrams, and bigrams in the corpus...' )
self.computeTokenCounts()
self.logger.info( 'Computing document co-occurrence likelihood...' )
self.similarity.document_g2 = self.getG2Stats( self.document_count, self.similarity.document_occurrence, self.similarity.document_cooccurrence )
self.logger.info( 'Computing sliding-window co-occurrence likelihood...' )
self.similarity.window_g2 = self.getG2Stats( self.window_count, self.similarity.window_occurrence, self.similarity.window_cooccurrence )
self.logger.info( 'Computing collocation likelihood...' )
self.similarity.collocation_g2 = self.getG2Stats( self.token_count, self.similarity.unigram_counts, self.similarity.bigram_counts )
self.combineSimilarityMatrices()
self.logger.info( 'Writing data to disk...' )
self.similarity.write()
self.logger.info( '--------------------------------------------------------------------------------' )
def incrementCount( self, occurrence, key ):
if key not in occurrence:
occurrence[ key ] = 1
else:
occurrence[ key ] += 1
def computeDocumentCooccurrence( self ):
document_count = 0
occurrence = {}
cooccurrence = {}
for docID, docTokens in self.tokens.data.iteritems():
self.logger.debug( ' %s (%d tokens)', docID, len(docTokens) )
tokenSet = frozenset(docTokens)
document_count += 1
for token in tokenSet:
self.incrementCount( occurrence, token )
for aToken in tokenSet:
for bToken in tokenSet:
if aToken < bToken:
self.incrementCount( cooccurrence, (aToken, bToken) )
self.document_count = document_count
self.similarity.document_occurrence = occurrence
self.similarity.document_cooccurrence = cooccurrence
def computeSlidingWindowCooccurrence( self, sliding_window_size ):
window_count = 0
occurrence = {}
cooccurrence = {}
for docID, docTokens in self.tokens.data.iteritems():
allWindowTokens = self.getSlidingWindowTokens( docTokens, sliding_window_size )
self.logger.debug( ' %s (%d tokens, %d windows)', docID, len(docTokens), len(allWindowTokens) )
for windowTokens in allWindowTokens:
tokenSet = frozenset(windowTokens)
window_count += 1
for token in tokenSet:
self.incrementCount( occurrence, token )
for aToken in tokenSet:
for bToken in tokenSet:
if aToken < bToken:
self.incrementCount( cooccurrence, (aToken, bToken) )
self.window_count = window_count
self.similarity.window_occurrence = occurrence
self.similarity.window_cooccurrence = cooccurrence
def getSlidingWindowTokens( self, tokens, sliding_window_size ):
allWindows = []
aIndex = 0 - sliding_window_size
bIndex = len(tokens) + sliding_window_size
for index in range( aIndex, bIndex ):
a = max( 0 , index - sliding_window_size )
b = min( len(tokens) , index + sliding_window_size )
allWindows.append( tokens[a:b] )
return allWindows
def computeTokenCounts( self ):
token_count = sum( len(docTokens) for docTokens in self.tokens.data.itervalues() )
unigram_counts = {}
for docTokens in self.tokens.data.itervalues():
for token in docTokens:
self.incrementCount( unigram_counts, token )
bigram_counts = {}
for docTokens in self.tokens.data.itervalues():
prevToken = None
for currToken in docTokens:
if prevToken is not None:
self.incrementCount( bigram_counts, (prevToken, currToken) )
prevToken = currToken
self.token_count = token_count
self.similarity.unigram_counts = unigram_counts
self.similarity.bigram_counts = bigram_counts
def getBinomial( self, B_given_A, any_given_A, B_given_notA, any_given_notA ):
assert B_given_A >= 0
assert B_given_notA >= 0
assert any_given_A >= B_given_A
assert any_given_notA >= B_given_notA
a = float( B_given_A )
b = float( B_given_notA )
c = float( any_given_A )
d = float( any_given_notA )
E1 = c * ( a + b ) / ( c + d )
E2 = d * ( a + b ) / ( c + d )
g2a = 0
g2b = 0
if a > 0:
g2a = a * math.log( a / E1 )
if b > 0:
g2b = b * math.log( b / E2 )
return 2 * ( g2a + g2b )
def getG2( self, freq_all, freq_ab, freq_a, freq_b ):
assert freq_all >= freq_a
assert freq_all >= freq_b
assert freq_a >= freq_ab
assert freq_b >= freq_ab
assert freq_all >= 0
assert freq_ab >= 0
assert freq_a >= 0
assert freq_b >= 0
B_given_A = freq_ab
B_given_notA = freq_b - freq_ab
any_given_A = freq_a
any_given_notA = freq_all - freq_a
return self.getBinomial( B_given_A, any_given_A, B_given_notA, any_given_notA )
def getG2Stats( self, max_count, occurrence, cooccurrence ):
g2_stats = {}
freq_all = max_count
for ( firstToken, secondToken ) in cooccurrence:
freq_a = occurrence[ firstToken ]
freq_b = occurrence[ secondToken ]
freq_ab = cooccurrence[ (firstToken, secondToken) ]
scale = ComputeSimilarity.MAX_FREQ / freq_all
rescaled_freq_all = freq_all * scale
rescaled_freq_a = freq_a * scale
rescaled_freq_b = freq_b * scale
rescaled_freq_ab = freq_ab * scale
if rescaled_freq_a > 1.0 and rescaled_freq_b > 1.0:
g2_stats[ (firstToken, secondToken) ] = self.getG2( freq_all, freq_ab, freq_a, freq_b )
return g2_stats
def combineSimilarityMatrices( self ):
self.logger.info( 'Combining similarity matrices...' )
self.similarity.combined_g2 = {}
keys_queued = []
for key in self.similarity.document_g2:
( firstToken, secondToken ) = key
otherKey = ( secondToken, firstToken )
keys_queued.append( key )
keys_queued.append( otherKey )
for key in self.similarity.window_g2:
( firstToken, secondToken ) = key
otherKey = ( secondToken, firstToken )
keys_queued.append( key )
keys_queued.append( otherKey )
for key in self.similarity.collocation_g2:
keys_queued.append( key )
keys_processed = {}
for key in keys_queued:
keys_processed[ key ] = False
for key in keys_queued:
if not keys_processed[ key ]:
keys_processed[ key ] = True
( firstToken, secondToken ) = key
if firstToken < secondToken:
orderedKey = key
else:
orderedKey = ( secondToken, firstToken )
score = 0.0
if orderedKey in self.similarity.document_g2:
score += self.similarity.document_g2[ orderedKey ]
if orderedKey in self.similarity.window_g2:
score += self.similarity.window_g2[ orderedKey ]
if key in self.similarity.collocation_g2:
score += self.similarity.collocation_g2[ key ]
if score > 0.0:
self.similarity.combined_g2[ key ] = score
