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fvt.py
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fvt.py
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#!/usr/bin/env python
# encoding: utf-8
"""
fvt.py
Copyright (c) 2010 Piick.com, Inc. All rights reserved.
"""
from lxml import etree
class Node(object):
def __init__(self, el=None):
if el is not None:
self.value = el.tag
self.children = []
self.size = 1
if el.text and el.text.strip():
text_node = Node()
text_node.value = el.text.strip()
self.children.append(text_node)
self.size += text_node.size
for c in el:
n = Node(c)
self.children.append(n)
self.size += n.size
if c.tail and c.tail.strip():
tail_node = Node()
tail_node.value = c.tail.strip()
self.children.append(tail_node)
self.size += tail_node.size
else:
self.children = []
self.value = None
self.size = 1
def __eq__(self, other):
return self.value == other.value
def __ne__(self, other):
return not self.__eq__(other)
def __iter__(self):
for c in self.children:
yield c
def __len__(self):
return len(self.children)
def iter(self):
yield self
for c in self.children:
for n in c.iter():
yield n
def tree_matching(A,B):
M = [[0.0 for j in xrange(len(B) + 1)] for i in xrange(len(A) + 1)]
for i, a in enumerate(A, 1):
for j, b in enumerate(B, 1):
M[i][j] = max(M[i][j-1], M[i-1][j], M[i-1][j-1] + tree_matching(a, b))
return M[len(A)][len(B)] + (1 if A == B else 0)
def fiva_tree_match_score(A, B):
if A != B:
return 0
if not len(A) or not len(B) or len(A) == len(B):
return 2 * tree_matching(A, B) / (A.size + B.size)
score = 0.0
for cA in A:
node_score = 0.0
match_no = 0
for cB in B:
tmp = 2 * tree_matching(cA, cB) / (cA.size + cB.size)
if tmp > 0.5:
node_score += tmp
match_no += 1
if match_no > 0:
node_score = node_score / match_no
score += node_score
return (score / len(A)) + 2 / (A.size + B.size)
def recognize_peer_node(M):
pass
def repeat_mining(child_list, N):
return []
def is_aligned(M):
return True
def matrix_alignment(M):
return []
def merge_optional(child_list):
pass
def multiple_tree_merge(T, P=None):
M = [[None for i in xrange(len(T))] for j in xrange(max([len(t) for t in T]))]
for i, t in enumerate(T):
for j, c in enumerate(t):
M[j][i] = c
recognize_peer_node(M)
child_list = repeat_mining(matrix_alignment(M), 1)
merge_optional(child_list)
for c in child_list:
P.append(multiple_tree_merge(peer_node(c, M), tag(c)) if len(c) else c)
return P
if __name__ == "__main__":
H = ["<html><head></head><body> <b>Book Name</b> Databases<br/> <b>Author</b> John </body></html>",
"<html><head></head><body> <b>Book Name</b> Operating Systems<br/> <b>Author</b> Jason </body></html>",
]
T = [Node(etree.fromstring(h)) for h in H]
# for a, b in zip(T[0].iter(), T[1].iter()): print "\t%s\t%s\t%s\t%d\t%d\t%d\t%d\t%f\t%f" % (a.value, b.value, str(a == b), len(a), len(b), a.size, b.size, fiva_tree_match_score(a, b), tree_matching(a,b))
print tree_matching(T[0], T[1])
print fiva_tree_match_score(T[0], T[1])
print multiple_tree_merge(T)