def __init__(self, parse_file):
counter = Counts()
for l in open(parse_file):
tree = json.loads(l)
counter.count(tree)
#N is 'Non-terminal'=> N[symbol] = count
self.N = counter.nonterm
#binary_R is 'binary Rule' binary_R[symbol, y1, y2] = count
self.binary_R = counter.binary
#unary will be on this form=> unary[symbol, word] = count
self.unary_R = counter.unary
#V is 'vocabulary' and there are 245 words in it.
self.V = counter.vocabulary #245
# pi[i, i, x] = probability
#'pi' is a dictionary, 'i' is the index of the word in the sentence, 'x' is the non-terminal symbol
#'pi[i, i, x]' = 0.03 where 0.03 is the probability of the word at 'i' being assigned to 'x'
self.pi = {}
#'bp' stands for 'back pointer' and it's a dictionary that maps the best rule and best_split to
#the binary table
#'bp[i, i, x]' = [(x,word),i]
self.bp = {}
#binary_table is a dictionary that collects the binary rules derived from one symbol
#binary_table['S'] = [ ('NP', 'VP'), ('NP', 'VP+VERB'), ...]
self.binary_table = defaultdict(set)
self.initialize_binary_table()
def __init__(self):
self.binary_q = {}
self.unary_q = {}
self.counter = Counts()
self.pi = []
self.bp = []
self.parser = {}
self.use_vert = False
def __init__(self):
self.binary_q = {}
self.unary_q = {}
self.counter = Counts()
self.counter_n = Counts()
self.pi = []
self.bp = []
self.parser = {}
self.use_vert = False
#0.95 curr best
self.beta = 0
self.vm = "\^\<[A-Z]+[+A-Z]*\>"
self.vocab = {}
def compute_cfg_frequency_in_train_file(self, train_file):
self.cfg_counter = Counts()
with open(train_file, 'r') as fd:
for line in fd:
try:
cfg_tree = json.loads(line)
self.cfg_counter.count(cfg_tree)
except:
print('Error: compute_cfg_frequency_in_train_file(): line: {0}'.format(line))
def __init__(self, rare_count_threshold=5):
self.rare_count_threshold = rare_count_threshold
self.cfg_counter = Counts()
self.word_freq = defaultdict(int)
class PCFG:
def __init__(self, rare_count_threshold=5):
self.rare_count_threshold = rare_count_threshold
self.cfg_counter = Counts()
self.word_freq = defaultdict(int)
# This tree is in Penn Tree Bank format not in Chomsky Normal Form
def create_parse_tree(self, elem_root_subtree):
if 'null' in elem_root_subtree.attrib:
return None
subtree_array = [elem_root_subtree.attrib['cat']]
if elem_root_subtree.tag == 'cons':
for child in elem_root_subtree:
child_subtree_array = self.create_parse_tree(child)
if child_subtree_array is not None:
subtree_array.append(child_subtree_array)
elif elem_root_subtree.tag == 'tok':
subtree_array.append(elem_root_subtree.text)
else:
# TBD: print error message
pass
return subtree_array
# right-factored
# TBD: Provide option for left-factored too
# TBD: Handle null as mentioned in section 6.3 Null Elements in GENIA corpus manual
def convert_PTB_to_CNF(self, tree_array):
if len(tree_array) > 3:
# convert multi-child tree into tree with two children
subtree_array = [tree_array[0]]
for i in range(2, len(tree_array)):
subtree_array.append(tree_array[i])
tree_array[2] = subtree_array
# tree_array[2] = [tree_array[0], tree_array[2], tree_array[3:]]
del tree_array[3:]
self.convert_PTB_to_CNF(tree_array)
elif len(tree_array) == 3:
# root of both the children should be non-terminals
assert (type(tree_array[1]) is list), "expected list for left child: {0}".format(tree_array[1])
assert (type(tree_array[2]) is list), "expected list for right child: {0}".format(tree_array[2])
# convert left child into CNF form if its not already in that form
self.convert_PTB_to_CNF(tree_array[1])
# convert right child into CNF form if its not already in that form
self.convert_PTB_to_CNF(tree_array[2])
elif (len(tree_array) == 2) and (type(tree_array[1]) is list):
# Form: X->Y where X,Y are non-terminals
tree_array[0] = tree_array[0] + '+' + tree_array[1][0]
if type(tree_array[1][1]) is list:
subtree_array = tree_array[1][1:]
del tree_array[1:]
for subtree in subtree_array:
tree_array.append(subtree)
self.convert_PTB_to_CNF(tree_array)
else:
# e.g. [NP [DET There]]
tree_array[1] = tree_array[1][1]
# Extract parse tree from the xml files. These parse trees are in Penn TreeBank format.
# Randomly assign xml files into training and validation set.
# Write the parse trees after converting them into Chomsky Normal Form
def create_train_and_test_parse_tree(self, treebank_folder, train_key_file, test_file, test_key_file, n_train_file):
file_list = glob.glob(treebank_folder+'/'+'*.xml')
# randomly select n_train_file for training and rest for testing
train_index_list = random.sample(range(len(file_list)), n_train_file)
# write the train file
count_parse_tree = 0
with open(train_key_file, 'w') as fd:
for file_i in train_index_list:
train_file = file_list[file_i]
try:
count_parse_tree_in_xml = self.write_parse_tree(fd, train_file)
count_parse_tree += count_parse_tree_in_xml
print('train file: {0} :: parse tree count till now: {1}'.format(train_file, count_parse_tree))
except:
err = sys.exc_info()[0]
print('Error in {0}: {1}'.format(train_file, err))
# test files are actually more of validation set
# write the test parse tree file
failure_parse_file_list = []
with open(test_key_file, 'w') as fd:
# optimize the search to O(n) by sorting the train_index_list first
for file_i in range(len(file_list)):
if file_i not in train_index_list:
test_xml_file = file_list[file_i]
try:
self.write_parse_tree(fd, test_xml_file)
except:
err = sys.exc_info()
print('Error in {0}: {1}'.format(test_xml_file, err))
parts_filename = os.path.split(test_xml_file)
failure_parse_file_list.append(parts_filename[1])
# Now write the test sentences
with open(test_file, 'w') as fd:
# optimize the search to O(n) by sorting the train_index_list first
for file_i in range(len(file_list)):
if file_i not in train_index_list:
test_xml_file = file_list[file_i]
parts_filename = os.path.split(test_xml_file)
if parts_filename[1] in failure_parse_file_list:
print('ignoring sentence extraction from {0}'.format(test_xml_file))
continue
try:
self.write_sentences(fd, test_xml_file)
except:
err = sys.exc_info()[0]
print('Error in extracting sentence from {0}: {1}'.format(test_xml_file, err))
# Create parse trees from xml file and write in the train/test file (fd)
def write_parse_tree(self, fd, xml_filename):
tree = ET.parse(xml_filename)
root = tree.getroot()
count_parse_tree_in_xml = 0
# reading the sentences only from the section: AbstractText
for abstractText in root.iter('AbstractText'):
# iterate over each of the sentences
for sentence in abstractText.iter('sentence'):
# TBD: Following should have a single iteration
# Need to check if the root is an actual root tag e.g. 'S'
for sentence_root in sentence:
tree_ptb_array = self.create_parse_tree(sentence_root)
tree_cnf_array = deepcopy(tree_ptb_array)
self.convert_PTB_to_CNF(tree_cnf_array)
# convert string into json (this converts single quotes to double quotes)
# required due to failure in json load of tree in count_cfg_freq.py
tree_cnf_json = json.dumps(tree_cnf_array)
fd.write('{0}\n'.format(tree_cnf_json))
count_parse_tree_in_xml += 1
return count_parse_tree_in_xml
# Extract sentences from xml file and write in fd
@staticmethod
def write_sentences(fd, xml_filename):
tree = ET.parse(xml_filename)
root = tree.getroot()
for abstractText in root.iter('AbstractText'):
for sentence in abstractText.iter('sentence'):
token_array = []
for token in sentence.iter('tok'):
token_array.append(token.text)
fd.write('{0}\n'.format(' '.join(token_array)))
def create_train_with_rare(self, orig_train_key_file, mod_train_key_file):
# First check if self.word_freq is already populated or not
if len(self.cfg_counter.unary) == 0:
self.compute_cfg_frequency_in_train_file(orig_train_key_file)
if len(self.word_freq) == 0:
self.compute_word_frequency_in_cfg()
# Now iterate through each parse tree and replace the rare word with rare symbol
# Write the changed parse trees into new train file
count_parse_tree = 0
with open(mod_train_key_file, 'w') as wfd:
with open(orig_train_key_file, 'r') as rfd:
for line in rfd:
count_parse_tree += 1
tree = json.loads(line)
try:
self.replace_infrequent_words_in_parse_tree(tree)
tree_json = json.dumps(tree)
wfd.write('{0}\n'.format(tree_json))
except:
print('Error: create_train_with_rare(): parse tree # {0} :: line: {1}\n'.format(count_parse_tree, line))
def compute_cfg_frequency_in_train_file(self, train_file):
self.cfg_counter = Counts()
with open(train_file, 'r') as fd:
for line in fd:
try:
cfg_tree = json.loads(line)
self.cfg_counter.count(cfg_tree)
except:
print('Error: compute_cfg_frequency_in_train_file(): line: {0}'.format(line))
def compute_word_frequency_in_cfg(self):
self.word_freq = defaultdict(int)
# Terminal word can be assigned to multiple non-Terminals
for (sym, word), count in self.cfg_counter.unary.iteritems():
self.word_freq[word] += count
def write_word_frequency_in_cfg(self, word_freq_file):
word_freq_sorted = sorted(self.word_freq.items(), key=lambda x: x[1], reverse=True)
with open(word_freq_file, 'w') as fd:
for word_freq in word_freq_sorted:
fd.write('{0} {1}\n'.format(word_freq[1], word_freq[0]))
def is_rare_word(self, word):
return self.word_freq[word.lower()] < self.rare_count_threshold
def replace_infrequent_words_in_parse_tree(self, tree):
if isinstance(tree, basestring):
return
if len(tree) == 3:
# binary rule
self.replace_infrequent_words_in_parse_tree(tree[1])
self.replace_infrequent_words_in_parse_tree(tree[2])
elif len(tree) == 2:
# unary rule
word = tree[1]
assert (type(tree[1]) is not list), "expected string: {0}".format(tree[1])
if self.is_rare_word(word):
tree[1] = '_RARE_'
# x -> y z
# x,y,z are non-terminals
def compute_binary_parameter(self, x, y, z):
key = (x, y, z)
if key not in self.cfg_counter.binary:
return 0.0
else:
return self.cfg_counter.binary[key]*1.0/self.cfg_counter.nonterm[x]
# x -> w
# x: non terminal
# w: terminal
def compute_unary_parameter(self, x, w):
key = (x, w)
if key not in self.cfg_counter.unary:
return 0.0
else:
return self.cfg_counter.unary[key]*1.0/self.cfg_counter.nonterm[x]
def compute_parse_tree_using_cky_algorithm(self, sentence):
tokens = sentence.strip().split(' ')
n = len(tokens)
# replace the rare token with rare symbol
for i in range(n):
if self.is_rare_word(tokens[i]):
tokens[i] = '_RARE_'.lower()
else:
tokens[i] = tokens[i].lower()
pi = {} # store the matrix in dict form
bp = {}
# initialize the dict
for i in range(n):
pi[i] = {}
bp[i] = {}
for j in range(i, n):
pi[i][j] = {}
bp[i][j] = {}
if i == j:
# initialize pi for x->w for each of the word
for x in self.cfg_counter.nonterm:
pi[i][i][x] = self.compute_unary_parameter(x, tokens[i])
bp[i][i][x] = None
else:
for x in self.cfg_counter.nonterm:
pi[i][j][x] = 0.0
bp[i][j][x] = None
# Now for x -> y z where x,y,z are non-terminals
for l in range(1, n):
for i in range(n-l):
j = i + l
# Here we only consider the (x,y,z) tuple which we have seen in training data
for (x, y, z) in self.cfg_counter.binary.keys():
q_x_to_yz = self.compute_binary_parameter(x, y, z)
if q_x_to_yz <= pi[i][j][x]:
# current x -> y z can't give better probability than already computed max prob with
# non-terminal x spanning words i..j inclusive
continue
max_arg_s, max_val_s_i_j = self.compute_best_split(pi, i, j, y, z)
# now check if the current value of pi[i][j][x] is better than the value computed earlier
val = q_x_to_yz * max_val_s_i_j
if pi[i][j][x] <= val:
pi[i][j][x] = val
bp[i][j][x] = [max_arg_s, y, z]
"""
# In the following we try all the (x,y,z) combinations
# This is a slower process
for x in self.cfg_counter.nonterm:
max_pi_i_j_x = 0.0
# [s,y,z]
arg_max_pi = [None, None, None]
for y in self.cfg_counter.nonterm:
for z in self.cfg_counter.nonterm:
q_x_to_yz = self.compute_binary_parameter(x, y, z)
max_val_s_i_j = 0.0
max_arg_s = None
for s in range(i, j):
val = pi[i][s][y] * pi[s+1][j][z]
if max_val_s_i_j < val:
max_val_s_i_j = val
max_arg_s = s
val = q_x_to_yz * max_val_s_i_j
if max_pi_i_j_x < val:
max_pi_i_j_x = val
arg_max_pi = [max_arg_s, y, z]
pi[i][j][x] = max_pi_i_j_x
bp[i][j][x] = arg_max_pi
"""
assert (pi[0][n-1]['S'] > 0), "pi[0][{0}]['S'] is zero".format(n-1)
# split the sentence into tokens again. In the beginning of the function we had replaced the rare tokens with rare symbol
tokens = sentence.strip().split(' ')
best_parse_tree = self.create_parse_tree_from_backpointers(tokens, bp, 0, n-1, 'S')
best_prob = pi[0][n-1]['S']
return best_parse_tree, best_prob
# For the given x -> y z for the words spanning i,..,j inclusive, compute the best split
# Select s which maximizes pi(i,s,y)*pi(s+1,j,z)
@staticmethod
def compute_best_split(pi, i, j, y, z):
max_val_s_i_j = 0.0
max_arg_s = None
for s in range(i, j):
val = pi[i][s][y] * pi[s + 1][j][z]
if max_val_s_i_j <= val:
max_val_s_i_j = val
max_arg_s = s
return max_arg_s, max_val_s_i_j
def create_parse_tree_from_backpointers(self, tokens, bp, i, j, x):
if i == j:
return [x, tokens[i]]
assert (bp[i][j][x] is not None), "bp[{0}][{1}][{2}] is None".format(i, j, x)
split_index, y, z = bp[i][j][x]
parse_tree_left_child = self.create_parse_tree_from_backpointers(tokens, bp, i, split_index, y)
parse_tree_right_child = self.create_parse_tree_from_backpointers(tokens, bp, split_index+1, j, z)
parse_tree = [x, parse_tree_left_child, parse_tree_right_child]
return parse_tree
def compute_parse_tree_for_test_sentences(self, test_sentence_file, test_key_file):
with open(test_key_file, 'w') as wfd:
with open(test_sentence_file, 'r') as rfd:
sent_i = 0
for sentence in rfd:
sent_i += 1
if sent_i % 20 == 19:
print('sent_i: {0}'.format(sent_i))
try:
tree, prob = self.compute_parse_tree_using_cky_algorithm(sentence)
# convert into json
tree_json = json.dumps(tree)
wfd.write('{0}\n'.format(tree_json))
print('prob of sent_i #{0}: {1}'.format(sent_i, prob))
except:
err = sys.exc_info()[0]
print('Error: compute_parse_tree_for_test_sentences(): sent_i: {0} :: sentence: {1} :: \
error: {2}\n'.format(sent_i, sentence, err))
def compute_cfg_frequency_in_train_file(self, train_file):
self.cfg_counter = Counts()
with open(train_file, 'r') as fd:
for line in fd:
cfg_tree = json.loads(line)
self.cfg_counter.count(cfg_tree)
def __init__(self, original_train_file, modified_train_file, count_threshold=5):
self.count_threshold = count_threshold
self.word_freq = defaultdict(int)
self.cfg_counter = Counts()
self.bp_table = {}
class PCFG:
def __init__(self, original_train_file, modified_train_file, count_threshold=5):
self.count_threshold = count_threshold
self.word_freq = defaultdict(int)
self.cfg_counter = Counts()
self.bp_table = {}
def compute_cfg_frequency_in_train_file(self, train_file):
self.cfg_counter = Counts()
with open(train_file, 'r') as fd:
for line in fd:
cfg_tree = json.loads(line)
self.cfg_counter.count(cfg_tree)
def compute_word_frequency_in_cfg(self):
self.word_freq = defaultdict(int)
# case sensitive
# https://class.coursera.org/nlangp-001/forum/thread?thread_id=631
# Terminal word can be assigned to multiple non-Terminals
# https://class.coursera.org/nlangp-001/forum/thread?thread_id=620#post-2613
for (sym, word), count in self.cfg_counter.unary.iteritems():
self.word_freq[word] += count
def is_rare_word(self, word):
return self.word_freq[word] < self.count_threshold
def replace_infrequent_words_in_parse_tree(self, tree):
if isinstance(tree, basestring):
return
if len(tree) == 3:
# binary rule
self.replace_infrequent_words_in_parse_tree(tree[1])
self.replace_infrequent_words_in_parse_tree(tree[2])
elif len(tree) == 2:
# unary rule
word = tree[1]
if self.is_rare_word(word):
tree[1] = '_RARE_'
def compute_binary_parameter(self, symbol, y1, y2):
return self.cfg_counter.binary[(symbol, y1, y2)]*1.0/self.cfg_counter.nonterm[symbol]
def compute_unary_parameter(self, symbol, word):
return self.cfg_counter.unary[(symbol, word)]*1.0/self.cfg_counter.nonterm[symbol]
def CKY_algorithm(self, sentence):
sentence_tokens = re.split(r'[ ]+', sentence.rstrip())
n_tokens = len(sentence_tokens)
max_prob_table = defaultdict(float) # pi table
self.bp_table = {}
# now build the dynamic programming table bottom-up
for symbol in self.cfg_counter.nonterm.iterkeys():
for i in range(0, n_tokens):
word = sentence_tokens[i]
if self.is_rare_word(word):
word = '_RARE_'
key = (symbol, word)
if key in self.cfg_counter.unary.keys():
max_prob_table[(i, i, symbol)] = self.compute_unary_parameter(symbol, word)
for step in range(1, n_tokens):
for i in range(0, n_tokens-step):
j = i + step
for (sym, y1, y2) in self.cfg_counter.binary.iterkeys():
binary_param = self.compute_binary_parameter(sym, y1, y2)
max_prob_mult = 0
max_prob_s = None
for s in range(i, j):
prob_mult = max_prob_table[(i, s, y1)]*max_prob_table[(s+1, j, y2)]
if max_prob_mult < prob_mult:
max_prob_mult = prob_mult
max_prob_s = s
prob_with_current_binary_rule_over_i_j = binary_param*max_prob_mult
if max_prob_table[(i, j, sym)] < prob_with_current_binary_rule_over_i_j:
max_prob_table[(i, j, sym)] = prob_with_current_binary_rule_over_i_j
self.bp_table[(i, j, sym)] = tuple([max_prob_s, y1, y2])
parse_tree = self.create_parse_tree(0, n_tokens-1, 'SBARQ', sentence_tokens)
return parse_tree
def create_parse_tree(self, i, j, sym, sentence_tokens):
# [sym, func(i,s,y1), func(s+1,j,y2) ]
parse_sub_tree = []
if i == j:
parse_sub_tree = [sym, sentence_tokens[i]]
# parse_sub_tree = '[' + sym + ', ' + sentence_tokens[i] + ']'
else:
split_tuple = self.bp_table[(i, j, sym)]
s = split_tuple[0]
y1 = split_tuple[1]
y2 = split_tuple[2]
parse_sub_tree = [sym, self.create_parse_tree(i, s, y1, sentence_tokens),
self.create_parse_tree(s+1, j, y2, sentence_tokens)]
'''
parse_sub_tree = '['
parse_sub_tree += sym
parse_sub_tree += ', '
parse_sub_tree += self.create_parse_tree(i, s, y1, sentence_tokens)
parse_sub_tree += ', '
parse_sub_tree += self.create_parse_tree(s+1, j, y2, sentence_tokens)
parse_sub_tree += ']'
'''
return parse_sub_tree
import json
from count_cfg_freq import Counts
counter = Counts()
vocab = {}
def loadData(corpus_file):
l = corpus_file.readline()
while (l):
t = json.loads(l)
counter.count(t)
wordList = [y for x, y in extract_tag(t)]
for word in wordList:
if word not in vocab:
vocab[word] = 0
vocab[word] += 1
#for tag in tagList
l = corpus_file.readline()
def extract_tag(t):
if len(t) == 3:
return extract_tag(t[1]) + extract_tag(t[2])
if len(t) == 2:
return [(t[0], t[1])]
def tagRareWord(corpus_file, new_corpus_file):
l = corpus_file.readline()
while (l):
class CKY:
def __init__(self):
self.binary_q = {}
self.unary_q = {}
self.counter = Counts()
self.pi = []
self.bp = []
self.parser = {}
self.use_vert = False
def loadData(self, corpus_file):
l = corpus_file.readline()
while (l):
t = json.loads(l)
self.counter.count(t)
l = corpus_file.readline()
def compute(self):
for (sym, word), count in self.counter.unary.iteritems():
self.unary_q[(sym, word)] = count / self.counter.nonterm[sym]
for (sym, y1, y2), count in self.counter.binary.iteritems():
self.binary_q[(sym, y1, y2)] = count / self.counter.nonterm[sym]
def buildNTDict(self):
self.binary_rule = {}
for x, y1, y2 in self.binary_q.keys():
if x not in self.binary_rule.keys():
self.binary_rule[x] = []
self.binary_rule[x].append((y1, y2))
def cky_init(self, wordList):
self.pi = []
self.bp = []
sent_len = len(wordList)
for i in range(0, sent_len):
self.pi.append([])
self.bp.append([])
for j in range(0, sent_len):
self.pi[i].append({})
self.bp[i].append({})
for (sym, word), q in self.unary_q.iteritems():
if wordList[i] == word:
self.pi[i][i][sym] = q
self.bp[i][i][sym] = word
pass
def clean_sent(self, sentence):
wordList = sentence.split()
vocabDict = [v for s, v in self.counter.unary.keys()]
for i, word in enumerate(wordList):
if word not in vocabDict:
wordList[i] = '_RARE_'
return wordList
def cky_algorithm(self, sentence):
self.inputWordList = sentence.split()
wordList = self.clean_sent(sentence)
sent_len = len(wordList)
self.cky_init(wordList)
#print self.pi[10][10]
for l in range(1, sent_len):
for i in range(0, sent_len - l):
j = i + l
#print i,j
for sym in self.binary_rule.keys():
for s in range(i, j):
derivations = self.binary_rule[sym]
for y1, y2 in derivations:
if y1 not in self.pi[i][s].keys(
) or y2 not in self.pi[s + 1][j].keys():
#if i == 0 and j == 4 :
#print s,'t'
#if sym=='S' and y1=='NP' and y2=='S':
# print sym,y1,y2
# print s
# print self.pi[0][1].keys()
# print self.pi[2][4].keys()
# print y1 in self.pi[i][s].keys()
# print y2 in self.pi[s+1][j].keys()
# print (sym,y1,y2) not in self.binary_q.keys()
#pass
continue
#print i,j
temp = self.binary_q.get(
(sym, y1, y2)) * self.pi[i][s].get(
y1) * self.pi[s + 1][j].get(y2)
self.bp[i][j][sym] = (
s, y1, y2) if temp > self.pi[i][j].get(
sym, 0) else self.bp[i][j].get(sym, 0)
self.pi[i][j][sym] = max(temp,
self.pi[i][j].get(sym, 0))
#print self.pi[1][sent_len-1]
#print self.pi[0][0]
#print self.binary_q['S','NP^<S>+NOUN','S']
#print self.bp
#try:
if 'S' in self.pi[0][sent_len - 1].keys():
root_score = 'S', self.pi[0][sent_len - 1]['S']
else:
root_score = max(self.pi[0][sent_len - 1].iteritems(),
key=lambda x: x[1])
self.parse_res = self.buildTreeHelper(0, sent_len - 1,
root_score[0]) #,self.parser
#except:
# self.parse_res = "cannot parse unk"
pass
#print 'start',root_score
def buildTreeHelper(self, start, end, root):
if start == end:
#self.parser[root]= self.bp[start][end][root]
#t=self.bp[start][end][root]
return '["' + root + '", "' + self.inputWordList[start] + '"]'
else:
s, y1, y2 = self.bp[start][end][root]
r1 = self.buildTreeHelper(start, s, y1)
r2 = self.buildTreeHelper(s + 1, end, y2)
return ('["' + root + '", ' + r1 + ', ' + r2 + ']')
def dev(self, dev_file, output_file):
output = file(output_file, 'w')
for l in open(dev_file):
print l.strip()
self.parse_res = ""
self.cky_algorithm(l.strip())
if self.use_vert:
new_parse_res = re.sub("\^\[[A-Z]+\]", "", self.parse_res)
self.parse_res = new_parse_res
output.write(self.parse_res)
output.write('\n')
def load_params(self, input):
self.counter = Counts()
self.counter.load(open(input))
class CKY(object):
def __init__(self):
self.counter = None
def load_params(self, input):
self.counter = Counts()
self.counter.load(open(input))
def cky(self, test_file):
for line in test_file:
self.parse(line)
def constract_result(self, tp, words, i, j, N):
#print t[(1, n, "S")]
tree = []
tree.append(N)
if i == j:
tree.append(words[i-1])
return tree
else:
(s, Y, Z) = tp[(i, j, N)]
#print s, Y, Z
tree.append(self.constract_result(tp, words, i, s, Y))
tree.append(self.constract_result(tp, words, s+1, j, Z))
return tree
def parse(self, line):
t = {}
tp = {}
#init
words = line.strip().split(" ")
n = len(words)
#print words
i = 1
for word in words:
if self.counter.unary_pob_reorg.has_key(word):
for N, freq in self.counter.unary_pob_reorg[word].iteritems():
t[(i, i, N)] = freq
else:
#print word, " mapped to ", rare_label
for N, freq in self.counter.unary_pob_reorg[rare_label].iteritems():
t[(i, i, N)] = freq
i += 1
#print t
#calculation
for l in range(1, n):
for i in range(1, n - l + 1):
j = i + l
for N in self.counter.nonterm.iterkeys():
if self.counter.binary_pob_reorg.has_key(N):
max = 0
max_s = i
max_Y = None
max_Z = None
for (Y, Z), freq in self.counter.binary_pob_reorg[N].iteritems():
for s in range(i, j):
if t.has_key((i, s, Y)) and t.has_key(((s+1, j, Z))):
p = freq * t[(i, s, Y)] * t[(s+1, j, Z)]
#print "none-value", i, j, N, p, s, Y, Z
if p > max:
max = p
max_s = s
max_Y = Y
max_Z = Z
if max > 0:
t[i, j, N] = max
tp[i, j, N] = (max_s, max_Y, max_Z)
#print i, j, N, max, (max_s, max_Y, max_Z)
#print t[(1, n, "SBARQ")]
#backpointer for result
tree = self.constract_result(tp, words, 1, n, "SBARQ")
print json.dumps(tree)
def __init__(self):
Counts.__init__(self)
self.wc = defaultdict(int)
self.rare_tag = '_RARE_'
self.rare_threshold = 5