def learn_triplets_cooccur_mat(triplets_file_path):
files = glob.glob(triplets_file_path)
np_voc = vocabulary.Vocabulary()
vp_voc = vocabulary.Vocabulary()
np_voc.load('../mat/np1.voc')
vp_voc.load('../mat/np2.voc')
num_np = np_voc.size()
num_vp = vp_voc.size()
cooccur_mat = zeros([num_np, num_vp])
for file_in in files:
with open(file_in, 'r') as f:
for line in f:
if (line[0] != '<'):
line = (line[:-2]).lower()
triplets = line.split('|')
np1 = cleansing.clean(triplets[0].split())
vp = cleansing.clean(triplets[1].split())
np2 = cleansing.clean(triplets[2].split())
for w in np2:
vp.append(w)
np1_new = [w for w in np1 if np_voc.contain(w)]
vp_new = [w for w in vp if vp_voc.contain(w)]
pairs = [(np_voc.get_word_index(u),
vp_voc.get_word_index(v)) for u in np1_new
for v in vp_new]
for pair in pairs:
cooccur_mat[pair[0], pair[1]] += 1
return cooccur_mat
def learn_triplets_cooccur_mat(triplets_file_path):
files = glob.glob(triplets_file_path)
np_voc = vocabulary.Vocabulary()
vp_voc = vocabulary.Vocabulary()
np_voc.load('../mat/np1.voc')
vp_voc.load('../mat/np2.voc')
num_np = np_voc.size()
num_vp = vp_voc.size()
cooccur_mat = zeros([num_np, num_vp])
for file_in in files:
with open(file_in, 'r') as f:
for line in f:
if(line[0] != '<'):
line = (line[:-2]).lower()
triplets = line.split('|')
np1 = cleansing.clean(triplets[0].split())
vp = cleansing.clean(triplets[1].split())
np2 = cleansing.clean(triplets[2].split())
for w in np2:
vp.append(w)
np1_new = [w for w in np1 if np_voc.contain(w)]
vp_new = [w for w in vp if vp_voc.contain(w)]
pairs = [(np_voc.get_word_index(u), vp_voc.get_word_index(v)) for u in np1_new for v in vp_new]
for pair in pairs:
cooccur_mat[pair[0], pair[1]] += 1
return cooccur_mat
def learn_story_histogram(file_in, words_voc, word_type='ALL', ocr_file=None):
"""word_type = 'NP1', 'VP', 'NP2', 'ALL'"""
words_voc_num = words_voc.size()
hist = np.zeros([1, words_voc_num])
document = []
with open(file_in, 'r') as f:
for line in f:
if(line[0] != '<'):
line = (line[:-2]).lower()
words = []
triplets = line.split('|')
if (word_type == 'NP1' or word_type == 'ALL'):
words.extend(cleansing.clean(triplets[0].split()))
if (word_type == 'VP' or word_type == 'ALL'):
words.extend(cleansing.clean(triplets[1].split()))
if (word_type == 'NP2' or word_type == 'ALL'):
words.extend(cleansing.clean(triplets[2].split()))
words_new = [w for w in words if words_voc.contain(w)]
document.extend(words_new)
for w in words_new:
hist[0, words_voc.get_word_index(w)] += 1
# Read OCR file and combine into histogram if provided.
ocr_words = []
if ocr_file is not None:
# TODO:
# read ocr file
# combine OCR into histogram
# each word count 2
# fill ocr_words
with open(ocr_file, 'r') as f:
for line in f:
if(line[0] != '<'):
line = (line[:-2]).lower()
ocr_words.extend(cleansing.clean(line.split()))
ocr_words_new = [w for w in ocr_words if words_voc.contain(w)]
document.extend(ocr_words_new)
for w in ocr_words_new:
hist[0, words_voc.get_word_index(w)] += 1
# Normalize.
sum_hist = hist.sum()
if sum_hist != 0:
hist = hist / sum_hist
try:
assert(hist.sum() > 0.9 and hist.sum() < 1.1)
except AssertionError:
print(hist)
print(hist.sum())
raise
return (hist, document, ocr_words)
def learn_story_histogram(file_in, words_voc, word_type="ALL", ocr_file=None):
"""word_type = 'NP1', 'VP', 'NP2', 'ALL'"""
words_voc_num = words_voc.size()
hist = np.zeros([1, words_voc_num])
document = []
with open(file_in, "r") as f:
for line in f:
if line[0] != "<":
line = (line[:-2]).lower()
words = []
triplets = line.split("|")
if word_type == "NP1" or word_type == "ALL":
words.extend(cleansing.clean(triplets[0].split()))
if word_type == "VP" or word_type == "ALL":
words.extend(cleansing.clean(triplets[1].split()))
if word_type == "NP2" or word_type == "ALL":
words.extend(cleansing.clean(triplets[2].split()))
words_new = [w for w in words if words_voc.contain(w)]
document.extend(words_new)
for w in words_new:
hist[0, words_voc.get_word_index(w)] += 1
# Read OCR file and combine into histogram if provided.
ocr_words = []
if ocr_file is not None:
# TODO:
# read ocr file
# combine OCR into histogram
# each word count 2
# fill ocr_words
with open(ocr_file, "r") as f:
for line in f:
if line[0] != "<":
line = (line[:-2]).lower()
ocr_words.extend(cleansing.clean(line.split()))
ocr_words_new = [w for w in ocr_words if words_voc.contain(w)]
document.extend(ocr_words_new)
for w in ocr_words_new:
hist[0, words_voc.get_word_index(w)] += 1
# Normalize.
sum_hist = hist.sum()
if sum_hist != 0:
hist = hist / sum_hist
try:
assert hist.sum() > 0.9 and hist.sum() < 1.1
except AssertionError:
print (hist)
print (hist.sum())
raise
return (hist, document, ocr_words)
def learn_triplets_cooccur_mat(file_in, co_mat_file):
learned_co_mat = CooccurMatrix()
learned_co_mat.load(co_mat_file)
np_voc = learned_co_mat.vocabulary
np1_matrix = learned_co_mat.matrix
np1_all = vocabulary.Vocabulary()
with open(file_in, 'r') as f:
for line in f:
if (line[0] != '<'):
line = (line[:-2]).lower()
triplets = line.split('|')
np1 = cleansing.clean(triplets[0].split())
# Delete words not in the similarity vocabulary.
np1_new = [w for w in np1 if np_voc.contain(w)]
for w in np1_new:
np1_all.add(w)
num_np1 = np1_all.size()
similarity_mat_np1 = zeros([num_np1, num_np1])
for i in range(num_np1):
for j in range(num_np1):
similarity_mat_np1[i, j] = np1_matrix[
np_voc.get_word_index(np1_all.get_word(i)),
np_voc.get_word_index(np1_all.get_word(j))]
return CooccurMatrix(similarity_mat_np1, np1_all)
def ocr_find_story(ocr_results, ocr_time, stories):
reprog = re.compile(r"""\d\d\d\d\d\d\d\d\d\d(\d\d)(\d\d).+""", re.I | re.X)
m = reprog.match(ocr_time)
g = m.groups()
start_time_minute = int(g[0])
start_time_second = int(g[1])
time_range_left_min = max([start_time_minute - 3, 0])
time_range_right_min = min([start_time_minute + 3, 59])
ocr_words = cleansing.clean(ocr_results)
ocr_words = [w for w in ocr_words if len(w) > 2]
if len(ocr_words) < 1:
return -1
overlap = 0
count = 0
for story in stories:
if time_overlap(time_range_left_min, time_range_right_min,
start_time_second, story):
#print count
p = words_overlap_percentage(ocr_words, story)
#print ocr_words[0]
#print p
if p > overlap:
overlap = p
story_id = count
break
count += 1
if overlap >= 0.5:
return story_id
else:
return -1
def learn_triplets_cooccur_mat(file_in, co_mat_file):
learned_co_mat = CooccurMatrix()
learned_co_mat.load(co_mat_file)
np_voc = learned_co_mat.vocabulary
np1_matrix = learned_co_mat.matrix
np1_all = vocabulary.Vocabulary()
with open(file_in, 'r') as f:
for line in f:
if(line[0] != '<'):
line = (line[:-2]).lower()
triplets = line.split('|')
np1 = cleansing.clean(triplets[0].split())
# Delete words not in the similarity vocabulary.
np1_new = [w for w in np1 if np_voc.contain(w)]
for w in np1_new:
np1_all.add(w)
num_np1 = np1_all.size()
similarity_mat_np1 = zeros([num_np1, num_np1])
for i in range(num_np1):
for j in range(num_np1):
similarity_mat_np1[i, j] = np1_matrix[np_voc.get_word_index(np1_all.get_word(i)), np_voc.get_word_index(np1_all.get_word(j))]
return CooccurMatrix(similarity_mat_np1, np1_all)
def ocr_find_story(ocr_results, ocr_time, stories):
reprog = re.compile(r"""\d\d\d\d\d\d\d\d\d\d(\d\d)(\d\d).+""", re.I | re.X)
m = reprog.match(ocr_time)
g = m.groups()
start_time_minute = int(g[0])
start_time_second = int(g[1])
time_range_left_min = max([start_time_minute - 3, 0])
time_range_right_min = min([start_time_minute + 3, 59])
ocr_words = cleansing.clean(ocr_results)
ocr_words = [w for w in ocr_words if len(w) > 2]
if len(ocr_words) < 1:
return -1
overlap = 0
count = 0
for story in stories:
if time_overlap(time_range_left_min, time_range_right_min, start_time_second, story):
#print count
p = words_overlap_percentage(ocr_words, story)
#print ocr_words[0]
#print p
if p > overlap:
overlap = p
story_id = count
break
count += 1
if overlap >= 0.5:
return story_id
else:
return -1
def generate_triplets_voc(files):
triplet_voc = triplet_vocabulary.TripletVocabulary()
for file_in in files:
with open(file_in, 'r') as f:
for line in f:
if(line[0] != '<'):
line = (line[:-2]).lower()
triplets = line.split('|')
# remove stop words and save NP1, NP2 -> np, VP -> vp
np1 = cleansing.clean(triplets[0].split())
for w in np1:
triplet_voc.add_np(w)
vp = cleansing.clean(triplets[1].split())
for w in vp:
triplet_voc.add_vp(w)
np2 = cleansing.clean(triplets[2].split())
for w in np2:
triplet_voc.add_vp(w)
return triplet_voc
def generate_triplets_voc(files):
triplet_voc = triplet_vocabulary.TripletVocabulary()
for file_in in files:
with open(file_in, 'r') as f:
for line in f:
if (line[0] != '<'):
line = (line[:-2]).lower()
triplets = line.split('|')
# remove stop words and save NP1, NP2 -> np, VP -> vp
np1 = cleansing.clean(triplets[0].split())
for w in np1:
triplet_voc.add_np(w)
vp = cleansing.clean(triplets[1].split())
for w in vp:
triplet_voc.add_vp(w)
np2 = cleansing.clean(triplets[2].split())
for w in np2:
triplet_voc.add_vp(w)
return triplet_voc
def build_vocabulary(input_triplet_files, word_type='ALL'):
vocab = vocabulary.Vocabulary()
for triplet_file in input_triplet_files:
with open(triplet_file, 'r') as f:
for line in f:
if(line[0] != '<'):
line = (line[:-2]).lower()
triplets = line.split('|')
words = []
if (word_type == 'NP1' or word_type == 'ALL'):
words.extend(cleansing.clean(triplets[0].split()))
if (word_type == 'VP' or word_type == 'ALL'):
words.extend(cleansing.clean(triplets[1].split()))
if (word_type == 'NP2' or word_type == 'ALL'):
words.extend(cleansing.clean(triplets[2].split()))
for w in words:
vocab.add(w)
logging.info('Vocabulary: {0}, {1}.'.format(word_type, vocab.size()))
return vocab
def build_vocabulary(input_triplet_files, word_type='ALL'):
vocab = vocabulary.Vocabulary()
for triplet_file in input_triplet_files:
with open(triplet_file, 'r') as f:
for line in f:
if (line[0] != '<'):
line = (line[:-2]).lower()
triplets = line.split('|')
words = []
if (word_type == 'NP1' or word_type == 'ALL'):
words.extend(cleansing.clean(triplets[0].split()))
if (word_type == 'VP' or word_type == 'ALL'):
words.extend(cleansing.clean(triplets[1].split()))
if (word_type == 'NP2' or word_type == 'ALL'):
words.extend(cleansing.clean(triplets[2].split()))
for w in words:
vocab.add(w)
logging.info('Vocabulary: {0}, {1}.'.format(word_type, vocab.size()))
return vocab
def add_line(self, line):
words = cleansing.clean(line.split())
for w in words:
self.line_list.append(w)