def url_similarity(url1, url2):
result = 0
if "://github.com" not in url1 and "://github.com" not in url2:
url1_main_part = extract_url_main_part(url1)
url2_main_part = extract_url_main_part(url2)
result = textdistance.jaccard(url1_main_part, url2_main_part)
elif "://github.com" in url1 and "://github.com" not in url2:
url1_main_part = get_name_by_github_url(url1)
#url2_main_part = extract_url_main_part(url2)
# print "url1: ", url1, " url1_main: ", url1_main_part, " url2: ", url2, " url2_main: ", url2
lcsstr = textdistance.lcsstr(url1_main_part, url2)
min_len = min(len(url1_main_part), len(url2))
if min_len == 0:
min_len = 1
result = len(lcsstr) * 1.0 / min_len
elif "://github.com" not in url1 and "://github.com" in url2:
#url1_main_part = extract_url_main_part(url1)
url2_main_part = get_name_by_github_url(url2)
# print "url1: ", url1, " url1_main: ", url1, " url2: ", url2, " url2_main: ", url2_main_part
lcsstr = textdistance.lcsstr(url1, url2_main_part)
min_len = min(len(url1), len(url2_main_part))
if min_len == 0:
min_len = 1
result = len(lcsstr) * 1.0 / min_len
else:
url1_main_part = get_name_by_github_url(url1)
url2_main_part = get_name_by_github_url(url2)
result = textdistance.jaccard(url1_main_part, url2_main_part)
return result
def jaccard_euclidean(df, sample, num=15):
vectors = np.zeros((len(df), len(sample)))
start = time()
checkpoint = time()
for idx, row in df.iterrows():
if idx % 10000 == 0:
print('Extracting sample ' + str(idx) + '/' + str(len(df)))
print('Time pass: '******'{:.1f}'.format(time() - start) +
'. Time left: ' + '{:.1f}'.format((time() - checkpoint) *
(len(df) - idx) / 10000))
checkpoint = time()
vectors[idx] = np.asarray(
[tdc.jaccard(row[key], sample[key]) for key in sample.keys()])
param1 = np.asarray([
7 / 66, 5 / 66, 11 / 66, 1 / 66, 3 / 66, 4 / 66, 6 / 66, 8 / 66,
9 / 66, 10 / 66, 2 / 66
])
param2 = np.asarray([
10 / 64, 5 / 64, 16 / 64, 1 / 64, 3 / 64, 4 / 64, 6 / 64, 3 / 64,
4 / 64, 10 / 64, 2 / 64
])
vectors = vectors * param1
score = np.linalg.norm(vectors, axis=1) / np.linalg.norm(param1)
# score /= np.max(score)
score_sorted = np.flip(np.sort(score)[-num:], axis=0)
indices = np.flip(np.argsort(score)[-num:], axis=0)
most_similar = df.loc[indices, :]
most_similar['SCORE'] = score_sorted
return most_similar
def add_query_features(df, inc, exc, k1list, k2list):
"""
Return a copy of a dataframe with summary features added for
the named text files defining the query
"""
df_new = df.copy()
k1lens = list(map(len, k1list))
k2lens = list(map(len, k2list))
k1max = max(k1lens)
k2max = max(k2lens)
k1count = len(k1list)
k2count = len(k2list)
df_new['k1_count'] = k1count
df_new['k2_count'] = k2count
df_new['k1_max'] = k1max
df_new['k2_max'] = k2max
jaro_dist = jellyfish.jaro_distance(inc, exc)
lev_dist = jellyfish.levenshtein_distance(inc, exc)
ji = textdistance.jaccard(inc, exc)
sd = textdistance.sorensen(inc, exc)
ro = textdistance.ratcliff_obershelp(inc, exc)
#jellyfish.damerau_levenshtein_distance(inc,exc)
#jellyfish.jaro_winkler(inc,exc)
df_new['inc_jaro_exc'] = jaro_dist
df_new['inc_lev_exc'] = lev_dist
df_new['inc_ji_exc'] = ji
df_new['inc_sd_exc'] = sd
df_new['inc_ro_exc'] = ro
return df_new
def compare_lines2headings(lines, headings):
if headings.shape[0] == 0:
print('Headings are empty')
return np.zeros(len(lines)), np.zeros(len(lines)), np.zeros(len(lines))
max_similarities = []
for line in lines:
ln_similarities = []
ln_words = line.lower().split()
for i, heading in headings.iterrows(
): # save info whether the best comparison is to a heading or subheading
hd = heading.Text.lower()
hd, _ = heading_id_toc.split_pagenum(hd)
hd_words = hd.split()
# compare words
similarity = textdistance.jaccard(
ln_words, hd_words
) # similarity defined by: intersection/union. see "jaccard index"
ln_similarities.append([similarity, heading.Heading, i])
max = np.array(ln_similarities)[:, 0].argmax()
bestsim = ln_similarities[max]
if bestsim[0] == 0: # if basically find no similarity
bestsim = np.array([0, 0, 0])
max_similarities.append(bestsim)
max_similarities = np.array(max_similarities)
return max_similarities[:,
0], max_similarities[:,
1], max_similarities[:,
2] # return similarity,type matched, and i of heading matched
def search(self, word, limit=0.7):
"""[Pesquisa palavra próxima no vocabulário utilizando um valor de aceitação de distancia.]
Arguments:
word {[string]} -- [Palavra de entrada]
Keyword Arguments:
limit {float} -- [Valor de distancia para aceitação de palavra (0,1)] (default: {0.7})
Returns:
[new_embed] -- []
"""
dist = list()
for index, token in enumerate(self.words):
if self.algorithm == "levenshtein":
distance = textdistance.levenshtein.normalized_similarity(
word, token)
if distance > limit:
return self.embeds[index]
dist.append(distance)
elif self.algorithm == "jaccard":
distance = textdistance.jaccard(word, token)
if distance > limit:
return self.embeds[index]
dist.append(distance)
elif self.algorithm == "ratcliff_obershelp":
distance = textdistance.ratcliff_obershelp(word, token)
if distance > limit:
return self.embeds[index]
dist.append(distance)
index = dist.index(max(dist))
return self.embeds[index]
def similarity(line1, line2):
"""
return a number
"""
distance = textdistance.jaccard(line1, line2)
closeness = 1 - 1 / (1 + math.exp(-30 * (distance - 0.5))) # Sigmoid mapping (0,1) to (0,1)
return closeness
def knn_search(self, word):
"""[Pesquisa uma palavra próxima no vocabulário utilizado a lógica do KNN]
Arguments:
word {[string]} -- [Palavra de entrada]
Returns:
[int] -- [Índice da palava mais próxima no vocabulário]
"""
dist = list()
for token in self.vocabulary:
if self.algorithm == "levenshtein":
dist.append(
textdistance.levenshtein.normalized_similarity(
word, token))
elif self.algorithm == "jaccard":
dist.append(textdistance.jaccard(word, token))
elif self.algorithm == "ratcliff_obershelp":
dist.append(textdistance.ratcliff_obershelp(word, token))
index = dist.index(max(dist))
return self.embeds[index]
def calculate_similarity_matrix(selected_data_list, vector_map, weight,
current_index, total_length):
full_result = []
lack_result = []
if selected_data_list is not None:
# print("there are total " + str(len(selected_data_list)) + " data.")
for i in range(0, len(selected_data_list)):
# print("execute the NO." + str(current_index) + " list now, there are total " + str(total_length) + " list.")
# print("calculate the NO." + str(i) + " data's similarity, " + "there are total " + str(len(selected_data_list)) + " data.")
first = str(selected_data_list[i]["paragraph_id"])
first_noun_phrase = selected_data_list[i]["noun_phrase"]
full_temp = []
lack_temp = []
for j in range(0, len(selected_data_list)):
second = str(selected_data_list[j]["paragraph_id"])
second_noun_phrase = selected_data_list[j]["noun_phrase"]
if i == j:
similarity = 0
else:
similarity = (vector_map.similarity(first, second) +
1) * weight / 2 + textdistance.jaccard(
first_noun_phrase,
second_noun_phrase) * (1 - weight)
similarity = round(similarity, 6) - 1
lack_temp.append(similarity)
full_temp.append(similarity)
full_result.append(full_temp)
lack_result.append(lack_temp)
return full_result, lack_result
def Jaccard_Index(Cell1, Cell2):
Cell1 = Cell1.lower().split(
) #removes punctuation and spilts string into list of words
Cell2 = Cell2.lower().split(
) #removes punctuation and spilts string into list of words
return jaccard(Cell1, Cell2) #calculates jaccard index of two strings
def extract_by_similarity(self,
concepts,
sentences,
alpha=0.75,
top_k=3,
min_sim=0.5):
alias2concept = {}
for concept in concepts:
for alias in concept.aliases:
alias2concept[alias.lower()] = concept
terms = set(alias2concept.keys())
term2sents = dict()
for sent in sentences:
rs = sent.find_spans(*terms, ignore_case=True)
for term in rs.keys():
if term not in term2sents:
term2sents[term] = set()
term2sents[term].add(sent)
term2emb = {
term: sum(sent.emb() for sent in sents) / len(sents)
for term, sents in term2sents.items()
}
concept2emb = {
concept: sum(term2emb[alias.lower()] for alias in concept.aliases)
for concept in concepts
}
def __cosine(vector1, vector2):
norm = (np.linalg.norm(vector1) * np.linalg.norm(vector2))
if norm == 0:
return 0
cos = np.dot(vector1, vector2) / norm
return 0.5 + 0.5 * cos
start2scores = dict()
for start, end in itertools.combinations(concepts, 2):
cos = __cosine(concept2emb[start], concept2emb[end])
start_words = set()
for alias in start.aliases:
start_words.update(self.name_handler.normalize(alias).split())
end_words = set()
for alias in end.aliases:
end_words.update(self.name_handler.normalize(alias).split())
jaccard = textdistance.jaccard(start_words, end_words)
score = alpha * cos + (1 - alpha) * jaccard
if start not in start2scores:
start2scores[start] = set()
start2scores[start].add((end, score))
relations = set()
for start, pairs in start2scores.items():
pairs = list(sorted(pairs, key=lambda item: item[1], reverse=True))
for end, score in pairs[:top_k]:
if score < min_sim:
break
relations.add(Relation(start, end, RelType.RELATED_TO))
return relations
def criteria_features(x, col):
raw_text = x[col].lower()
jd = jellyfish.jaro_distance(raw_text, crit)
ld = jellyfish.levenshtein_distance(raw_text, crit)
ji = textdistance.jaccard(raw_text, crit)
sd = textdistance.sorensen(raw_text, crit)
ro = textdistance.ratcliff_obershelp(raw_text, crit)
return jd, ld, ji, sd, ro
def similarity(phrase1, phrase2):
s1 = ""
s2 = ""
for note in phrase1:
s1 += chr(note)
for note in phrase2:
s2 += chr(note)
return jaccard(s1, s2)
def prova():
l = sorted(rf.keys())
dm = get_entry(l)
joint = {}
for word in dm:
joint[word] = [
td.jaccard(rf[word].split(), dm[word].split()), rf[word], dm[word]
]
return joint
def getSimilarity(self, desired, given):
# If the given string contains desired as a substring
if desired in given:
return 0.9
# Strings have to be split up into character arrays for this algorithm
desired_attr = [char for char in desired]
given_attr = [char for char in given]
# Returns a number representing how similar the two strings are
return textdistance.jaccard(desired_attr, given_attr)
def map_marie(input_data,
target_data,
input_bert_weights,
target_bert_weights,
string_match='edit',
alpha=0.8,
bert_layers=1,
top_n=5):
inp_txt2idx, inp_idx2txt = _create_txt2idx(input_data)
tgt_txt2idx, tgt_idx2txt = _create_txt2idx(target_data)
inp_bert_vectors = _get_bert_vectors(input_bert_weights, input_data,
inp_txt2idx, bert_layers)
tgt_bert_vectors = _get_bert_vectors(target_bert_weights, target_data,
tgt_txt2idx, bert_layers)
mapper = dict()
for cnt, (inp_txt, inp_idx) in enumerate(inp_txt2idx.items()):
inp_bert_vector = inp_bert_vectors[inp_idx]
cal_dist = []
cos_dist = [
alpha * (_cal_cosine(inp_bert_vector, tgt_bert_vectors[tgt_idx]))
for tgt_txt, tgt_idx in tgt_txt2idx.items()
]
if string_match == 'edit':
str_match = [
(1 - alpha) * (1 - editdistance.eval(inp_txt, tgt_txt) /
max(len(inp_txt), len(tgt_txt)))
for tgt_txt, tgt_idx in tgt_txt2idx.items()
]
if string_match == 'jaccard':
str_match = [(1 - alpha) * textdistance.jaccard(inp_txt, tgt_txt)
for tgt_txt, tgt_idx in tgt_txt2idx.items()]
if string_match == 'ob':
str_match = [(1 - alpha) *
textdistance.ratcliff_obershelp(inp_txt, tgt_txt)
for tgt_txt, tgt_idx in tgt_txt2idx.items()]
ord2idx = [tgt_idx for _, tgt_idx in tgt_txt2idx.items()]
cal_dist = np.add(cos_dist, str_match)
topn_ord_idx = cal_dist.argsort()[::-1][:top_n]
mapper[inp_idx] = [(ord2idx[idx], cal_dist[idx])
for idx in topn_ord_idx]
if cnt % 100 == 0:
print("...Processed %i mappings" % (cnt))
return mapper, inp_idx2txt, tgt_idx2txt
def answer(self, msg):
if not self.active or self.correct:
return
if not self.correct:
for ans in self.a:
ans = " ".join(ans.split()).strip().lower()
guess = " ".join(msg.args[1].split()).strip().lower()
if guess == ans:
self.correct = True
break
elif not self.correct:
answer = self.clean(ans)
guess = self.clean(guess)
if not self.correct and guess == answer:
self.correct = True
break
elif (not self.correct and self.flexibility < 1
and self.flexibility > 0.5):
dist = textdistance.jaro_winkler(guess, answer)
log.debug(
"Jeopardy: guess: {0}, answer: {1}, length: {2}, "
"distance: {3}, flexibility: {4}".format(
guess, answer, len(answer), dist,
self.flexibility))
if dist >= self.flexibility:
self.correct = True
break
elif (dist < self.flexibility and "," in self.a[0]
or "&" in self.a[0]):
dist = textdistance.jaccard(guess, answer)
if dist >= self.flexibility:
self.correct = True
break
if self.correct:
if not msg.nick in self.scores:
self.scores[msg.nick] = 0
self.scores[msg.nick] += self.p
if not msg.nick in self.roundscores:
self.roundscores[msg.nick] = 0
self.roundscores[msg.nick] += self.p
self.unanswered = 0
reply = self.correct_template.render(
nick=msg.nick,
answer=self.a[0],
points=self.p,
round=self.roundscores[msg.nick],
total=self.scores[msg.nick],
)
self.reply(reply)
self.correct = True
self.answered += 1
self.clear()
self.newquestion()
def check_match(data):
gr_row_no = data["gr_row_no"]
gr_row = data["gr_row"]
lib_rows = data["lib_rows"]
score = int(data["score"])
match_mode = data["match_mode"].upper().strip()
matches = []
for idx, lr in enumerate(lib_rows):
auth_score = int(TD.jaccard(gr_row["auth_tok"], lr["auth_tok"]) * 100)
title_score = int(
TD.jaccard(gr_row["title_tok"], lr["title_tok"]) * 100)
gr_comb = gr_row["auth_tok"] + gr_row["title_tok"]
lr_comb = lr["auth_tok"] + lr["title_tok"]
total_score = int(TD.jaccard(gr_comb, lr_comb) * 100)
cond = False
if match_mode == "TA":
cond = total_score > score
elif match_mode == "T":
cond = title_score > score
elif match_mode == "TTA":
cond = title_score > score or total_score > score
elif match_mode == "A":
cond = auth_score > score
else:
raise Exception("Unsupported matching condition: " + match_mode)
if cond:
row = dict(
zip(OUT_CSV_HEADER, [
gr_row_no, gr_row["author"], gr_row["title"], idx,
lr['author'], lr['title'], auth_score, title_score,
total_score
]))
matches.append(row)
return matches
def test_similarity_calculation(self):
str1 = "AbstractInputMethodService provides a abstract base class for inut methods."
str2 = "The default implementation in this abstract class returns 1.0 for all components."
vector_map = EntityVectorModel.load(
"mean_vector_api_paragraph.plain.txt", binary=False)
vector1 = vector_map.compute_mean_vector(str1)
vector2 = vector_map.compute_mean_vector(str2)
semantic_similarity = dot(matutils.unitvec(vector1),
matutils.unitvec(vector2))
print("semantic similarity is " + semantic_similarity)
structure_similarity = textdistance.jaccard(str1, str2)
print("structure similarity is " + structure_similarity)
def sm_features(x, col1, col2):
if (x[col1] != x[col1]) or (x[col2] != x[col2]):
jd = np.nan
ld = np.nan
ji = np.nan
sd = np.nan
else:
raw_text1 = x[col1].lower()
raw_text2 = x[col2].lower()
jd = jellyfish.jaro_distance(raw_text1, raw_text2)
ld = jellyfish.levenshtein_distance(raw_text1, raw_text2)
ji = textdistance.jaccard(raw_text1, raw_text2)
sd = textdistance.sorensen(raw_text1, raw_text2)
return jd, ld, ji, sd
def match_jaccard(ee, platforms):
for index, row in ee.iterrows():
if row.possible_stops == '':
subset = platforms[platforms.routes_wkd.str.contains(row.line)]
if subset.shape[0] > 0:
subset_stop_names = pd.DataFrame(subset.stop_name.unique(),columns=['stop_name'])
name_dist = [textdistance.jaccard(row.station_name,y) for y in subset_stop_names.stop_name]
matched_station_name = subset_stop_names.iloc[np.argmax(name_dist),0]
matched_stop_ids = subset[subset.stop_name == matched_station_name][['stop_id']]
score = max(name_dist)
if score >=0.8:
ee.loc[index,'possible_stops'] = ', '.join(matched_stop_ids.stop_id)
return ee
def similarity(str1, str2, type):
"""
Similarity score calculated by either Jaccard or Sorensen methods.
:param str1: string
:param str2: string
:param type: ['jaccard'|'sorensen']
:return: a float number between 0 (inclusive) and 1 (inclusive)
"""
tokens_1 = tokenize(str1)
tokens_2 = tokenize(str2)
if type == 'jaccard':
return textdistance.jaccard(tokens_1, tokens_2)
elif type == 'sorensen':
return textdistance.sorensen_dice(tokens_1, tokens_2)
return 0
def best_match_bert(orig_title, names_pool):
'''
Best matching using bert, some simple text distance measures
:param title: searched occupation name
:param names_pool: where are we searching
:return: best match occupation name from pool
'''
scores1 = []
scores2 = []
scores3 = []
scores4 = []
scores5 = []
scores6 = []
scores7 = []
# 1,2, 8 - scores > 1
title = pre_process(orig_title)
bb = BertEncoder()
l1 = bb.bert_encoder(title)
for name in names_pool:
proc_name = pre_process(name)
l2 = bb.bert_encoder(proc_name)
l1 = np.array(l1).reshape(-1, 1)
l2 = np.array(l2).reshape(-1, 1)
scores1.append(np.sum(euclidean_distances(l1, l2)))
scores2.append(np.sum(paired_distances(l1, l2)))
scores3.append(np.sum(cosine(l1, l2)))
scores4.append(np.sum(naive_metric(title, proc_name)))
scores5.append(np.sum(textdistance.jaccard(title, proc_name)))
scores6.append(np.sum(textdistance.sorensen_dice(title, proc_name)))
scores7.append(
np.sum(textdistance.damerau_levenshtein(title, proc_name)))
textdistance.j
chosen_idx = [
names_pool[np.argmin(scores1)], names_pool[np.argmin(scores2)],
names_pool[np.argmax(scores3)], names_pool[np.argmax(scores4)],
names_pool[np.argmax(scores5)], names_pool[np.argmax(scores6)],
names_pool[np.argmin(scores7)]
]
print(
"-----------------------------------------------------------------------------------------"
)
print("All title similarity candidates \n", chosen_idx)
c = Counter(chosen_idx)
frequency = c.most_common(1)[0][1]
if frequency < 3:
return names_pool[np.argmax(scores3)]
return c.most_common(1)[0][0]
def compare(self, str1, str2):
if self.debug:
self.log("jaccard comparison")
self.start_time()
self.result.distance = jaccard(str1, str2)
self.end_time()
self.result.nos = max(len(str1), len(str2))
self.result.threshold = 90
self.result.similarity = self.result.distance * 100
return self.result
def single_compare(bsl, our, j, label, count, id2vocab, id2response_dict):
# golden data
golden_context = [[id2vocab[w.item()] for w in sent if w != 0]
for sent in our[0]['context'][j] if not all(sent == 0)]
golden_query = [id2vocab[w.item()] for w in our[0]['query'][0] if w != 0]
golden_response = id2response_dict[our[0]['response_id'][j].item()]
golden_profile = vector2profile(our[0]['profile'][j])
golden_incomplete_profile = vector2profile(our[0]['incomplete_profile'][j])
# prediction
our_pred_response = id2response_dict[our[1]['pred_response_id'][j].item()]
# our_pred_profile = vector2profile(our[1]['pred_profile'][j])
our_pred_profile = vector2profile(
pred_profile_to_onehot_vec(
our[1]['pred_profile_prob'][j].unsqueeze(0)).squeeze(0))
our_pred_profile_prob = vector2probdict(our[1]['pred_profile_prob'][j])
bsl_pred_response = id2response_dict[bsl[1]['pred_response_id'][j].item()]
# similarity
# similarity = round(textdistance.hamming.normalized_similarity(tokenizer(bsl_pred_response, type=None), tokenizer(our_pred_response, type=None)), 3)
similarity = round(
textdistance.jaccard(tokenizer(bsl_pred_response, type='word'),
tokenizer(our_pred_response, type='word')), 3)
# similarity = round(textdistance.cosine(bsl_pred_response, our_pred_response), 3)
print_str = ''
if len(golden_incomplete_profile) < len(golden_profile) and len(
golden_incomplete_profile) != 0:
print_str = '%s %s %s\n' % (count, '=' * 50, label)
print_str += 'CONTEXT:\n'
for sent in golden_context: #[-5:]: # avoid too much context
print_str += '%s: %s\n' % (''.join(sent[-2:]), ' '.join(sent[:-2]))
# if sent[-2] == '$kb' and (sent[0] in our_pred_response or sent[0] in bsl_pred_response): # if restaurant mentioned in the response
# pass
# else:
# print_str += '%s: %s\n' % (''.join(sent[-2:]), ' '.join(sent[:-2]))
print_str += 'QUERY: %s\n' % ' '.join(golden_query)
print_str += 'INCOMPLETE:%s\n COMPLETE:%s\n PRED:%s\n PRED_PROB:%s\n' % (
golden_incomplete_profile, golden_profile, our_pred_profile,
our_pred_profile_prob)
print_str += 'GOLD: %s\n BSL: %s\n OUR: %s\n' % (
golden_response, bsl_pred_response, our_pred_response)
print_str += 'SIM: %s\n' % similarity
print(print_str)
# if label == 'GOOD':
# fw_good.write(print_str)
# elif label == 'BAD':
# fw_bad.write(print_str)
return similarity, print_str
def jaccardDist(name1, name2):
"""
info: calculate the jaccard distance between the two strings
name1, name2
input: name1:String, name2:String
output: distance value dist (real number within [0, 1])
"""
"""
dist = 0
for i in range(len(name1)):
if not (name1[i] == name2[i]):
dist += 1
#still have to calculate the real Jaccard distance
"""
dist = 1 - td.jaccard(name1, name2)
return dist
def compare(s_inp, s_out):
'''nlp = spacy.load("en_core_web_sm")
str_inp = nlp(s_inp)
srt_inp = " ".join([token.lemma_ for token in str_inp])
#print(str_inp)
#inp_lower = str_inp.lower()
print("Lower String Input: {}".format(str_inp))
str_out = nlp(s_out)
srt_out = " ".join([token.lemma_ for token in str_out])
#print(str_out)
#out_lower = str_out.lower()
print("Lower String Output: {}".format(str_out))
#print("Inside Compare")
#print("Str1: ", s_inp)
#print("Str2: ", s_out)'''
#Jaccard Index
jacc = textdistance.jaccard(s_inp, s_out)
print("jaccard: ", jacc)
#Sorens
soren = textdistance.sorensen(s_inp, s_out)
print("Sorensen: ", soren)
#TVR Value
tvr = textdistance.tversky(s_inp, s_out)
print("Tversky: ", tvr)
#Over Lap Index
overlap = textdistance.overlap(s_inp, s_out)
print("overlap_cofficient: ", overlap)
#Tanimoto Distance
#tanimoto_distance = textdistance.tanimoto(str_inp, str_out)
#print("Tanimoto: ", tanimoto_distance)
res = (jacc+soren+tvr+overlap)/4
if res == 0:
pass
else:
lst.append(res)
print("Result: {}".format(res))
'''if (res >= 0.6):
def fuzzy_match(term, term_list):
best_match = 0
if term == 'nan':
return False
if 'unnamed' in term:
return False
for t in term_list:
match = textdistance.jaccard(term, t)
#match = textdistance.damerau_levenshtein.normalized_similarity(term, t)
if match > best_match:
best_match = match
if best_match == 1:
print("Found match for: ", term, " with score of: ",
best_match)
return True # don't need to keep searching once find exact match
if best_match >= 0.8: # if best match is above a threhold for similarity - can modify this number
print("Found match for: ", term, " with score of: ", best_match)
return True
if best_match >= 0.6:
print("Didn't find match for: ", term, " with score of: ", best_match)
return False
def get_similarity_score(title, videos, movie_description, keywords=None):
'''
main method that take the list of video_data from a YouTube query
and sort the queries based on a set of similarities as defined
by accord index and description keywords
Parameters
==========
title:
the title of the movie
videos:
list of youtube video objects
keywords:
list of top-5 cast, top-5 characters, and director(s)
movie_description:
string of description from tmdb
Return
==========
list of YouTubeVideo objects with similarity score
'''
youtube_videos = []
for video in videos:
# first, all items will be converted to lowercase and tokenized
lowercase_title = title.lower()
movie_desc = strip_characters(movie_description.lower()).split()
trailer_desc = strip_characters(video.description.lower()).split()
movie_title = (strip_characters(lowercase_title) +
" official trailer").split()
trailer_title = strip_characters(video.title.lower()).split()
similarity_score = is_clip(
trailer_title, lowercase_title.split()) * 0.5 * (
textdistance.sorensen_dice(movie_desc, trailer_desc) +
textdistance.jaccard(movie_title, trailer_title))
video.set_similarity_score(similarity_score)
youtube_videos.append(video)
return youtube_videos
def super_similiar(es1, es2, sim_factor=0.8, sim_box=0.6):
"""Check if two elements are super similiar by text (Jaccad) and visually (compare bbox).
"""
text1 = only_text(es1)
text2 = only_text(es2)
points1 = only_points(es1)
points2 = only_points(es2)
if min(len(points1), len(points2)) < 4:
return False
logger.debug("points")
logger.debug(points1)
logger.debug(points2)
j_sim = jaccard(text1, text2)
b_sim = sim_bbox(points1, points2)
logger.debug(f"footer/header sims {j_sim} {b_sim}")
return j_sim > sim_factor and b_sim > sim_box
def calculate_similarity_matrix(selected_data_list, vector_map, weight):
full_result = []
id_list = []
if selected_data_list is not None:
vector_matrix = calculate_matrix(selected_data_list, vector_map).tolist()
for i in range(0, len(selected_data_list)):
first_noun_phrase = selected_data_list[i]["noun_phrase"]
id_list.append(selected_data_list[i]["id"])
full_temp = []
for j in range(0, len(selected_data_list)):
second_noun_phrase = selected_data_list[j]["noun_phrase"]
if i == j:
similarity = 0
else:
similarity = (vector_matrix[i][j] + 1) * weight / 2 + textdistance.jaccard(
first_noun_phrase,
second_noun_phrase) * (
1 - weight)
similarity = -round(similarity, 6) + 1
full_temp.append(similarity)
full_result.append(full_temp)
return full_result, id_list