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clustering_most_similar.py
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clustering_most_similar.py
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import os
import json
import gensim
import pandas as pd
import numpy as np
import pickle
from collections import Counter
from tqdm import tqdm
from khaiii import KhaiiiApi
from retrieval_utils import *
from sklearn.cluster import KMeans
def get_Kmeans_centroid(model, songs_in_dictionary):
kmeans = KMeans(n_clusters=2, n_init=5).fit([model.wv[song] for song in songs_in_dictionary])
return kmeans
with open(os.path.join('../media/bach3/dataset/melonPlaylist/src','unique_dict.json')) as f:
unique_dict = json.load(f)
with open(os.path.join('../media/bach3/dataset/melonPlaylist/src','filterSet')) as f:
filterSet = json.load(f)
filtered_tag = filterSet['tagsCum80']
filtered_song = filterSet['freq_cum90Filter']['songs']
def vector_most_similar(self, kmeans, all_words, topn, restrict_vocab=None):
self.init_sims()
mean = matutils.unitvec(kmeans).astype(REAL)
limited = self.vectors_norm if restrict_vocab is None else self.vectors_norm[restrict_vocab]
dists = dot(limited, mean)
best = matutils.argsort(dists, topn=topn + len(all_words), reverse=True)
# ignore (don't return) words from the input
if restrict_vocab:
result = [(self.index2word[restrict_vocab[sim]], float(dists[sim])) for sim in best if restrict_vocab[sim] not in all_words]
else:
result = [(self.index2word[sim], float(dists[sim])) for sim in best if sim not in all_words]
return result[:topn]
def cleanText(sent):
result = re.sub('[-=+,#/\?:;^$@*\"※~%ㆍ!』\\‘|\(\)\[\]\<\>`\'》]', '', sent)
result_with_space = re.sub('[.…]', '', result)
return result_with_space.lower()
def _get_candidate_set(songs_in_dictionary,filtered_song, threshold, topk, single_query=0):
mid_list = []
if len(songs_in_dictionary) > 1:
Kmeans = get_Kmeans_centroid(model, songs_in_dictionary)
centroid = Kmeans.cluster_centers_
centroid_label = Kmeans.labels_
for centroid_vector in centroid:
filter_songs = [int(x[0]) for x in vector_most_similar(model.wv, centroid_vector, songs_in_dictionary, topn=topk, restrict_vocab=search_song_indices) if x[1] >threshold]
mid_list.extend(filter_songs)
else:
for song in songs_in_dictionary:
filter_songs = [x[0] for x in custom_most_similar(model.wv, song, topn=topk, restrict_vocab=search_tag_indices) if x[1] > threshold]
mid_list.extend(filter_songs)
count_candidate_set = Counter(mid_list)
return count_candidate_set
def song_retrieval(songs_in_dictionary,filtered_song, threshold, topk):
count_candidate_set = []
while len(count_candidate_set) < 100:
threshold = threshold - 0.1
topk = topk + 100
count_candidate_set = _get_candidate_set(songs_in_dictionary,filtered_song, threshold, topk, single_query=0)
result100 = [i[0] for i in count_candidate_set.most_common(100)]
return result100
def _get_tag_candidate_set(tags_in_dictionary,filtered_tag, threshold, topk, single_query=0):
mid_list = []
if len(tags_in_dictionary) > 1:
Kmeans = get_Kmeans_centroid(model, tags_in_dictionary)
centroid_tag = Kmeans.cluster_centers_
centroid_label = Kmeans.labels_
for centroid_tag_vector in centroid_tag:
filter_tags = [x[0] for x in vector_most_similar(model.wv, centroid_tag_vector, tags_in_dictionary, topn=topk, restrict_vocab=search_song_indices) if x[1] > threshold]
mid_list.extend(filter_tags)
else:
for tag in tags_in_dictionary:
filter_tags = [x[0] for x in custom_most_similar(model.wv, tag, topn=topk, restrict_vocab=search_tag_indices) if x[1] > threshold]
mid_list.extend(filter_tags)
count_candidate_set = Counter(mid_list)
return count_candidate_set
def tag_retrieval(tags_in_dictionary,filtered_tag, threshold, topk):
count_candidate_set = []
while len(count_candidate_set) < 10:
threshold = threshold - 0.01
topk = topk + 100
count_candidate_set = _get_tag_candidate_set(tags_in_dictionary,filtered_tag, threshold, topk, single_query=0)
result10 = [i[0] for i in count_candidate_set.most_common(10)]
return result10
def tag_to_song_retrieval(tags_in_dictionary,filtered_song, threshold, topk):
count_candidate_set = []
while len(count_candidate_set) < 100:
threshold = threshold - 0.1
topk = topk + 100
count_candidate_set = _get_candidate_set(tags_in_dictionary,filtered_song, threshold, topk)
result100 = [i[0] for i in count_candidate_set.most_common(100)]
return result100
def song_to_tag_retrieval(songs_in_dictionary,filtered_tag, threshold, topk):
count_candidate_set = []
while len(count_candidate_set) < 10:
threshold = threshold - 0.01
topk = topk + 100
count_candidate_set = _get_tag_candidate_set(songs_in_dictionary,filtered_tag, threshold, topk)
result10 = [i[0] for i in count_candidate_set.most_common(10)]
return result10
def title_tokenizer(title):
token = api.analyze(title)
sentence = []
try:
for i in token:
if i.morphs[0].tag in ['NNG', 'NNP', 'VA', 'SL', 'XR', 'MAG'] and len(i.morphs[0].lex) > 1:
sentence.append(i.morphs[0].lex)
except:
pass
return sentence
def main(model, model_name, types, questions, search_song_indices, search_tag_indices, answers=[], index=0):
recommends = []
if index:
questions = questions[:index]
if answers != []:
answers = answers[:index]
for q in tqdm(questions):
song_list = []
tag_list = []
prior_list = []
songs_in_dictionary = [str(x) for x in q['songs'] if str(x) in model.wv.vocab]
if q['tags']:
tags_in_dictionary = [y for x in q['tags'] if x in unique_dict for y in unique_dict[x] if y in model.wv.vocab]
else:
tags_in_dictionary = []
if q['plylst_title']:
clean_title = cleanText(q['plylst_title']).strip()
if clean_title == "":
title_in_dictionary = []
else:
tokenizing = title_tokenizer(clean_title)
split = clean_title.split()
tokenize_title = list(set(tokenizing + split))
title_in_dictionary = [str(x) for x in tokenize_title if str(x) in model.wv.vocab]
else:
title_in_dictionary = []
# add title to tags
tags_in_dictionary += title_in_dictionary
for title_token in title_in_dictionary:
if title_token in filtered_tag:
prior_list.append(title_token)
if len(songs_in_dictionary) == 0 and len(tags_in_dictionary) == 0 and len(title_in_dictionary) == 0:
# plylist -> tag, song retrieval
song_list = MOST_POPULAR
tag_list = ["기분전환", "감성", "휴식", "발라드", "잔잔한", "드라이브", "힐링", "사랑", "새벽", "밤"]
elif len(songs_in_dictionary) == 0 and len(tags_in_dictionary) == 0:
song_list = tag_to_song_retrieval(title_in_dictionary,filtered_song, 0.7, 50)
title_tag_list = tag_retrieval(title_in_dictionary,filtered_tag, 0.3, 20)
total_tag_list = prior_list + title_tag_list
tag_list = total_tag_list[:10]
elif len(songs_in_dictionary) == 0:
song_list = tag_to_song_retrieval(tags_in_dictionary,filtered_song, 0.7, 50)
tag_list = tag_retrieval(tags_in_dictionary,filtered_tag, 0.3, 20)
total_tag_list = prior_list + tag_list
tag_list = total_tag_list[:10]
elif len(tags_in_dictionary) == 0:
song_list = song_retrieval(songs_in_dictionary,filtered_song, 0.7, 50)
tag_list = song_to_tag_retrieval(songs_in_dictionary,filtered_tag, 0.7, 50)
else:
song_list = song_retrieval(songs_in_dictionary,filtered_song, 0.7, 50)
tag_list = tag_retrieval(tags_in_dictionary,filtered_tag, 0.3, 20)
total_tag_list = prior_list + tag_list
tag_list = total_tag_list[:10]
recommends.append({
"id": q["id"],
"songs": song_list,
"tags": tag_list,
})
write_json(recommends, "results/cluster" + types + model_name + ".json")
if __name__ == '__main__':
val = load_json('../media/bach3/dataset/melonPlaylist/val.json')
test = load_json('../media/bach3/dataset/melonPlaylist/test.json')
model_name = "1003001050sg_model"
model = gensim.models.Word2Vec.load("../media/bach3/dataset/melonPlaylist/models/" + model_name)
search_song_indices = [model.wv.vocab[str(x)].index for x in filtered_song if str(x) in model.wv.vocab]
search_tag_indices = list(set([model.wv.vocab[x].index for x in filtered_tag if x in model.wv.vocab]))
main(model, model_name, "val", val, search_song_indices, search_tag_indices)
main(model, model_name, "test", test, search_song_indices, search_tag_indices)
# model_dir = Path('/media/bach3/dataset/melonPlaylist/models')
# model_lists = list(model_dir.glob('*50sg_model'))
# good_models = ['1003001050sg_model' ]
# for model_path in model_lists:
# print(str(model_path))
# model = gensim.models.Word2Vec.load(str(model_path))
# search_song_indices = [model.wv.vocab[str(x)].index for x in filtered_song if str(x) in model.wv.vocab]
# search_tag_indices = list(set([model.wv.vocab[x].index for x in filtered_tag if x in model.wv.vocab]))
# main(model, questions, search_song_indices, search_tag_indices, answers=answers, index=500)
def mix_two_lists(l1, l2):
if len(l1) > (l2):
long_list = l1
short_list = l2
else:
longer_list = l2
short_list = l1
length_ratio = long_list / short_list