video_recommender.py
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from gensim.models import Word2Vec
import numpy as np
from numpy import dot
from numpy.linalg import norm
import pandas as pd
import math
import activation as ac
def recommend_videos(tags, segments, tag_model_path, video_model_path, video_id_model, top_k, isPerSegment = True):
# 이 함수에서 모든걸 다 함
# tags는 label val 로 묶인 문자열 리스트임
# tags의 길이는 segment의 길이
# 비디오 벡터를 생성한 뒤 각 segment의 벡터와 비교해 가장 유사도 높은 segment의 인덱스 추출
# 그 후 그 인덱스 전후 2개 총 크기 5의 커널을 생성
# 벡터공간에서 비교한 후 추천할만한 영상의 아이디만 반환
#segments는 클래스 확률 클래스 확률... 일케 저장되어 있음
tag_vectors = Word2Vec.load(tag_model_path).wv
video_vectors = Word2Vec().wv.load(video_model_path)
video_ids = Word2Vec().wv.load(video_id_model)
error_tags = []
maxSimilarSegment = 0
maxSimilarity = -1
kernel = [np.zeros(100) for i in range(0,5)]
tagKernel = []
similar_ids = []
#우선은 비교를 뜰 입력 영상의 단일 비디오벡터를 구함
video_vector = np.zeros(100)
tag_preds =[]
videoTagList = []
for (tag, weight) in tags:
tag_preds.append(weight)
videoTagList.append(tag)
ac.softmax(tag_preds)
for (tag, weight),pred in zip(tags,tag_preds):
print(tag,pred)
if tag in tag_vectors.vocab:
video_vector = video_vector + (tag_vectors[tag] * float(pred))
else:
#print("unknown",tag)
# Pass if tag is unknown
if tag not in error_tags:
error_tags.append(tag)
if(isPerSegment == True):
#각 세그먼트마다 비교를 떠서 인덱스를 저장
midpos = math.floor(len(kernel)/2)
for i in range(0,midpos):
segments.insert(0,segments[0])
segments.append(segments[len(segments)-1])
currentIndex = midpos
for si in range(midpos,len(segments) - midpos - 1):
similarity = 0
for segi in range(-1,2):
segment = segments[si + segi]
segment_vector = np.zeros(100)
segTags = [segment[i] for i in range(0,len(segment),2)]
segProbs = [float(segment[i]) for i in range(1,len(segment),2)]#ac.softmax([float(segment[i]) for i in range(1,len(segment),2)])
for tag, weight in zip(segTags,segProbs):
if tag in tag_vectors.vocab:
segment_vector = segment_vector + (tag_vectors[tag] * float(weight))
else:
# Pass if tag is unknown
if tag not in error_tags:
error_tags.append(tag)
#비디오 벡터와 세그먼트 벡터 비교
#similarity = similarity + cos_sim(video_vector, segment_vector) #cos_sim(video_vector, segment_vector)#
for currentSegmentTag, videoVectorTag,videoVectorTagPred in zip(segTags,videoTagList,tag_preds):
if(currentSegmentTag in tag_vectors.vocab) and (videoVectorTag in tag_vectors.vocab):
prob = float(videoVectorTagPred)
if videoVectorTag not in segTags:
prob = 0
similarity = similarity + (tag_vectors.similarity(currentSegmentTag,videoVectorTag) * prob)
if similarity >= maxSimilarity:
maxSimilarSegment = currentIndex
maxSimilarity = similarity
if maxSimilarSegment < int(len(kernel)/2):
maxSimilarSegment = int(len(kernel)/2)
elif maxSimilarSegment == len(segments) - int(len(kernel)/2):
maxSimilarSegment = len(segments) - int(len(kernel)/2) - 1
#세그먼트 인덱스 증가
currentIndex = currentIndex + 1
#######################################print('maxSimilarSegment',maxSimilarSegment,'len',len(segments))
#커널 생성
for k in range (0,len(kernel)):
segment = segments[maxSimilarSegment - math.floor(len(kernel)/2) + k]
segment_vector = np.zeros(100)
segTags = [segment[i] for i in range(0,len(segment),2)]
tagKernel.append(segTags)
segProbs = ac.softmax([float(segment[i]) for i in range(1,len(segment),2)])
#print(segTags)
#print(segProbs)
for (tag, weight) in zip(segTags,segProbs):
if tag in tag_vectors.vocab:
#float(weight)
segment_vector = segment_vector + (tag_vectors[tag] * float(weight))
else:
#print("unknown",tag)
# Pass if tag is unknown
if tag not in error_tags:
error_tags.append(tag)
kernel[k] = segment_vector
#여기에서 유사한 벡터들을 뽑아냄
#현재는 비디오id로 영상을 얻을 수 없으므로 반환값으로 비디오 아이디와 태그들, 확률 사용
video_tags = pd.read_csv('/mnt/e/khuhub/2015104192/web/backend/yt8m/esot3ria/segment_tags.csv', encoding='utf8',error_bad_lines=False)
videoVectorList = []
segmentTagList = []
prevVideoId = ""
minimunVideoIds = [["",-1.0] for i in range(0,top_k)]
for i, row in video_tags.iterrows():
video_id = row[0]
if video_id == "vid_id":
continue
if prevVideoId == "":
prevVideoId = video_id
if video_id[0:4] != prevVideoId[0:4]:
#여기서 모다진걸로 컨볼루션 연산
#convmaxima, convidx = convolution(videoVectorList,kernel,prevVideoId)
maxima, idx = differenceMax(segmentTagList,tagKernel,tag_vectors,videoTagList)
#maxima = maxima + convmaxima
localMinima = 100
localMinimaIndex = -1
for seg in range(0,top_k):
if float(minimunVideoIds[seg][1]) < localMinima:
localMinima = float(minimunVideoIds[seg][1])
localMinimaIndex = seg
#print(maxima)
if localMinima < maxima:
#print(prevVideoId[0:4] + "_" + str(idx),localMinimaIndex,maxima)
minimunVideoIds[localMinimaIndex] = [prevVideoId[0:4] + "_" + str(idx),maxima]
videoVectorList.clear()
segmentTagList.clear()
prevVideoId = video_id
if video_id == "finished":
break
videoVectorList.append(video_vectors[video_id])
tagList = []
for i in range(1,top_k+1):
tagList.append([row[i].split(":")[0],row[i].split(":")[1]])
segmentTagList.append(tagList)
for i in range(0,top_k):
similar_ids.append(minimunVideoIds[i][0])
else:
similar_ids = [x[0] for x in video_ids.similar_by_vector(video_vector, top_k)]
return similar_ids
def cos_sim(A, B):
denom = norm(A)*norm(B)
if denom == 0:
return 0
else:
return dot(A, B)/(norm(A)*norm(B))
def sub_vec_size(A,B):
dir = A-B
return norm(dir)
def convolution(arrs, _kernel,vidId):
s = len(_kernel)
l = len(arrs)
result = []
midpos = math.floor(s/2)
for i in range(0,midpos):
arrs.insert(0,np.zeros(100))
arrs.append(np.zeros(100))
total = 0
for j in range(midpos,len(arrs) - midpos):
convResult = 0
for i in range(0, s):
if(i == int(len(_kernel)/2)):
convResult = convResult - sub_vec_size(arrs[j - midpos + i],_kernel[i]) + dot(arrs[j - midpos + i],_kernel[i])
result.append(convResult)
total = total + convResult
maxVal = max(result)
index = result.index(maxVal)
return total/l,index
def differenceMax(arrs, _kernel, w2v, videoTaglist):
s = len(_kernel)
result = []
midpos = math.floor(s/2)
for i in range(0,midpos):
arrs.insert(0,arrs[0])
arrs.append(arrs[len(arrs)-1])
prevIndex = 0
prevMax = -100
for j in range(midpos,len(arrs) - midpos):
convResult = 0
processed_vocabNum = 1
for i in range(0, s):
if(_kernel[i][0] not in arrs[j - midpos + i][0]):# and ((videoTaglist[0] not in arrs[j - midpos + i][0:2])) and ((videoTaglist[1] not in arrs[j - midpos + i][0:5])):
continue
for ind in range(0,5):
if(arrs[j - midpos + i][ind][0] in w2v.vocab) and (_kernel[i][ind] in w2v.vocab):
prob = float(arrs[j - midpos + i][ind][1])
if arrs[j - midpos + i][ind][0] not in videoTaglist:
prob = 0
convResult = convResult + (w2v.similarity(arrs[j - midpos + i][ind][0],_kernel[i][ind]) * prob)
processed_vocabNum = processed_vocabNum + 1
if prevMax < convResult:
prevMax = convResult
prevIndex = j - midpos
result.append(convResult)
#maxVal = max(result)
#index = result.index(maxVal)
return prevMax,prevIndex
def normalize(arrs):
maximum = max(arrs)
minimum = min(arrs)
denom = maximum - minimum
for i in range(0,len(arrs)):
arrs[i] = (arrs[i] - minimum)/ denom
def test(tag_model_path, video_model_path, video_id_model, video_tags_path, segment_tags_path,test_segment_tags,top_k):
tag_vectors = Word2Vec.load(tag_model_path).wv
video_tags = pd.read_csv(test_segment_tags, encoding='utf8',error_bad_lines=False)
segmentTagList = []
prevVideoId = ""
entire_video_tags = pd.read_csv(video_tags_path,encoding='utf8')
entire_segment_tags = pd.read_csv(segment_tags_path,encoding='utf8')
testResult = {}
totalIdNum = 0
for i, row in video_tags.iterrows():
video_id = row[0]
if video_id == "vid_id":
continue
if prevVideoId == "":
prevVideoId = video_id
if video_id[0:4] != prevVideoId[0:4]:
count = {}
cap1 = 0
cap2 = 0
totalSegmentTagProbList = []
for segTag in segmentTagList:
segmentTagProbList = []
for i in range(0,len(segTag)):
try: count[segTag[i][0]] += float(segTag[i][1])
except: count[segTag[i][0]] = float(segTag[i][1])
segmentTagProbList.append(segTag[i][0])
segmentTagProbList.append(segTag[i][1])
totalSegmentTagProbList.append(segmentTagProbList)
sorted(count.items(), key=lambda x: x[1], reverse=True)
tagnames = list(count.keys())[0:5]
tagprobs = list(count.values())[0:5]
tags = zip(tagnames,tagprobs)
result = recommend_videos(tags, totalSegmentTagProbList, tag_model_path, video_model_path, video_id_model, top_k,False)
score_avg = 0
print("input tags :",tagnames)
for ids in result:
score = 0
video_tags_info = entire_video_tags.loc[entire_video_tags["vid_id"] == ids]
tagList = []
for i in range(1, top_k + 1):
video_tag_tuple = video_tags_info["segment" + str(i)].values[0]# ex: "mobile-phone:0.361"
tag = video_tag_tuple.split(":")[0]
tagList.append(tag)
if tag in tag_vectors.vocab:
for vidTag,pr in zip(tagnames,tagprobs):
#if vidTag in tag_vectors.vocab:
# score = score + (tag_vectors.similarity(tag,vidTag) * float(pr))
if tag == vidTag:
score += 1
score_avg = score_avg + score
#print("result for id",ids,"is", str(score)," / tags ",tagList)
print("CAP - 1)score average = ",score_avg/5)
cap1 = score_avg/5
result = recommend_videos(tags, totalSegmentTagProbList, tag_model_path, video_model_path, video_id_model, top_k,True)
score_avg = 0
for ids in result:
score = 0
video_tags_info = entire_video_tags.loc[entire_video_tags["vid_id"] == ids[0:4]]#entire_segment_tags.loc[entire_segment_tags["vid_id"] == ids]
tagList = []
for i in range(1, top_k + 1):
video_tag_tuple = video_tags_info["segment" + str(i)].values[0]# ex: "mobile-phone:0.361"
tag = video_tag_tuple.split(":")[0]
tagList.append(tag)
#for vidTag in tagnames:
# if tag == vidTag:
# score += 1
if tag in tag_vectors.vocab:
for vidTag,pr in zip(tagnames,tagprobs):
#if vidTag in tag_vectors.vocab:
# score = score + (tag_vectors.similarity(tag,vidTag) * float(pr))
if tag == vidTag:
score += 1
score_avg = score_avg + score
#print("result for id",ids,"is", str(score)," / tags ",tagList)
print("CAP - 2)score average = ",score_avg/5)
cap2 = score_avg/5
totalIdNum += 1
if cap1 > cap2:
try: testResult['cap1'] += 1
except: testResult['cap1'] = 1
elif cap1 < cap2:
try: testResult['cap2'] += 1
except: testResult['cap2'] = 1
else:
try:
testResult['cap2'] += 0.5
testResult['cap1'] += 0.5
except:
testResult['cap2'] = 0.5
testResult['cap1'] = 0.5
print(totalIdNum, testResult)
segmentTagList.clear()
prevVideoId = video_id
if video_id == "finished":
break
tagList = []
for i in range(1,top_k+1):
tagList.append([row[i].split(":")[0],row[i].split(":")[1]])
segmentTagList.append(tagList)
#===========
VIDEO_TAGS_PATH = "/mnt/e/khuhub/2015104192/web/backend/yt8m/esot3ria/segment_tags.csv"
VIDEO_IDS_PATH = "/mnt/e/khuhub/2015104192/web/backend/yt8m/esot3ria/videoIds.csv"
TAG_VECTOR_MODEL_PATH = "/mnt/e/khuhub/2015104192/web/backend/yt8m/esot3ria/tag_vectors.model"
VIDEO_VECTOR_MODEL_PATH = "/mnt/e/khuhub/2015104192/web/backend/yt8m/esot3ria/video_vectors.model"
VIDEO_VECTOR_MODEL2_PATH = "/mnt/e/khuhub/2015104192/web/backend/yt8m/esot3ria/video_vectors2.model"
VIDEO_ID_MODEL_PATH = "/mnt/e/khuhub/2015104192/web/backend/yt8m/esot3ria/videoId_vectors.model"
TEST_TAGS_PATH = "/mnt/e/khuhub/2015104192/web/backend/yt8m/esot3ria/test_segement_tags.csv"
test(TAG_VECTOR_MODEL_PATH,
VIDEO_VECTOR_MODEL_PATH,
VIDEO_ID_MODEL_PATH,
VIDEO_IDS_PATH,
VIDEO_TAGS_PATH,
TEST_TAGS_PATH,
5)