def insert_new_relation(post1):
"""
Arguments:
- `post1`: newly added post
"""
posts = Post.objects()
if post1.post_type == "pdf":
return None
for post2 in posts:
if post2.post_type != "pdf" and post2.url != post1.url:
# text similarity
text1 = post1.content.lower()
text2 = post2.content.lower()
vector1 = text_to_vector(text1)
vector2 = text_to_vector(text2)
content_cosine = get_cosine(vector1, vector2)
# title similarity
title1 = post1.title.lower()
title2 = post2.title.lower()
tvector1 = text_to_vector(title1)
tvector2 = text_to_vector(title2)
title_cosine = get_cosine(tvector1, tvector2)
category_point = get_category_point(post1, post2)
cosine = content_cosine + title_cosine + category_point
if cosine > 0.1:
relation = Relation(post1, post2, cosine)
relation.save()
relation = Relation(post2, post1, cosine)
relation.save()
def delete_post():
post_data = json.loads(request.data)
post_id = post_data["postId"]
post = Post.objects(id=post_id).first()
author = User.objects(username=current_user.username).first()
if post.author != author:
return json.dumps({"status": "not-allowed"})
Relation.objects(post1=post).delete()
Relation.objects(post2=post).delete()
Post.objects(id=post_id).delete()
return json.dumps({"status": "success"})
def build_relation_db():
"""
Build a relation collection that includes
every similarity between posts.
Only includes relation when similarity > 0.2
This takes a lot of time, run this periodically.
Eg. once a week or everynight.
Use insert_new_relation() for new posts
"""
posts = Post.objects()
posts2 = Post.objects()
Relation.drop_collection()
counter = 0
print counter
for p1 in posts:
for p2 in posts2:
if p1.url != p2.url:
if p1.post_type != "pdf" and p2.post_type != "pdf":
counter = counter + 1
# text similarity
text1 = p1.content.lower()
text2 = p2.content.lower()
vector1 = text_to_vector(text1)
vector2 = text_to_vector(text2)
content_cosine = get_cosine(vector1, vector2)
# title similarity
title1 = p1.title.lower()
title2 = p2.title.lower()
tvector1 = text_to_vector(title1)
tvector2 = text_to_vector(title2)
title_cosine = get_cosine(tvector1, tvector2)
category_point = get_category_point(p1, p2)
cosine = content_cosine + title_cosine + category_point
if cosine > 0.1:
relation = Relation(p1, p2, cosine)
relation.save()
print counter
def single_post(seq, slug):
seq = int(seq)
user = get_user()
post = Post.objects(seq=seq).first()
related_posts = Relation.objects(post1=post).order_by("-similarity")[:3]
return render_template(
'single.html', user=user, post=post,
related_posts=related_posts
)
def after_tagging_calculation(post1):
"""
Arguments:
- `post1`: tag added to this post
"""
relation = Relation.objects(post1=post1)
if post1.post_type == "pdf":
return None
for r in relation:
category_point = get_category_point(r.post1, r.post2)
r.similarity += category_point
r.save()
relation = Relation.objects(post2=post1)
for r in relation:
category_point = get_category_point(r.post1, r.post2)
r.similarity += category_point
r.save()
def after_tagging_calculation(post1):
"""
Arguments:
- `post1`: tag added to this post
"""
relation = Relation.objects(post1=post1)
if post1.post_type == "pdf":
return None
for r in relation:
category_point = get_category_point(r.post1, r.post2)
r.similarity += category_point
r.save()
relation = Relation.objects(post2=post1)
for r in relation:
category_point = get_category_point(r.post1, r.post2)
r.similarity += category_point
r.save()
def insert_new_relation(post1):
"""
Arguments:
- `post1`: newly added post
"""
posts = Post.objects()
if post1.post_type == "pdf":
return None
for post2 in posts:
if post2.post_type != "pdf" and post2.url != post1.url:
# text similarity
text1 = post1.content.lower()
text2 = post2.content.lower()
vector1 = text_to_vector(text1)
vector2 = text_to_vector(text2)
content_cosine = get_cosine(vector1, vector2)
# title similarity
title1 = post1.title.lower()
title2 = post2.title.lower()
tvector1 = text_to_vector(title1)
tvector2 = text_to_vector(title2)
title_cosine = get_cosine(tvector1, tvector2)
category_point = get_category_point(post1, post2)
cosine = content_cosine + title_cosine + category_point
if cosine > 0.1:
relation = Relation(post1, post2, cosine)
relation.save()
relation = Relation(post2, post1, cosine)
relation.save()
def build_relation_db():
"""
Build a relation collection that includes
every similarity between posts.
Only includes relation when similarity > 0.2
This takes a lot of time, run this periodically.
Eg. once a week or everynight.
Use insert_new_relation() for new posts
"""
posts = Post.objects()
posts2 = Post.objects()
Relation.drop_collection()
counter = 0
print counter
for p1 in posts:
for p2 in posts2:
if p1.url != p2.url:
if p1.post_type != "pdf" and p2.post_type != "pdf":
counter = counter + 1
# text similarity
text1 = p1.content.lower()
text2 = p2.content.lower()
vector1 = text_to_vector(text1)
vector2 = text_to_vector(text2)
content_cosine = get_cosine(vector1, vector2)
# title similarity
title1 = p1.title.lower()
title2 = p2.title.lower()
tvector1 = text_to_vector(title1)
tvector2 = text_to_vector(title2)
title_cosine = get_cosine(tvector1, tvector2)
category_point = get_category_point(p1, p2)
cosine = content_cosine + title_cosine + category_point
if cosine > 0.1:
relation = Relation(p1, p2, cosine)
relation.save()
print counter
def get_related_posts(post1):
related_posts = Relation.objects(post1=post1).order_by("-similarity")[:4]
return related_posts
def get_related_posts(post1):
related_posts = Relation.objects(post1=post1).order_by("-similarity")[:4]
return related_posts
