def predict(movie_id):
target_movie = Movies.get(movie_id)
#target_movie = get_movie(movie_id)
target_ratings = make_target_ratings(movie_id)
# target_movie_rating = rating
my_movie_ids = movie_ids(0)
movies_to_compare = []
for id in my_movie_ids:
ratings = make_target_ratings(id)
movies_to_compare.append(ratings)
similarities = []
for ratings in movies_to_compare:
our_rating = ratings[0] # ratings is not a list, it is a dictionary
similarity = pearson(target_ratings, ratings)
tup = (similarity, our_rating)
similarities.append(tup)
# (similarity, ranking)
top_five = sorted(similarities)
top_five.reverse()
top_five = top_five[:5]
num = 0.0
den = 0.0
# Use a weighted mean rather than a strict top similarity
for sim, m in top_five:
num += (float(sim) * m)
den += sim
rating = num/den
print "Best guess for movie %d: %s is %.2f stars"%\
(movie_id, target_movie['title'], rating)
def load_movies():
"""Load movies from u.item into database."""
print "Movies"
# Delete all rows in table, so if we need to run this a second time,
# we won't be trying to add duplicate users
Movies.query.delete()
# Read u.user file and insert data
for i, row in enumerate(open("seed_data/u.item")):
row = row.rstrip()
row = row.split("|")[:-19]
movie_id, movie, release_date, __, imdb_url = row
title = movie.split("(")
title = title[0].rstrip()
title = title.decode("latin-1")
if release_date:
released_at = datetime.datetime.strptime(release_date, "%d-%b-%Y")
else:
released_at = None
movie = Movies(title=title, released_at=released_at, imdb_url=imdb_url)
# We need to add to the session or it won't ever be stored
db.session.add(movie)
if i % 100 == 0:
print i
# Once we're done, we should commit our work
db.session.commit()
def load_movies():
"""Load movies from u.item into database."""
print("Movies")
# Delete all rows in table, so if we need to run this a second time,
# we won't be trying to add duplicate users
Movies.query.delete()
# Read u.user file and insert data
for row in open("seed_data/u.item"):
row = row.rstrip()
movie_id, movie_title, release_at, space, imdb_url, *args = row.split(
"|")
release_date = datetime.strptime(release_at, "%d-%b-%Y")
for i, letter in enumerate(movie_title):
if letter == '(':
movie_title = movie_title[:i]
movie = Movies(movie_id=movie_id,
movie_title=movie_title,
release_at=release_date,
imdb_url=imdb_url)
# We need to add to the session or it won't ever be stored
db.session.add(movie)
# Once we're done, we should commit our work
db.session.commit()
def load_movies():
"""Load movies from u.item into database."""
print("Movies")
Movies.query.delete()
for row in open("seed_data/u.item"):
row = row.rstrip()
movie_id, title, release_date, junk, imdb = row.split("|")[:5]
title = title[:-7]
if release_date:
released_at = datetime.strptime(release_date, "%d-%b-%Y")
else:
released_at = None
movie = Movies(movie_id=movie_id,
title=title,
release_date=released_at,
imdb=imdb)
db.session.add(movie)
db.session.commit()
def post_movie(payload):
if request.data:
body = request.get_json()
print(body)
title = body['title']
releaseDate = body['releaseDate'].split('/')
year = int(releaseDate[0])
month = int(releaseDate[1])
day = int(releaseDate[2])
releaseDate = datetime(year, month, day)
try:
movie = Movies(title=title, releaseDate=releaseDate)
movie.insert()
return jsonify({
'success': True,
'movies': [movie.format()]
})
except:
abort(500)
def movie_details():
movie_id = int(request.form["movie_id"])
average_rating = movies.average_rating(movie_id)
md = Movies.get(movie_id)
return render_template("details.html", md=md, avg = average_rating)
