def read_csv_file(self):
with open(self.__file_name, mode='r', encoding='utf-8-sig') as csvfile:
movie_file_reader = csv.DictReader(csvfile)
index = 0
for row in movie_file_reader:
movie = Movie(row["Title"], int(row["Year"]))
movie.description = row["Description"]
movie.runtime_minutes = int(row["Runtime (Minutes)"])
self.__total_runtime_minutes += int(row["Runtime (Minutes)"])
self.__runtime_minutes_number_of_movies += 1
if row["Rating"] != "N/A":
movie.rating = float(row['Rating'])
self.__total_rating += float(row['Rating'])
self.__rating_number_of_movies += 1
if row["Votes"] != "N/A":
movie.votes = int(row["Votes"])
self.__total_votes += int(row["Votes"])
self.__votes_number_of_movies += 1
if row["Revenue (Millions)"] != "N/A":
movie.revenue_millions = float(row["Revenue (Millions)"])
self.__total_revenue_millions += float(
row["Revenue (Millions)"])
self.__revenue_millions_number_of_movies += 1
if row["Metascore"] != "N/A":
movie.metascore = int(row["Metascore"])
self.__total_metascore += int(row["Metascore"])
self.__metascore_number_of_movies += 1
self.__dataset_of_movies.append(movie)
self.__dataset_of_directors.add(Director(row["Director"]))
for actor in row["Actors"].split(","):
self.__dataset_of_actors.add(Actor(actor.strip()))
for genre in row["Genre"].split(","):
self.__dataset_of_genres.add(Genre(genre.strip()))
index += 1
def parseMoviesAndRatings():
inputFile = open("../dataset/movie_rating.txt")
lineString = inputFile.readline()
movieRatingVector = np.zeros(0)
global missing_count
while(lineString):
(movie_title,movie_year) = Movie.separateTitleAndYear(lineString.split("\t\t")[0].strip())
movie_rating = float(lineString.split("\t\t")[1].strip())
if(not movie_title in Movie.hashByTitle):
movie = Movie(movie_title,rating=movie_rating)
missing_count = missing_count + 1
else:
movie = Movie.hashByTitle[movie_title]
movie.rating = movie_rating
movieRatingVector = np.append(movieRatingVector,movie_rating)
lineString = inputFile.readline()
if(movie.id%100000 == 0):
print movie
arrayToSave = np.asarray(movieRatingVector)
np.savetxt("./movie_rating_vector.csv", arrayToSave, delimiter=",")
def test_rating_out_bounds_upper(movie):
movie = Movie("Moana", 2016)
with pytest.raises(ValueError):
movie.rating = 10.5
def test_rating_type(movie):
movie = Movie("Moana", 2016)
with pytest.raises(ValueError):
movie.rating = "banana"
def test_rating(movie):
movie = Movie("Moana", 2016)
movie.rating = 9.5
assert movie.rating == 9.5