def recommendations():
rec = Recommender()
rec.import_cosine_sim_from_pkl()
movies_enhanched = []
msg = ''
if request.method == "POST":
tconst_id = request.form['tconst']
dbhandler = DbHandler()
movie_title = dbhandler.exec_sql_cmd(
f"SELECT primaryTitle FROM title_basics WHERE tconst = '{tconst_id}'"
)
msg = f"Recommendations for '{movie_title[0][0]}'"
print(msg)
movies = rec.get_recommendation_titles_from_tconst(tconst=tconst_id,
limit=20)
for movie in movies[1:]:
movies_enhanched.append({
"tconst":
movie[0],
"title":
movie[1],
"imdburl":
f"https://www.imdb.com/title/{movie[0]}/"
})
return render_template("recommendations.html",
name="Apostolis",
movies=movies_enhanched,
result_message=msg)
def test_get_titles_from_tconst(self):
"""
Test the get_titles_from_tconst_list function
Grab the recommendation titles for a random movie and crosscheck with the titles in the title_basics table
:return:
"""
rec = Recommender()
rec.import_cosine_sim_from_pkl()
sample_tconst = random.choice(list(
rec.cosine_sim.tconst.values)) # grab a random movie to test
sample_recommendations_tconst = rec.get_recommendation_from_tconst(
sample_tconst)
sample_titles = get_titles_from_tconst_list(
sample_recommendations_tconst)
recommendation_titles = [result[1] for result in sample_titles]
# Make sure the titles returned are a subset of the titles in title_basics table
dbhandler = DbHandler()
dbhandler.connect()
# Use a join to limit the results only on those that interest us
all_titles = dbhandler.conn.execute(
text(
"SELECT tconst,primaryTitle from title_basics NATURAL JOIN title_keywords"
))
# Convert the result of the query to a df
all_titles_df = pd.DataFrame(data=[row for row in all_titles],
columns=['tconst', 'primaryTitle'])
# Find the titles that exist in both the df from the db and the results from the recommender
same_titles = all_titles_df.loc[all_titles_df['tconst'].isin(
sample_recommendations_tconst)]['primaryTitle']
# Make sure we found all the movie titles
assert len(recommendation_titles) == same_titles.size
def test_cosine_sim_import(self):
"""
Test that the import correctly initializes the self.cosine_sim dataframe
:return:
"""
rec = Recommender()
rec.import_cosine_sim_from_pkl()
print(rec.cosine_sim.head())
assert type(rec.cosine_sim) == pd.DataFrame
def test_get_recommendation_with_import(self):
rec = Recommender()
rec.import_cosine_sim_from_pkl()
sample_tconst = random.choice(list(
rec.cosine_sim.tconst.values)) # grab a random movie to test
print(f"Running test_get_recommendation for tconst: {sample_tconst}")
sample_recommendations = rec.get_recommendation_from_tconst(
sample_tconst)
assert sample_recommendations[
0] == sample_tconst # the best fit should be the movie itself
def test_cosine_sim_import_auto_create(self):
"""
Test that if auto_create is set then the file is successfully created
:return:
"""
rec = Recommender()
wrong_path = os.path.join(DATA_PATH,
'cosine_sim/wrong_file_auto_create.pkl')
rec.import_cosine_sim_from_pkl(pkl_path=wrong_path, auto_create=True)
assert os.path.exists(wrong_path)
os.remove(wrong_path)
def test_cosine_sim_import_error(self):
"""
Test that correct Exception is thrown in case of wrong filepath import
:return:
"""
rec = Recommender()
wrong_path = os.path.join(DATA_PATH, 'cosine_sim/wrong_file.pkl')
try:
rec.import_cosine_sim_from_pkl(pkl_path=wrong_path)
assert False
except FileNotFoundError:
assert True
def test_recommendation_limit_works(self):
"""
Test that the limit param in get_recommendation_from_tconst works as intended
:return:
"""
limit = random.randint(1, 50)
print(f"Testing for limit: {limit}")
rec = Recommender()
rec.import_cosine_sim_from_pkl()
sample_tconst = random.choice(list(
rec.cosine_sim.tconst.values)) # grab a random movie to test
sample_recommendations_tconst = rec.get_recommendation_from_tconst(
sample_tconst, limit=limit)
assert len(sample_recommendations_tconst) == limit
def main():
rec = Recommender()
rec.import_cosine_sim_from_pkl()
tprint("Movie Recommender")
while True:
ptable = PrettyTable()
tconst = input("Enter a tconst id to get recommendations or type exit to exit: ")
if tconst == 'exit':
sys.exit(0)
recommendation_titles = rec.get_recommendation_titles_from_tconst(tconst)[1:]
ptable.field_names = ['Tconst', 'Title']
ptable.add_rows(recommendation_titles)
print(ptable)
def test_cosine_sim_export(self):
"""
Test that export functionality after create_count_vectorizer() method is called works
:return:
"""
temp_test_path = os.path.join(DATA_PATH, 'cosine_sim/test_file.pkl')
rec = Recommender()
rec.create_cosine_sim() # Initialize self.cosine_sim
rec.export_cosine_sim_to_pkl(temp_test_path)
assert os.path.exists(temp_test_path)
os.remove(temp_test_path) # Remove test temp file
return
def test_wrong_tconst_str(self):
"""
Test that if a wrong tconst value is provided in the get_recommendation_from_tconst method
an Exception is raised
:return:
"""
rec = Recommender()
rec.import_cosine_sim_from_pkl()
try:
rec.get_recommendation_from_tconst('wrong_tconst')
# If Exception is raised then True
except Exception as e:
print(e)
assert True
return
assert False, "Exception not thrown for wrong tconst"
def test_recommender_init(self):
try:
rec = Recommender()
except Exception as e:
assert False, e