def get_meta(title, m_id):
rec = Recommendation()
rec.filter_genres()
rec.filter_productions()
df_movies = rec.md
df_credits = pd.read_csv(PATH_CREDITS)
df_imdb_link = pd.read_csv(PATH_MOVIELENS_TO_TMDB)
attributes = [
"id", "original_title", "genres", "homepage", "overview",
"release_date", "production_companies", "runtime", "tagline",
"vote_average", "vote_count"
]
df_title = df_movies.iloc[df_movies.index[df_movies["original_title"] ==
title][0]][attributes]
df_crew = df_credits.iloc[df_credits.index[df_credits["title"] == title]
[0]][["cast", "crew"]]
cast = [cast["name"] for cast in literal_eval(df_crew["cast"])[0:5]]
crew = [
crew["name"] for crew in literal_eval(df_crew["crew"])
if crew["job"] in ["Director"]
]
try:
imdb_link = str(df_imdb_link.iloc[df_imdb_link.index[
df_imdb_link["tmdbId"] == int(m_id)][0]]["imdbId"])[:-2]
imdb_link = ("https://www.imdb.com/title/tt" + "0" *
(IMDB_ID_LEN - len(imdb_link)) + imdb_link)
except:
imdb_link = "https://www.imdb.com/search/title/?title=" + title
return df_title, cast, crew, imdb_link
def main():
if (sys.argv[-1] == '--collect'):
url_pois = 'https://pt.foursquare.com/explore?mode=url&ne=-29.358988%2C-50.837817&q=Sele%C3%A7%C3%B5es%20principais&sw=-29.41889%2C-50.887942'
url_city = 'http://www.dataviva.info/pt/location/5rs020102'
e = Extraction(url_pois, url_city)
e.poi_data_extraction()
e.city_data_extraction()
# Gera relatório do dataset
file = 'foursquare_data.csv'
df = pd.read_csv(file, parse_dates=True, encoding='UTF-8')
profile = pandas_profiling.ProfileReport(df)
profile.to_file(outputfile='dataset_report.html')
P = Process(file)
df = P.method()
df_report = pd.read_csv('preprocessed.csv',
parse_dates=True,
encoding='UTF-8')
profile = pandas_profiling.ProfileReport(df_report)
profile.to_file(outputfile='preprocessed_dataset_report.html')
R = Recommendation(df)
R.pattern_recommendation()
R.new_recommendation()
R.compare()
# R.test_rec()
ont = Ontology()
ont.write_owl()
def test_recommendation(self):
recommendation = Recommendation(self.client)
recommendation.forward_to("book1", "book2")
recommendation.forward_to("book1", "book2")
recommendation.forward_to("book1", "book2")
recommendation.forward_to("book1", "book3")
recommendation.forward_to("book1", "book3")
recommendation.forward_to("book1", "book4")
recommendation.forward_to("book1", "book5")
recommendation.forward_to("book1", "book6")
recommendation.forward_to("book1", "book6")
assert recommendation.get_recommendations("book1", 1, 5) == [
"book2",
"book6",
"book3",
"book5",
"book4",
]
assert recommendation.get_recommendations("book1",
1,
5,
with_time=True) == [
("book2", 3),
("book6", 2),
("book3", 2),
("book5", 1),
("book4", 1),
]
def home():
if "recommend" in request.args:
try:
title = request.args["recommend"]
rec = ContentBased()
did_you_mean = False
df = rec.recommend(title,
DEFAULT_LIMIT,
full_search=True,
keywords_and_desc=False,
critics=False)
poster_paths = get_poster_paths(df["id"].tolist(),
df["original_title"].tolist())
if rec.changed_title != title and rec.changed_title != str():
did_you_mean = True
else:
rec.changed_title = title
rec_title_meta = get_meta(rec.changed_title, None)
rec_id = rec_title_meta[0]["id"]
return render_template('recommendations.html',
titles=df["original_title"].tolist(),
images=poster_paths,
votes=df["vote_average"].tolist(),
m_id=df["id"].tolist(),
rec_title=rec.changed_title,
rec_id=rec_id,
did_you_mean=did_you_mean)
except:
abort(404)
elif "genres" in request.args:
genre = request.args["genres"]
if genre == "All":
genre = None
offset = int(request.args["offset"])
gen_rec = Recommendation()
gen_rec.filter_genres()
df = gen_rec.top_movies(gen_rec.md,
percentile=0.85,
limit=DEFAULT_LIMIT,
offset=offset,
genre=genre)
poster_paths = get_poster_paths(df["id"].tolist(),
df["original_title"].tolist())
return render_template('recommendations.html',
titles=df["original_title"].tolist(),
images=poster_paths,
votes=df["vote_average"].tolist(),
m_id=df["id"].tolist(),
rec_title=request.args["genres"],
offset=offset,
next_offset=offset + DEFAULT_LIMIT,
prev_offset=offset - DEFAULT_LIMIT,
rec_id=None,
did_you_mean=None)
else:
return render_template('homepage.html')
def menu():
recommendation = Recommendation()
name_movie = None
while name_movie != "":
print("#################################")
print("RECOMMENDATION CONTENT BASED IMDB")
name_movie = input("Enter name movie: ")
if name_movie != "":
recommendation.recommendation_movies(name_movie)
def compute_recommendation(user):
"""
Compute the recommendation for a user. Return the recommended events
"""
recommendation = Recommendation()
recommendation.init_basic_matrix()
recommendation.init_frequency_matrix()
recommendation.compute_matrix()
return get_features_event(recommendation.compute_recommended_events(user))
def __init__(self):
self.recommendation = Recommendation()
def recommend(data):
recs = Recommendation(data, 5, 5)
return (recs.allRecommendations())
def setUp(self):
clean()
c = Category(name="C", external_id="1")
w = Weight(name="t", weight=1.0)
w.save()
c.save()
for i in range(0, 4):
User(name="U" + str(i), external_id=str(i),
token="t" + str(i)).save()
users = User.objects.all()
for i in range(0, 4):
Feature(name="F" + str(i)).save()
features = Feature.objects.all()
for i in range(0, 6):
Event(category=c,
external_id=str(i),
name='E' + str(i),
website='',
description='').save()
events = Event.objects.all()
Rating(event=events[1], user=users[0], rating=4).save()
Rating(event=events[4], user=users[0], rating=5).save()
Rating(event=events[1], user=users[1], rating=3).save()
Rating(event=events[3], user=users[1], rating=4).save()
Rating(event=events[5], user=users[2], rating=4).save()
Rating(event=events[0], user=users[3], rating=5).save()
Rating(event=events[2], user=users[3], rating=3).save()
EventFeature(event=events[0], feature=features[1], tf_idf=1,
weight=w).save()
EventFeature(event=events[1], feature=features[0], tf_idf=1,
weight=w).save()
EventFeature(event=events[1], feature=features[1], tf_idf=1,
weight=w).save()
EventFeature(event=events[2], feature=features[1], tf_idf=1,
weight=w).save()
EventFeature(event=events[2], feature=features[2], tf_idf=1,
weight=w).save()
EventFeature(event=events[3], feature=features[1], tf_idf=1,
weight=w).save()
EventFeature(event=events[4], feature=features[0], tf_idf=1,
weight=w).save()
EventFeature(event=events[4], feature=features[1], tf_idf=1,
weight=w).save()
EventFeature(event=events[4], feature=features[2], tf_idf=1,
weight=w).save()
EventFeature(event=events[5], feature=features[3], tf_idf=1,
weight=w).save()
from recommendation import Recommendation
self.recommendation = Recommendation()
def recommend(self,
title,
limit,
critics=False,
full_search=False,
use_pickle=True,
keywords_and_desc=False):
"""
param: title - movie title (as in TMDB dataset)
limit - no. of movies to display
critics - True - will display critically acclaimed movies
False - will not sort movies on basis of their imdb rankings
(DEFAULT - False)
full_search - True - will search using cast, crew, keywords
and genre as metadata
False - will search using overview and tagline
as metadata
(DEFAULT - False)
use_pickle - True - will use pickled results
False - will compute the results from scratch
(DEFAULT - True)
keywords_and_desc - True - will merge results of keywords
and description
False - will not merge results of keywords
and description
return: pandas DataFrame object with attributes -
original_title, id, vote_average, vote_count, popularity, release_date
"""
rec = Recommendation()
rec.filter_genres()
title_index = self.verify_title(rec.md, title)
if keywords_and_desc:
if isfile(PATH_PICKLE_KEYWORDS) and isfile(
PATH_PICKLE_DESC) and use_pickle:
df_keywords = pd.read_pickle(PATH_PICKLE_KEYWORDS)
df_desc = pd.read_pickle(PATH_PICKLE_DESC)
rec_matrix_keywords = self.countvectorize(df_keywords)
rec_matrix_desc = self.tfidf(df_desc)
rec_matrix = rec_matrix_keywords + rec_matrix_desc
df = df_keywords
elif full_search:
if isfile(PATH_PICKLE_KEYWORDS) and use_pickle:
df = pd.read_pickle(PATH_PICKLE_KEYWORDS)
else:
df = self.make_keywords(rec.md)
df.to_pickle(PATH_PICKLE_KEYWORDS)
rec_matrix = self.countvectorize(df)
else:
if isfile(PATH_PICKLE_DESC) and use_pickle:
df = pd.read_pickle(PATH_PICKLE_DESC)
else:
df = self.make_desc(rec.md)
df.to_pickle(PATH_PICKLE_DESC)
rec_matrix = self.tfidf(df)
rec_movie = rec_matrix[title_index]
ids = rec_movie.argsort()[::-1][1:SCAN_SIZE + 1]
if critics:
return rec.top_movies(df.iloc[ids],
percentile=0.50,
limit=limit,
offset=0)
else:
return df.iloc[ids[:limit]][[
"original_title",
"id",
"vote_average",
"vote_count",
"popularity",
"release_date",
]]