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import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from scipy import stats
from ast import literal_eval
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.metrics.pairwise import linear_kernel, cosine_similarity
from nltk.stem.snowball import SnowballStemmer
from nltk.stem.wordnet import WordNetLemmatizer
from nltk.corpus import wordnet
from surprise import Reader, Dataset, SVD
from surprise import NormalPredictor
from surprise.model_selection import cross_validate
from Levenshtein import distance
import warnings; warnings.simplefilter('ignore')
class Recommender_Model:
def __init__(self):
self.cleaned_data = None
self.cleaned_data1 = None
self.cosine_sim = None
self.titles = None
self.indices = None
self.index_movie_id = None
self.SVD = None
self.id_map = None
self.preprocessing()
def preprocessing(self):
movie_data = pd.read_csv("Datasets/movies_metadata.csv")
self.user_rating = pd.read_csv("Datasets/ratings_small.csv")
vote_counts = movie_data[movie_data['vote_count'].notnull()]['vote_count'].astype('int')
vote_averages = movie_data[movie_data['vote_average'].notnull()]['vote_average'].astype('int')
average_vote_score = vote_averages.mean()
percentile_80_cutoff = np.percentile(vote_counts,80)
cleand_data1 = movie_data[movie_data['vote_average']>=average_vote_score]
cleand_data1 = cleand_data1[cleand_data1['vote_count']>percentile_80_cutoff]
movie_data = movie_data.drop([19730, 29503, 35587])
links_small = pd.read_csv('Datasets/links_small.csv')
only_subset_movies = list(links_small['tmdbId'])
cleand_data1['id'] = cleand_data1['id'].astype('int')
self.cleaned_data = cleand_data1[cleand_data1['id'].isin(only_subset_movies)]
self.cleaned_data['tagline'] = self.cleaned_data['tagline'].fillna('')
### genres
self.cleaned_data['genres'] = self.cleaned_data['genres'].apply(literal_eval)
self.cleaned_data['genres'] = self.cleaned_data['genres'].apply(lambda x : [i['name'] for i in x])
stemmer = SnowballStemmer('english')
self.cleaned_data['genres'] = self.cleaned_data['genres'].apply(lambda x: [stemmer.stem(i) for i in x])
self.cleaned_data['genres'] = self.cleaned_data['genres'].apply(lambda x: [str.lower(i.replace(" ", "")) for i in x])
self.cleaned_data['genres'] = self.cleaned_data['genres'].apply(lambda x : list(set(x)))
# original_language
self.cleaned_data['original_language'].unique()
credits = pd.read_csv('Datasets/credits.csv')
keywords = pd.read_csv('Datasets/keywords.csv')
self.cleaned_data = self.cleaned_data.merge(credits, on='id')
self.cleaned_data = self.cleaned_data.merge(keywords, on='id')
self.cleaned_data['keywords'] = self.cleaned_data['keywords'].apply(literal_eval)
self.cleaned_data['keywords'] = self.cleaned_data['keywords'].apply(lambda x : [i['name'] for i in x])
self.cleaned_data['keywords'] = self.cleaned_data['keywords'].apply(lambda x: [stemmer.stem(i) for i in x])
self.cleaned_data['keywords'] = self.cleaned_data['keywords'].apply(lambda x: [str.lower(i.replace(" ", "")) for i in x])
self.cleaned_data['keywords'] = self.cleaned_data['keywords'].apply(lambda x : list(set(x)))
self.cleaned_data['cast'] = self.cleaned_data['cast'].apply(literal_eval)
self.cleaned_data['crew'] = self.cleaned_data['crew'].apply(literal_eval)
self.cleaned_data['top_crew'] = self.cleaned_data['cast'].apply(lambda x : [i['name'] for i in x])
self.cleaned_data['top_crew'] = self.cleaned_data['top_crew'].apply(lambda x : x[:2])
self.cleaned_data['director'] = self.cleaned_data['crew'].apply(get_director)
imp_cols = ['tagline', 'genres' ,'original_language' ,'keywords' ,'top_crew','director']
self.cleaned_data1 = self.cleaned_data[imp_cols]
self.cleaned_data1['tagline'] = self.cleaned_data1['tagline'].apply(lambda x : [x])
self.cleaned_data1['original_language'] = self.cleaned_data1['original_language'].apply(lambda x : [x])
self.cleaned_data1['director'] = self.cleaned_data1['director'].apply(lambda x : [x])
self.cleaned_data1['combine'] = self.cleaned_data1['genres'] + self.cleaned_data1['original_language'] +\
self.cleaned_data1['keywords'] + self.cleaned_data1['top_crew'] +\
self.cleaned_data1['director']
self.cleaned_data1['combine'] = self.cleaned_data1['combine'].apply(lambda x: ' '.join(x))
count = CountVectorizer(analyzer='word',ngram_range=(1, 2),min_df=0.01, stop_words='english')
count_matrix = count.fit_transform(self.cleaned_data1['combine'])
self.cosine_sim = cosine_similarity(count_matrix, count_matrix)
self.cleaned_data = self.cleaned_data.reset_index()
self.titles = self.cleaned_data['title']
self.indices = pd.Series(self.cleaned_data.index, index=self.cleaned_data['title'])
self.index_movie_id = self.cleaned_data[['index','id']]
reader = Reader()
data = Dataset.load_from_df(self.user_rating[['userId', 'movieId', 'rating']], reader)
cross_validate(NormalPredictor(), data, cv=4)
self.SVD = SVD()
trainset = data.build_full_trainset()
self.SVD.fit(trainset)
self.id_map = pd.read_csv('Datasets/links_small.csv')[['movieId', 'tmdbId']]
self.id_map['tmdbId'] = self.id_map['tmdbId'].apply(convert_int)
self.id_map.columns = ['movieId', 'id']
self.id_map = self.id_map.merge(self.cleaned_data[['title', 'id']], on='id').set_index('title')
self.indices_map = self.id_map.set_index('id')
self.user_rating.drop(columns=['timestamp'], inplace=True)
def hybrid2(self, userId, title1, title2, title3, number_of_suggestions: int):
idx1 = self.indices[title1]
idx2 = self.indices[title2]
idx3 = self.indices[title3]
tmdbId1 = self.id_map.loc[title1]['id']
tmdbId2 = self.id_map.loc[title2]['id']
tmdbId3 = self.id_map.loc[title3]['id']
movie_id1 = self.id_map.loc[title1]['movieId']
movie_id2 = self.id_map.loc[title2]['movieId']
movie_id3 = self.id_map.loc[title3]['movieId']
if type(idx1) == pd.Series:
idx1 = idx1.iloc[0]
if type(idx2) == pd.Series:
idx2 = idx2.iloc[0]
if type(idx3) == pd.Series:
idx3 = idx3.iloc[0]
sim_scores = list(enumerate(self.cosine_sim[int(idx1)] + self.cosine_sim[int(idx2)] + self.cosine_sim[int(idx3)]))
sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)
sim_scores = sim_scores[1:56]
movie_indices = [i[0] for i in sim_scores]
movies = self.cleaned_data.iloc[movie_indices][['title', 'vote_count', 'vote_average', 'id']]
movies['est'] = movies['id'].apply(lambda x: self.SVD.predict(userId, self.indices_smoother(x)).est)
movies = movies.sort_values('est', ascending=False)
return movies.head(number_of_suggestions + 3)
def get_similar_users(self, movie_names):
movie_ids = [self.cleaned_data.loc[self.cleaned_data['title'] == movie]['id'].iloc[0] for movie in movie_names]
new_user_ratings = pd.DataFrame({
'userId': [max(self.user_rating['userId']) + 1] * len(movie_ids),
'movieId': movie_ids,
'rating': [5.0] * len(movie_ids)
})
merged_ratings = pd.concat([self.user_rating, new_user_ratings], ignore_index=True)
user_item_matrix = merged_ratings.pivot_table(index='userId', columns='movieId', values='rating', fill_value=0)
new_user_vector = user_item_matrix.loc[user_item_matrix.index[-1]].values.reshape(1, -1)
user_similarity = cosine_similarity(user_item_matrix.values[:-1], new_user_vector)
similar_users_indices = user_similarity.argsort(axis=0)[-10:].flatten()[::-1]
similar_users_similarity = user_similarity[similar_users_indices].flatten()
similar_users = user_item_matrix.iloc[similar_users_indices]
similar_users_df = pd.DataFrame({'userId': similar_users.index, 'Similarity': similar_users_similarity})
return similar_users_df
def suggest(self, movies, number_of_suggestions) -> pd.DataFrame:
similar_id = self.get_similar_users(movies).iloc[0, 0]
return self.hybrid2(similar_id, movies[0], movies[1], movies[2], number_of_suggestions=number_of_suggestions)
def get_movie_info(self, movie_name: str) -> dict:
movie_info = {}
record = self.cleaned_data[self.cleaned_data['title'] == movie_name]
movie_info['title'] = record['title'].to_numpy()[0]
movie_info['overview'] = record['overview'].to_numpy()[0]
movie_info['language'] = get_language_name(record['original_language'].to_numpy()[0])
movie_info['genres'] = record['genres'].to_numpy()
return movie_info
def indices_smoother(self, ids):
if type(self.indices_map.loc[ids]['movieId']) == pd.Series:
return self.indices_map.loc[ids]['movieId'].iloc[0]
else:
return self.indices_map.loc[ids]['movieId']
def find_nearest_movie(self, movie_name: str) -> tuple:
lowest_distance = float('inf')
closest_movie = ''
for movie in self.cleaned_data['title']:
current_distance = levenshtein_distance(movie_name.replace(" ", '').lower(), movie.replace(" ", '').lower())
if current_distance < lowest_distance:
lowest_distance = current_distance
closest_movie = movie
return (closest_movie, lowest_distance)
def get_director(x):
for i in x:
if i['job'] == 'Director':
return i['name']
return ""
def convert_int(x):
try:
return int(x)
except:
return np.nan
def levenshtein_distance(name1: str, name2: str) -> int:
return distance(name1, name2)
def get_language_name(code:str) -> str:
language_dict = {
'en': 'English',
'fr': 'French',
'es': 'Spanish',
'de': 'German',
'ja': 'Japanese',
'zh-cn': 'Chinese',
'ru': 'Russian',
'pt': 'Portuguese',
'ar': 'Arabic',
'hi': 'Hindi'
}
if code in language_dict:
return language_dict[code]
else:
return 'Unknown Language' |