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import numpy as np
import pandas as pd
import random
from sklearn.feature_extraction.text import TfidfVectorizer #for TF-IDF
from sklearn.metrics.pairwise import linear_kernel
W_belongs_to_collection = 0.16
W_genres = 0.10
W_original_language = 0.01
W_title = 0.11
W_overview = 0.08
W_production_countries = 0.01
W_production_companies = 0.02
W_tagline = 0.10
W_keywords = 0.10
W_Director = 0.03
W_Writer = 0.02
W_Cast = 0.02
W_Top_Cast = 0.03
W_budget_categorized = 0.01
W_length = 0.02
W_average_vote_categorized = 0.08
W_count_vote_categorized = 0.07
W_era = 0.03
tfidf = TfidfVectorizer(stop_words='english') #defining tfidf model which removes additional words such as 'the', 'or', 'in'
df_popular_popularity = pd.read_csv('df_popular_popularity.csv', low_memory = False)
df_popular_WR_Q = pd.read_csv('df_popular_WR_Q.csv', low_memory = False)
df_cbf_Q = pd.read_csv('df_cbf_Q.csv', low_memory = False)
df_cbf_Q['belongs_to_collection'] = df_cbf_Q['belongs_to_collection'].fillna("")
df_cbf_Q['overview'] = df_cbf_Q['overview'].fillna("")
df_cbf_Q['spoken_languages'] = df_cbf_Q['spoken_languages'].fillna("")
df_cbf_Q['tagline'] = df_cbf_Q['tagline'].fillna("")
df_cbf_Q['Director'] = df_cbf_Q['Director'].fillna("")
df_cbf_Q['Writer'] = df_cbf_Q['Writer'].fillna("")
df_cbf1 = df_cbf_Q
df_cbf2 = df_cbf_Q
df_cbf_tfidf_belongs_to_collection = tfidf.fit_transform(df_cbf1['belongs_to_collection'])
cosine_sim_belongs_to_collection = linear_kernel(df_cbf_tfidf_belongs_to_collection, df_cbf_tfidf_belongs_to_collection)
df_cbf_tfidf_genres = tfidf.fit_transform(df_cbf1['genres'])
cosine_sim_genres = linear_kernel(df_cbf_tfidf_genres, df_cbf_tfidf_genres)
df_cbf_tfidf_original_language = tfidf.fit_transform(df_cbf1['original_language'])
cosine_sim_original_language = linear_kernel(df_cbf_tfidf_original_language, df_cbf_tfidf_original_language)
df_cbf_tfidf_title = tfidf.fit_transform(df_cbf1['title'])
cosine_sim_title = linear_kernel(df_cbf_tfidf_title, df_cbf_tfidf_title)
df_cbf_tfidf_overview = tfidf.fit_transform(df_cbf1['overview'])
cosine_sim_overview = linear_kernel(df_cbf_tfidf_overview, df_cbf_tfidf_overview)
df_cbf_tfidf_pruduction_countries = tfidf.fit_transform(df_cbf1['production_countries'])
cosine_sim_pruduction_countries = linear_kernel(df_cbf_tfidf_pruduction_countries, df_cbf_tfidf_pruduction_countries)
df_cbf_tfidf_pruduction_companies = tfidf.fit_transform(df_cbf1['production_companies'])
cosine_sim_pruduction_companies = linear_kernel(df_cbf_tfidf_pruduction_companies, df_cbf_tfidf_pruduction_companies)
df_cbf_tfidf_tagline = tfidf.fit_transform(df_cbf1['tagline'])
cosine_sim_tagline = linear_kernel(df_cbf_tfidf_tagline, df_cbf_tfidf_tagline)
df_cbf_tfidf_keywords = tfidf.fit_transform(df_cbf1['keywords'])
cosine_sim_keywords = linear_kernel(df_cbf_tfidf_keywords, df_cbf_tfidf_keywords)
df_cbf_tfidf_Director = tfidf.fit_transform(df_cbf1['Director'])
cosine_sim_Director = linear_kernel(df_cbf_tfidf_Director, df_cbf_tfidf_Director)
df_cbf_tfidf_Writer = tfidf.fit_transform(df_cbf1['Writer'])
cosine_sim_Writer = linear_kernel(df_cbf_tfidf_Writer, df_cbf_tfidf_Writer)
df_cbf_tfidf_Cast = tfidf.fit_transform(df_cbf1['Cast'])
cosine_sim_Cast = linear_kernel(df_cbf_tfidf_Cast, df_cbf_tfidf_Cast)
df_cbf_tfidf_Top_Cast = tfidf.fit_transform(df_cbf1['Top Cast'])
cosine_sim_Top_Cast = linear_kernel(df_cbf_tfidf_Top_Cast, df_cbf_tfidf_Top_Cast)
df_cbf_tfidf_budget_categorized = tfidf.fit_transform(df_cbf1['budget_categorized'])
cosine_sim_budget_categorized = linear_kernel(df_cbf_tfidf_budget_categorized, df_cbf_tfidf_budget_categorized)
df_cbf_tfidf_Length = tfidf.fit_transform(df_cbf1['Length'])
cosine_sim_Length = linear_kernel(df_cbf_tfidf_Length, df_cbf_tfidf_Length)
df_cbf_tfidf_average_vote_categorized = tfidf.fit_transform(df_cbf1['average_vote_categorized'])
cosine_sim_average_vote_categorized = linear_kernel(df_cbf_tfidf_average_vote_categorized, df_cbf_tfidf_average_vote_categorized)
df_cbf_tfidf_count_vote_categorized = tfidf.fit_transform(df_cbf1['count_vote_categorized'])
cosine_sim_count_vote_categorized = linear_kernel(df_cbf_tfidf_count_vote_categorized, df_cbf_tfidf_count_vote_categorized)
df_cbf_tfidf_era = tfidf.fit_transform(df_cbf1['era'])
cosine_sim_era = linear_kernel(df_cbf_tfidf_era, df_cbf_tfidf_era)
cosin_sim_final = np.multiply(cosine_sim_belongs_to_collection, W_belongs_to_collection) + np.multiply(cosine_sim_genres, W_genres) + np.multiply(cosine_sim_original_language, W_original_language) + np.multiply(cosine_sim_title, W_title) + np.multiply(cosine_sim_overview, W_overview) + np.multiply(cosine_sim_pruduction_countries, W_production_countries) + np.multiply(cosine_sim_pruduction_companies, W_production_companies) + np.multiply(cosine_sim_tagline, W_tagline) + np.multiply(cosine_sim_keywords, W_keywords) + np.multiply(cosine_sim_Director, W_Director) + np.multiply(cosine_sim_Writer, W_Writer) + np.multiply(cosine_sim_Cast, W_Cast) + np.multiply(cosine_sim_Top_Cast, W_Top_Cast) + np.multiply(cosine_sim_budget_categorized, W_budget_categorized) + np.multiply(cosine_sim_Length, W_length) + np.multiply(cosine_sim_average_vote_categorized, W_average_vote_categorized) + np.multiply(cosine_sim_count_vote_categorized, W_count_vote_categorized) + np.multiply(cosine_sim_era, W_era)
df_cbf2_indices = pd.Series(df_cbf2.index, index=df_cbf2['title'])
def final_recommender_hot_picks_now(Watched_movies_list):
recommended_list = []
for i in range(10):
recommended_list.append(df_popular_popularity.loc[i, 'title'])
return recommended_list
#recommend based on weighted ratings
def final_recommender_hot_picks_of_all_time(Watched_movies_list):
recommended_list = []
for i in range(10):
recommended_list.append(df_popular_WR_Q.loc[i, 'title'])
return recommended_list
#recommend based on content based
def final_recommender_for_you(Watched_movies_list):
recommended_list = []
if len(Watched_movies_list) < 3:
for i in range(10):
recommended_list.append(df_popular_WR_Q.loc[i, 'title'])
else:
recently_watched = Watched_movies_list[-3:]
for i in range(len(recently_watched)):
y = df_cbf2_indices[recently_watched[i]]
z = list(enumerate(cosin_sim_final[y]))
z = sorted(z, key=lambda x: x[1], reverse=True)
z = z[1:16]
k = [i[0] for i in z]
for j in k:
recommended_list.append(df_cbf2.loc[j, 'title'])
recommended_list = list(set(recommended_list))
random.shuffle(recommended_list)
recommended_list = recommended_list[:15]
return recommended_list
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