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| import pandas as pd | |
| from sklearn.metrics.pairwise import cosine_similarity | |
| from sklearn.metrics import mean_squared_error | |
| from xgboost import XGBRegressor | |
| def train_model(data,user_id, test=None, eval = False): | |
| # select only user data | |
| train_user = data[data['userId']==user_id] | |
| X_train = train_user.drop(columns=['userId','rating', 'Train', 'title']) | |
| y_train = train_user['rating'] | |
| model = XGBRegressor() | |
| model.fit(X_train,y_train) | |
| if eval: | |
| test_user = test[test['userId']== user_id] | |
| X_test = test_user.drop(columns=['userId','rating', 'Train', 'title']) | |
| y_test = test_user['rating'] | |
| y_pred = model.predict(X_test) | |
| rmse = np.sqrt(mean_squared_error(y_test, y_pred)) | |
| print(f'RMSE: {rmse:.4f}') | |
| # Model evaluation | |
| # print("Predected rating:", y_pred) | |
| # print("Actual rating:",y_test) | |
| # print(X_test) | |
| return model | |
| def get_user_recommendation_XGBoost(all_moves,model, user_id, n=10): | |
| # get all movies that the user has not seen | |
| user_seen_movies = all_moves[all_moves['userId'] == user_id]['title'] | |
| user_unseen_movies = all_moves[~all_moves['title'].isin(user_seen_movies)] | |
| # drop duplicates | |
| user_unseen_movies = user_unseen_movies.drop_duplicates(subset=['title']) | |
| # make predictions | |
| user_unseen_movies['Pred_rating'] = model.predict(user_unseen_movies.drop(columns=['userId', 'rating', 'Train', 'title'])) | |
| # only return movies with more than 100 ratings | |
| # get top 10 recommendations | |
| recommendations = user_unseen_movies.sort_values(by='Pred_rating', ascending=False).head(n)['title'] | |
| return recommendations ,user_seen_movies | |
| def seen_movies(dataBase,user_id): | |
| return dataBase[dataBase['userId'] == user_id]['title'].values | |
| def get_user_recommendation(DataBase, Matrix,user_id,l=10): | |
| user = Matrix[user_id] | |
| user = user.sort_values(ascending=False) | |
| # now we have a series of user similarities | |
| # we only want to recommend movies that the user has not seen | |
| # so we need to filter out movies that the user has seen | |
| user_seen_movies = DataBase[DataBase['userId'] == user_id]['title'] | |
| # Now we loop through user and get top 10 recommendations | |
| recommendations = [] | |
| print(len(user.index)) | |
| for U in user.index[1:10]: | |
| # get all rated movies by user U | |
| movies = DataBase[DataBase['userId'] == U]['title'] | |
| movies = movies[~movies.isin(user_seen_movies)] | |
| # get all movies that U has rated 4 or higher | |
| movies = movies[DataBase['rating'] >= 4] | |
| # sort by rating | |
| movies = movies.sort_values(ascending=False) | |
| for movie in movies[:4]: | |
| if movie not in recommendations: | |
| recommendations.append(movie) | |
| if len(recommendations) >= l: | |
| break | |
| return recommendations | |
| def get_recommendation_item(dataBase,matrix, movie_name, n=10): | |
| similar_scores = matrix[movie_name] | |
| similar_scores = similar_scores.sort_values(ascending=False) | |
| # only return movies with more than 100 ratings | |
| similar_scores = similar_scores[similar_scores.index.isin(dataBase[dataBase['number_of_ratings'] > 100].index)][:n] | |
| return similar_scores | |
| if __name__ == '__main__': | |
| import pickle | |
| def load_similarity_matrix(path): | |
| with open(path, 'rb') as f: | |
| similarity_df = pickle.load(f) | |
| return similarity_df | |
| # Load the data | |
| DataBaseCSV = r"D:\Study\ITI\Recommender Systems\Final\Movies-Recommender-System\Data\XGBoost_database.csv" | |
| DataBase = pd.read_csv(DataBaseCSV) | |
| # Load the similarity matrix | |
| MatrixCSV = r"D:\Study\ITI\Recommender Systems\Final\Movies-Recommender-System\Models\user_based_matrix.pkl" | |
| Matrix = load_similarity_matrix(MatrixCSV) | |
| recommendations = get_user_recommendation(DataBase, Matrix,1) | |
| print(recommendations) | |