addinh helper file

Helpers.py

@@ -0,0 +1,106 @@
+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 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
+
+
+
+
+
+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)
+

requirements.txt

@@ -3,3 +3,4 @@ requests==2.31.0
 pandas==2.2.2
 pickleshare==0.7.5
 gdown==5.1.0
+xgboost==2.0.3