import os
from surprise import KNNBaseline
import io
from surprise import Dataset

# step 1 : train model
def TrainModel():
    data = Dataset.load_builtin('ml-100k')
    trainset = data.build_full_trainset()
    # use pearson_baseline to compute similarity
    sim_options = {'name' : 'pearson_baseline', 'user_based' : False}
    algo = KNNBaseline(sim_options=sim_options)
    # train
    algo.fit(trainset)
    return algo

# step 2 : get id_name and name_id
def Get_Dict():
    file_name = os.path.expanduser('~/.surprise_data/ml-100k/ml-100k/u.item')
    id_name = {}
    name_id = {}
    with open(file_name, 'r', encoding='ISO-8859-1') as f:
        for line in f:
            line = line.split('|')
            id_name[line[0]] = line[1]
            name_id[line[1]] = line[0]
    return id_name, name_id

# step 3 : recommend movies based on the model
def RecommendMovie(movieName, algo, id_name, name_id, recommendNum):
    # get movie's raw id 
    raw_id = name_id[movieName]
    # translate raw_id to inner_id
    inner_id = algo.trainset.to_inner_iid(raw_id)
    # recommend movies
    recommendations = algo.get_neighbors(inner_id, recommendNum)
    # translate inner_id to raw_id
    raw_ids = [algo.trainset.to_raw_iid(inner_id) for inner_id in recommendations]
    # get movie name
    movies = [id_name[raw_id] for raw_id in raw_ids]
    for movie in movies:
        print(movie)

if __name__ == '__main__':
    id_name, name_id = Get_Dict()
    algo = TrainModel()
    showMovies = RecommendMovie('Toy Story (1995)', algo, id_name, name_id, 10)
 

Estimating biases using als…
Computing the pearson_baseline similarity matrix…
Done computing similarity matrix.
Beauty and the Beast (1991)
Raiders of the Lost Ark (1981)
That Thing You Do! (1996)
Lion King, The (1994)
Craft, The (1996)
Liar Liar (1997)
Aladdin (1992)
Cool Hand Luke (1967)
Winnie the Pooh and the Blustery Day (1968)
Indiana Jones and the Last Crusade (1989)