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- from surprise import Reader, Dataset, KNNBasic
- from surprise.model_selection import cross_validate
- import pandas as pd
- # Read the data from the test.csv file
- ratings = pd.read_csv('test.csv', sep=',');
- # Prepare the data to be used in Surprise
- ratings_dict = {'userid': list(ratings.userid),
- 'itemid': list(ratings.itemid),
- 'rating': list(ratings.rating)}
- df = pd.DataFrame(ratings_dict)
- reader = Reader(rating_scale=(0,5))
- data = Dataset.load_from_df(df[['userid', 'itemid', 'rating']], reader=reader)
- sim_options = {
- 'name': 'cosine',
- 'user_based': True
- }
- algo = KNNBasic(sim_options=sim_options)
- # Retrieve the trainset.
- trainset = data.build_full_trainset()
- algo.fit(trainset)
- # Predict
- print(algo.predict(1, 5, r_ui=None, verbose=True))
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