mf_surprise_example.py

import surprise

# there is pandas dataframe that contains the different ratings between user and item , indexed

# Build a reader
reader = surprise.Reader(rating_scale=(dfp_actions["rating"].min(),dfp_actions["rating"].max()))

#_id column are index id of the user and item
surprise_data = surprise.Dataset.load_from_df(dfp_actions[["user_id", "item_id", "rating"]], reader = reader)
train_set = surprise_train.build_full_trainset()

params = {
    "n_factors" : 2,
    "n_epochs" : 21,
    "biased" : True,
    "reg_pu" : 36,
    'reg_qi' : 57
}

model = surprise.prediction_algorithms.matrix_factorization.NMF(**params)
model = model.fit(train_set)

# Get the rating for a paur user item
model.predict(user_id, item_id).est