from algorithm import Algorithm
from data_Model import DataModel
# intialize classes
alg = Algorithm()
dat = DataModel()
# load file
dat.load_from_file("data.csv")
# split data set
dat.split_train_validate_test()
# get training, validation and test sets
training_data = dat.get_training_data()
validation_data = dat.get_validation_data()
test_data = dat.get_test_data()
# train data
alg.train(training_data, dat.get_size())
#calculate errors
alg.rmse(validation_data, dat.get_mean(), dat.get_biases())
alg.rmse(test_data, dat.get_mean(), dat.get_biases())
# run recommender system
rec = Recsys(alg.get_user_matrix(), alg.get_item_matrix(), dat.get_mean(), *dat.get_biases())
rec.predict(5, 600) # predict the user #5's rating for item #600
rec.similar(5, 10) # prints out the similarity between items #5 and #10
