def test_init(self): df = pd.DataFrame({ 'time': [30, 20, 10], 'item': [3, 2, 1], 'duration': [10, 10, 10] }) a = AvailabilityFilter(df) #todo: needs experiment environment r = rs.RecDat() r.time = 21
def test_init(self): df = pd.DataFrame({ 'time': [30, 20, 10], 'item': [3, 2, 1], 'duration': [10, 10, 10] }) a = prs.AvailabilityFilter(df) r = rs.RecDat() r.time = 21 a.run(r)
def predict(model, user, item): rd = rs.RecDat() rd.user = user rd.item = item return model.prediction(rd)
import alpenglow.Getter as rs data = pd.read_csv("../../python/test_alpenglow/test_data_4", sep=' ', header=None, names=['time', 'user', 'item', 'id', 'score', 'eval']) model = FactorModel( factor_seed=254938879, dimension=10, negative_rate=9, number_of_iterations=20, ) model.fit(data) model.model.write("output_file") #writes model to output_file rd = rs.RecDat() rd.user = 3 rd.item = 5 print("prediction for user=3, item=5:", model.model.prediction(rd)) #model2 must have the same dimension model2 = FactorModel( factor_seed=1234, dimension=10, negative_rate=0, number_of_iterations=0, ) #to create the inner model but avoid training, we need to run fit() #on an empty dataset data2 = pd.DataFrame(columns=['time', 'user', 'item']) model2.fit(data2)