def LoadMovieLensData(): ml = MovieLens() print("Loading movie ratings...") data = ml.loadMovieLensLatestSmall() print( "\nComputing movie popularity ranks so we can measure novelty later..." ) rankings = ml.getPopularityRanks() return (ml, data, rankings) np.random.seed(0) random.seed(0) # load up common data set for the recommender algorithms (ml, evaluationData, rankings) = LoadMovieLensData() # construct an evaluator to evaluator = Evaluator(evaluationData, rankings) contentKNN = ContentKNNAlgorithm() evaluator.addAlgorithm(contentKNN, "ContentKNN") # just make random recommendations Random = NormalPredictor() evaluator.addAlgorithm(Random, "Random") evaluator.evaluate(False) evaluator.sampleTopNRecs(ml)