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)