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, you know, evaluate them
evaluator = Evaluator(evaluationData, rankings)
contentKNN = ContentKNNAlgorithm()
evaluator.AddAlgorithm(contentKNN, "ContentKNN")
# Just make random recommendations
Random = NormalPredictor()
evaluator.AddAlgorithm(Random, "Random")
evaluator.Evaluate(True)
evaluator.SampleTopNRecs(ml)
# Load up common data set for the recommender algorithms (ml, evaluationData, rankings) = LoadMovieLensData() #(azr,evaluationData, rankings) = LoadAmazonData() # Construct an Evaluator to, you know, evaluate them ######### our evaluation code ######### evaluator = Evaluator(evaluationData, rankings, doTopN=True) SVDAlgorithm = SVDpp() evaluator.AddAlgorithm(SVDAlgorithm, "SVD++") #SVDppAlgorithm = SVDpp() #evaluator.AddAlgorithm(SVDppAlgorithm, "SVD++") evaluator.Evaluate() #evaluator.SampleTopNRecs(ml) ### buit-in fonction for evaluation #######" #algo = SVD() # Run 3-fold cross-validation and print results #cross_validate(algo, evaluationData, measures=['RMSE', 'MAE'], cv=3, verbose=True) # Retrieve the trainset. #trainset = evaluationData.build_full_trainset() # Build an algorithm, and train it. #algo = KNNBasic()
