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ProductionRecommend.py
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ProductionRecommend.py
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# -*- coding: utf-8 -*-
"""
Created on Fri May 4 16:25:39 2018
@author: Frank
"""
from MovieLens import MovieLens
from ContentKNNAlgorithm import ContentKNNAlgorithm
from Evaluator import Evaluator
from surprise import NormalPredictor
import random
import numpy as np
def LoadMovieLensData():
ml = MovieLens()
ml.actualizarRatings()
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)
recomendations = evaluator.globalRecommendation()
print("get_top_n")
evaluator.get_top_n(recomendations)
#evaluator.SampleTopNRecs(ml,268,10)