def test_explain_als(self):
sp = Splitter()
train, test = sp.split_leave_n_out(self.data, n=1)
self.assertTrue(self.als.fit(train))
recommender = Recommender(self.data, self.als)
recommendations = recommender.recommend_all()
evaluator = Evaluator(test)
evaluator.cal_hit_ratio(recommendations)
#explainer = ALSExplainer(self.als, recommendations, self.data)
#explainer.explain_recommendations()
KNNexplainer = KNNPostHocExplainer(self.als, recommendations, train)
KNNexpl = KNNexplainer.explain_recommendations()
def test_train_emf(self):
self.assertTrue(self.emf.fit(self.data))
recommender = Recommender(self.data, self.emf)
recommender.recommend_all()
def test_train_autoencoder(self):
self.assertTrue(self.autoencoder.fit(self.data))
recommender = Recommender(self.data, self.autoencoder)
recommender.recommend_all()
def test_train_recommend_als(self):
recommender = Recommender(self.data, self.als)
recommender.recommend_all()
def test_train_bpr(self):
self.assertTrue(self.bpr.fit(self.data))
recommender = Recommender(self.data, self.bpr)
recommender.recommend_all()
def test_train_als(self):
self.assertTrue(self.als.fit(self.data))
recommender = Recommender(self.data, self.als)
recommender.recommend_all()
def test_train_mlp(self):
self.assertTrue(self.mlp.fit(self.data))
recommender = Recommender(self.data, self.mlp)
recommender.recommend_all()