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()