def test_predict_recommend(self):
def gen_rand_user_item_feature(user_num, item_num, class_num):
user_id = random.randint(1, user_num)
item_id = random.randint(1, item_num)
rating = random.randint(1, class_num)
sample = Sample.from_ndarray(np.array([user_id, item_id]), np.array([rating]))
return UserItemFeature(user_id, item_id, sample)
model = NeuralCF(200, 80, 5)
data = self.sc.parallelize(range(0, 50))\
.map(lambda i: gen_rand_user_item_feature(200, 80, 5))
predictions = model.predict_user_item_pair(data).collect()
print(predictions[0])
recommended_items = model.recommend_for_user(data, max_items=3).collect()
print(recommended_items[0])
recommended_users = model.recommend_for_item(data, max_users=4).collect()
print(recommended_users[0])
def test_predict_recommend(self):
def gen_rand_user_item_feature(user_num, item_num, class_num):
user_id = random.randint(1, user_num)
item_id = random.randint(1, item_num)
rating = random.randint(1, class_num)
sample = Sample.from_ndarray(np.array([user_id, item_id]), np.array([rating]))
return UserItemFeature(user_id, item_id, sample)
model = NeuralCF(200, 80, 5)
data = self.sc.parallelize(range(0, 50))\
.map(lambda i: gen_rand_user_item_feature(200, 80, 5))
predictions = model.predict_user_item_pair(data).collect()
print(predictions[0])
recommended_items = model.recommend_for_user(data, max_items=3).collect()
print(recommended_items[0])
recommended_users = model.recommend_for_item(data, max_users=4).collect()
print(recommended_users[0])
# Train the model
optimizer.optimize()
# Prediction
results = ncf.predict(val_rdd)
results.take(5)
results_class = ncf.predict_class(val_rdd)
results_class.take(5)
userItemPairPrediction = ncf.predict_user_item_pair(valPairFeatureRdds)
for result in userItemPairPrediction.take(5):
print(result)
userRecs = ncf.recommend_for_user(valPairFeatureRdds, 3)
for result in userRecs.take(5):
print(result)
itemRecs = ncf.recommend_for_item(valPairFeatureRdds, 3)
for result in itemRecs.take(5):
print(result)
# Evaluation
#retrieve train and validation summary object and read the loss data into ndarray's.
loss = np.array(train_summary.read_scalar("Loss"))
val_loss = np.array(val_summary.read_scalar("Loss"))
#plot the train and validation curves
# each event data is a tuple in form of (iteration_count, value, timestamp)
plt.plot(loss[:,0],loss[:,1],label='train loss')
plt.plot(val_loss[:,0],val_loss[:,1],label='val loss',color='green')
plt.scatter(val_loss[:,0],val_loss[:,1],color='green')
plt.legend();