except:
print("batchSize learnRate")
exit()
print("parameter info:")
print("batch size:\t%d"%batchSize)
print("learn rate:\t%f"%learnRate)
print("="*20)
# hyper parameter
k = 10
epochCount = 100
# load data
import data
userCount, itemCount, trainSet, testSet = data.ml_1m()
print("dataset info:")
print("user count:\t%d"%(userCount))
print("item count:\t%d"%(itemCount))
print("train count:\t%d"%(trainSet.shape[0]))
print("test count:\t%d"%(testSet.shape[0]))
print("="*20)
# embedding layer
u = tf.placeholder(tf.int32, [None, 1])
v = tf.placeholder(tf.int32, [None, 1])
r = tf.placeholder(tf.float32, [None, 1])
U = tf.Variable(tf.random_uniform([userCount, k], -0.05, 0.05))
V = tf.Variable(tf.random_uniform([itemCount, k], -0.05, 0.05))
learnRate = 0.1
reLambda = 0.01
print("parameter info:")
print("batch size:\t%d" % batchSize)
print("learn rate:\t%f" % learnRate)
print("regular lambda:\t%f" % reLambda)
print("=" * 20)
# hyper parameter
k = 10
epochCount = 200
# load data
import data
userCount, itemCount, trainSet, testSet = data.ml_1m(seed=seed)
globalMean = trainSet[:, 2:3].mean()
print("dataset info:")
print("user count:\t%d" % (userCount))
print("item count:\t%d" % (itemCount))
print("train count:\t%d" % (trainSet.shape[0]))
print("test count:\t%d" % (testSet.shape[0]))
print("global mean:\t%.4f" % (globalMean))
print("=" * 20)
# matrix factorization
u = tf.placeholder(tf.int32, [None, 1])
v = tf.placeholder(tf.int32, [None, 1])
r = tf.placeholder(tf.float32, [None, 1])
except:
print("default batchSize learnRate")
batchSize = 512
learnRate = 0.05
#batchSize = 32
#learnRate = 0.1
# hyper parameter
k = 250
n_layer = [500, 250, 500]
epochCount = 300
dropout_rate = [0.8, 0.8, 1]
# load data
import data
userCount, itemCount, trainSet, testSet = data.ml_1m(should_shuffle=False)
# train data
trainData = defaultdict(lambda: [0] * itemCount)
trainMask = defaultdict(lambda: [0] * itemCount)
for t in trainSet:
userId = int(t[0])
itemId = int(t[1])
rating = float(t[2])
trainData[userId][itemId] = rating
trainMask[userId][itemId] = 1.0
# test data
missCnt = 0
testData = defaultdict(lambda: [0] * itemCount)
testMask = defaultdict(lambda: [0] * itemCount)
