FTRLFM
Training Factorization Machine with FTRL on Spark on Angel
FM(Factorization Machine) is an algorithm based on matrix decomposition which can predict any real-valued vector.
Its main advantages include:
- can handle highly sparse data;
- linear computational complexity
FTRL (Follow-the-regularized-leader) is an optimization algorithm which is widely deployed by online learning. Employing FTRL is easy in Spark-on-Angel and you can train a model with billions, even ten billions, dimensions once you have enough machines.
Here, we will use FTRL Optimizer to update the parameters of FM.
If you are not familiar with how to programming on Spark-on-Angel, please first refer to Programming Guide for Spark-on-Angel;
Factorization Model
where val optim = new FtrlFM(lambda1, lambda2, alpha, beta) // initializing the model optim.init(dim, factor)
There are four hyper-parameters for the FTRL optimizer, which are lambda1, lambda2, alpha and beta. We allocate a FTRL optimizer with these four hyper-parameters. The next step is to initialized a FtrlFM model. There are two matrixs for FtrlFM, including first
and second
, the first
contains the z, n and w in which z and n are used to init or update parameter w in FM, the second
contains the z, n and v in which z and n are used to init or update parameter v in FM. In the aboving code, we allocate first
a sparse distributed matrix with 3 rows and dim columns, and allocate second
a sparse distributed matrix with 3 * factor rows and dim columns.
set the dimension
In the scenaro of online learning, the index of features can be range from (int.min, int.max), which is usually generated by a hash function. In Spark-on-Angel, you can set the dim=-1 when your feature index range from (int.min, int.max) and rowType is sparse. If the feature index range from [0, n), you can set the dim=n.
Training with Spark
loading data
Using the interface of RDD to load data and parse them to vectors.
val data = sc.textFile(input).repartition(partNum)
.map(s => (DataLoader.parseIntFloat(s, dim), DataLoader.parseLabel(s, false)))
.map {
f =>
f._1.setY(f._2)
f._1
}
training model
val size = data.count()
for (epoch <- 1 to numEpoch) {
val totalLoss = data.mapPartitions {
case iterator =>
// for each partition
val loss = iterator
.sliding(batchSize, batchSize)
.zipWithIndex
.map(f => optim.optimize(f._2, f_1.toArray)).sum
Iterator.single(loss)
}.sum()
println(s"epoch=$epoch loss=${totalLoss / size}")
}
saving model
output = "hdfs://xxx"
optim.weight
optim.save(output + "/back")
optim.saveWeight(output)
The example code can be find at https://github.com/Angel-ML/angel/blob/master/spark-on-angel/examples/src/main/scala/com/tencent/angel/spark/examples/cluster/FtrlFMExample.scala
项目地址:https://github.com/Angel-ML/angel
官网:https://angelml.ai/