deepfm_sona_en.md

DeepFM

1. Introduction of Algorithm

The DeepFM algorithm adds a depth layer to the FM (Factorization Machine). Compared with the PNN and NFM algorithms, it preserves the second-order implicit feature intersection of FM and uses the deep network to obtain high-order feature intersections. The structure is as follows:

1.1 Description of the Embedding and BiInnerSumCross layer

Different from the traditional FM implementation, the combination of Embedding and BiInnerSumCross is used to implement the second-order implicit crossover. The expression of the traditional FM quadratic cross term is as follows::

In implementation, it is stored by Embedding, After calling Embedding's 'calOutput', computing and output result together. So the Embedding output of a sample is

The result of the original quadratic term can be re-expressed as::

The above is BiInnerSumCross's forward calculation method, which is implemented by Scala code:

val sumVector = VFactory.denseDoubleVector(mat.getSubDim)

(0 until batchSize).foreach { row =>
    val partitions = mat.getRow(row).getPartitions
    partitions.foreach { vectorOuter =>
    data(row) -= vectorOuter.dot(vectorOuter)
    sumVector.iadd(vectorOuter)
    }
    data(row) += sumVector.dot(sumVector)
    data(row) /= 2
    sumVector.clear()
}

1.2 Description of other layers

• SimpleInputLayer: Sparse data input layer, specially optimized for sparse high-dimensional data, essentially a FClayer
• FCLayer: The most common layer in DNN, linear transformation followed by transfer function
• SumPooling: Adding multiple input data as element-wise, requiring inputs have the same shape
• SimpleLossLayer: Loss layer, you can specify different loss functions

1.3 Building Network

  override def buildNetwork(): Unit = {
    ensureJsonAst()

    val wide = new SimpleInputLayer("input", 1, new Identity(),
      JsonUtils.getOptimizerByLayerType(jsonAst, "SparseInputLayer")
    )

    val embeddingParams = JsonUtils.getLayerParamsByLayerType(jsonAst, "Embedding")
      .asInstanceOf[EmbeddingParams]
    val embedding = new Embedding("embedding", embeddingParams.outputDim,
      embeddingParams.numFactors, embeddingParams.optimizer.build()
    )

    val innerSumCross = new BiInnerSumCross("innerSumPooling", embedding)

    val mlpLayer = JsonUtils.getFCLayer(jsonAst, embedding)

    val join = new SumPooling("sumPooling", 1, Array[Layer](wide, innerSumCross, mlpLayer))

    new SimpleLossLayer("simpleLossLayer", join, lossFunc)
  }

2. Running and performance

2.1 Explanation of Json configuration File

There are many parameters of DeepFM, which need to be specified by Json configuration file (for a complete description of Json configuration file, please refer toJson explanation), A typical example is:

{
  "data": {
    "format": "dummy",
    "indexrange": 148,
    "numfield": 13,
    "validateratio": 0.1,
    "sampleratio": 0.2
  },
  "model": {
    "modeltype": "T_DOUBLE_SPARSE_LONGKEY",
    "modelsize": 148
  },
  "train": {
    "epoch": 10,
    "numupdateperepoch": 10,
    "lr": 0.5,
    "decayclass": "StandardDecay",
    "decaybeta": 0.01
  },
  "default_optimizer": "Momentum",
  "layers": [
    {
      "name": "wide",
      "type": "simpleinputlayer",
      "outputdim": 1,
      "transfunc": "identity"
    },
    {
      "name": "embedding",
      "type": "embedding",
      "numfactors": 8,
      "outputdim": 104,
      "optimizer": {
        "type": "momentum",
        "momentum": 0.9,
        "reg2": 0.01
      }
    },
    {
      "name": "fclayer",
      "type": "FCLayer",
      "outputdims": [
        100,
        100,
        1
      ],
      "transfuncs": [
        "relu",
        "relu",
        "identity"
      ],
      "inputlayer": "embedding"
    },
    {
      "name": "biinnersumcross",
      "type": "BiInnerSumCross",
      "inputlayer": "embedding",
      "outputdim": 1
    },
    {
      "name": "sumPooling",
      "type": "SumPooling",
      "outputdim": 1,
      "inputlayers": [
        "wide",
        "biinnersumcross",
        "fclayer"
      ]
    },
    {
      "name": "simplelosslayer",
      "type": "simplelosslayer",
      "lossfunc": "logloss",
      "inputlayer": "sumPooling"
    }
  ]
}

2.2 Submitting script

Several steps must be done before editing the submitting script and running.

1. confirm Hadoop and Spark have ready in your environment
2. unzip sona--bin.zip to local directory (SONA_HOME)
3. upload sona--bin directory to HDFS (SONA_HDFS_HOME)
4. Edit $SONA_HOME/bin/spark-on-angel-env.sh, set SPARK_HOME, SONA_HOME, SONA_HDFS_HOME and ANGEL_VERSION

Here's an example of submitting scripts, remember to adjust the parameters and fill in the paths according to your own task.

#test description
actionType=train or predict
jsonFile=path-to-jsons/deepfm.json
modelPath=path-to-save-model
predictPath=path-to-save-predict-results
input=path-to-data
queue=your-queue

HADOOP_HOME=my-hadoop-home
source ./bin/spark-on-angel-env.sh
export HADOOP_HOME=$HADOOP_HOME

$SPARK_HOME/bin/spark-submit \
  --master yarn-cluster \
  --conf spark.ps.jars=$SONA_ANGEL_JARS \
  --conf spark.ps.instances=10 \
  --conf spark.ps.cores=2 \
  --conf spark.ps.memory=10g \
  --jars $SONA_SPARK_JARS \
  --files $jsonFile \
  --driver-memory 20g \
  --num-executors 20 \
  --executor-cores 5 \
  --executor-memory 30g \
  --queue $queue \
  --class org.apache.spark.angel.examples.JsonRunnerExamples \
  ./lib/angelml-$SONA_VERSION.jar \
  jsonFile:./deepfm.json \
  dataFormat:libsvm \
  data:$input \
  modelPath:$modelPath \
  predictPath:$predictPath \
  actionType:$actionType \
  numBatch:500 \
  maxIter:2 \
  lr:4.0 \
  numField:39