streaming.dsl.mmlib.algs.bigdl.BigDLFunctions.scala Maven / Gradle / Ivy
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package streaming.dsl.mmlib.algs.bigdl
import com.intel.analytics.bigdl.dlframes.{DLClassifier, DLModel}
import org.apache.spark.sql.{DataFrame, Row, SaveMode}
import org.apache.spark.sql.types._
import streaming.common.{HDFSOperator, ScalaObjectReflect}
import streaming.common.ScalaMethodMacros._
import streaming.dsl.mmlib.algs._
import streaming.log.{Logging, WowLog}
trait BigDLFunctions extends Functions with Logging with WowLog with Serializable {
def bigDLClassifyTrain[T](df: DataFrame, path: String, params: Map[String, String],
modelType: (Map[String, String]) => DLClassifier[T],
evaluate: (DLModel[T], Map[String, String]) => List[MetricValue]
) = {
val keepVersion = params.getOrElse("keepVersion", "true").toBoolean
val mf = (trainData: DataFrame, fitParam: Map[String, String], modelIndex: Int) => {
require(fitParam.contains("classNum"), "classNum is required")
require(fitParam.contains("featureSize"), "featureSize is required")
val newFitParam = Map(
str[BigDLDefaultConfig](_.batchSize) -> BigDLDefaultConfig().batchSize.toString,
str[BigDLDefaultConfig](_.maxEpoch) -> BigDLDefaultConfig().maxEpoch.toString
) ++ fitParam
val alg = modelType(newFitParam)
configureModel(alg, newFitParam)
logInfo(format(s"[training] [alg=${alg.getClass.getName}] [keepVersion=${keepVersion}]"))
var status = "success"
val modelTrainStartTime = System.currentTimeMillis()
val modelPath = SQLPythonFunc.getAlgModelPath(path, keepVersion) + "/" + modelIndex
var scores: List[MetricValue] = List()
try {
val newmodel = alg.fit(trainData)
newmodel.model.saveModule(HDFSOperator.getFilePath(modelPath))
scores = evaluate(newmodel, newFitParam)
logInfo(format(s"[trained] [alg=${alg.getClass.getName}] [metrics=${scores}] [model hyperparameters=${
newmodel.explainParams().replaceAll("\n", "\t")
}]"))
} catch {
case e: Exception =>
logInfo(format_exception(e))
status = "fail"
}
val modelTrainEndTime = System.currentTimeMillis()
val metrics = scores.map(score => Row.fromSeq(Seq(score.name, score.value))).toArray
Row.fromSeq(Seq(modelPath, modelIndex, alg.getClass.getName, metrics, status, modelTrainStartTime, modelTrainEndTime, fitParam))
}
var fitParam = arrayParamsWithIndex("fitParam", params)
if (fitParam.size == 0) {
fitParam = Array((0, Map[String, String]()))
}
val wowRes = fitParam.map { fp =>
mf(df, fp._2, fp._1)
}
val wowRDD = df.sparkSession.sparkContext.parallelize(wowRes, 1)
df.sparkSession.createDataFrame(wowRDD, StructType(Seq(
StructField("modelPath", StringType),
StructField("algIndex", IntegerType),
StructField("alg", StringType),
StructField("metrics", ArrayType(StructType(Seq(
StructField(name = "name", dataType = StringType),
StructField(name = "value", dataType = DoubleType)
)))),
StructField("status", StringType),
StructField("startTime", LongType),
StructField("endTime", LongType),
StructField("trainParams", MapType(StringType, StringType))
))).
write.
mode(SaveMode.Overwrite).
parquet(SQLPythonFunc.getAlgMetalPath(path, keepVersion) + "/0")
}
}