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Minor code cleanup #10

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Aug 13, 2018
Merged

Minor code cleanup #10

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21 changes: 0 additions & 21 deletions pom.xml
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Original file line number Diff line number Diff line change
Expand Up @@ -18,26 +18,6 @@
</properties>

<dependencies>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-core_2.10</artifactId>
<version>${spark.version}</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-sql_2.10</artifactId>
<version>${spark.version}</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-hive_2.10</artifactId>
<version>${spark.version}</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-graphx_2.10</artifactId>
<version>${spark.version}</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-mllib_2.10</artifactId>
Expand All @@ -63,7 +43,6 @@
<artifactId>scala-maven-plugin</artifactId>
<version>3.1.6</version>
<executions>

<execution>
<id>compile</id>
<goals>
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4 changes: 2 additions & 2 deletions src/main/scala/org/apache/spark/mllib/regression/FMWithLBFGS.scala
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Expand Up @@ -139,7 +139,7 @@ class FMWithLBFGS(private var task: Int,

/**
* Encode the FMModel to a dense vector, with its first numFeatures * numFactors elements representing the
* factorization matrix v, sequential numFeaturs elements representing the one-way interactions weights w if k1 is
* factorization matrix v, sequential numFeatures elements representing the one-way interactions weights w if k1 is
* set to true, and the last element representing the intercept w0 if k0 is set to true.
* The factorization matrix v is initialized by Gaussinan(0, initStd).
* v : numFeatures * numFactors + w : [numFeatures] + w0 : [1]
Expand All @@ -165,7 +165,7 @@ class FMWithLBFGS(private var task: Int,


/**
* Create a FMModle from an encoded vector.
* Create a FMModel from an encoded vector.
*/
private def createModel(weights: Vector): FMModel = {

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4 changes: 2 additions & 2 deletions src/main/scala/org/apache/spark/mllib/regression/FMWithSGD.scala
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Expand Up @@ -169,7 +169,7 @@ class FMWithSGD(private var task: Int,

/**
* Encode the FMModel to a dense vector, with its first numFeatures * numFactors elements representing the
* factorization matrix v, sequential numFeaturs elements representing the one-way interactions weights w if k1 is
* factorization matrix v, sequential numFeatures elements representing the one-way interactions weights w if k1 is
* set to true, and the last element representing the intercept w0 if k0 is set to true.
* The factorization matrix v is initialized by Gaussinan(0, initStd).
* v : numFeatures * numFactors + w : [numFeatures] + w0 : [1]
Expand All @@ -195,7 +195,7 @@ class FMWithSGD(private var task: Int,


/**
* Create a FMModle from an encoded vector.
* Create a FMModel from an encoded vector.
*/
private def createModel(weights: Vector): FMModel = {

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4 changes: 2 additions & 2 deletions src/main/scala/org/apache/spark/mllib/regression/FactorizationMachine.scala
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Expand Up @@ -107,13 +107,13 @@ object FMModel extends Loader[FMModel] {

// Create Parquet data.
val dataRDD: DataFrame = sc.parallelize(Seq(data), 1).toDF()
dataRDD.saveAsParquetFile(dataPath(path))
dataRDD.write.parquet(dataPath(path))
}

def load(sc: SparkContext, path: String): FMModel = {
val sqlContext = new SQLContext(sc)
// Load Parquet data.
val dataRDD = sqlContext.parquetFile(dataPath(path))
val dataRDD = sqlContext.read.parquet(dataPath(path))
// Check schema explicitly since erasure makes it hard to use match-case for checking.
checkSchema[Data](dataRDD.schema)
val dataArray = dataRDD.select("task", "factorMatrix", "weightVector", "intercept", "min", "max").take(1)
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