在SPARK Scala中创建两列邻接矩阵及其计数的方法如下:
import org.apache.spark.sql.SparkSession
import org.apache.spark.ml.feature.CountVectorizer
import org.apache.spark.ml.linalg.Vector
val spark = SparkSession.builder().appName("AdjacencyMatrix").getOrCreate()
val countVectorizer = new CountVectorizer()
.setInputCol("source")
.setOutputCol("sourceVector")
.setVocabSize(1000) // 设置词汇表大小,根据实际情况调整
val sourceVectorizerModel = countVectorizer.fit(data)
val sourceVectorized = sourceVectorizerModel.transform(data)
val targetVectorizerModel = countVectorizer.setInputCol("target").setOutputCol("targetVector").fit(data)
val targetVectorized = targetVectorizerModel.transform(sourceVectorized)
val adjacencyMatrix = targetVectorized.select("sourceVector", "targetVector")
val adjacencyMatrixCount = adjacencyMatrix.groupBy("sourceVector", "targetVector").count()
完整代码示例:
import org.apache.spark.sql.SparkSession
import org.apache.spark.ml.feature.CountVectorizer
import org.apache.spark.ml.linalg.Vector
val spark = SparkSession.builder().appName("AdjacencyMatrix").getOrCreate()
// 准备数据集
val data = spark.createDataFrame(Seq(
(1, 2),
(1, 3),
(2, 3),
(3, 4),
(4, 5)
)).toDF("source", "target")
// 创建CountVectorizer模型
val countVectorizer = new CountVectorizer()
.setInputCol("source")
.setOutputCol("sourceVector")
.setVocabSize(1000)
// 对source列进行向量化转换
val sourceVectorizerModel = countVectorizer.fit(data)
val sourceVectorized = sourceVectorizerModel.transform(data)
// 创建CountVectorizer模型并对target列进行向量化转换
val targetVectorizerModel = countVectorizer.setInputCol("target").setOutputCol("targetVector").fit(data)
val targetVectorized = targetVectorizerModel.transform(sourceVectorized)
// 创建邻接矩阵
val adjacencyMatrix = targetVectorized.select("sourceVector", "targetVector")
// 计算邻接矩阵的计数
val adjacencyMatrixCount = adjacencyMatrix.groupBy("sourceVector", "targetVector").count()
adjacencyMatrixCount.show()
这段代码使用了SPARK的ML库中的CountVectorizer模型来将source和target列中的数据转换为向量表示,然后通过对向量化后的数据进行分组计数,得到了邻接矩阵及其计数。
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