Spark Scala - 如何对数据帧行进行分组并将复杂功能应用于组？
Nor*_*n D 4 parallel-processing aggregate-functions dataframe apache-spark custom-function
我正在努力解决这个超级简单的问题,我已经厌倦了,我希望有人可以帮我解决这个问题.我有一个像这样的形状的数据框:
--------------------------- | Category | Product_ID | |------------+------------+ | a | product 1 | | a | product 2 | | a | product 3 | | a | product 1 | | a | product 4 | | b | product 5 | | b | product 6 | ---------------------------
如何按类别对这些行进行分组并在Scala中应用复杂的功能？也许是这样的:
val result = df.groupBy("Category").apply(myComplexFunction)
这个myComplexFunction应该为每个类别生成下表,并上传成Hive表中的成对相似性或将其保存到HDFS中:
+--------------------------------------------------+ | | Product_1 | Product_2 | Product_3 | +------------+------------+------------------------+ | Product_1 | 1.0 | 0.1 | 0.8 | | Product_2 | 0.1 | 1.0 | 0.5 | | Product_3 | 0.8 | 0.5 | 1.0 | +--------------------------------------------------+
这是我想要应用的函数(它只是计算每个类别中的项目项余弦相似度):
def myComplexFunction(context_data : DataFrame, country_name: String,
context_id: String, table_name_correlations: String,
context_layer: String, context_index: String) : Boolean = {
val unique_identifier = country_name + "_" + context_layer + "_" + context_index
val temp_table_vocabulary = "temp_vocabulary_" + unique_identifier
val temp_table_similarities = "temp_similarities_" + unique_identifier
val temp_table_correlations = "temp_correlations_" + unique_identifier
//context.count()
// fit a CountVectorizerModel from the corpus
//println("Creating sparse incidence matrix")
val cvModel: CountVectorizerModel = new CountVectorizer().setInputCol("words").setOutputCol("features").fit(context_data)
val incidence = cvModel.transform(context_data)
// ========================================================================================
// create dataframe of mapping from indices into the item id
//println("Creating vocabulary")
val vocabulary_rdd = sc.parallelize(cvModel.vocabulary)
val rows_vocabulary_rdd = vocabulary_rdd.zipWithIndex.map{ case (s,i) => Row(s,i)}
val vocabulary_field1 = StructField("Product_ID", StringType, true)
val vocabulary_field2 = StructField("Product_Index", LongType, true)
val schema_vocabulary = StructType(Seq(vocabulary_field1, vocabulary_field2))
val df_vocabulary = hiveContext.createDataFrame(rows_vocabulary_rdd, schema_vocabulary)
// ========================================================================================
//println("Computing similarity matrix")
val myvectors = incidence.select("features").rdd.map(r => r(0).asInstanceOf[Vector])
val mat: RowMatrix = new RowMatrix(myvectors)
val sims = mat.columnSimilarities(0.0)
// ========================================================================================
// Convert records of the Matrix Entry RDD into Rows
//println("Extracting paired similarities")
val rowRdd = sims.entries.map{case MatrixEntry(i, j, v) => Row(i, j, v)}
// ========================================================================================
// create dataframe schema
//println("Creating similarity dataframe")
val field1 = StructField("Product_Index", LongType, true)
val field2 = StructField("Neighbor_Index", LongType, true)
var field3 = StructField("Similarity_Score", DoubleType, true)
val schema_similarities = StructType(Seq(field1, field2, field3))
// create the dataframe
val df_similarities = hiveContext.createDataFrame(rowRdd, schema_similarities)
// ========================================================================================
//println("Register vocabulary and correlations as spark temp tables")
df_vocabulary.registerTempTable(temp_table_vocabulary)
df_similarities.registerTempTable(temp_table_similarities)
// ========================================================================================
//println("Extracting Product_ID")
val temp_corrs = hiveContext.sql(
s"SELECT T1.Product_ID, T2.Neighbor_ID, T1.Similarity_Score " +
s"FROM " +
s"(SELECT Product_ID, Neighbor_Index, Similarity_Score " +
s"FROM $temp_table_similarities LEFT JOIN $temp_table_vocabulary " +
s"WHERE $temp_table_similarities.Product_Index = $temp_table_vocabulary.Product_Index) AS T1 " +
s"LEFT JOIN " +
s"(SELECT Product_ID AS Neighbor_ID, Product_Index as Neighbor_Index FROM $temp_table_vocabulary) AS T2 " +
s"ON " +
s"T1.Neighbor_Index = T2.Neighbor_Index")
// ========================================================================================
val context_corrs = temp_corrs.withColumn("Context_Layer", lit(context_layer)).withColumn("Context_ID", lit(context_id)).withColumn("Country", lit(country_name))
context_corrs.registerTempTable(temp_table_correlations)
// ========================================================================================
hiveContext.sql(s"INSERT INTO TABLE $table_name_correlations SELECT * FROM $temp_table_correlations")
// ========================================================================================
// clean up environment
//println("Cleaning up temp tables")
hiveContext.dropTempTable(temp_table_correlations)
hiveContext.dropTempTable(temp_table_similarities)
hiveContext.dropTempTable(temp_table_vocabulary)
return true
}
val partitioned = tokenized.repartition(tokenized("context_id"))
val context_counts = partitioned.mapPartitions()
//val context_counts = model_code_ids.zipWithIndex.map{case (model_code_id, context_index) => compute_similarity(tokenized.filter(tokenized("context_id") === model_code_id), country_name, model_code_id.asInstanceOf[String], table_name_correlations, context_layer, context_index.toString)}
}
我已经尝试过以下内容:
val category_ids = df.select("Category").distinct.collect()
val result = category_ids.map(category_id => myComplexFunction(df.filter(df("Category") <=> category_id)))
我不知道为什么,但这种方法是顺序运行而不是并行运行.
余弦相似度不是一个复杂的函数,可以使用标准SQL聚合表示.让我们考虑以下示例:
val df = Seq(
("feat1", 1.0, "item1"),
("feat2", 1.0, "item1"),
("feat6", 1.0, "item1"),
("feat1", 1.0, "item2"),
("feat3", 1.0, "item2"),
("feat4", 1.0, "item3"),
("feat5", 1.0, "item3"),
("feat1", 1.0, "item4"),
("feat6", 1.0, "item4")
).toDF("feature", "value", "item")
其中feature是特征标识符,value是对应的值,item是对象标识符feature,item对只有一个对应的值.
余弦相似度定义为:
其中分子可以计算为:
val numer = df.as("this").withColumnRenamed("item", "this")
.join(df.as("other").withColumnRenamed("item", "other"), Seq("feature"))
.where($"this" < $"other")
.groupBy($"this", $"other")
.agg(sum($"this.value" * $"other.value").alias("dot"))
和分母中使用的规范如下:
import org.apache.spark.sql.functions.sqrt
val norms = df.groupBy($"item").agg(sqrt(sum($"value" * $"value")).alias("norm"))
//组合在一起:
val cosine = ($"dot" / ($"this_norm.norm" * $"other_norm.norm")).as("cosine")
val similarities = numer
.join(norms.alias("this_norm").withColumnRenamed("item", "this"), Seq("this"))
.join(norms.alias("other_norm").withColumnRenamed("item", "other"), Seq("other"))
.select($"this", $"other", cosine)
结果表示上三角矩阵的非零项忽略对角线(这是微不足道的):
+-----+-----+-------------------+
| this|other| cosine|
+-----+-----+-------------------+
|item1|item4| 0.8164965809277259|
|item1|item2|0.40824829046386296|
|item2|item4| 0.4999999999999999|
+-----+-----+-------------------+
这相当于:
import org.apache.spark.sql.functions.array
import org.apache.spark.mllib.linalg.distributed.{IndexedRow, IndexedRowMatrix}
import org.apache.spark.mllib.linalg.Vectors
val pivoted = df.groupBy("item").pivot("feature").sum()
.na.fill(0.0)
.orderBy("item")
val mat = new IndexedRowMatrix(pivoted
.select(array(pivoted.columns.tail.map(col): _*))
.rdd
.zipWithIndex
.map {
case (row, idx) =>
new IndexedRow(idx, Vectors.dense(row.getSeq[Double](0).toArray))
})
mat.toCoordinateMatrix.transpose
.toIndexedRowMatrix.columnSimilarities
.toBlockMatrix.toLocalMatrix
0.0 0.408248290463863 0.0 0.816496580927726
0.0 0.0 0.0 0.4999999999999999
0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0
关于你的代码:
- 执行是顺序执行的,因为您的代码在local(
collected)集合上运行. myComplexFunction无法进一步分发,因为它是分布式数据结构和上下文.