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op3.2\jars\RoaringBitmap-0.9.0.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\scala-collection-compat_2.12-2.1.1.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\scala-compiler-2.12.10.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\scala-library-2.12.10.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\scala-parser-combinators_2.12-1.1.2.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\scala-reflect-2.12.10.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\scala-xml_2.12-1.2.0.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\shapeless_2.12-2.3.3.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\shims-0.9.0.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\slf4j-api-1.7.30.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\slf4j-log4j12-1.7.30.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\snakeyaml-1.24.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\snappy-java-1.1.8.2.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\spark-catalyst_2.12-3.1.1.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\spark-core_2.12-3.1.1.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\spark-graphx_2.12-3.1.1.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\spark-hive-thriftserver_2.12-3.1.1.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\spark-hive_2.12-3.1.1.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\spark-kubernetes_2.12-3.1.1.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\spark-kvstore_2.12-3.1.1.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\spark-launcher_2.12-3.1.1.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\spark-mesos_2.12-3.1.1.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\spark-mllib-local_2.12-3.1.1.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\spark-mllib_2.12-3.1.1.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\spark-network-common_2.12-3.1.1.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\spark-network-shuffle_2.12-3.1.1.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\spark-repl_2.12-3.1.1.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\spark-sketch_2.12-3.1.1.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\spark-sql_2.12-3.1.1.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\spark-streaming_2.12-3.1.1.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\spark-tags_2.12-3.1.1-tests.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\spark-tags_2.12-3.1.1.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\spark-unsafe_2.12-3.1.1.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\spark-yarn_2.12-3.1.1.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\spire-macros_2.12-0.17.0-M1.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\spire-platform_2.12-0.17.0-M1.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\spire-util_2.12-0.17.0-M1.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\spire_2.12-0.17.0-M1.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\ST4-4.0.4.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\stax-api-1.0.1.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\stax2-api-3.1.4.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\stream-2.9.6.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\super-csv-2.2.0.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\threeten-extra-1.5.0.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\token-provider-1.0.1.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\transaction-api-1.1.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\univocity-parsers-2.9.1.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\velocity-1.5.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\woodstox-core-5.0.3.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\xbean-asm7-shaded-4.15.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\xz-1.5.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\zjsonpatch-0.3.0.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\zookeeper-3.4.14.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\zstd-jni-1.4.8-1.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\arrow-vector-2.0.0.jar" car.LoadModelRideHailing
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
25/06/08 17:05:07 INFO SparkContext: Running Spark version 3.1.1
25/06/08 17:05:07 INFO ResourceUtils: ==============================================================
25/06/08 17:05:07 INFO ResourceUtils: No custom resources configured for spark.driver.
25/06/08 17:05:07 INFO ResourceUtils: ==============================================================
25/06/08 17:05:07 INFO SparkContext: Submitted application: LoadModelRideHailing
25/06/08 17:05:07 INFO ResourceProfile: Default ResourceProfile created, executor resources: Map(cores -> name: cores, amount: 1, script: , vendor: , memory -> name: memory, amount: 1024, script: , vendor: , offHeap -> name: offHeap, amount: 0, script: , vendor: ), task resources: Map(cpus -> name: cpus, amount: 1.0)
25/06/08 17:05:07 INFO ResourceProfile: Limiting resource is cpu
25/06/08 17:05:07 INFO ResourceProfileManager: Added ResourceProfile id: 0
25/06/08 17:05:07 INFO SecurityManager: Changing view acls to: wyatt
25/06/08 17:05:07 INFO SecurityManager: Changing modify acls to: wyatt
25/06/08 17:05:07 INFO SecurityManager: Changing view acls groups to:
25/06/08 17:05:07 INFO SecurityManager: Changing modify acls groups to:
25/06/08 17:05:07 INFO SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(wyatt); groups with view permissions: Set(); users with modify permissions: Set(wyatt); groups with modify permissions: Set()
25/06/08 17:05:07 INFO Utils: Successfully started service 'sparkDriver' on port 59361.
25/06/08 17:05:07 INFO SparkEnv: Registering MapOutputTracker
25/06/08 17:05:07 INFO SparkEnv: Registering BlockManagerMaster
25/06/08 17:05:08 INFO BlockManagerMasterEndpoint: Using org.apache.spark.storage.DefaultTopologyMapper for getting topology information
25/06/08 17:05:08 INFO BlockManagerMasterEndpoint: BlockManagerMasterEndpoint up
25/06/08 17:05:08 INFO SparkEnv: Registering BlockManagerMasterHeartbeat
25/06/08 17:05:08 INFO DiskBlockManager: Created local directory at C:\Users\wyatt\AppData\Local\Temp\blockmgr-8fe065e2-024c-4e2f-8662-45d2fe3de444
25/06/08 17:05:08 INFO MemoryStore: MemoryStore started with capacity 1899.0 MiB
25/06/08 17:05:08 INFO SparkEnv: Registering OutputCommitCoordinator
25/06/08 17:05:08 INFO Utils: Successfully started service 'SparkUI' on port 4040.
25/06/08 17:05:08 INFO SparkUI: Bound SparkUI to 0.0.0.0, and started at http://windows10.microdone.cn:4040
25/06/08 17:05:08 INFO Executor: Starting executor ID driver on host windows10.microdone.cn
25/06/08 17:05:08 INFO Utils: Successfully started service 'org.apache.spark.network.netty.NettyBlockTransferService' on port 59392.
25/06/08 17:05:08 INFO NettyBlockTransferService: Server created on windows10.microdone.cn:59392
25/06/08 17:05:08 INFO BlockManager: Using org.apache.spark.storage.RandomBlockReplicationPolicy for block replication policy
25/06/08 17:05:08 INFO BlockManagerMaster: Registering BlockManager BlockManagerId(driver, windows10.microdone.cn, 59392, None)
25/06/08 17:05:08 INFO BlockManagerMasterEndpoint: Registering block manager windows10.microdone.cn:59392 with 1899.0 MiB RAM, BlockManagerId(driver, windows10.microdone.cn, 59392, None)
25/06/08 17:05:08 INFO BlockManagerMaster: Registered BlockManager BlockManagerId(driver, windows10.microdone.cn, 59392, None)
25/06/08 17:05:08 INFO BlockManager: Initialized BlockManager: BlockManagerId(driver, windows10.microdone.cn, 59392, None)
Exception in thread "main" java.lang.IllegalArgumentException: 测试数据中不包含 features 列，请检查数据！
at car.LoadModelRideHailing$.main(LoadModelRideHailing.scala:23)
at car.LoadModelRideHailing.main(LoadModelRideHailing.scala)
进程已结束，退出代码为 1
package car
import org.apache.spark.ml.classification.{LogisticRegressionModel, RandomForestClassificationModel}
import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
import org.apache.spark.sql.{SparkSession, functions => F}
object LoadModelRideHailing {
def main(args: Array[String]): Unit = {
val spark = SparkSession.builder()
.master("local[3]")
.appName("LoadModelRideHailing")
.getOrCreate()
spark.sparkContext.setLogLevel("Error")
// 使用经过特征工程处理后的测试数据
val TestData = spark.read.option("header", "true").csv("C:\\Users\\wyatt\\Documents\\ride_hailing_test_data.csv")
// 将 label 列转换为数值类型
val testDataWithNumericLabel = TestData.withColumn("label", F.col("label").cast("double"))
// 检查 features 列是否存在
if (!testDataWithNumericLabel.columns.contains("features")) {
throw new IllegalArgumentException("测试数据中不包含 features 列，请检查数据！")
}
// 修正后的模型路径（确保文件夹存在且包含元数据）
val LogisticModel = LogisticRegressionModel.load("C:\\Users\\wyatt\\Documents\\ride_hailing_logistic_model") // 示例路径
val LogisticPre = LogisticModel.transform(testDataWithNumericLabel)
val LogisticAcc = new MulticlassClassificationEvaluator()
.setLabelCol("label")
.setPredictionCol("prediction")
.setMetricName("accuracy")
.evaluate(LogisticPre)
println("逻辑回归模型后期数据准确率：" + LogisticAcc)
// 随机森林模型路径同步修正
val RandomForest = RandomForestClassificationModel.load("C:\\Users\\wyatt\\Documents\\ride_hailing_random_forest_model") // 示例路径
val RandomForestPre = RandomForest.transform(testDataWithNumericLabel)
val RandomForestAcc = new MulticlassClassificationEvaluator()
.setLabelCol("label")
.setPredictionCol("prediction")
.setMetricName("accuracy")
.evaluate(RandomForestPre)
println("随机森林模型后期数据准确率：" + RandomForestAcc)
spark.stop()
}
}
本文探讨了在Hadoop环境中高效采集数据的关键因素，包括优化读取速率、处理各种文件格式（如CSV、JSON和变长文件）、选择合适的压缩方式以及整合关系型数据库管理系统中的数据。同时介绍了流式数据和日志文件的最佳采集方法。
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