决策树分类红酒数据的pyspark.ml的pipeline

最新推荐文章于 2025-09-17 19:49:44 发布

原创最新推荐文章于 2025-09-17 19:49:44 发布 · 495 阅读

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#python #决策树 #大数据 #spark

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该博客演示了如何使用PySpark MLlib库构建决策树分类器来处理葡萄酒数据集。首先，对数据进行预处理，包括特征和标签的编码。接着，创建决策树分类器，并构建管道进行训练。通过随机分割数据进行训练和测试，最后评估模型的多类分类准确性。

数据地址：http://archive.ics.uci.edu/ml/datasets/Wine

from pyspark.ml.classification import DecisionTreeClassificationModel
from pyspark.ml.classification import DecisionTreeClassifier
from pyspark.ml import Pipeline,PipelineModel
from pyspark.ml.evaluation import MulticlassClassificationEvaluator
from pyspark.ml.linalg import Vector,Vectors
from pyspark.sql import Row
from pyspark.ml.feature import IndexToString,StringIndexer,VectorIndexer

def getFeaAndLab(x):
    res = {}
    res['features'] = Vectors.dense(float(x[1]), float(x[2]), 
                                   float(x[3]), float(x[4]),
                                   float(x[5]), float(x[6]), 
                                   float(x[7]), float(x[8]),
                                   float(x[9]), float(x[10]),
                                   float(x[11]), float(x[12]),
                                   float(x[13]))
                                  
    res['label'] = str(x[0])
    return res

def model(data):
    # ------------------------data procesing-------------------------
    labelIndexer = StringIndexer(inputCol='label', outputCol='indexedLabel').fit(data)
    featureIndexer = VectorIndexer(inputCol='features', outputCol='indexedFeatures').fit(data)
    labelConverter = IndexToString(inputCol='prediction', outputCol='predictedLabel', labels=labelIndexer.labels)
    
    # -----------------------choose your model--------------------------------
    dtClassifier = DecisionTreeClassifier().setLabelCol("indexedLabel").setFeaturesCol("indexedFeatures")
    
#     print("DecisionTree parameters:\n" + dtClassifier.explainParams()) # 参数解释
    # ------------------------------pipeline-------------------------------------------------
    dtPipeline = Pipeline().setStages([labelIndexer, featureIndexer, dtClassifier, labelConverter])
    
    # ------------------------------split-------------------------------------------------
    trainingData, testData = data.randomSplit([0.7, 0.3]) # 自带打乱功能
    
    # ------------------------------Train-------------------------------------------------
    dtPipelineModel = dtPipeline.fit(trainingData)
    
    # ------------------------------test-------------------------------------------------
    dtPredictions = dtPipelineModel.transform(testData)
    
    # ------------------------------show-------------------------------------------------    
    dtPredictions.select("predictedLabel", "label", "features").show(20)
    
    # ------------------------------evaluate-------------------------------------------------    
    evaluator = MulticlassClassificationEvaluator().setLabelCol("indexedLabel").setPredictionCol("prediction")
    dtAccuracy = evaluator.evaluate(dtPredictions)
    print(dtAccuracy)
    
def main():
    # 读取数据
    rdd = spark.sparkContext.textFile('file:///usr/local/spark/mycode/ml/wine.txt')
    # 将数据进行分割并转换为dataframe
    data = rdd.map(lambda x: x.split(',')).map(lambda x: Row(**getFeaAndLab(x))).toDF()
    model(data)
if __name__ == '__main__':
	main()