zoukankan      html  css  js  c++  java
  • Python+Spark2.0+hadoop学习笔记——Python Spark MLlib决策树回归

    机器学习领域中分类方法和回归方法是相对的,大多数的方法可以相互转换,即一般的机器学习方法如果可以分类的话,也会可以做回归预测。在本例的回归方法中,使用的评价指标是RMSE。

    第一步:导入数据库

    import sys
    from time import time
    import pandas as pd
    import matplotlib.pyplot as plt
    from pyspark import SparkConf, SparkContext
    from pyspark.mllib.tree import DecisionTree
    from pyspark.mllib.regression import LabeledPoint
    import numpy as np
    from pyspark.mllib.evaluation import RegressionMetrics
    import math

    第二步:数据准备

    def extract_label(record):
    label=(record[-1])
    return float(label)

    def convert_float(x):
    return (0 if x=="?" else float(x))

    def extract_features(record,featureEnd):
    featureSeason=[convert_float(field) for field in record[2]]
    features=[convert_float(field) for field in record[4: featureEnd-2]]
    return np.concatenate( (featureSeason, features))

    def PrepareData(sc):
    print("Data loading...")
    rawDataWithHeader = sc.textFile(Path+"data/hour.csv")
    header = rawDataWithHeader.first()
    rawData = rawDataWithHeader.filter(lambda x:x !=header)
    lines = rawData.map(lambda x: x.split(","))
    print (lines.first())
    print("The number of data:" + str(lines.count()))
    labelpointRDD = lines.map(lambda r:LabeledPoint(
    extract_label(r),
    extract_features(r,len(r) - 1)))

    print(labelpointRDD.first())
    (trainData, validationData, testData) = labelpointRDD.randomSplit([8, 1, 1])
    print("TrainData:" + str(trainData.count()) +
    "ValidationData:" + str(validationData.count()) +
    "TestData:" + str(testData.count()))
    return (trainData, validationData, testData)

    第三步:对模型进行训练

    def PredictData(sc,model):
    print("Data loading...")
    rawDataWithHeader = sc.textFile(Path+"data/hour.csv")
    header = rawDataWithHeader.first()
    rawData = rawDataWithHeader.filter(lambda x:x !=header)
    lines = rawData.map(lambda x: x.split(","))
    print("The number of data:" + str(lines.count()))
    labelpointRDD = lines.map(lambda r: LabeledPoint(
    extract_label(r),
    extract_features(r,len(r) - 1)))
    SeasonDict = { 1 : "春", 2 : "夏", 3 :"秋", 4 : "冬" }
    HoildayDict={ 0 : "非假日", 1 : "假日" }
    WeekDict = {0:"一",1:"二",2:"三",3:"四",4 :"五",5:"六",6:"日"}
    WorkDayDict={ 1 : "工作日", 0 : "非工作日" }
    WeatherDict={ 1 : "晴", 2 : "阴", 3 : "小雨", 4 : "大雨" }
    for lp in labelpointRDD.take(100):
    predict = int(model.predict(lp.features))
    label=lp.label
    features=lp.features
    result = ("True" if (label == predict) else "False")
    error = math.fabs(label - predict)
    dataDesc="Feature: "+SeasonDict[features[0]] +"季,"+
    str(features[1]) + "月," +
    str(features[2]) + "时,"+
    HoildayDict[features[3]] +","+
    "Week"+WeekDict[features[4]]+","+
    WorkDayDict[features[5]]+","+
    WeatherDict[features[6]]+","+
    str(features[7] * 41)+ "度,"+
    "Temperature" + str(features[8] * 50) + "度," +
    "Humidity" + str(features[9] * 100) + ","+
    "Wind speed" + str(features[10] * 67) +
    "Predict result:" + str(predict )+
    "Actual:" + str(label) + result +", Error:" + str(error)
    print(dataDesc)

    第四步:对模型进行评估

    def evaluateModel(model, validationData):
    score = model.predict(validationData.map(lambda p: p.features))
    scoreAndLabels=score.zip(validationData.map(lambda p: p.label))
    metrics = RegressionMetrics(scoreAndLabels)
    RMSE=metrics.rootMeanSquaredError
    return(RMSE)

    def trainEvaluateModel(trainData,validationData,
    impurityParm, maxDepthParm, maxBinsParm):
    startTime = time()
    model = DecisionTree.trainRegressor(trainData,
    categoricalFeaturesInfo={},
    impurity=impurityParm,
    maxDepth=maxDepthParm,
    maxBins=maxBinsParm)
    RMSE = evaluateModel(model, validationData)
    duration = time() - startTime
    print (" impurityParm= %s"%impurityParm+
    " maxDepthParm= %s"%maxDepthParm+
    " maxBinsParm = %d."%maxBinsParm +
    " Time=%d"%duration +
    " RMSE = %f " % RMSE )
    return (RMSE,duration, impurityParm, maxDepthParm, maxBinsParm,model)

    def evalParameter(trainData, validationData, evaparm,impurityList, maxDepthList, maxBinsList):
    metrics = [trainEvaluateModel(trainData, validationData, impurity,maxdepth, maxBins )
    for impurity in impurityList
    for maxdepth in maxDepthList
    for maxBins in maxBinsList ]
    if evaparm=="impurity":
    IndexList=impurityList[:]
    elif evaparm=="maxDepth":
    IndexList=maxDepthList[:]
    elif evaparm=="maxBins":
    IndexList=maxBinsList[:]
    df = pd.DataFrame(metrics,index=IndexList,
    columns=['RMSE', 'duration','impurityParm', 'maxDepthParm', 'maxBinsParm','model'])
    showchart(df,evaparm,'RMSE','duration',0,200 )

    def showchart(df,evalparm ,barData,lineData,yMin,yMax):
    ax = df[barData].plot(kind='bar', title =evalparm,figsize=(10,6),legend=True, fontsize=12)
    ax.set_xlabel(evalparm,fontsize=12)
    ax.set_ylim([yMin,yMax])
    ax.set_ylabel(barData,fontsize=12)
    ax2 = ax.twinx()
    ax2.plot(df[[lineData ]].values, linestyle='-', marker='o', linewidth=2.0,color='r')
    plt.show()

    def evalAllParameter(training_RDD, validation_RDD, impurityList, maxDepthList, maxBinsList):
    metrics = [trainEvaluateModel(trainData, validationData, impurity,maxdepth, maxBins )
    for impurity in impurityList
    for maxdepth in maxDepthList
    for maxBins in maxBinsList ]
    Smetrics = sorted(metrics, key=lambda k: k[0])
    bestParameter=Smetrics[0]
    print("impurity:" + str(bestParameter[2]) +
    " ,maxDepth:" + str(bestParameter[3]) +
    " ,maxBins:" + str(bestParameter[4]) +
    " ,RMSE = " + str(bestParameter[0]))
    return bestParameter[5]

    def parametersEval(training_RDD, validation_RDD):
    print("-----MaxDepth---------")
    evalParameter(training_RDD, validation_RDD,"maxDepth",
    impurityList=["variance"],
    maxDepthList =[3, 5, 10, 15, 20, 25] ,
    maxBinsList=[10])
    print("----MaxBins---------")
    evalParameter(training_RDD, validation_RDD,"maxBins",
    impurityList=["variance"],
    maxDepthList=[10],
    maxBinsList=[3, 5, 10, 50, 100, 200 ])

    第五步:Spark相关设置

    def SetLogger( sc ):
    logger = sc._jvm.org.apache.log4j
    logger.LogManager.getLogger("org"). setLevel( logger.Level.ERROR )
    logger.LogManager.getLogger("akka").setLevel( logger.Level.ERROR )
    logger.LogManager.getRootLogger().setLevel(logger.Level.ERROR)

    def SetPath(sc):
    global Path
    if sc.master[0:5]=="local" :
    Path="file:/home/jorlinlee/pythonsparkexample/PythonProject/"
    else:
    Path="hdfs://master:9000/user/jorlinlee/"

    def CreateSparkContext():
    sparkConf = SparkConf()
    .setAppName("RunDecisionTreeRegression")
    .set("spark.ui.showConsoleProgress", "false")
    sc = SparkContext(conf = sparkConf)
    print ("master="+sc.master)
    SetLogger(sc)
    SetPath(sc)
    return (sc)

    sc.stop()

    第六步:运行主程序

    if __name__ == "__main__":
    print("RunDecisionTreeRegression")
    sc=CreateSparkContext()
    print("Preparing")
    (trainData, validationData, testData) =PrepareData(sc)
    trainData.persist(); validationData.persist(); testData.persist()
    print("Testing")
    (AUC,duration, impurityParm, maxDepthParm, maxBinsParm,model)=
    trainEvaluateModel(trainData, validationData, "variance", 10, 100)
    if (len(sys.argv) == 2) and (sys.argv[1]=="-e"):
    parametersEval(trainData, validationData)
    elif (len(sys.argv) == 2) and (sys.argv[1]=="-a"):
    print("Best parameters")
    model=evalAllParameter(trainData, validationData,
    ["variance"],
    [3, 5, 10, 15, 20, 25],
    [3, 5, 10, 50, 100, 200 ])
    print("Testing")
    RMSE = evaluateModel(model, testData)
    print("RMSE:" + str(RMSE))
    print("Predict")
    PredictData(sc, model)

    结果:

    Feature: 春季,1.0月,0.0时,非假日,Week日,非工作日,晴,9.84度,Temperature14.395度,Humidity81.0,Wind speed0.0Predict result:41Actual:16.0False, Error:25.0
    Feature: 春季,1.0月,1.0时,非假日,Week日,非工作日,晴,9.02度,Temperature13.635度,Humidity80.0,Wind speed0.0Predict result:26Actual:40.0False, Error:14.0
    Feature: 春季,1.0月,2.0时,非假日,Week日,非工作日,晴,9.02度,Temperature13.635度,Humidity80.0,Wind speed0.0Predict result:25Actual:32.0False, Error:7.0
    Feature: 春季,1.0月,3.0时,非假日,Week日,非工作日,晴,9.84度,Temperature14.395度,Humidity75.0,Wind speed0.0Predict result:12Actual:13.0False, Error:1.0
    Feature: 春季,1.0月,4.0时,非假日,Week日,非工作日,晴,9.84度,Temperature14.395度,Humidity75.0,Wind speed0.0Predict result:3Actual:1.0False, Error:2.0
    Feature: 春季,1.0月,5.0时,非假日,Week日,非工作日,阴,9.84度,Temperature12.879999999999999度,Humidity75.0,Wind speed6.0032Predict result:3Actual:1.0False, Error:2.0
    Feature: 春季,1.0月,6.0时,非假日,Week日,非工作日,晴,9.02度,Temperature13.635度,Humidity80.0,Wind speed0.0Predict result:2Actual:2.0True, Error:0.0
    Feature: 春季,1.0月,7.0时,非假日,Week日,非工作日,晴,8.200000000000001度,Temperature12.879999999999999度,Humidity86.0,Wind speed0.0Predict result:15Actual:3.0False, Error:12.0
    Feature: 春季,1.0月,8.0时,非假日,Week日,非工作日,晴,9.84度,Temperature14.395度,Humidity75.0,Wind speed0.0Predict result:64Actual:8.0False, Error:56.0
    Feature: 春季,1.0月,9.0时,非假日,Week日,非工作日,晴,13.120000000000001度,Temperature17.424999999999997度,Humidity76.0,Wind speed0.0Predict result:156Actual:14.0False, Error:142.0
    Feature: 春季,1.0月,10.0时,非假日,Week日,非工作日,晴,15.58度,Temperature19.695度,Humidity76.0,Wind speed16.997899999999998Predict result:197Actual:36.0False, Error:161.0
    Feature: 春季,1.0月,11.0时,非假日,Week日,非工作日,晴,14.76度,Temperature16.665度,Humidity81.0,Wind speed19.0012Predict result:197Actual:56.0False, Error:141.0
    Feature: 春季,1.0月,12.0时,非假日,Week日,非工作日,晴,17.22度,Temperature21.21度,Humidity77.0,Wind speed19.0012Predict result:124Actual:84.0False, Error:40.0
    Feature: 春季,1.0月,13.0时,非假日,Week日,非工作日,阴,18.86度,Temperature22.725度,Humidity72.0,Wind speed19.999499999999998Predict result:205Actual:94.0False, Error:111.0
    Feature: 春季,1.0月,14.0时,非假日,Week日,非工作日,阴,18.86度,Temperature22.725度,Humidity72.0,Wind speed19.0012Predict result:205Actual:106.0False, Error:99.0
    Feature: 春季,1.0月,15.0时,非假日,Week日,非工作日,阴,18.04度,Temperature21.97度,Humidity77.0,Wind speed19.999499999999998Predict result:205Actual:110.0False, Error:95.0
    Feature: 春季,1.0月,16.0时,非假日,Week日,非工作日,阴,17.22度,Temperature21.21度,Humidity82.0,Wind speed19.999499999999998Predict result:205Actual:93.0False, Error:112.0
    Feature: 春季,1.0月,17.0时,非假日,Week日,非工作日,阴,18.04度,Temperature21.97度,Humidity82.0,Wind speed19.0012Predict result:55Actual:67.0False, Error:12.0
    Feature: 春季,1.0月,18.0时,非假日,Week日,非工作日,小雨,17.22度,Temperature21.21度,Humidity88.0,Wind speed16.997899999999998Predict result:98Actual:35.0False, Error:63.0
    Feature: 春季,1.0月,19.0时,非假日,Week日,非工作日,小雨,17.22度,Temperature21.21度,Humidity88.0,Wind speed16.997899999999998Predict result:98Actual:37.0False, Error:61.0
    Feature: 春季,1.0月,20.0时,非假日,Week日,非工作日,阴,16.400000000000002度,Temperature20.455000000000002度,Humidity87.0,Wind speed16.997899999999998Predict result:70Actual:36.0False, Error:34.0
    Feature: 春季,1.0月,21.0时,非假日,Week日,非工作日,阴,16.400000000000002度,Temperature20.455000000000002度,Humidity87.0,Wind speed12.998000000000001Predict result:70Actual:34.0False, Error:36.0
    Feature: 春季,1.0月,22.0时,非假日,Week日,非工作日,阴,16.400000000000002度,Temperature20.455000000000002度,Humidity94.0,Wind speed15.001299999999999Predict result:96Actual:28.0False, Error:68.0
    Feature: 春季,1.0月,23.0时,非假日,Week日,非工作日,阴,18.86度,Temperature22.725度,Humidity88.0,Wind speed19.999499999999998Predict result:96Actual:39.0False, Error:57.0
    Feature: 春季,1.0月,0.0时,非假日,Week一,非工作日,阴,18.86度,Temperature22.725度,Humidity88.0,Wind speed19.999499999999998Predict result:17Actual:17.0True, Error:0.0
    Feature: 春季,1.0月,1.0时,非假日,Week一,非工作日,阴,18.04度,Temperature21.97度,Humidity94.0,Wind speed16.997899999999998Predict result:17Actual:17.0True, Error:0.0
    Feature: 春季,1.0月,2.0时,非假日,Week一,非工作日,阴,17.22度,Temperature21.21度,Humidity100.0,Wind speed19.0012Predict result:9Actual:9.0True, Error:0.0
    Feature: 春季,1.0月,3.0时,非假日,Week一,非工作日,阴,18.86度,Temperature22.725度,Humidity94.0,Wind speed12.998000000000001Predict result:25Actual:6.0False, Error:19.0
    Feature: 春季,1.0月,4.0时,非假日,Week一,非工作日,阴,18.86度,Temperature22.725度,Humidity94.0,Wind speed12.998000000000001Predict result:7Actual:3.0False, Error:4.0
    Feature: 春季,1.0月,6.0时,非假日,Week一,非工作日,小雨,17.22度,Temperature21.21度,Humidity77.0,Wind speed19.999499999999998Predict result:2Actual:2.0True, Error:0.0
    Feature: 春季,1.0月,7.0时,非假日,Week一,非工作日,阴,16.400000000000002度,Temperature20.455000000000002度,Humidity76.0,Wind speed12.998000000000001Predict result:100Actual:1.0False, Error:99.0
    Feature: 春季,1.0月,8.0时,非假日,Week一,非工作日,小雨,16.400000000000002度,Temperature20.455000000000002度,Humidity71.0,Wind speed15.001299999999999Predict result:45Actual:8.0False, Error:37.0
    Feature: 春季,1.0月,9.0时,非假日,Week一,非工作日,阴,15.58度,Temperature19.695度,Humidity76.0,Wind speed15.001299999999999Predict result:100Actual:20.0False, Error:80.0
    Feature: 春季,1.0月,10.0时,非假日,Week一,非工作日,阴,14.76度,Temperature17.424999999999997度,Humidity81.0,Wind speed15.001299999999999Predict result:100Actual:53.0False, Error:47.0
    Feature: 春季,1.0月,11.0时,非假日,Week一,非工作日,阴,14.76度,Temperature16.665度,Humidity71.0,Wind speed16.997899999999998Predict result:100Actual:70.0False, Error:30.0
    Feature: 春季,1.0月,12.0时,非假日,Week一,非工作日,阴,14.76度,Temperature16.665度,Humidity66.0,Wind speed19.999499999999998Predict result:100Actual:93.0False, Error:7.0
    Feature: 春季,1.0月,13.0时,非假日,Week一,非工作日,阴,14.76度,Temperature17.424999999999997度,Humidity66.0,Wind speed8.9981Predict result:100Actual:75.0False, Error:25.0
    Feature: 春季,1.0月,14.0时,非假日,Week一,非工作日,小雨,14.76度,Temperature17.424999999999997度,Humidity76.0,Wind speed12.998000000000001Predict result:45Actual:59.0False, Error:14.0
    Feature: 春季,1.0月,15.0时,非假日,Week一,非工作日,小雨,13.940000000000001度,Temperature16.665度,Humidity81.0,Wind speed11.0014Predict result:45Actual:74.0False, Error:29.0
    Feature: 春季,1.0月,16.0时,非假日,Week一,非工作日,小雨,13.940000000000001度,Temperature16.665度,Humidity71.0,Wind speed11.0014Predict result:45Actual:76.0False, Error:31.0
    Feature: 春季,1.0月,17.0时,非假日,Week一,非工作日,晴,13.940000000000001度,Temperature16.665度,Humidity56.99999999999999,Wind speed12.998000000000001Predict result:116Actual:65.0False, Error:51.0
    Feature: 春季,1.0月,18.0时,非假日,Week一,非工作日,阴,14.76度,Temperature16.665度,Humidity46.0,Wind speed22.0028Predict result:116Actual:53.0False, Error:63.0
    Feature: 春季,1.0月,19.0时,非假日,Week一,非工作日,晴,13.120000000000001度,Temperature14.395度,Humidity42.0,Wind speed30.002599999999997Predict result:111Actual:30.0False, Error:81.0
    Feature: 春季,1.0月,20.0时,非假日,Week一,非工作日,晴,12.299999999999999度,Temperature13.635度,Humidity39.0,Wind speed23.9994Predict result:87Actual:22.0False, Error:65.0
    Feature: 春季,1.0月,21.0时,非假日,Week一,非工作日,晴,10.66度,Temperature11.365度,Humidity44.0,Wind speed22.0028Predict result:49Actual:31.0False, Error:18.0
    Feature: 春季,1.0月,22.0时,非假日,Week一,非工作日,晴,9.84度,Temperature10.605度,Humidity44.0,Wind speed19.999499999999998Predict result:34Actual:9.0False, Error:25.0
    Feature: 春季,1.0月,23.0时,非假日,Week一,非工作日,晴,9.02度,Temperature11.365度,Humidity47.0,Wind speed11.0014Predict result:22Actual:8.0False, Error:14.0
    Feature: 春季,1.0月,0.0时,非假日,Week二,工作日,晴,9.02度,Temperature9.85度,Humidity44.0,Wind speed23.9994Predict result:9Actual:5.0False, Error:4.0
    Feature: 春季,1.0月,1.0时,非假日,Week二,工作日,晴,8.200000000000001度,Temperature8.334999999999999度,Humidity44.0,Wind speed27.999299999999998Predict result:1Actual:2.0False, Error:1.0
    Feature: 春季,1.0月,4.0时,非假日,Week二,工作日,晴,6.5600000000000005度,Temperature6.819999999999999度,Humidity47.0,Wind speed26.0027Predict result:2Actual:1.0False, Error:1.0
    Feature: 春季,1.0月,5.0时,非假日,Week二,工作日,晴,6.5600000000000005度,Temperature6.819999999999999度,Humidity47.0,Wind speed19.0012Predict result:5Actual:3.0False, Error:2.0

    Feature: 春季,1.0月,6.0时,非假日,Week二,工作日,晴,5.74度,Temperature5.305度,Humidity50.0,Wind speed26.0027Predict result:30Actual:30.0True, Error:0.0
    Feature: 春季,1.0月,7.0时,非假日,Week二,工作日,晴,5.74度,Temperature6.819999999999999度,Humidity50.0,Wind speed12.998000000000001Predict result:88Actual:64.0False, Error:24.0
    Feature: 春季,1.0月,8.0时,非假日,Week二,工作日,晴,5.74度,Temperature6.0600000000000005度,Humidity50.0,Wind speed19.0012Predict result:279Actual:154.0False, Error:125.0
    Feature: 春季,1.0月,9.0时,非假日,Week二,工作日,晴,6.5600000000000005度,Temperature6.819999999999999度,Humidity43.0,Wind speed26.0027Predict result:152Actual:88.0False, Error:64.0
    Feature: 春季,1.0月,10.0时,非假日,Week二,工作日,晴,7.38度,Temperature8.334999999999999度,Humidity43.0,Wind speed16.997899999999998Predict result:71Actual:44.0False, Error:27.0
    Feature: 春季,1.0月,11.0时,非假日,Week二,工作日,晴,8.200000000000001度,Temperature9.09度,Humidity40.0,Wind speed22.0028Predict result:71Actual:51.0False, Error:20.0
    Feature: 春季,1.0月,12.0时,非假日,Week二,工作日,晴,9.02度,Temperature10.605度,Humidity35.0,Wind speed19.999499999999998Predict result:71Actual:61.0False, Error:10.0
    Feature: 春季,1.0月,13.0时,非假日,Week二,工作日,晴,9.84度,Temperature10.605度,Humidity35.0,Wind speed19.0012Predict result:71Actual:61.0False, Error:10.0
    Feature: 春季,1.0月,14.0时,非假日,Week二,工作日,晴,10.66度,Temperature12.120000000000001度,Humidity30.0,Wind speed19.0012Predict result:158Actual:77.0False, Error:81.0
    Feature: 春季,1.0月,15.0时,非假日,Week二,工作日,晴,10.66度,Temperature12.120000000000001度,Humidity30.0,Wind speed16.997899999999998Predict result:158Actual:72.0False, Error:86.0
    Feature: 春季,1.0月,16.0时,非假日,Week二,工作日,晴,10.66度,Temperature12.120000000000001度,Humidity30.0,Wind speed16.997899999999998Predict result:158Actual:76.0False, Error:82.0
    Feature: 春季,1.0月,17.0时,非假日,Week二,工作日,晴,9.84度,Temperature11.365度,Humidity30.0,Wind speed15.001299999999999Predict result:190Actual:157.0False, Error:33.0
    Feature: 春季,1.0月,18.0时,非假日,Week二,工作日,晴,9.84度,Temperature12.879999999999999度,Humidity32.0,Wind speed7.0015Predict result:190Actual:157.0False, Error:33.0
    Feature: 春季,1.0月,19.0时,非假日,Week二,工作日,晴,8.200000000000001度,Temperature12.879999999999999度,Humidity47.0,Wind speed0.0Predict result:134Actual:110.0False, Error:24.0
    Feature: 春季,1.0月,20.0时,非假日,Week二,工作日,晴,8.200000000000001度,Temperature11.365度,Humidity47.0,Wind speed7.0015Predict result:95Actual:52.0False, Error:43.0
    Feature: 春季,1.0月,21.0时,非假日,Week二,工作日,晴,7.38度,Temperature9.85度,Humidity64.0,Wind speed8.9981Predict result:48Actual:52.0False, Error:4.0
    Feature: 春季,1.0月,22.0时,非假日,Week二,工作日,晴,5.74度,Temperature7.575度,Humidity69.0,Wind speed8.9981Predict result:48Actual:20.0False, Error:28.0
    Feature: 春季,1.0月,23.0时,非假日,Week二,工作日,晴,7.38度,Temperature10.605度,Humidity55.00000000000001,Wind speed7.0015Predict result:21Actual:12.0False, Error:9.0
    Feature: 春季,1.0月,0.0时,非假日,Week三,工作日,晴,6.5600000000000005度,Temperature9.09度,Humidity55.00000000000001,Wind speed7.0015Predict result:9Actual:5.0False, Error:4.0
    Feature: 春季,1.0月,1.0时,非假日,Week三,工作日,晴,6.5600000000000005度,Temperature9.09度,Humidity59.0,Wind speed7.0015Predict result:5Actual:2.0False, Error:3.0
    Feature: 春季,1.0月,2.0时,非假日,Week三,工作日,晴,5.74度,Temperature7.575度,Humidity63.0,Wind speed8.9981Predict result:2Actual:1.0False, Error:1.0
    Feature: 春季,1.0月,4.0时,非假日,Week三,工作日,晴,5.74度,Temperature9.09度,Humidity63.0,Wind speed6.0032Predict result:2Actual:2.0True, Error:0.0
    Feature: 春季,1.0月,5.0时,非假日,Week三,工作日,晴,4.92度,Temperature7.575度,Humidity68.0,Wind speed7.0015Predict result:7Actual:4.0False, Error:3.0
    Feature: 春季,1.0月,6.0时,非假日,Week三,工作日,晴,4.92度,Temperature7.575度,Humidity74.0,Wind speed7.0015Predict result:53Actual:36.0False, Error:17.0
    Feature: 春季,1.0月,7.0时,非假日,Week三,工作日,晴,4.92度,Temperature7.575度,Humidity74.0,Wind speed8.9981Predict result:88Actual:94.0False, Error:6.0
    Feature: 春季,1.0月,8.0时,非假日,Week三,工作日,晴,5.74度,Temperature7.575度,Humidity69.0,Wind speed11.0014Predict result:279Actual:179.0False, Error:100.0
    Feature: 春季,1.0月,9.0时,非假日,Week三,工作日,晴,6.5600000000000005度,Temperature7.575度,Humidity64.0,Wind speed15.001299999999999Predict result:152Actual:100.0False, Error:52.0
    Feature: 春季,1.0月,10.0时,非假日,Week三,工作日,阴,6.5600000000000005度,Temperature6.819999999999999度,Humidity69.0,Wind speed22.0028Predict result:71Actual:42.0False, Error:29.0
    Feature: 春季,1.0月,11.0时,非假日,Week三,工作日,晴,9.02度,Temperature10.605度,Humidity51.0,Wind speed19.999499999999998Predict result:71Actual:57.0False, Error:14.0
    Feature: 春季,1.0月,12.0时,非假日,Week三,工作日,晴,9.02度,Temperature11.365度,Humidity51.0,Wind speed11.0014Predict result:71Actual:78.0False, Error:7.0
    Feature: 春季,1.0月,13.0时,非假日,Week三,工作日,晴,9.84度,Temperature11.365度,Humidity56.00000000000001,Wind speed12.998000000000001Predict result:71Actual:97.0False, Error:26.0
    Feature: 春季,1.0月,14.0时,非假日,Week三,工作日,晴,10.66度,Temperature12.879999999999999度,Humidity52.0,Wind speed15.001299999999999Predict result:158Actual:63.0False, Error:95.0
    Feature: 春季,1.0月,15.0时,非假日,Week三,工作日,晴,11.48度,Temperature13.635度,Humidity52.0,Wind speed16.997899999999998Predict result:158Actual:65.0False, Error:93.0
    Feature: 春季,1.0月,16.0时,非假日,Week三,工作日,晴,12.299999999999999度,Temperature14.395度,Humidity49.0,Wind speed16.997899999999998Predict result:158Actual:83.0False, Error:75.0
    Feature: 春季,1.0月,17.0时,非假日,Week三,工作日,晴,11.48度,Temperature13.635度,Humidity48.0,Wind speed15.001299999999999Predict result:158Actual:212.0False, Error:54.0
    Feature: 春季,1.0月,18.0时,非假日,Week三,工作日,晴,10.66度,Temperature12.879999999999999度,Humidity48.0,Wind speed12.998000000000001Predict result:158Actual:182.0False, Error:24.0
    Feature: 春季,1.0月,19.0时,非假日,Week三,工作日,晴,9.84度,Temperature12.879999999999999度,Humidity48.0,Wind speed7.0015Predict result:134Actual:112.0False, Error:22.0
    Feature: 春季,1.0月,20.0时,非假日,Week三,工作日,晴,9.84度,Temperature12.879999999999999度,Humidity48.0,Wind speed7.0015Predict result:95Actual:54.0False, Error:41.0
    Feature: 春季,1.0月,21.0时,非假日,Week三,工作日,晴,9.02度,Temperature13.635度,Humidity64.0,Wind speed0.0Predict result:95Actual:48.0False, Error:47.0
    Feature: 春季,1.0月,22.0时,非假日,Week三,工作日,晴,9.02度,Temperature12.879999999999999度,Humidity64.0,Wind speed6.0032Predict result:48Actual:35.0False, Error:13.0
    Feature: 春季,1.0月,23.0时,非假日,Week三,工作日,晴,8.200000000000001度,Temperature11.365度,Humidity69.0,Wind speed6.0032Predict result:35Actual:11.0False, Error:24.0
    Feature: 春季,1.0月,0.0时,非假日,Week四,工作日,晴,8.200000000000001度,Temperature12.879999999999999度,Humidity64.0,Wind speed0.0Predict result:17Actual:6.0False, Error:11.0
    Feature: 春季,1.0月,1.0时,非假日,Week四,工作日,晴,6.5600000000000005度,Temperature9.85度,Humidity74.0,Wind speed6.0032Predict result:5Actual:6.0False, Error:1.0
    Feature: 春季,1.0月,2.0时,非假日,Week四,工作日,晴,6.5600000000000005度,Temperature9.85度,Humidity74.0,Wind speed6.0032Predict result:2Actual:2.0True, Error:0.0
    Feature: 春季,1.0月,4.0时,非假日,Week四,工作日,晴,9.84度,Temperature11.365度,Humidity48.0,Wind speed15.001299999999999Predict result:2Actual:2.0True, Error:0.0
    Feature: 春季,1.0月,5.0时,非假日,Week四,工作日,晴,9.02度,Temperature11.365度,Humidity47.0,Wind speed11.0014Predict result:16Actual:3.0False, Error:13.0
    Feature: 春季,1.0月,6.0时,非假日,Week四,工作日,晴,8.200000000000001度,Temperature9.85度,Humidity47.0,Wind speed15.001299999999999Predict result:53Actual:33.0False, Error:20.0
    Feature: 春季,1.0月,7.0时,非假日,Week四,工作日,晴,7.38度,Temperature9.09度,Humidity43.0,Wind speed12.998000000000001Predict result:147Actual:88.0False, Error:59.0
    Feature: 春季,1.0月,8.0时,非假日,Week四,工作日,晴,8.200000000000001度,Temperature9.09度,Humidity40.0,Wind speed19.999499999999998Predict result:279Actual:195.0False, Error:84.0

  • 相关阅读:
    工作日时间,每10分钟执行一次磁盘空间检查,一旦发现任何分区利用率高 于80%,就发送邮件报警
    编写脚本,使用for和while分别实现192.168.0.0/24网段内,地址是否能够ping通,若ping通则输出"success!",若ping不通则输出"fail!"
    显示统计占用系统内存最多的进程,并排序
    总结IP配置方法
    总结ip分类以及每个分类可以分配的IP数量
    总结描述TCP三次握手四次挥手
    描述TCP和UDP区别
    简述osi七层模型和TCP/IP五层模型
    创建一个至少有两个PV组成的大小为20G的名为testvg的VG;要求PE大小 为16MB, 而后在卷组中创建大小为5G的逻辑卷testlv;挂载至/users目录
    【转载】Centos升级gcc至5.4.0
  • 原文地址:https://www.cnblogs.com/zhuozige/p/12642314.html
Copyright © 2011-2022 走看看