python spark pyspark——回归预测习题整理

特征量选区：age，enducation，race，sex。目标值：income。

from pyspark.mllib.linalg import Vectors,Vector
from pyspark import SparkContext
from pyspark.ml.regression import LinearRegression
from pyspark.ml.feature import VectorAssembler
from pyspark.python.pyspark.shell import spark
from pyspark.ml.feature import StringIndexer
from pyspark.sql.types import *
from pyspark.sql.functions  import *

#1.读取数据
sc=SparkContext.getOrCreate()
train_data=sc.textFile("hdfs://master:9000/income.CSV")
def GetParts(line):
    parts = line.split(',')
    return parts[0],parts[2],parts[5],parts[6],parts[10]
train = train_data.map(lambda line: GetParts(line)) #根据逗号切割返回需要的特征变量
df = spark.createDataFrame(train,["age", "enducation",'race','sex','income'])#创建DataFrame格式
df.show()
indexer_1 = StringIndexer(inputCol="race", outputCol="race_index")
indexer_2 = StringIndexer(inputCol="sex", outputCol="sex_index")
indexer_3 = StringIndexer(inputCol="income", outputCol="income_index") #离散化处理
indexed_1 = indexer_1.fit(df).transform(df)
indexed_2=indexer_2.fit(indexed_1).transform(indexed_1)
indexed=indexer_3.fit(indexed_2).transform(indexed_2)
indexed=indexed.drop('race')#删除原始数据
indexed=indexed.drop('sex')
indexed=indexed.drop('income')   
indexed.show()


d1 = indexed.withColumn("age", indexed["age"].cast(FloatType())) #spark不支持str类型将其转换为float类型
indexed = d1.withColumn("enducation", indexed["enducation"].cast(FloatType()))
assembler = VectorAssembler(inputCols=["age", "enducation",'race_index','sex_index'],outputCol="features")
output = assembler.transform(indexed)
label_features = output.select("features", "income_index").toDF('features','label')#分类将特征量放一列，另一列为目标值
label_features.show(truncate=False)

#创建回归模型
lr = LinearRegression(maxIter=10, regParam=0.3, elasticNetParam=0.8)
lrModel = lr.fit(label_features)
print("Coefficients: %s" % str(lrModel.coefficients))
print("Intercept: %s" % str(lrModel.intercept))
trainingSummary = lrModel.summary
print("numIteraotins: %d" % trainingSummary.totalIterations)
print("objectiveHistory: %s" % str(trainingSummary.objectiveHistory))
trainingSummary.residuals.show()
print("RMSE: %f" % trainingSummary.rootMeanSquaredError)
print("r2: %f" % trainingSummary.r2)
#构造测式集数据
df1 = indexed.head(5)
df1 = spark.createDataFrame(df1)
df1.show()
test_output = assembler.transform(df1)
test_label_features = test_output.select("features", "income_index").toDF('features','label')
test_label_features.show(truncate=False)
#预测
predictions = lrModel.transform(test_label_features)
print (predictions.collect())

1.提取后的数据

2.离散化后的数据

3.分类后的数据

4.模拟预测数据

5.预测结果返回

标签：features,pyspark,python,label,income,indexed,import,习题
来源： https://blog.csdn.net/weixin_41895381/article/details/89710507