# -*-coding=utf-8 -*-
from pyspark import SparkConf, SparkContext
sc = SparkContext('local')import math
from pyspark.mllib.regression import LabeledPoint, IsotonicRegression, IsotonicRegressionModel
from pyspark.mllib.util import MLUtils# Load and parse the data 加载和解析数据
def parsePoint(labeledData):return (labeledData.label, labeledData.features[0], 1.0)data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_isotonic_regression_libsvm_data.txt")# Create label, feature, weight tuples from input data with weight set to default value 1.0. 创建标签，特征，权重的元组，并设置权重默认为1.0
parsedData = data.map(parsePoint)# Split data into training (60%) and test (40%) sets. 分割数据集
training, test = parsedData.randomSplit([0.6, 0.4], 11)# Create isotonic regression model from training data. 创建保序回归模型
# Isotonic parameter defaults to true so it is only shown for demonstration 参数默认为true，这里只是用于展示
model = IsotonicRegression.train(training)# Create tuples of predicted and real labels. 创建预测和真实标签的元组
predictionAndLabel = test.map(lambda p: (model.predict(p[1]), p[0]))# Calculate mean squared error between predicted and real labels.计算预测和真实标签的均方误差
meanSquaredError = predictionAndLabel.map(lambda pl: math.pow((pl[0] - pl[1]), 2)).mean()
print("Mean Squared Error = " + str(meanSquaredError)) #Mean Squared Error = 0.00863040529956# Save and load model
model.save(sc, "myIsotonicRegressionModel")
sameModel = IsotonicRegressionModel.load(sc, "myIsotonicRegressionModel")
print sameModel.predict(data.collect()[0].features) #0.14987251