【Spark机器学习速成宝典】模型篇08保序回归【IsotonicRegression】（。。。

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# -*-coding=utf-8 -*-

from pyspark import SparkConf, SparkContextsc = SparkContext('local')

import math

from pyspark.mllib.regression import LabeledPoint, IsotonicRegression, IsotonicRegressionModelfrom 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1.0parsedData = 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