Python regression.RidgeRegressionWithSGD类代码示例

本文整理汇总了Python中pyspark.mllib.regression.RidgeRegressionWithSGD类的典型用法代码示例。如果您正苦于以下问题：Python RidgeRegressionWithSGD类的具体用法？Python RidgeRegressionWithSGD怎么用？Python RidgeRegressionWithSGD使用的例子？那么恭喜您, 这里精选的类代码示例或许可以为您提供帮助。

在下文中一共展示了RidgeRegressionWithSGD类的9个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。

示例1: test_regression

    def test_regression(self):
        from pyspark.mllib.regression import LinearRegressionWithSGD, LassoWithSGD, \
            RidgeRegressionWithSGD
        from pyspark.mllib.tree import DecisionTree, RandomForest, GradientBoostedTrees
        data = [
            LabeledPoint(-1.0, [0, -1]),
            LabeledPoint(1.0, [0, 1]),
            LabeledPoint(-1.0, [0, -2]),
            LabeledPoint(1.0, [0, 2])
        ]
        rdd = self.sc.parallelize(data)
        features = [p.features.tolist() for p in data]

        lr_model = LinearRegressionWithSGD.train(rdd, iterations=10)
        self.assertTrue(lr_model.predict(features[0]) <= 0)
        self.assertTrue(lr_model.predict(features[1]) > 0)
        self.assertTrue(lr_model.predict(features[2]) <= 0)
        self.assertTrue(lr_model.predict(features[3]) > 0)

        lasso_model = LassoWithSGD.train(rdd, iterations=10)
        self.assertTrue(lasso_model.predict(features[0]) <= 0)
        self.assertTrue(lasso_model.predict(features[1]) > 0)
        self.assertTrue(lasso_model.predict(features[2]) <= 0)
        self.assertTrue(lasso_model.predict(features[3]) > 0)

        rr_model = RidgeRegressionWithSGD.train(rdd, iterations=10)
        self.assertTrue(rr_model.predict(features[0]) <= 0)
        self.assertTrue(rr_model.predict(features[1]) > 0)
        self.assertTrue(rr_model.predict(features[2]) <= 0)
        self.assertTrue(rr_model.predict(features[3]) > 0)

        categoricalFeaturesInfo = {0: 2}  # feature 0 has 2 categories
        dt_model = DecisionTree.trainRegressor(
            rdd, categoricalFeaturesInfo=categoricalFeaturesInfo, maxBins=4)
        self.assertTrue(dt_model.predict(features[0]) <= 0)
        self.assertTrue(dt_model.predict(features[1]) > 0)
        self.assertTrue(dt_model.predict(features[2]) <= 0)
        self.assertTrue(dt_model.predict(features[3]) > 0)

        rf_model = RandomForest.trainRegressor(
            rdd, categoricalFeaturesInfo=categoricalFeaturesInfo, numTrees=10, maxBins=4, seed=1)
        self.assertTrue(rf_model.predict(features[0]) <= 0)
        self.assertTrue(rf_model.predict(features[1]) > 0)
        self.assertTrue(rf_model.predict(features[2]) <= 0)
        self.assertTrue(rf_model.predict(features[3]) > 0)

        gbt_model = GradientBoostedTrees.trainRegressor(
            rdd, categoricalFeaturesInfo=categoricalFeaturesInfo, numIterations=4)
        self.assertTrue(gbt_model.predict(features[0]) <= 0)
        self.assertTrue(gbt_model.predict(features[1]) > 0)
        self.assertTrue(gbt_model.predict(features[2]) <= 0)
        self.assertTrue(gbt_model.predict(features[3]) > 0)

        try:
            LinearRegressionWithSGD.train(rdd, initialWeights=array([1.0, 1.0]), iterations=10)
            LassoWithSGD.train(rdd, initialWeights=array([1.0, 1.0]), iterations=10)
            RidgeRegressionWithSGD.train(rdd, initialWeights=array([1.0, 1.0]), iterations=10)
        except ValueError:
            self.fail()

开发者ID:1ambda，项目名称:spark，代码行数:59，代码来源:tests.py

示例2: iterateRidge

def iterateRidge(iterNums, stepSizes, regParam, train, valid):
  from pyspark.mllib.regression import RidgeRegressionWithSGD
  import math
  for numIter in iterNums:
    for step in stepSizes:
      alg = RidgeRegressionWithSGD()
      model = alg.train(train, intercept=True, regParam=regParam, iterations=numIter, step=step)
      rescaledPredicts = train.map(lambda x: (model.predict(x.features), x.label))
      validPredicts = valid.map(lambda x: (model.predict(x.features), x.label))
      meanSquared = math.sqrt(rescaledPredicts.map(lambda p: pow(p[0]-p[1],2)).mean())
      meanSquaredValid = math.sqrt(validPredicts.map(lambda p: pow(p[0]-p[1],2)).mean())
      print("%d, %5.3f -> %.4f, %.4f" % (numIter, step, meanSquared, meanSquaredValid))

开发者ID:AkiraKane，项目名称:first-edition，代码行数:12，代码来源:ch07-listings.py

示例3: test_regression

    def test_regression(self):
        from pyspark.mllib.regression import LinearRegressionWithSGD, LassoWithSGD, \
                RidgeRegressionWithSGD
        data = [
            LabeledPoint(-1.0, self.scipy_matrix(2, {1: -1.0})),
            LabeledPoint(1.0, self.scipy_matrix(2, {1: 1.0})),
            LabeledPoint(-1.0, self.scipy_matrix(2, {1: -2.0})),
            LabeledPoint(1.0, self.scipy_matrix(2, {1: 2.0}))
        ]
        rdd = self.sc.parallelize(data)
        features = [p.features for p in data]

        lr_model = LinearRegressionWithSGD.train(rdd)
        self.assertTrue(lr_model.predict(features[0]) <= 0)
        self.assertTrue(lr_model.predict(features[1]) > 0)
        self.assertTrue(lr_model.predict(features[2]) <= 0)
        self.assertTrue(lr_model.predict(features[3]) > 0)

        lasso_model = LassoWithSGD.train(rdd)
        self.assertTrue(lasso_model.predict(features[0]) <= 0)
        self.assertTrue(lasso_model.predict(features[1]) > 0)
        self.assertTrue(lasso_model.predict(features[2]) <= 0)
        self.assertTrue(lasso_model.predict(features[3]) > 0)

        rr_model = RidgeRegressionWithSGD.train(rdd)
        self.assertTrue(rr_model.predict(features[0]) <= 0)
        self.assertTrue(rr_model.predict(features[1]) > 0)
        self.assertTrue(rr_model.predict(features[2]) <= 0)
        self.assertTrue(rr_model.predict(features[3]) > 0)

开发者ID:EronWright，项目名称:spark，代码行数:29，代码来源:tests.py

示例4: test_regression

    def test_regression(self):
        from pyspark.mllib.regression import LinearRegressionWithSGD, LassoWithSGD, \
            RidgeRegressionWithSGD
        from pyspark.mllib.tree import DecisionTree, RandomForest, GradientBoostedTrees
        data = [
            LabeledPoint(-1.0, [0, -1]),
            LabeledPoint(1.0, [0, 1]),
            LabeledPoint(-1.0, [0, -2]),
            LabeledPoint(1.0, [0, 2])
        ]
        rdd = self.sc.parallelize(data)
        features = [p.features.tolist() for p in data]

        lr_model = LinearRegressionWithSGD.train(rdd)
        self.assertTrue(lr_model.predict(features[0]) <= 0)
        self.assertTrue(lr_model.predict(features[1]) > 0)
        self.assertTrue(lr_model.predict(features[2]) <= 0)
        self.assertTrue(lr_model.predict(features[3]) > 0)

        lasso_model = LassoWithSGD.train(rdd)
        self.assertTrue(lasso_model.predict(features[0]) <= 0)
        self.assertTrue(lasso_model.predict(features[1]) > 0)
        self.assertTrue(lasso_model.predict(features[2]) <= 0)
        self.assertTrue(lasso_model.predict(features[3]) > 0)

        rr_model = RidgeRegressionWithSGD.train(rdd)
        self.assertTrue(rr_model.predict(features[0]) <= 0)
        self.assertTrue(rr_model.predict(features[1]) > 0)
        self.assertTrue(rr_model.predict(features[2]) <= 0)
        self.assertTrue(rr_model.predict(features[3]) > 0)

        categoricalFeaturesInfo = {0: 2}  # feature 0 has 2 categories
        dt_model = DecisionTree.trainRegressor(
            rdd, categoricalFeaturesInfo=categoricalFeaturesInfo)
        self.assertTrue(dt_model.predict(features[0]) <= 0)
        self.assertTrue(dt_model.predict(features[1]) > 0)
        self.assertTrue(dt_model.predict(features[2]) <= 0)
        self.assertTrue(dt_model.predict(features[3]) > 0)

        rf_model = RandomForest.trainRegressor(
            rdd, categoricalFeaturesInfo=categoricalFeaturesInfo, numTrees=100)
        self.assertTrue(rf_model.predict(features[0]) <= 0)
        self.assertTrue(rf_model.predict(features[1]) > 0)
        self.assertTrue(rf_model.predict(features[2]) <= 0)
        self.assertTrue(rf_model.predict(features[3]) > 0)

        gbt_model = GradientBoostedTrees.trainRegressor(
            rdd, categoricalFeaturesInfo=categoricalFeaturesInfo)
        self.assertTrue(gbt_model.predict(features[0]) <= 0)
        self.assertTrue(gbt_model.predict(features[1]) > 0)
        self.assertTrue(gbt_model.predict(features[2]) <= 0)
        self.assertTrue(gbt_model.predict(features[3]) > 0)

开发者ID:greatyan，项目名称:spark，代码行数:52，代码来源:tests.py

示例5: linearRegression_f

def linearRegression_f(mode):
    if   mode == "no_reg":
         model = LinearRegressionWithSGD.train(parsedData)
    elif mode == "L1_reg":
         model = LassoWithSGD.train(parsedData)
    elif mode == "L2_reg":
         model = RidgeRegressionWithSGD.train(parsedData)
    else:
        print("ERROR Mode")
        
    #Evaluate the model on training data
    # parsedData map method to get {train_data, predict_data} pairs 
    valuesAndPreds = parsedData.map(lambda p: (p.label, model.predict(p.features)))
    
    #calculate the key-value pairs to get MSE
    MSE = valuesAndPreds.map(lambda (v, p): (v-p)**2).reduce(lambda x, y: x+y)/valuesAndPreds.count()
    
  
    return MSE

开发者ID:ZaphyrRobin，项目名称:linear_regression_bill_vs_tip，代码行数:19，代码来源:tip_linear_regression.py

示例6: test_regression

    def test_regression(self):
        from pyspark.mllib.regression import LinearRegressionWithSGD, LassoWithSGD, \
            RidgeRegressionWithSGD
        from pyspark.mllib.tree import DecisionTree
        data = [
            LabeledPoint(-1.0, self.scipy_matrix(2, {1: -1.0})),
            LabeledPoint(1.0, self.scipy_matrix(2, {1: 1.0})),
            LabeledPoint(-1.0, self.scipy_matrix(2, {1: -2.0})),
            LabeledPoint(1.0, self.scipy_matrix(2, {1: 2.0}))
        ]
        rdd = self.sc.parallelize(data)
        features = [p.features for p in data]

        lr_model = LinearRegressionWithSGD.train(rdd)
        self.assertTrue(lr_model.predict(features[0]) <= 0)
        self.assertTrue(lr_model.predict(features[1]) > 0)
        self.assertTrue(lr_model.predict(features[2]) <= 0)
        self.assertTrue(lr_model.predict(features[3]) > 0)

        lasso_model = LassoWithSGD.train(rdd)
        self.assertTrue(lasso_model.predict(features[0]) <= 0)
        self.assertTrue(lasso_model.predict(features[1]) > 0)
        self.assertTrue(lasso_model.predict(features[2]) <= 0)
        self.assertTrue(lasso_model.predict(features[3]) > 0)

        rr_model = RidgeRegressionWithSGD.train(rdd)
        self.assertTrue(rr_model.predict(features[0]) <= 0)
        self.assertTrue(rr_model.predict(features[1]) > 0)
        self.assertTrue(rr_model.predict(features[2]) <= 0)
        self.assertTrue(rr_model.predict(features[3]) > 0)

        categoricalFeaturesInfo = {0: 2}  # feature 0 has 2 categories
        dt_model = DecisionTree.trainRegressor(rdd, categoricalFeaturesInfo=categoricalFeaturesInfo)
        self.assertTrue(dt_model.predict(features[0]) <= 0)
        self.assertTrue(dt_model.predict(features[1]) > 0)
        self.assertTrue(dt_model.predict(features[2]) <= 0)
        self.assertTrue(dt_model.predict(features[3]) > 0)

开发者ID:drewrobb，项目名称:spark，代码行数:37，代码来源:test_linalg.py

示例7: parsePoint

from pyspark.mllib.regression import LabeledPoint, RidgeRegressionWithSGD
from numpy import array
from pyspark import SparkContext
from pyspark.mllib.classification import LogisticRegressionWithLBFGS

# Load and parse the data
def parsePoint(line):
    values = [float(x) for x in line.split(";")]
    return LabeledPoint(values[11], values[0:10])


sc = SparkContext("local", "Simple App")
data = sc.textFile("../winequality.csv")
parsedData = data.map(parsePoint)

# Build the model
model = RidgeRegressionWithSGD.train(parsedData)

# Evaluating the model on training data
labelsAndPreds = parsedData.map(lambda p: (p.label, model.predict(p.features)))
trainErr = labelsAndPreds.filter(lambda (v, p): v != p).count() / float(parsedData.count())
print("Training Error = " + str(trainErr))

开发者ID:zhangning95，项目名称:CSYE7374Assignment1_Group6，代码行数:22，代码来源:RidgeRegressionWithSGD.py

示例8: SparkContext

import sys
from pyspark import SparkContext
from pyspark.mllib.regression import LabeledPoint
from pyspark.mllib.regression import RidgeRegressionWithSGD, RidgeRegressionModel
from pyspark.mllib.util import MLUtils

sc = SparkContext(appName="PythonWordCount")
data=MLUtils.loadLibSVMFile(sc, '/usr/hadoop/para_avg_halfsqrsum.txt')
traindata=MLUtils.loadLibSVMFile(sc, '/usr/hadoop/train_para.txt')
data_720=MLUtils.loadLibSVMFile(sc, '/usr/hadoop/para_avg_halfsqrsum_720.txt')
data_540=MLUtils.loadLibSVMFile(sc, '/usr/hadoop/para_avg_halfsqrsum_540.txt')
data_360=MLUtils.loadLibSVMFile(sc, '/usr/hadoop/para_avg_halfsqrsum_360.txt')

model = RidgeRegressionWithSGD.train(traindata)

predictions = model.predict(data.map(lambda x:x.features))
labelsandpredictions=data.map(lambda lp: lp.label).zip(predictions)
MSE = labelsandpredictions.map(lambda (v,p): (v-p)*(v-p)).sum()/float(data.count())
print("training MSE = "+str(MSE))
labelsandpredictions.saveAsTextFile("/usr/hadoop/hf_Ridge")
predictions_720 = model.predict(data_720.map(lambda x:x.features))
labelsandpredictions_720=data_720.map(lambda lp: lp.label).zip(predictions_720)
MSE_720 = labelsandpredictions_720.map(lambda (v,p): (v-p)*(v-p)).sum()/float(data_720.count())
print("training MSE_720 = "+str(MSE_720))
labelsandpredictions_720.saveAsTextFile("/usr/hadoop/hf_720_Ridge")
predictions_540 = model.predict(data_540.map(lambda x:x.features))
labelsandpredictions_540=data_540.map(lambda lp: lp.label).zip(predictions_540)
MSE_540 = labelsandpredictions_540.map(lambda (v,p): (v-p)*(v-p)).sum()/float(data_540.count())
print("training MSE_540 = "+str(MSE_540))
labelsandpredictions_540.saveAsTextFile("/usr/hadoop/hf_540_Ridge")
predictions_360 = model.predict(data_360.map(lambda x:x.features))

开发者ID:zjucsxxd，项目名称:Image-Quality-Assessment-For-Different-Resolution，代码行数:31，代码来源:Ridgereg.py

示例9: RidgeRegressionModel

def RidgeRegressionModel(dataPath, label, normalize, character, master, ispca):

    pca_n = 2
    sc = SparkContext(master)
    data = sc.textFile(dataPath)

# not RDD data 

    ndata = data.map(lambda line: line.split(character)).map(lambda part: (map(lambda x: float(x) ,part[0: len(part)])))

    if label == 0:
        ndata = ndata.map(lambda line: line[::-1])

    if normalize == 1:
        test_data = norm(ndata.collect())    
        norm_data = sc.parallelize(test_data)
        train_data = norm_data.map(lambda part: lbp(part[0], part[1]))   
     #raw_data = data.map(lambda line: line.split(character))

    else:
        test_data = ndata.map(lambda part: (part[0], part[1:len(part) - 1])).collect()
        train_data = ndata.map(lambda part: lbp(part[0], part[1: len(part) - 1]))

    if ispca == 1:
        pca = PCA(n_components = pca_n)
        pca_train = [test_data[i][1] for i in range(len(test_data))]
        pca_data = pca.fit(pca_train).transform(pca_train)

        test = []
        for i in range(len(pca_data)):
            test.append([test_data[i][0], pca_data[i]])

        train_data = sc.parallelize(test).map(lambda part: lbp(part[0], part[1]))
        test_data = test
    

    model_rr = rr.train(train_data)
    err_rr = 0.0
    size = len(train_data.collect())

 
    for i in range(size):
        err_rr = err_rr + abs(model_rr.predict(test_data[i][1]) - test_data[i][0]) 
    
    print "result:", err_rr/size

    String = "Ridge Regression Result:\n"
    String = String + str(model_rr.weights) + '\n'
    String = String + "Error: " + str(err_rr / size)

    sc.stop() 

    return String

开发者ID:Tomlong，项目名称:MLlib-UI，代码行数:53，代码来源:mlRidgeRegression.py

注：本文中的pyspark.mllib.regression.RidgeRegressionWithSGD类示例由纯净天空整理自Github/MSDocs等开源代码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。