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Merge pull request #155 from jiangzhonglian/master

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更新 15章 理论和项目案例
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片刻
2017-09-15 17:16:52 +08:00
committed by GitHub
parent 3a8513bcf4 a901e7f775
commit fc0e30aa91
4 changed files with 123 additions and 54 deletions
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41
docs/15.大数据与MapReduce.md
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@@ -143,10 +143,47 @@ Pegasos是指原始估计梯度求解器(Peimal Estimated sub-GrAdient Solver)
> 训练算法
[完整代码地址](https://github.com/apachecn/MachineLearning/blob/master/src/python/2.KNN/kNN.py): <https://github.com/apachecn/MachineLearning/blob/master/src/python/2.KNN/kNN.py>
```python
def batchPegasos(dataSet, labels, lam, T, k):
"""batchPegasos()
Args:
dataMat 特征集合
labels 分类结果集合
lam 固定值，微调的空间
T 迭代次数
k 待处理列表大小
Returns:
w 权重向量
"""
m, n = shape(dataSet)
w = zeros(n)
dataIndex = range(m)
for t in range(1, T+1):
wDelta = mat(zeros(n)) # 重置 wDelta
我们继续看 Python 版本的代码实现。
# 它是学习率，代表了权重调整幅度的大小。（也可以理解为随机梯度的步长）
# 输入T和K分别设定了迭代次数和待处理列表的大小。在T次迭代过程中，每次需要重新计算eta
eta = 1.0/(lam*t)
random.shuffle(dataIndex)
for j in range(k): # 全部的训练集 内循环中执行批处理，将分类错误的值全部做累加后更新权重向量
i = dataIndex[j]
p = predict(w, dataSet[i, :]) # mapper 代码
# 如果预测正确，并且预测结果的绝对值>=1, 认为没问题。
# 否则算是预测错误, 通过预测错误的结果，来累计更新w.
if labels[i]*p < 1: # mapper 代码
wDelta += labels[i]*dataSet[i, :].A # 累积变化
# w通过不断的随机梯度的方式来优化
w = (1.0 - 1/t)*w + (eta/k)*wDelta # 在每个 T上应用更改
# print '-----', w
# print '++++++', w
return w
```
[完整代码地址](https://github.com/apachecn/MachineLearning/blob/master/src/python/15.BigData_MapReduce/pegasos.py): <https://github.com/apachecn/MachineLearning/blob/master/src/python/15.BigData_MapReduce/pegasos.py>
[MR版本的代码位置](https://github.com/apachecn/MachineLearning/blob/master/src/python/15.BigData_MapReduce/mrSVM.py): <https://github.com/apachecn/MachineLearning/blob/master/src/python/15.BigData_MapReduce/mrSVM.py>
* * *

24
src/python/15.BigData_MapReduce/mrSVM.py
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@@ -2,9 +2,11 @@
# coding:utf8
'''
Created on 2017-04-07
Update on 2017-06-20
MapReduce version of Pegasos SVM
Using mrjob to automate job flow
@author: Peter/ApacheCN-xy
@author: Peter/ApacheCN-xy/片刻
《机器学习实战》更新地址：https://github.com/apachecn/MachineLearning
'''
from mrjob.job import MRJob
@@ -17,14 +19,14 @@ class MRsvm(MRJob):
def __init__(self, *args, **kwargs):
super(MRsvm, self).__init__(*args, **kwargs)
self.data = pickle.load(open('input/15.BigData_MapReduce/svmDat27'))
self.data = pickle.load(open('/opt/git/MachineLearning/input/15.BigData_MapReduce/svmDat27'))
self.w = 0
self.eta = 0.69
self.dataList = []
self.k = self.options.batchsize
self.numMappers = 1
self.t = 1 # iteration number
def configure_options(self):
super(MRsvm, self).configure_options()
self.add_passthrough_option(
@@ -42,20 +44,20 @@ class MRsvm(MRJob):
self.w = inVals[1]
elif inVals[0] == 'x':
self.dataList.append(inVals[1]) # 累积数据点计算
elif inVals[0] == 't':
elif inVals[0] == 't': # 迭代次数
self.t = inVals[1]
else:
self.eta = inVals # 这用于 debug， eta未在map中使用
self.eta = inVals # 这用于 debug， eta未在map中使用
def map_fin(self):
labels = self.data[:,-1]
X = self.data[:, 0:-1] # 将数据重新形成 X 和 Y
if self.w == 0:
labels = self.data[:, -1]
X = self.data[:, :-1] # 将数据重新形成 X 和 Y
if self.w == 0:
self.w = [0.001] * shape(X)[1] # 在第一次迭代时，初始化 w
for index in self.dataList:
p = mat(self.w)*X[index, :].T # calc p=w*dataSet[key].T
p = mat(self.w)*X[index, :].T # calc p=w*dataSet[key].T
if labels[index]*p < 1.0:
yield (1, ['u', index]) # 确保一切数据包含相同的key
yield (1, ['u', index]) # 确保一切数据包含相同的key
yield (1, ['w', self.w]) # 它们将在同一个 reducer
yield (1, ['t', self.t])
@@ -66,7 +68,7 @@ class MRsvm(MRJob):
elif valArr[0] == 'w':
self.w = valArr[1]
elif valArr[0] == 't':
self.t = valArr[1]
self.t = valArr[1]
labels = self.data[:, -1]
X = self.data[:, 0:-1]

81
src/python/15.BigData_MapReduce/pegasos.py
View File

@@ -10,24 +10,26 @@ from numpy import *
def loadDataSet(fileName):
dataMat = []; labelMat = []
dataMat = []
labelMat = []
fr = open(fileName)
for line in fr.readlines():
lineArr = line.strip().split('\t')
#dataMat.append([float(lineArr[0]), float(lineArr[1]), float(lineArr[2])])
# dataMat.append([float(lineArr[0]), float(lineArr[1]), float(lineArr[2])])
dataMat.append([float(lineArr[0]), float(lineArr[1])])
labelMat.append(float(lineArr[2]))
return dataMat,labelMat
return dataMat, labelMat
def seqPegasos(dataSet, labels, lam, T):
m,n = shape(dataSet); w = zeros(n)
m, n = shape(dataSet)
w = zeros(n)
for t in range(1, T+1):
i = random.randint(m)
eta = 1.0/(lam*t)
p = predict(w, dataSet[i,:])
p = predict(w, dataSet[i, :])
if labels[i]*p < 1:
w = (1.0 - 1/t)*w + eta*labels[i]*dataSet[i,:]
w = (1.0 - 1/t)*w + eta*labels[i]*dataSet[i, :]
else:
w = (1.0 - 1/t)*w
print w
@@ -35,29 +37,49 @@ def seqPegasos(dataSet, labels, lam, T):
def predict(w, x):
return w*x.T
return w*x.T # 就是预测 y 的值
def batchPegasos(dataSet, labels, lam, T, k):
m,n = shape(dataSet); w = zeros(n);
"""batchPegasos()
Args:
dataMat 特征集合
labels 分类结果集合
lam 固定值，微调的空间
T 迭代次数
k 待处理列表大小
Returns:
w 权重向量
"""
m, n = shape(dataSet)
w = zeros(n)
dataIndex = range(m)
for t in range(1, T+1):
wDelta = mat(zeros(n)) # 重置 wDelta
wDelta = mat(zeros(n)) # 重置 wDelta
# 它是学习率，代表了权重调整幅度的大小。（也可以理解为随机梯度的步长）
# 输入T和K分别设定了迭代次数和待处理列表的大小。在T次迭代过程中，每次需要重新计算eta
eta = 1.0/(lam*t)
random.shuffle(dataIndex)
for j in range(k):# 全部的训练集
for j in range(k): # 全部的训练集 内循环中执行批处理，将分类错误的值全部做累加后更新权重向量
i = dataIndex[j]
p = predict(w, dataSet[i,:]) # mapper 代码
if labels[i]*p < 1: # mapper 代码
wDelta += labels[i]*dataSet[i,:].A # 累积变化
w = (1.0 - 1/t)*w + (eta/k)*wDelta # 在每个 T上应用更改
p = predict(w, dataSet[i, :]) # mapper 代码
# 如果预测正确，并且预测结果的绝对值>=1, 认为没问题。
# 否则算是预测错误, 通过预测错误的结果，来累计更新w.
if labels[i]*p < 1: # mapper 代码
wDelta += labels[i]*dataSet[i, :].A # 累积变化
# w通过不断的随机梯度的方式来优化
w = (1.0 - 1/t)*w + (eta/k)*wDelta # 在每个 T上应用更改
# print '-----', w
# print '++++++', w
return w
datArr,labelList = loadDataSet('testSet.txt')
datArr, labelList = loadDataSet('input/15.BigData_MapReduce/testSet.txt')
datMat = mat(datArr)
#finalWs = seqPegasos(datMat, labelList, 2, 5000)
# finalWs = seqPegasos(datMat, labelList, 2, 5000)
finalWs = batchPegasos(datMat, labelList, 2, 50, 100)
print finalWs
@@ -65,20 +87,25 @@ import matplotlib
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111)
x1=[]; y1=[]; xm1=[]; ym1=[]
x1 = []
y1 = []
xm1 = []
ym1 = []
for i in range(len(labelList)):
if labelList[i] == 1.0:
x1.append(datMat[i,0]); y1.append(datMat[i,1])
x1.append(datMat[i, 0])
y1.append(datMat[i, 1])
else:
xm1.append(datMat[i,0]); ym1.append(datMat[i,1])
xm1.append(datMat[i, 0])
ym1.append(datMat[i, 1])
ax.scatter(x1, y1, marker='s', s=90)
ax.scatter(xm1, ym1, marker='o', s=50, c='red')
x = arange(-6.0, 8.0, 0.1)
y = (-finalWs[0,0]*x - 0)/finalWs[0,1]
#y2 = (0.43799*x)/0.12316
y2 = (0.498442*x)/0.092387 #2 iterations
ax.plot(x,y)
ax.plot(x,y2,'g-.')
ax.axis([-6,8,-4,5])
ax.legend(('50 Iterations', '2 Iterations') )
y = (-finalWs[0, 0]*x - 0)/finalWs[0, 1]
# y2 = (0.43799*x)/0.12316
y2 = (0.498442*x)/0.092387 # 2 iterations
ax.plot(x, y)
ax.plot(x, y2, 'g-.')
ax.axis([-6, 8, -4, 5])
ax.legend(('50 Iterations', '2 Iterations'))
plt.show()

31
src/python/15.BigData_MapReduce/proximalSVM.py
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@@ -1,7 +1,10 @@
#!/usr/bin/python
# coding:utf8
'''
Created on Feb 25, 2011
@author: Peter
Created on 2011-02-25
Update on 2017-06-20
@author: Peter/ApacheCN-xy/片刻
《机器学习实战》更新地址：https://github.com/apachecn/MachineLearning
'''
import numpy
@@ -9,28 +12,28 @@ def map(key, value):
# input key= class for one training example, e.g. "-1.0"
classes = [float(item) for item in key.split(",")] # e.g. [-1.0]
D = numpy.diag(classes)
# input value = feature vector for one training example, e.g. "3.0, 7.0, 2.0"
featurematrix = [float(item) for item in value.split(",")]
A = numpy.matrix(featurematrix)
# create matrix E and vector e
e = numpy.matrix(numpy.ones(len(A)).reshape(len(A),1))
E = numpy.matrix(numpy.append(A,-e,axis=1))
e = numpy.matrix(numpy.ones(len(A)).reshape(len(A), 1))
E = numpy.matrix(numpy.append(A, -e, axis=1))
# create a tuple with the values to be used by reducer
# and encode it with base64 to avoid potential trouble with '\t' and '\n' used
# as default separators in Hadoop Streaming
producedvalue = base64.b64encode(pickle.dumps( (E.T*E, E.T*D*e) )
producedvalue = base64.b64encode(pickle.dumps( (E.T*E, E.T*D*e))
# note: a single constant key "producedkey" sends to only one reducer
# somewhat "atypical" due to low degree of parallism on reducer side
print "producedkey\t%s" % (producedvalue)
def reduce(key, values, mu=0.1):
sumETE = None
sumETDe = None
# key isn't used, so ignoring it with _ (underscore).
for _, value in values:
# unpickle values
@@ -39,13 +42,13 @@ def reduce(key, values, mu=0.1):
# create the I/mu with correct dimensions
sumETE = numpy.matrix(numpy.eye(ETE.shape[1])/mu)
sumETE += ETE
if sumETDe == None:
# create sumETDe with correct dimensions
sumETDe = ETDe
else:
sumETDe += ETDe
# note: omega = result[:-1] and gamma = result[-1]
# but printing entire vector as output
result = sumETE.I*sumETDe