添加15章代码和测试数据

src/python/15.BigData_MapReduce/mrSVM.py

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+'''
+Created on 2017-04-07
+MapReduce version of Pegasos SVM
+Using mrjob to automate job flow
+@author: Peter/ApacheCN-xy
+'''
+from mrjob.job import MRJob
+
+import pickle
+from numpy import *
+
+class MRsvm(MRJob):
+    DEFAULT_INPUT_PROTOCOL = 'json_value'
+    
+    def __init__(self, *args, **kwargs):
+        super(MRsvm, self).__init__(*args, **kwargs)
+        self.data = pickle.load(open('C:\Users\Peter\machinelearninginaction\Ch15\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(
+            '--iterations', dest='iterations', default=2, type='int',
+            help='T: number of iterations to run')
+        self.add_passthrough_option(
+            '--batchsize', dest='batchsize', default=100, type='int',
+            help='k: number of data points in a batch')
+    
+    def map(self, mapperId, inVals): # 需要 2 个参数
+        #input: nodeId, ('w', w-vector) OR nodeId, ('x', int)
+        if False: yield
+        if inVals[0]=='w':                  # 积累 w向量
+            self.w = inVals[1]
+        elif inVals[0]=='x':
+            self.dataList.append(inVals[1])# 累积数据点计算
+        elif inVals[0]=='t': self.t = inVals[1] 
+        else: 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: 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 
+            if labels[index]*p < 1.0:
+                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])
+
+    def reduce(self, _, packedVals):
+        for valArr in packedVals: # 从流输入获取值
+            if valArr[0]=='u':  self.dataList.append(valArr[1])
+            elif valArr[0]=='w': self.w = valArr[1]
+            elif valArr[0]=='t':  self.t = valArr[1] 
+        labels = self.data[:,-1]; X=self.data[:,0:-1]
+        wMat = mat(self.w);   wDelta = mat(zeros(len(self.w)))
+        for index in self.dataList:
+            wDelta += float(labels[index])*X[index,:] #wDelta += label*dataSet
+        eta = 1.0/(2.0*self.t)       #calc new: eta
+        #calc new: w = (1.0 - 1/t)*w + (eta/k)*wDelta
+        wMat = (1.0 - 1.0/self.t)*wMat + (eta/self.k)*wDelta
+        for mapperNum in range(1,self.numMappers+1):
+            yield (mapperNum, ['w', wMat.tolist()[0] ]) #发出 w
+            if self.t < self.options.iterations:
+                yield (mapperNum, ['t', self.t+1])# 增量 T
+                for j in range(self.k/self.numMappers):#emit random ints for mappers iid
+                    yield (mapperNum, ['x', random.randint(shape(self.data)[0]) ])
+        
+    def steps(self):
+        return ([self.mr(mapper=self.map, reducer=self.reduce, 
+                         mapper_final=self.map_fin)]*self.options.iterations)
+
+if __name__ == '__main__':
+    MRsvm.run()