Create FM_train.py

Author: zhaozhiyong19890102

Chapter_3 Factorization Machine/FM_train.py

@@ -0,0 +1,182 @@
+# coding:UTF-8
+'''
+Date:20160831
+@author: zhaozhiyong
+'''
+import numpy as np
+from random import normalvariate  # 正态分布
+
+def loadDataSet(data):
+    '''导入训练数据
+    input:  data(string)训练数据
+    output: dataMat(list)特征
+            labelMat(list)标签
+    '''
+    dataMat = []
+    labelMat = []   
+    fr = open(data)  # 打开文件  
+    for line in fr.readlines():
+        lines = line.strip().split("\t")
+        lineArr = []
+        
+        for i in xrange(len(lines) - 1):
+            lineArr.append(float(lines[i]))
+        dataMat.append(lineArr)
+        
+        #labelMat.append(float(lines[-1]) * 2 - 1)  # 转换成{-1,1}
+        labelMat.append(float(lines[-1]))
+    fr.close()
+    return dataMat, labelMat
+
+def sigmoid(inx):
+    return 1.0 / (1 + np.exp(-inx))
+
+def initialize_v(n, k):
+    '''初始化交叉项
+    input:  n(int)特征的个数
+            k(int)FM模型的度
+    output: v(mat):交叉项的系数权重
+    '''
+    v = np.mat(np.zeros((n, k)))
+    
+    for i in xrange(n):
+        for j in xrange(k):
+            # 利用正态分布生成每一个权重
+            v[i, j] = normalvariate(0, 0.2)
+    return v
+
+def stocGradAscent(dataMatrix, classLabels, k, max_iter, alpha):
+    '''利用随机梯度下降法训练FM模型
+    input:  dataMatrix(mat)特征
+            classLabels(mat)标签
+            k(int)v的维数
+            max_iter(int)最大迭代次数
+            alpha(float)学习率
+    output: w0(float),w(mat),v(mat):权重
+    '''
+    m, n = np.shape(dataMatrix)
+    # 1、初始化参数
+    w = np.zeros((n, 1))  # 其中n是特征的个数
+    w0 = 0  # 偏置项
+    v = initialize_v(n, k)  # 初始化V
+    
+    # 2、训练
+    for it in xrange(max_iter):
+        for x in xrange(m):  # 随机优化，对每一个样本而言的
+            inter_1 = dataMatrix[x] * v
+            inter_2 = np.multiply(dataMatrix[x], dataMatrix[x]) * \
+             np.multiply(v, v)  # multiply对应元素相乘
+            # 完成交叉项
+            interaction = np.sum(np.multiply(inter_1, inter_1) - inter_2) / 2.
+            p = w0 + dataMatrix[x] * w + interaction  # 计算预测的输出
+            loss = sigmoid(classLabels[x] * p[0, 0]) - 1
+        
+            w0 = w0 - alpha * loss * classLabels[x]
+            for i in xrange(n):
+                if dataMatrix[x, i] != 0:
+                    w[i, 0] = w[i, 0] - alpha * loss * classLabels[x] * dataMatrix[x, i]
+                    
+                    for j in xrange(k):
+                        v[i, j] = v[i, j] - alpha * loss * classLabels[x] * \
+                        (dataMatrix[x, i] * inter_1[0, j] -\
+                          v[i, j] * dataMatrix[x, i] * dataMatrix[x, i])
+        
+        # 计算损失函数的值
+        if it % 1000 == 0:
+            print "\t------- iter: ", it, " , cost: ", \
+            getCost(getPrediction(np.mat(dataTrain), w0, w, v), classLabels)
+    
+    # 3、返回最终的FM模型的参数
+    return w0, w, v
+
+def getCost(predict, classLabels):
+    '''计算预测准确性
+    input:  predict(list)预测值
+            classLabels(list)标签
+    output: error(float)计算损失函数的值
+    '''
+    m = len(predict)
+    error = 0.0
+    for i in xrange(m):
+        error -=  np.log(sigmoid(predict[i] * classLabels[i] ))  
+    return error
+
+def getPrediction(dataMatrix, w0, w, v):
+    '''得到预测值
+    input:  dataMatrix(mat)特征
+            w(int)常数项权重
+            w0(int)一次项权重
+            v(float)交叉项权重
+    output: result(list)预测的结果
+    '''
+    m = np.shape(dataMatrix)[0]   
+    result = []
+    for x in xrange(m):
+        
+        inter_1 = dataMatrix[x] * v
+        inter_2 = np.multiply(dataMatrix[x], dataMatrix[x]) * \
+         np.multiply(v, v)  # multiply对应元素相乘
+        # 完成交叉项
+        interaction = np.sum(np.multiply(inter_1, inter_1) - inter_2) / 2.
+        p = w0 + dataMatrix[x] * w + interaction  # 计算预测的输出        
+        pre = sigmoid(p[0, 0])        
+        result.append(pre)        
+    return result
+
+def getAccuracy(predict, classLabels):
+    '''计算预测准确性
+    input:  predict(list)预测值
+            classLabels(list)标签
+    output: float(error) / allItem(float)错误率
+    '''
+    m = len(predict)
+    allItem = 0
+    error = 0
+    for i in xrange(m):
+        allItem += 1
+        if float(predict[i]) < 0.5 and classLabels[i] == 1.0:
+            error += 1
+        elif float(predict[i]) >= 0.5 and classLabels[i] == -1.0:
+            error += 1
+        else:
+            continue
+    return float(error) / allItem      
+
+def save_model(file_name, w0, w, v):
+    '''保存训练好的FM模型
+    input:  file_name(string):保存的文件名
+            w0(float):偏置项
+            w(mat):一次项的权重
+            v(mat):交叉项的权重
+    '''
+    f = open(file_name, "w")
+    # 1、保存w0
+    f.write(str(w0) + "\n")
+    # 2、保存一次项的权重
+    w_array = []
+    m = np.shape(w)[0]
+    for i in xrange(m):
+        w_array.append(str(w[i, 0]))
+    f.write("\t".join(w_array) + "\n")
+    # 3、保存交叉项的权重
+    m1 , n1 = np.shape(v)
+    for i in xrange(m1):
+        v_tmp = []
+        for j in xrange(n1):
+            v_tmp.append(str(v[i, j]))
+        f.write("\t".join(v_tmp) + "\n")
+    f.close()
+     
+   
+if __name__ == "__main__":
+    # 1、导入训练数据
+    print "---------- 1.load data ---------"
+    dataTrain, labelTrain = loadDataSet("data_1.txt")
+    print "---------- 2.learning ---------"
+    # 2、利用随机梯度训练FM模型
+    w0, w, v = stocGradAscent(np.mat(dataTrain), labelTrain, 2, 20000, 0.01)
+    predict_result = getPrediction(np.mat(dataTrain), w0, w, v)  # 得到训练的准确性
+    print "----------training accuracy: %f" % (1 - getAccuracy(predict_result, labelTrain))
+    print "---------- 3.save result ---------"
+    # 3、保存训练好的FM模型
+    save_model("weights", w0, w, v)