邏輯回歸解決異網用戶分類問題
邏輯回歸演算法可以高效的解決二分類問題，也可以解決多分類只是沒有KNN高效，KNN天生就是解決多分類，不過KNN過於簡單，適用性沒有邏輯回歸好。我是想通過移動用戶的交際圈來判斷異網用戶是否25歲以下，想看看到底年輕人有多少去了聯通。這裡就提取了兩個特徵，一個是交際圈前5名的平均年齡，再一個就是平均聯繫親密度。然後利用網內對網內的數據進行模型訓練，準確率上去後再開始應用。
這塊能用到的地方還是挺多的，比如判斷用戶是否會訂購業務，是否即將離網，是否套餐降檔等等，但是，處理實際的用戶多分類問題，最佳的還是神經網路深度學習，但是運算量太大了，我的機器配置過低了。實際應用邏輯回歸需要改進的地方比較多，不像理論數據，簡單運算就能90以上準確率。還得繼續學學別人的優化演算法。
用的是python3.6，參考代碼是2.7的，花了點時間看懂然後改代碼，改參數，還是不熟，報錯就百度，搞了一個多小時終於OK了。代碼和結果如下
# -*- coding: utf-8 -*-
"""
Created on Wed Apr 11 19:49:13 2018
from numpy import *
import matplotlib.pyplot as plt
import time
# calculate the sigmoid function
def sigmoid(inX):
return 1.0 / (1 + exp(-inX))
# train a logistic regression model using some optional optimize algorithm
# input: train_x is a mat datatype, each row stands for one sample
# train_y is mat datatype too, each row is the corresponding label
# opts is optimize option include step and maximum number of iterations
def trainLogRegres(train_x, train_y, opts):
# calculate training time
#startTime = time.time()
numSamples, numFeatures = shape(train_x)
alpha = opts[alpha]; maxIter = opts[maxIter]
weights = ones((numFeatures, 1))
# optimize through gradient descent algorilthm
for k in range(maxIter):
if opts[optimizeType] == gradDescent: # gradient descent algorilthm
output = sigmoid(train_x * weights)
error = train_y - output
weights = weights + alpha * train_x.transpose() * error
elif opts[optimizeType] == stocGradDescent: # stochastic gradient descent
for i in range(numSamples):
output = sigmoid(train_x[i, :] * weights)
error = train_y[i, 0] - output
weights = weights + alpha * train_x[i, :].transpose() * error
elif opts[optimizeType] == smoothStocGradDescent: # smooth stochastic gradient descent
# randomly select samples to optimize for reducing cycle fluctuations
dataIndex = list(range(numSamples))
for i in range(numSamples):
alpha = 4.0 / (1.0 + k + i) + 0.01
randIndex = int(random.uniform(0, len(dataIndex)))
output = sigmoid(train_x[randIndex, :] * weights)
error = train_y[randIndex, 0] - output
weights = weights + alpha * train_x[randIndex, :].transpose() * error
del(dataIndex[randIndex]) # during one interation, delete the optimized sample
else:
raise NameError(Not support optimize method type!)
print( Congratulations, training complete! )
return weights
# test your trained Logistic Regression model given test set
def testLogRegres(weights, test_x, test_y):
numSamples, numFeatures = shape(test_x)
matchCount = 0
for i in range(numSamples):
predict = sigmoid(test_x[i, :] * weights)[0, 0] > 0.5
if predict == bool(test_y[i, 0]):
matchCount += 1
accuracy = float(matchCount) / numSamples
return accuracy
# show your trained logistic regression model only available with 2-D data
def showLogRegres(weights, train_x, train_y):
# notice: train_x and train_y is mat datatype
numSamples, numFeatures = shape(train_x)
if numFeatures != 3:
print ("Sorry! I can not draw because the dimension of your data is not 2!")
return 1
# draw all samples
for i in range(numSamples):
if int(train_y[i, 0]) == 0:
plt.plot(train_x[i, 1], train_x[i, 2], or)
elif int(train_y[i, 0]) == 1:
plt.plot(train_x[i, 1], train_x[i, 2], ob)
# draw the classify line
min_x = min(train_x[:, 1])[0, 0]
max_x = max(train_x[:, 1])[0, 0]
weights = weights.getA() # convert mat to array
y_min_x = float(-weights[0] - weights[1] * min_x) / weights[2]
y_max_x = float(-weights[0] - weights[1] * max_x) / weights[2]
plt.plot([min_x, max_x], [y_min_x, y_max_x], -g)
plt.xlabel(X1); plt.ylabel(X2)
plt.show()
from numpy import *
import matplotlib.pyplot as plt
import time
def loadData():
train_x = []
train_y = []
fileIn = open(E:/ceshi/luoji001.txt)
for line in fileIn.readlines():
lineArr = line.strip().split()
train_x.append([1.0, float(lineArr[0]), float(lineArr[1])])
train_y.append(float(lineArr[2]))
return mat(train_x), mat(train_y).transpose()
## step 1: load data
print ("step 1: load data...")
train_x, train_y = loadData()
test_x = train_x; test_y = train_y
## step 2: training...
print ("step 2: training...")
opts = {alpha: 0.01, maxIter: 20, optimizeType: smoothStocGradDescent}
optimalWeights = trainLogRegres(train_x, train_y, opts)
## step 3: testing
print ("step 3: testing...")
accuracy = testLogRegres(optimalWeights, test_x, test_y)
## step 4: show the result
print ( "step 4: show the result...")
print ( The classify accuracy is: %.3f%% % (accuracy * 100))
showLogRegres(optimalWeights, train_x, train_y)
輸出：step 1: load data...
step 2: training...Congratulations, training complete! step 3: testing...step 4: show the result...The classify accuracy is: 86.262%推薦閱讀：