ClassificationExperiment.ipynb

﻿{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7637737239727289\n"
     ]
    },
    {
     "data": {
      "image/png": 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GTsYCBLtZUVGlI9un5tFcxfviDBFZu6NRaZjdazbTu09n95ndjNswjjOFZ+o9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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18b88f4fc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %load SVM2_ALL.py\n",
    "#本代码用于完成机器学习实验一中SVM线性分类的操作\n",
    "#by 王松盛\n",
    "from sklearn.datasets import load_svmlight_file\n",
    "from sklearn.model_selection import train_test_split\n",
    "from matplotlib.ticker import LogLocator,FormatStrFormatter\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from numpy import linalg\n",
    "import random\n",
    "import math\n",
    "import copy\n",
    "#先读取数据集\n",
    "def GetData():\n",
    "    train_x,train_y = load_svmlight_file('./dataset/a9a.txt', 123)\n",
    "    train_x = train_x.toarray()\n",
    "    test_x,test_y = load_svmlight_file('./dataset/a9a_test.txt',123)\n",
    "    test_x = test_x.toarray()\n",
    "    return train_x,train_y,test_x,test_y\n",
    "#初始化模型:随机初始化\n",
    "def getInitData(seedNum):\n",
    "    np.random.seed(seedNum)\n",
    "    w = np.random.rand(124)\n",
    "    return w\n",
    "#输出线性分类的结果\n",
    "def linearClassifier(x,w,b):\n",
    "    #print ('w',w,'x',x,'b',b)\n",
    "    #input()\n",
    "    val = (w*x).sum()+b\n",
    "    if val > 0:\n",
    "        return 1,val\n",
    "    else:\n",
    "        return -1,val\n",
    "#本代码用于完成机器学习实验一中SVM线性分类的操作\n",
    "#by 王松盛\n",
    "from sklearn.datasets import load_svmlight_file\n",
    "from sklearn.model_selection import train_test_split\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from numpy import linalg\n",
    "import random\n",
    "import math\n",
    "import copy\n",
    "#先读取数据集\n",
    "def GetData():\n",
    "    train_x,train_y = load_svmlight_file('./dataset/a9a.txt', 123)\n",
    "    train_x = train_x.toarray()\n",
    "    test_x,test_y = load_svmlight_file('./dataset/a9a_test.txt',123)\n",
    "    test_x = test_x.toarray()\n",
    "    return train_x,train_y,test_x,test_y\n",
    "#初始化模型:随机初始化\n",
    "def getInitData(seedNum):\n",
    "    np.random.seed(seedNum)\n",
    "    w = np.random.rand(124)\n",
    "    return w\n",
    "#输出线性分类的结果\n",
    "def linearClassifier(x,w,b):\n",
    "    #print ('w',w,'x',x,'b',b)\n",
    "    #input()\n",
    "    val = (w*x).sum()+b\n",
    "    if val > 0:\n",
    "        return 1,val\n",
    "    else:\n",
    "        return -1,val\n",
    "#输出w和b梯度\n",
    "def GetGradient(x,y,w,c,start,batch):\n",
    "    b = w[-1]\n",
    "    w = np.array(w.tolist()[:-1])\n",
    "    wGrad = np.array([0.0] * len(w))\n",
    "    bGrad = 0.0\n",
    "    for i in range(start,start + batch):\n",
    "        temp = 1-y[i] *((w * x[i]).sum() + b)\n",
    "        if temp >= 0:\n",
    "            wGrad += w - c * y[i] * x[i]\n",
    "            bGrad -= c * y.sum()\n",
    "        else:\n",
    "            wGrad += w\n",
    "    wGrad /= batch\n",
    "    bGrad /= batch\n",
    "    return np.array(wGrad.tolist() + [b])\n",
    "#梯度下降算法\n",
    "def GradientDescent(x,y,w,c,steplen,n_iteration,batch):\n",
    "    w_Group = [w]\n",
    "    for i in range(n_iteration):\n",
    "        #if i % 100 == 0:\n",
    "           # print (i)\n",
    "        start = int(random.random() * (len(x) - batch))\n",
    "        w = np.copy(w_Group[-1])\n",
    "        wGrad = GetGradient(x,y,w,c,start,batch)\n",
    "        w -= wGrad * steplen\n",
    "        # temp = wGrad.tolist() + [bGrad]\n",
    "        # temp = np.array(temp)\n",
    "        # temp /= linalg.norm(temp) * steplen\n",
    "        # w -= temp[:-1]\n",
    "        # b -= temp[-1]\n",
    "        w_Group.append(w)\n",
    "    return w_Group\n",
    "def GradientDescent_NAG(x,y,w,c,learningRate,n_iteration,batch,gamma):\n",
    "    w_Group = [w]\n",
    "    v = np.array([0.0]*124)\n",
    "    for i in range(n_iteration):\n",
    "        #if i % 1000 == 0:\n",
    "            #print (i)\n",
    "        index = int(random.random() * (len(x)-batch))\n",
    "        w = np.copy(w)\n",
    "        xGroup = []\n",
    "        yGroup = []\n",
    "        wGrad = GetGradient(x,y,w-gamma * v,c,index,batch)\n",
    "        v = gamma * v + learningRate * wGrad\n",
    "        # wGrad1,bGrad1 = GetGradient1(x,y,w,b)\n",
    "        # if bGrad != bGrad1:\n",
    "        #     print (i)\n",
    "        #     print (bGrad,bGrad1)\n",
    "        #     print (x,y,w,b)\n",
    "        #     input()\n",
    "        w -= v\n",
    "        w_Group.append(w)\n",
    "    return w_Group\n",
    "def GradientDescent_RMSProp(x,y,w,c,learningRate,n_iteration,batch,gamma,ita):\n",
    "    w_Group = [w]\n",
    "    Gt = 0\n",
    "    for i in range(n_iteration):\n",
    "        #if i % 1000 == 0:\n",
    "            #print (i)\n",
    "        index = int(random.random() * (len(x)-batch))\n",
    "        w = np.copy(w)\n",
    "        xGroup = []\n",
    "        yGroup = []\n",
    "        wGrad = GetGradient(x,y,w,c,index,batch)\n",
    "        Gt = gamma * Gt + (1-gamma)* wGrad.dot(wGrad)\n",
    "        w -= learningRate / math.sqrt(Gt + ita) * wGrad\n",
    "        w_Group.append(w)\n",
    "    return w_Group\n",
    "def GradientDescent_AdaDelta(x,y,w,c,n_iteration,batch,gamma,ita):\n",
    "    w_Group = [w]\n",
    "    Gt = 0.0\n",
    "    delta = 0.0\n",
    "    for i in range(n_iteration):\n",
    "        #if i % 1000 == 0:\n",
    "            #print (i)\n",
    "        index = int(random.random() * (len(x)-batch))\n",
    "        w = np.copy(w)\n",
    "        wGrad = GetGradient(x,y,w,c,index,batch)\n",
    "        Gt = gamma * Gt + (1-gamma)* wGrad.dot(wGrad)\n",
    "        deltaTheta = - (math.sqrt(delta+ita) / math.sqrt(Gt + ita)) * wGrad\n",
    "        w += deltaTheta\n",
    "        delta = gamma * delta + (1-gamma) * deltaTheta.dot(deltaTheta)\n",
    "        w_Group.append(w)\n",
    "    return w_Group\n",
    "def GradientDescent_Adam(x,y,w,c,learningRate,n_iteration,batch,gamma,beta,ita):\n",
    "    w_Group = [w]\n",
    "    Gt = 0\n",
    "    m = np.array([0.0]*len(w))\n",
    "    for i in range(n_iteration):\n",
    "        #if i % 1000 == 0:\n",
    "            #print (i)\n",
    "        index = int(random.random() * (len(x)-batch))\n",
    "        w = np.copy(w)\n",
    "        wGrad = GetGradient(x,y,w,c,index,batch)\n",
    "        m = beta * m + (1-beta) * wGrad\n",
    "        Gt = gamma * Gt + (1-gamma)* wGrad.dot(wGrad)\n",
    "        a = learningRate * (math.sqrt(1.0 - pow(gamma,i))/ (1.0 - pow(beta,i+1)))\n",
    "        w -= a * m / math.sqrt(Gt + ita)\n",
    "        w_Group.append(w)\n",
    "    return w_Group\n",
    "def loss(x,y,w,c):\n",
    "    sumWr = 0\n",
    "    b = w[-1]\n",
    "    w = np.array(w.tolist()[:-1])\n",
    "    for i in range(len(x)):\n",
    "        temp = 1 - y[i] * ((w * x[i]).sum() + b)\n",
    "        if temp <= 0:\n",
    "            continue\n",
    "        else:\n",
    "            sumWr += temp\n",
    "    return ((w*w).sum()*0.5 + c* sumWr) / len(x)\n",
    "def Precision(x,y,w):\n",
    "    b = w[-1]\n",
    "    w = np.array(w.tolist()[:-1])\n",
    "    right = 0\n",
    "    for i in range(len(x)):\n",
    "        ans = linearClassifier(x[i],w,b)[0]\n",
    "        if ans == y[i]:\n",
    "            right += 1\n",
    "    return float(right) / len(x)\n",
    "#主程序\n",
    "def main(c,iteration,seed,batch):\n",
    "    random.seed(seed)\n",
    "    train_x,train_y,test_x,test_y = GetData()\n",
    "    w = getInitData(seed)\n",
    "    w_group = GradientDescent(train_x,train_y,w,c,0.002,iteration,batch)\n",
    "    w_group_NAG = GradientDescent_NAG(train_x,train_y,w,c,0.002,iteration,batch,0.9)\n",
    "    w_group_RMSProp = GradientDescent_RMSProp(train_x, train_y, w, c, 0.002, iteration, batch, 0.9, 0.0000001)\n",
    "    w_group_AdaDelta = GradientDescent_AdaDelta(train_x, train_y, w, c, iteration, batch, 0.95, 0.00001)\n",
    "    w_group_Adam = GradientDescent_Adam(train_x, train_y, w, c, 0.002, iteration, batch, 0.999, 0.9, 0.001)\n",
    "    test_dif_Group = []\n",
    "    test_dif_Group_NAG = []\n",
    "    test_dif_Group_RMSProp = []\n",
    "    test_dif_Group_AdaDelta = []\n",
    "    test_dif_Group_Adam = []\n",
    "    for i in range(len(w_group)):\n",
    "        #print ('w',w_group[i])\n",
    "        #print ('b',b_Group[i])\n",
    "        if i % 10 != 0:\n",
    "            continue\n",
    "        test_dif_Group.append(loss(test_x,test_y,w_group[i],c))\n",
    "        test_dif_Group_NAG.append(loss(test_x,test_y,w_group_NAG[i],c))\n",
    "        test_dif_Group_RMSProp.append(loss(test_x, test_y, w_group_RMSProp[i], c))\n",
    "        test_dif_Group_AdaDelta.append(loss(test_x, test_y, w_group_AdaDelta[i], c))\n",
    "        test_dif_Group_Adam.append(loss(test_x, test_y, w_group_Adam[i], c))\n",
    "        #print (train_dif_Group[-1])\n",
    "        #print (test_dif_Group[-1])\n",
    "    plt.plot([i*10 for i in range(len(test_dif_Group_AdaDelta))], test_dif_Group_AdaDelta, label='AdaDelta')\n",
    "    plt.plot([i*10 for i in range(len(test_dif_Group_NAG))],test_dif_Group_NAG,label = 'NAG')\n",
    "    plt.plot([i*10 for i in range(len(test_dif_Group_RMSProp))], test_dif_Group_RMSProp, label='RMSProp')\n",
    "    plt.plot([i*10 for i in range(len(test_dif_Group))], test_dif_Group, label='SGD')\n",
    "    plt.plot([i*10 for i in range(len(test_dif_Group_Adam))], test_dif_Group_Adam, label='Adam')\n",
    "    #print (Precision(test_x,test_y,w_group[-1]))\n",
    "    plt.yscale('log')\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "main(0.958,4000,0,30)"
   ]
  }
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