random-forest-usps.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
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    "collapsed": true
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   "source": [
    "import scipy.io as scio\n",
    "import numpy as np\n",
    "from functools import reduce\n",
    "import operator\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 读数据集函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "xfile: x文件\n",
    "yfile: y文件\n",
    "test_rate: 测试集比例\n",
    "\"\"\"\n",
    "def readData(xfile, yfile, test_rate):\n",
    "    # 加载文件， 此处是dict\n",
    "    x = scio.loadmat(xfile)\n",
    "    y = scio.loadmat(yfile)\n",
    "    \n",
    "    x = x.get(xfile[8:-4])\n",
    "    y = y.get(yfile[8:-4])\n",
    "    \n",
    "    # 二值化\n",
    "    x = np.where(x > 0, 1, 0)\n",
    "    \n",
    "    num = x.shape[0]\n",
    "    index = int(num * (1-test_rate))\n",
    "    \n",
    "    x_train = x[0:index]\n",
    "    y_train = y[0:index]\n",
    "    x_test = x[index:num]\n",
    "    y_test = y[index:num]\n",
    "    \n",
    "    y_train = y_train.ravel()\n",
    "    y_test = y_test.ravel()\n",
    "\n",
    "    return x_train, y_train, x_test, y_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 读取数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train.shape = (4261, 256)\n",
      "y_train.shape = (4261,)\n",
      "x_test.shape = (388, 256)\n",
      "y_test.shape = (388,)\n"
     ]
    }
   ],
   "source": [
    "x_train, y_train, x_test, y_test = readData(\"dataset/usps_train.mat\", \"dataset/usps_train_labels.mat\", 1/12)\n",
    "print(\"x_train.shape = {}\".format(x_train.shape))\n",
    "print(\"y_train.shape = {}\".format(y_train.shape))\n",
    "print(\"x_test.shape = {}\".format(x_test.shape))\n",
    "print(\"y_test.shape = {}\".format(y_test.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 开始训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rfc = RandomForestClassifier(n_estimators=500, oob_score=True, n_jobs=4)\n",
    "model = rfc.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 准确率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集-带外准确率 Acc_train_usps= 0.9380427129781741\n",
      "测试集-准确率 Acc_test_usps = 0.9407216494845361\n"
     ]
    }
   ],
   "source": [
    "print(\"训练集-带外准确率 Acc_train_usps= {}\".format(rfc.oob_score_))\n",
    "print(\"测试集-准确率 Acc_test_usps = {}\".format(rfc.score(x_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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