handout_test.ipynb

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     "text": [
      "/home/lmolhw/anaconda3/lib/python3.5/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "#导入自带数据集\n",
    "from sklearn import datasets\n",
    "#导入交叉验证库\n",
    "from sklearn import cross_validation\n",
    "#导入SVM分类算法库\n",
    "from sklearn import svm\n",
    "#导入图表库\n",
    "import matplotlib.pyplot as plt\n",
    "#生成预测结果准确率的混淆矩阵\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#读取自带数据集并赋值给digits\n",
    "digits = datasets.load_digits()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0.,   0.,  11.,  12.,   0.,   0.,   0.,   0.,   0.,   2.,  16.,\n",
       "        16.,  16.,  13.,   0.,   0.,   0.,   3.,  16.,  12.,  10.,  14.,\n",
       "         0.,   0.,   0.,   1.,  16.,   1.,  12.,  15.,   0.,   0.,   0.,\n",
       "         0.,  13.,  16.,   9.,  15.,   2.,   0.,   0.,   0.,   0.,   3.,\n",
       "         0.,   9.,  11.,   0.,   0.,   0.,   0.,   0.,   9.,  15.,   4.,\n",
       "         0.,   0.,   0.,   9.,  12.,  13.,   3.,   0.,   0.])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看数据集中数字9的矩阵\n",
    "digits.data[9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'plt' is not defined",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-c3612984064b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#绘制图表查看数据集中数字9的图像\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdigits\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimages\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgray_r\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minterpolation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'nearest'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'digits.target[0]'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'plt' is not defined"
     ],
     "output_type": "error"
    }
   ],
   "source": [
    "#绘制图表查看数据集中数字9的图像\n",
    "plt.imshow(digits.images[9], cmap=plt.cm.gray_r, interpolation='nearest')\n",
    "plt.title('digits.target[9]')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, ..., 8, 9, 8])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#数据集中的目标分类\n",
    "digits.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#将数据集中的目标赋给Y\n",
    "Y=digits.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#使用reshape函数对矩阵进行转换，并赋值给X\n",
    "n_samples = len(digits.images)\n",
    "X = digits.images.reshape((n_samples, 64))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1797, 64), (1797,))"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看X和Y的行数\n",
    "X.shape,Y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#随机抽取生成训练集和测试集，其中训练集的比例为60%，测试集40%\n",
    "X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, Y, test_size=0.4, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1078, 64), (1078,))"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看训练集的行数\n",
    "X_train.shape,y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#生成SVM分类模型\n",
    "clf = svm.SVC(gamma=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape=None, degree=3, gamma=0.001, kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#使用训练集对svm分类模型进行训练\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#对测试集数据进行预测\n",
    "predicted=clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 8, 2, 6, 6, 7, 1, 9, 8, 5, 2, 8, 6, 6, 6, 6, 1, 0, 5, 8])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看前20个测试集的预测结果\n",
    "predicted[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#查看测试集中的真实结果\n",
    "expected=y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 8, 2, 6, 6, 7, 1, 9, 8, 5, 2, 8, 6, 6, 6, 6, 1, 0, 5, 8])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看测试集中前20个真实结果\n",
    "expected[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[60,  0,  0,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0, 73,  0,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0,  1, 69,  0,  0,  0,  0,  1,  0,  0],\n",
       "       [ 0,  0,  0, 70,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0,  0,  0,  0, 63,  0,  0,  0,  0,  0],\n",
       "       [ 0,  0,  0,  0,  0, 87,  1,  0,  0,  1],\n",
       "       [ 0,  0,  0,  0,  0,  0, 76,  0,  0,  0],\n",
       "       [ 0,  0,  0,  0,  0,  0,  0, 65,  0,  0],\n",
       "       [ 0,  1,  0,  0,  0,  0,  0,  0, 77,  0],\n",
       "       [ 0,  0,  0,  0,  0,  0,  0,  0,  0, 74]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#生成准确率的混淆矩阵(Confusion matrix)\n",
    "metrics.confusion_matrix(expected, predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    ""
   ]
  }
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