3. SVM.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.model_selection import RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read files\n",
    "df = pd.read_csv('norm_selected_pixels.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "\n",
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     "metadata": {},
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   "source": [
    "df.head()"
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  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df.emotion\n",
    "X = df.drop('emotion',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split dataset into training set and test set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, stratify=df['emotion'], random_state=88)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "\n",
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       "18388  0.764706  0.788235  0.756863  ...  0.658824  0.713725  0.678431   \n",
       "10101  0.745098  0.737255  0.733333  ...  0.443137  0.658824  0.521569   \n",
       "\n",
       "          px_14     px_29   px_1217   px_1612     px_36     px_28     px_83  \n",
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       "10101  0.607843  0.729412  0.764706  0.576471  0.596078  0.745098  0.627451  \n",
       "\n",
       "[5 rows x 149 columns]"
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     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "      <th>px_1360</th>\n",
       "      <th>px_1409</th>\n",
       "      <th>...</th>\n",
       "      <th>px_1746</th>\n",
       "      <th>px_82</th>\n",
       "      <th>px_12</th>\n",
       "      <th>px_14</th>\n",
       "      <th>px_29</th>\n",
       "      <th>px_1217</th>\n",
       "      <th>px_1612</th>\n",
       "      <th>px_36</th>\n",
       "      <th>px_28</th>\n",
       "      <th>px_83</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9780</th>\n",
       "      <td>0.125490</td>\n",
       "      <td>0.490196</td>\n",
       "      <td>0.113725</td>\n",
       "      <td>0.149020</td>\n",
       "      <td>0.117647</td>\n",
       "      <td>0.780392</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.721569</td>\n",
       "      <td>0.752941</td>\n",
       "      <td>0.643137</td>\n",
       "      <td>...</td>\n",
       "      <td>0.211765</td>\n",
       "      <td>0.639216</td>\n",
       "      <td>0.737255</td>\n",
       "      <td>0.764706</td>\n",
       "      <td>0.745098</td>\n",
       "      <td>0.270588</td>\n",
       "      <td>0.921569</td>\n",
       "      <td>0.678431</td>\n",
       "      <td>0.721569</td>\n",
       "      <td>0.647059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7860</th>\n",
       "      <td>0.847059</td>\n",
       "      <td>0.870588</td>\n",
       "      <td>0.831373</td>\n",
       "      <td>0.835294</td>\n",
       "      <td>0.858824</td>\n",
       "      <td>0.819608</td>\n",
       "      <td>0.819608</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>0.878431</td>\n",
       "      <td>0.858824</td>\n",
       "      <td>...</td>\n",
       "      <td>0.815686</td>\n",
       "      <td>0.576471</td>\n",
       "      <td>0.670588</td>\n",
       "      <td>0.686275</td>\n",
       "      <td>0.654902</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.447059</td>\n",
       "      <td>0.670588</td>\n",
       "      <td>0.533333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25340</th>\n",
       "      <td>0.172549</td>\n",
       "      <td>0.258824</td>\n",
       "      <td>0.250980</td>\n",
       "      <td>0.219608</td>\n",
       "      <td>0.152941</td>\n",
       "      <td>0.250980</td>\n",
       "      <td>0.258824</td>\n",
       "      <td>0.356863</td>\n",
       "      <td>0.349020</td>\n",
       "      <td>0.325490</td>\n",
       "      <td>...</td>\n",
       "      <td>0.290196</td>\n",
       "      <td>0.109804</td>\n",
       "      <td>0.086275</td>\n",
       "      <td>0.090196</td>\n",
       "      <td>0.117647</td>\n",
       "      <td>0.262745</td>\n",
       "      <td>0.282353</td>\n",
       "      <td>0.101961</td>\n",
       "      <td>0.101961</td>\n",
       "      <td>0.113725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5006</th>\n",
       "      <td>0.368627</td>\n",
       "      <td>0.384314</td>\n",
       "      <td>0.317647</td>\n",
       "      <td>0.337255</td>\n",
       "      <td>0.298039</td>\n",
       "      <td>0.317647</td>\n",
       "      <td>0.211765</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.258824</td>\n",
       "      <td>0.372549</td>\n",
       "      <td>...</td>\n",
       "      <td>0.070588</td>\n",
       "      <td>0.305882</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.172549</td>\n",
       "      <td>0.176471</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>0.235294</td>\n",
       "      <td>0.247059</td>\n",
       "      <td>0.121569</td>\n",
       "      <td>0.294118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23588</th>\n",
       "      <td>0.392157</td>\n",
       "      <td>0.505882</td>\n",
       "      <td>0.454902</td>\n",
       "      <td>0.388235</td>\n",
       "      <td>0.474510</td>\n",
       "      <td>0.380392</td>\n",
       "      <td>0.454902</td>\n",
       "      <td>0.529412</td>\n",
       "      <td>0.639216</td>\n",
       "      <td>0.364706</td>\n",
       "      <td>...</td>\n",
       "      <td>0.552941</td>\n",
       "      <td>0.290196</td>\n",
       "      <td>0.329412</td>\n",
       "      <td>0.443137</td>\n",
       "      <td>0.517647</td>\n",
       "      <td>0.603922</td>\n",
       "      <td>0.678431</td>\n",
       "      <td>0.094118</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>0.188235</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 149 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        px_1456   px_1408   px_1504   px_1503   px_1455   px_1551   px_1552  \\\n",
       "9780   0.125490  0.490196  0.113725  0.149020  0.117647  0.780392  0.666667   \n",
       "7860   0.847059  0.870588  0.831373  0.835294  0.858824  0.819608  0.819608   \n",
       "25340  0.172549  0.258824  0.250980  0.219608  0.152941  0.250980  0.258824   \n",
       "5006   0.368627  0.384314  0.317647  0.337255  0.298039  0.317647  0.211765   \n",
       "23588  0.392157  0.505882  0.454902  0.388235  0.474510  0.380392  0.454902   \n",
       "\n",
       "        px_1361   px_1360   px_1409  ...   px_1746     px_82     px_12  \\\n",
       "9780   0.721569  0.752941  0.643137  ...  0.211765  0.639216  0.737255   \n",
       "7860   0.866667  0.878431  0.858824  ...  0.815686  0.576471  0.670588   \n",
       "25340  0.356863  0.349020  0.325490  ...  0.290196  0.109804  0.086275   \n",
       "5006   0.333333  0.258824  0.372549  ...  0.070588  0.305882  0.000000   \n",
       "23588  0.529412  0.639216  0.364706  ...  0.552941  0.290196  0.329412   \n",
       "\n",
       "          px_14     px_29   px_1217   px_1612     px_36     px_28     px_83  \n",
       "9780   0.764706  0.745098  0.270588  0.921569  0.678431  0.721569  0.647059  \n",
       "7860   0.686275  0.654902  0.866667  0.466667  0.447059  0.670588  0.533333  \n",
       "25340  0.090196  0.117647  0.262745  0.282353  0.101961  0.101961  0.113725  \n",
       "5006   0.172549  0.176471  0.533333  0.235294  0.247059  0.121569  0.294118  \n",
       "23588  0.443137  0.517647  0.603922  0.678431  0.094118  0.533333  0.188235  \n",
       "\n",
       "[5 rows x 149 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(25120, 149) (25120,) (10767, 149) (10767,)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape, y_train.shape, X_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa11b2b3c50>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(y_train)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa100984438>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(y_test)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# param_rand setting for randomizedSearch\n",
    "param_rand = {'C':[1e3,5e5,1e4,5e4,1e5],\n",
    "              'gamma':[0.0001,0.0005,0.001,0.005,0.01,0.1]}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model\n",
    "search_svm = SVC(kernel='rbf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 1 candidates, totalling 5 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   5 | elapsed: 54.0min remaining: 81.0min\n",
      "[Parallel(n_jobs=-1)]: Done   5 out of   5 | elapsed: 58.1min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, error_score='raise-deprecating',\n",
       "          estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
       "  kernel='rbf', max_iter=-1, probability=False, random_state=None,\n",
       "  shrinking=True, tol=0.001, verbose=False),\n",
       "          fit_params=None, iid='warn', n_iter=1, n_jobs=-1,\n",
       "          param_distributions={'C': [1000.0, 500000.0, 10000.0, 50000.0, 100000.0], 'gamma': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1]},\n",
       "          pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
       "          return_train_score='warn', scoring='accuracy', verbose=1)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rand_search = RandomizedSearchCV(search_svm,param_rand, cv = 5,scoring = 'accuracy',n_iter=1,n_jobs = -1,verbose=1)\n",
    "rand_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best score is: 0.31962579617834397\n",
      "Best params are: {'gamma': 0.0001, 'C': 500000.0}\n"
     ]
    }
   ],
   "source": [
    "print('Best score is:', rand_search.best_score_)\n",
    "print('Best params are:', rand_search.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build svm model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=500000.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma=0.0001, kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# svm model\n",
    "svm = SVC(kernel='rbf',\n",
    "          C=rand_search.best_params_['C'], \n",
    "          gamma=rand_search.best_params_['gamma']) # take the best parameter to build model\n",
    "svm.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 5 6 ... 6 5 2]\n"
     ]
    }
   ],
   "source": [
    "y_pred = svm.predict(X_test)\n",
    "print(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.32200241478591995\n"
     ]
    }
   ],
   "source": [
    "# accuracy\n",
    "scores = svm.score(X_test,y_test)\n",
    "print('accuracy:',scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 173,    0,   74,  649,  210,   82,  298],\n",
       "       [  13,    3,   18,   79,   17,   11,   23],\n",
       "       [  91,    0,  108,  703,  192,  153,  289],\n",
       "       [ 105,    0,   91, 1860,  242,  121,  278],\n",
       "       [ 106,    0,   91,  796,  368,  112,  350],\n",
       "       [  34,    0,   74,  424,   85,  308,  276],\n",
       "       [  85,    0,   70,  663,  227,  168,  647]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Predicted</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>173</td>\n",
       "      <td>0</td>\n",
       "      <td>74</td>\n",
       "      <td>649</td>\n",
       "      <td>210</td>\n",
       "      <td>82</td>\n",
       "      <td>298</td>\n",
       "      <td>1486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "      <td>79</td>\n",
       "      <td>17</td>\n",
       "      <td>11</td>\n",
       "      <td>23</td>\n",
       "      <td>164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>91</td>\n",
       "      <td>0</td>\n",
       "      <td>108</td>\n",
       "      <td>703</td>\n",
       "      <td>192</td>\n",
       "      <td>153</td>\n",
       "      <td>289</td>\n",
       "      <td>1536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>105</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>1860</td>\n",
       "      <td>242</td>\n",
       "      <td>121</td>\n",
       "      <td>278</td>\n",
       "      <td>2697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>106</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>796</td>\n",
       "      <td>368</td>\n",
       "      <td>112</td>\n",
       "      <td>350</td>\n",
       "      <td>1823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>74</td>\n",
       "      <td>424</td>\n",
       "      <td>85</td>\n",
       "      <td>308</td>\n",
       "      <td>276</td>\n",
       "      <td>1201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>85</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>663</td>\n",
       "      <td>227</td>\n",
       "      <td>168</td>\n",
       "      <td>647</td>\n",
       "      <td>1860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>607</td>\n",
       "      <td>3</td>\n",
       "      <td>526</td>\n",
       "      <td>5174</td>\n",
       "      <td>1341</td>\n",
       "      <td>955</td>\n",
       "      <td>2161</td>\n",
       "      <td>10767</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predicted    0  1    2     3     4    5     6    All\n",
       "True                                                \n",
       "0          173  0   74   649   210   82   298   1486\n",
       "1           13  3   18    79    17   11    23    164\n",
       "2           91  0  108   703   192  153   289   1536\n",
       "3          105  0   91  1860   242  121   278   2697\n",
       "4          106  0   91   796   368  112   350   1823\n",
       "5           34  0   74   424    85  308   276   1201\n",
       "6           85  0   70   663   227  168   647   1860\n",
       "All        607  3  526  5174  1341  955  2161  10767"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# confusion matrix\n",
    "confusion_matrix(y_test, y_pred)\n",
    "pd.crosstab(y_test, y_pred, rownames=['True'], colnames=['Predicted'], margins=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.29      0.12      0.17      1486\n",
      "           1       1.00      0.02      0.04       164\n",
      "           2       0.21      0.07      0.10      1536\n",
      "           3       0.36      0.69      0.47      2697\n",
      "           4       0.27      0.20      0.23      1823\n",
      "           5       0.32      0.26      0.29      1201\n",
      "           6       0.30      0.35      0.32      1860\n",
      "\n",
      "   micro avg       0.32      0.32      0.32     10767\n",
      "   macro avg       0.39      0.24      0.23     10767\n",
      "weighted avg       0.31      0.32      0.28     10767\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# classification report\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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