{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 本教程教你如何使用 SVM 分类 [Mnist 数据集](http://yann.lecun.com/exdb/mnist/)\n",
    "\n",
    "MNIST 有一个 60，000 个示例的训练集和 10，000 个示例的测试集。这些数字已经尺寸归一化，并在固定大小(28*28)的图像中居中。\n",
    "\n",
    "#### 1. 下载数据集\n",
    "\n",
    "简单的话，可以通过 Scikit-Learn 获取 MNIST。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/h/miniconda3/lib/python3.8/site-packages/sklearn/datasets/_openml.py:932: FutureWarning: The default value of `parser` will change from `'liac-arff'` to `'auto'` in 1.4. You can set `parser='auto'` to silence this warning. Therefore, an `ImportError` will be raised from 1.4 if the dataset is dense and pandas is not installed. Note that the pandas parser may return different data types. See the Notes Section in fetch_openml's API doc for details.\n",
      "  warn(\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import fetch_openml\n",
    "\n",
    "mnist = fetch_openml(\"mnist_784\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. 分析数据集\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['data', 'target', 'frame', 'categories', 'feature_names', 'target_names', 'DESCR', 'details', 'url'])\n",
      "**Author**: Yann LeCun, Corinna Cortes, Christopher J.C. Burges  \n",
      "**Source**: [MNIST Website](http://yann.lecun.com/exdb/mnist/) - Date unknown  \n",
      "**Please cite**:  \n",
      "\n",
      "The MNIST database of handwritten digits with 784 features, raw data available at: http://yann.lecun.com/exdb/mnist/. It can be split in a training set of the first 60,000 examples, and a test set of 10,000 examples  \n",
      "\n",
      "It is a subset of a larger set available from NIST. The digits have been size-normalized and centered in a fixed-size image. It is a good database for people who want to try learning techniques and pattern recognition methods on real-world data while spending minimal efforts on preprocessing and formatting. The original black and white (bilevel) images from NIST were size normalized to fit in a 20x20 pixel box while preserving their aspect ratio. The resulting images contain grey levels as a result of the anti-aliasing technique used by the normalization algorithm. the images were centered in a 28x28 image by computing the center of mass of the pixels, and translating the image so as to position this point at the center of the 28x28 field.  \n",
      "\n",
      "With some classification methods (particularly template-based methods, such as SVM and K-nearest neighbors), the error rate improves when the digits are centered by bounding box rather than center of mass. If you do this kind of pre-processing, you should report it in your publications. The MNIST database was constructed from NIST's NIST originally designated SD-3 as their training set and SD-1 as their test set. However, SD-3 is much cleaner and easier to recognize than SD-1. The reason for this can be found on the fact that SD-3 was collected among Census Bureau employees, while SD-1 was collected among high-school students. Drawing sensible conclusions from learning experiments requires that the result be independent of the choice of training set and test among the complete set of samples. Therefore it was necessary to build a new database by mixing NIST's datasets.  \n",
      "\n",
      "The MNIST training set is composed of 30,000 patterns from SD-3 and 30,000 patterns from SD-1. Our test set was composed of 5,000 patterns from SD-3 and 5,000 patterns from SD-1. The 60,000 pattern training set contained examples from approximately 250 writers. We made sure that the sets of writers of the training set and test set were disjoint. SD-1 contains 58,527 digit images written by 500 different writers. In contrast to SD-3, where blocks of data from each writer appeared in sequence, the data in SD-1 is scrambled. Writer identities for SD-1 is available and we used this information to unscramble the writers. We then split SD-1 in two: characters written by the first 250 writers went into our new training set. The remaining 250 writers were placed in our test set. Thus we had two sets with nearly 30,000 examples each. The new training set was completed with enough examples from SD-3, starting at pattern # 0, to make a full set of 60,000 training patterns. Similarly, the new test set was completed with SD-3 examples starting at pattern # 35,000 to make a full set with 60,000 test patterns. Only a subset of 10,000 test images (5,000 from SD-1 and 5,000 from SD-3) is available on this site. The full 60,000 sample training set is available.\n",
      "\n",
      "Downloaded from openml.org.\n"
     ]
    }
   ],
   "source": [
    "print(mnist.keys())  # 看下数据集中有啥键，'data', 'target' 分别为 特征(就是每张图像)和标签\n",
    "print(mnist['DESCR']) # 打印下说明"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "查看下 数据维度和类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((70000, 784), (70000,), pandas.core.series.Series)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mnist[\"data\"].shape, mnist[\"target\"].shape, type(mnist[\"target\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "为了方便，我们将 panda 对象转为 numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((70000, 784), (70000,))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = mnist[\"data\"].to_numpy()\n",
    "y = mnist[\"target\"].to_numpy()\n",
    "x.shape, y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "画图显示下数据和对应的标签"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x1000 with 64 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 8 行 8 列\n",
    "fig, axes = plt.subplots(8, 8, figsize=(10, 10),\n",
    "                         subplot_kw={'xticks':[], 'yticks':[]},\n",
    "                         gridspec_kw=dict(hspace=0.1, wspace=0.1))\n",
    "# 绿的是 标签值\n",
    "for i, ax in enumerate(axes.flat):\n",
    "    ax.imshow(x[i].reshape(28, 28), cmap='binary', interpolation='nearest')\n",
    "    ax.text(0.05, 0.05, str(y[i]),\n",
    "            transform=ax.transAxes, color='green')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. 数据划分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((52500, 784), (52500,))"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "Xtrain, Xtest, ytrain, ytest = train_test_split(x, y, random_state=42)\n",
    "Xtrain.shape, ytrain.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4. 定义模型对象"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "model = SVC(kernel='rbf', class_weight='balanced')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5. 训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(class_weight=&#x27;balanced&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(class_weight=&#x27;balanced&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC(class_weight='balanced')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(Xtrain, ytrain) # 需要花一些时间"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "将训练好的模型来跑下测试集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "ypred = model.predict(Xtest) # 需要花一些时间"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "统计下模型分类精度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9765142857142857"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(ytest, ypred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "默认的参数看来模型不太行啊，没有达到 99%。下面画以下 混淆矩阵"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(113.9222222222222, 0.5, 'true value')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "import seaborn as sns\n",
    "\n",
    "mat = confusion_matrix(ytest, ypred)\n",
    "\n",
    "sns.heatmap(mat, square=True, annot=True, cbar=False, fmt='.20g', annot_kws={\"fontsize\":8}) # fmt：不用科学计数法\n",
    "plt.xlabel('predicted value')\n",
    "plt.ylabel('true value')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "预测错误的 标注为 红色，对的为 绿色"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2800x2800 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(5, 5, figsize=(28, 28),\n",
    "                         subplot_kw={'xticks':[], 'yticks':[]},\n",
    "                         gridspec_kw=dict(hspace=0.1, wspace=0.1))\n",
    "\n",
    "test_images = Xtest.reshape(-1, 28, 28)\n",
    "\n",
    "for i, ax in enumerate(axes.flat):\n",
    "    ax.imshow(test_images[i], cmap='binary', interpolation='nearest')\n",
    "    ax.text(0.05, 0.05, str(ypred[i]),\n",
    "            transform=ax.transAxes,\n",
    "            color='green' if (ytest[i] == ypred[i]) else 'red',\n",
    "            fontsize=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "统计结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.99      0.99      1714\n",
      "           1       0.98      0.99      0.99      1977\n",
      "           2       0.97      0.98      0.97      1761\n",
      "           3       0.97      0.97      0.97      1806\n",
      "           4       0.97      0.98      0.97      1587\n",
      "           5       0.98      0.97      0.98      1607\n",
      "           6       0.98      0.99      0.99      1761\n",
      "           7       0.97      0.97      0.97      1878\n",
      "           8       0.98      0.96      0.97      1657\n",
      "           9       0.97      0.96      0.97      1752\n",
      "\n",
      "    accuracy                           0.98     17500\n",
      "   macro avg       0.98      0.98      0.98     17500\n",
      "weighted avg       0.98      0.98      0.98     17500\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "target_names = list(mnist['target'].cat.categories)\n",
    "print(classification_report(ytest, ypred,\n",
    "                            target_names=target_names))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PCA 降维+SVM 加速训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.decomposition import PCA as RandomizedPCA\n",
    "from sklearn.pipeline import make_pipeline\n",
    "\n",
    "# 28*28 降维为 10\n",
    "pca = RandomizedPCA(n_components=10, whiten=True, random_state=42)\n",
    "svc = SVC(kernel='rbf', class_weight='balanced')\n",
    "model = make_pipeline(pca, svc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;pca&#x27;, PCA(n_components=10, random_state=42, whiten=True)),\n",
       "                (&#x27;svc&#x27;, SVC(class_weight=&#x27;balanced&#x27;))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;pca&#x27;, PCA(n_components=10, random_state=42, whiten=True)),\n",
       "                (&#x27;svc&#x27;, SVC(class_weight=&#x27;balanced&#x27;))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">PCA</label><div class=\"sk-toggleable__content\"><pre>PCA(n_components=10, random_state=42, whiten=True)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(class_weight=&#x27;balanced&#x27;)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('pca', PCA(n_components=10, random_state=42, whiten=True)),\n",
       "                ('svc', SVC(class_weight='balanced'))])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(Xtrain, ytrain) # 速度明显加快，可以统计下时间"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "ypred = model.predict(Xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9339428571428572"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(ytest, ypred)  # 降低了，看来 svm 的默认参数确实效果一般"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "其余步骤请参考上面内容..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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    "name": "ipython",
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