{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "07463a5f",
   "metadata": {},
   "source": [
    "# サポートベクトルマシン\n",
    "\n",
    "サポートベクトルマシン全般については次を確認してください。\n",
    "[https://scikit-learn.org/stable/modules/svm.html](https://scikit-learn.org/stable/modules/svm.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22ae72e6",
   "metadata": {},
   "source": [
    "**データとモジュールのロード**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4f42c570",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn import model_selection\n",
    "\n",
    "data = pd.read_csv(\"input/pn_same_judge_preprocessed.csv\")\n",
    "train, test = model_selection.train_test_split(data, test_size=0.1, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "55fdf55a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.metrics import ConfusionMatrixDisplay\n",
    "from sklearn.metrics import PrecisionRecallDisplay"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "310d666d",
   "metadata": {},
   "source": [
    "## SVC"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea30df74",
   "metadata": {},
   "source": [
    "[sklearn.svm.LinearSVC](https://scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVC.html)\n",
    "を使います。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "79cc90a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import LinearSVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bb3dbb21",
   "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>Pipeline(steps=[(&#x27;vect&#x27;,\n",
       "                 TfidfVectorizer(tokenizer=&lt;method &#x27;split&#x27; of &#x27;str&#x27; objects&gt;)),\n",
       "                (&#x27;clf&#x27;, LinearSVC())])</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-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;vect&#x27;,\n",
       "                 TfidfVectorizer(tokenizer=&lt;method &#x27;split&#x27; of &#x27;str&#x27; objects&gt;)),\n",
       "                (&#x27;clf&#x27;, LinearSVC())])</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-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">TfidfVectorizer</label><div class=\"sk-toggleable__content\"><pre>TfidfVectorizer(tokenizer=&lt;method &#x27;split&#x27; of &#x27;str&#x27; objects&gt;)</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-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearSVC</label><div class=\"sk-toggleable__content\"><pre>LinearSVC()</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('vect',\n",
       "                 TfidfVectorizer(tokenizer=<method 'split' of 'str' objects>)),\n",
       "                ('clf', LinearSVC())])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe = Pipeline([\n",
    "    (\"vect\", TfidfVectorizer(tokenizer=str.split)),\n",
    "    (\"clf\", LinearSVC())\n",
    "])\n",
    "\n",
    "pipe.fit(train[\"tokens\"], train[\"label_num\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "441429d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f1c61f412b0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred = pipe.predict(test[\"tokens\"])\n",
    "ConfusionMatrixDisplay.from_predictions(y_true=test[\"label_num\"], y_pred=pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dd63ab0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# SVC では predict_proba ではなく decision_function を使います\n",
    "score = pipe.decision_function(test[\"tokens\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2b58a9c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.precision_recall_curve.PrecisionRecallDisplay at 0x7f1c5e01e7f0>"
      ]
     },
     "execution_count": 7,
     "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": [
    "PrecisionRecallDisplay.from_predictions(\n",
    "    y_true=test[\"label_num\"],\n",
    "    y_pred=score,\n",
    "    name=\"SVC\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b54610a",
   "metadata": {},
   "source": [
    "## 不均衡データに対応する\n",
    "\n",
    "`class_weight` パラメータで不均衡データに対応できます。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b0ed478e",
   "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;vect&#x27;,\n",
       "                 TfidfVectorizer(tokenizer=&lt;method &#x27;split&#x27; of &#x27;str&#x27; objects&gt;)),\n",
       "                (&#x27;clf&#x27;, LinearSVC(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-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;vect&#x27;,\n",
       "                 TfidfVectorizer(tokenizer=&lt;method &#x27;split&#x27; of &#x27;str&#x27; objects&gt;)),\n",
       "                (&#x27;clf&#x27;, LinearSVC(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-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">TfidfVectorizer</label><div class=\"sk-toggleable__content\"><pre>TfidfVectorizer(tokenizer=&lt;method &#x27;split&#x27; of &#x27;str&#x27; objects&gt;)</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-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearSVC</label><div class=\"sk-toggleable__content\"><pre>LinearSVC(class_weight=&#x27;balanced&#x27;)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('vect',\n",
       "                 TfidfVectorizer(tokenizer=<method 'split' of 'str' objects>)),\n",
       "                ('clf', LinearSVC(class_weight='balanced'))])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe_weight = Pipeline([\n",
    "    (\"vect\", TfidfVectorizer(tokenizer=str.split)),\n",
    "    (\"clf\", LinearSVC(class_weight=\"balanced\"))\n",
    "])\n",
    "\n",
    "pipe_weight.fit(train[\"tokens\"], train[\"label_num\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0a600cfd",
   "metadata": {},
   "outputs": [],
   "source": [
    "score_weight = pipe_weight.decision_function(test[\"tokens\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d160f50",
   "metadata": {},
   "source": [
    "class_weightオプションを付けないモデルと比較します。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9935b0d8",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "import  matplotlib.pyplot as plt\n",
    "\n",
    "_, ax = plt.subplots()\n",
    "for name, pred in [\n",
    "    (\"SVC\", score),\n",
    "    (\"SVC+balanced\", score_weight),\n",
    "]:\n",
    "    PrecisionRecallDisplay.from_predictions(ax=ax, y_true=test[\"label_num\"], y_pred=pred, name=name)"
   ]
  }
 ],
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