16.2 Tfidf 随机森林 朴素贝叶斯 自然语言处理 航空公司评论分类.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "90b7bf43",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adbe8c11",
   "metadata": {},
   "source": [
    "# 数据处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d22ce8d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('./dataset/Tweets.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b1dac007",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>airline_sentiment</th>\n",
       "      <th>airline_sentiment_confidence</th>\n",
       "      <th>negativereason</th>\n",
       "      <th>negativereason_confidence</th>\n",
       "      <th>airline</th>\n",
       "      <th>airline_sentiment_gold</th>\n",
       "      <th>name</th>\n",
       "      <th>negativereason_gold</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>text</th>\n",
       "      <th>tweet_coord</th>\n",
       "      <th>tweet_created</th>\n",
       "      <th>tweet_location</th>\n",
       "      <th>user_timezone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>570306133677760513</td>\n",
       "      <td>neutral</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cairdin</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@VirginAmerica What @dhepburn said.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-24 11:35:52 -0800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Eastern Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>570301130888122368</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.3486</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jnardino</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@VirginAmerica plus you've added commercials t...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-24 11:15:59 -0800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pacific Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>570301083672813571</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.6837</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>yvonnalynn</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@VirginAmerica I didn't today... Must mean I n...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-24 11:15:48 -0800</td>\n",
       "      <td>Lets Play</td>\n",
       "      <td>Central Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>570301031407624196</td>\n",
       "      <td>negative</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>Bad Flight</td>\n",
       "      <td>0.7033</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jnardino</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@VirginAmerica it's really aggressive to blast...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-24 11:15:36 -0800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pacific Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>570300817074462722</td>\n",
       "      <td>negative</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>Can't Tell</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jnardino</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@VirginAmerica and it's a really big bad thing...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-24 11:14:45 -0800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pacific Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id airline_sentiment  airline_sentiment_confidence  \\\n",
       "0  570306133677760513           neutral                        1.0000   \n",
       "1  570301130888122368          positive                        0.3486   \n",
       "2  570301083672813571           neutral                        0.6837   \n",
       "3  570301031407624196          negative                        1.0000   \n",
       "4  570300817074462722          negative                        1.0000   \n",
       "\n",
       "  negativereason  negativereason_confidence         airline  \\\n",
       "0            NaN                        NaN  Virgin America   \n",
       "1            NaN                     0.0000  Virgin America   \n",
       "2            NaN                        NaN  Virgin America   \n",
       "3     Bad Flight                     0.7033  Virgin America   \n",
       "4     Can't Tell                     1.0000  Virgin America   \n",
       "\n",
       "  airline_sentiment_gold        name negativereason_gold  retweet_count  \\\n",
       "0                    NaN     cairdin                 NaN              0   \n",
       "1                    NaN    jnardino                 NaN              0   \n",
       "2                    NaN  yvonnalynn                 NaN              0   \n",
       "3                    NaN    jnardino                 NaN              0   \n",
       "4                    NaN    jnardino                 NaN              0   \n",
       "\n",
       "                                                text tweet_coord  \\\n",
       "0                @VirginAmerica What @dhepburn said.         NaN   \n",
       "1  @VirginAmerica plus you've added commercials t...         NaN   \n",
       "2  @VirginAmerica I didn't today... Must mean I n...         NaN   \n",
       "3  @VirginAmerica it's really aggressive to blast...         NaN   \n",
       "4  @VirginAmerica and it's a really big bad thing...         NaN   \n",
       "\n",
       "               tweet_created tweet_location               user_timezone  \n",
       "0  2015-02-24 11:35:52 -0800            NaN  Eastern Time (US & Canada)  \n",
       "1  2015-02-24 11:15:59 -0800            NaN  Pacific Time (US & Canada)  \n",
       "2  2015-02-24 11:15:48 -0800      Lets Play  Central Time (US & Canada)  \n",
       "3  2015-02-24 11:15:36 -0800            NaN  Pacific Time (US & Canada)  \n",
       "4  2015-02-24 11:14:45 -0800            NaN  Pacific Time (US & Canada)  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "44841fec",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data[['airline_sentiment', 'text']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bade399c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 14640 entries, 0 to 14639\n",
      "Data columns (total 2 columns):\n",
      " #   Column             Non-Null Count  Dtype \n",
      "---  ------             --------------  ----- \n",
      " 0   airline_sentiment  14640 non-null  object\n",
      " 1   text               14640 non-null  object\n",
      "dtypes: object(2)\n",
      "memory usage: 228.9+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "995c3991",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['neutral', 'positive', 'negative'], dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.airline_sentiment.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2d7f4732",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "negative    9178\n",
       "neutral     3099\n",
       "positive    2363\n",
       "Name: airline_sentiment, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.airline_sentiment.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66a6e979",
   "metadata": {},
   "source": [
    "树算法对处理不均匀数据效果较好"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9a9793d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import re # 正则表达式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5e4ae8ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "token = re.compile(r'[A-Za-z]+|[!?.:,()]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "00336daf",
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_text(text):\n",
    "    new_text = token.findall(text)\n",
    "    new_text = ' '.join([x.lower() for x in new_text]) # 变为小写，注意join前要有空格，否则会拼成一个单词\n",
    "    return new_text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "437870a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = data.text.apply(extract_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3bad1808",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = data.airline_sentiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d9a5d9cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "41058b5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "703a75a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((10980,), (3660,))"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape, x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "32d3276b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7af536de",
   "metadata": {},
   "outputs": [],
   "source": [
    "vect = TfidfVectorizer(ngram_range=(1,3), stop_words='english', min_df=3)\n",
    "# (ngram_range=(1,3) 1-3个单词作为一个序列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "55afc334",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_vect = vect.fit_transform(x_train)  # 文本向量化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "efee4f05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<10980x7698 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 116278 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train_vect # 10980x361"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "dab6da8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test_vect = vect.transform(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f1b517a",
   "metadata": {},
   "source": [
    "# 模型构建"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c63f7e8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6b2b8c7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "83f1a7e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train_vect, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "72cfcc51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9934426229508196"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(x_train_vect, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ea7c880b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.753551912568306"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(x_test_vect, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "5da9c771",
   "metadata": {},
   "outputs": [],
   "source": [
    "model2 = RandomForestClassifier(n_estimators=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4fe9d130",
   "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>RandomForestClassifier(n_estimators=500)</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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(n_estimators=500)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(n_estimators=500)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2.fit(x_train_vect, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "41c5a8e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9934426229508196"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2.score(x_train_vect, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7ee9ca54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7543715846994535"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2.score(x_test_vect, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d29e3849",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "eb311bc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "param = {\n",
    "    'max_depth': range(1,500,10),\n",
    "    'criterion': ['gini', 'entropy']\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "9cc6970b",
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_s = GridSearchCV(RandomForestClassifier(n_jobs=8),\n",
    "                      param_grid=param,\n",
    "                      cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "408e2854",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_vect = vect.transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "82e2f3bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 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-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 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-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 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-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 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-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=RandomForestClassifier(n_jobs=8),\n",
       "             param_grid={&#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n",
       "                         &#x27;max_depth&#x27;: range(1, 500, 10)})</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-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=RandomForestClassifier(n_jobs=8),\n",
       "             param_grid={&#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n",
       "                         &#x27;max_depth&#x27;: range(1, 500, 10)})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><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\">estimator: RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(n_jobs=8)</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\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(n_jobs=8)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5, estimator=RandomForestClassifier(n_jobs=8),\n",
       "             param_grid={'criterion': ['gini', 'entropy'],\n",
       "                         'max_depth': range(1, 500, 10)})"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_s.fit(x_vect, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "438ddfba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<14640x7698 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 152642 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_vect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "8c39d483",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 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-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 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-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 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-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 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-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(criterion=&#x27;entropy&#x27;, max_depth=371, n_jobs=8)</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-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(criterion=&#x27;entropy&#x27;, max_depth=371, n_jobs=8)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(criterion='entropy', max_depth=371, n_jobs=8)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_s.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b750af8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6948087431693989"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_s.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "510caaa7",
   "metadata": {},
   "source": [
    "# 朴素贝叶斯"
   ]
  },
  {
   "cell_type": "raw",
   "id": "02d6722b",
   "metadata": {},
   "source": [
    "多项式分布的贝叶斯算法\n",
    "参数：alpha平滑参数\n",
    "    越小越容易过拟合\n",
    "    越大越容易欠拟合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "329395d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "ba805e2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = MultinomialNB(alpha=0.0001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f84e3c4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 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-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 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-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 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-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 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-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MultinomialNB(alpha=0.0001)</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-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MultinomialNB</label><div class=\"sk-toggleable__content\"><pre>MultinomialNB(alpha=0.0001)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "MultinomialNB(alpha=0.0001)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train_vect, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "6a10ce70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8883424408014572"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(x_train_vect, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "bc4bf5e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7502732240437159"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(x_test_vect, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "93c2c29a",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_score = []\n",
    "alpha = np.linspace(0.00001, 0.01, 100)  # 等距\n",
    "for a in alpha:\n",
    "    model = MultinomialNB(alpha=a)\n",
    "    model.fit(x_train_vect, y_train)\n",
    "    test_score.append(model.score(x_test_vect, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "74704cac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7573770491803279"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_score = max(test_score)\n",
    "max_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "cf1df01b",
   "metadata": {},
   "outputs": [],
   "source": [
    "index = test_score.index(max_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "2e1819df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "94"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "87ac8d05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.009495454545454547"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alpha[index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "061f877b",
   "metadata": {},
   "outputs": [],
   "source": [
    "best_alpha = alpha[index]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54b1fb18",
   "metadata": {},
   "source": [
    "# 模型评价"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2237a71f",
   "metadata": {},
   "source": [
    "## F1-Score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efd9238c",
   "metadata": {},
   "source": [
    "使用classification_report函数来查看针对每个类别的预测准确性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "a217c441",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "647e38d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 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-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 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-6 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-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 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-6 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-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 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-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 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-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 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-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MultinomialNB(alpha=0.009495454545454547)</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-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MultinomialNB</label><div class=\"sk-toggleable__content\"><pre>MultinomialNB(alpha=0.009495454545454547)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "MultinomialNB(alpha=0.009495454545454547)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = MultinomialNB(alpha=best_alpha)\n",
    "model.fit(x_train_vect, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "06279b94",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = model.predict(x_test_vect)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "22f11910",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('              precision    recall  f1-score   support\\n'\n",
      " '\\n'\n",
      " '    negative       0.78      0.94      0.85      2288\\n'\n",
      " '     neutral       0.63      0.38      0.47       781\\n'\n",
      " '    positive       0.77      0.54      0.63       591\\n'\n",
      " '\\n'\n",
      " '    accuracy                           0.76      3660\\n'\n",
      " '   macro avg       0.73      0.62      0.65      3660\\n'\n",
      " 'weighted avg       0.74      0.76      0.74      3660\\n')\n"
     ]
    }
   ],
   "source": [
    "import pprint  # 打印\n",
    "pprint.pprint(classification_report(y_test, pred))"
   ]
  },
  {
   "cell_type": "raw",
   "id": "c5d3f9a9",
   "metadata": {},
   "source": [
    "查准率 precision 召回率 recall\n",
    "比如预测是否阳性\n",
    "TP：将正类预测为正类数\n",
    "FN：将正类预测为负类数\n",
    "FP：将负类预测为正类数\n",
    "TN：将负类预测为负类数\n",
    "查准率 precision = TP/(TP+FP)\n",
    "召回率 recall = TP/(TP+FN)\n",
    "scikit-learn 评估性能的算法都在sklearn.metrics包里\n",
    "查准率 sklearn.metrics.precision_score()\n",
    "召回率 sklearn.metrics.recall_score()\n",
    "F1-Score 同时兼顾了分类模型的精确率和召回率。F1-Score可以看作是模型准确率和召回率的一种加权平均"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c594260",
   "metadata": {},
   "source": [
    "## 混淆矩阵"
   ]
  },
  {
   "cell_type": "raw",
   "id": "6c322af3",
   "metadata": {},
   "source": [
    "混淆矩阵\n",
    "观察每一个类别被错误分类的情况，以矩阵形式将数据集中的记录按照真实的类别与分类模型预测的类别判断两个标准进行汇总。其中矩阵行表示真实值，列表示预测值。对角线上是预测正确的值。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "cc7e0bcc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "9d9fffe3",
   "metadata": {},
   "outputs": [],
   "source": [
    "cm = confusion_matrix(y_test, pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "fb25b50e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2158,   99,   31],\n",
       "       [ 422,  297,   62],\n",
       "       [ 198,   76,  317]], dtype=int64)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "bc85ee81",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "c7b9c63b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x24dd97653c0>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 480x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.matshow(cm)\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ee630ee9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}