建模算法/pipline/mlFlow.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a1e521e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import missingno as msno\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import roc_auc_score, roc_curve\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "14371260",
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'train.csv.zip'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-2-50b5f44443cd>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mtrain_df\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"train.csv.zip\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mindex_col\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mtest_df\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"test.csv.zip\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mindex_col\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda3\\envs\\mathmodel\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36mread_csv\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[0;32m    686\u001b[0m     )\n\u001b[0;32m    687\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 688\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    689\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    690\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda3\\envs\\mathmodel\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    452\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    453\u001b[0m     \u001b[1;31m# Create the parser.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 454\u001b[1;33m     \u001b[0mparser\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfp_or_buf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    455\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    456\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda3\\envs\\mathmodel\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m    946\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    947\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 948\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    949\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    950\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda3\\envs\\mathmodel\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[1;34m(self, engine)\u001b[0m\n\u001b[0;32m   1178\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"c\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1179\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"c\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1180\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1181\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1182\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"python\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda3\\envs\\mathmodel\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, src, **kwds)\u001b[0m\n\u001b[0;32m   2008\u001b[0m         \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"usecols\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0musecols\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2009\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2010\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2011\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munnamed_cols\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reader\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munnamed_cols\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2012\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda3\\envs\\mathmodel\\lib\\zipfile.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, file, mode, compression, allowZip64)\u001b[0m\n\u001b[0;32m   1111\u001b[0m             \u001b[1;32mwhile\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1112\u001b[0m                 \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1113\u001b[1;33m                     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mio\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfilemode\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1114\u001b[0m                 \u001b[1;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1115\u001b[0m                     \u001b[1;32mif\u001b[0m \u001b[0mfilemode\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mmodeDict\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'train.csv.zip'"
     ]
    }
   ],
   "source": [
    "train_df = pd.read_csv(\"train.csv.zip\",index_col=0)\n",
    "test_df = pd.read_csv(\"test.csv.zip\",index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7bbdee24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='target', ylabel='count'>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train_df['target'], palette='Set3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "081e5458",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    179902\n",
       "1     20098\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[\"target\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "76788b11",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df_0 = train_df[train_df['target'] == 0]\n",
    "train_df_1 = train_df[train_df['target'] == 1]\n",
    "train_df_1_resample = train_df_1.sample(n=1000,replace=False,axis=0)\n",
    "train_df_0_resample = train_df_0.sample(n=1000,replace=False,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "df96f8df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>target</th>\n",
       "      <th>var_0</th>\n",
       "      <th>var_1</th>\n",
       "      <th>var_2</th>\n",
       "      <th>var_3</th>\n",
       "      <th>var_4</th>\n",
       "      <th>var_5</th>\n",
       "      <th>var_6</th>\n",
       "      <th>var_7</th>\n",
       "      <th>var_8</th>\n",
       "      <th>...</th>\n",
       "      <th>var_190</th>\n",
       "      <th>var_191</th>\n",
       "      <th>var_192</th>\n",
       "      <th>var_193</th>\n",
       "      <th>var_194</th>\n",
       "      <th>var_195</th>\n",
       "      <th>var_196</th>\n",
       "      <th>var_197</th>\n",
       "      <th>var_198</th>\n",
       "      <th>var_199</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID_code</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>train_182441</th>\n",
       "      <td>0</td>\n",
       "      <td>10.9737</td>\n",
       "      <td>-2.7212</td>\n",
       "      <td>12.2556</td>\n",
       "      <td>3.5929</td>\n",
       "      <td>13.3702</td>\n",
       "      <td>9.3316</td>\n",
       "      <td>4.5349</td>\n",
       "      <td>20.3147</td>\n",
       "      <td>-0.6414</td>\n",
       "      <td>...</td>\n",
       "      <td>4.2496</td>\n",
       "      <td>10.6792</td>\n",
       "      <td>1.7368</td>\n",
       "      <td>0.2051</td>\n",
       "      <td>11.6572</td>\n",
       "      <td>1.1973</td>\n",
       "      <td>9.4878</td>\n",
       "      <td>8.1458</td>\n",
       "      <td>13.8475</td>\n",
       "      <td>9.1632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>train_47096</th>\n",
       "      <td>0</td>\n",
       "      <td>11.9998</td>\n",
       "      <td>6.1510</td>\n",
       "      <td>8.4010</td>\n",
       "      <td>7.6555</td>\n",
       "      <td>11.2702</td>\n",
       "      <td>7.7612</td>\n",
       "      <td>5.1604</td>\n",
       "      <td>21.5015</td>\n",
       "      <td>0.3788</td>\n",
       "      <td>...</td>\n",
       "      <td>6.9136</td>\n",
       "      <td>7.7628</td>\n",
       "      <td>2.4661</td>\n",
       "      <td>12.2814</td>\n",
       "      <td>17.2701</td>\n",
       "      <td>0.6449</td>\n",
       "      <td>-3.7681</td>\n",
       "      <td>9.3441</td>\n",
       "      <td>22.8392</td>\n",
       "      <td>-13.8421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>train_46427</th>\n",
       "      <td>0</td>\n",
       "      <td>10.6003</td>\n",
       "      <td>-2.9563</td>\n",
       "      <td>7.3665</td>\n",
       "      <td>10.1899</td>\n",
       "      <td>12.2038</td>\n",
       "      <td>1.7290</td>\n",
       "      <td>4.2858</td>\n",
       "      <td>11.9045</td>\n",
       "      <td>6.1558</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5593</td>\n",
       "      <td>3.6345</td>\n",
       "      <td>0.2184</td>\n",
       "      <td>-2.2726</td>\n",
       "      <td>21.8692</td>\n",
       "      <td>0.9281</td>\n",
       "      <td>-5.1831</td>\n",
       "      <td>9.1242</td>\n",
       "      <td>18.4134</td>\n",
       "      <td>8.8957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>train_55935</th>\n",
       "      <td>0</td>\n",
       "      <td>12.3167</td>\n",
       "      <td>-4.4945</td>\n",
       "      <td>6.7338</td>\n",
       "      <td>6.5256</td>\n",
       "      <td>10.6927</td>\n",
       "      <td>-2.2523</td>\n",
       "      <td>6.4094</td>\n",
       "      <td>15.6645</td>\n",
       "      <td>-3.9562</td>\n",
       "      <td>...</td>\n",
       "      <td>6.7441</td>\n",
       "      <td>6.2868</td>\n",
       "      <td>0.9003</td>\n",
       "      <td>-1.1641</td>\n",
       "      <td>20.1415</td>\n",
       "      <td>0.9274</td>\n",
       "      <td>9.7951</td>\n",
       "      <td>8.2044</td>\n",
       "      <td>13.4595</td>\n",
       "      <td>-1.4031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>train_174337</th>\n",
       "      <td>0</td>\n",
       "      <td>16.8291</td>\n",
       "      <td>-9.0097</td>\n",
       "      <td>14.0495</td>\n",
       "      <td>5.3397</td>\n",
       "      <td>12.4134</td>\n",
       "      <td>0.1958</td>\n",
       "      <td>4.4049</td>\n",
       "      <td>19.4997</td>\n",
       "      <td>-4.6313</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.3697</td>\n",
       "      <td>6.8628</td>\n",
       "      <td>1.8962</td>\n",
       "      <td>4.8207</td>\n",
       "      <td>14.1539</td>\n",
       "      <td>1.0073</td>\n",
       "      <td>5.0522</td>\n",
       "      <td>8.6131</td>\n",
       "      <td>16.4658</td>\n",
       "      <td>-16.2425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>train_72916</th>\n",
       "      <td>1</td>\n",
       "      <td>14.7208</td>\n",
       "      <td>1.0362</td>\n",
       "      <td>12.3238</td>\n",
       "      <td>8.0755</td>\n",
       "      <td>8.6288</td>\n",
       "      <td>-3.9484</td>\n",
       "      <td>6.6357</td>\n",
       "      <td>19.2845</td>\n",
       "      <td>5.7397</td>\n",
       "      <td>...</td>\n",
       "      <td>2.3906</td>\n",
       "      <td>7.4373</td>\n",
       "      <td>-0.2916</td>\n",
       "      <td>4.6268</td>\n",
       "      <td>19.0622</td>\n",
       "      <td>-0.5872</td>\n",
       "      <td>4.5890</td>\n",
       "      <td>7.8241</td>\n",
       "      <td>20.5936</td>\n",
       "      <td>10.2743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>train_40696</th>\n",
       "      <td>1</td>\n",
       "      <td>16.0456</td>\n",
       "      <td>3.0133</td>\n",
       "      <td>8.0936</td>\n",
       "      <td>8.1017</td>\n",
       "      <td>9.1416</td>\n",
       "      <td>-4.8372</td>\n",
       "      <td>6.2008</td>\n",
       "      <td>14.3469</td>\n",
       "      <td>3.5453</td>\n",
       "      <td>...</td>\n",
       "      <td>4.3149</td>\n",
       "      <td>10.4263</td>\n",
       "      <td>1.2603</td>\n",
       "      <td>5.7981</td>\n",
       "      <td>10.8781</td>\n",
       "      <td>0.0391</td>\n",
       "      <td>-9.3372</td>\n",
       "      <td>10.1673</td>\n",
       "      <td>18.6318</td>\n",
       "      <td>4.7522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>train_177082</th>\n",
       "      <td>1</td>\n",
       "      <td>9.4869</td>\n",
       "      <td>1.0786</td>\n",
       "      <td>13.2960</td>\n",
       "      <td>1.7995</td>\n",
       "      <td>9.3621</td>\n",
       "      <td>1.9421</td>\n",
       "      <td>6.4929</td>\n",
       "      <td>16.3955</td>\n",
       "      <td>2.6899</td>\n",
       "      <td>...</td>\n",
       "      <td>12.6956</td>\n",
       "      <td>6.2124</td>\n",
       "      <td>-0.0469</td>\n",
       "      <td>-3.9557</td>\n",
       "      <td>22.6041</td>\n",
       "      <td>-1.1672</td>\n",
       "      <td>5.8291</td>\n",
       "      <td>7.6355</td>\n",
       "      <td>14.6637</td>\n",
       "      <td>-10.7085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>train_187887</th>\n",
       "      <td>1</td>\n",
       "      <td>18.6744</td>\n",
       "      <td>1.2014</td>\n",
       "      <td>8.3349</td>\n",
       "      <td>7.7951</td>\n",
       "      <td>11.0403</td>\n",
       "      <td>3.7869</td>\n",
       "      <td>4.6571</td>\n",
       "      <td>13.8060</td>\n",
       "      <td>-0.8665</td>\n",
       "      <td>...</td>\n",
       "      <td>5.9171</td>\n",
       "      <td>6.8054</td>\n",
       "      <td>-0.6964</td>\n",
       "      <td>-3.6390</td>\n",
       "      <td>18.9357</td>\n",
       "      <td>0.3819</td>\n",
       "      <td>1.2320</td>\n",
       "      <td>6.8870</td>\n",
       "      <td>16.3279</td>\n",
       "      <td>-22.6802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>train_142335</th>\n",
       "      <td>1</td>\n",
       "      <td>11.2977</td>\n",
       "      <td>3.3056</td>\n",
       "      <td>6.7860</td>\n",
       "      <td>6.9166</td>\n",
       "      <td>10.5414</td>\n",
       "      <td>8.9998</td>\n",
       "      <td>5.2983</td>\n",
       "      <td>18.5309</td>\n",
       "      <td>3.8250</td>\n",
       "      <td>...</td>\n",
       "      <td>11.7655</td>\n",
       "      <td>7.2742</td>\n",
       "      <td>2.0753</td>\n",
       "      <td>6.2444</td>\n",
       "      <td>14.6237</td>\n",
       "      <td>1.1213</td>\n",
       "      <td>2.8922</td>\n",
       "      <td>9.1055</td>\n",
       "      <td>11.7777</td>\n",
       "      <td>-8.7520</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2000 rows × 201 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              target    var_0   var_1    var_2    var_3    var_4   var_5  \\\n",
       "ID_code                                                                    \n",
       "train_182441       0  10.9737 -2.7212  12.2556   3.5929  13.3702  9.3316   \n",
       "train_47096        0  11.9998  6.1510   8.4010   7.6555  11.2702  7.7612   \n",
       "train_46427        0  10.6003 -2.9563   7.3665  10.1899  12.2038  1.7290   \n",
       "train_55935        0  12.3167 -4.4945   6.7338   6.5256  10.6927 -2.2523   \n",
       "train_174337       0  16.8291 -9.0097  14.0495   5.3397  12.4134  0.1958   \n",
       "...              ...      ...     ...      ...      ...      ...     ...   \n",
       "train_72916        1  14.7208  1.0362  12.3238   8.0755   8.6288 -3.9484   \n",
       "train_40696        1  16.0456  3.0133   8.0936   8.1017   9.1416 -4.8372   \n",
       "train_177082       1   9.4869  1.0786  13.2960   1.7995   9.3621  1.9421   \n",
       "train_187887       1  18.6744  1.2014   8.3349   7.7951  11.0403  3.7869   \n",
       "train_142335       1  11.2977  3.3056   6.7860   6.9166  10.5414  8.9998   \n",
       "\n",
       "               var_6    var_7   var_8  ...  var_190  var_191  var_192  \\\n",
       "ID_code                                ...                              \n",
       "train_182441  4.5349  20.3147 -0.6414  ...   4.2496  10.6792   1.7368   \n",
       "train_47096   5.1604  21.5015  0.3788  ...   6.9136   7.7628   2.4661   \n",
       "train_46427   4.2858  11.9045  6.1558  ...   0.5593   3.6345   0.2184   \n",
       "train_55935   6.4094  15.6645 -3.9562  ...   6.7441   6.2868   0.9003   \n",
       "train_174337  4.4049  19.4997 -4.6313  ...  -0.3697   6.8628   1.8962   \n",
       "...              ...      ...     ...  ...      ...      ...      ...   \n",
       "train_72916   6.6357  19.2845  5.7397  ...   2.3906   7.4373  -0.2916   \n",
       "train_40696   6.2008  14.3469  3.5453  ...   4.3149  10.4263   1.2603   \n",
       "train_177082  6.4929  16.3955  2.6899  ...  12.6956   6.2124  -0.0469   \n",
       "train_187887  4.6571  13.8060 -0.8665  ...   5.9171   6.8054  -0.6964   \n",
       "train_142335  5.2983  18.5309  3.8250  ...  11.7655   7.2742   2.0753   \n",
       "\n",
       "              var_193  var_194  var_195  var_196  var_197  var_198  var_199  \n",
       "ID_code                                                                      \n",
       "train_182441   0.2051  11.6572   1.1973   9.4878   8.1458  13.8475   9.1632  \n",
       "train_47096   12.2814  17.2701   0.6449  -3.7681   9.3441  22.8392 -13.8421  \n",
       "train_46427   -2.2726  21.8692   0.9281  -5.1831   9.1242  18.4134   8.8957  \n",
       "train_55935   -1.1641  20.1415   0.9274   9.7951   8.2044  13.4595  -1.4031  \n",
       "train_174337   4.8207  14.1539   1.0073   5.0522   8.6131  16.4658 -16.2425  \n",
       "...               ...      ...      ...      ...      ...      ...      ...  \n",
       "train_72916    4.6268  19.0622  -0.5872   4.5890   7.8241  20.5936  10.2743  \n",
       "train_40696    5.7981  10.8781   0.0391  -9.3372  10.1673  18.6318   4.7522  \n",
       "train_177082  -3.9557  22.6041  -1.1672   5.8291   7.6355  14.6637 -10.7085  \n",
       "train_187887  -3.6390  18.9357   0.3819   1.2320   6.8870  16.3279 -22.6802  \n",
       "train_142335   6.2444  14.6237   1.1213   2.8922   9.1055  11.7777  -8.7520  \n",
       "\n",
       "[2000 rows x 201 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_train_df = pd.concat([train_df_0_resample,train_df_1_resample],axis=0)\n",
    "new_train_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c175f4fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='target', ylabel='count'>"
      ]
     },
     "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": [
    "sns.countplot(new_train_df['target'], palette='Set3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "80e4a2fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2000, 200)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = new_train_df.iloc[:,1:].values\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f0e01a87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2000,)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = new_train_df.iloc[:,0].values\n",
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3f2a87f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2000, 40)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_selection import GenericUnivariateSelect, chi2,f_classif,mutual_info_classif\n",
    "\n",
    "#### 特征选择 ####\n",
    "## sklearn的 特征选择\n",
    "## 分类问题：\n",
    "## chi2(非负特征的卡方统计) 指标要求特征都是非负的\n",
    "## f_classif(方差分析F值)\n",
    "## mutual_info_classif(互信息)\n",
    "## 回归问题：\n",
    "\"\"\"\n",
    "#### 特征选择 ####\n",
    "## sklearn的 特征选择\n",
    "\n",
    "过滤式单变量特征选择\n",
    "\n",
    "第一个参数: score_func\n",
    "\n",
    "    分类问题：\n",
    "    chi2(非负特征的卡方统计) 指标要求特征都是非负的\n",
    "    f_classif(方差分析F值)\n",
    "    mutual_info_classif(互信息)   ### 跑起来比较慢\n",
    "    回归问题：\n",
    "    f_regression\n",
    "    mutual_info_regression\n",
    "\n",
    "第二个参数: mode \n",
    "    percentile : 要保留的百分比   param： 0-100\n",
    "    k_best: 要保留的数目          param： feature_num\n",
    "    fpr:\n",
    "    fdr:\n",
    "    fwe:\n",
    "    \n",
    "\"\"\"\n",
    "\n",
    "transformer = GenericUnivariateSelect(score_func = f_classif, mode='percentile', param=20)\n",
    "X_new = transformer.fit_transform(X, y)\n",
    "X_new.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1a5b0bc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2000, 44)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "\"\"\"\n",
    "    根据模型得到特征的重要性\n",
    "    \n",
    "\"\"\"\n",
    "selector = SelectFromModel(estimator=LogisticRegression(solver='liblinear')).fit(X, y)\n",
    "# selector.estimator_.feature_importances_\n",
    "# selector.threshold_\n",
    "X_new = selector.transform(X)\n",
    "X_new.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a84ac246",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import SequentialFeatureSelector\n",
    "\n",
    "\"\"\"\n",
    "    顺序特征选择(前向，后向)\n",
    "    ### 很慢，别用\n",
    "\"\"\"\n",
    "\n",
    "sfs = SequentialFeatureSelector(estimator = RandomForestClassifier(), n_features_to_select=20,direction='forward')\n",
    "sfs.fit(X, y)\n",
    "X_new = sfs.transform(X)\n",
    "X_new.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1c9c30a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.feature_selection import RFECV\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "\"\"\"\n",
    "嵌入式递归特征选择\n",
    "    SVM 好像只能用线性核，很慢\n",
    "    \n",
    "    REFCV 通过交叉验证选择特征数，以时间为代价\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "estimator = RandomForestClassifier()  \n",
    "# selector = RFE(estimator,n_features_to_select=20,step=0.5)\n",
    "selector = RFECV(estimator, step=0.2, cv=5)\n",
    "selector = selector.fit(X, y)\n",
    "X_new = selector.transform(X)\n",
    "X_new.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5e363150",
   "metadata": {},
   "outputs": [],
   "source": [
    "from ITMO_FS.filters.univariate import select_k_best\n",
    "from ITMO_FS.filters.univariate import UnivariateFilter\n",
    "from ITMO_FS.filters.univariate import f_ratio_measure,gini_index, su_measure,spearman_corr,pearson_corr, fechner_corr,kendall_corr,reliefF_measure,chi2_measure,information_gain\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "    ITMO_FS , 中的  单变量过滤式  特征选择方法，只列举一些sklearn没有的\n",
    "    第一个参数 ： f_ratio_measure,gini_index, su_measure,spearman_corr,pearson_corr, \n",
    "            fechner_corr,kendall_corr,reliefF_measure,chi2_measure,information_gain\n",
    "            \n",
    "            以前试过relieF,西瓜书上有讲，不过好像有点慢\n",
    "            \n",
    "    第二个参数 ：select_best_by_value(), select_k_best(), select_best_percentage() \n",
    "    \n",
    "    \n",
    "\"\"\"\n",
    "\n",
    "ufilter = UnivariateFilter(reliefF_measure, select_k_best(10))\n",
    "ufilter.fit(X,y)\n",
    "X_new = ufilter.transform(X)\n",
    "X_new.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "397fab3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from ITMO_FS.filters.multivariate import MultivariateFilter\n",
    "\"\"\"\n",
    "\n",
    "    第一个参数：'MIM','MRMR','JMI'   MRMR好像更有名一点\n",
    "\n",
    "\"\"\"\n",
    "model = MultivariateFilter('MRMR', 8)\n",
    "model.fit(X,y)\n",
    "X_new = model.fit_transform(X)\n",
    "X_new.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb431e69",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "    数据降维的方法\n",
    "    LLE : 西瓜书上有讲\n",
    "\n",
    "\"\"\"\n",
    "from sklearn.manifold import LocallyLinearEmbedding\n",
    "embedding = LocallyLinearEmbedding(n_components=12)\n",
    "embedding.fit(X,y)\n",
    "X_new = embedding.transform(X)\n",
    "X_new.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "9e138bec",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "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": [
    "\"\"\"\n",
    "    各种不同的PCA\n",
    "    PCA\n",
    "    KernelPCA : 针对线性不可分的数据集，使用非线性的核函数把样本空间映射到线性可分的高维空间，然后在这个高维空间进行主成分分析\n",
    "    SparsePCA : 针对主成分分析结果解释性弱的问题，通过提取最能重建数据的稀疏分量， 凸显主成分中的主要组成部分，\n",
    "                容易解释哪些原始变量导致了样本之间的差异\n",
    "    IncrementalPCA : 针对大型数据集，为了解决内存限制问题，将数据分成多批，通过增量方式逐步调用主成分分析算法，最终完成整个数据集的降维。\n",
    "    \n",
    "\n",
    "\"\"\"\n",
    "from sklearn.decomposition import PCA,KernelPCA,SparsePCA,IncrementalPCA\n",
    "\n",
    "pca =  PCA(n_components=3)\n",
    "pca.fit(X,y)\n",
    "new_X = pca.transform(X)\n",
    "pca.components_.shape\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D \n",
    "\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = Axes3D(fig)\n",
    "colors = ['navy', 'turquoise',]\n",
    "lw = 2\n",
    "\n",
    "for color, i in zip(colors, [0, 1]):\n",
    "    ax.scatter(X_new[y == i, 0], X_new[y == i, 1], X_new[y==i,2],color=color, alpha=.8, lw=lw,)\n",
    "ax.legend(loc='best', shadow=False, scatterpoints=1)\n",
    "plt.title('PCA ')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d3ea17ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "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": [
    "from sklearn.decomposition import PCA,KernelPCA,SparsePCA,IncrementalPCA\n",
    "\n",
    "pca =  SparsePCA(n_components=3)\n",
    "pca.fit(X,y)\n",
    "new_X = pca.transform(X)\n",
    "pca.components_.shape\n",
    "\n",
    "plt.figure()\n",
    "colors = ['navy', 'turquoise',]\n",
    "lw = 2\n",
    "\n",
    "for color, i in zip(colors, [0, 1]):\n",
    "    plt.scatter(X_new[y == i, 0], X_new[y == i, 1], color=color, alpha=.8, lw=lw,)\n",
    "plt.legend(loc='best', shadow=False, scatterpoints=1)\n",
    "plt.title('PCA ')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "4d316a5d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "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": [
    "from sklearn.decomposition import PCA,KernelPCA,SparsePCA,IncrementalPCA\n",
    "\n",
    "pca =  KernelPCA(n_components=3,kernel='rbf')\n",
    "pca.fit(X,y)\n",
    "new_X = pca.transform(X)\n",
    "new_X.shape\n",
    "\n",
    "plt.figure()\n",
    "colors = ['navy', 'turquoise',]\n",
    "lw = 2\n",
    "\n",
    "for color, i in zip(colors, [0, 1]):\n",
    "    plt.scatter(X_new[y == i, 0], X_new[y == i, 1], color=color, alpha=.8, lw=lw,)\n",
    "plt.legend(loc='best', shadow=False, scatterpoints=1)\n",
    "plt.title('PCA ')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8cea3930",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "    降维后的可视化\n",
    "    \n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "1aedcfac",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "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": [
    "\"\"\"\n",
    "    数据降维的方法\n",
    "    T-SNE : 不太懂，用于可视化比较多吧\n",
    "\n",
    "\"\"\"\n",
    "from sklearn.manifold import TSNE\n",
    "embedding = TSNE(n_components=3)\n",
    "X_new = embedding.fit_transform(X)\n",
    "X_new.shape\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D \n",
    "\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = Axes3D(fig)\n",
    "colors = ['navy', 'turquoise',]\n",
    "lw = 2\n",
    "\n",
    "for color, i in zip(colors, [0, 1]):\n",
    "    ax.scatter(X_new[y == i, 0], X_new[y == i, 1], X_new[y==i,2],color=color, alpha=.8, lw=lw,)\n",
    "ax.legend(loc='best', shadow=False, scatterpoints=1)\n",
    "plt.title('PCA ')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "76358f65",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "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": [
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "\n",
    "\"\"\"\n",
    "  LDA : \n",
    "\"\"\"\n",
    "\n",
    "clf = LinearDiscriminantAnalysis()\n",
    "clf.fit(X, y)\n",
    "X_new = clf.transform(X)\n",
    "\n",
    "plt.figure()\n",
    "colors = ['navy', 'turquoise',]\n",
    "lw = 2\n",
    "\n",
    "for color, i in zip(colors, [0, 1]):\n",
    "    plt.scatter(X_new[y == i, 0],np.zeros_like(X_new[y==i,0]),color=color, alpha=.8, lw=lw,)\n",
    "plt.legend(loc='best', shadow=False, scatterpoints=1)\n",
    "plt.title('LDA ')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "21100ef6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 for training set: 1.0\n",
      "R2 for validation set: 0.6983333333333334\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'bootstrap': True,\n",
       " 'ccp_alpha': 0.0,\n",
       " 'class_weight': None,\n",
       " 'criterion': 'gini',\n",
       " 'max_depth': None,\n",
       " 'max_features': 'auto',\n",
       " 'max_leaf_nodes': None,\n",
       " 'max_samples': None,\n",
       " 'min_impurity_decrease': 0.0,\n",
       " 'min_impurity_split': None,\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_samples_split': 2,\n",
       " 'min_weight_fraction_leaf': 0.0,\n",
       " 'n_estimators': 100,\n",
       " 'n_jobs': None,\n",
       " 'oob_score': False,\n",
       " 'random_state': None,\n",
       " 'verbose': 0,\n",
       " 'warm_start': False}"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "    随机搜索 + 网格搜索\n",
    "    \n",
    "\n",
    "\"\"\"\n",
    "from sklearn.model_selection import train_test_split \n",
    "\n",
    "X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.3) \n",
    "forest = RandomForestClassifier()\n",
    "forest.fit(X_train, y_train) \n",
    "print(f\"R2 for training set: {forest.score(X_train, y_train)}\") \n",
    "print(f\"R2 for validation set: {forest.score(X_valid, y_valid)}\\n\")\n",
    "forest.get_params() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "82a7ba42",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best params:\n",
      "\n",
      "{'n_estimators': 1900, 'min_samples_split': 8, 'min_samples_leaf': 4, 'max_features': 'log2', 'max_depth': 60, 'bootstrap': True}\n"
     ]
    }
   ],
   "source": [
    "n_estimators = np.arange(100, 2000, step=100) \n",
    "max_features = [\"auto\", \"sqrt\", \"log2\"] \n",
    "max_depth = list(np.arange(10, 100, step=10)) + [None] \n",
    "min_samples_split = np.arange(2, 10, step=2) \n",
    "min_samples_leaf = [1, 2, 4] \n",
    "bootstrap = [True, False]\n",
    "\n",
    "param_grid = { \n",
    "    \"n_estimators\": n_estimators, \n",
    "    \"max_features\": max_features, \n",
    "    \"max_depth\": max_depth, \n",
    "    \"min_samples_split\": min_samples_split, \n",
    "    \"min_samples_leaf\": min_samples_leaf, \n",
    "    \"bootstrap\": bootstrap, \n",
    "} \n",
    "\n",
    "from sklearn.model_selection import RandomizedSearchCV \n",
    " \n",
    "forest = RandomForestClassifier() \n",
    " \n",
    "random_cv = RandomizedSearchCV( \n",
    "    forest, param_grid, n_iter=100, cv=3, scoring=\"accuracy\", n_jobs=-1 \n",
    ")\n",
    "random_cv.fit(X, y) \n",
    " \n",
    "print(\"Best params:\\n\") \n",
    "print(random_cv.best_params_) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "69dc453e",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_params = { \n",
    "    \"n_estimators\": [1600, 1700, 1800, 1900, 2000,2100], \n",
    "    \"max_features\": ['logw'], \n",
    "    \"max_depth\": [50, 55, 60, 65, 70], \n",
    "    \"min_samples_split\": [6, 8, 10], \n",
    "    \"min_samples_leaf\": [2, 4,6], \n",
    "    \"bootstrap\": [True], \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "d77e9bac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7705028866947906"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_cv.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "047f02cd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "82d44c99",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best params:\n",
      "\n",
      "{'bootstrap': True, 'max_depth': 50, 'max_features': 'log2', 'min_samples_leaf': 2, 'min_samples_split': 8, 'n_estimators': 1700} \n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV \n",
    "\n",
    "forest = RandomForestClassifier() \n",
    "\n",
    "new_params = { \n",
    "    \"n_estimators\": [1600, 1700, 1800, 1900, 2000,2100], \n",
    "    \"max_features\": ['log2'], \n",
    "    \"max_depth\": [50, 55, 60, 65, 70], \n",
    "    \"min_samples_split\": [6, 8, 10], \n",
    "    \"min_samples_leaf\": [2, 4,6], \n",
    "    \"bootstrap\": [True], \n",
    "}\n",
    "\n",
    "\n",
    "grid_cv = GridSearchCV(forest, new_params,cv=3, n_jobs=-1)\n",
    "grid_cv.fit(X, y) \n",
    "print('Best params:\\n') \n",
    "print(grid_cv.best_params_, '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "94c06ea7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.77650213932073"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_cv.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "4d3a6b21",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6trial [04:13, 42.20s/trial, best loss: -0.7685053869461665]                                                           \n",
      "best\n",
      "{'max_depth': 1, 'max_features': 0, 'n_estimators': 3}\n",
      "{'state': 2, 'tid': 0, 'spec': None, 'result': {'loss': -0.7675043859451657, 'status': 'ok'}, 'misc': {'tid': 0, 'cmd': ('domain_attachment', 'FMinIter_Domain'), 'workdir': None, 'idxs': {'n_estimators': [0], 'max_features': [0], 'max_depth': [0]}, 'vals': {'n_estimators': [1], 'max_features': [0], 'max_depth': [1]}}, 'exp_key': None, 'owner': None, 'version': 0, 'book_time': datetime.datetime(2021, 8, 27, 8, 29, 47, 588000), 'refresh_time': datetime.datetime(2021, 8, 27, 8, 30, 13, 458000)}\n",
      "{'state': 2, 'tid': 1, 'spec': None, 'result': {'loss': -0.7685053869461665, 'status': 'ok'}, 'misc': {'tid': 1, 'cmd': ('domain_attachment', 'FMinIter_Domain'), 'workdir': None, 'idxs': {'max_depth': [1], 'max_features': [1], 'n_estimators': [1]}, 'vals': {'max_depth': [1], 'max_features': [0], 'n_estimators': [3]}}, 'exp_key': None, 'owner': None, 'version': 0, 'book_time': datetime.datetime(2021, 8, 27, 8, 30, 13, 462000), 'refresh_time': datetime.datetime(2021, 8, 27, 8, 30, 43, 340000)}\n",
      "{'state': 2, 'tid': 2, 'spec': None, 'result': {'loss': -0.7554983769376573, 'status': 'ok'}, 'misc': {'tid': 2, 'cmd': ('domain_attachment', 'FMinIter_Domain'), 'workdir': None, 'idxs': {'max_depth': [2], 'max_features': [2], 'n_estimators': [2]}, 'vals': {'max_depth': [3], 'max_features': [1], 'n_estimators': [2]}}, 'exp_key': None, 'owner': None, 'version': 0, 'book_time': datetime.datetime(2021, 8, 27, 8, 30, 43, 344000), 'refresh_time': datetime.datetime(2021, 8, 27, 8, 31, 37, 911000)}\n",
      "{'state': 2, 'tid': 3, 'spec': None, 'result': {'loss': -0.7594993794394093, 'status': 'ok'}, 'misc': {'tid': 3, 'cmd': ('domain_attachment', 'FMinIter_Domain'), 'workdir': None, 'idxs': {'max_depth': [3], 'max_features': [3], 'n_estimators': [3]}, 'vals': {'max_depth': [3], 'max_features': [0], 'n_estimators': [4]}}, 'exp_key': None, 'owner': None, 'version': 0, 'book_time': datetime.datetime(2021, 8, 27, 8, 31, 37, 914000), 'refresh_time': datetime.datetime(2021, 8, 27, 8, 32, 10, 661000)}\n",
      "{'state': 2, 'tid': 4, 'spec': None, 'result': {'loss': -0.7579986283134709, 'status': 'ok'}, 'misc': {'tid': 4, 'cmd': ('domain_attachment', 'FMinIter_Domain'), 'workdir': None, 'idxs': {'max_depth': [4], 'max_features': [4], 'n_estimators': [4]}, 'vals': {'max_depth': [0], 'max_features': [1], 'n_estimators': [1]}}, 'exp_key': None, 'owner': None, 'version': 0, 'book_time': datetime.datetime(2021, 8, 27, 8, 32, 10, 663000), 'refresh_time': datetime.datetime(2021, 8, 27, 8, 32, 58, 993000)}\n",
      "{'state': 2, 'tid': 5, 'spec': None, 'result': {'loss': -0.7555028791910351, 'status': 'ok'}, 'misc': {'tid': 5, 'cmd': ('domain_attachment', 'FMinIter_Domain'), 'workdir': None, 'idxs': {'max_depth': [5], 'max_features': [5], 'n_estimators': [5]}, 'vals': {'max_depth': [1], 'max_features': [1], 'n_estimators': [4]}}, 'exp_key': None, 'owner': None, 'version': 0, 'book_time': datetime.datetime(2021, 8, 27, 8, 32, 58, 996000), 'refresh_time': datetime.datetime(2021, 8, 27, 8, 34, 0, 800000)}\n"
     ]
    }
   ],
   "source": [
    "from hyperopt import hp, STATUS_OK, Trials, fmin, tpe\n",
    "from hyperopt.fmin import generate_trials_to_calculate\n",
    "\n",
    "from sklearn.model_selection import cross_validate\n",
    "\n",
    "def hyperopt_train(params,X,Y):\n",
    "    clf = RandomForestClassifier(**params)\n",
    "    res = cross_validate(clf, X, Y, scoring=['accuracy'], cv=3)\n",
    "    return res['test_accuracy'].mean()\n",
    "\n",
    "\n",
    "space_knn = {'n_estimators': hp.choice('n_estimators', range(1500, 2000,100)),\n",
    "             'max_features':hp.choice('max_features',['log2','sqrt','auto']),\n",
    "             \"max_depth\": hp.choice('max_depth',range(50,80,5)) \n",
    "             \n",
    "            }\n",
    "\n",
    "\n",
    "def f(parmas):\n",
    "    acc = hyperopt_train(parmas,X,y)\n",
    "    return {'loss': -acc, 'status': STATUS_OK}\n",
    "\n",
    "\n",
    "# trials = Trials()\n",
    "\n",
    "trials = generate_trials_to_calculate([{'n_estimators':1,'max_features':0,'max_depth':1}])\n",
    "best = fmin(f, space_knn, algo=tpe.suggest, max_evals=5, trials=trials)\n",
    "print('best')\n",
    "print(best)\n",
    "for trial in trials.trials:\n",
    "    print(trial)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "4d66efbe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameter (CV score=0.735):\n",
      "{'rf__n_estimators': 1500, 'rf__min_samples_split': 4, 'rf__min_samples_leaf': 2, 'rf__max_features': 'sqrt', 'rf__max_depth': 60, 'rf__bootstrap': False, 'pca__n_components': 60}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.model_selection import GridSearchCV,RandomizedSearchCV\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "\n",
    "# Define a pipeline to search for the best combination of PCA truncation\n",
    "# and classifier regularization.\n",
    "scaler = StandardScaler()\n",
    "pca = PCA()\n",
    "# set the tolerance to a large value to make the example faster\n",
    "rf = RandomForestClassifier()\n",
    "pipe = Pipeline(steps=[('scaler',scaler),('pca', pca), ('rf', rf)])\n",
    "\n",
    "\n",
    "\n",
    "n_estimators = np.arange(100, 2000, step=100) \n",
    "max_features = [\"auto\", \"sqrt\", \"log2\"] \n",
    "max_depth = list(np.arange(10, 100, step=10)) + [None] \n",
    "min_samples_split = np.arange(2, 10, step=2) \n",
    "min_samples_leaf = [1, 2, 4] \n",
    "bootstrap = [True, False]\n",
    "\n",
    "# Parameters of pipelines can be set using ‘__’ separated parameter names:\n",
    "param_grid = {\n",
    "    'pca__n_components': [5, 15, 20, 30, 60],\n",
    "    'rf__n_estimators': n_estimators,\n",
    "    \"rf__max_features\": max_features, \n",
    "    \"rf__max_depth\": max_depth, \n",
    "    \"rf__min_samples_split\": min_samples_split, \n",
    "    \"rf__min_samples_leaf\": min_samples_leaf, \n",
    "    \"rf__bootstrap\": bootstrap, \n",
    "}\n",
    "search = RandomizedSearchCV(pipe, param_grid,n_iter=100,cv = 3,n_jobs=-1)\n",
    "search.fit(X, y)\n",
    "print(\"Best parameter (CV score=%0.3f):\" % search.best_score_)\n",
    "print(search.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "659eab21",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---start----Nearest Neighbors\n",
      "---start----Linear SVM\n",
      "---start----RBF SVM\n",
      "---start----Gaussian Process\n",
      "---start----Decision Tree\n",
      "---start----Random Forest\n",
      "---start----GDBT\n",
      "---start----Neural Net\n",
      "---start----AdaBoost\n",
      "---start----Naive Bayes\n",
      "---start----QDA\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>acc</th>\n",
       "      <th>f1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Nearest Neighbors</th>\n",
       "      <td>0.540909</td>\n",
       "      <td>0.433645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Linear SVM</th>\n",
       "      <td>0.743939</td>\n",
       "      <td>0.745865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RBF SVM</th>\n",
       "      <td>0.69697</td>\n",
       "      <td>0.691358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gaussian Process</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.574242</td>\n",
       "      <td>0.561622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random Forest</th>\n",
       "      <td>0.739394</td>\n",
       "      <td>0.728707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GDBT</th>\n",
       "      <td>0.706061</td>\n",
       "      <td>0.696875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neural Net</th>\n",
       "      <td>0.657576</td>\n",
       "      <td>0.646875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AdaBoost</th>\n",
       "      <td>0.686364</td>\n",
       "      <td>0.684932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Naive Bayes</th>\n",
       "      <td>0.783333</td>\n",
       "      <td>0.783661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QDA</th>\n",
       "      <td>0.563636</td>\n",
       "      <td>0.580175</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        acc        f1\n",
       "Nearest Neighbors  0.540909  0.433645\n",
       "Linear SVM         0.743939  0.745865\n",
       "RBF SVM             0.69697  0.691358\n",
       "Gaussian Process        0.5       0.0\n",
       "Decision Tree      0.574242  0.561622\n",
       "Random Forest      0.739394  0.728707\n",
       "GDBT               0.706061  0.696875\n",
       "Neural Net         0.657576  0.646875\n",
       "AdaBoost           0.686364  0.684932\n",
       "Naive Bayes        0.783333  0.783661\n",
       "QDA                0.563636  0.580175"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#### 多种分类器的一个测试\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.gaussian_process import GaussianProcessClassifier\n",
    "from sklearn.gaussian_process.kernels import RBF\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier,GradientBoostingClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis\n",
    "from sklearn.metrics import accuracy_score,roc_auc_score,f1_score\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.33, stratify=y)\n",
    "\n",
    "\n",
    "names = [\"Nearest Neighbors\", \"Linear SVM\", \"RBF SVM\", \"Gaussian Process\",\n",
    "         \"Decision Tree\", \"Random Forest\",\"GDBT\", \"Neural Net\", \"AdaBoost\",\n",
    "         \"Naive Bayes\", \"QDA\"]\n",
    "\n",
    "classifiers = [\n",
    "    KNeighborsClassifier(),\n",
    "    SVC(kernel=\"linear\"),\n",
    "    SVC(kernel=\"rbf\"),\n",
    "    GaussianProcessClassifier(1.0 * RBF(1.0)),\n",
    "    DecisionTreeClassifier(),\n",
    "    RandomForestClassifier(),\n",
    "    GradientBoostingClassifier(n_estimators=200),\n",
    "    MLPClassifier(alpha=1, max_iter=1000),\n",
    "    AdaBoostClassifier(),\n",
    "    GaussianNB(),\n",
    "    QuadraticDiscriminantAnalysis()]\n",
    "\n",
    "classifiers_df = df = pd.DataFrame(index = names,columns=['acc','f1'])\n",
    "\n",
    "for name, clf in zip(names, classifiers):\n",
    "    print(\"---start\\t\" + name + \"\\t----\")\n",
    "    clf.fit(X_train, y_train)\n",
    "    y_pred = clf.predict(X_test)\n",
    "    acc = accuracy_score(y_pred,y_test)\n",
    "    f1 = f1_score(y_test,y_pred)\n",
    "    classifiers_df.loc[name]['acc']=acc\n",
    "    classifiers_df.loc[name]['f1'] = f1\n",
    "\n",
    "classifiers_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "72d22478",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "总结:\n",
    "sklearn中的特征选择和降维方法\n",
    "    \n",
    "1.过滤式单变量特征选择\n",
    "    \n",
    "    from sklearn.feature_selection import GenericUnivariateSelect, chi2,f_classif,mutual_info_classi\n",
    "    \n",
    "    transformer = GenericUnivariateSelect(score_func = f_classif, mode='percentile', param=20)\n",
    "    X_new = transformer.fit_transform(X, y)\n",
    "    X_new.shape\n",
    "\n",
    "    第一个参数: score_func\n",
    "\n",
    "        分类问题：\n",
    "        chi2(非负特征的卡方统计) 指标要求特征都是非负的\n",
    "        f_classif(方差分析F值)\n",
    "        mutual_info_classif(互信息)   ### 跑起来比较慢\n",
    "        回归问题：\n",
    "        f_regression\n",
    "        mutual_info_regression\n",
    "\n",
    "    第二个参数: mode \n",
    "        percentile : 要保留的百分比   param： 0-100\n",
    "        k_best: 要保留的数目          param： feature_num\n",
    "        fpr:\n",
    "        fdr:\n",
    "        fwe:\n",
    "\n",
    "2. 根据模型得到特征的重要性进行特征选择\n",
    "    from sklearn.linear_model import LogisticRegression\n",
    "    from sklearn.ensemble import RandomForestClassifier\n",
    "    from sklearn.feature_selection import SelectFromModel\n",
    "\n",
    "    selector = SelectFromModel(estimator=LogisticRegression(solver='liblinear')).fit(X, y)\n",
    "    X_new = selector.transform(X)\n",
    "    X_new.shape\n",
    "    \n",
    "\n",
    "3. 顺序特征选择(前向，后向)  ### 很慢，别用    0.24.2版本才有的\n",
    "    \n",
    "    from sklearn.feature_selection import SequentialFeatureSelector\n",
    "    sfs = SequentialFeatureSelector(estimator = RandomForestClassifier(), n_features_to_select=20,direction='forward')\n",
    "    sfs.fit(X, y)\n",
    "    X_new = sfs.transform(X)\n",
    "   \n",
    "4.  嵌入式递归特征选择\n",
    "\n",
    "    SVM 好像只能用线性核，很慢\n",
    "    \n",
    "    REFCV 通过交叉验证选择特征数，以时间为代价 \n",
    "\n",
    "    from sklearn.feature_selection import RFE\n",
    "    from sklearn.feature_selection import RFECV\n",
    "    from sklearn.svm import SVR\n",
    "    from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "\n",
    "    estimator = RandomForestClassifier()  \n",
    "    # selector = RFE(estimator,n_features_to_select=20,step=0.5)\n",
    "    selector = RFECV(estimator, step=0.2, cv=5)\n",
    "    selector = selector.fit(X, y)\n",
    "    X_new = selector.transform(X)\n",
    "\n",
    "ITMO_FSZ中的特征选择方法更多，但好多看不懂：\n",
    "    \n",
    "    1. ITMO_FS , 中的单变量过滤式  特征选择方法，只列举一些sklearn没有的\n",
    "        \n",
    "    ufilter = UnivariateFilter(reliefF_measure, select_k_best(10))\n",
    "    \n",
    "    第一个参数 ： f_ratio_measure,gini_index, su_measure,spearman_corr,pearson_corr, \n",
    "            fechner_corr,kendall_corr,reliefF_measure,chi2_measure,information_gain\n",
    "            \n",
    "            以前试过relieF,西瓜书上有讲，不过好像有点慢\n",
    "            \n",
    "    第二个参数 ：select_best_by_value(), select_k_best(), select_best_percentage() \n",
    "    \n",
    "    \n",
    "    from ITMO_FS.filters.univariate import select_k_best\n",
    "    from ITMO_FS.filters.univariate import UnivariateFilter\n",
    "    from ITMO_FS.filters.univariate import f_ratio_measure,gini_index, su_measure,spearman_corr,pearson_corr, fechner_corr,kendall_corr,reliefF_measure,chi2_measure,information_gain\n",
    "\n",
    "    \n",
    "    ufilter = UnivariateFilter(reliefF_measure, select_k_best(10))\n",
    "    ufilter.fit(X,y)\n",
    "    X_new = ufilter.transform(X)\n",
    "    \n",
    "    \n",
    "    2. ITMO_FS，中的多变量特征选择，只听过MRMR，名气很大\n",
    "    \n",
    "        第一个参数：'MIM','MRMR','JMI'   MRMR好像更有名一点\n",
    "        第二个参数：需要的特征数\n",
    "    \n",
    "    from ITMO_FS.filters.multivariate import MultivariateFilter\n",
    "    \n",
    "    model = MultivariateFilter('MRMR', 8)\n",
    "    model.fit(X,y)\n",
    "    X_new = model.fit_transform(X)\n",
    "    X_new.shape\n",
    "    \n",
    "    \n",
    "\n",
    "上述特征选择的方法都不知道最后保留多少个特征合适\n",
    "\n",
    "\n",
    "sklearn中的降维方法：\n",
    "    1. 主成分分析系列：\n",
    "        \n",
    "        PCA :\n",
    "        KernelPCA : 针对线性不可分的数据集，使用非线性的核函数把样本空间映射到线性可分的高维空间，然后在这个高维空间进行主成分分析\n",
    "        SparsePCA : 针对主成分分析结果解释性弱的问题，通过提取最能重建数据的稀疏分量， 凸显主成分中的主要组成部分，\n",
    "                    容易解释哪些原始变量导致了样本之间的差异\n",
    "        IncrementalPCA : 针对大型数据集，为了解决内存限制问题，将数据分成多批，通过增量方式逐步调用主成分分析算法，最终完成整个数据集的降维。\n",
    "        \n",
    "    2.  局部线性嵌入\n",
    "        \n",
    "        from sklearn.manifold import LocallyLinearEmbedding\n",
    "        embedding = LocallyLinearEmbedding(n_components=12)\n",
    "        embedding.fit(X,y)\n",
    "        X_new = embedding.transform(X)\n",
    "        \n",
    "    3.  T-SNE  论文里都用来作高维数据的可视化，聚类后用T-SNE展示用\n",
    "               但是用来降维作可视化好像不这样做\n",
    "               \n",
    "        主要是为了作可视化\n",
    "        \n",
    "\n",
    "        \n",
    "\n",
    "sklearn 中的两个参数搜索函数，随机搜索和网格搜索，一般先在一个大的参数空间中用随机搜索，再在随即搜索最优附近较小的参数空间用网格搜索\n",
    "    \n",
    "    随机搜索：\n",
    "    \n",
    "    from sklearn.model_selection import RandomizedSearchCV \n",
    "    \n",
    "    n_estimators = np.arange(100, 2000, step=100) \n",
    "    max_features = [\"auto\", \"sqrt\", \"log2\"] \n",
    "    max_depth = list(np.arange(10, 100, step=10)) + [None] \n",
    "    min_samples_split = np.arange(2, 10, step=2) \n",
    "    min_samples_leaf = [1, 2, 4] \n",
    "    bootstrap = [True, False]\n",
    "\n",
    "    param_grid = { \n",
    "        \"n_estimators\": n_estimators, \n",
    "        \"max_features\": max_features, \n",
    "        \"max_depth\": max_depth, \n",
    "        \"min_samples_split\": min_samples_split, \n",
    "        \"min_samples_leaf\": min_samples_leaf, \n",
    "        \"bootstrap\": bootstrap, \n",
    "    } \n",
    " \n",
    "    forest = RandomForestClassifier() \n",
    "\n",
    "    random_cv = RandomizedSearchCV( \n",
    "        forest, param_grid, n_iter=100, cv=3, scoring=\"accuracy\", n_jobs=-1 \n",
    "    )\n",
    "    random_cv.fit(X, y) \n",
    "\n",
    "    print(\"Best params:\\n\") \n",
    "    print(random_cv.best_params_) \n",
    "    \n",
    "    \n",
    "    网格搜索：\n",
    "    \n",
    "    from sklearn.model_selection import GridSearchCV \n",
    "\n",
    "    forest = RandomForestClassifier() \n",
    "\n",
    "    new_params = { \n",
    "        \"n_estimators\": [1600, 1700, 1800, 1900, 2000,2100], \n",
    "        \"max_features\": ['log2'], \n",
    "        \"max_depth\": [50, 55, 60, 65, 70], \n",
    "        \"min_samples_split\": [6, 8, 10], \n",
    "        \"min_samples_leaf\": [2, 4,6], \n",
    "        \"bootstrap\": [True], \n",
    "    }\n",
    "\n",
    "\n",
    "    grid_cv = GridSearchCV(forest, new_params,cv=3, n_jobs=-1)\n",
    "    grid_cv.fit(X, y) \n",
    "    print('Best params:\\n') \n",
    "    print(grid_cv.best_params_, '\\n')\n",
    "    print(grid_cv.best_score_)\n",
    "    \n",
    "\n",
    "hyperopt库中的超参数优化，贝叶斯优化，之前试过，什么都记不清了\n",
    "    \n",
    "    \n",
    "    from hyperopt import hp, STATUS_OK, Trials, fmin, tpe\n",
    "    from hyperopt.fmin import generate_trials_to_calculate\n",
    "\n",
    "    from sklearn.model_selection import cross_validate\n",
    "\n",
    "    def hyperopt_train(params,X,Y):\n",
    "        clf = RandomForestClassifier(**params)\n",
    "        res = cross_validate(clf, X, Y, scoring=['accuracy'], cv=3)\n",
    "        return res['test_accuracy'].mean()\n",
    "\n",
    "\n",
    "    space_params = {'n_estimators': hp.choice('n_estimators', range(1500, 2000,100)),\n",
    "                 'max_features':hp.choice('max_features',['log2','sqrt','auto']),\n",
    "                 \"max_depth\": hp.choice('max_depth',range(50,80,5)) \n",
    "\n",
    "                }\n",
    "\n",
    "\n",
    "    def f(parmas):\n",
    "        acc = hyperopt_train(parmas,X,y)\n",
    "        return {'loss': -acc, 'status': STATUS_OK}\n",
    "\n",
    "\n",
    "    # trials = Trials()\n",
    "\n",
    "    trials = generate_trials_to_calculate([{'n_estimators':1,'max_features':0,'max_depth':1}])         ### 从特定的起点开始搜索\n",
    "    best = fmin(f, space_params, algo=tpe.suggest, max_evals=5, trials=trials)\n",
    "    print('best')\n",
    "    print(best)\n",
    "    for trial in trials.trials:\n",
    "        print(trial)\n",
    "\n",
    "\n",
    "\n",
    "pipeline + 参数搜索:\n",
    "\n",
    "\n",
    "    from sklearn.decomposition import PCA\n",
    "    from sklearn.linear_model import LogisticRegression\n",
    "    from sklearn.pipeline import Pipeline\n",
    "    from sklearn.model_selection import GridSearchCV,RandomizedSearchCV\n",
    "    from sklearn.ensemble import RandomForestClassifier\n",
    "    from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "\n",
    "    scaler = StandardScaler()\n",
    "    pca = PCA()\n",
    "    # set the tolerance to a large value to make the example faster\n",
    "    rf = RandomForestClassifier()\n",
    "    pipe = Pipeline(steps=[('scaler',scaler),('pca', pca), ('rf', rf)])\n",
    "\n",
    "\n",
    "\n",
    "    n_estimators = np.arange(100, 2000, step=100) \n",
    "    max_features = [\"auto\", \"sqrt\", \"log2\"] \n",
    "    max_depth = list(np.arange(10, 100, step=10)) + [None] \n",
    "    min_samples_split = np.arange(2, 10, step=2) \n",
    "    min_samples_leaf = [1, 2, 4] \n",
    "    bootstrap = [True, False]\n",
    "\n",
    "    # Parameters of pipelines can be set using ‘__’ separated parameter names:\n",
    "    param_grid = {\n",
    "        'pca__n_components': [5, 15, 20, 30, 60],\n",
    "        'rf__n_estimators': n_estimators,\n",
    "        \"rf__max_features\": max_features, \n",
    "        \"rf__max_depth\": max_depth, \n",
    "        \"rf__min_samples_split\": min_samples_split, \n",
    "        \"rf__min_samples_leaf\": min_samples_leaf, \n",
    "        \"rf__bootstrap\": bootstrap, \n",
    "    }\n",
    "    \n",
    "    search = RandomizedSearchCV(pipe, param_grid,n_iter=100,cv = 3,n_jobs=-1)\n",
    "    search.fit(X, y)\n",
    "    print(\"Best parameter (CV score=%0.3f):\" % search.best_score_)\n",
    "    print(search.best_params_)\n",
    "    \n",
    "    \n",
    "    \n",
    "### 测试多种分类器  应该放在标准化和特征选择(过滤式特征选择)之后，选出最优的分类器，然后调参，是吧？？，\n",
    "    \n",
    "    #### 多种分类器的一个测试\n",
    "\n",
    "    from sklearn.model_selection import train_test_split\n",
    "    from sklearn.preprocessing import StandardScaler\n",
    "    from sklearn.neural_network import MLPClassifier\n",
    "    from sklearn.neighbors import KNeighborsClassifier\n",
    "    from sklearn.svm import SVC\n",
    "    from sklearn.gaussian_process import GaussianProcessClassifier\n",
    "    from sklearn.gaussian_process.kernels import RBF\n",
    "    from sklearn.tree import DecisionTreeClassifier\n",
    "    from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier,GradientBoostingClassifier\n",
    "    from sklearn.naive_bayes import GaussianNB\n",
    "    from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis\n",
    "    from sklearn.metrics import accuracy_score,roc_auc_score,f1_score\n",
    "\n",
    "    X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.33, stratify=y)\n",
    "\n",
    "\n",
    "    names = [\"Nearest Neighbors\", \"Linear SVM\", \"RBF SVM\", \"Gaussian Process\",\n",
    "             \"Decision Tree\", \"Random Forest\",\"GDBT\", \"Neural Net\", \"AdaBoost\",\n",
    "             \"Naive Bayes\", \"QDA\"]\n",
    "\n",
    "    classifiers = [\n",
    "        KNeighborsClassifier(),\n",
    "        SVC(kernel=\"linear\"),\n",
    "        SVC(kernel=\"rbf\"),\n",
    "        GaussianProcessClassifier(1.0 * RBF(1.0)),\n",
    "        DecisionTreeClassifier(),\n",
    "        RandomForestClassifier(),\n",
    "        GradientBoostingClassifier(n_estimators=200),\n",
    "        MLPClassifier(alpha=1, max_iter=1000),\n",
    "        AdaBoostClassifier(),\n",
    "        GaussianNB(),\n",
    "        QuadraticDiscriminantAnalysis()]\n",
    "\n",
    "    classifiers_df = df = pd.DataFrame(index = names,columns=['acc','f1'])\n",
    "\n",
    "    for name, clf in zip(names, classifiers):\n",
    "        print(\"---start\\t\" + name + \"\\t----\")\n",
    "        clf.fit(X_train, y_train)\n",
    "        y_pred = clf.predict(X_test)\n",
    "        acc = accuracy_score(y_pred,y_test)\n",
    "        f1 = f1_score(y_test,y_pred)\n",
    "        classifiers_df.loc[name]['acc']=acc\n",
    "        classifiers_df.loc[name]['f1'] = f1\n",
    "\n",
    "    classifiers_df\n",
    "    \n",
    "\n",
    "\"\"\""
   ]
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