dj_Beeline_algs.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# разные модели в sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "%pylab inline\n",
    "plt.style.use('seaborn-dark')\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\") # отключение варнингов\n",
    "pd.set_option('display.max_columns', None) # pd.options.display.max_columns = None \n",
    "# pd.set_option('display.max_rows', None) # не прятать столбцы при выводе дата-фреймов\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "plt.rc('font', size=14)\n",
    "\n",
    "from mlxtend.plotting import plot_decision_regions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.162435</td>\n",
       "      <td>-0.061176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.945128</td>\n",
       "      <td>-0.043227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.078331</td>\n",
       "      <td>-0.102277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.156040</td>\n",
       "      <td>0.115038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.999199</td>\n",
       "      <td>0.228718</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1\n",
       "0  1.162435 -0.061176\n",
       "1  0.945128 -0.043227\n",
       "2  1.078331 -0.102277\n",
       "3  1.156040  0.115038\n",
       "4  0.999199  0.228718"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 504x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets import make_moons\n",
    "\n",
    "n_samples=100\n",
    "\n",
    "X, y = make_moons(n_samples=n_samples, shuffle=False, noise=0.1, random_state=1)\n",
    "\n",
    "# прореживаем, чтобы был дисбаланс\n",
    "i = (y==0) | (np.random.rand(n_samples) > 0.5)\n",
    "X = X[i, :]\n",
    "y = y[i]\n",
    "\n",
    "plt.figure(figsize=(7, 3))\n",
    "plt.scatter(X[:,0], X[:,1], 40, y)\n",
    "pd.DataFrame(X[:5,:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SGDClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2c6e602f50>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import SGDClassifier\n",
    "sgd = SGDClassifier()\n",
    "sgd.fit(X, y)\n",
    "plot_decision_regions(X, y, sgd)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2c6e540810>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.svm import LinearSVC\n",
    "svm = LinearSVC()\n",
    "svm.fit(X, y)\n",
    "plot_decision_regions(X, y, svm)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2c6e4d5c90>"
      ]
     },
     "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": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "lda = LinearDiscriminantAnalysis(solver=\"svd\", store_covariance=True)\n",
    "lda.fit(X, y)\n",
    "plot_decision_regions(X, y, lda)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2c6e451e90>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "svm = SVC(kernel='rbf', gamma=1.0)\n",
    "svm.fit(X, y)\n",
    "plot_decision_regions(X, y, svm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## своя модель с нелинейностями"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator, ClassifierMixin\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "class myC(BaseEstimator, ClassifierMixin):  \n",
    "    \"\"\"пример классификатора\"\"\"\n",
    "\n",
    "    def __init__(self, n_estimators=10):\n",
    "        \"\"\"\n",
    "        Инициализация\n",
    "        \"\"\"\n",
    "        self.model = LogisticRegression(C=1)\n",
    "    @staticmethod\n",
    "    def addX(X):\n",
    "        \"\"\"\n",
    "        Добавить 'случайные нелинейности'\n",
    "        \"\"\"\n",
    "        XX = np.hstack([X,\n",
    "                        (np.abs(X.dot([0.5, 0.5])) ** 0.5)[:, np.newaxis],\n",
    "                        (np.abs(X.dot([0.25, 0.75])) ** 0.75)[:, np.newaxis],\n",
    "                        (np.abs(X.dot([0.75, 0.25])) ** 1.5)[:, np.newaxis],\n",
    "                        ])\n",
    "        return (XX)\n",
    "        \n",
    "\n",
    "\n",
    "    def fit(self, X, y=None):\n",
    "        \"\"\"\n",
    "        Обучение\n",
    "        \"\"\"\n",
    "        self.model.fit(self.addX(X), y)\n",
    "\n",
    "        return self\n",
    "\n",
    "\n",
    "    def predict(self, X, y=None):\n",
    "        \"\"\"\n",
    "        классификация\n",
    "        \"\"\"\n",
    "        \n",
    "        return(self.model.predict(self.addX(X)))\n",
    "\n",
    "    def predict_proba(self, X, y=None):\n",
    "        \"\"\"\n",
    "        вероятность\n",
    "        \"\"\"\n",
    "        \n",
    "        return(self.model.predict_proba(self.addX(X)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2c6c1ab150>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ToANhgDuQd5ZMh6YwHxYLcO3fC6qPu3n5IWzMMxJWZh8nf/wS47sB3dr6Ynw3naxH4c4mL+siMo/uQZThGkICffHm5H6CjLZJWAxwidVdju3mGwiLxQIo3BF6/zwAQO62V5C39RVEdrhFylIFVTX6fm1yEABgSmwQ4rdxFC61ohvXkbb3U3Tx1eKvw3vilqhhktbD1ZlNY4CLrO4NL01hPiKnvAY3N3f4mk3V+25kb1pYvXReHxbZ4MMG5Kxq9B3qX7mUOtTfE+O7QbajcDnfTDboK5Dy3YfwMxTCBxX4IH6Qw2woxdWZTXOMvyUXUvemY8q6Z+Ed1rHWfhvWcOR+5aY6TK6cPoyz6gp8kZJd67h/2GFZBrjcbiZrNUXIuZSCkusX0EaThtkjeuP2Lo617J+rM5vHAJc5Rx7dNdVh8sSKf0tUlWszGvQ4uf1DKAqvYMawLmhzpy96dRwldVkN4urM5jHAyS7YYeIYLL8/CPvyke9RnnEURn05lj/UD+3bOvb9FK7ObBkGuAOpuXVpzUd+OcJ0iLVcucMk93omzGYTgMp7HFW7Flb9PYoxV24yGXHl3AmoDn2BziHeGNg5HI88MUCw89sbV2e2DANcYh7efsjetBBGbSGCQsKrj8v5kV+u3mFirnEz2kMZUj03XvUL2Z5z5dfOHoHq7G/QFeRg2qBIDE8YCqWvtyDnFhNXZ7YMA1xkdW86egPw9nKHv4wDuy5n6zBpiZp/rzX3RhFjX5TyMi1Of/U2lJ5A92AFXp1yGxSK7nBzc2v+mx0UV2e2DANcZC0JaTm3pAHO12HSEjX/Xl5/ZmKDo2whFeZmo7RIjZyjOxHtWYo3JvZHWJC854bZ8209BrgDkltLWl3sMLEPfUU5zu7+DIayUkSab2Bor0j0HtsBnSJDmv9mGWDPt/UY4EQO7uKvO1CedQqGslK8+FAMwtuEwt+nh9RlCYo9363DACe7kvN2sa3V2OIqbZEaZrMJJ//5WK3jVQ/rqJKbdRnlN3W4fuBTtAvywsBbQvHotP52r1tK7PluHQY42ZU128U6S9g3dp+isbnxjA3P4fFFiTi142OU5mfjrnZARJAflj41BP4NPPzX2Vy4lof3vtyHH2e1A8Ceb2vI9zY1ObyqdsLVE6Jx5bft0JUUN/n6mmHvCixmE/IOfYGK4lxofvoQiSMDsC4+BrMfuAMT7urtEuENAH/76Ft0DDBjZ6oWQO2eb2oaR+AOyJH3N7GGNYt5nH3lpsVigcmgx838LOjV13Ez8zjcvXzQod8QIG0fVj5+l9QliqJup4lao0NaRibevtcb878vxqdnjPBwrxxXsue7eQxwBySHVsHmWLuYx1lXbuZnXcCN1KMoyr4EtwoN3G+cQ6CPH7o+8ITUpUni4+0HkZ11GRu2H8SL0+7Hlj1JmDU4BPffHoS5Wg0MbfsztK3AABdJQ73dJQW5ULi5IyA4rNZxufR7N8WaxTzOtnLTaNDj9Dfr4GEsQ6i7Dkvv6ws/nxjcdSwJ7W61/qG/clZzxG2xANt/SsIbI73w0oEkTBwZy/1ObMQAF0lDvd0p655F2LjFiOrYtdZxufR7N8WaxTxyXrmpKciDyWiAtigfql8+Q1igL4wGI/4ypje6RHWv9dowpRdSP3qh3jnClF4YufhDqLV6AEBukRbm3zehUlhMiAxtU/26n95y7J9HXTV7u8vK9Rjd0YiRt/hi9JWbWLr+f9zvxEYMcLILaxbzyG3lpqYgD9dP/4LSvGvo6qlGVLA/Onq649VZw5t8XmRT4Rsz6z30eXp15R+u5sEnrAMA4PqmRejz9BsA0GD4O7Kavd1Pf5uM0rKb2DLBCz6ebpjZ3wv3bM7EjbwAbDmjr/V9nPtuOQa4nVVNnWgK85Gy7tnq42LskSEXcli5mXF0D4ovn4ACgEd5ARaMjYF/bCQiQ51rQY2QavZ2t3G/iQEdjOgRVrmxVo8wD8Tf6gm/rnfgxWn3S1ypfDHA7UynLYX//Yvhpa+Awu2PH3fu1pcBi0nCyqguk9FQ3ep45fAO+GoyoPh9Q6jbopWIn9ZPyvJkpe5+3llFeqTlmnAwqxSK319TUmGBb/YpBrgNGOAiMJtN8AyJhsLjj75ed2UwTNoiCasiraYQWWd+rQ6UgnMHMbBzABRQIL57OAb3jpO0Pjmru5935xBPpOdZUGL0QKCfLwAgwAuIDA+WskzZEyTAVSoVEhMTcfLkSfj4+OCee+7B0qVL4enpKcTpnYbF+Mdcn8VkBMwm5G19BTcb6EJxNmm/fIt2MUMQWGPPczGVl2lxZvsHcK/xqUehy8e8+3rB/fdRdvTAQQjw85GkPmdTfz9vHwQE+CAyPIRbxQpIkACfN28eunfvjr1796K0tBTz5s3DmjVr8MIL8rrpYk+eXrU31Xf38ERguPM9bb4x+kuHoL6eBJXCE2W+Ueg1emr113z8A6BQKJr47oYZDXoYKsrrHTeZTDi3/X0Eexqrj5kN5Vj5p1iH3XK1ZodK3S6UquNhSvmszGRIi8PmAE9JSUFqaio++ugjBAYGIjAwELNnz8by5cuxePFiWW8qT8Lx9/PByvjKKYnkC9k4eOAtAIDJbMFxtQLhfa1biWg2mZB77DtcSj+P8aMGwc+n9i/Ivz3QHe3byqeHXG7tgeQYbA7wc+fOISoqCiEhf+xJ3LdvX2g0GmRlZaFz5862XkLW/JUByPni5eontFTx8PYDLBUSVSW+G1oz8ou1CG+jxIAe0RjQI7r6a3lFpchRZ1h9zgPtPFGSoUOQux7zJwwVslwiWbA5wIuLixEYGFjrWFBQ5Yq6oqIilw/whW9ubvgJO5YKp5zrbkyvB+fg26RPkDDm9npfaxscgLbB1v0s1Bod9h055fD7R9dcoFOTHBflkOOxSxeKpWr+rhXzms5I7sviheDt54+r+SUtfn1zj9eSy/7Raq3+jwU6NTS3KIfBTy1hc4CHhISgqKh2O5xGo6n+GhEAKINCcN63K66oCnBLVGizr2/q8Vp1e4ydcQ+N1gY/uRabAzwmJga5ubnIy8tD27ZtAQBnzpxBaGgoOnToYHOB5DyUIVGoMOQ3+7rmHq9Vt8dYzntoNDbSvlFQjD4S1EPyYnOA9+nTB7fffjtWr16NV155BcXFxVi/fj3i4+M5hUKt0tz0SP0e40py3EOjsZG2ahXb8Kh5gsyBr1mzBomJiRg9ejT8/PwwduxYzJkzR4hTk4tpyfQIe4yJKgkS4BEREVi3bp0QpyIXt2VPEkZ1BJ756gY+eDRK1tMjQNNbyBLZinuhkEM5dPoizmeUoo2XHnHvZSM4oHLfDDlOjwCtX6DjplAw+KlZDHByKO8vmYYnX12HdQ/6Y+5OHTavmOs0nSXWiAhW4uyH80S5VnMtm+S4GODkUOTS3y0UR5hiaaplsy6GvWNhgJPDcIX+7rqkXpTTXMtmXdaEPdkfd5oih9FUfzfZR+1PPE3/rKvC/v2Hw7D71+MoKNGJWCk1hAFOojGbm34C0aHTF7HljB4D1+VX/7PljB6HTl8UqULXUhXIj/evHHE/3t+/yWC2JuzrXif+bx8z8O2AUygkioqbOujSfkbXEaMafQ37uxtnj7lna1a02jK9xWkX++EInERRpi3BoG5h8PRo/Knt1LiaISgUaz7xtHZ6i9Mu9sUROInCYjZLXYJsWXujsaWs+cTT2u0LXK2rSGwMcBJF2q4P8drDPaQuQ5YcIQRbM73lil1FYuMUCokiwteM6HD5POLMUVh7o9GRsKvI/hjgJIrfn/EhOrl3QMg5BNlVZH+cQiG7O7f7UzzUL0qSa8u9A0LOW+eyq8j+GOBkVwZ9BcqyUnD3AyNEv7a9bv6JyV4hyCXxzoFTKGRXKXv+g5ce6ivJtVu78KQpQkzJOMK0jj3aEkl8DHCyqwptMcLbKEW/ri03/5oKWCGCT+zwrPt+2JvtPBjgZDf5OVfRxV2NAD8f0a9ty82/hgJWrdFh8ssfYsfPyS0OvoZ+EUgRnnXfjz0+mZA0GOBkNxm//A+ThnSX5Nqt7YBoLGC37ElCUW4Wwr3LWxx8Df0iEDs8676fi9fzZNuWSPXxJibZTZhRhZ4dpVm809qbfw0tmnnsvkHYdfAYlg/3xMpDehToTM0uSmnoBqrFAtEXttR9P69++G2L9z9pCd4MlRZH4GQXBTlZCFV6S12GVerOmz/Q0wfvfbkP7321HyOj9egf5YGHe3rik2PFzU7JNDTSFrunu6H7ACfSM/HZqXLBerN5M1RaHIGTXVz6/gOsnxkndRlWqRuwu1K1iPIz4bsDSfhkoi8Ky8yIi1bg8a+L8ekZIzzc3Rrsx25sCbmXjz8KioTt6W5qBNzQL4yFw0NgaNtfkB5yZ2jTlDsGOAku88RPGNQpQHY7D9ZcNGM0mVGq1WL0Le7w9bTg9s7h8HCv/MA6d5imyRBsbKRtaNtL8MU3TS1UsvciIEfYo8XVMcBJUBaLBVeTd+P1uUOlLsVqNefN3/1yPzzzTuLniyVIv6rHrrevI9DPt/rrTYWgWKsn1Roddh08hlcGu+PvB4/VGwHbcyUkN6pyDAxwEtTlYz/hsTvCpC7DJjXDadHQIKh1Rvx5Wyk2r5jbonASawn5lj1JGBmtR6dAM0ZGW0QdAVvzMAiyH97EJMEYDXrcOL4Lw2I6S12KTeSwgVTV6PvBLmZ0bOOBB7uYsevgMdHaAblRlWPgCJwEo8o4j9G9QhHoL/7CHSHJYQOpqtF352B3+Hi6oXOwO0ZG60UbAXOjKsfAACdB5GamQXv4M0x9crjUpdhMDuG0/3ga0jK02HHeDe4KwGQB1GVm9C5Kc5hfMmR/DHASROaBz/HhzOFwc+OsnBjuvqMXxnS4iUVDg6qPvX1IA0PbXhJWRWJjgJPNMk/8hLs6+zG8RSSHaR6yPwY42cRsNiMzeTdefmqQ1KW4FDlM85D9cchENkn67A28eH83+Hp7Sl0KkcthgFOr6UqK0clHi1u7REpdiuw4wkMdSP4Y4NRqKf99C4seukPqMmSJm0CREBjg1Co5aSfRPVgh+55vKfCJOCQUBjhZLS/rIownt+LFiQOkLkWW+EQcEgoDnKyWcXgXEkb3hUKhkLoU2bHlWZ1EdTHAySpndm3Cw13N6BQZInUpsiSHfVZIPtgHTi1mNOjhqU7DQ+OHSV2KbHEBDgmJAU4tYjTocXhTIhIn9JW6FFnjAhwSEgOcmmXQV+DIv/+G1x/tiw5tg6Uuh4h+xzlwalbW+VOYFBvK8CZyMAxwapI6OxPaY9sw5o7uUpdCRHVwCoWalLH7I6ybOUJ2DygmcgUcgVOjLh35AQM7KRneRA5KkADPycnB1KlT0bNnTyFORw7gwqHv0E13Ak/fd6vUpRBRI2wO8KNHj2Ly5MmIiooSoh5yALrSYnhnHcLT990mdSlE1ASbA7yoqAgbNmzAuHHjhKiHJGY2m3Hm2w8wZRg/TRE5OptvYo4ZMwYAoFKpbC6GpGU2mZD0n1VYMDwCMZ0jpC6HiJrBLhQCAJhMRhz59B9Yck8H9O3cVupyiKgFmg3wAwcOYM6cOQ1+bd68eZg/f77gRZH4rl88hwe7ezO8iWSk2QAfNWoU0tPTxaiFJFJSmA/VT5vx0syhUpdCRFbgFIqL0xTkIf2rN7D2qWHw9/GSuhwisgID3IUV5eXg8jersfap4XyqPJEM2RzgM2fORHJyMiwWCwCgX79+AICNGzdiwAA+cstR6SvKkfbff2L9rFHw9uLvcSI5svn/3I0bNwpRB4ks69RBjIvtwPAmkjHuheKCMpJ/RDv1YUwc0kvqUojIBhx+uZhLR35Ap+JjmPvQHVKXQkQ2YoC7kAuHvkOPihQ8/WB/qUshIgEwwF3EhYPfoo8hFTO4QRWR0+AcuAswm80ovXgEM+7l1rBEzoQB7uTMJhOOfLYSc+7pJnUpRCQwTqE4MZPJiKRPV+KFe9pzjxMiJ8QAd1ImowFHPn0dS8d0Rs8O4VKXQ0R2wAB3QkaDHoc3/x3Lx3VDt+gwqcshIjthgDsZo0GPw5sSkTihF26JDJG6HCKyIwa4E9FXlCNpcyJef6QvOrQNlrocIrIzBriT0JffxNHNiXhj8q1oFxYkdTlEJFn3rOIAAAZuSURBVAIGuBOouKlD8uZE/POx/ogICZS6HCISCQNc5srLtDj2SSJWx9+J8DZKqcshIhExwGXsprYEJz5bgbceH4jQQH+pyyEikTHAZUpXUozTn/8dbz8Rh+AAP6nLISIJMMBlSKspRMqW1/DOk0MQ5O8rdTlEJBEGuMyUFOYjddtKrJ05FEpfb6nLISIJMcBlpFidiwv/XcUnyBMRAAa4bBTlq/gEeSKqhQEuA4Wqa8jc8Q7WzhzOhxATUTWmgYNTZ2ci+4f3sOap4fDy5F8XEf2BieDA8q9fRu7eD/D2zOHw9HCXuhwicjAMcAeVdzUdBQc24K0Zw+HuzgcnEVF9DHAHdCMjFSW/bsKbTzK8iahxDHAHk3PpDMqT/oNVTwyDmxvDm4gaxwB3IDlpJ2E4uRX/eHwYFAqF1OUQkYNjgDuI6+eSoTj3Nf4eP5ThTUQtwgB3AFkph+GTvhN/nTKE4U1ELcYAl1jmqYMIyvwRSybHMbyJyCoMcAldOX4AYTm/YPEjA6UuhYhkiAEukctJexFdcATzJ9wpdSlEJFMMcJHlZqbh6t6NuKt7KJ4cHyt1OUQkYwxwEeVeSYXm0CZ88PQwLtAhIpspLBaLReoiiIjIehwGEhHJFAOciEimGOBERDLFACcikilJA7yoqAhLly7F0KFDMWDAACQkJODy5ctSliS6nJwcTJ06FT179pS6FFGoVCrMmTMHgwYNwogRI7BixQoYDAapyxJVeno6xo0bh7vvvlvqUkSXnZ2N+fPnY9CgQYiLi8OCBQuQm5srdVmiOXXqFKZNm4bY2FjcddddWLx4MfLz81t9PkkDfOnSpVCpVPjmm29w4MABBAUFYcGCBVKWJKqjR49i8uTJiIqKkroU0cybNw9t2rTB3r178fnnn+PkyZNYs2aN1GWJZteuXUhISECnTp2kLkUSc+bMgbe3N/bt24edO3eiuLgYy5cvl7osUWg0GsycORP33nsvkpKSsH37duTn5+PVV19t9TklC3CLxYKIiAi89NJLCAsLg1KpxOOPP46LFy+iuLhYqrJEVVRUhA0bNmDcuHFSlyKKlJQUpKam4i9/+QsCAwMRHR2N2bNnY9u2bTCbzVKXJwqdToetW7di8ODBUpciupKSEsTExGDJkiVQKpUIDQ3F5MmTkZycLHVpotDr9Vi2bBmmT58OT09PhIaG4t5770VaWlqrzynZQh6FQoHExMRax1QqFfz8/KBUKiWqSlxjxowBUPm+XcG5c+cQFRWFkJCQ6mN9+/aFRqNBVlYWOnfuLF1xIpk0aZLUJUgmMDAQK1eurHVMpVIhIiJCoorEFR4ejkceeQRA5QA2IyMDX3/9NR588MFWn9NhbmIWFBRg1apVmDNnDjw8uEDUGRUXFyMwMLDWsaCgIACVn0bItWRkZGD9+vWYO3eu1KWIKi0tDTExMRg3bhz69euHhQsXtvpcdg3wAwcOoGfPng3+8+6771a/7tq1a5g6dSqGDBmCWbNm2bMkUbX0/buyqoXA3ErXtZw9exbTpk3DjBkzMH78eKnLEVWvXr1w9uxZ7NixA1euXMHixYtbfS67DnVHjRqF9PT0Jl+TmpqKhIQETJo0CQsXLnSq/5Fb8v5dSUhISL2Rtkajqf4auYaDBw9i4cKFeP755zF16lSpy5GEQqFA165dsXjxYkyZMgX5+fkIDw+3+jySTqFkZWUhISEBzz77LBYtWuRU4U31xcTEIDc3F3l5edXHzpw5g9DQUHTo0EHCykgsp0+fxqJFi7Bq1SqXC+/vv/8eEydOrHWs6sHlrZ02ljTAExMTMW7cOMTHx0tZBomkT58+uP3227F69WqUlpbi2rVrWL9+PeLj4/nL2wUYjUYsW7YM8+fPx+jRo6UuR3SxsbG4evUq3n//fZSXl6OgoADvvvsuYmNjERwc3KpzSrYboUqlwsiRI+Hp6Vnvf96NGzdiwIABUpQlqpkzZyI5ORkWiwUGgwFeXl4AnPv95+bmIjExEcePH4efnx/Gjh2L559/Hu7u7lKXJooxY8YgJycHZrMZRqOx+u/8hx9+QHR0tMTV2dexY8cQHx9f/Z5rcoX3D1R+Alm5ciVSU1OhVCoRFxeHF198sdWdONxOlohIphymjZCIiKzDACcikikGOBGRTDHAiYhkigFORCRTDHAiIpligBMRyRQDnIhIphjgREQy9f8tf6RMgn2eagAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "myc = myC()\n",
    "myc.fit(X, y)\n",
    "plot_decision_regions(X, y, myc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator, ClassifierMixin\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "class myC(BaseEstimator, ClassifierMixin):  \n",
    "    \"\"\"пример классификатора\"\"\"\n",
    "\n",
    "    def __init__(self, alpha=1.0, k=5):\n",
    "        \"\"\"\n",
    "        Инициализация\n",
    "        \"\"\"\n",
    "        self.model = LogisticRegression(C=1)\n",
    "        self.cluster = KMeans(n_clusters=k)\n",
    "        self.alpha = alpha\n",
    "    \n",
    "    @staticmethod\n",
    "    def addX(self, X):\n",
    "        \"\"\"\n",
    "        Добавить 'случайные нелинейности'\n",
    "        \"\"\"\n",
    "        XX = X.copy()\n",
    "        for x in self.cluster.cluster_centers_:\n",
    "            XX = np.hstack([XX,\n",
    "                            np.exp(-self.alpha*((X - x) ** 2).sum(axis=1, keepdims=True))])\n",
    "        return (XX)\n",
    "        \n",
    "\n",
    "\n",
    "    def fit(self, X, y=None):\n",
    "        \"\"\"\n",
    "        Обучение\n",
    "        \"\"\"\n",
    "        self.cluster.fit(X)\n",
    "        self.model.fit(self.addX(self, X), y)\n",
    "\n",
    "        return self\n",
    "\n",
    "\n",
    "    def predict(self, X, y=None):\n",
    "        \"\"\"\n",
    "        классификация\n",
    "        \"\"\"\n",
    "        \n",
    "        return(self.model.predict(self.addX(self, X)))\n",
    "\n",
    "    def predict_proba(self, X, y=None):\n",
    "        \"\"\"\n",
    "        вероятность\n",
    "        \"\"\"\n",
    "        \n",
    "        return(self.model.predict_proba(self.addX(self, X)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2c6d619750>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "myc = myC(alpha=1.01, k=10)\n",
    "myc.fit(X, y)\n",
    "plot_decision_regions(X, y, myc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# деревья"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2c5fc26350>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "tree = DecisionTreeClassifier()\n",
    "tree.fit(X, y)\n",
    "plot_decision_regions(X, y, tree)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# голосование"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2c5f7e5550>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import VotingClassifier\n",
    "\n",
    "estimators = [(str(i), DecisionTreeClassifier(max_depth=3, splitter='random', random_state=i)) for i in range(9)]\n",
    "\n",
    "vc = VotingClassifier(estimators, voting =\"soft\")\n",
    "vc.fit(X, y)\n",
    "plot_decision_regions(X, y, vc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# бэггинг"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2c5fa1edd0>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import BaggingClassifier\n",
    "clf = BaggingClassifier(base_estimator=DecisionTreeClassifier(),\n",
    "                        n_estimators=10,\n",
    "                        random_state=0).fit(X, y)\n",
    "clf.fit(X, y)\n",
    "plot_decision_regions(X, y, clf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AdaBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2c5f8ff650>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.datasets import make_classification\n",
    "ada = AdaBoostClassifier(base_estimator=DecisionTreeClassifier(max_depth=2),\n",
    "                         n_estimators=100, random_state=0)\n",
    "ada.fit(X, y)\n",
    "plot_decision_regions(X, y, ada)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# стекинг"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2c5f41c810>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import StackingClassifier, StackingRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "estimators = [(str(i), DecisionTreeClassifier(max_depth=3, splitter='random', random_state=i)) for i in range(9)]\n",
    "# не работает! estimators = [(str(i), DecisionTreeRegressor(max_depth=3, splitter='random', random_state=i)) for i in range(9)]\n",
    "\n",
    "stack = StackingClassifier(estimators,\n",
    "                           final_estimator=LogisticRegression(C=1.0))\n",
    "stack.fit(X, y)\n",
    "plot_decision_regions(X, y, stack)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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