From 8798a804dc787570caf86466295b0c8b48bdff70 Mon Sep 17 00:00:00 2001 From: monkapolla Date: Fri, 23 May 2025 15:12:35 +0600 Subject: [PATCH 1/5] =?UTF-8?q?=D0=9C=D0=B5=D1=82=D0=BE=D0=B4=20=D0=BC?= =?UTF-8?q?=D0=B0=D0=BA=D1=81=D0=B8=D0=BC=D0=B8=D0=B7=D0=B0=D1=86=D0=B8?= =?UTF-8?q?=D0=B8=20=D0=BF=D1=80=D0=B0=D0=B2=D0=B4=D0=BE=D0=B1=D0=BE=D0=B4?= =?UTF-8?q?=D0=BE=D0=B1=D0=BD=D0=BE=D1=81=D1=82=D0=B8?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- MLE.ipynb | 183 ++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 183 insertions(+) create mode 100644 MLE.ipynb diff --git a/MLE.ipynb b/MLE.ipynb new file mode 100644 index 0000000..12d66f5 --- /dev/null +++ b/MLE.ipynb @@ -0,0 +1,183 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "b2e1ba7e-7656-4f51-a693-270e1df2a97c", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np \n", + "import matplotlib.pyplot as plt\n", + "from sklearn.datasets import make_classification\n", + "from sklearn.preprocessing import StandardScaler\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "98917f90-965c-4b46-b7c8-bdd8c2fb7bf7", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAj0AAAHHCAYAAABUcOnjAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABKeUlEQVR4nO3deVyU5f7/8TfDaiKosbmQmJqWe5IcLFOTxCXL9qyvopW2aJmeFq2U6pwjlR2X0rLlpJ3TZnrMU2maaZYZqbllpqWmuQVlJeAGyly/P+4fEyMDzMAg0v16Ph73Q7jnuu/7c933zczbe5sAY4wRAADAn5yjugsAAAA4HQg9AADAFgg9AADAFgg9AADAFgg9AADAFgg9AADAFgg9AADAFgg9AADAFgg9AADAFgg9AIAKef3117V7927X77Nnz9b+/furryCgHISeP5nZs2crICBAX331lcfXu3fvrjZt2pzmqgD8Ga1cuVIPPvigdu/erSVLlmjEiBFyOPhYwZkrqLoLAADUTKNHj1b37t3VtGlTSdKYMWPUoEGDaq4KKB2hBwBQIa1atdLOnTv1zTffKCoqSs2aNavukoAycRwSmjVrli677DLFxMQoNDRUF1xwgV544YUS7RISEnTFFVe4jRs5cqQCAgLcxn388ccKCAjQBx984BrXvXt3de/e3a3d2rVrFRAQUGL6shS1P3VYsWKFq01BQYEmTJigTp06KTIyUrVr11bXrl31ySefuM1r9+7dCggI0DPPPFNiOW3atHGrd8WKFQoICNC8efNKtA0PD9eQIUNcv5d3ilHyvD7y8/OVnp6u5s2bKzQ0VPHx8XrwwQeVn59f9kopxpv1U2TIkCEe2z722GNu7ebNm6fExETVqVPHrZ2n9VZaXz0tZ/bs2W7t5s6dq06dOqlWrVqKiorS//3f/5W4PqS0mps3b+5qk5CQ4LY9iuYdEBCghIQEt/FOp1PTpk1T27ZtFRYWpujoaPXu3du17Upbn0VD0TYs2j9OXc/9+vUrsU49bfuiffHUdbJt2zZdd911ql+/vsLCwpSYmKj33nuvxDo+dOiQRo8erYSEBIWGhqpx48YaPHiwDh48WGp9Bw4cUEJCghITE3X48GHX+J9//lm33XabYmNjFRYWpvbt2+u1114rtd7atWsrKSlJzZo104gRIxQQEFBi/Z+q+N/elClT1KRJE9WqVUvdunXTN998U6K9N+uh6O/O07Bv3z5J1v4THh6uH374Qampqapdu7YaNmyoJ554QsYYt/k5nU5NnTpVrVu3VlhYmGJjY3XHHXfo999/L1Hfhx9+qG7duqlOnTqKiIjQRRddpDfffNOtzerVq9W3b1/Vq1dPtWvXVrt27TRt2jTX60OGDCmxf+7du1e1atVSQECA27VTvuzjcMeRnj+pnJwc1xtecSdOnCgx7oUXXlDr1q115ZVXKigoSO+//77uvvtuOZ1OjRgxospqfOihhyo03eWXX67BgwdLsoLTs88+6/Z6bm6uXnnlFQ0cOFDDhg1TXl6e/vWvfyk1NVVr1qxRhw4dKlu63zmdTl155ZX6/PPPNXz4cJ1//vnavHmzpkyZou+//14LFizwel7lrZ/ioqKiNGXKFNfvgwYNcns9MzNTN9xwg9q3b68nn3xSkZGROnjwoEaPHu1T/1q1aqVHHnlEkjxOP3v2bA0dOlQXXXSRMjIylJ2drWnTpmnVqlXasGGD6tat62obGhqqV155xW36OnXqlLrskydPupZ9qttuu02zZ89Wnz59dPvtt+vkyZNauXKlvvzySyUmJuo///mPq+3KlSv10ksvacqUKYqKipIkxcbGlrrczz77TIsWLSr19fJs2bJFF198sRo1aqSxY8eqdu3aeueddzRgwAD997//1dVXXy1JOnz4sLp27aqtW7fq1ltv1YUXXqiDBw/qvffe0759+1y1FpeTk6M+ffooODhYixYtUnh4uCTp2LFj6t69u3bs2KGRI0eqadOmmjt3roYMGaJDhw5p1KhRpda7Y8cOvfzyyz718d///rfy8vI0YsQIHT9+XNOmTdNll12mzZs3u9att+uhyBNPPOE63Vakfv36rp8LCwvVu3dv/eUvf9HTTz+txYsXKz09XSdPntQTTzzhanfHHXe49st7771Xu3bt0vTp07VhwwatWrVKwcHBkqx999Zbb1Xr1q01btw41a1bVxs2bNDixYt18803S5KWLl2qK664Qg0aNNCoUaMUFxenrVu36oMPPihznU6YMEHHjx8vdz2WtY/jFAZ/KrNmzTKSyhxat27tNs3Ro0dLzCc1NdWce+65buOaNGli+vXr5zZuxIgR5tTdaOnSpUaSef/9913junXrZrp16+b6fdGiRUaS6d27d4npS1NQUGAkmZEjR7rGzZ0710gyn3zyiWvcyZMnTX5+vtu0v//+u4mNjTW33nqra9yuXbuMJDNp0qQSy2rdurVbvZ988omRZObOnVuibe3atU1aWprr96JtsHbt2lL7cur6+M9//mMcDodZuXKlW7uZM2caSWbVqlWlzquIt+unyC233GKaNm3qNk6SSU9Pd/0+btw4I8n89NNPrnFlrTdPLr74YtOjR48S08+aNctVd0xMjGnTpo05duyYq90HH3xgJJkJEya4xqWlpZnatWuXubwmTZq4bY/nn3/ehIaGmh49epgmTZq4xi9fvtxIMvfee2+JeTidzhLjirbrrl27SrxWtH8UX89JSUmmT58+JdZpjx49zKWXXuo2/anrxBhjevbsadq2bWuOHz/uVleXLl1MixYtXOMmTJhgJJn58+eX2o/i9R0/ftx0797dxMTEmB07dri1nzp1qpFkXn/9dde4goICk5ycbMLDw01ubm6p9d5www2mTZs2Jj4+3m39e1I0fa1atcy+fftc41evXm0kmdGjR/u8Hrz5u0tLSzOSzD333OM2r379+pmQkBDzyy+/GGOMWblypZFk3njjDbfpFy9e7Db+0KFDpk6dOiYpKclt3y2arzHW+1HTpk1NkyZNzO+//+6xTVFtxffPb775xjgcDtc+VHy/83YfR0mc3vqTmjFjhpYuXVpiaNeuXYm2tWrVcv1cdISoW7du+uGHH5STk+P32owxGjdunK699lolJSV5PV3R/3jCwsLKbBcYGKiQkBBJ1hGU3377TSdPnlRiYqLWr19fov3Ro0d18OBBt6GwsNDjvPPy8kq0LU3RuszLyyu3b3PnztX555+vVq1auc37sssuk6QSp+Y88Xb9FCkoKFBoaGiZbfLy8uRwONyOtPiqvOV89dVX+vnnn3X33Xe71d6vXz+1atVKCxcurPCyjx49qieeeEIjR47UOeec4/baf//7XwUEBCg9Pb3EdL6ccvVk/vz5Wrt2rZ588skSr8XExLhOt5Tmt99+0/Lly3XDDTe47XO//vqrUlNTtX37dtepv//+979q3759iSMenvrhdDo1ePBgffnll1q0aFGJa3AWLVqkuLg4DRw40DUuODhY9957rw4fPqxPP/3UY73r1q3T3LlzlZGR4dPdWwMGDFCjRo1cv3fu3FlJSUmuI2S+rAdfjBw50vVzQECARo4cqYKCAn388ceSrL/HyMhIXX755W5/j506dVJ4eLjr73Hp0qXKy8vT2LFjS/zdFa37DRs2aNeuXbrvvvtK/B2VtZ+NGzdOF154oa6//voy+1LWPo6SCD1/Up07d1ZKSkqJoV69eiXarlq1SikpKapdu7bq1q2r6OhoPfzww5JUJaHnjTfe0JYtWzRx4kSfpisKGJGRkeW2fe2119SuXTuFhYXp7LPPVnR0tBYuXOixP+np6YqOjnYbtm3b5nG+t956a4m2R44c8dg2JSVF0dHRioiIUL169XT33XeX2nb79u3asmVLiXmfd955kqzrLMrjy/qRrOtAik5rlCY5OVlOp1OjRo3Szp07dfDgQY/XNFRmOT/++KMkqWXLliVea9Wqlev1ipg8ebKOHz/u2p+L27lzpxo2bOh26sMfCgsL9fDDD+uWW27x+J+MLl266IcfftDUqVOVlZXlcZ3u2LFDxhiNHz++xD5RFNKK9omdO3d6/RiKRx55RO+8847y8/N19OjREq//+OOPatGiRYngcv7557te92Ts2LHq2rVriWv+ytOiRYsS48477zzX9Su+rAdvORwOnXvuuSWWKcm13O3btysnJ0cxMTEllnv48GG3dS+pzPXvTZtTff7553r//ff11FNPlRvAy9rHURLX9Njczp071bNnT7Vq1UqTJ09WfHy8QkJCtGjRIk2ZMkVOp9OvyysoKND48eN12223ud5ovFX0hlTehXqvv/66hgwZogEDBuiBBx5QTEyMAgMDlZGR4XoDKm748OEl/jc1bNgwj/OeMGGCunbt6jauf//+HtvOmDFD5513nvLz87VixQrXhb/PP/98ibZOp1Nt27bV5MmTPc4rPj7e4/jivF0/RbKystSkSZMy29x0001av369nnvuOb300ktezdfTclJTUys0bWUcPHhQkyZN0rhx4/webMryr3/9y/XcGk+GDx+uJUuWaPTo0aVeG1X0d3f//feXuu6KX7ztrdWrV2v27NmaPn26hg8fro0bN5Z7tK88H330kT7++GNlZmZWaj6eVNV68Ga5MTExeuONNzy+Hh0d7fdlFvfQQw8pNTVVl112WYmL24urrn28JiP02Nz777+v/Px8vffee26HRr05nVIRzz//vH7++ecSdwh5o+iOmsTExDLbzZs3T+eee67mz5/v9r8kT6cxJOt/mykpKW7jateu7bFt27ZtS7QNDAz02LZz586uWvv166dNmzZp8eLFHts2a9ZMmzZtUs+ePSt8asXb9SNZF7Tv2LFDvXv3LrOdw+HQM888o82bN2vXrl16/vnnlZ2drf/7v//zqqZ9+/YpLy/PdaTAk6Lg9d1337lO5xX57rvvyg1mpfn73/+uOnXqlHqhaLNmzbRkyRL99ttvfvvAOHr0qB5//HHdfffdpdYdFhamhQsX6vvvv9fevXtljCmxTouORAQHB5fY3zz1w9MdT548/vjjSktLU4cOHZSYmKi///3v+tvf/uZ6vUmTJvr666/ldDrdjvYUHfk8tU/GGI0dO1ZXX321/vKXv3hVQ3Hbt28vMe777793BXdf1oO3nE6nfvjhB7f/dH3//feS/vgPQ7NmzfTxxx/r4osvdjv9f6qi04PffPNNqeGreBtv+rBgwQJlZmZ6PBV/qvL2cZTE6S2bK/rANsVu18zJydGsWbP8vqy8vDz94x//0OjRoxUXF+fz9PPmzVPLli3VqlWrMtt56tPq1aur5H+ivnA6naUGpBtuuEH79+/3ePfLsWPHSj0tVpy360eS/ve//+nYsWMlQoYnzz33nJYvX6433nhDKSkpuvjii8udpsjbb78tSWUuJzExUTExMZo5c6bb7fkffvihtm7dqn79+nm9vCK7d+/WCy+8oMcee6zUD61rr71Wxhg9/vjjJV4zp9y+7K1p06bpyJEjXt1Jc95556lnz54e12lMTIy6d++uF198UT/99FOJaX/55RfXz9dee602bdqkd999t0S7U/tRdJSyffv2uv/++/XUU0+5Baa+ffsqKytLc+bMcY07efKknnvuOYWHh6tbt25u83v77bf19ddfKyMjo9z+erJgwQK3a3LWrFmj1atXq0+fPpJ8Ww++mD59uutnY4ymT5+u4OBg9ezZU5L191hYWOgWCIucPHlShw4dkiT16tVLderUUUZGRom7rIrW/YUXXqimTZtq6tSprulObVOk6NTozTffXO5dpt7s4yiJIz0216tXL4WEhKh///664447dPjwYb388suKiYkp9U2m+NGKPXv2SJLbuI0bN3pc1vr16xUVFaUHH3zQpxp/+OEHPf3001qzZo2uueYavf76667X1q5dK8m6oPCcc87RueeeqyuuuELz58/X1VdfrX79+mnXrl2aOXOmLrjgArfnkVS1zMxMHTx40HV6a9myZbr//vs9th00aJDeeecd3Xnnnfrkk0908cUXq7CwUNu2bdM777yjJUuWlHoEx5f1ExcXp/T0dD3//PPq0qWLevXqVWYftmzZogcffFCPPfaYLrroIq/7np2drfT0dL3yyiu66aabygxiwcHBeuqppzR06FB169ZNAwcOdN2ynpCQ4PPt8ZL06aef6vzzz9fQoUNLbdOjRw8NGjRIzz77rLZv367evXvL6XRq5cqV6tGjh9vFrt766KOP9I9//ENnn322z9OeasaMGbrkkkvUtm1bDRs2TOeee66ys7OVmZmpffv2adOmTZKkBx54QPPmzdP111+vW2+9VZ06ddJvv/2m9957TzNnzlT79u09zj89PV3//e9/NWzYMK1atUoOh0PDhw/Xiy++qCFDhmjdunVKSEjQvHnztGrVKk2dOrXEowE++ugjDRs2zOP1WN5o3ry5LrnkEt11113Kz8/X1KlTdfbZZ7u9R3i7HrwVFhamxYsXKy0tTUlJSfrwww+1cOFCPfzww67TVt26ddMdd9yhjIwMbdy4Ub169VJwcLC2b9+uuXPnatq0abruuusUERGhKVOm6Pbbb9dFF12km2++WfXq1dOmTZt09OhRvfbaa3I4HHrhhRfUv39/dejQQUOHDlWDBg20bds2bdmyxe006L59+1yXF5THm30cHlTLPWOoMuXdttmtW7cSt6y/9957pl27diYsLMwkJCSYp556yrz66qseb5NUObfDFx9OvWVdkpkyZYrbstPT08u9Zd2b2/BV7PZZp9NpJk6caJo0aWJCQ0NNx44dzQcffFDiltCqvmW9aAgJCTHNmzc3EyZMcN1Kf+ot68ZYtwY/9dRTpnXr1iY0NNTUq1fPdOrUyTz++OMmJyfHL+tn3759Jj4+3tx3330e56lit1cfP37ctGvXzlxyySXm5MmTXq23IqtWrTLNmzc3jz32WInHB3i63dkYY+bMmWM6duxoQkNDTf369c0tt9zidjuzMd7fsi7JvPvuuyWmPfV23pMnT5pJkyaZVq1amZCQEBMdHW369Olj1q1bV2K+3tyy3qBBA3PkyBG314qv09KUtk527txpBg8ebOLi4kxwcLBp1KiRueKKK8y8efPc2v36669m5MiRplGjRiYkJMQ0btzYpKWlmYMHD7rVd+qjC1asWGECAgLMtGnTXOOys7PN0KFDTVRUlAkJCTFt27YtUVfxW87379/v9tqpt1OX1d9JkyaZf/7znyY+Pt6Ehoaarl27mk2bNpVo78168PaW9dq1a5udO3eaXr16mbPOOsvExsaa9PR0U1hYWKL9Sy+9ZDp16mRq1apl6tSpY9q2bWsefPBBc+DAAbd27733nunSpYupVauWiYiIMJ07dzZvvfWWW5vPP//cXH755aZOnTqmdu3apl27dua5555zq02SGTVqlNt0nvY7X/ZxuAswpoLHcYHTZPbs2Xrsscfcnkh6qu7du2vIkCHlPgn2z4j1g5pm9+7datq0qSZNmlTq0c+qMGTIEM2bN++0HvHFmYVregAAgC0QenDGa9asmccHrxV3+eWX2/bLDlk/AOAdLmTGGa9r164lno1zKjt/7wzrBwC8wzU9AADAFji9BQAAbIHQAwAAbMF21/Q4nU4dOHBAderUqfQ3KQMAgNPDGKO8vDw1bNiwxJfiest2oefAgQNefXkjAAA48+zdu1eNGzeu0LS2Cz1Fj1Hfu3evIiIiqrkaAADgjdzcXMXHx5f4OhRf2C70FJ3SioiIIPQAAFDDVObSFC5kBgAAtkDoAQAAtkDoAQAAtkDoAQAAtkDoAQAAtkDoAQAAtkDoAQAAtkDoAQAAtkDoAQAAtkDoAQCcOU6elGbPljp3lurWlRo3lh58UNq3z7f5bNokDR0qRUdL9etLfftKixdbr23YIA0cKNWuLTkcUkDAH0Pt2tZ0//iHNV3x14oGh0MKDpYiIqSzz7Z+9tTOmyEkRGrQQIqMtPpbu3bZ7aOirLoiIqxpitcfGCiFhUkJCdLf/ib9+qv7OikslF5/XerSxVpWo0bSX/8q7d4tZWdL48ZZ/QkMtIazz5YeeUT6+efy13dhofTGG57nfSYx1ejTTz81V1xxhWnQoIGRZN59991yp/nkk09Mx44dTUhIiGnWrJmZNWuWT8vMyckxkkxOTk7FigYAVI38fGN69zZGMsbhsP6VjAkMNCYy0ph167ybz9tvW9MEBbnPQzKmf3/3ef9Zh4AAYxo1MmbXLmudnDhhzFVXeV63Z51lrd/S5hUdbcz335e+vsuad3i4MZmZldotivjj87taj/QcOXJE7du314wZM7xqv2vXLvXr1089evTQxo0bdd999+n222/XkiVLqrhSAECVe/JJ6aOPrJ+dzj/GFxZKhw9LV11lHQkqy9690qBB1jTF2xYWWv++/777vP+sjLGO3gwcaP0+ebL03nvWz6eu26NHpZyc0ud18KB07bXWPD2ZMqX0eR87Jl15pVRQUPG++FGAMaX14vQKCAjQu+++qwEDBpTa5qGHHtLChQv1zTffuMbddNNNOnTokBYXHbYsR25uriIjI5WTk8MXjgLAmeLECalhQ+sDtiwLFljhpzTjx0sZGX+EHEhr11rr7MCBys1n1Srr9FVxTqd0zjnS/v1lT/vWW9JNN1Vq8f74/K5R1/RkZmYqJSXFbVxqaqoyMzNLnSY/P1+5ubluAwDgDPPjj+UHnuBgqYz3e0nSF18QeIoLCJCWLKl84HE4PK/7n34qP/B4s91OkxoVerKyshQbG+s2LjY2Vrm5uTp27JjHaTIyMhQZGeka4uPjT0epAABfBAaW38YYKSio8vOxE2Osi6X9MR9P697b9X2GbJcaFXoqYty4ccrJyXENe/fure6SAACnatJEatrUOjJRmpMnpVOO9peQmlr2POzo6qulVq0qt16M8bzuY2Ol888ve94nTkiXX17xZftRjQo9cXFxys7OdhuXnZ2tiIgI1apVy+M0oaGhioiIcBsAAGcYh8O6Nb20y0yDgqT27aVu3cqez9ChUp061vzsLjDQuoi4eXPpoYdKX7flcTiswNO6dcnXAgLKnndQkNSypRVGzwA1aq9ITk7WsmXL3MYtXbpUycnJ1VQRAMBv7rhDuvde6+eiUylF4aVJE+vOq/KOVtSvL334oRQe7h58AgOt3x96yHrNDi68UHrtNevntDQrVEp/rNuiddm4sXTeeaXPp00b60Lk0gwebK1XT/Nu2FBatOiMCaHVevfW4cOHtWPHDklSx44dNXnyZPXo0UP169fXOeeco3Hjxmn//v3697//Lcm6Zb1NmzYaMWKEbr31Vi1fvlz33nuvFi5cqFQvUyR3bwHAGe7LL6UXX5S2brUedHfjjdadP6Uc0ffo4EHp1VelhQut0ytdukh33mkd9fj5Z+nll60P8v37pePHrVNnQUFSfLz1AR4bK/3979LGjVJ+/h/zDQ216ggPtx58GBRkze/XX612TucfT7iRyj66EhRkPZiwaVOrBofDqvvHH62aTxUaKiUmWj/n51vT7Nr1x7Th4VJMjNSihRVyrrrKuoi4uK++kmbOlL75xnrA4Q03WLe1BwVJ8+ZJzz0n/f/PZbVoYYXQa6/17rqgU+d9/fXSzTdbD1z0A398fldr6FmxYoV69OhRYnxaWppmz56tIUOGaPfu3VqxYoXbNKNHj9a3336rxo0ba/z48RoyZIjXyyT0AABQ89T40FMdCD0AANQ8tntODwAAQEURegAAgC0QegAAgC0QegAAgC0QegAAgC0QegAAgC0QegAAgC0QegAAgC0QegAAgC0QegAAgC0QegAAgC0QegAAgC0QegAAgC0QegAAgC0QegAAgC0QegAAgC0QegAAgC0QegAAgC0QegAAgC0QegAAgC0QegAAgC0QegAAgC0QegAAgC0QegAAgC0QegAAgC0QegAAgC0QegAAgC0QegAAgC0QegAAgC0QegAAgC0QegAAgC0QegAAgC0QegAAgC0QegAAgC0QegAAgC0QegAAgC0QegAAgC0QegAAgC0QegAAgC0QegAAgC0QegAAgC0QegAAgC0QegAAgC0QegAAgC0QegAAgC0QegAAgC0QegAAgC0QegAAgC0QegAAgC0QegAAgC0QegAAgC0QegAAgC0QegAAgC0QegAAgC0QegAAgC0QegAAgC1Ue+iZMWOGEhISFBYWpqSkJK1Zs6bM9lOnTlXLli1Vq1YtxcfHa/To0Tp+/PhpqhYAANRU1Rp65syZozFjxig9PV3r169X+/btlZqaqp9//tlj+zfffFNjx45Venq6tm7dqn/961+aM2eOHn744dNcOQAAqGmqNfRMnjxZw4YN09ChQ3XBBRdo5syZOuuss/Tqq696bP/FF1/o4osv1s0336yEhAT16tVLAwcOLPfoEAAAQLWFnoKCAq1bt04pKSl/FONwKCUlRZmZmR6n6dKli9atW+cKOT/88IMWLVqkvn37lrqc/Px85ebmug0AAMB+gqprwQcPHlRhYaFiY2PdxsfGxmrbtm0ep7n55pt18OBBXXLJJTLG6OTJk7rzzjvLPL2VkZGhxx9/3K+1AwCAmqfaL2T2xYoVKzRx4kQ9//zzWr9+vebPn6+FCxfqb3/7W6nTjBs3Tjk5Oa5h7969p7FiAABwpqi2Iz1RUVEKDAxUdna22/js7GzFxcV5nGb8+PEaNGiQbr/9dklS27ZtdeTIEQ0fPlyPPPKIHI6SGS40NFShoaH+7wAAAKhRqu1IT0hIiDp16qRly5a5xjmdTi1btkzJyckepzl69GiJYBMYGChJMsZUXbEAAKDGq7YjPZI0ZswYpaWlKTExUZ07d9bUqVN15MgRDR06VJI0ePBgNWrUSBkZGZKk/v37a/LkyerYsaOSkpK0Y8cOjR8/Xv3793eFHwAAAE+qNfTceOON+uWXXzRhwgRlZWWpQ4cOWrx4sevi5j179rgd2Xn00UcVEBCgRx99VPv371d0dLT69++vf/zjH9XVBQAAUEMEGJudF8rNzVVkZKRycnIUERFR3eUAAAAv+OPzu0bdvQUAAFBRhB4AAGALhB4AAGALhB4AAGALhB4AAGALhB4AAGALhB4AAGALhB4AAGALhB4AAGALhB4AAGALhB4AAGALhB4AAGALhB4AAGALhB4AAGALhB4AAGALhB4AAGALhB4AAGALhB4AAGALhB4AAGALhB4AAGALhB4AAGALhB4AAGALhB4AAGALhB4AAGALhB4AAGALhB4AAGALhB4AAGALhB4AAGALhB4AAGALhB4AAGALhB4AAGALhB4AAGALhB4AAGALhB4AAGALhB4AAGALhB4AAGALhB4AAGALhB4AAGALhB4AAGALhB4AAGALhB4AAGALhB4AAGALhB4AAGALhB4AAGALhB4AAGALhB4AAGALhB4AAGALhB4AAGALhB4AAGALhB4AAGALhB4AAGALhB4AAGALhB4AAGALhB4AAGALhB4AAGALhB4AAGAL1R56ZsyYoYSEBIWFhSkpKUlr1qwps/2hQ4c0YsQINWjQQKGhoTrvvPO0aNGi01QtAACoqYKqc+Fz5szRmDFjNHPmTCUlJWnq1KlKTU3Vd999p5iYmBLtCwoKdPnllysmJkbz5s1To0aN9OOPP6pu3bqnv3gAAFCjBBhjTHUtPCkpSRdddJGmT58uSXI6nYqPj9c999yjsWPHlmg/c+ZMTZo0Sdu2bVNwcHCFlpmbm6vIyEjl5OQoIiKiUvUDAIDTwx+f39V2equgoEDr1q1TSkrKH8U4HEpJSVFmZqbHad577z0lJydrxIgRio2NVZs2bTRx4kQVFhaWupz8/Hzl5ua6DQAAwH6qLfQcPHhQhYWFio2NdRsfGxurrKwsj9P88MMPmjdvngoLC7Vo0SKNHz9e//znP/X3v/+91OVkZGQoMjLSNcTHx/u1HwAAoGao9guZfeF0OhUTE6OXXnpJnTp10o033qhHHnlEM2fOLHWacePGKScnxzXs3bv3NFYMAADOFNV2IXNUVJQCAwOVnZ3tNj47O1txcXEep2nQoIGCg4MVGBjoGnf++ecrKytLBQUFCgkJKTFNaGioQkND/Vs8AACocartSE9ISIg6deqkZcuWucY5nU4tW7ZMycnJHqe5+OKLtWPHDjmdTte477//Xg0aNPAYeAAAAIr4dKTH6XTq008/1cqVK/Xjjz/q6NGjio6OVseOHZWSkuLz9TJjxoxRWlqaEhMT1blzZ02dOlVHjhzR0KFDJUmDBw9Wo0aNlJGRIUm66667NH36dI0aNUr33HOPtm/frokTJ+ree+/1abkAAMB+vAo9x44d0z//+U+98MIL+u2339ShQwc1bNhQtWrV0o4dO7RgwQINGzZMvXr10oQJE/SXv/zFq4XfeOON+uWXXzRhwgRlZWWpQ4cOWrx4sevi5j179sjh+ONgVHx8vJYsWaLRo0erXbt2atSokUaNGqWHHnqoAl0HAAB24tVzeuLj45WcnKwhQ4bo8ssv9/iMnB9//FFvvvmmXnzxRT3yyCMaNmxYlRRcWTynBwCAmscfn99ehZ6tW7fq/PPP92qGJ06c0J49e9SsWbMKFVTVCD0AANQ8p+3hhN4GHkkKDg4+YwMPAACwL5/v3po1a5bmzp1bYvzcuXP12muv+aUoAAAAf/M59GRkZCgqKqrE+JiYGE2cONEvRQEAAPibz6Fnz549atq0aYnxTZo00Z49e/xSFAAAgL/5HHpiYmL09ddflxi/adMmnX322X4pCgAAwN98Dj0DBw7Uvffeq08++USFhYUqLCzU8uXLNWrUKN10001VUSMAAECl+fzdW3/729+0e/du9ezZU0FB1uROp1ODBw/mmh4AAHDG8uo5PZ5s375dGzduVK1atdS2bVs1adLE37VVCZ7TAwBAzeOPz+8Kf8t6ixYt1KJFi4pODgAAcFr5fE3Ptddeq6eeeqrE+KefflrXX3+9X4oCAADwN59Dz2effaa+ffuWGN+nTx999tlnfikKAADA33wOPYcPH1ZISEiJ8cHBwcrNzfVLUQAAAP7mc+hp27at5syZU2L822+/rQsuuMAvRQEAAPibzxcyjx8/Xtdcc4127typyy67TJK0bNkyvfXWWx6/kwsAAOBM4HPo6d+/vxYsWKCJEydq3rx5qlWrltq1a6ePP/5Y3bp1q4oaAQAAKq3Cz+mpqXhODwAANY8/Pr99vqYHAACgJvL59FZhYaGmTJmid955R3v27FFBQYHb67/99pvfigMAAPAXn4/0PP7445o8ebJuvPFG5eTkaMyYMbrmmmvkcDj02GOPVUGJAAAAledz6HnjjTf08ssv669//auCgoI0cOBAvfLKK5owYYK+/PLLqqgRAACg0nwOPVlZWWrbtq0kKTw8XDk5OZKkK664QgsXLvRvdQAAAH7ic+hp3LixfvrpJ0lSs2bN9NFHH0mS1q5dq9DQUP9WBwAA4Cc+h56rr75ay5YtkyTdc889Gj9+vFq0aKHBgwfr1ltv9XuBAAAA/lDp5/RkZmYqMzNTLVq0UP/+/f1VV5XhOT0AANQ8/vj89vmW9VMlJycrOTm5srMBAACoUj6HnmeffbbM1++9994KFwMAAFBVfD695XA41LhxYwUGBpacWUCAfvjhB78VVxU4vQUAQM1Tbae3vvrqK8XExFRogQAAANXB57u3AgICFBAQUBW1AAAAVBmfj/QYYzR+/HhFRkaqdu3aatiwoTp27KhOnTpVRX0AAAB+4XPoufTSS7Vt2zadOHFCubm5OnDggH7//Xe1b99eCxcuVMOGDauiTgAAgEqp9HN6JGnnzp0aPHiwzjnnHL311lv+qKvKcCEzAAA1jz8+v32+pseTZs2aadq0adq/f78/ZgcAAOB3fgk9kpSYmKhPP/3UX7MDAADwK59Dz5IlSzyO37lzp7p3717ZegAAAKqEz6Hnuuuu07x589zGTZs2Te3bt9d5553nt8IAAAD8yee7t9555x3deOONysnJUbdu3TR06FDt2bNH8+bNU+/evauiRgAAgErzOfT06dNHCxcu1JVXXqn8/HzdcsstWrhwIXdCAQCAM1qFLmTu2rWrli9frvDwcMXExBB4AADAGc/nIz3XXHON6+eGDRvqySef1BdffKF69epJkubPn++/6gAAAPzE59ATGRnp+rljx47q2LGjXwsCAACoCj6HnlmzZlVFHQAAAFXKbw8nBAAAOJN5FXp69+6tL7/8stx2eXl5euqppzRjxoxKFwYAAOBPXp3euv7663XttdcqMjJS/fv3V2Jioho2bKiwsDD9/vvv+vbbb/X5559r0aJF6tevnyZNmlTVdQMAAPjE629Zz8/P19y5czVnzhx9/vnnysnJsWYQEKALLrhAqampuu2223T++edXacGVxbesAwBQ8/jj89vr0HOqnJwcHTt2TGeffbaCg4MrtPDqQOgBAKDm8cfnt893bxWJjIx0u30dAADgTMbdWwAAwBYIPQAAwBYIPQAAwBYIPQAAwBZ8Dj1paWn67LPPqqIWAACAKuNz6MnJyVFKSopatGihiRMnav/+/VVRFwAAgF/5HHoWLFig/fv366677tKcOXOUkJCgPn36aN68eTpx4kRV1AgAAFBpFbqmJzo6WmPGjNGmTZu0evVqNW/eXIMGDVLDhg01evRobd++3af5zZgxQwkJCQoLC1NSUpLWrFnj1XRvv/22AgICNGDAgAr0AgAA2EmlLmT+6aeftHTpUi1dulSBgYHq27evNm/erAsuuEBTpkzxah5z5szRmDFjlJ6ervXr16t9+/ZKTU3Vzz//XOZ0u3fv1v3336+uXbtWpgsAAMAmfP4aihMnTui9997TrFmz9NFHH6ldu3a6/fbbdfPNN7seC/3uu+/q1ltv1e+//17u/JKSknTRRRdp+vTpkiSn06n4+Hjdc889Gjt2rMdpCgsLdemll+rWW2/VypUrdejQIS1YsMCr+vkaCgAAap5q+RqKBg0ayOl0auDAgVqzZo06dOhQok2PHj1Ut27dcudVUFCgdevWady4ca5xDodDKSkpyszMLHW6J554QjExMbrtttu0cuVKX7sAAABsyOfQM2XKFF1//fUKCwsrtU3dunW1a9eucud18OBBFRYWKjY21m18bGystm3b5nGazz//XP/617+0ceNGr+rNz89Xfn6+6/fc3FyvpgMAAH8uPl/TM2jQoDIDT1XKy8vToEGD9PLLLysqKsqraTIyMlxfjhoZGan4+PgqrhIAAJyJKvwt6/4QFRWlwMBAZWdnu43Pzs5WXFxcifY7d+7U7t271b9/f9c4p9MpSQoKCtJ3332nZs2auU0zbtw4jRkzxvV7bm4uwQcAABuq1tATEhKiTp06admyZa7bzp1Op5YtW6aRI0eWaN+qVStt3rzZbdyjjz6qvLw8TZs2zWOYCQ0NVWhoaJXUDwAAao5qDT2SNGbMGKWlpSkxMVGdO3fW1KlTdeTIEQ0dOlSSNHjwYDVq1EgZGRkKCwtTmzZt3KYvumD61PEAAADFVXvoufHGG/XLL79owoQJysrKUocOHbR48WLXxc179uyRw8H3ogIAgMrx+Tk9NR3P6QEAoObxx+c3h1AAAIAtEHoAAIAtEHoAAIAtEHoAAIAtEHoAAIAtEHoAAIAtEHoAAIAtEHoAAIAtEHoAAIAtEHoAAIAtEHoAAIAtEHoAAIAtEHoAAIAtEHoAAIAtEHoAAIAtEHoAAIAtEHoAAIAtEHoAAIAtEHoAAIAtEHoAAIAtEHoAAIAtEHoAAIAtEHoAAIAtEHoAAIAtEHoAAIAtEHoAAIAtEHoAAIAtEHoAAIAtEHoAAIAtEHoAAIAtEHoAAIAtEHoAAIAtEHoAAIAtEHoAAIAtEHoAAIAtEHoAAIAtEHoAAIAtEHoAAIAtEHoAAIAtEHoAAIAtEHoAAIAtEHoAAIAtEHoAAIAtEHoAAIAtEHoAAIAtEHoAAIAtEHoAAIAtEHoAAIAtEHoAAIAtEHoAAIAtEHoAAIAtEHoAAIAtEHoAAIAtEHoAAIAtEHoAAIAtEHoAAIAtnBGhZ8aMGUpISFBYWJiSkpK0Zs2aUtu+/PLL6tq1q+rVq6d69eopJSWlzPYAAADSGRB65syZozFjxig9PV3r169X+/btlZqaqp9//tlj+xUrVmjgwIH65JNPlJmZqfj4ePXq1Uv79+8/zZUDAICaJMAYY6qzgKSkJF100UWaPn26JMnpdCo+Pl733HOPxo4dW+70hYWFqlevnqZPn67BgweX2z43N1eRkZHKyclRREREpesHAABVzx+f39V6pKegoEDr1q1TSkqKa5zD4VBKSooyMzO9msfRo0d14sQJ1a9fv6rKBAAAfwJB1bnwgwcPqrCwULGxsW7jY2NjtW3bNq/m8dBDD6lhw4Zuwam4/Px85efnu37Pzc2teMEAAKDGqvZreirjySef1Ntvv613331XYWFhHttkZGQoMjLSNcTHx5/mKgEAwJmgWkNPVFSUAgMDlZ2d7TY+OztbcXFxZU77zDPP6Mknn9RHH32kdu3aldpu3LhxysnJcQ179+71S+0AAKBmqdbQExISok6dOmnZsmWucU6nU8uWLVNycnKp0z399NP629/+psWLFysxMbHMZYSGhioiIsJtAAAA9lOt1/RI0pgxY5SWlqbExER17txZU6dO1ZEjRzR06FBJ0uDBg9WoUSNlZGRIkp566ilNmDBBb775phISEpSVlSVJCg8PV3h4eLX1AwAAnNmqPfTceOON+uWXXzRhwgRlZWWpQ4cOWrx4sevi5j179sjh+OOA1AsvvKCCggJdd911bvNJT0/XY489djpLBwAANUi1P6fndOM5PQAA1Dw1/jk9AAAApwuhBwAA2AKhBwAA2AKhBwAA2AKhBwAA2AKhBwAA2AKhBwAA2AKhBwAA2AKhBwAA2AKhBwAA2AKhBwAA2AKhBwAA2AKhBwAA2AKhBwAA2AKhBwAA2AKhBwAA2AKhBwAA2AKhBwAA2AKhBwAA2AKhBwAA2AKhBwAA2AKhBwAA2AKhBwAA2AKhBwAA2AKhBwAA2AKhBwAA2AKhBwAA2AKhBwAA2AKhBwAA2AKhBwAA2AKhBwAA2AKhBwAA2AKhBwAA2AKhBwAA2AKhBwAA2AKhBwAA2AKhBwAA2AKhBwAA2AKhBwAA2AKhBwAA2AKhBwAA2AKhBwAA2AKhBwAA2AKhBwAA2AKhBwAA2AKhBwAA2AKhBwAA2AKhBwAA2AKhBwAA2AKhBwAA2AKhBwAA2AKhBwAA2AKhBwAA2AKhBwAA2AKhBwAA2AKhBwAA2AKhp5KMkd59V7rsMqlePSk2VrrrLmnbNt/ms2mTNHSoFB0t1a8v9ekjffhh1dR8psvPl154QYqLkwIC3Ie6daW775a++67kNC+9JHXsKEVGSg0aSOecIwUHW9MFBkqhoVJEhNS7t7RokbR5s3TNNVJYmORwWG2bN7e2QfFlOhxSSIj1enCwtZ3vuEPassW7bb9zp/R//yeddZY1r6AgqXVr6fbbrX8jI6WEBOnyy615BAVZ9UZESP36SX/5i1VjYKBVR5Mm1nR160oxMdLw4VYtklRYKL35ptSli/V6w4ZS165WnxwOa2jYUJowQRo1yvq5bl2r/fTp0kMPSfHx1rIbNbKmi4iQ2rSRnn1W+uknadIkqWVLq+6WLa3fc3PL3p4vvSR16CDVqmX1ITTUWvall0qNG1vrpk4d699T+yRZf2fz5kndu1v1xsVJI0dK33/vvpwXX7T2gbPOspbjcPyx3zzwgLV+vGGM9N//Sq1aWevd4bDqu/NOKTvbu3mUJStLGj/e2paRkVbNL71k9cGTlSul666z3hvOOsvaJmFhUu3a1jo9+2zp4ov/2C5Nm0qPPSb9/HPlay3uq6+sfTkqyqrlyiulZcv8uwygSpkzwPTp002TJk1MaGio6dy5s1m9enWZ7d955x3TsmVLExoaatq0aWMWLlzo9bJycnKMJJOTk1PZso3TacywYcZIxgQGWv9KxgQFGRMSYsyHH3o3nzlzrOmDgv6YR9H8Hnyw0mXWKEeOGHPxxX+sB09DQIAxoaHGLFniPk1AgDWUNa1kjMNRfpvyhuLL8rTtFy2yavvsM+v3yi6vrCEoyBrmzzfmmmsq1seivpS1/gICrL6c2sbhMKZFC2Oysjxvz0su8W67eOrTggXGFBYaM3iw53UdFmbMxx8bc/jwH/tAWfNt3tyYkyfL3gedTmOGDy99HpGRxmzdWvF9/NtvjYmKcu9L0f50ySXWOivun/8s2XdvBofDmNhYY77/vuK1FjdrllVj8fepop+feMI/ywDK4o/Pb/mxngp5++23TUhIiHn11VfNli1bzLBhw0zdunVNdna2x/arVq0ygYGB5umnnzbffvutefTRR01wcLDZvHmzV8vzZ+h57bWyPyDOOsuY334rex579xoTHFz2m9d771W61Bpj9GjvPyBr1zbm0CFrGn8EGX8NRdt+3z7rA/J0LTMoyPdw4a8hMNCY/v1Lbs8xYyq+bYrCbdGHvqfB4TCmTh1j7rrL+1AwZEjZ++Dbb5c/jxYtrHDkK6fTmFatSq/V4bDWWZHVqyu3XYKCjGnfvmK1Frd9e/nbcfnyyi0DKM+fIvR07tzZjBgxwvV7YWGhadiwocnIyPDY/oYbbjD9+vVzG5eUlGTuuOMOr5bnz9DToUPZbwQBAcZMmVL2PCZMKPvNOjDQmJ49K11qjXD4sBVkfPlQfOYZ36Y5XUNAgDE33VT9dZzuPu/a9cf2PHLEmPDwys3T4TAmOrr8MFfefxyKD6GhZYeAxETv5rNihe/7+PLl5c83PNz6WzDGmFtucT+yUtHhiy98r7W4MWPKfp8KCjJmwIDKLQMojz8+v6v1mp6CggKtW7dOKSkprnEOh0MpKSnKzMz0OE1mZqZbe0lKTU0ttX1+fr5yc3PdBn84eVLauFFyOktvExAglVKWy6pVZV9nUFgoffFFhUqscbZulY4c8b69wyF9/LFv05wuAQHSmjXVXcXpZYx7n7dulQ4frtw8nU7pl1+seZcmMFA6ccL7eebnS4cOeX7NGGnduvLn4c3ftidffmnVW5bDh/+4Luzzz633mspwOCpWa3Gff172+9TJk9Z1R8CZrlpDz8GDB1VYWKjY2Fi38bGxscrKyvI4TVZWlk/tMzIyFBkZ6Rri4+P9UnvRRa7ltQkKKrtNea972+bPoLwPA0/O1HVTdPF0efvIn03x7VGR7XkqRxW9Q5W133izTGMqtu95u06K5u2PdVh8flU5/Zn6twgU96e/e2vcuHHKyclxDXv37vXLfAMDrTtJynpTKiy07sgpS2pq2R+MQUFWGzto08a6K8RbhYXW3VfR0VVXU0UVFkq9epV9hKIqVGfICgqy7hQr0rp15beNMdadSGUFkcJCKTzc+3lGRFh3YnkSECD17OndfE454OyVyy8v/w6ymBjpggusn/v2rXzwcTorVmtxvXuXvQ2Cgqw7ToEzXbWGnqioKAUGBir7lHtAs7OzFRcX53GauLg4n9qHhoYqIiLCbfCXBx8s/Q0sMNC6/fjGG8uex5Ah1ptwaW8ohYXSmDGVKrPGCA6W/vpX79oGBFi3pQ8caK2fM+mIStG2z8iwbkk+XcsMDz/9IauIw2Hty8VDTtH2rOi2CQy0boueOLH008iBgdYt7w884P1y7r237Ncfeqj8eXTtat2C76uOHa3b9Es7KhIQYO3PwcHW7yNHVm7fDgy0wndRiKqoYcP+eLSDJ06ndN99lVsGcFr48RqjCuncubMZOXKk6/fCwkLTqFGjMi9kvuKKK9zGJScnV8uFzMb8cWdJ0cWGRbeeRkUZs3Gjd/P44gtjIiLcL4oODLR+f+UVv5RZY5w8aUxaWvkXZkZHG1N0w17xaby56DMw8I87gypzcWitWiWXGRBgzNln/7Htv/vO2hfKuvi3shepBgQYU7++MWvXGvPww96vh+LLL36RqqeL84vGRUe7ty/6NyWl5K3Wxli3mw8Z4ntfAwKMqVfPmK++suYzcaLnv7PYWGO2bLH2gaLb2su6uaBvX+/2w2nTSp9Hs2bGlHJzqVeys41p3dq91qJ+DRlirbPi3n3XulDbl7vgitq2b2/MwYMVr7W45cutuxJPfZ8KCjLmzTf9swygLP74/A4wprr+b2iZM2eO0tLS9OKLL6pz586aOnWq3nnnHW3btk2xsbEaPHiwGjVqpIyMDEnSF198oW7duunJJ59Uv3799Pbbb2vixIlav3692rRpU+7ycnNzFRkZqZycHL8d9dmyRZo503pw11lnSVddJQ0ebD0QzVsHD0qzZkkLF0oFBVJysvUgtBYt/FJijWKM9Nln1sPV1qyRjh+3/rcbHi6df750883W+o2MLDnNK69I27dbbQMDpa+/lvLyrP85161rHYm45BJr3UZFSTNmSLNnWxfL1q5tvZaVJa1ebV0cGxBgnQqpV8+6ANbhsI7e3HCDlJZmPaxv5kxp7Vpr2195pTW++LbPy7MePPfii9aD7cLCrFMol10mLV0q7d5tHRk6/3xruVu3WheGxsdb+9Ivv0gffST99pvVr7ZtrX/37LHmdeWV1lGWevWs5a1bZ9W0ebNVe9u21sXwRRfHtm0rPfKINf28edaDBdu2teaxY4f01lvS779b/SkstPqdkCDddpvUo4e0ZIm1r+7bZx1lGTrUOrVR2mkYY6yLXF9+2aotJ8equ3lz6bzzrGXu32/t94GB1umd/v2teurX/2M+X39trcP1661tdfXV0qBB1pHS4vvAyy9L33xjbce8PGubJSRIjz9unQ711tat0qOPSp9+atXWuLE0erS174WGej8fT/LzrXX/xhvWdm3Rwjqa0rWr5yM7e/da+9Dy5dZF2EXv2kXrLDbWOj38yy/Wdo2Oth4ieM011kMa/SU72/obW7LE2je6drUe1Nm0qf+WAZTGH5/f1R56JGn69OmaNGmSsrKy1KFDBz377LNKSkqSJHXv3l0JCQmaPXu2q/3cuXP16KOPavfu3WrRooWefvpp9e3b16tlVUXoAQAAVetPE3pOJ0IPAAA1jz8+v//0d28BAABIhB4AAGAThB4AAGALhB4AAGALhB4AAGALhB4AAGALhB4AAGALhB4AAGALhB4AAGALpXzX759X0QOoc3Nzq7kSAADgraLP7cp8kYTtQk9eXp4kKT4+vporAQAAvsrLy1Nk8W+c9oHtvnvL6XTqwIEDqlOnjgI8fZ1xDZCbm6v4+Hjt3bvXdt8fRt/pO323Fzv3n767990Yo7y8PDVs2FAOR8WuzrHdkR6Hw6HGjRtXdxl+ERERYbs/hCL0nb7bjZ37Ltm7//T9j75X9AhPES5kBgAAtkDoAQAAtkDoqYFCQ0OVnp6u0NDQ6i7ltKPv9N1u7Nx3yd79p+/+77vtLmQGAAD2xJEeAABgC4QeAABgC4QeAABgC4QeAABgC4SeM9zu3bt12223qWnTpqpVq5aaNWum9PR0FRQUlDnd8ePHNWLECJ199tkKDw/Xtddeq+zs7NNUtf/84x//UJcuXXTWWWepbt26Xk0zZMgQBQQEuA29e/eu2kKrSEX6b4zRhAkT1KBBA9WqVUspKSnavn171RZaBX777TfdcsstioiIUN26dXXbbbfp8OHDZU7TvXv3Etv+zjvvPE0VV9yMGTOUkJCgsLAwJSUlac2aNWW2nzt3rlq1aqWwsDC1bdtWixYtOk2VVg1f+j979uwS2zgsLOw0Vusfn332mfr376+GDRsqICBACxYsKHeaFStW6MILL1RoaKiaN2+u2bNnV3mdVcXX/q9YsaLEdg8ICFBWVpZPyyX0nOG2bdsmp9OpF198UVu2bNGUKVM0c+ZMPfzww2VON3r0aL3//vuaO3euPv30Ux04cEDXXHPNaarafwoKCnT99dfrrrvu8mm63r1766effnINb731VhVVWLUq0v+nn35azz77rGbOnKnVq1erdu3aSk1N1fHjx6uwUv+75ZZbtGXLFi1dulQffPCBPvvsMw0fPrzc6YYNG+a27Z9++unTUG3FzZkzR2PGjFF6errWr1+v9u3bKzU1VT///LPH9l988YUGDhyo2267TRs2bNCAAQM0YMAAffPNN6e5cv/wtf+S9ZTe4tv4xx9/PI0V+8eRI0fUvn17zZgxw6v2u3btUr9+/dSjRw9t3LhR9913n26//XYtWbKkiiutGr72v8h3333ntu1jYmJ8W7BBjfP000+bpk2blvr6oUOHTHBwsJk7d65r3NatW40kk5mZeTpK9LtZs2aZyMhIr9qmpaWZq666qkrrOd287b/T6TRxcXFm0qRJrnGHDh0yoaGh5q233qrCCv3r22+/NZLM2rVrXeM+/PBDExAQYPbv31/qdN26dTOjRo06DRX6T+fOnc2IESNcvxcWFpqGDRuajIwMj+1vuOEG069fP7dxSUlJ5o477qjSOquKr/335b2gppBk3n333TLbPPjgg6Z169Zu42688UaTmppahZWdHt70/5NPPjGSzO+//16pZXGkpwbKyclR/fr1S3193bp1OnHihFJSUlzjWrVqpXPOOUeZmZmno8Rqt2LFCsXExKhly5a666679Ouvv1Z3SafFrl27lJWV5bbtIyMjlZSUVKO2fWZmpurWravExETXuJSUFDkcDq1evbrMad944w1FRUWpTZs2GjdunI4ePVrV5VZYQUGB1q1b57a9HA6HUlJSSt1emZmZbu0lKTU1tUZt3yIV6b8kHT58WE2aNFF8fLyuuuoqbdmy5XSUW63+TNu9Mjp06KAGDRro8ssv16pVq3ye3nZfOFrT7dixQ88995yeeeaZUttkZWUpJCSkxDUgsbGxPp//rIl69+6ta665Rk2bNtXOnTv18MMPq0+fPsrMzFRgYGB1l1elirZvbGys2/iatu2zsrJKHLYOCgpS/fr1y+zHzTffrCZNmqhhw4b6+uuv9dBDD+m7777T/Pnzq7rkCjl48KAKCws9bq9t27Z5nCYrK6vGb98iFel/y5Yt9eqrr6pdu3bKycnRM888oy5dumjLli1/mi+T9qS07Z6bm6tjx46pVq1a1VTZ6dGgQQPNnDlTiYmJys/P1yuvvKLu3btr9erVuvDCC72eD0d6qsnYsWM9XpRVfDj1j37//v3q3bu3rr/+eg0bNqyaKq+8ivTdFzfddJOuvPJKtW3bVgMGDNAHH3ygtWvXasWKFf7rRCVUdf/PZFXd9+HDhys1NVVt27bVLbfcon//+9969913tXPnTj/2AtUpOTlZgwcPVocOHdStWzfNnz9f0dHRevHFF6u7NFShli1b6o477lCnTp3UpUsXvfrqq+rSpYumTJni03w40lNN/vrXv2rIkCFltjn33HNdPx84cEA9evRQly5d9NJLL5U5XVxcnAoKCnTo0CG3oz3Z2dmKi4urTNl+4WvfK+vcc89VVFSUduzYoZ49e/ptvhVVlf0v2r7Z2dlq0KCBa3x2drY6dOhQoXn6k7d9j4uLK3Eh68mTJ/Xbb7/5tA8nJSVJso6QNmvWzOd6q1pUVJQCAwNL3FlZ1t9qXFycT+3PZBXp/6mCg4PVsWNH7dixoypKPGOUtt0jIiL+9Ed5StO5c2d9/vnnPk1D6Kkm0dHRio6O9qrt/v371aNHD3Xq1EmzZs2Sw1H2AbpOnTopODhYy5Yt07XXXivJuuJ9z549Sk5OrnTtleVL3/1h3759+vXXX91CQHWqyv43bdpUcXFxWrZsmSvk5ObmavXq1T7fAVcVvO17cnKyDh06pHXr1qlTp06SpOXLl8vpdLqCjDc2btwoSWfMtj9VSEiIOnXqpGXLlmnAgAGSJKfTqWXLlmnkyJEep0lOTtayZct03333ucYtXbr0jPjb9lVF+n+qwsJCbd68WX379q3CSqtfcnJyiUcT1NTt7i8bN270/W+7UpdBo8rt27fPNG/e3PTs2dPs27fP/PTTT66heJuWLVua1atXu8bdeeed5pxzzjHLly83X331lUlOTjbJycnV0YVK+fHHH82GDRvM448/bsLDw82GDRvMhg0bTF5enqtNy5Ytzfz5840xxuTl5Zn777/fZGZmml27dpmPP/7YXHjhhaZFixbm+PHj1dWNCvO1/8YY8+STT5q6deua//3vf+brr782V111lWnatKk5duxYdXShwnr37m06duxoVq9ebT7//HPTokULM3DgQNfrp+73O3bsME888YT56quvzK5du8z//vc/c+6555pLL720urrglbffftuEhoaa2bNnm2+//dYMHz7c1K1b12RlZRljjBk0aJAZO3asq/2qVatMUFCQeeaZZ8zWrVtNenq6CQ4ONps3b66uLlSKr/1//PHHzZIlS8zOnTvNunXrzE033WTCwsLMli1bqqsLFZKXl+f6e5ZkJk+ebDZs2GB+/PFHY4wxY8eONYMGDXK1/+GHH8xZZ51lHnjgAbN161YzY8YMExgYaBYvXlxdXagUX/s/ZcoUs2DBArN9+3azefNmM2rUKONwOMzHH3/s03IJPWe4WbNmGUkehyK7du0ykswnn3ziGnfs2DFz9913m3r16pmzzjrLXH311W5BqaZIS0vz2PfifZVkZs2aZYwx5ujRo6ZXr14mOjraBAcHmyZNmphhw4a53kBrGl/7b4x12/r48eNNbGysCQ0NNT179jTffffd6S++kn799VczcOBAEx4ebiIiIszQoUPdwt6p+/2ePXvMpZdeaurXr29CQ0NN8+bNzQMPPGBycnKqqQfee+6558w555xjQkJCTOfOnc2XX37peq1bt24mLS3Nrf0777xjzjvvPBMSEmJat25tFi5ceJor9i9f+n/fffe52sbGxpq+ffua9evXV0PVlVN0C/apQ1Ff09LSTLdu3UpM06FDBxMSEmLOPfdct7/7msbX/j/11FOmWbNmJiwszNSvX990797dLF++3OflBhhjTMUOLAEAANQc3L0FAABsgdADAABsgdADAABsgdADAABsgdADAABsgdADAABsgdADAABsgdADAABsgdADAABsgdADAABsgdADoMb75ZdfFBcXp4kTJ7rGffHFFwoJCdGyZcuqsTIAZxK+ewvAn8KiRYs0YMAAffHFF2rZsqU6dOigq666SpMnT67u0gCcIQg9AP40RowYoY8//liJiYnavHmz1q5dq9DQ0OouC8AZgtAD4E/j2LFjatOmjfbu3at169apbdu21V0SgDMI1/QA+NPYuXOnDhw4IKfTqd27d1d3OQDOMBzpAfCnUFBQoM6dO6tDhw5q2bKlpk6dqs2bNysmJqa6SwNwhiD0APhTeOCBBzRv3jxt2rRJ4eHh6tatmyIjI/XBBx9Ud2kAzhCc3gJQ461YsUJTp07Vf/7zH0VERMjhcOg///mPVq5cqRdeeKG6ywNwhuBIDwAAsAWO9AAAAFsg9AAAAFsg9AAAAFsg9AAAAFsg9AAAAFsg9AAAAFsg9AAAAFsg9AAAAFsg9AAAAFsg9AAAAFsg9AAAAFsg9AAAAFv4fx7pAZXVFx7rAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x, y = make_classification(n_samples=100, n_features=1, n_informative=1, n_redundant=0,\n", + " n_clusters_per_class=1, random_state=1)\n", + "scaler=StandardScaler()\n", + "x = scaler.fit_transform(x)\n", + "\n", + "plt.scatter(x, y, c=y, cmap='bwr')\n", + "plt.xlabel('x')\n", + "plt.ylabel('y (класс)')\n", + "plt.title('Наши данные для логистической регрессии')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "d77813c6-a8fb-4c18-9d21-d0706709ecc4", + "metadata": {}, + "outputs": [], + "source": [ + "def sigmoid(z):\n", + " return 1 / (1 + np.exp(-z))\n", + "\n", + "def log_likelihood(x, y, w, b):\n", + " z = x @ w + b\n", + " p = sigmoid(z)\n", + " eps = 1e-9\n", + " return np.sum(y * np.log(p + eps) + (1 - y) * np.log(1 - p + eps))\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "d03ef821-65cf-4344-bff9-fd0520b99004", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "w = 4.9121, b = -0.9997\n" + ] + } + ], + "source": [ + "w = np.random.randn(1)\n", + "b = 0\n", + "lr = 0.1\n", + "losses = []\n", + "for i in range(1000):\n", + " z = x @ w + b\n", + " p = sigmoid(z)\n", + " grad_w = (x.T @ (p - y)) / len(y)\n", + " grad_b = np.mean(p - y)\n", + "\n", + " w -= lr * grad_w\n", + " b -= lr * grad_b\n", + "\n", + " ll = log_likelihood(x, y, w, b)\n", + " losses.append(ll)\n", + "print(f'w = {w[0]:.4f}, b = {b:.4f}')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "9316f63d-a9cb-405b-872d-1c551bced104", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(losses)\n", + "plt.title('Рост лог-правдоподия (целевая функция)')\n", + "plt.xlabel('Эпоха')\n", + "plt.ylabel('Log-likelihood')\n", + "plt.grid()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "3aa76964-5f2f-411a-b7c2-f9cb384efaec", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x_range = np.linspace(-3, 3, 100).reshape(-1, 1)\n", + "probs = sigmoid(x_range @ w + b)\n", + "\n", + "plt.scatter(x, y, c=y, cmap='bwr', alpha=0.5)\n", + "plt.plot(x_range, probs, color='black', label='Модель')\n", + "plt.title(\"Логистическая регрессия после обучения\")\n", + "plt.xlabel(\"x\")\n", + "plt.ylabel(\"P(y=1|x)\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6fedaa2e-6e31-4609-a9fb-ff376e839e29", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 742faad6af0d03be83c31f975fd14aca20d09f6e Mon Sep 17 00:00:00 2001 From: monkapolla Date: Sat, 24 May 2025 17:44:08 +0600 Subject: [PATCH 2/5] =?UTF-8?q?=D0=9B=D0=BE=D0=B3=D0=B8=D1=81=D1=82=D0=B8?= =?UTF-8?q?=D1=87=D0=B5=D1=81=D0=BA=D0=B0=D1=8F=20=D1=80=D0=B5=D0=B3=D1=80?= =?UTF-8?q?=D0=B5=D1=81=D1=81=D0=B8=D1=8F?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- LogisticRegression.ipynb | 375 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 375 insertions(+) create mode 100644 LogisticRegression.ipynb diff --git a/LogisticRegression.ipynb b/LogisticRegression.ipynb new file mode 100644 index 0000000..2bde7ba --- /dev/null +++ b/LogisticRegression.ipynb @@ -0,0 +1,375 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 6, + "id": "3e38cbcb-c667-4b64-b1b9-1f648ad15da2", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Отчет о классификации: \n", + " precision recall f1-score support\n", + "\n", + " 0 0.96 0.79 0.87 34\n", + " 1 0.78 0.96 0.86 26\n", + "\n", + " accuracy 0.87 60\n", + " macro avg 0.87 0.88 0.87 60\n", + "weighted avg 0.88 0.87 0.87 60\n", + "\n", + "Матрица ошибок:\n" + ] + }, + { + "ename": "ValueError", + "evalue": "Classification metrics can't handle a mix of continuous-multioutput and binary targets", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[6], line 25\u001b[0m\n\u001b[0;32m 23\u001b[0m \u001b[38;5;28mprint\u001b[39m(classification_report(y_test, y_pred))\n\u001b[0;32m 24\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mМатрица ошибок:\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m---> 25\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43mconfusion_matrix\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx_test\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_pred\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[0;32m 27\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mplot_decision_boundary\u001b[39m(model, x, y):\n\u001b[0;32m 28\u001b[0m h\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m.02\u001b[39m\n", + "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py:317\u001b[0m, in \u001b[0;36mconfusion_matrix\u001b[1;34m(y_true, y_pred, labels, sample_weight, normalize)\u001b[0m\n\u001b[0;32m 232\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mconfusion_matrix\u001b[39m(\n\u001b[0;32m 233\u001b[0m y_true, y_pred, \u001b[38;5;241m*\u001b[39m, labels\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, sample_weight\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, normalize\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 234\u001b[0m ):\n\u001b[0;32m 235\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Compute confusion matrix to evaluate the accuracy of a classification.\u001b[39;00m\n\u001b[0;32m 236\u001b[0m \n\u001b[0;32m 237\u001b[0m \u001b[38;5;124;03m By definition a confusion matrix :math:`C` is such that :math:`C_{i, j}`\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 315\u001b[0m \u001b[38;5;124;03m (0, 2, 1, 1)\u001b[39;00m\n\u001b[0;32m 316\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m--> 317\u001b[0m y_type, y_true, y_pred \u001b[38;5;241m=\u001b[39m \u001b[43m_check_targets\u001b[49m\u001b[43m(\u001b[49m\u001b[43my_true\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_pred\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 318\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m y_type \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m (\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbinary\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmulticlass\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m 319\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m is not supported\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m y_type)\n", + "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py:95\u001b[0m, in \u001b[0;36m_check_targets\u001b[1;34m(y_true, y_pred)\u001b[0m\n\u001b[0;32m 92\u001b[0m y_type \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmulticlass\u001b[39m\u001b[38;5;124m\"\u001b[39m}\n\u001b[0;32m 94\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(y_type) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m---> 95\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m 96\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mClassification metrics can\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt handle a mix of \u001b[39m\u001b[38;5;132;01m{0}\u001b[39;00m\u001b[38;5;124m and \u001b[39m\u001b[38;5;132;01m{1}\u001b[39;00m\u001b[38;5;124m targets\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\n\u001b[0;32m 97\u001b[0m type_true, type_pred\n\u001b[0;32m 98\u001b[0m )\n\u001b[0;32m 99\u001b[0m )\n\u001b[0;32m 101\u001b[0m \u001b[38;5;66;03m# We can't have more than one value on y_type => The set is no more needed\u001b[39;00m\n\u001b[0;32m 102\u001b[0m y_type \u001b[38;5;241m=\u001b[39m y_type\u001b[38;5;241m.\u001b[39mpop()\n", + "\u001b[1;31mValueError\u001b[0m: Classification metrics can't handle a mix of continuous-multioutput and binary targets" + ] + } + ], + "source": [ + "import numpy as np \n", + "import matplotlib.pyplot as plt\n", + "from sklearn.datasets import make_classification\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import classification_report, confusion_matrix\n", + "\n", + "x, y = make_classification(n_samples=200, n_features=2, n_redundant=0, n_informative=2,\n", + " n_clusters_per_class=1, random_state=42)\n", + "plt.scatter(x[:,0], x[:, 1], c=y, cmap='bwr', edgecolors='k')\n", + "\n", + "plt.title(\"Сгенерированные данные для классификации\")\n", + "plt.xlabel('Признак 1')\n", + "plt.ylabel('Признак 2')\n", + "plt.show()\n", + "\n", + "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=42)\n", + "model = LogisticRegression()\n", + "model.fit(x_train, y_train)\n", + "\n", + "y_pred = model.predict(x_test)\n", + "print('Отчет о классификации: ')\n", + "print(classification_report(y_test, y_pred))\n", + "print('Матрица ошибок:')\n", + "print(confusion_matrix(x_test, y_pred))\n", + "\n", + "def plot_decision_boundary(model, x, y):\n", + " h=.02\n", + "\n", + " x_min, x_max = x[:, 0].min() - 1, x[:, 0].max() + 1\n", + " y_min, y_max = x[:, 0].min() - 1, x[:, 0].max() + 1\n", + " xx, yy = np.mashgard(np.arange(x_min, x_max, h),\n", + " np.arange(y_min, y_max, h))\n", + " z = model.predict(np.c_[xx.ravel(), yy.ravel()])\n", + " z = z.reshape(xx.shape)\n", + " plt.contourf(xx, yy, z, cmap='bwr', alpha=0.2)\n", + " plt.scatter(x[:, 0], x[:, 1], c=y, cmap='bwr', edgecolors='k')\n", + " plt.title(\"Логистическая регрессия - разделяющая граница\")\n", + " plt.ylabel(\"Признак 1\")\n", + " plt.xlabel(\"Признак 2\")\n", + " plt.show()\n", + "plot_decision_boundary(model, x_test, y_test)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "23701dfd-2480-4a2f-8432-91648de55617", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Отчет о классификации:\n", + " precision recall f1-score support\n", + "\n", + " 0 0.96 0.79 0.87 34\n", + " 1 0.78 0.96 0.86 26\n", + "\n", + " accuracy 0.87 60\n", + " macro avg 0.87 0.88 0.87 60\n", + "weighted avg 0.88 0.87 0.87 60\n", + "\n", + "Матрица ошибок:\n", + "[[27 7]\n", + " [ 1 25]]\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# 1. Импорт библиотек\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.datasets import make_classification\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import classification_report, confusion_matrix\n", + "\n", + "# 2. Генерация данных\n", + "X, y = make_classification(n_samples=200, n_features=2, \n", + " n_redundant=0, n_informative=2,\n", + " n_clusters_per_class=1, random_state=42)\n", + "\n", + "# 3. Визуализация данных\n", + "plt.scatter(X[:, 0], X[:, 1], c=y, cmap='bwr', edgecolors='k')\n", + "plt.title(\"Сгенерированные данные для классификации\")\n", + "plt.xlabel(\"Признак 1\")\n", + "plt.ylabel(\"Признак 2\")\n", + "plt.show()\n", + "\n", + "# 4. Разделение на обучающую и тестовую выборки\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n", + "\n", + "# 5. Обучение модели\n", + "model = LogisticRegression()\n", + "model.fit(X_train, y_train)\n", + "\n", + "# 6. Предсказания\n", + "y_pred = model.predict(X_test)\n", + "\n", + "# 7. Оценка качества модели\n", + "print(\"Отчет о классификации:\")\n", + "print(classification_report(y_test, y_pred))\n", + "\n", + "print(\"Матрица ошибок:\")\n", + "print(confusion_matrix(y_test, y_pred))\n", + "\n", + "# 8. Визуализация разделяющей границы\n", + "def plot_decision_boundary(model, X, y):\n", + " h = .02 # шаг сетки\n", + " x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n", + " y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n", + " xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n", + " np.arange(y_min, y_max, h))\n", + " Z = model.predict(np.c_[xx.ravel(), yy.ravel()])\n", + " Z = Z.reshape(xx.shape)\n", + " \n", + " plt.contourf(xx, yy, Z, cmap='bwr', alpha=0.2)\n", + " plt.scatter(X[:, 0], X[:, 1], c=y, cmap='bwr', edgecolors='k')\n", + " plt.title(\"Логистическая регрессия — разделяющая граница\")\n", + " plt.xlabel(\"Признак 1\")\n", + " plt.ylabel(\"Признак 2\")\n", + " plt.show()\n", + "\n", + "plot_decision_boundary(model, X_test, y_test)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "c856cf67-1884-4a5c-958f-da72d0534b7e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " mean radius mean texture mean perimeter mean area mean smoothness \\\n", + "0 17.99 10.38 122.80 1001.0 0.11840 \n", + "1 20.57 17.77 132.90 1326.0 0.08474 \n", + "2 19.69 21.25 130.00 1203.0 0.10960 \n", + "3 11.42 20.38 77.58 386.1 0.14250 \n", + "4 20.29 14.34 135.10 1297.0 0.10030 \n", + "\n", + " mean compactness mean concavity mean concave points mean symmetry \\\n", + "0 0.27760 0.3001 0.14710 0.2419 \n", + "1 0.07864 0.0869 0.07017 0.1812 \n", + "2 0.15990 0.1974 0.12790 0.2069 \n", + "3 0.28390 0.2414 0.10520 0.2597 \n", + "4 0.13280 0.1980 0.10430 0.1809 \n", + "\n", + " mean fractal dimension ... worst texture worst perimeter worst area \\\n", + "0 0.07871 ... 17.33 184.60 2019.0 \n", + "1 0.05667 ... 23.41 158.80 1956.0 \n", + "2 0.05999 ... 25.53 152.50 1709.0 \n", + "3 0.09744 ... 26.50 98.87 567.7 \n", + "4 0.05883 ... 16.67 152.20 1575.0 \n", + "\n", + " worst smoothness worst compactness worst concavity worst concave points \\\n", + "0 0.1622 0.6656 0.7119 0.2654 \n", + "1 0.1238 0.1866 0.2416 0.1860 \n", + "2 0.1444 0.4245 0.4504 0.2430 \n", + "3 0.2098 0.8663 0.6869 0.2575 \n", + "4 0.1374 0.2050 0.4000 0.1625 \n", + "\n", + " worst symmetry worst fractal dimension target \n", + "0 0.4601 0.11890 0 \n", + "1 0.2750 0.08902 0 \n", + "2 0.3613 0.08758 0 \n", + "3 0.6638 0.17300 0 \n", + "4 0.2364 0.07678 0 \n", + "\n", + "[5 rows x 31 columns]\n", + "\n", + "Отчет о классификации:\n", + " precision recall f1-score support\n", + "\n", + " 0 0.97 0.97 0.97 63\n", + " 1 0.98 0.98 0.98 108\n", + "\n", + " accuracy 0.98 171\n", + " macro avg 0.97 0.97 0.97 171\n", + "weighted avg 0.98 0.98 0.98 171\n", + "\n", + "Матрица ошибок:\n", + "[[ 61 2]\n", + " [ 2 106]]\n" + ] + } + ], + "source": [ + "# 1. Импорт библиотек\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.datasets import load_breast_cancer\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import classification_report, confusion_matrix\n", + "\n", + "# 2. Загрузка данных\n", + "data = load_breast_cancer()\n", + "X = data.data\n", + "y = data.target\n", + "\n", + "# 3. Создание DataFrame для удобства\n", + "df = pd.DataFrame(X, columns=data.feature_names)\n", + "df[\"target\"] = y\n", + "print(df.head())\n", + "\n", + "# 4. Разделение данных\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n", + "\n", + "# 5. Обучение модели\n", + "model = LogisticRegression(max_iter=10000)\n", + "model.fit(X_train, y_train)\n", + "\n", + "# 6. Предсказание и метрики\n", + "y_pred = model.predict(X_test)\n", + "\n", + "print(\"\\nОтчет о классификации:\")\n", + "print(classification_report(y_test, y_pred))\n", + "\n", + "print(\"Матрица ошибок:\")\n", + "print(confusion_matrix(y_test, y_pred))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "f01cfc43-597d-4e3b-b68c-08cd6c393986", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.decomposition import PCA\n", + "\n", + "# 1. Преобразуем данные в 2D с помощью PCA\n", + "pca = PCA(n_components=2)\n", + "X_pca = pca.fit_transform(X)\n", + "\n", + "# 2. Разделим на train и test заново на новых признаках\n", + "X_train_pca, X_test_pca, y_train_pca, y_test_pca = train_test_split(X_pca, y, test_size=0.3, random_state=42)\n", + "\n", + "# 3. Обучим модель на 2D-признаках\n", + "model_pca = LogisticRegression()\n", + "model_pca.fit(X_train_pca, y_train_pca)\n", + "\n", + "# 4. Построим предсказания\n", + "y_pred_pca = model_pca.predict(X_test_pca)\n", + "\n", + "# 5. Визуализация результатов\n", + "plt.figure(figsize=(10, 6))\n", + "plt.scatter(X_test_pca[:, 0], X_test_pca[:, 1], c=y_pred_pca, cmap='bwr', alpha=0.7, edgecolors='k')\n", + "plt.title(\"Визуализация предсказаний логистической регрессии после PCA\")\n", + "plt.xlabel(\"Первая компонента\")\n", + "plt.ylabel(\"Вторая компонента\")\n", + "plt.colorbar(label='Предсказанный класс')\n", + "plt.grid(True)\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6b892ff8-99c7-41cc-b3d0-1dc61f1590b2", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 53b179a4a72d49c33b1422b328b86418120bb99d Mon Sep 17 00:00:00 2001 From: monkapolla Date: Sat, 24 May 2025 22:03:11 +0600 Subject: [PATCH 3/5] =?UTF-8?q?ML=20=D0=9D=D0=B5=D0=B4=D0=B5=D0=BB=D1=8F2?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../House_price_predictor-checkpoint.ipynb | 6 ++++ .../Linear_algebra-checkpoint.ipynb | 6 ++++ .../LogisticRegression-checkpoint.ipynb | 6 ++++ .ipynb_checkpoints/MLE-checkpoint.ipynb | 6 ++++ .../OptimizationMethod-checkpoint.ipynb | 6 ++++ .ipynb_checkpoints/Untitled-checkpoint.ipynb | 33 ++++++++++++++++++ .ipynb_checkpoints/gradient-checkpoint.ipynb | 6 ++++ Linear_algebra.ipynb | 33 ++++++++++++++++++ Untitled.ipynb | 33 ++++++++++++++++++ model.pkl | Bin 0 -> 1168 bytes 10 files changed, 135 insertions(+) create mode 100644 .ipynb_checkpoints/House_price_predictor-checkpoint.ipynb create mode 100644 .ipynb_checkpoints/Linear_algebra-checkpoint.ipynb create mode 100644 .ipynb_checkpoints/LogisticRegression-checkpoint.ipynb create mode 100644 .ipynb_checkpoints/MLE-checkpoint.ipynb create mode 100644 .ipynb_checkpoints/OptimizationMethod-checkpoint.ipynb create mode 100644 .ipynb_checkpoints/Untitled-checkpoint.ipynb create mode 100644 .ipynb_checkpoints/gradient-checkpoint.ipynb create mode 100644 Linear_algebra.ipynb create mode 100644 Untitled.ipynb create mode 100644 model.pkl diff --git a/.ipynb_checkpoints/House_price_predictor-checkpoint.ipynb b/.ipynb_checkpoints/House_price_predictor-checkpoint.ipynb new file mode 100644 index 0000000..363fcab --- /dev/null +++ b/.ipynb_checkpoints/House_price_predictor-checkpoint.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/.ipynb_checkpoints/Linear_algebra-checkpoint.ipynb b/.ipynb_checkpoints/Linear_algebra-checkpoint.ipynb new file mode 100644 index 0000000..363fcab --- /dev/null +++ b/.ipynb_checkpoints/Linear_algebra-checkpoint.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/.ipynb_checkpoints/LogisticRegression-checkpoint.ipynb b/.ipynb_checkpoints/LogisticRegression-checkpoint.ipynb new file mode 100644 index 0000000..363fcab --- /dev/null +++ b/.ipynb_checkpoints/LogisticRegression-checkpoint.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/.ipynb_checkpoints/MLE-checkpoint.ipynb b/.ipynb_checkpoints/MLE-checkpoint.ipynb new file mode 100644 index 0000000..363fcab --- /dev/null +++ b/.ipynb_checkpoints/MLE-checkpoint.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/.ipynb_checkpoints/OptimizationMethod-checkpoint.ipynb b/.ipynb_checkpoints/OptimizationMethod-checkpoint.ipynb new file mode 100644 index 0000000..363fcab --- /dev/null +++ b/.ipynb_checkpoints/OptimizationMethod-checkpoint.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/.ipynb_checkpoints/Untitled-checkpoint.ipynb b/.ipynb_checkpoints/Untitled-checkpoint.ipynb new file mode 100644 index 0000000..5c65fc2 --- /dev/null +++ b/.ipynb_checkpoints/Untitled-checkpoint.ipynb @@ -0,0 +1,33 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "748d0729-1a67-4fcd-9094-5443f125208b", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/.ipynb_checkpoints/gradient-checkpoint.ipynb b/.ipynb_checkpoints/gradient-checkpoint.ipynb new file mode 100644 index 0000000..363fcab --- /dev/null +++ b/.ipynb_checkpoints/gradient-checkpoint.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Linear_algebra.ipynb b/Linear_algebra.ipynb new file mode 100644 index 0000000..8f8c391 --- /dev/null +++ b/Linear_algebra.ipynb @@ -0,0 +1,33 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "9da7b68b-0fb5-46da-9883-f1475d520698", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Untitled.ipynb b/Untitled.ipynb new file mode 100644 index 0000000..5c65fc2 --- /dev/null +++ b/Untitled.ipynb @@ -0,0 +1,33 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "748d0729-1a67-4fcd-9094-5443f125208b", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/model.pkl b/model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..b5ce7f0851a197e09889924fc4129e4c1f2d8533 GIT binary patch literal 1168 zcmZ`(U2GIp6yDu-x7&e*ZOgU@Qi)1fQ6{!3V64s{302oDWMk4z2wv~Z-rX6PxwH3Y z7q$TflhST-eUaOsL3l8P)Zmj*s|m^K3qg&E7=5ydwK3tz2SQ8}e}Z>sYmG$D!`yGq zeCOWt%{k|u>YjZr8VOBoVn;m$Db_s~s}=Y`33)oOAVX55UksfERH2A5H^B0&eCV`1 zDYGI~b~$h{N7P1DE@wo-4yp}MY$FVg1QwHdk*o&HL$H%{3C~ zu(XwwH4@E5rbx^Nw)*Wh(-3Y0jdT^;^|45IF`-ruau<(~_!*KYke+FhERf!8E~-=# zZDqqypGMXcV~T%Z4_T}9kyL@KQwqbqPLTC?t0ZZwd6@KNi`^>5rmj!=!|+K=28waz zb8)H0$cD~$2Qrc=b}2Uwj+4QVNqsUDx?ly7jUg(zjNB6<3QJ^Dh&Vh>HWzzTXq8i? zB_Q`Y-QfuyHfK=Ze4AiJ~^~z{QHZ>jfJfbTz~(P z@dd)o>HP`w<~9BLkB^Ofw!HjF*UO*xnY+$UF6{cU(JH^=FY+DBt@&4f9lZS2H^zrM zf2#lf^;BzVVt(mJ?w8gFV>dE$xu)@6v@Un=Uu?}j+0hpfaSHKKp!!twG8b2Bo|?S> z*S38%?|7KA_tDhOKOeth-t0X%@y2&=nUg;hFCX0fx|zr<+%cZ#=7pX*xH@i`w}1O~ z{%v~5d~~cb|7PJObMD;XN1i^PH_34SmTT`mYu^5O+ureGPnZ{yDP#1N2h9!k*^gga z9565bab@n%;w@tq{GG41I_YX^GymD?QY4rSl@K+D&h~xD9obL&A2yyb Date: Sun, 25 May 2025 17:22:52 +0600 Subject: [PATCH 4/5] =?UTF-8?q?=D0=94=D0=B5=D1=80=D0=B5=D0=B2=D0=BE=20?= =?UTF-8?q?=D1=80=D0=B5=D1=88=D0=B5=D0=BD=D0=B8=D0=B9?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- decision_tree_01.ipynb | 212 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 212 insertions(+) create mode 100644 decision_tree_01.ipynb diff --git a/decision_tree_01.ipynb b/decision_tree_01.ipynb new file mode 100644 index 0000000..0e7596a --- /dev/null +++ b/decision_tree_01.ipynb @@ -0,0 +1,212 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "id": "ad31f2a8-6eb9-404f-a2cf-4a08aafbc0ec", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.tree import DecisionTreeClassifier, plot_tree\n", + "import numpy as np \n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "93353c21-e6d5-43ac-af16-ca9ab4c7ec86", + "metadata": {}, + "outputs": [], + "source": [ + "data = {\n", + " 'Погода':['Солнечно','Солнечно', 'Облачно', 'Дождь', 'Дождь', 'Дождь', 'Облачно','Солнечно'],\n", + " 'Влажность':['Высокая', 'Высокая', 'Высокая', 'Средняя', 'Низкая', 'Низкая', 'Средняя', 'Средняя'],\n", + " 'Пойти гулять':['Нет', 'Нет', 'Да', 'Да', 'Да', 'Нет', 'Да', 'Да']\n", + " \n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "21929f5e-b81b-48ba-85ec-1a19e0140515", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Погода Влажность Пойти гулять\n", + "0 Солнечно Высокая Нет\n", + "1 Солнечно Высокая Нет\n", + "2 Облачно Высокая Да\n", + "3 Дождь Средняя Да\n", + "4 Дождь Низкая Да\n", + "5 Дождь Низкая Нет\n", + "6 Облачно Средняя Да\n", + "7 Солнечно Средняя Да\n" + ] + } + ], + "source": [ + "path = pd.DataFrame(data)\n", + "print(path)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "f0f7797a-2ae2-4949-ae2d-1fa03eea3bb5", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\monka\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", + " if is_sparse(pd_dtype):\n", + "C:\\Users\\monka\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", + " if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n", + "C:\\Users\\monka\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", + " if is_sparse(pd_dtype):\n", + "C:\\Users\\monka\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", + " if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n", + "C:\\Users\\monka\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", + " if is_sparse(pd_dtype):\n", + "C:\\Users\\monka\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", + " if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n" + ] + } + ], + "source": [ + "from sklearn.preprocessing import LabelEncoder\n", + "le = LabelEncoder()\n", + "\n", + "path_encoded = path.copy()\n", + "path_encoded['Погода'] = le.fit_transform(path_encoded['Погода'])\n", + "path['Влажность'] = le.fit_transform(path_encoded['Влажность'])\n", + "path['Пойти гулять'] = le.fit_transform(path_encoded['Пойти гулять'])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "44ecf489-d2b7-4bbc-a204-42119399bbfc", + "metadata": {}, + "outputs": [], + "source": [ + "x = path_encoded[['Влажность', 'Погода']]\n", + "y = path_encoded['Пойти гулять']\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "a478a805-0729-47ee-b5c3-9de4f9c999f5", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\monka\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\utils\\validation.py:767: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", + " if not hasattr(array, \"sparse\") and array.dtypes.apply(is_sparse).any():\n", + "C:\\Users\\monka\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", + " if is_sparse(pd_dtype):\n", + "C:\\Users\\monka\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", + " if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n" + ] + }, + { + "ename": "ValueError", + "evalue": "could not convert string to float: 'Высокая'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_6048\\3521119663.py\u001b[0m in \u001b[0;36m?\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mclf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mDecisionTreeClassifier\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcriterion\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'entropy'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmax_depth\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mclf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\tree\\_classes.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(self, X, y, sample_weight, check_input)\u001b[0m\n\u001b[0;32m 885\u001b[0m \u001b[0mself\u001b[0m \u001b[1;33m:\u001b[0m \u001b[0mDecisionTreeClassifier\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 886\u001b[0m \u001b[0mFitted\u001b[0m \u001b[0mestimator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 887\u001b[0m \"\"\"\n\u001b[0;32m 888\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 889\u001b[1;33m super().fit(\n\u001b[0m\u001b[0;32m 890\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 891\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 892\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\tree\\_classes.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(self, X, y, sample_weight, check_input)\u001b[0m\n\u001b[0;32m 182\u001b[0m \u001b[1;31m# We can't pass multi_output=True because that would allow y to be\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 183\u001b[0m \u001b[1;31m# csr.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 184\u001b[0m \u001b[0mcheck_X_params\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mDTYPE\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"csc\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 185\u001b[0m \u001b[0mcheck_y_params\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mensure_2d\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 186\u001b[1;33m X, y = self._validate_data(\n\u001b[0m\u001b[0;32m 187\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalidate_separately\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcheck_X_params\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcheck_y_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 188\u001b[0m )\n\u001b[0;32m 189\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0missparse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\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;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[0;32m 575\u001b[0m \u001b[1;31m# :(\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 576\u001b[0m \u001b[0mcheck_X_params\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcheck_y_params\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvalidate_separately\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 577\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m\"estimator\"\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mcheck_X_params\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 578\u001b[0m \u001b[0mcheck_X_params\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mdefault_check_params\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mcheck_X_params\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 579\u001b[1;33m \u001b[0mX\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput_name\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"X\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mcheck_X_params\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 580\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m\"estimator\"\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mcheck_y_params\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 581\u001b[0m \u001b[0mcheck_y_params\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mdefault_check_params\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mcheck_y_params\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 582\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput_name\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"y\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mcheck_y_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)\u001b[0m\n\u001b[0;32m 876\u001b[0m )\n\u001b[0;32m 877\u001b[0m \u001b[0marray\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mxp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 878\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 879\u001b[0m \u001b[0marray\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_asarray_with_order\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mxp\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mxp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 880\u001b[1;33m \u001b[1;32mexcept\u001b[0m \u001b[0mComplexWarning\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mcomplex_warning\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 881\u001b[0m raise ValueError(\n\u001b[0;32m 882\u001b[0m \u001b[1;34m\"Complex data not supported\\n{}\\n\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 883\u001b[0m ) from complex_warning\n", + "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\utils\\_array_api.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(array, dtype, order, copy, xp)\u001b[0m\n\u001b[0;32m 181\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mxp\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 182\u001b[0m \u001b[0mxp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_namespace\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 183\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mxp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;34m\"numpy\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"numpy.array_api\"\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 184\u001b[0m \u001b[1;31m# Use NumPy API to support order\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 185\u001b[1;33m \u001b[0marray\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\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 186\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mxp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 187\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 188\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mxp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(self, dtype)\u001b[0m\n\u001b[0;32m 2082\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m__array__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mnpt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDTypeLike\u001b[0m \u001b[1;33m|\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2083\u001b[0m \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2084\u001b[1;33m \u001b[0marr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\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 2085\u001b[0m if (\n\u001b[0;32m 2086\u001b[0m \u001b[0mastype_is_view\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2087\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0musing_copy_on_write\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;31mValueError\u001b[0m: could not convert string to float: 'Высокая'" + ] + } + ], + "source": [ + "clf = DecisionTreeClassifier(criterion='entropy', max_depth=3)\n", + "clf.fit(x, y)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "141c4e05-aa46-4da9-b0ab-fdfd88a80cb9", + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'DecisionTreeClassifier' object has no attribute 'tree_'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[13], line 2\u001b[0m\n\u001b[0;32m 1\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m10\u001b[39m, \u001b[38;5;241m6\u001b[39m))\n\u001b[1;32m----> 2\u001b[0m \u001b[43mplot_tree\u001b[49m\u001b[43m(\u001b[49m\u001b[43mclf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfeature_names\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mclass_names\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfilled\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[0;32m 3\u001b[0m plt\u001b[38;5;241m.\u001b[39mtitle(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mДерево решений\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m 4\u001b[0m plt\u001b[38;5;241m.\u001b[39mshow()\n", + "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\tree\\_export.py:194\u001b[0m, in \u001b[0;36mplot_tree\u001b[1;34m(decision_tree, max_depth, feature_names, class_names, label, filled, impurity, node_ids, proportion, rounded, precision, ax, fontsize)\u001b[0m\n\u001b[0;32m 179\u001b[0m check_is_fitted(decision_tree)\n\u001b[0;32m 181\u001b[0m exporter \u001b[38;5;241m=\u001b[39m _MPLTreeExporter(\n\u001b[0;32m 182\u001b[0m max_depth\u001b[38;5;241m=\u001b[39mmax_depth,\n\u001b[0;32m 183\u001b[0m feature_names\u001b[38;5;241m=\u001b[39mfeature_names,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 192\u001b[0m fontsize\u001b[38;5;241m=\u001b[39mfontsize,\n\u001b[0;32m 193\u001b[0m )\n\u001b[1;32m--> 194\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mexporter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexport\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdecision_tree\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43max\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43max\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\tree\\_export.py:655\u001b[0m, in \u001b[0;36m_MPLTreeExporter.export\u001b[1;34m(self, decision_tree, ax)\u001b[0m\n\u001b[0;32m 653\u001b[0m ax\u001b[38;5;241m.\u001b[39mclear()\n\u001b[0;32m 654\u001b[0m ax\u001b[38;5;241m.\u001b[39mset_axis_off()\n\u001b[1;32m--> 655\u001b[0m my_tree \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_make_tree(\u001b[38;5;241m0\u001b[39m, \u001b[43mdecision_tree\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtree_\u001b[49m, decision_tree\u001b[38;5;241m.\u001b[39mcriterion)\n\u001b[0;32m 656\u001b[0m draw_tree \u001b[38;5;241m=\u001b[39m buchheim(my_tree)\n\u001b[0;32m 658\u001b[0m \u001b[38;5;66;03m# important to make sure we're still\u001b[39;00m\n\u001b[0;32m 659\u001b[0m \u001b[38;5;66;03m# inside the axis after drawing the box\u001b[39;00m\n\u001b[0;32m 660\u001b[0m \u001b[38;5;66;03m# this makes sense because the width of a box\u001b[39;00m\n\u001b[0;32m 661\u001b[0m \u001b[38;5;66;03m# is about the same as the distance between boxes\u001b[39;00m\n", + "\u001b[1;31mAttributeError\u001b[0m: 'DecisionTreeClassifier' object has no attribute 'tree_'" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10, 6))\n", + "plot_tree(clf, feature_names=['', ''], class_names=['', ''], filled=True)\n", + "plt.title('Дерево решений')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cfde3d73-7c0f-4d18-8c5e-0b18386bc0bc", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 7b6dc90257e341bee040d7882aa187d88675bacb Mon Sep 17 00:00:00 2001 From: monkapolla Date: Sun, 25 May 2025 17:43:55 +0600 Subject: [PATCH 5/5] =?UTF-8?q?=D0=94=D0=B5=D1=80=D0=B5=D0=B2=D0=BE=20?= =?UTF-8?q?=D1=80=D0=B5=D1=88=D0=B5=D0=BD=D0=B8=D0=B9=202.0?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- decision_tree_01.ipynb | 142 +++++++++++++++++++++++++++++++++++++---- 1 file changed, 130 insertions(+), 12 deletions(-) diff --git a/decision_tree_01.ipynb b/decision_tree_01.ipynb index 0e7596a..3b4c61c 100644 --- a/decision_tree_01.ipynb +++ b/decision_tree_01.ipynb @@ -103,7 +103,19 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 21, + "id": "cfde3d73-7c0f-4d18-8c5e-0b18386bc0bc", + "metadata": {}, + "outputs": [], + "source": [ + "le_weather = LabelEncoder()\n", + "le_humidity = LabelEncoder()\n", + "le_target = LabelEncoder()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, "id": "a478a805-0729-47ee-b5c3-9de4f9c999f5", "metadata": {}, "outputs": [ @@ -144,28 +156,27 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 23, "id": "141c4e05-aa46-4da9-b0ab-fdfd88a80cb9", - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "ename": "AttributeError", - "evalue": "'DecisionTreeClassifier' object has no attribute 'tree_'", + "evalue": "'LabelEncoder' object has no attribute 'classes_'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[13], line 2\u001b[0m\n\u001b[0;32m 1\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m10\u001b[39m, \u001b[38;5;241m6\u001b[39m))\n\u001b[1;32m----> 2\u001b[0m \u001b[43mplot_tree\u001b[49m\u001b[43m(\u001b[49m\u001b[43mclf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfeature_names\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mclass_names\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfilled\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[0;32m 3\u001b[0m plt\u001b[38;5;241m.\u001b[39mtitle(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mДерево решений\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m 4\u001b[0m plt\u001b[38;5;241m.\u001b[39mshow()\n", - "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\tree\\_export.py:194\u001b[0m, in \u001b[0;36mplot_tree\u001b[1;34m(decision_tree, max_depth, feature_names, class_names, label, filled, impurity, node_ids, proportion, rounded, precision, ax, fontsize)\u001b[0m\n\u001b[0;32m 179\u001b[0m check_is_fitted(decision_tree)\n\u001b[0;32m 181\u001b[0m exporter \u001b[38;5;241m=\u001b[39m _MPLTreeExporter(\n\u001b[0;32m 182\u001b[0m max_depth\u001b[38;5;241m=\u001b[39mmax_depth,\n\u001b[0;32m 183\u001b[0m feature_names\u001b[38;5;241m=\u001b[39mfeature_names,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 192\u001b[0m fontsize\u001b[38;5;241m=\u001b[39mfontsize,\n\u001b[0;32m 193\u001b[0m )\n\u001b[1;32m--> 194\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mexporter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexport\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdecision_tree\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43max\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43max\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\tree\\_export.py:655\u001b[0m, in \u001b[0;36m_MPLTreeExporter.export\u001b[1;34m(self, decision_tree, ax)\u001b[0m\n\u001b[0;32m 653\u001b[0m ax\u001b[38;5;241m.\u001b[39mclear()\n\u001b[0;32m 654\u001b[0m ax\u001b[38;5;241m.\u001b[39mset_axis_off()\n\u001b[1;32m--> 655\u001b[0m my_tree \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_make_tree(\u001b[38;5;241m0\u001b[39m, \u001b[43mdecision_tree\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtree_\u001b[49m, decision_tree\u001b[38;5;241m.\u001b[39mcriterion)\n\u001b[0;32m 656\u001b[0m draw_tree \u001b[38;5;241m=\u001b[39m buchheim(my_tree)\n\u001b[0;32m 658\u001b[0m \u001b[38;5;66;03m# important to make sure we're still\u001b[39;00m\n\u001b[0;32m 659\u001b[0m \u001b[38;5;66;03m# inside the axis after drawing the box\u001b[39;00m\n\u001b[0;32m 660\u001b[0m \u001b[38;5;66;03m# this makes sense because the width of a box\u001b[39;00m\n\u001b[0;32m 661\u001b[0m \u001b[38;5;66;03m# is about the same as the distance between boxes\u001b[39;00m\n", - "\u001b[1;31mAttributeError\u001b[0m: 'DecisionTreeClassifier' object has no attribute 'tree_'" + "Cell \u001b[1;32mIn[23], line 4\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtree\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m plot_tree\n\u001b[0;32m 3\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m10\u001b[39m, \u001b[38;5;241m6\u001b[39m))\n\u001b[1;32m----> 4\u001b[0m plot_tree(clf, feature_names\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mПогода\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mВлажность\u001b[39m\u001b[38;5;124m'\u001b[39m], class_names\u001b[38;5;241m=\u001b[39m\u001b[43mle_target\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclasses_\u001b[49m, filled\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m 5\u001b[0m plt\u001b[38;5;241m.\u001b[39mtitle(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mДерево решений\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m 6\u001b[0m plt\u001b[38;5;241m.\u001b[39mshow()\n", + "\u001b[1;31mAttributeError\u001b[0m: 'LabelEncoder' object has no attribute 'classes_'" ] }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxoAAAHiCAYAAABvHroPAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAKAUlEQVR4nO3XwQkAIBDAMHX/nc8lCoIkE/TbPTOzAAAAQud1AAAA8B+jAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAADmjAQAA5IwGAACQMxoAAEDOaAAAALkLGFEHwJCUOL0AAAAASUVORK5CYII=", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -173,16 +184,123 @@ } ], "source": [ + "from sklearn.tree import plot_tree\n", + "\n", "plt.figure(figsize=(10, 6))\n", - "plot_tree(clf, feature_names=['', ''], class_names=['', ''], filled=True)\n", + "plot_tree(clf, feature_names=['Погода', 'Влажность'], class_names=le_target.classes_, filled=True)\n", "plt.title('Дерево решений')\n", "plt.show()" ] }, + { + "cell_type": "code", + "execution_count": 24, + "id": "6e5baff6-9a0c-4889-8253-e3ea40b3f256", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Погода Влажность Гулять\n", + "0 Солнечно Высокая Нет\n", + "1 Солнечно Высокая Нет\n", + "2 Облачно Высокая Да\n", + "3 Дождь Средняя Да\n", + "4 Дождь Низкая Да\n", + "5 Дождь Низкая Нет\n", + "6 Облачно Средняя Да\n", + "7 Солнечно Средняя Нет\n", + "8 Солнечно Высокая Да\n", + "9 Дождь Средняя Да\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\monka\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", + " if is_sparse(pd_dtype):\n", + "C:\\Users\\monka\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", + " if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n", + "C:\\Users\\monka\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", + " if is_sparse(pd_dtype):\n", + "C:\\Users\\monka\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", + " if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n", + "C:\\Users\\monka\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", + " if is_sparse(pd_dtype):\n", + "C:\\Users\\monka\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", + " if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n", + "C:\\Users\\monka\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\utils\\validation.py:767: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", + " if not hasattr(array, \"sparse\") and array.dtypes.apply(is_sparse).any():\n", + "C:\\Users\\monka\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", + " if is_sparse(pd_dtype):\n", + "C:\\Users\\monka\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", + " if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n", + "C:\\Users\\monka\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", + " if is_sparse(pd_dtype):\n", + "C:\\Users\\monka\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", + " if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Импорт необходимых библиотек\n", + "import pandas as pd\n", + "from sklearn.preprocessing import LabelEncoder\n", + "from sklearn.tree import DecisionTreeClassifier, plot_tree\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Шаг 1. Данные\n", + "data = {\n", + " 'Погода': ['Солнечно', 'Солнечно', 'Облачно', 'Дождь', 'Дождь', 'Дождь', 'Облачно', 'Солнечно', 'Солнечно', 'Дождь'],\n", + " 'Влажность': ['Высокая', 'Высокая', 'Высокая', 'Средняя', 'Низкая', 'Низкая', 'Средняя', 'Средняя', 'Высокая', 'Средняя'],\n", + " 'Гулять': ['Нет', 'Нет', 'Да', 'Да', 'Да', 'Нет', 'Да', 'Нет', 'Да', 'Да']\n", + "}\n", + "\n", + "df = pd.DataFrame(data)\n", + "print(df)\n", + "\n", + "# Шаг 2. Кодирование категориальных признаков\n", + "le_weather = LabelEncoder()\n", + "le_humidity = LabelEncoder()\n", + "le_target = LabelEncoder()\n", + "\n", + "df['Погода'] = le_weather.fit_transform(df['Погода'])\n", + "df['Влажность'] = le_humidity.fit_transform(df['Влажность'])\n", + "df['Гулять'] = le_target.fit_transform(df['Гулять'])\n", + "\n", + "# Шаг 3. Обучение модели\n", + "x = df[['Погода', 'Влажность']]\n", + "y = df['Гулять']\n", + "\n", + "clf = DecisionTreeClassifier(criterion='entropy', max_depth=3)\n", + "clf.fit(x, y)\n", + "\n", + "# Шаг 4. Визуализация дерева\n", + "plt.figure(figsize=(10, 6))\n", + "plot_tree(clf, \n", + " feature_names=['Погода', 'Влажность'], \n", + " class_names=le_target.classes_, \n", + " filled=True)\n", + "plt.title('Дерево решений: Пойти гулять?')\n", + "plt.show()\n" + ] + }, { "cell_type": "code", "execution_count": null, - "id": "cfde3d73-7c0f-4d18-8c5e-0b18386bc0bc", + "id": "4aff4f67-47f8-4da4-9136-062fe1068765", "metadata": {}, "outputs": [], "source": []