additional_task.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn as sk\n",
    "\n",
    "from sklearn import linear_model\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Species  Weight  Length1  Length2  Length3   Height   Width\n",
      "0     Bream   242.0     23.2     25.4     30.0  11.5200  4.0200\n",
      "1     Bream   290.0     24.0     26.3     31.2  12.4800  4.3056\n",
      "2     Bream   340.0     23.9     26.5     31.1  12.3778  4.6961\n",
      "3     Bream   363.0     26.3     29.0     33.5  12.7300  4.4555\n",
      "4     Bream   430.0     26.5     29.0     34.0  12.4440  5.1340\n",
      "..      ...     ...      ...      ...      ...      ...     ...\n",
      "154   Smelt    12.2     11.5     12.2     13.4   2.0904  1.3936\n",
      "155   Smelt    13.4     11.7     12.4     13.5   2.4300  1.2690\n",
      "156   Smelt    12.2     12.1     13.0     13.8   2.2770  1.2558\n",
      "157   Smelt    19.7     13.2     14.3     15.2   2.8728  2.0672\n",
      "158   Smelt    19.9     13.8     15.0     16.2   2.9322  1.8792\n",
      "\n",
      "[159 rows x 7 columns]\n"
     ]
    }
   ],
   "source": [
    "# Набор данных взят с https://www.kaggle.com/aungpyaeap/fish-market\n",
    "# Параметры нескольких популярных промысловых рыб\n",
    "# length 1 = Body height\n",
    "# length 2 = Total Length\n",
    "# length 3 = Diagonal Length\n",
    "fish_data = pd.read_csv(\"datasets/Fish.csv\", delimiter=',')\n",
    "print(fish_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Length1  Length2  Length3   Height   Width  Weight\n",
      "0       23.2     25.4     30.0  11.5200  4.0200   242.0\n",
      "1       24.0     26.3     31.2  12.4800  4.3056   290.0\n",
      "2       23.9     26.5     31.1  12.3778  4.6961   340.0\n",
      "3       26.3     29.0     33.5  12.7300  4.4555   363.0\n",
      "4       26.5     29.0     34.0  12.4440  5.1340   430.0\n",
      "..       ...      ...      ...      ...     ...     ...\n",
      "154     11.5     12.2     13.4   2.0904  1.3936    12.2\n",
      "155     11.7     12.4     13.5   2.4300  1.2690    13.4\n",
      "156     12.1     13.0     13.8   2.2770  1.2558    12.2\n",
      "157     13.2     14.3     15.2   2.8728  2.0672    19.7\n",
      "158     13.8     15.0     16.2   2.9322  1.8792    19.9\n",
      "\n",
      "[159 rows x 6 columns]\n"
     ]
    }
   ],
   "source": [
    "# Выделим необходимые переменные\n",
    "x_labels = ['Length1', 'Length2', 'Length3', 'Height', 'Width']\n",
    "y_label = 'Weight'\n",
    "data = fish_data[x_labels + [y_label]]\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32\n"
     ]
    }
   ],
   "source": [
    "# Определим размер валидационной и тестовой выборок\n",
    "val_test_size = round(0.2*len(data))\n",
    "print(val_test_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Генерируем уникальный seed\n",
    "my_code = \"Johnson\"\n",
    "seed_limit = 2 ** 32\n",
    "my_seed = int.from_bytes(my_code.encode(), \"little\") % seed_limit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "95 32 32\n"
     ]
    }
   ],
   "source": [
    "# Создадим обучающую, валидационную и тестовую выборки\n",
    "random_state = my_seed\n",
    "train_val, test = train_test_split(data, test_size=val_test_size, random_state=random_state)\n",
    "train, val = train_test_split(train_val, test_size=val_test_size, random_state=random_state)\n",
    "print(len(train), len(val), len(test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Преобразуем данные к ожидаемому библиотекой skleran формату\n",
    "#train_x = np.array(train[x_labels]).reshape(-1,1)\n",
    "train_x = train[x_labels]\n",
    "train_y = np.array(train[y_label]).reshape(-1,1)\n",
    "\n",
    "#val_x = np.array(val[x_labels]).reshape(-1,1)\n",
    "val_x = val[x_labels]\n",
    "val_y = np.array(val[y_label]).reshape(-1,1)\n",
    "\n",
    "#test_x = np.array(test[x_labels]).reshape(-1,1)\n",
    "test_x = test[x_labels]\n",
    "test_y = np.array(test[y_label]).reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Length1  Length2  Length3   Height   Width\n",
      "18      30.9     33.5     38.6  15.6330  5.1338\n",
      "134     40.0     42.5     45.5   7.2800  4.3225\n",
      "128     30.0     32.3     34.8   5.5680  3.3756\n",
      "98      22.6     24.6     26.2   6.7334  4.1658\n",
      "60      37.3     40.0     43.5  12.3540  6.5250\n",
      "..       ...      ...      ...      ...     ...\n",
      "158     13.8     15.0     16.2   2.9322  1.8792\n",
      "84      19.0     21.0     22.5   5.6925  3.6675\n",
      "46      21.0     22.5     25.0   6.5500  3.3250\n",
      "154     11.5     12.2     13.4   2.0904  1.3936\n",
      "66      19.0     20.7     23.2   8.5376  3.2944\n",
      "\n",
      "[95 rows x 5 columns]\n",
      "Length1\n",
      "Length2\n",
      "Length3\n",
      "Height\n",
      "Width\n"
     ]
    }
   ],
   "source": [
    "print(train_x)\n",
    "for x in train_x :\n",
    "    print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Нормируем значения\n",
    "scalers = []\n",
    "scaled_train_x = []\n",
    "for x in train_x :\n",
    "    scaler_x = MinMaxScaler()\n",
    "    scaler_x.fit(x)\n",
    "    scaled_x = scaler_x.transform(x)\n",
    "    scalers.add(scaler_x)\n",
    "    scaled_train_x.add[scaled_x]\n",
    "\n",
    "scaler_y = MinMaxScaler()\n",
    "scaler_y.fit(train_y)\n",
    "scaled_train_y = scaler_y.transform(train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Строим модель и выводим результаты для нормированных данных\n",
    "model2 = linear_model.LinearRegression()\n",
    "model2.fit(scaled_train_x, scaled_train_y)\n",
    "a = model2.coef_[0]\n",
    "b = model2.intercept_\n",
    "\n",
    "x = np.linspace(min(scaled_train_x), max(scaled_train_x), 100)\n",
    "y = a * x + b\n",
    "\n",
    "plt.plot(scaled_train_x, scaled_train_y, 'o')\n",
    "plt.plot(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.008373125246063868\n"
     ]
    }
   ],
   "source": [
    "# Проверим результат на валидационной выборке\n",
    "scaled_val_x = scaler_x.transform(val_x)\n",
    "scaled_val_y = scaler_y.transform(val_y)\n",
    "\n",
    "val_predicted = model2.predict(scaled_val_x)\n",
    "\n",
    "mse2 = mean_squared_error(scaled_val_y, val_predicted)\n",
    "print(mse2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Построим модель линейной регресси с L1-регуляризацией и выведем результаты для нормированных данных\n",
    "model3 = linear_model.Lasso()\n",
    "model3.fit(scaled_train_x, scaled_train_y)\n",
    "a = model3.coef_[0]\n",
    "b = model3.intercept_\n",
    "\n",
    "x = np.linspace(min(scaled_train_x), max(scaled_train_x), 100)\n",
    "y = a * x + b\n",
    "\n",
    "plt.plot(scaled_train_x, scaled_train_y, 'o')\n",
    "plt.plot(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.055926483371156764\n"
     ]
    }
   ],
   "source": [
    "# Проверим результат на валидационной выборке\n",
    "scaled_val_x = scaler_x.transform(val_x)\n",
    "scaled_val_y = scaler_y.transform(val_y)\n",
    "\n",
    "val_predicted = model3.predict(scaled_val_x)\n",
    "\n",
    "mse3 = mean_squared_error(scaled_val_y, val_predicted)\n",
    "print(mse3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Построим модель линейной регресси с L2-регуляризацией и выведем результаты для нормированных данных\n",
    "model4 = linear_model.Ridge()\n",
    "model4.fit(scaled_train_x, scaled_train_y)\n",
    "a = model4.coef_[0]\n",
    "b = model4.intercept_\n",
    "\n",
    "x = np.linspace(min(scaled_train_x), max(scaled_train_x), 100)\n",
    "y = a * x + b\n",
    "\n",
    "plt.plot(scaled_train_x, scaled_train_y, 'o')\n",
    "plt.plot(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.009317697850441578\n"
     ]
    }
   ],
   "source": [
    "# Проверим результат на валидационной выборке\n",
    "scaled_val_x = scaler_x.transform(val_x)\n",
    "scaled_val_y = scaler_y.transform(val_y)\n",
    "\n",
    "val_predicted = model4.predict(scaled_val_x)\n",
    "\n",
    "mse4 = mean_squared_error(scaled_val_y, val_predicted)\n",
    "print(mse4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Построим модель линейной регресси с ElasticNet-регуляризацией и выведем результаты для нормированных данных\n",
    "model5 = linear_model.ElasticNet()\n",
    "model5.fit(scaled_train_x, scaled_train_y)\n",
    "a = model5.coef_[0]\n",
    "b = model5.intercept_\n",
    "\n",
    "x = np.linspace(min(scaled_train_x), max(scaled_train_x), 100)\n",
    "y = a * x + b\n",
    "\n",
    "plt.plot(scaled_train_x, scaled_train_y, 'o')\n",
    "plt.plot(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.055926483371156764\n"
     ]
    }
   ],
   "source": [
    "# Проверим результат на валидационной выборке\n",
    "scaled_val_x = scaler_x.transform(val_x)\n",
    "scaled_val_y = scaler_y.transform(val_y)\n",
    "\n",
    "val_predicted = model5.predict(scaled_val_x)\n",
    "\n",
    "mse5 = mean_squared_error(scaled_val_y, val_predicted)\n",
    "print(mse5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.008373125246063868 0.055926483371156764 0.009317697850441578 0.055926483371156764\n"
     ]
    }
   ],
   "source": [
    "# Выведем ошибки для моделей на нормированных данных\n",
    "print(mse2, mse3, mse4, mse5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.007290890601151484\n"
     ]
    }
   ],
   "source": [
    "# Минимальное значение достигается для второй модели, получим итоговую величину ошибки на тестовой выборке\n",
    "scaled_test_x = scaler_x.transform(test_x)\n",
    "scaled_test_y = scaler_y.transform(test_y)\n",
    "\n",
    "test_predicted = model2.predict(scaled_test_x)\n",
    "\n",
    "mse_test = mean_squared_error(scaled_test_y, test_predicted)\n",
    "print(mse_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Повторите выделение данных, нормирование, и анализ 4 моделей\n",
    "# (обычная линейная регрессия, L1-регуляризация, L2-регуляризация, ElasticNet-регуляризация)\n",
    "# для x = Length2 и y = Width."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}