{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "543-uGN-9jAZ"
   },
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "HnjQZLuC9jAY"
   },
   "source": [
    "<h2 style=\"text-align: center;\"><b>Перцептрон Розенблатта<br><br></b></h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "1JBsLVMI9jAa"
   },
   "source": [
    "В данном ноутбуке мы:  \n",
    "\n",
    "- реализуем класс **`Perceptron()`** -- нейрон пороговой функцией активации\n",
    "- обучим и протестируем перцептрон на сгенерированных и реальных данных (файлы с реальными данными помещены в папку /data в этой же директории)\n",
    "- сравним качество работы Вашего класса с классом из библиотеки `scikit-learn` (`sklearn.linear_model.Perceptron()`)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "cOAHk8eO9jAb"
   },
   "source": [
    "<h2 style=\"text-align: center;\"><b>Введение</b></h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "bF22tUW79jAc"
   },
   "source": [
    "Почти любой алгоритм машинного обучения, решающий задачу *классификации* или *регрессии*, работает так:\n",
    "\n",
    "1. (*стадия инициализации*) Задаются его **гиперпараметры**, то есть те величины, которые не \"выучиваются\" алгоритмом в процессе обучения самостоятельно \n",
    "2. (*стадия обучения*) Алгоритм запускается на данных, **обучаясь** на них и меняя свои **параметры** (не путать с *гипер*параметрами) каким-то определённым образом (например, с помощью *метода градиентного спуска* или *метода коррекции ошибки*), исходя из функции потерь (её называют *loss function*). Функция потерь, по сути, говорит, где и как ошибается модель\n",
    "3.  (*стадия предсказания*) Модель готова, и теперь с помощью неё можно делать **предсказания** на новых объектах"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3hxoVvmN9jAd"
   },
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.colors import ListedColormap\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "jHd4CZjS9jAg"
   },
   "source": [
    "<h2 style=\"text-align: center;\"><b>Класс Perceptron</b></h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "PObIs0OB9jAh"
   },
   "source": [
    "В даном разделе будет решаться задача **бинарной классификации** с помощью перцептрона:  \n",
    "- *Входные данные*: матрица $X$ размера $(n, m)$ и столбец $y$ из нулей и единиц размера $(n, 1)$. Строкам матрицы соответствуют объекты, столбцам - признаки (то есть строка $i$ есть набор признаков (*признаковое описание*) объекта $X_i$).\n",
    "- *Выходные данные*: столбец $\\hat{y}$ из нулей и единиц размера $(n, 1)$ - предсказания алгоритма."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "wkd_24Zr9jAi"
   },
   "source": [
    "Модель нейрона в биологии и в deep learning:  \n",
    "\n",
    "![title](http://lamda.nju.edu.cn/weixs/project/CNNTricks/imgs/neuron.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "82qIny-49jAi"
   },
   "source": [
    "Чтобы понять, как мы будем обновлять параметры модели (веса), нужно знать, какую функцию потерь мы оптимизируем (находим минимум). В данном случае мы решаем задачу бинарной классификации (2 класса: 1 или 0), возьмём в качестве функции потерь среднеквадратичную ошибку:  \n",
    "\n",
    "$$Loss(\\hat{y}, y) = \\frac{1}{2n}\\sum_{i=1}^{n} (\\hat{y_i} - y_i)^2 = \\frac{1}{2n}\\sum_{i=1}^{n} (f(w \\cdot X_i + b) - y_i)^2$$  \n",
    "\n",
    "Здесь $w \\cdot X_i$ - скалярное произведение, а $f()$ - пороговая функция:  \n",
    "\n",
    "$$\n",
    "f(w \\cdot X_i + b) =\n",
    "\\begin{cases}\n",
    "1, &\\text{если } w \\cdot X_i + b > 0 \\\\\n",
    "0, &\\text{если } w \\cdot X_i + b \\le 0\n",
    "\\end{cases}\n",
    "$$  \n",
    "\n",
    "**Примечание:** На самом деле можно считать, что $b$ - свободный член - является частью вектора весов: $w_0$, приписав к $X$ слева единичный столбец, тогда в скалярном произведении с каждым объектом $b$ будет именно как свободный член. При реализации класса `Perceptron()` мы будем обновлять $b$ отдельно от $w$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6wGYvpsv9jAj"
   },
   "source": [
    "Реализуем функцию потерь:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "KIMPzh0B9jAk"
   },
   "outputs": [],
   "source": [
    "def Loss(y_pred, y):\n",
    "    '''\n",
    "    Считаем среднеквадратичную ошибку\n",
    "    '''\n",
    "    y_pred = y_pred.reshape(-1,1)\n",
    "    y = np.array(y).reshape(-1,1)\n",
    "    return 0.5 * np.mean((y_pred - y) ** 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5QrUrljB9jAn"
   },
   "source": [
    "Поскольку у *пороговой функции* не существует производной в нуле, то мы не можем использовать градиентный спуск, ведь:  \n",
    "\n",
    "$$ \\frac{\\partial Loss}{\\partial w} = \\frac{1}{n} X^T\\left(f(w \\cdot X) - y\\right)f'(w \\cdot X)$$  \n",
    "\n",
    "где $f^{'}(w \\cdot X)$ -- в точке 0 посчитать не получится. Но хочется как-то обновлять веса, иначе обучения не случиться.\n",
    "\n",
    "Поэтому предлагается обновлять так:   \n",
    "\n",
    "$$w^{j+1} = w^{j} - \\alpha\\Delta{w^{j}}$$ \n",
    "$$b^{j+1} = b^{j} - \\alpha\\Delta{b^{j}}$$ \n",
    "\n",
    "где:  \n",
    "\n",
    "$$\\Delta{w} = \\frac{1}{n}X^T(\\hat{y} - y) = \\frac{1}{n}X^T(f(X \\cdot w^j + b^j) - y)$$  \n",
    "$$\\Delta{b} = \\frac{1}{n}X^T(\\hat{y} - y) = \\frac{1}{n}1^T(f(X \\cdot w^j + b^j) - y)$$  \n",
    "\n",
    "где $w \\cdot X$ - матричное произведение столбца весов $w$ на матрицу объектов-признаков $X$, $1^T$ -- вектор-строка из единиц, а индекс $j$ -- номер итерации градиентного спуска.\n",
    "\n",
    "Это правило является неким частным случаем градиентного спуска для данного случая (см. *[правило Хебба](https://ru.wikipedia.org/wiki/%D0%94%D0%B5%D0%BB%D1%8C%D1%82%D0%B0-%D0%BF%D1%80%D0%B0%D0%B2%D0%B8%D0%BB%D0%BE)*, *[метод коррекции ошибки](https://ru.wikipedia.org/wiki/%D0%9C%D0%B5%D1%82%D0%BE%D0%B4_%D0%BA%D0%BE%D1%80%D1%80%D0%B5%D0%BA%D1%86%D0%B8%D0%B8_%D0%BE%D1%88%D0%B8%D0%B1%D0%BA%D0%B8)*)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Zrm01BR69jAo"
   },
   "source": [
    "Вооружившись всеми формулами, напишем свой класс **`Perceptron()`**. \n",
    "\n",
    "*Примечание*: В коде ниже `y_pred` -- это $\\hat{y}$ из формул выше"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "rLBDN2G89jAo"
   },
   "outputs": [],
   "source": [
    "class Perceptron:\n",
    "    def __init__(self, w=None, b=0):\n",
    "        \"\"\"\n",
    "        :param: w -- вектор весов\n",
    "        :param: b -- смещение\n",
    "        \"\"\"\n",
    "        # Пока что мы не знаем размер матрицы X, а значит не знаем, сколько будет весов\n",
    "        self.w = w\n",
    "        self.b = b\n",
    "        \n",
    "    def activate(self, x):\n",
    "        return np.array(x > 0, dtype=np.int64)\n",
    "        \n",
    "    def forward_pass(self, X):\n",
    "        \"\"\"\n",
    "        Эта функция рассчитывает ответ перцептрона при предъявлении набора объектов\n",
    "        :param: X -- матрица объектов размера (n, m), каждая строка - отдельный объект\n",
    "        :return: вектор размера (n, 1) из нулей и единиц с ответами перцептрона \n",
    "        \"\"\"\n",
    "        n = X.shape[0]\n",
    "        y_pred = np.zeros((n, 1))  # y_pred == y_predicted - предсказанные классы\n",
    "        y_pred = self.activate(X @ self.w.reshape(X.shape[1], 1) + self.b)\n",
    "        return y_pred.reshape(-1, 1)\n",
    "    \n",
    "    def backward_pass(self, X, y, y_pred, learning_rate=0.005):\n",
    "        \"\"\"\n",
    "        Обновляет значения весов перцептрона в соответствии с этим объектом\n",
    "        :param: X -- матрица объектов размера (n, m)\n",
    "                y -- вектор правильных ответов размера (n, 1)\n",
    "                learning_rate - \"скорость обучения\" (символ alpha в формулах выше)\n",
    "        В этом методе ничего возвращать не нужно, только правильно поменять веса\n",
    "        с помощью градиентного спуска.\n",
    "        \"\"\"\n",
    "        n = len(y)\n",
    "        y = np.array(y).reshape(-1, 1)\n",
    "        self.w = self.w - learning_rate * (X.T @ (y_pred - y) / n)\n",
    "        self.b = self.b - learning_rate * np.mean(y_pred - y)\n",
    "    \n",
    "    def fit(self, X, y, num_epochs=300):\n",
    "        \"\"\"\n",
    "        Спускаемся в минимум\n",
    "        :param: X -- матрица объектов размера (n, m)\n",
    "                y -- вектор правильных ответов размера (n, 1)\n",
    "                num_epochs -- количество итераций обучения\n",
    "        :return: losses -- вектор значений функции потерь\n",
    "        \"\"\"\n",
    "        self.w = np.zeros((X.shape[1], 1))  # столбец (m, 1)\n",
    "        self.b = 0  # смещение\n",
    "        losses = []  # значения функции потерь на различных итерациях обновления весов\n",
    "        \n",
    "        for i in range(num_epochs):\n",
    "            # предсказания с текущими весами\n",
    "            y_pred = self.forward_pass(X)\n",
    "            # считаем функцию потерь с текущими весами\n",
    "            losses.append(Loss(y_pred, y))\n",
    "            # обновляем веса в соответсвие с тем, где ошиблись раньше\n",
    "            self.backward_pass(X, y, y_pred)\n",
    "\n",
    "        return losses"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "XlWXLoHQ9jAr"
   },
   "source": [
    "Класс готов. Посмотрим, правильно ли ведёт себя Ваш перцептрон. Далее идут несколько ячеек с тестовым кодом, Вам нужно просто запустить их и проверить, чтобы результаты запуска совпадали с соответствующими числами из таблиц:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "GnrccB6H9jAs"
   },
   "source": [
    "**Проверка forward_pass():**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 551,
     "status": "ok",
     "timestamp": 1539450353886,
     "user": {
      "displayName": "Маза Факер",
      "photoUrl": "",
      "userId": "00428038539447448724"
     },
     "user_tz": -180
    },
    "id": "9LZgkcHv9jAt",
    "outputId": "59bd0c97-a549-476f-9fb1-243b1ae07ef6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y_pred = [[1]\n",
      " [1]\n",
      " [0]]\n"
     ]
    }
   ],
   "source": [
    "w = np.array([1., 2.]).reshape(2, 1)\n",
    "b = 2.\n",
    "X = np.array([[1., 2., -1.], [3., 4., -3.2]])\n",
    "\n",
    "perceptron = Perceptron(w, b)\n",
    "y_pred = perceptron.forward_pass(X.T)\n",
    "print (\"y_pred = \" + str(y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "1rgBqV9D9jAv"
   },
   "source": [
    "**Проверка backward_pass():**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9RkAnK0P9jAw"
   },
   "outputs": [],
   "source": [
    "y = np.array([1, 0, 1]).reshape(3, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 72
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 612,
     "status": "ok",
     "timestamp": 1539450382319,
     "user": {
      "displayName": "Sir. Klop300channel",
      "photoUrl": "",
      "userId": "17461207290813147689"
     },
     "user_tz": -180
    },
    "id": "Be7OJp8c9jA1",
    "outputId": "67b51d16-7a2b-4a75-d649-c25169bfcec9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w = [[0.995]\n",
      " [1.988]]\n",
      "b = 2.0\n"
     ]
    }
   ],
   "source": [
    "perceptron.backward_pass(X.T, y, y_pred)\n",
    "\n",
    "print (\"w = \" + str(perceptron.w))\n",
    "print (\"b = \" + str(perceptron.b))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "EDjsmpZp9jA5"
   },
   "source": [
    "Посмотрим, как меняется функция потерь в течение процесса обучения на реальных данных - датасет \"Яблоки и Груши\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 900
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 598,
     "status": "error",
     "timestamp": 1539450431613,
     "user": {
      "displayName": "Sir. Klop300channel",
      "photoUrl": "",
      "userId": "17461207290813147689"
     },
     "user_tz": -180
    },
    "id": "aPzhL2L99jA5",
    "outputId": "ec67cd75-7d6e-4699-ee31-14c06f54e15b"
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"./data/apples_pears.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 163
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 680,
     "status": "error",
     "timestamp": 1539450471450,
     "user": {
      "displayName": "BrainsExplotion",
      "photoUrl": "",
      "userId": "13698855170200822423"
     },
     "user_tz": -180
    },
    "id": "q7cWGg5S9jA7",
    "outputId": "bb042b57-e5ad-494e-a507-8f74dcb20dd0"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>yellowness</th>\n",
       "      <th>symmetry</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.779427</td>\n",
       "      <td>0.257305</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.777005</td>\n",
       "      <td>0.015915</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.977092</td>\n",
       "      <td>0.304210</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.043032</td>\n",
       "      <td>0.140899</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.760433</td>\n",
       "      <td>0.193123</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   yellowness  symmetry  target\n",
       "0    0.779427  0.257305     1.0\n",
       "1    0.777005  0.015915     1.0\n",
       "2    0.977092  0.304210     1.0\n",
       "3    0.043032  0.140899     0.0\n",
       "4    0.760433  0.193123     1.0"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 231
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1184,
     "status": "error",
     "timestamp": 1539448612121,
     "user": {
      "displayName": "Кирилл Куликов",
      "photoUrl": "",
      "userId": "13750676750728975449"
     },
     "user_tz": -180
    },
    "id": "V6T8WK2w9jA-",
    "outputId": "6f5f394b-8d31-43c9-f659-2ed7e9a96c34"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 8))\n",
    "plt.scatter(data.iloc[:, 0], data.iloc[:, 1], c=data['target'], cmap='rainbow')\n",
    "plt.title('Яблоки и груши', fontsize=15)\n",
    "plt.xlabel('симметричность', fontsize=14)\n",
    "plt.ylabel('желтизна', fontsize=14)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "JYSpUvQM9jBE"
   },
   "source": [
    "**Вопрос:** Какой класс соответствует яблокам (какого они цвета на графике)?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "X6m0IAdu9jBF"
   },
   "source": [
    "Обозначим, что здесь признаки, а что - классы:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ARYN13Io9jBG"
   },
   "outputs": [],
   "source": [
    "X = data.iloc[:,:2].values  # матрица объекты-признаки\n",
    "y = data['target'].values.reshape((-1, 1))  # классы (столбец из нулей и единиц)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "MRCQeKtH9jBI"
   },
   "source": [
    "**Вывод функции потерь**  \n",
    "Функция потерь должна убывать и в итоге стать близкой к 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "sIR0g6mQ9jBJ"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 2.15 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "perceptron = Perceptron()\n",
    "losses = perceptron.fit(X, y)\n",
    "\n",
    "plt.figure(figsize=(10, 8))\n",
    "plt.plot(losses)\n",
    "plt.title('Функция потерь', fontsize=15)\n",
    "plt.xlabel('номер итерации', fontsize=14)\n",
    "plt.ylabel('$Loss(\\hat{y}, y)$', fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "gnzSyV_j9jBN"
   },
   "source": [
    "Посмотрим, как перцептрон классифицировал объекты из выборки:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "bNhsJbuY9jBO"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 8))\n",
    "plt.scatter(data.iloc[:, 0], data.iloc[:, 1], c=perceptron.forward_pass(X).ravel(), cmap='spring')\n",
    "plt.title('Яблоки и груши', fontsize=15)\n",
    "plt.xlabel('симметричность', fontsize=14)\n",
    "plt.ylabel('желтизна', fontsize=14)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Справился идеально. Однако просьба обратить внимание, что это очень простая, *линейно разделимая*, выборка."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "aE8iMuT39jBQ"
   },
   "source": [
    "<h3 style=\"text-align: center;\"><b>Предсказание пола по голосу</b></h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "hQHD1i1W9jBR"
   },
   "source": [
    "Сравнить качество работы нашего перцептрона и алгоритма из библиотеки `sklearn` на датасете с сайта [Kaggle](https://www.kaggle.com) - [Gender Recognition by Voice](https://www.kaggle.com/primaryobjects/voicegender). В данном датасете в качестве признаков выступают различные звуковые характеристики голоса, а в качестве классов - пол (мужчина/женщина). Подробнее о самих признаках можно почитать [на странице датасета](https://www.kaggle.com/primaryobjects/voicegender) (на английском). Нашей целью пока что является просто протестировать на этих данных два алгоритма."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "YaLaxBHR9jBS"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.linear_model import Perceptron as skPerceptron\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 936
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 625,
     "status": "error",
     "timestamp": 1539451209225,
     "user": {
      "displayName": "Sir. Klop300channel",
      "photoUrl": "",
      "userId": "17461207290813147689"
     },
     "user_tz": -180
    },
    "id": "IaNjHU7Q9jBU",
    "outputId": "ff3a76eb-da48-464e-95bd-d343fb260d16"
   },
   "outputs": [],
   "source": [
    "data_path = './data/voice.csv'\n",
    "data = pd.read_csv(data_path)\n",
    "data['label'] = data['label'].apply(lambda x: 1 if x == 'male' else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "eU1EZFzM9jBW"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>meanfreq</th>\n",
       "      <th>sd</th>\n",
       "      <th>median</th>\n",
       "      <th>Q25</th>\n",
       "      <th>Q75</th>\n",
       "      <th>IQR</th>\n",
       "      <th>skew</th>\n",
       "      <th>kurt</th>\n",
       "      <th>sp.ent</th>\n",
       "      <th>sfm</th>\n",
       "      <th>...</th>\n",
       "      <th>centroid</th>\n",
       "      <th>meanfun</th>\n",
       "      <th>minfun</th>\n",
       "      <th>maxfun</th>\n",
       "      <th>meandom</th>\n",
       "      <th>mindom</th>\n",
       "      <th>maxdom</th>\n",
       "      <th>dfrange</th>\n",
       "      <th>modindx</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.059781</td>\n",
       "      <td>0.064241</td>\n",
       "      <td>0.032027</td>\n",
       "      <td>0.015071</td>\n",
       "      <td>0.090193</td>\n",
       "      <td>0.075122</td>\n",
       "      <td>12.863462</td>\n",
       "      <td>274.402906</td>\n",
       "      <td>0.893369</td>\n",
       "      <td>0.491918</td>\n",
       "      <td>...</td>\n",
       "      <td>0.059781</td>\n",
       "      <td>0.084279</td>\n",
       "      <td>0.015702</td>\n",
       "      <td>0.275862</td>\n",
       "      <td>0.007812</td>\n",
       "      <td>0.007812</td>\n",
       "      <td>0.007812</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.066009</td>\n",
       "      <td>0.067310</td>\n",
       "      <td>0.040229</td>\n",
       "      <td>0.019414</td>\n",
       "      <td>0.092666</td>\n",
       "      <td>0.073252</td>\n",
       "      <td>22.423285</td>\n",
       "      <td>634.613855</td>\n",
       "      <td>0.892193</td>\n",
       "      <td>0.513724</td>\n",
       "      <td>...</td>\n",
       "      <td>0.066009</td>\n",
       "      <td>0.107937</td>\n",
       "      <td>0.015826</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.009014</td>\n",
       "      <td>0.007812</td>\n",
       "      <td>0.054688</td>\n",
       "      <td>0.046875</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.077316</td>\n",
       "      <td>0.083829</td>\n",
       "      <td>0.036718</td>\n",
       "      <td>0.008701</td>\n",
       "      <td>0.131908</td>\n",
       "      <td>0.123207</td>\n",
       "      <td>30.757155</td>\n",
       "      <td>1024.927705</td>\n",
       "      <td>0.846389</td>\n",
       "      <td>0.478905</td>\n",
       "      <td>...</td>\n",
       "      <td>0.077316</td>\n",
       "      <td>0.098706</td>\n",
       "      <td>0.015656</td>\n",
       "      <td>0.271186</td>\n",
       "      <td>0.007990</td>\n",
       "      <td>0.007812</td>\n",
       "      <td>0.015625</td>\n",
       "      <td>0.007812</td>\n",
       "      <td>0.046512</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.151228</td>\n",
       "      <td>0.072111</td>\n",
       "      <td>0.158011</td>\n",
       "      <td>0.096582</td>\n",
       "      <td>0.207955</td>\n",
       "      <td>0.111374</td>\n",
       "      <td>1.232831</td>\n",
       "      <td>4.177296</td>\n",
       "      <td>0.963322</td>\n",
       "      <td>0.727232</td>\n",
       "      <td>...</td>\n",
       "      <td>0.151228</td>\n",
       "      <td>0.088965</td>\n",
       "      <td>0.017798</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.201497</td>\n",
       "      <td>0.007812</td>\n",
       "      <td>0.562500</td>\n",
       "      <td>0.554688</td>\n",
       "      <td>0.247119</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.135120</td>\n",
       "      <td>0.079146</td>\n",
       "      <td>0.124656</td>\n",
       "      <td>0.078720</td>\n",
       "      <td>0.206045</td>\n",
       "      <td>0.127325</td>\n",
       "      <td>1.101174</td>\n",
       "      <td>4.333713</td>\n",
       "      <td>0.971955</td>\n",
       "      <td>0.783568</td>\n",
       "      <td>...</td>\n",
       "      <td>0.135120</td>\n",
       "      <td>0.106398</td>\n",
       "      <td>0.016931</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>0.712812</td>\n",
       "      <td>0.007812</td>\n",
       "      <td>5.484375</td>\n",
       "      <td>5.476562</td>\n",
       "      <td>0.208274</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   meanfreq        sd    median       Q25       Q75       IQR       skew  \\\n",
       "0  0.059781  0.064241  0.032027  0.015071  0.090193  0.075122  12.863462   \n",
       "1  0.066009  0.067310  0.040229  0.019414  0.092666  0.073252  22.423285   \n",
       "2  0.077316  0.083829  0.036718  0.008701  0.131908  0.123207  30.757155   \n",
       "3  0.151228  0.072111  0.158011  0.096582  0.207955  0.111374   1.232831   \n",
       "4  0.135120  0.079146  0.124656  0.078720  0.206045  0.127325   1.101174   \n",
       "\n",
       "          kurt    sp.ent       sfm  ...    centroid   meanfun    minfun  \\\n",
       "0   274.402906  0.893369  0.491918  ...    0.059781  0.084279  0.015702   \n",
       "1   634.613855  0.892193  0.513724  ...    0.066009  0.107937  0.015826   \n",
       "2  1024.927705  0.846389  0.478905  ...    0.077316  0.098706  0.015656   \n",
       "3     4.177296  0.963322  0.727232  ...    0.151228  0.088965  0.017798   \n",
       "4     4.333713  0.971955  0.783568  ...    0.135120  0.106398  0.016931   \n",
       "\n",
       "     maxfun   meandom    mindom    maxdom   dfrange   modindx  label  \n",
       "0  0.275862  0.007812  0.007812  0.007812  0.000000  0.000000      1  \n",
       "1  0.250000  0.009014  0.007812  0.054688  0.046875  0.052632      1  \n",
       "2  0.271186  0.007990  0.007812  0.015625  0.007812  0.046512      1  \n",
       "3  0.250000  0.201497  0.007812  0.562500  0.554688  0.247119      1  \n",
       "4  0.266667  0.712812  0.007812  5.484375  5.476562  0.208274      1  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "QCSK3sfX9jBY"
   },
   "outputs": [],
   "source": [
    "# Чтобы перемешать данные. Изначально там сначала идут все мужчины, потом все женщины\n",
    "data = data.sample(frac=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "VKY1jHT79jBZ"
   },
   "outputs": [],
   "source": [
    "X_train = data.iloc[:int(len(data)*0.7), :-1]  # матрица объекты-признаки\n",
    "y_train = data.iloc[:int(len(data)*0.7), -1]  # истинные значения пола (мужчина/женщина)\n",
    "\n",
    "X_test = data.iloc[int(len(data)*0.7):, :-1]  # матрица объекты-признаки\n",
    "y_test = data.iloc[int(len(data)*0.7):, -1]  # истинные значения пола (мужчина/женщина)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "DDsWavYZ9jBe"
   },
   "source": [
    "Натренируем наш перцептрон и перцептрон из `sklearn` на этих данных:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4Z8Rh-bu9jBf"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\izakharkin\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.perceptron.Perceptron'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Perceptron(alpha=0.0001, class_weight=None, eta0=1.0, fit_intercept=True,\n",
       "      max_iter=None, n_iter=None, n_jobs=1, penalty=None, random_state=42,\n",
       "      shuffle=True, tol=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RANDOM_SEED = 42\n",
    "\n",
    "perceptron = Perceptron()\n",
    "perceptron.fit(X_train.values, y_train.values)\n",
    "\n",
    "sk_perceptron = skPerceptron(random_state=RANDOM_SEED)\n",
    "sk_perceptron.fit(X_train.values, y_train.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3qsolz149jBh"
   },
   "source": [
    "Сравним доли правильных ответов (на тестовых данных):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 129
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 703,
     "status": "error",
     "timestamp": 1539448636600,
     "user": {
      "displayName": "Кирилл Куликов",
      "photoUrl": "",
      "userId": "13750676750728975449"
     },
     "user_tz": -180
    },
    "id": "l6R3cXLO9jBi",
    "outputId": "cbb39ebe-965e-407e-ff55-6e5bf388667e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Точность (доля правильных ответов, из 100%) нашего перцептрона: 48.791 %\n",
      "Точность (доля правильных ответов) перцептрона из sklearn: 51.209 %\n"
     ]
    }
   ],
   "source": [
    "print('Точность (доля правильных ответов, из 100%) нашего перцептрона: {:.3f} %'.format(\n",
    "    accuracy_score(y_test.values, perceptron.forward_pass(X_test)) * 100))\n",
    "print('Точность (доля правильных ответов) перцептрона из sklearn: {:.3f} %'.format(\n",
    "    accuracy_score(y_test.values, sk_perceptron.predict(X_test)) * 100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Попробуем поставить число итераций побольше:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Perceptron(alpha=0.0001, class_weight=None, eta0=1.0, fit_intercept=True,\n",
       "      max_iter=5000, n_iter=None, n_jobs=1, penalty=None, random_state=42,\n",
       "      shuffle=True, tol=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RANDOM_SEED = 42\n",
    "\n",
    "perceptron = Perceptron()\n",
    "perceptron.fit(X_train.values, y_train.values, num_epochs=5000)\n",
    "\n",
    "sk_perceptron = skPerceptron(random_state=RANDOM_SEED, max_iter=5000)\n",
    "sk_perceptron.fit(X_train.values, y_train.values, )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Точность (доля правильных ответов, из 100%) нашего перцептрона: 53.733 %\n",
      "Точность (доля правильных ответов) перцептрона из sklearn: 73.712 %\n"
     ]
    }
   ],
   "source": [
    "print('Точность (доля правильных ответов, из 100%) нашего перцептрона: {:.3f} %'.format(\n",
    "    accuracy_score(y_test.values, perceptron.forward_pass(X_test)) * 100))\n",
    "print('Точность (доля правильных ответов) перцептрона из sklearn: {:.3f} %'.format(\n",
    "    accuracy_score(y_test.values, sk_perceptron.predict(X_test)) * 100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "CSasTfW09jBj"
   },
   "source": [
    "**Вопрос:** Хорошее ли качество показывает перцептрон? Как Вы думаете, почему?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "yyX2G7VvdvMC"
   },
   "source": [
    "### Важно"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "sTZFLUqFdv57"
   },
   "source": [
    "Стоит понимать, что перцептрон сам по себе не используется в приложениях. Мы продемонстрровали его вам, чтобы вы знали, с чего всё начиналось. На самом деле это просто один нейрон с пороговой функцией активации, который не используется в многослойных нейросетях и каких-либо прикладных задачах, но всё же является хорошим учебным примером, помогающим понять то, как обновляются веса в соответствие с ошибками и перейти к рассмотрению более полезных моделей (нейронов с другими функциями активации)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "MC0FNrfq9jBl"
   },
   "source": [
    "<h2 style=\"text-align: center;\"><b>Полезные ссылки</b></h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "AZLMhq149jBl"
   },
   "source": [
    "1). Lecture Notes Стэнфордского университета: http://cs231n.github.io/neural-networks-1/  \n",
    "2). [Википедия про перцептрон](https://ru.wikipedia.org/wiki/%D0%9F%D0%B5%D1%80%D1%86%D0%B5%D0%BF%D1%82%D1%80%D0%BE%D0%BD)"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "[seminar]perceptron.ipynb",
   "provenance": [],
   "version": "0.3.2"
  },
  "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}