Logistic Regression and random forest.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Майнор Интеллектуальный анализ данных / Введение в анализ данных\n",
    "\n",
    "## Домашнее задание 3. Логистическая регрессия и случайный лес.\n",
    "\n",
    "В этом задании вам предстоит:\n",
    "- поработать с логистической регрессией (полная реализация не потребуется, однако можно заработать бонусные баллы)\n",
    "- воспроизвести подход случайного леса через одно решающее дерево, поэкспериментировать со случайным лесом.\n",
    "\n",
    "На все вопросы требуется отвечать развёрнуто, аппелируя к полученным значениям или графикам, ответы вписывать в отдельную ячейку, выбрав для неё тип \"Markdown\". От полноты и качества ответов будет во многом зависеть ваша итоговая оценка.\n",
    "\n",
    "Задание выполняется самостоятельно, плагиат будет стандартно наказываться лишением всех баллов за задание.\n",
    "- Максимальная оценка за задание: 10 баллов.\n",
    "- Дата выдачи: 28.04.2018\n",
    "- Срок сдачи: 23:59 18.05.2018"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "% matplotlib inline\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Часть 0. Подготовка данных."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Для наших экспериентов возьмём обучающую выборку [отсюда](https://www.kaggle.com/iabhishekofficial/mobile-price-classification)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv('train.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Решается задача многоклассовой классификации — определение ценовой категории телефона. Для простоты перейдём к задаче бинарной классификации — пусть исходные классы 0 и 1 соответствуют классу 0 новой целевой переменной, а остальные классу 1.\n",
    "\n",
    "Замените целевую переменную, отделите её в отдельную переменную и удалите из исходной выборки."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = train.price_range.replace([0, 1, 2, 3], [0, 0, 1, 1])\n",
    "X = train.iloc[:, :-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Разделите выборку на обучающую и тестовую части в соотношении 7 к 3. Для этого можно использовать `train_test_split` из scikit-learn. Не забудьте зафиксировать сид для разбиения."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Часть 1. Логистическая регрессия.\n",
    "\n",
    "[4 балла]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В этой части вы будете обучать самый простой бинарный классификатор — логистическую регрессию. Будем использовать готовую реализацию `LogisticRegression` из scikit-learn.\n",
    "\n",
    "Логистическая регрессия — линейный метод, то есть в нём предсказание алгоритма вычислаяется как скалярное произведение признаков и весов алгоритма: \n",
    "\n",
    "$$\n",
    "b(x) = w_0 + \\langle w, x \\rangle = w_0 + \\sum_{i=1}^{d} w_i x_i\n",
    "$$\n",
    "\n",
    "Для вычисления вероятности положительного класса применяется сигмода. В результате предсказание вероятности принадлежности объекта к положительному классу можно записать как: \n",
    "\n",
    "$$\n",
    "P(y = +1 | x) = \\frac{1}{1 + \\exp(- w_0 - \\langle w, x \\rangle )}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Не забывайте, что для линейных методов матрицу объекты-признаки необходимо предварительно нормировать (то есть привести каждый признак к одному и тому же масштабу одним из способов). Для этого можно воспользоваться `StandardScaler` или сделать это вручную."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.fit_transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Обучите логистическую регрессию. Сделайте предсказания для тестовой части, посчитайте по ним ROC-AUC и Accuracy (порог 0.5). Хорошо ли удаётся предсказывать целевую переменную?\n",
    "\n",
    "Не забывайте, что метод `predict_proba` вычисляет вероятности обоих классов выборки, а в бинарной классификации нас интересует в первую очередь вероятность принадлежности к положительному классу."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy score: 0.9833333333333333\n",
      "roc-auc score : 0.9997333066639997\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, roc_auc_score\n",
    "\n",
    "lr = LogisticRegression()\n",
    "lr.fit(X_train, y_train)\n",
    "y_score = lr.predict_proba(np.array(X_test))\n",
    "\n",
    "y_pred = 1*(y_score[:, 1] > 0.5)\n",
    "\n",
    "print(\"accuracy score: {0}\".format(accuracy_score(y_test, y_pred)))\n",
    "print(\"roc-auc score : {0}\".format(roc_auc_score(y_test, y_score[:, 1])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " - <i>Целевую переменную хорошо удаётся предсказать, так как значения acuuracy и roc-auc очень близки к 1</i>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "У обученной логистической регрессии есть два аттрибута: `coef_` и `intercept_`, которые соответствуют весам $w$ и $w_0$. Это и есть результат обучения логистической регрессии. Попробуйте с помощью них (с помощью всё той же обученной ранее логистической регрессии) посчитать \"сырое\" предсказание алгоритма $b(x)$. \n",
    "\n",
    "Постройте гистограмму полученных значений и ответьте на вопросы:\n",
    "- Какие значения принимает такое предсказание?\n",
    "- Похожи ли эти значения на вероятность классов?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Количество')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdc5549d860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "raw_y = []\n",
    "for x in X_test:\n",
    "    raw_y.append(np.dot(lr.coef_, x)[0] + lr.intercept_[0])\n",
    "\n",
    "plt.hist(raw_y)\n",
    "plt.title(\"Значения сырого предсказания\")\n",
    "plt.xlabel(\"Значение\")\n",
    "plt.ylabel(\"Количество\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Как видно из гистрограммы, сырое предсказание принимает значение от -20 до 20, что не похоже на вероятность классов по принимаемым значениям (вероятность классов лежит в отрезке $[0, 1]$)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Реализуйте сигмоиду и постройте её график. Что вы можете сказать об этой функции?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'y')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdc5539f898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import math\n",
    "\n",
    "def sigmoid(x):\n",
    "    return 1/(1 + math.exp(-x))\n",
    "\n",
    "x = np.linspace(-6, 6)\n",
    "y = [sigmoid(cur_x) for cur_x in x ]\n",
    "plt.plot(x, y)\n",
    "plt.title('Сигмоида')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Сигмо́ида — это гладкая монотонная нелинейная функция, имеющая форму буквы \"S\", которая часто применяется для «сглаживания» значений некоторой величины. Возрастающая функция.\n",
    "#### Применение в логистических функциях\n",
    "Логистическая функция $f(x) = \\frac{1}{1 + exp(-x)}$ используется в логистической регрессии следующим образом. В ней решается задача классификации с двумя классами ($y=0$ и $y=1$, где $y$ - переменная, указывающая класс объекта) и делается предположение о том, что вероятность принадлежности объекта к одному из классов выражается через значения приззнаков этого объекта $x_1, x_2, x_3, ..., x_n$ (действительные числа). Уравнение сигмоиды - непрерывная версия логистического отображения. Можно легко найти решение этого уравнения и получить две наиболее распространенных формы записи логистической зависимости после интегрирования, так как функция гладкая, имеет производные на всей области определения. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Примените реализованную сигмоиду к $b(x)$. Вы должны получить вероятности принадлежности к положительному классу. Проверьте, что ваши значения совпали с теми, которые получены с помощью `predict_proba`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Значения совпадают до 14 знака\n"
     ]
    }
   ],
   "source": [
    "acc = 1\n",
    "values_match = [round(sigmoid(x), acc) for x in raw_y] == [round(x, acc) for x in y_score[:, 1]]\n",
    "while values_match:\n",
    "    acc += 1\n",
    "    values_match = [round(sigmoid(x), acc) for x in raw_y] == [round(x, acc) for x in y_score[:, 1]]\n",
    "print(\"Значения совпадают до {0} знака\".format(acc))    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Таким образом, обучение логистической регрессии — настройка параметров $w$ и $w_0$, а применение — подсчёт вероятностей принадлежности положительному классу как применение сигмоды к скалярному произведению признаков и параметров.\n",
    "\n",
    "Постройте для обученной логистической регрессии ROC-кривую `roc_curve` и PR-кривую `precision_recall_curve`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'Recall')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdc553bd2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, precision_recall_curve\n",
    "\n",
    "fpr, tpr, _ = roc_curve(y_test, y_score[:, 1])\n",
    "p, r, _ = precision_recall_curve(y_test, y_score[:, 1])\n",
    "\n",
    "f, (ax1, ax2) = plt.subplots(1, 2, figsize=(15,5))\n",
    "\n",
    "ax1.plot(fpr, tpr)\n",
    "ax1.set_title(\"Precision recall curve\")\n",
    "ax1.set_xlabel(\"False positive rate\")\n",
    "ax1.set_ylabel(\"True positive rate\")\n",
    "\n",
    "ax2.plot(r, p)\n",
    "ax2.set_title(\"Precision recall curve\")\n",
    "ax2.set_ylabel(\"Precision\")\n",
    "ax2.set_xlabel(\"Recall\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Бонусное задание. Обучение логистической регрессии.\n",
    "\n",
    "[2 бонусных балла]\n",
    "\n",
    "Если выше вручную мы только применяли логистическую регрессию, то здесь предлагается реализовать обучение с помощью полного градиентного спуска.\n",
    "\n",
    "Если кратко, то обучение логистической регрессии с $L_2$-регуляризацией можно записать следующим образом:\n",
    "\n",
    "$$\n",
    "Q(w, X) = \\frac{1}{l} \\sum_{i=1}^{l} \\log (1 + \\exp(- y_i \\langle w, x_i \\rangle )) + \\frac{\\lambda_2}{2} \\lVert w \\rVert _2^2 \\to \\min_w\n",
    "$$\n",
    "\n",
    "Считаем, что $y_i \\in \\{-1, +1\\}$, а нулевым признаком сделан единичный (то есть $w_0$ соответствует свободному члену). Искать $w$ будем с помощью градиентного спуска:\n",
    "\n",
    "$$\n",
    "w^{(k+1)} = w^{(k)} - \\alpha \\nabla_w Q(w, X)\n",
    "$$\n",
    "\n",
    "В случае полного градиентного спуска $\\nabla_w Q(w, X)$ считается напрямую (как есть, то есть, используя все объекты выборки). Длину шага $\\alpha > 0$ в рамках данного задания предлагается брать равной некоторой малой константе.\n",
    "\n",
    "Градиент по объекту $x_i$ считается по следующей формуле:\n",
    "\n",
    "$$\n",
    "\\nabla_w Q(w, x_i) = - \\frac{y_i x_i}{1 + \\exp(y_i \\langle w, x_i \\rangle)} + \\lambda_2 w\n",
    "$$\n",
    "\n",
    "На самом деле неправильно регуляризировать свободный член $w_0$ (то есть при добавлении градиента для $w_0$ не надо учитывать слагаемое с $\\lambda_2$). Но в рамках этого задания мы не обращаем на это внимания и работаем со всеми вектором весов одинаково. \n",
    "\n",
    "В качестве критерия останова необходимо использовать (одновременно):\n",
    "- проверку на евклидовую норму разности весов на двух соседних итерациях (например, меньше некоторого малого числа порядка $10^{-6}$) — параметр tolerance\n",
    "- достижение максимального числа итераций (например, 10000) — параметр max\\_iter.\n",
    "\n",
    "Инициализировать веса можно случайным образом или нулевым вектором.\n",
    "\n",
    "Реализуйте обучение логистической регрессии. Для удобства ниже предоставлен прототип с необходимыми методами. В `loss_history` необходимо сохранять вычисленное на каждой итерации значение функции потерь. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator\n",
    "\n",
    "class LogReg(BaseEstimator):\n",
    "    def __init__(self, lambda_2=1.0, tolerance=1e-4, max_iter=1000, alpha=1e-3):\n",
    "        \"\"\"\n",
    "        lambda_2: L2 regularization param\n",
    "        tolerance: for stopping gradient descent\n",
    "        max_iter: maximum number of steps in gradient descent\n",
    "        alpha: learning rate\n",
    "        \"\"\"\n",
    "        self.lambda_2 = lambda_2\n",
    "        self.tolerance = tolerance\n",
    "        self.max_iter = max_iter\n",
    "        self.alpha = alpha\n",
    "        self.w = None\n",
    "        self.loss_history = None\n",
    "    \n",
    "    def fit(self, X, y):\n",
    "        \"\"\"\n",
    "        X: np.array of shape (l, d)\n",
    "        y: np.array of shape (l)\n",
    "        ---\n",
    "        output: self\n",
    "        \"\"\"\n",
    "        self.loss_history = []\n",
    "        \n",
    "        pass\n",
    "        \n",
    "        return self\n",
    "    \n",
    "    def predict_proba(self, X):\n",
    "        \"\"\"\n",
    "        X: np.array of shape (l, d)\n",
    "        ---\n",
    "        output: np.array of shape (l, 2) where\n",
    "        first column has probabilities of -1\n",
    "        second column has probabilities of +1\n",
    "        \"\"\"\n",
    "        if self.w is None:\n",
    "            raise Exception('Not trained yet')\n",
    "        \n",
    "        pass\n",
    "    \n",
    "    def calc_gradient(self, X, y):\n",
    "        \"\"\"\n",
    "        X: np.array of shape (l, d) (l can be equal to 1 if stochastic)\n",
    "        y: np.array of shape (l)\n",
    "        ---\n",
    "        output: np.array of shape (d)\n",
    "        \"\"\"\n",
    "        pass\n",
    "\n",
    "    def calc_loss(self, X, y):\n",
    "        \"\"\"\n",
    "        X: np.array of shape (l, d)\n",
    "        y: np.array of shape (l)\n",
    "        ---\n",
    "        output: float \n",
    "        \"\"\" \n",
    "        pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Примените логистическую регресиию на той же выборке.\n",
    "2. Посчитайте качество по тем же метрикам.\n",
    "3. Визуализируйте изменение значений функции потерь от номера итераций."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "### ╰( ͡° ͜ʖ ͡° )つ──☆*:・ﾟ"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Часть 2. Случайные леса.\n",
    "\n",
    "[6 баллов]\n",
    "\n",
    "Случайный лес — алгоритм машинного обучения, представляющий собой бэггинг над решающими деревьями (усреднение ответов множества слабых алгоритмов) с 2 основными идеями:\n",
    "- Использование подмножества признаков при построении каждого сплита дерева.\n",
    "- Бутстрап обучающей выборки для построения каждого дерева (с повторениями).\n",
    "\n",
    "В этом задании мы попробуем оценить пользу каждой из идей. Будем использовать ту же выборку с тем же разбиением на 2 части. Для начала обучите решающее дерево `DecisionTreeClassifier` из scikit-learn и посчитайте ROC-AUC и Accuracy (порог 0.5). Не забудьте зафикиксировать сид для построения дерева (несмотря на то, что в классической реализации никакой случайности нет, при большой глубине дерева может возникать неоднозначность в выборке признака в сплите). Используйте этот сид для всех заданий ниже."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy score: 0.935\n",
      "roc-auc score : 0.934910157682435\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "clf = DecisionTreeClassifier(random_state=42)\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "y_tr_score = clf.predict_proba(X_test)[:, 1]\n",
    "y_tr_pred = 1 * (y_tr_score > 0.5)\n",
    "\n",
    "\n",
    "print(\"accuracy score: {0}\".format(accuracy_score(y_test, y_tr_pred)))\n",
    "print(\"roc-auc score : {0}\".format(roc_auc_score(y_test, y_tr_score)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 Бэггинг классификаторов\n",
    "\n",
    "Реализуйте бэггинг над решающими деревьями (усреднение предсказанных вероятностей всего ансамбля). \n",
    "В качестве основы используйте всё тот же `DecisionTreeClassifier`. Количество базовых алгоритмов предлагается брать равным 100. \n",
    "\n",
    "Посчитайте качество с помощью тех же метрик. Ответьте на следующие вопросы:\n",
    "- Что интересного вы видите?\n",
    "- С чем это связано?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy score: 0.9633333333333334\n",
      "roc-auc score : 0.9924492449244925\n"
     ]
    }
   ],
   "source": [
    "forest_pred = []\n",
    "subsample_size = 800\n",
    "for i in range(100):\n",
    "    seq = np.random.permutation(X_train.shape[0])[:subsample_size]\n",
    "    tree = DecisionTreeClassifier(random_state=42)\n",
    "    tree.fit(np.array(X_train)[seq], np.array(y_train)[seq])\n",
    "    forest_pred.append(tree.predict_proba(X_test)[:, 1])\n",
    "    \n",
    "ans = np.mean(np.array(forest_pred), 0)\n",
    "ans_pred = 1*(ans > 0.5)\n",
    "print(\"accuracy score: {0}\".format(accuracy_score(y_test, ans_pred)))\n",
    "print(\"roc-auc score : {0}\".format(roc_auc_score(y_test, ans)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " - Качество алгоритма возрасло (параметры accuracy и roc-auc стали больше, чем при обучении одного дерева)\n",
    " - Это связано с тем, что процедура бэггинг показывает высокий прирост обобщающей способности по сравнению с алгоритмом, обученным с помощью базового метода по исходной обучающей выборке, в тех случаях, когда вариационная составляющая ошибки базового метода высока. К таким моделям относятся в частности решающие деревья и нейросетевые методы. При использовании в качестве базового метода решающих деревьев процедура бэггинг приводит к построению ансамблей решающих деревьев (решающих лесов)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Сэмплирование обучающей выборки"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Добавим к нашему бэггингу бутстрап выборки (генерация случайной выборки того же размера с возвращением). Для этого может пригодиться `numpy.random.randint`.\n",
    "\n",
    "Посчитайте качество. Что вы видите?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy score: 0.96\n",
      "roc-auc score : 0.9929937438188263\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(123)  # для одинакового бутстрапа в каждом запуске\n",
    "\n",
    "forest_pred = []\n",
    "subsample_size = X_train.shape[0]\n",
    "for i in range(100):\n",
    "    seq = numpy.random.randint(0, X_train.shape[0], X_train.shape[0])[:subsample_size]\n",
    "    tree = DecisionTreeClassifier(random_state=42)\n",
    "    tree.fit(np.array(X_train)[seq], np.array(y_train)[seq])\n",
    "    forest_pred.append(tree.predict_proba(X_test)[:, 1])\n",
    "    \n",
    "ans = np.mean(np.array(forest_pred), 0)\n",
    "ans_pred = 1*(ans > 0.5)\n",
    "print(\"accuracy score: {0}\".format(accuracy_score(y_test, ans_pred)))\n",
    "print(\"roc-auc score : {0}\".format(roc_auc_score(y_test, ans)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Качество алгоритма возрасло по метрике roc-auc и упало по accuracy в сравнении с бэггингом классификаторов без семплирования обучающей выборки "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 Выбор случайного подмножества признаков"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Временно забудем о бутстрапе выборки и добавим выбор случайного подмножества признаков при построении каждого сплита. В `DecisionTreeClassifier` за это отвечает параметр `max_features`. По умолчанию он имеет значение `None`, что обозначает использование всех возможных признаков. Для задачи классификации рекоменуется использовать квадратный корень от количества признаков. Попробуйте выставить такое значение. На этот раз надо отключить фиксированный сид в построении дерева, так как иначе каждый раз мы будем выбирать одинаковые подмножества признаков. \n",
    "\n",
    "Посчитайте качество. Что вы видите?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy score: 0.955\n",
      "roc-auc score : 0.9918769654743252\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "np.random.seed(123)  # для воспроизводимости построения случайных подмножеств признаков\n",
    "\n",
    "forest_pred = []\n",
    "max_feat = int(math.sqrt(X_train.shape[1]))\n",
    "\n",
    "subsample_size = 800\n",
    "for i in range(100):\n",
    "    tree = DecisionTreeClassifier(max_features=max_feat)\n",
    "    tree.fit(np.array(X_train), np.array(y_train))\n",
    "    forest_pred.append(tree.predict_proba(X_test)[:, 1])\n",
    "    \n",
    "ans = np.mean(np.array(forest_pred), 0)\n",
    "ans_pred = 1*(ans > 0.5)\n",
    "print(\"accuracy score: {0}\".format(accuracy_score(y_test, ans_pred)))\n",
    "print(\"roc-auc score : {0}\".format(roc_auc_score(y_test, ans)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " - Качество алгоритма выше, чем у одного обученного дерева, но ниже, чем при бэггинге классификатора по всем измеряемым метрикам (accuracy и roc-auc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4 = 2.2 + 2.3\n",
    "\n",
    "Объединим два подхода (бутстрап + выбор подмножества признаков). Получим случайный лес.\n",
    "\n",
    "Посчитайте качество. Что вы видите?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy score: 0.965\n",
      "roc-auc score : 0.9935437988243269\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "np.random.seed(123)  # для одинакового бутстрапа и выбора подмножеств признаков в каждом запуске\n",
    "\n",
    "forest_pred = []\n",
    "max_feat = int(math.sqrt(X_train.shape[1]))\n",
    "\n",
    "subsample_size = 800\n",
    "for i in range(100):\n",
    "    seq = np.random.permutation(X_train.shape[0])[:subsample_size]\n",
    "    tree = DecisionTreeClassifier(max_features=max_feat)\n",
    "    tree.fit(np.array(X_train)[seq], np.array(y_train)[seq])\n",
    "    forest_pred.append(tree.predict_proba(X_test)[:, 1])\n",
    "    \n",
    "ans = np.mean(np.array(forest_pred), 0)\n",
    "ans_pred = 1*(ans > 0.5)\n",
    "print(\"accuracy score: {0}\".format(accuracy_score(y_test, ans_pred)))\n",
    "print(\"roc-auc score : {0}\".format(roc_auc_score(y_test, ans)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " - Модель обладает самым высоким accuracy и roc-auc. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "То, что мы сделали уже реализовано в `RandomForestClassifier`. Попробуйте воспользоваться им. Количество используемых деревьев передаётся в параметре `n_estimators`.\n",
    "\n",
    "Посчитайте качество. Что вы видите?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy score: 0.9616666666666667\n",
      "roc-auc score : 0.993727150492827\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "np.random.seed(123)  # для одинакового бутстрапа и выбора подмножеств признаков в каждом запуске\n",
    "\n",
    "\n",
    "rfc = RandomForestClassifier(n_estimators=100, max_features=4)\n",
    "rfc.fit(X_train, y_train)\n",
    "\n",
    "rfc_score = rfc.predict_proba(X_test)[:, 1]\n",
    "rfc_pred = 1*(rfc_score > 0.5)\n",
    "\n",
    "\n",
    "print(\"accuracy score: {0}\".format(accuracy_score(y_test, rfc_pred)))\n",
    "print(\"roc-auc score : {0}\".format(roc_auc_score(y_test, rfc_score)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " - Метрики качества немного отличаются, связано с тем, что модель из sklearn устроена сложнее, чем созданная нами"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.5 Влияние количества используемых деревьев в случайном лесе\n",
    "\n",
    "Один из параметров случайного леса — количество деревьев, используемых в бэггинге. Оценим, как влияет этот параметр на финальное качество. Для этого обучите случайные леса с разным количество деревьев (например, перебирайте от 10 до 1000 с шагом в 10), оцените качество с помощью ROC-AUC. Постройте график зависимости ROC-AUC от количества используемых деревьев. Что вы видите?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "roc_auc = []\n",
    "x = []\n",
    "\n",
    "for i in range(10, 1010, 10):\n",
    "    rfc = RandomForestClassifier(n_estimators=i, max_features=4)\n",
    "    rfc.fit(X_train, y_train)\n",
    "\n",
    "    rfc_score = rfc.predict_proba(X_test)[:, 1]\n",
    "    rfc_pred = 1*(rfc_score > 0.5)\n",
    "\n",
    "    x.append(i)\n",
    "    roc_auc.append(roc_auc_score(y_test, rfc_score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'roc-auc')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3973b8a588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, roc_auc)\n",
    "plt.title('roc-auc показатель случайного леса от количества используемых деревьев')\n",
    "plt.xlabel('количество дереьвев')\n",
    "plt.ylabel('roc-auc')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " - roc-auc показатель стремительно растёт при количестве деревьев в промежутке от 10 до 200, после этого график становится пологим с некоторыми колебаниями, это говорит о том, что у случайного дерева есть асимптота качества по метрике auc-roc от количества используемых деревьев, а это значит, что достаточно обучить 200 деревьев и результат будет примерно такой же, как если бы мы обучили более 200 деревьев"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.6 Важность признаков\n",
    "\n",
    "Случайный лес позволяет оценить важность признаков. У обученного случайного леса есть аттрибут `feature_importances_`, где храниться важность для каждого признака. Постройте `barplot` с важностью признаков (удобно использовать библиотеку `seaborn`, где можно для каждого столбца передать название признака `train.columns`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'Важность')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3974d2b0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "rfc = RandomForestClassifier(n_estimators=100, max_features=max_feat)\n",
    "rfc.fit(X_train, y_train)\n",
    "\n",
    "ax = plt.axes()\n",
    "sns.barplot(y=train.columns[:-1], x=rfc.feature_importances_, ax=ax)\n",
    "ax.set_title(\"Важность признаков\")\n",
    "ax.set_ylabel(\"Признаки\")\n",
    "ax.set_xlabel(\"Важность\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Выводы\n",
    "\n",
    "Напишите, что интересного вы узнали в этой работе, в каких экспериментах какие результаты получились.\n",
    "\n",
    "- Мы обучили три типа моделей : логистическую регрессию, решающее дерево и случайный лес и сравнивали качество этих алгоритмов по двум метрикам : accuracy и roc-auc\n",
    "    - Наилучший результат показала логистическая регрессия\n",
    "- При рассмотрении случайного леса мы сравнивали различные подходы : строили решающее дерево, использовали бэггинг над 100 решающими деревьями, делали семплирование обучающей выборки и выбор случайного множества признаков и сравнивали полученные модели с стандартной из библиотеки sklearn\n",
    "    - При выборе подмножества признаков их количество стоит принять как корень квадратный от количества признаков\n",
    "    - Комбинация всех методов (бэггинг, бутстрап выборки и выбор подмножества признаков) даёт лучший результат по accuracy и auc-roc\n",
    "    - Полученная итоговая модель случайного леса отличается от стандартной из библиотеки, потому что последняя устроена сложнее, чем реализованная нами\n",
    "- В работе была рассмотрено влияние количества используемых деревьев на качество алгоритма по метрике auc-roc. Было установлено, что у качества есть асимптота и при определённом количестве деревьев значительного выигрыша в качестве не получить, это одно из важнейших свойств случайного леса, которое ограничивает итерации, необходимые для получения оптимальной модели\n",
    "- Также было рассмотрено свойство случайного леса по нахождению важных признаков, это является большим плюсом алгоритма, так как позволяет интерпретировать его результат, в нашем примере самым важным признаком оказалось количество оперативной памяти в телефоне далее объем батареи, но он выделяется не так, как первый признак. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}