09 Logistic_Regression_raw.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from math import exp\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data\n",
    "def create_data():\n",
    "    iris = load_iris()\n",
    "    df = pd.DataFrame(iris.data, columns=iris.feature_names)\n",
    "    df['label'] = iris.target\n",
    "    df.columns = ['sepal length', 'sepal width', 'petal length', 'petal width', 'label']\n",
    "    data = np.array(df.iloc[:100, [0,1,-1]])\n",
    "    # print(data)\n",
    "    return data[:,:2], data[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = create_data()\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LogisticReressionClassifier:\n",
    "    def __init__(self, max_iter=200, learning_rate=0.01):\n",
    "        self.max_iter = max_iter\n",
    "        self.learning_rate = learning_rate\n",
    "        \n",
    "    def sigmoid(self, x):\n",
    "        return 1 / (1 + exp(-x))\n",
    "\n",
    "    def data_matrix(self, X):\n",
    "        data_mat = []\n",
    "        for d in X:\n",
    "            data_mat.append([1.0, *d])\n",
    "        return data_mat\n",
    "    \n",
    "    def train(self, X, y):\n",
    "        data_mat = self.data_matrix(X)\n",
    "        self.weights = np.zeros((len(data_mat[0]), 1), dtype=np.float32)\n",
    "        \n",
    "        for _ in range(self.max_iter):\n",
    "            for i in range(len(X)):\n",
    "                y_hat = self.sigmoid(np.dot(data_mat[i], self.weights))\n",
    "                error = y[i] - y_hat\n",
    "                self.weights += self.learning_rate * error * np.transpose([data_mat[i]])\n",
    "    \n",
    "    def score(self, X_test, y_test):\n",
    "        right = 0\n",
    "        X_test = self.data_matrix(X_test)\n",
    "        for x, y in zip(X_test, y_test):\n",
    "            result = np.dot(x, self.weights)\n",
    "            if (result > 0 and y == 1) or (result < 0 and y == 0):\n",
    "                right += 1\n",
    "        return right / len(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_clf = LogisticReressionClassifier()\n",
    "lr_clf.train(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9666666666666667"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_clf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fa2ddbce438>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_ponits = np.arange(4, 8)\n",
    "y_ = -(lr_clf.weights[1]*x_ponits + lr_clf.weights[0])/lr_clf.weights[2]\n",
    "plt.plot(x_ponits, y_)\n",
    "\n",
    "#lr_clf.show_graph()\n",
    "plt.scatter(X[:50,0],X[:50,1], label='0')\n",
    "plt.scatter(X[50:,0],X[50:,1], label='1')\n",
    "plt.legend()"
   ]
  },
  {
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}