Algorithms/logistic_regression/code/custom_implementation_vs_sklearn.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Use Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from logistic_regression import LogisticRegression\n",
    "from one_vs_all_logistic_regression import LogisticRegressionOneVsAll\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression as LogisticRegressionSklearn\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import datasets\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Two Class Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/heart.csv')\n",
    "X, y = [np.array(df.drop('target', axis=1)), np.array(df['target'])]\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 86.88524590163934\n",
      "CPU times: user 3 s, sys: 0 ns, total: 3 s\n",
      "Wall time: 3 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "model = LogisticRegression(0.0001, X_train.shape[1])\n",
    "model.fit(X_train, y_train, 50000)\n",
    "\n",
    "predictions = model.predict(X_test)\n",
    "print('Accuracy:', (predictions == y_test).sum()/len(predictions)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 88.52459016393442\n",
      "CPU times: user 23.7 ms, sys: 268 µs, total: 24 ms\n",
      "Wall time: 20.7 ms\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "model = LogisticRegressionSklearn()\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "predictions = model.predict(X_test)\n",
    "print('Accuracy:', (predictions == y_test).sum()/len(predictions)*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multiclass Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = datasets.load_iris()\n",
    "X = iris.data[:]\n",
    "y = iris.target\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 100.0\n",
      "CPU times: user 394 ms, sys: 28.4 ms, total: 423 ms\n",
      "Wall time: 389 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "model = LogisticRegressionOneVsAll(0.01, X_train.shape[1], len(np.unique(y)))\n",
    "model.fit(X_train, y_train, 3000)\n",
    "predictions = model.predict(X_test)\n",
    "print('Accuracy:', (predictions == y_test).sum()/len(predictions)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 100.0\n",
      "CPU times: user 63.7 ms, sys: 3.86 ms, total: 67.6 ms\n",
      "Wall time: 61.1 ms\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "model = LogisticRegressionSklearn()\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "predictions = model.predict(X_test)\n",
    "print('Accuracy:', (predictions == y_test).sum()/len(predictions)*100)"
   ]
  }
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