{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Simple Logit Example in Python" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [], "source": [ "\n", "#basic imports\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "#matplotlib inline\n", "\n", "from sklearn.linear_model import LogisticRegression\n", "import statsmodels.api as sm\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Read in Data" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(10000, 4)\n" ] }, { "data": { "text/html": [ "
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defaultstudentbalanceincome
0NoNo729.52649544361.625074
1NoYes817.18040712106.134700
2NoNo1073.54916431767.138947
3NoNo529.25060535704.493935
4NoNo785.65588338463.495879
\n", "
" ], "text/plain": [ " default student balance income\n", "0 No No 729.526495 44361.625074\n", "1 No Yes 817.180407 12106.134700\n", "2 No No 1073.549164 31767.138947\n", "3 No No 529.250605 35704.493935\n", "4 No No 785.655883 38463.495879" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dd = pd.read_csv(\"ISLR-Default.csv\")\n", "print(dd.shape)\n", "dd.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot the data" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "dd.boxplot(column=\"balance\",by=\"default\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fit the Logit" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [], "source": [ "## get x and y as numpy arrays\n", "X = dd[\"balance\"].to_numpy().reshape((dd.shape[0],1))\n", "y = np.where(dd[\"default\"]=='No',0,1)" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[-10.65132867] [[0.00549892]]\n" ] } ], "source": [ "#sklearn\n", "lfit = LogisticRegression(penalty='none')\n", "lfit.fit(X,y)\n", "print(lfit.intercept_,lfit.coef_)" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [], "source": [ "phat = lfit.predict_proba(X)[:,1]" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.scatter(X,phat)" ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Optimization terminated successfully.\n", " Current function value: 0.079823\n", " Iterations 10\n", " Logit Regression Results \n", "==============================================================================\n", "Dep. Variable: y No. Observations: 10000\n", "Model: Logit Df Residuals: 9998\n", "Method: MLE Df Model: 1\n", "Date: Sun, 02 Jan 2022 Pseudo R-squ.: 0.4534\n", "Time: 16:04:32 Log-Likelihood: -798.23\n", "converged: True LL-Null: -1460.3\n", "Covariance Type: nonrobust LLR p-value: 6.233e-290\n", "==============================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const -10.6513 0.361 -29.491 0.000 -11.359 -9.943\n", "x1 0.0055 0.000 24.952 0.000 0.005 0.006\n", "==============================================================================\n", "\n", "Possibly complete quasi-separation: A fraction 0.13 of observations can be\n", "perfectly predicted. This might indicate that there is complete\n", "quasi-separation. In this case some parameters will not be identified.\n" ] } ], "source": [ "# statsmodels\n", "XX = sm.add_constant(X)\n", "lfit2 = sm.Logit(y, XX).fit()\n", "print(lfit2.summary())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Check" ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "count 10000.0\n", "mean 0.0\n", "std 0.0\n", "min 0.0\n", "25% 0.0\n", "50% 0.0\n", "75% 0.0\n", "max 0.0\n", "dtype: float64" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" } ], "source": [ "eta = lfit.intercept_ + lfit.coef_[0] * X[:,0]\n", "phat0 = 1.0/(1.0 + np.exp(-eta))\n", "diff = pd.Series(phat-phat0)\n", "diff.describe()" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 0.0\n", "1 0.0\n", "2 0.0\n", "3 0.0\n", "4 0.0\n", "5 0.0\n", "6 0.0\n", "7 0.0\n", "8 0.0\n", "9 0.0\n", "10 0.0\n", "11 0.0\n", "12 0.0\n", "13 0.0\n", "14 0.0\n", "15 0.0\n", "16 0.0\n", "17 0.0\n", "18 0.0\n", "19 0.0\n", "dtype: float64" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "diff[:20]" ] } ], "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": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 4 }