{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "from patsy import ContrastMatrix\n", "from sklearn.model_selection import train_test_split\n", "import statsmodels.api as sm\n", "from statsmodels.graphics.mosaicplot import mosaic\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "data = pd.read_csv('./data/adult.data')\n", "cols = ['workclass', 'sex', 'age', 'education_num', 'capital_gain',\n", " 'capital_loss', 'hours_per_week', 'label']\n", "data = data[cols]\n", "data['label_code'] = pd.Categorical(data.label).codes" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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label<=50K>50K
sex
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" ], "text/plain": [ "label <=50K >50K\n", "sex \n", " Female 9592 1179\n", " Male 15128 6662" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 计算sex, label交叉报表\n", "cross1 = pd.crosstab(data['sex'], data['label'])\n", "cross1" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 将交叉报表图形化\n", "props = lambda key: {'color': '0.45'} if ' >50K' in key else {'color': '#C6E2FF'}\n", "mosaic(cross1[[' >50K', ' <=50K']].stack(), properties=props, axes_label=False)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# 将数据分为训练集和测试集\n", "train_set, test_set = train_test_split(data, test_size=0.2, random_state=2310)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Optimization terminated successfully.\n", " Current function value: 0.409236\n", " Iterations 8\n", " Logit Regression Results \n", "==============================================================================\n", "Dep. Variable: label_code No. Observations: 26048\n", "Model: Logit Df Residuals: 26042\n", "Method: MLE Df Model: 5\n", "Date: Mon, 06 Nov 2023 Pseudo R-squ.: 0.2582\n", "Time: 20:42:17 Log-Likelihood: -10660.\n", "converged: True LL-Null: -14370.\n", "Covariance Type: nonrobust LLR p-value: 0.000\n", "===================================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "-----------------------------------------------------------------------------------\n", "Intercept -7.1944 0.112 -64.011 0.000 -7.415 -6.974\n", "C(sex)[T. Male] 1.2221 0.044 27.602 0.000 1.135 1.309\n", "education_num 0.3329 0.008 43.145 0.000 0.318 0.348\n", "capital_gain 0.0003 1.08e-05 31.073 0.000 0.000 0.000\n", "capital_loss 0.0007 3.6e-05 20.798 0.000 0.001 0.001\n", "hours_per_week 0.0308 0.001 20.857 0.000 0.028 0.034\n", "===================================================================================\n" ] } ], "source": [ "# 加入sex，搭建逻辑回归模型，并训练模型\n", "formula = 'label_code ~ C(sex) + education_num + capital_gain + capital_loss + hours_per_week'\n", "model = sm.Logit.from_formula(formula, data=train_set)\n", "re = model.fit()\n", "print(re.summary())" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Warning: Maximum number of iterations has been exceeded.\n", " Current function value: 0.422077\n", " Iterations: 35\n", " Logit Regression Results \n", "==============================================================================\n", "Dep. Variable: label_code No. Observations: 26048\n", "Model: Logit Df Residuals: 26035\n", "Method: MLE Df Model: 12\n", "Date: Mon, 06 Nov 2023 Pseudo R-squ.: 0.2349\n", "Time: 20:42:17 Log-Likelihood: -10994.\n", "converged: False LL-Null: -14370.\n", "Covariance Type: nonrobust LLR p-value: 0.000\n", "=====================================================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "-----------------------------------------------------------------------------------------------------\n", "Intercept -6.8672 0.138 -49.714 0.000 -7.138 -6.596\n", "C(workclass)[T. Federal-gov] 1.1092 0.129 8.616 0.000 0.857 1.362\n", "C(workclass)[T. Local-gov] 0.5202 0.116 4.491 0.000 0.293 0.747\n", "C(workclass)[T. Never-worked] -26.5153 1.14e+06 -2.32e-05 1.000 -2.24e+06 2.24e+06\n", "C(workclass)[T. Private] 0.5357 0.100 5.376 0.000 0.340 0.731\n", "C(workclass)[T. Self-emp-inc] 1.4538 0.127 11.462 0.000 1.205 1.702\n", "C(workclass)[T. Self-emp-not-inc] 0.5330 0.115 4.646 0.000 0.308 0.758\n", "C(workclass)[T. State-gov] 0.3562 0.128 2.787 0.005 0.106 0.607\n", "C(workclass)[T. Without-pay] -13.9687 945.497 -0.015 0.988 -1867.108 1839.171\n", "education_num 0.3167 0.008 40.970 0.000 0.302 0.332\n", "capital_gain 0.0003 1.08e-05 31.211 0.000 0.000 0.000\n", "capital_loss 0.0008 3.54e-05 21.500 0.000 0.001 0.001\n", "hours_per_week 0.0354 0.001 23.797 0.000 0.032 0.038\n", "=====================================================================================================\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/tgbaggio/opt/anaconda3/lib/python3.8/site-packages/statsmodels/base/model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n", " warnings.warn(\"Maximum Likelihood optimization failed to \"\n" ] } ], "source": [ "# 加入workclass，搭建逻辑回归模型，并训练模型\n", "formula = 'label_code ~ C(workclass) + education_num + capital_gain + capital_loss + hours_per_week'\n", "model = sm.Logit.from_formula(formula, data=train_set)\n", "re = model.fit()\n", "print(re.summary())" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Optimization terminated successfully.\n", " Current function value: 0.405935\n", " Iterations 8\n", " Logit Regression Results \n", "==============================================================================\n", "Dep. Variable: label_code No. Observations: 26048\n", "Model: Logit Df Residuals: 26036\n", "Method: MLE Df Model: 11\n", "Date: Mon, 06 Nov 2023 Pseudo R-squ.: 0.2642\n", "Time: 20:42:18 Log-Likelihood: -10574.\n", "converged: True LL-Null: -14370.\n", "Covariance Type: nonrobust LLR p-value: 0.000\n", "=========================================================================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "-------------------------------------------------------------------------------------------------------------------------\n", "Intercept -7.5784 0.145 -52.287 0.000 -7.862 -7.294\n", "C(workclass, contrast_mat, levels=l) State-gov 0.3520 0.130 2.699 0.007 0.096 0.608\n", "C(workclass, contrast_mat, levels=l) Self-emp-not-inc 0.3687 0.116 3.165 0.002 0.140 0.597\n", "C(workclass, contrast_mat, levels=l) Private 0.5058 0.101 4.984 0.000 0.307 0.705\n", "C(workclass, contrast_mat, levels=l) Federal-gov 1.0792 0.132 8.196 0.000 0.821 1.337\n", "C(workclass, contrast_mat, levels=l) Local-gov 0.6012 0.118 5.106 0.000 0.370 0.832\n", "C(workclass, contrast_mat, levels=l) Self-emp-inc 1.2921 0.129 10.026 0.000 1.040 1.545\n", "C(sex)[T. Male] 1.2097 0.045 27.092 0.000 1.122 1.297\n", "education_num 0.3284 0.008 41.737 0.000 0.313 0.344\n", "capital_gain 0.0003 1.08e-05 30.770 0.000 0.000 0.000\n", "capital_loss 0.0007 3.62e-05 20.617 0.000 0.001 0.001\n", "hours_per_week 0.0287 0.002 18.937 0.000 0.026 0.032\n", "=========================================================================================================================\n" ] } ], "source": [ "# 剔除掉不显著的workclass，搭建逻辑回归模型，并训练模型\n", "# 定义workclass的类别顺序，数组里的前3个类别为基准类别\n", "l = [' ?', ' Never-worked', ' Without-pay', ' State-gov',\n", " ' Self-emp-not-inc', ' Private', ' Federal-gov',\n", " ' Local-gov', ' Self-emp-inc']\n", "# 定义各个类别对应的虚拟变量\n", "contrast = np.eye(9, 6, k=-3)\n", "# 为每个虚拟变量命名\n", "contrast_mat = ContrastMatrix(contrast, l[3:])\n", "formula = '''label_code ~ C(workclass, contrast_mat, levels=l)\n", " + C(sex) + education_num + capital_gain\n", " + capital_loss + hours_per_week'''\n", "model = sm.Logit.from_formula(formula, data=train_set)\n", "re = model.fit()\n", "print(re.summary())" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Optimization terminated successfully.\n", " Current function value: 0.409236\n", " Iterations 8\n", " Logit Regression Results \n", "==============================================================================\n", "Dep. Variable: label_code No. Observations: 26048\n", "Model: Logit Df Residuals: 26042\n", "Method: MLE Df Model: 5\n", "Date: Mon, 06 Nov 2023 Pseudo R-squ.: 0.2582\n", "Time: 20:42:18 Log-Likelihood: -10660.\n", "converged: True LL-Null: -14370.\n", "Covariance Type: nonrobust LLR p-value: 0.000\n", "============================================================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------------------------------------\n", "Intercept -6.3756 0.106 -59.909 0.000 -6.584 -6.167\n", "C(sex, contrast_mat, levels=l)Ridit(sex) -1.2221 0.044 -27.602 0.000 -1.309 -1.135\n", "education_num 0.3329 0.008 43.145 0.000 0.318 0.348\n", "capital_gain 0.0003 1.08e-05 31.073 0.000 0.000 0.000\n", "capital_loss 0.0007 3.6e-05 20.798 0.000 0.001 0.001\n", "hours_per_week 0.0308 0.001 20.857 0.000 0.028 0.034\n", "============================================================================================================\n" ] } ], "source": [ "# 使用sex变量的Ridit scoring搭建逻辑回归模型，并训练模型\n", "l = [' Male', ' Female']\n", "contrast = [[-0.33], [0.67]]\n", "contrast_mat = ContrastMatrix(contrast, ['Ridit(sex)'])\n", "formula = '''label_code ~ C(sex, contrast_mat, levels=l) + education_num\n", " + capital_gain + capital_loss + hours_per_week'''\n", "model = sm.Logit.from_formula(formula, data=train_set)\n", "re = model.fit()\n", "print(re.summary())" ] } ], "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": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 4 }