{ "cells": [ { "cell_type": "markdown", "source": [ "## HW2 Question 2\r\n", "\r\n", "Your job is to predict democracy index by real GDP per capita and other demographic features. Inorder to predict the democracy index we are going to use the following models:\r\n", "1. Ridge Regression (10 points)\r\n", "2. Lasso Regression (10 points)\r\n", "3. Adaptive Lasso Regression (10 points)\r\n", "4. Elastic Net Regression (10 points)" ], "metadata": {} }, { "cell_type": "code", "execution_count": 40, "source": [ "#Required libraries\r\n", "import pandas as pd\r\n", "import asgl\r\n", "from sklearn.linear_model import ElasticNetCV, RidgeCV, LassoCV\r\n", "import logging\r\n", "from sklearn.model_selection import GridSearchCV, RepeatedKFold, cross_val_score, train_test_split\r\n", "from sklearn.metrics import mean_squared_error\r\n", "from sklearn.datasets import make_regression\r\n", "from dataclasses import dataclass\r\n", "import numpy as np\r\n", "import matplotlib.pyplot as plt\r\n" ], "outputs": [], "metadata": {} }, { "cell_type": "code", "execution_count": 54, "source": [ "income_cleansed" ], "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " dem_ind log_gdppc log_pop age_1 age_2 age_3 age_4 \\\n", "38 0.530000 8.905374 9.933823 0.307737 0.241766 0.210866 0.151395 \n", "39 0.166667 9.016160 10.011580 0.302024 0.239600 0.207557 0.152852 \n", "40 0.833333 9.133990 10.084220 0.293686 0.245023 0.199240 0.154751 \n", "41 0.166667 9.202840 10.167740 0.292191 0.248464 0.190341 0.154945 \n", "42 0.833333 9.271142 10.243310 0.305165 0.237141 0.186167 0.152458 \n", "... ... ... ... ... ... ... ... \n", "1364 0.666667 7.831994 8.716208 0.487388 0.249146 0.135395 0.081204 \n", "1365 0.500000 7.876341 8.872487 0.490522 0.261934 0.124533 0.075827 \n", "1366 0.166667 7.913739 9.061492 0.477027 0.272603 0.132026 0.073196 \n", "1367 0.333333 7.977293 9.234155 0.462648 0.277990 0.141861 0.072411 \n", "1368 0.166667 7.882274 9.347926 0.454770 0.282207 0.146730 0.070168 \n", "\n", " age_5 educ age_median \n", "38 0.088237 4.988 26.799999 \n", "39 0.097967 5.214 27.200001 \n", "40 0.107299 5.876 27.400000 \n", "41 0.114059 5.845 27.299999 \n", "42 0.119069 6.618 27.200001 \n", "... ... ... ... \n", "1364 0.046867 2.147 15.600000 \n", "1365 0.047184 2.816 15.400000 \n", "1366 0.045149 2.828 16.000000 \n", "1367 0.045090 4.087 16.700001 \n", "1368 0.046125 4.428 17.000000 \n", "\n", "[679 rows x 10 columns]" ], "text/html": [ "
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dem_indlog_gdppclog_popage_1age_2age_3age_4age_5educage_median
380.5300008.9053749.9338230.3077370.2417660.2108660.1513950.0882374.98826.799999
390.1666679.01616010.0115800.3020240.2396000.2075570.1528520.0979675.21427.200001
400.8333339.13399010.0842200.2936860.2450230.1992400.1547510.1072995.87627.400000
410.1666679.20284010.1677400.2921910.2484640.1903410.1549450.1140595.84527.299999
420.8333339.27114210.2433100.3051650.2371410.1861670.1524580.1190696.61827.200001
.................................
13640.6666677.8319948.7162080.4873880.2491460.1353950.0812040.0468672.14715.600000
13650.5000007.8763418.8724870.4905220.2619340.1245330.0758270.0471842.81615.400000
13660.1666677.9137399.0614920.4770270.2726030.1320260.0731960.0451492.82816.000000
13670.3333337.9772939.2341550.4626480.2779900.1418610.0724110.0450904.08716.700001
13680.1666677.8822749.3479260.4547700.2822070.1467300.0701680.0461254.42817.000000
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679 rows × 10 columns

\n", "
" ] }, "metadata": {}, "execution_count": 54 } ], "metadata": {} }, { "cell_type": "code", "execution_count": 6, "source": [ "#Read in and cleanse the data\r\n", "income_democracy = pd.read_csv('./data/income_democracy.csv')\r\n", "exclude_cols = ['country','year','code']\r\n", "income_cleansed = income_democracy.drop(exclude_cols, axis=1)\r\n", "for col in income_cleansed.columns:\r\n", " income_cleansed = income_cleansed[~pd.isnull(income_cleansed[col])]\r\n" ], "outputs": [], "metadata": {} }, { "cell_type": "code", "execution_count": 53, "source": [ "income_cleansed.shape" ], "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "(679, 10)" ] }, "metadata": {}, "execution_count": 53 } ], "metadata": {} }, { "cell_type": "code", "execution_count": 7, "source": [ "y = income_cleansed.dem_ind\r\n", "X = income_cleansed.drop(\"dem_ind\", axis=1)" ], "outputs": [], "metadata": {} }, { "cell_type": "code", "execution_count": 8, "source": [ "#Split dataset into train/test with a train size of 80%\r\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)" ], "outputs": [], "metadata": {} }, { "cell_type": "markdown", "source": [ "## Fit ridge regression\r\n", "### 2A) " ], "metadata": {} }, { "cell_type": "code", "execution_count": 21, "source": [ "ridge = RidgeCV(alphas=[1e-3, 1e-2, 1e-1, 1, 10],store_cv_values=True, alpha_per_target=True).fit(X_train, y_train)" ], "outputs": [], "metadata": {} }, { "cell_type": "code", "execution_count": 22, "source": [ "ridge.alpha_" ], "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "10.0" ] }, "metadata": {}, "execution_count": 22 } ], "metadata": {} }, { "cell_type": "code", "execution_count": 30, "source": [ "y_pred_ridge = ridge.predict(X_test)\r\n", "ridge_mse = mean_squared_error(y_test,y_pred_ridge)\r\n", "print(ridge_mse)" ], "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "0.05953832584294149\n" ] } ], "metadata": {} }, { "cell_type": "code", "execution_count": 11, "source": [ "fig = plt.figure()\r\n", "ax = fig.add_axes([0,0,1,1])\r\n", "ax.bar(list(X_train.columns),list(ridge.coef_))\r\n", "#set parameters for tick labels\r\n", "plt.tick_params(axis='x', which='major', labelsize=9)" ], "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
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" }, "metadata": { "needs_background": "light" } } ], "metadata": {} }, { "cell_type": "markdown", "source": [ "## Fit lasso regression\r\n", "### 2B) " ], "metadata": {} }, { "cell_type": "code", "execution_count": 12, "source": [ "lasso = LassoCV(cv=10).fit(X_train, y_train)" ], "outputs": [], "metadata": {} }, { "cell_type": "code", "execution_count": 13, "source": [ "lasso.alpha_" ], "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "0.0015182975900542828" ] }, "metadata": {}, "execution_count": 13 } ], "metadata": {} }, { "cell_type": "code", "execution_count": 37, "source": [ "y_pred_lasso = lasso.predict(X_test)\r\n", "lasso_mse = mean_squared_error(y_test,y_pred_lasso)\r\n", "print(lasso_mse)" ], "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "0.059578045325377305\n" ] } ], "metadata": {} }, { "cell_type": "code", "execution_count": 14, "source": [ "fig = plt.figure()\r\n", "ax = fig.add_axes([0,0,1,1])\r\n", "ax.bar(list(X_train.columns),list(lasso.coef_))\r\n", "#set parameters for tick labels\r\n", "plt.tick_params(axis='x', which='major', labelsize=9)\r\n", "plt.show()" ], "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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" }, "metadata": { "needs_background": "light" } } ], "metadata": {} }, { "cell_type": "markdown", "source": [ "## Fit adaptive lasso regression\r\n", "### 2C) " ], "metadata": {} }, { "cell_type": "code", "execution_count": 49, "source": [ "#lambda1 = 10.0 ** np.arange(-3, 1.51, 0.1)\r\n", "tvt_alasso = asgl.TVT(model='lm', penalization='alasso', lambda1=lasso.alpha_, parallel=True,\r\n", " weight_technique='lasso', error_type='MSE', random_state=1, \r\n", " train_size=80)\r\n", "\r\n", "#alasso_result = tvt_alasso.train_validate_test(x=X_train, y=y_train)\r\n", "#alasso_prediction_error = alasso_result['test_error']\r\n", "#alasso_betas = alasso_result['optimal_betas'][1:]\r\n" ], "outputs": [], "metadata": {} }, { "cell_type": "code", "execution_count": 50, "source": [ "tvt_alasso.alpha" ], "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "0.5" ] }, "metadata": {}, "execution_count": 50 } ], "metadata": {} }, { "cell_type": "code", "execution_count": 52, "source": [ "tvt_alasso.coef_" ], "outputs": [], "metadata": {} }, { "cell_type": "markdown", "source": [ "## Fit elastic net regression\r\n", "### 2D) " ], "metadata": {} }, { "cell_type": "code", "execution_count": 15, "source": [ "elastic = ElasticNetCV(cv=10).fit(X_train, y_train)" ], "outputs": [], "metadata": {} }, { "cell_type": "code", "execution_count": 16, "source": [ "elastic.alpha_" ], "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "0.003036595180108563" ] }, "metadata": {}, "execution_count": 16 } ], "metadata": {} }, { "cell_type": "markdown", "source": [], "metadata": {} }, { "cell_type": "code", "execution_count": 17, "source": [ "fig = plt.figure()\r\n", "ax = fig.add_axes([0,0,1,1])\r\n", "ax.bar(list(X_train.columns),list(elastic.coef_))\r\n", "#set parameters for tick labels\r\n", "plt.tick_params(axis='x', which='major', labelsize=9)\r\n", "plt.show()" ], "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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qU1U7kzwCHNq3f3Zo7OHDK0hyNnA2wJFHHjlq7SO594LXz+vy9oYV665f6BL+zjg+f9L+Yhxfn+NY83xYFDdIVdWlVTVZVZMTExMLXY4kSfNqlLC9Hzhi4PHyvm3WPkmWAgcDD444VpKkfdooYbsFWJVkZZJldDc8bRzqsxE4o58+BbixqqpvP62/W3klsAr43PyULknSeJjzmm1/DfYcYDOwBLiiqrYlWQ9sraqNwOXA1UmmgIfoApm+37XAV4CdwNur6vFG2yJJ0qI0yg1SVNUmYNNQ27kD048Bp+5i7PnA+XtQoyRJY21R3CAlSdK+zLCVJKkxw1aSpMYMW0mSGjNsJUlqzLCVJKkxw1aSpMYMW0mSGjNsJUlqzLCVJKkxw1aSpMZG+mxk7V376z9XlqR9lUe2kiQ1ZthKktSYYStJUmOGrSRJjRm2kiQ1ZthKktSYYStJUmOGrSRJjRm2kiQ1ZthKktSYYStJUmOGrSRJjaWqFrqGJ0kyDXxjoeuYxWHAAwtdxG4at5rHrV4Yv5rHrV6w5r1h3OqFxVnzS6pqYrYZiy5sF6skW6tqcqHr2B3jVvO41QvjV/O41QvWvDeMW70wfjV7GlmSpMYMW0mSGjNsR3fpQhfwNIxbzeNWL4xfzeNWL1jz3jBu9cKY1ew1W0mSGvPIVpKkxgxbSZIa2+fCNsmKJH+5l9Y11XDZe207xkWS30xyd8vnfb4l+ViSW5LcluTMha5nLkl+P8mnk3wuye8vdD27I8lNSS5b6DpGkeTKJHf0NX9sAet4c5LzFmr9e0uSY5Nc2U+vS3L03q5h6d5eobQH/ifwp8CdC13IbnhPVX0tyTOBLyfZUFWPLXRRT+G9VbUDoA/dl1XVtoUuai5J/hHwvYWuYze9o6puXugi9jdVdcFCrHefDdskL6W7Wy3A/wPOrKofJvnPwGuAvwJOqqqX7GL8PwPeA0wBBwG/V1U39e/2jwW+Cizr+x4PvA/4LrASOL+qPta/Y1wNPIfu007eWlVfSfKrwPuBHwN3VNU7G27HTcBXgJcCjwGnVdWjSd4HrKE7u7G+qq7fVb27qu2pJDkMuAZYAhwAvBV4MXAx8E3gR8D1VXVlklOBf9Nv46eqav1sy6yqb/fLfjolLVTNX+sndwCPA/N2R2KjemeC9gDgUeBb81Vvq5qTPAN4O/Ah4JT5rLdVzb0PJvkR8IdVdc181z1Q/+/R7SuWAecD9wBXAtPA9+n2DySZqqqj+unLgI/0+7zfAt4I/AC4sqqueop1tfj53gR8EXg53RuqTcAb+u05sap+kOQdfdtS4PKquizJTwMbgB8C36Z/7fVHuJdV1c1JNgMHAs8Gfquqbu33g6uA5wJH0u0zvzrnEz2XqtqnvoAVwF8C/ws4rm87t/+h/jzwyb7tJcCPd7GMJcDd/ZN9ALANOB74uYHxK2bG9/O+3P+gn9ePfQZwHvDhvs8/6GsKXVC/cGZdrbajn38TcHo//V7g3wKv7Jcd4JCnqncPfg4HAMv66ZOAK4DbgSP69X4KOBN4PvBZ4IC+78eBo+dY9lSj352WNb8X+N1xqBf4r8B9wOXAMxZ7zXQ79H9F9zq8bBx+L4DD+u8vAO4AfqbR7/Qa4E/66WfTvTn/c+CX+rY/Bc7rp6cGxl3WP58vBz4NLO3bZ91fNX6ubgJO7qc/Cbyzn74Y+HXgZ4Hr++UvAW4BDqV78zW477uyn74SOLaffk7//WeBG/vp84CL++k3An8wHz+LffbIlu5I7pZ++hbgn9K9u9kCUFXfSPLtXYw9DPh2VX0PIMkdA8ucGX/v0Pg7qmon8LdJvgPMfD7m5/rvt/XjJ4AHqz9Kq6rHG27HjMEafgP4G+Cz1f02fbev97Bd1Pt0HQJckuRFdO9Avwc8t6ruA0gys56j6N4w3NAfsR7SP/7SHqz76TqEBjUneQvwCuD0cai3qt6R5J10p+3X0B1JLMqa+9Pzb+rrPHYe62xWM0BVPdB/fyjJDcDfpzvinG9HA7/SHx1CdxS3iie/zpfPMm7m9NFq4OZ+3zbK/uoQ2rzuZ/bB24EvDEy/gO4NwWrgf/ftz6ML95cC/2VgO1c9aQOTZwEfSvL36M46HT4w+/b++zeB186xzSPZ526QGnA33akT+u930Z0S/gWAJEcCL9zF2AeAFyY5KMlSuiNBgK89xfhXJlma5Ll9+3TfPvPZna/qx08DL0gy0S9nrp/BnmzHjMEa7u6/fjGdQ4Cf4icf6D1c79P1Zro3IMcB6+levI8mmXlhz6znnn57/mFVHU931P6JPVjvnpj3mpOspXt3/M+r6okxqPeZAP3O9ft0pw4Xc80r6XbUfwF8ADgxyb9c5DXTv+5IsozuLNLd81zzjG10p2iP72t6RV/j4Ot8xiNJXpRkCT/Z520DXtO3jbK/avW6r11Mh+4ejjuAX+2X9XNV9QW6/dds2zljDfB4Vf0y8Jv85A3GbOvYY/vyke064MPp3jZ9h25n98N0d7PeSnfa9/7ZBlbV4/15+5uBr/fjd1TV55PcOTB+8HrWt4CP0b34/2NVPdG/YzsoySfojhzPrKpK8nZgY3+95g5gl9ds92Q7BvxSkrPprhu+oaq+l+QW4Fa6N1y/s6t651juU/kU8NEkx9G9YAHeBfxFkvvprh/vqKoHk1wM3Jjkcbrr2G+huz79JP01nrcBL053p/a5VXXLcL/FVDPw3+kuG3yqf37fVFVz/bwWtN4kh9KdDvy/VXXTPNXapOaqupN+h5ru3ok3V9V835Hc4nm+JslBdM/zR6rRTWhVtSnJa/oj26I7GnwPcEWSB3nyf835AHAD3TZ+px+/LcmfA7ck+T5wVf+1Ky2eq7m28cv9/uDT/bJ+mORk4ELgfyT5F8z+n+RuBd7dj/3M7q53t83Huehx+uIn1wheAnxhhH4H0F2cf9FT9D2eWa4V0Z37f/MCb8dNwPIRl9ms3qGaA2wGJhf692Ffq3nc6rXmff/L56r72pePbHfl4iQvp7vD+F1JVgN/NNTnUuA5Sd5Ed/7/z6pqt99xNTbqdiwmJyb5d3Q3atxcVVtn67Srbamqj7YucBbjVvO41QvWvK/zucLPRpYkqbl9+QYpSZIWBcNWkqTGDFtJkhozbCVJasywlSSpsf8PFlyx4yKSwUMAAAAASUVORK5CYII=" }, "metadata": { "needs_background": "light" } } ], "metadata": {} }, { "cell_type": "code", "execution_count": 34, "source": [ "y_pred_elastic = lasso.predict(X_test)\r\n", "elastic_mse = mean_squared_error(y_test,y_pred_elastic)\r\n", "print(elastic_mse)" ], "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "0.059578045325377305\n" ] } ], "metadata": {} }, { "cell_type": "code", "execution_count": 35, "source": [ "fig = plt.figure()\r\n", "ax = fig.add_axes([0,0,1,1])\r\n", "mse_x = [\"Ridge MSE\",\"Lasso MSE\",\"Elastic MSE\"]\r\n", "mse_y=[ridge_mse,lasso_mse,elastic_mse]\r\n", "ax.bar(mse_x,mse_y)\r\n", "#set parameters for tick labels\r\n", "plt.tick_params(axis='x', which='major', labelsize=9)\r\n", "plt.show()" ], "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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" 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