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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# Linear regression (following Chapter 3 of ISL, http://www-bcf.usc.edu/~gareth/ISL/index.html)\n",
"\n",
"# Here we use two different python packages which have built-in functions : statsmodels and scikit-learn\n",
"\n",
"# scikit-learn: has a large variety of different statistical learning algorithms \n",
"# http://scikit-learn.org/stable/\n",
"\n",
"# statsmodels: I found that it gives esay+complete overview of different values of the fitting result \n",
"#(apparently it is inspired by the lm() function in R)\n",
"# https://www.statsmodels.org/stable/index.html"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import os, sys\n",
"import csv\n",
"import matplotlib\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import statsmodels.api as sm\n",
"import statsmodels.formula.api as smf # http://www.statsmodels.org/dev/example_formulas.html\n",
"from sklearn import datasets, linear_model\n",
"from sklearn.metrics import mean_squared_error, r2_score\n",
"from sklearn.preprocessing import PolynomialFeatures"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Unnamed: 0</th>\n",
" <th>crim</th>\n",
" <th>zn</th>\n",
" <th>indus</th>\n",
" <th>chas</th>\n",
" <th>nox</th>\n",
" <th>rm</th>\n",
" <th>age</th>\n",
" <th>dis</th>\n",
" <th>rad</th>\n",
" <th>tax</th>\n",
" <th>ptratio</th>\n",
" <th>black</th>\n",
" <th>lstat</th>\n",
" <th>medv</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>0.00632</td>\n",
" <td>18.0</td>\n",
" <td>2.31</td>\n",
" <td>0</td>\n",
" <td>0.538</td>\n",
" <td>6.575</td>\n",
" <td>65.2</td>\n",
" <td>4.0900</td>\n",
" <td>1</td>\n",
" <td>296</td>\n",
" <td>15.3</td>\n",
" <td>396.90</td>\n",
" <td>4.98</td>\n",
" <td>24.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>0.02731</td>\n",
" <td>0.0</td>\n",
" <td>7.07</td>\n",
" <td>0</td>\n",
" <td>0.469</td>\n",
" <td>6.421</td>\n",
" <td>78.9</td>\n",
" <td>4.9671</td>\n",
" <td>2</td>\n",
" <td>242</td>\n",
" <td>17.8</td>\n",
" <td>396.90</td>\n",
" <td>9.14</td>\n",
" <td>21.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>0.02729</td>\n",
" <td>0.0</td>\n",
" <td>7.07</td>\n",
" <td>0</td>\n",
" <td>0.469</td>\n",
" <td>7.185</td>\n",
" <td>61.1</td>\n",
" <td>4.9671</td>\n",
" <td>2</td>\n",
" <td>242</td>\n",
" <td>17.8</td>\n",
" <td>392.83</td>\n",
" <td>4.03</td>\n",
" <td>34.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>0.03237</td>\n",
" <td>0.0</td>\n",
" <td>2.18</td>\n",
" <td>0</td>\n",
" <td>0.458</td>\n",
" <td>6.998</td>\n",
" <td>45.8</td>\n",
" <td>6.0622</td>\n",
" <td>3</td>\n",
" <td>222</td>\n",
" <td>18.7</td>\n",
" <td>394.63</td>\n",
" <td>2.94</td>\n",
" <td>33.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>0.06905</td>\n",
" <td>0.0</td>\n",
" <td>2.18</td>\n",
" <td>0</td>\n",
" <td>0.458</td>\n",
" <td>7.147</td>\n",
" <td>54.2</td>\n",
" <td>6.0622</td>\n",
" <td>3</td>\n",
" <td>222</td>\n",
" <td>18.7</td>\n",
" <td>396.90</td>\n",
" <td>5.33</td>\n",
" <td>36.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>6</td>\n",
" <td>0.02985</td>\n",
" <td>0.0</td>\n",
" <td>2.18</td>\n",
" <td>0</td>\n",
" <td>0.458</td>\n",
" <td>6.430</td>\n",
" <td>58.7</td>\n",
" <td>6.0622</td>\n",
" <td>3</td>\n",
" <td>222</td>\n",
" <td>18.7</td>\n",
" <td>394.12</td>\n",
" <td>5.21</td>\n",
" <td>28.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>7</td>\n",
" <td>0.08829</td>\n",
" <td>12.5</td>\n",
" <td>7.87</td>\n",
" <td>0</td>\n",
" <td>0.524</td>\n",
" <td>6.012</td>\n",
" <td>66.6</td>\n",
" <td>5.5605</td>\n",
" <td>5</td>\n",
" <td>311</td>\n",
" <td>15.2</td>\n",
" <td>395.60</td>\n",
" <td>12.43</td>\n",
" <td>22.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>8</td>\n",
" <td>0.14455</td>\n",
" <td>12.5</td>\n",
" <td>7.87</td>\n",
" <td>0</td>\n",
" <td>0.524</td>\n",
" <td>6.172</td>\n",
" <td>96.1</td>\n",
" <td>5.9505</td>\n",
" <td>5</td>\n",
" <td>311</td>\n",
" <td>15.2</td>\n",
" <td>396.90</td>\n",
" <td>19.15</td>\n",
" <td>27.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>9</td>\n",
" <td>0.21124</td>\n",
" <td>12.5</td>\n",
" <td>7.87</td>\n",
" <td>0</td>\n",
" <td>0.524</td>\n",
" <td>5.631</td>\n",
" <td>100.0</td>\n",
" <td>6.0821</td>\n",
" <td>5</td>\n",
" <td>311</td>\n",
" <td>15.2</td>\n",
" <td>386.63</td>\n",
" <td>29.93</td>\n",
" <td>16.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>10</td>\n",
" <td>0.17004</td>\n",
" <td>12.5</td>\n",
" <td>7.87</td>\n",
" <td>0</td>\n",
" <td>0.524</td>\n",
" <td>6.004</td>\n",
" <td>85.9</td>\n",
" <td>6.5921</td>\n",
" <td>5</td>\n",
" <td>311</td>\n",
" <td>15.2</td>\n",
" <td>386.71</td>\n",
" <td>17.10</td>\n",
" <td>18.9</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0 crim zn indus chas nox rm age dis rad \\\n",
"0 1 0.00632 18.0 2.31 0 0.538 6.575 65.2 4.0900 1 \n",
"1 2 0.02731 0.0 7.07 0 0.469 6.421 78.9 4.9671 2 \n",
"2 3 0.02729 0.0 7.07 0 0.469 7.185 61.1 4.9671 2 \n",
"3 4 0.03237 0.0 2.18 0 0.458 6.998 45.8 6.0622 3 \n",
"4 5 0.06905 0.0 2.18 0 0.458 7.147 54.2 6.0622 3 \n",
"5 6 0.02985 0.0 2.18 0 0.458 6.430 58.7 6.0622 3 \n",
"6 7 0.08829 12.5 7.87 0 0.524 6.012 66.6 5.5605 5 \n",
"7 8 0.14455 12.5 7.87 0 0.524 6.172 96.1 5.9505 5 \n",
"8 9 0.21124 12.5 7.87 0 0.524 5.631 100.0 6.0821 5 \n",
"9 10 0.17004 12.5 7.87 0 0.524 6.004 85.9 6.5921 5 \n",
"\n",
" tax ptratio black lstat medv \n",
"0 296 15.3 396.90 4.98 24.0 \n",
"1 242 17.8 396.90 9.14 21.6 \n",
"2 242 17.8 392.83 4.03 34.7 \n",
"3 222 18.7 394.63 2.94 33.4 \n",
"4 222 18.7 396.90 5.33 36.2 \n",
"5 222 18.7 394.12 5.21 28.7 \n",
"6 311 15.2 395.60 12.43 22.9 \n",
"7 311 15.2 396.90 19.15 27.1 \n",
"8 311 15.2 386.63 29.93 16.5 \n",
"9 311 15.2 386.71 17.10 18.9 "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"'''following 3.6 Lab: Linear Regression (page 109)'''\n",
"\n",
"#read in Boston housing data set and getting it into Pandas DataFrame\n",
"fname = 'Boston.csv'\n",
"df = pd.read_csv(fname)\n",
"\n",
"df.head(10) #show first 10 lines of dataset"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"simpletable\">\n",
"<caption>OLS Regression Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>medv</td> <th> R-squared: </th> <td> 0.544</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>OLS</td> <th> Adj. R-squared: </th> <td> 0.543</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Method:</th> <td>Least Squares</td> <th> F-statistic: </th> <td> 601.6</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Mon, 16 Jul 2018</td> <th> Prob (F-statistic):</th> <td>5.08e-88</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>08:43:10</td> <th> Log-Likelihood: </th> <td> -1641.5</td>\n",
"</tr>\n",
"<tr>\n",
" <th>No. Observations:</th> <td> 506</td> <th> AIC: </th> <td> 3287.</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Df Residuals:</th> <td> 504</td> <th> BIC: </th> <td> 3295.</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Df Model:</th> <td> 1</td> <th> </th> <td> </td> \n",
"</tr>\n",
"<tr>\n",
" <th>Covariance Type:</th> <td>nonrobust</td> <th> </th> <td> </td> \n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>t</th> <th>P>|t|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>const</th> <td> 34.5538</td> <td> 0.563</td> <td> 61.415</td> <td> 0.000</td> <td> 33.448</td> <td> 35.659</td>\n",
"</tr>\n",
"<tr>\n",
" <th>lstat</th> <td> -0.9500</td> <td> 0.039</td> <td> -24.528</td> <td> 0.000</td> <td> -1.026</td> <td> -0.874</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <th>Omnibus:</th> <td>137.043</td> <th> Durbin-Watson: </th> <td> 0.892</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Prob(Omnibus):</th> <td> 0.000</td> <th> Jarque-Bera (JB): </th> <td> 291.373</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Skew:</th> <td> 1.453</td> <th> Prob(JB): </th> <td>5.36e-64</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Kurtosis:</th> <td> 5.319</td> <th> Cond. No. </th> <td> 29.7</td>\n",
"</tr>\n",
"</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
],
"text/plain": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: medv R-squared: 0.544\n",
"Model: OLS Adj. R-squared: 0.543\n",
"Method: Least Squares F-statistic: 601.6\n",
"Date: Mon, 16 Jul 2018 Prob (F-statistic): 5.08e-88\n",
"Time: 08:43:10 Log-Likelihood: -1641.5\n",
"No. Observations: 506 AIC: 3287.\n",
"Df Residuals: 504 BIC: 3295.\n",
"Df Model: 1 \n",
"Covariance Type: nonrobust \n",
"==============================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"const 34.5538 0.563 61.415 0.000 33.448 35.659\n",
"lstat -0.9500 0.039 -24.528 0.000 -1.026 -0.874\n",
"==============================================================================\n",
"Omnibus: 137.043 Durbin-Watson: 0.892\n",
"Prob(Omnibus): 0.000 Jarque-Bera (JB): 291.373\n",
"Skew: 1.453 Prob(JB): 5.36e-64\n",
"Kurtosis: 5.319 Cond. No. 29.7\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
"\"\"\""
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#doing the linear regression using the package statsmodels \n",
"\n",
"'''Lab: 3.6.2 Simple Linear Regression (page 110)'''\n",
"\n",
"X = df[\"lstat\"] #independent variable (or predictor)\n",
"y = df[\"medv\"] #dependent variable (which we want to fit / predict)\n",
"X0 = sm.add_constant(X) # we have to manually specify that we want an intercept (beta_0) in our model\n",
"model = sm.OLS(y, X0).fit() #doing the actual linear regression, using the OLS = ordinary least squared\n",
"\n",
"model.summary() #showing the results in a table"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0,0.5,'medv')"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"Y_hat = model.predict(X0) #predicted Y accodring to linear fit\n",
"\n",
"fig1, ax1 = plt.subplots(1,1,figsize=(6,4))\n",
"ax1.scatter(X,y, color='b')\n",
"ax1.plot(X,Y_hat,color='r',linewidth=3)\n",
"ax1.set_xlabel('lstat')\n",
"ax1.set_ylabel('medv')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"coefficients: \n",
" const 34.553841\n",
"lstat -0.950049\n",
"dtype: float64\n",
"\n",
"R^2: 0.544146297586\n",
"\n",
"pvalues: \n",
" const 3.743081e-236\n",
"lstat 5.081103e-88\n",
"dtype: float64\n"
]
}
],
"source": [
"# to access the important values\"\n",
"print(\"coefficients: \\n\", model.params)\n",
"print()\n",
"print(\"R^2: \",model.rsquared)\n",
"print()\n",
"print(\"pvalues: \\n\",model.pvalues)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"simpletable\">\n",
"<caption>OLS Regression Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>medv</td> <th> R-squared: </th> <td> 0.551</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>OLS</td> <th> Adj. R-squared: </th> <td> 0.549</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Method:</th> <td>Least Squares</td> <th> F-statistic: </th> <td> 309.0</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Mon, 16 Jul 2018</td> <th> Prob (F-statistic):</th> <td>2.98e-88</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>08:43:10</td> <th> Log-Likelihood: </th> <td> -1637.5</td>\n",
"</tr>\n",
"<tr>\n",
" <th>No. Observations:</th> <td> 506</td> <th> AIC: </th> <td> 3281.</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Df Residuals:</th> <td> 503</td> <th> BIC: </th> <td> 3294.</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Df Model:</th> <td> 2</td> <th> </th> <td> </td> \n",
"</tr>\n",
"<tr>\n",
" <th>Covariance Type:</th> <td>nonrobust</td> <th> </th> <td> </td> \n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>t</th> <th>P>|t|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>const</th> <td> 33.2228</td> <td> 0.731</td> <td> 45.458</td> <td> 0.000</td> <td> 31.787</td> <td> 34.659</td>\n",
"</tr>\n",
"<tr>\n",
" <th>lstat</th> <td> -1.0321</td> <td> 0.048</td> <td> -21.416</td> <td> 0.000</td> <td> -1.127</td> <td> -0.937</td>\n",
"</tr>\n",
"<tr>\n",
" <th>age</th> <td> 0.0345</td> <td> 0.012</td> <td> 2.826</td> <td> 0.005</td> <td> 0.011</td> <td> 0.059</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <th>Omnibus:</th> <td>124.288</td> <th> Durbin-Watson: </th> <td> 0.945</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Prob(Omnibus):</th> <td> 0.000</td> <th> Jarque-Bera (JB): </th> <td> 244.026</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Skew:</th> <td> 1.362</td> <th> Prob(JB): </th> <td>1.02e-53</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Kurtosis:</th> <td> 5.038</td> <th> Cond. No. </th> <td> 201.</td>\n",
"</tr>\n",
"</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
],
"text/plain": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: medv R-squared: 0.551\n",
"Model: OLS Adj. R-squared: 0.549\n",
"Method: Least Squares F-statistic: 309.0\n",
"Date: Mon, 16 Jul 2018 Prob (F-statistic): 2.98e-88\n",
"Time: 08:43:10 Log-Likelihood: -1637.5\n",
"No. Observations: 506 AIC: 3281.\n",
"Df Residuals: 503 BIC: 3294.\n",
"Df Model: 2 \n",
"Covariance Type: nonrobust \n",
"==============================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"const 33.2228 0.731 45.458 0.000 31.787 34.659\n",
"lstat -1.0321 0.048 -21.416 0.000 -1.127 -0.937\n",
"age 0.0345 0.012 2.826 0.005 0.011 0.059\n",
"==============================================================================\n",
"Omnibus: 124.288 Durbin-Watson: 0.945\n",
"Prob(Omnibus): 0.000 Jarque-Bera (JB): 244.026\n",
"Skew: 1.362 Prob(JB): 1.02e-53\n",
"Kurtosis: 5.038 Cond. No. 201.\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
"\"\"\""
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"'''Lab: 3.6.3 Multiple Linear Regression (page 113)'''\n",
"\n",
"#with two predictor variables\n",
"X = df[['lstat', 'age']]\n",
"y = df[\"medv\"]\n",
"X0 = sm.add_constant(X) # we have to manually specify that we want an intercept (beta_0) in our model\n",
"\n",
"model = sm.OLS(y, X0).fit()\n",
"model.summary()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"simpletable\">\n",
"<caption>OLS Regression Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>medv</td> <th> R-squared: </th> <td> 0.741</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>OLS</td> <th> Adj. R-squared: </th> <td> 0.734</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Method:</th> <td>Least Squares</td> <th> F-statistic: </th> <td> 108.1</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Mon, 16 Jul 2018</td> <th> Prob (F-statistic):</th> <td>6.72e-135</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>08:43:10</td> <th> Log-Likelihood: </th> <td> -1498.8</td> \n",
"</tr>\n",
"<tr>\n",
" <th>No. Observations:</th> <td> 506</td> <th> AIC: </th> <td> 3026.</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Df Residuals:</th> <td> 492</td> <th> BIC: </th> <td> 3085.</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Df Model:</th> <td> 13</td> <th> </th> <td> </td> \n",
"</tr>\n",
"<tr>\n",
" <th>Covariance Type:</th> <td>nonrobust</td> <th> </th> <td> </td> \n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>t</th> <th>P>|t|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>const</th> <td> 36.4595</td> <td> 5.103</td> <td> 7.144</td> <td> 0.000</td> <td> 26.432</td> <td> 46.487</td>\n",
"</tr>\n",
"<tr>\n",
" <th>crim</th> <td> -0.1080</td> <td> 0.033</td> <td> -3.287</td> <td> 0.001</td> <td> -0.173</td> <td> -0.043</td>\n",
"</tr>\n",
"<tr>\n",
" <th>zn</th> <td> 0.0464</td> <td> 0.014</td> <td> 3.382</td> <td> 0.001</td> <td> 0.019</td> <td> 0.073</td>\n",
"</tr>\n",
"<tr>\n",
" <th>indus</th> <td> 0.0206</td> <td> 0.061</td> <td> 0.334</td> <td> 0.738</td> <td> -0.100</td> <td> 0.141</td>\n",
"</tr>\n",
"<tr>\n",
" <th>chas</th> <td> 2.6867</td> <td> 0.862</td> <td> 3.118</td> <td> 0.002</td> <td> 0.994</td> <td> 4.380</td>\n",
"</tr>\n",
"<tr>\n",
" <th>nox</th> <td> -17.7666</td> <td> 3.820</td> <td> -4.651</td> <td> 0.000</td> <td> -25.272</td> <td> -10.262</td>\n",
"</tr>\n",
"<tr>\n",
" <th>rm</th> <td> 3.8099</td> <td> 0.418</td> <td> 9.116</td> <td> 0.000</td> <td> 2.989</td> <td> 4.631</td>\n",
"</tr>\n",
"<tr>\n",
" <th>age</th> <td> 0.0007</td> <td> 0.013</td> <td> 0.052</td> <td> 0.958</td> <td> -0.025</td> <td> 0.027</td>\n",
"</tr>\n",
"<tr>\n",
" <th>dis</th> <td> -1.4756</td> <td> 0.199</td> <td> -7.398</td> <td> 0.000</td> <td> -1.867</td> <td> -1.084</td>\n",
"</tr>\n",
"<tr>\n",
" <th>rad</th> <td> 0.3060</td> <td> 0.066</td> <td> 4.613</td> <td> 0.000</td> <td> 0.176</td> <td> 0.436</td>\n",
"</tr>\n",
"<tr>\n",
" <th>tax</th> <td> -0.0123</td> <td> 0.004</td> <td> -3.280</td> <td> 0.001</td> <td> -0.020</td> <td> -0.005</td>\n",
"</tr>\n",
"<tr>\n",
" <th>ptratio</th> <td> -0.9527</td> <td> 0.131</td> <td> -7.283</td> <td> 0.000</td> <td> -1.210</td> <td> -0.696</td>\n",
"</tr>\n",
"<tr>\n",
" <th>black</th> <td> 0.0093</td> <td> 0.003</td> <td> 3.467</td> <td> 0.001</td> <td> 0.004</td> <td> 0.015</td>\n",
"</tr>\n",
"<tr>\n",
" <th>lstat</th> <td> -0.5248</td> <td> 0.051</td> <td> -10.347</td> <td> 0.000</td> <td> -0.624</td> <td> -0.425</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <th>Omnibus:</th> <td>178.041</td> <th> Durbin-Watson: </th> <td> 1.078</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Prob(Omnibus):</th> <td> 0.000</td> <th> Jarque-Bera (JB): </th> <td> 783.126</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Skew:</th> <td> 1.521</td> <th> Prob(JB): </th> <td>8.84e-171</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Kurtosis:</th> <td> 8.281</td> <th> Cond. No. </th> <td>1.51e+04</td> \n",
"</tr>\n",
"</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.51e+04. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
],
"text/plain": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: medv R-squared: 0.741\n",
"Model: OLS Adj. R-squared: 0.734\n",
"Method: Least Squares F-statistic: 108.1\n",
"Date: Mon, 16 Jul 2018 Prob (F-statistic): 6.72e-135\n",
"Time: 08:43:10 Log-Likelihood: -1498.8\n",
"No. Observations: 506 AIC: 3026.\n",
"Df Residuals: 492 BIC: 3085.\n",
"Df Model: 13 \n",
"Covariance Type: nonrobust \n",
"==============================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"const 36.4595 5.103 7.144 0.000 26.432 46.487\n",
"crim -0.1080 0.033 -3.287 0.001 -0.173 -0.043\n",
"zn 0.0464 0.014 3.382 0.001 0.019 0.073\n",
"indus 0.0206 0.061 0.334 0.738 -0.100 0.141\n",
"chas 2.6867 0.862 3.118 0.002 0.994 4.380\n",
"nox -17.7666 3.820 -4.651 0.000 -25.272 -10.262\n",
"rm 3.8099 0.418 9.116 0.000 2.989 4.631\n",
"age 0.0007 0.013 0.052 0.958 -0.025 0.027\n",
"dis -1.4756 0.199 -7.398 0.000 -1.867 -1.084\n",
"rad 0.3060 0.066 4.613 0.000 0.176 0.436\n",
"tax -0.0123 0.004 -3.280 0.001 -0.020 -0.005\n",
"ptratio -0.9527 0.131 -7.283 0.000 -1.210 -0.696\n",
"black 0.0093 0.003 3.467 0.001 0.004 0.015\n",
"lstat -0.5248 0.051 -10.347 0.000 -0.624 -0.425\n",
"==============================================================================\n",
"Omnibus: 178.041 Durbin-Watson: 1.078\n",
"Prob(Omnibus): 0.000 Jarque-Bera (JB): 783.126\n",
"Skew: 1.521 Prob(JB): 8.84e-171\n",
"Kurtosis: 8.281 Cond. No. 1.51e+04\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
"[2] The condition number is large, 1.51e+04. This might indicate that there are\n",
"strong multicollinearity or other numerical problems.\n",
"\"\"\""
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# multiple linear regression with all predictor variables \n",
"\n",
"def_new=df.drop([df.columns[0], 'medv'], axis=1) #drop first column (just name of indices) and predicted variable column \n",
"Xall = def_new\n",
"y = df[\"medv\"]\n",
"Xall0 = sm.add_constant(Xall) # we have to manually specify that we want an intercept (beta_0) in our model\n",
"model = sm.OLS(y, Xall0).fit()\n",
"model.summary()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"simpletable\">\n",
"<caption>OLS Regression Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>medv</td> <th> R-squared: </th> <td> 0.556</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>OLS</td> <th> Adj. R-squared: </th> <td> 0.553</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Method:</th> <td>Least Squares</td> <th> F-statistic: </th> <td> 209.3</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Mon, 16 Jul 2018</td> <th> Prob (F-statistic):</th> <td>4.86e-88</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>08:43:10</td> <th> Log-Likelihood: </th> <td> -1635.0</td>\n",
"</tr>\n",
"<tr>\n",
" <th>No. Observations:</th> <td> 506</td> <th> AIC: </th> <td> 3278.</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Df Residuals:</th> <td> 502</td> <th> BIC: </th> <td> 3295.</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Df Model:</th> <td> 3</td> <th> </th> <td> </td> \n",
"</tr>\n",
"<tr>\n",
" <th>Covariance Type:</th> <td>nonrobust</td> <th> </th> <td> </td> \n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>t</th> <th>P>|t|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>Intercept</th> <td> 36.0885</td> <td> 1.470</td> <td> 24.553</td> <td> 0.000</td> <td> 33.201</td> <td> 38.976</td>\n",
"</tr>\n",
"<tr>\n",
" <th>lstat</th> <td> -1.3921</td> <td> 0.167</td> <td> -8.313</td> <td> 0.000</td> <td> -1.721</td> <td> -1.063</td>\n",
"</tr>\n",
"<tr>\n",
" <th>age</th> <td> -0.0007</td> <td> 0.020</td> <td> -0.036</td> <td> 0.971</td> <td> -0.040</td> <td> 0.038</td>\n",
"</tr>\n",
"<tr>\n",
" <th>lstat:age</th> <td> 0.0042</td> <td> 0.002</td> <td> 2.244</td> <td> 0.025</td> <td> 0.001</td> <td> 0.008</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <th>Omnibus:</th> <td>135.601</td> <th> Durbin-Watson: </th> <td> 0.965</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Prob(Omnibus):</th> <td> 0.000</td> <th> Jarque-Bera (JB): </th> <td> 296.955</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Skew:</th> <td> 1.417</td> <th> Prob(JB): </th> <td>3.29e-65</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Kurtosis:</th> <td> 5.461</td> <th> Cond. No. </th> <td>6.88e+03</td>\n",
"</tr>\n",
"</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 6.88e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
],
"text/plain": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: medv R-squared: 0.556\n",
"Model: OLS Adj. R-squared: 0.553\n",
"Method: Least Squares F-statistic: 209.3\n",
"Date: Mon, 16 Jul 2018 Prob (F-statistic): 4.86e-88\n",
"Time: 08:43:10 Log-Likelihood: -1635.0\n",
"No. Observations: 506 AIC: 3278.\n",
"Df Residuals: 502 BIC: 3295.\n",
"Df Model: 3 \n",
"Covariance Type: nonrobust \n",
"==============================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"Intercept 36.0885 1.470 24.553 0.000 33.201 38.976\n",
"lstat -1.3921 0.167 -8.313 0.000 -1.721 -1.063\n",
"age -0.0007 0.020 -0.036 0.971 -0.040 0.038\n",
"lstat:age 0.0042 0.002 2.244 0.025 0.001 0.008\n",
"==============================================================================\n",
"Omnibus: 135.601 Durbin-Watson: 0.965\n",
"Prob(Omnibus): 0.000 Jarque-Bera (JB): 296.955\n",
"Skew: 1.417 Prob(JB): 3.29e-65\n",
"Kurtosis: 5.461 Cond. No. 6.88e+03\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
"[2] The condition number is large, 6.88e+03. This might indicate that there are\n",
"strong multicollinearity or other numerical problems.\n",
"\"\"\""
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"'''Lab: 3.6.4 Interaction terms (page 115)'''\n",
"\n",
"# multiple linear regression including interaction terms (here between lstat and age)\n",
"\n",
"# just for illustration I use smf.ols() which uses the formula syntax known from R, \n",
"# see http://www.statsmodels.org/devel/examples/notebooks/generated/formulas.html\n",
"\n",
"model_interac = smf.ols(formula='medv ~ lstat * age', data=df).fit() #important: because we use formula, we need smf instead of sm, and ols in lower case\n",
"#equivalent: model_interac = smf.ols(formula='medv ~ lstat + age + lstat:age', data=df).fit()\n",
"model_interac.summary()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"simpletable\">\n",
"<caption>OLS Regression Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>medv</td> <th> R-squared: </th> <td> 0.641</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>OLS</td> <th> Adj. R-squared: </th> <td> 0.639</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Method:</th> <td>Least Squares</td> <th> F-statistic: </th> <td> 448.5</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Mon, 16 Jul 2018</td> <th> Prob (F-statistic):</th> <td>1.56e-112</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>08:43:10</td> <th> Log-Likelihood: </th> <td> -1581.3</td> \n",
"</tr>\n",
"<tr>\n",
" <th>No. Observations:</th> <td> 506</td> <th> AIC: </th> <td> 3169.</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Df Residuals:</th> <td> 503</td> <th> BIC: </th> <td> 3181.</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Df Model:</th> <td> 2</td> <th> </th> <td> </td> \n",
"</tr>\n",
"<tr>\n",
" <th>Covariance Type:</th> <td>nonrobust</td> <th> </th> <td> </td> \n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>t</th> <th>P>|t|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>Intercept</th> <td> 42.8620</td> <td> 0.872</td> <td> 49.149</td> <td> 0.000</td> <td> 41.149</td> <td> 44.575</td>\n",
"</tr>\n",
"<tr>\n",
" <th>lstat</th> <td> -2.3328</td> <td> 0.124</td> <td> -18.843</td> <td> 0.000</td> <td> -2.576</td> <td> -2.090</td>\n",
"</tr>\n",
"<tr>\n",
" <th>I(lstat ** 2)</th> <td> 0.0435</td> <td> 0.004</td> <td> 11.628</td> <td> 0.000</td> <td> 0.036</td> <td> 0.051</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <th>Omnibus:</th> <td>107.006</td> <th> Durbin-Watson: </th> <td> 0.921</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Prob(Omnibus):</th> <td> 0.000</td> <th> Jarque-Bera (JB): </th> <td> 228.388</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Skew:</th> <td> 1.128</td> <th> Prob(JB): </th> <td>2.55e-50</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Kurtosis:</th> <td> 5.397</td> <th> Cond. No. </th> <td>1.13e+03</td>\n",
"</tr>\n",
"</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.13e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
],
"text/plain": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: medv R-squared: 0.641\n",
"Model: OLS Adj. R-squared: 0.639\n",
"Method: Least Squares F-statistic: 448.5\n",
"Date: Mon, 16 Jul 2018 Prob (F-statistic): 1.56e-112\n",
"Time: 08:43:10 Log-Likelihood: -1581.3\n",
"No. Observations: 506 AIC: 3169.\n",
"Df Residuals: 503 BIC: 3181.\n",
"Df Model: 2 \n",
"Covariance Type: nonrobust \n",
"=================================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"---------------------------------------------------------------------------------\n",
"Intercept 42.8620 0.872 49.149 0.000 41.149 44.575\n",
"lstat -2.3328 0.124 -18.843 0.000 -2.576 -2.090\n",
"I(lstat ** 2) 0.0435 0.004 11.628 0.000 0.036 0.051\n",
"==============================================================================\n",
"Omnibus: 107.006 Durbin-Watson: 0.921\n",
"Prob(Omnibus): 0.000 Jarque-Bera (JB): 228.388\n",
"Skew: 1.128 Prob(JB): 2.55e-50\n",
"Kurtosis: 5.397 Cond. No. 1.13e+03\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
"[2] The condition number is large, 1.13e+03. This might indicate that there are\n",
"strong multicollinearity or other numerical problems.\n",
"\"\"\""
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"'''Lab: 3.6.5 Non-linear transfomartions of predictos (page 115)'''\n",
"\n",
"# e.g. including quadratic terms of predictors\n",
"\n",
"model_nonlin = smf.ols(formula='medv ~ lstat + I(lstat**2)', data=df).fit() #important: because we use formula, we need smf instead of sm, and ols in lower case\n",
"model_nonlin.summary()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0,0.5,'medv')"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"X = df[\"lstat\"] \n",
"Y_hat_nonlin = model_nonlin.predict(X)\n",
"\n",
"fig2, ax2 = plt.subplots(1,1,figsize=(6,4))\n",
"ax2.scatter(X,y, color='b')\n",
"ax2.scatter(X,Y_hat_nonlin,color='r')#,ls='none')#linewidth=3) \n",
"ax2.set_xlabel('lstat')\n",
"ax2.set_ylabel('medv')"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Coefficients [-0.95004935]\n",
"Intercept: 34.5538408794\n",
"Variance score: 0.54\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:14: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
" \n",
"/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:15: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
" from ipykernel import kernelapp as app\n"
]
},
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7fbbb5b46470>]"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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quSlEhxGl+MCLgF8CtwG3Ax+qnl+CV854L/ANYKeovvL00Pv7g73LIG91+jFp3+UVkQ3fwWemJRvnz4/2zKO8Xr9wRbNlkmneLw9diGJAq0IuzRx5CfroqFlPz0wh6u6evoVc3OMU1kQ2+gl/aY4d08Q9yVELfYRVpjQrrknj2pogJEQxmNWCHiTY/f3e635CFefYh/tiNVzEbxOLek2Yk95smpkkFEfgldwUov3MakGPkwBM46nXjgrb7b/ZP7Lhit2viNVfvdeb9kaQFHnfQnQOcQW9sxfnCiBOArC2A1CtRjsJXd1zOLDnThzG+zgvsN3nNp6I4fgqb/R93S8BmnQWZ9DuSFGk2QqvjGgRMVEq4qh+VkeeMfS43mcaj7iWpKx5+M7FK3s0sOfxeKhnnSYclMazbtc6L+2iFYlmIfKC2RxyMYsf+00SdqlUouPMC7qetqeIVuTvn/2jSNtbGXqZTRUsQcIdVgklRJGY9YIelyCPOKi8sHEtlMYbR33bC3l3tBJ/5Suh9sUV9KSe9WzyTvNMNAvRCiToCRgZmRmC6O426+oK/+L39Hjt/AS//jiK6yJV5N4j3mIvGHhmxhNFXDFK41U2W8HSKRUweSWahWgVEvQEpK12SXrsxqbIRutZbHsz8Zy3HCem3g7PupM8/LAy1k75DGJ2E1fQS1nlkpRWTGXv7/d+Ojd17nH6cBiOST7KWb7v25uHWM8gT291/PA9Vz+3YmIY7dhkupOqZIJWf/zUp/zXtNeG3aJjiaP6WR2d5qH7eXBJH9tHRyOm+994Y3RnZ55pQwM7ChUe6LQqmU4JDwnhB/LQ4xPHg/Ojpye4z5rXPzzsLb4VuD74oYcyNGgsZCO3sNy/s3/7Nx5YX2GtewkL2TizjzbQrt2Q0lKbdzA56f2UFy7KiAQd/+3kTj3VCx+ccoo3eae7e+b7tm0L7tNsaqJKrf9aGAZg3ryp31etgj/0LuRgbqGLHYHhmBfbWjayB4bjMH4yrY+80SYWQhSQOG58Vkc7Qi5pHrXTrvXid/T0TNU7N4Yp6hNwvnZ+5zuRA7yfjxlMtiWMoDCGEPmAqlzSVWKExbxbcUTFwJ0zW8x6u5+h0I6+z8ttj3lPhN8gMrqmWtBLiHyRoFvy2ZBZeuZxj6Akot9s0Tlss9X8r8hOr1611vdGNjLSfN151A2yk8oZhegUJOiWvBIjaCp4Hh56vVfb3z9zwlLj8Qa+Gtn52/j8jM/djNDGuUHOpiUFhMiLuIJe6qRokkqMsTHYtKm19vixatXU9nUTE578bdoE27eHv+9S3ojD2J87eZIFvm2+wNufW+2xh2cwm/761q1wxhnxVxuMsyWdtq2bnWjVyoIQR/WzOooaQ887bl5/jIxkM/Y8nrYrK68JbbSBhbaEe0P7CfPa5aELPxRmaz0o5OIRlaBrR9w8LAyS9qhfd+ad/HvkG07k8sCXG8U3bPXH+iUKgtr09GT75Y6bdFVyNh90E289EvSY5LWOSzuOZdwS2ej/8i7r4tkZL9VXywTd8JzznjCiboq1vVyzIMlTl7zGfOi0WcOdSFxBd17bfFi+fLmNj4/nNl4c6tdaKSu78jhX8bccwU8C29zJ/vwV1/Moez53rjYRKiy3UJtFOzERbsPgoDdDs1mGhvzHauw/bjvRPLrWrcc5d6uZBUwln6LUSdEoxsZmh6BvYTeO5Mc4JjmHD/u2+XN+w+9YhOH4K64DPCGPShSvXx8v4RnUJmkyLaifiYnpfSg5mx+aNVwg4rjxWR1FC7mUOdwSdRzNDyIbfYgPG0yGNhscjHcd/eKpacIiccbSbkT5o3xFa0Ex9GiySkh28rEnD9t/s39ooxs4/Lm9UOuP7u54tftBIp0mmRY3ia21zkWZiCvoszrkUtSVAfPkdyxiKXcyh+38O+/ybXMEP2ELu2E4luHlQPr7vXBVVEjGb43xWpglKO4eFp6prcNeqYSPu3lza9c6V921KCRxVD+ro2geertLFot21EofT+TyyMb/PP8/IvurVGY+gse55knCM/Pnx++jlf9v5P2LVoJCLvGor5/OOgTT39++CUtpj/prsIR7bQMLQ99wBa+xuWyN7LcmeFEx8KThGb8jyzJJP1R3LfImrqDP6pALTG18YAaXXBK8mUVSnPM2yPDb3CIvRkaSv8ds6vf7+VP2YCNz+QNf4w2+7V/Lf/IHenmCnfkz7grst7Y9XViVSVhYJEl1yi67tHYDC1XQiKIy6wW9npq4j44Gi/D8+VPx4/5+mDPHv93RR3v91W+ekTfXXJNNP88wlzfxNRzG2/Hf4HRnnuIu9sdwvJ5LfdusXx+ct6jVLAcJcZJ8x+bN8dumodN2axKzBwm6D347DNUw8zzvyUl47DH48pdhgc/aWD/72VSibHjYq8n166+VTExkfyP5Am/HYbyYtYFtLuWNGI7VvJ05TK0y1tfn7f7kx1NPhScW/Wqdg+jri9cuLaq7FoUlKiYD7A1cD9wJ3AGcUT3fB1wL3FP9uVtUX0WMoYcRN1Ya1a5dyddKxRu7lWPszO/t+7w8tNH9DNk+3Q9GLgkcZ/OR+lrnoIRof38r/jeE2xJ3JyzVaos0kFVSFFgEHFT9fWfgbmApcD5wVvX8WcB5UX11mqDHXaMiql07JzC1WtCnjkl7Hx+LbHgs34nsK67YddIaIqqMEc2QmaDPeANcCfw1cBewyKZE/66o93aaoGflobdrAlPcWZxZH4dzQ2Sjf+Usc+wIbdbfn27WaO2GkLU33EyfqowRzdASQQeGgPXALsCWhtceD3jPCmAcGB8YGMjn02dEViv7tUNUa+O3czbsQjbYLzgwtNHNLLc+Hov8HEn+fUZGsveGm/Ww4zxNKCQjgshc0IEFwK3AidW/Ywl6/dFpHrpZNmtvj45GbymX5eFce28mM+xhh53PmZEND+FG35fCPO762vZazX9Q7X8z3nDQdazlKdK+PyzPopCMqJGpoAPdwPeA99adK33IJUvy3K+00esr0mzYV/L/Ihu9l3+zqEXBah7v4KC/Rx51XZIS9qQTR3jTPsUpJCPMMhR0wAFfAS5sOP/xhqTo+VF9zWZBjxKE+r+z8Obr489xZmjmfQywztazOLTRNRxn83kyllDHGbMVHnqSvsOe4jopwSvyJ0tBPxww4DZgbfU4HugHflgtW/wh0BfV12wW9CAPvavL8zAbv+gjI82LZnf39L7ziKf39CR7GunmGbuYt4Q2eoZuW8rtTV+LrGPoWQqvPHQRRsuqXJo5JOj+R9AjexYCm3dStJnwzimsiWx0Kl9K1XcWtelhm4k3K7yKobeWTk84S9ALRpSw+glC3mGSnp5kN4AgcavfsDrN8efcYVuZG9roK5xs3Tzz3Kkou7MKXbRSeIsmOkWzJy1luFlK0AtGlDj7CU6eCc1acjFO21r4otXefy9P2VW8KrTRw+xpgzxgS5eG25Pl7NGyCF0YZRDBGmUIZ0nQC0aUOAf956oXj/5+76gJSRaVM7Uv6chIPIGeP791JZFhSw2/mwsjO3g1V8YS9GYEuUhiHmWLJkJ5lCHhLEEvIKOj/iKc1vOJukksWOBfQVN/UxgdTeZt13+hs3yCqF2DqKUKDubnkZ1dwD9aF8/6fnmb8TyL5LVG2ZLHRKhOoQw3Jwl6gcnSywsTwNoEo6ixknjajV/oZkoi62vJ6+2K8+SxG5vsRg4JbXQbB9ge/G7GtUj75S6SMETZ0qytRfqszVKkG3FaJOgdRjsfj5PEwoO2h0saT/ebYRnn5jB/fuOXc9I+OufsyAGP4Eeh9f1xPM8iea1RtjRra7NPMkUJS9Uook1JkKB3EM16EHHe7/cfOql3ncX2cEHCkjR8U6ukqf9y/uD934t849nu/5jfLNT+/vRPMmX00M3SLxHc6d5wEZGgdxCt/vL5fcm6u70yxbgCGvaFTlPtEnfFyrBEaU+Pv0178ZDdzQtCDbiOo2wXtjzXT6P3nmYRtjxpdQw9LUW66ZUJCXoH0epH+WarUaK+jEn7r188rEZY+7CYelj9foXtdhGnRRr0suf9MnbfRXp0j7JlZGT6gmUjI/Hfm5YihaXKhAS9g2i1V9NsvXjUlzFpDP2YY5p7f+PhZ0/j08ffcWlkRyv4bOzPXSRh9yPMQ2+l9y4PvTVI0DuIVj8et9pDN2uuv2bta6zPr4nWXJ/Jpi+ae5f9sXfX0A4v4yTbiT+Ezg0oSugliDBhbaXodsK16UQk6B1GUo8vSftm6sXrvbqw8Zopfcx6xmltk4vQG8rWrWYnnRTa0WP02ZHPvyf2Zy2SFxoW+mh1WKToTy+diAS9xKTxghq/ZFEedP3M1NqXPWw8P5uChKNR+FqxVnzYejKN68Wv7Pt0ZIc3/MNlz72nE+LEeXroEvDWI0EvMVl8IaP6iOPVN47X+MWOuxVcnpt/hH3Gl3Br9JtPO82WDGzPVBBbQV4x9KR9tUv8O/2mI0EvMVl4iFFfxDghlDjjhdW/x3laCCtbTHvUPmPQjWQXtth1HBXayd1uX1vEb5sWxDjXqxV9ZTVOEueiXfH1MsT1JeglJqtH5rAvdZy4dtzx6sfp709W/x53e7m4x4IFUzZFt5+0lfxLZMM37XFtInEIuu7tFp40Ip/EuWhX7iHMaWi1t57VjVOCXmLy+OJHec7NLGiV5Oju9kQ4CzGvn4iUdIbsBX97fXTDs882m5xMfD1q17Kdyda0/6eS2Nyu3EOUc9Kqm2aW31MJeslpdUwwLMmZZLxmSxKzOtI8gdQ+8zHHTF2LPfid3cYB4W869FCzzZsTXY/av2UrBS/s/0xQ+CnqZpJEtIroobfShiw/rwRdNE0WN428t8BrPBqXBwjzhIMOvzh+F8/aJzkj+s033TTtekTdPPzOZ7V9XliSNMymOH3H+X9SpBh6q26a9WR5g5agi0LQbg+9fqPsMNFs5jiBb0U2+kDvhamrebIQ9DRljLXXs6TdVS55fU4zeeiihPh5R/WbbLSiisXPI4pqMzgYHHaIY6NzZnb//WZ77hna8EpebfN4OrH9zZJmohF0ViVIHPJ8SlAMXZSSZqtpshDsqKNmp98XME6lzTSv649/tG/OPzn0DU/Ra/vz37Fsq1RaW14Y9FqW+7AWiTyfElTlUnI6fYJD1jQbkqkJbjP9VCpT9gStUDg6GuzB1zbNrqd2k/l7vhhpwDCXJL55Jf2/k9dEI9EaJOgFZLZ+caImuKQta4zTV1zvPej9fksc1At7f3+8io4DuM22MSfUiIt5i81h23OnwpYvSPN/J+uJRnJO8kOCXkA6YVGnrEkjko3HggXRa8nU9xW1/EDQ9Q8LPaQRO79x5/OkXcNxoQatY8D2ZiL2Ta0dzFbnpF1I0AtIJyzqlDVJbmIjI/7CnbZGukZ9hUPYjSGuN59mb836JX69fibtTM6PHOx4rg5t0q7/O7PROWknEvQCUvYvgd8jeNKbWBZ9JLWxRpI4fE2k04QbGsc5lJ9GDng+Z5pjR2H+78xG56SdSNALSJkfU4M+W7PetVl+N8Jm4vnNbuoNZv1stJtZHjrQOAdZPxvb/n+n7M5J0ZCgF5SyJpLC4s/N3sRGR2cu6BW0QXSzNP77JJkMlCTWHjo7kx32Md4XOeD3PvSTVJ8pyY0nSTK7LM5JEclM0IGLgQ3A7XXn+oBrgXuqP3eLM5gEvbyEPYL7JSqTCMzoqFcaWN+vX6lgK2il1x4W4qlUqu/99rejB/rYxwIXBUsrvHGT2Un/HcvozORBloJ+JHBQg6CfD5xV/f0s4Lw4g0nQy0vcR/A0AtPux/vR0fQzWsNsTLSGyvr1ZkND4YO94hVmTzwx7W1pr13W11wefXNkGnIBhhoE/S5gUfX3RcBdcfqRoJeXuF/YNEIRlYDLw/NL66lHJQkT5xi2bTN729tCB93husx+9SszS5+8zDrp2e6bcqfTakHf0vD643H6kaCXmzjCmkYowsQg77U56ssQm/XQa32mXqY4zMWvHv/c94VCeOiqimmOwgg6sAIYB8YHBgZy+fCiuKQRijDRbqfnF1XmmLRevV7M4/YxOGi2H3faE4TvAjLKm6yHP8a2K+sbpTz05lDIRRSSZpJ0ft5/Kz2/qCeOprxrH5q8Z1+1AAAMQUlEQVQNR83jafsWJ4QK+4auPew/L7gvk8+fhDwrlcpIqwX94w1J0fPj9CNBF2bZCkUWnp+fPXFvPFl+lizDUefs9qlQYTew/zrjilxXHWxXpVIZyLLK5WvAI8B24CHgbUA/8MNq2eIPgb44g0nQRdY0Gxpo1XID9fbFFc0gcX6uhDHh5x8dNTt8p5sjhf1T/IN18WxLq07aEXIpU5mkJhaJWUMzE2eSrqWeJJST9GYTVkkT9T6/z18voruy2X7MYaEf7naW2vLFj8T/gDHtMMs/KVq2MkkJuhARJFm7JY1HmTYBHFTzntSb9RfRSfsIH4r+oNddl2isKAHN20MvWxI2rqB3IcQsZf364Nf6+6G3d/q53l5Ytar5/sPGHR6Gycnk7xsbg6Eh6Oryfo6NwcCAX0vHxYMfYWjQOIYfBHd49NHgHJx7rqeFEaxcCVu3Tj+3dat3Hrzr1uz1TEKaa18GJOhi1uIveB6bNsG8eZ6wOweDg7B6tSe4zfYfNm6a942NwYoVMDHhae/EhPf38ccHi+iqVfDz3mNwGIt4mN+wn3/n55zj3SVe9jLYsuW58RpvHlECOjzsXb/+/qnX5s3zf0/QGElIe+07njhufFaHQi6iSMSZ/dlM3DVNDD1NPXrUxKuwBbbqXxtbs93s9NMjwzF/OXd8hm1xkshJKoeyWNAtjxh6XolXFEMXIpr6L2RWseug/sO+8M3UtLck4fiNb0QK++n8x3N/xllVM25cO6v4d6vFNs/EqwRdiIS0c3p6MyLW0gTgPfeYLVwYKuyXc6LNY2ukgMa9vp2yTECeide4gq4YuhBV2hl3bSaJ19KE4wteABs3wh/+AH/3d75N/idXsJVehk9/Huu+fzeXXOKdP+WU6fHvuNe3U+LfRUy8StCFqJJ3JUY9zYhYLeE4OJg+gRvJ3Lnw9a8zNmq8q+dz/m2eeAL224/hkx2HTFyK2VSCdmws/vVt579DEgp544njxmd1KOQiik67Zhd20kSY2jU6kF+GhmIM7LOssArbnwtDJMkpFH2Wp2LoEnQhAmmliLVUIH//e7Ojjw4V9ntZYvbggxkOWgyKVuXivLb5sHz5chsfH89tPCHEVJ16/cSf3t4WhGXMOL/vY7xvywfD2333u3DssRkOXH6cc7ea2fKodoqhC5EDzU6UaYaoWZyZ4Rx7/ccHmN9rHMENwe2OO84L9n/wg8HTYkUqJOhCtJigmZx5iXqe1Ri1BO2Dg0fQ5Yxlix/lof4X+Tf+6EehUoGXvtSbmiuaRoIuRIvJzUMOIO9qjOFhWLfOc75vfXAPDl/wKyo8yyd4r/8bbr4ZFi70vPaf/7w1Rs0SJOhCtJh21yu3uwxw/XqYpMKZfAKH8WquCm586KGesF9wgfc4IxIhQReixbS7XjmXOvUQGj/n1bwah3H4Xg/A85/v/6Z/+icv4fDKV8LTT7feyJIgQReixbTbQ4bpYZB16/ITcwj+/CPnDcFvfwvPPANvfrP/m6+5BhYsgJ12gjvuaLmtnY4EXYgW024Pud1Efv6eHlizxguxfPnL/p1s2wYHHOB1sGZNXqZ3HKpDF0IUjzvugGXLPO89iDe/GT7/ee+GUHJUhy6E6Fxe+EL44x/hySe9nTr8+MpXvFDMXnt5cSQhQRdCFJgFC+Db3/aC/xdc4N/m4Ydhn328cMxVIRU0swAJuhCi+DgH73mPF2f/2c+C251wgtf2ve+dlbNQJehCiM7ikEM8YX/sMTj4YP82n/ykNwv1xS+GDRvyta+NSNCFEJ1Jfz/cdBPs2OGtC+PHbbfBn/yJ57XfELK+TEmQoAshOpuuLq/Y3cxbyTGIl73ME/Z//dfSzkKVoAshysOxx3pi/eCDsGSJf5uVK72bwDHHeLsslQgJuhCifCxeDPfd501IWrHCv81118Hznud57WvX5mtfi5CgCyHKS3c3fO5zntd+6aXB7V7yEk/YV6/Oz7YWIEEXQswOXv96T9h/8xvYZRf/Nu94hyfsr3+9N7Gpw2hK0J1zxznn7nLO3eucOysro4QQomXstx/8/vfeovQnnujf5rLLYN48r5Lm3nvzta8JUgu6c64CfBr4G2Ap8Ebn3NKsDBNCiJYybx5885ue1/7pT/u32bwZ9t3X89ovvzxf+1LQjId+MHCvmd1vZtuAS4ETsjFLCCFy5PTTPWEPWzzwda/zhP2d74Rnn83PtgQ0I+h7AQ/W/f1Q9ZwQQnQmy5Z5wv7443Dkkf5tLrrIS7but5+3jkyBaEbQnc+5GdX6zrkVzrlx59z4xo0bmxhOCCFyYtdd4Uc/8taDOfdc/zZ33+2t9Ogc/OAH+doXQDOC/hCwd93fi4EZtyszW21my81s+e67797EcEIIkTPOwf/+357Xfv31we3++q+9tuec09ZZqM0I+i3Avs65fZxzPcAbIGz3VyGE6GCOOsoT60cegaUB9R/nnuvNQj3sMC9skzOpBd3MngXeBXwPuBO4zMy06Z8Qotzsuae3o9Kzz8K73+3f5sYboa/P89pvuSU305qqQzeza8zsz8zsT80sxy1vhRCizVQq8KlPeV77FVcEtzv4YE/YN21quUmaKSqEEM3y2td6wn7ffbDHHv5tTj+95WZI0IUQIiuWLIFHH/WWDXjTm6a/1tfX8uHntHwEIYSYbey0E4yNecfVV3ve+7HHtnxYCboQQrSSV70qt6EUchFCiJIgQRdCiJIgQRdCiJIgQRdCiJIgQRdCiJIgQRdCiJIgQRdCiJLgLMelHp1zG4GngcdyGzQ9C5GdWdIJdnaCjSA7s6YT7Bw0s8j1x3MVdADn3LiZLc910BTIzmzpBDs7wUaQnVnTKXbGQSEXIYQoCRJ0IYQoCe0Q9NVtGDMNsjNbOsHOTrARZGfWdIqdkeQeQxdCCNEaFHIRQoiSkJugO+eOc87d5Zy71zl3Vl7jJsU5t84592vn3Frn3Hi77anhnLvYObfBOXd73bk+59y1zrl7qj93a6eNVZv87Pywc+631Wu61jl3fDttrNq0t3Pueufcnc65O5xzZ1TPF+qahthZqGvqnJvrnLvZOferqp0fqZ7fxzl3U/V6fr26oXwR7fyyc+6Buut5YDvtTI2ZtfwAKsB9wBKgB/gVsDSPsVPYug5Y2G47fOw6EjgIuL3u3PnAWdXfzwLOK6idHwbObLdtDXYuAg6q/r4zcDewtGjXNMTOQl1TwAELqr93AzcBhwCXAW+onv8sMFJQO78MnNTu69jskZeHfjBwr5ndb2bbgEuBE3IauxSY2Q3A5obTJwBrqr+vAV6Tq1E+BNhZOMzsETP7RfX3J4E7gb0o2DUNsbNQmMdT1T+7q4cBRwOXV88X4XoG2VkK8hL0vYAH6/5+iAL+p6xiwPedc7c651a025gI/sTMHgHviw8E7E5bCN7lnLutGpJpe2ioHufcEPASPG+tsNe0wU4o2DV1zlWcc2uBDcC1eE/lW8zs2WqTQnzvG+00s9r1XFW9np90zu3URhNTk5egO59zRb0rHmZmBwF/A7zTOXdkuw0qAZ8B/hQ4EHgE+ER7zZnCObcA+Cbwj2b2RLvtCcLHzsJdUzPbYWYHAovxnsr/3K9Zvlb5GNBgp3PuAOADwP7A/wD6gPe30cTU5CXoDwF71/29GHg4p7ETYWYPV39uAL6F9x+zqDzqnFsEUP25oc32+GJmj1a/RJPA5ynINXXOdeOJ5JiZXVE9Xbhr6mdnUa8pgJltAf4LLza9q3Outndxob73dXYeVw1tmZk9A3yJAl3PJOQl6LcA+1Yz3j3AG4Crcho7Ns65+c65nWu/A68Abg9/V1u5Cji1+vupwJVttCWQmkBWeS0FuKbOOQd8EbjTzC6oe6lQ1zTIzqJdU+fc7s65Xau/zwNejhfvvx44qdqsCNfTz87f1N3EHV6cv+3/R9OQ28SialnVhXgVLxeb2apcBk6Ac24JnlcOMAf4alHsdM59DTgKb2W4R4FzgP/EqyIYANYDrzOztiYkA+w8Ci80YHhVRO+oxanbhXPucODHwK+ByerpD+LFpwtzTUPsfCMFuqbOuRfhJT0reI7iZWZ2bvU7dSleGOOXwMlVL7hodl4H7I4XHl4LnFaXPO0YNFNUCCFKgmaKCiFESZCgCyFESZCgCyFESZCgCyFESZCgCyFESZCgCyFESZCgCyFESZCgCyFESfj/GSpDZnVEgkQAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Now lets have a look at scikit learn, another python package for all kinds of statistical learning algorithms\n",
"# In order to directly compare it to statsmodels, I first do again the simple linear regression from Lab 3.6.2\n",
"\n",
"fname = 'Boston.csv'\n",
"df = pd.read_csv(fname)\n",
"\n",
"'''Lab: 3.6.2 Simple Linear Regression (page 110)'''\n",
"\n",
"X = df[\"lstat\"] #independent variable (or predictor)\n",
"y = df[\"medv\"] #dependent variable (which we want to fit / predict)\n",
"\n",
"\n",
"#sklearn does not really handle pandas dataframes (at least it did not work for me) => convert it to numpy array\n",
"X_np = pd.DataFrame.as_matrix(X)\n",
"y_np = pd.DataFrame.as_matrix(y)\n",
"\n",
"#necessary to convert from 1D to 2D array since sklearn needs 2D input for predictor matrix\n",
"X_np2 = X_np[:,np.newaxis]\n",
"\n",
"\n",
"regr = linear_model.LinearRegression()\n",
"regr.fit(X_np2, y_np) #performing actual linear regression\n",
"\n",
"y_hat = regr.predict(X_np2) #predicting values using fit\n",
"\n",
"# we dont get a nice table with sklearn but we can acces the results like this:\n",
"# (note that there is no implementation of STDs, p-values,..., so in my opinion statsmodels is better for linear regressions)\n",
"print('Coefficients', regr.coef_)\n",
"print('Intercept:', regr.intercept_)\n",
"print('Variance score: %.2f' % r2_score(y_np, y_hat))\n",
"\n",
"\n",
"# Plot outputs\n",
"plt.scatter(X_np2, y_np, color='blue')\n",
"plt.plot(X_np2, y_hat, color='red', linewidth=3)\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:20: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
"/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:21: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Coefficients [-0.15784473]\n",
"Intercept: 39.9358610212\n",
"Variance score: 0.61\n"
]
},
{
"data": {
"text/plain": [
"Text(0,0.5,'mpg')"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"'''Exercise: 3.7.8 Simple Linear Regression (page 121)'''\n",
"\n",
"\n",
"fname = 'Auto.csv'\n",
"df = pd.read_csv(fname)\n",
"\n",
"#convert strings to float in case of e.g. '?' convert it to NaN\n",
"df['horsepower'] = df['horsepower'].apply(pd.to_numeric, errors='coerce') \n",
"\n",
"\n",
"# Drop NaN values, listing the converted columns explicitly, so NaN values in other columns aren't dropped\n",
"df = df.dropna(how='any',subset = ['horsepower'])\n",
"\n",
"\n",
"X = df[\"horsepower\"] #independent variable (or predictor)\n",
"Y = df[\"mpg\"] #dependent variable (which we want to fit / predict)\n",
"\n",
"\n",
"#sklearn does not really handle pandas dataframes (at leat it did not work for me) => convert to numpy array\n",
"X_np=pd.DataFrame.as_matrix(X)\n",
"Y_np =pd.DataFrame.as_matrix(Y)\n",
"\n",
"#necessary to convert from 1D to 2D array since sklearn needs 2D input for predictor matrix\n",
"X_np2 = X_np[:,np.newaxis]\n",
"\n",
"\n",
"regr = linear_model.LinearRegression()\n",
"regr.fit(X_np2, Y_np) #performing actual linear regression\n",
"\n",
"Y_hat = regr.predict(X_np2) #predicting values using fit\n",
"\n",
"print('Coefficients', regr.coef_)\n",
"print('Intercept:', regr.intercept_)\n",
"print('Variance score: %.2f' % r2_score(Y_np, Y_hat))\n",
"\n",
"# Plot outputs\n",
"plt.scatter(X_np2, Y_np, color='blue')\n",
"plt.plot(X_np2, Y_hat, color='red', linewidth=3) \n",
"plt.xlabel('horespower')\n",
"plt.ylabel('mpg')"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1440x1440 with 64 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"'''Exercise: 3.7.9 Multiple linear Regression (page 122)'''\n",
"\n",
"\n",
"fname = 'Auto.csv'\n",
"df = pd.read_csv(fname)\n",
"\n",
"#plot all binary variable combinations as scatter plots\n",
"f, axarr = plt.subplots(8,8,figsize=(20,20))\n",
"\n",
"col_to_use = ['mpg','cylinders','displacement','horsepower','weight','acceleration','year','origin']\n",
"df_new = df[col_to_use]\n",
"\n",
"c_row = 0\n",
"for row in df_new:\n",
" c_col = 0\n",
" for col in df_new:\n",
" var1 = df_new[col]\n",
" var2 = df_new[row]\n",
" axarr[c_row,c_col].scatter(var1,var2)\n",
" axarr[c_row,c_col].set_xlabel(col)\n",
" axarr[c_row,c_col].set_ylabel(row)\n",
" c_col += 1\n",
" c_row += 1\n",
"f.subplots_adjust(hspace=0.3, wspace=0.3)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>mpg</th>\n",
" <th>cylinders</th>\n",
" <th>displacement</th>\n",
" <th>weight</th>\n",
" <th>acceleration</th>\n",
" <th>year</th>\n",
" <th>origin</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>mpg</th>\n",
" <td>1.000000</td>\n",
" <td>-0.776260</td>\n",
" <td>-0.804443</td>\n",
" <td>-0.831739</td>\n",
" <td>0.422297</td>\n",
" <td>0.581469</td>\n",
" <td>0.563698</td>\n",
" </tr>\n",
" <tr>\n",
" <th>cylinders</th>\n",
" <td>-0.776260</td>\n",
" <td>1.000000</td>\n",
" <td>0.950920</td>\n",
" <td>0.897017</td>\n",
" <td>-0.504061</td>\n",
" <td>-0.346717</td>\n",
" <td>-0.564972</td>\n",
" </tr>\n",
" <tr>\n",
" <th>displacement</th>\n",
" <td>-0.804443</td>\n",
" <td>0.950920</td>\n",
" <td>1.000000</td>\n",
" <td>0.933104</td>\n",
" <td>-0.544162</td>\n",
" <td>-0.369804</td>\n",
" <td>-0.610664</td>\n",
" </tr>\n",
" <tr>\n",
" <th>weight</th>\n",
" <td>-0.831739</td>\n",
" <td>0.897017</td>\n",
" <td>0.933104</td>\n",
" <td>1.000000</td>\n",
" <td>-0.419502</td>\n",
" <td>-0.307900</td>\n",
" <td>-0.581265</td>\n",
" </tr>\n",
" <tr>\n",
" <th>acceleration</th>\n",
" <td>0.422297</td>\n",
" <td>-0.504061</td>\n",
" <td>-0.544162</td>\n",
" <td>-0.419502</td>\n",
" <td>1.000000</td>\n",
" <td>0.282901</td>\n",
" <td>0.210084</td>\n",
" </tr>\n",
" <tr>\n",
" <th>year</th>\n",
" <td>0.581469</td>\n",
" <td>-0.346717</td>\n",
" <td>-0.369804</td>\n",
" <td>-0.307900</td>\n",
" <td>0.282901</td>\n",
" <td>1.000000</td>\n",
" <td>0.184314</td>\n",
" </tr>\n",
" <tr>\n",
" <th>origin</th>\n",
" <td>0.563698</td>\n",
" <td>-0.564972</td>\n",
" <td>-0.610664</td>\n",
" <td>-0.581265</td>\n",
" <td>0.210084</td>\n",
" <td>0.184314</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" mpg cylinders displacement weight acceleration \\\n",
"mpg 1.000000 -0.776260 -0.804443 -0.831739 0.422297 \n",
"cylinders -0.776260 1.000000 0.950920 0.897017 -0.504061 \n",
"displacement -0.804443 0.950920 1.000000 0.933104 -0.544162 \n",
"weight -0.831739 0.897017 0.933104 1.000000 -0.419502 \n",
"acceleration 0.422297 -0.504061 -0.544162 -0.419502 1.000000 \n",
"year 0.581469 -0.346717 -0.369804 -0.307900 0.282901 \n",
"origin 0.563698 -0.564972 -0.610664 -0.581265 0.210084 \n",
"\n",
" year origin \n",
"mpg 0.581469 0.563698 \n",
"cylinders -0.346717 -0.564972 \n",
"displacement -0.369804 -0.610664 \n",
"weight -0.307900 -0.581265 \n",
"acceleration 0.282901 0.210084 \n",
"year 1.000000 0.184314 \n",
"origin 0.184314 1.000000 "
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#correlation matrix\n",
"df.corr()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Coefficients [-0.49337632 0.01989564 -0.01695114 -0.00647404 0.08057584 0.75077268\n",
" 1.4261405 ]\n",
"Intercept: -17.218434622\n",
"Variance score: 0.82\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:20: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n"
]
}
],
"source": [
"#doing multiple linear regression fit\n",
"\n",
"col_to_use_fit = ['cylinders','displacement','horsepower','weight','acceleration','year','origin']\n",
"\n",
"\n",
"#for using sklearn we have to use floats, however rows containing strings as '?' have to be deleted\n",
"\n",
"#convert strings to float and in case of e.g. '?' convert it to NaN\n",
"df[col_to_use_fit] = df[col_to_use_fit].apply(pd.to_numeric, errors='coerce') \n",
"\n",
"\n",
"# Drop NaN values, listing the converted columns explicitly, so NaN values in other columns aren't dropped\n",
"df = df.dropna(how='any',subset = col_to_use_fit)\n",
"\n",
"X = df[col_to_use_fit] #independent variables (or predictors)\n",
"Y = df[\"mpg\"] #dependent variable (which we want to fit / predict)\n",
"\n",
"\n",
"#sklearn does not really handle pandas dataframes (at least it did not work for me)\n",
"X_np=pd.DataFrame.as_matrix(X)\n",
"\n",
"\n",
"regr = linear_model.LinearRegression()\n",
"regr.fit(X_np, Y_np)\n",
"\n",
"Y_hat = regr.predict(X_np) #predicting values using fit\n",
"\n",
"print('Coefficients', regr.coef_)\n",
"print('Intercept:', regr.intercept_)\n",
"print('Variance score: %.2f' % r2_score(Y_np, Y_hat))\n"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Coefficients [ 6.98857616e+00 -4.78538689e-01 5.03433939e-01 4.13288566e-03\n",
" -5.85917321e+00 6.97430284e-01 -2.08955704e+01 -3.38326306e-03\n",
" 1.16133262e-02 3.57462990e-04 2.77871993e-01 -1.74125857e-01\n",
" 4.02168216e-01 -8.49062164e-05 2.47186800e-05 -3.47899523e-03\n",
" 5.93380246e-03 2.39811277e-02 -1.96842975e-05 -7.21273895e-03\n",
" -5.83750571e-03 2.23250718e-03 2.34619450e-04 -2.24523733e-04\n",
" -5.78847492e-04 5.56215079e-02 4.58316099e-01 1.39257020e-01]\n",
"Intercept: 35.4788874758\n",
"Variance score: 0.89\n"
]
}
],
"source": [
"# in order to get all interaction terms, obtain all combinations of predictors (of second order), see https://stackoverflow.com/questions/45828964/how-to-add-interaction-term-in-python-sklearn\n",
"poly = PolynomialFeatures(interaction_only=True,include_bias = False)\n",
"X_interact = poly.fit_transform(X_np) #new feature space: [x1,x2,...xN,x1*x2,...x1*XN,x2*x3,...x2*xN,...,...x(N-1)*xN] \n",
"\n",
"regr_interact = linear_model.LinearRegression()\n",
"regr_interact.fit(X_interact, Y_np)\n",
"\n",
"Y_hat_interact = regr_interact.predict(X_interact) #predicting values using fit\n",
"\n",
"\n",
"print('Coefficients', regr_interact.coef_)\n",
"print('Intercept:', regr_interact.intercept_)\n",
"print('Variance score: %.2f' % r2_score(Y_np, Y_hat_interact))\n",
"\n"
]
},
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