6.2 Logistic Regression - Coefficients.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression - Coefficients\n",
    "### Diabetes Prediction\n",
    "\n",
    "Boise State University\\\n",
    "Department of Computer Science\\\n",
    "CS 334 Algorithms of Machine Learning\\\n",
    "Conrad Kennington\\\n",
    "Fall 2020"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Simple Logistic Regression Example using scikit-learn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import pandas\n",
    "import pandas as pd\n",
    "\n",
    "col_names = ['pregnant', 'glucose', 'bp', 'skin', 'insulin', 'bmi', 'pedigree', 'age', 'label']\n",
    "\n",
    "# load dataset\n",
    "patients = pd.read_csv(\"diabetes.csv\", header=None, names=col_names, skiprows=[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnant</th>\n",
       "      <th>glucose</th>\n",
       "      <th>bp</th>\n",
       "      <th>skin</th>\n",
       "      <th>insulin</th>\n",
       "      <th>bmi</th>\n",
       "      <th>pedigree</th>\n",
       "      <th>age</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pregnant  glucose  bp  skin  insulin   bmi  pedigree  age  label\n",
       "0         6      148  72    35        0  33.6     0.627   50      1\n",
       "1         1       85  66    29        0  26.6     0.351   31      0\n",
       "2         8      183  64     0        0  23.3     0.672   32      1\n",
       "3         1       89  66    23       94  28.1     0.167   21      0\n",
       "4         0      137  40    35      168  43.1     2.288   33      1"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patients.head() # Note all the features are numeric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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17WtOLwNen+QLwC3Acyc7sjQe75IqSep8piBJ6oyCJKkzCpKkzihIkjqjIEnqjIIkqTMKkqTu/wEO5VxtE/wPuwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "ax = sns.countplot(x=\"label\", data=patients) # Without vs with Diabetes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_cols = ['pregnant', 'insulin', 'bmi', 'age','glucose','bp','pedigree']\n",
    "X = patients[feature_cols] # Features\n",
    "y = patients.label # Target variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test=train_test_split(X, y, test_size=0.25, random_state=25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the class\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "# instantiate the model (using the default parameters)\n",
    "logreg = LogisticRegression(max_iter=5000)\n",
    "\n",
    "# fit the model with data\n",
    "logreg.fit(X_train,y_train)\n",
    "\n",
    "\n",
    "y_pred = logreg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>pregnant</th>\n",
       "      <th>insulin</th>\n",
       "      <th>bmi</th>\n",
       "      <th>age</th>\n",
       "      <th>glucose</th>\n",
       "      <th>bp</th>\n",
       "      <th>pedigree</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>459</th>\n",
       "      <td>9</td>\n",
       "      <td>60</td>\n",
       "      <td>25.9</td>\n",
       "      <td>81</td>\n",
       "      <td>134</td>\n",
       "      <td>74</td>\n",
       "      <td>0.460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>4</td>\n",
       "      <td>207</td>\n",
       "      <td>37.1</td>\n",
       "      <td>56</td>\n",
       "      <td>111</td>\n",
       "      <td>72</td>\n",
       "      <td>1.390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>36.8</td>\n",
       "      <td>57</td>\n",
       "      <td>95</td>\n",
       "      <td>72</td>\n",
       "      <td>0.485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>48.8</td>\n",
       "      <td>37</td>\n",
       "      <td>137</td>\n",
       "      <td>108</td>\n",
       "      <td>0.227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>700</th>\n",
       "      <td>2</td>\n",
       "      <td>200</td>\n",
       "      <td>35.9</td>\n",
       "      <td>26</td>\n",
       "      <td>122</td>\n",
       "      <td>76</td>\n",
       "      <td>0.483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>669</th>\n",
       "      <td>9</td>\n",
       "      <td>100</td>\n",
       "      <td>30.9</td>\n",
       "      <td>45</td>\n",
       "      <td>154</td>\n",
       "      <td>78</td>\n",
       "      <td>0.164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>53.2</td>\n",
       "      <td>25</td>\n",
       "      <td>162</td>\n",
       "      <td>76</td>\n",
       "      <td>0.759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>4</td>\n",
       "      <td>51</td>\n",
       "      <td>23.2</td>\n",
       "      <td>21</td>\n",
       "      <td>99</td>\n",
       "      <td>76</td>\n",
       "      <td>0.223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>582</th>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>26.5</td>\n",
       "      <td>62</td>\n",
       "      <td>121</td>\n",
       "      <td>78</td>\n",
       "      <td>0.259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>9</td>\n",
       "      <td>240</td>\n",
       "      <td>45.4</td>\n",
       "      <td>54</td>\n",
       "      <td>171</td>\n",
       "      <td>110</td>\n",
       "      <td>0.721</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>192 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     pregnant  insulin   bmi  age  glucose   bp  pedigree\n",
       "459         9       60  25.9   81      134   74     0.460\n",
       "39          4      207  37.1   56      111   72     1.390\n",
       "344         8        0  36.8   57       95   72     0.485\n",
       "84          5        0  48.8   37      137  108     0.227\n",
       "700         2      200  35.9   26      122   76     0.483\n",
       "..        ...      ...   ...  ...      ...  ...       ...\n",
       "669         9      100  30.9   45      154   78     0.164\n",
       "120         0      100  53.2   25      162   76     0.759\n",
       "119         4       51  23.2   21       99   76     0.223\n",
       "582        12        0  26.5   62      121   78     0.259\n",
       "43          9      240  45.4   54      171  110     0.721\n",
       "\n",
       "[192 rows x 7 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.78125\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lets plot the logit curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.scatter(X['pedigree'],y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# This might seem like a bad candidate for logistic regression... "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most samples cluster on the left, with or without diabetes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But consider that most people do not get diabetes, even if their parents have it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.regplot(x = \"pedigree\", y = \"label\", data=patients, logistic = True)\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Coefficients are the learned weights applied to the model. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### You can access them and print them out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Importance')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 792x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "coeff = list(logreg.coef_[0])\n",
    "labels = list(X_train.columns)\n",
    "features = pd.DataFrame()\n",
    "features['Features'] = labels\n",
    "features['importance'] = coeff\n",
    "features.sort_values(by=['importance'], ascending=True, inplace=True)\n",
    "features['positive'] = features['importance'] > 0\n",
    "features.set_index('Features', inplace=True)\n",
    "features.importance.plot(kind='barh', figsize=(11, 6),color = features.positive.map({True: 'blue', False: 'red'}))\n",
    "plt.xlabel('Importance')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Glucose level, BMI, pregnancies and diabetes pedigree function have significant influence on the model, specially glucose level and BMI. \n",
    "\n",
    "Blood pressure has a negative influence on the prediction, i.e. higher blood pressure is correlated with a person not being diabetic. (also, note that blood pressure is more important as a feature than age, because the magnitude is higher for blood pressure)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## When you think about it... a train Logistic Regression model is the just coefficients, or weights. The algorithm remains the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
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