Man1130/jupyter/Man1130-python-comission/course_materials/Note/CH8B_Logit.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IRIS data set\n",
    "> - **label**: species   \n",
    "> - **features**: sepal_length, sepal_width, petal_length, petal_width"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width species\n",
       "0           5.1          3.5           1.4          0.2  setosa\n",
       "1           4.9          3.0           1.4          0.2  setosa\n",
       "2           4.7          3.2           1.3          0.2  setosa\n",
       "3           4.6          3.1           1.5          0.2  setosa\n",
       "4           5.0          3.6           1.4          0.2  setosa"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "iris = sns.load_dataset('iris')\n",
    "iris.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width species\n",
       "0           5.1          3.5           1.4          0.2  setosa\n",
       "1           4.9          3.0           1.4          0.2  setosa\n",
       "2           4.7          3.2           1.3          0.2  setosa\n",
       "3           4.6          3.1           1.5          0.2  setosa\n",
       "4           5.0          3.6           1.4          0.2  setosa"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make it binary\n",
    "data=iris[iris['species']!='virginica']\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>5.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>5.7</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>6.2</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>5.1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>5.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    sepal_length  sepal_width  petal_length  petal_width     species\n",
       "95           5.7          3.0           4.2          1.2  versicolor\n",
       "96           5.7          2.9           4.2          1.3  versicolor\n",
       "97           6.2          2.9           4.3          1.3  versicolor\n",
       "98           5.1          2.5           3.0          1.1  versicolor\n",
       "99           5.7          2.8           4.1          1.3  versicolor"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Estimation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100, 2)\n",
      "(100,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "X = data[['sepal_width','petal_width']]\n",
    "y = data['species']\n",
    "print(X.shape)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2.42148338]\n",
      "[[-1.76171746  3.815637  ]]\n"
     ]
    }
   ],
   "source": [
    "clf = LogisticRegression(solver='lbfgs', multi_class='ovr')\n",
    "clf.fit(X, y)\n",
    "\n",
    "print(clf.intercept_)\n",
    "print(clf.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "y1 = LabelEncoder().fit_transform(y)\n",
    "y1[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "sw = np.linspace(2,5,20)\n",
    "pw = (-clf.intercept_[0]/clf.coef_[0][1]) - (clf.coef_[0][0]/clf.coef_[0][1])*sw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f526a26dfd0>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.figure()\n",
    "\n",
    "plt.scatter(X['sepal_width'],X['petal_width'], c=y1)\n",
    "plt.xlabel('sepal_width')\n",
    "plt.ylabel('petal_width')\n",
    "plt.plot(sw,pw,color='red')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa',\n",
       "       'setosa', 'setosa', 'setosa', 'setosa'], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ypred = clf.predict(X)\n",
    "ypred[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.95172675, 0.04827325],\n",
       "       [0.89095581, 0.10904419],\n",
       "       [0.92077218, 0.07922782],\n",
       "       [0.90693   , 0.09307   ],\n",
       "       [0.95920605, 0.04079395],\n",
       "       [0.94896983, 0.05103017],\n",
       "       [0.91861416, 0.08138584],\n",
       "       [0.94295771, 0.05704229],\n",
       "       [0.87262494, 0.12737506],\n",
       "       [0.93451931, 0.06548069]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ypred_prob = clf.predict_proba(X)\n",
    "ypred_prob[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[50,  0],\n",
       "       [ 0, 50]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix,accuracy_score\n",
    "confusion_matrix(ypred,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(ypred,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Activity 1\n",
    "> Use the data generated by the next cell to do the Logistic Regression.  \n",
    "> Split the data into training (80%) and testing (20%).  \n",
    "> Do the Logistic Regression.  \n",
    "> Plot the training data and the fitted line (x2 on y-axis, and x1 on x-axis).  \n",
    "> plot the testing data and the fitted line (x2 on y-axis, and x1 on x-axis).  \n",
    "> Report the confusion matrix.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>x1</th>\n",
       "      <th>x2</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.217679</td>\n",
       "      <td>-1.424184</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.821455</td>\n",
       "      <td>0.427360</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.481278</td>\n",
       "      <td>0.668849</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.331864</td>\n",
       "      <td>-0.015787</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.361865</td>\n",
       "      <td>-0.491017</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         x1        x2  y\n",
       "0 -0.217679 -1.424184  0\n",
       "1  0.821455  0.427360  1\n",
       "2  1.481278  0.668849  1\n",
       "3  1.331864 -0.015787  1\n",
       "4 -0.361865 -0.491017  0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.random.seed(2019)\n",
    "\n",
    "x1 = np.random.standard_normal(1000)           # some continuous variables \n",
    "x2 = np.random.standard_normal(1000)\n",
    "z = 1 + 2*x1 + 3*x2        # linear combination with a bias\n",
    "pr = 1/(1+np.exp(-z))         # pass through an inv-logit function\n",
    "y = (pr > 0.5).astype('uint8') \n",
    "\n",
    "import pandas as pd\n",
    "df = pd.DataFrame({'x1':x1,'x2':x2,'y':y})\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[['x1','x2']]\n",
    "y = df['y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Splitting Data\n",
    "from sklearn.model_selection import train_test_split\n",
    "Xtrain,Xtest,ytrain,ytest = train_test_split(X, y, train_size=0.8, test_size=0.2,random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(800, 2)\n",
      "(200, 2)\n"
     ]
    }
   ],
   "source": [
    "print(Xtrain.shape)\n",
    "print(Xtest.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2.17556822]\n",
      "[[4.37776481 6.53152735]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "clf = LogisticRegression(solver='lbfgs', multi_class='ovr')\n",
    "\n",
    "clf.fit(Xtrain, ytrain)\n",
    "\n",
    "print(clf.intercept_)\n",
    "print(clf.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predictions\n",
    "ypred = clf.predict(Xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 83,   0],\n",
       "       [  0, 117]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, accuracy_score\n",
    "confusion_matrix(ytest,ypred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(ytest,ypred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "z1 = np.linspace(-3,3,20)\n",
    "z2 = (-clf.intercept_[0]/clf.coef_[0][1]) - (clf.coef_[0][0]/clf.coef_[0][1])*z1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f5266d300d0>]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure()\n",
    "plt.scatter(df['x1'],df['x2'],c=df['y'])\n",
    "plt.xlabel('x1')\n",
    "plt.ylabel('x2')\n",
    "plt.plot(z1,z2,color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}