softmax-demo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9408b672",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score,confusion_matrix\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "81bbf3dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = sns.load_dataset('iris')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ae40a49b",
   "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a4d1835a",
   "metadata": {},
   "outputs": [],
   "source": [
    "encoder = LabelEncoder()\n",
    "df['species'] = encoder.fit_transform(df['species'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "aefda19d",
   "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>0</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>0</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>0</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>0</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>0</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        0\n",
       "1           4.9          3.0           1.4          0.2        0\n",
       "2           4.7          3.2           1.3          0.2        0\n",
       "3           4.6          3.1           1.5          0.2        0\n",
       "4           5.0          3.6           1.4          0.2        0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0b3b80f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[['sepal_length','petal_length','species']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f151b597",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        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>sepal_length</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  petal_length  species\n",
       "0           5.1           1.4        0\n",
       "1           4.9           1.4        0\n",
       "2           4.7           1.3        0\n",
       "3           4.6           1.5        0\n",
       "4           5.0           1.4        0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "28766276",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.iloc[:,0:2]\n",
    "y = df.iloc[:,-1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2d799863",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7e07861a",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = LogisticRegression(multi_class='multinomial')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "98a9c657",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(multi_class='multinomial')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3ef268f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ac7a921e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9666666666666667\n"
     ]
    }
   ],
   "source": [
    "print(accuracy_score(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0ca5e137",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    0  1  2\n",
       "0  14  0  0\n",
       "1   0  7  1\n",
       "2   0  0  8"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(confusion_matrix(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "474bdcd5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6a1097b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[7.25957888e-01, 2.73627865e-01, 4.14246954e-04]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#query=clf.predict([[3.4,2.7]])\n",
    "clf.predict_proba([[3.4,2.7]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e72bedfb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[7.25957888e-01, 2.73627865e-01, 4.14246954e-04]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# prediction\n",
    "query = np.array([[3.4,2.7]])\n",
    "clf.predict_proba(query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3667c802",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.predict(query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c2c1cb5c",
   "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": [
    "from mlxtend.plotting import plot_decision_regions\n",
    "\n",
    "plot_decision_regions(X.values, y.values, clf, legend=2)\n",
    "\n",
    "# Adding axes annotations\n",
    "plt.xlabel('sepal length [cm]')\n",
    "plt.xlabel('petal length [cm]')\n",
    "plt.title('Softmax on Iris')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a72764e8",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}