softmax regression.ipynb

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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [],
   "source": [
    "df=pd.read_csv('IRIS.csv')"
   ],
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   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "data": {
      "text/plain": "   sepal_length  sepal_width  petal_length  petal_width      species\n0           5.1          3.5           1.4          0.2  Iris-setosa\n1           4.9          3.0           1.4          0.2  Iris-setosa\n2           4.7          3.2           1.3          0.2  Iris-setosa\n3           4.6          3.1           1.5          0.2  Iris-setosa\n4           5.0          3.6           1.4          0.2  Iris-setosa",
      "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>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>Iris-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>Iris-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>Iris-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>Iris-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>Iris-setosa</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 3,
     "metadata": {},
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    {
     "data": {
      "text/plain": "   sepal_length  sepal_width  petal_length  petal_width      species\n0           5.1          3.5           1.4          0.2  Iris-setosa\n1           4.9          3.0           1.4          0.2  Iris-setosa\n2           4.7          3.2           1.3          0.2  Iris-setosa\n3           4.6          3.1           1.5          0.2  Iris-setosa\n4           5.0          3.6           1.4          0.2  Iris-setosa",
      "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>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>Iris-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>Iris-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>Iris-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>Iris-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>Iris-setosa</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 6,
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   ],
   "source": [
    "df.head()"
   ],
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  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [],
   "source": [
    "encoder=LabelEncoder()\n",
    "df['species']=encoder.fit_transform(df['species'])"
   ],
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    "ExecuteTime": {
     "start_time": "2023-04-24T22:35:46.990713Z",
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   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "   sepal_length  sepal_width  petal_length  petal_width  species\n0           5.1          3.5           1.4          0.2        0\n1           4.9          3.0           1.4          0.2        0\n2           4.7          3.2           1.3          0.2        0\n3           4.6          3.1           1.5          0.2        0\n4           5.0          3.6           1.4          0.2        0",
      "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>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>"
     },
     "execution_count": 8,
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   ],
   "source": [
    "df.head()"
   ],
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   "cell_type": "code",
   "execution_count": 9,
   "outputs": [],
   "source": [
    "df=df[['sepal_length','petal_length','species']]"
   ],
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     "start_time": "2023-04-24T22:35:47.051647Z",
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  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "data": {
      "text/plain": "   sepal_length  petal_length  species\n0           5.1           1.4        0\n1           4.9           1.4        0\n2           4.7           1.3        0\n3           4.6           1.5        0\n4           5.0           1.4        0",
      "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>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>"
     },
     "execution_count": 10,
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   ],
   "source": [
    "df.head()"
   ],
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     "end_time": "2023-04-24T22:35:47.465860Z"
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   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [],
   "source": [
    "X=df.iloc[:,0:2]\n",
    "y=df.iloc[:,-1]"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-24T22:35:47.052285Z",
     "end_time": "2023-04-24T22:35:47.466170Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=2)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-24T22:35:47.094466Z",
     "end_time": "2023-04-24T22:35:47.466478Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Softmax regression"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [],
   "source": [
    "clf=LogisticRegression(multi_class='multinomial')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-24T22:35:47.094745Z",
     "end_time": "2023-04-24T22:35:47.466696Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-24T22:35:47.094995Z",
     "end_time": "2023-04-24T22:35:47.466920Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [],
   "source": [
    "model=clf.fit(X_train,y_train)\n",
    "y_pred=model.predict(X_test)"
   ],
   "metadata": {
    "collapsed": false,
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     "start_time": "2023-04-24T22:35:47.095191Z",
     "end_time": "2023-04-24T22:35:47.467166Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score 0.9666666666666667\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy Score\", accuracy_score(y_test,y_pred))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-24T22:35:47.110166Z",
     "end_time": "2023-04-24T22:35:47.468243Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix [[14  0  0]\n",
      " [ 0  7  1]\n",
      " [ 0  0  8]]\n"
     ]
    }
   ],
   "source": [
    "print(\"Confusion Matrix\", confusion_matrix(y_test,y_pred))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-24T22:35:47.155467Z",
     "end_time": "2023-04-24T22:35:47.468782Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# prediction"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/zuhaib/anaconda3/lib/python3.10/site-packages/sklearn/base.py:420: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": "array([[7.27439607e-01, 2.72148017e-01, 4.12375708e-04]])"
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query=np.array([[3.4,2.7]])\n",
    "clf.predict_proba(query)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-24T22:35:47.155833Z",
     "end_time": "2023-04-24T22:35:47.539248Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/zuhaib/anaconda3/lib/python3.10/site-packages/sklearn/base.py:420: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": "Text(0.5, 1.0, 'Softmax on Iris')"
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "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",
    "plt.xlabel('sepal length')\n",
    "plt.ylabel('petal length')\n",
    "plt.title('Softmax on Iris')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-24T22:37:16.046297Z",
     "end_time": "2023-04-24T22:37:16.503938Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}