regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline \n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn.datasets import load_digits\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "x, y = load_digits(return_X_y=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  0.,  0., 12., 13.,  5.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., 11., 16.,  9.,  0.,  0.],\n",
       "       [ 0.,  0.,  3., 15., 16.,  6.,  0.,  0.],\n",
       "       [ 0.,  7., 15., 16., 16.,  2.,  0.,  0.],\n",
       "       [ 0.,  0.,  1., 16., 16.,  3.,  0.,  0.],\n",
       "       [ 0.,  0.,  1., 16., 16.,  6.,  0.,  0.],\n",
       "       [ 0.,  0.,  1., 16., 16.,  6.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., 11., 16., 10.,  0.,  0.]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[1].reshape(8,8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(x[1].reshape(8,8), interpolation='nearest')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Standardizing data \n",
    "scaler = StandardScaler()\n",
    "x_train = scaler.fit_transform(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LogisticRegression(solver='saga', C=10, multi_class='multinomial',\n",
    "                           random_state=0, max_iter=2000).fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test = scaler.transform(x_test)\n",
    "y_pred = model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9611111111111111"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 8))\n",
    "ax.imshow(cm)\n",
    "ax.grid(False)\n",
    "ax.set_xlabel('Predicted outputs', color='black')\n",
    "ax.set_ylabel('Actual outputs', color='black')\n",
    "ax.xaxis.set(ticks=range(10))\n",
    "ax.yaxis.set(ticks=range(10))\n",
    "ax.set_ylim(9.5, -0.5)\n",
    "for i in range(10):\n",
    "    for j in range(10):\n",
    "        ax.text(j, i, cm[i, j], ha='center', va='center', color='white')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        27\n",
      "           1       0.91      0.89      0.90        35\n",
      "           2       0.97      0.97      0.97        36\n",
      "           3       0.97      1.00      0.98        29\n",
      "           4       0.94      1.00      0.97        30\n",
      "           5       0.97      0.93      0.95        40\n",
      "           6       1.00      0.98      0.99        44\n",
      "           7       1.00      0.97      0.99        39\n",
      "           8       0.95      0.92      0.94        39\n",
      "           9       0.91      0.98      0.94        41\n",
      "\n",
      "    accuracy                           0.96       360\n",
      "   macro avg       0.96      0.96      0.96       360\n",
      "weighted avg       0.96      0.96      0.96       360\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.1 ('venv': venv)",
   "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": "3.9.1"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "b63b40442186946a02b4e800957036ca262ee8f6bf374aad2e72ddc4a71cf0ff"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}