LogReg(Multiclass)DIGIT IMAGES.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import load_digits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "digits = load_digits()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['DESCR', 'data', 'feature_names', 'frame', 'images', 'target', 'target_names']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(digits)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "digits.data[0]  will give 8*8 matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  0.,  5., 13.,  9.,  1.,  0.,  0.,  0.,  0., 13., 15., 10.,\n",
       "       15.,  5.,  0.,  0.,  3., 15.,  2.,  0., 11.,  8.,  0.,  0.,  4.,\n",
       "       12.,  0.,  0.,  8.,  8.,  0.,  0.,  5.,  8.,  0.,  0.,  9.,  8.,\n",
       "        0.,  0.,  4., 11.,  0.,  1., 12.,  7.,  0.,  0.,  2., 14.,  5.,\n",
       "       10., 12.,  0.,  0.,  0.,  0.,  6., 13., 10.,  0.,  0.,  0.])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digits.data[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "plt.gray() will show image in gray shades "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "index 0 will give image of digit at this index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1e5d723f730>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.gray()\n",
    "plt.matshow(digits.images[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TARGET GIVE OUTPUT OF DIGITS AT INDEXES MENTIONED IN THE SQUARE BRACKETS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digits.target[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.gray()\n",
    "for i in range(5):\n",
    "    plt.matshow(digits.images[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test = train_test_split(digits.data,digits.target,test_size = 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1797"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(digits.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1437"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "360"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import linear_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = linear_model.LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1437, 64)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "CHECKING ACCURACY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9722222222222222"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "VISUALINZING FEW DIGITS AT DIFFERNET INDEXES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1e5d96a69d0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.matshow(digits.images[100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digits.target[100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1e5d97093a0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.matshow(digits.images[1000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digits.target[1000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict([digits.data[1000]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PREDICTING MORE THAN ONE DIGIT VALUES AT A TIME"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 4, 0, 5, 3])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(digits.data[1000:1005])                #here we don't use the square brackets around digits.data coz our range makes it 2D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predicted = model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "confu_mat = confusion_matrix(y_test , y_predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[40,  0,  0,  0,  1,  0,  0,  0,  0,  0],\n",
       "       [ 0, 37,  0,  0,  1,  0,  0,  0,  0,  0],\n",
       "       [ 0,  0, 39,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0,  0,  0, 36,  0,  0,  0,  0,  0,  1],\n",
       "       [ 0,  2,  0,  0, 33,  0,  0,  0,  0,  0],\n",
       "       [ 0,  0,  0,  1,  0, 34,  0,  0,  0,  0],\n",
       "       [ 0,  0,  0,  0,  0,  1, 35,  0,  0,  0],\n",
       "       [ 0,  0,  0,  1,  0,  0,  0, 35,  0,  0],\n",
       "       [ 0,  0,  1,  0,  0,  1,  0,  0, 32,  0],\n",
       "       [ 0,  0,  0,  0,  0,  0,  0,  0,  0, 29]], dtype=int64)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confu_mat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "VISUALIZING CONFUSION MATRIX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(69.0, 0.5, 'truth')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,7))\n",
    "sn.heatmap(confu_mat,annot = True)\n",
    "plt.xlabel('predicted')\n",
    "plt.ylabel('truth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}