KNN_Classifier_for_MNIST_dataset.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b11e65ad-be10-4a64-a973-730eb113bae8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'frame', 'categories', 'feature_names', 'target_names', 'DESCR', 'details', 'url'])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load dataset\n",
    "from sklearn.datasets import fetch_openml\n",
    "mnist = fetch_openml('mnist_784', version=1, as_frame=False)\n",
    "mnist.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9c29bbc3-fb8a-4d10-a412-c21a121e5e8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "X,y = mnist['data'], mnist['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b16c08ab-a57a-432e-a7e5-7611283bef9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70000, 784)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "55d675b0-a235-42a0-b8bb-767ed0323488",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70000,)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1f86e46c-648f-431d-91f9-10f4f5f112a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7i37sEnO15HHj7bJAELyDDgiCsgNBUHYgCMoOBEHZgSAoOxAEZQeC+D+ypTV9clByEAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "some_digit = X[0]\n",
    "some_digit_image = some_digit.reshape(28,28)\n",
    "plt.imshow(some_digit_image, cmap=mpl.cm.binary)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9a3b9d8a-571b-4b3e-946c-2f006ad6ba3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'5'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a0ef1da4-0476-417f-af04-ae8f13082a0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "y = y.astype(np.uint8)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b44336e9-67b3-4703-ad7d-65115bf17973",
   "metadata": {},
   "source": [
    "we want to see few instances from the dataset . For this we need to plot of those instances. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "51fc0283-2db8-4bec-9b08-b3272cfa2e35",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_digit(data):\n",
    "    image = data.reshape(28, 28)\n",
    "    plt.imshow(image, cmap=mpl.cm.binary, interpolation='nearest')\n",
    "    plt.axis('off')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "da57f214-1079-4f78-af4c-9349e980e2ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_digits(instances, images_per_row=10, **options):\n",
    "    size = 28\n",
    "    images_per_row = min(len(instances), images_per_row)\n",
    "    n_rows = (len(instances) - 1) // images_per_row + 1\n",
    "    \n",
    "    n_empty = n_rows * images_per_row - len(instances)\n",
    "    padded_instances = np.concatenate([instances, np.zeros((n_empty, size * size))], axis=0)\n",
    "    \n",
    "    images_grid = padded_instances.reshape((n_rows, images_per_row, size, size))\n",
    "    \n",
    "    big_image = images_grid.transpose(0, 2, 1, 3).reshape(n_rows * size, images_per_row * size)\n",
    "    \n",
    "    plt.imshow(big_image, cmap=plt.cm.binary, **options)\n",
    "    plt.axis('off')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1d4e719c-8aeb-4ce3-9ee9-11cea6793682",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize=(8,4))\n",
    "example_images = X[:100]\n",
    "plot_digits(example_images, images_per_row=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8211652a-f2df-44f0-9056-ed0cf0415fda",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8f287b8b-38a4-45c7-94e3-7faf6d6e43e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = X[:60000], X[60000:], y[:60000], y[60000:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b26e1b82-6364-4e38-b42a-8f3d1ef493e6",
   "metadata": {},
   "source": [
    "we have selected KNN classifier for this classification task. we need to find good hyperperameter values. we'll use #gridSearchCv for this task ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3eca596b-96de-40da-9816-00270673fdfc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 6 candidates, totalling 30 fits\n",
      "[CV 1/5] END ....n_neighbors=3, weights=uniform;, score=0.972 total time=  30.1s\n",
      "[CV 2/5] END ....n_neighbors=3, weights=uniform;, score=0.971 total time=  29.4s\n",
      "[CV 3/5] END ....n_neighbors=3, weights=uniform;, score=0.969 total time=  33.5s\n",
      "[CV 4/5] END ....n_neighbors=3, weights=uniform;, score=0.969 total time=  32.6s\n",
      "[CV 5/5] END ....n_neighbors=3, weights=uniform;, score=0.970 total time=  30.0s\n",
      "[CV 1/5] END ...n_neighbors=3, weights=distance;, score=0.972 total time=  31.1s\n",
      "[CV 2/5] END ...n_neighbors=3, weights=distance;, score=0.972 total time=  31.4s\n",
      "[CV 3/5] END ...n_neighbors=3, weights=distance;, score=0.970 total time=  31.2s\n",
      "[CV 4/5] END ...n_neighbors=3, weights=distance;, score=0.970 total time=  31.5s\n",
      "[CV 5/5] END ...n_neighbors=3, weights=distance;, score=0.971 total time=  32.2s\n",
      "[CV 1/5] END ....n_neighbors=4, weights=uniform;, score=0.969 total time=  35.5s\n",
      "[CV 2/5] END ....n_neighbors=4, weights=uniform;, score=0.968 total time=  36.4s\n",
      "[CV 3/5] END ....n_neighbors=4, weights=uniform;, score=0.968 total time=  34.6s\n",
      "[CV 4/5] END ....n_neighbors=4, weights=uniform;, score=0.967 total time=  34.7s\n",
      "[CV 5/5] END ....n_neighbors=4, weights=uniform;, score=0.970 total time=  36.0s\n",
      "[CV 1/5] END ...n_neighbors=4, weights=distance;, score=0.973 total time=  34.5s\n",
      "[CV 2/5] END ...n_neighbors=4, weights=distance;, score=0.972 total time=  35.0s\n",
      "[CV 3/5] END ...n_neighbors=4, weights=distance;, score=0.970 total time=  34.7s\n",
      "[CV 4/5] END ...n_neighbors=4, weights=distance;, score=0.971 total time=  34.4s\n",
      "[CV 5/5] END ...n_neighbors=4, weights=distance;, score=0.972 total time=  35.7s\n",
      "[CV 1/5] END ....n_neighbors=5, weights=uniform;, score=0.970 total time=  36.4s\n",
      "[CV 2/5] END ....n_neighbors=5, weights=uniform;, score=0.970 total time=  36.3s\n",
      "[CV 3/5] END ....n_neighbors=5, weights=uniform;, score=0.969 total time=  35.0s\n",
      "[CV 4/5] END ....n_neighbors=5, weights=uniform;, score=0.968 total time=  36.2s\n",
      "[CV 5/5] END ....n_neighbors=5, weights=uniform;, score=0.969 total time=  35.5s\n",
      "[CV 1/5] END ...n_neighbors=5, weights=distance;, score=0.970 total time=  34.7s\n",
      "[CV 2/5] END ...n_neighbors=5, weights=distance;, score=0.971 total time=  35.8s\n",
      "[CV 3/5] END ...n_neighbors=5, weights=distance;, score=0.970 total time=  34.6s\n",
      "[CV 4/5] END ...n_neighbors=5, weights=distance;, score=0.969 total time=  34.9s\n",
      "[CV 5/5] END ...n_neighbors=5, weights=distance;, score=0.971 total time=  35.5s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=KNeighborsClassifier(),\n",
       "             param_grid=[{'n_neighbors': [3, 4, 5],\n",
       "                          'weights': ['uniform', 'distance']}],\n",
       "             verbose=3)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "param_grid = [{'weights':['uniform', 'distance'], 'n_neighbors':[3, 4, 5]}]\n",
    "\n",
    "knn_clf = KNeighborsClassifier()\n",
    "grid_search = GridSearchCV(knn_clf, param_grid, cv=5, verbose=3)\n",
    "grid_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "bf9abcb5-4445-4886-8e39-7d421fcafc5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_neighbors': 4, 'weights': 'distance'}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "32bac873-d7e9-4160-baa6-263a9cdd0cf3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9716166666666666"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e89eca54-8f63-4bea-a977-a16013b56e01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9714"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "y_pred = grid_search.predict(X_test)\n",
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "31c00f16-8ec8-4a56-a1a1-d13e6e31d69f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec71c2fe-2a11-45a4-9680-f7c66fae1a9e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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