04-perian-digit-classification-with-knn.ipynb

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  {
   "cell_type": "markdown",
   "id": "67c1d1fd",
   "metadata": {},
   "source": [
    "## K Nieghborss Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ad8ddbb1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SYSTEM_WGETRC = c:/progra~1/wget/etc/wgetrc\n",
      "syswgetrc = C:\\Program Files (x86)\\GnuWin32/etc/wgetrc\n",
      "--2022-07-16 11:59:40--  https://raw.githubusercontent.com/soroushfathi/ML-notebooks/main/dataset.py\n",
      "Resolving raw.githubusercontent.com... 185.199.109.133, 185.199.111.133, 185.199.110.133, ...\n",
      "Connecting to raw.githubusercontent.com|185.199.109.133|:443... connected.\n",
      "Unable to establish SSL connection.\n"
     ]
    }
   ],
   "source": [
    "!wget https://raw.githubusercontent.com/soroushfathi/ML-notebooks/main/dataset.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "285aa6e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "import matplotlib.pyplot as plt \n",
    "import numpy as np\n",
    "from mydataset import load_hoda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2e620484",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 49)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, Y_train, X_test, Y_test = load_hoda()\n",
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3e2cb775",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=3)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=3)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neigh = KNeighborsClassifier(n_neighbors=3)\n",
    "neigh.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e9e3121f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n",
      "(1, 49)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([5], dtype=uint8)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# predict i'th data of dataset\n",
    "i = 4\n",
    "print(Y_test[i])\n",
    "b = np.array([X_test[i]])\n",
    "print(b.shape)\n",
    "neigh.predict(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9c635e00",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample 24 is a 9, and prediction: [9]\n"
     ]
    }
   ],
   "source": [
    "sample = 24\n",
    "X = [X_test[sample]]\n",
    "predict_class = neigh.predict(X)\n",
    "print(\"Sample {} is a {}, and prediction: {}\".format(sample, Y_test[sample], predict_class))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0d52f710",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.         0.         0.         0.         0.         0.\n",
      "  0.33333333 0.         0.         0.66666667]]\n"
     ]
    }
   ],
   "source": [
    "print(neigh.predict_proba(X))\n",
    "# prediction probability would be [0, 0.333, 0.666, 1], because of knn is 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bfe6ec14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7, 2, 3, 1, 5, 5, 4, 7, 3, 3, 0, 8, 8, 0, 2, 9, 3, 6, 7, 4, 0, 3,\n",
       "       6, 3, 9, 1, 7, 5, 2, 9, 7, 5, 5, 8, 9, 6, 5, 1, 4, 8, 8, 4, 7, 7,\n",
       "       1, 2, 7, 9, 0, 3, 7, 4, 7, 5, 2, 9, 8, 2, 9, 8, 8, 6, 6, 6, 6, 6,\n",
       "       2, 4, 2, 4, 4, 5, 9, 1, 8, 2, 0, 5, 6, 2, 4, 3, 3, 7, 7, 7, 7, 1,\n",
       "       8, 1, 7, 8, 7, 7, 8, 9, 4, 2, 4, 1, 0, 2, 9, 6, 3, 5, 5, 0, 0, 2,\n",
       "       6, 7, 9, 3, 9, 9, 8, 7, 9, 2, 0, 2, 5, 5, 9, 6, 9, 3, 0, 3, 7, 6,\n",
       "       5, 2, 9, 0, 4, 1, 8, 2, 2, 3, 0, 2, 9, 3, 8, 6, 7, 0, 9, 9, 0, 7,\n",
       "       6, 4, 3, 7, 9, 3, 7, 0, 7, 1, 9, 4, 7, 3, 4, 1, 5, 6, 7, 9, 0, 3,\n",
       "       5, 3, 5, 7, 4, 1, 3, 3, 1, 1, 3, 3, 8, 9, 6, 7, 7, 2, 3, 0, 1, 4,\n",
       "       9, 5], dtype=uint8)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_classes = neigh.predict(X_test)\n",
    "pred_classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5ddfab8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.93"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(pred_classes == Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7cf53e9d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is 93.00 %\n"
     ]
    }
   ],
   "source": [
    "acc = neigh.score(X_test, Y_test)\n",
    "print(\"Accuracy is %.2f %%\" % (acc*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ecefb22b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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