CHAPTER 3- Classification- 3rd notebook  ( minst data ) .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d0ae38f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "798415bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# as this project required this version \n",
    "import sys\n",
    "\n",
    "assert sys.version_info >= (3, 7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5272e7a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# it also requires Scikit-Learn ≥ 1.0.1:\n",
    "from packaging import version\n",
    "import sklearn\n",
    "\n",
    "assert version.parse(sklearn.__version__) >= version.parse(\"1.0.1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a6fc6df9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# defiing the font size \n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rc('font', size=14)\n",
    "plt.rc('axes', labelsize=14, titlesize=14)\n",
    "plt.rc('legend', fontsize=14)\n",
    "plt.rc('xtick', labelsize=10)\n",
    "plt.rc('ytick', labelsize=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2d05592f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "IMAGES_PATH = Path() / \"images\" / \"classification\"\n",
    "IMAGES_PATH.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n",
    "    path = IMAGES_PATH / f\"{fig_id}.{fig_extension}\"\n",
    "    if tight_layout:\n",
    "        plt.tight_layout()\n",
    "    plt.savefig(path, format=fig_extension, dpi=resolution)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82318273",
   "metadata": {},
   "source": [
    "# loading the MNIST data set "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1c67bc80",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_openml\n",
    "\n",
    "mnist = fetch_openml('mnist_784', as_frame=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "43efed4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'frame', 'categories', 'feature_names', 'target_names', 'DESCR', 'details', 'url'])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mnist.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f902dd0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       ...,\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X, y = mnist.data, mnist.target\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "837fe3e9",
   "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": "b4387ce4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['5', '0', '4', ..., '4', '5', '6'], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "735c38da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70000,)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a660e99c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def plot_digit(image_data):\n",
    "    image = image_data.reshape(28, 28)\n",
    "    plt.imshow(image, cmap=\"binary\")\n",
    "    plt.axis(\"off\")\n",
    "\n",
    "some_digit = X[0]\n",
    "plot_digit(some_digit)\n",
    "save_fig(\"some_digit_plot\")  # extra code\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6e3700ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'5'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "aae8de96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   3.,  18.,\n",
       "        18.,  18., 126., 136., 175.,  26., 166., 255., 247., 127.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "        30.,  36.,  94., 154., 170., 253., 253., 253., 253., 253., 225.,\n",
       "       172., 253., 242., 195.,  64.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,  49., 238., 253., 253., 253., 253.,\n",
       "       253., 253., 253., 253., 251.,  93.,  82.,  82.,  56.,  39.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "        18., 219., 253., 253., 253., 253., 253., 198., 182., 247., 241.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,  80., 156., 107., 253.,\n",
       "       253., 205.,  11.,   0.,  43., 154.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,  14.,   1., 154., 253.,  90.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "       139., 253., 190.,   2.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,  11., 190., 253.,  70.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,  35., 241., 225., 160., 108.,   1.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,  81., 240.,\n",
       "       253., 253., 119.,  25.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,  45., 186., 253., 253., 150.,  27.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,  16.,  93., 252., 253., 187.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0., 249., 253.,\n",
       "       249.,  64.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,  46., 130., 183., 253., 253., 207.,   2.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,  39., 148., 229., 253., 253., 253.,\n",
       "       250., 182.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,  24., 114.,\n",
       "       221., 253., 253., 253., 253., 201.,  78.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,  23.,  66., 213., 253., 253., 253., 253., 198.,  81.,\n",
       "         2.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,  18., 171., 219., 253., 253.,\n",
       "       253., 253., 195.,  80.,   9.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,  55.,\n",
       "       172., 226., 253., 253., 253., 253., 244., 133.,  11.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0., 136., 253., 253., 253., 212., 135.,\n",
       "       132.,  16.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "53fb41bb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.]]),\n",
       " array(['5', '0', '4', ..., '4', '5', '6'], dtype=object))"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X,y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "de72a814",
   "metadata": {},
   "outputs": [],
   "source": [
    "# y = y.astype(float)\n",
    "# y = y.astype(int)\n",
    "# y = y.astype(complex)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "357f8d20",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = y.astype(np.uint8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "69f2e2ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "# splitting the data in training and test set before exploring the data further \n",
    "X_train, X_test, y_train, y_test = X[:60000], X[60000:], y[:60000], y[60000:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a2e0b39",
   "metadata": {},
   "source": [
    "# training a binary classifier "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8588af94",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_5 = (y_train == 5) # True for all 5s, False for all other digits.\n",
    "y_test_5 = (y_test == 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "87ef748c",
   "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>SGDClassifier(random_state=42)</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\">SGDClassifier</label><div class=\"sk-toggleable__content\"><pre>SGDClassifier(random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SGDClassifier(random_state=42)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import SGDClassifier\n",
    "sgd_clf = SGDClassifier(random_state=42)\n",
    "sgd_clf.fit(X_train, y_train_5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "94506097",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sgd_clf.predict([some_digit])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4920f66e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.95035, 0.96035, 0.9604 ])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "cross_val_score(sgd_clf, X_train, y_train_5, cv=3, scoring=\"accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f26315fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.95936795, 0.89060092, 0.74963109])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "cross_val_score(sgd_clf, X_train, y_train_5, cv=3, scoring=\"precision\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfb74378",
   "metadata": {},
   "source": [
    "# confuson matrix "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "dfdcb1d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_predict\n",
    "\n",
    "y_train_pred = cross_val_predict(sgd_clf, X_train, y_train_5, cv=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6e0a3bb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[53892,   687],\n",
       "       [ 1891,  3530]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "cm = confusion_matrix(y_train_5, y_train_pred)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "248bdbde",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[54579,     0],\n",
       "       [    0,  5421]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_perfect_predictions = y_train_5 \n",
    "confusion_matrix(y_train_5, y_train_perfect_predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c3f5c52",
   "metadata": {},
   "source": [
    "# precision and recall "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "fbc6a8cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8370879772350012"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import precision_score, recall_score\n",
    "\n",
    "precision_score(y_train_5, y_train_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7f2e9f6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6511713705958311"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(y_train_5, y_train_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "bd30a0e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7325171197343846"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "\n",
    "f1_score(y_train_5, y_train_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "009698a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2164.22030239])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_scores = sgd_clf.decision_function([some_digit])\n",
    "y_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d7cdc07f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2164.22030239])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_scores = sgd_clf.decision_function([some_digit])\n",
    "y_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "431465bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "threshold = 0\n",
    "y_some_digit_pred = (y_scores > threshold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e5ec20b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_some_digit_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d9d70667",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_scores > 0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "3e6d9371",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "threshold =  5000\n",
    "y_some_digit_pred = (y_scores > threshold)\n",
    "y_some_digit_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "050b8681",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_scores = cross_val_predict(sgd_clf, X_train, y_train_5, cv=3,\n",
    "                             method=\"decision_function\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "8c0d24e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_recall_curve\n",
    "\n",
    "precisions, recalls, thresholds = precision_recall_curve(y_train_5, y_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "f96fbbc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 4))  # extra code – it's not needed, just formatting\n",
    "plt.plot(thresholds, precisions[:-1], \"b--\", label=\"Precision\", linewidth=2)\n",
    "plt.plot(thresholds, recalls[:-1], \"r-\", label=\"Recall\", linewidth=2)\n",
    "plt.vlines(threshold, 0, 1.0, \"k\", \"dotted\", label=\"threshold\")\n",
    "plt.xlabel(\"Threshold\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b50a0a3",
   "metadata": {},
   "source": [
    "# ROC curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "9528a8dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_curve\n",
    "fpr, tpr, thresholds = roc_curve(y_train_5, y_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "80badf25",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_roc_curve(fpr, tpr, label=None):\n",
    "    plt.plot(fpr, tpr, linewidth=2, label=label)\n",
    "    plt.plot([0, 1], [0, 1], 'k--')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "005b9f7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_roc_curve(fpr, tpr)\n",
    "plt.xlabel('False Positive Rate (Fall-Out)')\n",
    "plt.ylabel('True Positive Rate (Recall)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "04277368",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9604938554008616"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "roc_auc_score(y_train_5, y_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "4b27bf59",
   "metadata": {},
   "outputs": [],
   "source": [
    "# SGDClassifier\n",
    "# RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "5233e71c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "forest_clf = RandomForestClassifier(random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "6adcbbe7",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'y_probas_forest' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn [45], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m y_scores_forest \u001b[38;5;241m=\u001b[39m \u001b[43my_probas_forest\u001b[49m[:, \u001b[38;5;241m1\u001b[39m] \n",
      "\u001b[0;31mNameError\u001b[0m: name 'y_probas_forest' is not defined"
     ]
    }
   ],
   "source": [
    "y_scores_forest = y_probas_forest[:, 1] \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "88d2805f",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_probas_forest = cross_val_predict(forest_clf, X_train, y_train_5, cv=3,\n",
    "                                    method=\"predict_proba\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "4007bb81",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'y_train_pred_forest' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn [47], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m recall_score(y_train_5, \u001b[43my_train_pred_forest\u001b[49m)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'y_train_pred_forest' is not defined"
     ]
    }
   ],
   "source": [
    "recall_score(y_train_5, y_train_pred_forest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "82014c85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.11, 0.89],\n",
       "       [0.99, 0.01]])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_probas_forest[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "2d826cf5",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_scores_forest = y_probas_forest[:, 1]\n",
    "precisions_forest, recalls_forest, thresholds_forest = precision_recall_curve(\n",
    "    y_train_5, y_scores_forest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "f6965f4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 5))  # extra code – not needed, just formatting\n",
    "\n",
    "plt.plot(recalls_forest, precisions_forest, \"b-\", linewidth=2,\n",
    "         label=\"Random Forest\")\n",
    "plt.plot(recalls, precisions, \"--\", linewidth=2, label=\"SGD\")\n",
    "\n",
    "# extra code – just beautifies and saves Figure 3–8\n",
    "plt.xlabel(\"Recall\")\n",
    "plt.ylabel(\"Precision\")\n",
    "plt.axis([0, 1, 0, 1])\n",
    "plt.grid()\n",
    "plt.legend(loc=\"lower left\")\n",
    "save_fig(\"pr_curve_comparison_plot\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "a39c600c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9274509803921569"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_pred_forest = y_probas_forest[:, 1] >= 0.5  # positive proba ≥ 50%\n",
    "f1_score(y_train_5, y_train_pred_forest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "92483fee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9983436731328145"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_train_5, y_scores_forest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "b0204fdd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9897468089558485"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision_score(y_train_5, y_train_pred_forest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "e544ec74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8725327430363402"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(y_train_5, y_train_pred_forest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "da17b18d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Support Vector Machine ( SVM) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "b1c91a60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(random_state=42)</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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC(random_state=42)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "svm_clf = SVC(random_state=42)\n",
    "svm_clf.fit(X_train, y_train_5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "a3b08dd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_clf.predict([some_digit])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "f1b94b19",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "clf = make_pipeline(StandardScaler(), SVC())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "147e8b2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 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-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 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-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 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-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 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-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(random_state=42)</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-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC(random_state=42)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "# Create an instance of StandardScaler\n",
    "scaler = StandardScaler()\n",
    "\n",
    "# Fit the scaler to the training data\n",
    "scaler.fit(X_train)\n",
    "\n",
    "# Transform the training and test data using the fitted scaler\n",
    "X_train_scaled = scaler.transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "# Create an instance of SVC and fit it to the scaled training data\n",
    "svm_clf = SVC(random_state=42)\n",
    "svm_clf.fit(X_train_scaled, y_train_5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "4d30cdeb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_clf.predict([some_digit])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "db7e89c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 106, in __call__\n",
      "    score = scorer._score(cached_call, estimator, *args, **kwargs)\n",
      "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 267, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 1776, in precision_score\n",
      "    p, _, _, _ = precision_recall_fscore_support(\n",
      "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 1563, in precision_recall_fscore_support\n",
      "    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\n",
      "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 1381, in _check_set_wise_labels\n",
      "    raise ValueError(\n",
      "ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\n",
      "\n",
      "  warnings.warn(\n",
      "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 106, in __call__\n",
      "    score = scorer._score(cached_call, estimator, *args, **kwargs)\n",
      "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 267, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 1776, in precision_score\n",
      "    p, _, _, _ = precision_recall_fscore_support(\n",
      "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 1563, in precision_recall_fscore_support\n",
      "    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\n",
      "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 1381, in _check_set_wise_labels\n",
      "    raise ValueError(\n",
      "ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\n",
      "\n",
      "  warnings.warn(\n",
      "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 106, in __call__\n",
      "    score = scorer._score(cached_call, estimator, *args, **kwargs)\n",
      "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 267, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 1776, in precision_score\n",
      "    p, _, _, _ = precision_recall_fscore_support(\n",
      "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 1563, in precision_recall_fscore_support\n",
      "    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\n",
      "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 1381, in _check_set_wise_labels\n",
      "    raise ValueError(\n",
      "ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\n",
      "\n",
      "  warnings.warn(\n",
      "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 106, in __call__\n",
      "    score = scorer._score(cached_call, estimator, *args, **kwargs)\n",
      "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 267, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 1776, in precision_score\n",
      "    p, _, _, _ = precision_recall_fscore_support(\n",
      "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 1563, in precision_recall_fscore_support\n",
      "    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\n",
      "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 1381, in _check_set_wise_labels\n",
      "    raise ValueError(\n",
      "ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\n",
      "\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-validation scores:\n",
      "Accuracy: nan\n",
      "Precision: nan\n",
      "Recall: nan\n",
      "F1 score: nan\n",
      "ROC AUC: nan\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 106, in __call__\n",
      "    score = scorer._score(cached_call, estimator, *args, **kwargs)\n",
      "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 267, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 1776, in precision_score\n",
      "    p, _, _, _ = precision_recall_fscore_support(\n",
      "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 1563, in precision_recall_fscore_support\n",
      "    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\n",
      "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 1381, in _check_set_wise_labels\n",
      "    raise ValueError(\n",
      "ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\n",
      "\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_validate\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "svm_clf = SVC(random_state=42)\n",
    "\n",
    "scoring = ['accuracy', 'precision', 'recall', 'f1', 'roc_auc']\n",
    "results = cross_validate(svm_clf, X, y, cv=5, scoring=scoring)\n",
    "\n",
    "print(\"Cross-validation scores:\")\n",
    "print(\"Accuracy:\", results['test_accuracy'].mean())\n",
    "print(\"Precision:\", results['test_precision'].mean())\n",
    "print(\"Recall:\", results['test_recall'].mean())\n",
    "print(\"F1 score:\", results['test_f1'].mean())\n",
    "print(\"ROC AUC:\", results['test_roc_auc'].mean())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14fd4880",
   "metadata": {},
   "source": [
    "# multiclass classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "7053ff8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3], dtype=uint8)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sgd_clf.fit(X_train, y_train) # y_train, not y_train_5\n",
    "sgd_clf.predict([some_digit])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "39d853e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-31893.03095419, -34419.69069632,  -9530.63950739,\n",
       "          1823.73154031, -22320.14822878,  -1385.80478895,\n",
       "        -26188.91070951, -16147.51323997,  -4604.35491274,\n",
       "        -12050.767298  ]])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "some_digit_scores = sgd_clf.decision_function([some_digit])\n",
    "some_digit_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "d0df4735",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 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-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 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-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 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-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 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-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(random_state=42)</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-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC(random_state=42)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "svm_clf = SVC(random_state=42)\n",
    "svm_clf.fit(X_train[:2000], y_train[:2000])  # y_train, not y_train_5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "cf61467b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5], dtype=uint8)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_clf.predict([some_digit])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7ec5fd69",
   "metadata": {},
   "outputs": [],
   "source": [
    "some_digit_scores = svm_clf.decision_function([some_digit])\n",
    "some_digit_scores.round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "62dbc64f",
   "metadata": {},
   "outputs": [],
   "source": [
    "class_id = some_digit_scores.argmax()\n",
    "class_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ef55ab8",
   "metadata": {},
   "outputs": [],
   "source": [
    "svm_clf.classes_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c946de3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "svm_clf.classes_[class_id]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4768a96c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# extra code – shows how to get all 45 OvO scores if needed\n",
    "svm_clf.decision_function_shape = \"ovo\"\n",
    "some_digit_scores_ovo = svm_clf.decision_function([some_digit])\n",
    "some_digit_scores_ovo.round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a37d201",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "\n",
    "ovr_clf = OneVsRestClassifier(SVC(random_state=42))\n",
    "ovr_clf.fit(X_train[:2000], y_train[:2000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29bf76b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "ovr_clf.predict([some_digit])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "38c6d974",
   "metadata": {},
   "outputs": [],
   "source": [
    "len(ovr_clf.estimators_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "157324bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "sgd_clf = SGDClassifier(random_state=42)\n",
    "sgd_clf.fit(X_train, y_train)\n",
    "sgd_clf.predict([some_digit])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4613b9fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "sgd_clf.decision_function([some_digit]).round()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "83e75b83",
   "metadata": {},
   "outputs": [],
   "source": [
    "cross_val_score(sgd_clf, X_train, y_train, cv=3, scoring=\"accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e3a4164d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train.astype(\"float64\"))\n",
    "cross_val_score(sgd_clf, X_train_scaled, y_train, cv=3, scoring=\"accuracy\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25bb710a",
   "metadata": {},
   "source": [
    "# ERror analysis "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "39d24a9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_pred = cross_val_predict(sgd_clf, X_train_scaled, y_train, cv=3)\n",
    "conf_mx = confusion_matrix(y_train, y_train_pred)\n",
    "conf_mx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "45983e5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.matshow(conf_mx, cmap=plt.cm.gray)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "63377cd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "row_sums = conf_mx.sum(axis=1, keepdims=True)\n",
    "norm_conf_mx = conf_mx / row_sums"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ee9876b",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.fill_diagonal(norm_conf_mx, 0)\n",
    "plt.matshow(norm_conf_mx, cmap=plt.cm.gray)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "43744566",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6cd5bd36",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import ConfusionMatrixDisplay\n",
    "\n",
    "y_train_pred = cross_val_predict(sgd_clf, X_train_scaled, y_train, cv=3)\n",
    "plt.rc('font', size=9)  # extra code – make the text smaller\n",
    "ConfusionMatrixDisplay.from_predictions(y_train, y_train_pred)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bdee0bc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rc('font', size=10)  # extra code\n",
    "ConfusionMatrixDisplay.from_predictions(y_train, y_train_pred,\n",
    "                                        normalize=\"true\", values_format=\".0%\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "682aaf09",
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_weight = (y_train_pred != y_train)\n",
    "plt.rc('font', size=10)  # extra code\n",
    "ConfusionMatrixDisplay.from_predictions(y_train, y_train_pred,\n",
    "                                        sample_weight=sample_weight,\n",
    "                                        normalize=\"true\", values_format=\".0%\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03e3faed",
   "metadata": {},
   "source": [
    "# multilabel classification "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "21faf347",
   "metadata": {},
   "outputs": [],
   "source": [
    "# more than 1 labels by giving condition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "10579724",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "y_train_large = (y_train >= '7')\n",
    "y_train_odd = (y_train.astype('int8') % 2 == 1)\n",
    "y_multilabel = np.c_[y_train_large, y_train_odd]\n",
    "\n",
    "knn_clf = KNeighborsClassifier()\n",
    "knn_clf.fit(X_train, y_multilabel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c04c6490",
   "metadata": {},
   "outputs": [],
   "source": [
    "knn_clf.predict([some_digit])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb533ef2",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_knn_pred = cross_val_predict(knn_clf, X_train, y_multilabel, cv=3)\n",
    "f1_score(y_multilabel, y_train_knn_pred, average=\"macro\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb275230",
   "metadata": {},
   "outputs": [],
   "source": [
    "f1_score(y_multilabel, y_train_knn_pred, average=\"weighted\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9e09507f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.multioutput import ClassifierChain\n",
    "\n",
    "chain_clf = ClassifierChain(SVC(), cv=3, random_state=42)\n",
    "chain_clf.fit(X_train[:2000], y_multilabel[:2000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "450ab524",
   "metadata": {},
   "outputs": [],
   "source": [
    "chain_clf.predict([some_digit])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e592a383",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "fa0a9cbe",
   "metadata": {},
   "source": [
    "# multiout put classification "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "249cbfb8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# example in text is removing the noice of the image "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8ca333b4",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'np' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn [6], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mnp\u001b[49m\u001b[38;5;241m.\u001b[39mrandom\u001b[38;5;241m.\u001b[39mseed(\u001b[38;5;241m42\u001b[39m)  \u001b[38;5;66;03m# to make this code example reproducible\u001b[39;00m\n\u001b[1;32m      2\u001b[0m noise \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mrandom\u001b[38;5;241m.\u001b[39mrandint(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m100\u001b[39m, (\u001b[38;5;28mlen\u001b[39m(X_train), \u001b[38;5;241m784\u001b[39m))\n\u001b[1;32m      3\u001b[0m X_train_mod \u001b[38;5;241m=\u001b[39m X_train \u001b[38;5;241m+\u001b[39m noise\n",
      "\u001b[0;31mNameError\u001b[0m: name 'np' is not defined"
     ]
    }
   ],
   "source": [
    "np.random.seed(42)  # to make this code example reproducible\n",
    "noise = np.random.randint(0, 100, (len(X_train), 784))\n",
    "X_train_mod = X_train + noise\n",
    "noise = np.random.randint(0, 100, (len(X_test), 784))\n",
    "X_test_mod = X_test + noise\n",
    "y_train_mod = X_train\n",
    "y_test_mod = X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e96d71d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.subplot(121); plot_digit(X_test_mod[0])\n",
    "plt.subplot(122); plot_digit(y_test_mod[0])\n",
    "save_fig(\"noisy_digit_example_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "07ec37cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "knn_clf = KNeighborsClassifier()\n",
    "knn_clf.fit(X_train_mod, y_train_mod)\n",
    "clean_digit = knn_clf.predict([X_test_mod[0]])\n",
    "plot_digit(clean_digit)\n",
    "save_fig(\"cleaned_digit_example_plot\")  # extra code – saves Figure 3–13\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df1d9b5b",
   "metadata": {},
   "source": [
    "# excercise solutions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6552352",
   "metadata": {},
   "source": [
    "# 1) Try to build a classifier for the MNIST dataset that achieves over 97%accuracy\n",
    "# on the test set. Hint: the KNeighborsClassifier works quite well for this task;\n",
    "# you just need to find good hyperparameter values (try a grid search on the\n",
    "# weights and n_neighbors hyperparameters)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f745b7ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.neighbors import KNeighborsClassifier\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d0a2723",
   "metadata": {},
   "outputs": [],
   "source": [
    "knn_clf = KNeighborsClassifier()\n",
    "knn_clf.fit(X_train, y_train)\n",
    "baseline_accuracy = knn_clf.score(X_test, y_test)\n",
    "baseline_accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fdbd3c47",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "param_grid = [{'weights': [\"uniform\", \"distance\"], 'n_neighbors': [3, 4, 5, 6]}]\n",
    "\n",
    "knn_clf = KNeighborsClassifier()\n",
    "grid_search = GridSearchCV(knn_clf, param_grid, cv=5)\n",
    "grid_search.fit(X_train[:10_000], y_train[:10_000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8c07b027",
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0aac49a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_search.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5e8d3d8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_search.best_estimator_.fit(X_train, y_train)\n",
    "tuned_accuracy = grid_search.score(X_test, y_test)\n",
    "tuned_accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "ed04bef3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#2) Write a function that can shift an MNIST image in any direction (left, right, up, or down) by one pixel.5 \n",
    "# Then, for each image in the training set, create four shif‐ted copies (one per direction) and add them to the training set. Finally, train your\n",
    "#best model on this expanded training set and measure its accuracy on the test set.\n",
    "#You should observe that your model performs even better now! This technique of\n",
    "#artificially growing the training set is called data augmentation or training set\n",
    "# expansion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "7a912a0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import numpy as np\n",
    "# from scipy.ndimage import shift\n",
    "# from sklearn.datasets import fetch_openml\n",
    "# from sklearn.model_selection import train_test_split\n",
    "# from sklearn.metrics import accuracy_score\n",
    "# from sklearn.svm import SVC\n",
    "\n",
    "# def shift_images(images, labels, shift_directions):\n",
    "#     shifted_images = []\n",
    "#     shifted_labels = []\n",
    "\n",
    "#     for image, label in zip(images, labels):\n",
    "#         for direction in shift_directions:\n",
    "#             shifted_image = shift(image.reshape(28, 28), direction, cval=0).flatten()\n",
    "#             shifted_images.append(shifted_image)\n",
    "#             shifted_labels.append(label)\n",
    "\n",
    "#     return np.array(shifted_images), np.array(shifted_labels)\n",
    "\n",
    "# # Load MNIST dataset\n",
    "# mnist = fetch_openml('mnist_784', version=1)\n",
    "# X, y = mnist[\"data\"], mnist[\"target\"]\n",
    "\n",
    "# # Split the dataset into training and test sets\n",
    "# X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# # Define shift directions\n",
    "# shift_directions = [(0, 1), (0, -1), (1, 0), (-1, 0)]  # Right, Left, Down, Up\n",
    "\n",
    "# # Shift the training images and augment the training set\n",
    "# X_train_shifted, y_train_shifted = shift_images(X_train, y_train, shift_directions)\n",
    "\n",
    "# # Concatenate original and shifted training data\n",
    "# X_train_augmented = np.concatenate((X_train, X_train_shifted))\n",
    "# y_train_augmented = np.concatenate((y_train, y_train_shifted))\n",
    "\n",
    "# # Train your best model on the augmented training set\n",
    "# best_model = SVC(random_state=42)\n",
    "# best_model.fit(X_train_augmented, y_train_augmented)\n",
    "\n",
    "# # Evaluate the model on the test set\n",
    "# y_pred = best_model.predict(X_test)\n",
    "# accuracy = accuracy_score(y_test, y_pred)\n",
    "\n",
    "# print(\"Accuracy on test set:\", accuracy)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "f9a8e24d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.ndimage import shift"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "26e333d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def shift_image(image, dx, dy):\n",
    "    image = image.reshape((28, 28))\n",
    "    shifted_image = shift(image, [dy, dx], cval=0, mode=\"constant\")\n",
    "    return shifted_image.reshape([-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "f610be7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1200x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image = X_train[1233]\n",
    "shifted_image_down = shift_image(image, 0, 8)\n",
    "shifted_image_left = shift_image(image, -8, 0)\n",
    "\n",
    "plt.figure(figsize=(12,3))\n",
    "plt.subplot(131)\n",
    "plt.title(\"Original\", fontsize=14)\n",
    "plt.imshow(image.reshape(28, 28), interpolation=\"nearest\", cmap=\"Greys\")\n",
    "plt.subplot(132)\n",
    "plt.title(\"Shifted down\", fontsize=14)\n",
    "plt.imshow(shifted_image_down.reshape(28, 28), interpolation=\"nearest\", cmap=\"Greys\")\n",
    "plt.subplot(133)\n",
    "plt.title(\"Shifted left\", fontsize=14)\n",
    "plt.imshow(shifted_image_left.reshape(28, 28), interpolation=\"nearest\", cmap=\"Greys\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11659ac0",
   "metadata": {},
   "source": [
    "# 3) Tackle the Titanic dataset. A great place to start is on Kaggle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fcb5eaef",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "877c5f71",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "772e48b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#4) Build a spam classifier (a more challenging exercise):\n",
    "# • Download examples of spam and ham from Apache SpamAssassin’s public datasets.\n",
    "# • Unzip the datasets and familiarize yourself with the data format.\n",
    "# • Split the datasets into a training set and a test set.\n",
    "# • Write a data preparation pipeline to convert each email into a feature vector.\n",
    "#Your preparation pipeline should transform an email into a (sparse) vector\n",
    "#indicating the presence or absence of each possible word. For example, if all\n",
    "# emails only ever contain four words, “Hello,” “how,” “are,” “you,” then the email\n",
    "# “Hello you Hello Hello you” would be converted into a vector [1, 0, 0, 1]\n",
    "# (meaning [“Hello” is present, “how” is absent, “are” is absent, “you” is\n",
    "# present]), or [3, 0, 0, 2] if you prefer to count the number of occurrences of\n",
    "# each word.\n",
    "# • You may want to add hyperparameters to your preparation pipeline to control\n",
    "# whether or not to strip off email headers, convert each email to lowercase,\n",
    "# remove punctuation, replace all URLs with “URL,” replace all numbers with\n",
    "# “NUMBER,” or even perform stemming (i.e., trim off word endings; there are\n",
    "# Python libraries available to do this).\n",
    "# • Then try out several classifiers and see if you can build a great spam classifier,\n",
    "# with both high recall and high precision."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a27dc005",
   "metadata": {},
   "outputs": [],
   "source": [
    "# loading the data set\n",
    "import os\n",
    "import tarfile\n",
    "import urllib.request\n",
    "\n",
    "DOWNLOAD_ROOT = \"http://spamassassin.apache.org/old/publiccorpus/\"\n",
    "HAM_URL = DOWNLOAD_ROOT + \"20030228_easy_ham.tar.bz2\"\n",
    "SPAM_URL = DOWNLOAD_ROOT + \"20030228_spam.tar.bz2\"\n",
    "SPAM_PATH = os.path.join(\"datasets\", \"spam\")\n",
    "\n",
    "def fetch_spam_data(ham_url=HAM_URL, spam_url=SPAM_URL, spam_path=SPAM_PATH):\n",
    "    if not os.path.isdir(spam_path):\n",
    "        os.makedirs(spam_path)\n",
    "    for filename, url in ((\"ham.tar.bz2\", ham_url), (\"spam.tar.bz2\", spam_url)):\n",
    "        path = os.path.join(spam_path, filename)\n",
    "        if not os.path.isfile(path):\n",
    "            urllib.request.urlretrieve(url, path)\n",
    "        tar_bz2_file = tarfile.open(path)\n",
    "        tar_bz2_file.extractall(path=spam_path)\n",
    "        tar_bz2_file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3e872920",
   "metadata": {},
   "outputs": [],
   "source": [
    "fetch_spam_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "16837219",
   "metadata": {},
   "outputs": [],
   "source": [
    "HAM_DIR = os.path.join(SPAM_PATH, \"easy_ham\")\n",
    "SPAM_DIR = os.path.join(SPAM_PATH, \"spam\")\n",
    "ham_filenames = [name for name in sorted(os.listdir(HAM_DIR)) if len(name) > 20]\n",
    "spam_filenames = [name for name in sorted(os.listdir(SPAM_DIR)) if len(name) > 20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "12f675ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2500"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ham_filenames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "086b058b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "500"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(spam_filenames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "76811050",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "50c85d09",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "36cd5b14",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d98a08ae",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c517578c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "182659d8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ee2687b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f71f7ed1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "69192faa",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cbca9601",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "643adc29",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d1a74a89",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "713bf0fc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8813750f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b197c03b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af257fdd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "27fbf6ed",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "27f93c5d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "518aa2ac",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "28329b06",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fee3c635",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "57af51ba",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f40f5820",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a98fe491",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e9341541",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}