03.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Python ≥3.5 is required\n",
    "import sys\n",
    "assert sys.version_info >= (3, 5)\n",
    "\n",
    "# Scikit-Learn ≥0.20 is required\n",
    "import sklearn\n",
    "assert sklearn.__version__ >= \"0.20\"\n",
    "\n",
    "# Common imports\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "# to make this notebook's output stable across runs\n",
    "np.random.seed(42)\n",
    "\n",
    "# To plot pretty figures\n",
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "mpl.rc('axes', labelsize=14)\n",
    "mpl.rc('xtick', labelsize=12)\n",
    "mpl.rc('ytick', labelsize=12)\n",
    "\n",
    "# Where to save the figures\n",
    "PROJECT_ROOT_DIR = \".\"\n",
    "CHAPTER_ID = \"classification\"\n",
    "IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID)\n",
    "os.makedirs(IMAGES_PATH, exist_ok=True)\n",
    "\n",
    "def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n",
    "    path = os.path.join(IMAGES_PATH, fig_id + \".\" + fig_extension)\n",
    "    print(\"Saving figure\", fig_id)\n",
    "    if tight_layout:\n",
    "        plt.tight_layout()\n",
    "    plt.savefig(path, format=fig_extension, dpi=resolution)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_openml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'frame', 'feature_names', 'target_names', 'DESCR', 'details', 'categories', 'url'])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mnist = fetch_openml(\"mnist_784\", version=1)\n",
    "mnist.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "        [0., 0., 0., ..., 0., 0., 0.]]),\n",
       " 'target': array(['5', '0', '4', ..., '4', '5', '6'], dtype=object),\n",
       " 'frame': None,\n",
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       "  'pixel643',\n",
       "  'pixel644',\n",
       "  'pixel645',\n",
       "  'pixel646',\n",
       "  'pixel647',\n",
       "  'pixel648',\n",
       "  'pixel649',\n",
       "  'pixel650',\n",
       "  'pixel651',\n",
       "  'pixel652',\n",
       "  'pixel653',\n",
       "  'pixel654',\n",
       "  'pixel655',\n",
       "  'pixel656',\n",
       "  'pixel657',\n",
       "  'pixel658',\n",
       "  'pixel659',\n",
       "  'pixel660',\n",
       "  'pixel661',\n",
       "  'pixel662',\n",
       "  'pixel663',\n",
       "  'pixel664',\n",
       "  'pixel665',\n",
       "  'pixel666',\n",
       "  'pixel667',\n",
       "  'pixel668',\n",
       "  'pixel669',\n",
       "  'pixel670',\n",
       "  'pixel671',\n",
       "  'pixel672',\n",
       "  'pixel673',\n",
       "  'pixel674',\n",
       "  'pixel675',\n",
       "  'pixel676',\n",
       "  'pixel677',\n",
       "  'pixel678',\n",
       "  'pixel679',\n",
       "  'pixel680',\n",
       "  'pixel681',\n",
       "  'pixel682',\n",
       "  'pixel683',\n",
       "  'pixel684',\n",
       "  'pixel685',\n",
       "  'pixel686',\n",
       "  'pixel687',\n",
       "  'pixel688',\n",
       "  'pixel689',\n",
       "  'pixel690',\n",
       "  'pixel691',\n",
       "  'pixel692',\n",
       "  'pixel693',\n",
       "  'pixel694',\n",
       "  'pixel695',\n",
       "  'pixel696',\n",
       "  'pixel697',\n",
       "  'pixel698',\n",
       "  'pixel699',\n",
       "  'pixel700',\n",
       "  'pixel701',\n",
       "  'pixel702',\n",
       "  'pixel703',\n",
       "  'pixel704',\n",
       "  'pixel705',\n",
       "  'pixel706',\n",
       "  'pixel707',\n",
       "  'pixel708',\n",
       "  'pixel709',\n",
       "  'pixel710',\n",
       "  'pixel711',\n",
       "  'pixel712',\n",
       "  'pixel713',\n",
       "  'pixel714',\n",
       "  'pixel715',\n",
       "  'pixel716',\n",
       "  'pixel717',\n",
       "  'pixel718',\n",
       "  'pixel719',\n",
       "  'pixel720',\n",
       "  'pixel721',\n",
       "  'pixel722',\n",
       "  'pixel723',\n",
       "  'pixel724',\n",
       "  'pixel725',\n",
       "  'pixel726',\n",
       "  'pixel727',\n",
       "  'pixel728',\n",
       "  'pixel729',\n",
       "  'pixel730',\n",
       "  'pixel731',\n",
       "  'pixel732',\n",
       "  'pixel733',\n",
       "  'pixel734',\n",
       "  'pixel735',\n",
       "  'pixel736',\n",
       "  'pixel737',\n",
       "  'pixel738',\n",
       "  'pixel739',\n",
       "  'pixel740',\n",
       "  'pixel741',\n",
       "  'pixel742',\n",
       "  'pixel743',\n",
       "  'pixel744',\n",
       "  'pixel745',\n",
       "  'pixel746',\n",
       "  'pixel747',\n",
       "  'pixel748',\n",
       "  'pixel749',\n",
       "  'pixel750',\n",
       "  'pixel751',\n",
       "  'pixel752',\n",
       "  'pixel753',\n",
       "  'pixel754',\n",
       "  'pixel755',\n",
       "  'pixel756',\n",
       "  'pixel757',\n",
       "  'pixel758',\n",
       "  'pixel759',\n",
       "  'pixel760',\n",
       "  'pixel761',\n",
       "  'pixel762',\n",
       "  'pixel763',\n",
       "  'pixel764',\n",
       "  'pixel765',\n",
       "  'pixel766',\n",
       "  'pixel767',\n",
       "  'pixel768',\n",
       "  'pixel769',\n",
       "  'pixel770',\n",
       "  'pixel771',\n",
       "  'pixel772',\n",
       "  'pixel773',\n",
       "  'pixel774',\n",
       "  'pixel775',\n",
       "  'pixel776',\n",
       "  'pixel777',\n",
       "  'pixel778',\n",
       "  'pixel779',\n",
       "  'pixel780',\n",
       "  'pixel781',\n",
       "  'pixel782',\n",
       "  'pixel783',\n",
       "  'pixel784'],\n",
       " 'target_names': ['class'],\n",
       " 'DESCR': \"**Author**: Yann LeCun, Corinna Cortes, Christopher J.C. Burges  \\n**Source**: [MNIST Website](http://yann.lecun.com/exdb/mnist/) - Date unknown  \\n**Please cite**:  \\n\\nThe MNIST database of handwritten digits with 784 features, raw data available at: http://yann.lecun.com/exdb/mnist/. It can be split in a training set of the first 60,000 examples, and a test set of 10,000 examples  \\n\\nIt is a subset of a larger set available from NIST. The digits have been size-normalized and centered in a fixed-size image. It is a good database for people who want to try learning techniques and pattern recognition methods on real-world data while spending minimal efforts on preprocessing and formatting. The original black and white (bilevel) images from NIST were size normalized to fit in a 20x20 pixel box while preserving their aspect ratio. The resulting images contain grey levels as a result of the anti-aliasing technique used by the normalization algorithm. the images were centered in a 28x28 image by computing the center of mass of the pixels, and translating the image so as to position this point at the center of the 28x28 field.  \\n\\nWith some classification methods (particularly template-based methods, such as SVM and K-nearest neighbors), the error rate improves when the digits are centered by bounding box rather than center of mass. If you do this kind of pre-processing, you should report it in your publications. The MNIST database was constructed from NIST's NIST originally designated SD-3 as their training set and SD-1 as their test set. However, SD-3 is much cleaner and easier to recognize than SD-1. The reason for this can be found on the fact that SD-3 was collected among Census Bureau employees, while SD-1 was collected among high-school students. Drawing sensible conclusions from learning experiments requires that the result be independent of the choice of training set and test among the complete set of samples. Therefore it was necessary to build a new database by mixing NIST's datasets.  \\n\\nThe MNIST training set is composed of 30,000 patterns from SD-3 and 30,000 patterns from SD-1. Our test set was composed of 5,000 patterns from SD-3 and 5,000 patterns from SD-1. The 60,000 pattern training set contained examples from approximately 250 writers. We made sure that the sets of writers of the training set and test set were disjoint. SD-1 contains 58,527 digit images written by 500 different writers. In contrast to SD-3, where blocks of data from each writer appeared in sequence, the data in SD-1 is scrambled. Writer identities for SD-1 is available and we used this information to unscramble the writers. We then split SD-1 in two: characters written by the first 250 writers went into our new training set. The remaining 250 writers were placed in our test set. Thus we had two sets with nearly 30,000 examples each. The new training set was completed with enough examples from SD-3, starting at pattern # 0, to make a full set of 60,000 training patterns. Similarly, the new test set was completed with SD-3 examples starting at pattern # 35,000 to make a full set with 60,000 test patterns. Only a subset of 10,000 test images (5,000 from SD-1 and 5,000 from SD-3) is available on this site. The full 60,000 sample training set is available.\\n\\nDownloaded from openml.org.\",\n",
       " 'details': {'id': '554',\n",
       "  'name': 'mnist_784',\n",
       "  'version': '1',\n",
       "  'format': 'ARFF',\n",
       "  'upload_date': '2014-09-29T03:28:38',\n",
       "  'licence': 'Public',\n",
       "  'url': 'https://www.openml.org/data/v1/download/52667/mnist_784.arff',\n",
       "  'file_id': '52667',\n",
       "  'default_target_attribute': 'class',\n",
       "  'tag': ['AzurePilot',\n",
       "   'OpenML-CC18',\n",
       "   'OpenML100',\n",
       "   'study_1',\n",
       "   'study_123',\n",
       "   'study_41',\n",
       "   'study_99',\n",
       "   'vision'],\n",
       "  'visibility': 'public',\n",
       "  'status': 'active',\n",
       "  'processing_date': '2018-10-03 21:23:30',\n",
       "  'md5_checksum': '0298d579eb1b86163de7723944c7e495'},\n",
       " 'categories': {},\n",
       " 'url': 'https://www.openml.org/d/554'}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mnist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((70000, 784), (70000,))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X, y = mnist[\"data\"], mnist[\"target\"]\n",
    "X.shape, y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "some_digit = X[0]\n",
    "some_digit_image = some_digit.reshape(28, 28)\n",
    "\n",
    "plt.imshow(some_digit_image, cmap=\"binary\")\n",
    "plt.axis(\"off\")\n",
    "print(y[0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = y.astype(np.uint8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = X[:60000], X[60000:], y[:60000], y[60000:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_5 = (y_train == 5) \n",
    "y_test_5 = (y_test == 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SGDClassifier(alpha=0.0001, average=False, class_weight=None,\n",
       "              early_stopping=False, epsilon=0.1, eta0=0.0, fit_intercept=True,\n",
       "              l1_ratio=0.15, learning_rate='optimal', loss='hinge',\n",
       "              max_iter=1000, n_iter_no_change=5, n_jobs=None, penalty='l2',\n",
       "              power_t=0.5, random_state=42, shuffle=True, tol=0.001,\n",
       "              validation_fraction=0.1, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import SGDClassifier\n",
    "\n",
    "sgd_clf = SGDClassifier(random_state=42)\n",
    "sgd_clf.fit(X_train, y_train_5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sgd_clf.predict([some_digit])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.base import clone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/biowar1337/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_split.py:296: FutureWarning: Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.95035\n",
      "0.96035\n",
      "0.9604\n"
     ]
    }
   ],
   "source": [
    "skfolds = StratifiedKFold(n_splits=3, random_state=42)\n",
    "for train_index, test_index in skfolds.split(X_train, y_train_5):\n",
    "    clone_clf = clone(sgd_clf)\n",
    "    X_train_folds = X_train[train_index]\n",
    "    y_train_folds = y_train_5[train_index]\n",
    "    X_test_fold = X_train[test_index]\n",
    "    y_test_fold = y_train_5[test_index]\n",
    "    \n",
    "    clone_clf.fit(X_train_folds, y_train_folds)\n",
    "    y_pred = clone_clf.predict(X_test_fold)\n",
    "    n_correct = sum(y_pred == y_test_fold)\n",
    "    print(n_correct / len(y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.95035, 0.96035, 0.9604 ])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "cross_val_score(sgd_clf, X_train, y_train_5, cv=3, scoring=\"accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator\n",
    "\n",
    "class Never5Classifier(BaseEstimator):\n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "    def predict(self, X):\n",
    "        return np.zeros((len(X), 1), dtype=bool)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.91125, 0.90855, 0.90915])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "never_5_clf = Never5Classifier()\n",
    "cross_val_score(never_5_clf, X_train, y_train_5, cv=3, scoring=\"accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((60000,), (60000,))"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_pred.shape, y_train_5.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[53892,   687],\n",
       "       [ 1891,  3530]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "confusion_matrix(y_train_5, y_train_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_perfect_predictions = y_train_5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[54579,     0],\n",
       "       [    0,  5421]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_train_5, y_train_perfect_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8370879772350012"
      ]
     },
     "execution_count": 22,
     "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": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6511713705958311"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(y_train_5, y_train_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7325171197343846"
      ]
     },
     "execution_count": 24,
     "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": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2164.22030239])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_scores = sgd_clf.decision_function([some_digit])\n",
    "y_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "threshold = 0\n",
    "y_some_digit_pred = (y_scores > threshold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_some_digit_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "threshold = 8000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_some_digit_pred = (y_scores > threshold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_some_digit_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure precision_recall_vs_threshold_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import precision_recall_curve\n",
    "\n",
    "precisions, recalls, thresholds = precision_recall_curve(y_train_5, y_scores)\n",
    "\n",
    "def plot_precision_recall_vs_threshold(precisions, recalls, thresholds):\n",
    "    plt.plot(thresholds, precisions[:-1], \"b--\", label=\"Precision\", linewidth=2)\n",
    "    plt.plot(thresholds, recalls[:-1], \"g-\", label=\"Recall\", linewidth=2)\n",
    "    plt.legend(loc=\"center right\", fontsize=16) # Not shown in the book\n",
    "    plt.xlabel(\"Threshold\", fontsize=16)        # Not shown\n",
    "    plt.grid(True)                              # Not shown\n",
    "    plt.axis([-50000, 50000, 0, 1])             # Not shown\n",
    "\n",
    "plt.figure(figsize=(8, 4))                      # Not shown\n",
    "plot_precision_recall_vs_threshold(precisions, recalls, thresholds)\n",
    "plt.plot([7813, 7813], [0., 0.9], \"r:\")         # Not shown\n",
    "plt.plot([-50000, 7813], [0.9, 0.9], \"r:\")      # Not shown\n",
    "plt.plot([-50000, 7813], [0.4368, 0.4368], \"r:\")# Not shown\n",
    "plt.plot([7813], [0.9], \"ro\")                   # Not shown\n",
    "plt.plot([7813], [0.4368], \"ro\")                # Not shown\n",
    "save_fig(\"precision_recall_vs_threshold_plot\")  # Not shown\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure precision_vs_recall_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_precision_vs_recall(precisions, recalls):\n",
    "    plt.plot(recalls, precisions, \"b-\", linewidth=2)\n",
    "    plt.xlabel(\"Recall\", fontsize=16)\n",
    "    plt.ylabel(\"Precision\", fontsize=16)\n",
    "    plt.axis([0, 1, 0, 1])\n",
    "    plt.grid(True)\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "plot_precision_vs_recall(precisions, recalls)\n",
    "plt.plot([0.4368, 0.4368], [0., 0.9], \"r:\")\n",
    "plt.plot([0.0, 0.4368], [0.9, 0.9], \"r:\")\n",
    "plt.plot([0.4368], [0.9], \"ro\")\n",
    "save_fig(\"precision_vs_recall_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] (59967,)\n",
      "[INFO] (59967,)\n",
      "[INFO] (59966,)\n"
     ]
    }
   ],
   "source": [
    "for i in (precisions, recalls, thresholds):\n",
    "    print(f\"[INFO] {i.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9000345901072293"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "threshold_90_precision = thresholds[np.argmax(precisions>=0.90)]\n",
    "\n",
    "y_train_pred_90 = (y_scores >= threshold_90_precision)\n",
    "precision_score(y_train_5, y_train_pred_90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4799852425751706"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(y_train_5, y_train_pred_90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000,)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_pred_90.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure roc_curve_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve\n",
    "\n",
    "fpr, tpr, thresholds = roc_curve(y_train_5, y_scores)\n",
    "\n",
    "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--') # dashed diagonal\n",
    "    plt.axis([0, 1, 0, 1])                                    # Not shown in the book\n",
    "    plt.xlabel('False Positive Rate (Fall-Out)', fontsize=16) # Not shown\n",
    "    plt.ylabel('True Positive Rate (Recall)', fontsize=16)    # Not shown\n",
    "    plt.grid(True)                                            # Not shown\n",
    "\n",
    "plt.figure(figsize=(8, 6))                         # Not shown\n",
    "plot_roc_curve(fpr, tpr)\n",
    "plt.plot([4.837e-3, 4.837e-3], [0., 0.4368], \"r:\") # Not shown\n",
    "plt.plot([0.0, 4.837e-3], [0.4368, 0.4368], \"r:\")  # Not shown\n",
    "plt.plot([4.837e-3], [0.4368], \"ro\")               # Not shown\n",
    "save_fig(\"roc_curve_plot\")                         # Not shown\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9604938554008616"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "roc_auc_score(y_train_5, y_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 2)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "forest_clf = RandomForestClassifier(random_state=42)\n",
    "y_probas_forest = cross_val_predict(forest_clf, X_train, y_train_5, cv=3,\n",
    "                                    method=\"predict_proba\")\n",
    "y_probas_forest.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0.11, 0.89]), array([0.99, 0.01]))"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_probas_forest[0], y_probas_forest[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure roc_curve_comparison_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_scores_forest = y_probas_forest[:, 1] # score = proba of positive class\n",
    "fpr_forest, tpr_forest, thresholds_forest = roc_curve(y_train_5,y_scores_forest)\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(fpr, tpr, \"b:\", linewidth=2, label=\"SGD\")\n",
    "plot_roc_curve(fpr_forest, tpr_forest, \"Random Forest\")\n",
    "plt.plot([4.837e-3, 4.837e-3], [0., 0.4368], \"r:\")\n",
    "plt.plot([0.0, 4.837e-3], [0.4368, 0.4368], \"r:\")\n",
    "plt.plot([4.837e-3], [0.4368], \"ro\")\n",
    "plt.plot([4.837e-3, 4.837e-3], [0., 0.9487], \"r:\")\n",
    "plt.plot([4.837e-3], [0.9487], \"ro\")\n",
    "plt.grid(True)\n",
    "plt.legend(loc=\"lower right\", fontsize=16)\n",
    "save_fig(\"roc_curve_comparison_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9983436731328145, 0.89)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_train_5, y_scores_forest), y_scores_forest[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_pred_forest = cross_val_predict(forest_clf, X_train, y_train_5, cv=3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((60000,), True)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_pred_forest.shape, y_train_pred_forest[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9905083315756169, 0.8662608374838591)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision_score(y_train_5, y_train_pred_forest), recall_score(y_train_5, y_train_pred_forest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5], dtype=uint8)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "svm_clf = SVC()\n",
    "svm_clf.fit(X_train, y_train)\n",
    "svm_clf.predict([some_digit])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['svm_number_clf_ovo.pkl']"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import joblib\n",
    "\n",
    "joblib.dump(svm_clf, \"svm_number_clf_ovo.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.72501977,  2.72809088,  7.2510018 ,  8.3076379 , -0.31087254,\n",
       "         9.3132482 ,  1.70975103,  2.76765202,  6.23049537,  4.84771048]])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "some_digit_scores = svm_clf.decision_function([some_digit])\n",
    "some_digit_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(some_digit_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_clf.classes_[5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5], dtype=uint8)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "\n",
    "ovr_clf = OneVsRestClassifier(SVC())\n",
    "ovr_clf.fit(X_train, y_train)\n",
    "ovr_clf.predict([some_digit])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['svm_number_clf_OvR.pkl']"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(ovr_clf, \"svm_number_clf_OvR.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3], dtype=uint8)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sgd_clf.fit(X_train, y_train)\n",
    "sgd_clf.predict([some_digit])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sgd_clf.decision_function([some_digit])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.87365, 0.85835, 0.8689 ])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(sgd_clf, X_train, y_train, cv=3, scoring=\"accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.8983, 0.891 , 0.9018])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train.astype(np.float64))\n",
    "cross_val_score(sgd_clf, X_train_scaled, y_train, cv=3, scoring=\"accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5577,    0,   22,    5,    8,   43,   36,    6,  225,    1],\n",
       "       [   0, 6400,   37,   24,    4,   44,    4,    7,  212,   10],\n",
       "       [  27,   27, 5220,   92,   73,   27,   67,   36,  378,   11],\n",
       "       [  22,   17,  117, 5227,    2,  203,   27,   40,  403,   73],\n",
       "       [  12,   14,   41,    9, 5182,   12,   34,   27,  347,  164],\n",
       "       [  27,   15,   30,  168,   53, 4444,   75,   14,  535,   60],\n",
       "       [  30,   15,   42,    3,   44,   97, 5552,    3,  131,    1],\n",
       "       [  21,   10,   51,   30,   49,   12,    3, 5684,  195,  210],\n",
       "       [  17,   63,   48,   86,    3,  126,   25,   10, 5429,   44],\n",
       "       [  25,   18,   30,   64,  118,   36,    1,  179,  371, 5107]])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# EXTRA\n",
    "def plot_digits(instances, images_per_row=10, **options):\n",
    "    size = 28\n",
    "    images_per_row = min(len(instances), images_per_row)\n",
    "    images = [instance.reshape(size,size) for instance in instances]\n",
    "    n_rows = (len(instances) - 1) // images_per_row + 1\n",
    "    row_images = []\n",
    "    n_empty = n_rows * images_per_row - len(instances)\n",
    "    images.append(np.zeros((size, size * n_empty)))\n",
    "    for row in range(n_rows):\n",
    "        rimages = images[row * images_per_row : (row + 1) * images_per_row]\n",
    "        row_images.append(np.concatenate(rimages, axis=1))\n",
    "    image = np.concatenate(row_images, axis=0)\n",
    "    plt.imshow(image, cmap = mpl.cm.binary, **options)\n",
    "    plt.axis(\"off\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.matshow(conf_mx, cmap=plt.cm.Greens)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPwAAAEFCAYAAADHQYoCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAANHklEQVR4nO3dW4ic93mA8efVrqKjD7JWVo3sSMYoJc1FlWZLLlLXpnHqA4SWOKUipoTeKNgkkJBcxuADuXAvEkLsuAhUExKT4BY3DY7rBNISSIvbbOsaauwYuz5JsYhGkS1L1nnfXsyausqu9lt7/vpmeZ8fCKPd4eXVep/9Zndm/xOZiaQaVvS9gKTzx+ClQgxeKsTgpUIMXirE4KVCDF4qpNfgI+KSiPj7iDgaES9FxKf63GcxEbEqIvbM7fpGRDwRETf2vVcXEbE9Io5HxHf63qWLiNgZEU/PfW48HxFX973TuUTEtoh4NCIORcT+iLg3Iib73utsfV/h7wNOApuBW4D7I+ID/a50TpPAK8A1wEXA7cBDEbGtx526ug/4ed9LdBERHwPuAf4SuAD4Q+B/el1qcd8EfgVcBuxg+DlyW68bzaO34CNiHXAzcHtmHsnMnwE/AP6ir50Wk5lHM/OOzHwxM2cz8xHgBeBDfe92LhGxE3gN+Enfu3R0J3BXZj4+93Hel5n7+l5qEVcCD2Xm8czcDzwGjN3Fq88r/PuAM5n57Nve9iRj+EFaSERsZvjveKrvXRYSERcCdwFf7HuXLiJiApgGNkXEcxGxd+7u8Zq+d1vE14GdEbE2IrYANzKMfqz0Gfx64PWz3vY6w7twYy8iVgIPAt/KzGf63ucc7gb2ZOYrfS/S0WZgJfBJ4GqGd48/CHy5z6U6+CnDi9VhYC8wA3y/143m0WfwR4ALz3rbhcAbPeyyJBGxAvg2w58/fLbndRYUETuA64Cv9b3LEhyb++83MvPVzBwAXwVu6nGnc5r7fPgR8DCwDpgCNjD8OcRY6TP4Z4HJiNj+trf9LmN89xggIgLYw/BKdHNmnup5pXO5FtgGvBwR+4EvATdHxH/2udS5ZOYhhlfI5fRrnJcAVwD3ZuaJzDwIPMAYfpHqLfjMPMrwK+JdEbEuIj4C/AnDK+c4ux94P/DxzDy22I17thu4iuHd4h3AXwM/BK7vc6kOHgA+FxGXRsQG4PPAIz3vtKC5eyEvALdGxGREXAx8muHPpMZK3w/L3QasYfhwxneBWzNzbK/wEbEV+AzDePZHxJG5P7f0vNq8MvPNzNz/1h+G30Ydz8wDfe+2iLsZPoT4LPA08ATwlV43WtwngBuAA8BzwGngC71uNI/wAAypjr6v8JLOI4OXCjF4qRCDlwoxeKkQg5cKGYvgI2JX3zss1XLbebntC+7cwlgED4z1B2kBy23n5bYvuPPIjUvwks6DZs+0m5qayq3b3tvptgcODNi0aarJHksxu4SPxcHBgI1T3XYO4p2uNDKDwYCpjvsCzDLbZI+lfCQGBw4ytWnjEma3uX79+sTBzrc9cugI6zes73z7jau7//u6eunFlxkMBvN+qJudubV123v5l3/7WavxTZycPdlk7uT4HW22qGNn3mwyd0XDO5WrJlY3mfu95x5sMhfgU9tHf8DTRz78Bwu+z7v0UiEGLxVi8FIhBi8VYvBSIZ2CX26vECNpfl0fL3r7K8TsAH4YEU+O83FUkn7Tolf45fgKMZLm1+Uu/bJ/hRhJQ12C7/wKMRGxKyJmImLmwIHBKPaTNEJdgu/8CjGZuTszpzNzehyeGy/p/+sS/LJ8hRhJv2nR4JfxK8RIOkvXJ94sq1eIkTS/To/DZ+avgT9tvIukxnxqrVSIwUuFGLxUiMFLhSy/w9Yamp093WTu6YZfVlfGyiZzWx28uXpiTZO5AEmbA1k3rRn9QZNvmc3RHxZ6rrNYvcJLhRi8VIjBS4UYvFSIwUuFGLxUiMFLhRi8VIjBS4UYvFSIwUuFGLxUiMFLhRi8VIjBS4UYvFSIwUuFGLxUiMFLhRi8VIjBS4UYvFRIs2OqZ3OWE2eOtxrfxOrJtU3m/vLoy03mAqyebHPs87rJC5rMfe3kwSZzAVZPtPn/91f/+nCTuQB/9GfXN5s9H6/wUiEGLxVi8FIhBi8VYvBSIQYvFWLwUiGLBh8RqyJiT0S8FBFvRMQTEXHj+VhO0mh1ucJPAq8A1wAXAbcDD0XEtnZrSWph0WfaZeZR4I63vemRiHgB+BDwYpu1JLWw5O/hI2Iz8D7gqdGvI6mlJQUfESuBB4FvZeYz87x/V0TMRMTMwUG750xLemc6Bx8RK4BvAyeBz853m8zcnZnTmTm9cWrjiFaUNCqdflsuIgLYA2wGbsrMU023ktRE11+PvR94P3BdZh5ruI+khro8Dr8V+AywA9gfEUfm/tzSfDtJI9XlYbmXgDgPu0hqzKfWSoUYvFSIwUuFGLxUSLNTayEYPldntFY0/Pnh84d/0WTuVRf+dpO5LT368g+azP3olj9uMhfgTJ5pMvfUydNN5gIcPnlo5DPP5ML7eoWXCjF4qRCDlwoxeKkQg5cKMXipEIOXCjF4qRCDlwoxeKkQg5cKMXipEIOXCjF4qRCDlwoxeKkQg5cKMXipEIOXCjF4qRCDlwoxeKmQZsdUBzDR4OtJjnzi//mttVuazJ3N2SZzAVY0OAoc4PrLb2oy9+TsySZzASZiosncSy5a32QuwNOHnhr5zONnFn6BZ6/wUiEGLxVi8FIhBi8VYvBSIQYvFWLwUiFLCj4itkfE8Yj4TquFJLWz1Cv8fcDPWywiqb3OwUfETuA14Cft1pHUUqfgI+JC4C7gi23XkdRS1yv83cCezHzlXDeKiF0RMRMRM4PBwXe/naSRWjT4iNgBXAd8bbHbZubuzJzOzOmpqY2j2E/SCHX5bblrgW3AyxEBsB6YiIjfyczfa7eapFHrEvxu4Htv+/uXGH4BuLXFQpLaWTT4zHwTePOtv0fEEeB4Zh5ouZik0VvyARiZeUeDPSSdBz61VirE4KVCDF4qxOClQpqdWpskp/P0yOe2OpkU2pyyC/BP+37cZC7A71/64SZzL3rPhiZzj5xo9+DOhlWbmsz9x/va/f/7m3+4c+QzV0+sWfB9XuGlQgxeKsTgpUIMXirE4KVCDF4qxOClQgxeKsTgpUIMXirE4KVCDF4qxOClQgxeKsTgpUIMXirE4KVCDF4qxOClQgxeKsTgpUKanVoLw5NrRy8azBw6w2yTuR/dcn2TuQBvnj7SZO7eoy82mXv5um1N5gJktvh8g//47t82mQtw8arRv6z65IqFs/YKLxVi8FIhBi8VYvBSIQYvFWLwUiEGLxXSOfiI2BkRT0fE0Yh4PiKubrmYpNHr9MSbiPgYcA/w58C/A5e1XEpSG12faXcncFdmPj73932N9pHU0KJ36SNiApgGNkXEcxGxNyLujYiFX3Ve0ljq8j38ZmAl8EngamAH8EHgy2ffMCJ2RcRMRMwMBgdHuqikd69L8Mfm/vuNzHw1MwfAV4Gbzr5hZu7OzOnMnJ6aGv0vBUh6dxYNPjMPAXuhya++STqPuj4s9wDwuYi4NCI2AJ8HHmm3lqQWuv6U/m5gCngWOA48BHyl1VKS2ugUfGaeAm6b+yNpmfKptVIhBi8VYvBSIQYvFWLwUiHNjqkOgomYGP3caHdMdWabY6pPzp5oMhdaHQUOl629osncVkdJQ7vPjV8efbXJXIAXDr848pmvnXhtwfd5hZcKMXipEIOXCjF4qRCDlwoxeKkQg5cKMXipEIOXCjF4qRCDlwoxeKkQg5cKMXipEIOXCjF4qRCDlwoxeKkQg5cKMXipEIOXCml2ai0MT64dtTdOvT7ymW+ZjDYfjkMnBk3mAmxes6XJ3F+8/t9N5l55wfYmcwEmV6xsMve6y29oMhfg6Kk3Rj7zgvdcsOD7vMJLhRi8VIjBS4UYvFSIwUuFGLxUiMFLhXQKPiK2RcSjEXEoIvZHxL0RjR60ltRM1yv8N4FfAZcBO4BrgNtaLSWpja7BXwk8lJnHM3M/8BjwgXZrSWqha/BfB3ZGxNqI2ALcyDB6SctI1+B/yvCKfhjYC8wA3z/7RhGxKyJmImJmMDg4ui0ljcSiwUfECuBHwMPAOmAK2ADcc/ZtM3N3Zk5n5vTU1MZR7yrpXepyhb8EuAK4NzNPZOZB4AHgpqabSRq5RYPPzAHwAnBrRExGxMXAp4EnWy8nabS6fg//CeAG4ADwHHAa+EKrpSS10enJM5n5X8C1bVeR1JpPrZUKMXipEIOXCjF4qRCDlwpp9iuuCczm7Mjnrp9c+Ajed+t0nm4yd/Xk2iZzAZ4//EyTuVvXX9Vk7qqJ1U3mAvzzvh83mXvP43/XZC7AYzfvHvnMiZhY8H1e4aVCDF4qxOClQgxeKsTgpUIMXirE4KVCDF4qxOClQgxeKsTgpUIMXirE4KVCDF4qxOClQgxeKsTgpUIMXirE4KVCDF4qxOClQiIz2wyOOAC81PHmU8CgySLtLLedl9u+4M7v1NbM3DTfO5oFvxQRMZOZ033vsRTLbeflti+4cwvepZcKMXipkHEJfvQvv9Hectt5ue0L7jxyY/E9vKTzY1yu8JLOA4OXCjF4qRCDlwoxeKmQ/wVexOdd0Y0QwgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "row_sums = conf_mx.sum(axis=1, keepdims=True)\n",
    "norm_conf_mx = conf_mx / row_sums\n",
    "\n",
    "np.fill_diagonal(norm_conf_mx, 0)\n",
    "plt.matshow(norm_conf_mx, cmap=plt.cm.Greens)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cl_a, cl_b = 3, 5\n",
    "X_aa = X_train[(y_train == cl_a) & (y_train_pred == cl_a)]\n",
    "X_ab = X_train[(y_train == cl_a) & (y_train_pred == cl_b)]\n",
    "X_ba = X_train[(y_train == cl_b) & (y_train_pred == cl_a)]\n",
    "X_bb = X_train[(y_train == cl_b) & (y_train_pred == cl_b)]\n",
    "\n",
    "plt.figure(figsize=(8,8))\n",
    "plt.subplot(221); plot_digits(X_aa[:25], images_per_row=5)\n",
    "plt.subplot(222); plot_digits(X_ab[:25], images_per_row=5)\n",
    "plt.subplot(223); plot_digits(X_ba[:25], images_per_row=5)\n",
    "plt.subplot(224); plot_digits(X_bb[:25], images_per_row=5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                     metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
       "                     weights='uniform')"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "y_train_large = (y_train >= 7)\n",
    "y_train_odd = (y_train % 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": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[False,  True]])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_clf.predict([some_digit])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.976410265560605"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "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": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure noisy_digit_example_plot\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_digit(data):\n",
    "    image = data.reshape(28, 28)\n",
    "    plt.imshow(image, cmap = mpl.cm.binary,\n",
    "               interpolation=\"nearest\")\n",
    "    plt.axis(\"off\")\n",
    "    \n",
    "some_index = 42\n",
    "plt.subplot(121); plot_digit(X_test_mod[some_index])\n",
    "plt.subplot(122); plot_digit(y_test_mod[some_index])\n",
    "save_fig(\"noisy_digit_example_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAOcAAADnCAYAAADl9EEgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAE8UlEQVR4nO3dsWpUWRzA4Z0QQQULxWewtLIQ0QexU5vY6lPYCLYK9vaChZ1obHyAQGwES7USbRRn62Un9+7mzmR+M/m+Mn/ucIj55UAO5zqbz+d/AT07614AsJg4IUqcECVOiBInRO2OzP0pF1ZvtuiLdk6IEidEiROixAlR4oQocUKUOCFKnBAlTogSJ0SJE6LECVHihChxQpQ4IUqcECVOiBInRIkTosQJUeKEKHFClDghSpwQJU6IEidEiROixAlR4oQocUKUOCFKnBAlTogSJ0SJE6LECVHihChxQpQ4IUqcECVOiBInRIkTosQJUeKEKHFC1O66F8D2ePr06eD88PBwcP748eNlLmfj2TkhSpwQJU6IEidEiROixAlRjlKOYW9vb3D+6NGjwfnFixeXuZwT8+XLl8H5/fv3B+dPnjxZ5nK2np0TosQJUeKEKHFClDghSpwQJU6Ics65wO/fvwfnz549G5zfvXt3cH79+vX/vaaCV69eTXr+4OBgSSs5HeycECVOiBInRIkTosQJUeKEKHFClHPOBd6/fz/p+atXry5pJSfv8+fPR87u3Lkz6bNv3rw56fnTxs4JUeKEKHFClDghSpwQJU6IEidEzebz+dB8cLitZrPZpOdHvqdpHz9+PHJ25cqVSZ/98+fPwfm5c+cmff4GW/gDZ+eEKHFClDghSpwQJU6IEidEiROi3Odc4PLly4Pzr1+/Ds6/f/8+OL9w4cL/XtNJ2d/fX9lnn+JzzGOxc0KUOCFKnBAlTogSJ0SJE6JcGVtg7MrY8+fPB+f37t1b5nJO1NTrckM2+SrdirkyBptEnBAlTogSJ0SJE6LECVHihChXxo7h8PBw3Us4tjdv3qx7CfxHdk6IEidEiROixAlR4oQocUKUOCHqVN7n/Pbt2+B87NWYY1Z5b/Hdu3eD87HXT167dm2Zy/mHX79+Dc53dx2rH8F9Ttgk4oQocUKUOCFKnBAlTogSJ0SdynPOMWPvbj1z5szgfOy87+3bt0fObt26Nfhs2Y8fPwbn58+fP6GVbBznnLBJxAlR4oQocUKUOCFKnBAlTohywe4Yxs4xx6zyLHPsLumLFy8G57dv3z5ytre3N/jszo7f9cvkuwlR4oQocUKUOCFKnBAlTohylLLAKl9tuW4fPnw49rMPHz4cnJ89e/bYn82/2TkhSpwQJU6IEidEiROixAlR4oQor8Y8ZcZe+zlkm89/18yrMWGTiBOixAlR4oQocUKUOCFKnBDlPueW+fTp07qXwJLYOSFKnBAlTogSJ0SJE6LECVHihCjnnFvm4OBg0vMvX75c0kqYys4JUeKEKHFClDghSpwQJU6IcpSyZfb39yc9f+PGjSWthKnsnBAlTogSJ0SJE6LECVHihChxQpRzzi3z+vXrSc9funRpSSthKjsnRIkTosQJUeKEKHFClDghSpwQNZvP50PzwSE9OzvDv29H/r1H56zEbNEX7ZwQJU6IEidEiROixAlR4oQocUKU+5xb5s+fP4PzBw8enNBKmMrOCVHihChxQpQ4IUqcECVOiBInRLnPCevnPidsEnFClDghSpwQJU6IEidEiROixAlR4oQocUKUOCFKnBAlTogSJ0SJE6LECVHihChxQpQ4IUqcECVOiBInRI39F4ALX9kHrJ6dE6LECVHihChxQpQ4IUqcEPU3Ft2f2rK6ydEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "knn_clf.fit(X_train_mod, y_train_mod)\n",
    "clean_digit = knn_clf.predict([X_test_mod[some_index]])\n",
    "plot_digit(clean_digit)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Home Ex."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 2 folds for each of 4 candidates, totalling 8 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-82-cdff76f4681e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0mknn_clf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mKNeighborsClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0mgrid_search\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGridSearchCV\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mknn_clf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparam_grid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mgrid_search\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, groups, **fit_params)\u001b[0m\n\u001b[1;32m    710\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    711\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 712\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_run_search\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevaluate_candidates\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    713\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    714\u001b[0m         \u001b[0;31m# For multi-metric evaluation, store the best_index_, best_params_ and\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36m_run_search\u001b[0;34m(self, evaluate_candidates)\u001b[0m\n\u001b[1;32m   1151\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_run_search\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevaluate_candidates\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1152\u001b[0m         \u001b[0;34m\"\"\"Search all candidates in param_grid\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1153\u001b[0;31m         \u001b[0mevaluate_candidates\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mParameterGrid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparam_grid\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1154\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1155\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36mevaluate_candidates\u001b[0;34m(candidate_params)\u001b[0m\n\u001b[1;32m    689\u001b[0m                                \u001b[0;32mfor\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    690\u001b[0m                                in product(candidate_params,\n\u001b[0;32m--> 691\u001b[0;31m                                           cv.split(X, y, groups)))\n\u001b[0m\u001b[1;32m    692\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    693\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   1015\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1016\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieval_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1017\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1018\u001b[0m             \u001b[0;31m# Make sure that we get a last message telling us we are done\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1019\u001b[0m             \u001b[0melapsed_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_start_time\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36mretrieve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    907\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    908\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'supports_timeout'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 909\u001b[0;31m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    910\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    911\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36mwrap_future_result\u001b[0;34m(future, timeout)\u001b[0m\n\u001b[1;32m    560\u001b[0m         AsyncResults.get from multiprocessing.\"\"\"\n\u001b[1;32m    561\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 562\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfuture\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    563\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mLokyTimeoutError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    564\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mTimeoutError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/concurrent/futures/_base.py\u001b[0m in \u001b[0;36mresult\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    428\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__get_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    429\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 430\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_condition\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    431\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    432\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_state\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mCANCELLED\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCANCELLED_AND_NOTIFIED\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    294\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m    \u001b[0;31m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    295\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 296\u001b[0;31m                 \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    297\u001b[0m                 \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    298\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "param_grid = [\n",
    "    {\"n_neighbors\": [5, 10], \"weights\":[\"uniform\", \"distance\"]},\n",
    "]\n",
    "\n",
    "knn_clf = KNeighborsClassifier()\n",
    "grid_search = GridSearchCV(knn_clf, param_grid, cv=2, n_jobs=-1, verbose=3)\n",
    "grid_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "Invalid shape (784,) for image data",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-129-9210d826ed7e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m121\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Original\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minterpolation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"nearest\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Greys\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m122\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Shifted\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m  \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage_shifted\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minterpolation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"nearest\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Greys\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     13\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mimshow\u001b[0;34m(X, cmap, norm, aspect, interpolation, alpha, vmin, vmax, origin, extent, shape, filternorm, filterrad, imlim, resample, url, data, **kwargs)\u001b[0m\n\u001b[1;32m   2681\u001b[0m         \u001b[0mfilternorm\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfilternorm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilterrad\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfilterrad\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimlim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mimlim\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2682\u001b[0m         resample=resample, url=url, **({\"data\": data} if data is not\n\u001b[0;32m-> 2683\u001b[0;31m         None else {}), **kwargs)\n\u001b[0m\u001b[1;32m   2684\u001b[0m     \u001b[0msci\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m__ret\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2685\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0m__ret\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1599\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1600\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1601\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msanitize_sequence\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1602\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1603\u001b[0m         \u001b[0mbound\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_sig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/matplotlib/cbook/deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    367\u001b[0m                 \u001b[0;34mf\"%(removal)s.  If any parameter follows {name!r}, they \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    368\u001b[0m                 f\"should be pass as keyword, not positionally.\")\n\u001b[0;32m--> 369\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    370\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    371\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/matplotlib/cbook/deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    367\u001b[0m                 \u001b[0;34mf\"%(removal)s.  If any parameter follows {name!r}, they \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    368\u001b[0m                 f\"should be pass as keyword, not positionally.\")\n\u001b[0;32m--> 369\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    370\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    371\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mimshow\u001b[0;34m(self, X, cmap, norm, aspect, interpolation, alpha, vmin, vmax, origin, extent, shape, filternorm, filterrad, imlim, resample, url, **kwargs)\u001b[0m\n\u001b[1;32m   5669\u001b[0m                               resample=resample, **kwargs)\n\u001b[1;32m   5670\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5671\u001b[0;31m         \u001b[0mim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5672\u001b[0m         \u001b[0mim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_alpha\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5673\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_clip_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/matplotlib/image.py\u001b[0m in \u001b[0;36mset_data\u001b[0;34m(self, A)\u001b[0m\n\u001b[1;32m    688\u001b[0m                 or self._A.ndim == 3 and self._A.shape[-1] in [3, 4]):\n\u001b[1;32m    689\u001b[0m             raise TypeError(\"Invalid shape {} for image data\"\n\u001b[0;32m--> 690\u001b[0;31m                             .format(self._A.shape))\n\u001b[0m\u001b[1;32m    691\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    692\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_A\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: Invalid shape (784,) for image data"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.ndimage.interpolation import shift\n",
    "\n",
    "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])\n",
    "\n",
    "image = X_train[1223].reshape((28, 28))\n",
    "image_shifted = shift_image(image, 6, 6)\n",
    "\n",
    "plt.subplot(121); plt.title(\"Original\"); plt.imshow(image, interpolation=\"nearest\", cmap=\"Greys\")\n",
    "plt.subplot(122); plt.title(\"Shifted\");  plt.imshow(image_shifted, interpolation=\"nearest\", cmap=\"Greys\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_augmented = [image for image in X_train]\n",
    "y_train_augmented = [label for label in y_train]\n",
    "\n",
    "for dx, dy in ((1, 0), (-1, 0), (0, 1), (0, -1)):\n",
    "    for image, label in zip(X_train, y_train):\n",
    "        X_train_augmented.append(shift_image(image, dx, dy))\n",
    "        y_train_augmented.append(label)\n",
    "\n",
    "X_train_augmented = np.array(X_train_augmented)\n",
    "y_train_augmented = np.array(y_train_augmented)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "shuffle_idx = np.random.permutation(len(X_train_augmented))\n",
    "X_train_augmented = X_train_augmented[shuffle_idx]\n",
    "y_train_augmented = y_train_augmented[shuffle_idx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn_clf = KNeighborsClassifier(n_neighbors=4, weights=\"distance\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        2.45000000e+02,  5.60000000e+01, -5.38195700e-17, -8.46730706e-16,\n",
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       "        1.72568020e-19, -1.25364086e-19, -5.44086037e-20,  9.25677973e-21,\n",
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       "        5.30000000e+01,  2.53000000e+02,  2.53000000e+02,  1.90000000e+02,\n",
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       "       -4.21931749e-19,  2.54682917e-18, -9.27749397e-18,  4.08244142e-17,\n",
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       "       -2.74023160e-17, -5.45631269e-18,  6.59107804e-19, -4.52642575e-19,\n",
       "       -8.76067118e-20, -6.04378363e-21,  2.51515455e-21,  7.48138831e-22,\n",
       "       -5.97565756e-22, -3.01750247e-22, -4.79042390e-23, -1.94847772e-23,\n",
       "        3.47830215e-20,  1.22495710e-19, -2.94372901e-19,  9.46575677e-19,\n",
       "       -2.93533398e-19,  1.33464042e-17,  7.09405985e-17, -1.48398048e-16,\n",
       "       -3.36606252e-15,  4.54828282e-15, -1.26429265e-14,  1.63507039e-15,\n",
       "        4.31852413e-16,  4.08316131e-16,  7.10219963e-17,  8.00281489e-18,\n",
       "       -1.84171326e-18,  2.05034083e-18,  3.78466646e-19, -1.55719041e-19,\n",
       "       -3.03103862e-20, -4.65496385e-21,  1.74674271e-21,  1.31007031e-21,\n",
       "        3.57394432e-23, -9.43839283e-23, -2.46447415e-23, -4.31968183e-24])"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_augmented[0].reshape((784,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "for idx in range(len(X_train_augmented)):\n",
    "    X_train_augmented[idx] = X_train_augmented[idx].reshape((784,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(784,)"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_augmented[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                     metric_params=None, n_jobs=None, n_neighbors=4, p=2,\n",
       "                     weights='distance')"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_clf.fit(X_train_augmented, y_train_augmented)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = knn_clf.predict(X_test)\n",
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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