Classification Model V1.2.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5bc8d18a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import warnings as wr\n",
    "wr.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3012d1ee",
   "metadata": {},
   "outputs": [
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       "  '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",
       "  'description_version': '1',\n",
       "  'format': 'ARFF',\n",
       "  'creator': ['Yann LeCun', 'Corinna Cortes', 'Christopher J.C. Burges'],\n",
       "  'upload_date': '2014-09-29T03:28:38',\n",
       "  'language': 'English',\n",
       "  'licence': 'Public',\n",
       "  'url': 'https://api.openml.org/data/v1/download/52667/mnist_784.arff',\n",
       "  'parquet_url': 'http://openml1.win.tue.nl/dataset554/dataset_554.pq',\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",
       "  'minio_url': 'http://openml1.win.tue.nl/dataset554/dataset_554.pq',\n",
       "  'status': 'active',\n",
       "  'processing_date': '2020-11-20 20:12:09',\n",
       "  'md5_checksum': '0298d579eb1b86163de7723944c7e495'},\n",
       " 'url': 'https://www.openml.org/d/554'}"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import fetch_openml\n",
    "\n",
    "# Fetch the MNIST dataset\n",
    "mnist = fetch_openml('mnist_784')\n",
    "\n",
    "mnist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9b9648d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y =mnist[\"data\"], mnist[\"target\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fc9c37e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70000, 784)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bec91c1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70000,)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "73330139",
   "metadata": {},
   "outputs": [
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       "4     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "\n",
       "   pixel10  ...  pixel775  pixel776  pixel777  pixel778  pixel779  pixel780  \\\n",
       "0      0.0  ...       0.0       0.0       0.0       0.0       0.0       0.0   \n",
       "1      0.0  ...       0.0       0.0       0.0       0.0       0.0       0.0   \n",
       "2      0.0  ...       0.0       0.0       0.0       0.0       0.0       0.0   \n",
       "3      0.0  ...       0.0       0.0       0.0       0.0       0.0       0.0   \n",
       "4      0.0  ...       0.0       0.0       0.0       0.0       0.0       0.0   \n",
       "\n",
       "   pixel781  pixel782  pixel783  pixel784  \n",
       "0       0.0       0.0       0.0       0.0  \n",
       "1       0.0       0.0       0.0       0.0  \n",
       "2       0.0       0.0       0.0       0.0  \n",
       "3       0.0       0.0       0.0       0.0  \n",
       "4       0.0       0.0       0.0       0.0  \n",
       "\n",
       "[5 rows x 784 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8bc565ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    5\n",
       "1    0\n",
       "2    4\n",
       "3    1\n",
       "4    9\n",
       "Name: class, dtype: category\n",
       "Categories (10, object): ['0', '1', '2', '3', ..., '6', '7', '8', '9']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3860deec",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "some_digit = X.iloc[40222]\n",
    "some_digit_image = some_digit.values.reshape(28, 28)\n",
    "plt.imshow(some_digit_image, cmap='gray')\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c4ca0b6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'7'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[40222]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "314bc413",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "The number of classes has to be greater than one; got 1 class",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[18], line 4\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlinear_model\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m SGDClassifier\n\u001b[0;32m      3\u001b[0m sgd_clf \u001b[38;5;241m=\u001b[39m SGDClassifier(max_iter\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5\u001b[39m, tol\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1e-3\u001b[39m, random_state\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m42\u001b[39m)\n\u001b[1;32m----> 4\u001b[0m \u001b[43msgd_clf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train_5\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:894\u001b[0m, in \u001b[0;36mBaseSGDClassifier.fit\u001b[1;34m(self, X, y, coef_init, intercept_init, sample_weight)\u001b[0m\n\u001b[0;32m    891\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_params()\n\u001b[0;32m    892\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_more_validate_params()\n\u001b[1;32m--> 894\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fit\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    895\u001b[0m \u001b[43m    \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    896\u001b[0m \u001b[43m    \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    897\u001b[0m \u001b[43m    \u001b[49m\u001b[43malpha\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43malpha\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    898\u001b[0m \u001b[43m    \u001b[49m\u001b[43mC\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1.0\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m    899\u001b[0m \u001b[43m    \u001b[49m\u001b[43mloss\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloss\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    900\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlearning_rate\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlearning_rate\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    901\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcoef_init\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcoef_init\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    902\u001b[0m \u001b[43m    \u001b[49m\u001b[43mintercept_init\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mintercept_init\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    903\u001b[0m \u001b[43m    \u001b[49m\u001b[43msample_weight\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msample_weight\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    904\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:683\u001b[0m, in \u001b[0;36mBaseSGDClassifier._fit\u001b[1;34m(self, X, y, alpha, C, loss, learning_rate, coef_init, intercept_init, sample_weight)\u001b[0m\n\u001b[0;32m    680\u001b[0m \u001b[38;5;66;03m# Clear iteration count for multiple call to fit.\u001b[39;00m\n\u001b[0;32m    681\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mt_ \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1.0\u001b[39m\n\u001b[1;32m--> 683\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_partial_fit\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    684\u001b[0m \u001b[43m    \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    685\u001b[0m \u001b[43m    \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    686\u001b[0m \u001b[43m    \u001b[49m\u001b[43malpha\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    687\u001b[0m \u001b[43m    \u001b[49m\u001b[43mC\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    688\u001b[0m \u001b[43m    \u001b[49m\u001b[43mloss\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    689\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlearning_rate\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    690\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmax_iter\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    691\u001b[0m \u001b[43m    \u001b[49m\u001b[43mclasses\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    692\u001b[0m \u001b[43m    \u001b[49m\u001b[43msample_weight\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    693\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcoef_init\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    694\u001b[0m \u001b[43m    \u001b[49m\u001b[43mintercept_init\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    695\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    697\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[0;32m    698\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtol \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    699\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtol \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m-\u001b[39mnp\u001b[38;5;241m.\u001b[39minf\n\u001b[0;32m    700\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_iter_ \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmax_iter\n\u001b[0;32m    701\u001b[0m ):\n\u001b[0;32m    702\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[0;32m    703\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMaximum number of iteration reached before \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    704\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mconvergence. Consider increasing max_iter to \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    705\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mimprove the fit.\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m    706\u001b[0m         ConvergenceWarning,\n\u001b[0;32m    707\u001b[0m     )\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:637\u001b[0m, in \u001b[0;36mBaseSGDClassifier._partial_fit\u001b[1;34m(self, X, y, alpha, C, loss, learning_rate, max_iter, classes, sample_weight, coef_init, intercept_init)\u001b[0m\n\u001b[0;32m    627\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fit_binary(\n\u001b[0;32m    628\u001b[0m         X,\n\u001b[0;32m    629\u001b[0m         y,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    634\u001b[0m         max_iter\u001b[38;5;241m=\u001b[39mmax_iter,\n\u001b[0;32m    635\u001b[0m     )\n\u001b[0;32m    636\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 637\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    638\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe number of classes has to be greater than one; got \u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[38;5;124m class\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    639\u001b[0m         \u001b[38;5;241m%\u001b[39m n_classes\n\u001b[0;32m    640\u001b[0m     )\n\u001b[0;32m    642\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n",
      "\u001b[1;31mValueError\u001b[0m: The number of classes has to be greater than one; got 1 class"
     ]
    }
   ],
   "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\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "49e1f4e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_digits(instances, images_per_row, **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='binary', **options)\n",
    "    plt.axis('off')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9af01ac5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5, 5))\n",
    "example_images = np.r_[X[:12000:600], X[13000:30600:600], X[30600:60000:590]]\n",
    "plot_digits(example_images, images_per_row=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3c3ca8fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Spliting the datatset into training and testing sets\n",
    "X_train, X_test, y_train, y_test = X[:60000], X[60000:], y[:60000], y[60000:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "45e07002",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Shuffling the training set to ensure that all cross-validation folds will be similar.\n",
    "#We do not want one fold to be missing some digits\n",
    "shuffle_index = np.random.permutation(60000)\n",
    "X_train = X_train.iloc[shuffle_index]\n",
    "y_train = y_train.iloc[shuffle_index]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7476b9c1",
   "metadata": {},
   "source": [
    "## Binary Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1e72c38a",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_5 = (y_train == 5)\n",
    "y_test_5 = (y_test == 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d8d55972",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "The number of classes has to be greater than one; got 1 class",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[20], line 7\u001b[0m\n\u001b[0;32m      4\u001b[0m y_test_5 \u001b[38;5;241m=\u001b[39m (y_test \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m5\u001b[39m)\n\u001b[0;32m      6\u001b[0m sgd_clf \u001b[38;5;241m=\u001b[39m SGDClassifier(max_iter\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5\u001b[39m, tol\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1e-3\u001b[39m, random_state\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m42\u001b[39m)\n\u001b[1;32m----> 7\u001b[0m \u001b[43msgd_clf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train_5\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:894\u001b[0m, in \u001b[0;36mBaseSGDClassifier.fit\u001b[1;34m(self, X, y, coef_init, intercept_init, sample_weight)\u001b[0m\n\u001b[0;32m    891\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_params()\n\u001b[0;32m    892\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_more_validate_params()\n\u001b[1;32m--> 894\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fit\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    895\u001b[0m \u001b[43m    \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    896\u001b[0m \u001b[43m    \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    897\u001b[0m \u001b[43m    \u001b[49m\u001b[43malpha\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43malpha\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    898\u001b[0m \u001b[43m    \u001b[49m\u001b[43mC\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1.0\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m    899\u001b[0m \u001b[43m    \u001b[49m\u001b[43mloss\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloss\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    900\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlearning_rate\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlearning_rate\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    901\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcoef_init\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcoef_init\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    902\u001b[0m \u001b[43m    \u001b[49m\u001b[43mintercept_init\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mintercept_init\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    903\u001b[0m \u001b[43m    \u001b[49m\u001b[43msample_weight\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msample_weight\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    904\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:683\u001b[0m, in \u001b[0;36mBaseSGDClassifier._fit\u001b[1;34m(self, X, y, alpha, C, loss, learning_rate, coef_init, intercept_init, sample_weight)\u001b[0m\n\u001b[0;32m    680\u001b[0m \u001b[38;5;66;03m# Clear iteration count for multiple call to fit.\u001b[39;00m\n\u001b[0;32m    681\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mt_ \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1.0\u001b[39m\n\u001b[1;32m--> 683\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_partial_fit\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    684\u001b[0m \u001b[43m    \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    685\u001b[0m \u001b[43m    \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    686\u001b[0m \u001b[43m    \u001b[49m\u001b[43malpha\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    687\u001b[0m \u001b[43m    \u001b[49m\u001b[43mC\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    688\u001b[0m \u001b[43m    \u001b[49m\u001b[43mloss\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    689\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlearning_rate\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    690\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmax_iter\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    691\u001b[0m \u001b[43m    \u001b[49m\u001b[43mclasses\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    692\u001b[0m \u001b[43m    \u001b[49m\u001b[43msample_weight\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    693\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcoef_init\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    694\u001b[0m \u001b[43m    \u001b[49m\u001b[43mintercept_init\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    695\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    697\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[0;32m    698\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtol \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    699\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtol \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m-\u001b[39mnp\u001b[38;5;241m.\u001b[39minf\n\u001b[0;32m    700\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_iter_ \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmax_iter\n\u001b[0;32m    701\u001b[0m ):\n\u001b[0;32m    702\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[0;32m    703\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMaximum number of iteration reached before \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    704\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mconvergence. Consider increasing max_iter to \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    705\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mimprove the fit.\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m    706\u001b[0m         ConvergenceWarning,\n\u001b[0;32m    707\u001b[0m     )\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:637\u001b[0m, in \u001b[0;36mBaseSGDClassifier._partial_fit\u001b[1;34m(self, X, y, alpha, C, loss, learning_rate, max_iter, classes, sample_weight, coef_init, intercept_init)\u001b[0m\n\u001b[0;32m    627\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fit_binary(\n\u001b[0;32m    628\u001b[0m         X,\n\u001b[0;32m    629\u001b[0m         y,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    634\u001b[0m         max_iter\u001b[38;5;241m=\u001b[39mmax_iter,\n\u001b[0;32m    635\u001b[0m     )\n\u001b[0;32m    636\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 637\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    638\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe number of classes has to be greater than one; got \u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[38;5;124m class\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    639\u001b[0m         \u001b[38;5;241m%\u001b[39m n_classes\n\u001b[0;32m    640\u001b[0m     )\n\u001b[0;32m    642\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n",
      "\u001b[1;31mValueError\u001b[0m: The number of classes has to be greater than one; got 1 class"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import SGDClassifier\n",
    "\n",
    "y_train_5 = (y_train == 5)\n",
    "y_test_5 = (y_test == 5)\n",
    "\n",
    "sgd_clf = SGDClassifier(max_iter=5, tol=1e-3, random_state=42)\n",
    "sgd_clf.fit(X_train, y_train_5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a13e06a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2', '7', '1', '4', '7'], dtype='<U1')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sgd_clf.predict(X_train[15:20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9285c00c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "54768    2\n",
       "23220    7\n",
       "59768    1\n",
       "30954    4\n",
       "16299    7\n",
       "Name: class, dtype: category\n",
       "Categories (10, object): ['0', '1', '2', '3', ..., '6', '7', '8', '9']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train[15:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8c0b027c",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = sgd_clf.predict(X_test[20:25])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "064abfb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60020    9\n",
       "60021    6\n",
       "60022    6\n",
       "60023    5\n",
       "60024    4\n",
       "Name: class, dtype: category\n",
       "Categories (10, object): ['0', '1', '2', '3', ..., '6', '7', '8', '9']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test[20:25]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f3abfe51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy = accuracy_score(y_test[20:25], y_pred)\n",
    "\n",
    "#print(\"Accuracy:\", accuracy)\n",
    "accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cf3d809",
   "metadata": {},
   "source": [
    "## Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5eb3b625",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "cross_val_score(sgd_clf, X_train, y_train, cv=3, scoring=\"accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8208cdff",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.base import clone\n",
    "\n",
    "skfolds = StratifiedKFold(n_splits=3, random_state=42, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2c35de1b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0993\n",
      "0.1015\n",
      "0.10485\n"
     ]
    }
   ],
   "source": [
    "for train_index, test_index in skfolds.split(X_train, y_train):\n",
    "    clone_clf = clone(sgd_clf)\n",
    "    \n",
    "    \n",
    "    X_train_folds = X_train.iloc[train_index]\n",
    "    y_train_folds = y_train[train_index]\n",
    "    X_test_folds = X_train.iloc[test_index]\n",
    "    y_test_folds = y_train[test_index]\n",
    "    \n",
    "    clone_clf.fit(X_train_folds, y_train_folds)\n",
    "    y_pred = clone_clf.predict(X_test_folds)\n",
    "    n_correct = sum(y_pred == y_test_folds)\n",
    "    print(n_correct/len(y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3d8e51d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.8726 , 0.86495, 0.877  ])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "cross_val_score(sgd_clf, X_train, y_train, cv=3, scoring=\"accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f5a0b324",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_predict\n",
    "y_train_pred = cross_val_predict(sgd_clf, X_train, y_train, cv=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "449254cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5720,    0,   46,   15,    8,   47,   26,    2,   50,    9],\n",
       "       [   3, 6476,   92,   36,   12,   37,    3,   15,   48,   20],\n",
       "       [  53,   35, 5379,  119,   53,   46,   45,   59,  132,   37],\n",
       "       [  29,   16,  313, 5094,   16,  307,   11,   51,  167,  127],\n",
       "       [  24,   29,   76,   34, 5235,   54,   30,   12,   48,  300],\n",
       "       [  90,   18,   87,  267,   76, 4428,   48,   17,  272,  118],\n",
       "       [  75,   19,  260,   21,   86,  193, 5178,    1,   80,    5],\n",
       "       [  35,   17,  121,   76,   97,   29,    8, 5385,   28,  469],\n",
       "       [  64,   79,  368,  169,   58,  510,   21,   15, 4277,  290],\n",
       "       [  37,   22,   39,  127,  262,  104,    4,  169,   66, 5119]],\n",
       "      dtype=int64)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix (y_train, y_train_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "575839db",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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