Naive_bayes_classifier.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a1613128",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73303022",
   "metadata": {},
   "source": [
    "## Fair dirichlet prior\n",
    "The line below toggles dirichlet to true and false. If you want to disable smoothing, turn dirichlet value to *false*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "54245255",
   "metadata": {},
   "outputs": [],
   "source": [
    "dirichlet = True"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae9f9de6",
   "metadata": {},
   "source": [
    "## The input data\n",
    "If you want to apply the test with another dataset, change the below lines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "85006a12",
   "metadata": {},
   "outputs": [],
   "source": [
    "training_data = pd.read_csv(r'bbcsports_train.csv')\n",
    "validation_data = pd.read_csv(r'bbcsports_val.csv')\n",
    "# You may uncomment the line below and comment the line above to test with different data\n",
    "#validation_data = pd.read_csv(r'bbcsports_test.csv') "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6cd7b0b",
   "metadata": {},
   "source": [
    "# Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e3211893",
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_value(argument):\n",
    "    max_result = label_probabilities[0]\n",
    "    for j in range(4613):\n",
    "        max_result += argument[j] * word_probabilities.iloc[0][j]\n",
    "    class_id = 0\n",
    "    for k in range(1, 5):\n",
    "        result = label_probabilities[k]\n",
    "        for j in range(4613):\n",
    "            result += argument[j] * word_probabilities.iloc[k][j]\n",
    "        if result > max_result:\n",
    "            max_result = result\n",
    "            class_id = k\n",
    "    return class_id"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7614ff90",
   "metadata": {},
   "source": [
    "$P(Dear|y_i)$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "66ae6feb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "class_label\n",
       "0     77\n",
       "1     86\n",
       "2    198\n",
       "3    114\n",
       "4     77\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "label_document_count = training_data.groupby(['class_label']).size()\n",
    "label_document_count.plot()\n",
    "label_count = int(label_document_count.count())\n",
    "display(label_document_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b64abcb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "class_label\n",
       "0    24\n",
       "1    38\n",
       "2    67\n",
       "3    33\n",
       "4    23\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "val_label_document_count = validation_data.groupby(['class_label']).size()\n",
    "val_label_document_count.plot()\n",
    "display(val_label_document_count)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3470d8b",
   "metadata": {},
   "source": [
    "Calculation of $π_y=y_k ≡ P (Y=y_k)$ and keeping it in **class_probabilities**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4add157a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "class_label\n",
       "0    0.139493\n",
       "1    0.155797\n",
       "2    0.358696\n",
       "3    0.206522\n",
       "4    0.139493\n",
       "dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "label_probabilities = label_document_count / label_document_count.sum()\n",
    "display(label_probabilities) \n",
    "label_probabilities = np.log(label_probabilities)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fce8e2d9",
   "metadata": {},
   "source": [
    "Sum the words accross documents into **occurances_of_words**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c746f23c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>claxton</th>\n",
       "      <th>hunt</th>\n",
       "      <th>first</th>\n",
       "      <th>major</th>\n",
       "      <th>medal</th>\n",
       "      <th>british</th>\n",
       "      <th>hurdler</th>\n",
       "      <th>sarah</th>\n",
       "      <th>confid</th>\n",
       "      <th>win</th>\n",
       "      <th>...</th>\n",
       "      <th>kooyong</th>\n",
       "      <th>massu</th>\n",
       "      <th>mcenro</th>\n",
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       "      <th>fiveset</th>\n",
       "      <th>mario</th>\n",
       "      <th>ancic</th>\n",
       "      <th>lundgren</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>class_label</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>10</td>\n",
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       "      <td>73</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>141</td>\n",
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       "      <td>0</td>\n",
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       "      <td>122</td>\n",
       "      <td>27</td>\n",
       "      <td>7</td>\n",
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       "      <th>3</th>\n",
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      "text/plain": [
       "             claxton  hunt  first  major  medal  british  hurdler  sarah  \\\n",
       "class_label                                                                \n",
       "0                 10     1     37     17     38       44        5      5   \n",
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       "3                  0     0    124     13      1        8        0      0   \n",
       "4                  0     0    102      7      0       20        0      0   \n",
       "\n",
       "             confid  win  ...  kooyong  massu  mcenro  mauresmo  ameli  hip  \\\n",
       "class_label               ...                                                 \n",
       "0                18   73  ...        0      0       0         0      0    0   \n",
       "1                17   43  ...        0      0       0         0      0    0   \n",
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       "3                17  146  ...        0      0       0         0      0    0   \n",
       "4                22  130  ...        4      3       5        10      3    6   \n",
       "\n",
       "             fiveset  mario  ancic  lundgren  \n",
       "class_label                                   \n",
       "0                  0      0      0         0  \n",
       "1                  0      0      0         0  \n",
       "2                  0      0      0         0  \n",
       "3                  0      0      0         0  \n",
       "4                  0      3      3         3  \n",
       "\n",
       "[5 rows x 4613 columns]"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "class_label\n",
       "0    11572\n",
       "1    17179\n",
       "2    31533\n",
       "3    20755\n",
       "4    10819\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "occurances_of_words = training_data.groupby(['class_label']).sum()\n",
    "\n",
    "# The line below is the fair dirichlet prior addition with alpha value of 1\n",
    "if(dirichlet):\n",
    "    occurances_of_words += 1\n",
    "\n",
    "display(occurances_of_words)\n",
    "label_word_count = occurances_of_words.sum(axis=1)\n",
    "display(label_word_count)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fd933f4",
   "metadata": {},
   "source": [
    "Calculating the $P(x_i|y_j)$ values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "24cb6954",
   "metadata": {},
   "outputs": [
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       "              claxton      hunt     first     major     medal   british  \\\n",
       "class_label                                                               \n",
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       "\n",
       "              hurdler     sarah    confid       win  ...  kooyong     massu  \\\n",
       "class_label                                          ...                      \n",
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       "\n",
       "               mcenro  mauresmo     ameli       hip  fiveset     mario  \\\n",
       "class_label                                                              \n",
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       "\n",
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     "text": [
      "/home/emre/anaconda3/lib/python3.9/site-packages/pandas/core/internals/blocks.py:402: RuntimeWarning: divide by zero encountered in log\n",
      "  result = func(self.values, **kwargs)\n"
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       "      <td>-1.797693e+308</td>\n",
       "      <td>-1.797693e+308</td>\n",
       "      <td>-6.198017</td>\n",
       "      <td>-4.421525</td>\n",
       "      <td>...</td>\n",
       "      <td>-7.902765e+00</td>\n",
       "      <td>-8.190447e+00</td>\n",
       "      <td>-7.679621e+00</td>\n",
       "      <td>-6.986474e+00</td>\n",
       "      <td>-8.190447e+00</td>\n",
       "      <td>-7.497300e+00</td>\n",
       "      <td>-1.797693e+308</td>\n",
       "      <td>-8.190447e+00</td>\n",
       "      <td>-8.190447e+00</td>\n",
       "      <td>-8.190447e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 4613 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   claxton           hunt     first     major          medal  \\\n",
       "class_label                                                                    \n",
       "0            -7.053759e+00  -9.356344e+00 -5.745426 -6.523130  -5.718758e+00   \n",
       "1           -1.797693e+308  -8.365149e+00 -4.802683 -8.365149 -1.797693e+308   \n",
       "2           -1.797693e+308  -9.665643e+00 -5.554769 -7.062953  -8.412880e+00   \n",
       "3           -1.797693e+308 -1.797693e+308 -5.120261 -7.375593  -9.940542e+00   \n",
       "4           -1.797693e+308 -1.797693e+308 -4.664086 -7.343149 -1.797693e+308   \n",
       "\n",
       "                   british        hurdler          sarah    confid       win  \\\n",
       "class_label                                                                    \n",
       "0            -5.572154e+00  -7.746906e+00  -7.746906e+00 -6.465972 -5.065884   \n",
       "1            -9.058296e+00 -1.797693e+308 -1.797693e+308 -6.918230 -5.990243   \n",
       "2           -1.797693e+308 -1.797693e+308 -1.797693e+308 -6.832429 -5.205498   \n",
       "3            -7.861101e+00 -1.797693e+308 -1.797693e+308 -7.107329 -4.956936   \n",
       "4            -6.293327e+00 -1.797693e+308 -1.797693e+308 -6.198017 -4.421525   \n",
       "\n",
       "             ...        kooyong          massu         mcenro       mauresmo  \\\n",
       "class_label  ...                                                               \n",
       "0            ... -1.797693e+308 -1.797693e+308 -1.797693e+308 -1.797693e+308   \n",
       "1            ... -1.797693e+308 -1.797693e+308 -1.797693e+308 -1.797693e+308   \n",
       "2            ... -1.797693e+308 -1.797693e+308 -1.797693e+308 -1.797693e+308   \n",
       "3            ... -1.797693e+308 -1.797693e+308 -1.797693e+308 -1.797693e+308   \n",
       "4            ...  -7.902765e+00  -8.190447e+00  -7.679621e+00  -6.986474e+00   \n",
       "\n",
       "                     ameli            hip        fiveset          mario  \\\n",
       "class_label                                                               \n",
       "0           -1.797693e+308 -1.797693e+308 -1.797693e+308 -1.797693e+308   \n",
       "1           -1.797693e+308 -1.797693e+308 -1.797693e+308 -1.797693e+308   \n",
       "2           -1.797693e+308 -1.797693e+308 -1.797693e+308 -1.797693e+308   \n",
       "3           -1.797693e+308 -1.797693e+308 -1.797693e+308 -1.797693e+308   \n",
       "4            -8.190447e+00  -7.497300e+00 -1.797693e+308  -8.190447e+00   \n",
       "\n",
       "                     ancic       lundgren  \n",
       "class_label                                \n",
       "0           -1.797693e+308 -1.797693e+308  \n",
       "1           -1.797693e+308 -1.797693e+308  \n",
       "2           -1.797693e+308 -1.797693e+308  \n",
       "3           -1.797693e+308 -1.797693e+308  \n",
       "4            -8.190447e+00  -8.190447e+00  \n",
       "\n",
       "[5 rows x 4613 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "word_probabilities = occurances_of_words.div(label_word_count, axis='rows')\n",
    "display(word_probabilities)\n",
    "word_probabilities = np.log(word_probabilities)\n",
    "word_probabilities = word_probabilities.replace(-np.inf, np.nan_to_num(-np.inf))\n",
    "#word_probabilities = np.nan_to_num(word_probabilities)\n",
    "#word_probabilities = pd.DataFrame(word_probabilities)\n",
    "display(word_probabilities)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb7f3d23",
   "metadata": {},
   "source": [
    "### Predicting on the validation data\n",
    "Each document in the validation (or test) dataset is fed into the predict_value function individually. Their results are appended to a numpy array called predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0c6a648a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_18500/1651131820.py:4: RuntimeWarning: overflow encountered in double_scalars\n",
      "  max_result += argument[j] * word_probabilities.iloc[0][j]\n",
      "/tmp/ipykernel_18500/1651131820.py:4: RuntimeWarning: overflow encountered in multiply\n",
      "  max_result += argument[j] * word_probabilities.iloc[0][j]\n",
      "/tmp/ipykernel_18500/1651131820.py:9: RuntimeWarning: overflow encountered in double_scalars\n",
      "  result += argument[j] * word_probabilities.iloc[k][j]\n",
      "/tmp/ipykernel_18500/1651131820.py:9: RuntimeWarning: overflow encountered in multiply\n",
      "  result += argument[j] * word_probabilities.iloc[k][j]\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/tmp/ipykernel_18500/696012258.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidation_row_count\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      6\u001b[0m     \u001b[0mrow\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalidation_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\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----> 7\u001b[0;31m     \u001b[0mrow_prediction\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpredict_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\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      8\u001b[0m     \u001b[0mpredictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpredictions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow_prediction\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/tmp/ipykernel_18500/1651131820.py\u001b[0m in \u001b[0;36mpredict_value\u001b[0;34m(argument)\u001b[0m\n\u001b[1;32m      7\u001b[0m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlabel_probabilities\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m4613\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----> 9\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0margument\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mword_probabilities\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mj\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     10\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mmax_result\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m             \u001b[0mmax_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    965\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    966\u001b[0m             \u001b[0mmaybe_callable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_if_callable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 967\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmaybe_callable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\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    968\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    969\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_is_scalar_access\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtuple\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/python3.9/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_axis\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1523\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1524\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1525\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ixs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\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   1526\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1527\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_slice_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslice_obj\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mslice\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\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/python3.9/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_ixs\u001b[0;34m(self, i, axis)\u001b[0m\n\u001b[1;32m   3426\u001b[0m             \u001b[0;31m# if we are a copy, mark as such\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3427\u001b[0m             \u001b[0mcopy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_values\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mnew_values\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbase\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3428\u001b[0;31m             result = self._constructor_sliced(\n\u001b[0m\u001b[1;32m   3429\u001b[0m                 \u001b[0mnew_values\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3430\u001b[0m                 \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\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/python3.9/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, index, dtype, name, copy, fastpath)\u001b[0m\n\u001b[1;32m    457\u001b[0m                     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSingleArrayManager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    458\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 459\u001b[0;31m         \u001b[0mNDFrame\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\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    460\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    461\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfastpath\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\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/python3.9/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, copy, attrs)\u001b[0m\n\u001b[1;32m    253\u001b[0m             \u001b[0mattrs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    254\u001b[0m         \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"_attrs\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattrs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 255\u001b[0;31m         \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"_flags\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mFlags\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallows_duplicate_labels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\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    256\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    257\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mclassmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pandas/core/flags.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, obj, allows_duplicate_labels)\u001b[0m\n\u001b[1;32m     45\u001b[0m     \u001b[0m_keys\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m\"allows_duplicate_labels\"\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     46\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 47\u001b[0;31m     \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallows_duplicate_labels\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     48\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_allows_duplicate_labels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mallows_duplicate_labels\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     49\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_obj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mweakref\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mref\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\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": [
    "validation_labels = pd.DataFrame(validation_data[[\"class_label\"]], dtype='int32')\n",
    "validation_row_count = validation_labels.count()['class_label'].astype(int)\n",
    "\n",
    "predictions = np.array([])\n",
    "for i in range(0, validation_row_count):\n",
    "    row = validation_data.loc[i,:]\n",
    "    row_prediction = predict_value(row)\n",
    "    predictions = np.append(predictions, [int(row_prediction)])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdb69b3d",
   "metadata": {},
   "source": [
    "### Create confusion matrix\n",
    "- First, a table of predicted values versus the actual values is created. This is assigned to the variable confusion_matrix.\n",
    "- To obtain the confusion matrix with labels TP, TN, FP and FN, these values are calculated for each label, and then the summed to get the total TP, TN, FP and FN values. \n",
    "- The accuracy is then calculated using this table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "41e0b750",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# turn predictions array into dataframe\n",
    "prediction_labels = pd.DataFrame(predictions, dtype='int32', columns=['predicted_label'])\n",
    "\n",
    "# create the confusion matrix after concatinating validation labels and prediction labels into one dataframe\n",
    "pred_results = pd.concat([validation_labels, prediction_labels], axis=1, ignore_index=True)\n",
    "pred_results = pred_results.rename(columns={0: 'y_actual', 1: 'y_predicted'})\n",
    "confusion_matrix = pd.crosstab(pred_results['y_actual'], pred_results['y_predicted'], rownames=['Actual'], colnames=['Predicted'])\n",
    "display(confusion_matrix)\n",
    "\n",
    "confusion_data = np.full((label_count, 4), 0, dtype=int)\n",
    "confusion_values = pd.DataFrame(confusion_data, columns=['TP', 'TN', 'FP', 'FN'])\n",
    "for i in range(label_count):\n",
    "    TP = 0\n",
    "    TN = 0\n",
    "    FP = 0\n",
    "    FN = 0\n",
    "    for j in range(label_count):\n",
    "        for k in range(label_count):\n",
    "            if j == i and k == i:\n",
    "                TP = confusion_matrix[j][k]\n",
    "            elif j == i:\n",
    "                FP += confusion_matrix[j][k]\n",
    "            elif k == i:\n",
    "                FN += confusion_matrix[j][k]\n",
    "            else:\n",
    "                TN += confusion_matrix[j][k]\n",
    "    confusion_values['TP'][i] = TP\n",
    "    confusion_values['TN'][i] = TN\n",
    "    confusion_values['FP'][i] = FP\n",
    "    confusion_values['FN'][i] = FN\n",
    "display(confusion_values)\n",
    "\n",
    "confusion_sum = confusion_values.sum()\n",
    "TP = confusion_sum['TP']\n",
    "TN = confusion_sum['TN']\n",
    "FP = confusion_sum['FP']\n",
    "FN = confusion_sum['FN']\n",
    "accuracy = (TP + TN) / (TP + TN + FP + FN)\n",
    "print(\"Accuracy: \" + str(accuracy))"
   ]
  }
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