classification_using_joint_probability.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classification Using Joint Probability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.datasets import load_iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = load_iris()\n",
    "mini_iris = np.round(iris.data[:, :2]).astype(int)\n",
    "data = pd.DataFrame(mini_iris, columns = ['length', 'width'])\n",
    "data['type'] = iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "raw_mimetype": "text/html",
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<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",
       "    }\n",
       "\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>length</th>\n",
       "      <th>width</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     length  width  type\n",
       "114       6      3     2\n",
       "62        6      2     1\n",
       "33        6      4     0\n",
       "107       7      3     2\n",
       "7         5      3     0\n",
       "..      ...    ...   ...\n",
       "9         5      3     0\n",
       "103       6      3     2\n",
       "67        6      3     1\n",
       "117       8      4     2\n",
       "47        5      3     0\n",
       "\n",
       "[150 rows x 3 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# shuffle data\n",
    "data = data.sample(frac = 1, random_state = 0)\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 1. Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2277b31aa00>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed = 10\n",
    "data_with_noise = data.iloc[:, :2] + np.random.normal(loc = 0, scale = 0.1, size = (150, 2), )\n",
    "plt.scatter(data_with_noise.length, data_with_noise.width, c = [\"bgr\"[k] for k in data.iloc[:,2]], s = 200, alpha = 0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test = data[:120], data[120:]\n",
    "\n",
    "y_train = X_train['type']\n",
    "y_test = X_test['type']\n",
    "\n",
    "X_train = X_train.drop('type', axis = 1)\n",
    "X_test = X_test.drop('type', axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=4)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;DecisionTreeClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier(max_depth=4)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(max_depth=4)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier = DecisionTreeClassifier(max_depth = 4)\n",
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 0, 0, 2, 0, 1, 1, 1, 1, 1, 2, 1, 2, 0, 1, 2, 1, 0, 2, 1, 1,\n",
       "       1, 0, 0, 0, 1, 1, 2, 0])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 2. Probabilistic Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "X_train, X_test = data[:120], data[120:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    length  width  type     count\n",
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       "3        5      2     2  0.008333\n",
       "4        5      3     0  0.141667\n",
       "5        5      3     1  0.016667\n",
       "6        5      4     0  0.125000\n",
       "7        6      2     1  0.050000\n",
       "8        6      2     2  0.025000\n",
       "9        6      3     1  0.175000\n",
       "10       6      3     2  0.166667\n",
       "11       6      4     0  0.025000\n",
       "12       7      2     2  0.008333\n",
       "13       7      3     1  0.050000\n",
       "14       7      3     2  0.091667\n",
       "15       7      4     2  0.008333\n",
       "16       8      3     2  0.025000\n",
       "17       8      4     2  0.008333"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# computing the joint probability distribution over the training data\n",
    "joint_prob = X_train.groupby(['length', 'width', 'type']).size() / 120\n",
    "joint_prob = joint_prob.reset_index(name = 'count')\n",
    "joint_prob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    length  width  type     count\n",
      "26       6      4     2  0.000001\n",
      "33       7      4     0  0.000001\n",
      "4        5      3     0  0.141667\n",
      "3        5      2     2  0.008333\n",
      "27       7      2     0  0.000001\n",
      "38       8      2     2  0.000001\n",
      "14       5      3     2  0.000001\n",
      "25       6      4     1  0.000001\n",
      "9        5      2     0  0.000001\n",
      "1        4      2     1  0.000001\n",
      "Cardinality: 45\n"
     ]
    }
   ],
   "source": [
    "p_full = pd.DataFrame(data = [(l, w, t, s) for l in [4, 5, 6, 7, 8] for w in [2, 3, 4] for t in [0, 1, 2] for s in [0.000001]],\n",
    "                      columns = ['length', 'width', 'type', 'count'])\n",
    "joint_prob = pd.concat([joint_prob, p_full], axis = 0)\n",
    "joint_prob = joint_prob.drop_duplicates(subset = ['length', 'width', 'type'], keep = 'first')\n",
    "print(joint_prob.sample(10))\n",
    "print(\"Cardinality: {}\".format(p_full.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_features = X_test.iloc[:, :2].values\n",
    "X_test_actual_results = X_test.iloc[:, 2].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted_values = []\n",
    "\n",
    "for i in range(X_test_features.shape[0]):\n",
    "    mask = (joint_prob.length.isin([X_test_features[i][0]])) & \\\n",
    "           (joint_prob.width.isin([X_test_features[i][1]]))\n",
    "    predicted_values.append(joint_prob[mask].sort_values(['type'])['count'].argmax())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 0, 0, 2, 0, 1, 1, 1, 1, 1, 2, 1, 2, 0, 1, 2, 1, 0, 2, 1, 1,\n",
       "       1, 0, 0, 0, 1, 1, 2, 0], dtype=int64)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted_values = np.array(predicted_values)\n",
    "predicted_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True,  True,  True,  True, False,  True,  True,\n",
       "        True, False,  True,  True, False,  True,  True,  True, False,\n",
       "        True, False,  True,  True,  True,  True,  True,  True, False,\n",
       "        True,  True,  True])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted_values == X_test_actual_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "import seaborn as sns\n",
    "\n",
    "sns.heatmap(confusion_matrix(X_test_actual_results, predicted_values),\n",
    "            cmap = 'viridis',\n",
    "            annot = True,\n",
    "            annot_kws = {'fontsize':15})\n",
    "plt.xlabel('\"Predicted')\n",
    "plt.ylabel('Acutal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8\n"
     ]
    }
   ],
   "source": [
    "score = (predicted_values == X_test_actual_results).sum() / 30\n",
    "print(score)"
   ]
  }
 ],
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