{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plus proches voisins - évaluation\n",
    "\n",
    "\n",
    "Comment évaluer la pertinence d'un modèle des plus proches voisins."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from teachpyx.datasets import load_wines_dataset\n",
    "\n",
    "df = load_wines_dataset()\n",
    "X = df.drop([\"quality\", \"color\"], axis=1)\n",
    "y = df[\"quality\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "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>KNeighborsRegressor(n_neighbors=1)</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;KNeighborsRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsRegressor.html\">?<span>Documentation for KNeighborsRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsRegressor(n_neighbors=1)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsRegressor(n_neighbors=1)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "\n",
    "knn = KNeighborsRegressor(n_neighbors=1)\n",
    "knn.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = knn.predict(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Le modèle ne fait pas d'erreur sur tous les exemples de la base de vins. C'est normal puisque le plus proche voisin d'un vin est nécessairement lui-même, la note prédite et la sienne."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 0.0)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min(prediction - y), max(prediction - y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Il est difficile dans ces conditions de dire si la prédiction et de bonne qualité. On pourrait estimer la qualité de la prédiction sur un vin nouveau mais il n'y en a aucun pour le moment et ce n'est pas l'ordinateur qui va les fabriquer. On peut peut-être regarder combien de fois le plus proche voisin d'un vin autre que le vin lui-même partage la même note."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\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-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 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-2 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-2 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-2 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-2 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-2 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-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 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-2 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-2 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-2 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-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 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-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 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-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 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-2 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-2 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-2 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-2 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-2 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-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 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-2 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-2 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-2 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-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 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-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 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-2 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-2 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-2 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-2 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-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>NearestNeighbors(n_neighbors=2)</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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;NearestNeighbors<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.NearestNeighbors.html\">?<span>Documentation for NearestNeighbors</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>NearestNeighbors(n_neighbors=2)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "NearestNeighbors(n_neighbors=2)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import NearestNeighbors\n",
    "\n",
    "nn = NearestNeighbors(n_neighbors=2)\n",
    "nn.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "distance, index = nn.kneighbors(X)\n",
    "proche = index[:, 1].ravel()\n",
    "note_proche = [y[i] for i in proche]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Il ne reste plus qu'à calculer la différence entre la note d'un vin et celle de son plus proche voisin autre que lui-même."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Histogramme des différences\\nde prédiction')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lEn/88QeAsvfsHqZSqdC7d28sWrQIOp0OTz/9NJKTk5GZmfkYfivV4+3tjQULFmD27Nm4ePEihgwZAnt7e2RmZmLbtm2YMGEC/vOf/5T5/JiYGOzZswe9evXCG2+8gaKiIqxYsQLt2rXDr7/+WuntHDhwAJGRkXj55ZfxzDPPoKioCOvXr4e5ufkjz+dYWFhgwYIFmDhxIvr27YtXXnkFmZmZWLduXY2f81KpVFi9ejVGjRqFLl26YMSIEXB2dkZWVhaSkpLQo0cPkz+SJfXp0wcTJ05EbGws0tLSEBQUBAsLC5w/fx6bN2/GsmXLMGzYsEr1a8aMGdiyZQtefvlljB07Fj4+Prhx4wa+++47JCQkoFOnThg1ahQ2bdqESZMm4eDBg+jRoweKi4tx7tw5bNq0CXv37kXXrl0xb948HDlyBCEhIfD09ERubi5WrVoFDw8P6VrGCqnWZ5V1RFkfRRv06dOn3EslFixYILp37y4cHR2FtbW1aN26tXj//feNLhEoKioSU6ZMEc7OzkKhUBh9XH/79m0xbdo04e7uLiwsLETLli3FRx99JH2MbFBQUCDUarVwcnISdnZ2YsiQISIjI0MAMLp0obSP0Q3++ecf8dJLLwlHR0fh4OAgXn75ZXHlypUyL7couY6wsDBha2tb7u/JcKlEyUsLKvPRvxBCbN26VfTs2VPY2toKW1tb0bp1a6FWq0VGRoZJH0o6fPiw8PHxEZaWlqJ58+YiISFB2k5J5W3nr7/+EmPHjhXe3t7CyspKODk5icDAQLFv375y+yGEEKtWrRJeXl5CqVSKrl27iiNHjog+ffrU6KUSBgcPHhTBwcHCwcFBWFlZCW9vbxEeHi5Onjwp1ZT1OhqsWbNG+Pj4CGtra2Fvby86dOggZs6cKa5cuSLVeHp6lnqJUMlxCSHE9evXRWRkpHj66aeFpaWl8PDwEGFhYeLatWtSTWFhofjwww9Fu3bthFKpFA0bNhQ+Pj4iJiZG5OXlCSGE2L9/v3jxxReFu7u7sLS0FO7u7uLVV18Vf/zxR5ljKY1CCE46W9vS0tLw7LPP4quvvpKukCaiR+M5r3/ZvXv3TNqWLl0KMzMzkyvbiahsPOf1L1u0aBFSU1MRGBiIBg0aYPfu3di9ezcmTJjwyK+ZEJExHjb+yzQaDWJiYnD27FncuXMHTZs2xahRo/DOO++YXMNDRGVjeBGRLPGcFxHJEsOL6pwVK1aY3HaIqCSG1xOkLk1cUZYVK1Zg3rx50l0yymO4YeKhQ4ekNsPN/mqKYY6Eixcv1tg6qfoYXlRnnDhxAnPmzMH3339f5h0iHqcPPvhAumEe1X0ML6oz0tPTsXXr1grvdZXl008/NfnydkWUFV6jRo3CvXv3pC9oU93Az+apVuj1ehQWFhpNVlFT82g+6j5hVWFubl7h27TQv4d7XvVUTU9cURrDObRz585h+PDhUKlUaNSoEd58803cv3/fqFahUCAyMhIbNmxAu3btoFQqpTvLXr58GWPHjoWrqyuUSiXatWuHzz//3GR7//zzD4YMGQJbW1u4uLhg2rRppd6FoLRzXoYpxjp06AArKys4Oztj4MCB0lyfCoUCBQUF+OKLL6TJVQxhWtY5r1WrVkljcXd3h1qtNrljg2HO0LNnzyIwMBA2NjZ4+umnsWjRonJ/v/Ro3POqh2p64oryDB8+HM2aNZNu6rd8+XLcvHnTZAaaAwcOYNOmTYiMjETjxo3RrFkz5OTkwM/PTwo3Z2dn7N69GxEREcjPz0dUVBSAB1+r6tevH7KysjB16lS4u7tj/fr1OHDgQIV+JxEREUhMTMSgQYMwbtw4FBUV4ejRozh+/Di6du2K9evXS5OvTJgwAcD/3VurNNHR0YiJiUH//v0xefJkZGRkYPXq1Thx4oTJBBc3b97EwIEDMXToUAwfPhxbtmzB22+/jQ4dOki3S6IqqNTXuEkWanriirIY7oowePBgo/Y33nhDABC//PKL1AZAmJmZifT0dKPaiIgI8dRTTxndmUAIIUaMGCEcHBykyUaWLl0qAIhNmzZJNQUFBaJFixYCgDh48KDUHhYWZjQRyIEDBwQAMXXqVJMxPHzXD1tbW6M7jRgY7gqRmZkphHgwQYWlpaUICgoSxcXFUt3KlSsFAPH5559LbX369BEAxJdffim1abVa4ebmJkJDQ022RRXHw8Z65nFMXFEetVpt9NgwocjD95kHHtxnqm3bttJjIQS2bt2KF154AUIIoz4EBwcjLy9PmlBk165deOqpp4zuQ2VjYyPtJT3K1q1boVAopHkSH1aVS0f27duHwsJCREVFGd2Cefz48VCpVEhKSjKqt7Ozw+uvvy49trS0RPfu3Uud7IQqjoeN9czjmLiiPCWf7+3tDTMzM5NzRCUnE7l69Spu3bqFNWvWYM2aNaWu2zBpw99//40WLVqYhE2rVq3K7d+ff/4Jd3d3ODk5lVtbEYYJOUpu29LSEs2bN5eWG3h4eJj0u2HDhkY3VKTKY3g9wR7XBBll7c2UvJ+6YbKJ119/XZpVuqSKzN5U19XUBBdkjOFVzzzOiSvKcv78eaO9qgsXLkCv15d7lbuzszPs7e1RXFxc7qQLnp6e+O233yCEMArHilzP5e3tjb179+LGjRuP3Puq6CGk4XqvjIwMo9tAFxYWIjMzs8oTZ1Dl8JxXPfM4Jq4oT3x8vNFjw4Qi5X2SZrh3/NatW0udmebhySaee+45XLlyBVu2bJHa7t69W+bh5sNCQ0MhhEBMTIzJsofHbWtrW6HJKfr37w9LS0ssX77c6Plr165FXl4eQkJCyl0HVR/3vOqhmp64ojyZmZkYPHgwBg4ciJSUFHz11Vd47bXXKjQl2MKFC3Hw4EH4+vpi/PjxaNu2LW7cuIFTp05h37590kwz48ePx8qVKzF69Gikpqbiqaeewvr162FjY1PuNgIDAzFq1CgsX74c58+fl+a3PHr0KAIDA6VJVnx8fLBv3z4sWbIE7u7u8PLygq+vr8n6nJ2dMXv2bMTExGDgwIEYPHgwMjIysGrVKnTr1s3o5Dw9RrX0KSc9ZjU5cUVZDOs7e/asGDZsmLC3txcNGzYUkZGR4t69e0a1AIRarS51PTk5OUKtVosmTZoICwsL4ebmJvr16yfWrFljVPf333+LwYMHCxsbG9G4cWPx5ptvij179pR7qYQQDyZP+eijj0Tr1q2FpaWlcHZ2FoMGDRKpqalSzblz50Tv3r2FtbW1ACBdNlHyUgmDlStXitatWwsLCwvh6uoqJk+eLG7evGlUU9rkL2X1kSqHNyOkKjNcqHn16lU0bty4trtDTxie8yIiWWJ4EZEsMbyISJZ4zouIZIl7XkQkS/X2Oi+9Xo8rV67A3t6+zt+3nehJI4TA7du34e7ubvTl9sqot+F15coVzkBNVMddunQJHh4eVXpuvQ0ve3t7AA9+OSqVqsw6nU6H5ORkBAUF1fjtg2sLxyQPT/KY8vPz0aRJE+nfaVXU2/AyHCqqVKpyw8vGxgYqlapevYE4prqPY6ra/dQMeMKeiGSJ4UVEssTwIiJZYngRkSwxvIhIlqoVXgsXLoRCoZDm1gOA+/fvQ61Wo1GjRrCzs0NoaChycnKMnpeVlYWQkBDY2NjAxcUFM2bMQFFRkVHNoUOH0KVLFyiVSrRo0QKJiYnV6SoR1TNVDq8TJ07gk08+MZkgYdq0afj++++xefNmHD58GFeuXMHQoUOl5cXFxQgJCUFhYSF+/PFHfPHFF0hMTMScOXOkmszMTISEhCAwMBBpaWmIiorCuHHjTG5jTERPripd53Xnzh2MHDkSn376KRYsWCC15+XlYe3atdi4cSP69u0LAFi3bh3atGmD48ePw8/PD8nJyTh79iz27dsHV1dXdO7cGfPnz8fbb7+N6OhoWFpaIiEhAV5eXli8eDGAB3MOHjt2DHFxcSZzDxpotVqjqd/z8/MBPLjuRKfTlTkWw7JH1cgNxyQPT/KYamLMVQovtVqNkJAQ9O/f3yi8UlNTodPpjGZPad26NZo2bYqUlBT4+fkhJSUFHTp0MJp6Pjg4GJMnT0Z6ejqeffZZpKSkmMzAEhwcbHR4WlJsbGypEywkJydX6D7nGo2m3Bq54Zjk4Ukc0927d6u9jUqH19dff41Tp07hxIkTJsuys7NhaWkJR0dHo3ZXV1dkZ2dLNQ8Hl2G5YdmjavLz83Hv3j2T+f8AYPbs2Zg+fbr02PD1g6CgoHKvsNdoNBgwYEC9usq5vo7pvZNm0OrLvyr7t+jS99Drkvr8OpU3JsORUXVUKrwuXbqEN998ExqNBlZWVtXeeE1SKpVQKpUm7RYWFhV6Y1S0Tk7q45i0egW0xeWHl5zGXR9fp/LGVBPjrdQJ+9TUVOTm5qJLly5o0KABGjRogMOHD2P58uVo0KABXF1dUVhYaDL3XU5ODtzc3AAAbm5uJp8+Gh6XV6NSqUrd6yKiJ0+lwqtfv344c+YM0tLSpJ+uXbti5MiR0v9bWFhg//790nMyMjKQlZUFf39/AIC/vz/OnDmD3NxcqUaj0UClUqFt27ZSzcPrMNQY1kFEVKnDRnt7e7Rv396ozdbWFo0aNZLaIyIiMH36dDg5OUGlUmHKlCnw9/eHn58fACAoKAht27bFqFGjsGjRImRnZ+Pdd9+FWq2WDvsmTZqElStXYubMmRg7diwOHDiATZs2ISkpqSbGTET1QI3fEicuLg5mZmYIDQ2FVqtFcHAwVq1aJS03NzfHzp07MXnyZPj7+8PW1hZhYWGYN2+eVOPl5YWkpCRMmzYNy5Ytg4eHBz777LMyL5MgoidPtcPr0KFDRo+trKwQHx+P+Pj4Mp/j6emJXbt2PXK9AQEBOH36dHW7R0T1FL/bSESyxPAiIllieBGRLDG8iEiWGF5EJEsMLyKSJYYXEckSw4uIZInhRUSyxPAiIllieBGRLDG8iEiWGF5EJEsMLyKSJYYXEckSw4uIZInhRUSyxPAiIllieBGRLDG8iEiWGF5EJEsMLyKSJYYXEckSw4uIZInhRUSyxPAiIllieBGRLDG8iEiWGF5EJEsMLyKSJYYXEckSw4uIZInhRUSyxPAiIllieBGRLDG8iEiWGF5EJEsMLyKSJYYXEckSw4uIZInhRUSyxPAiIllieBGRLDG8iEiWKhVeq1evRseOHaFSqaBSqeDv74/du3dLy+/fvw+1Wo1GjRrBzs4OoaGhyMnJMVpHVlYWQkJCYGNjAxcXF8yYMQNFRUVGNYcOHUKXLl2gVCrRokULJCYmVn2ERFQvVSq8PDw8sHDhQqSmpuLkyZPo27cvXnzxRaSnpwMApk2bhu+//x6bN2/G4cOHceXKFQwdOlR6fnFxMUJCQlBYWIgff/wRX3zxBRITEzFnzhypJjMzEyEhIQgMDERaWhqioqIwbtw47N27t4aGTET1QYPKFL/wwgtGj99//32sXr0ax48fh4eHB9auXYuNGzeib9++AIB169ahTZs2OH78OPz8/JCcnIyzZ89i3759cHV1RefOnTF//ny8/fbbiI6OhqWlJRISEuDl5YXFixcDANq0aYNjx44hLi4OwcHBZfZNq9VCq9VKj/Pz8wEAOp0OOp2uzOcZlj2qRm7q85iUZqJS9XVZfX6dyhtTTYy5UuH1sOLiYmzevBkFBQXw9/dHamoqdDod+vfvL9W0bt0aTZs2RUpKCvz8/JCSkoIOHTrA1dVVqgkODsbkyZORnp6OZ599FikpKUbrMNRERUU9sj+xsbGIiYkxaU9OToaNjU2549FoNOXWyE19HNP8rvoK1e3atesx96Tm1MfXqbwx3b17t9rbqHR4nTlzBv7+/rh//z7s7Oywbds2tG3bFmlpabC0tISjo6NRvaurK7KzswEA2dnZRsFlWG5Y9qia/Px83Lt3D9bW1qX2a/bs2Zg+fbr0OD8/H02aNEFQUBBUKlWZ49HpdNBoNBgwYAAsLCwq9kuo4+rzmN47aQatXlFu/W/RZe+l1xX1+XUqb0yGI6PqqHR4tWrVCmlpacjLy8OWLVsQFhaGw4cPV7sj1aVUKqFUKk3aLSwsKvTGqGidnNTHMWn1CmiLyw8vOY27Pr5O5Y2pJsZb6fCytLREixYtAAA+Pj44ceIEli1bhldeeQWFhYW4deuW0d5XTk4O3NzcAABubm74+eefjdZn+DTy4ZqSn1Dm5ORApVKVuddFRE+eal/npdfrodVq4ePjAwsLC+zfv19alpGRgaysLPj7+wMA/P39cebMGeTm5ko1Go0GKpUKbdu2lWoeXoehxrAOIiKgkntes2fPxqBBg9C0aVPcvn0bGzduxKFDh7B37144ODggIiIC06dPh5OTE1QqFaZMmQJ/f3/4+fkBAIKCgtC2bVuMGjUKixYtQnZ2Nt59912o1WrpkG/SpElYuXIlZs6cibFjx+LAgQPYtGkTkpKSan70RCRblQqv3NxcjB49Gv/7v/8LBwcHdOzYEXv37sWAAQMAAHFxcTAzM0NoaCi0Wi2Cg4OxatUq6fnm5ubYuXMnJk+eDH9/f9ja2iIsLAzz5s2Tary8vJCUlIRp06Zh2bJl8PDwwGefffbIyySI6MlTqfBau3btI5dbWVkhPj4e8fHxZdZ4enqW+zF2QEAATp8+XZmuEdETht9tJCJZYngRkSwxvIhIlhheRCRLDC8ikiWGFxHJEsOLiGSJ4UVEssTwIiJZYngRkSwxvIhIlhheRCRLDC8ikiWGFxHJEsOLiGSJ4UVEssTwIiJZYngRkSwxvIhIlhheRCRLDC8ikiWGFxHJEsOLiGSJ4UVEssTwIiJZYngRkSwxvIhIlhheRCRLDC8ikiWGFxHJEsOLiGSJ4UVEssTwIiJZYngRkSwxvIhIlhheRCRLDC8ikiWGFxHJEsOLiGSJ4UVEssTwIiJZYngRkSwxvIhIlhheRCRLlQqv2NhYdOvWDfb29nBxccGQIUOQkZFhVHP//n2o1Wo0atQIdnZ2CA0NRU5OjlFNVlYWQkJCYGNjAxcXF8yYMQNFRUVGNYcOHUKXLl2gVCrRokULJCYmVm2ERFQvVSq8Dh8+DLVajePHj0Oj0UCn0yEoKAgFBQVSzbRp0/D9999j8+bNOHz4MK5cuYKhQ4dKy4uLixESEoLCwkL8+OOP+OKLL5CYmIg5c+ZINZmZmQgJCUFgYCDS0tIQFRWFcePGYe/evTUwZCKqDxpUpnjPnj1GjxMTE+Hi4oLU1FT07t0beXl5WLt2LTZu3Ii+ffsCANatW4c2bdrg+PHj8PPzQ3JyMs6ePYt9+/bB1dUVnTt3xvz58/H2228jOjoalpaWSEhIgJeXFxYvXgwAaNOmDY4dO4a4uDgEBweX2jetVgutVis9zs/PBwDodDrodLoyx2RY9qgauanPY1KaiUrV12X1+XUqb0w1MeZKhVdJeXl5AAAnJycAQGpqKnQ6Hfr37y/VtG7dGk2bNkVKSgr8/PyQkpKCDh06wNXVVaoJDg7G5MmTkZ6ejmeffRYpKSlG6zDUREVFldmX2NhYxMTEmLQnJyfDxsam3LFoNJpya+SmPo5pfld9hep27dr1mHtSc+rj61TemO7evVvtbVQ5vPR6PaKiotCjRw+0b98eAJCdnQ1LS0s4Ojoa1bq6uiI7O1uqeTi4DMsNyx5Vk5+fj3v37sHa2tqkP7Nnz8b06dOlx/n5+WjSpAmCgoKgUqnKHIdOp4NGo8GAAQNgYWFRwdHXbfV5TO+dNINWryi3/rfo0vfQ65L6/DqVNybDkVF1VDm81Go1fvvtNxw7dqzanagJSqUSSqXSpN3CwqJCb4yK1slJfRyTVq+Atrj88JLTuOvj61TemGpivFW6VCIyMhI7d+7EwYMH4eHhIbW7ubmhsLAQt27dMqrPycmBm5ubVFPy00fD4/JqVCpVqXtdRPTkqVR4CSEQGRmJbdu24cCBA/Dy8jJa7uPjAwsLC+zfv19qy8jIQFZWFvz9/QEA/v7+OHPmDHJzc6UajUYDlUqFtm3bSjUPr8NQY1gHEVGlDhvVajU2btyIHTt2wN7eXjpH5eDgAGtrazg4OCAiIgLTp0+Hk5MTVCoVpkyZAn9/f/j5+QEAgoKC0LZtW4waNQqLFi1CdnY23n33XajVaumwb9KkSVi5ciVmzpyJsWPH4sCBA9i0aROSkpJqePhEJFeV2vNavXo18vLyEBAQgKeeekr6+eabb6SauLg4PP/88wgNDUXv3r3h5uaGb7/9Vlpubm6OnTt3wtzcHP7+/nj99dcxevRozJs3T6rx8vJCUlISNBoNOnXqhMWLF+Ozzz4r8zIJInryVGrPS4jyr7GxsrJCfHw84uPjy6zx9PQs96PsgIAAnD59ujLdI6InCL/bSESyxPAiIllieBGRLDG8iEiWGF5EJEsMLyKSJYYXEckSw4uIZInhRUSyxPAiIllieBGRLDG8iEiWGF5EJEsMLyKSJYYXEckSw4uIZInhRUSyxPAiIllieBGRLDG8iEiWGF5EJEsMLyKSJYYXEckSw4uIZInhRUSyxPAiIllieBGRLDG8iEiWGF5EJEsMLyKSJYYXEckSw4uIZInhRUSyxPAiIllieBGRLDG8iEiWGF5EJEsMLyKSJYYXEckSw4uIZInhRUSyxPAiIllieBGRLDG8iEiWKh1eR44cwQsvvAB3d3coFAps377daLkQAnPmzMFTTz0Fa2tr9O/fH+fPnzequXHjBkaOHAmVSgVHR0dERETgzp07RjW//vorevXqBSsrKzRp0gSLFi2q/OiIqN6qdHgVFBSgU6dOiI+PL3X5okWLsHz5ciQkJOCnn36Cra0tgoODcf/+falm5MiRSE9Ph0ajwc6dO3HkyBFMmDBBWp6fn4+goCB4enoiNTUVH330EaKjo7FmzZoqDJGI6qMGlX3CoEGDMGjQoFKXCSGwdOlSvPvuu3jxxRcBAF9++SVcXV2xfft2jBgxAr///jv27NmDEydOoGvXrgCAFStW4LnnnsPHH38Md3d3bNiwAYWFhfj8889haWmJdu3aIS0tDUuWLDEKuYdptVpotVrpcX5+PgBAp9NBp9OVOR7DskfVyE19HpPSTFSqvi6rz69TeWOqiTFXOrweJTMzE9nZ2ejfv7/U5uDgAF9fX6SkpGDEiBFISUmBo6OjFFwA0L9/f5iZmeGnn37CSy+9hJSUFPTu3RuWlpZSTXBwMD788EPcvHkTDRs2NNl2bGwsYmJiTNqTk5NhY2NTbt81Gk1lh1vn1ccxze+qr1Ddrl27HnNPak59fJ3KG9Pdu3ervY0aDa/s7GwAgKurq1G7q6urtCw7OxsuLi7GnWjQAE5OTkY1Xl5eJuswLCstvGbPno3p06dLj/Pz89GkSRMEBQVBpVKV2WedTgeNRoMBAwbAwsKiokOt0x7XmNpH761w7W/RwTW2XeD/xvTeSTNo9Yp/ffuPw5P83jMcGVVHjYZXbVIqlVAqlSbtFhYWFXpjVLROTmp6TNri8kPj4W0/Dlq9okL9kNNr+SS+92pivDV6qYSbmxsAICcnx6g9JydHWubm5obc3Fyj5UVFRbhx44ZRTWnreHgbRPRkq9E9Ly8vL7i5uWH//v3o3LkzgAe7hz/99BMmT54MAPD398etW7eQmpoKHx8fAMCBAweg1+vh6+sr1bzzzjvQ6XRSQms0GrRq1arUQ0aiqmo2K6nCtRcXhjzGnlBlVXrP686dO0hLS0NaWhqAByfp09LSkJWVBYVCgaioKCxYsADfffcdzpw5g9GjR8Pd3R1DhgwBALRp0wYDBw7E+PHj8fPPP+OHH35AZGQkRowYAXd3dwDAa6+9BktLS0RERCA9PR3ffPMNli1bZnROi4iebJXe8zp58iQCAwOlx4ZACQsLQ2JiImbOnImCggJMmDABt27dQs+ePbFnzx5YWVlJz9mwYQMiIyPRr18/mJmZITQ0FMuXL5eWOzg4IDk5GWq1Gj4+PmjcuDHmzJlT5mUSRPTkqXR4BQQEQIiyr7VRKBSYN28e5s2bV2aNk5MTNm7c+MjtdOzYEUePHq1s94joCcHvNhKRLDG8iEiWGF5EJEsMLyKSJYYXEckSw4uIZInhRUSyxPAiIllieBGRLDG8iEiWGF5EJEsMLyKSJYYXEckSw4uIZInhRUSyxPAiIllieBGRLDG8iEiWGF5EJEsMLyKSJYYXEckSw4uIZInhRUSyxPAiIllieBGRLDG8iEiWGF5EJEsMLyKSJYYXEckSw4uIZInhRUSyxPAiIllqUNsdoMenffReaIsV5dZdXBjyL/SGqGZxz4uIZIl7XkS1jHvIVcM9LyKSJYYXEckSw4uIZInhRUSyxPAiIllieBGRLDG8iEiWGF5EJEsMLyKSpTodXvHx8WjWrBmsrKzg6+uLn3/+uba7RER1RJ39etA333yD6dOnIyEhAb6+vli6dCmCg4ORkZEBFxeX2u5ehTSblVShOn7tg6jy6mx4LVmyBOPHj8eYMWMAAAkJCUhKSsLnn3+OWbNmmdRrtVpotVrpcV5eHgDgxo0b0Ol0ZW5Hp9Ph7t27uH79OiwsLGp0DA2KCipUd/369RrdrmFMDXRmKNaX/525im6/ouOpzDorimOq+PZ9Y/dXuA8/ze5X4dqKqOi/p9u3bwMAhBBV35iog7RarTA3Nxfbtm0zah89erQYPHhwqc+ZO3euAMAf/vBHRj+XLl2qck7UyT2va9euobi4GK6urkbtrq6uOHfuXKnPmT17NqZPny491uv1uHHjBho1agSFouy/avn5+WjSpAkuXboElUpVMwOoZRyTPDzJYxJC4Pbt23B3d6/ytupkeFWFUqmEUqk0anN0dKzw81UqVb15AxlwTPLwpI7JwcGhWtuok582Nm7cGObm5sjJyTFqz8nJgZubWy31iojqkjoZXpaWlvDx8cH+/f934lGv12P//v3w9/evxZ4RUV1RZw8bp0+fjrCwMHTt2hXdu3fH0qVLUVBQIH36WFOUSiXmzp1rcsgpZxyTPHBM1aMQojqfVT5eK1euxEcffYTs7Gx07twZy5cvh6+vb213i4jqgDodXkREZamT57yIiMrD8CIiWWJ4EZEsMbyISJYYXmXQarXo3LkzFAoF0tLSars7VXbx4kVERETAy8sL1tbW8Pb2xty5c1FYWFjbXauU+nR7pNjYWHTr1g329vZwcXHBkCFDkJGRUdvdqjELFy6EQqFAVFTUY90Ow6sMM2fOrNb3ruqKc+fOQa/X45NPPkF6ejri4uKQkJCA//73v7XdtQoz3B5p7ty5OHXqFDp16oTg4GDk5ubWdteq5PDhw1Cr1Th+/Dg0Gg10Oh2CgoJQUFDxO1zUVSdOnMAnn3yCjh07Pv6NVfkr3fXYrl27ROvWrUV6eroAIE6fPl3bXapRixYtEl5eXrXdjQrr3r27UKvV0uPi4mLh7u4uYmNja7FXNSc3N1cAEIcPH67trlTL7du3RcuWLYVGoxF9+vQRb7755mPdHve8SsjJycH48eOxfv162NjY1HZ3Hou8vDw4OTnVdjcqpLCwEKmpqejfv7/UZmZmhv79+yMlJaUWe1ZzDPeek8trUha1Wo2QkBCj1+pxqrNfD6oNQgiEh4dj0qRJ6Nq1Ky5evFjbXapxFy5cwIoVK/Dxxx/XdlcqpCq3R5ITvV6PqKgo9OjRA+3bt6/t7lTZ119/jVOnTuHEiRP/2jafiD2vWbNmQaFQPPLn3LlzWLFiBW7fvo3Zs2fXdpfLVdExPezy5csYOHAgXn75ZYwfP76Wek4PU6vV+O233/D111/Xdleq7NKlS3jzzTexYcMGWFlZ/WvbfSK+HnT16tVyb6HbvHlzDB8+HN9//73RzQuLi4thbm6OkSNH4osvvnjcXa2wio7J0tISAHDlyhUEBATAz88PiYmJMDOTx9+twsJC2NjYYMuWLRgyZIjUHhYWhlu3bmHHjh2117lqioyMxI4dO3DkyBF4eXnVdneqbPv27XjppZdgbm4utRUXF0OhUMDMzAxardZoWU15IsKrorKyspCfny89vnLlCoKDg7Flyxb4+vrCw8OjFntXdZcvX0ZgYCB8fHzw1VdfPZY30uPk6+uL7t27Y8WKFQAeHGo1bdoUkZGRpc5nUNcJITBlyhRs27YNhw4dQsuWLWu7S9Vy+/Zt/P3330ZtY8aMQevWrfH2228/tsNhnvN6SNOmTY0e29nZAQC8vb1lHVwBAQHw9PTExx9/jKtXr0rL5HJjx3/r9kj/FrVajY0bN2LHjh2wt7dHdnY2gAd3FrW2tq7l3lWevb29SUDZ2tqiUaNGj/U8HsOrntNoNLhw4QIuXLhgEsBy2el+5ZVXcPXqVcyZM0e6PdKePXtMTuLLxerVqwEAAQEBRu3r1q1DeHj4v98hmeJhIxHJkjzO2hIRlcDwIiJZYngRkSwxvIhIlhheRCRLDC8ikiWGFxHJEsOLiGSJ4UVEssTwIiJZYngRkSz9f5ftYtAdkXLTAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "diff = y - note_proche\n",
    "ax = diff.hist(bins=20, figsize=(3, 3))\n",
    "ax.set_title(\"Histogramme des différences\\nde prédiction\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ca marche pour les deux tiers de la base, pour le tiers restant, les notes diffèrent. On peut maintenant regarder si la distance entre ces deux voisins pourrait être corrélée à cette différence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas\n",
    "\n",
    "dif = pandas.DataFrame(dict(dist=distance[:, 1], diff=diff))\n",
    "ax = dif.plot(x=\"dist\", y=\"diff\", kind=\"scatter\", figsize=(3, 3))\n",
    "ax.set_title(\"Graphe XY - distance / différence\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ce n'est pas très lisible. Essayons un autre type de graphique."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from seaborn import violinplot, boxplot\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, ax = plt.subplots(1, 2, figsize=(8, 3))\n",
    "violinplot(x=\"diff\", y=\"dist\", data=dif, ax=ax[0])\n",
    "ax[0].set_ylim([0, 25])\n",
    "ax[0].set_title(\"Violons distribution\\ndifférence / distance\")\n",
    "boxplot(x=\"diff\", y=\"dist\", data=dif, ax=ax[1])\n",
    "ax[1].set_title(\"Boxplots distribution\\ndifférence / distance\")\n",
    "ax[1].set_ylim([0, 25]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A priori le modèle n'est pas si mauvais, les voisins partageant la même note ont l'air plus proches que ceux qui ont des notes différentes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy\n",
    "\n",
    "dif[\"abs_diff\"] = numpy.abs(dif[\"diff\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/xadupre/.local/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/home/xadupre/.local/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/home/xadupre/.local/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/home/xadupre/.local/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from seaborn import jointplot\n",
    "\n",
    "ax = jointplot(\n",
    "    dif[dif.dist <= 10], x=\"dist\", y=\"abs_diff\", kind=\"kde\", space=0, color=\"g\"\n",
    ")\n",
    "ax.ax_marg_y.set_title(\"Heatmap distribution distance / différence\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Les vins proches se ressemblent pour la plupart. C'est rassurant pour la suite. 61% des vins ont un voisin proche partageant la même note."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6152070186239803"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dif[dif[\"abs_diff\"] == 0]) / dif.shape[0]"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}