KNN-Recommendation.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "zqXqk3nRiYQt"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy.sparse import csr_matrix\n",
    "from sklearn.neighbors import NearestNeighbors\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "U1dWSB1Zit0K"
   },
   "outputs": [],
   "source": [
    "# path config\n",
    "df = pd.read_csv(\"newfood.csv\",usecols=['id','title','userId','stars',],dtype={'id': 'int32', 'title': 'str', 'userId' : 'int32', 'stars' : 'int32' })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "bLqMgSS3j1PA",
    "outputId": "7a50faf2-9ff0-4c65-d8c3-ca70297b6520"
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>title</th>\n",
       "      <th>stars</th>\n",
       "      <th>userId</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Fried Anchovies with Sage</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Anchovies Appetizer With Breadcrumbs &amp; Scallions</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Carrots, Cauliflower And Anchovies</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Bap Story Stir Fried Anchovies (Myulchi Bokkeum)</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Bread, Butter And Anchovies</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id                                             title  stars  userId\n",
       "0   1                         Fried Anchovies with Sage      3       1\n",
       "1   2  Anchovies Appetizer With Breadcrumbs & Scallions      1       1\n",
       "2   3                Carrots, Cauliflower And Anchovies      4       1\n",
       "3   4  Bap Story Stir Fried Anchovies (Myulchi Bokkeum)      3       1\n",
       "4   5                       Bread, Butter And Anchovies      5       1"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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    },
    "id": "DDKvBtom75QU",
    "outputId": "90894dc1-574a-41f5-b622-dc3bd5abb1c2"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>totalRatingCount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tonight Caesar salad</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Tonight Cornmeal-Crusted Pan-Fried Trout</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tonight Grilled Caesar salad</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Tonight Mackerel with Currants</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Tonight Marinated Chickpea salad</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       title  totalRatingCount\n",
       "0                       Tonight Caesar salad                 1\n",
       "1   Tonight Cornmeal-Crusted Pan-Fried Trout                 1\n",
       "2               Tonight Grilled Caesar salad                 1\n",
       "3             Tonight Mackerel with Currants                 1\n",
       "4           Tonight Marinated Chickpea salad                 1"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combine_recipe_rating = df.dropna(axis = 0, subset = ['title'])\n",
    "recipe_ratingCount = (combine_recipe_rating.\n",
    "     groupby(by = ['title'])['stars'].\n",
    "     count().\n",
    "     reset_index().\n",
    "     rename(columns = {'stars': 'totalRatingCount'})\n",
    "     [['title', 'totalRatingCount']]\n",
    "    )\n",
    "recipe_ratingCount.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "7ZGN1fyz75i5",
    "outputId": "8642e42f-a350-4914-cd75-6b3fe2fa2a3b"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>title</th>\n",
       "      <th>stars</th>\n",
       "      <th>userId</th>\n",
       "      <th>totalRatingCount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Fried Anchovies with Sage</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Anchovies Appetizer With Breadcrumbs &amp; Scallions</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Carrots, Cauliflower And Anchovies</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Bap Story Stir Fried Anchovies (Myulchi Bokkeum)</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Bread, Butter And Anchovies</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id                                             title  stars  userId  \\\n",
       "0   1                         Fried Anchovies with Sage      3       1   \n",
       "1   2  Anchovies Appetizer With Breadcrumbs & Scallions      1       1   \n",
       "2   3                Carrots, Cauliflower And Anchovies      4       1   \n",
       "3   4  Bap Story Stir Fried Anchovies (Myulchi Bokkeum)      3       1   \n",
       "4   5                       Bread, Butter And Anchovies      5       1   \n",
       "\n",
       "   totalRatingCount  \n",
       "0                 1  \n",
       "1                 1  \n",
       "2                 1  \n",
       "3                 1  \n",
       "4                 1  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rating_with_totalRatingCount = combine_recipe_rating.merge(recipe_ratingCount, left_on = 'title', right_on = 'title', how = 'left')\n",
    "rating_with_totalRatingCount.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "eYlu0OFI9llI",
    "outputId": "46fb6447-ecc7-4654-d29e-43455be5f59b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count   1645.000\n",
      "mean       1.047\n",
      "std        0.348\n",
      "min        1.000\n",
      "25%        1.000\n",
      "50%        1.000\n",
      "75%        1.000\n",
      "max        8.000\n",
      "Name: totalRatingCount, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "pd.set_option('display.float_format', lambda x: '%.3f' % x)\n",
    "print(recipe_ratingCount['totalRatingCount'].describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "j9fSCPfv9xwS"
   },
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "DQhEnlJX9yxc",
    "outputId": "92706473-2401-4e3d-a75b-3c75ddddafb8"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>title</th>\n",
       "      <th>stars</th>\n",
       "      <th>userId</th>\n",
       "      <th>totalRatingCount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Fried Anchovies with Sage</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Anchovies Appetizer With Breadcrumbs &amp; Scallions</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Carrots, Cauliflower And Anchovies</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Bap Story Stir Fried Anchovies (Myulchi Bokkeum)</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Bread, Butter And Anchovies</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id                                             title  stars  userId  \\\n",
       "0   1                         Fried Anchovies with Sage      3       1   \n",
       "1   2  Anchovies Appetizer With Breadcrumbs & Scallions      1       1   \n",
       "2   3                Carrots, Cauliflower And Anchovies      4       1   \n",
       "3   4  Bap Story Stir Fried Anchovies (Myulchi Bokkeum)      3       1   \n",
       "4   5                       Bread, Butter And Anchovies      5       1   \n",
       "\n",
       "   totalRatingCount  \n",
       "0                 1  \n",
       "1                 1  \n",
       "2                 1  \n",
       "3                 1  \n",
       "4                 1  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "popularity_threshold = 1\n",
    "rating_popular_recipe= rating_with_totalRatingCount.query('totalRatingCount >= @popularity_threshold')\n",
    "rating_popular_recipe.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "rating_count=rating_popular_recipe.sort_values('totalRatingCount',ascending=False)\n",
    "plt.figure(figsize=(12,6))\n",
    "axis1=sns.barplot(x=rating_count['totalRatingCount'].head(10), y=rating_count['title'].head(10), data=rating_count)\n",
    "plt.xlim(2, 5)\n",
    "plt.title('Best Recipes by average votes', weight='bold')\n",
    "plt.xlabel('Rating Average Score', weight='bold')\n",
    "plt.ylabel('Recipe Title', weight='bold')\n",
    "plt.savefig('best_recipes.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "IJF1ayau-I_O",
    "outputId": "475ccf48-32af-469d-b7c8-dd5b42d5ac9d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1722, 5)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rating_popular_recipe.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 238
    },
    "id": "wDmWB1wF-NoC",
    "outputId": "1cd9c0fb-3845-42d9-8c82-77b9a9807db5"
   },
   "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>userId</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Tonight Caesar salad</th>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>5.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tonight Cornmeal-Crusted Pan-Fried Trout</th>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>4.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tonight Grilled Caesar salad</th>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>4.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tonight Mackerel with Currants</th>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>3.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tonight Marinated Chickpea salad</th>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>4.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "userId                                       1     2     3     4     5     6   \\\n",
       "title                                                                           \n",
       " Tonight Caesar salad                     1.000 1.000 1.000 1.000 1.000 1.000   \n",
       " Tonight Cornmeal-Crusted Pan-Fried Trout 1.000 1.000 1.000 1.000 1.000 1.000   \n",
       " Tonight Grilled Caesar salad             1.000 1.000 1.000 1.000 1.000 1.000   \n",
       " Tonight Mackerel with Currants           1.000 1.000 1.000 1.000 1.000 1.000   \n",
       " Tonight Marinated Chickpea salad         1.000 1.000 1.000 1.000 1.000 1.000   \n",
       "\n",
       "userId                                       7     8     9     10    11    12  \\\n",
       "title                                                                           \n",
       " Tonight Caesar salad                     5.000 1.000 1.000 1.000 1.000 1.000   \n",
       " Tonight Cornmeal-Crusted Pan-Fried Trout 1.000 1.000 1.000 1.000 4.000 1.000   \n",
       " Tonight Grilled Caesar salad             4.000 1.000 1.000 1.000 1.000 1.000   \n",
       " Tonight Mackerel with Currants           1.000 1.000 1.000 1.000 3.000 1.000   \n",
       " Tonight Marinated Chickpea salad         4.000 1.000 1.000 1.000 1.000 1.000   \n",
       "\n",
       "userId                                       13    14  \n",
       "title                                                  \n",
       " Tonight Caesar salad                     1.000 1.000  \n",
       " Tonight Cornmeal-Crusted Pan-Fried Trout 1.000 1.000  \n",
       " Tonight Grilled Caesar salad             1.000 1.000  \n",
       " Tonight Mackerel with Currants           1.000 1.000  \n",
       " Tonight Marinated Chickpea salad         1.000 1.000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## First lets create a Pivot matrix\n",
    "\n",
    "recipe_features_df=rating_popular_recipe.pivot_table(index='title',columns='userId',values='stars').fillna(1)\n",
    "recipe_features_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NearestNeighbors(algorithm='brute', metric='cosine')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import NearestNeighbors\n",
    "from scipy.sparse import csr_matrix\n",
    "\n",
    "recipe_features_df_matrix = csr_matrix(recipe_features_df.values)\n",
    "\n",
    "model_knn = NearestNeighbors(metric = 'cosine', algorithm = 'brute')\n",
    "model_knn.fit(recipe_features_df_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "iH5BUxVB-kgM",
    "outputId": "112a70ec-9c91-4cb3-c5af-3f4d64bd5adf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1645, 14)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recipe_features_df_matrix.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_kv0bHcr-vFu",
    "outputId": "a15b0a56-3b55-4e5d-8e2f-74d5d5b529f3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "213\n"
     ]
    }
   ],
   "source": [
    "query_index = np.random.choice(recipe_features_df.shape[0])\n",
    "print(query_index)\n",
    "distances, indices = model_knn.kneighbors(recipe_features_df.iloc[query_index,:].values.reshape(1, -1), n_neighbors = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 238
    },
    "id": "iI1raLk0-1xY",
    "outputId": "ffd74849-5a1e-42b1-91b3-caa5cab228c9"
   },
   "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>userId</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
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       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Tonight Caesar salad</th>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>5.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tonight Cornmeal-Crusted Pan-Fried Trout</th>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>4.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tonight Grilled Caesar salad</th>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>4.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tonight Mackerel with Currants</th>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>3.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tonight Marinated Chickpea salad</th>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>4.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "userId                                       1     2     3     4     5     6   \\\n",
       "title                                                                           \n",
       " Tonight Caesar salad                     1.000 1.000 1.000 1.000 1.000 1.000   \n",
       " Tonight Cornmeal-Crusted Pan-Fried Trout 1.000 1.000 1.000 1.000 1.000 1.000   \n",
       " Tonight Grilled Caesar salad             1.000 1.000 1.000 1.000 1.000 1.000   \n",
       " Tonight Mackerel with Currants           1.000 1.000 1.000 1.000 1.000 1.000   \n",
       " Tonight Marinated Chickpea salad         1.000 1.000 1.000 1.000 1.000 1.000   \n",
       "\n",
       "userId                                       7     8     9     10    11    12  \\\n",
       "title                                                                           \n",
       " Tonight Caesar salad                     5.000 1.000 1.000 1.000 1.000 1.000   \n",
       " Tonight Cornmeal-Crusted Pan-Fried Trout 1.000 1.000 1.000 1.000 4.000 1.000   \n",
       " Tonight Grilled Caesar salad             4.000 1.000 1.000 1.000 1.000 1.000   \n",
       " Tonight Mackerel with Currants           1.000 1.000 1.000 1.000 3.000 1.000   \n",
       " Tonight Marinated Chickpea salad         4.000 1.000 1.000 1.000 1.000 1.000   \n",
       "\n",
       "userId                                       13    14  \n",
       "title                                                  \n",
       " Tonight Caesar salad                     1.000 1.000  \n",
       " Tonight Cornmeal-Crusted Pan-Fried Trout 1.000 1.000  \n",
       " Tonight Grilled Caesar salad             1.000 1.000  \n",
       " Tonight Mackerel with Currants           1.000 1.000  \n",
       " Tonight Marinated Chickpea salad         1.000 1.000  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recipe_features_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ghRa588q-5ZH",
    "outputId": "fdbf6250-f4f9-4a28-a1ec-6d83638757d4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recommendations for Caesar Soup:\n",
      "\n",
      "1: Seafood Stoup Fra Diavolo, with distance of 4.440892098500626e-16:\n",
      "2: Roast Lamb With Anchovies, with distance of 4.440892098500626e-16:\n",
      "3: Orecchiette With Broccoli, Anchovies, And Chiles, with distance of 4.440892098500626e-16:\n",
      "4: Green Herb & Yogurt Dip, with distance of 4.440892098500626e-16:\n",
      "5: Broccoli Rabe Saute With Chicken Sausage, with distance of 4.440892098500626e-16:\n",
      "6: Red Mullet With Tomatoes, Olives & Anchovies, with distance of 4.440892098500626e-16:\n",
      "7: Radicchio Slaw With Green Beans And Cauliflower, with distance of 4.440892098500626e-16:\n",
      "8: French Onion Tart, with distance of 4.440892098500626e-16:\n",
      "9: Kale salad With Walnuts And Soft-boiled Eggs, with distance of 4.440892098500626e-16:\n"
     ]
    }
   ],
   "source": [
    "for i in range(0, len(distances.flatten())):\n",
    "    if i == 0:\n",
    "        print('Recommendations for {0}:\\n'.format(recipe_features_df.index[query_index]))\n",
    "    else:\n",
    "        print('{0}: {1}, with distance of {2}:'.format(i, recipe_features_df.index[indices.flatten()[i]], distances.flatten()[i]))"
   ]
  }
 ],
 "metadata": {
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   "name": "KNN.ipynb",
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  },
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   "language": "python",
   "name": "python3"
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