Deployment.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pip install bentoml==1.0.0a4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pip install ruamel_yaml_conda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pip install ruamel-yaml-conda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "conda install ruamel_yaml_conda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "conda config --add channels conda-forge\n",
    "conda config --set channel_priority strict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "conda config --add channels conda-forge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "conda config --set channel_priority strict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "conda install ruamel.yaml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import bentoml.sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "python -m pylint --load-plugins=pylint_json2html --confidence=HIGH --output-format=jsonextended"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pip install pylint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import bentoml.sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pip install bentoml --pre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import bentoml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pip install typing-extensions --upgrade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "conda update conda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "conda update --all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pip install typing-extensions --upgrade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import bentoml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy.sparse import csr_matrix\n",
    "from sklearn.neighbors import NearestNeighbors\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "movies = pd.read_csv(\"DataSet/movies.csv\")\n",
    "ratings = pd.read_csv(\"DataSet/ratings.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   movieId                               title  \\\n",
       "0        1                    Toy Story (1995)   \n",
       "1        2                      Jumanji (1995)   \n",
       "2        3             Grumpier Old Men (1995)   \n",
       "3        4            Waiting to Exhale (1995)   \n",
       "4        5  Father of the Bride Part II (1995)   \n",
       "\n",
       "                                        genres  \n",
       "0  Adventure|Animation|Children|Comedy|Fantasy  \n",
       "1                   Adventure|Children|Fantasy  \n",
       "2                               Comedy|Romance  \n",
       "3                         Comedy|Drama|Romance  \n",
       "4                                       Comedy  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   userId  movieId  rating  timestamp\n",
       "0       1        1     4.0  964982703\n",
       "1       1        3     4.0  964981247\n",
       "2       1        6     4.0  964982224\n",
       "3       1       47     5.0  964983815\n",
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     "execution_count": 7,
     "metadata": {},
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   ],
   "source": [
    "ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "<p>5 rows × 610 columns</p>\n",
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      "text/plain": [
       "userId   1    2    3    4    5    6    7    8    9    10   ...  601  602  603  \\\n",
       "movieId                                                    ...                  \n",
       "1        4.0  NaN  NaN  NaN  4.0  NaN  4.5  NaN  NaN  NaN  ...  4.0  NaN  4.0   \n",
       "2        NaN  NaN  NaN  NaN  NaN  4.0  NaN  4.0  NaN  NaN  ...  NaN  4.0  NaN   \n",
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       "5        NaN  NaN  NaN  NaN  NaN  5.0  NaN  NaN  NaN  NaN  ...  NaN  NaN  NaN   \n",
       "\n",
       "userId   604  605  606  607  608  609  610  \n",
       "movieId                                     \n",
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       "2        5.0  3.5  NaN  NaN  2.0  NaN  NaN  \n",
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       "\n",
       "[5 rows x 610 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset = ratings.pivot(index='movieId',columns='userId',values='rating')\n",
    "final_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 610 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "userId   1    2    3    4    5    6    7    8    9    10   ...  601  602  603  \\\n",
       "movieId                                                    ...                  \n",
       "1        4.0  0.0  0.0  0.0  4.0  0.0  4.5  0.0  0.0  0.0  ...  4.0  0.0  4.0   \n",
       "2        0.0  0.0  0.0  0.0  0.0  4.0  0.0  4.0  0.0  0.0  ...  0.0  4.0  0.0   \n",
       "3        4.0  0.0  0.0  0.0  0.0  5.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "4        0.0  0.0  0.0  0.0  0.0  3.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "5        0.0  0.0  0.0  0.0  0.0  5.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "\n",
       "userId   604  605  606  607  608  609  610  \n",
       "movieId                                     \n",
       "1        3.0  4.0  2.5  4.0  2.5  3.0  5.0  \n",
       "2        5.0  3.5  0.0  0.0  2.0  0.0  0.0  \n",
       "3        0.0  0.0  0.0  0.0  2.0  0.0  0.0  \n",
       "4        0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "5        3.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "\n",
       "[5 rows x 610 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.fillna(0,inplace=True)\n",
    "final_dataset.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "no_user_voted = ratings.groupby('movieId')['rating'].agg('count')\n",
    "no_movies_voted = ratings.groupby('userId')['rating'].agg('count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,ax = plt.subplots(1,1,figsize=(16,4))\n",
    "# ratings['rating'].plot(kind='hist')\n",
    "plt.scatter(no_user_voted.index,no_user_voted,color='mediumseagreen')\n",
    "plt.axhline(y=10,color='r')\n",
    "plt.xlabel('MovieId')\n",
    "plt.ylabel('No. of users voted')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_dataset = final_dataset.loc[no_user_voted[no_user_voted > 10].index,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,ax = plt.subplots(1,1,figsize=(16,4))\n",
    "plt.scatter(no_movies_voted.index,no_movies_voted,color='mediumseagreen')\n",
    "plt.axhline(y=50,color='r')\n",
    "plt.xlabel('UserId')\n",
    "plt.ylabel('No. of votes by user')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "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>4</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>...</th>\n",
       "      <th>600</th>\n",
       "      <th>601</th>\n",
       "      <th>602</th>\n",
       "      <th>603</th>\n",
       "      <th>604</th>\n",
       "      <th>605</th>\n",
       "      <th>606</th>\n",
       "      <th>607</th>\n",
       "      <th>608</th>\n",
       "      <th>610</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movieId</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",
       "      <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>1</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>...</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174055</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176371</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177765</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179819</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187593</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2121 rows × 378 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "userId   1    4    6    7    10   11   15   16   17   18   ...  600  601  602  \\\n",
       "movieId                                                    ...                  \n",
       "1        4.0  0.0  0.0  4.5  0.0  0.0  2.5  0.0  4.5  3.5  ...  2.5  4.0  0.0   \n",
       "2        0.0  0.0  4.0  0.0  0.0  0.0  0.0  0.0  0.0  3.0  ...  4.0  0.0  4.0   \n",
       "3        4.0  0.0  5.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "5        0.0  0.0  5.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  2.5  0.0  0.0   \n",
       "6        4.0  0.0  4.0  0.0  0.0  5.0  0.0  0.0  0.0  4.0  ...  0.0  0.0  3.0   \n",
       "...      ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...   \n",
       "174055   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  4.0  0.0   \n",
       "176371   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  4.0  0.0   \n",
       "177765   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  4.5  0.0   \n",
       "179819   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "187593   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "\n",
       "userId   603  604  605  606  607  608  610  \n",
       "movieId                                     \n",
       "1        4.0  3.0  4.0  2.5  4.0  2.5  5.0  \n",
       "2        0.0  5.0  3.5  0.0  0.0  2.0  0.0  \n",
       "3        0.0  0.0  0.0  0.0  0.0  2.0  0.0  \n",
       "5        0.0  3.0  0.0  0.0  0.0  0.0  0.0  \n",
       "6        4.0  3.0  0.0  0.0  0.0  0.0  5.0  \n",
       "...      ...  ...  ...  ...  ...  ...  ...  \n",
       "174055   0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "176371   0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "177765   0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "179819   0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "187593   0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "\n",
       "[2121 rows x 378 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset=final_dataset.loc[:,no_movies_voted[no_movies_voted > 50].index]\n",
    "final_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7333333333333334\n"
     ]
    }
   ],
   "source": [
    "sample = np.array([[0,0,3,0,0],[4,0,0,0,2],[0,0,0,0,1]])\n",
    "sparsity = 1.0 - ( np.count_nonzero(sample) / float(sample.size) )\n",
    "print(sparsity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  (0, 2)\t3\n",
      "  (1, 0)\t4\n",
      "  (1, 4)\t2\n",
      "  (2, 4)\t1\n"
     ]
    }
   ],
   "source": [
    "csr_sample = csr_matrix(sample)\n",
    "print(csr_sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "csr_data = csr_matrix(final_dataset.values)\n",
    "final_dataset.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn = NearestNeighbors(metric='cosine', algorithm='brute', n_neighbors=20, n_jobs=-1)\n",
    "model = knn.fit(csr_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[12:13:54] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> <span style=\"font-weight: bold\">[</span>boot<span style=\"font-weight: bold\">]</span> Successfully saved <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">Model</span><span style=\"font-weight: bold\">(</span><span style=\"color: #808000; text-decoration-color: #808000\">tag</span>=<span style=\"color: #008000; text-decoration-color: #008000\">\"recommender_model:675q3huuoobtz6he\"</span>,\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>         <span style=\"color: #808000; text-decoration-color: #808000\">path</span>=<span style=\"color: #800080; text-decoration-color: #800080\">\"C</span>:\\Users\\siddh\\bentoml\\models\\recommender_model\\675q3huuoobtz6he\\\"<span style=\"font-weight: bold\">)</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[12:13:54]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m \u001b[1m[\u001b[0mboot\u001b[1m]\u001b[0m Successfully saved \u001b[1;35mModel\u001b[0m\u001b[1m(\u001b[0m\u001b[33mtag\u001b[0m=\u001b[32m\"recommender_model\u001b[0m\u001b[32m:675q3huuoobtz6he\"\u001b[0m,\n",
       "\u001b[2;36m           \u001b[0m         \u001b[33mpath\u001b[0m=\u001b[35m\"C\u001b[0m:\\Users\\siddh\\bentoml\\models\\recommender_model\\675q3huuoobtz6he\\\"\u001b[1m)\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Tag(name='recommender_model', version='675q3huuoobtz6he')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bentoml.sklearn.save(\"recommender_model\", model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "recommender_runner = bentoml.sklearn.load_runner(\"recommender_model:latest\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-28-36790fee9776>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-28-36790fee9776>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m    bentoml model list recommender_model\u001b[0m\n\u001b[1;37m            ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "bentoml model list recommender_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-29-3b6cfa05eb50>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-29-3b6cfa05eb50>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m    bentoml models --help\u001b[0m\n\u001b[1;37m            ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "bentoml models --help"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-35-1ea3d2298677>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-35-1ea3d2298677>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m    bentoml models list\u001b[0m\n\u001b[1;37m            ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "bentoml models list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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