09-01. Подготовка RecSys.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf               # TesorFlow\n",
    "import numpy as np                    # NumPy\n",
    "import matplotlib.pyplot as plt       # Графики-то строить будем\n",
    "import pandas as pd                   # Для работы с таблицами и CSV-файлами\n",
    "import sklearn.metrics                # Для построения confusion matrix\n",
    "import seaborn as sns                 # Для отображения confusion matrix\n",
    "import os                             # Для возможности выбора GPU\n",
    "\n",
    "import sklearn.decomposition          # Понадобится для SVD-разложений\n",
    "import sklearn.metrics                # Понадобится для оценки разложений"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Используем GPU 0\n",
    "os.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\" \n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n",
    "\n",
    "# Отключаем отладочные сообщения в TensorFlow\n",
    "tf.get_logger().setLevel('ERROR')\n",
    "os.environ['TF_CPP_MIN_LOG_LEVEL'] = \"2\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATASET_DIR = '/opt/datasets/movielens'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "        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",
       "4            1       50     5.0   964982931\n",
       "...        ...      ...     ...         ...\n",
       "100831     610   166534     4.0  1493848402\n",
       "100832     610   168248     5.0  1493850091\n",
       "100833     610   168250     5.0  1494273047\n",
       "100834     610   168252     5.0  1493846352\n",
       "100835     610   170875     3.0  1493846415\n",
       "\n",
       "[100836 rows x 4 columns]"
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     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(DATASET_DIR + '/ratings.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "userId   1    2    3    4    5    6    7    8    9    10   ...  601  602  603  \\\n",
       "movieId                                                    ...                  \n",
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       "\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",
       "193581   0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "193583   0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "193585   0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "193587   0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "193609   0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "\n",
       "[9724 rows x 610 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "utility_matrix = df.pivot(index=\"movieId\", columns=\"userId\", values='rating').fillna(0)\n",
    "utility_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![alt text](https://miro.medium.com/max/720/0*TIp1NS_fHguLx1mn.png \"Title\")\n",
    "\n",
    "![alt text](https://miro.medium.com/max/640/0*lB7TP90s4s-JJT9u)\n",
    "\n",
    "![alt text](https://miro.medium.com/max/720/1*FfF24cL1TLT4fdu3CAciHA.png)\n",
    "\n",
    "![alt text](https://miro.medium.com/max/640/1*gomd5YFOrW2SJmnHSNCEXg.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = sklearn.decomposition.TruncatedSVD()\n",
    "model.n_components = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.fit(utility_matrix)\n",
    "W = model.transform(utility_matrix)\n",
    "H = model.components_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Utility Matrix: (9724, 610)\n",
      "W: (9724, 3)\n",
      "H: (3, 610)\n"
     ]
    }
   ],
   "source": [
    "print('Utility Matrix: {}'.format(utility_matrix.shape))\n",
    "print('W: {}'.format(W.shape))\n",
    "print('H: {}'.format(H.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2.64791183e+00,  1.73667552e-01,  3.84194818e-02, ...,\n",
       "         4.59313742e+00,  9.69395463e-01,  2.55339449e+00],\n",
       "       [ 1.29163425e+00,  1.60162434e-01,  1.02215895e-02, ...,\n",
       "         2.50283822e+00,  5.94525958e-01,  1.76016060e+00],\n",
       "       [ 8.62154178e-01, -3.63581223e-02,  1.74293216e-02, ...,\n",
       "         1.08859917e+00,  2.79878995e-01, -2.91757237e-01],\n",
       "       ...,\n",
       "       [-6.78037596e-03,  2.58086646e-03, -2.10487485e-04, ...,\n",
       "         2.21518297e-03, -2.30320399e-03,  3.42667375e-02],\n",
       "       [-6.78037596e-03,  2.58086646e-03, -2.10487485e-04, ...,\n",
       "         2.21518297e-03, -2.30320399e-03,  3.42667375e-02],\n",
       "       [-9.82222643e-03,  6.17423263e-03, -4.52061800e-04, ...,\n",
       "         1.36029407e-02, -2.02725857e-03,  7.74872863e-02]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reconstructed_matrix = np.dot(W, H)\n",
    "reconstructed_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "reconstructed_matrix[reconstructed_matrix < 0] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>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>...</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>609</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>2.65</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.04</td>\n",
       "      <td>1.05</td>\n",
       "      <td>1.31</td>\n",
       "      <td>3.74</td>\n",
       "      <td>1.66</td>\n",
       "      <td>1.73</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.50</td>\n",
       "      <td>...</td>\n",
       "      <td>1.12</td>\n",
       "      <td>2.86</td>\n",
       "      <td>2.13</td>\n",
       "      <td>1.68</td>\n",
       "      <td>1.47</td>\n",
       "      <td>2.57</td>\n",
       "      <td>2.34</td>\n",
       "      <td>4.59</td>\n",
       "      <td>0.97</td>\n",
       "      <td>2.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.29</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.77</td>\n",
       "      <td>2.19</td>\n",
       "      <td>1.02</td>\n",
       "      <td>1.05</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.40</td>\n",
       "      <td>...</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1.69</td>\n",
       "      <td>0.22</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.82</td>\n",
       "      <td>1.22</td>\n",
       "      <td>2.50</td>\n",
       "      <td>0.59</td>\n",
       "      <td>1.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.86</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1.17</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.88</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.09</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.09</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.55</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.36</td>\n",
       "      <td>1.04</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.04</td>\n",
       "      <td>...</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.00</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>193581</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>...</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193583</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>...</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193585</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>...</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193587</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>...</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193609</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>...</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9724 rows × 610 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "userId    1     2     3     4     5     6     7     8     9     10   ...  \\\n",
       "movieId                                                              ...   \n",
       "1        2.65  0.17  0.04  1.05  1.31  3.74  1.66  1.73  0.19  0.50  ...   \n",
       "2        1.29  0.16  0.01  0.32  0.77  2.19  1.02  1.05  0.10  0.40  ...   \n",
       "3        0.86  0.00  0.02  0.47  0.40  1.17  0.33  0.51  0.04  0.00  ...   \n",
       "4        0.09  0.00  0.00  0.03  0.07  0.21  0.04  0.09  0.00  0.00  ...   \n",
       "5        0.55  0.01  0.01  0.18  0.36  1.04  0.32  0.48  0.02  0.04  ...   \n",
       "...       ...   ...   ...   ...   ...   ...   ...   ...   ...   ...  ...   \n",
       "193581   0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.01  ...   \n",
       "193583   0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.01  ...   \n",
       "193585   0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.01  ...   \n",
       "193587   0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.01  ...   \n",
       "193609   0.00  0.01  0.00  0.00  0.00  0.00  0.01  0.00  0.00  0.01  ...   \n",
       "\n",
       "userId    601   602   603   604   605   606   607   608   609   610  \n",
       "movieId                                                              \n",
       "1        1.12  2.86  2.13  1.68  1.47  2.57  2.34  4.59  0.97  2.55  \n",
       "2        0.80  1.69  0.22  1.00  0.90  0.82  1.22  2.50  0.59  1.76  \n",
       "3        0.04  0.88  1.25  0.51  0.30  0.88  0.75  1.09  0.28  0.00  \n",
       "4        0.00  0.16  0.04  0.09  0.04  0.00  0.09  0.07  0.05  0.00  \n",
       "5        0.13  0.79  0.29  0.47  0.29  0.22  0.54  0.78  0.27  0.00  \n",
       "...       ...   ...   ...   ...   ...   ...   ...   ...   ...   ...  \n",
       "193581   0.01  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.04  \n",
       "193583   0.01  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.03  \n",
       "193585   0.01  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.03  \n",
       "193587   0.01  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.03  \n",
       "193609   0.02  0.00  0.00  0.00  0.01  0.00  0.00  0.01  0.00  0.08  \n",
       "\n",
       "[9724 rows x 610 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rm = pd.DataFrame(np.round(reconstructed_matrix, 2), columns=utility_matrix.columns, index=utility_matrix.index)\n",
    "df_rm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sparsity(data):\n",
    "    return 1.0 - np.count_nonzero(data) / data.size\n",
    "\n",
    "def rel_spars(data1, data2):\n",
    "    #print(sparsity(data1))\n",
    "    #print(sparsity(data2))\n",
    "    return (sparsity(data1) - sparsity(data2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 0.1628230777612288\n",
      "MAE: 0.09550094999402964\n",
      "EVS: 0.16969030166821553\n",
      "SPARS: 0.6711722221847584\n"
     ]
    }
   ],
   "source": [
    "MSE_val = sklearn.metrics.mean_squared_error(utility_matrix, reconstructed_matrix)\n",
    "MAE_val = sklearn.metrics.mean_absolute_error(utility_matrix, reconstructed_matrix)\n",
    "EVS_val = sklearn.metrics.explained_variance_score(utility_matrix, reconstructed_matrix)\n",
    "SPARS_val = rel_spars(utility_matrix, reconstructed_matrix)\n",
    "\n",
    "print('MSE: {}'.format(MSE_val))\n",
    "print('MAE: {}'.format(MAE_val))\n",
    "print('EVS: {}'.format(EVS_val))\n",
    "print('SPARS: {}'.format(SPARS_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "MSE = []\n",
    "MAE = []\n",
    "EVS = []\n",
    "SPARS = []\n",
    "\n",
    "for k in range(1,600):\n",
    "    model = sklearn.decomposition.TruncatedSVD()\n",
    "    model.n_components = k\n",
    "    model.fit(utility_matrix)\n",
    "    W = model.transform(utility_matrix)\n",
    "    H = model.components_\n",
    "    reconstructed_matrix = np.dot(W, H)\n",
    "    reconstructed_matrix[reconstructed_matrix < 0] = 0\n",
    "    MSE_val = sklearn.metrics.mean_squared_error(utility_matrix, reconstructed_matrix)\n",
    "    MAE_val = sklearn.metrics.mean_absolute_error(utility_matrix, reconstructed_matrix)\n",
    "    EVS_val = sklearn.metrics.explained_variance_score(utility_matrix, reconstructed_matrix)\n",
    "    SPARS_val = rel_spars(utility_matrix, reconstructed_matrix)\n",
    "    MSE.append(MSE_val)\n",
    "    MAE.append(MAE_val)\n",
    "    EVS.append(EVS_val)\n",
    "    SPARS.append(SPARS_val)\n",
    "    \n",
    "plt.plot(MSE)\n",
    "plt.plot(MAE)\n",
    "plt.plot(EVS)\n",
    "plt.plot(SPARS)\n",
    "plt.legend(['MSE', 'MAE', 'EVS', 'SPARS'], loc='upper right')\n",
    "plt.xlabel('n_components')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8",
   "language": "python",
   "name": "python38"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}