HeriGrap_Construction_Venezia(sparse)_np.ipynb

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   "source": [
    "import json\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from argparse import Namespace\n",
    "from collections import Counter\n",
    "import pickle\n",
    "import os\n",
    "import urllib\n",
    "import networkx as nx"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c85136ec",
   "metadata": {},
   "source": [
    "## Venezia"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f280ce8d",
   "metadata": {},
   "source": [
    "### Importing Useful Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "92cc8562",
   "metadata": {},
   "outputs": [],
   "source": [
    "GEO_metadata = pd.read_csv('Venezia/data_storage/GEO_metadata.csv', sep='\\t', index_col='Unnamed: 0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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       "                    y          x  street_count        pr        bc        dg   \n",
       "osmid                                                                          \n",
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       "                  cl highway                       geometry  \n",
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     "execution_count": 6,
     "metadata": {},
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   "source": [
    "GEO_nodes = pd.read_csv('Venezia/data_storage/GEO_nodes.csv', sep='\\t').set_index('osmid')\n",
    "GEO_nodes"
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  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "47eadde5",
   "metadata": {},
   "outputs": [],
   "source": [
    "social_links = pd.read_csv('Venezia/data_storage/social_links.csv', sep='\\t').rename(columns={'Unnamed: 0': 'User0', \n",
    "                                                                                              'Unnamed: 1': 'User1'})"
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     "execution_count": 5,
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    "social_links"
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  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "27d10afb",
   "metadata": {},
   "outputs": [],
   "source": [
    "GEO_node_dist = pd.read_csv('Venezia/data_storage/GEO_node_dist.csv', sep='\\t', index_col='Unnamed: 0')"
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       "      <td>206.8</td>\n",
       "      <td>190.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>43.5</td>\n",
       "      <td>117.3</td>\n",
       "      <td>168.2</td>\n",
       "      <td>32.4</td>\n",
       "      <td>60.4</td>\n",
       "      <td>88.8</td>\n",
       "      <td>75.4</td>\n",
       "      <td>...</td>\n",
       "      <td>154.4</td>\n",
       "      <td>102.0</td>\n",
       "      <td>71.3</td>\n",
       "      <td>78.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>159.4</td>\n",
       "      <td>570.3</td>\n",
       "      <td>153.7</td>\n",
       "      <td>149.3</td>\n",
       "      <td>53.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4437784469</th>\n",
       "      <td>213.1</td>\n",
       "      <td>190.6</td>\n",
       "      <td>43.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>106.1</td>\n",
       "      <td>135.0</td>\n",
       "      <td>11.1</td>\n",
       "      <td>60.5</td>\n",
       "      <td>100.4</td>\n",
       "      <td>76.0</td>\n",
       "      <td>...</td>\n",
       "      <td>166.0</td>\n",
       "      <td>73.2</td>\n",
       "      <td>48.2</td>\n",
       "      <td>45.2</td>\n",
       "      <td>21.7</td>\n",
       "      <td>119.1</td>\n",
       "      <td>570.9</td>\n",
       "      <td>113.4</td>\n",
       "      <td>160.9</td>\n",
       "      <td>71.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27231707</th>\n",
       "      <td>179.3</td>\n",
       "      <td>160.3</td>\n",
       "      <td>117.3</td>\n",
       "      <td>106.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>152.3</td>\n",
       "      <td>106.3</td>\n",
       "      <td>60.0</td>\n",
       "      <td>142.3</td>\n",
       "      <td>79.0</td>\n",
       "      <td>...</td>\n",
       "      <td>207.9</td>\n",
       "      <td>49.2</td>\n",
       "      <td>60.9</td>\n",
       "      <td>77.1</td>\n",
       "      <td>116.2</td>\n",
       "      <td>202.9</td>\n",
       "      <td>535.6</td>\n",
       "      <td>197.2</td>\n",
       "      <td>177.4</td>\n",
       "      <td>88.3</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>944189412</th>\n",
       "      <td>327.8</td>\n",
       "      <td>305.3</td>\n",
       "      <td>159.4</td>\n",
       "      <td>119.1</td>\n",
       "      <td>202.9</td>\n",
       "      <td>185.8</td>\n",
       "      <td>127.0</td>\n",
       "      <td>175.2</td>\n",
       "      <td>216.3</td>\n",
       "      <td>190.7</td>\n",
       "      <td>...</td>\n",
       "      <td>281.9</td>\n",
       "      <td>153.7</td>\n",
       "      <td>150.4</td>\n",
       "      <td>132.4</td>\n",
       "      <td>105.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>685.6</td>\n",
       "      <td>5.7</td>\n",
       "      <td>276.8</td>\n",
       "      <td>187.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1327380181</th>\n",
       "      <td>366.8</td>\n",
       "      <td>373.3</td>\n",
       "      <td>563.3</td>\n",
       "      <td>563.9</td>\n",
       "      <td>528.6</td>\n",
       "      <td>660.8</td>\n",
       "      <td>552.8</td>\n",
       "      <td>506.9</td>\n",
       "      <td>485.1</td>\n",
       "      <td>494.1</td>\n",
       "      <td>...</td>\n",
       "      <td>439.9</td>\n",
       "      <td>577.0</td>\n",
       "      <td>554.2</td>\n",
       "      <td>565.2</td>\n",
       "      <td>581.2</td>\n",
       "      <td>678.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>672.9</td>\n",
       "      <td>432.6</td>\n",
       "      <td>528.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>944189121</th>\n",
       "      <td>322.1</td>\n",
       "      <td>299.6</td>\n",
       "      <td>153.7</td>\n",
       "      <td>113.4</td>\n",
       "      <td>197.2</td>\n",
       "      <td>180.1</td>\n",
       "      <td>121.3</td>\n",
       "      <td>169.5</td>\n",
       "      <td>210.6</td>\n",
       "      <td>185.0</td>\n",
       "      <td>...</td>\n",
       "      <td>276.2</td>\n",
       "      <td>148.0</td>\n",
       "      <td>144.7</td>\n",
       "      <td>126.7</td>\n",
       "      <td>99.7</td>\n",
       "      <td>5.7</td>\n",
       "      <td>679.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>271.1</td>\n",
       "      <td>181.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4471751053</th>\n",
       "      <td>69.9</td>\n",
       "      <td>62.0</td>\n",
       "      <td>149.3</td>\n",
       "      <td>160.9</td>\n",
       "      <td>177.4</td>\n",
       "      <td>271.3</td>\n",
       "      <td>149.8</td>\n",
       "      <td>117.4</td>\n",
       "      <td>64.9</td>\n",
       "      <td>104.6</td>\n",
       "      <td>...</td>\n",
       "      <td>51.1</td>\n",
       "      <td>195.4</td>\n",
       "      <td>164.7</td>\n",
       "      <td>175.7</td>\n",
       "      <td>179.4</td>\n",
       "      <td>276.8</td>\n",
       "      <td>439.6</td>\n",
       "      <td>271.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>121.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271343269</th>\n",
       "      <td>178.1</td>\n",
       "      <td>155.6</td>\n",
       "      <td>53.9</td>\n",
       "      <td>71.2</td>\n",
       "      <td>88.3</td>\n",
       "      <td>178.7</td>\n",
       "      <td>60.1</td>\n",
       "      <td>28.3</td>\n",
       "      <td>61.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>...</td>\n",
       "      <td>126.6</td>\n",
       "      <td>102.8</td>\n",
       "      <td>72.1</td>\n",
       "      <td>83.1</td>\n",
       "      <td>89.7</td>\n",
       "      <td>187.1</td>\n",
       "      <td>535.9</td>\n",
       "      <td>181.4</td>\n",
       "      <td>121.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>935 rows × 935 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            7636418620  1851468091  7887753052  4437784469  27231707   \n",
       "7636418620         0.0        24.0       206.8       213.1     179.3  \\\n",
       "1851468091        24.0         0.0       190.0       190.6     160.3   \n",
       "7887753052       206.8       190.0         0.0        43.5     117.3   \n",
       "4437784469       213.1       190.6        43.5         0.0     106.1   \n",
       "27231707         179.3       160.3       117.3       106.1       0.0   \n",
       "...                ...         ...         ...         ...       ...   \n",
       "944189412        327.8       305.3       159.4       119.1     202.9   \n",
       "1327380181       366.8       373.3       563.3       563.9     528.6   \n",
       "944189121        322.1       299.6       153.7       113.4     197.2   \n",
       "4471751053        69.9        62.0       149.3       160.9     177.4   \n",
       "271343269        178.1       155.6        53.9        71.2      88.3   \n",
       "\n",
       "            1996193041  1863759288  245128972  3586376601  251147479  ...   \n",
       "7636418620       310.0       202.0      156.1       122.4      143.3  ...  \\\n",
       "1851468091       287.5       179.5      133.6       116.8      120.8  ...   \n",
       "7887753052       168.2        32.4       60.4        88.8       75.4  ...   \n",
       "4437784469       135.0        11.1       60.5       100.4       76.0  ...   \n",
       "27231707         152.3       106.3       60.0       142.3       79.0  ...   \n",
       "...                ...         ...        ...         ...        ...  ...   \n",
       "944189412        185.8       127.0      175.2       216.3      190.7  ...   \n",
       "1327380181       660.8       552.8      506.9       485.1      494.1  ...   \n",
       "944189121        180.1       121.3      169.5       210.6      185.0  ...   \n",
       "4471751053       271.3       149.8      117.4        64.9      104.6  ...   \n",
       "271343269        178.7        60.1       28.3        61.0       41.0  ...   \n",
       "\n",
       "            1833539390  248119073  248776130  1920433297  3586674306   \n",
       "7636418620        77.2      227.7      203.4       214.4       230.4  \\\n",
       "1851468091        71.6      208.7      180.9       191.9       207.9   \n",
       "7887753052       154.4      102.0       71.3        78.0        62.0   \n",
       "4437784469       166.0       73.2       48.2        45.2        21.7   \n",
       "27231707         207.9       49.2       60.9        77.1       116.2   \n",
       "...                ...        ...        ...         ...         ...   \n",
       "944189412        281.9      153.7      150.4       132.4       105.4   \n",
       "1327380181       439.9      577.0      554.2       565.2       581.2   \n",
       "944189121        276.2      148.0      144.7       126.7        99.7   \n",
       "4471751053        51.1      195.4      164.7       175.7       179.4   \n",
       "271343269        126.6      102.8       72.1        83.1        89.7   \n",
       "\n",
       "            944189412  1327380181  944189121  4471751053  271343269  \n",
       "7636418620      327.8       372.2      322.1        69.9      178.1  \n",
       "1851468091      305.3       380.3      299.6        62.0      155.6  \n",
       "7887753052      159.4       570.3      153.7       149.3       53.9  \n",
       "4437784469      119.1       570.9      113.4       160.9       71.2  \n",
       "27231707        202.9       535.6      197.2       177.4       88.3  \n",
       "...               ...         ...        ...         ...        ...  \n",
       "944189412         0.0       685.6        5.7       276.8      187.1  \n",
       "1327380181      678.6         0.0      672.9       432.6      528.9  \n",
       "944189121         5.7       679.9        0.0       271.1      181.4  \n",
       "4471751053      276.8       439.6      271.1         0.0      121.5  \n",
       "271343269       187.1       535.9      181.4       121.5        0.0  \n",
       "\n",
       "[935 rows x 935 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GEO_node_dist"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58a41e20",
   "metadata": {},
   "source": [
    "## Sparse Matrix Version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0fb59233",
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "dc7515d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "935"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(GEO_metadata['GEO_node'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bc5d9377",
   "metadata": {},
   "outputs": [],
   "source": [
    "GEO_nodes = GEO_metadata['GEO_node'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "f2ecc564",
   "metadata": {},
   "outputs": [],
   "source": [
    "GEO_dict = {GEO_nodes[i]:i for i in range(len(GEO_nodes))}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b0587838",
   "metadata": {},
   "outputs": [],
   "source": [
    "GEO_node_dist_s = GEO_node_dist[GEO_nodes.astype(str)].loc[GEO_nodes]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "2a4868c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "GEO_node_dist_sym = (np.array(GEO_node_dist_s) + np.array(GEO_node_dist_s).T)/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "0e499adb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0. ,  24. , 206.8, ..., 322.1,  69.9, 178.1],\n",
       "       [ 24. ,   0. , 190. , ..., 299.6,  62. , 155.6],\n",
       "       [206.8, 190. ,   0. , ..., 153.7, 149.3,  53.9],\n",
       "       ...,\n",
       "       [322.1, 299.6, 153.7, ...,   0. , 271.1, 181.4],\n",
       "       [ 69.9,  62. , 149.3, ..., 271.1,   0. , 121.5],\n",
       "       [178.1, 155.6,  53.9, ..., 181.4, 121.5,   0. ]])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GEO_node_dist_sym"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "6a94c345",
   "metadata": {},
   "outputs": [],
   "source": [
    "SPA_dense =  ((20 - GEO_node_dist_sym)/20 * ((GEO_node_dist_sym<20).astype(int)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "340d4038",
   "metadata": {},
   "outputs": [],
   "source": [
    "SPA_sparse = scipy.sparse.csr_matrix(SPA_dense)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "36d46f91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 1., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 1., ..., 0., 0., 0.],\n",
       "       ...,\n",
       "       [0., 0., 0., ..., 1., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 1., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 1.]])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SPA_sparse.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "48af1cfa",
   "metadata": {},
   "outputs": [],
   "source": [
    "owners = GEO_metadata['owner'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8b7a48ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "social_links['relationship'] = (((social_links['self'].astype(int)*2 + (social_links['friend']=='True').astype(int) + (social_links['interest']>0.05).astype(int))).astype(int))/3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "dc8330a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.000000    106358\n",
       "0.333333      2126\n",
       "1.000000       330\n",
       "0.666667        86\n",
       "Name: relationship, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "social_links['relationship'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0fdd52bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "social_links = social_links[social_links['relationship']>0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "43a477d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "user_dict = {owners[i]:i for i in range(len(owners))}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6a5c637b",
   "metadata": {},
   "outputs": [],
   "source": [
    "row = np.array(social_links['User0'].apply(lambda x: user_dict[x]))\n",
    "col = np.array(social_links['User1'].apply(lambda x: user_dict[x]))\n",
    "data = np.array(social_links['relationship'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6050b966",
   "metadata": {},
   "outputs": [],
   "source": [
    "SOC_sparse = scipy.sparse.coo_matrix((data, (row, col)), shape=(len(owners), len(owners))).tocsr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "df4ff4ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.        , 1.        , 0.        , ..., 0.        , 0.33333333,\n",
       "        0.        ],\n",
       "       [0.        , 0.        , 1.        , ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       ...,\n",
       "       [0.        , 0.        , 0.        , ..., 1.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.        , 0.33333333, 0.        , ..., 0.        , 1.        ,\n",
       "        0.        ],\n",
       "       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
       "        1.        ]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SOC_sparse.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "114f9ccd",
   "metadata": {},
   "outputs": [],
   "source": [
    "times = sorted(GEO_metadata['adjusted_week'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a5609df7",
   "metadata": {},
   "outputs": [],
   "source": [
    "time_dict = {times[i]:i for i in range(len(times))}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "283c53d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.sparse import diags"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a7025200",
   "metadata": {},
   "outputs": [],
   "source": [
    "TEM_sparse = diags([0.5, 1, 0.5], [-1, 0, 1], shape=(len(times), len(times))).tocsr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "680b8853",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1. , 0.5, 0. , ..., 0. , 0. , 0. ],\n",
       "       [0.5, 1. , 0.5, ..., 0. , 0. , 0. ],\n",
       "       [0. , 0.5, 1. , ..., 0. , 0. , 0. ],\n",
       "       ...,\n",
       "       [0. , 0. , 0. , ..., 1. , 0.5, 0. ],\n",
       "       [0. , 0. , 0. , ..., 0.5, 1. , 0.5],\n",
       "       [0. , 0. , 0. , ..., 0. , 0.5, 1. ]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TEM_sparse.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "52e0d53e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         0\n",
       "1         0\n",
       "2         1\n",
       "3         2\n",
       "4         3\n",
       "       ... \n",
       "2946    278\n",
       "2947    461\n",
       "2948    459\n",
       "2949    932\n",
       "2950    930\n",
       "Name: GEO_node, Length: 2951, dtype: int64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SPA = GEO_metadata['GEO_node'].apply(lambda x: GEO_dict[x])\n",
    "SPA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "f7f8f42d",
   "metadata": {},
   "outputs": [],
   "source": [
    "SPA_enc = scipy.sparse.coo_matrix((np.ones(len(SPA)), (np.array(SPA), range(len(SPA)))), shape=(len(GEO_nodes), len(SPA))).tocsr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "fcdcd647",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 1., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       ...,\n",
       "       [0., 0., 0., ..., 0., 1., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SPA_enc.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "3762d00d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         0\n",
       "1         0\n",
       "2         0\n",
       "3         1\n",
       "4         2\n",
       "       ... \n",
       "2946    136\n",
       "2947    136\n",
       "2948    136\n",
       "2949    136\n",
       "2950    136\n",
       "Name: owner, Length: 2951, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SOC = GEO_metadata['owner'].apply(lambda x: user_dict[x])\n",
    "SOC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a0544977",
   "metadata": {},
   "outputs": [],
   "source": [
    "SOC_enc = scipy.sparse.coo_matrix((np.ones(len(SOC)), (np.array(SOC), range(len(SOC)))), shape=(len(owners), len(SOC))).tocsr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "23cfc33e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       254\n",
       "1       254\n",
       "2       254\n",
       "3       164\n",
       "4       268\n",
       "       ... \n",
       "2946    234\n",
       "2947    234\n",
       "2948    234\n",
       "2949    234\n",
       "2950    234\n",
       "Name: adjusted_week, Length: 2951, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TEM = GEO_metadata['adjusted_week'].apply(lambda x: time_dict[x])\n",
    "TEM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "83d31c7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "TEM_enc = scipy.sparse.coo_matrix((np.ones(len(TEM)), (np.array(TEM), range(len(TEM)))), shape=(len(times), len(TEM))).tocsr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "3ffd9424",
   "metadata": {},
   "outputs": [],
   "source": [
    "A_SPA = SPA_enc.T * SPA_sparse * SPA_enc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "448bd393",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<2951x2951 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 445779 stored elements in Compressed Sparse Column format>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A_SPA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "01ad73e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.   , 1.   , 0.   , ..., 0.   , 0.   , 0.   ],\n",
       "       [1.   , 1.   , 0.   , ..., 0.   , 0.   , 0.   ],\n",
       "       [0.   , 0.   , 1.   , ..., 0.   , 0.   , 0.   ],\n",
       "       ...,\n",
       "       [0.   , 0.   , 0.   , ..., 1.   , 0.3  , 0.405],\n",
       "       [0.   , 0.   , 0.   , ..., 0.3  , 1.   , 0.715],\n",
       "       [0.   , 0.   , 0.   , ..., 0.405, 0.715, 1.   ]])"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A_SPA.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "ae6db396",
   "metadata": {},
   "outputs": [],
   "source": [
    "A_SOC = SOC_enc.T * SOC_sparse * SOC_enc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "b99ba350",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<2951x2951 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 488103 stored elements in Compressed Sparse Column format>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A_SOC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "3ff11649",
   "metadata": {},
   "outputs": [],
   "source": [
    "A_TEM = TEM_enc.T * TEM_sparse * TEM_enc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "b92ac873",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<2951x2951 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 501191 stored elements in Compressed Sparse Column format>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A_TEM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "9186b5be",
   "metadata": {},
   "outputs": [],
   "source": [
    "A_simp = (((A_SOC>0) + (A_TEM>0) + (A_SPA>0))>0).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "e080a296",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<2951x2951 sparse matrix of type '<class 'numpy.int64'>'\n",
       "\twith 1071977 stored elements in Compressed Sparse Column format>"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A_simp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "c9ca712e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 1, 1, ..., 0, 0, 0],\n",
       "       [1, 1, 1, ..., 0, 0, 0],\n",
       "       [1, 1, 1, ..., 0, 0, 0],\n",
       "       ...,\n",
       "       [0, 0, 0, ..., 1, 1, 1],\n",
       "       [0, 0, 0, ..., 1, 1, 1],\n",
       "       [0, 0, 0, ..., 1, 1, 1]])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A_simp.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "fe56d428",
   "metadata": {},
   "outputs": [],
   "source": [
    "scipy.sparse.save_npz('dataset_np/Venice/A_SOC.npz', A_SOC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "fccfe9e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "scipy.sparse.save_npz('dataset_np/Venice/A_SPA.npz', A_SPA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "eb80982e",
   "metadata": {},
   "outputs": [],
   "source": [
    "scipy.sparse.save_npz('dataset_np/Venice/A_TEM.npz', A_TEM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "b509e26d",
   "metadata": {},
   "outputs": [],
   "source": [
    "scipy.sparse.save_npz('dataset_np/Venice/A_simp.npz', A_simp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "27f968d4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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