examples/basic_examples.ipynb

{
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  {
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import sys\n",
    "sys.path.insert(0, '../')\n",
    "from SimRank import SimRank"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SimRank on Directive Graphs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare data\n",
    "Data used in this section: BTS Flight dataset(https://www.transtats.bts.gov/DL_SelectFields.asp?Table_ID=236)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ORIGIN_AIRPORT_ID</th>\n",
       "      <th>DEST_AIRPORT_ID</th>\n",
       "      <th>flights</th>\n",
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       "      <td>466</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>11298</td>\n",
       "      <td>508</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>465</td>\n",
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       "      <th>4</th>\n",
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       "      <td>387</td>\n",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>14107</td>\n",
       "      <td>11298</td>\n",
       "      <td>397</td>\n",
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       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>14107</td>\n",
       "      <td>12266</td>\n",
       "      <td>246</td>\n",
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       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>14107</td>\n",
       "      <td>12889</td>\n",
       "      <td>584</td>\n",
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       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>14107</td>\n",
       "      <td>12892</td>\n",
       "      <td>561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>14107</td>\n",
       "      <td>13930</td>\n",
       "      <td>473</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>84 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    ORIGIN_AIRPORT_ID  DEST_AIRPORT_ID  flights\n",
       "0               10397            11057      506\n",
       "1               10397            11292      466\n",
       "2               10397            11298      508\n",
       "3               10397            12266      465\n",
       "4               10397            12889      387\n",
       "..                ...              ...      ...\n",
       "79              14107            11298      397\n",
       "80              14107            12266      246\n",
       "81              14107            12889      584\n",
       "82              14107            12892      561\n",
       "83              14107            13930      473\n",
       "\n",
       "[84 rows x 3 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "\"\"\"\n",
    "Load data\n",
    "keep only top 10 ORIGIN_AIRPORT_ID with the largest airline counts, and top 10 DEST_AIRPORT_ID with the largest airline counts\n",
    "Count total number of flights for each arilines\n",
    "\"\"\"\n",
    "flights = pd.read_csv('data/flights.csv')\n",
    "origin = flights.groupby('ORIGIN_AIRPORT_ID')['DEST_AIRPORT_ID'].count().nlargest(10).index\n",
    "dest = flights.groupby('DEST_AIRPORT_ID')['ORIGIN_AIRPORT_ID'].count().nlargest(10).index\n",
    "sample_data1 = flights[flights.ORIGIN_AIRPORT_ID.isin(origin) & flights.DEST_AIRPORT_ID.isin(dest)][['ORIGIN_AIRPORT_ID', 'DEST_AIRPORT_ID']]\n",
    "sample_data1['flights'] = 1\n",
    "sample_data1 = sample_data1.groupby(['ORIGIN_AIRPORT_ID', 'DEST_AIRPORT_ID']).flights.count().to_frame().reset_index()\n",
    "sample_data1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simple SimRank on Directive Graphs\n",
    "Use the SimRank class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start iterating...\n",
      "Percent: [##############################] 100% Complete! \n",
      "Converged at iteration 22"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Init instance of SimRank class to run the basic SimRank on the directive graph\n",
    "Not considering weights(flights) on this data sample ended up in an barely differentiated similarity results\n",
    "(\"weight_column\" is not required if \"weighted\" is set to False)\n",
    "\"\"\"\n",
    "sr = SimRank.SimRank()\n",
    "s_airports = sr.fit(\n",
    "    sample_data1, \n",
    "    weighted = False, \n",
    "    from_node_column = 'ORIGIN_AIRPORT_ID', \n",
    "    to_node_column = 'DEST_AIRPORT_ID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "      <td>0.282750</td>\n",
       "      <td>0.282750</td>\n",
       "      <td>0.282750</td>\n",
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       "    <tr>\n",
       "      <th>11298</th>\n",
       "      <td>0.279489</td>\n",
       "      <td>0.282750</td>\n",
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       "      <th>12266</th>\n",
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       "          12953     12889     14107     11292     10397     12892     11298  \\\n",
       "12953  1.000000  0.298554  0.298554  0.279489  0.279489  0.298554  0.279489   \n",
       "12889  0.298554  1.000000  0.289676  0.282750  0.282750  0.289676  0.282750   \n",
       "14107  0.298554  0.289676  1.000000  0.282750  0.282750  0.289676  0.282750   \n",
       "11292  0.279489  0.282750  0.282750  1.000000  0.284122  0.282750  0.284122   \n",
       "10397  0.279489  0.282750  0.282750  0.284122  1.000000  0.282750  0.284122   \n",
       "12892  0.298554  0.289676  0.289676  0.282750  0.282750  1.000000  0.282750   \n",
       "11298  0.279489  0.282750  0.282750  0.284122  0.284122  0.282750  1.000000   \n",
       "12266  0.279489  0.282750  0.282750  0.284122  0.284122  0.282750  0.284122   \n",
       "13930  0.279489  0.282750  0.282750  0.284122  0.284122  0.282750  0.284122   \n",
       "11057  0.279489  0.282750  0.282750  0.284122  0.284122  0.282750  0.284122   \n",
       "\n",
       "          12266     13930     11057  \n",
       "12953  0.279489  0.279489  0.279489  \n",
       "12889  0.282750  0.282750  0.282750  \n",
       "14107  0.282750  0.282750  0.282750  \n",
       "11292  0.284122  0.284122  0.284122  \n",
       "10397  0.284122  0.284122  0.284122  \n",
       "12892  0.282750  0.282750  0.282750  \n",
       "11298  0.284122  0.284122  0.284122  \n",
       "12266  1.000000  0.284122  0.284122  \n",
       "13930  0.284122  1.000000  0.284122  \n",
       "11057  0.284122  0.284122  1.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "s_airports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start iterating...\n",
      "Percent: [##############################] 100% Complete! \n",
      "Converged at iteration 1"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Init instance of SimRank class to run the basic SimRank on the directive graph\n",
    "Now, use the count of flights as the weight and run the SimRank again\n",
    "\"\"\"\n",
    "sr = SimRank.SimRank()\n",
    "s_airports = sr.fit(\n",
    "    sample_data1, \n",
    "    weighted = True, \n",
    "    from_node_column = 'ORIGIN_AIRPORT_ID', \n",
    "    to_node_column = 'DEST_AIRPORT_ID',\n",
    "    weight_column = 'flights')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
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       "      <th>11292</th>\n",
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       "      <td>2.525765e-07</td>\n",
       "      <td>4.348164e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11298</th>\n",
       "      <td>2.628607e-07</td>\n",
       "      <td>3.335487e-07</td>\n",
       "      <td>3.744519e-07</td>\n",
       "      <td>3.544493e-07</td>\n",
       "      <td>3.268490e-07</td>\n",
       "      <td>2.897426e-07</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>4.573679e-07</td>\n",
       "      <td>3.024365e-07</td>\n",
       "      <td>5.206516e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12266</th>\n",
       "      <td>3.465275e-07</td>\n",
       "      <td>4.397150e-07</td>\n",
       "      <td>4.936375e-07</td>\n",
       "      <td>4.672682e-07</td>\n",
       "      <td>4.308829e-07</td>\n",
       "      <td>3.819658e-07</td>\n",
       "      <td>4.573679e-07</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>3.987001e-07</td>\n",
       "      <td>6.863715e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13930</th>\n",
       "      <td>2.291428e-07</td>\n",
       "      <td>2.907634e-07</td>\n",
       "      <td>3.264200e-07</td>\n",
       "      <td>3.089831e-07</td>\n",
       "      <td>2.849232e-07</td>\n",
       "      <td>2.525765e-07</td>\n",
       "      <td>3.024365e-07</td>\n",
       "      <td>3.987001e-07</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>4.538661e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11057</th>\n",
       "      <td>3.944747e-07</td>\n",
       "      <td>5.005561e-07</td>\n",
       "      <td>5.619396e-07</td>\n",
       "      <td>5.319216e-07</td>\n",
       "      <td>4.905020e-07</td>\n",
       "      <td>4.348164e-07</td>\n",
       "      <td>5.206516e-07</td>\n",
       "      <td>6.863715e-07</td>\n",
       "      <td>4.538661e-07</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              12953         12889         14107         11292         10397  \\\n",
       "12953  1.000000e+00  3.465807e-07  3.890822e-07  2.685506e-07  2.476391e-07   \n",
       "12889  3.465807e-07  1.000000e+00  4.114278e-07  3.407687e-07  3.142337e-07   \n",
       "14107  3.890822e-07  4.114278e-07  1.000000e+00  3.825574e-07  3.527684e-07   \n",
       "11292  2.685506e-07  3.407687e-07  3.825574e-07  1.000000e+00  3.339240e-07   \n",
       "10397  2.476391e-07  3.142337e-07  3.527684e-07  3.339240e-07  1.000000e+00   \n",
       "12892  3.010631e-07  3.183537e-07  3.573936e-07  2.960144e-07  2.729643e-07   \n",
       "11298  2.628607e-07  3.335487e-07  3.744519e-07  3.544493e-07  3.268490e-07   \n",
       "12266  3.465275e-07  4.397150e-07  4.936375e-07  4.672682e-07  4.308829e-07   \n",
       "13930  2.291428e-07  2.907634e-07  3.264200e-07  3.089831e-07  2.849232e-07   \n",
       "11057  3.944747e-07  5.005561e-07  5.619396e-07  5.319216e-07  4.905020e-07   \n",
       "\n",
       "              12892         11298         12266         13930         11057  \n",
       "12953  3.010631e-07  2.628607e-07  3.465275e-07  2.291428e-07  3.944747e-07  \n",
       "12889  3.183537e-07  3.335487e-07  4.397150e-07  2.907634e-07  5.005561e-07  \n",
       "14107  3.573936e-07  3.744519e-07  4.936375e-07  3.264200e-07  5.619396e-07  \n",
       "11292  2.960144e-07  3.544493e-07  4.672682e-07  3.089831e-07  5.319216e-07  \n",
       "10397  2.729643e-07  3.268490e-07  4.308829e-07  2.849232e-07  4.905020e-07  \n",
       "12892  1.000000e+00  2.897426e-07  3.819658e-07  2.525765e-07  4.348164e-07  \n",
       "11298  2.897426e-07  1.000000e+00  4.573679e-07  3.024365e-07  5.206516e-07  \n",
       "12266  3.819658e-07  4.573679e-07  1.000000e+00  3.987001e-07  6.863715e-07  \n",
       "13930  2.525765e-07  3.024365e-07  3.987001e-07  1.000000e+00  4.538661e-07  \n",
       "11057  4.348164e-07  5.206516e-07  6.863715e-07  4.538661e-07  1.000000e+00  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_airports"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SimRank++ on Directive Graphs\n",
    "Use the SimRankPP class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start iterating...\n",
      "Percent: [##############################] 100% Complete! \n",
      "Converged at iteration 22"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Init instance of SimRankPP class to run the SimRank++ on the directive graph\n",
    "Weight matrixs and Evidence matrixs will be included during the iteration\n",
    "Not considering weights(flights) on this data sample ended up in an barely differentiated similarity results\n",
    "(\"weight_column\" is not required if \"weighted\" is set to False)\n",
    "\"\"\"\n",
    "srpp = SimRank.SimRankPP()\n",
    "s_airports = sr.fit(\n",
    "    sample_data1, \n",
    "    weighted = False, \n",
    "    from_node_column = 'ORIGIN_AIRPORT_ID', \n",
    "    to_node_column = 'DEST_AIRPORT_ID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "      <td>1.000000</td>\n",
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       "      <td>0.282750</td>\n",
       "      <td>0.282750</td>\n",
       "      <td>0.282750</td>\n",
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       "      <th>11298</th>\n",
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       "      <td>0.284122</td>\n",
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       "      <td>0.284122</td>\n",
       "      <td>1.000000</td>\n",
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       "          12953     12889     14107     11292     10397     12892     11298  \\\n",
       "12953  1.000000  0.298554  0.298554  0.279489  0.279489  0.298554  0.279489   \n",
       "12889  0.298554  1.000000  0.289676  0.282750  0.282750  0.289676  0.282750   \n",
       "14107  0.298554  0.289676  1.000000  0.282750  0.282750  0.289676  0.282750   \n",
       "11292  0.279489  0.282750  0.282750  1.000000  0.284122  0.282750  0.284122   \n",
       "10397  0.279489  0.282750  0.282750  0.284122  1.000000  0.282750  0.284122   \n",
       "12892  0.298554  0.289676  0.289676  0.282750  0.282750  1.000000  0.282750   \n",
       "11298  0.279489  0.282750  0.282750  0.284122  0.284122  0.282750  1.000000   \n",
       "12266  0.279489  0.282750  0.282750  0.284122  0.284122  0.282750  0.284122   \n",
       "13930  0.279489  0.282750  0.282750  0.284122  0.284122  0.282750  0.284122   \n",
       "11057  0.279489  0.282750  0.282750  0.284122  0.284122  0.282750  0.284122   \n",
       "\n",
       "          12266     13930     11057  \n",
       "12953  0.279489  0.279489  0.279489  \n",
       "12889  0.282750  0.282750  0.282750  \n",
       "14107  0.282750  0.282750  0.282750  \n",
       "11292  0.284122  0.284122  0.284122  \n",
       "10397  0.284122  0.284122  0.284122  \n",
       "12892  0.282750  0.282750  0.282750  \n",
       "11298  0.284122  0.284122  0.284122  \n",
       "12266  1.000000  0.284122  0.284122  \n",
       "13930  0.284122  1.000000  0.284122  \n",
       "11057  0.284122  0.284122  1.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_airports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initializing Weight matrix...\n",
      "Finished in 0.0029380321502685547s!\n",
      "Initializing Evidence matrix...\n",
      "Finished in 0.001386880874633789s!\n",
      "Start iterating...\n",
      "Percent: [##############################] 100% Complete! \n",
      "Converged at iteration 1"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Init instance of SimRankPP class to run the SimRank++ on the directive graph\n",
    "Weight matrixs and Evidence matrixs will be included during the iteration\n",
    "Now, use the count of flights as the weight and run the SimRank again\n",
    "\"\"\"\n",
    "srpp = SimRank.SimRankPP()\n",
    "s_airports = srpp.fit(\n",
    "    sample_data1, \n",
    "    weighted = True, \n",
    "    from_node_column = 'ORIGIN_AIRPORT_ID', \n",
    "    to_node_column = 'DEST_AIRPORT_ID',\n",
    "    weight_column = 'flights')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
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       "      <td>3.238698e-07</td>\n",
       "      <td>5.575494e-07</td>\n",
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       "      <td>3.255723e-07</td>\n",
       "      <td>4.291998e-07</td>\n",
       "      <td>2.838102e-07</td>\n",
       "      <td>4.885859e-07</td>\n",
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       "      <th>12892</th>\n",
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       "      <td>3.158665e-07</td>\n",
       "      <td>3.546014e-07</td>\n",
       "      <td>2.937018e-07</td>\n",
       "      <td>2.708318e-07</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.874790e-07</td>\n",
       "      <td>3.789817e-07</td>\n",
       "      <td>2.506033e-07</td>\n",
       "      <td>4.314194e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11298</th>\n",
       "      <td>2.546463e-07</td>\n",
       "      <td>3.309428e-07</td>\n",
       "      <td>3.715265e-07</td>\n",
       "      <td>3.530647e-07</td>\n",
       "      <td>3.255723e-07</td>\n",
       "      <td>2.874790e-07</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>4.555814e-07</td>\n",
       "      <td>3.012551e-07</td>\n",
       "      <td>5.186178e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12266</th>\n",
       "      <td>3.356986e-07</td>\n",
       "      <td>4.362797e-07</td>\n",
       "      <td>4.897810e-07</td>\n",
       "      <td>4.654429e-07</td>\n",
       "      <td>4.291998e-07</td>\n",
       "      <td>3.789817e-07</td>\n",
       "      <td>4.555814e-07</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>3.971427e-07</td>\n",
       "      <td>6.836904e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13930</th>\n",
       "      <td>2.219821e-07</td>\n",
       "      <td>2.884918e-07</td>\n",
       "      <td>3.238698e-07</td>\n",
       "      <td>3.077761e-07</td>\n",
       "      <td>2.838102e-07</td>\n",
       "      <td>2.506033e-07</td>\n",
       "      <td>3.012551e-07</td>\n",
       "      <td>3.971427e-07</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>4.520932e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11057</th>\n",
       "      <td>3.821474e-07</td>\n",
       "      <td>4.966455e-07</td>\n",
       "      <td>5.575494e-07</td>\n",
       "      <td>5.298438e-07</td>\n",
       "      <td>4.885859e-07</td>\n",
       "      <td>4.314194e-07</td>\n",
       "      <td>5.186178e-07</td>\n",
       "      <td>6.836904e-07</td>\n",
       "      <td>4.520932e-07</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              12953         12889         14107         11292         10397  \\\n",
       "12953  1.000000e+00  3.411654e-07  3.830028e-07  2.601584e-07  2.399004e-07   \n",
       "12889  3.411654e-07  1.000000e+00  4.082135e-07  3.381064e-07  3.117788e-07   \n",
       "14107  3.830028e-07  4.082135e-07  1.000000e+00  3.795686e-07  3.500124e-07   \n",
       "11292  2.601584e-07  3.381064e-07  3.795686e-07  1.000000e+00  3.326196e-07   \n",
       "10397  2.399004e-07  3.117788e-07  3.500124e-07  3.326196e-07  1.000000e+00   \n",
       "12892  2.963590e-07  3.158665e-07  3.546014e-07  2.937018e-07  2.708318e-07   \n",
       "11298  2.546463e-07  3.309428e-07  3.715265e-07  3.530647e-07  3.255723e-07   \n",
       "12266  3.356986e-07  4.362797e-07  4.897810e-07  4.654429e-07  4.291998e-07   \n",
       "13930  2.219821e-07  2.884918e-07  3.238698e-07  3.077761e-07  2.838102e-07   \n",
       "11057  3.821474e-07  4.966455e-07  5.575494e-07  5.298438e-07  4.885859e-07   \n",
       "\n",
       "              12892         11298         12266         13930         11057  \n",
       "12953  2.963590e-07  2.546463e-07  3.356986e-07  2.219821e-07  3.821474e-07  \n",
       "12889  3.158665e-07  3.309428e-07  4.362797e-07  2.884918e-07  4.966455e-07  \n",
       "14107  3.546014e-07  3.715265e-07  4.897810e-07  3.238698e-07  5.575494e-07  \n",
       "11292  2.937018e-07  3.530647e-07  4.654429e-07  3.077761e-07  5.298438e-07  \n",
       "10397  2.708318e-07  3.255723e-07  4.291998e-07  2.838102e-07  4.885859e-07  \n",
       "12892  1.000000e+00  2.874790e-07  3.789817e-07  2.506033e-07  4.314194e-07  \n",
       "11298  2.874790e-07  1.000000e+00  4.555814e-07  3.012551e-07  5.186178e-07  \n",
       "12266  3.789817e-07  4.555814e-07  1.000000e+00  3.971427e-07  6.836904e-07  \n",
       "13930  2.506033e-07  3.012551e-07  3.971427e-07  1.000000e+00  4.520932e-07  \n",
       "11057  4.314194e-07  5.186178e-07  6.836904e-07  4.520932e-07  1.000000e+00  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_airports"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SimRank on Bipartitle Graphs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare data\n",
    "Data used in this section: MovieLens(https://grouplens.org/datasets/movielens/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1225858</th>\n",
       "      <td>8405</td>\n",
       "      <td>110</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1160464030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1225954</th>\n",
       "      <td>8405</td>\n",
       "      <td>260</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1160463829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1225976</th>\n",
       "      <td>8405</td>\n",
       "      <td>296</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1160463818</td>\n",
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       "    <tr>\n",
       "      <th>1225992</th>\n",
       "      <td>8405</td>\n",
       "      <td>318</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1160463940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1226021</th>\n",
       "      <td>8405</td>\n",
       "      <td>356</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1344220422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19063141</th>\n",
       "      <td>131904</td>\n",
       "      <td>480</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1241145479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19063152</th>\n",
       "      <td>131904</td>\n",
       "      <td>527</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1265933408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19063168</th>\n",
       "      <td>131904</td>\n",
       "      <td>589</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1241145534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19063171</th>\n",
       "      <td>131904</td>\n",
       "      <td>593</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1239074621</td>\n",
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       "    <tr>\n",
       "      <th>19063645</th>\n",
       "      <td>131904</td>\n",
       "      <td>2571</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1424306872</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          userId  movieId  rating   timestamp\n",
       "1225858     8405      110     4.0  1160464030\n",
       "1225954     8405      260     5.0  1160463829\n",
       "1225976     8405      296     5.0  1160463818\n",
       "1225992     8405      318     4.5  1160463940\n",
       "1226021     8405      356     4.5  1344220422\n",
       "...          ...      ...     ...         ...\n",
       "19063141  131904      480     4.0  1241145479\n",
       "19063152  131904      527     4.0  1265933408\n",
       "19063168  131904      589     4.0  1241145534\n",
       "19063171  131904      593     4.0  1239074621\n",
       "19063645  131904     2571     4.5  1424306872\n",
       "\n",
       "[100 rows x 4 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Load data\n",
    "keep only top 10 users with the largest rating counts, and top 10 movies with the largest rating counts\n",
    "\"\"\"\n",
    "ratings = pd.read_csv('data/ratings.csv')\n",
    "users = ratings.groupby('userId')['movieId'].count().nlargest(10).index\n",
    "movies = ratings.groupby('movieId')['userId'].count().nlargest(10).index\n",
    "sample_data2 = ratings[ratings.userId.isin(users) & ratings.movieId.isin(movies)]\n",
    "sample_data2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simple SimRank on Bipartitle Graphs\n",
    "Use the BipartiteSimRank class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start iterating...\n",
      "Percent: [##############################] 100% Complete! \n",
      "Converged at iteration 13"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Init instance of BipartiteSimRank class to run the basic SimRank on the bipartitle graph\n",
    "Not considering weights(rating) on this data sample ended up in an undifferentiated similarity results\n",
    "That's because every user viewed exactly the same movies and vice versa\n",
    "(\"weight_column\" is not required if \"weighted\" is set to False)\n",
    "\"\"\"\n",
    "bsr = SimRank.BipartiteSimRank()\n",
    "s_users, s_movies = bsr.fit(\n",
    "    sample_data2, \n",
    "    weighted = False, \n",
    "    node_group1_column = 'userId', \n",
    "    node_group2_column = 'movieId', \n",
    "    weight_column = 'rating')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1.000000</td>\n",
       "      <td>0.285637</td>\n",
       "      <td>0.285637</td>\n",
       "      <td>0.285637</td>\n",
       "      <td>0.285637</td>\n",
       "      <td>0.285637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59477</th>\n",
       "      <td>0.285637</td>\n",
       "      <td>0.285637</td>\n",
       "      <td>0.285637</td>\n",
       "      <td>0.285637</td>\n",
       "      <td>0.285637</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.285637</td>\n",
       "      <td>0.285637</td>\n",
       "      <td>0.285637</td>\n",
       "      <td>0.285637</td>\n",
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       "    <tr>\n",
       "      <th>8405</th>\n",
       "      <td>0.285637</td>\n",
       "      <td>0.285637</td>\n",
       "      <td>0.285637</td>\n",
       "      <td>0.285637</td>\n",
       "      <td>0.285637</td>\n",
       "      <td>0.285637</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.285637</td>\n",
       "      <td>0.285637</td>\n",
       "      <td>0.285637</td>\n",
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       "    <tr>\n",
       "      <th>118205</th>\n",
       "      <td>0.285637</td>\n",
       "      <td>0.285637</td>\n",
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       "      <td>0.285637</td>\n",
       "      <td>0.285637</td>\n",
       "      <td>0.285637</td>\n",
       "      <td>0.285637</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.285637</td>\n",
       "      <td>0.285637</td>\n",
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       "      <th>74142</th>\n",
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       "      <td>0.285637</td>\n",
       "      <td>0.285637</td>\n",
       "      <td>0.285637</td>\n",
       "      <td>0.285637</td>\n",
       "      <td>0.285637</td>\n",
       "      <td>0.285637</td>\n",
       "      <td>0.285637</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.285637</td>\n",
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       "    <tr>\n",
       "      <th>121535</th>\n",
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       "      <td>0.285637</td>\n",
       "      <td>0.285637</td>\n",
       "      <td>0.285637</td>\n",
       "      <td>0.285637</td>\n",
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       "      <td>1.000000</td>\n",
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       "</table>\n",
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       "          131904    125794    34576     82418     83090     59477     8405    \\\n",
       "131904  1.000000  0.285637  0.285637  0.285637  0.285637  0.285637  0.285637   \n",
       "125794  0.285637  1.000000  0.285637  0.285637  0.285637  0.285637  0.285637   \n",
       "34576   0.285637  0.285637  1.000000  0.285637  0.285637  0.285637  0.285637   \n",
       "82418   0.285637  0.285637  0.285637  1.000000  0.285637  0.285637  0.285637   \n",
       "83090   0.285637  0.285637  0.285637  0.285637  1.000000  0.285637  0.285637   \n",
       "59477   0.285637  0.285637  0.285637  0.285637  0.285637  1.000000  0.285637   \n",
       "8405    0.285637  0.285637  0.285637  0.285637  0.285637  0.285637  1.000000   \n",
       "118205  0.285637  0.285637  0.285637  0.285637  0.285637  0.285637  0.285637   \n",
       "74142   0.285637  0.285637  0.285637  0.285637  0.285637  0.285637  0.285637   \n",
       "121535  0.285637  0.285637  0.285637  0.285637  0.285637  0.285637  0.285637   \n",
       "\n",
       "          118205    74142     121535  \n",
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  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start iterating...\n",
      "Percent: [##############################] 100% Complete! \n",
      "Converged at iteration 3"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Init instance of BipartiteSimRank class to run the basic SimRank on the bipartitle graph\n",
    "Considering weights(rating) this time\n",
    "And we have differentiated results now\n",
    "\"\"\"\n",
    "bsr = SimRank.BipartiteSimRank()\n",
    "s_users, s_movies = bsr.fit(\n",
    "    sample_data2, \n",
    "    weighted = True, \n",
    "    node_group1_column = 'userId', \n",
    "    node_group2_column = 'movieId', \n",
    "    weight_column = 'rating')"
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  {
   "cell_type": "code",
   "execution_count": 16,
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       "      <td>0.004788</td>\n",
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       "    <tr>\n",
       "      <th>356</th>\n",
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       "      <th>296</th>\n",
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       "      <td>0.004519</td>\n",
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       "      <td>0.005069</td>\n",
       "      <td>0.005371</td>\n",
       "      <td>0.005435</td>\n",
       "      <td>0.005013</td>\n",
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       "    <tr>\n",
       "      <th>589</th>\n",
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       "      <td>0.005161</td>\n",
       "      <td>0.005224</td>\n",
       "      <td>0.004817</td>\n",
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       "    <tr>\n",
       "      <th>110</th>\n",
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       "      <td>0.004077</td>\n",
       "      <td>0.004215</td>\n",
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       "    <tr>\n",
       "      <th>593</th>\n",
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       "      <td>0.004372</td>\n",
       "      <td>0.004519</td>\n",
       "      <td>0.005435</td>\n",
       "      <td>0.005224</td>\n",
       "      <td>0.004519</td>\n",
       "      <td>0.004788</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.004469</td>\n",
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       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>0.004817</td>\n",
       "      <td>0.004416</td>\n",
       "      <td>0.004032</td>\n",
       "      <td>0.004168</td>\n",
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       "      <td>0.004817</td>\n",
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       "      <td>0.004469</td>\n",
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       "          480       260       356       296       2571      589       110   \\\n",
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       "\n",
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       "527   1.000000  0.004788  0.004416  \n",
       "593   0.004788  1.000000  0.004469  \n",
       "318   0.004416  0.004469  1.000000  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_movies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SimRank++ on Bipartitle Graphs\n",
    "The BipartiteSimRankPP class will calculate the Evidence and Weight matrix in order to run the SimRank++ algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initializing Weight matrix...\n",
      "Finished in 0.0013289451599121094s!\n",
      "Initializing Weight matrix...\n",
      "Finished in 0.0012862682342529297s!\n",
      "Initializing Evidence matrix...\n",
      "Finished in 0.001260995864868164s!\n",
      "Initializing Evidence matrix...\n",
      "Finished in 0.0012061595916748047s!\n",
      "Start iterating...\n",
      "Percent: [##############################] 100% Complete! \n",
      "Converged at iteration 13"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Init instance of BipartiteSimRankPP class to run the SimRank++ on the bipartitle graph\n",
    "Weight matrixs and Evidence matrixs will be included during the iteration\n",
    "Not considering weights(rating) on this data sample ended up in an undifferentiated similarity results\n",
    "That's because every user viewed exactly the same movies and vice versa\n",
    "(\"weight_column\" is not required if \"weighted\" is set to False)\n",
    "\"\"\"\n",
    "bsrpp = SimRank.BipartiteSimRankPP()\n",
    "s_users, s_movies = bsrpp.fit(\n",
    "    sample_data2, \n",
    "    weighted = False, \n",
    "    node_group1_column = 'userId', \n",
    "    node_group2_column = 'movieId', \n",
    "    weight_column = 'rating')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
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  {
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   "execution_count": 20,
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     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initializing Weight matrix...\n",
      "Finished in 0.001653909683227539s!\n",
      "Initializing Weight matrix...\n",
      "Finished in 0.0015270709991455078s!\n",
      "Initializing Evidence matrix...\n",
      "Finished in 0.0012881755828857422s!\n",
      "Initializing Evidence matrix...\n",
      "Finished in 0.0031511783599853516s!\n",
      "Start iterating...\n",
      "Percent: [##############################] 100% Complete! \n",
      "Converged at iteration 3"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Init instance of BipartiteSimRankPP class to run the SimRank++ on the bipartitle graph\n",
    "Weight matrixs and Evidence matrixs will be included during the iteration\n",
    "Considering weights(rating) this time\n",
    "And we have differentiated results now\n",
    "\"\"\"\n",
    "bsrpp = SimRank.BipartiteSimRankPP()\n",
    "s_users, s_movies = bsrpp.fit(\n",
    "    sample_data2, \n",
    "    weighted = True, \n",
    "    node_group1_column = 'userId', \n",
    "    node_group2_column = 'movieId', \n",
    "    weight_column = 'rating')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
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  {
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   "execution_count": 23,
   "metadata": {},
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       "          480       260       356       296       2571      589       110   \\\n",
       "480   1.000000  0.005156  0.004708  0.004866  0.005853  0.005625  0.004866   \n",
       "260   0.005156  1.000000  0.004315  0.004461  0.005365  0.005156  0.004461   \n",
       "356   0.004708  0.004315  1.000000  0.004073  0.004899  0.004708  0.004073   \n",
       "296   0.004866  0.004461  0.004073  1.000000  0.005064  0.004866  0.004210   \n",
       "2571  0.005853  0.005365  0.004899  0.005064  1.000000  0.005853  0.005064   \n",
       "589   0.005625  0.005156  0.004708  0.004866  0.005853  1.000000  0.004866   \n",
       "110   0.004866  0.004461  0.004073  0.004210  0.005064  0.004866  1.000000   \n",
       "527   0.005156  0.004726  0.004315  0.004461  0.005365  0.005156  0.004461   \n",
       "593   0.005218  0.004783  0.004367  0.004515  0.005430  0.005218  0.004515   \n",
       "318   0.004812  0.004411  0.004028  0.004164  0.005008  0.004812  0.004164   \n",
       "\n",
       "          527       593       318   \n",
       "480   0.005156  0.005218  0.004812  \n",
       "260   0.004726  0.004783  0.004411  \n",
       "356   0.004315  0.004367  0.004028  \n",
       "296   0.004461  0.004515  0.004164  \n",
       "2571  0.005365  0.005430  0.005008  \n",
       "589   0.005156  0.005218  0.004812  \n",
       "110   0.004461  0.004515  0.004164  \n",
       "527   1.000000  0.004783  0.004411  \n",
       "593   0.004783  1.000000  0.004465  \n",
       "318   0.004411  0.004465  1.000000  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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