notebooks/learn_from_bel/learning_from_bel.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://colab.research.google.com/github/pykeen/pykeen/blob/master/notebooks/learning_from_bel/learning_from_bel.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Learning from BEL\n",
    "\n",
    "This notebook is about learning from biological knowledge graphs that are encoded using the [Biological Expression Language](https://biological-expression-language.github.io/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "import pykeen\n",
    "from pykeen.pipeline import pipeline\n",
    "from pykeen.triples import TriplesFactory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th>Key            </th><th>Value                   </th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>OS             </td><td>posix                   </td></tr>\n",
       "<tr><td>Platform       </td><td>Darwin                  </td></tr>\n",
       "<tr><td>Release        </td><td>23.5.0                  </td></tr>\n",
       "<tr><td>Time           </td><td>Fri Aug  9 10:57:23 2024</td></tr>\n",
       "<tr><td>Python         </td><td>3.12.4                  </td></tr>\n",
       "<tr><td>PyKEEN         </td><td>1.10.3-dev              </td></tr>\n",
       "<tr><td>PyKEEN Hash    </td><td>6f7c83ba                </td></tr>\n",
       "<tr><td>PyKEEN Branch  </td><td>snekpackify             </td></tr>\n",
       "<tr><td>PyTorch        </td><td>2.3.1                   </td></tr>\n",
       "<tr><td>CUDA Available?</td><td>false                   </td></tr>\n",
       "<tr><td>CUDA Version   </td><td>N/A                     </td></tr>\n",
       "<tr><td>cuDNN Version  </td><td>N/A                     </td></tr>\n",
       "</tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pykeen.env()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ras_triples_path = \"ras_machine_triples.tsv\"\n",
    "missing_ras_triples = not os.path.exists(ras_triples_path)\n",
    "missing_ras_triples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "if missing_ras_triples:\n",
    "    import pybel\n",
    "\n",
    "    print(pybel.get_version(with_git_hash=True))\n",
    "    graph = pybel.from_emmaa(\"rasmachine\")\n",
    "    graph.summarize()\n",
    "    triples = pybel.to_triples(graph)\n",
    "    np.savetxt(ras_triples_path, triples, fmt=\"%s\", delimiter=\"\\t\")\n",
    "else:\n",
    "    triples = np.loadtxt(ras_triples_path, dtype=str, delimiter=\"\\t\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf = TriplesFactory.from_labeled_triples(triples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "training, testing = tf.split(random_state=1234)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
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   ],
   "source": [
    "results = pipeline(\n",
    "    training=training,\n",
    "    testing=testing,\n",
    "    model=\"RotatE\",\n",
    "    training_kwargs=dict(num_epochs=100),\n",
    "    random_seed=1235,\n",
    "    device=\"cpu\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:pykeen.triples.triples_factory:Stored TriplesFactory(num_entities=4072, num_relations=8, create_inverse_triples=False, num_triples=6197) to file:///Users/cthoyt/dev/pykeen/notebooks/learn_from_bel/results/training_triples\n",
      "INFO:pykeen.pipeline.api:Saved to directory: /Users/cthoyt/dev/pykeen/notebooks/learn_from_bel/results\n"
     ]
    }
   ],
   "source": [
    "results.save_to_directory(\"results\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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></th>\n",
       "      <th>Side</th>\n",
       "      <th>Rank_type</th>\n",
       "      <th>Metric</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>head</td>\n",
       "      <td>optimistic</td>\n",
       "      <td>inverse_arithmetic_mean_rank</td>\n",
       "      <td>0.002992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tail</td>\n",
       "      <td>optimistic</td>\n",
       "      <td>inverse_arithmetic_mean_rank</td>\n",
       "      <td>0.000768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>both</td>\n",
       "      <td>optimistic</td>\n",
       "      <td>inverse_arithmetic_mean_rank</td>\n",
       "      <td>0.001223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>head</td>\n",
       "      <td>realistic</td>\n",
       "      <td>inverse_arithmetic_mean_rank</td>\n",
       "      <td>0.002992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tail</td>\n",
       "      <td>realistic</td>\n",
       "      <td>inverse_arithmetic_mean_rank</td>\n",
       "      <td>0.000768</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>220</th>\n",
       "      <td>tail</td>\n",
       "      <td>realistic</td>\n",
       "      <td>adjusted_hits_at_k</td>\n",
       "      <td>0.196722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>both</td>\n",
       "      <td>realistic</td>\n",
       "      <td>adjusted_hits_at_k</td>\n",
       "      <td>0.257200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>head</td>\n",
       "      <td>pessimistic</td>\n",
       "      <td>adjusted_hits_at_k</td>\n",
       "      <td>0.317677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>tail</td>\n",
       "      <td>pessimistic</td>\n",
       "      <td>adjusted_hits_at_k</td>\n",
       "      <td>0.196722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>both</td>\n",
       "      <td>pessimistic</td>\n",
       "      <td>adjusted_hits_at_k</td>\n",
       "      <td>0.257200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>225 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Side    Rank_type                        Metric     Value\n",
       "0    head   optimistic  inverse_arithmetic_mean_rank  0.002992\n",
       "1    tail   optimistic  inverse_arithmetic_mean_rank  0.000768\n",
       "2    both   optimistic  inverse_arithmetic_mean_rank  0.001223\n",
       "3    head    realistic  inverse_arithmetic_mean_rank  0.002992\n",
       "4    tail    realistic  inverse_arithmetic_mean_rank  0.000768\n",
       "..    ...          ...                           ...       ...\n",
       "220  tail    realistic            adjusted_hits_at_k  0.196722\n",
       "221  both    realistic            adjusted_hits_at_k  0.257200\n",
       "222  head  pessimistic            adjusted_hits_at_k  0.317677\n",
       "223  tail  pessimistic            adjusted_hits_at_k  0.196722\n",
       "224  both  pessimistic            adjusted_hits_at_k  0.257200\n",
       "\n",
       "[225 rows x 4 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.metric_results.to_df()"
   ]
  }
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