BoxETraining.ipynb

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      "INFO:pykeen.nn.node_piece.tokenization:Selecting anchors according to MixtureAnchorSelection(num_anchors=85, selections=[DegreeAnchorSelection(num_anchors=34), PageRankAnchorSelection(num_anchors=34), RandomAnchorSelection(num_anchors=17)])\n",
      "INFO:pykeen.nn.node_piece.tokenization:Searching closest anchors with ScipySparseAnchorSearcher(max_iter=5)\n"
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      "WARNING:pykeen.nn.combination:No symbolic computation of output shape.\n"
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   "source": [
    "from pykeen.models import NodePiece\n",
    "from pykeen.nn import AutoSFInteraction\n",
    "model = NodePiece(\n",
    "    triples_factory=training,\n",
    "    interaction = 'RotatE',\n",
    "    tokenizers=[\"AnchorTokenizer\", \"RelationTokenizer\"],\n",
    "    num_tokens=[25, 8],\n",
    "    random_seed = 21159,\n",
    "    tokenizers_kwargs=[\n",
    "        dict(\n",
    "            selection=\"MixtureAnchorSelection\",\n",
    "            selection_kwargs=dict(\n",
    "                selections=[\"degree\", \"pagerank\",\"random\"],\n",
    "                ratios=[0.4, 0.4, 0.2],\n",
    "                num_anchors=85,\n",
    "            ),\n",
    "            searcher=\"ScipySparse\",\n",
    "        ),\n",
    "        dict(),  # empty dict for the RelationTokenizer - it doesn't need any kwargs\n",
    "    ],\n",
    "    embedding_dim=32,\n",
    ")"
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   "source": [
    "from pykeen.triples import TriplesFactory\n",
    "from pykeen.pipeline import pipeline\n",
    "from pykeen.models import BoxE\n",
    "from torch.optim import Adam\n",
    "import wandb\n",
    "\n",
    "training = TriplesFactory.from_path(\n",
    "    './data/X4/train.txt',\n",
    "    create_inverse_triples = False\n",
    ")"
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      "WARNING:pykeen.triples.triples_factory:You're trying to map triples with 2 entities and 0 relations that are not in the training set. These triples will be excluded from the mapping.\n",
      "WARNING:pykeen.triples.triples_factory:In total 2 from 3334 triples were filtered out\n",
      "WARNING:pykeen.triples.triples_factory:You're trying to map triples with 1 entities and 0 relations that are not in the training set. These triples will be excluded from the mapping.\n",
      "WARNING:pykeen.triples.triples_factory:In total 1 from 1808 triples were filtered out\n",
      "INFO:pykeen.pipeline.api:loaded random seed 21159 from checkpoint.\n",
      "INFO:pykeen.pipeline.api:Using device: cuda\n",
      "INFO:pykeen.training.training_loop:=> loading checkpoint '/checkpoint/new/cpx.pt'\n",
      "INFO:pykeen.training.training_loop:=> loaded checkpoint '/checkpoint/new/cpx.pt' stopped after having finished epoch 1024\n",
      "INFO:pykeen.stoppers.stopper:=> loading stopper summary dict from training loop checkpoint in '/checkpoint/new/cpx.pt'\n",
      "INFO:pykeen.stoppers.stopper:=> loaded stopper summary dictionary from checkpoint in '/checkpoint/new/cpx.pt'\n",
      "WARNING:pykeen.training.training_loop:the training loop was configured with a stopper but no stopper configuration was saved in the checkpoint\n"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:pykeen.training.training_loop:=> Saved checkpoint after having finished epoch 1424.\n",
      "INFO:pykeen.evaluation.evaluator:Starting batch_size search for evaluation now...\n",
      "INFO:pykeen.evaluation.evaluator:Concluded batch_size search with batch_size=4096.\n"
     ]
    },
    {
     "data": {
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       "model_id": "78e37734c4ed46fe9215526f62b59226",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Evaluating on cuda:0:   0%|          | 0.00/3.33k [00:00<?, ?triple/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:pykeen.evaluation.evaluator:Evaluation took 0.43s seconds\n",
      "INFO:pykeen.triples.triples_factory:Stored TriplesFactory(num_entities=2948, num_relations=18, create_inverse_triples=False, num_triples=22341, path=\"/output/data/X4/train.txt\") to file:///output/Xiangyu_BoxE2/training_triples\n",
      "INFO:pykeen.pipeline.api:Saved to directory: file:///output/Xiangyu_BoxE2\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#wandb.init(project=\"pykeen_project\", entity=\"sheshan\")\n",
    "\n",
    "model = BoxE(\n",
    "    triples_factory = training,\n",
    "    embedding_dim = 64,\n",
    "    tanh_map = True,\n",
    "    random_seed = 21159\n",
    ")\n",
    "#model = ComplEx(\n",
    "#    triples_factory = training,\n",
    "#    embedding_dim = 64,\n",
    "#    tanh_map = True,\n",
    "#    random_seed = 21159\n",
    "#)\n",
    "\n",
    "testing = TriplesFactory.from_path(\n",
    "    './data/X4/test.txt',\n",
    "    entity_to_id=training.entity_to_id,\n",
    "    relation_to_id=training.relation_to_id,\n",
    "    #create_inverse_triples = True,\n",
    "                        )\n",
    "\n",
    "validation = TriplesFactory.from_path(\n",
    "    path='./data/X4/valid.txt',\n",
    "    entity_to_id=training.entity_to_id,\n",
    "    relation_to_id=training.relation_to_id,\n",
    "                        )\n",
    "\n",
    "result = pipeline(\n",
    "    training=training,\n",
    "    testing=testing,\n",
    "    device='cuda',\n",
    "    optimizer = Adam,\n",
    "    model=model,\n",
    "    evaluator='RankBasedEvaluator',\n",
    "    random_seed = 21159,\n",
    "    validation= validation,\n",
    "    training_kwargs=dict(\n",
    "        num_epochs=1424,\n",
    "        checkpoint_name='cpx.pt',\n",
    "        checkpoint_directory='/checkpoint/new',\n",
    "        checkpoint_frequency=16,\n",
    "    ),\n",
    ")\n",
    "\n",
    "result.save_to_directory('Xiangyu_BoxE2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1496fbac-e1ea-417f-aa9a-2d4846d1ee8c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-27T13:31:55.213959Z",
     "iopub.status.busy": "2023-01-27T13:31:55.213530Z",
     "iopub.status.idle": "2023-01-27T13:31:55.250321Z",
     "shell.execute_reply": "2023-01-27T13:31:55.249509Z",
     "shell.execute_reply.started": "2023-01-27T13:31:55.213925Z"
    },
    "tags": []
   },
   "outputs": [
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       "    }\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>tail_id</th>\n",
       "      <th>score</th>\n",
       "      <th>tail_label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2828</th>\n",
       "      <td>2828</td>\n",
       "      <td>-4.627784</td>\n",
       "      <td>dʑ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2946</th>\n",
       "      <td>2946</td>\n",
       "      <td>-4.745692</td>\n",
       "      <td>ʑ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2936</th>\n",
       "      <td>2936</td>\n",
       "      <td>-4.747501</td>\n",
       "      <td>ɣ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2825</th>\n",
       "      <td>2825</td>\n",
       "      <td>-5.053135</td>\n",
       "      <td>d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2935</th>\n",
       "      <td>2935</td>\n",
       "      <td>-5.096650</td>\n",
       "      <td>ɡ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2684</th>\n",
       "      <td>2684</td>\n",
       "      <td>-13.281719</td>\n",
       "      <td>8721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>999</td>\n",
       "      <td>-13.318329</td>\n",
       "      <td>17401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1930</th>\n",
       "      <td>1930</td>\n",
       "      <td>-13.322428</td>\n",
       "      <td>25517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>30</td>\n",
       "      <td>-13.528795</td>\n",
       "      <td>10165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1791</th>\n",
       "      <td>1791</td>\n",
       "      <td>-13.655204</td>\n",
       "      <td>23851</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2948 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      tail_id      score tail_label\n",
       "2828     2828  -4.627784         dʑ\n",
       "2946     2946  -4.745692          ʑ\n",
       "2936     2936  -4.747501          ɣ\n",
       "2825     2825  -5.053135          d\n",
       "2935     2935  -5.096650          ɡ\n",
       "...       ...        ...        ...\n",
       "2684     2684 -13.281719       8721\n",
       "999       999 -13.318329      17401\n",
       "1930     1930 -13.322428      25517\n",
       "30         30 -13.528795      10165\n",
       "1791     1791 -13.655204      23851\n",
       "\n",
       "[2948 rows x 3 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pykeen import predict\n",
    "predict.predict_target(model=model, head=\"p\", relation=\"+voi\", triples_factory=training).df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "a76dd3c3-017f-4ed6-9f5c-7c9f00d14b83",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-25T08:54:34.842997Z",
     "iopub.status.busy": "2023-01-25T08:54:34.842604Z",
     "iopub.status.idle": "2023-01-25T08:54:34.850489Z",
     "shell.execute_reply": "2023-01-25T08:54:34.850049Z",
     "shell.execute_reply.started": "2023-01-25T08:54:34.842963Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BoxE(\n",
       "  (loss): NSSALoss()\n",
       "  (interaction): BoxEInteraction()\n",
       "  (entity_representations): ModuleList(\n",
       "    (0): Embedding(\n",
       "      (_embeddings): Embedding(2946, 64)\n",
       "    )\n",
       "    (1): Embedding(\n",
       "      (_embeddings): Embedding(2946, 64)\n",
       "    )\n",
       "  )\n",
       "  (relation_representations): ModuleList(\n",
       "    (0): Embedding(\n",
       "      (_embeddings): Embedding(12, 64)\n",
       "    )\n",
       "    (1): Embedding(\n",
       "      (_embeddings): Embedding(12, 64)\n",
       "    )\n",
       "    (2): Embedding(\n",
       "      (_embeddings): Embedding(12, 1)\n",
       "    )\n",
       "    (3): Embedding(\n",
       "      (_embeddings): Embedding(12, 64)\n",
       "    )\n",
       "    (4): Embedding(\n",
       "      (_embeddings): Embedding(12, 64)\n",
       "    )\n",
       "    (5): Embedding(\n",
       "      (_embeddings): Embedding(12, 1)\n",
       "    )\n",
       "  )\n",
       "  (weight_regularizers): ModuleList()\n",
       ")"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "ae45d07b-c786-42ff-93fa-111dff3b06d1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-25T09:00:13.656337Z",
     "iopub.status.busy": "2023-01-25T09:00:13.655939Z",
     "iopub.status.idle": "2023-01-25T09:00:13.677671Z",
     "shell.execute_reply": "2023-01-25T09:00:13.676885Z",
     "shell.execute_reply.started": "2023-01-25T09:00:13.656303Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "522b889d-dbfc-4195-917b-e15ca033b21c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-25T09:00:16.925156Z",
     "iopub.status.busy": "2023-01-25T09:00:16.924809Z",
     "iopub.status.idle": "2023-01-25T09:00:16.933397Z",
     "shell.execute_reply": "2023-01-25T09:00:16.932716Z",
     "shell.execute_reply.started": "2023-01-25T09:00:16.925124Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "         4.16853875e-01, -4.11055565e+00, -3.72676873e+00,\n",
       "         4.29990578e+00,  9.87419796e+00, -3.67606953e-02,\n",
       "        -6.38212025e-01, -7.04266608e-01, -1.13351595e+00,\n",
       "        -3.49425435e+00, -3.89173484e+00, -3.32424688e+00,\n",
       "         8.29137638e-02, -5.86011946e-01, -6.79415703e-01,\n",
       "        -4.88927174e+00, -2.63132000e+00, -3.79996490e+00,\n",
       "        -9.73580182e-01, -3.25357246e+00, -1.03267694e+00,\n",
       "        -2.74440980e+00, -9.33735073e-02,  1.23634589e+00,\n",
       "         9.85263348e+00]], dtype=float32)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embs.weight.detach().numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "96c89f89-44e0-408a-a005-03e1747cbc89",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-25T08:53:38.106746Z",
     "iopub.status.busy": "2023-01-25T08:53:38.106349Z",
     "iopub.status.idle": "2023-01-25T08:53:38.110444Z",
     "shell.execute_reply": "2023-01-25T08:53:38.109735Z",
     "shell.execute_reply.started": "2023-01-25T08:53:38.106708Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "fd516da1-0fb1-4c9c-ac50-4c2575080997",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-06T05:30:33.376043Z",
     "iopub.status.busy": "2022-07-06T05:30:33.375122Z",
     "iopub.status.idle": "2022-07-06T05:30:33.530409Z",
     "shell.execute_reply": "2022-07-06T05:30:33.529201Z",
     "shell.execute_reply.started": "2022-07-06T05:30:33.375982Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Xy_ConvE.model']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import joblib as jl\n",
    "jl.dump(result,'Xy_ConvE.model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7b52eddf-f3e3-4907-a632-c6377613e345",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "with open('./data/dict\n",
    "checkpoint['entity_to_id_dict']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e88031d7-8a7f-40e4-bbf0-214a65ddf495",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-05T13:03:26.531870Z",
     "iopub.status.busy": "2022-07-05T13:03:26.531092Z",
     "iopub.status.idle": "2022-07-05T13:03:26.556040Z",
     "shell.execute_reply": "2022-07-05T13:03:26.554447Z",
     "shell.execute_reply.started": "2022-07-05T13:03:26.531812Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "a = checkpoint['model_state_dict']['relation_representations.0._embeddings.weight'].cpu().numpy()\n",
    "b = checkpoint['model_state_dict']['relation_representations.1._embeddings.weight'].cpu().numpy()\n",
    "c = checkpoint['model_state_dict']['relation_representations.2._embeddings.weight'].cpu().numpy()\n",
    "d = checkpoint['model_state_dict']['relation_representations.3._embeddings.weight'].cpu().numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5ae47492-625d-4fc0-aa0d-7a1aeb58cdc1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-05T12:59:21.751414Z",
     "iopub.status.busy": "2022-07-05T12:59:21.750742Z",
     "iopub.status.idle": "2022-07-05T12:59:21.761165Z",
     "shell.execute_reply": "2022-07-05T12:59:21.759854Z",
     "shell.execute_reply.started": "2022-07-05T12:59:21.751358Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4824, 256)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f94c297a-81ab-42b5-a47e-f0f5a4274a4c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-05T13:03:33.882323Z",
     "iopub.status.busy": "2022-07-05T13:03:33.881636Z",
     "iopub.status.idle": "2022-07-05T13:03:33.890988Z",
     "shell.execute_reply": "2022-07-05T13:03:33.889685Z",
     "shell.execute_reply.started": "2022-07-05T13:03:33.882266Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "np.savez('./data/rels.txt',a,b,c,d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "082c7993-53ec-4782-991f-1aa9de448fe2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-25T07:32:58.125532Z",
     "iopub.status.busy": "2023-01-25T07:32:58.125139Z",
     "iopub.status.idle": "2023-01-25T07:32:58.146743Z",
     "shell.execute_reply": "2023-01-25T07:32:58.145618Z",
     "shell.execute_reply.started": "2023-01-25T07:32:58.125501Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'pykeen.models.predict'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[30], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpykeen\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodels\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpredict\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m predict\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'pykeen.models.predict'"
     ]
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "175c93e6-4e7e-4b13-a7cc-6d819d39130e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-25T09:30:30.402005Z",
     "iopub.status.busy": "2023-01-25T09:30:30.401628Z",
     "iopub.status.idle": "2023-01-25T09:30:31.110779Z",
     "shell.execute_reply": "2023-01-25T09:30:31.109704Z",
     "shell.execute_reply.started": "2023-01-25T09:30:30.401987Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Complex data not supported\n[[ 0.3781345 -3.6084655e-01j  0.09390053+4.3499310e-02j\n  -1.0443127 -3.2956937e-01j ... -0.20330974+1.0654817e+00j\n  -0.20248395-3.9709845e-01j  0.7661117 +1.1174473e-03j]\n [ 0.7572415 +8.4394449e-01j -1.1377153 +6.7232960e-01j\n  -2.2249918 -1.3278307e-01j ...  0.04713264+7.2618312e-01j\n   0.87000316+4.8082000e-01j -1.635183  +6.6057360e-01j]\n [-0.82199085+1.2415135e-01j  0.21124898-9.1059297e-02j\n  -1.6013912 -5.3352004e-01j ... -0.08804699-9.4887815e-02j\n  -0.54006475-1.0844716e+00j  1.755571  -1.3291166e+00j]\n ...\n [ 0.7884725 +1.9453542e-01j  1.2218179 +7.5266087e-01j\n  -1.5205196 -7.5470078e-01j ... -0.3012225 -2.4313221e-02j\n   2.006678  +7.2385943e-01j -1.6055052 -9.8005724e-01j]\n [ 0.2621836 +1.6384976e+00j  0.49529266+2.5210388e-02j\n  -1.4453493 -6.9120735e-01j ... -0.0028849 -7.2415091e-02j\n  -0.04615272-1.9769661e-01j  2.1367304 +1.4294608e-01j]\n [ 0.29790193+2.3023668e-01j -0.50511813+1.2130161e-01j\n  -1.3373092 -9.6548563e-01j ...  0.93450856-4.4077992e-01j\n  -1.2920572 +4.2834905e-01j -0.13854231-1.3528819e-01j]]\n",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mComplexWarning\u001b[0m                            Traceback (most recent call last)",
      "File \u001b[0;32m/output/pykeen/lib/python3.10/site-packages/sklearn/utils/validation.py:879\u001b[0m, in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)\u001b[0m\n\u001b[1;32m    878\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 879\u001b[0m         array \u001b[38;5;241m=\u001b[39m \u001b[43m_asarray_with_order\u001b[49m\u001b[43m(\u001b[49m\u001b[43marray\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morder\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43morder\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mxp\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mxp\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    880\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m ComplexWarning \u001b[38;5;28;01mas\u001b[39;00m complex_warning:\n",
      "File \u001b[0;32m/output/pykeen/lib/python3.10/site-packages/sklearn/utils/_array_api.py:185\u001b[0m, in \u001b[0;36m_asarray_with_order\u001b[0;34m(array, dtype, order, copy, xp)\u001b[0m\n\u001b[1;32m    183\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m xp\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m \u001b[38;5;129;01min\u001b[39;00m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnumpy\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnumpy.array_api\u001b[39m\u001b[38;5;124m\"\u001b[39m}:\n\u001b[1;32m    184\u001b[0m     \u001b[38;5;66;03m# Use NumPy API to support order\u001b[39;00m\n\u001b[0;32m--> 185\u001b[0m     array \u001b[38;5;241m=\u001b[39m \u001b[43mnumpy\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43masarray\u001b[49m\u001b[43m(\u001b[49m\u001b[43marray\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morder\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43morder\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    186\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m xp\u001b[38;5;241m.\u001b[39masarray(array, copy\u001b[38;5;241m=\u001b[39mcopy)\n",
      "\u001b[0;31mComplexWarning\u001b[0m: Casting complex values to real discards the imaginary part",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[17], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mresult\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/output/pykeen/lib/python3.10/site-packages/pykeen/pipeline/api.py:366\u001b[0m, in \u001b[0;36mPipelineResult.plot\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m    359\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Plot all plots.\u001b[39;00m\n\u001b[1;32m    360\u001b[0m \n\u001b[1;32m    361\u001b[0m \u001b[38;5;124;03m:param kwargs: The keyword arguments passed to :func:`pykeen.pipeline_plot.plot`\u001b[39;00m\n\u001b[1;32m    362\u001b[0m \u001b[38;5;124;03m:returns: The axis\u001b[39;00m\n\u001b[1;32m    363\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    364\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mplot_utils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m plot\n\u001b[0;32m--> 366\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/output/pykeen/lib/python3.10/site-packages/pykeen/pipeline/plot_utils.py:305\u001b[0m, in \u001b[0;36mplot\u001b[0;34m(pipeline_result, er_kwargs, figsize)\u001b[0m\n\u001b[1;32m    302\u001b[0m fig, axes \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39msubplots(\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m2\u001b[39m, figsize\u001b[38;5;241m=\u001b[39mfigsize)\n\u001b[1;32m    304\u001b[0m pipeline_result\u001b[38;5;241m.\u001b[39mplot_losses(ax\u001b[38;5;241m=\u001b[39maxes[\u001b[38;5;241m0\u001b[39m])\n\u001b[0;32m--> 305\u001b[0m \u001b[43mpipeline_result\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot_er\u001b[49m\u001b[43m(\u001b[49m\u001b[43max\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxes\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mer_kwargs\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    307\u001b[0m plt\u001b[38;5;241m.\u001b[39mtight_layout()\n\u001b[1;32m    308\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m fig, axes\n",
      "File \u001b[0;32m/output/pykeen/lib/python3.10/site-packages/pykeen/pipeline/api.py:356\u001b[0m, in \u001b[0;36mPipelineResult.plot_er\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m    344\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Plot the reduced entities and relation vectors in 2D.\u001b[39;00m\n\u001b[1;32m    345\u001b[0m \n\u001b[1;32m    346\u001b[0m \u001b[38;5;124;03m:param kwargs: The keyword arguments passed to :func:`pykeen.pipeline.plot_utils.plot_er`\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    352\u001b[0m \u001b[38;5;124;03m    meaningful for translational distance models like TransE.\u001b[39;00m\n\u001b[1;32m    353\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    354\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mplot_utils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m plot_er\n\u001b[0;32m--> 356\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mplot_er\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/output/pykeen/lib/python3.10/site-packages/pykeen/pipeline/plot_utils.py:189\u001b[0m, in \u001b[0;36mplot_er\u001b[0;34m(pipeline_result, model, entities, relations, apply_limits, margin, plot_entities, plot_relations, annotation_x_offset, annotation_y_offset, entity_embedding_getter, relation_embedding_getter, ax, subtitle, **kwargs)\u001b[0m\n\u001b[1;32m    187\u001b[0m     ymin \u001b[38;5;241m=\u001b[39m r_embeddings[:, \u001b[38;5;241m1\u001b[39m]\u001b[38;5;241m.\u001b[39mmin() \u001b[38;5;241m-\u001b[39m margin\n\u001b[1;32m    188\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m plot_entities:\n\u001b[0;32m--> 189\u001b[0m     e_embeddings, e_reduced \u001b[38;5;241m=\u001b[39m \u001b[43m_reduce_embeddings\u001b[49m\u001b[43m(\u001b[49m\u001b[43mentity_embedding_getter\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpipeline_result\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreducer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfit\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m    190\u001b[0m     r_embeddings, r_reduced \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m    192\u001b[0m     xmax \u001b[38;5;241m=\u001b[39m e_embeddings[:, \u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mmax() \u001b[38;5;241m+\u001b[39m margin\n",
      "File \u001b[0;32m/output/pykeen/lib/python3.10/site-packages/pykeen/pipeline/plot_utils.py:258\u001b[0m, in \u001b[0;36m_reduce_embeddings\u001b[0;34m(embedding, reducer, fit)\u001b[0m\n\u001b[1;32m    256\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m embeddings_numpy, \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m    257\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m fit:\n\u001b[0;32m--> 258\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mreducer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit_transform\u001b[49m\u001b[43m(\u001b[49m\u001b[43membeddings_numpy\u001b[49m\u001b[43m)\u001b[49m, \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m    259\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    260\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m reducer\u001b[38;5;241m.\u001b[39mtransform(embeddings_numpy), \u001b[38;5;28;01mTrue\u001b[39;00m\n",
      "File \u001b[0;32m/output/pykeen/lib/python3.10/site-packages/sklearn/utils/_set_output.py:142\u001b[0m, in \u001b[0;36m_wrap_method_output.<locals>.wrapped\u001b[0;34m(self, X, *args, **kwargs)\u001b[0m\n\u001b[1;32m    140\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(f)\n\u001b[1;32m    141\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapped\u001b[39m(\u001b[38;5;28mself\u001b[39m, X, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m--> 142\u001b[0m     data_to_wrap \u001b[38;5;241m=\u001b[39m \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    143\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data_to_wrap, \u001b[38;5;28mtuple\u001b[39m):\n\u001b[1;32m    144\u001b[0m         \u001b[38;5;66;03m# only wrap the first output for cross decomposition\u001b[39;00m\n\u001b[1;32m    145\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m (\n\u001b[1;32m    146\u001b[0m             _wrap_data_with_container(method, data_to_wrap[\u001b[38;5;241m0\u001b[39m], X, \u001b[38;5;28mself\u001b[39m),\n\u001b[1;32m    147\u001b[0m             \u001b[38;5;241m*\u001b[39mdata_to_wrap[\u001b[38;5;241m1\u001b[39m:],\n\u001b[1;32m    148\u001b[0m         )\n",
      "File \u001b[0;32m/output/pykeen/lib/python3.10/site-packages/sklearn/decomposition/_pca.py:462\u001b[0m, in \u001b[0;36mPCA.fit_transform\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m    439\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Fit the model with X and apply the dimensionality reduction on X.\u001b[39;00m\n\u001b[1;32m    440\u001b[0m \n\u001b[1;32m    441\u001b[0m \u001b[38;5;124;03mParameters\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    458\u001b[0m \u001b[38;5;124;03mC-ordered array, use 'np.ascontiguousarray'.\u001b[39;00m\n\u001b[1;32m    459\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    460\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_params()\n\u001b[0;32m--> 462\u001b[0m U, S, Vt \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    463\u001b[0m U \u001b[38;5;241m=\u001b[39m U[:, : \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_components_]\n\u001b[1;32m    465\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mwhiten:\n\u001b[1;32m    466\u001b[0m     \u001b[38;5;66;03m# X_new = X * V / S * sqrt(n_samples) = U * sqrt(n_samples)\u001b[39;00m\n",
      "File \u001b[0;32m/output/pykeen/lib/python3.10/site-packages/sklearn/decomposition/_pca.py:485\u001b[0m, in \u001b[0;36mPCA._fit\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    479\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m issparse(X):\n\u001b[1;32m    480\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[1;32m    481\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPCA does not support sparse input. See \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    482\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTruncatedSVD for a possible alternative.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    483\u001b[0m     )\n\u001b[0;32m--> 485\u001b[0m X \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_data\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    486\u001b[0m \u001b[43m    \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfloat64\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfloat32\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mensure_2d\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcopy\u001b[49m\n\u001b[1;32m    487\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    489\u001b[0m \u001b[38;5;66;03m# Handle n_components==None\u001b[39;00m\n\u001b[1;32m    490\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_components \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[0;32m/output/pykeen/lib/python3.10/site-packages/sklearn/base.py:546\u001b[0m, in \u001b[0;36mBaseEstimator._validate_data\u001b[0;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[1;32m    544\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mValidation should be done on X, y or both.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    545\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m no_val_X \u001b[38;5;129;01mand\u001b[39;00m no_val_y:\n\u001b[0;32m--> 546\u001b[0m     X \u001b[38;5;241m=\u001b[39m \u001b[43mcheck_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minput_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mX\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mcheck_params\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    547\u001b[0m     out \u001b[38;5;241m=\u001b[39m X\n\u001b[1;32m    548\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m no_val_X \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m no_val_y:\n",
      "File \u001b[0;32m/output/pykeen/lib/python3.10/site-packages/sklearn/utils/validation.py:881\u001b[0m, in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)\u001b[0m\n\u001b[1;32m    879\u001b[0m             array \u001b[38;5;241m=\u001b[39m _asarray_with_order(array, order\u001b[38;5;241m=\u001b[39morder, dtype\u001b[38;5;241m=\u001b[39mdtype, xp\u001b[38;5;241m=\u001b[39mxp)\n\u001b[1;32m    880\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m ComplexWarning \u001b[38;5;28;01mas\u001b[39;00m complex_warning:\n\u001b[0;32m--> 881\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    882\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mComplex data not supported\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(array)\n\u001b[1;32m    883\u001b[0m         ) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcomplex_warning\u001b[39;00m\n\u001b[1;32m    885\u001b[0m \u001b[38;5;66;03m# It is possible that the np.array(..) gave no warning. This happens\u001b[39;00m\n\u001b[1;32m    886\u001b[0m \u001b[38;5;66;03m# when no dtype conversion happened, for example dtype = None. The\u001b[39;00m\n\u001b[1;32m    887\u001b[0m \u001b[38;5;66;03m# result is that np.array(..) produces an array of complex dtype\u001b[39;00m\n\u001b[1;32m    888\u001b[0m \u001b[38;5;66;03m# and we need to catch and raise exception for such cases.\u001b[39;00m\n\u001b[1;32m    889\u001b[0m _ensure_no_complex_data(array)\n",
      "\u001b[0;31mValueError\u001b[0m: Complex data not supported\n[[ 0.3781345 -3.6084655e-01j  0.09390053+4.3499310e-02j\n  -1.0443127 -3.2956937e-01j ... -0.20330974+1.0654817e+00j\n  -0.20248395-3.9709845e-01j  0.7661117 +1.1174473e-03j]\n [ 0.7572415 +8.4394449e-01j -1.1377153 +6.7232960e-01j\n  -2.2249918 -1.3278307e-01j ...  0.04713264+7.2618312e-01j\n   0.87000316+4.8082000e-01j -1.635183  +6.6057360e-01j]\n [-0.82199085+1.2415135e-01j  0.21124898-9.1059297e-02j\n  -1.6013912 -5.3352004e-01j ... -0.08804699-9.4887815e-02j\n  -0.54006475-1.0844716e+00j  1.755571  -1.3291166e+00j]\n ...\n [ 0.7884725 +1.9453542e-01j  1.2218179 +7.5266087e-01j\n  -1.5205196 -7.5470078e-01j ... -0.3012225 -2.4313221e-02j\n   2.006678  +7.2385943e-01j -1.6055052 -9.8005724e-01j]\n [ 0.2621836 +1.6384976e+00j  0.49529266+2.5210388e-02j\n  -1.4453493 -6.9120735e-01j ... -0.0028849 -7.2415091e-02j\n  -0.04615272-1.9769661e-01j  2.1367304 +1.4294608e-01j]\n [ 0.29790193+2.3023668e-01j -0.50511813+1.2130161e-01j\n  -1.3373092 -9.6548563e-01j ...  0.93450856-4.4077992e-01j\n  -1.2920572 +4.2834905e-01j -0.13854231-1.3528819e-01j]]\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "38bdadc2-2061-4656-a466-ddc3ef4b4d6c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-25T08:40:13.881179Z",
     "iopub.status.busy": "2023-01-25T08:40:13.880959Z",
     "iopub.status.idle": "2023-01-25T08:40:13.891393Z",
     "shell.execute_reply": "2023-01-25T08:40:13.890807Z",
     "shell.execute_reply.started": "2023-01-25T08:40:13.881161Z"
    }
   },
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'matplotlib'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[44], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'matplotlib'"
     ]
    }
   ],
   "source": [
    "import matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "9e9f1f28-1806-4ccb-aa58-6d268c9604df",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-25T09:02:47.545742Z",
     "iopub.status.busy": "2023-01-25T09:02:47.545316Z",
     "iopub.status.idle": "2023-01-25T09:02:47.561834Z",
     "shell.execute_reply": "2023-01-25T09:02:47.561186Z",
     "shell.execute_reply.started": "2023-01-25T09:02:47.545710Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'TriplesFactory' object has no attribute 'get_entities'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[76], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mtraining\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_entities\u001b[49m()\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'TriplesFactory' object has no attribute 'get_entities'"
     ]
    }
   ],
   "source": [
    "training.get_entities()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "acecf5d1-74ee-43c9-b62b-8cc827a75bc6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-25T09:32:17.775113Z",
     "iopub.status.busy": "2023-01-25T09:32:17.774762Z",
     "iopub.status.idle": "2023-01-25T09:32:17.799139Z",
     "shell.execute_reply": "2023-01-25T09:32:17.798265Z",
     "shell.execute_reply.started": "2023-01-25T09:32:17.775082Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "30648d31-272e-411e-a223-b8098ffc891d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-25T09:32:19.207805Z",
     "iopub.status.busy": "2023-01-25T09:32:19.207413Z",
     "iopub.status.idle": "2023-01-25T09:32:19.213722Z",
     "shell.execute_reply": "2023-01-25T09:32:19.213052Z",
     "shell.execute_reply.started": "2023-01-25T09:32:19.207768Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BoxE(\n",
       "  (loss): NSSALoss()\n",
       "  (interaction): BoxEInteraction()\n",
       "  (entity_representations): ModuleList(\n",
       "    (0): Embedding(\n",
       "      (_embeddings): Embedding(2946, 64)\n",
       "    )\n",
       "    (1): Embedding(\n",
       "      (_embeddings): Embedding(2946, 64)\n",
       "    )\n",
       "  )\n",
       "  (relation_representations): ModuleList(\n",
       "    (0): Embedding(\n",
       "      (_embeddings): Embedding(12, 64)\n",
       "    )\n",
       "    (1): Embedding(\n",
       "      (_embeddings): Embedding(12, 64)\n",
       "    )\n",
       "    (2): Embedding(\n",
       "      (_embeddings): Embedding(12, 1)\n",
       "    )\n",
       "    (3): Embedding(\n",
       "      (_embeddings): Embedding(12, 64)\n",
       "    )\n",
       "    (4): Embedding(\n",
       "      (_embeddings): Embedding(12, 64)\n",
       "    )\n",
       "    (5): Embedding(\n",
       "      (_embeddings): Embedding(12, 1)\n",
       "    )\n",
       "  )\n",
       "  (weight_regularizers): ModuleList()\n",
       ")"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9d8664eb-833a-4a46-9a51-ff0f04bca423",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-25T09:29:52.082772Z",
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      "text/plain": [
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      ]
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     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
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