Embeddings_OMIM2ORDO FinetuneSbert.ipynb

{"cells":[{"cell_type":"markdown","metadata":{"id":"TN1XndwXxLGI"},"source":["#Install Transformers"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":40385,"status":"ok","timestamp":1690492081503,"user":{"displayName":"Netra Kulkarni","userId":"06886571954059412853"},"user_tz":-60},"id":"qs7Y8fH9L4wf","outputId":"5356bf02-333d-4a9e-841e-3549f8279a58"},"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting datasets\n","  Downloading datasets-2.14.1-py3-none-any.whl (492 kB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m492.4/492.4 kB\u001b[0m \u001b[31m4.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from datasets) (1.22.4)\n","Requirement already satisfied: pyarrow>=8.0.0 in /usr/local/lib/python3.10/dist-packages (from datasets) 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Kulkarni","userId":"06886571954059412853"},"user_tz":-60},"id":"VqIu7Fh42NSP","outputId":"c54ac203-72e7-45c8-b323-e2f2cd33a45b"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}],"source":["from google.colab import drive\n","drive.mount('/content/drive')"]},{"cell_type":"markdown","metadata":{"id":"RR9taWXGxUBc"},"source":["#Import libraries"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"3Enbw7k6LPar"},"outputs":[],"source":["from torch.utils.data import DataLoader\n","import math\n","from sentence_transformers import SentenceTransformer, LoggingHandler, losses, util, InputExample\n","from sentence_transformers.evaluation import EmbeddingSimilarityEvaluator\n","import logging\n","from datetime import datetime\n","import os\n","import gzip\n","import csv\n","from datasets import load_dataset\n","import pandas as pd\n","import numpy as np"]},{"cell_type":"code","source":["# Parameter Settings\n","base_model_name = 'FremyCompany/BioLORD-STAMB2-v1'\n","train_batch_size = 16\n","num_epochs = 10\n","#model_save_path = '/content/drive/MyDrive/Thesis/output/finetuneSbert-'+model_name+'-'+datetime.now().strftime(\"%Y-%m-%d_%H-%M-%S\")\n","model_save_path = '/content/drive/MyDrive/Thesis/OMIM2ORDO/output/finetuneSbert-'+base_model_name"],"metadata":{"id":"6Gmgt2DHb6Yg"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"6dzqs0p2049Z"},"source":["#Loading Ontology Data Files for embedding"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4420,"status":"ok","timestamp":1690492172289,"user":{"displayName":"Netra Kulkarni","userId":"06886571954059412853"},"user_tz":-60},"id":"pYkKE1CYILXG","outputId":"36049b26-3bc8-4a91-e196-68246d94a2e1"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["((9642, 2),\n"," (9755, 2),\n"," (8838, 2),\n"," (17150, 2),\n"," (9755, 2),\n"," (17150, 2),\n"," (9642, 2),\n"," (8838, 2))"]},"metadata":{},"execution_count":5}],"source":["sourceOntBL = pd.read_csv('/content/drive/MyDrive/Thesis/OMIM2ORDO/sourceOntBL.csv', header=None)\n","sourceOntBL.rename(columns={0: \"entity\", 1: \"text\"}, inplace=True)\n","sourceOntKI = pd.read_csv('/content/drive/MyDrive/Thesis/OMIM2ORDO/sourceOntKI.csv', header=None)\n","sourceOntKI.rename(columns={0: \"entity\", 1: \"text\"}, inplace=True)\n","targetOntBL = pd.read_csv('/content/drive/MyDrive/Thesis/OMIM2ORDO/targetOntBL.csv', header=None)\n","targetOntBL.rename(columns={0: \"entity\", 1: \"text\"}, inplace=True)\n","targetOntKI = pd.read_csv('/content/drive/MyDrive/Thesis/OMIM2ORDO/targetOntKI.csv', header=None)\n","targetOntKI.rename(columns={0: \"entity\", 1: \"text\"}, inplace=True)\n","sourceOntKS = pd.read_csv('/content/drive/MyDrive/Thesis/OMIM2ORDO/sourceOntKISym.csv', header=None)\n","sourceOntKS.rename(columns={0: \"entity\", 1: \"text\"}, inplace=True)\n","targetOntKS = pd.read_csv('/content/drive/MyDrive/Thesis/OMIM2ORDO/targetOntKISym.csv', header=None)\n","targetOntKS.rename(columns={0: \"entity\", 1: \"text\"}, inplace=True)\n","sourceOntKJ = pd.read_csv('/content/drive/MyDrive/Thesis/OMIM2ORDO/sourceOntKIJoin.csv', header=None)\n","sourceOntKJ.rename(columns={0: \"entity\", 1: \"text\"}, inplace=True)\n","targetOntKJ = pd.read_csv('/content/drive/MyDrive/Thesis/OMIM2ORDO/targetOntKIJoin.csv', header=None)\n","targetOntKJ.rename(columns={0: \"entity\", 1: \"text\"}, inplace=True)\n","\n","sourceOntBL.shape, sourceOntKI.shape, targetOntBL.shape, targetOntKI.shape, sourceOntKS.shape, targetOntKS.shape, sourceOntKJ.shape, targetOntKJ.shape"]},{"cell_type":"markdown","metadata":{"id":"1HbKvXMbw2M1"},"source":["#Load the Baseline Model - Model = BL"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":414,"referenced_widgets":["955f63809fa74b6f988ddbe06c9e41f1","f54feb3fb2eb44d0a91525e7f24fa49b","b0481b6c350c45f7848f4fe05db4a563","eaea494ea7ad4aafb21353f465aeb0c3","afa3253baf2145938a653e9ebb3a5670","a503d684470b44d092f60b59d3351baf","ad9602ba4fe54e738b619dae56f882f7","6c833d72280b49e89fedb3c5d3484440","8ca72fc72ffc4559b08dc6ee0b2d9080","4ca579de28f149c9b3456760d29f1696","919591a5e5f0452b9844435515308a2c","311b911bb98644269b4b7f9244e31727","79e6acbc647646a5849b7eec6374d92e","1324807a93654be184c2ba5adaea8add","ff8ff0f74a3744d396302e4ef9250c38","951524b8c73c4da99877b745f4c4f886","7993c50607c64e18a5142aae0d125b6b","147b5caedeeb45c181eb3786c3a1f1d2","f17bdc91b74444b1aa337035b92cecca","f95c8203138c4890a8352edf7460e437","81b06b9338cb45d6985f11006765ffa3","4b423b0306e44799aa6fe8db75083b69","9e255ee1783949eb8f5e96881ab67e1a","f51d605f15ac48fcafe2afc9f7e76042","b97ca5da2ead4891a38ef38bfda16308","b6028fc0cdb7458e806d04b46e78c444","0f6fd316c26b4b369c4fb9e46806ee0d","2a239e8bc7144a2fa6624e4c1965cf75","c5f4232409154e3ea8501badbebc3d01","bfef2944105c47bab2f48aaf4592a95f","9c28d8ae0a80424199058df3690e588f","33443df21336440bb2a4d6d13e5732db","6f7a79dc8647493489b7b4322d32ab44","0385a80251f7459199fbf24c455592fa","01df77cfc3ad47f0ae185c36fb248b65","c9431ce775f44578a82fcfd3103dcebf","502cbbbc28ad46f1884af0442c5da1cd","a1b94b01bbbd4201b05d3ac48d69b62e","820e69ec831c4b4ea30c5be9cfaaf54c","e43951b72e2f49a2a19ebae2ba6d1c10","0374e94bc18544fa9027933f47e8f95c","fcfc1e12e40d45758fd543a75cd4db0d","bc6b5e430b89447c857f19cd45c63f7b","d6878bf4a0194514b0aed9af5522c0f9","39e8894d59d74cf296647e274da0d651","d734dd17a0e84fadac06b4987f4dca86","9d32cc1d72284215b2a5669f4872f181","f26799c607f44e44810fdbef745e8af0","5626e5d57a914060b38ed375f99c57d6","205924d478c043a1aa7f4766de98d705","e0aaea2d26074a66992806d148cc1282","d64800c38f9d487199efcaae73a13cad","d29383d973f94716a3878e643c7901d4","df5975318484442da63894719e480869","52dc373466fc4c81bcd6f8c965f65cac","62c8383878ab4d079e258ce26fdd0dab","6c4efd7a09bc48e5986f3855bde9575a","e2c6552fb68f45ca96953655f45ca7dd","5cde284595a741aa8f3db81869d502d0","d6a6f3d86d6c4de88eb8dfb04518d2f3","91de492354a8400182af249a13de33f9","9dc3165a8f2c4d8b827b0cda24fd9ed9","95bfe02a019f442e8e261aeffd3dc25d","adf6e2e78ae74727adf2ebde9ffafba8","ac2ca1ad6c0b46c4b42d5b53f71dea1d","ce3e4c9bcf1d4d6f8d9540219c1da28b","8b93ad4d7f01403d9e661e1e6acf2017","66d1e0cf43a44925bc1d32d41e4e2484","d32802e2341f4c7a88f6e7dd668a1ea2","60c47c1484ef40d186510d0854225123","8205001fe17a4c91b985965dbf45109d","fd256bdee73142689611396945f0c4c1","af8cb9cb6e094411ab41c28369564f0a","e6a0ada3fd69407ebd29b7f5a200ac2e","b4e8920bfeb34e7e876bbc5e3115e52b","827321fdfdf94f4aaae850733df54376","e7cb03b69fee4f34b84990b81cb7ac57","787d176ead3948e6aca6374f9bf6ad53","9f1f7331b9734066a7a59d529df23c58","a0e274099fb642d886c6867f9246cb10","39b98a8b1c8b421d8664a8245fdddb79","36af48c6235c4c88bbdf860ba493696d","239e956d40194b56be98bf216493f71a","b2c56f8f5b48425fb26fa322ad47460b","5dc71b22978e482ebc7651f187096236","7adff1a354a145c0a723638733c04b9b","47ef0a39de504a15882881214a212a2c","6287de206499482797a676e332edc647","6ce3ff5954ec48bfb8ee3c9108add76e","b57b6620a2844d7296010396b37209a5","5dd78c8fe0054844b55cfe801afc4b8f","fe8a8c43e33f4bd68320a2647ded5af3","1e6466f5cb884f329be7c11b3077ebb8","196b54f96b9844ed94f9e6010324270a","13194394a4c34e44bda49bad880bc01d","f27a6165a3024f3c8313a63c7242d9d1","8f858ff2ef934bb48944947f47e1dcc2","fd38aa64e9ff457e92b2ddca15be70d6","508454b15b104f1db29ebce502f7996a"]},"executionInfo":{"elapsed":9184,"status":"ok","timestamp":1690482227590,"user":{"displayName":"Netra Kulkarni","userId":"06886571954059412853"},"user_tz":-60},"id":"Y_b00p_3w1R3","outputId":"4eff2a15-fe66-484f-f48f-0dcd4c0e08c2"},"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading (…)e6e96/.gitattributes:   0%|          | 0.00/1.43k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"955f63809fa74b6f988ddbe06c9e41f1"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)f5ea6e6e96/README.md:   0%|          | 0.00/6.80k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"311b911bb98644269b4b7f9244e31727"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)ea6e6e96/config.json:   0%|          | 0.00/693 [00:00<?, 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?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"6ce3ff5954ec48bfb8ee3c9108add76e"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["WARNING:sentence_transformers.SentenceTransformer:No sentence-transformers model found with name /root/.cache/torch/sentence_transformers/FremyCompany_BioLORD-STAMB2-v1. Creating a new one with MEAN pooling.\n"]},{"output_type":"stream","name":"stdout","text":["SentenceTransformer(\n","  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel \n","  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})\n",")\n"]}],"source":["model_name = 'FremyCompany/BioLORD-STAMB2-v1'\n","modelBL = SentenceTransformer(model_name)\n","#embeddings = model.encode(sentences)\n","print(modelBL)"]},{"cell_type":"markdown","metadata":{"id":"T-pqn2UbxaO_"},"source":["#Embeddings using BL Model, OntFiles - BL, KI, KS\n","\n","BLBL, BLKI, BLKS"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":53},"executionInfo":{"elapsed":25177,"status":"ok","timestamp":1690482258649,"user":{"displayName":"Netra Kulkarni","userId":"06886571954059412853"},"user_tz":-60},"id":"sYM8ysrVxfKO","outputId":"999076a5-624b-476f-b3c6-f90e6f0784a3"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["'embeddingsSrcBLKI = modelBL.encode(sourceOntKI[\"text\"])\\nembeddingsTgtBLKI = modelBL.encode(targetOntKI[\"text\"])\\nembeddingsSrcBLKS = modelBL.encode(sourceOntKS[\"text\"])\\nembeddingsTgtBLKS = modelBL.encode(targetOntKS[\"text\"])'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":7}],"source":["embeddingsSrcBLBL = modelBL.encode(sourceOntBL[\"text\"])\n","embeddingsTgtBLBL = modelBL.encode(targetOntBL[\"text\"])\n","\"\"\"embeddingsSrcBLKI = modelBL.encode(sourceOntKI[\"text\"])\n","embeddingsTgtBLKI = modelBL.encode(targetOntKI[\"text\"])\n","embeddingsSrcBLKS = modelBL.encode(sourceOntKS[\"text\"])\n","embeddingsTgtBLKS = modelBL.encode(targetOntKS[\"text\"])\"\"\"\n","\n","#sourceOntBL.shape, embeddingsSrcBLBL.shape, targetOntBL.shape, embeddingsTgtBLBL.shape, sourceOntKS.shape, embeddingsSrcBLKI.shape, targetOntKS.shape, embeddingsTgtBLKI.shape"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"zwAPIlSMLzW8","colab":{"base_uri":"https://localhost:8080/","height":143},"executionInfo":{"status":"ok","timestamp":1690482286981,"user_tz":-60,"elapsed":18471,"user":{"displayName":"Netra Kulkarni","userId":"06886571954059412853"}},"outputId":"63508d82-e93e-481b-80a4-0da682ce927b"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["\"srcBLKI = pd.DataFrame()\\nsrcBLKI['entity-name'] = pd.DataFrame(sourceOntKI).apply(lambda row: '«'.join(row.values.astype(str)), axis=1)\\nsrcBLKI['vector'] = pd.DataFrame(embeddingsSrcBLKI).apply(lambda row: ','.join(row.values.astype(str)), axis=1)\\nsrcBLKI['combined'] = pd.DataFrame(srcBLKI).apply(lambda row: '|['.join(row.values.astype(str)), axis=1)+ ']'\\n\\ntgtBLKI = pd.DataFrame()\\ntgtBLKI['entity-name'] = pd.DataFrame(targetOntKI).apply(lambda row: '«'.join(row.values.astype(str)), axis=1)\\ntgtBLKI['vector'] = pd.DataFrame(embeddingsTgtBLKI).apply(lambda row: ','.join(row.values.astype(str)), axis=1)\\ntgtBLKI['combined'] = pd.DataFrame(tgtBLKI).apply(lambda row: '|['.join(row.values.astype(str)), axis=1)+ ']'\\n\\nsrcBLKS = pd.DataFrame()\\nsrcBLKS['entity-name'] = pd.DataFrame(sourceOntKI).apply(lambda row: '«'.join(row.values.astype(str)), axis=1)\\nsrcBLKS['vector'] = pd.DataFrame(embeddingsSrcBLKS).apply(lambda row: ','.join(row.values.astype(str)), axis=1)\\nsrcBLKS['combined'] = pd.DataFrame(srcBLKS).apply(lambda row: '|['.join(row.values.astype(str)), axis=1)+ ']'\\n\\ntgtBLKS = pd.DataFrame()\\ntgtBLKS['entity-name'] = pd.DataFrame(targetOntKI).apply(lambda row: '«'.join(row.values.astype(str)), axis=1)\\ntgtBLKS['vector'] = pd.DataFrame(embeddingsTgtBLKS).apply(lambda row: ','.join(row.values.astype(str)), axis=1)\\ntgtBLKS['combined'] = pd.DataFrame(tgtBLKS).apply(lambda row: '|['.join(row.values.astype(str)), axis=1)+ ']'\""],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":8}],"source":["#\"Entity«Name|[vector]«Name|[vector] \\n\"\n","\n","srcBLBL = pd.DataFrame()\n","srcBLBL['entity-name'] = pd.DataFrame(sourceOntBL).apply(lambda row: '«'.join(row.values.astype(str)), axis=1)\n","srcBLBL['vector'] = pd.DataFrame(embeddingsSrcBLBL).apply(lambda row: ','.join(row.values.astype(str)), axis=1)\n","srcBLBL['combined'] = pd.DataFrame(srcBLBL).apply(lambda row: '|['.join(row.values.astype(str)), axis=1)+ ']'\n","\n","tgtBLBL = pd.DataFrame()\n","tgtBLBL['entity-name'] = pd.DataFrame(targetOntBL).apply(lambda row: '«'.join(row.values.astype(str)), axis=1)\n","tgtBLBL['vector'] = pd.DataFrame(embeddingsTgtBLBL).apply(lambda row: ','.join(row.values.astype(str)), axis=1)\n","tgtBLBL['combined'] = pd.DataFrame(tgtBLBL).apply(lambda row: '|['.join(row.values.astype(str)), axis=1)+ ']'\n","\n","\"\"\"srcBLKI = pd.DataFrame()\n","srcBLKI['entity-name'] = pd.DataFrame(sourceOntKI).apply(lambda row: '«'.join(row.values.astype(str)), axis=1)\n","srcBLKI['vector'] = pd.DataFrame(embeddingsSrcBLKI).apply(lambda row: ','.join(row.values.astype(str)), axis=1)\n","srcBLKI['combined'] = pd.DataFrame(srcBLKI).apply(lambda row: '|['.join(row.values.astype(str)), axis=1)+ ']'\n","\n","tgtBLKI = pd.DataFrame()\n","tgtBLKI['entity-name'] = pd.DataFrame(targetOntKI).apply(lambda row: '«'.join(row.values.astype(str)), axis=1)\n","tgtBLKI['vector'] = pd.DataFrame(embeddingsTgtBLKI).apply(lambda row: ','.join(row.values.astype(str)), axis=1)\n","tgtBLKI['combined'] = pd.DataFrame(tgtBLKI).apply(lambda row: '|['.join(row.values.astype(str)), axis=1)+ ']'\n","\n","srcBLKS = pd.DataFrame()\n","srcBLKS['entity-name'] = pd.DataFrame(sourceOntKI).apply(lambda row: '«'.join(row.values.astype(str)), axis=1)\n","srcBLKS['vector'] = pd.DataFrame(embeddingsSrcBLKS).apply(lambda row: ','.join(row.values.astype(str)), axis=1)\n","srcBLKS['combined'] = pd.DataFrame(srcBLKS).apply(lambda row: '|['.join(row.values.astype(str)), axis=1)+ ']'\n","\n","tgtBLKS = pd.DataFrame()\n","tgtBLKS['entity-name'] = pd.DataFrame(targetOntKI).apply(lambda row: '«'.join(row.values.astype(str)), axis=1)\n","tgtBLKS['vector'] = pd.DataFrame(embeddingsTgtBLKS).apply(lambda row: ','.join(row.values.astype(str)), axis=1)\n","tgtBLKS['combined'] = pd.DataFrame(tgtBLKS).apply(lambda row: '|['.join(row.values.astype(str)), axis=1)+ ']'\"\"\"\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"6L7M0Vm_T-S_","colab":{"base_uri":"https://localhost:8080/","height":89},"executionInfo":{"status":"ok","timestamp":1690482357934,"user_tz":-60,"elapsed":1951,"user":{"displayName":"Netra Kulkarni","userId":"06886571954059412853"}},"outputId":"a054918a-6ea3-4531-f07a-ea606965cd91"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["\"np.savetxt(r'/content/drive/MyDrive/Thesis/OMIM2ORDO/output/srcBLKI.txt', srcBLKI['combined'].values, fmt='%s')\\nnp.savetxt(r'/content/drive/MyDrive/Thesis/OMIM2ORDO/output/tgtBLKI.txt', tgtBLKI['combined'].values, fmt='%s')\\nnp.savetxt(r'/content/drive/MyDrive/Thesis/OMIM2ORDO/output/srcBLKS.txt', srcBLKS['combined'].values, fmt='%s')\\nnp.savetxt(r'/content/drive/MyDrive/Thesis/OMIM2ORDO/output/tgtBLKS.txt', tgtBLKS['combined'].values, fmt='%s')\""],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":9}],"source":["np.savetxt(r'/content/drive/MyDrive/Thesis/OMIM2ORDO/output/srcBLBL.txt', srcBLBL['combined'].values, fmt='%s')\n","np.savetxt(r'/content/drive/MyDrive/Thesis/OMIM2ORDO/output/tgtBLBL.txt', tgtBLBL['combined'].values, fmt='%s')\n","\"\"\"np.savetxt(r'/content/drive/MyDrive/Thesis/OMIM2ORDO/output/srcBLKI.txt', srcBLKI['combined'].values, fmt='%s')\n","np.savetxt(r'/content/drive/MyDrive/Thesis/OMIM2ORDO/output/tgtBLKI.txt', tgtBLKI['combined'].values, fmt='%s')\n","np.savetxt(r'/content/drive/MyDrive/Thesis/OMIM2ORDO/output/srcBLKS.txt', srcBLKS['combined'].values, fmt='%s')\n","np.savetxt(r'/content/drive/MyDrive/Thesis/OMIM2ORDO/output/tgtBLKS.txt', tgtBLKS['combined'].values, fmt='%s')\"\"\""]},{"cell_type":"markdown","metadata":{"id":"nokBeBsEvOoo"},"source":["#Load the Model = FT\n","\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"1S6LrFB32A3k"},"outputs":[],"source":["model_save_path = '/content/drive/MyDrive/Thesis/OMIM2ORDO/output/finetuneSbert-'+base_model_name+'FT'"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":13973,"status":"ok","timestamp":1690482378312,"user":{"displayName":"Netra Kulkarni","userId":"06886571954059412853"},"user_tz":-60},"id":"Xs9tfFx37ON0","outputId":"fc13430c-d62a-425c-df73-465ddef8be5f"},"outputs":[{"output_type":"stream","name":"stdout","text":["SentenceTransformer(\n","  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel \n","  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})\n",")\n"]}],"source":["modelFT = SentenceTransformer(model_save_path)\n","#embeddings = model.encode(sentences)\n","print(modelFT)"]},{"cell_type":"markdown","metadata":{"id":"koT46QzXgR8b"},"source":["#Embeddings using FT model, OntFiles - BL, KI, KS\n","\n","FTBL, FTKI, FTKS"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":53},"executionInfo":{"elapsed":18690,"status":"ok","timestamp":1690482423079,"user":{"displayName":"Netra Kulkarni","userId":"06886571954059412853"},"user_tz":-60},"id":"bWhlnZlRr8t6","outputId":"10c77c52-6329-4661-acbc-ca9979d01337"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["'embeddingsSrcFTKI = modelFT.encode(sourceOntKI[\"text\"])\\nembeddingsTgtFTKI = modelFT.encode(targetOntKI[\"text\"])\\nembeddingsSrcFTKS = modelFT.encode(sourceOntKS[\"text\"])\\nembeddingsTgtFTKS = modelFT.encode(targetOntKS[\"text\"])'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":12}],"source":["embeddingsSrcFTBL = modelFT.encode(sourceOntBL[\"text\"])\n","embeddingsTgtFTBL = modelFT.encode(targetOntBL[\"text\"])\n","\"\"\"embeddingsSrcFTKI = modelFT.encode(sourceOntKI[\"text\"])\n","embeddingsTgtFTKI = modelFT.encode(targetOntKI[\"text\"])\n","embeddingsSrcFTKS = modelFT.encode(sourceOntKS[\"text\"])\n","embeddingsTgtFTKS = modelFT.encode(targetOntKS[\"text\"])\"\"\"\n","#sourceOntBL.shape, embeddingsSrcFTBL.shape, targetOntBL.shape, embeddingsTgtFTBL.shape, sourceOntKI.shape, embeddingsSrcFTKI.shape, targetOntKI.shape, embeddingsTgtFTKI.shape, sourceOntKS.shape, embeddingsSrcFTKS.shape, targetOntKS.shape, embeddingsTgtFTKS.shape"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"12SyS7kvxsoZ","colab":{"base_uri":"https://localhost:8080/","height":143},"executionInfo":{"status":"ok","timestamp":1690482486973,"user_tz":-60,"elapsed":15661,"user":{"displayName":"Netra Kulkarni","userId":"06886571954059412853"}},"outputId":"be3aefd6-0efa-4bd8-ecba-642457c514a9"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["\"srcFTKI = pd.DataFrame()\\nsrcFTKI['entity-name'] = pd.DataFrame(sourceOntKI).apply(lambda row: '«'.join(row.values.astype(str)), axis=1)\\nsrcFTKI['vector'] = pd.DataFrame(embeddingsSrcFTKI).apply(lambda row: ','.join(row.values.astype(str)), axis=1)\\nsrcFTKI['combined'] = pd.DataFrame(srcFTKI).apply(lambda row: '|['.join(row.values.astype(str)), axis=1)+ ']'\\n\\ntgtFTKI = pd.DataFrame()\\ntgtFTKI['entity-name'] = pd.DataFrame(targetOntKI).apply(lambda row: '«'.join(row.values.astype(str)), axis=1)\\ntgtFTKI['vector'] = pd.DataFrame(embeddingsTgtFTKI).apply(lambda row: ','.join(row.values.astype(str)), axis=1)\\ntgtFTKI['combined'] = pd.DataFrame(tgtFTKI).apply(lambda row: '|['.join(row.values.astype(str)), axis=1)+ ']'\\n\\nsrcFTKS = pd.DataFrame()\\nsrcFTKS['entity-name'] = pd.DataFrame(sourceOntKS).apply(lambda row: '«'.join(row.values.astype(str)), axis=1)\\nsrcFTKS['vector'] = pd.DataFrame(embeddingsSrcFTKS).apply(lambda row: ','.join(row.values.astype(str)), axis=1)\\nsrcFTKS['combined'] = pd.DataFrame(srcFTKS).apply(lambda row: '|['.join(row.values.astype(str)), axis=1)+ ']'\\n\\ntgtFTKS = pd.DataFrame()\\ntgtFTKS['entity-name'] = pd.DataFrame(targetOntKS).apply(lambda row: '«'.join(row.values.astype(str)), axis=1)\\ntgtFTKS['vector'] = pd.DataFrame(embeddingsTgtFTKS).apply(lambda row: ','.join(row.values.astype(str)), axis=1)\\ntgtFTKS['combined'] = pd.DataFrame(tgtFTKS).apply(lambda row: '|['.join(row.values.astype(str)), axis=1)+ ']'\""],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":13}],"source":["#\"Entity«Name|[vector]«Name|[vector] \\n\"\n","\n","srcFTBL = pd.DataFrame()\n","srcFTBL['entity-name'] = pd.DataFrame(sourceOntBL).apply(lambda row: '«'.join(row.values.astype(str)), axis=1)\n","srcFTBL['vector'] = pd.DataFrame(embeddingsSrcFTBL).apply(lambda row: ','.join(row.values.astype(str)), axis=1)\n","srcFTBL['combined'] = pd.DataFrame(srcFTBL).apply(lambda row: '|['.join(row.values.astype(str)), axis=1)+ ']'\n","\n","tgtFTBL = pd.DataFrame()\n","tgtFTBL['entity-name'] = pd.DataFrame(targetOntBL).apply(lambda row: '«'.join(row.values.astype(str)), axis=1)\n","tgtFTBL['vector'] = pd.DataFrame(embeddingsTgtFTBL).apply(lambda row: ','.join(row.values.astype(str)), axis=1)\n","tgtFTBL['combined'] = pd.DataFrame(tgtFTBL).apply(lambda row: '|['.join(row.values.astype(str)), axis=1)+ ']'\n","\n","\"\"\"srcFTKI = pd.DataFrame()\n","srcFTKI['entity-name'] = pd.DataFrame(sourceOntKI).apply(lambda row: '«'.join(row.values.astype(str)), axis=1)\n","srcFTKI['vector'] = pd.DataFrame(embeddingsSrcFTKI).apply(lambda row: ','.join(row.values.astype(str)), axis=1)\n","srcFTKI['combined'] = pd.DataFrame(srcFTKI).apply(lambda row: '|['.join(row.values.astype(str)), axis=1)+ ']'\n","\n","tgtFTKI = pd.DataFrame()\n","tgtFTKI['entity-name'] = pd.DataFrame(targetOntKI).apply(lambda row: '«'.join(row.values.astype(str)), axis=1)\n","tgtFTKI['vector'] = pd.DataFrame(embeddingsTgtFTKI).apply(lambda row: ','.join(row.values.astype(str)), axis=1)\n","tgtFTKI['combined'] = pd.DataFrame(tgtFTKI).apply(lambda row: '|['.join(row.values.astype(str)), axis=1)+ ']'\n","\n","srcFTKS = pd.DataFrame()\n","srcFTKS['entity-name'] = pd.DataFrame(sourceOntKS).apply(lambda row: '«'.join(row.values.astype(str)), axis=1)\n","srcFTKS['vector'] = pd.DataFrame(embeddingsSrcFTKS).apply(lambda row: ','.join(row.values.astype(str)), axis=1)\n","srcFTKS['combined'] = pd.DataFrame(srcFTKS).apply(lambda row: '|['.join(row.values.astype(str)), axis=1)+ ']'\n","\n","tgtFTKS = pd.DataFrame()\n","tgtFTKS['entity-name'] = pd.DataFrame(targetOntKS).apply(lambda row: '«'.join(row.values.astype(str)), axis=1)\n","tgtFTKS['vector'] = pd.DataFrame(embeddingsTgtFTKS).apply(lambda row: ','.join(row.values.astype(str)), axis=1)\n","tgtFTKS['combined'] = pd.DataFrame(tgtFTKS).apply(lambda row: '|['.join(row.values.astype(str)), axis=1)+ ']'\"\"\""]},{"cell_type":"code","execution_count":null,"metadata":{"id":"-c5swDNZvSC2","colab":{"base_uri":"https://localhost:8080/","height":89},"executionInfo":{"status":"ok","timestamp":1690482494893,"user_tz":-60,"elapsed":1264,"user":{"displayName":"Netra Kulkarni","userId":"06886571954059412853"}},"outputId":"fc9b6bc8-8efe-45c7-fe59-8ba283fd1b0d"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["\"np.savetxt(r'/content/drive/MyDrive/Thesis/OMIM2ORDO/output/srcFTKI.txt', srcFTKI['combined'].values, fmt='%s')\\nnp.savetxt(r'/content/drive/MyDrive/Thesis/OMIM2ORDO/output/tgtFTKI.txt', tgtFTKI['combined'].values, fmt='%s')\\nnp.savetxt(r'/content/drive/MyDrive/Thesis/OMIM2ORDO/output/srcFTKS.txt', srcFTKS['combined'].values, fmt='%s')\\nnp.savetxt(r'/content/drive/MyDrive/Thesis/OMIM2ORDO/output/tgtFTKS.txt', tgtFTKS['combined'].values, fmt='%s')\""],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":14}],"source":["np.savetxt(r'/content/drive/MyDrive/Thesis/OMIM2ORDO/output/srcFTBL.txt', srcFTBL['combined'].values, fmt='%s')\n","np.savetxt(r'/content/drive/MyDrive/Thesis/OMIM2ORDO/output/tgtFTBL.txt', tgtFTBL['combined'].values, fmt='%s')\n","\"\"\"np.savetxt(r'/content/drive/MyDrive/Thesis/OMIM2ORDO/output/srcFTKI.txt', srcFTKI['combined'].values, fmt='%s')\n","np.savetxt(r'/content/drive/MyDrive/Thesis/OMIM2ORDO/output/tgtFTKI.txt', tgtFTKI['combined'].values, fmt='%s')\n","np.savetxt(r'/content/drive/MyDrive/Thesis/OMIM2ORDO/output/srcFTKS.txt', srcFTKS['combined'].values, fmt='%s')\n","np.savetxt(r'/content/drive/MyDrive/Thesis/OMIM2ORDO/output/tgtFTKS.txt', tgtFTKS['combined'].values, fmt='%s')\"\"\""]},{"cell_type":"markdown","metadata":{"id":"s3YjGUJMtx7w"},"source":["#Load the Model = FS"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"JGzUS939FLQZ"},"outputs":[],"source":["model_save_path = '/content/drive/MyDrive/Thesis/OMIM2ORDO/output/finetuneSbert-'+base_model_name +'FS'"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":12794,"status":"ok","timestamp":1690492193251,"user":{"displayName":"Netra Kulkarni","userId":"06886571954059412853"},"user_tz":-60},"id":"DgWRyID2t2gq","outputId":"ff75f19d-e09d-424b-b3aa-d5bc9d96bac0"},"outputs":[{"output_type":"stream","name":"stdout","text":["SentenceTransformer(\n","  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel \n","  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})\n",")\n"]}],"source":["modelFS = SentenceTransformer(model_save_path)\n","#embeddings = model.encode(sentences)\n","print(modelFS)"]},{"cell_type":"markdown","metadata":{"id":"5QzGDcFLpLjB"},"source":["#Embeddings using FS model, OntFiles - BL, KI, KS"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"L52ILt2PuE5N"},"outputs":[],"source":["\"\"\"embeddingsSrcFSBL = modelFS.encode(sourceOntBL[\"text\"])\n","embeddingsTgtFSBL = modelFS.encode(targetOntBL[\"text\"])\n","embeddingsSrcFSKI = modelFS.encode(sourceOntKI[\"text\"])\n","embeddingsTgtFSKI = modelFS.encode(targetOntKI[\"text\"])\"\"\"\n","embeddingsSrcFSKS = modelFS.encode(sourceOntKS[\"text\"])\n","embeddingsTgtFSKS = modelFS.encode(targetOntKS[\"text\"])\n","#sourceOntBL.shape, embeddingsSrcFSBL.shape, targetOntBL.shape, embeddingsTgtFSBL.shape, sourceOntKI.shape, embeddingsSrcFSKI.shape, targetOntKI.shape, embeddingsTgtFSKI.shape, sourceOntKS.shape, embeddingsSrcFSKS.shape, targetOntKS.shape, embeddingsTgtFSKS.shape"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"xGDY5LuMuobF"},"outputs":[],"source":["#\"Entity«Name|[vector]«Name|[vector] \\n\"\n","\n","\"\"\"srcFSBL = pd.DataFrame()\n","srcFSBL['entity-name'] = pd.DataFrame(sourceOntBL).apply(lambda row: '«'.join(row.values.astype(str)), axis=1)\n","srcFSBL['vector'] = pd.DataFrame(embeddingsSrcFSBL).apply(lambda row: ','.join(row.values.astype(str)), axis=1)\n","srcFSBL['combined'] = pd.DataFrame(srcFSBL).apply(lambda row: '|['.join(row.values.astype(str)), axis=1)+ ']'\n","\n","tgtFSBL = pd.DataFrame()\n","tgtFSBL['entity-name'] = pd.DataFrame(targetOntBL).apply(lambda row: '«'.join(row.values.astype(str)), axis=1)\n","tgtFSBL['vector'] = pd.DataFrame(embeddingsTgtFSBL).apply(lambda row: ','.join(row.values.astype(str)), axis=1)\n","tgtFSBL['combined'] = pd.DataFrame(tgtFSBL).apply(lambda row: '|['.join(row.values.astype(str)), axis=1)+ ']'\n","\n","srcFSKI = pd.DataFrame()\n","srcFSKI['entity-name'] = pd.DataFrame(sourceOntKI).apply(lambda row: '«'.join(row.values.astype(str)), axis=1)\n","srcFSKI['vector'] = pd.DataFrame(embeddingsSrcFSKI).apply(lambda row: ','.join(row.values.astype(str)), axis=1)\n","srcFSKI['combined'] = pd.DataFrame(srcFSKI).apply(lambda row: '|['.join(row.values.astype(str)), axis=1)+ ']'\n","\n","tgtFSKI = pd.DataFrame()\n","tgtFSKI['entity-name'] = pd.DataFrame(targetOntKI).apply(lambda row: '«'.join(row.values.astype(str)), axis=1)\n","tgtFSKI['vector'] = pd.DataFrame(embeddingsTgtFSKI).apply(lambda row: ','.join(row.values.astype(str)), axis=1)\n","tgtFSKI['combined'] = pd.DataFrame(tgtFSKI).apply(lambda row: '|['.join(row.values.astype(str)), axis=1)+ ']'\"\"\"\n","\n","srcFSKS = pd.DataFrame()\n","srcFSKS['entity-name'] = pd.DataFrame(sourceOntKS).apply(lambda row: '«'.join(row.values.astype(str)), axis=1)\n","srcFSKS['vector'] = pd.DataFrame(embeddingsSrcFSKS).apply(lambda row: ','.join(row.values.astype(str)), axis=1)\n","srcFSKS['combined'] = pd.DataFrame(srcFSKS).apply(lambda row: '|['.join(row.values.astype(str)), axis=1)+ ']'\n","\n","tgtFSKS = pd.DataFrame()\n","tgtFSKS['entity-name'] = pd.DataFrame(targetOntKS).apply(lambda row: '«'.join(row.values.astype(str)), axis=1)\n","tgtFSKS['vector'] = pd.DataFrame(embeddingsTgtFSKS).apply(lambda row: ','.join(row.values.astype(str)), axis=1)\n","tgtFSKS['combined'] = pd.DataFrame(tgtFSKS).apply(lambda row: '|['.join(row.values.astype(str)), axis=1)+ ']'\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"vdLuUqksvEMO"},"outputs":[],"source":["\"\"\"np.savetxt(r'/content/drive/MyDrive/Thesis/OMIM2ORDO/output/srcFSBL.txt', srcFSBL['combined'].values, fmt='%s')\n","np.savetxt(r'/content/drive/MyDrive/Thesis/OMIM2ORDO/output/tgtFSBL.txt', tgtFSBL['combined'].values, fmt='%s')\n","np.savetxt(r'/content/drive/MyDrive/Thesis/OMIM2ORDO/output/srcFSKI.txt', srcFSKI['combined'].values, fmt='%s')\n","np.savetxt(r'/content/drive/MyDrive/Thesis/OMIM2ORDO/output/tgtFSKI.txt', tgtFSKI['combined'].values, fmt='%s')\"\"\"\n","np.savetxt(r'/content/drive/MyDrive/Thesis/OMIM2ORDO/output/srcFSKS.txt', srcFSKS['combined'].values, fmt='%s')\n","np.savetxt(r'/content/drive/MyDrive/Thesis/OMIM2ORDO/output/tgtFSKS.txt', tgtFSKS['combined'].values, fmt='%s')"]},{"cell_type":"markdown","metadata":{"id":"d6R0eDMNcusT"},"source":["# Embeddings using FT model, OntFiles - KD\n","\n","FTKD (Knowledge injection KI WITHOUT Duplicates)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"p8To9yYIR6U4"},"outputs":[],"source":["# FTKD : FT + KI without duplicates [KD]\n","\n","model_save_path = '/content/drive/MyDrive/Thesis/OMIM2ORDO/output/finetuneSbert-'+base_model_name+'FT'\n","modelFT = SentenceTransformer(model_save_path)\n","#print(modelFT)\n","\n","sourceOntKD = pd.DataFrame()\n","targetOntKD = pd.DataFrame()\n","sourceOntKD[\"entity\"] = sourceOntKI[\"entity\"]\n","sourceOntKD[\"text\"] = sourceOntKI[\"text\"].apply(lambda row: (' '.join(dict.fromkeys(row.split()))).replace(\" :\",\"\"))\n","targetOntKD[\"entity\"] = targetOntKI[\"entity\"]\n","targetOntKD[\"text\"] = targetOntKI[\"text\"].apply(lambda row: (' '.join(dict.fromkeys(row.split()))).replace(\" :\",\"\"))\n","#sourceOntKD, sourceOntKI, targetOntKD, targetOntKI\n","\n","embeddingsSrcFTKD = modelFT.encode(sourceOntKD[\"text\"])\n","embeddingsTgtFTKD = modelFT.encode(targetOntKD[\"text\"])\n","\n","srcFTKD = pd.DataFrame()\n","srcFTKD['entity-name'] = pd.DataFrame(sourceOntKD).apply(lambda row: '«'.join(row.values.astype(str)), axis=1)\n","srcFTKD['vector'] = pd.DataFrame(embeddingsSrcFTKD).apply(lambda row: ','.join(row.values.astype(str)), axis=1)\n","srcFTKD['combined'] = pd.DataFrame(srcFTKD).apply(lambda row: '|['.join(row.values.astype(str)), axis=1)+ ']'\n","\n","tgtFTKD = pd.DataFrame()\n","tgtFTKD['entity-name'] = pd.DataFrame(targetOntKD).apply(lambda row: '«'.join(row.values.astype(str)), axis=1)\n","tgtFTKD['vector'] = pd.DataFrame(embeddingsTgtFTKD).apply(lambda row: ','.join(row.values.astype(str)), axis=1)\n","tgtFTKD['combined'] = pd.DataFrame(tgtFTKD).apply(lambda row: '|['.join(row.values.astype(str)), axis=1)+ ']'\n","\n","np.savetxt(r'/content/drive/MyDrive/Thesis/OMIM2ORDO/output/srcFTKD.txt', srcFTKD['combined'].values, fmt='%s')\n","np.savetxt(r'/content/drive/MyDrive/Thesis/OMIM2ORDO/output/tgtFTKD.txt', tgtFTKD['combined'].values, fmt='%s')\n","\n","\n"]},{"cell_type":"markdown","metadata":{"id":"xxZMCIRml3uu"},"source":["#FDKD"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"VU0b9uMHFsb9"},"outputs":[],"source":["# FDKD : FD + KI without duplicates [KD]\n","\n","model_save_path = '/content/drive/MyDrive/Thesis/OMIM2ORDO/output/finetuneSbert-'+base_model_name +'FD'\n","modelFD = SentenceTransformer(model_save_path)\n","#print(modelFT)\n","\n","sourceOntKD = pd.DataFrame()\n","targetOntKD = pd.DataFrame()\n","sourceOntKD[\"entity\"] = sourceOntKI[\"entity\"]\n","sourceOntKD[\"text\"] = sourceOntKI[\"text\"].apply(lambda row: (' '.join(dict.fromkeys(row.split()))).replace(\" :\",\"\"))\n","targetOntKD[\"entity\"] = targetOntKI[\"entity\"]\n","targetOntKD[\"text\"] = targetOntKI[\"text\"].apply(lambda row: (' '.join(dict.fromkeys(row.split()))).replace(\" :\",\"\"))\n","#sourceOntKD, sourceOntKI, targetOntKD, targetOntKI\n","\n","embeddingsSrcFDKD = modelFD.encode(sourceOntKD[\"text\"])\n","embeddingsTgtFDKD = modelFD.encode(targetOntKD[\"text\"])\n","\n","srcFDKD = pd.DataFrame()\n","srcFDKD['entity-name'] = pd.DataFrame(sourceOntKD).apply(lambda row: '«'.join(row.values.astype(str)), axis=1)\n","srcFDKD['vector'] = pd.DataFrame(embeddingsSrcFDKD).apply(lambda row: ','.join(row.values.astype(str)), axis=1)\n","srcFDKD['combined'] = pd.DataFrame(srcFDKD).apply(lambda row: '|['.join(row.values.astype(str)), axis=1)+ ']'\n","\n","tgtFDKD = pd.DataFrame()\n","tgtFDKD['entity-name'] = pd.DataFrame(targetOntKD).apply(lambda row: '«'.join(row.values.astype(str)), axis=1)\n","tgtFDKD['vector'] = pd.DataFrame(embeddingsTgtFDKD).apply(lambda row: ','.join(row.values.astype(str)), axis=1)\n","tgtFDKD['combined'] = pd.DataFrame(tgtFDKD).apply(lambda row: '|['.join(row.values.astype(str)), axis=1)+ ']'\n","\n","np.savetxt(r'/content/drive/MyDrive/Thesis/OMIM2ORDO/output/srcFDKD.txt', srcFDKD['combined'].values, fmt='%s')\n","np.savetxt(r'/content/drive/MyDrive/Thesis/OMIM2ORDO/output/tgtFDKD.txt', tgtFDKD['combined'].values, fmt='%s')\n","\n","\n"]},{"cell_type":"markdown","metadata":{"id":"oXEH1gTChIHT"},"source":["#FCKC\n","\n","Fine Tune Model = FC,\n","Training Files = KS (KS + colon replaced with comma),\n","OntFiles = (KS + colon replaced with comma)"]},{"cell_type":"code","source":["# FCKC : FC + KS + colon replaced with comma (KC)\n","\n","model_save_path = '/content/drive/MyDrive/Thesis/OMIM2ORDO/output/finetuneSbert-'+base_model_name +'FC'\n","modelFC = SentenceTransformer(model_save_path)\n","#print(modelFT)\n","\n","sourceOntKC = pd.DataFrame()\n","targetOntKC = pd.DataFrame()\n","sourceOntKC[\"entity\"] = sourceOntKS[\"entity\"]\n","sourceOntKC[\"text\"] = sourceOntKS[\"text\"].replace(\":\",\",\")\n","targetOntKC[\"entity\"] = targetOntKS[\"entity\"]\n","targetOntKC[\"text\"] = targetOntKS[\"text\"].replace(\":\",\",\")\n","#sourceOntKD, sourceOntKI, targetOntKD, targetOntKI\n","\n","embeddingsSrcFCKC = modelFC.encode(sourceOntKC[\"text\"])\n","embeddingsTgtFCKC = modelFC.encode(targetOntKC[\"text\"])\n","\n","srcFCKC = pd.DataFrame()\n","srcFCKC['entity-name'] = pd.DataFrame(sourceOntKC).apply(lambda row: '«'.join(row.values.astype(str)), axis=1)\n","srcFCKC['vector'] = pd.DataFrame(embeddingsSrcFCKC).apply(lambda row: ','.join(row.values.astype(str)), axis=1)\n","srcFCKC['combined'] = pd.DataFrame(srcFCKC).apply(lambda row: '|['.join(row.values.astype(str)), axis=1)+ ']'\n","\n","tgtFCKC = pd.DataFrame()\n","tgtFCKC['entity-name'] = pd.DataFrame(targetOntKC).apply(lambda row: '«'.join(row.values.astype(str)), axis=1)\n","tgtFCKC['vector'] = pd.DataFrame(embeddingsTgtFCKC).apply(lambda row: ','.join(row.values.astype(str)), axis=1)\n","tgtFCKC['combined'] = pd.DataFrame(tgtFCKC).apply(lambda row: '|['.join(row.values.astype(str)), axis=1)+ ']'\n","\n","np.savetxt(r'/content/drive/MyDrive/Thesis/OMIM2ORDO/output/srcFCKC.txt', srcFCKC['combined'].values, fmt='%s')\n","np.savetxt(r'/content/drive/MyDrive/Thesis/OMIM2ORDO/output/tgtFCKC.txt', tgtFCKC['combined'].values, fmt='%s')\n","\n","\n"],"metadata":{"id":"Mzgu31dV6yYz"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"7G-785c7Gz0Z"},"source":["#FSDKSD\n","\n","(KI with sym colon but no duplicates)"]},{"cell_type":"code","source":["# FSDKSD : FSD + KS without duplicates [KD]\n","\n","model_save_path = '/content/drive/MyDrive/Thesis/OMIM2ORDO/output/finetuneSbert-'+base_model_name +'FSD'\n","modelFSD = SentenceTransformer(model_save_path)\n","#print(modelFT)\n","\n","sourceOntKSD = pd.DataFrame()\n","targetOntKSD = pd.DataFrame()\n","sourceOntKSD[\"entity\"] = sourceOntKS[\"entity\"]\n","sourceOntKSD[\"text\"] = sourceOntKS[\"text\"].apply(lambda row: (' '.join(dict.fromkeys(row.split()))))\n","targetOntKSD[\"entity\"] = targetOntKS[\"entity\"]\n","targetOntKSD[\"text\"] = targetOntKS[\"text\"].apply(lambda row: (' '.join(dict.fromkeys(row.split()))))\n","#sourceOntKD, sourceOntKI, targetOntKD, targetOntKI\n","\n","embeddingsSrcFSDKSD = modelFSD.encode(sourceOntKSD[\"text\"])\n","embeddingsTgtFSDKSD = modelFSD.encode(targetOntKSD[\"text\"])\n","\n","srcFSDKSD = pd.DataFrame()\n","srcFSDKSD['entity-name'] = pd.DataFrame(sourceOntKSD).apply(lambda row: '«'.join(row.values.astype(str)), axis=1)\n","srcFSDKSD['vector'] = pd.DataFrame(embeddingsSrcFSDKSD).apply(lambda row: ','.join(row.values.astype(str)), axis=1)\n","srcFSDKSD['combined'] = pd.DataFrame(srcFSDKSD).apply(lambda row: '|['.join(row.values.astype(str)), axis=1)+ ']'\n","\n","tgtFSDKSD = pd.DataFrame()\n","tgtFSDKSD['entity-name'] = pd.DataFrame(targetOntKSD).apply(lambda row: '«'.join(row.values.astype(str)), axis=1)\n","tgtFSDKSD['vector'] = pd.DataFrame(embeddingsTgtFSDKSD).apply(lambda row: ','.join(row.values.astype(str)), axis=1)\n","tgtFSDKSD['combined'] = pd.DataFrame(tgtFSDKSD).apply(lambda row: '|['.join(row.values.astype(str)), axis=1)+ ']'\n","\n","np.savetxt(r'/content/drive/MyDrive/Thesis/OMIM2ORDO/output/srcFSDKSD.txt', srcFSDKSD['combined'].values, fmt='%s')\n","np.savetxt(r'/content/drive/MyDrive/Thesis/OMIM2ORDO/output/tgtFSDKSD.txt', tgtFSDKSD['combined'].values, fmt='%s')\n","\n","\n"],"metadata":{"id":"ViCKFnts87eq"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"Zh_y1sgyRm5_"},"source":["#FIKI"]},{"cell_type":"code","source":["# FIKI : FI + KI\n","\n","model_save_path = '/content/drive/MyDrive/Thesis/OMIM2ORDO/output/finetuneSbert-'+base_model_name +'FI'\n","modelFI = SentenceTransformer(model_save_path)\n","#print(modelFT)\n","\n","#sourceOntKD, sourceOntKI, targetOntKD, targetOntKI\n","\n","embeddingsSrcFIKI = modelFI.encode(sourceOntKI[\"text\"])\n","embeddingsTgtFIKI = modelFI.encode(targetOntKI[\"text\"])\n","\n","srcFIKI = pd.DataFrame()\n","srcFIKI['entity-name'] = pd.DataFrame(sourceOntKI).apply(lambda row: '«'.join(row.values.astype(str)), axis=1)\n","srcFIKI['vector'] = pd.DataFrame(embeddingsSrcFIKI).apply(lambda row: ','.join(row.values.astype(str)), axis=1)\n","srcFIKI['combined'] = pd.DataFrame(srcFIKI).apply(lambda row: '|['.join(row.values.astype(str)), axis=1)+ ']'\n","\n","tgtFIKI = pd.DataFrame()\n","tgtFIKI['entity-name'] = pd.DataFrame(targetOntKI).apply(lambda row: '«'.join(row.values.astype(str)), axis=1)\n","tgtFIKI['vector'] = pd.DataFrame(embeddingsTgtFIKI).apply(lambda row: ','.join(row.values.astype(str)), axis=1)\n","tgtFIKI['combined'] = pd.DataFrame(tgtFIKI).apply(lambda row: '|['.join(row.values.astype(str)), axis=1)+ ']'\n","\n","np.savetxt(r'/content/drive/MyDrive/Thesis/OMIM2ORDO/output/srcFIKI.txt', srcFIKI['combined'].values, fmt='%s')\n","np.savetxt(r'/content/drive/MyDrive/Thesis/OMIM2ORDO/output/tgtFIKI.txt', tgtFIKI['combined'].values, fmt='%s')\n","\n","\n"],"metadata":{"id":"fWgQX_eq-1Ll"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"ziunf6w74ibb"},"source":["#FJKJ"]},{"cell_type":"code","source":["# FJKJ : FJ + KJ\n","\n","model_save_path = '/content/drive/MyDrive/Thesis/OMIM2ORDO/output/finetuneSbert-'+base_model_name +'FJ'\n","modelFJ = SentenceTransformer(model_save_path)\n","#print(modelFT)\n","\n","#sourceOntKD, sourceOntKI, targetOntKD, targetOntKI\n","\n","embeddingsSrcFJKJ = modelFJ.encode(sourceOntKJ[\"text\"])\n","embeddingsTgtFJKJ = modelFJ.encode(targetOntKJ[\"text\"])\n","\n","srcFJKJ = pd.DataFrame()\n","srcFJKJ['entity-name'] = pd.DataFrame(sourceOntKJ).apply(lambda row: '«'.join(row.values.astype(str)), axis=1)\n","srcFJKJ['vector'] = pd.DataFrame(embeddingsSrcFJKJ).apply(lambda row: ','.join(row.values.astype(str)), axis=1)\n","srcFJKJ['combined'] = pd.DataFrame(srcFJKJ).apply(lambda row: '|['.join(row.values.astype(str)), axis=1)+ ']'\n","\n","tgtFJKJ = pd.DataFrame()\n","tgtFJKJ['entity-name'] = pd.DataFrame(targetOntKJ).apply(lambda row: '«'.join(row.values.astype(str)), axis=1)\n","tgtFJKJ['vector'] = pd.DataFrame(embeddingsTgtFJKJ).apply(lambda row: ','.join(row.values.astype(str)), axis=1)\n","tgtFJKJ['combined'] = pd.DataFrame(tgtFJKJ).apply(lambda row: '|['.join(row.values.astype(str)), axis=1)+ ']'\n","\n","np.savetxt(r'/content/drive/MyDrive/Thesis/OMIM2ORDO/output/srcFJKJ.txt', srcFJKJ['combined'].values, fmt='%s')\n","np.savetxt(r'/content/drive/MyDrive/Thesis/OMIM2ORDO/output/tgtFJKJ.txt', tgtFJKJ['combined'].values, fmt='%s')\n","\n","\n"],"metadata":{"id":"-_RzU7HZ_2MO"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"E99sTHHN3Pt4"},"source":["#FJDKJD"]},{"cell_type":"code","source":["# FJDKJD : FJD + KJ without duplicates\n","\n","model_save_path = '/content/drive/MyDrive/Thesis/OMIM2ORDO/output/finetuneSbert-'+base_model_name +'FJD'\n","modelFJD = SentenceTransformer(model_save_path)\n","#print(modelFT)\n","\n","sourceOntKJD = pd.DataFrame()\n","targetOntKJD = pd.DataFrame()\n","sourceOntKJD[\"entity\"] = sourceOntKJ[\"entity\"]\n","sourceOntKJD[\"text\"] = sourceOntKJ[\"text\"].apply(lambda row: (' '.join(dict.fromkeys(row.split()))))\n","targetOntKJD[\"entity\"] = targetOntKJ[\"entity\"]\n","targetOntKJD[\"text\"] = targetOntKJ[\"text\"].apply(lambda row: (' '.join(dict.fromkeys(row.split()))))\n","#sourceOntKD, sourceOntKI, targetOntKD, targetOntKI\n","\n","embeddingsSrcFJDKJD = modelFJD.encode(sourceOntKJD[\"text\"])\n","embeddingsTgtFJDKJD = modelFJD.encode(targetOntKJD[\"text\"])\n","\n","srcFJDKJD = pd.DataFrame()\n","srcFJDKJD['entity-name'] = pd.DataFrame(sourceOntKJD).apply(lambda row: '«'.join(row.values.astype(str)), axis=1)\n","srcFJDKJD['vector'] = pd.DataFrame(embeddingsSrcFJDKJD).apply(lambda row: ','.join(row.values.astype(str)), axis=1)\n","srcFJDKJD['combined'] = pd.DataFrame(srcFJDKJD).apply(lambda row: '|['.join(row.values.astype(str)), axis=1)+ ']'\n","\n","tgtFJDKJD = pd.DataFrame()\n","tgtFJDKJD['entity-name'] = pd.DataFrame(targetOntKJD).apply(lambda row: '«'.join(row.values.astype(str)), axis=1)\n","tgtFJDKJD['vector'] = pd.DataFrame(embeddingsTgtFJDKJD).apply(lambda row: ','.join(row.values.astype(str)), axis=1)\n","tgtFJDKJD['combined'] = pd.DataFrame(tgtFJDKJD).apply(lambda row: '|['.join(row.values.astype(str)), axis=1)+ ']'\n","\n","np.savetxt(r'/content/drive/MyDrive/Thesis/OMIM2ORDO/output/srcFJDKJD.txt', srcFJDKJD['combined'].values, fmt='%s')\n","np.savetxt(r'/content/drive/MyDrive/Thesis/OMIM2ORDO/output/tgtFJDKJD.txt', tgtFJDKJD['combined'].values, 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