KG_RAG/notebooks/disease_retrieval_accuracy.ipynb

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   "source": [
    "import os\n",
    "os.chdir('..')"
   ]
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  {
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     "text": [
      "/root/anaconda3/envs/kg_rag_test_2/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from kg_rag.utility import *\n",
    "from tqdm import tqdm\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "19fc98b9-64a8-40c0-9e5a-92b4392e6969",
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   "source": [
    "data = pd.read_csv('data/dataset_for_entity_retrieval_accuracy_analysis.csv')\n"
   ]
  },
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   "cell_type": "code",
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   "id": "2851be4c-2a76-4f6d-b5f4-118e8122b155",
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   "source": [
    "VECTOR_DB_PATH = config_data[\"VECTOR_DB_PATH\"]\n",
    "SENTENCE_EMBEDDING_MODEL_FOR_NODE_RETRIEVAL = config_data[\"SENTENCE_EMBEDDING_MODEL_FOR_NODE_RETRIEVAL\"]\n",
    "\n",
    "vectorstore = load_chroma(VECTOR_DB_PATH, SENTENCE_EMBEDDING_MODEL_FOR_NODE_RETRIEVAL)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
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     "text": [
      "322it [00:05, 56.20it/s]"
     ]
    },
    {
     "name": "stdout",
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     "text": [
      "CPU times: user 4.74 s, sys: 896 ms, total: 5.64 s\n",
      "Wall time: 5.73 s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
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   ],
   "source": [
    "%%time\n",
    "\n",
    "correct_retrieval = 0\n",
    "\n",
    "for index, row in tqdm(data.iterrows()):\n",
    "    question = row['text']\n",
    "    entities = disease_entity_extractor_v2(question)  \n",
    "    for entity in entities:\n",
    "        node_search_result = vectorstore.similarity_search_with_score(entity, k=1)\n",
    "        if node_search_result[0][0].page_content == row['node_hits']:\n",
    "            correct_retrieval += 1 \n",
    "            break\n",
    "            \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2f997335-bff7-431c-bbd8-608513eddcc7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Retrieval accuracy is 99.7%\n"
     ]
    }
   ],
   "source": [
    "retrieval_accuracy = 100*correct_retrieval/data.shape[0]\n",
    "print(f'Retrieval accuracy is {round(retrieval_accuracy,1)}%')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "afe971ab-b8b9-4c88-9657-c588813b412f",
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