papers-semantic-swearch.ipynb

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   "source": [
    "Semantic search\n",
    "\n",
    "Reference: https://huggingface.co/learn/nlp-course/chapter5/6?fw=tf\n",
    "\n",
    "Dataset: https://www.kaggle.com/datasets/arplusman/papers-by-subject"
   ]
  },
  {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/kaggle/input/papers-by-subject/arXiv-DataFrame.csv\n"
     ]
    }
   ],
   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
   ]
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "  Downloading faiss_gpu-1.7.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (85.5 MB)\r\n",
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     ]
    }
   ],
   "source": [
    "!pip install datasets evaluate transformers[sentencepiece]\n",
    "!pip install faiss-gpu\n",
    "!pip install datasets"
   ]
  },
  {
   "cell_type": "code",
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   "id": "dab05752",
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   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Title</th>\n",
       "      <th>Summary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dynamic Backtracking</td>\n",
       "      <td>Because of their occasional need to return to ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A Market-Oriented Programming Environment and ...</td>\n",
       "      <td>Market price systems constitute a well-underst...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>An Empirical Analysis of Search in GSAT</td>\n",
       "      <td>We describe an extensive study of search in GS...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>The Difficulties of Learning Logic Programs wi...</td>\n",
       "      <td>As real logic programmers normally use cut (!)...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Software Agents: Completing Patterns and Const...</td>\n",
       "      <td>To support the goal of allowing users to recor...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               Title  \\\n",
       "0                               Dynamic Backtracking   \n",
       "1  A Market-Oriented Programming Environment and ...   \n",
       "2            An Empirical Analysis of Search in GSAT   \n",
       "3  The Difficulties of Learning Logic Programs wi...   \n",
       "4  Software Agents: Completing Patterns and Const...   \n",
       "\n",
       "                                             Summary  \n",
       "0  Because of their occasional need to return to ...  \n",
       "1  Market price systems constitute a well-underst...  \n",
       "2  We describe an extensive study of search in GS...  \n",
       "3  As real logic programmers normally use cut (!)...  \n",
       "4  To support the goal of allowing users to recor...  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('/kaggle/input/papers-by-subject/arXiv-DataFrame.csv',\n",
    "                usecols=['Title', 'Summary'])\n",
    "df = df[:537]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "16ddacf5",
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    {
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      "text/plain": [
       "(537, 2)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dfea487b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-15T16:17:59.370765Z",
     "iopub.status.busy": "2023-08-15T16:17:59.370352Z",
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    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['Title', 'Summary'],\n",
       "    num_rows: 537\n",
       "})"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datasets import Dataset\n",
    "\n",
    "ds = Dataset.from_pandas(df)\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8bd7acb9",
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    },
    "tags": []
   },
   "outputs": [
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       "  0%|          | 0/537 [00:00<?, ?ex/s]"
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['Title', 'Summary', 'text'],\n",
       "    num_rows: 537\n",
       "})"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def concatenate_text(examples):\n",
    "    return {\n",
    "        \"text\": examples[\"Title\"] + \" \\n \" + examples[\"Summary\"]\n",
    "    }\n",
    "\n",
    "\n",
    "ds_concat = ds.map(concatenate_text)\n",
    "ds_concat"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "709a3b06",
   "metadata": {
    "papermill": {
     "duration": 0.012334,
     "end_time": "2023-08-15T16:18:00.678056",
     "exception": false,
     "start_time": "2023-08-15T16:18:00.665722",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Create embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ef25d6fc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-15T16:18:00.705111Z",
     "iopub.status.busy": "2023-08-15T16:18:00.704668Z",
     "iopub.status.idle": "2023-08-15T16:18:30.214337Z",
     "shell.execute_reply": "2023-08-15T16:18:30.213136Z"
    },
    "papermill": {
     "duration": 29.526797,
     "end_time": "2023-08-15T16:18:30.217476",
     "exception": false,
     "start_time": "2023-08-15T16:18:00.690679",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ba07e569d40848ddb489c5905013b36d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)okenizer_config.json:   0%|          | 0.00/363 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2b749f7244de4601ab5f910dcc86c502",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fed844842c014b2cae8bcf2625829895",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5ea05bb7b9da4f28a25295b6538b74d3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/239 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cfc074c74ce048e098861b03bd6762f7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)lve/main/config.json:   0%|          | 0.00/571 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.5\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n",
      "/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/__init__.py:98: UserWarning: unable to load libtensorflow_io_plugins.so: unable to open file: libtensorflow_io_plugins.so, from paths: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io_plugins.so']\n",
      "caused by: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io_plugins.so: undefined symbol: _ZN3tsl6StatusC1EN10tensorflow5error4CodeESt17basic_string_viewIcSt11char_traitsIcEENS_14SourceLocationE']\n",
      "  warnings.warn(f\"unable to load libtensorflow_io_plugins.so: {e}\")\n",
      "/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/__init__.py:104: UserWarning: file system plugins are not loaded: unable to open file: libtensorflow_io.so, from paths: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io.so']\n",
      "caused by: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io.so: undefined symbol: _ZTVN10tensorflow13GcsFileSystemE']\n",
      "  warnings.warn(f\"file system plugins are not loaded: {e}\")\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4f07e1587aff46ba8bb526a274f1d773",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading pytorch_model.bin:   0%|          | 0.00/438M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the PyTorch model were not used when initializing the TF 2.0 model TFMPNetModel: ['embeddings.position_ids']\n",
      "- This IS expected if you are initializing TFMPNetModel from a PyTorch model trained on another task or with another architecture (e.g. initializing a TFBertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing TFMPNetModel from a PyTorch model that you expect to be exactly identical (e.g. initializing a TFBertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "All the weights of TFMPNetModel were initialized from the PyTorch model.\n",
      "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFMPNetModel for predictions without further training.\n"
     ]
    }
   ],
   "source": [
    "from transformers import AutoTokenizer, TFAutoModel\n",
    "\n",
    "model_ckpt = \"sentence-transformers/multi-qa-mpnet-base-dot-v1\"\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_ckpt)\n",
    "model = TFAutoModel.from_pretrained(model_ckpt, from_pt=True)\n",
    "# from_pt=True will automatically convert PyTorch weights to the TensorFlow format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "659b6f89",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-15T16:18:30.248523Z",
     "iopub.status.busy": "2023-08-15T16:18:30.248133Z",
     "iopub.status.idle": "2023-08-15T16:18:30.255052Z",
     "shell.execute_reply": "2023-08-15T16:18:30.253826Z"
    },
    "papermill": {
     "duration": 0.025652,
     "end_time": "2023-08-15T16:18:30.257958",
     "exception": false,
     "start_time": "2023-08-15T16:18:30.232306",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def cls_pooling(model_output):\n",
    "    return model_output.last_hidden_state[:, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "89f4254a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-15T16:18:30.291203Z",
     "iopub.status.busy": "2023-08-15T16:18:30.289660Z",
     "iopub.status.idle": "2023-08-15T16:18:30.297399Z",
     "shell.execute_reply": "2023-08-15T16:18:30.295996Z"
    },
    "papermill": {
     "duration": 0.027678,
     "end_time": "2023-08-15T16:18:30.300105",
     "exception": false,
     "start_time": "2023-08-15T16:18:30.272427",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_embeddings(text_list):\n",
    "    encoded_input = tokenizer(\n",
    "        text_list, padding=True, truncation=True, return_tensors=\"tf\"\n",
    "    )\n",
    "    encoded_input = {k: v for k, v in encoded_input.items()}\n",
    "    model_output = model(**encoded_input)\n",
    "    return cls_pooling(model_output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c964b36f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-15T16:18:30.331630Z",
     "iopub.status.busy": "2023-08-15T16:18:30.331168Z",
     "iopub.status.idle": "2023-08-15T16:18:30.935006Z",
     "shell.execute_reply": "2023-08-15T16:18:30.933817Z"
    },
    "papermill": {
     "duration": 0.623055,
     "end_time": "2023-08-15T16:18:30.937530",
     "exception": false,
     "start_time": "2023-08-15T16:18:30.314475",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TensorShape([1, 768])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embedding = get_embeddings(ds_concat[\"text\"][0])\n",
    "embedding.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "958fd672",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-15T16:18:30.969162Z",
     "iopub.status.busy": "2023-08-15T16:18:30.968711Z",
     "iopub.status.idle": "2023-08-15T16:25:30.437534Z",
     "shell.execute_reply": "2023-08-15T16:25:30.436095Z"
    },
    "papermill": {
     "duration": 419.488597,
     "end_time": "2023-08-15T16:25:30.441324",
     "exception": false,
     "start_time": "2023-08-15T16:18:30.952727",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4b9bec375bc64f08afd883d8ebb71053",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/537 [00:00<?, ?ex/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "embeddings_dataset = ds_concat.map(\n",
    "    lambda x: {\"embeddings\": get_embeddings(x[\"text\"]).numpy()[0]}\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6b7a01b8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-15T16:25:30.475309Z",
     "iopub.status.busy": "2023-08-15T16:25:30.474917Z",
     "iopub.status.idle": "2023-08-15T16:25:30.540981Z",
     "shell.execute_reply": "2023-08-15T16:25:30.539726Z"
    },
    "papermill": {
     "duration": 0.08702,
     "end_time": "2023-08-15T16:25:30.543538",
     "exception": false,
     "start_time": "2023-08-15T16:25:30.456518",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "de6ce3cffd4144ea8cb2225f39320568",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['Title', 'Summary', 'text', 'embeddings'],\n",
       "    num_rows: 537\n",
       "})"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embeddings_dataset.add_faiss_index(column=\"embeddings\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "18a6d7df",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-15T16:25:30.576202Z",
     "iopub.status.busy": "2023-08-15T16:25:30.575793Z",
     "iopub.status.idle": "2023-08-15T16:25:30.585746Z",
     "shell.execute_reply": "2023-08-15T16:25:30.584728Z"
    },
    "papermill": {
     "duration": 0.029598,
     "end_time": "2023-08-15T16:25:30.588288",
     "exception": false,
     "start_time": "2023-08-15T16:25:30.558690",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Dynamic Backtracking \\n Because of their occasional need to return to shallow points in a search tree, existing backtracking methods can sometimes erase meaningful progress toward solving a search problem. In this paper, we present a method by which backtrack points can be moved deeper in the search space, thereby avoiding this difficulty. The technique developed is a variant of dependency-directed backtracking that uses only polynomial space while still providing useful control information and retaining the completeness guarantees provided by earlier approaches.'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# https://huggingface.co/docs/datasets/access\n",
    "embeddings_dataset[0]['text']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7293f7d7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-15T16:25:30.622197Z",
     "iopub.status.busy": "2023-08-15T16:25:30.621748Z",
     "iopub.status.idle": "2023-08-15T16:25:30.944893Z",
     "shell.execute_reply": "2023-08-15T16:25:30.943614Z"
    },
    "papermill": {
     "duration": 0.343748,
     "end_time": "2023-08-15T16:25:30.947653",
     "exception": false,
     "start_time": "2023-08-15T16:25:30.603905",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 768)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "question = \"How does Dynamic Backtracking improve upon traditional backtracking methods?\"\n",
    "question_embedding = get_embeddings([question]).numpy()\n",
    "question_embedding.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e840f55a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-15T16:25:30.982026Z",
     "iopub.status.busy": "2023-08-15T16:25:30.981504Z",
     "iopub.status.idle": "2023-08-15T16:25:30.992279Z",
     "shell.execute_reply": "2023-08-15T16:25:30.990988Z"
    },
    "papermill": {
     "duration": 0.031521,
     "end_time": "2023-08-15T16:25:30.995209",
     "exception": false,
     "start_time": "2023-08-15T16:25:30.963688",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "scores, samples = embeddings_dataset.get_nearest_examples(\n",
    "    \"embeddings\", question_embedding, k=5\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f8cef098",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-15T16:25:31.028862Z",
     "iopub.status.busy": "2023-08-15T16:25:31.028409Z",
     "iopub.status.idle": "2023-08-15T16:25:31.042184Z",
     "shell.execute_reply": "2023-08-15T16:25:31.040895Z"
    },
    "papermill": {
     "duration": 0.034514,
     "end_time": "2023-08-15T16:25:31.045280",
     "exception": false,
     "start_time": "2023-08-15T16:25:31.010766",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "samples_df = pd.DataFrame.from_dict(samples)\n",
    "samples_df[\"scores\"] = scores\n",
    "samples_df.sort_values(\"scores\", ascending=False, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "51d1afc2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-15T16:25:31.080158Z",
     "iopub.status.busy": "2023-08-15T16:25:31.079197Z",
     "iopub.status.idle": "2023-08-15T16:25:31.088610Z",
     "shell.execute_reply": "2023-08-15T16:25:31.086869Z"
    },
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     "duration": 0.029643,
     "end_time": "2023-08-15T16:25:31.091119",
     "exception": false,
     "start_time": "2023-08-15T16:25:31.061476",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TITLE: Quantum Computing and Phase Transitions in Combinatorial Search\n",
      "Summary: We introduce an algorithm for combinatorial search on quantum computers that is capable of significantly concentrating amplitude into solutions for some NP search problems, on average. This is done by exploiting the same aspects of problem structure as used by classical backtrack methods to avoid unproductive search choices. This quantum algorithm is much more likely to find solutions than the simple direct use of quantum parallelism. Furthermore, empirical evaluation on small problems shows this quantum algorithm displays the same phase transition behavior, and at the same location, as seen in many previously studied classical search methods. Specifically, difficult problem instances are concentrated near the abrupt change from underconstrained to overconstrained problems.\n",
      "SCORE: 46.565040588378906\n",
      "==================================================\n",
      "\n",
      "TITLE: Iterative Deepening Branch and Bound\n",
      "Summary: In tree search problem the best-first search algorithm needs too much of space . To remove such drawbacks of these algorithms the IDA* was developed which is both space and time cost efficient. But again IDA* can give an optimal solution for real valued problems like Flow shop scheduling, Travelling Salesman and 0/1 Knapsack due to their real valued cost estimates. Thus further modifications are done on it and the Iterative Deepening Branch and Bound Search Algorithms is developed which meets the requirements. We have tried using this algorithm for the Flow Shop Scheduling Problem and have found that it is quite effective.\n",
      "SCORE: 43.8082160949707\n",
      "==================================================\n",
      "\n",
      "TITLE: Bidirectional Heuristic Search Reconsidered\n",
      "Summary: The assessment of bidirectional heuristic search has been incorrect since it was first published more than a quarter of a century ago. For quite a long time, this search strategy did not achieve the expected results, and there was a major misunderstanding about the reasons behind it. Although there is still wide-spread belief that bidirectional heuristic search is afflicted by the problem of search frontiers passing each other, we demonstrate that this conjecture is wrong. Based on this finding, we present both a new generic approach to bidirectional heuristic search and a new approach to dynamically improving heuristic values that is feasible in bidirectional search only. These approaches are put into perspective with both the traditional and more recently proposed approaches in order to facilitate a better overall understanding. Empirical results of experiments with our new approaches show that bidirectional heuristic search can be performed very efficiently and also with limited memory. These results suggest that bidirectional heuristic search appears to be better for solving certain difficult problems than corresponding unidirectional search. This provides some evidence for the usefulness of a search strategy that was long neglected. In summary, we show that bidirectional heuristic search is viable and consequently propose that it be reconsidered.\n",
      "SCORE: 38.716156005859375\n",
      "==================================================\n",
      "\n",
      "TITLE: The Design and Experimental Analysis of Algorithms for Temporal\n",
      "  Reasoning\n",
      "Summary: Many applications -- from planning and scheduling to problems in molecular biology -- rely heavily on a temporal reasoning component. In this paper, we discuss the design and empirical analysis of algorithms for a temporal reasoning system based on Allen's influential interval-based framework for representing temporal information. At the core of the system are algorithms for determining whether the temporal information is consistent, and, if so, finding one or more scenarios that are consistent with the temporal information. Two important algorithms for these tasks are a path consistency algorithm and a backtracking algorithm. For the path consistency algorithm, we develop techniques that can result in up to a ten-fold speedup over an already highly optimized implementation. For the backtracking algorithm, we develop variable and value ordering heuristics that are shown empirically to dramatically improve the performance of the algorithm. As well, we show that a previously suggested reformulation of the backtracking search problem can reduce the time and space requirements of the backtracking search. Taken together, the techniques we develop allow a temporal reasoning component to solve problems that are of practical size.\n",
      "SCORE: 38.160362243652344\n",
      "==================================================\n",
      "\n",
      "TITLE: Dynamic Backtracking\n",
      "Summary: Because of their occasional need to return to shallow points in a search tree, existing backtracking methods can sometimes erase meaningful progress toward solving a search problem. In this paper, we present a method by which backtrack points can be moved deeper in the search space, thereby avoiding this difficulty. The technique developed is a variant of dependency-directed backtracking that uses only polynomial space while still providing useful control information and retaining the completeness guarantees provided by earlier approaches.\n",
      "SCORE: 20.720169067382812\n",
      "==================================================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for _, row in samples_df.iterrows():\n",
    "    print(f\"TITLE: {row.Title}\")\n",
    "    print(f\"Summary: {row.Summary}\")\n",
    "    print(f\"SCORE: {row.scores}\")\n",
    "    print(\"=\" * 50)\n",
    "    print()"
   ]
  }
 ],
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