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SciDMT/SciDMT.tar

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a8579e90d1560fe33fcb9f503c964698b86431df8833ab69c5cc8f7c4b7a465d
+size 3031326720

SciDMT_demo.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "import pickle\n",
+    "from const import *\n",
+    "import pandas as pd\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "SCIDMT_PATH = {\n",
+    "    'DICT': './SciDMT/SciDMT_dict.json',\n",
+    "\n",
+    "    # machine learning inputs at sentence level\n",
+    "    'sent_xy': './SciDMT/SciDMT_sentences.p', \n",
+    "    'sent_eval': './SciDMT/SciDMT_E_sentences.json',\n",
+    "    'sent_split': './SciDMT/SciDMT_sentences_split.json',\n",
+    "\n",
+    "    # document level inputs\n",
+    "    'doc_split': './SciDMT/SciDMT_split.json',\n",
+    "    'doc_eval': './SciDMT/SciDMT_E_human_annotations.json',\n",
+    "    'doc_text_and_meta': './SciDMT/SciDMT_papers.csv',\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def print_dict_structure(d, indent=0, indent_str='   '):\n",
+    "    for key, value in d.items():\n",
+    "        print(indent_str * indent + ' ' +str(key))\n",
+    "        if isinstance(value, dict):\n",
+    "            print_dict_structure(value, indent+1)\n",
+    "        else:\n",
+    "            if type(value) == list:\n",
+    "                des = f'| len={len(value)} | first 3 entries={value[:3]}'\n",
+    "            else:\n",
+    "                des = ''\n",
+    "            print(indent_str * (indent+1) + ' ' + str(type(value)) + des)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dict_keys(['datasets', 'methods', 'tasks'])\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Load SciDMT dictionary for entities\n",
+    "DICT = json.load(open(SCIDMT_PATH['DICT'], 'r'))\n",
+    "print(DICT.keys())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load SciDMT evaluation set in sentence level\n",
+    "scidmt_e = pd.read_json(SCIDMT_PATH['sent_eval'])\n",
+    "\n",
+    "# load x_test, y_test\n",
+    "X_test, y_test = scidmt_e['X'].to_list(), scidmt_e['y'].to_list()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " is_contain\n",
+      "    datasets\n",
+      "       <class 'list'>| len=48049 | first 3 entries=[True, False, True]\n",
+      "    tasks\n",
+      "       <class 'list'>| len=48049 | first 3 entries=[True, True, True]\n",
+      "    methods\n",
+      "       <class 'list'>| len=48049 | first 3 entries=[True, True, True]\n",
+      "    all\n",
+      "       <class 'list'>| len=48049 | first 3 entries=[True, True, True]\n",
+      " is_test\n",
+      "    datasets\n",
+      "       <class 'list'>| len=48049 | first 3 entries=[False, False, False]\n",
+      "    tasks\n",
+      "       <class 'list'>| len=48049 | first 3 entries=[True, False, False]\n",
+      "    methods\n",
+      "       <class 'list'>| len=48049 | first 3 entries=[False, True, False]\n",
+      "    all\n",
+      "       <class 'list'>| len=48049 | first 3 entries=[False, False, False]\n",
+      " doc_pids\n",
+      "    <class 'list'>| len=48049 | first 3 entries=[51881821, 51881855, 51881912]\n",
+      " is_0shot\n",
+      "    datasets\n",
+      "       <class 'list'>| len=48049 | first 3 entries=[False, False, False]\n",
+      "    methods\n",
+      "       <class 'list'>| len=48049 | first 3 entries=[False, False, False]\n",
+      "    tasks\n",
+      "       <class 'list'>| len=48049 | first 3 entries=[False, False, False]\n",
+      "    all\n",
+      "       <class 'list'>| len=48049 | first 3 entries=[False, False, False]\n",
+      " SHOT0\n",
+      "    datasets\n",
+      "       entity_indexs\n",
+      "          <class 'list'>| len=10 | first 3 entries=[1561, 2889, 2810]\n",
+      "       ml_inputs_idxs\n",
+      "          <class 'list'>| len=147 | first 3 entries=[461223, 461224, 461225]\n",
+      "    tasks\n",
+      "       entity_indexs\n",
+      "          <class 'list'>| len=10 | first 3 entries=[766, 1487, 1548]\n",
+      "       ml_inputs_idxs\n",
+      "          <class 'list'>| len=305 | first 3 entries=[646887, 646888, 646889]\n",
+      "    methods\n",
+      "       entity_indexs\n",
+      "          <class 'list'>| len=10 | first 3 entries=[605, 1324, 1099]\n",
+      "       ml_inputs_idxs\n",
+      "          <class 'list'>| len=128 | first 3 entries=[1154530, 1154548, 1154550]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# load document level split\n",
+    "DOC_SPLIT = json.load(open(SCIDMT_PATH['doc_split'], 'r'))\n",
+    "print_dict_structure(DOC_SPLIT)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " is_contain\n",
+      "    datasets\n",
+      "       <class 'list'>| len=1128148 | first 3 entries=[True, False, False]\n",
+      "    tasks\n",
+      "       <class 'list'>| len=1128148 | first 3 entries=[False, True, True]\n",
+      "    methods\n",
+      "       <class 'list'>| len=1128148 | first 3 entries=[False, False, False]\n",
+      "    all\n",
+      "       <class 'list'>| len=1128148 | first 3 entries=[True, True, True]\n",
+      " is_test\n",
+      "    datasets\n",
+      "       <class 'list'>| len=1128148 | first 3 entries=[False, False, False]\n",
+      "    tasks\n",
+      "       <class 'list'>| len=1128148 | first 3 entries=[True, True, True]\n",
+      "    methods\n",
+      "       <class 'list'>| len=1128148 | first 3 entries=[True, True, True]\n",
+      "    all\n",
+      "       <class 'list'>| len=1128148 | first 3 entries=[False, False, False]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# load sentence level train/test split\n",
+    "with open(SCIDMT_PATH['sent_split'], 'r') as f:\n",
+    "    SPLIT = json.load(f)\n",
+    "print_dict_structure(SPLIT)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_1610900/687978490.py:4: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
+      "  X_train = np.array(X)[is_train]#[:5000] ##debug\n",
+      "/tmp/ipykernel_1610900/687978490.py:5: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
+      "  y_train = np.array(y)[is_train]#[:5000] ##debug\n"
+     ]
+    }
+   ],
+   "source": [
+    "# load x_train, y_train\n",
+    "# is_test = true are not all human annotated. Thus, we have a seperate test file\n",
+    "is_train = np.logical_and(SPLIT['is_test']['all']==False, SPLIT['is_contain']['all'] == True)\n",
+    "X, y, _, _, _ = pickle.load(open(SCIDMT_PATH['sent_xy'], 'rb'))\n",
+    "X_train = np.array(X)[is_train]#[:5000] ##debug\n",
+    "y_train = np.array(y)[is_train]#[:5000] ##debug"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "py39",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}