Datasets:
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import json\n",
"import pickle\n",
"import pandas as pd\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"SCIDMT_PATH = {\n",
" 'DICT': './SciDMT_dict.json',\n",
"\n",
" # machine learning inputs at sentence level\n",
" 'sent_xy': './SciDMT_sentences.p', \n",
" 'sent_eval': './SciDMT_E_sentences.json',\n",
" 'sent_split': './SciDMT_sentences_split.json',\n",
"\n",
" # document level inputs\n",
" 'doc_split': './SciDMT_split.json',\n",
" 'doc_eval': './SciDMT_E_human_annotations.json',\n",
" 'doc_text_and_meta': './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"
]
}
],
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