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{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# Train / validation / test split\n",
"\n",
"This is a notebook that was used to split the CC dataset into train/valid/test samples for the reference annotation NLP task."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from sec_certs.dataset import CCDataset\n",
"from sec_certs.sample import CCCertificate\n",
"import pandas as pd\n",
"from sklearn.model_selection import train_test_split\n",
"import json"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"dset = CCDataset.from_web()\n",
"df = dset.to_pandas()\n",
"reference_rich_certs = {x.dgst for x in dset if (x.heuristics.st_references.directly_referencing and x.state.st_txt_path) or (x.heuristics.report_references.directly_referencing and x.state.report_txt_path)}\n",
"df = df.loc[df.index.isin(reference_rich_certs)]\n",
"\n",
"# The following certs go straight to the test set as they represent super rare categories that we cannot split\n",
"certs_from_rare_categories = df.loc[df.category.isin({\"Multi-Function Devices\", \"Mobility\", \"Data Protection\"})].index.tolist()\n",
"df = df.loc[~df.index.isin(certs_from_rare_categories)]"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# This splits 30/20/50 (train, valid, test)\n",
"x_train, x_test = train_test_split(df.index, test_size=0.5, shuffle=True, stratify=df.category)\n",
"x_train, x_valid = train_test_split(x_train, test_size=0.4, shuffle=True, stratify=df.loc[df.index.isin(x_train)].category)\n",
"x_test = list(x_test) + list(certs_from_rare_categories)\n",
"\n",
"with open(\"../../../data/reference_annotations_split/train.json\", \"w\") as handle:\n",
" json.dump(x_train.tolist(), handle, indent=4)\n",
"\n",
"with open(\"../../../data/reference_annotations_split/valid.json\", \"w\") as handle:\n",
" json.dump(x_valid.tolist(), handle, indent=4)\n",
"\n",
"with open(\"../../../data/reference_annotations_split/test.json\", \"w\") as handle:\n",
" json.dump(x_test, handle, indent=4)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "venv",
"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": "3.8.13"
},
"orig_nbformat": 4,
"vscode": {
"interpreter": {
"hash": "a5b8c5b127d2cfe5bc3a1c933e197485eb9eba25154c3661362401503b4ef9d4"
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|
