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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Data Preparation "
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [],
"source": [
"!poetry add -qqq python-dotenv datasets wandb didkit\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"from dotenv import load_dotenv, find_dotenv\n",
"if os.path.exists('../env'):\n",
" load_dotenv(find_dotenv())\n",
"import wandb"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m Calling wandb.login() after wandb.init() has no effect.\n"
]
},
{
"data": {
"text/html": [
"Finishing last run (ID:pnvhnkh8) before initializing another..."
],
"text/plain": [
"<IPython.core.display.HTML object>"
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"VBox(children=(Label(value='0.001 MB of 0.001 MB uploaded\\r'), FloatProgress(value=1.0, max=1.0)))"
]
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"output_type": "display_data"
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{
"data": {
"text/html": [
" View run <strong style=\"color:#cdcd00\">verida_data_raw</strong> at: <a href='https://wandb.ai/orion-agents/verida-pii/runs/pnvhnkh8' target=\"_blank\">https://wandb.ai/orion-agents/verida-pii/runs/pnvhnkh8</a><br/> View project at: <a href='https://wandb.ai/orion-agents/verida-pii' target=\"_blank\">https://wandb.ai/orion-agents/verida-pii</a><br/>Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"Find logs at: <code>./wandb/run-20240825_035519-pnvhnkh8/logs</code>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"The new W&B backend becomes opt-out in version 0.18.0; try it out with `wandb.require(\"core\")`! See https://wandb.me/wandb-core for more information."
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"Successfully finished last run (ID:pnvhnkh8). Initializing new run:<br/>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "76495a6b8c1843b09162b9ec31d99dfe",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"VBox(children=(Label(value='Waiting for wandb.init()...\\r'), FloatProgress(value=0.01112003148947325, max=1.0)…"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"Tracking run with wandb version 0.17.7"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"Run data is saved locally in <code>/Users/nullzero/Documents/repos/github.com/privacy-identity/vda-simulation-medical/vda-sim-medical/wandb/run-20240825_035604-69f6mbdr</code>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"Syncing run <strong><a href='https://wandb.ai/orion-agents/verida-pii/runs/69f6mbdr' target=\"_blank\">verida_data_raw</a></strong> to <a href='https://wandb.ai/orion-agents/verida-pii' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
" View project at <a href='https://wandb.ai/orion-agents/verida-pii' target=\"_blank\">https://wandb.ai/orion-agents/verida-pii</a>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
" View run at <a href='https://wandb.ai/orion-agents/verida-pii/runs/69f6mbdr' target=\"_blank\">https://wandb.ai/orion-agents/verida-pii/runs/69f6mbdr</a>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001b[34m\u001b[1mwandb\u001b[0m: Network error resolved after 1:13:41.968146, resuming normal operation.\n",
"\u001b[34m\u001b[1mwandb\u001b[0m: Network error resolved after 0:42:49.841123, resuming normal operation.\n",
"\u001b[34m\u001b[1mwandb\u001b[0m: Network error resolved after 0:18:54.049113, resuming normal operation.\n"
]
}
],
"source": [
"wandb.login(key=os.getenv('WANDB_API_KEY'))\n",
"run = wandb.init(project=\"verida-pii\", name=\"verida_data_raw\")"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"539\n"
]
}
],
"source": [
"from datasets import load_dataset\n",
"import pandas as pd\n",
"data_name=\"Ezi/medical_and_legislators_synthetic\"\n",
"data = load_dataset(path=data_name, split='train')\n",
"data_df = data.to_pandas()\n",
"data_df.head()\n",
"print(len(data_df))\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'mps'"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"device = \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n",
"device"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [],
"source": [
"# DiD Generator\n",
"import didkit\n",
"\n",
"def generate_did():\n",
" key = didkit.generate_ed25519_key()\n",
" did = didkit.key_to_did(\"key\", key)\n",
" return did, key"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [],
"source": [
"from tqdm import tqdm, tqdm_notebook, tqdm_pandas\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['last_name', 'first_name', 'middle_name', 'suffix', 'nickname',\n",
" 'full_name', 'birthday', 'gender', 'type', 'state', 'district',\n",
" 'senate_class', 'party', 'url', 'address', 'phone', 'contact_form',\n",
" 'rss_url', 'twitter', 'facebook', 'youtube', 'youtube_id',\n",
" 'bioguide_id', 'thomas_id', 'opensecrets_id', 'lis_id', 'fec_ids',\n",
" 'cspan_id', 'govtrack_id', 'votesmart_id', 'ballotpedia_id',\n",
" 'washington_post_id', 'icpsr_id', 'wikipedia_id', 'last_name.1',\n",
" 'first_name.1', 'middle_name.1', 'suffix.1', 'nickname.1',\n",
" 'full_name.1', 'birthday.1', 'gender.1', 'type.1', 'state.1',\n",
" 'district.1', 'senate_class.1', 'party.1', 'url.1', 'address.1',\n",
" 'phone.1', 'contact_form.1', 'rss_url.1', 'twitter.1', 'facebook.1',\n",
" 'youtube.1', 'youtube_id.1', 'bioguide_id.1', 'thomas_id.1',\n",
" 'opensecrets_id.1', 'lis_id.1', 'fec_ids.1', 'cspan_id.1',\n",
" 'govtrack_id.1', 'votesmart_id.1', 'ballotpedia_id.1',\n",
" 'washington_post_id.1', 'icpsr_id.1', 'wikipedia_id.1'],\n",
" dtype='object')"
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#data_df['did'] = data_df.apply(lambda x: generate_did()[0], axis=1)\n",
"#data_df['key'] = data_df.apply(lambda x: generate_did()[1], axis=1)\n",
"cleaned_df = data_df.copy()\n",
"cleaned_df.head()\n",
"cleaned_df.isna().sum()\n",
"cleaned_df.isna().dropna()\n",
"cleaned_df.describe()\n",
"cleaned_df.shape\n",
"cleaned_df.columns"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {},
"outputs": [],
"source": [
"data_did = data_df.copy()\n",
"data_did.to_csv(\"data_did.csv\")\n",
"data_did"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {},
"outputs": [
{
"ename": "ModuleNotFoundError",
"evalue": "No module named 'DatasetDict'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[76], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Back to dataset\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mDatasetDict\u001b[39;00m \n\u001b[1;32m 3\u001b[0m secure_mode \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m 4\u001b[0m train_split \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.8\u001b[39m\n",
"\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'DatasetDict'"
]
}
],
"source": [
"# Back to dataset\n",
"\n",
"secure_mode = False\n",
"train_split = 0.8\n",
"test_every = 5\n",
"batch_size = 800\n",
"\n",
"ds = data_did\n",
"train_len = int(train_split * len(ds))\n",
"test_len = len(ds) - train_len\n",
"\n",
"print(f\"{train_len} samples for training, {test_len} for testing\")\n",
"\n",
"train_ds, test_ds = torch.utils.data.random_split(ds, [train_len, test_len])\n"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "expected str, bytes or os.PathLike object, not Subset",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[78], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mdatasets\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m load_dataset\n\u001b[0;32m----> 2\u001b[0m ds \u001b[38;5;241m=\u001b[39m \u001b[43mload_dataset\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain_ds\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtest_ds\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msplit\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtrain\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtest\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/verida-differential-privacy-OB45ac0m-py3.11/lib/python3.11/site-packages/datasets/load.py:2588\u001b[0m, in \u001b[0;36mload_dataset\u001b[0;34m(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, trust_remote_code, **config_kwargs)\u001b[0m\n\u001b[1;32m 2586\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m data_files \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m data_files:\n\u001b[1;32m 2587\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mEmpty \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdata_files\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m: \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdata_files\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m. It should be either non-empty or None (default).\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m-> 2588\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[43mPath\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mDATASET_STATE_JSON_FILENAME\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mexists():\n\u001b[1;32m 2589\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 2590\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mYou are trying to load a dataset that was saved using `save_to_disk`. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 2591\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPlease use `load_from_disk` instead.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 2592\u001b[0m )\n\u001b[1;32m 2594\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m streaming \u001b[38;5;129;01mand\u001b[39;00m num_proc \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
"File \u001b[0;32m/opt/homebrew/Cellar/[email protected]/3.11.9_1/Frameworks/Python.framework/Versions/3.11/lib/python3.11/pathlib.py:871\u001b[0m, in \u001b[0;36mPath.__new__\u001b[0;34m(cls, *args, **kwargs)\u001b[0m\n\u001b[1;32m 869\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mcls\u001b[39m \u001b[38;5;129;01mis\u001b[39;00m Path:\n\u001b[1;32m 870\u001b[0m \u001b[38;5;28mcls\u001b[39m \u001b[38;5;241m=\u001b[39m WindowsPath \u001b[38;5;28;01mif\u001b[39;00m os\u001b[38;5;241m.\u001b[39mname \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnt\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m PosixPath\n\u001b[0;32m--> 871\u001b[0m \u001b[38;5;28mself\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_from_parts\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 872\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_flavour\u001b[38;5;241m.\u001b[39mis_supported:\n\u001b[1;32m 873\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcannot instantiate \u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m on your system\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 874\u001b[0m \u001b[38;5;241m%\u001b[39m (\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m,))\n",
"File \u001b[0;32m/opt/homebrew/Cellar/[email protected]/3.11.9_1/Frameworks/Python.framework/Versions/3.11/lib/python3.11/pathlib.py:509\u001b[0m, in \u001b[0;36mPurePath._from_parts\u001b[0;34m(cls, args)\u001b[0m\n\u001b[1;32m 504\u001b[0m \u001b[38;5;129m@classmethod\u001b[39m\n\u001b[1;32m 505\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_from_parts\u001b[39m(\u001b[38;5;28mcls\u001b[39m, args):\n\u001b[1;32m 506\u001b[0m \u001b[38;5;66;03m# We need to call _parse_args on the instance, so as to get the\u001b[39;00m\n\u001b[1;32m 507\u001b[0m \u001b[38;5;66;03m# right flavour.\u001b[39;00m\n\u001b[1;32m 508\u001b[0m \u001b[38;5;28mself\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mobject\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__new__\u001b[39m(\u001b[38;5;28mcls\u001b[39m)\n\u001b[0;32m--> 509\u001b[0m drv, root, parts \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_parse_args\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 510\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_drv \u001b[38;5;241m=\u001b[39m drv\n\u001b[1;32m 511\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_root \u001b[38;5;241m=\u001b[39m root\n",
"File \u001b[0;32m/opt/homebrew/Cellar/[email protected]/3.11.9_1/Frameworks/Python.framework/Versions/3.11/lib/python3.11/pathlib.py:493\u001b[0m, in \u001b[0;36mPurePath._parse_args\u001b[0;34m(cls, args)\u001b[0m\n\u001b[1;32m 491\u001b[0m parts \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m a\u001b[38;5;241m.\u001b[39m_parts\n\u001b[1;32m 492\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 493\u001b[0m a \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mfspath(a)\n\u001b[1;32m 494\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(a, \u001b[38;5;28mstr\u001b[39m):\n\u001b[1;32m 495\u001b[0m \u001b[38;5;66;03m# Force-cast str subclasses to str (issue #21127)\u001b[39;00m\n\u001b[1;32m 496\u001b[0m parts\u001b[38;5;241m.\u001b[39mappend(\u001b[38;5;28mstr\u001b[39m(a))\n",
"\u001b[0;31mTypeError\u001b[0m: expected str, bytes or os.PathLike object, not Subset"
]
}
],
"source": [
"from datasets import load_dataset\n",
"ds = load_dataset(train_ds, test_ds, split=[\"train\", \"test\"])\n"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"import torch.nn as nn\n",
"\n",
"class CharByteEncoder(nn.Module):\n",
" \"\"\"\n",
" This encoder takes a UTF-8 string and encodes its bytes into a Tensor. It can also\n",
" perform the opposite operation to check a result.\n",
" Examples:\n",
" >>> encoder = CharByteEncoder()\n",
" >>> t = encoder('Ślusàrski') # returns tensor([256, 197, 154, 108, 117, 115, 195, 160, 114, 115, 107, 105, 257])\n",
" >>> encoder.decode(t) # returns \"<s>Ślusàrski</s>\"\n",
" \"\"\"\n",
"\n",
" def __init__(self):\n",
" super().__init__()\n",
" self.start_token = \"<s>\"\n",
" self.end_token = \"</s>\"\n",
" self.pad_token = \"<pad>\"\n",
"\n",
" self.start_idx = 256\n",
" self.end_idx = 257\n",
" self.pad_idx = 258\n",
"\n",
" def forward(self, s: str, pad_to=0) -> torch.LongTensor:\n",
" \"\"\"\n",
" Encodes a string. It will append a start token <s> (id=self.start_idx) and an end token </s>\n",
" (id=self.end_idx).\n",
" Args:\n",
" s: The string to encode.\n",
" pad_to: If not zero, pad by appending self.pad_idx until string is of length `pad_to`.\n",
" Defaults to 0.\n",
" Returns:\n",
" The encoded LongTensor of indices.\n",
" \"\"\"\n",
" encoded = s.encode()\n",
" n_pad = pad_to - len(encoded) if pad_to > len(encoded) else 0\n",
" return torch.LongTensor(\n",
" [self.start_idx]\n",
" + [c for c in encoded] # noqa\n",
" + [self.end_idx]\n",
" + [self.pad_idx for _ in range(n_pad)]\n",
" )\n",
"\n",
" def decode(self, char_ids_tensor: torch.LongTensor) -> str:\n",
" \"\"\"\n",
" The inverse of `forward`. Keeps the start, end, and pad indices.\n",
" \"\"\"\n",
" char_ids = char_ids_tensor.cpu().detach().tolist()\n",
"\n",
" out = []\n",
" buf = []\n",
" for c in char_ids:\n",
" if c < 256:\n",
" buf.append(c)\n",
" else:\n",
" if buf:\n",
" out.append(bytes(buf).decode())\n",
" buf = []\n",
" if c == self.start_idx:\n",
" out.append(self.start_token)\n",
" elif c == self.end_idx:\n",
" out.append(self.end_token)\n",
" elif c == self.pad_idx:\n",
" out.append(self.pad_token)\n",
"\n",
" if buf: # in case some are left\n",
" out.append(bytes(buf).decode())\n",
" return \"\".join(out)\n",
"\n",
" def __len__(self):\n",
" \"\"\"\n",
" The length of our encoder space. This is fixed to 256 (one byte) + 3 special chars\n",
" (start, end, pad).\n",
" Returns:\n",
" 259\n",
" \"\"\"\n",
" return 259"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {},
"outputs": [],
"source": [
"from torch.nn.utils.rnn import pad_sequence\n",
"\n",
"def padded_collate(batch, padding_idx=0):\n",
" x = pad_sequence(\n",
" [elem[0] for elem in batch], batch_first=True, padding_value=padding_idx\n",
" )\n",
" y = torch.stack([elem[1] for elem in batch]).long()\n",
"\n",
" return x, y"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [],
"source": [
"from torch.utils.data import Dataset\n",
"from pathlib import Path\n",
"\n",
"\n",
"class NamesDataset(Dataset):\n",
" def __init__(self, root):\n",
" self.root = Path(root)\n",
"\n",
" self.labels = list({langfile.stem for langfile in self.root.iterdir()})\n",
" self.labels_dict = {label: i for i, label in enumerate(self.labels)}\n",
" self.encoder = CharByteEncoder()\n",
" self.samples = self.construct_samples()\n",
"\n",
" def __getitem__(self, i):\n",
" return self.samples[i]\n",
"\n",
" def __len__(self):\n",
" return len(self.samples)\n",
"\n",
" def construct_samples(self):\n",
" samples = []\n",
" for langfile in self.root.iterdir():\n",
" label_name = langfile.stem\n",
" label_id = self.labels_dict[label_name]\n",
" with open(langfile, \"r\") as fin:\n",
" for row in fin:\n",
" samples.append(\n",
" (self.encoder(row.strip()), torch.tensor(label_id).long())\n",
" )\n",
" return samples\n",
"\n",
" def label_count(self):\n",
" cnt = Counter()\n",
" for _x, y in self.samples:\n",
" label = self.labels[int(y)]\n",
" cnt[label] += 1\n",
" return cnt\n",
"\n",
"\n",
"VOCAB_SIZE = 256 + 3 # 256 alternatives in one byte, plus 3 special characters."
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {},
"outputs": [],
"source": [
"# Data Loaders\n",
"from torch.utils.data import DataLoader\n",
"\n",
"train_loader = DataLoader(\n",
" train_ds,\n",
" batch_size=batch_size,\n",
" pin_memory=True,\n",
" collate_fn=padded_collate,\n",
")\n",
"\n",
"test_loader = DataLoader(\n",
" test_ds,\n",
" batch_size=2 * batch_size,\n",
" shuffle=False,\n",
" pin_memory=True,\n",
" collate_fn=padded_collate,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"\n",
"df = pd.read_csv(\"hf://datasets/synavate/medical_records_did/data_did.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['last_name', 'first_name', 'full_name', 'birthday', 'gender', 'type',\n",
" 'state', 'district', 'senate_class', 'party', 'url', 'address', 'phone',\n",
" 'contact_form', 'rss_url', 'twitter', 'facebook', 'youtube',\n",
" 'youtube_id', 'bioguide_id', 'thomas_id', 'opensecrets_id', 'lis_id',\n",
" 'fec_ids', 'cspan_id', 'govtrack_id', 'votesmart_id', 'ballotpedia_id',\n",
" 'washington_post_id', 'icpsr_id', 'wikipedia_id', 'last_name.1',\n",
" 'first_name.1', 'middle_name.1', 'suffix.1', 'nickname.1',\n",
" 'full_name.1', 'birthday.1', 'gender.1', 'type.1', 'state.1',\n",
" 'district.1', 'senate_class.1', 'party.1', 'url.1', 'address.1',\n",
" 'phone.1', 'contact_form.1', 'rss_url.1', 'twitter.1', 'facebook.1',\n",
" 'youtube.1', 'youtube_id.1', 'bioguide_id.1', 'thomas_id.1',\n",
" 'opensecrets_id.1', 'lis_id.1', 'fec_ids.1', 'cspan_id.1',\n",
" 'govtrack_id.1', 'votesmart_id.1', 'ballotpedia_id.1',\n",
" 'washington_post_id.1', 'icpsr_id.1', 'wikipedia_id.1'],\n",
" dtype='object')"
]
},
"execution_count": 93,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_drop=df.copy()\n",
"df_drop.isnull().drop(index=1)\n",
"df_drop.isna().sum()\n",
"df_drop.drop(columns=['Unnamed: 0', 'middle_name', 'suffix', 'nickname'], inplace=True)\n",
"df_drop.columns"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!pip install scikit-learn\n",
"from sklearn import train_test_split\n",
"\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.11.9"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|