Upload generate_ds.ipynb

utils/generate_ds.ipynb

@@ -0,0 +1,156 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from datasets import load_dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_files = {\n",
+    "    \"train\": \"./train.txt\",\n",
+    "    \"val\": \"./val.txt\",\n",
+    "    \"test\": \"./test.txt\",\n",
+    "}\n",
+    "ds = load_dataset(\"text\", data_files=data_files)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds['train'] = ds['train'].rename_column('text', 'SMILE')\n",
+    "ds['val'] = ds['val'].rename_column('text', 'SMILE')\n",
+    "ds['test'] = ds['test'].rename_column('text', 'SMILE')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import selfies as sf\n",
+    "\n",
+    "def try_convert(row):\n",
+    "    selfie = None\n",
+    "    try:\n",
+    "        selfie = sf.encoder(row['SMILE'])\n",
+    "    except:\n",
+    "        pass\n",
+    "\n",
+    "    return {'SELFIE': selfie}\n",
+    "\n",
+    "# Alongside the SMILES, we also need to convert them to SELFIES\n",
+    "# ds['train'] = ds['train'].add_column('SELFIE', ds['train'].map(try_convert, num_proc=8))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds['train'] = ds['train'].map(try_convert, num_proc=8)\n",
+    "ds['val'] = ds['val'].map(try_convert, num_proc=8)\n",
+    "ds['test'] = ds['test'].map(try_convert, num_proc=8)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Drop the rows where the conversion failed\n",
+    "ds['train'] = ds['train'].filter(lambda row: row['SELFIE'] is not None)\n",
+    "ds['val'] = ds['val'].filter(lambda row: row['SELFIE'] is not None)\n",
+    "ds['test'] = ds['test'].filter(lambda row: row['SELFIE'] is not None)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tokenizers import Tokenizer\n",
+    "\n",
+    "tokenizer = Tokenizer.from_pretrained(\"haydn-jones/GuacamolSELFIETokenizer\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "unk_id = tokenizer.token_to_id('<UNK>')\n",
+    "\n",
+    "# Drop any rows where the tokenization has an <UNK> token\n",
+    "ds['train'] = ds['train'].filter(lambda row: unk_id not in tokenizer.encode(row['SELFIE']).ids, num_proc=8)\n",
+    "ds['val'] = ds['val'].filter(lambda row: unk_id not in tokenizer.encode(row['SELFIE']).ids, num_proc=8)\n",
+    "ds['test'] = ds['test'].filter(lambda row: unk_id not in tokenizer.encode(row['SELFIE']).ids, num_proc=8)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds.save_to_disk('./guacamol')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "repo_id = \"haydn-jones/Guacamol\"\n",
+    "\n",
+    "# Push the dataset to the repo\n",
+    "ds.push_to_hub(repo_id)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "ddpm",
+   "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": "3.11.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}