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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "829ddc03",
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"sys.path.append('..')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ea820e23",
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"import load"
]
},
{
"cell_type": "markdown",
"id": "b9a51fa8",
"metadata": {},
"source": [
"# Load MHG-GNN"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c6ea1fc8",
"metadata": {},
"outputs": [],
"source": [
"model_ckp = \"models/model_checkpoints/mhg_model/pickles/mhggnn_pretrained_model_radius7_1116_2023.pickle\"\n",
"\n",
"model = load.load(model_name = model_ckp)\n",
"if model is None:\n",
" print(\"Model not loaded, please check you have MHG pickle file\")\n",
"else:\n",
" print(\"MHG model loaded\")"
]
},
{
"cell_type": "markdown",
"id": "b4a0b557",
"metadata": {},
"source": [
"# Embeddings\n",
"\n",
"※ replace the smiles exaple list with your dataset"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c63a6be6",
"metadata": {},
"outputs": [],
"source": [
"with torch.no_grad():\n",
" repr = model.encode([\"CCO\", \"O=C=O\", \"OC(=O)c1ccccc1C(=O)O\"])\n",
" \n",
"# Print the latent vectors\n",
"print(repr)"
]
},
{
"cell_type": "markdown",
"id": "a59f9442",
"metadata": {},
"source": [
"# Decoding"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6a0d8a41",
"metadata": {},
"outputs": [],
"source": [
"orig = model.decode(repr)\n",
"print(orig)"
]
}
],
"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": "3.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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