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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "73c4a5d6-a444-43c0-9812-298113480923",
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"from tqdm import tqdm\n",
"\n",
"def generate_from_extrapolated_embeddings(model, tokenizer, base_embeddings,\n",
" target_properties, extrapolation_factor=0.2):\n",
" \"\"\"\n",
" Generate molecules from extrapolated embeddings\n",
" \n",
" Args:\n",
" model: Trained encoder-decoder model\n",
" tokenizer: SMILES tokenizer\n",
" base_embeddings: Base embeddings to extrapolate from\n",
" target_properties: Target property values for extrapolation direction\n",
" extrapolation_factor: How much to extrapolate (0.2 = 20% increase)\n",
" \"\"\"\n",
" model.eval()\n",
" device = next(model.parameters()).device\n",
" \n",
" # Calculate property gradient direction in embedding space\n",
" # This is a simplified approach - you might want to use more sophisticated methods\n",
" mean_embedding = torch.mean(base_embeddings, dim=0)\n",
" \n",
" # Find direction of increasing properties\n",
" high_prop_mask = target_properties > torch.median(target_properties)\n",
" low_prop_mask = target_properties < torch.median(target_properties)\n",
" \n",
" high_prop_embeddings = base_embeddings[high_prop_mask].mean(dim=0)\n",
" low_prop_embeddings = base_embeddings[low_prop_mask].mean(dim=0)\n",
" \n",
" property_direction = high_prop_embeddings - low_prop_embeddings\n",
" property_direction = property_direction / torch.norm(property_direction)\n",
" \n",
" # Generate extrapolated embeddings\n",
" extrapolated_embeddings = mean_embedding + extrapolation_factor * property_direction\n",
" extrapolated_embeddings = extrapolated_embeddings.unsqueeze(0).to(device)\n",
" \n",
" # Generate SMILES from extrapolated embeddings\n",
" with torch.no_grad():\n",
" generated_ids = model.generate(extrapolated_embeddings)\n",
" generated_smiles = tokenizer.decode(generated_ids[0], skip_special_tokens=True)\n",
" \n",
" return generated_smiles, extrapolated_embeddings\n",
"\n",
"def generate_slerp(model, tokenizer, base_embeddings,\n",
" df, target_col, extrapolation_factor=0.2):\n",
" \"\"\"\n",
" Generate molecules from extrapolated embeddings\n",
" \n",
" Args:\n",
" model: Trained encoder-decoder model\n",
" tokenizer: SMILES tokenizer\n",
" base_embeddings: Base embeddings to extrapolate from\n",
" target_properties: Target property values for extrapolation direction\n",
" extrapolation_factor: How much to extrapolate (0.2 = 20% increase)\n",
" \"\"\"\n",
" model.eval()\n",
" device = next(model.parameters()).device\n",
"\n",
" co2_properties = torch.tensor(df['CO2'].values, dtype=torch.float32)\n",
" ch4_properties = torch.tensor(df['CH4'].values, dtype=torch.float32)\n",
" \n",
" # Normalize properties to same scale before combining\n",
" co2_norm = (co2_properties - co2_properties.mean()) / co2_properties.std()\n",
" ch4_norm = (ch4_properties - ch4_properties.mean()) / ch4_properties.std()\n",
" \n",
" # Combined property (equal weighting)\n",
" target_properties = co2_norm + ch4_norm\n",
" \n",
" extrapolated_embeddings = slerp_extrapolation(base_embeddings, target_properties, factor=extrapolation_factor)\n",
" generated_molecules = []\n",
" # Generate SMILES from extrapolated embeddings\n",
" with torch.no_grad():\n",
" generated_ids = model.generate(extrapolated_embeddings.cuda())\n",
" generated_smiles = tokenizer.decode(generated_ids[0], skip_special_tokens=True)\n",
"\n",
" mol = Chem.MolFromSmiles(generated_smiles)\n",
" is_valid = mol is not None\n",
" \n",
" if is_valid:\n",
" canonical_smiles = MolToSmiles(mol, canonical=True)\n",
" else:\n",
" canonical_smiles = \"INVALID\"\n",
" \n",
" generated_molecules.append({\n",
" 'generated_smiles': generated_smiles,\n",
" 'is_valid': is_valid,\n",
" 'target_property': 'slerp',\n",
" 'extrapolation_factor': extrapolation_factor\n",
" })\n",
" \n",
" print(f\"Generation: {generated_smiles} (Valid: {is_valid})\")\n",
" return generated_molecules\n",
"\n",
"\n",
"\n",
"\n",
"def generate_dual_enhanced_molecules(model, tokenizer, val_df, base_embeddings, \n",
" extrapolation_factor=0.2, num_generations=10):\n",
" \"\"\"Generate molecules with enhanced both CO₂ and CH₄ permeability\"\"\"\n",
" \n",
" # Combine both properties (you can weight them differently if needed)\n",
" co2_properties = torch.tensor(val_df['CO2'].values, dtype=torch.float32)\n",
" ch4_properties = torch.tensor(val_df['CH4'].values, dtype=torch.float32)\n",
" \n",
" # Normalize properties to same scale before combining\n",
" co2_norm = (co2_properties - co2_properties.mean()) / co2_properties.std()\n",
" ch4_norm = (ch4_properties - ch4_properties.mean()) / ch4_properties.std()\n",
" \n",
" # Combined property (equal weighting)\n",
" combined_properties = co2_norm + ch4_norm\n",
" \n",
" generated_molecules = []\n",
" \n",
" print(f\"Generating {num_generations} molecules with enhanced dual permeability...\")\n",
" print(f\"Extrapolation factor: {extrapolation_factor}\")\n",
" \n",
" for i in range(num_generations):\n",
" generated_smiles, extrapolated_embedding = generate_from_extrapolated_embeddings(\n",
" model, tokenizer, base_embeddings, combined_properties, extrapolation_factor\n",
" )\n",
" \n",
" # Validate generated molecule\n",
" mol = Chem.MolFromSmiles(generated_smiles)\n",
" is_valid = mol is not None\n",
" \n",
" if is_valid:\n",
" canonical_smiles = MolToSmiles(mol, canonical=True)\n",
" else:\n",
" canonical_smiles = \"INVALID\"\n",
" \n",
" generated_molecules.append({\n",
" 'generation_id': i + 1,\n",
" 'generated_smiles': generated_smiles,\n",
" 'canonical_smiles': canonical_smiles,\n",
" 'is_valid': is_valid,\n",
" 'target_property': 'DUAL_enhanced',\n",
" 'extrapolation_factor': extrapolation_factor\n",
" })\n",
" \n",
" print(f\"Generation {i+1}: {generated_smiles} (Valid: {is_valid})\")\n",
" \n",
" return generated_molecules\n",
"\n",
"ch4_results = generate_enhanced_molecules_ch4(\n",
" model, tokenizer, val_df, base_embeddings, \n",
" extrapolation_factor=factor, num_generations=1\n",
" )\n",
" \n",
" # Dual enhanced\n",
" dual_results = generate_dual_enhanced_molecules(\n",
" model, tokenizer, val_df, base_embeddings, \n",
" extrapolation_factor=factor, num_generations=1\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b088097d-26b1-45c8-848f-b02784092e75",
"metadata": {},
"outputs": [],
"source": [
"from safetensors import safe_open\n",
"\n",
"checkpoint_path = '/home/jovyan/simson_training_bolgov/regression/decoder_checkpoints/checkpoint-110000/model.safetensors'\n",
"\n",
"state_dict = {}\n",
"with safe_open(checkpoint_path, framework=\"pt\", device=\"cpu\") as f:\n",
" for k in f.keys():\n",
" state_dict[k] = f.get_tensor(k)\n",
"\n",
"model.load_state_dict(state_dict)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b9abe169-0a17-4728-be0c-4befb2439102",
"metadata": {},
"outputs": [],
"source": [
"print(property_loss.detach().item(), scale_factor * (lambda_reg * regularization_loss).detach().item())"
]
}
],
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"display_name": "Python [conda env:.mlspace-bolgov_simson_training]",
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"name": "conda-env-.mlspace-bolgov_simson_training-py"
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"codemirror_mode": {
"name": "ipython",
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
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