example_notebooks/example_decoy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d23c9cd7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-02-01 17:27:48.580449: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F AVX512_VNNI FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2024-02-01 17:27:48.702841: I tensorflow/core/util/port.cc:104] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
      "2024-02-01 17:27:49.788465: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\n",
      "2024-02-01 17:27:49.788527: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\n",
      "2024-02-01 17:27:49.788532: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n",
      "/state/partition1/slurm_tmp/24924496.0.0/ipykernel_2303667/1433951569.py:17: DeprecationWarning: Please use `pearsonr` from the `scipy.stats` namespace, the `scipy.stats.stats` namespace is deprecated.\n",
      "  from scipy.stats.stats import pearsonr\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "os.chdir('..')\n",
    "import src.models_and_optimizers as model_utils\n",
    "import yaml\n",
    "from types import SimpleNamespace\n",
    "from clip_main import get_wds_loaders\n",
    "from transformers import EsmTokenizer\n",
    "import src.data_utils as data_utils\n",
    "import torch\n",
    "import sys\n",
    "import pickle\n",
    "from tqdm import tqdm\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import json\n",
    "from torch.cuda.amp import autocast\n",
    "from scipy.stats.stats import pearsonr \n",
    "import matplotlib.pyplot as plt\n",
    "import webdataset as wds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7f6bee06",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Load model parameters\n",
    "\n",
    "model_dir = \"/c/example/path\"\n",
    "path = os.path.join(model_dir, \"checkpoints/checkpoint_latest.pt\")\n",
    "data_root = \"/data1/groups/keating_madry/wds/decoys\"\n",
    "args_path = os.path.join(model_dir, \n",
    "                         [u for u in os.listdir(model_dir) if u.endswith('.pt')][0])\n",
    "\n",
    "hparams = torch.load(args_path)\n",
    "hparams['args']['blacklist_file'] = \"pdb_blacklist.txt\"\n",
    "hparams['args']['coordinator_hparams'] = \"terminator_configs/standard.json\"\n",
    "args_dict = hparams['args']\n",
    "args_dict['batch_size'] = 50\n",
    "args_dict['distributed'] = 0\n",
    "args_dict['train_wds_path'] = 'wds_decoy'\n",
    "args_dict['val_wds_path'] = 'wds_decoy'\n",
    "args_dict['data_root'] = \"example_data/wds\"\n",
    "args_dict['arch'] = '../esm_model_150'\n",
    "args_dict['num_workers'] = 1\n",
    "\n",
    "args = SimpleNamespace(**args_dict)\n",
    "coordinator_params = data_utils.get_coordinator_params(args.coordinator_hparams)\n",
    "coordinator_params['num_positional_embeddings'] = args.gnn_num_pos_embs\n",
    "coordinator_params['zero_out_pos_embs']= args.gnn_zero_out_pos_embs\n",
    "coordinator_params['clip_mode'] = True\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ca033edd",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading state dict from /data1/groups/keating_madry/runs/new_blacklist/version_0/checkpoints/checkpoint_latest.pt\n",
      "building model based on path\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at /data1/groups/keating_madry/huggingface/esm2_t30_150M_UR50D were not used when initializing EsmModel: ['lm_head.layer_norm.weight', 'lm_head.dense.bias', 'lm_head.dense.weight', 'lm_head.bias', 'lm_head.layer_norm.bias', 'lm_head.decoder.weight']\n",
      "- This IS expected if you are initializing EsmModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing EsmModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "Some weights of EsmModel were not initialized from the model checkpoint at /data1/groups/keating_madry/huggingface/esm2_t30_150M_UR50D and are newly initialized: ['esm.pooler.dense.bias', 'esm.pooler.dense.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ZERO OUT POS EMB False\n",
      "GNN Potts Model Encoder hidden dimensionality is 128\n",
      "freeze_llm False\n",
      "{'training_args': {'epochs': 10, 'lr': 0.001, 'weight_decay': 0.001, 'momentum': 0.9, 'label_smoothing': 0.0, 'lr_peak_epoch': 2, 'eval_epochs': 2, 'only_non_bn_weight_decay': False, 'opt': 'ADAM', 'lr_scheduler': 'cosine', 'burn_in': 0, 'mixed_precision': True, 'max_len': 1024, 'self_supervised': True}, 'epoch': 9, 'training_metrics': {'loss': 0.6333152562079322, 'acc': 0.6356185298161776}, 'val_metrics': {'loss': 1.6227540006807477, 'acc': 0.5308778800395427}, 'model_building_args': {'esm_arch': 'facebook/esm2_t30_150M_UR50D', 'terminator_hparams': {'cov_features': 'all_raw', 'cov_compress': 'project', 'term_use_mpnn': True, 'matches': 'transformer', 'energies_style': 'mpnn', 'energies_use_mpnn': True, 'energies_full_graph': True, 'contact_idx': True, 'energies_encoder_layers': 3, 'energies_hidden_dim': 128, 'resnet_linear': True, 'matches_linear': True, 'transformer_linear': True, 'term_mpnn_linear': True, 'use_terms': False, 'res_embed_linear': True, 'model': 'multichain', 'term_hidden_dim': 32, 'gradient_checkpointing': True, 'num_pair_stats': 28, 'num_sing_stats': 0, 'resnet_blocks': 4, 'term_layers': 4, 'term_heads': 4, 'conv_filter': 3, 'matches_layers': 4, 'matches_num_heads': 4, 'k_neighbors': 30, 'k_cutoff': None, 'cie_dropout': 0.1, 'cie_scaling': 500, 'cie_offset': 0, 'transformer_dropout': 0.1, 'energies_protein_features': 'full', 'energies_augment_eps': 0, 'energies_dropout': 0.1, 'energies_output_dim': 400, 'energies_gvp': False, 'struct2seq_linear': False, 'use_coords': True, 'num_features': 9, 'condense_options': 'residue_local_reduce_edges_expand_transformer_reduce_log_probs', 'chain_handle': 'replace', 'merge_dups': 'broken', 'num_positional_embeddings': 16, 'zero_out_pos_embs': False, 'energies_type': 'MPNN', 'energies_geometric': False, 'graphformer_config': {'num_layers': 5, 'num_heads': 4, 'embed_per_head': 64, 'mlp_multiplier': 4}, 'clip_mode': True, 'energies_input_dim': 0}, 'self_supervised': True, 'gnn_checkpoint': None, 'freeze_llm': False, 'freeze_text_proj': False, 'language_head': False, 'language_head_type': 'MLP'}}\n"
     ]
    }
   ],
   "source": [
    "## Load model\n",
    "\n",
    "trained_model = model_utils.load_model(path, args_dict['arch'], 'cuda')\n",
    "tokenizer = EsmTokenizer.from_pretrained(args_dict['arch'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8e4cedeb",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Get sequence and structure embeddings from RLA\n",
    "def get_seq_and_struct_features(model, tokenizer, batch):\n",
    "    seq_batch, coords_batch = batch\n",
    "    seqs = seq_batch['string_sequence']\n",
    "    text_inp = tokenizer(seqs, return_tensors='pt', padding=True, truncation=True, max_length=1024+2)\n",
    "    text_inp['position_ids'] = seq_batch['pos_embs'][0]\n",
    "    text_inp = {k: v.to('cuda') for k, v in text_inp.items()}\n",
    "    coord_data = data_utils.construct_gnn_inp(coords_batch, device='cuda', half_precision=True)\n",
    "    gnn_features, text_features, logit_scale = model(text_inp, coord_data) # Get features\n",
    "    new_text_features, _, new_text_mask = data_utils.postprocess_text_features(\n",
    "        text_features=text_features, \n",
    "        inp_dict=text_inp, \n",
    "        tokenizer=tokenizer, \n",
    "        placeholder_mask=seq_batch['placeholder_mask'][0])\n",
    "    return {\n",
    "        'text': new_text_features, # text feature\n",
    "        'gnn': gnn_features, # gnn feature\n",
    "        'seq_mask_with_burn_in': seq_batch['seq_loss_mask'][0], # sequence mask of what's supervised\n",
    "        'coord_mask_with_burn_in': coords_batch['coords_loss_mask'][0], # coord mask of what's supervised\n",
    "        'seq_mask_no_burn_in': new_text_mask.bool(), # sequence mask of what's valid (e.g., not padded)\n",
    "        'coord_mask_no_burn_in': coords_batch['coords'][1], # coord mask of what's valid\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f6bf9057",
   "metadata": {},
   "outputs": [],
   "source": [
    "decoys = ['1a32', '1aaj']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3bf55ad0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running on  1a32\n",
      "{'max_coords_len': 2000, 'shuffle_coords': False, 'max_seq_len': 1024, 'pos_offset': 128, 'burn_in': 0, 'k_neighbors': 30, 'crop_type': 'absolute', 'shuffle_chains': False, 'num_mutations': -1, 'masked_rate': -1.0, 'masked_mode': 'MASK'}\n",
      "added select filtering... 30\n",
      "0 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "20it [00:03,  5.14it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9497209486923339\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running on  1aaj\n",
      "{'max_coords_len': 2000, 'shuffle_coords': False, 'max_seq_len': 1024, 'pos_offset': 128, 'burn_in': 0, 'k_neighbors': 30, 'crop_type': 'absolute', 'shuffle_chains': False, 'num_mutations': -1, 'masked_rate': -1.0, 'masked_mode': 'MASK'}\n",
      "added select filtering... 30\n",
      "0 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "33it [00:07,  4.30it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9337255493916822\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Iterate over decoys, calculate RLA scores, and compare to TM-scores\n",
    "for decoy in decoys:\n",
    "    print(\"running on \", decoy)\n",
    "    \n",
    "    ## Calculate TM score to native (or load if pre-calculated)\n",
    "    out_path = os.path.join(\"example_data/TM_scores\",decoy + 'scores.json')\n",
    "    with open(out_path, 'r') as f:\n",
    "        TM_scores = json.load(f)\n",
    "    TM_scores = np.array(list(TM_scores.values()))\n",
    "    TM_scores = np.array(TM_scores)\n",
    "    \n",
    "    args.train_wds_path = \"wds_decoy\" + decoy + '.tar'\n",
    "    args.val_wds_path = \"wds_decoy\" + decoy + '.tar'\n",
    "    train_loader, val_loader, train_len, val_len = get_wds_loaders(args, coordinator_params, gpu=None, shuffle_train=False, val_only=True, return_count=False) # Load data\n",
    "\n",
    "    all_scores = []\n",
    "    for i, batch in enumerate(tqdm(val_loader, total=val_len)):\n",
    "        with torch.no_grad():\n",
    "            with autocast(dtype=torch.float16):\n",
    "                output_dict = get_seq_and_struct_features(trained_model, tokenizer, batch)\n",
    "                text_feat = output_dict['text']\n",
    "                gnn_feat =  output_dict['gnn'][:, :text_feat.shape[1]] # Remove tail padding\n",
    "                scores = (text_feat.unsqueeze(2) @ gnn_feat.unsqueeze(-1)).squeeze(-1).squeeze(-1)\n",
    "                scores = (scores * output_dict['seq_mask_no_burn_in'].float()).sum(1)/output_dict['seq_mask_no_burn_in'].sum(1) # Calculate RLA score\n",
    "                all_scores.append(scores.cpu())\n",
    "    all_sims_list = torch.cat(all_scores).numpy()\n",
    "     \n",
    "    \n",
    "    ## Plot results\n",
    "    if len(TM_scores) != len(all_sims_list):\n",
    "        print(\"Length issue with \", decoy)\n",
    "        continue\n",
    "    nas = np.logical_or(np.isnan(TM_scores), np.isnan(all_sims_list))\n",
    "    corr = pearsonr(TM_scores[~nas], all_sims_list[~nas])[0]\n",
    "    print(corr)\n",
    "    fig = plt.figure()\n",
    "    plt.scatter([TM_scores], all_sims_list, c= \"k\", s= 0.5)\n",
    "    plt.xlabel('TM scores')\n",
    "    plt.ylabel('Embedding Cosine Similarity')\n",
    "    plt.title(decoy + ' Pearson correlation: ' + str(corr))\n",
    "#     plt.savefig(os.path.join(decoy_folder, decoy + '_correlation.png'))\n",
    "    plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1e66555e-e0f5-457b-aab1-bec44a62dea7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:.conda-rla-public]",
   "language": "python",
   "name": "conda-env-.conda-rla-public-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}