proteinbind_model_3modalities.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "82940d47-8ec8-498e-b5ce-9a0bca790cf3",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import torch\n",
    "from tqdm import tqdm\n",
    "from torch.utils.data import Subset\n",
    "from torch.utils.data import DataLoader\n",
    "from pytorch_metric_learning.losses import SelfSupervisedLoss, NTXentLoss\n",
    "\n",
    "from proteinbind_new import EmbeddingDataset, DualEmbeddingDataset\n",
    "from proteinbind_new import create_proteinbind\n",
    "from transformers import pipeline\n",
    "\n",
    "device = torch.device(\"cpu\")\n",
    "if torch.cuda.is_available():\n",
    "    device = torch.device(\"cuda\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "76cca8b4-8814-465a-907d-6f291325b79d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "batch_size = 64\n",
    "\n",
    "# GO\n",
    "go_dataset = DualEmbeddingDataset('GO_files/go_AA_embeddings.pt', 'GO_files/GO_embeddings.pt', \"go\")\n",
    "\n",
    "train_count = int(len(go_dataset) * 0.95)\n",
    "indices = list(range(len(go_dataset)))\n",
    "train_indices = indices[:train_count]\n",
    "test_indices = indices[train_count:]\n",
    "\n",
    "go_train_dataloader = DataLoader(Subset(go_dataset, train_indices), batch_size=batch_size)\n",
    "go_test_dataloader = DataLoader(Subset(go_dataset, test_indices), batch_size=batch_size)\n",
    "\n",
    "go_train_iter = iter(go_train_dataloader)\n",
    "go_test_iter = iter(go_test_dataloader)\n",
    "\n",
    "# DNA\n",
    "dna_dataset = DualEmbeddingDataset('DNA_files/dna_AA_embeddings.pt', 'DNA_files/uniprot_nuc_embeddings.pt', \"dna\")\n",
    "dna_train_dataloader = DataLoader(Subset(dna_dataset, train_indices), batch_size=batch_size)\n",
    "dna_test_dataloader = DataLoader(Subset(dna_dataset, test_indices), batch_size=batch_size)\n",
    "\n",
    "dna_train_iter = iter(dna_train_dataloader)\n",
    "\n",
    "# Text\n",
    "text_dataset = DualEmbeddingDataset('Text_files/text_AA_embeddings.pt', 'Text_files/protein_whole_text_embedding.pt', \"text\")\n",
    "text_train_dataloader = DataLoader(Subset(text_dataset, train_indices), batch_size=batch_size)\n",
    "text_test_dataloader = DataLoader(Subset(text_dataset, test_indices), batch_size=batch_size)\n",
    "\n",
    "text_train_iter = iter(text_train_dataloader)\n",
    "\n",
    "# MSA\n",
    "#msa_dataset = DualEmbeddingDataset('MSA_files/msa_AA_embeddings.pt', 'MSA_files/MSA_embeddings.pt', \"msa\")\n",
    "#msa_dataloader = DataLoader(dna_dataset, batch_size=batch_size)\n",
    "\n",
    "# AA \n",
    "aa_dataset = DualEmbeddingDataset('Text_files/text_AA_embeddings.pt', 'GO_files/go_AA_embeddings.pt', \"text\")\n",
    "aa_train_dataloader = DataLoader(Subset(aa_dataset, train_indices), batch_size=batch_size)\n",
    "aa_test_dataloader = DataLoader(Subset(aa_dataset, test_indices), batch_size=batch_size)\n",
    "\n",
    "aa_train_iter = iter(aa_train_dataloader)\n",
    "\n",
    "\n",
    "model = create_proteinbind()\n",
    "loss_func = SelfSupervisedLoss(NTXentLoss())"
   ]
  },
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      "Epoch 671: joint loss 2.4172581553851304\n"
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      "Epoch 968: joint loss 1.473242151011762\n"
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      "Epoch 974: joint loss 1.5158970542252064\n"
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      "Epoch 975: joint loss 1.497865871085148\n"
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      "Epoch 976: joint loss 1.5186610703209513\n"
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      "Epoch 977: joint loss 1.504070325901634\n"
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      "Epoch 978: joint loss 1.5102887824177742\n"
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      "Epoch 979: joint loss 1.521283231479557\n"
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      "Epoch 980: joint loss 1.513444903649782\n"
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      "Epoch 981: joint loss 1.4927624992438053\n"
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      "Epoch 982: joint loss 1.478477269803223\n"
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      "Epoch 984: joint loss 1.4887410001339096\n"
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      "Epoch 985: joint loss 1.4707259387758218\n"
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      "Epoch 986: joint loss 1.4524592652328705\n"
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      "Epoch 987: joint loss 1.4472744901125367\n"
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      "Epoch 988: joint loss 1.4338137735857774\n"
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      "Epoch 989: joint loss 1.428962920547316\n"
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      "Epoch 990: joint loss 1.4387878584430407\n"
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      "Epoch 991: joint loss 1.445055645841517\n"
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      "Epoch 992: joint loss 1.4552888607508259\n"
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      "Epoch 993: joint loss 1.4522439736480777\n"
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      "Epoch 995: joint loss 1.4414364773976176\n"
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      "Epoch 996: joint loss 1.4333256519350566\n"
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      "Epoch 997: joint loss 1.421843871474266\n"
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      "Epoch 998: joint loss 1.4085704030371027\n"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 999: joint loss 1.4089930797681998\n"
     ]
    },
    {
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      "100%|██████████| 1000/1000 [3:49:43<00:00, 13.78s/it]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1000: joint loss 1.4253748347492594\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "num_epochs = 1000\n",
    "model.to(device)\n",
    "model.train()\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=0.0001)\n",
    "losses = []\n",
    "\n",
    "# Get the total number of mini-batches per epoch based on the smallest data loader\n",
    "num_batches = min(len(go_train_dataloader), len(text_train_dataloader), len(dna_train_dataloader), len(aa_train_dataloader))\n",
    "\n",
    "for epoch in tqdm(range(num_epochs)):\n",
    "    for batch_idx, (go_batch, text_batch, dna_batch, aa_batch) in enumerate(zip(go_train_dataloader, text_train_dataloader, dna_train_dataloader, aa_train_dataloader)):\n",
    "        batch = {\n",
    "            'go': go_batch['go'].type(torch.float32),\n",
    "            'text': text_batch['text'].type(torch.float32),\n",
    "            'dna': dna_batch['dna'].type(torch.float32),\n",
    "            'AA': aa_batch['AA'].type(torch.float32)\n",
    "        }\n",
    "\n",
    "        input_data = {\n",
    "            'go': batch['go'].to(device),\n",
    "            'dna': batch['dna'].to(device),\n",
    "            'text': batch['text'].to(device),\n",
    "            'aa': batch['AA'].to(device)\n",
    "        }\n",
    "\n",
    "        output = model(input_data)\n",
    "        loss = loss_func(output[\"aa\"], output[\"go\"]) + loss_func(output[\"go\"], output[\"aa\"]) + loss_func(output[\"aa\"], output[\"dna\"]) + loss_func(output[\"dna\"], output[\"aa\"]) + loss_func(output[\"aa\"], output[\"text\"]) + loss_func(output[\"text\"], output[\"aa\"])\n",
    "        losses.append(loss.item())\n",
    "\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "    if epoch != 0 and (epoch + 1) % 1 == 0:\n",
    "        print(f\"Epoch {epoch+1}: joint loss {sum(losses) / num_batches}\")\n",
    "        losses = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "1b4d70ba-0ca0-4d97-86d1-77287cc2ca04",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "torch.save(model, 'GO_DNA_Text_model.pth')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "feafbec5-9daa-471c-a4c6-e7662a02b344",
   "metadata": {},
   "source": [
    "GO 9k 1000 epochs loss 1.2401675581932068"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18f82858-e199-41b4-a42b-7557ffe0113b",
   "metadata": {},
   "source": [
    "20 steps, ~6 minutes:  GO loss 8.317567825317383"
   ]
  }
 ],
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