explain.ipynb

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   "id": "f3089086-5a6d-468c-a2b5-5630c4a2a147",
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     "text": [
      "Global seed set to 42\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "42"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pytorch_lightning as pl\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from captum.attr import IntegratedGradients, DeepLift, GradientShap, NoiseTunnel, FeatureAblation\n",
    "import plotly.express as px\n",
    "import wandb\n",
    "from src.esm_embedder import ESMEmbedder\n",
    "from pytorch_lightning import seed_everything\n",
    "from pathlib import Path\n",
    "import pandas as pd\n",
    "import pickle\n",
    "\n",
    "seed_everything(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e4521578",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33milsenatorov\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
     ]
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    {
     "data": {
      "text/html": [
       "Tracking run with wandb version 0.15.8"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Run data is saved locally in <code>/home/jupyter-il.senatorov/SMTB_2023/wandb/run-20230822_085931-d6wiw5dt</code>"
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       "<IPython.core.display.HTML object>"
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     "data": {
      "text/html": [
       "Syncing run <strong><a href='https://wandb.ai/ilsenatorov/SMTB_2023/runs/d6wiw5dt' target=\"_blank\">youthful-gorge-9</a></strong> to <a href='https://wandb.ai/ilsenatorov/SMTB_2023' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
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       "<IPython.core.display.HTML object>"
      ]
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    {
     "data": {
      "text/html": [
       " View project at <a href='https://wandb.ai/ilsenatorov/SMTB_2023' target=\"_blank\">https://wandb.ai/ilsenatorov/SMTB_2023</a>"
      ],
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       "<IPython.core.display.HTML object>"
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     "data": {
      "text/html": [
       " View run at <a href='https://wandb.ai/ilsenatorov/SMTB_2023/runs/d6wiw5dt' target=\"_blank\">https://wandb.ai/ilsenatorov/SMTB_2023/runs/d6wiw5dt</a>"
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   "source": [
    "api = wandb.Api()\n",
    "current_run = wandb.init()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "aa3cf511-806c-449a-be22-3a217dbf5413",
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   "source": [
    "class Model(pl.LightningModule):\n",
    "    def __init__(self, input_dim, hidden_dim: int):\n",
    "        super().__init__()\n",
    "        self.model = nn.Sequential(\n",
    "            nn.Linear(input_dim, hidden_dim),\n",
    "            nn.ReLU(),\n",
    "            nn.Dropout(0.5),\n",
    "            nn.LazyLinear(1),\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        return self.model(x).squeeze(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "faf728ab-a61a-47f7-8dfe-a79f3bf629a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_dims = {\n",
    "    \"esm2_t33_650M_UR50D\": 1280,\n",
    "    \"esm2_t30_150M_UR50D\": 640,\n",
    "    \"esm2_t12_35M_UR50D\": 480,\n",
    "    \"esm2_t6_8M_UR50D\": 320,\n",
    "}\n",
    "model_names = {\n",
    "    33: \"esm2_t33_650M_UR50D\",\n",
    "    30: \"esm2_t30_150M_UR50D\",\n",
    "    12: \"esm2_t12_35M_UR50D\",\n",
    "    6: \"esm2_t6_8M_UR50D\",\n",
    "}"
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  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e5429449-c938-45ac-bbda-370cbd4b4ef2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_baseline(num_layers: int, current_layer: int):\n",
    "    data = [(\"prot1\", \"\")]\n",
    "    embedder = ESMEmbedder(num_layers)\n",
    "    reprs = embedder.run(data)[0][\"representations\"]\n",
    "    return torch.stack([reprs[x].squeeze(0).mean(0) for x in range(num_layers + 1)])[current_layer]\n",
    "\n",
    "\n",
    "def get_data(dataset: str, num_layers: str, current_layer: int, num_samples: int = 1000):\n",
    "    p = Path(\"/shared\") / dataset / model_names[num_layers] / \"test\"\n",
    "    assert p.exists()\n",
    "    embeddings = []\n",
    "    fluorescence_values = {}\n",
    "    for idx, i in enumerate(p.glob(\"*.pt\")):\n",
    "        fluorescence_values[str(i)] = float(i.stem.split(\"|\")[-1])\n",
    "    fluorescence_values = pd.Series(fluorescence_values).sort_values()\n",
    "    fluorescence_values = fluorescence_values.index.to_list()\n",
    "    for idx, i in enumerate(fluorescence_values[: num_samples // 2] + fluorescence_values[-num_samples // 2 :]):\n",
    "        t = torch.load(i)\n",
    "        emb = torch.stack([t[\"mean_representations\"][x] for x in range(num_layers + 1)])\n",
    "        embeddings.append(emb)\n",
    "    return torch.stack(embeddings)[:, current_layer, :]"
   ]
  },
  {
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   "execution_count": null,
   "id": "252c12e4-af99-428e-80ea-6c4013aad7d7",
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    "scrolled": true
   },
   "outputs": [
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      "  warnings.warn('Lazy modules are a new feature under heavy development '\n",
      "/opt/tljh/user/lib/python3.10/site-packages/captum/_utils/gradient.py:57: UserWarning: Input Tensor 0 did not already require gradients, required_grads has been set automatically.\n",
      "  warnings.warn(\n",
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     ]
    }
   ],
   "source": [
    "for ds_name in [\"stability\", \"fluorescence\"]:\n",
    "    runs = api.runs(f\"smtb2023/{ds_name}\")\n",
    "\n",
    "    run_attributions = []\n",
    "\n",
    "    for model_run in runs:\n",
    "        if model_run.state != \"finished\":\n",
    "            continue\n",
    "        run_id = model_run.id\n",
    "        model_name = model_run.config[\"model_name\"]\n",
    "        # if model_name != \"esm2_t6_8M_UR50D\":\n",
    "        #     continue\n",
    "        model_layer = model_run.config[\"layer_num\"]\n",
    "        total_num_layers = int(model_name.split(\"_\")[1][1:])\n",
    "        data = get_data(ds_name, total_num_layers, model_layer)\n",
    "        baseline = get_baseline(total_num_layers, model_layer)\n",
    "        baseline = torch.stack([baseline] * data.size(0))\n",
    "        artifact = current_run.use_artifact(f\"smtb2023/{ds_name}/model-{run_id}:v0\", type=\"model\")\n",
    "        artifact_dir = artifact.download()\n",
    "        model = Model.load_from_checkpoint(\n",
    "            f\"artifacts/model-{run_id}:v0/model.ckpt\", input_dim=input_dims[model_name], hidden_dim=512\n",
    "        )\n",
    "        model.eval()\n",
    "        model = model.cuda()\n",
    "        data = data.cuda()\n",
    "        baseline = baseline.cuda()\n",
    "\n",
    "        # ig = IntegratedGradients(model)\n",
    "        # ig_nt = NoiseTunnel(ig)\n",
    "        dl = DeepLift(model)\n",
    "        # gs = GradientShap(model)\n",
    "        # fa = FeatureAblation(model)\n",
    "\n",
    "        # ig_attr = ig.attribute(data, baselines=baseline, n_steps=50)\n",
    "        # ig_nt_attr = ig_nt.attribute(data, baselines=baseline)\n",
    "        dl_attr = dl.attribute(data, baselines=baseline)\n",
    "        # gs_attr = gs.attribute(data, baselines=baseline, X_train)\n",
    "        # fa_attr = fa.attribute(data, baselines=baseline)\n",
    "        run_attributions.append(\n",
    "            {\n",
    "                \"model_name\": model_name,\n",
    "                \"layer\": model_layer,\n",
    "                \"run_id\": run_id,\n",
    "                # \"ig_attr\": ig_attr.mean(0).cpu(),\n",
    "                # \"ig_nt_attr\": ig_nt_attr.mean(0).cpu(),\n",
    "                \"dl_attr\": dl_attr.detach().cpu(),\n",
    "                # \"gs_attr\": gs_attr.mean(0).cpu(),\n",
    "                # \"fa_attr\": fa_attr.mean(0).cpu(),\n",
    "                \"data\": data.cpu(),\n",
    "            }\n",
    "        )\n",
    "    with open(f\"{ds_name}.pkl\", \"wb\") as f:\n",
    "        pickle.dump(run_attributions, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9ab77773-a381-4f94-8a67-2ba375f4b8c9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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