test_prot_models.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "91e8c0ac-a0fe-414d-a54f-b5100af16caf",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at Rostlab/prot_t5_xl_uniref50 were not used when initializing T5EncoderModel: ['decoder.block.18.layer.0.SelfAttention.q.weight', 'decoder.block.17.layer.2.layer_norm.weight', 'decoder.block.0.layer.0.SelfAttention.v.weight', 'decoder.block.15.layer.0.SelfAttention.q.weight', 'decoder.block.19.layer.2.DenseReluDense.wi.weight', 'decoder.block.5.layer.0.SelfAttention.k.weight', 'decoder.block.13.layer.2.layer_norm.weight', 'decoder.block.20.layer.0.layer_norm.weight', 'decoder.block.20.layer.2.DenseReluDense.wi.weight', 'decoder.block.21.layer.0.SelfAttention.q.weight', 'decoder.block.15.layer.1.EncDecAttention.q.weight', 'decoder.block.15.layer.0.SelfAttention.o.weight', 'decoder.block.0.layer.0.SelfAttention.q.weight', 'decoder.block.16.layer.2.layer_norm.weight', 'decoder.block.9.layer.0.SelfAttention.v.weight', 'decoder.block.7.layer.2.DenseReluDense.wo.weight', 'decoder.block.8.layer.1.EncDecAttention.q.weight', 'decoder.block.22.layer.0.SelfAttention.o.weight', 'decoder.block.6.layer.2.DenseReluDense.wi.weight', 'decoder.block.16.layer.1.layer_norm.weight', 'decoder.block.6.layer.0.SelfAttention.k.weight', 'lm_head.weight', 'decoder.block.20.layer.0.SelfAttention.v.weight', 'decoder.block.9.layer.0.layer_norm.weight', 'decoder.block.2.layer.0.SelfAttention.q.weight', 'decoder.block.13.layer.0.SelfAttention.q.weight', 'decoder.block.5.layer.0.SelfAttention.o.weight', 'decoder.block.2.layer.2.DenseReluDense.wo.weight', 'decoder.block.23.layer.0.SelfAttention.v.weight', 'decoder.block.5.layer.2.DenseReluDense.wi.weight', 'decoder.block.16.layer.1.EncDecAttention.q.weight', 'decoder.block.9.layer.0.SelfAttention.o.weight', 'decoder.block.0.layer.2.DenseReluDense.wo.weight', 'decoder.block.18.layer.1.EncDecAttention.o.weight', 'decoder.block.3.layer.0.SelfAttention.k.weight', 'decoder.block.3.layer.1.EncDecAttention.v.weight', 'decoder.block.4.layer.1.EncDecAttention.q.weight', 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'decoder.block.5.layer.2.DenseReluDense.wo.weight', 'decoder.block.18.layer.0.SelfAttention.o.weight', 'decoder.block.20.layer.1.EncDecAttention.k.weight', 'decoder.block.2.layer.1.EncDecAttention.o.weight', 'decoder.block.8.layer.1.layer_norm.weight', 'decoder.block.15.layer.2.DenseReluDense.wo.weight', 'decoder.block.7.layer.0.SelfAttention.v.weight', 'decoder.block.0.layer.0.SelfAttention.relative_attention_bias.weight', 'decoder.block.4.layer.1.EncDecAttention.v.weight', 'decoder.block.17.layer.1.EncDecAttention.v.weight', 'decoder.block.10.layer.2.DenseReluDense.wi.weight', 'decoder.block.6.layer.1.EncDecAttention.k.weight', 'decoder.block.22.layer.2.layer_norm.weight', 'decoder.block.5.layer.0.SelfAttention.v.weight', 'decoder.block.3.layer.2.DenseReluDense.wi.weight', 'decoder.block.2.layer.1.EncDecAttention.q.weight', 'decoder.block.17.layer.0.SelfAttention.o.weight', 'decoder.block.0.layer.1.EncDecAttention.v.weight', 'decoder.block.15.layer.1.EncDecAttention.v.weight', 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'decoder.block.20.layer.0.SelfAttention.k.weight', 'decoder.block.6.layer.1.EncDecAttention.o.weight', 'decoder.block.11.layer.2.layer_norm.weight', 'decoder.block.4.layer.1.EncDecAttention.o.weight', 'decoder.block.19.layer.0.SelfAttention.k.weight', 'decoder.block.8.layer.1.EncDecAttention.o.weight', 'decoder.block.14.layer.2.DenseReluDense.wo.weight', 'decoder.block.18.layer.1.EncDecAttention.v.weight', 'decoder.block.23.layer.2.layer_norm.weight', 'decoder.block.8.layer.0.SelfAttention.o.weight', 'decoder.block.11.layer.1.EncDecAttention.q.weight', 'decoder.block.1.layer.1.EncDecAttention.q.weight', 'decoder.block.12.layer.0.SelfAttention.q.weight', 'decoder.block.7.layer.0.SelfAttention.k.weight', 'decoder.block.11.layer.0.layer_norm.weight', 'decoder.block.12.layer.2.layer_norm.weight', 'decoder.block.4.layer.0.SelfAttention.o.weight', 'decoder.block.18.layer.1.EncDecAttention.q.weight', 'decoder.block.9.layer.1.EncDecAttention.v.weight', 'decoder.block.0.layer.1.EncDecAttention.k.weight', 'decoder.block.7.layer.0.layer_norm.weight', 'decoder.block.5.layer.1.EncDecAttention.q.weight', 'decoder.block.17.layer.1.EncDecAttention.k.weight', 'decoder.block.5.layer.1.EncDecAttention.k.weight', 'decoder.block.22.layer.1.EncDecAttention.k.weight', 'decoder.block.20.layer.2.DenseReluDense.wo.weight', 'decoder.block.16.layer.1.EncDecAttention.o.weight', 'decoder.block.7.layer.0.SelfAttention.o.weight', 'decoder.block.12.layer.0.layer_norm.weight', 'decoder.block.21.layer.1.layer_norm.weight', 'decoder.block.1.layer.1.EncDecAttention.o.weight', 'decoder.block.3.layer.1.EncDecAttention.o.weight', 'decoder.block.4.layer.0.SelfAttention.v.weight', 'decoder.block.10.layer.1.EncDecAttention.k.weight', 'decoder.block.19.layer.0.SelfAttention.v.weight', 'decoder.block.7.layer.1.EncDecAttention.v.weight', 'decoder.block.17.layer.1.layer_norm.weight', 'decoder.block.3.layer.0.SelfAttention.q.weight', 'decoder.block.7.layer.1.layer_norm.weight', 'decoder.block.20.layer.1.layer_norm.weight', 'decoder.block.0.layer.1.layer_norm.weight', 'decoder.block.18.layer.2.DenseReluDense.wi.weight', 'decoder.block.2.layer.2.DenseReluDense.wi.weight', 'decoder.block.19.layer.1.EncDecAttention.q.weight', 'decoder.block.2.layer.1.layer_norm.weight', 'decoder.block.8.layer.0.layer_norm.weight', 'decoder.block.15.layer.1.layer_norm.weight', 'decoder.block.13.layer.2.DenseReluDense.wi.weight', 'decoder.block.16.layer.2.DenseReluDense.wi.weight', 'decoder.block.12.layer.2.DenseReluDense.wo.weight', 'decoder.block.22.layer.1.layer_norm.weight', 'decoder.block.0.layer.0.SelfAttention.k.weight', 'decoder.block.22.layer.1.EncDecAttention.v.weight', 'decoder.block.23.layer.1.EncDecAttention.o.weight', 'decoder.block.6.layer.0.SelfAttention.q.weight', 'decoder.block.1.layer.2.DenseReluDense.wo.weight', 'decoder.block.23.layer.2.DenseReluDense.wi.weight', 'decoder.block.8.layer.2.DenseReluDense.wi.weight', 'decoder.block.3.layer.0.SelfAttention.o.weight', 'decoder.block.15.layer.1.EncDecAttention.o.weight', 'decoder.block.11.layer.0.SelfAttention.o.weight', 'decoder.block.6.layer.0.SelfAttention.v.weight', 'decoder.block.10.layer.1.EncDecAttention.v.weight', 'decoder.block.2.layer.0.SelfAttention.v.weight', 'decoder.block.0.layer.0.layer_norm.weight', 'decoder.block.23.layer.1.layer_norm.weight']\n",
      "- This IS expected if you are initializing T5EncoderModel 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 T5EncoderModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
     ]
    }
   ],
   "source": [
    "from transformers import T5Tokenizer, T5Model, T5EncoderModel\n",
    "import re\n",
    "import time\n",
    "import torch\n",
    "\n",
    "\n",
    "tokenizer = T5Tokenizer.from_pretrained('Rostlab/prot_t5_xl_uniref50', do_lower_case=False)\n",
    "\n",
    "model = T5EncoderModel.from_pretrained(\"Rostlab/prot_t5_xl_uniref50\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f51aece-bfc3-4ee6-a4ed-ae0283a22079",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "4da9d8cf-167b-4293-bd52-c84cda741153",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "i_seq = 0\n",
    "sequences = []\n",
    "seq_df = pd.read_csv(\"data/genbank_spike_prot_clades.csv\", sep=\",\")\n",
    "\n",
    "seq_df = seq_df.sample(frac=1).reset_index(drop=True)\n",
    "n_seqs = 10\n",
    "batch_size = 10\n",
    "\n",
    "for row in seq_df.iterrows():\n",
    "    seq = row[1][\"Sequence\"]\n",
    "    seq = re.sub(\"\\*\", \"\", seq)\n",
    "    sequences.append(\" \".join(seq))\n",
    "    i_seq += 1\n",
    "    if i_seq == n_seqs:\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "c157f40c-e5eb-4a01-b29d-09c8fd853acc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(sequences)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "4712aac4-ef66-4f03-ac4d-e6919a37be29",
   "metadata": {},
   "outputs": [],
   "source": [
    "device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n",
    "\n",
    "model = model.to(device)\n",
    "model = model.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "f7cad8a2-7569-4b0d-9c7d-03b208941d03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mapping to rare amino acids...\n",
      "tokenization...\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(torch.Size([10, 1274]), torch.Size([10, 1274]))"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# map rarely occured amino acids (U,Z,O,B) to (X)\n",
    "print(\"mapping to rare amino acids...\")\n",
    "#sequences = [\"A E T C Z A O\", \"S K T Z P M V\"]\n",
    "sequences = [re.sub(r\"[UZOJB]\", \"X\", sequence) for sequence in sequences]\n",
    "# Tokenize, encode sequences and load it into the GPU if possibile\n",
    "\n",
    "print(\"tokenization...\")\n",
    "ids = tokenizer.batch_encode_plus(\n",
    "    sequences, add_special_tokens=True, padding=True\n",
    ")\n",
    "input_ids = torch.tensor(ids[\"input_ids\"]).to(device)\n",
    "attention_mask = torch.tensor(ids[\"attention_mask\"]).to(device)\n",
    "\n",
    "input_ids.shape, attention_mask.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "160c5a20-a304-418a-b4ff-92b8936f69c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing embedding...\n",
      "embedding computed\n",
      "Time elapsed: 0.28317283391952514 minutes\n"
     ]
    }
   ],
   "source": [
    "print(\"computing embedding...\")\n",
    "\n",
    "agg_embedding = list()\n",
    "s_time = time.time()\n",
    "\n",
    "for i in range(int(len(sequences) / batch_size)):\n",
    "    curr = i * batch_size\n",
    "    nex = i * batch_size + batch_size\n",
    "    with torch.no_grad():\n",
    "        embedding = model(input_ids=input_ids[curr:nex,], attention_mask=attention_mask[curr:nex,])\n",
    "    embedding = embedding.last_hidden_state.cpu().numpy()\n",
    "    agg_embedding.extend(embedding)\n",
    "e_time = time.time()\n",
    "print(\"embedding computed\")\n",
    "print(\"Time elapsed: {} minutes\".format(str((e_time - s_time)/float(60))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "0bf6046e-51ee-4dff-97ee-034c020a3984",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 1274, 1024)"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg_embedding = np.array(agg_embedding)\n",
    "agg_embedding.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "88bc543c-eb14-49e3-8a85-5ea96f5e9e6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_embedding():\n",
    "    import h5py\n",
    "    hf = h5py.File('data/save_agg_embedding_{}.h5'.format(str(len(sequences))), 'w')\n",
    "    hf.create_dataset('embedding', data=agg_embedding)\n",
    "    hf.close()\n",
    "save_embedding()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "023cf0f2-8c95-499b-b88b-05b1fe361a54",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "features = []\n",
    "agg_features = []\n",
    "for seq_num in range(len(agg_embedding)):\n",
    "    seq_len = (attention_mask[seq_num] == 1).sum()\n",
    "    seq_emd = agg_embedding[seq_num][: seq_len - 1]\n",
    "    agg_features.append(np.mean(seq_emd, axis=0))\n",
    "    features.append(seq_emd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "42ebd284-743d-442b-a503-1c33de82d9fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500, 1024)"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.manifold import TSNE\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "X = np.array(agg_features)\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "f59ebca6-4ac6-4a9e-8f42-1a78c760acb5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-172-5ea5ee5a9730>:27: UserWarning: The palette list has more values (15) than needed (10), which may not be intended.\n",
      "  sns.scatterplot(x=\"comp-1\", y=\"comp-2\", hue=new_hue_list, palette=sns.color_palette(\"hls\", n_colors), data=df).set(title=\"T-SNE projection\")\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'T-SNE projection')]"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.pyplot import figure\n",
    "\n",
    "perplexity = 30\n",
    "n_colors = 15\n",
    "figsize = (8, 8)\n",
    "\n",
    "figure(figsize=figsize, dpi=150)\n",
    "\n",
    "z = TSNE(n_components=2, learning_rate='auto', init='random', perplexity=perplexity).fit_transform(X)\n",
    "\n",
    "df = pd.DataFrame()\n",
    "df[\"comp-1\"] = z[:,0]\n",
    "df[\"comp-2\"] = z[:,1]\n",
    "\n",
    "hue_list = seq_df[\"Clade\"].tolist()[:n_seqs]\n",
    "\n",
    "print(hue_list)\n",
    "\n",
    "new_hue_list = hue_list\n",
    "\n",
    "new_hue_list[-5:] = ['X', 'X', 'X', 'X', 'X'] \n",
    "# 20C','21L (Omicron)','21K (Omicron)','21J (Delta)','21K (Omicron)'\n",
    "\n",
    "print()\n",
    "\n",
    "sns.scatterplot(x=\"comp-1\", y=\"comp-2\", hue=new_hue_list, palette=sns.color_palette(\"hls\", n_colors), data=df).set(title=\"T-SNE projection\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "3cd61f41-f3cb-4cb9-85f6-1e998611d061",
   "metadata": {},
   "outputs": [],
   "source": [
    "#'20C', '21L (Omicron)', '21K (Omicron)', '21J (Delta)', '21K (Omicron)'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "f9fb62b1-a2ae-4a96-9237-f24144b8a5e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-177-25f7f8a0f8a2>:21: UserWarning: The palette list has more values (15) than needed (10), which may not be intended.\n",
      "  sns.scatterplot(x=\"comp-1\", y=\"comp-2\", hue=new_hue_list, palette=sns.color_palette(\"hls\", n_colors), data=df_umap).set(title=\"UMAP projection\")\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'UMAP projection')]"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from umap import UMAP\n",
    "\n",
    "figure(figsize=figsize, dpi=150)\n",
    "\n",
    "n_colors = 15\n",
    "#n_neighbors=15\n",
    "#min_dist=0.01\n",
    "n_components=2\n",
    "metric='euclidean'\n",
    "\n",
    "umap_2d = UMAP(n_components=n_components, n_neighbors=50, min_dist=0.1, metric=metric, init='random', random_state=0)\n",
    "\n",
    "proj_2d = umap_2d.fit_transform(X)\n",
    "\n",
    "df_umap = pd.DataFrame()\n",
    "df_umap[\"comp-1\"] = proj_2d[:,0]\n",
    "df_umap[\"comp-2\"] = proj_2d[:,1]\n",
    "\n",
    "#new_hue_list = seq_df[\"Clade\"].tolist()[:n_seqs]\n",
    "\n",
    "sns.scatterplot(x=\"comp-1\", y=\"comp-2\", hue=new_hue_list, palette=sns.color_palette(\"hls\", n_colors), data=df_umap).set(title=\"UMAP projection\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "70818de8-e520-4a20-ba87-0fbecc9881da",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4745ffdf-ffda-48e5-8def-4685d83308ec",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4728ad79-fee7-4895-9b7f-c17cb74e6ea9",
   "metadata": {},
   "outputs": [],
   "source": []
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