run_trained_models.ipynb

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   "outputs": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[easydl] tensorflow not available!\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import numpy as np\n",
    "import pytorch_lightning as pl\n",
    "import torchvision.transforms as tf\n",
    "from tqdm import tqdm\n",
    "from predict import *\n",
    "from HIST2ST import *\n",
    "from dataset import ViT_HER2ST, ViT_SKIN\n",
    "from scipy.stats import pearsonr,spearmanr\n",
    "from torch.utils.data import DataLoader\n",
    "from pytorch_lightning.loggers import TensorBoardLogger\n",
    "from copy import deepcopy as dcp\n",
    "from collections import defaultdict as dfd\n",
    "from sklearn.metrics import adjusted_rand_score as ari_score\n",
    "from sklearn.metrics.cluster import normalized_mutual_info_score as nmi_score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b132bfb4-bd29-44fa-bbca-3790b0848fd4",
   "metadata": {},
   "source": [
    "# Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6281d8f2-e5f7-4c26-88bf-5256197b1418",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "name=[*[f'A{i}' for i in range(2,7)],*[f'B{i}' for i in range(1,7)],\n",
    "      *[f'C{i}' for i in range(1,7)],*[f'D{i}' for i in range(1,7)],\n",
    "      *[f'E{i}' for i in range(1,4)],*[f'F{i}' for i in range(1,4)],*[f'G{i}' for i in range(1,4)]]\n",
    "patients = ['P2', 'P5', 'P9', 'P10']\n",
    "reps = ['rep1', 'rep2', 'rep3']\n",
    "skinname = []\n",
    "for i in patients:\n",
    "    for j in reps:\n",
    "        skinname.append(i+'_ST_'+j)\n",
    "device='cuda'\n",
    "tag='5-7-2-8-4-16-32'\n",
    "k,p,d1,d2,d3,h,c=map(lambda x:int(x),tag.split('-'))\n",
    "dropout=0.2\n",
    "random.seed(12000)\n",
    "np.random.seed(12000)\n",
    "torch.manual_seed(12000)\n",
    "torch.cuda.manual_seed(12000)\n",
    "torch.cuda.manual_seed_all(12000)  \n",
    "torch.backends.cudnn.benchmark = False\n",
    "torch.backends.cudnn.deterministic = True"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "\n",
    "# Hist2ST Prediction\n",
    "\n",
    "### To run the trained model, please select the trained model and replace the value of the variable fold with the number in the name of the selected trained model."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0f19d5d8-6510-493b-9287-ba7215155dc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "fold=5\n",
    "data='her2st'\n",
    "prune='Grid' if data=='her2st' else 'NA'\n",
    "genes=171 if data=='cscc' else 785"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "76cfcfb9-3169-46da-9288-2e2be7942d84",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['B1']\n",
      "Loading imgs...\n",
      "Loading metadata...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████| 1/1 [00:02<00:00,  2.85s/it]\n"
     ]
    }
   ],
   "source": [
    "testset = pk_load(fold,'test',dataset=data,flatten=False,adj=True,ori=True,prune=prune)\n",
    "test_loader = DataLoader(testset, batch_size=1, num_workers=0, shuffle=False)\n",
    "label=testset.label[testset.names[0]]\n",
    "genes=785\n",
    "model=Hist2ST(\n",
    "    depth1=d1, depth2=d2,depth3=d3,n_genes=genes, \n",
    "    kernel_size=k, patch_size=p,\n",
    "    heads=h, channel=c, dropout=0.2,\n",
    "    zinb=0.25, nb=False,\n",
    "    bake=5, lamb=0.5, \n",
    ")\n",
    "model.load_state_dict(torch.load(f'./model/{fold}-Hist2ST.ckpt'))\n",
    "pred, gt = test(model, test_loader,'cuda')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4266cce3-6bc7-4f5b-81d5-d03d3ae089c4",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pearson Correlation: 0.2887870599966082\n",
      "ARI: 0.431\n"
     ]
    }
   ],
   "source": [
    "R=get_R(pred,gt)[0]\n",
    "print('Pearson Correlation:',np.nanmean(R))\n",
    "\n",
    "\n",
    "# clus,ARI=cluster(pred,label)\n",
    "# print('ARI:',ARI)\n"
   ]
  }
 ],
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