evaluate_performance.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/emaad/cs294-learning_genome_context/env/lib/python3.8/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n",
      "100%|██████████| 150/150 [23:20<00:00,  9.33s/it]\n"
     ]
    }
   ],
   "source": [
    "SUBSAMPLE_LENGTH = 300\n",
    "#more depth = better performance but slower and more memory required\n",
    "DEPTH = 11\n",
    "BATCH_SIZE = 60\n",
    "DEVICE = 'cuda:3'\n",
    "EPOCHS = 200\n",
    "#\"bsubtilis\" or \"ecoli\"\n",
    "IMG_SET = \"bsubtilis\"\n",
    "#odd number\n",
    "MAX_NUM_GENES = 13\n",
    "INCLUDE_POSITION = False\n",
    "INCLUDE_SEQUENCE = False\n",
    "\n",
    "import torch\n",
    "from torch.utils.data import DataLoader\n",
    "from operon_transformer import operon_transformer, operon_transformer_classifier\n",
    "from torch.optim import AdamW\n",
    "from torch.utils.data import DataLoader\n",
    "from dataloader import OperonLoader\n",
    "import math\n",
    "from tqdm import tqdm\n",
    "import numpy as np\n",
    "\n",
    "torch.random.manual_seed(42)\n",
    "\n",
    "val_data = OperonLoader('data.csv',\n",
    "                        subsample_length=SUBSAMPLE_LENGTH,\n",
    "                        include_position=INCLUDE_POSITION,\n",
    "                        include_sequence=INCLUDE_SEQUENCE,\n",
    "                        max_num_genes=MAX_NUM_GENES,\n",
    "                        split_key=\"valid\",\n",
    "                        im_set=IMG_SET,\n",
    "                        device=DEVICE)\n",
    "\n",
    "test_data = OperonLoader('data.csv',\n",
    "                        subsample_length=SUBSAMPLE_LENGTH,\n",
    "                        include_position=INCLUDE_POSITION,\n",
    "                        include_sequence=INCLUDE_SEQUENCE,\n",
    "                        max_num_genes=MAX_NUM_GENES,\n",
    "                        split_key=\"test\",\n",
    "                        im_set=IMG_SET,\n",
    "                        device=DEVICE)\n",
    "\n",
    "val_dataloader = DataLoader(val_data, batch_size=BATCH_SIZE)\n",
    "test_dataloader = DataLoader(test_data, batch_size=BATCH_SIZE)\n",
    "\n",
    "\n",
    "model = operon_transformer_classifier(\n",
    "    depth=DEPTH,\n",
    "    ckpt_path='saved_models/operon_transformer_stable_last.pt',\n",
    "    max_num_genes=MAX_NUM_GENES,\n",
    "    alignment_length=SUBSAMPLE_LENGTH,\n",
    "    include_position=INCLUDE_POSITION,\n",
    "    include_sequence=INCLUDE_SEQUENCE,\n",
    "    stable=False,\n",
    "    attn_dropout=0,\n",
    "    ff_dropout=0).to(DEVICE)\n",
    "\n",
    "model.load_state_dict(torch.load('saved_models/stable_operon_transformer_classifier_last.pt'))\n",
    "\n",
    "model.eval()\n",
    "\n",
    "preds = []\n",
    "labels = []\n",
    "\n",
    "with torch.no_grad():\n",
    "    for batch in tqdm(val_dataloader):\n",
    "        msa, score, label = batch\n",
    "        out = model(msa, score)\n",
    "        if len(preds) == 0:\n",
    "            preds = out[0]\n",
    "            labels = label.T[0]\n",
    "        else:\n",
    "            preds = torch.cat((preds, out[0]), dim=0)\n",
    "            labels = torch.cat((labels, label.T[0]), dim=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics\n",
    "fpr, tpr, thresholds = metrics.roc_curve(labels.cpu(),preds.cpu())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "auc = metrics.roc_auc_score(labels.cpu(),preds.cpu())\n",
    "with plt.style.context('ggplot'):\n",
    "    plt.figure(dpi=150, clear=True,edgecolor='white', facecolor='white', frameon=False, tight_layout=True)\n",
    "    plt.title(f'ROC Curve for Validation Set\\nAUC={str(auc)}', color='black')\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.plot(fpr,tpr)\n",
    "    plt.savefig('roc_curve.png')\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "cutoff = thresholds[np.argmax(tpr-fpr)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 12/12 [01:42<00:00,  8.58s/it]\n"
     ]
    }
   ],
   "source": [
    "preds_test = []\n",
    "labels_test = []\n",
    "\n",
    "with torch.no_grad():\n",
    "    for batch in tqdm(test_dataloader):\n",
    "        msa, score, label = batch\n",
    "        out = model(msa, score)\n",
    "        if len(preds_test) == 0:\n",
    "            preds_test = out[0] >= cutoff\n",
    "            labels_test = label.T[0]\n",
    "        else:\n",
    "            preds_test = torch.cat((preds_test >= cutoff, out[0]), dim=0)\n",
    "            labels_test = torch.cat((labels_test, label.T[0]), dim=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "tn, fp, fn, tp = confusion_matrix(labels_test.long().cpu(), preds_test.long().cpu(), labels=[0, 1]).ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sensitivity: 0.8924731182795699\n",
      "Precision: 0.8668407310704961\n",
      "Specificity: 0.8311258278145696\n",
      "Accuracy: 0.8649851632047477\n",
      "MCC: 0.7264855585454512\n",
      "F1: 0.8794701986754967\n"
     ]
    }
   ],
   "source": [
    "sensitivity = tp/(tp+fn)\n",
    "precision = tp/(tp+fp)\n",
    "specificity = tn/(tn+fp)\n",
    "accuracy = (tp+tn)/(tp+tn+fp+fn)\n",
    "mcc = (tp*tn-fp*fn)/(math.sqrt((tp+fp)*(tp+fn)*(tn+fp)*(tn+fn)))\n",
    "f1 = 2*tp/(2*tp+fp+fn)\n",
    "\n",
    "print(f'Sensitivity: {sensitivity}')\n",
    "print(f'Precision: {precision}')\n",
    "print(f'Specificity: {specificity}')\n",
    "print(f'Accuracy: {accuracy}')\n",
    "print(f'MCC: {mcc}')\n",
    "print(f'F1: {f1}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.757"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cutoff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "674"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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