BrainModel-CD276.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "# Author: Guoqing Bao\n",
    "# School of Computer Science, The University of Sydney\n",
    "# Date: 2019-12-12\n",
    "# GitHub Project Link: https://github.com/guoqingbao/Pathofusion\n",
    "# Please cite our work if you found it is useful for your research or clinical practice\n",
    "\n",
    "import os\n",
    "# os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\"\n",
    "import gc\n",
    "gc.enable()\n",
    "import warnings;\n",
    "warnings.filterwarnings('ignore');\n",
    "import tensorflow as tf\n",
    "import sqlite3\n",
    "%matplotlib inline \n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy import interp\n",
    "import matplotlib.pyplot as plt\n",
    "from itertools import cycle\n",
    "from sklearn.metrics import roc_curve, auc, roc_auc_score, precision_recall_fscore_support\n",
    "# from sklearn import cross_validation, metrics\n",
    "from sklearn.metrics import f1_score,confusion_matrix, classification_report, accuracy_score\n",
    "\n",
    "\n",
    "import torch.optim as optim\n",
    "import torch.backends.cudnn as cudnn\n",
    "from tqdm import tqdm\n",
    "\n",
    "# config = tf.ConfigProto(allow_soft_placement=True)\n",
    "# config.gpu_options.allocator_type = 'BFC'\n",
    "# config.gpu_options.per_process_gpu_memory_fraction = 0.40\n",
    "# config.gpu_options.allow_growth=True\n",
    "from io import StringIO\n",
    "\n",
    "# import helper function (database manupulation, image augmentation, plot performance, train, etc.)\n",
    "from project.models.helper import *\n",
    "\n",
    "# import our BCNN from models\n",
    "from project.models.bcnn import BCNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Schema exists\n",
      "\n",
      "Schema exists\n",
      "\n"
     ]
    }
   ],
   "source": [
    "project_path = './project/brain/'\n",
    "path = project_path + \"results/bcnn/\"\n",
    "\n",
    "conn_256 = create_or_open_db(project_path + \"data/cd276_labeling_256.db\")\n",
    "conn_512 = create_or_open_db(project_path + \"data/cd276_labeling_512.db\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Schema exists\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# for ihc images, \"load_data\" convert type to positive (type>0) and negative expression (type==0)\n",
    "x_train, y_train, x_test, y_test = load_data(project_path + \"data/cd276_labeling_256.db\", test_ids=test_patient_ids, ihc=True) # patch ID and type\n",
    "trainLoader = DataGenerator(x_train, y_train, connections=[conn_256, conn_512], image_sizes=[256,512], augment=True, classes=2)\n",
    "testLoader = DataGenerator(x_test, y_test, connections=[conn_256, conn_512], image_sizes=[256,512], augment=False, classes=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of model parameters: 11617250\n"
     ]
    }
   ],
   "source": [
    "model = BCNN(2, False)\n",
    "print('Number of model parameters: {}'.format(\n",
    "        sum([p.data.nelement() for p in model.parameters()])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load the model if we have trained\n",
    "if os.path.exists(path + 'torch_model_cd276.h5'):\n",
    "    checkPoint = torch.load(path + 'torch_model_cd276.h5')\n",
    "    model.load_state_dict(checkPoint)\n",
    "    model = nn.DataParallel(model).cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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     "output_type": "stream",
     "text": [
      "                                                                                \r"
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      "Epoch\tLearningRate\tTrainLoss\tTestLoss\tTrainAcc.\tTestAcc.\tEndTime.\n"
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "42\t0.000050\t0.070170\t0.112900\t99.233821\t96.050448\t12/12 15:12:03\n"
     ]
    },
    {
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      "43\t0.000050\t0.065437\t0.113942\t99.343275\t96.150017\t12/12 15:14:31\n"
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    {
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      "46\t0.000050\t0.062846\t0.112238\t99.411684\t96.216396\t12/12 15:21:54\n"
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      "  0%|          | 0/229 [00:00<?, ?it/s]                                         "
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      "47\t0.000050\t0.063911\t0.115335\t99.343275\t96.050448\t12/12 15:24:22\n"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "48\t0.000050\t0.064203\t0.114232\t99.356957\t96.216396\t12/12 15:26:51\n"
     ]
    },
    {
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     "output_type": "stream",
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      "  0%|          | 0/229 [00:00<?, ?it/s]                                         "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "49\t0.000050\t0.065531\t0.114944\t99.261185\t96.083638\t12/12 15:29:18\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50\t0.000050\t0.060522\t0.116470\t99.480093\t96.116827\t12/12 15:31:46\n"
     ]
    }
   ],
   "source": [
    "# otherwise, train the model and save (we only save the last epoch model)\n",
    "if not os.path.exists(path + 'torch_model_cd276.h5'):\n",
    "    history = train(model, trainLoader, testLoader, multiinputs=True, epochs=50, base_lr=0.0001, weight_decay=0.005, log_path=path, log_file='cd276_train_test.log')\n",
    "    torch.save(model.state_dict(), path + 'torch_model_cd276.h5')\n",
    "    np.save(path + 'train_test_history_cd276.npy', np.array(history.history))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "history = np.load(path + 'train_test_history_cd276.npy',allow_pickle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n",
      "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's plot the train/test history\n",
    "trains, tests = show_train_history([history.item()], 'acc', 'val_acc', path, 'train_test_history_cd276')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "External Testing accuracy 0.9611682708264189\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#let's see the test results\n",
    "model.eval()\n",
    "probas_ = []\n",
    "with torch.no_grad():\n",
    "    for i in range(len(testLoader)):\n",
    "        [x1, x2], t = testLoader[i]\n",
    "        m1, m2, t = Variable(torch.FloatTensor(x1).cuda()), Variable(torch.FloatTensor(x2).cuda()), \\\n",
    "                        Variable(torch.LongTensor(t.tolist()).cuda())\n",
    "        y = model(m1, m2)\n",
    "        probas_.extend(F.softmax(y).cpu().numpy().tolist())\n",
    "probas_ = np.array(probas_)\n",
    "pred = np.argmax(probas_,axis=1) \n",
    "ac = accuracy_score(y_test, pred)\n",
    "print(\"External Testing accuracy {}\\r\\n\".format(ac))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mdaRbxZwWER+UtBIgIt6Q5JP+zcwy0pGOweyU1JXkMcmSDgd2p1qVmZk1qqMd5P8O8FOgn6T/S+Huyv+calVmZtakDhMwEfFjScsp3LJfwMci4oXUKzMzs0bl5SyyUh44diTwDvBo8byIeDnNwszMbG8dbYjscQrHX0ThkcnHAOuAYSnWZWZmTegwARMRJxdPJ3dZvjq1iszMrFkdJmAaiogVkkanUYyZmbWsIx2D+fuiyS7AB4FXU6vIzMya1NGOwRxU9HoXhWMyP0mnHDMza0mHCJjkAsveEfGFdqrHzMxakPuAkdQtInY19+hkMzNrf7kPGGAJheMtlZLmAnOA7XULI+KhlGszM7MGJHWcg/zAocBrwEd473qYABwwZmbWpOYCpl9yBtka3guWOpFqVWZm1qSOMETWFehN/WCp44AxM8tIRwiYLRHxtXarxMzMStIRAiYfn8DMrBPJ00H+5qo8p92qMDOzkqXxyGRJ4yWtk7RB0hebWW+0pFpJF7W0zSY7mIh4vcWKzMys3bX1EFlyUf0dwDigBlgqaW5EVDWy3jeAhaVsNx99lpmZ7ZFCBzMG2BARv4uIHcBsYHIj611P4VZhr5RSpwPGzCxnUgiYCmBj0XRNMq94nxXAhcBdpdbZ6tv1m5lZdvbjIP9hkpYVTc+MiJl1m21k/YaXo3wbuCkiaksdonPAmJnlzD4eg9kaEaOaWFYDDCyaHgBsbrDOKGB2su/DgImSdkXEw03t0AFjZpYzKVwHsxQ4XtIxwCZgKvCJ4hUi4pii/f8AeKy5cAEHjJlZ7rR1wCR3zr+OwtlhXYFZEbFW0jXJ8pKPuxRzwJiZ5UhaF1pGxDxgXoN5jQZLRFxeyjYdMGZmOZOXW8X4NGUzM0uFA8bMzFLhITIzs5zJyxCZA8bMLGccMGZm1uZKvTtyOXDAmJnljAPGzMxS4YAxM7NUOGDMzCwVeQkYXwdjZmapcAdjZpYjPovMzMxS44AxM7NUOGDMzCwVDhgzM0tFXgLGZ5GZmVkq3MGYmeWIzyIzM7PU5CVgPERmZmapcAdjZpYzeelgHDBmZjnjgDEzs1Q4YMzMrM35LDIzM0uNA8bMzFLhgDEzs1Q4YMzMLBV5CRhfaGlmZqlwB2NmliN5OovMHYzVs2DBAoYOHcrgwYP5xje+sdfyiODGG29k8ODBnHrqqaxYsWLPsuOOO44RI0YwcuRITjvttPYs29rReeedx5o1a6iqquILX/jCXsv79u3LnDlzWL58Ob/61a8YNmzYnmUvvfQSK1asYOnSpfzmN79pz7ItA6kFjKSQdFvR9D9K+koK+/lSg+lft/U+Oova2lqmT5/OY489xurVq3nggQeoqqqqt878+fNZv349L774It/73ve49tpr6y1/8sknWb58Oc8991x7lm7tpEuXLsyYMYMLLriA4cOHc/HFFzNkyJB669x0002sWrWKkSNHMm3aNG677bZ6y8eNG8fo0aM544wz2rP0DqWui2nNVxbS7GD+Anxc0mEp7gOgXsBExIdS3l+HtWTJEo477jiOPfZYevTowZQpU5g7d269dR599FE++clPIonTTz+dt956iy1btmRUsbW30aNH89vf/pbq6mp27tzJgw8+yAUXXFBvnSFDhvD0008DsG7dOo466ij69euXRbkdlgMGdgEzgc83XCDpcEk/kbQ0+fqrovk/k7RC0t2S/lAXUJIelrRc0lpJVyXzbgF6SqqU9ONk3tvJvw9Imli0zx9I+ltJXSXdmuz3eUlXp/g9yJXNmzczcODAPdMDBgxg8+bN9dbZtGkTAwYM2DNdUVHBpk2bgMIP/YQJExgzZgz33HNP+xRt7aqiooKampo905s2beKII46ot87q1av52Mc+BsCoUaM46qijqKioAApDrPPmzWPx4sVcccUV7Vd4B5OXgEn7IP8dwPOSvtlg/gzgXyPiWUlHAguBIcDNwNMR8S+SxgNXFb1nWkS8LqknsFTSTyLii5Kui4gRjex7NnAxME9SD+Ac4LPAFcBbETFa0gHAryQ9ERHVxW9OQuwqgCOPPHI/vw35EBF7zWv4g9ncOosWLeKII47glVdeYfz48QwePJizzjornWItE439omr4M/HNb36Tb33rWyxdupQ1a9ZQWVlJbW0tAGPHjmXLli0cfvjhzJ8/n3Xr1vHss8+2S+0dSV4O8qcaMBHxJ0n3AdOBd4sWnQsMLfomHSzpIOBM4MLkvQskvVH0numSLkxeDwSOB15rZvfzge8kITIeWBQR70o6DzhF0kXJen2SbdULmIiYSaEDY9SoUXv/Vu2AKioq2Lhx457pmpoa+vfvX2+dAQMGNPkXbN2//fr1Y/LkySxdutQB08HU1NTs1cE2HCLdtm0bn/nMZ/ZMv/TSS1RXF/571a376quv8sgjjzB69GgHTCv5LLL6vk2haziwwX7PiIgRyVdFRGwDGv2uSRpLIZTOiIjhwErgfc3tNCL+DDwDnE+hk5ldtzng+qJ9HxMRT+zzp+tARo8ezYYNG6iurmbHjh2Njq9/9KMf5Uc/+hERweLFizn44IPp378/27dvZ9u2bQBs376dn/3sZ/XOHrKOYdmyZQwaNIijjz6a7t27M2XKFB577LF66/Tp04fu3bsDMG3aNJ599lm2bdtGr1696N27NwC9evXi3HPPZe3ate3+Gaz9pH4dTDKs9SCFkJmVzH4CuA64FUDSiIioBJ4FpgDfSDqNQ5L1+wBvRMQ7kk4ETi/axU5J3SNiZyO7nw1cCYwCLk/mLQQ+K+npiNgp6QRgU0Rsb6OPnFvdunVjxowZTJw4kdraWi6//HKGDRvG3XffDcDVV1/NxIkTWbBgAYMHD6ZXr17ce++9APzxj3/koosKTeGuXbuYOnUq48ePz+yzWDpqa2u58cYbefzxx+nSpQs//OEPqaqq2tOx3HPPPZx44onMmjWL3bt388ILL3DVVYWR7g984APMmTMHKPyszZ49myee8N92+yIvHYwaG1Nvkw1Lb0dE7+T1BygMQX0zIr6SHLi/g8Jxl24Uhq+ukdQPuJ9CsPyCQudxTLLJh4EKYB1wOPCViHhG0jeAScCKiLi0wX67A/8FzI2ITyfzugD/B7iAQjfzKvCxiHirqc8yatSo8Gm31pyePXtmXYLlwM6dO5dHxKj92cbJJ58cDz/8cKvfN2jQoP3ed2ul1sHU/ZJPXv8R6FU0vZVCeDT0FnB+ROySdAZwdkT8JVk2oYn93ATc1MR+dwLvb7D+bgqnNtc7vdnMzNpWud0q5kjgwaTL2AF8poX1zcw6nbwMkZVVwETEeuDUrOswMytnDhgzM2tzPk3ZzMw6PXcwZmY54w7GzMw6NXcwZmY54w7GzMw6NXcwZmY5k5cOxgFjZpYzeQkYD5GZmeXIvjxsrJRAkjRe0jpJGyR9sZHllyYPaXxe0q8lDW9pmw4YM7NOTlJXCjcgngAMBS6RNLTBatXAhyPiFODrJM/Lao6HyMzMciaFIbIxwIaI+F2y/dnAZKCqboWI+HXR+ouBAbTAAWNmljP7GDCHSVpWND0zeXIvFB6FsrFoWQ1wWjPbuoLCU4Ob5YAxM8uZfQyYrc08D6axDTb6sDBJZ1MImDNb2qEDxszMaoCBRdMDgM0NV5J0CnAvMCEiXmtpow4YM7OcSeEYzFLgeEnHAJuAqcAnGuzzSOAh4JMR8VIpG3XAmJnlSBq360+eInwdsBDoCsyKiLWSrkmW3wV8mcITgu9M9r+rpUcwO2DMzIyImAfMazDvrqLXVwJXtmabDhgzs5zxlfxmZtapuYMxM8uZvHQwDhgzs5zJS8B4iMzMzFLhDsbMLGfy0sE4YMzMciSN62DS4iEyMzNLhTsYM7OcyUsH44AxM8uZvASMh8jMzCwV7mDMzHLGHYyZmXVq7mDMzHLEpymbmVmn5w7GzCxn3MGYmVmn5oAxM7NUeIjMzCxnPERmZmadmjsYM7OccQdjZmadmjsYM7OccQdjZmadmjsYM7Mc8a1izMys03PAmJlZKjxEZmaWMx4iMzOzTs0djJlZzriDMTOzTs0BY2ZmqfAQmZlZzniIzMzMOjV3MGZmOeMOxszMOjV3MGZmOeJ7kZmZWafnDsbMLGfcwZiZWafmDsbMLGfcwZiZWafmDsbMLGfcwZiZWaemiMi6hrIn6VXgD1nXUWYOA7ZmXYSVNf+M7O2oiDh8fzYgaQGF721rbY2I8fuz79ZywNg+kbQsIkZlXYeVL/+MmIfIzMwsFQ4YMzNLhQPG9tXMrAuwsuefkU7Ox2DMzCwV7mDMzCwVDhgzM0uFA8bMzFLhgDEzs1Q4YKxkkk6Q9JSkNcn0KZL+Oeu6rLxIOkrSucnrnpIOyromy4YDxlrjHuCfgJ0AEfE8MDXTiqysSPoM8J/A3cmsAcDD2VVkWXLAWGv0ioglDebtyqQSK1fXAn8F/AkgItYD/TKtyDLjgLHW2CrpOCAAJF0EbMm2JCszf4mIHXUTkrqR/LxY5+PnwVhrXEvh6uwTJW0CqoFLsy3JyswvJH0J6ClpHPA54NGMa7KM+Ep+K5mkrhFRK+lAoEtEbMu6JisvkroAVwDnAQIWAveGf9F0Sg4YK5mkl4EFwAPA0/6lYQ1JuhCYFxF/yboWy56PwVhrDAaepDBUVi3pdklnZlyTlZdJwEuSfiTpb5JjMNZJuYOxfSLpEGAGcGlEdM26HisfkroDE4CLgTOBn0XEldlWZVlwB2OtIunDku4EVgDvA6ZkXJKVmYjYCcwHZgPLgcnZVmRZcQdjJZNUDVQCDwJzI2J7xiVZmZE0nsLFt2cDz1A4XvdERPh6qU7IAWMlk3RwRPwp6zqsfEmaTaFzme8D/eaAsRZJ+p8R8U1J36WRi+YiYnoGZZlZmfMZHlaKF5J/l2VahZUtSc9GxJmStlH/jxABEREHZ1SaZcgBYy2KiLorsd+JiDnFyyT9XQYlWZmJiDOTf33nZNvDZ5FZa/xTifOsk5L0o1LmWefgDsZaJGkCMBGokPSdokUH47spW33DiieSCy1HZlSLZcwBY6XYTOH4yyQK1zXU2QZ8PpOKrKxI+ieg7iaXdWcaCthB4Qap1gn5LDIrmaRuvp7BmiPpXyLCw6YGOGCsBJIejIgpklbT+BlCp2RUmpWh5DZCx1O40wMAEbEou4osKw4Ya5Gk/hGxRdJRjS2PiD+0d01WniRdCdxA4VHJlcDpwG8i4iOZFmaZ8Flk1qKIqHtq5VZgYxIoBwDDKRyfMatzAzAa+ENEnA2cCryabUmWFQeMtcYi4H2SKoCngE8DP8i0Iis3f46IPwNIOiAiXqTwmAfrhBww1hqKiHeAjwPfjYgLgaEZ12TlpUZSX+Bh4GeSHsFdbqfl05StNSTpDOBSCo/FBf8MWZHkjw6Ar0j6OdCHwlNQrRPyLwdrjRspXLn/04hYK+lY4OcZ12RlRNKhRZOrk399JlEn5bPIrNUkHUTh9OS3s67Fyouk3wMDgTconMbeF9gCvAJ8JiKWN/1u62h8DMZKJulkSSuBNUCVpOWShrX0PutUFgATI+KwiHg/hUcnPwh8Drgz08qs3bmDsZJJ+jXwvyLi58n0WOD/RcSHMi3MyoakZRExqrF5kiojYkRWtVn78zEYa40D68IFICKekXRglgVZ2Xld0k0UnmoJcDHwhqSuwO7syrIseIjMWuN3kv63pKOTr38GqrMuysrKJyhcxf9w8jUwmdcVmJJhXZYBD5FZyZJ7TH0VODOZtQj4akS8kV1VVo4k9fZJIOaAsRZJeh9wDTCIwqmnsyJiZ7ZVWTmS9CHgXqB3RBwpaThwdUR8LuPSLAMeIrNS/BAYRSFcJgC3ZluOlbF/Bc4HXgOIiFXAWZlWZJnxQX4rxdCIOBlA0r8BSzKux8pYRGyUVDyrNqtaLFsOGCvFnuGwiNjV4JeHWbGNyTBZSOoBTAdeyLgmy4iPwViLJNUC2+smgZ7AO7z3wLGDs6rNyoukw4AZwLkUfj6eAG6IiNcyLcwy4YAxM7NUeIjMzPabpC83szgi4uvtVoyVDXcwZrbfJP1DI7MPpPBYh/dHRO92LsnKgAPGzNpUcrftGyiEy4PAbRHxSrZVWRY8RGZmbSJ5FszfU8G6lNEAAANtSURBVHgg3Q+BD/ouD52bA8bM9pukWyk8SnsmcLJvE2PgITIzawOSdgN/AXZR/wmWPpW9E3PAmJlZKnwvMjMzS4UDxszMUuGAsVySVCupUtIaSXMk9dqPbf1A0kXJ63slDW1m3bHJvbZau4/fJ7dRKWl+g3VadcBc0lck/WNrazRraw4Yy6t3I2JERJwE7KDwvJo9kkf0tlpEXBkRVc2sMhZodcCYdUYOGOsIfgkMSrqLn0v6D2C1pK6SbpW0VNLzkq4GUMHtkqokPQ70q9uQpGckjUpej5e0QtIqSU9JOppCkH0+6Z7+WtLhkn6S7GOppL9K3vt+SU9IWinpbgpnUzVL0sOSlktaK+mqBstuS2p5StLhybzjJC1I3vNLSSe2xTfTrK34OhjLNUndKDwEbUEyawxwUkRUJ7+k34qI0ZIOAH4l6QngVGAwcDLwAaAKmNVgu4cD9wBnJds6NCJel3QX8HZE/P9kvf8A/jUinpV0JLAQGALcDDwbEV+T9DdAvcBowrRkHz2BpZJ+ktyF+EBgRUT8Q3LPr5uB6yhcc3JNRKyXdBpwJ/CRffg2mqXCAWN51VNSZfL6l8C/URi6WhIR1cn884BT6o6vAH2A4yk8YfH+iKgFNkt6upHtnw4sqttWRLzeRB3nAkOLnpFzcHKrlLMoXHhIRDwuqZQr2qdLujB5PTCp9TVgN/BAMv/fgYck9U4+75yifR9Qwj7M2o0DxvLq3YgYUTwj+UW7vXgWcH1ELGyw3kTqXwzYGJWwDhSGmc+IiHcbqaXki8wkjaUQVmdExDuSngHe18Tqkez3zYbfA7Ny4mMw1pEtBD4rqTuApBMkHQgsAqYmx2j6A2c38t7fAB+WdEzy3kOT+duAg4rWe4LCcBXJenW/8BdRuCcXkiYAh7RQax/gjSRcTqTQQdXpAtR1YZ+gMPT2J6Ba0t8l+5Ck4S3sw6xdOWCsI7uXwvGVFZLWAHdT6Np/CqwHVgPfA37R8I0R8SqF4yYPSVrFe0NUjwIX1h3kp/BI4FHJSQRVvHc221eBsyStoDBU93ILtS4Aukl6Hvg6sLho2XZgmKTlFI6xfC2ZfylwRVLfWmByCd8Ts3bjW8WYmVkq3MGYmVkqHDBmZpYKB4yZmaXCAWNmZqlwwJiZWSocMGZmlgoHjJmZpeK/AYSL9EDPlhvnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import multilabel_confusion_matrix,confusion_matrix, ConfusionMatrixDisplay\n",
    "from keras.utils import to_categorical\n",
    "fig = plt.figure(figsize=(6,5))\n",
    "ax1 = fig.add_subplot(1,1,1)\n",
    "classesnames = ['Positive', 'Negative']\n",
    "\n",
    "cm = confusion_matrix(y_test, pred, labels=[0,1], normalize='true')\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=classesnames)\n",
    "disp.plot(include_values=True,cmap='Greys', ax=ax1, xticks_rotation=90,values_format='.2f')\n",
    "\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.savefig(path + 'confusion_matrix_cd276.svg',format='svg')\n",
    "\n",
    "# confusion_matrix(to_categorical(y_test,num_classes=6,dtype='int32'), to_categorical(pred,num_classes=6,dtype='int32'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1-score</th>\n",
       "      <th>accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.962177</td>\n",
       "      <td>0.961168</td>\n",
       "      <td>0.961339</td>\n",
       "      <td>0.961168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Average</th>\n",
       "      <td>0.962177</td>\n",
       "      <td>0.961168</td>\n",
       "      <td>0.961339</td>\n",
       "      <td>0.961168</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         precision    recall  f1-score  accuracy\n",
       "0         0.962177  0.961168  0.961339  0.961168\n",
       "Average   0.962177  0.961168  0.961339  0.961168"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# and other test metrics\n",
    "\n",
    "precision_recall_fscore = []\n",
    "\n",
    "prf = precision_recall_fscore_support(y_test, pred,average = \"weighted\")\n",
    "ac = accuracy_score(y_test, pred)\n",
    "\n",
    "precision_recall_fscore.append([prf[0],prf[1],prf[2],ac])\n",
    "\n",
    "metrics = pd.DataFrame(np.array(precision_recall_fscore), columns=['precision','recall','f1-score','accuracy'])\n",
    "mean_values = []\n",
    "for i in range(4):\n",
    "    mean_values.append(np.mean(np.array(precision_recall_fscore)[:,i]))\n",
    "metrics = metrics.append(pd.Series(mean_values, index=metrics.columns, name=\"Average\"))\n",
    "metrics.to_excel(path + 'test_metrics_cd276.xlsx')\n",
    "metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def roc_plot(n_classes_, y_tests_,y_prediction_proba_, path, filename ):\n",
    "    # plt.rcParams['font.sans-serif']=['Arial']\n",
    "    plt.rcParams['axes.unicode_minus']=False \n",
    "#     plt.grid(linestyle = \"--\")\n",
    "    ax = plt.gca()\n",
    "#     ax.spines['top'].set_visible(False)\n",
    "#     ax.spines['right'].set_visible(False)\n",
    "    fig = plt.gcf()\n",
    "    fig.set_size_inches(5.5, 4.5)\n",
    "\n",
    "    tprs_all = []\n",
    "    aucs_all = []\n",
    "    mean_fpr = np.linspace(0, 1, 100)\n",
    "\n",
    "    auc_values = []\n",
    "    classesnames = ['Positive', 'Negative']\n",
    "    colors = ['red', 'cyan','green']\n",
    "    for j in range(n_classes_):\n",
    "        tprs = []\n",
    "        aucs = []        \n",
    "        for i in range(len(y_tests_)):\n",
    "            fpr, tpr, thresholds = roc_curve(to_categorical(y_tests_[i],num_classes=n_classes_)[:, j], y_prediction_proba_[i][:, j])\n",
    "            tprs.append(interp(mean_fpr, fpr, tpr))\n",
    "            tprs_all.append(interp(mean_fpr, fpr, tpr))\n",
    "\n",
    "            tprs[-1][0] = 0.0\n",
    "            roc_auc = auc(fpr, tpr)\n",
    "            aucs.append(roc_auc)\n",
    "            aucs_all.append(roc_auc)\n",
    "\n",
    "\n",
    "        if len(y_tests_)== 1:\n",
    "            mean_tpr = tprs[0]\n",
    "            mean_tpr[-1] = 1.0\n",
    "            mean_auc = auc(mean_fpr, mean_tpr)\n",
    "            plt.plot(mean_fpr, mean_tpr, color=colors[j],\n",
    "                     label=r'%d. %s - ROC (AUC=%0.3f)' % (j+1,classesnames[j], mean_auc), linestyle='--',\n",
    "                     lw=1., alpha=1)\n",
    "            auc_values.append(mean_auc)\n",
    "            \n",
    "        else:\n",
    "            mean_tpr = np.mean(tprs, axis=0)\n",
    "            mean_tpr[-1] = 1.0\n",
    "            mean_auc = auc(mean_fpr, mean_tpr)\n",
    "            std_auc = np.std(aucs)\n",
    "            plt.plot(mean_fpr, mean_tpr, color=colors[j],\n",
    "                     label=r' %d. %s - Mean ROC (AUC=%0.3f$\\pm$%0.3f)' % (j+1, classesnames[j], mean_auc, std_auc),linestyle='--',\n",
    "                     lw=1., alpha=1)\n",
    "            auc_values.append(mean_auc)\n",
    "\n",
    "    mean_tpr = np.mean(tprs_all, axis=0)\n",
    "    mean_tpr[0] = .0\n",
    "    mean_tpr[-1] = 1.0\n",
    "    \n",
    "    if n_classes_ > 1:\n",
    "        plt.plot(mean_fpr, mean_tpr, color=colors[-1],\n",
    "                 label=r'Mean ROC (AUC=%0.3f$\\pm$%0.3f)' % (np.mean(auc_values),  np.std(auc_values)),\n",
    "                 lw=2, alpha=1)\n",
    "        \n",
    "    plt.plot([0, 1], [0, 1], linestyle='--', lw=2, color='black',label='Chance', alpha=1)\n",
    "\n",
    "\n",
    "\n",
    "    std_tpr = np.std(tprs_all, axis=0)\n",
    "    tprs_upper = np.minimum(mean_tpr + std_tpr, 1)\n",
    "    tprs_lower = np.maximum(mean_tpr - std_tpr, 0)\n",
    "    plt.fill_between(mean_fpr, tprs_lower, tprs_upper, color='grey', alpha=.1,\n",
    "                     label=r'$\\pm$ 1 std. dev.')\n",
    "\n",
    "    plt.xlim([0., 1.0])\n",
    "    plt.ylim([0., 1.0])\n",
    "\n",
    "    plt.xlabel('False Positive Rate',fontsize=14)\n",
    "    plt.ylabel('True Positive Rate',fontsize=14)\n",
    "    plt.legend(loc=\"lower right\", fontsize=12)\n",
    "\n",
    "\n",
    "    ax.xaxis.set_tick_params(labelsize=14)\n",
    "    ax.yaxis.set_tick_params(labelsize=14)\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(path + filename + '.svg',format='svg')\n",
    "\n",
    "    plt.show()\n",
    "    return mean_tpr, auc_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 396x324 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the test roc/auc\n",
    "\n",
    "yts=[]\n",
    "yts.append(y_test)\n",
    "pbs=[]\n",
    "pbs.append(probas_)\n",
    "mean_tpr, auc_values = roc_plot(2,yts,pbs, path,'roc_2class_test_cd276_conference')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 396x324 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the test roc/auc\n",
    "classesnames = ['Positive', 'Negative']\n",
    "colors = ['red', 'cyan','black']\n",
    "yts=[]\n",
    "yts.append(y_test)\n",
    "pbs=[]\n",
    "pbs.append(probas_)\n",
    "mean_tpr, auc_values = roc_plot(2,yts,pbs, classesnames, colors, path,'roc_2class_test_cd276')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#save for later comparison\n",
    "np.save(path + 'roc_2class_test_mean_tpr_cd276.npy',mean_tpr)\n",
    "np.save(path + 'roc_2class_test_auc_values_cd276.npy',auc_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9882052380736424"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.mean(np.load(path + 'roc_2class_test_auc_values_cd276.npy'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# For Conference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "classesnames = ['Positive', 'Negative']\n",
    "def roc_plot(n_classes_, y_tests_,y_prediction_proba_, path, filename ):\n",
    "    # plt.rcParams['font.sans-serif']=['Arial']\n",
    "    plt.rcParams['axes.unicode_minus']=False \n",
    "#     plt.grid(linestyle = \"--\")\n",
    "    ax = plt.gca()\n",
    "#     ax.spines['top'].set_visible(False)\n",
    "#     ax.spines['right'].set_visible(False)\n",
    "    fig = plt.gcf()\n",
    "    fig.set_size_inches(6, 5)\n",
    "\n",
    "    tprs_all = []\n",
    "    aucs_all = []\n",
    "    mean_fpr = np.linspace(0, 1, 100)\n",
    "\n",
    "    auc_values = []\n",
    "    colors = ['red', 'cyan','yellow','lightgrey','green','green']\n",
    "    for j in range(n_classes_):\n",
    "        tprs = []\n",
    "        aucs = []        \n",
    "        for i in range(len(y_tests_)):\n",
    "            fpr, tpr, thresholds = roc_curve(to_categorical(y_tests_[i],num_classes=n_classes_)[:, j], y_prediction_proba_[i][:, j])\n",
    "            tprs.append(interp(mean_fpr, fpr, tpr))\n",
    "            tprs_all.append(interp(mean_fpr, fpr, tpr))\n",
    "\n",
    "            tprs[-1][0] = 0.0\n",
    "            roc_auc = auc(fpr, tpr)\n",
    "            aucs.append(roc_auc)\n",
    "            aucs_all.append(roc_auc)\n",
    "\n",
    "\n",
    "        if len(y_tests_)== 1:\n",
    "            mean_tpr = tprs[0]\n",
    "            mean_tpr[-1] = 1.0\n",
    "            mean_auc = auc(mean_fpr, mean_tpr)\n",
    "            plt.plot(mean_fpr, mean_tpr, color=colors[j],\n",
    "                     label=r'%d. %s - ROC (AUC=%0.3f)' % (j+1,classesnames[j], mean_auc), linestyle='--',\n",
    "                     lw=1., alpha=1)\n",
    "            auc_values.append(mean_auc)\n",
    "            \n",
    "        else:\n",
    "            mean_tpr = np.mean(tprs, axis=0)\n",
    "            mean_tpr[-1] = 1.0\n",
    "            mean_auc = auc(mean_fpr, mean_tpr)\n",
    "            std_auc = np.std(aucs)\n",
    "            plt.plot(mean_fpr, mean_tpr, color=colors[j],\n",
    "                     label=r' %d. %s - Mean ROC (AUC=%0.3f$\\pm$%0.3f)' % (j+1, classesnames[j], mean_auc, std_auc),linestyle='--',\n",
    "                     lw=1., alpha=1)\n",
    "            auc_values.append(mean_auc)\n",
    "\n",
    "    mean_tpr = np.mean(tprs_all, axis=0)\n",
    "    mean_tpr[0] = .0\n",
    "    mean_tpr[-1] = 1.0\n",
    "    \n",
    "    if n_classes_ > 1:\n",
    "        plt.plot(mean_fpr, mean_tpr, color=colors[-1],\n",
    "                 label=r'Mean ROC (AUC=%0.3f$\\pm$%0.3f)' % (np.mean(auc_values),  np.std(auc_values)),\n",
    "                 lw=2, alpha=1)\n",
    "        \n",
    "    plt.plot([0, 1], [0, 1], linestyle='--', lw=2, color='black',label='Chance', alpha=1)\n",
    "\n",
    "\n",
    "\n",
    "    std_tpr = np.std(tprs_all, axis=0)\n",
    "    tprs_upper = np.minimum(mean_tpr + std_tpr, 1)\n",
    "    tprs_lower = np.maximum(mean_tpr - std_tpr, 0)\n",
    "    plt.fill_between(mean_fpr, tprs_lower, tprs_upper, color='grey', alpha=.1,\n",
    "                     label=r'$\\pm$ 1 std. dev.')\n",
    "\n",
    "    plt.xlim([0., 1.0])\n",
    "    plt.ylim([0., 1.0])\n",
    "\n",
    "    plt.xlabel('False Positive Rate',fontsize=10)\n",
    "    plt.ylabel('True Positive Rate',fontsize=10)\n",
    "    plt.legend(loc=\"lower right\")\n",
    "\n",
    "\n",
    "    ax.xaxis.set_tick_params(labelsize=10)\n",
    "    ax.yaxis.set_tick_params(labelsize=10)\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(path + filename + '.svg',format='svg')\n",
    "\n",
    "    plt.show()\n",
    "    return mean_tpr, auc_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "yts=[]\n",
    "yts.append(y_test)\n",
    "pbs=[]\n",
    "pbs.append(probas_)\n",
    "mean_tpr, auc_values = roc_plot(2,yts,pbs, path, 'roc_2class_test_cd276_conference')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}