Train Model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f8e937c5",
   "metadata": {},
   "source": [
    "# Train model using Mouse Brain dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df641d71",
   "metadata": {},
   "source": [
    "In this notebook, we're going to train our model using the Mouse Brain dataset (GSE60361). \n",
    "\n",
    "This assumes that you've made the graph using the ```Infer GRN.ipynb``` code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "65a9a75a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Torch version: 1.8.0+cu111\n",
      "Cuda available: True\n",
      "Torch geometric version: 2.0.3\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import torch\n",
    "import torch_geometric\n",
    "from torch_geometric.data import Data, Dataset\n",
    "from tqdm import tqdm\n",
    "from datasets.datasetMouseBrain import MouseBrainDataset\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.manifold import TSNE\n",
    "from tqdm import tqdm\n",
    "from scipy.special import softmax\n",
    "from sklearn.metrics import (auc, precision_recall_curve, roc_auc_score,\n",
    "                             roc_curve)\n",
    "from statistics import mean"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "419e7c62",
   "metadata": {},
   "source": [
    "Load up the dataset. Read ```datasetMouseBrain.py``` on how the dataset was built. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2da40b66",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 243075/243075 [00:09<00:00, 26305.11it/s]\n"
     ]
    }
   ],
   "source": [
    "dataset = MouseBrainDataset(\"/gpfs/data/rsingh47/hzaki1/data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fefcae56",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Dataset: MouseBrainDataset(3005):\n",
      "====================\n",
      "Number of graphs: 3005\n",
      "Number of features: 1\n",
      "\n",
      "Data(x=[19972], edge_index=[2, 243075], y=[1])\n",
      "=============================================================\n",
      "Number of nodes: 19972\n",
      "Number of edges: 243075\n",
      "Average node degree: 12.17\n",
      "Contains isolated nodes: True\n",
      "Contains self-loops: False\n",
      "Is undirected: False\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/gpfs/home/hzaki1/celltypefromgrn/env-gpu/lib/python3.7/site-packages/torch_geometric/deprecation.py:13: UserWarning: 'contains_isolated_nodes' is deprecated, use 'has_isolated_nodes' instead\n",
      "  warnings.warn(out)\n",
      "/gpfs/home/hzaki1/celltypefromgrn/env-gpu/lib/python3.7/site-packages/torch_geometric/deprecation.py:13: UserWarning: 'contains_self_loops' is deprecated, use 'has_self_loops' instead\n",
      "  warnings.warn(out)\n"
     ]
    }
   ],
   "source": [
    "print()\n",
    "print(f'Dataset: {dataset}:')\n",
    "print('====================')\n",
    "print(f'Number of graphs: {len(dataset)}')\n",
    "print(f'Number of features: {dataset.num_features}')\n",
    "\n",
    "data = dataset[0]  # Get the first graph object.\n",
    "\n",
    "print()\n",
    "print(data)\n",
    "print('=============================================================')\n",
    "\n",
    "# Gather some statistics about the first graph.\n",
    "print(f'Number of nodes: {data.num_nodes}')\n",
    "print(f'Number of edges: {data.num_edges}')\n",
    "print(f'Average node degree: {data.num_edges / data.num_nodes:.2f}')\n",
    "print(f'Contains isolated nodes: {data.contains_isolated_nodes()}')\n",
    "print(f'Contains self-loops: {data.contains_self_loops()}')\n",
    "print(f'Is undirected: {data.is_undirected()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8a66c7cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of training graphs: 2404\n",
      "Number of test graphs: 301\n"
     ]
    }
   ],
   "source": [
    "torch.manual_seed(12345)\n",
    "#dataset = dataset.shuffle()\n",
    "\n",
    "\n",
    "shuffle_index = np.loadtxt('shuffle_indices/shuffleIndex_MouseBrain.txt')\n",
    "shuffle_index = shuffle_index.astype(np.int32)\n",
    "train_size, val_size = int(len(shuffle_index)* 0.8), int(len(shuffle_index)* 0.9)\n",
    "train_dataset = [dataset[i] for i in shuffle_index[0:train_size]]\n",
    "val_dataset = [dataset[i] for i in shuffle_index[train_size: val_size]]\n",
    "test_dataset =  [dataset[i] for i in shuffle_index[val_size:]]\n",
    "\n",
    "# train_dataset = torch.load('trainDataset.pt')\n",
    "# test_dataset = torch.load('testDataset.pt')\n",
    "\n",
    "# train_dataset = dataset[:2403]\n",
    "# test_dataset = dataset[2403:]\n",
    "#test_dataset = dataset[-12:]\n",
    "\n",
    "\n",
    "# train_dataset = dataset[:40]\n",
    "# test_dataset = dataset[40:60]\n",
    "\n",
    "print(f'Number of training graphs: {len(train_dataset)}')\n",
    "print(f'Number of test graphs: {len(test_dataset)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5dce17cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/gpfs/home/hzaki1/celltypefromgrn/env-gpu/lib/python3.7/site-packages/torch_geometric/deprecation.py:13: UserWarning: 'data.DataLoader' is deprecated, use 'loader.DataLoader' instead\n",
      "  warnings.warn(out)\n"
     ]
    }
   ],
   "source": [
    "from torch_geometric.data import DataLoader\n",
    "\n",
    "train_loader = DataLoader(train_dataset, batch_size=30, shuffle=True)\n",
    "train_loader_testing = DataLoader(train_dataset, batch_size=1, shuffle=True)\n",
    "val_loader = DataLoader(val_dataset, batch_size=1, shuffle=False)\n",
    "test_loader = DataLoader(test_dataset, batch_size=1, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ad8a9de2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GCN(\n",
      "  (conv1): SAGEConv(1, 128)\n",
      "  (conv2): SAGEConv(128, 128)\n",
      "  (conv3): SAGEConv(128, 128)\n",
      "  (lin): Linear(in_features=128, out_features=7, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "from gcnmodel import GCN\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "\n",
    "model = GCN(hidden_channels=128, data=dataset, output_size=7).to(device)\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "783ea868",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 001, Train Acc: 0.5017, Train AUC: 0.8089, Train AUPR: 0.3697, Test Acc: 0.4700, Test Auc: 0.7945, Test AUPR: 0.3649,  Loss: 1.6595\n"
     ]
    },
    {
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     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 002, Train Acc: 0.5391, Train AUC: 0.8563, Train AUPR: 0.4429, Test Acc: 0.5133, Test Auc: 0.8398, Test AUPR: 0.4432,  Loss: 1.3150\n"
     ]
    },
    {
     "name": "stderr",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 003, Train Acc: 0.6019, Train AUC: 0.8748, Train AUPR: 0.4751, Test Acc: 0.5567, Test Auc: 0.8574, Test AUPR: 0.4668,  Loss: 1.1130\n"
     ]
    },
    {
     "name": "stderr",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 004, Train Acc: 0.6102, Train AUC: 0.8930, Train AUPR: 0.5025, Test Acc: 0.5567, Test Auc: 0.8714, Test AUPR: 0.4788,  Loss: 1.0164\n"
     ]
    },
    {
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 005, Train Acc: 0.6560, Train AUC: 0.9129, Train AUPR: 0.5404, Test Acc: 0.6033, Test Auc: 0.8943, Test AUPR: 0.5351,  Loss: 0.9553\n"
     ]
    },
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 006, Train Acc: 0.6622, Train AUC: 0.9195, Train AUPR: 0.5605, Test Acc: 0.6233, Test Auc: 0.9025, Test AUPR: 0.5557,  Loss: 0.9114\n"
     ]
    },
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    },
    {
     "name": "stdout",
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     "text": [
      "Epoch: 007, Train Acc: 0.6647, Train AUC: 0.9251, Train AUPR: 0.5834, Test Acc: 0.6300, Test Auc: 0.9062, Test AUPR: 0.5676,  Loss: 0.8964\n"
     ]
    },
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    },
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     "text": [
      "Epoch: 008, Train Acc: 0.6814, Train AUC: 0.9277, Train AUPR: 0.5857, Test Acc: 0.6267, Test Auc: 0.9073, Test AUPR: 0.5663,  Loss: 0.8385\n"
     ]
    },
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     "text": [
      "Epoch: 009, Train Acc: 0.7184, Train AUC: 0.9350, Train AUPR: 0.6178, Test Acc: 0.6500, Test Auc: 0.9176, Test AUPR: 0.6008,  Loss: 0.8123\n"
     ]
    },
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    },
    {
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     "text": [
      "Epoch: 010, Train Acc: 0.6976, Train AUC: 0.9378, Train AUPR: 0.6166, Test Acc: 0.6600, Test Auc: 0.9196, Test AUPR: 0.5936,  Loss: 0.7837\n"
     ]
    },
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     "text": [
      "Epoch: 011, Train Acc: 0.7067, Train AUC: 0.9393, Train AUPR: 0.6209, Test Acc: 0.6433, Test Auc: 0.9184, Test AUPR: 0.5939,  Loss: 0.7619\n"
     ]
    },
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     "text": [
      "Epoch: 012, Train Acc: 0.7209, Train AUC: 0.9385, Train AUPR: 0.6278, Test Acc: 0.6767, Test Auc: 0.9212, Test AUPR: 0.6057,  Loss: 0.7439\n"
     ]
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     "text": [
      "Epoch: 013, Train Acc: 0.7400, Train AUC: 0.9451, Train AUPR: 0.6445, Test Acc: 0.6933, Test Auc: 0.9255, Test AUPR: 0.6279,  Loss: 0.7387\n"
     ]
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     "text": [
      "Epoch: 014, Train Acc: 0.7363, Train AUC: 0.9459, Train AUPR: 0.6479, Test Acc: 0.6833, Test Auc: 0.9272, Test AUPR: 0.6271,  Loss: 0.7213\n"
     ]
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     "text": [
      "Epoch: 015, Train Acc: 0.7463, Train AUC: 0.9472, Train AUPR: 0.6517, Test Acc: 0.6767, Test Auc: 0.9300, Test AUPR: 0.6268,  Loss: 0.7195\n"
     ]
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     "text": [
      "Epoch: 016, Train Acc: 0.7533, Train AUC: 0.9466, Train AUPR: 0.6540, Test Acc: 0.7167, Test Auc: 0.9292, Test AUPR: 0.6384,  Loss: 0.7238\n"
     ]
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     "text": [
      "Epoch: 017, Train Acc: 0.7479, Train AUC: 0.9469, Train AUPR: 0.6550, Test Acc: 0.6867, Test Auc: 0.9286, Test AUPR: 0.6285,  Loss: 0.7536\n"
     ]
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    },
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     "text": [
      "Epoch: 018, Train Acc: 0.7483, Train AUC: 0.9483, Train AUPR: 0.6606, Test Acc: 0.6800, Test Auc: 0.9310, Test AUPR: 0.6457,  Loss: 0.6979\n"
     ]
    },
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     "text": [
      "Epoch: 019, Train Acc: 0.7567, Train AUC: 0.9506, Train AUPR: 0.6721, Test Acc: 0.6833, Test Auc: 0.9371, Test AUPR: 0.6501,  Loss: 0.7046\n"
     ]
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    },
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     "text": [
      "Epoch: 020, Train Acc: 0.7267, Train AUC: 0.9480, Train AUPR: 0.6660, Test Acc: 0.6467, Test Auc: 0.9314, Test AUPR: 0.6341,  Loss: 0.6973\n"
     ]
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     "text": [
      "Epoch: 021, Train Acc: 0.7512, Train AUC: 0.9516, Train AUPR: 0.6732, Test Acc: 0.6767, Test Auc: 0.9321, Test AUPR: 0.6409,  Loss: 0.7080\n"
     ]
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     "text": [
      "Epoch: 022, Train Acc: 0.7691, Train AUC: 0.9515, Train AUPR: 0.6807, Test Acc: 0.7033, Test Auc: 0.9361, Test AUPR: 0.6594,  Loss: 0.7108\n"
     ]
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     "text": [
      "Epoch: 023, Train Acc: 0.7700, Train AUC: 0.9540, Train AUPR: 0.6863, Test Acc: 0.7033, Test Auc: 0.9400, Test AUPR: 0.6641,  Loss: 0.7029\n"
     ]
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     "text": [
      "Epoch: 024, Train Acc: 0.7567, Train AUC: 0.9540, Train AUPR: 0.6842, Test Acc: 0.6967, Test Auc: 0.9370, Test AUPR: 0.6537,  Loss: 0.6904\n"
     ]
    },
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     "text": [
      "Epoch: 025, Train Acc: 0.7662, Train AUC: 0.9553, Train AUPR: 0.6941, Test Acc: 0.6967, Test Auc: 0.9383, Test AUPR: 0.6645,  Loss: 0.6605\n"
     ]
    },
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    },
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     "text": [
      "Epoch: 026, Train Acc: 0.7417, Train AUC: 0.9527, Train AUPR: 0.6784, Test Acc: 0.6800, Test Auc: 0.9374, Test AUPR: 0.6573,  Loss: 0.6654\n"
     ]
    },
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     "text": [
      "Epoch: 027, Train Acc: 0.7542, Train AUC: 0.9546, Train AUPR: 0.6931, Test Acc: 0.6767, Test Auc: 0.9361, Test AUPR: 0.6735,  Loss: 0.6641\n"
     ]
    },
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    },
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      "Epoch: 028, Train Acc: 0.7679, Train AUC: 0.9580, Train AUPR: 0.7005, Test Acc: 0.7067, Test Auc: 0.9401, Test AUPR: 0.6723,  Loss: 0.6663\n"
     ]
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     ]
    }
   ],
   "source": [
    "optimizer = torch.optim.Adam(model.parameters(), lr=0.001)\n",
    "criterion = torch.nn.CrossEntropyLoss()\n",
    "\n",
    "    \n",
    "def test(loader, size):\n",
    "    model.eval()\n",
    "    output = np.zeros((len(loader), size))\n",
    "    actual = np.zeros((len(loader), size))\n",
    "    accuracy = 0\n",
    "    for ind, data in enumerate(loader):  # Iterate in batches over the training/test dataset.\n",
    "        data.x = torch.reshape(data.x, (data.x.shape[0], 1))\n",
    "        data.x = data.x.type(torch.FloatTensor)\n",
    "        data = data.to(device)\n",
    "        out = model(data.x, data.edge_index, data.batch)\n",
    "        output[ind] = softmax(out.cpu().detach().numpy())\n",
    "        actual[ind][data.y] = 1\n",
    "        accuracy += int((out.argmax(dim=1) == data.y).sum())\n",
    "    all_labels = list(dataset.cellToIndex.keys())\n",
    "    actual = np.array(actual)\n",
    "    precision = dict()\n",
    "    recall = dict()\n",
    "    averageAUROC = []\n",
    "    averageAUPR = []\n",
    "    for (idx, c_label) in enumerate(all_labels):\n",
    "        fpr, tpr, thresholds = roc_curve(actual[:,idx].astype(int), output[:,idx])\n",
    "        precision[idx], recall[idx], _ = precision_recall_curve(actual[:, idx],\n",
    "                                                        output[:, idx])\n",
    "        averageAUROC.append(auc(fpr, tpr))\n",
    "        averageAUPR.append(round(auc(recall[idx], precision[idx]),4))\n",
    "\n",
    "    return accuracy/len(loader.dataset), mean(averageAUROC), mean(averageAUPR)\n",
    "\n",
    "\n",
    "def train():\n",
    "    model.train()\n",
    "    avgLoss = 0\n",
    "    for data in tqdm(train_loader, total=81):  # Iterate in batches over the training dataset.\n",
    "        data.x = torch.reshape(data.x, (data.x.shape[0], 1))\n",
    "        data.x = data.x.type(torch.FloatTensor)\n",
    "        data = data.to(device)\n",
    "        out = model(data.x, data.edge_index, data.batch)# Perform a single forward pass.\n",
    "        loss = criterion(out, data.y)  # Compute the loss.\n",
    "        loss.backward()  # Derive gradients.\n",
    "        optimizer.step()  # Update parameters based on gradients.\n",
    "        optimizer.zero_grad()  # Clear gradients.\n",
    "        avgLoss += loss\n",
    "    return avgLoss / 81\n",
    "\n",
    "\n",
    "for epoch in range(1, 250):\n",
    "    loss = train()\n",
    "    train_acc, trainAUC, trainAUPR = test(train_loader_testing, 7)\n",
    "    test_acc,testAUC, testAUPR = test(val_loader, 7)\n",
    "    print(f'Epoch: {epoch:03d}, Train Acc: {train_acc:.4f}, Train AUC: {trainAUC:.4f}, Train AUPR: {trainAUPR:.4f}, Test Acc: {test_acc:.4f}, Test Auc: {testAUC:.4f}, Test AUPR: {testAUPR:.4f},  Loss: {loss:.4f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b97aba3d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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