tugda_mtl_example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TUGDA on Multi-task Learning settings\n",
    "In this notebook we present how TUGDA on MTL settings (Figure 1, preprint) can be trained using the GDSC dataset (*in vitro* settings). Here we trained and evaluated TUGDA on set of best hyperparameters (previously found on validation data) using 3-fold cross-validation (Figure 2, TUGDA model)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "#install required libs\n",
    "!pip install pandas\n",
    "!pip install torch==1.5.1+cu101  -f https://download.pytorch.org/whl/torch_stable.html\n",
    "#in this version we require pytorch-lightning==0.9.0\n",
    "!pip install pytorch-lightning==0.9.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random\n",
    "import torch\n",
    "from torch import nn\n",
    "from torch.utils.data import DataLoader, TensorDataset\n",
    "#call pytorch lightning functions\n",
    "import pytorch_lightning as pl\n",
    "from pytorch_lightning import Callback\n",
    "from pytorch_lightning import Trainer, seed_everything"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#get list of drugs to be trained and predicted\n",
    "folder = 'data/'\n",
    "drug_list = pd.read_csv('{}/cl_y_test_o_k1.csv'.format(folder), index_col=0 )\n",
    "drug_list = drug_list.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#3-fold training and test data;\n",
    "train_data_report = {}\n",
    "test_data_report = {}\n",
    "\n",
    "for k in range(1,4):\n",
    "    train_data_report['x_k_fold{}'.format(k)] = pd.read_csv('{}/cl_x_train_o_k{}.csv'.format(folder, k), index_col=0)\n",
    "    train_data_report['y_k_fold{}'.format(k)] = pd.read_csv('{}/cl_y_train_o_k{}.csv'.format(folder, k), index_col=0)\n",
    "    \n",
    "    test_data_report['x_k_fold{}'.format(k)] = pd.read_csv('{}/cl_x_test_o_k{}.csv'.format(folder, k), index_col=0)\n",
    "    test_data_report['y_k_fold{}'.format(k)] = pd.read_csv('{}/cl_y_test_o_k{}.csv'.format(folder, k), index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda\n"
     ]
    }
   ],
   "source": [
    "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
    "print(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MetricsCallback(Callback):\n",
    "    \"\"\"PyTorch Lightning metric callback.\"\"\"\n",
    "\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.metrics = []\n",
    "\n",
    "    def on_test_end(self, trainer, pl_module):\n",
    "        self.metrics.append(trainer.callback_metrics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "class tugda_mtl(pl.LightningModule):\n",
    "    def __init__(self, params, train_data, y_train,\n",
    "                 test_data, y_test\n",
    "                ):\n",
    "        super(tugda_mtl, self).__init__()\n",
    "        \n",
    "        self.learning_rate = params['lr']\n",
    "        self.batch_size = params['bs']\n",
    "        self.mu = params['mu']\n",
    "        self.lambda_ = params['lambda_']\n",
    "        self.gamma = params['gamma']\n",
    "        self.num_tasks = params['num_tasks']\n",
    "        self.passes = params['passes']\n",
    "\n",
    "        self.train_data = train_data\n",
    "        self.y_train = y_train\n",
    "        self.test_data = test_data\n",
    "        self.y_test = y_test\n",
    "        \n",
    "        input_dim = self.train_data.shape[1]\n",
    "        \n",
    "        feature_extractor = [nn.Linear(input_dim, params['hidden_units_1']), \n",
    "                             nn.Dropout(p=params['dropout']),\n",
    "                             nn.ReLU()]\n",
    "        \n",
    "        self.feature_extractor = nn.Sequential(*feature_extractor)\n",
    "\n",
    "        latent_basis =  [nn.Linear(params['hidden_units_1'], params['latent_space']),\n",
    "                         nn.Dropout(p=params['dropout']),\n",
    "                         nn.ReLU()]\n",
    "        \n",
    "        self.latent_basis = nn.Sequential(*latent_basis)\n",
    "        \n",
    "        #task-specific weights\n",
    "        self.S = nn.Linear(params['latent_space'], self.num_tasks)\n",
    "        \n",
    "        #decoder weights\n",
    "        A = [nn.Linear( self.num_tasks , params['latent_space']), nn.ReLU()]\n",
    "        self.A = nn.Sequential(*A)\n",
    "        \n",
    "        #uncertainty (aleatoric)\n",
    "        self.log_vars = torch.zeros(self.num_tasks, requires_grad=True, device=device)\n",
    "        \n",
    "    def forward(self, input_data):\n",
    "        x = self.feature_extractor(input_data)\n",
    "        h = self.latent_basis(x)\n",
    "        preds = self.S(h)\n",
    "        h_hat = self.A(preds)\n",
    "        return preds, h, h_hat\n",
    "\n",
    "    def prepare_data(self):\n",
    "        train_dataset = TensorDataset(torch.FloatTensor(self.train_data),\n",
    "                                      torch.FloatTensor(self.y_train))\n",
    "\n",
    "        test_dataset = TensorDataset(torch.FloatTensor(self.test_data),\n",
    "                                     torch.FloatTensor(self.y_test))\n",
    "\n",
    "        self.train_dataset = train_dataset\n",
    "        self.test_dataset = test_dataset\n",
    "\n",
    "    def train_dataloader(self):\n",
    "        return DataLoader(self.train_dataset, batch_size=self.batch_size, shuffle=True, num_workers=8)\n",
    "    \n",
    "    def test_dataloader(self):\n",
    "        return DataLoader(self.test_dataset, batch_size=len(self.test_dataset), shuffle=False, num_workers=8)\n",
    "\n",
    "    def configure_optimizers(self):\n",
    "        params = ([p for p in self.parameters()] + [self.log_vars])\n",
    "        optimizer = torch.optim.Adagrad(params, lr=self.learning_rate)\n",
    "        return optimizer\n",
    "    \n",
    "    def optimizer_step(self, current_epoch, batch_nb, optimizer, optimizer_idx, \n",
    "                       second_order_closure=None, on_tpu=False, using_native_amp=False, using_lbfgs=False):\n",
    "        # update params\n",
    "        optimizer.step()\n",
    "        optimizer.zero_grad()\n",
    "        \n",
    "    def mse_ignore_nan(self, preds, labels):\n",
    "        mse_loss = torch.nn.MSELoss(reduction='none')\n",
    "        per_task_loss = torch.zeros(labels.size(1), device=device)\n",
    "\n",
    "        for k in range(labels.size(1)):\n",
    "            precision = torch.exp(-self.log_vars[k])\n",
    "            diff = mse_loss(preds[~torch.isnan(labels[:,k]), k], labels[~torch.isnan(labels[:,k]), k])\n",
    "            per_task_loss[k] = torch.mean(precision * diff + self.log_vars[k])\n",
    "\n",
    "        return torch.mean(per_task_loss[~torch.isnan(per_task_loss)]), per_task_loss\n",
    "    \n",
    "    def mse_ignore_nan_test(self, preds, labels):\n",
    "        mse_loss = torch.nn.MSELoss(reduction='mean')\n",
    "        per_task_loss = torch.zeros(labels.size(1), device=device)\n",
    "        \n",
    "        for k in range(labels.size(1)):\n",
    "            per_task_loss[k] = mse_loss(preds[~torch.isnan(labels[:,k]), k], labels[~torch.isnan(labels[:,k]), k])\n",
    "            \n",
    "        return torch.mean(per_task_loss[~torch.isnan(per_task_loss)]), per_task_loss \n",
    "    \n",
    "    #autoencoder loss\n",
    "    def MSE_loss(self, x, x_hat):\n",
    "        mse_loss = torch.nn.MSELoss()\n",
    "        return mse_loss(x, x_hat)\n",
    "    \n",
    "    def forward_pass(self, fw_batch, batch_idx):\n",
    "        \n",
    "        x, y = fw_batch\n",
    "        \n",
    "        preds_simulation = torch.torch.zeros(y.size(0),y.size(1), self.passes, device=device)\n",
    "        for simulation in range(self.passes):\n",
    "            \n",
    "            preds, h, h_hat = self.forward(x)\n",
    "            preds_simulation[:,:, simulation]=preds\n",
    "        \n",
    "        preds_mean = torch.mean(preds_simulation, axis=2)\n",
    "        preds_var = torch.var(preds_simulation, axis=2)\n",
    "        total_unc = torch.mean(preds_var, axis=0)\n",
    "        \n",
    "        #prediction loss\n",
    "        local_loss, task_loss = self.mse_ignore_nan(preds_mean, y)\n",
    "        #autoencoder loss\n",
    "        recon_loss = self.gamma * self.MSE_loss(h, h_hat)\n",
    "        \n",
    "        a = 1 + (total_unc + torch.sum(torch.abs(self.A[0].weight.T),1))\n",
    "        loss_weight = ( a[~torch.isnan(task_loss)]  ) * task_loss[~torch.isnan(task_loss)] \n",
    "        loss_weight = torch.sum(loss_weight)\n",
    "        l1_S = self.mu * self.S.weight.norm(1)\n",
    "        L = self.latent_basis[0].weight.norm(2) + self.feature_extractor[0].weight.norm(2)\n",
    "        l2_L = self.lambda_ * L\n",
    "\n",
    "        #total loss\n",
    "        total_loss = loss_weight + recon_loss + l1_S + l2_L\n",
    "        return total_loss, task_loss\n",
    "        \n",
    "    def training_step(self, train_batch, batch_idx):\n",
    "        \n",
    "        loss, task_loss = self.forward_pass(train_batch, batch_idx)\n",
    "        \n",
    "        logs = {'train_loss': loss, 'task_loss': task_loss}\n",
    "        return {'loss': loss, 'log': logs}\n",
    "    \n",
    "    def test_step(self, test_batch, batch_idx):\n",
    "        x, y = test_batch\n",
    "        \n",
    "        #enable dropouts\n",
    "        self.feature_extractor[1].train()\n",
    "        self.latent_basis[1].train()\n",
    "        \n",
    "        preds_simulation = torch.torch.zeros(y.size(0),y.size(1), self.passes, device=device)\n",
    "        for simulation in range(self.passes):\n",
    "            \n",
    "            seed = simulation\n",
    "            #to reproduce predictions\n",
    "            random.seed(seed)\n",
    "            np.random.seed(seed)\n",
    "            torch.manual_seed(seed)\n",
    "            torch.backends.cudnn.deterministic = True\n",
    "            torch.backends.cudnn.benchmark = False\n",
    "            \n",
    "            preds, _, _  = self.forward(x)\n",
    "            preds_simulation[:,:, simulation]=preds\n",
    "        \n",
    "        preds_mean = torch.mean(preds_simulation, axis=2)\n",
    "        loss, task_losses_per_class = self.mse_ignore_nan_test(preds_mean, y)\n",
    "        \n",
    "        #disable dropouts\n",
    "        self.feature_extractor[1].eval()\n",
    "        self.latent_basis[1].eval()\n",
    "        \n",
    "        return {'test_loss': loss,\n",
    "               'test_task_losses_per_class': task_losses_per_class.detach().cpu().numpy(),\n",
    "               'test_preds': preds_mean.detach().cpu().numpy(),\n",
    "                }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#best set of hyperparamters found on this dataset setting (GDSC)\n",
    "net_params = {\n",
    " #tunned hyperparameters\n",
    " 'hidden_units_1': 1024,\n",
    " 'latent_space': 700,\n",
    " 'lr': 0.001,\n",
    " 'dropout': 0.1,\n",
    " 'mu': 0.01,\n",
    " 'lambda_': 0.001,\n",
    " 'gamma': 0.0001,\n",
    " 'bs': 300,\n",
    " 'passes': 50,\n",
    " 'num_tasks': 200,\n",
    " 'epochs': 100}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: True, used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name              | Type       | Params\n",
      "-------------------------------------------------\n",
      "0 | feature_extractor | Sequential | 1 M   \n",
      "1 | latent_basis      | Sequential | 717 K \n",
      "2 | S                 | Linear     | 140 K \n",
      "3 | A                 | Sequential | 140 K \n"
     ]
    },
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       "Training: 0it [00:00, ?it/s]"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Saving latest checkpoint..\n"
     ]
    },
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       "model_id": "649e4e15bad84a83bfdb05ab75a1d4c1",
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       "Testing: 0it [00:00, ?it/s]"
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: True, used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name              | Type       | Params\n",
      "-------------------------------------------------\n",
      "0 | feature_extractor | Sequential | 1 M   \n",
      "1 | latent_basis      | Sequential | 717 K \n",
      "2 | S                 | Linear     | 140 K \n",
      "3 | A                 | Sequential | 140 K \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------------------------\n",
      "DATALOADER:0 TEST RESULTS\n",
      "{'test_loss': tensor(1.8383, device='cuda:0'),\n",
      " 'test_preds': array([[ 5.5901303 ,  3.5795941 ,  3.8705695 , ...,  1.2044197 ,\n",
      "         3.9309645 ,  8.334039  ],\n",
      "       [ 1.2868376 ,  1.8721877 ,  2.4557369 , ...,  1.2568722 ,\n",
      "        -0.61222005,  6.030042  ],\n",
      "       [ 4.191142  ,  2.702798  ,  4.1943035 , ...,  0.48288536,\n",
      "         2.7910597 ,  7.878966  ],\n",
      "       ...,\n",
      "       [ 4.408374  ,  1.7468319 ,  3.9545064 , ...,  1.9170301 ,\n",
      "         2.4439368 ,  6.893946  ],\n",
      "       [ 5.5004554 ,  3.5991457 ,  4.3986564 , ...,  1.3953224 ,\n",
      "         4.053058  ,  9.278416  ],\n",
      "       [ 4.348958  ,  2.8188868 ,  3.2787073 , ...,  3.7183821 ,\n",
      "         3.2583432 ,  7.179334  ]], dtype=float32),\n",
      " 'test_task_losses_per_class': array([1.4914658 , 1.3344768 , 0.88051796, 2.2467782 , 1.5596995 ,\n",
      "       3.5224605 , 2.424226  , 2.3212197 , 1.6756608 , 2.6915495 ,\n",
      "       1.6154008 , 1.9730062 , 1.7105626 , 1.9959676 , 1.0190631 ,\n",
      "       2.9768188 , 1.5461088 , 1.102366  , 0.931433  , 0.82602704,\n",
      "       2.28387   , 1.5159544 , 1.8722813 , 0.5711951 , 1.7915198 ,\n",
      "       2.194051  , 2.7518792 , 1.8263147 , 1.347743  , 2.352855  ,\n",
      "       1.5221233 , 1.6027234 , 1.1670179 , 3.8893757 , 0.91850275,\n",
      "       1.4904368 , 0.85229653, 1.9946803 , 3.0042245 , 1.7425665 ,\n",
      "       2.2510684 , 1.2749488 , 2.0592015 , 1.0267596 , 6.6181293 ,\n",
      "       1.4451417 , 1.0931648 , 1.4989623 , 1.2671918 , 0.8872875 ,\n",
      "       1.9936559 , 1.3416718 , 2.564696  , 1.2083482 , 0.64319843,\n",
      "       1.9810653 , 1.6958852 , 1.4946179 , 2.7681384 , 1.2584802 ,\n",
      "       1.2298619 , 1.9783152 , 1.8387939 , 1.1989739 , 1.3086188 ,\n",
      "       0.9535423 , 2.5214164 , 1.3153939 , 1.5696398 , 0.9527564 ,\n",
      "       1.235768  , 1.7796938 , 1.3902647 , 1.7621764 , 1.9073557 ,\n",
      "       1.879757  , 0.98642063, 1.9330039 , 1.4555621 , 2.9774153 ,\n",
      "       0.8890094 , 1.1052568 , 1.3479999 , 1.144791  , 3.0485234 ,\n",
      "       3.1419046 , 3.1664984 , 1.8137443 , 1.0493797 , 1.8974339 ,\n",
      "       2.8537753 , 1.1863081 , 1.2162344 , 1.7596557 , 1.1305084 ,\n",
      "       2.282558  , 1.8525871 , 1.4171493 , 1.6714058 , 1.5504037 ,\n",
      "       1.8624858 , 1.8197768 , 3.237362  , 2.6708772 , 2.9559674 ,\n",
      "       1.5369862 , 1.5036252 , 0.80178756, 1.176437  , 1.2637811 ,\n",
      "       1.2529837 , 1.8116759 , 0.73546785, 0.9342989 , 1.2344885 ,\n",
      "       1.045831  , 2.5454693 , 0.91366833, 1.0346955 , 1.0892396 ,\n",
      "       0.8783416 , 0.9776893 , 0.77604514, 0.72134113, 0.8774352 ,\n",
      "       1.9944516 , 2.2644236 , 3.298804  , 1.0354307 , 2.2640421 ,\n",
      "       1.4481244 , 2.819952  , 0.9850197 , 2.5256965 , 1.4371397 ,\n",
      "       1.7313886 , 1.0978574 , 2.0406086 , 1.1886925 , 2.579776  ,\n",
      "       1.7549223 , 2.400164  , 0.73224413, 2.3319244 , 1.6959089 ,\n",
      "       0.9232797 , 1.9613534 , 2.033264  , 0.6532379 , 0.8613608 ,\n",
      "       0.9888231 , 1.7576929 , 1.8930027 , 1.1785876 , 3.054566  ,\n",
      "       0.63196844, 1.8492525 , 1.4654012 , 1.849014  , 3.0310946 ,\n",
      "       2.8172967 , 1.0274436 , 1.9420953 , 1.8391694 , 1.2753922 ,\n",
      "       2.0958717 , 2.2738595 , 2.445934  , 4.4321275 , 1.9760387 ,\n",
      "       4.0376387 , 1.2490084 , 3.8097146 , 2.1521795 , 1.4918312 ,\n",
      "       1.8566419 , 2.618912  , 1.5748388 , 1.5421085 , 1.6746308 ,\n",
      "       0.8313271 , 2.3621082 , 1.5535059 , 3.4717596 , 2.1192024 ,\n",
      "       5.0659018 , 1.4469955 , 1.1502858 , 2.2680037 , 3.2370958 ,\n",
      "       1.9155369 , 6.8768997 , 1.263119  , 1.4517041 , 2.8453424 ,\n",
      "       4.3818836 , 0.44229668, 2.9155166 , 2.391824  , 1.5195389 ],\n",
      "      dtype=float32)}\n",
      "--------------------------------------------------------------------------------\n"
     ]
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       "Training: 0it [00:00, ?it/s]"
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     "metadata": {},
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Saving latest checkpoint..\n"
     ]
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       "Testing: 0it [00:00, ?it/s]"
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     "output_type": "stream",
     "text": [
      "GPU available: True, used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name              | Type       | Params\n",
      "-------------------------------------------------\n",
      "0 | feature_extractor | Sequential | 1 M   \n",
      "1 | latent_basis      | Sequential | 717 K \n",
      "2 | S                 | Linear     | 140 K \n",
      "3 | A                 | Sequential | 140 K \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------------------------\n",
      "DATALOADER:0 TEST RESULTS\n",
      "{'test_loss': tensor(1.7584, device='cuda:0'),\n",
      " 'test_preds': array([[3.3611708 , 2.166865  , 2.6094744 , ..., 3.636953  , 3.1647692 ,\n",
      "        8.365946  ],\n",
      "       [4.6335254 , 2.4563174 , 3.911688  , ..., 1.1904571 , 2.5462885 ,\n",
      "        6.9986057 ],\n",
      "       [3.7746596 , 2.6394184 , 3.618499  , ..., 0.30261213, 1.8948536 ,\n",
      "        6.629425  ],\n",
      "       ...,\n",
      "       [5.496294  , 1.7671915 , 5.0049424 , ..., 1.4731125 , 2.834544  ,\n",
      "        9.644545  ],\n",
      "       [1.7615435 , 1.7464737 , 2.256626  , ..., 2.788451  , 0.7374495 ,\n",
      "        5.73632   ],\n",
      "       [4.406636  , 1.975105  , 3.4456518 , ..., 2.7866516 , 3.4155595 ,\n",
      "        7.1431456 ]], dtype=float32),\n",
      " 'test_task_losses_per_class': array([1.411931  , 1.1247501 , 0.65603256, 1.9193974 , 1.4431998 ,\n",
      "       3.5096447 , 2.6746562 , 2.1912496 , 1.9675112 , 2.7095287 ,\n",
      "       1.4945976 , 2.19008   , 1.803638  , 1.9738765 , 0.74348086,\n",
      "       2.3029485 , 1.206946  , 1.0373294 , 0.7817543 , 0.77575904,\n",
      "       2.4785202 , 1.6443913 , 1.596918  , 0.67104256, 1.3974379 ,\n",
      "       1.9217087 , 2.5982883 , 1.6729393 , 1.2379909 , 2.1727252 ,\n",
      "       1.5919839 , 1.2558098 , 1.146972  , 2.5560067 , 0.9957336 ,\n",
      "       1.6143291 , 0.58179164, 2.141091  , 2.8992786 , 1.7891201 ,\n",
      "       2.1703787 , 0.8420178 , 1.5719233 , 0.93032795, 7.014588  ,\n",
      "       1.3892189 , 1.3392451 , 1.3616893 , 1.4583719 , 0.8125712 ,\n",
      "       1.7046754 , 1.2133325 , 2.4235573 , 1.1210371 , 0.5836595 ,\n",
      "       1.7112392 , 1.7214895 , 1.6282169 , 3.041409  , 1.22816   ,\n",
      "       1.1479626 , 1.7259535 , 1.7013035 , 1.2911185 , 1.035787  ,\n",
      "       0.7489546 , 2.6705813 , 1.0811805 , 1.8899711 , 0.8437652 ,\n",
      "       1.0735735 , 1.5856005 , 1.6621875 , 1.5768121 , 1.5526913 ,\n",
      "       2.4375043 , 0.9433983 , 1.7364492 , 0.9341074 , 3.0390055 ,\n",
      "       0.7399434 , 0.9864524 , 1.1471162 , 1.0704358 , 2.6229293 ,\n",
      "       2.792941  , 2.659168  , 1.0409667 , 0.81431735, 1.5974395 ,\n",
      "       3.379586  , 0.9781918 , 1.1670227 , 1.1722785 , 0.9391657 ,\n",
      "       1.8925818 , 1.5344816 , 0.9869674 , 1.4985245 , 1.5903804 ,\n",
      "       1.6116548 , 1.2828991 , 2.9064722 , 2.756918  , 2.8535254 ,\n",
      "       1.400365  , 1.049993  , 0.7179657 , 0.99016434, 1.549074  ,\n",
      "       1.0248748 , 1.3899145 , 0.5461772 , 0.82629   , 1.1749092 ,\n",
      "       0.9958003 , 2.774438  , 0.86286   , 0.7920295 , 1.0002937 ,\n",
      "       0.8859016 , 0.85848475, 0.7495441 , 0.6116207 , 0.8644066 ,\n",
      "       1.5339781 , 1.7295899 , 3.1921353 , 0.90342635, 2.4466245 ,\n",
      "       1.0753864 , 2.7532182 , 0.95359373, 2.5142183 , 1.7255839 ,\n",
      "       1.5429983 , 0.9730561 , 1.830049  , 1.0960387 , 2.2405887 ,\n",
      "       1.7190584 , 2.2604938 , 0.66035527, 2.382899  , 1.2835267 ,\n",
      "       0.92956585, 1.8508623 , 1.674437  , 0.54878503, 0.6582659 ,\n",
      "       1.1183054 , 1.7163192 , 1.640145  , 1.0307102 , 2.9928582 ,\n",
      "       0.6237704 , 1.5205331 , 1.0687221 , 2.0435636 , 2.6295328 ,\n",
      "       2.5418408 , 0.88658303, 2.128992  , 1.7864616 , 1.167636  ,\n",
      "       2.261164  , 2.6673214 , 2.53122   , 3.8046417 , 2.0409837 ,\n",
      "       3.976319  , 1.2422318 , 5.0131865 , 3.5198562 , 1.5415595 ,\n",
      "       2.1665993 , 1.5815398 , 1.6199259 , 2.197957  , 1.817273  ,\n",
      "       2.293498  , 1.3708096 , 1.2631412 , 4.7576504 , 2.2254992 ,\n",
      "       3.6015713 , 1.6035948 , 1.4138101 , 0.8633534 , 1.7446946 ,\n",
      "       5.1770277 , 6.819554  , 1.238606  , 1.7242321 , 3.3884213 ,\n",
      "       4.575054  , 0.46663168, 2.5017393 , 2.4957352 , 1.8836225 ],\n",
      "      dtype=float32)}\n",
      "--------------------------------------------------------------------------------\n"
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       "Training: 0it [00:00, ?it/s]"
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     "text": [
      "Saving latest checkpoint..\n"
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       "Testing: 0it [00:00, ?it/s]"
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     "text": [
      "--------------------------------------------------------------------------------\n",
      "DATALOADER:0 TEST RESULTS\n",
      "{'test_loss': tensor(1.7833, device='cuda:0'),\n",
      " 'test_preds': array([[3.8143811 , 1.8225929 , 3.7388253 , ..., 2.9014597 , 2.376441  ,\n",
      "        7.878894  ],\n",
      "       [3.5160081 , 2.1327918 , 3.0487273 , ..., 0.37313232, 1.8789779 ,\n",
      "        6.543551  ],\n",
      "       [4.3462915 , 2.7900941 , 3.8394225 , ..., 0.5774908 , 2.6929893 ,\n",
      "        6.9358087 ],\n",
      "       ...,\n",
      "       [4.8131986 , 1.4028168 , 3.9412835 , ..., 1.5468093 , 3.3820288 ,\n",
      "        7.945622  ],\n",
      "       [1.7894878 , 2.346606  , 2.2630565 , ..., 2.6876516 , 0.93378997,\n",
      "        5.619823  ],\n",
      "       [2.663619  , 1.5849105 , 2.5142999 , ..., 2.1392007 , 0.89365065,\n",
      "        6.041117  ]], dtype=float32),\n",
      " 'test_task_losses_per_class': array([2.0588343 , 1.33407   , 0.83493567, 2.2446628 , 1.5029362 ,\n",
      "       3.7772398 , 2.755147  , 2.6765773 , 1.8384305 , 2.6004894 ,\n",
      "       1.8577863 , 2.2867053 , 1.8857011 , 1.905037  , 0.9244382 ,\n",
      "       2.5472755 , 1.4170343 , 1.2643775 , 0.9555448 , 0.89057636,\n",
      "       2.5087438 , 2.0635421 , 1.867621  , 0.60330725, 1.5043118 ,\n",
      "       1.9191492 , 2.6958299 , 1.6610651 , 1.0458853 , 2.2683642 ,\n",
      "       1.5042922 , 1.4876146 , 1.346603  , 3.1560571 , 0.86985964,\n",
      "       1.8578676 , 0.7833495 , 2.2161257 , 2.9790144 , 2.2265904 ,\n",
      "       2.2036173 , 1.1709198 , 2.0404644 , 1.052524  , 6.9955664 ,\n",
      "       2.1489477 , 1.0502082 , 1.7589418 , 1.1489388 , 0.775901  ,\n",
      "       1.7194536 , 1.1013573 , 1.8462031 , 1.4479281 , 0.59125245,\n",
      "       1.7598563 , 1.6435312 , 1.492579  , 2.8935375 , 1.2434754 ,\n",
      "       1.0106174 , 1.8397372 , 1.7742908 , 1.3181165 , 1.0949365 ,\n",
      "       0.9635578 , 2.4744275 , 1.1420093 , 1.7019485 , 0.9858905 ,\n",
      "       1.3415142 , 1.5137447 , 1.5867199 , 1.5623181 , 1.467628  ,\n",
      "       2.6300204 , 0.8925077 , 1.6676841 , 1.3904654 , 2.8173227 ,\n",
      "       0.8106916 , 0.9940881 , 1.228617  , 1.1195829 , 2.7828293 ,\n",
      "       3.290999  , 3.0730593 , 1.377523  , 1.0741105 , 1.7111192 ,\n",
      "       2.9756663 , 0.97114867, 1.0644329 , 1.5985085 , 0.8428373 ,\n",
      "       2.1021225 , 1.6496376 , 1.1418691 , 1.6399162 , 1.6883571 ,\n",
      "       1.9341978 , 1.9812878 , 3.387973  , 2.6733553 , 3.5811067 ,\n",
      "       1.5530596 , 1.2857168 , 0.7578118 , 1.0925231 , 1.444989  ,\n",
      "       1.2227088 , 1.7551816 , 0.665869  , 0.9690669 , 1.4134165 ,\n",
      "       1.1485823 , 2.8122025 , 1.0907563 , 1.2342894 , 1.2732788 ,\n",
      "       1.0780659 , 1.0424427 , 0.94928014, 0.74217623, 1.0369676 ,\n",
      "       1.7524673 , 2.028869  , 3.198164  , 1.0122726 , 2.219993  ,\n",
      "       1.2399212 , 2.6620436 , 1.1077615 , 2.753184  , 1.8763115 ,\n",
      "       1.8245757 , 1.1276968 , 2.153228  , 1.440687  , 2.3763006 ,\n",
      "       1.9244263 , 2.3190627 , 0.7967456 , 2.329055  , 1.5610714 ,\n",
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      "       2.729401  , 0.9803765 , 2.3031843 , 1.8665376 , 1.1036224 ,\n",
      "       2.1465485 , 1.8296636 , 2.2820323 , 3.7865546 , 2.3385775 ,\n",
      "       4.44162   , 1.308104  , 4.0749993 , 2.6727557 , 1.8617008 ,\n",
      "       2.3514233 , 1.8013041 , 1.1643937 , 0.7251484 , 1.7118953 ,\n",
      "       1.3643792 , 0.7667595 , 1.0977575 , 1.8141856 , 0.787192  ,\n",
      "       3.857925  , 0.8456603 , 3.0118241 , 1.143691  , 0.93019736,\n",
      "       3.5324786 , 2.8279223 , 1.1122533 , 1.3888165 , 3.0167186 ,\n",
      "       4.4748263 , 0.45942745, 2.6677227 , 2.4625762 , 1.708776  ],\n",
      "      dtype=float32)}\n",
      "--------------------------------------------------------------------------------\n"
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   ],
   "source": [
    "#training and testing\n",
    "error_list = []\n",
    "pcorr_list = []\n",
    "\n",
    "metrics_callback = MetricsCallback()\n",
    "\n",
    "for k in range(1,4):\n",
    "    \n",
    "    X_train = train_data_report['x_k_fold{}'.format(k)].values\n",
    "    X_test = test_data_report['x_k_fold{}'.format(k)].values\n",
    "\n",
    "    y_train = train_data_report['y_k_fold{}'.format(k)].values\n",
    "    y_test = test_data_report['y_k_fold{}'.format(k)].values\n",
    "\n",
    "    trainer = pl.Trainer(\n",
    "        max_epochs=net_params['epochs'],\n",
    "        gpus=1 if torch.cuda.is_available() else None,\n",
    "        callbacks=[metrics_callback],\n",
    "        deterministic=True,\n",
    "        reload_dataloaders_every_epoch=True\n",
    "    )\n",
    "\n",
    "    seed = 42\n",
    "    random.seed(seed)\n",
    "    np.random.seed(seed)\n",
    "    torch.manual_seed(seed)\n",
    "    torch.backends.cudnn.deterministic = True\n",
    "    torch.backends.cudnn.benchmark = False\n",
    "\n",
    "    seed_everything(seed)\n",
    "    model = tugda_mtl(net_params, X_train, y_train,\n",
    "                  X_test, y_test)\n",
    "\n",
    "    trainer.fit(model)\n",
    "    \n",
    "    # use model after training or load weights\n",
    "    results = trainer.test(model)\n",
    "\n",
    "    #get error per drug\n",
    "    error_mtl_nn_results = np.concatenate((np.array(drug_list, ndmin=2).T,\n",
    "                            np.array(results[0]['test_task_losses_per_class'], ndmin=2).T), axis=1)\n",
    "\n"
   ]
  }
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