J.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import spectral as sp\n",
    "import cv2\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "import matplotlib.pyplot as plt\n",
    "import spectral.io.envi as envi\n",
    "from spectral import imshow, view_cube\n",
    "from tqdm import tqdm\n",
    "import pandas as pd\n",
    "import utm\n",
    "import json\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_x = json.load(open('data_x_J.json'))\n",
    "data_y = json.load(open('data_y_J.json'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 21, 82)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(data_x).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Flatten the data\n",
    "data_x = np.array(data_x).reshape(210, 82)\n",
    "data_y = np.array(data_y).reshape(210)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "S1_x, S1_y = data_x[:90], data_y[:90]\n",
    "S2_x, S2_y = data_x[90:150], data_y[90:150]\n",
    "S3_x, S3_y = data_x[150:], data_y[150:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train_x, test_x, train_y, test_y = train_test_split(data_x, data_y, test_size=0.2, random_state=42)\n",
    "# train_x, val_x, train_y, val_y = train_test_split(train_x, train_y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import Dataset, DataLoader\n",
    "from torchvision import transforms\n",
    "\n",
    "class HyperDataset(Dataset):\n",
    "    def __init__(self, data_x, data_y):\n",
    "        self.data_x = np.array(data_x)\n",
    "        self.data_y = np.array(data_y)\n",
    "        self.m_x = np.mean(self.data_x)\n",
    "        self.s_x = np.std(self.data_x)\n",
    "        self.m_y = np.mean(self.data_y)\n",
    "        self.s_y = np.std(self.data_y)\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.data_x)\n",
    "    \n",
    "    def mean_x(self):\n",
    "        return self.m_x\n",
    "    def std_x(self):\n",
    "        return self.s_x\n",
    "    def mean_y(self):\n",
    "        return self.m_y\n",
    "    def std_y(self):\n",
    "        return self.s_y\n",
    "        \n",
    "    def __getitem__(self, index):\n",
    "        x = self.data_x[index]\n",
    "        y = self.data_y[index]\n",
    "        return torch.Tensor(x), torch.FloatTensor([y])\n",
    "    \n",
    "train_data = HyperDataset(S1_x, S1_y)\n",
    "val_data = HyperDataset(S2_x, S2_y)\n",
    "test_data = HyperDataset(S3_x, S3_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([82])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[0][0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_loader = DataLoader(train_data, batch_size=32, shuffle=False)\n",
    "val_loader = DataLoader(val_data, batch_size=32, shuffle=False)\n",
    "test_loader = DataLoader(test_data, batch_size=1, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the model\n",
    "class RegressionModel(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(RegressionModel, self).__init__()\n",
    "        \n",
    "        self.sequence = nn.Sequential(\n",
    "            # nn.Conv1d(1, 16, 3),\n",
    "            # nn.ReLU(),\n",
    "            # nn.Conv1d(16, 32, 3),\n",
    "            # nn.ReLU(),\n",
    "            # nn.Flatten(),\n",
    "            # nn.Dropout(0.5),\n",
    "            # nn.GELU(),\n",
    "            # nn.Linear(64, 32),\n",
    "            # nn.Dropout(0.5),\n",
    "            # nn.ReLU(),\n",
    "            nn.Linear(82, 1),\n",
    "            # nn.ReLU(),\n",
    "            # nn.Linear(10, 5),\n",
    "            # nn.ReLU(),\n",
    "            # nn.Linear(5, 1)\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.sequence(x)\n",
    "        return x\n",
    "    \n",
    "model = RegressionModel().to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the Gradient Boosting model\n",
    "class GradientBoostingRegressor(nn.Module):\n",
    "    def __init__(self, n_estimators=100, device='cuda'):\n",
    "        super(GradientBoostingRegressor, self).__init__()\n",
    "        self.n_estimators = n_estimators\n",
    "        self.device = device\n",
    "        \n",
    "        # Create a list of weak learners (fully-connected neural networks)\n",
    "        self.weak_learners = nn.ModuleList([self.create_weak_learner() for _ in range(n_estimators)])\n",
    "        \n",
    "        # Initialize the output weights\n",
    "        self.output_weights = torch.ones(n_estimators, 1, requires_grad=True, device=self.device)\n",
    "\n",
    "    def create_weak_learner(self):\n",
    "        return nn.Sequential(\n",
    "            nn.Linear(82, 1),\n",
    "            # nn.GELU(),\n",
    "            # nn.Dropout(0.3),\n",
    "            # nn.Linear(64, 32),\n",
    "            # nn.Sigmoid(),\n",
    "            # nn.Dropout(0.3),\n",
    "            # nn.Linear(32, 1),\n",
    "        ).to(self.device)\n",
    "\n",
    "    def forward(self, x):\n",
    "        batch_size = x.size(0)\n",
    "        outputs = torch.zeros(batch_size, 1, device=self.device)\n",
    "        # print(outputs.shape)\n",
    "\n",
    "        for i, weak_learner in enumerate(self.weak_learners):\n",
    "            outputs += self.output_weights[i] * weak_learner(x)\n",
    "\n",
    "        return outputs\n",
    "    \n",
    "model = GradientBoostingRegressor(n_estimators=10, device=device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def r2_loss(y_true, y_pred):\n",
    "    ss_total = torch.sum((y_true - torch.mean(y_true)) ** 2)\n",
    "    ss_residual = torch.sum((y_true - y_pred) ** 2)\n",
    "    r2 = 1 - (ss_residual / (ss_total + 1e-6))  # Avoid division by zero\n",
    "    return -r2  # Return negative R^2 for minimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mae_loss(y_true, y_pred):\n",
    "    return torch.mean(torch.abs(y_true - y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def adjusted_r2_loss(y_true, y_pred, n_features):\n",
    "    r2 = r2_loss(y_true, y_pred)\n",
    "    n = y_true.size(0)\n",
    "    p = n_features\n",
    "    adjusted_r2 = 1 - (1 - r2) * (n - 1) / (n - p - 1)\n",
    "    return -adjusted_r2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([32, 82])\n",
      "torch.Size([32, 1])\n",
      "31587236.0\n",
      "torch.Size([32, 82])\n",
      "torch.Size([32, 1])\n",
      "40504824.0\n",
      "torch.Size([26, 82])\n",
      "torch.Size([26, 1])\n",
      "28193418.0\n",
      "33428492.666666668\n"
     ]
    }
   ],
   "source": [
    "# model.load_state_dict(torch.load('runs/100000_J_conv_best.pth'))\n",
    "model.eval()\n",
    "out = []\n",
    "y = []\n",
    "with torch.no_grad():\n",
    "    loss = 0\n",
    "    for i, (x, y) in enumerate(train_loader):\n",
    "        x, y = x.to(device), y.to(device)\n",
    "        # x = x.unsqueeze(1)\n",
    "        print(x.shape)\n",
    "        output = model(x)\n",
    "        print(output.shape)\n",
    "        \n",
    "        loss += nn.MSELoss()(y, output).item()\n",
    "        print(nn.MSELoss()(y, output).item())\n",
    "        \n",
    "    loss /= len(train_loader)\n",
    "    print(loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.optim.lr_scheduler import ReduceLROnPlateau, CyclicLR\n",
    "\n",
    "#Set up the training loop\n",
    "loss_fn = adjusted_r2_loss\n",
    "# loss_fn = nn.MSELoss()\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=1)\n",
    "num_epochs = 20000\n",
    "lr1 = ReduceLROnPlateau\n",
    "lr2 = CyclicLR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'adjusted_r2_loss'"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loss_fn.__name__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_checkpoint(state, is_best, epoch):\n",
    "    torch.save(state[f'state_dict'], f'runs/{epoch}_J_conv_grad_last.pth')\n",
    "    if is_best:\n",
    "        # print('Found best')\n",
    "        torch.save(state[f'state_dict'], f'runs/{epoch}_J_conv_grad_best.pth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import wandb\n",
    "# from IPython.display import clear_output\n",
    "def train(model, epoch, train_dataloader, val_dataloader, optimizer, lr_list, loss_fn, resume = False):\n",
    "    if resume == False:\n",
    "        best_loss = float('inf')\n",
    "        result = {'epoch':[] ,'train_loss_total':[], \"val_loss_total\":[], 'lr':[]}\n",
    "        lr_ROP = None\n",
    "        lr_else = []\n",
    "        for lr in lr_list:\n",
    "            if lr.__name__ == 'ReduceLROnPlateau':\n",
    "                lr_ROP = lr(optimizer, mode='max', factor=0.1, patience=10, threshold=0, eps=0)\n",
    "            elif lr.__name__ == 'CyclicLR':\n",
    "                lr_else.append(lr(optimizer, base_lr=1e-4, max_lr=1e-2, step_size_up=2000, cycle_momentum=False))\n",
    "    else:\n",
    "        r_prev = pd.read_csv(f'runs/result.csv')\n",
    "        result = r_prev.to_dict('list')\n",
    "        best_loss = min(r_prev['val_loss_total'])\n",
    "        optimizer = torch.optim.Adam(model.parameters(), lr=float(eval(result['lr'][-1])[0]))\n",
    "        \n",
    "        lr_ROP = None\n",
    "        lr_else = []\n",
    "        for lr in lr_list:\n",
    "            if lr.__name__ == 'ReduceLROnPlateau':\n",
    "                lr_ROP = lr(optimizer, mode='max', factor=0.1, patience=10, threshold=0, eps=0)\n",
    "            elif lr.__name__ == 'CyclicLR':\n",
    "                lr_else.append(lr(optimizer, base_lr=1e-4, max_lr=1e-2, step_size_up=2000, cycle_momentum=False))\n",
    "        model.load_state_dict(torch.load(f\"runs/{epoch}_last.pth\"))\n",
    "        \n",
    "        \n",
    "    early_stop = 1000\n",
    "    count_stop = 0\n",
    "    \n",
    "            \n",
    "    # Initialize Weights & Biases\n",
    "    wandb.init(\n",
    "        project=\"J1-regressor\", \n",
    "        config={\n",
    "            \"learning_rate\": lr_ROP.get_last_lr(),\n",
    "            \"epochs\": epoch,\n",
    "        })\n",
    "     \n",
    "    with tqdm(range(epoch), desc=\"Epoch\", unit=\"epoch\") as pbar:   \n",
    "        for e in pbar:\n",
    "            # Training loop\n",
    "            if resume == False:\n",
    "                result['epoch'].append(e+1)\n",
    "            else:\n",
    "                result['epoch'].append(e+1 + len(result['epoch']))\n",
    "            loss_epoch = 0\n",
    "            model.train()\n",
    "\n",
    "            for batch_data, batch_labels in train_dataloader:\n",
    "\n",
    "                batch_data = batch_data.to(device).unsqueeze(1)\n",
    "                # batch_data = (batch_data - train_data.mean_x())/train_data.std_x() # Normalize the data\n",
    "                #batch_labels = batch_labels.movedim(0, 1)\n",
    "\n",
    "                # Forward pass\n",
    "                label = batch_labels.to(device)\n",
    "                # label = (label - train_data.mean())/train_data.std() # Normalize the labels\n",
    "\n",
    "                output = model(batch_data)\n",
    "\n",
    "                # Optim\n",
    "                optimizer.zero_grad()\n",
    "                if loss_fn.__name__ == 'adjusted_r2_loss':\n",
    "                    loss = loss_fn(output, label, 82)\n",
    "                # else:\n",
    "                #     loss = loss_fn(output, label)\n",
    "                loss_epoch += loss.item()\n",
    "                loss.backward()\n",
    "                optimizer.step()\n",
    "                \n",
    "\n",
    "\n",
    "            # Combined loss\n",
    "            loss_epoch /= len(train_dataloader)\n",
    "            result['train_loss_total'].append(loss_epoch)\n",
    "\n",
    "            # print('Loss: ', loss_epoch)\n",
    "\n",
    "            # Vaidating\n",
    "            num_batches = len(val_dataloader)\n",
    "            model.eval()\n",
    "\n",
    "\n",
    "            test_loss = 0\n",
    "        \n",
    "            with torch.no_grad():\n",
    "                # print('Val------')\n",
    "                for X, y in val_dataloader:\n",
    "\n",
    "                    X = X.to(device).unsqueeze(1)\n",
    "                    # X = (X - train_data.mean_x())/train_data.std_x() # Normalize the data\n",
    "                    pred = model(X)\n",
    "\n",
    "                    label = y.to(device)\n",
    "                    # label = (label - train_data.mean())/train_data.std() # Normalize the labels   \n",
    "                    if loss_fn.__name__ == 'adjusted_r2_loss':\n",
    "                        loss = loss_fn(pred, label, 82)\n",
    "                    # else:\n",
    "                    #     loss = loss_fn(pred, label)\n",
    "                    test_loss += loss.item()\n",
    "                    \n",
    "                    for lr in lr_else:\n",
    "                        lr.step()\n",
    "\n",
    "            test_loss /= num_batches\n",
    "            lr_ROP.step(test_loss)\n",
    "            result['lr'].append(lr_ROP.get_last_lr())\n",
    "            \n",
    "            result['val_loss_total'].append(test_loss)\n",
    "            \n",
    "            # Update the progress bar with 100*n epoch, train loss, and val loss\n",
    "            if e % 10 == 0:\n",
    "                pbar.set_postfix(train_loss=f\"{loss_epoch:.4f}\", val_loss=f\"{test_loss:.4f}\")\n",
    "                wandb.log({\"train_loss\": loss_epoch, \"val_loss\": test_loss})\n",
    "            \n",
    "            # remember best acc@ and save checkpoint\n",
    "            \n",
    "            is_best = test_loss < best_loss\n",
    "            if is_best:\n",
    "                count_stop = 0\n",
    "                pbar.set_description(f'Epoch{e}: Good with lr: {lr_ROP.get_last_lr()}', refresh=True)\n",
    "            else:\n",
    "                count_stop += 1\n",
    "                if count_stop > early_stop:\n",
    "                    print('Early stopping')\n",
    "                    break\n",
    "            best_loss = min(test_loss, best_loss)\n",
    "            save_checkpoint({\n",
    "                f'state_dict': model.state_dict(),\n",
    "            }, is_best, epoch)\n",
    "            \n",
    "            \n",
    "\n",
    "            # print(f\"\"\"Val Error:\n",
    "            # Avg loss: {test_loss}\n",
    "            # \"\"\")\n",
    "            # if (e+1) % 10 == 0:\n",
    "            #     clear_output(wait=True)\n",
    "    r = pd.DataFrame(result)\n",
    "    r.to_csv(f'runs/resultJ.csv')\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([32, 82])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "next(iter(train_loader))[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_x, train_y = torch.tensor(S1_x, dtype=torch.float).to(device), torch.tensor(S1_y, dtype=torch.float).unsqueeze(dim=1).to(device)\n",
    "val_x, val_y = torch.tensor(S2_x, dtype=torch.float).to(device), torch.tensor(S2_y, dtype=torch.float).unsqueeze(dim=1).to(device)\n",
    "test_x, test_y = torch.tensor(S3_x, dtype=torch.float).to(device), torch.tensor(S3_y, dtype=torch.float).unsqueeze(dim=1).to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([90, 82])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([90, 1])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([90, 1])\n",
      "tensor(33729804., device='cuda:0', grad_fn=<MseLossBackward0>)\n"
     ]
    }
   ],
   "source": [
    "model = RegressionModel().to(device)   \n",
    "model.eval()\n",
    "out = model(train_x)\n",
    "print(out.shape)\n",
    "loss = nn.MSELoss()(train_y, out)\n",
    "print(loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m Calling wandb.login() after wandb.init() has no effect.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "Finishing last run (ID:c844oq8c) before initializing another..."
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       " View run <strong style=\"color:#cdcd00\">rose-salad-19</strong> at: <a href='https://wandb.ai/grpprj/J1-gradboostregressor/runs/c844oq8c' target=\"_blank\">https://wandb.ai/grpprj/J1-gradboostregressor/runs/c844oq8c</a><br/> View project at: <a href='https://wandb.ai/grpprj/J1-gradboostregressor' target=\"_blank\">https://wandb.ai/grpprj/J1-gradboostregressor</a><br/>Synced 6 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Find logs at: <code>./wandb/run-20240816_122505-c844oq8c/logs</code>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "The new W&B backend becomes opt-out in version 0.18.0; try it out with `wandb.require(\"core\")`! See https://wandb.me/wandb-core for more information."
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Successfully finished last run (ID:c844oq8c). Initializing new run:<br/>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "wandb version 0.17.7 is available!  To upgrade, please run:\n",
       " $ pip install wandb --upgrade"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Tracking run with wandb version 0.17.6"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Run data is saved locally in <code>/storage/student5/minh_chu/PhosphoreHyper/wandb/run-20240816_123733-g8pkx6ip</code>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Syncing run <strong><a href='https://wandb.ai/grpprj/J1-gradboostregressor/runs/g8pkx6ip' target=\"_blank\">vital-serenity-22</a></strong> to <a href='https://wandb.ai/grpprj/J1-gradboostregressor' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       " View project at <a href='https://wandb.ai/grpprj/J1-gradboostregressor' target=\"_blank\">https://wandb.ai/grpprj/J1-gradboostregressor</a>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       " View run at <a href='https://wandb.ai/grpprj/J1-gradboostregressor/runs/g8pkx6ip' target=\"_blank\">https://wandb.ai/grpprj/J1-gradboostregressor/runs/g8pkx6ip</a>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Estimator1: Good with loss: 9103672.0:  13%|█▎        | 12632/100000 [00:45<05:12, 279.77epoch/s, train_loss=Estimator1:5594864.5000, val_loss=Estimator1:9548383.0000]  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Estimator2: Good with loss: 9545950.0:  11%|█▏        | 11442/100000 [00:34<04:27, 331.52epoch/s, train_loss=Estimator2:3023771.7500, val_loss=Estimator2:11735312.0000]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Estimator3: Good with loss: 13771435.0:  20%|█▉        | 19703/100000 [01:11<04:52, 274.68epoch/s, train_loss=Estimator3:1704968.6250, val_loss=Estimator3:13906197.0000]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Estimator4: Good with loss: 10273758.0:  55%|█████▌    | 55097/100000 [03:50<03:08, 238.66epoch/s, train_loss=Estimator4:1108736.2500, val_loss=Estimator4:10560498.0000]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Estimator5: Good with loss: 13426900.0:  16%|█▋        | 16475/100000 [01:02<05:19, 261.82epoch/s, train_loss=Estimator5:975531.8750, val_loss=Estimator5:13800593.0000]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Estimator6: Good with loss: 14255553.0:  10%|█         | 10001/100000 [00:30<04:30, 332.76epoch/s, train_loss=Estimator6:924238.0625, val_loss=Estimator6:15397236.0000]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Estimator7: Good with loss: 16509317.0:  12%|█▏        | 11825/100000 [00:39<04:55, 298.72epoch/s, train_loss=Estimator7:881715.7500, val_loss=Estimator7:17292764.0000]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Estimator8: Good with loss: 18312876.0:  10%|█         | 10001/100000 [00:33<04:58, 301.11epoch/s, train_loss=Estimator8:838348.4375, val_loss=Estimator8:19079038.0000]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Estimator9: Good with loss: 19504354.0:  12%|█▏        | 12465/100000 [00:44<05:10, 281.48epoch/s, train_loss=Estimator9:809552.3750, val_loss=Estimator9:19871804.0000]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Estimator10: Good with loss: 20463766.0:  10%|█         | 10072/100000 [00:33<04:56, 303.42epoch/s, train_loss=Estimator10:764337.6875, val_loss=Estimator10:21026322.0000]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Estimator11: Good with loss: 21256332.0:  12%|█▏        | 11575/100000 [00:40<05:07, 287.52epoch/s, train_loss=Estimator11:740882.6250, val_loss=Estimator11:21529866.0000]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Estimator12: Good with loss: 22076906.0:  10%|█         | 10001/100000 [00:32<04:53, 306.80epoch/s, train_loss=Estimator12:693248.1250, val_loss=Estimator12:22594918.0000]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Estimator13: Good with loss: 22722074.0:  12%|█▏        | 12486/100000 [00:43<05:07, 284.37epoch/s, train_loss=Estimator13:674443.3125, val_loss=Estimator13:22816450.0000]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Estimator14: Good with loss: 22933186.0:  10%|█         | 10001/100000 [00:32<04:50, 309.93epoch/s, train_loss=Estimator14:625488.1250, val_loss=Estimator14:23349658.0000]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Estimator15: Good with loss: 23418528.0:  12%|█▏        | 11815/100000 [00:42<05:13, 281.22epoch/s, train_loss=Estimator15:609475.4375, val_loss=Estimator15:23448666.0000]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Estimator16: Good with loss: 23498278.0:  10%|█         | 10043/100000 [00:32<04:52, 307.96epoch/s, train_loss=Estimator16:560078.2500, val_loss=Estimator16:23780340.0000]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Estimator17: Good with loss: 23825456.0:  15%|█▌        | 15386/100000 [00:53<04:54, 287.71epoch/s, train_loss=Estimator17:542965.5000, val_loss=Estimator17:23839974.0000]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Estimator18: Good with loss: 24032102.0:  10%|█         | 10001/100000 [00:32<04:50, 309.80epoch/s, train_loss=Estimator18:471681.2188, val_loss=Estimator18:24391286.0000]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Estimator19: Good with loss: 24012224.0: 100%|██████████| 100000/100000 [06:18<00:00, 263.91epoch/s, train_loss=Estimator19:402708.0938, val_loss=Estimator19:24012226.0000]\n",
      "Estimator20: Good with loss: 26767098.0:  10%|█         | 10001/100000 [00:33<04:57, 302.94epoch/s, train_loss=Estimator20:287731.4375, val_loss=Estimator20:26833668.0000]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Estimator21: Good with loss: 25748816.0: 100%|██████████| 100000/100000 [05:45<00:00, 289.28epoch/s, train_loss=Estimator21:286276.6875, val_loss=Estimator21:25751348.0000]\n",
      "Estimator22: Good with loss: 25071934.0:  24%|██▍       | 23929/100000 [01:19<04:11, 302.35epoch/s, train_loss=Estimator22:285848.6562, val_loss=Estimator22:25115656.0000]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Estimator23: Good with loss: 25077362.0:  10%|█         | 10051/100000 [00:29<04:21, 344.26epoch/s, train_loss=Estimator23:285774.2812, val_loss=Estimator23:25167332.0000]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Estimator24: Good with loss: 25198024.0:  24%|██▍       | 23830/100000 [01:17<04:08, 306.95epoch/s, train_loss=Estimator24:285711.4062, val_loss=Estimator24:25236038.0000]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Estimator25: Good with loss: 25199882.0:  10%|█         | 10087/100000 [00:33<04:55, 304.56epoch/s, train_loss=Estimator25:285626.5312, val_loss=Estimator25:25281832.0000]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Estimator26: Good with loss: 25244904.0:  10%|█         | 10001/100000 [00:31<04:42, 318.17epoch/s, train_loss=Estimator26:285612.0000, val_loss=Estimator26:25322804.0000]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Estimator27: Good with loss: 25372462.0:  16%|█▌        | 15701/100000 [00:51<04:36, 304.37epoch/s, train_loss=Estimator27:285565.1250, val_loss=Estimator27:25404230.0000]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Estimator28: Good with loss: 25381106.0:  10%|█         | 10001/100000 [00:30<04:35, 326.79epoch/s, train_loss=Estimator28:285549.2500, val_loss=Estimator28:25427690.0000]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Estimator29: Good with loss: 25390312.0:  10%|█         | 10001/100000 [00:33<04:57, 302.10epoch/s, train_loss=Estimator29:285526.4375, val_loss=Estimator29:25474282.0000]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Estimator30: Good with loss: 25461510.0:  10%|█         | 10036/100000 [00:32<04:47, 312.79epoch/s, train_loss=Estimator30:285513.0000, val_loss=Estimator30:25502042.0000]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Estimator31: Good with loss: 25540584.0:  10%|█         | 10086/100000 [00:32<04:45, 314.56epoch/s, train_loss=Estimator31:285480.5312, val_loss=Estimator31:25571990.0000]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Estimator32: Good with loss: 25551038.0:  10%|█         | 10001/100000 [00:33<05:02, 297.61epoch/s, train_loss=Estimator32:285471.0312, val_loss=Estimator32:25596990.0000]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Estimator33: Good with loss: 25686106.0:  12%|█▏        | 12005/100000 [00:39<04:48, 304.86epoch/s, train_loss=Estimator33:285422.8438, val_loss=Estimator33:25743876.0000]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Estimator34: Good with loss: 25723940.0:  10%|█         | 10025/100000 [00:33<04:58, 301.64epoch/s, train_loss=Estimator34:285415.9062, val_loss=Estimator34:25768014.0000]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Estimator35: Good with loss: 25863156.0:  12%|█▏        | 11876/100000 [00:36<04:30, 325.53epoch/s, train_loss=Estimator35:285376.9375, val_loss=Estimator35:25913960.0000]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Estimator36: Good with loss: 25843184.0:  10%|█         | 10001/100000 [00:35<05:15, 285.43epoch/s, train_loss=Estimator36:285372.6562, val_loss=Estimator36:25935306.0000]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Estimator37: Good with loss: 25958006.0:  16%|█▌        | 15527/100000 [00:50<04:36, 305.39epoch/s, train_loss=Estimator37:285357.8750, val_loss=Estimator37:26002948.0000]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Estimator38: Good with loss: 25998462.0:  10%|█         | 10001/100000 [00:32<04:52, 307.78epoch/s, train_loss=Estimator38:285349.3438, val_loss=Estimator38:26041616.0000]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Estimator39: Good with loss: 26040180.0:  11%|█▏        | 11338/100000 [00:38<04:59, 296.42epoch/s, train_loss=Estimator39:285341.7500, val_loss=Estimator39:26078294.0000]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Estimator40: Good with loss: 26048222.0:  10%|█         | 10001/100000 [00:31<04:46, 314.18epoch/s, train_loss=Estimator40:285330.7812, val_loss=Estimator40:26133434.0000]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Estimator41: Good with loss: 26078966.0:  10%|█         | 10001/100000 [00:30<04:33, 328.91epoch/s, train_loss=Estimator41:285326.0938, val_loss=Estimator41:26159246.0000]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Estimator42: Good with loss: 26155730.0:  10%|█         | 10103/100000 [00:31<04:38, 322.22epoch/s, train_loss=Estimator42:285318.9375, val_loss=Estimator42:26198550.0000]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Estimator43: Good with loss: 26196952.0:  12%|█▏        | 11501/100000 [00:35<04:32, 325.17epoch/s, train_loss=Estimator43:285313.7500, val_loss=Estimator43:26234104.0000]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Estimator44: Good with loss: 26287020.0:  16%|█▌        | 15937/100000 [00:50<04:27, 314.40epoch/s, train_loss=Estimator44:285298.4688, val_loss=Estimator44:26338138.0000]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Estimator45: Good with loss: 26319668.0:  10%|█         | 10067/100000 [00:34<05:10, 289.24epoch/s, train_loss=Estimator45:285294.7500, val_loss=Estimator45:26359818.0000]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Estimator46: Good with loss: 26373576.0:  10%|█         | 10001/100000 [00:32<04:54, 305.52epoch/s, train_loss=Estimator46:285287.3438, val_loss=Estimator46:26411552.0000]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Estimator47: Good with loss: 26314494.0:  10%|█         | 10001/100000 [00:34<05:06, 293.36epoch/s, train_loss=Estimator47:285284.5625, val_loss=Estimator47:26433542.0000]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Estimator48: Good with loss: 26430240.0:  12%|█▏        | 11786/100000 [00:37<04:42, 312.41epoch/s, train_loss=Estimator48:285280.2188, val_loss=Estimator48:26464370.0000]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Estimator49: Good with loss: 26466434.0:  10%|█         | 10067/100000 [00:35<05:15, 285.27epoch/s, train_loss=Estimator49:285274.4375, val_loss=Estimator49:26510294.0000]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Estimator50: Good with loss: 26497734.0:  12%|█▏        | 11992/100000 [00:37<04:37, 317.28epoch/s, train_loss=Estimator50:285271.2188, val_loss=Estimator50:26539472.0000]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Estimator51: Good with loss: 26483610.0:  10%|█         | 10001/100000 [00:33<05:05, 295.00epoch/s, train_loss=Estimator51:285265.1250, val_loss=Estimator51:26597546.0000]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Estimator52: Good with loss: 26554066.0:  10%|█         | 10001/100000 [00:32<04:49, 310.77epoch/s, train_loss=Estimator52:285263.5312, val_loss=Estimator52:26613648.0000]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Estimator53: Good with loss: 26622150.0:  12%|█▏        | 11686/100000 [00:39<04:58, 295.39epoch/s, train_loss=Estimator53:285257.5625, val_loss=Estimator53:26666650.0000]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Estimator54: Good with loss: 26632176.0:  10%|█         | 10001/100000 [00:29<04:28, 335.19epoch/s, train_loss=Estimator54:285255.4375, val_loss=Estimator54:26685118.0000]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Estimator55: Good with loss: 26679024.0:  10%|█         | 10001/100000 [00:32<04:56, 303.67epoch/s, train_loss=Estimator55:285250.7812, val_loss=Estimator55:26729170.0000]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Estimator56: Good with loss: 26704400.0:  10%|█         | 10001/100000 [00:34<05:09, 291.05epoch/s, train_loss=Estimator56:285249.3438, val_loss=Estimator56:26743920.0000]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Estimator57: Good with loss: 26736190.0:  10%|█         | 10130/100000 [00:34<05:09, 290.19epoch/s, train_loss=Estimator57:285246.5000, val_loss=Estimator57:26774086.0000]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Estimator58: Good with loss: 26667752.0:  10%|█         | 10001/100000 [00:32<04:48, 311.97epoch/s, train_loss=Estimator58:285245.3750, val_loss=Estimator58:26782654.0000]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Estimator59: Good with loss: 26776838.0:  12%|█▏        | 11609/100000 [00:38<04:56, 298.29epoch/s, train_loss=Estimator59:285242.0625, val_loss=Estimator59:26817110.0000]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Estimator60: Good with loss: 26795300.0:  12%|█▏        | 12040/100000 [00:37<04:32, 322.59epoch/s, train_loss=Estimator60:285239.9062, val_loss=Estimator60:26842318.0000]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Estimator61: Good with loss: 26832702.0:  24%|██▍       | 23868/100000 [01:17<04:06, 309.38epoch/s, train_loss=Estimator61:285236.5000, val_loss=Estimator61:26878244.0000]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Estimator62: Good with loss: 26860776.0:  10%|█         | 10001/100000 [00:33<05:04, 295.66epoch/s, train_loss=Estimator62:285232.3125, val_loss=Estimator62:26927102.0000]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Estimator63: Good with loss: 26905730.0:  16%|█▌        | 15639/100000 [00:52<04:45, 295.42epoch/s, train_loss=Estimator63:285230.8125, val_loss=Estimator63:26948114.0000]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Estimator64: Good with loss: 27051286.0:  12%|█▏        | 11940/100000 [00:38<04:47, 306.76epoch/s, train_loss=Estimator64:285222.0625, val_loss=Estimator64:27088834.0000]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Estimator65: Good with loss: 27063270.0:  24%|██▍       | 23863/100000 [01:18<04:11, 302.22epoch/s, train_loss=Estimator65:285220.8125, val_loss=Estimator65:27109638.0000]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Estimator66: Good with loss: 27204320.0:  19%|█▉        | 19247/100000 [01:02<04:21, 308.33epoch/s, train_loss=Estimator66:285214.2188, val_loss=Estimator66:27234942.0000]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Estimator67: Good with loss: 27202062.0:  12%|█▏        | 11703/100000 [00:39<04:55, 299.27epoch/s, train_loss=Estimator67:285213.5625, val_loss=Estimator67:27242710.0000]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Estimator68: Good with loss: 27215622.0:  16%|█▌        | 15795/100000 [00:45<04:02, 347.70epoch/s, train_loss=Estimator68:285212.6250, val_loss=Estimator68:27261206.0000]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Estimator69: Good with loss: 27255152.0:   1%|          | 581/100000 [00:01<02:37, 630.23epoch/s, train_loss=Estimator69:285213.4062, val_loss=Estimator69:27255570.0000]"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import wandb\n",
    "# from IPython.display import clear_output\n",
    "# def train_grad_boost(model, epoch, train_dataloader, val_dataloader, optimizer, lr_list, loss_fn, resume = False):\n",
    "\n",
    "train_x, train_y = torch.tensor(S1_x, dtype=torch.float).to(device), torch.tensor(S1_y, dtype=torch.float).unsqueeze(dim=1).to(device)\n",
    "val_x, val_y = torch.tensor(S2_x, dtype=torch.float).to(device), torch.tensor(S2_y, dtype=torch.float).unsqueeze(dim=1).to(device)\n",
    "test_x, test_y = torch.tensor(S3_x, dtype=torch.float).to(device), torch.tensor(S3_y, dtype=torch.float).unsqueeze(dim=1).to(device)\n",
    "\n",
    "\n",
    "def save_checkpoint_boost(state, is_best, index):\n",
    "    torch.save(state, f'runs/grad_boost/grad_J_best_{index}.pth')\n",
    "    if is_best:\n",
    "        # print('Found best')\n",
    "        torch.save(state, f'runs/grad_boost/grad_J_last_{index}.pth')\n",
    "    \n",
    "wandb.login()\n",
    "\n",
    "\n",
    "num_model = 100\n",
    "epoch = 100000\n",
    "lr_list = [ReduceLROnPlateau, CyclicLR]\n",
    "models = []\n",
    "gammas = []\n",
    "    \n",
    "early_stop = 10000\n",
    "count_stop = 0\n",
    "\n",
    "\n",
    "train_output = torch.zeros_like(train_y).to(device)\n",
    "val_output = 0\n",
    "test_output = 0\n",
    "train_y_new = train_y.clone()\n",
    "val_y_new = val_y.clone()\n",
    "\n",
    "# Initialize Weights & Biases\n",
    "wandb.init(\n",
    "    project=\"J1-gradboostregressor\", \n",
    "    config={\n",
    "        \"epochs\": epoch*num_model,\n",
    "    })  \n",
    "\n",
    "     \n",
    "\n",
    "for i in range(num_model):\n",
    "    with tqdm(range(epoch), desc=f\"Estimator{i+1}\", unit='epoch') as pbar:\n",
    "        \n",
    "        # Inintialize the model\n",
    "        model = RegressionModel().to(device)\n",
    "        loss_fn = nn.MSELoss\n",
    "        optimizer = torch.optim.Adam(model.parameters(), lr=1)\n",
    "        \n",
    "        \n",
    "        best_loss = float('inf')\n",
    "        result = {'epoch':[] ,'train_loss_total':[], \"val_loss_total\":[], 'lr':[]}\n",
    "        lr_ROP = None\n",
    "        lr_else = []\n",
    "        for lr in lr_list:\n",
    "            if lr.__name__ == 'ReduceLROnPlateau':\n",
    "                lr_ROP = lr(optimizer, mode='max', factor=0.1, patience=10, threshold=0, eps=0)\n",
    "            elif lr.__name__ == 'CyclicLR':\n",
    "                lr_else.append(lr(optimizer, base_lr=1e-4, max_lr=1e-2, step_size_up=2000, cycle_momentum=False))\n",
    "            \n",
    "        for e in pbar:\n",
    "            # Training loop\n",
    "            result['epoch'].append(e*(i+1)+1)\n",
    "\n",
    "            # loss_epoch = 0\n",
    "            model.train()\n",
    "\n",
    "            # batch_data = batch_data.to(device)\n",
    "            # batch_data = (batch_data - train_data.mean_x())/train_data.std_x() # Normalize the data\n",
    "            #batch_labels = batch_labels.movedim(0, 1)\n",
    "\n",
    "            # Forward pass\n",
    "            # label = batch_labels.to(device)\n",
    "            # label = (label - train_data.mean())/train_data.std() # Normalize the labels\n",
    "\n",
    "            output = model(train_x)\n",
    "            # Optim\n",
    "            optimizer.zero_grad()\n",
    "            if loss_fn.__name__ == 'adjusted_r2_loss':\n",
    "                loss = loss_fn(output, train_y_new, 82)\n",
    "            elif loss_fn.__name__ == 'MSELoss':\n",
    "                loss = loss_fn()(output, train_y_new)\n",
    "            loss_epoch = loss.item()\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "                \n",
    "\n",
    "\n",
    "            # Combined loss\n",
    "            # loss_epoch /= len(train_loader)\n",
    "            result['train_loss_total'].append(loss_epoch)\n",
    "\n",
    "            # print('Loss: ', loss_epoch)\n",
    "\n",
    "            # Vaidating\n",
    "            model.eval()\n",
    "        \n",
    "            with torch.no_grad():\n",
    "                # print('Val------')\n",
    "                # X = (X - train_data.mean_x())/train_data.std_x() # Normalize the data\n",
    "                pred = model(val_x)\n",
    "                # label = (label - train_data.mean())/train_data.std() # Normalize the labels   \n",
    "                if loss_fn.__name__ == 'adjusted_r2_loss':\n",
    "                    loss = loss_fn(pred, val_y_new, 82)\n",
    "                elif loss_fn.__name__ == 'MSELoss':\n",
    "                    loss = loss_fn()(pred, val_y_new)\n",
    "                test_loss = loss.item()\n",
    "                \n",
    "                for lr in lr_else:\n",
    "                    lr.step()\n",
    "\n",
    "            lr_ROP.step(test_loss)\n",
    "            result['lr'].append(lr_ROP.get_last_lr())\n",
    "            \n",
    "            result['val_loss_total'].append(test_loss)\n",
    "            \n",
    "            # Update the progress bar with 100*n epoch, train loss, and val loss\n",
    "            # if e % 10 == 0:\n",
    "            pbar.set_postfix(train_loss=f\"Estimator{i+1}:{loss_epoch:.4f}\", val_loss=f\"Estimator{i+1}:{test_loss:.4f}\")\n",
    "                # wandb.log({f\"Estimator{i+1}_train_loss\": loss_epoch, f\"Estimator{i+1}:_val_loss\": test_loss})\n",
    "            \n",
    "            # remember best acc@ and save checkpoint\n",
    "            \n",
    "            is_best = test_loss < best_loss\n",
    "            if is_best:\n",
    "                count_stop = 0\n",
    "                pbar.set_description(f'Estimator{i+1}: Good with loss: {test_loss}', refresh=True)\n",
    "            else:\n",
    "                count_stop += 1\n",
    "                if count_stop > early_stop:\n",
    "                    print('Early stopping')\n",
    "                    break\n",
    "            best_loss = min(test_loss, best_loss)\n",
    "            # save_checkpoint_boost({\n",
    "            #     f'state_dict': model.state_dict(),\n",
    "            # }, is_best, i)\n",
    "            \n",
    "        # Create new y_train\n",
    "        with torch.no_grad():  \n",
    "            models.append(model)\n",
    "            gamma = 1* -(train_output*model(train_x) - model(train_x)*train_y).sum()/(model(train_x)**2).sum().item()\n",
    "            gammas.append(gamma.item())\n",
    "            train_output += gamma*model(train_x).detach()\n",
    "            val_output += gamma*model(val_x).detach()\n",
    "            \n",
    "\n",
    "                \n",
    "            if loss_fn.__name__ == 'adjusted_r2_loss':\n",
    "                val_loss = loss_fn(val_output, val_y, 82).item()\n",
    "                train_loss = loss_fn(train_output, train_y, 82).item()\n",
    "            elif loss_fn.__name__ == 'MSELoss':\n",
    "                val_loss = loss_fn()(val_y, val_output).item()\n",
    "                train_loss = loss_fn()(train_y, train_output).item()\n",
    "            wandb.log({f\"Total_train_loss\": train_loss, f'Total_val_loss': val_loss})\n",
    "                \n",
    "            train_y_new -= gamma*model(train_x).detach()\n",
    "            val_y_new -= gamma*model(val_x).detach()\n",
    "\n",
    "            # print(f\"\"\"Val Error:\n",
    "            # Avg loss: {test_loss}\n",
    "            # \"\"\")\n",
    "            # if (e+1) % 10 == 0:\n",
    "            #     clear_output(wait=True)\n",
    "r = pd.DataFrame(result)\n",
    "r.to_csv(f'runs/resultJ.csv')\n",
    "# return result\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "output=0\n",
    "for model in models:\n",
    "    model.eval()\n",
    "    with torch.no_grad():\n",
    "        # train_output += model(train_x)\n",
    "        # val_output += model(val_x)\n",
    "        output += model(train_x)\n",
    "        \n",
    "plt.plot(output.cpu().numpy(), label='Predicted')\n",
    "plt.plot(train_y.cpu().numpy(), label='True')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m Calling wandb.login() after wandb.init() has no effect.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "Finishing last run (ID:cg4hnrme) before initializing another..."
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>\n",
       "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: left ; width: auto;} td:nth-child(2) {text-align: left ; width: 100%}\n",
       "    .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; justify-content: flex-start; width: 100% }\n",
       "    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
       "    </style>\n",
       "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>train_loss</td><td>█▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>val_loss</td><td>█▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>train_loss</td><td>-1.52906</td></tr><tr><td>val_loss</td><td>-1.53723</td></tr></table><br/></div></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       " View run <strong style=\"color:#cdcd00\">avid-sunset-22</strong> at: <a href='https://wandb.ai/grpprj/J1-regressor/runs/cg4hnrme' target=\"_blank\">https://wandb.ai/grpprj/J1-regressor/runs/cg4hnrme</a><br/> View project at: <a href='https://wandb.ai/grpprj/J1-regressor' target=\"_blank\">https://wandb.ai/grpprj/J1-regressor</a><br/>Synced 6 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Find logs at: <code>./wandb/run-20240816_001745-cg4hnrme/logs</code>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "The new W&B backend becomes opt-out in version 0.18.0; try it out with `wandb.require(\"core\")`! See https://wandb.me/wandb-core for more information."
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Successfully finished last run (ID:cg4hnrme). Initializing new run:<br/>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "wandb version 0.17.7 is available!  To upgrade, please run:\n",
       " $ pip install wandb --upgrade"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Tracking run with wandb version 0.17.6"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Run data is saved locally in <code>/storage/student5/minh_chu/PhosphoreHyper/wandb/run-20240816_002704-bt25mfl2</code>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Syncing run <strong><a href='https://wandb.ai/grpprj/J1-regressor/runs/bt25mfl2' target=\"_blank\">gentle-frost-23</a></strong> to <a href='https://wandb.ai/grpprj/J1-regressor' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       " View project at <a href='https://wandb.ai/grpprj/J1-regressor' target=\"_blank\">https://wandb.ai/grpprj/J1-regressor</a>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       " View run at <a href='https://wandb.ai/grpprj/J1-regressor/runs/bt25mfl2' target=\"_blank\">https://wandb.ai/grpprj/J1-regressor/runs/bt25mfl2</a>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch17000: Good with lr: [0.009990099999999988]:  18%|█▊        | 18001/100000 [10:36<48:17, 28.30epoch/s, train_loss=-1.5514, val_loss=-1.5439]      \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping\n"
     ]
    }
   ],
   "source": [
    "wandb.login()\n",
    "result = train(model, 100000, train_loader, val_loader, optimizer, [lr1, lr2],  loss_fn, resume = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1475"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(result['epoch'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 90/90 [00:00<00:00, 1328.77it/s]\n"
     ]
    }
   ],
   "source": [
    "# model.load_state_dict(torch.load(f\"runs/100000_J_conv_last.pth\"))\n",
    "model.eval()\n",
    "# model(torch.Tensor([train_x[1]]).to(device)), train_y[1]\n",
    "with torch.no_grad():\n",
    "    predict = []\n",
    "    for x in tqdm(S1_x):\n",
    "        # x = (x - train_data.mean_x())/train_data.std_x()\n",
    "        predict.append(model(torch.Tensor([x]).to(device).unsqueeze(1)).item())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 90/90 [00:00<00:00, 1440.24it/s]\n"
     ]
    }
   ],
   "source": [
    "# model.load_state_dict(torch.load(f\"runs/100000_J_conv_best.pth\"))\n",
    "model.eval()\n",
    "# model(torch.Tensor([train_x[1]]).to(device)), train_y[1]\n",
    "with torch.no_grad():\n",
    "    predict1 = []\n",
    "    for x in tqdm(S1_x):\n",
    "        # x = (x - train_data.mean_x())/train_data.std_x()\n",
    "        predict1.append(model(torch.Tensor([x]).to(device).unsqueeze(1)).item())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f8bc414bd50>]"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([i for i in range(len(S1_y))],S1_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.plot([i for i in range(len(predict))], predict, c = 'green', label = 'Last')\n",
    "plt.plot([i for i in range(len(predict1))], predict1, c = 'red', label = 'Best')\n",
    "plt.plot([i for i in range(len(S1_y))], S1_y, c = 'blue', label = 'True')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(result['epoch'], result['train_loss_total'], label='train')\n",
    "plt.plot(result['epoch'], result['val_loss_total'], label='val')\n",
    "plt.y_ticks = np.arange(10, 100, 1000)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f0d118694d0>]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.load_state_dict(torch.load('runs/20000_best.pth'))\n",
    "model.eval()\n",
    "predict = []\n",
    "for i in test_x:\n",
    "    i = (i - train_data.mean_x())/train_data.std_x()\n",
    "    predict.append(model(torch.Tensor(i).to(device)).item())\n",
    "# model(torch.Tensor(test_x[1]).to(device)), test_y[1]\n",
    "\n",
    "plt.plot([i for i in range(len(test_x))], predict, c='blue')\n",
    "plt.plot([i for i in range(len(test_y))], test_y, c='red')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "minhchu1",
   "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": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}