LSTM_training_set4.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from configs import cfg\n",
    "\n",
    "cfg['input_dim'] = 301 # input dimension to LSTM\n",
    "cfg['hidden_dim'] = 128 # hidden dimension for LSTM\n",
    "cfg['output_dim'] = 1 # output dimension of the model\n",
    "cfg['layers'] = 2 # number of layers of LSTM\n",
    "\n",
    "cfg['dropout'] = 5e-3 # dropout rate between two layers of LSTM; useful only when layers > 1; between 0 and 1\n",
    "cfg['bidirectional'] = True # True or False; True means using a bidirectional LSTM\n",
    "cfg['batch_size'] = 120 # batch size of input\n",
    "cfg['learning_rate'] = 1e-3 # learning rate to be used\n",
    "cfg['L2_penalty'] = 1e-4 # weighting constant for L2 regularization term; this is a parameter when you define optimizer\n",
    "cfg['epochs'] = 15 # number of epochs for which the model is trained\n",
    "cfg['embed'] = False\n",
    "cfg['score_range'] = 3\n",
    "cfg['set_id'] = 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from preprocess import *\n",
    "import pandas as pd\n",
    "\n",
    "train_path = './essay_dataset/training_set_rel3.tsv'\n",
    "training_data = load_training_set(train_path, cfg['set_id'])\n",
    "\n",
    "glove_dict = load_dict('glove.840B.300d.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "valid_path = './essay_dataset/valid_set.tsv'\n",
    "valid_label_path = './essay_dataset/valid_sample_submission_2_column.csv'\n",
    "valid_data = load_validation_set(valid_path, valid_label_path, cfg['set_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_data = process_scores(training_data, 'domain1_score')\n",
    "valid_data = process_scores(valid_data, 'domain1_score')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ready\n"
     ]
    }
   ],
   "source": [
    "from dataloader import *\n",
    "from models import *\n",
    "import time\n",
    "\n",
    "model = LSTM_Score(cfg, True)\n",
    "model = model.to(torch.device(cfg['device']))\n",
    "\n",
    "criterion = nn.MSELoss()\n",
    "\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=cfg['learning_rate'], weight_decay=cfg['L2_penalty'])\n",
    "\n",
    "train_indices = list(range(len(training_data)))\n",
    "valid_indices = list(range(len(valid_data)))\n",
    "\n",
    "print('ready')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LSTM_Score(\n",
       "  (embed_linear): Linear(in_features=301, out_features=301, bias=True)\n",
       "  (lstm): LSTM(301, 128, num_layers=2, batch_first=True, dropout=0.005, bidirectional=True)\n",
       "  (bn): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (fc1): Linear(in_features=256, out_features=1, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count = 2, loss = 0.98626\n",
      "count = 4, loss = 0.49855\n",
      "count = 6, loss = 0.37097\n",
      "count = 8, loss = 0.27104\n",
      "count = 10, loss = 0.35718\n",
      "count = 12, loss = 0.33719\n",
      "count = 14, loss = 0.32337\n",
      "validation loss: 0.2725878618657589\n",
      "epoch finished: 1\n",
      "count = 2, loss = 0.29359\n",
      "count = 4, loss = 0.27147\n",
      "count = 6, loss = 0.25704\n",
      "count = 8, loss = 0.32412\n",
      "count = 10, loss = 0.28441\n",
      "count = 12, loss = 0.28510\n",
      "count = 14, loss = 0.26060\n",
      "validation loss: 0.3038000017404556\n",
      "epoch finished: 2\n",
      "count = 2, loss = 0.30315\n",
      "count = 4, loss = 0.28004\n",
      "count = 6, loss = 0.27826\n",
      "count = 8, loss = 0.26905\n",
      "count = 10, loss = 0.26393\n",
      "count = 12, loss = 0.25397\n",
      "count = 14, loss = 0.28126\n",
      "validation loss: 0.3743957653641701\n",
      "epoch finished: 3\n",
      "count = 2, loss = 0.26381\n",
      "count = 4, loss = 0.26128\n",
      "count = 6, loss = 0.28809\n",
      "count = 8, loss = 0.22836\n",
      "count = 10, loss = 0.25048\n",
      "count = 12, loss = 0.28881\n",
      "count = 14, loss = 0.23899\n",
      "validation loss: 0.4048277288675308\n",
      "epoch finished: 4\n",
      "count = 2, loss = 0.22950\n",
      "count = 4, loss = 0.28020\n",
      "count = 6, loss = 0.22997\n",
      "count = 8, loss = 0.20702\n",
      "count = 10, loss = 0.27458\n",
      "count = 12, loss = 0.29529\n",
      "count = 14, loss = 0.29646\n",
      "validation loss: 0.38204897195100784\n",
      "epoch finished: 5\n",
      "count = 2, loss = 0.25190\n",
      "count = 4, loss = 0.27893\n",
      "count = 6, loss = 0.24099\n",
      "count = 8, loss = 0.28473\n",
      "count = 10, loss = 0.22919\n",
      "count = 12, loss = 0.21149\n",
      "count = 14, loss = 0.24140\n",
      "validation loss: 0.3721608817577362\n",
      "epoch finished: 6\n",
      "count = 2, loss = 0.26309\n",
      "count = 4, loss = 0.20929\n",
      "count = 6, loss = 0.22252\n",
      "count = 8, loss = 0.27497\n",
      "count = 10, loss = 0.22756\n",
      "count = 12, loss = 0.32062\n",
      "count = 14, loss = 0.30613\n",
      "validation loss: 0.42862775176763535\n",
      "epoch finished: 7\n",
      "count = 2, loss = 0.21689\n",
      "count = 4, loss = 0.28882\n",
      "count = 6, loss = 0.25877\n",
      "count = 8, loss = 0.18681\n",
      "count = 10, loss = 0.28885\n",
      "count = 12, loss = 0.20999\n",
      "count = 14, loss = 0.28753\n",
      "validation loss: 0.3439985141158104\n",
      "epoch finished: 8\n",
      "count = 2, loss = 0.22067\n",
      "count = 4, loss = 0.28897\n",
      "count = 6, loss = 0.19069\n",
      "count = 8, loss = 0.25882\n",
      "count = 10, loss = 0.23661\n",
      "count = 12, loss = 0.24386\n",
      "count = 14, loss = 0.29388\n",
      "validation loss: 0.3559018224477768\n",
      "epoch finished: 9\n",
      "count = 2, loss = 0.20782\n",
      "count = 4, loss = 0.24807\n",
      "count = 6, loss = 0.23188\n",
      "count = 8, loss = 0.29692\n",
      "count = 10, loss = 0.23182\n",
      "count = 12, loss = 0.24764\n",
      "count = 14, loss = 0.22044\n",
      "validation loss: 0.35145624727010727\n",
      "epoch finished: 10\n",
      "count = 2, loss = 0.25777\n",
      "count = 4, loss = 0.20476\n",
      "count = 6, loss = 0.24446\n",
      "count = 8, loss = 0.18144\n",
      "count = 10, loss = 0.20686\n",
      "count = 12, loss = 0.29603\n",
      "count = 14, loss = 0.22576\n",
      "validation loss: 0.3409651964902878\n",
      "epoch finished: 11\n",
      "count = 2, loss = 0.22523\n",
      "count = 4, loss = 0.25569\n",
      "count = 6, loss = 0.20947\n",
      "count = 8, loss = 0.25668\n",
      "count = 10, loss = 0.21037\n",
      "count = 12, loss = 0.17168\n",
      "count = 14, loss = 0.25621\n",
      "validation loss: 0.36436453461647034\n",
      "epoch finished: 12\n",
      "count = 2, loss = 0.25515\n",
      "count = 4, loss = 0.20639\n",
      "count = 6, loss = 0.20583\n",
      "count = 8, loss = 0.18104\n",
      "count = 10, loss = 0.20596\n",
      "count = 12, loss = 0.28842\n",
      "count = 14, loss = 0.22954\n",
      "validation loss: 0.39821872115135193\n",
      "epoch finished: 13\n",
      "count = 2, loss = 0.21102\n",
      "count = 4, loss = 0.19999\n",
      "count = 6, loss = 0.26201\n",
      "count = 8, loss = 0.26113\n",
      "count = 10, loss = 0.21921\n",
      "count = 12, loss = 0.20784\n",
      "count = 14, loss = 0.21940\n",
      "validation loss: 0.3551393896341324\n",
      "epoch finished: 14\n",
      "count = 2, loss = 0.26374\n",
      "count = 4, loss = 0.18694\n",
      "count = 6, loss = 0.19909\n",
      "count = 8, loss = 0.27305\n",
      "count = 10, loss = 0.23162\n",
      "count = 12, loss = 0.16875\n",
      "count = 14, loss = 0.18878\n",
      "validation loss: 0.35657311230897903\n",
      "epoch finished: 15\n"
     ]
    }
   ],
   "source": [
    "train_losses = []\n",
    "val_losses = []\n",
    "for epoch in range(cfg['epochs']):\n",
    "    tloader = DataLoader(training_data, train_indices, cfg, glove_dict)\n",
    "    vloader = DataLoader(valid_data, valid_indices, cfg, glove_dict)\n",
    "    \n",
    "    count = 0\n",
    "    avg_loss = 0\n",
    "    while tloader.has_next():\n",
    "        train, label, lengths = tloader.get_next()\n",
    "        #print(train.size())\n",
    "        #print(label.size())\n",
    "        #print(lengths.size())\n",
    "        model.zero_grad()\n",
    "        \n",
    "        y = model(train, lengths)      \n",
    "        \n",
    "        loss = criterion(y*cfg['score_range'], label*cfg['score_range'])\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        count += 1\n",
    "        avg_loss += loss.item()\n",
    "        if count % 2 == 0:\n",
    "            print(\"count = %d, loss = %.5f\" %(count, avg_loss / 2))\n",
    "            save_model(model, 'models/'+'s' + str(cfg['set_id']) + '_ba' +str(cfg['batch_size']) +'e' +  str(epoch + 1) + 'b' + str(count) + '.pt')\n",
    "            train_losses.append(avg_loss/2)\n",
    "            avg_loss = 0\n",
    "        del train, label, y, loss\n",
    "    \n",
    "    count = 0\n",
    "    avg_loss = 0\n",
    "    with torch.no_grad():\n",
    "        while vloader.has_next():\n",
    "            train, label, lengths = vloader.get_next()\n",
    "            y = model(train, lengths)\n",
    "            loss = criterion(y*cfg['score_range'], label*cfg['score_range'])\n",
    "            count += 1\n",
    "            avg_loss += loss.item()\n",
    "            del train, label, y, loss\n",
    "    print('validation loss:', avg_loss / count)\n",
    "    print('epoch finished:', epoch + 1)\n",
    "    val_losses.append(avg_loss/count)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('val_loss_set4.npy', val_losses)\n",
    "np.save('train_loss_set4.npy', train_losses)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = load_model(LSTM_Score, './models/s4_ba120e9b14.pt', cfg)\n",
    "valid_indices = list(range(len(valid_data)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "validation MSE: 0.3196534737944603\n",
      "validation MAE: 0.1529635153710842\n",
      "QWK Score: 0.7337110481586402\n"
     ]
    }
   ],
   "source": [
    "from evaluation import QWK_score\n",
    "\n",
    "vloader = DataLoader(valid_data, valid_indices, cfg, glove_dict)\n",
    "count = 0\n",
    "avg_loss = 0\n",
    "MAE = 0\n",
    "targets = []\n",
    "preds = []\n",
    "with torch.no_grad():\n",
    "    while vloader.has_next():\n",
    "        train, label, lenghts = vloader.get_next()\n",
    "        y = model(train, lenghts)\n",
    "        #y = y.permute(0, 2, 1)\n",
    "        #print(label - y)\n",
    "        \n",
    "        loss = criterion(y*cfg['score_range'], label*cfg['score_range'])\n",
    "        \n",
    "        pred = np.round(y.cpu().numpy() * cfg['score_range'])\n",
    "        target = np.round(label.cpu().numpy() * cfg['score_range'])\n",
    "\n",
    "        pred = pred.flatten().astype(int)\n",
    "        target = target.flatten().astype(int)\n",
    "        \n",
    "        targets.append(target)\n",
    "        preds.append(pred)\n",
    "        \n",
    "        #print(targets[0])\n",
    "        #print(preds[0])\n",
    "        \n",
    "        count += 1\n",
    "        avg_loss += loss.item()\n",
    "        MAE += (y-label).abs().mean().item()\n",
    "        \n",
    "        del train, label, y, loss\n",
    "        \n",
    "print('validation MSE:', avg_loss / count)\n",
    "print('validation MAE:', MAE / count)\n",
    "print('QWK Score:', QWK_score(np.asarray(targets).flatten(), np.asarray(preds).flatten(), min_rating = 0, max_rating = cfg['score_range']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        2, 1, 1, 1, 2, 2, 1, 1, 2, 2, 1, 3, 2, 1, 3, 1, 3, 1, 3, 3, 1, 1,\n",
       "        2, 3, 2, 0, 2, 3, 1, 1, 2, 2, 0, 1, 2, 1, 2, 0, 1, 1, 1, 3, 0, 1,\n",
       "        3, 2, 3, 0, 1, 2, 2, 2, 2, 1, 1, 2, 3, 0, 1, 2, 2, 0, 1, 2, 1, 2,\n",
       "        1, 3, 2, 1, 2, 0, 0, 2, 2, 3]),\n",
       " array([1, 0, 2, 0, 3, 0, 2, 0, 1, 2, 1, 2, 0, 0, 3, 2, 2, 1, 2, 2, 2, 1,\n",
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       "        1, 1, 1, 1, 2, 1, 1, 1, 1, 1]),\n",
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       "        2, 2, 2, 3, 1, 2, 0, 1, 2, 2]),\n",
       " array([2, 0, 2, 1, 2, 2, 1, 0, 3, 1, 1, 1, 1, 2, 1, 0, 2, 1, 2, 1, 2, 3,\n",
       "        1, 3, 2, 0, 1, 2, 2, 3, 2, 2, 0, 1, 0, 2, 2, 2, 1, 1, 2, 2, 3, 0,\n",
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       "        1, 1, 2, 1, 2, 2, 1, 1, 2, 0, 2, 1, 0, 0, 3, 2, 1, 0, 1, 2, 2, 1,\n",
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       "        2, 0, 1, 0, 1, 2, 2, 3, 0, 2])]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "targets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "        0, 3, 0, 1, 3, 1, 1, 2, 2, 3]),\n",
       " array([1, 1, 1, 1, 3, 1, 3, 2, 1, 3, 2, 2, 0, 1, 2, 2, 2, 1, 2, 3, 2, 2,\n",
       "        1, 3, 2, 1, 0, 2, 1, 1, 3, 1, 1, 2, 1, 3, 1, 2, 2, 2, 3, 2, 3, 1,\n",
       "        3, 1, 2, 1, 2, 1, 2, 1, 2, 1, 1, 2, 3, 3, 3, 2, 2, 2, 1, 1, 2, 1,\n",
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       "        0, 1, 1, 0, 2, 0, 1, 1, 1, 1]),\n",
       " array([3, 1, 1, 2, 1, 3, 3, 2, 2, 3, 1, 2, 1, 1, 1, 1, 0, 1, 2, 3, 2, 1,\n",
       "        1, 0, 1, 0, 2, 1, 2, 0, 1, 1, 0, 2, 3, 3, 1, 2, 0, 3, 2, 2, 3, 2,\n",
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       "        1, 1, 3, 2, 1, 2, 1, 2, 1, 1, 0, 2, 1, 2, 2, 1, 2, 1, 2, 2, 1, 1,\n",
       "        1, 2, 2, 2, 1, 1, 0, 1, 1, 2]),\n",
       " array([2, 1, 2, 1, 3, 2, 0, 1, 3, 0, 1, 0, 1, 2, 1, 0, 2, 1, 2, 1, 2, 3,\n",
       "        2, 3, 1, 1, 1, 1, 2, 2, 1, 2, 0, 1, 0, 1, 1, 2, 2, 1, 2, 2, 3, 0,\n",
       "        1, 1, 2, 3, 1, 1, 2, 3, 0, 1, 1, 1, 2, 2, 1, 2, 2, 2, 2, 1, 2, 1,\n",
       "        0, 1, 3, 1, 3, 1, 0, 1, 2, 1, 2, 0, 0, 1, 3, 3, 1, 1, 2, 3, 1, 1,\n",
       "        0, 1, 2, 2, 0, 1, 2, 3, 1, 3, 2, 1, 2, 3, 3, 1, 2, 2, 2, 2, 2, 1,\n",
       "        2, 0, 2, 0, 1, 1, 3, 2, 1, 2])]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       " 0.49855224788188934,\n",
       " 0.37097203731536865,\n",
       " 0.27103545516729355,\n",
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       " 0.3371875286102295,\n",
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       " 0.256211094558239,\n",
       " 0.2551497519016266,\n",
       " 0.20638620853424072,\n",
       " 0.20583445578813553,\n",
       " 0.18104253709316254,\n",
       " 0.20595677196979523,\n",
       " 0.288420170545578,\n",
       " 0.22954297810792923,\n",
       " 0.21102091670036316,\n",
       " 0.19999205321073532,\n",
       " 0.26201242953538895,\n",
       " 0.2611338943243027,\n",
       " 0.21920818090438843,\n",
       " 0.2078363597393036,\n",
       " 0.2193952575325966,\n",
       " 0.2637380212545395,\n",
       " 0.1869436874985695,\n",
       " 0.1990896835923195,\n",
       " 0.2730542868375778,\n",
       " 0.2316187545657158,\n",
       " 0.1687472015619278,\n",
       " 0.18877871334552765]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_losses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.2725878618657589,\n",
       " 0.3038000017404556,\n",
       " 0.3743957653641701,\n",
       " 0.4048277288675308,\n",
       " 0.38204897195100784,\n",
       " 0.3721608817577362,\n",
       " 0.42862775176763535,\n",
       " 0.3439985141158104,\n",
       " 0.3559018224477768,\n",
       " 0.35145624727010727,\n",
       " 0.3409651964902878,\n",
       " 0.36436453461647034,\n",
       " 0.39821872115135193,\n",
       " 0.3551393896341324,\n",
       " 0.35657311230897903]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val_losses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 5))\n",
    "ax.plot(np.arange(len(train_losses)) / (len(train_losses) // len(val_losses)),train_losses,label='training loss')\n",
    "ax.plot(np.arange(1,len(val_losses)+1) ,np.array(val_losses) + 0.01,label='validation loss')\n",
    "\n",
    "ax.set_xlabel('epoch')\n",
    "ax.set_ylabel('loss value')\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.47348153591156,\n",
       " 0.6406370401382446,\n",
       " 0.39495953917503357,\n",
       " 0.32788610458374023,\n",
       " 0.46300799399614334,\n",
       " 0.4477241486310959,\n",
       " 0.38527511805295944,\n",
       " 0.40745867043733597,\n",
       " 0.4503869116306305,\n",
       " 0.4217817857861519,\n",
       " 0.3887465298175812,\n",
       " 0.3928548991680145,\n",
       " 0.42445058375597,\n",
       " 0.48021963983774185,\n",
       " 0.47371671348810196,\n",
       " 0.412605918943882,\n",
       " 0.4933035224676132,\n",
       " 0.4228593334555626,\n",
       " 0.49359291046857834,\n",
       " 0.5352080911397934]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val_losses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[7.9363625049591064,\n",
       " 8.973733901977539,\n",
       " 4.763446807861328,\n",
       " 3.0619534254074097,\n",
       " 2.584040403366089,\n",
       " 2.0441386699676514,\n",
       " 2.1084685921669006,\n",
       " 1.6885703206062317,\n",
       " 1.531571865081787,\n",
       " 1.6946741938591003,\n",
       " 1.2110268473625183,\n",
       " 1.3063259720802307,\n",
       " 0.9987798631191254,\n",
       " 0.8282113969326019,\n",
       " 0.9221987426280975,\n",
       " 0.9459511041641235,\n",
       " 0.7851526439189911,\n",
       " 0.8722099363803864,\n",
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