XLMfinetuned.ipynb

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     "text": [
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\r\n",
      "Requirement already satisfied: numba in /opt/conda/lib/python3.7/site-packages (0.54.1)\r\n",
      "Requirement already satisfied: setuptools in /opt/conda/lib/python3.7/site-packages (from numba) (59.5.0)\r\n",
      "Requirement already satisfied: llvmlite<0.38,>=0.37.0rc1 in /opt/conda/lib/python3.7/site-packages (from numba) (0.37.0)\r\n",
      "Requirement already satisfied: numpy<1.21,>=1.17 in /opt/conda/lib/python3.7/site-packages (from numba) (1.20.3)\r\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\r\n"
     ]
    }
   ],
   "source": [
    "!pip install -q transformers\n",
    "!pip install numba "
   ]
  },
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   "source": [
    "import json, time\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
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     "text": [
      "Cloning into 'NLP_Shared_Task_2022'...\r\n",
      "remote: Enumerating objects: 45988, done.\u001b[K\r\n",
      "remote: Counting objects: 100% (8470/8470), done.\u001b[K\r\n",
      "remote: Compressing objects: 100% (8294/8294), done.\u001b[K\r\n",
      "remote: Total 45988 (delta 179), reused 8466 (delta 176), pack-reused 37518\u001b[K\r\n",
      "Receiving objects: 100% (45988/45988), 640.61 MiB | 18.68 MiB/s, done.\r\n",
      "Resolving deltas: 100% (1327/1327), done.\r\n",
      "Updating files: 100% (48295/48295), done.\r\n",
      "/kaggle/working/NLP_Shared_Task_2022\n"
     ]
    }
   ],
   "source": [
    "!git clone https://github.com/AdarshKumar712/NLP_Shared_Task_2022\n",
    "%cd NLP_Shared_Task_2022/"
   ]
  },
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   "source": [
    "#Loading Train and Test Dataset Files\n",
    "train_data = pd.read_csv(\"train_v2.csv\")\n",
    "test_data = pd.read_csv(\"eval_with_result.csv\")"
   ]
  },
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       "      <td>https://gadgets.ndtv.com/internet/news/indian-...</td>\n",
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       "      <td>1.0</td>\n",
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       "  url1_lang url2_lang                pair_id  \\\n",
       "0        en        en  1484084337_1484110209   \n",
       "1        en        en  1484396422_1483924666   \n",
       "2        en        en  1484698254_1483758694   \n",
       "3        en        en  1576314516_1576455088   \n",
       "4        en        en  1484036253_1483894099   \n",
       "\n",
       "                                               link1  \\\n",
       "0  https://www.washingtonpost.com/local/virginia-...   \n",
       "1  https://www.stlucianewsonline.com/guyana-three...   \n",
       "2  https://www.teaparty.org/trump-brings-in-2020-...   \n",
       "3  https://gadgets.ndtv.com/apps/news/zomato-uber...   \n",
       "4  https://news.yahoo.com/india-approves-third-mo...   \n",
       "\n",
       "                                               link2  \\\n",
       "0  https://www.washingtonpost.com/world/the_ameri...   \n",
       "1  https://www.thestar.com/news/world/europe/2020...   \n",
       "2  https://www.timesofisrael.com/trump-says-he-do...   \n",
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       "3  https://web.archive.org/web/gadgets.ndtv.com/a...   \n",
       "4  https://web.archive.org/web/news.yahoo.com/ind...   \n",
       "\n",
       "                                            ia_link2  Geography  Entities  \\\n",
       "0  https://web.archive.org/web/www.washingtonpost...        4.0  4.000000   \n",
       "1  https://web.archive.org/web/www.thestar.com/ne...        4.0  4.000000   \n",
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       "\n",
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      ]
     },
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    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
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   },
   "source": [
    "# Data Processing"
   ]
  },
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   "source": [
    "def text_preprocessing(text):\n",
    "    text = re.sub(r'(@.*?)[\\s]', ' ', text)\n",
    "    text = re.sub(r'&amp;', '&', text)\n",
    "    text = re.sub(r'\\s+', ' ', text).strip()\n",
    "    return text"
   ]
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   "outputs": [
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   "source": [
    "import re\n",
    "from transformers import AutoTokenizer, AutoModel\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# tokenizer = AutoTokenizer.from_pretrained('bert-base-multilingual-cased')\n",
    "# tokenizer_name = \"mbert\"\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained('xlm-roberta-base')\n",
    "tokenizer_name = \"xlm\"\n",
    "\n",
    "def load_data(input_data, input_dir):\n",
    "  files_present,files_absent = 0,0\n",
    "  inputs1, inputs2 = list(), list()\n",
    "  masks1, masks2 = list(), list()\n",
    "  targets = list()\n",
    "  for i in range(len(input_data)):\n",
    "    row = input_data.iloc[i]\n",
    "    pair = row['pair_id']\n",
    "    \n",
    "    y_target = row['Overall']\n",
    "    # y_scaler = StandardScaler()\n",
    "    # y_target = y_scaler.transform(y_target.reshape(-1, 1))\n",
    "\n",
    "    f1, f2 = pair.split(\"_\")\n",
    "    folder1 = f1[-2:]\n",
    "    folder2 = f2[-2:]\n",
    "    try:\n",
    "      with open(input_dir+\"/\"+folder1+\"/\"+f1+\".json\",\"r\") as f:\n",
    "        d1 = json.load(f)\n",
    "      with open(input_dir+\"/\"+folder2+\"/\"+f2+\".json\",\"r\") as f:\n",
    "        d2 = json.load(f)\n",
    "    except Exception as E:\n",
    "      files_absent = files_absent + 1\n",
    "      continue\n",
    "    \n",
    "    files_present = files_present+1\n",
    "\n",
    "    s1 = text_preprocessing(d1['text'])\n",
    "    s2 = text_preprocessing(d2['text'])\n",
    "    encoded_input1 = tokenizer.encode_plus(s1, return_tensors='pt',return_attention_mask=True, pad_to_max_length=True,max_length=256)\n",
    "    encoded_input2 = tokenizer.encode_plus(s2, return_tensors='pt',return_attention_mask=True, pad_to_max_length=True,max_length=256)\n",
    "\n",
    "    input_ids1 = encoded_input1['input_ids']\n",
    "    input_ids2 = encoded_input2['input_ids']\n",
    "    attention_masks1 = encoded_input1['attention_mask']\n",
    "    attention_masks2 = encoded_input2['attention_mask']\n",
    "    \n",
    "    inputs1.append(input_ids1)\n",
    "    inputs2.append(input_ids2)\n",
    "    masks1.append(attention_masks1)\n",
    "    masks2.append(attention_masks2)\n",
    "    targets.append(y_target)\n",
    "\n",
    "  print(f\"Files present: {files_present} and Files absent: {files_absent}. % missing = {(files_absent*100)/(files_present+files_absent)}%\")\n",
    "  return inputs1, inputs2, masks1, masks2, targets  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1cf9248e",
   "metadata": {
    "execution": {
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch.utils.data import TensorDataset, DataLoader\n",
    "\n",
    "def create_dataloaders(inputs1, inputs2, masks1, masks2, targets, batch_size):\n",
    "    input_tensor1 = torch.tensor([t.numpy() for t in inputs1])\n",
    "    input_tensor2 = torch.tensor([t.numpy() for t in inputs2])\n",
    "    mask_tensor1 = torch.tensor([t.numpy() for t in masks1])\n",
    "    mask_tensor2 = torch.tensor([t.numpy() for t in masks2])\n",
    "    targets_tensor = torch.tensor(targets)\n",
    "    dataset = TensorDataset(input_tensor1, input_tensor2, mask_tensor1, mask_tensor2, targets_tensor)\n",
    "    dataloader = DataLoader(dataset, batch_size=batch_size,shuffle=True)\n",
    "    return dataloader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "277d2ea2",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2022-03-15T06:49:04.793183Z",
     "shell.execute_reply.started": "2022-03-15T06:41:08.962494Z"
    },
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     "end_time": "2022-03-15T06:49:04.793429",
     "exception": false,
     "start_time": "2022-03-15T06:47:52.007699",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation=True` to explicitly truncate examples to max length. Defaulting to 'longest_first' truncation strategy. If you encode pairs of sequences (GLUE-style) with the tokenizer you can select this strategy more precisely by providing a specific strategy to `truncation`.\n",
      "/opt/conda/lib/python3.7/site-packages/transformers/tokenization_utils_base.py:2232: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
      "  FutureWarning,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files present: 4940 and Files absent: 24. % missing = 0.48348106365834004%\n",
      "Files present: 4659 and Files absent: 243. % missing = 4.957160342717258%\n"
     ]
    }
   ],
   "source": [
    "batch_size = 16\n",
    "\n",
    "#Train Set\n",
    "train_inputs1, train_inputs2, train_masks1, train_masks2, train_targets = load_data(train_data, \"articles\")\n",
    "train_dataloader = create_dataloaders(train_inputs1, train_inputs2, train_masks1, train_masks2, train_targets, batch_size)\n",
    "\n",
    "#Test Set\n",
    "test_inputs1, test_inputs2, test_masks1, test_masks2, test_targets = load_data(test_data, \"eval_data\")\n",
    "test_dataloader = create_dataloaders(test_inputs1, test_inputs2, test_masks1, test_masks2, test_targets, batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c9fa6e69",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-15T06:49:05.067420Z",
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     "shell.execute_reply": "2022-03-15T06:49:05.070055Z",
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    },
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     "duration": 0.160644,
     "end_time": "2022-03-15T06:49:05.070209",
     "exception": false,
     "start_time": "2022-03-15T06:49:04.909565",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using GPU.\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "\n",
    "if torch.cuda.is_available():       \n",
    "  device = torch.device(\"cuda\")\n",
    "  print(\"Using GPU.\")\n",
    "else:\n",
    "  print(\"No GPU available, using the CPU instead.\")\n",
    "  device = torch.device(\"cpu\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b02f8702",
   "metadata": {
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     "start_time": "2022-03-15T06:49:05.172082",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Modeling Baselines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9a0060ab",
   "metadata": {
    "execution": {
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     "shell.execute_reply.started": "2022-03-15T06:42:10.446783Z"
    },
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     "duration": 0.114667,
     "end_time": "2022-03-15T06:49:05.489136",
     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#Model Architecure\n",
    "\n",
    "#Baseline 1: mBERT\n",
    "class mBERTRegressor(nn.Module):\n",
    "    \n",
    "    def __init__(self, drop_rate=0.2, freeze_camembert=False):        \n",
    "        super(mBERTRegressor, self).__init__()\n",
    "        D_in, D_out = 1536, 1\n",
    "        self.mbert = AutoModel.from_pretrained(\"bert-base-multilingual-cased\")   \n",
    "        self.regressor = nn.Sequential(\n",
    "            nn.Dropout(drop_rate),\n",
    "            nn.Linear(D_in, D_out)) \n",
    "           \n",
    "    def forward(self, input_ids1, input_ids2, attention_masks1, attention_masks2):\n",
    "      outputs1 = self.mbert(input_ids1, attention_masks1)\n",
    "      outputs2 = self.mbert(input_ids2, attention_masks2)\n",
    "      #outputs = torch.dot(outputs1[1],outputs2[1])\n",
    "      #outputs = outputs1[1]+outputs2[1]\n",
    "      outputs = torch.cat((outputs1[1], outputs2[1]), 1)\n",
    "    \n",
    "      # last_hidden_state_cls = outputs[0][:, 0, :]\n",
    "      last_hidden_state_cls = outputs\n",
    "      final_outputs = self.regressor(last_hidden_state_cls)\n",
    "      return final_outputs\n",
    "\n",
    "      \n",
    "#Baseline 2: XLM\n",
    "class XLMRegressor(nn.Module):\n",
    "    \n",
    "    def __init__(self, drop_rate=0.2, freeze_camembert=False):        \n",
    "        super(XLMRegressor, self).__init__()\n",
    "        D_in, D_out = 1536, 1\n",
    "        self.xlm = AutoModel.from_pretrained(\"xlm-roberta-base\")\n",
    "        self.regressor = nn.Sequential(\n",
    "            nn.Dropout(drop_rate),\n",
    "            nn.Linear(D_in, D_out)) \n",
    "           \n",
    "    def forward(self, input_ids1, input_ids2, attention_masks1, attention_masks2):\n",
    "      outputs1 = self.xlm(input_ids1, attention_masks1)\n",
    "      outputs2 = self.xlm(input_ids2, attention_masks2)\n",
    "      #outputs = torch.dot(outputs1[1],outputs2[1])\n",
    "      #outputs = outputs1[1]+outputs2[1]\n",
    "      outputs = torch.cat((outputs1[1], outputs2[1]), 1)\n",
    "      # last_hidden_state_cls = outputs[0][:, 0, :]\n",
    "      last_hidden_state_cls = outputs\n",
    "      final_outputs = self.regressor(last_hidden_state_cls)\n",
    "      return final_outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5f0b8174",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2022-03-15T06:49:38.929438Z",
     "shell.execute_reply.started": "2022-03-15T06:42:10.463283Z"
    },
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     "exception": false,
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     "status": "completed"
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   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b3f3862fcfca40609e8ac679e02f7781",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/1.04G [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at xlm-roberta-base were not used when initializing XLMRobertaModel: ['lm_head.bias', 'lm_head.layer_norm.bias', 'lm_head.dense.bias', 'lm_head.dense.weight', 'lm_head.decoder.weight', 'lm_head.layer_norm.weight']\n",
      "- This IS expected if you are initializing XLMRobertaModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing XLMRobertaModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
     ]
    }
   ],
   "source": [
    "# model = mBERTRegressor(drop_rate=0.2).to(device)\n",
    "model = XLMRegressor(drop_rate=0.2).to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "32c15c50",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-15T06:49:39.194369Z",
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   "outputs": [],
   "source": [
    "from torch.nn.utils.clip_grad import clip_grad_norm\n",
    "from transformers import get_linear_schedule_with_warmup\n",
    "from transformers import AdamW\n",
    "\n",
    "\n",
    "freeze = False #set to True for 3rd baseline\n",
    "\n",
    "epochs = 10\n",
    "total_steps = len(train_dataloader) * epochs\n",
    "if freeze:\n",
    "  optimizer = AdamW(model.regressor.parameters(), lr=5e-5, eps=1e-8) \n",
    "else:\n",
    "  optimizer = AdamW(model.parameters(), lr=5e-5, eps=1e-8)    \n",
    "scheduler = get_linear_schedule_with_warmup(optimizer,num_warmup_steps=0, num_training_steps=total_steps)\n",
    "loss_function = nn.MSELoss()\n",
    "\n",
    "\n",
    "def train(model, optimizer, scheduler, loss_function, epochs, train_dataloader, device, clip_value=2):\n",
    "    for epoch_i in range(epochs):\n",
    "        print(f\"Epoch {epoch_i}\")\n",
    "        print(\"------------------------------------------------------------\")\n",
    "        best_loss = 1e10\n",
    "\n",
    "        t0_epoch, t0_batch = time.time(), time.time()\n",
    "        total_loss, batch_loss, batch_counts = 0, 0, 0\n",
    "        \n",
    "        model.train()\n",
    "        for step, batch in enumerate(train_dataloader): \n",
    "            batch_counts +=1\n",
    "            \n",
    "            batch_inputs1, batch_inputs2, batch_masks1, batch_masks2, batch_targets = tuple(b.to(device) for b in batch)\n",
    "            batch_inputs1 = batch_inputs1.squeeze(1)\n",
    "            batch_inputs2 = batch_inputs2.squeeze(1)\n",
    "            batch_masks1 = batch_masks1.squeeze(1)\n",
    "            batch_masks2 = batch_masks2.squeeze(1)\n",
    "            \n",
    "            model.zero_grad()\n",
    "            \n",
    "            outputs = model(batch_inputs1, batch_inputs2, batch_masks1, batch_masks2)           \n",
    "            \n",
    "            loss = loss_function(outputs.squeeze(), batch_targets.float().squeeze())\n",
    "            batch_loss += loss.item()\n",
    "            total_loss += loss.item()\n",
    "            \n",
    "            loss.backward()\n",
    "            \n",
    "            if freeze:\n",
    "              clip_grad_norm(model.regressor.parameters(), clip_value)\n",
    "            else:\n",
    "              clip_grad_norm(model.parameters(), clip_value)\n",
    "            \n",
    "            optimizer.step()\n",
    "            scheduler.step()\n",
    "            \n",
    "            if (step % 20 == 0 and step != 0) or (step == len(train_dataloader) - 1):\n",
    "                # Calculate time elapsed for 20 batches\n",
    "                time_elapsed = time.time() - t0_batch\n",
    "\n",
    "                # Print training results\n",
    "                print(f\"{epoch_i + 1:^7} | {step:^7} | {batch_loss / batch_counts:^12.6f} | {'-':^10} | {'-':^9} | {time_elapsed:^9.2f}\")\n",
    "\n",
    "                # Reset batch tracking variables\n",
    "                batch_loss, batch_counts = 0, 0\n",
    "                t0_batch = time.time()\n",
    "              \n",
    "#             with torch.no_grad():\n",
    "#                 del outputs\n",
    "#                 torch.cuda.empty_cache()\n",
    "        \n",
    "#             show_gpu(f'{epoch_i}: GPU memory usage after training model:')\n",
    "            del loss, outputs\n",
    "            torch.cuda.empty_cache()\n",
    "            torch.cuda.synchronize()\n",
    "#             show_gpu(f'{epoch_i}: GPU memory usage after clearing cache:')\n",
    "        \n",
    "        avg_train_loss = total_loss / len(train_dataloader)\n",
    "\n",
    "        print(\"-\"*70)\n",
    "        print(f\"{epoch_i + 1:^7} | {'-':^7} | {avg_train_loss:^12.6f}\")\n",
    "        print();\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6c0c2d92",
   "metadata": {
    "execution": {
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   "source": [
    "# from numba import cuda \n",
    "# dev = cuda.get_current_device()\n",
    "# dev.reset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "643b4955",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-15T06:49:44.274870Z",
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    },
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0\n",
      "------------------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:50: UserWarning: torch.nn.utils.clip_grad_norm is now deprecated in favor of torch.nn.utils.clip_grad_norm_.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   1    |   20    |   2.906416   |     -      |     -     |   26.15  \n",
      "   1    |   40    |   1.404530   |     -      |     -     |   23.07  \n",
      "   1    |   60    |   1.401613   |     -      |     -     |   23.06  \n",
      "   1    |   80    |   1.397575   |     -      |     -     |   23.03  \n",
      "   1    |   100   |   1.290578   |     -      |     -     |   23.06  \n",
      "   1    |   120   |   1.300371   |     -      |     -     |   23.06  \n",
      "   1    |   140   |   1.362193   |     -      |     -     |   23.04  \n",
      "   1    |   160   |   1.273066   |     -      |     -     |   23.07  \n",
      "   1    |   180   |   1.353890   |     -      |     -     |   23.06  \n",
      "   1    |   200   |   1.370127   |     -      |     -     |   23.05  \n",
      "   1    |   220   |   1.260719   |     -      |     -     |   23.07  \n",
      "   1    |   240   |   1.261810   |     -      |     -     |   23.05  \n",
      "   1    |   260   |   1.292420   |     -      |     -     |   23.07  \n",
      "   1    |   280   |   1.237755   |     -      |     -     |   23.03  \n",
      "   1    |   300   |   1.266345   |     -      |     -     |   23.05  \n",
      "   1    |   308   |   1.379404   |     -      |     -     |   9.04   \n",
      "----------------------------------------------------------------------\n",
      "   1    |    -    |   1.428899  \n",
      "\n",
      "Epoch 1\n",
      "------------------------------------------------------------\n",
      "   2    |   20    |   1.211237   |     -      |     -     |   23.87  \n",
      "   2    |   40    |   1.123212   |     -      |     -     |   23.06  \n",
      "   2    |   60    |   1.148271   |     -      |     -     |   23.05  \n",
      "   2    |   80    |   1.148855   |     -      |     -     |   23.07  \n",
      "   2    |   100   |   1.208064   |     -      |     -     |   23.06  \n",
      "   2    |   120   |   1.156666   |     -      |     -     |   23.04  \n",
      "   2    |   140   |   1.272161   |     -      |     -     |   23.07  \n",
      "   2    |   160   |   0.996844   |     -      |     -     |   23.07  \n",
      "   2    |   180   |   1.215080   |     -      |     -     |   23.08  \n",
      "   2    |   200   |   1.224364   |     -      |     -     |   23.05  \n",
      "   2    |   220   |   1.266903   |     -      |     -     |   23.09  \n",
      "   2    |   240   |   1.247411   |     -      |     -     |   23.06  \n",
      "   2    |   260   |   1.197235   |     -      |     -     |   23.03  \n",
      "   2    |   280   |   1.174488   |     -      |     -     |   23.07  \n",
      "   2    |   300   |   1.226361   |     -      |     -     |   23.10  \n",
      "   2    |   308   |   0.988491   |     -      |     -     |   9.03   \n",
      "----------------------------------------------------------------------\n",
      "   2    |    -    |   1.182726  \n",
      "\n",
      "Epoch 2\n",
      "------------------------------------------------------------\n",
      "   3    |   20    |   1.179917   |     -      |     -     |   23.88  \n",
      "   3    |   40    |   1.002642   |     -      |     -     |   23.03  \n",
      "   3    |   60    |   1.139808   |     -      |     -     |   23.06  \n",
      "   3    |   80    |   1.094252   |     -      |     -     |   23.07  \n",
      "   3    |   100   |   1.078611   |     -      |     -     |   23.05  \n",
      "   3    |   120   |   1.023417   |     -      |     -     |   23.05  \n",
      "   3    |   140   |   1.060306   |     -      |     -     |   23.06  \n",
      "   3    |   160   |   1.082602   |     -      |     -     |   23.05  \n",
      "   3    |   180   |   1.091626   |     -      |     -     |   23.03  \n",
      "   3    |   200   |   1.190749   |     -      |     -     |   23.07  \n",
      "   3    |   220   |   1.031956   |     -      |     -     |   23.09  \n",
      "   3    |   240   |   1.138438   |     -      |     -     |   23.02  \n",
      "   3    |   260   |   1.123011   |     -      |     -     |   23.05  \n",
      "   3    |   280   |   1.127639   |     -      |     -     |   23.07  \n",
      "   3    |   300   |   1.100801   |     -      |     -     |   23.06  \n",
      "   3    |   308   |   1.041812   |     -      |     -     |   9.05   \n",
      "----------------------------------------------------------------------\n",
      "   3    |    -    |   1.096537  \n",
      "\n",
      "Epoch 3\n",
      "------------------------------------------------------------\n",
      "   4    |   20    |   1.126781   |     -      |     -     |   23.87  \n",
      "   4    |   40    |   1.048482   |     -      |     -     |   23.05  \n",
      "   4    |   60    |   0.933797   |     -      |     -     |   23.08  \n",
      "   4    |   80    |   0.966091   |     -      |     -     |   23.08  \n",
      "   4    |   100   |   1.108211   |     -      |     -     |   23.04  \n",
      "   4    |   120   |   1.017481   |     -      |     -     |   23.06  \n",
      "   4    |   140   |   1.127442   |     -      |     -     |   23.07  \n",
      "   4    |   160   |   0.993707   |     -      |     -     |   23.05  \n",
      "   4    |   180   |   0.868265   |     -      |     -     |   23.07  \n",
      "   4    |   200   |   0.944547   |     -      |     -     |   23.07  \n",
      "   4    |   220   |   1.026885   |     -      |     -     |   23.07  \n",
      "   4    |   240   |   1.053446   |     -      |     -     |   23.04  \n",
      "   4    |   260   |   1.084796   |     -      |     -     |   23.06  \n",
      "   4    |   280   |   1.000114   |     -      |     -     |   23.06  \n",
      "   4    |   300   |   0.949653   |     -      |     -     |   23.03  \n",
      "   4    |   308   |   0.996253   |     -      |     -     |   9.06   \n",
      "----------------------------------------------------------------------\n",
      "   4    |    -    |   1.016475  \n",
      "\n",
      "Epoch 4\n",
      "------------------------------------------------------------\n",
      "   5    |   20    |   0.915232   |     -      |     -     |   23.87  \n",
      "   5    |   40    |   1.030122   |     -      |     -     |   23.08  \n",
      "   5    |   60    |   0.781235   |     -      |     -     |   23.05  \n",
      "   5    |   80    |   1.032534   |     -      |     -     |   23.07  \n",
      "   5    |   100   |   0.980901   |     -      |     -     |   23.06  \n",
      "   5    |   120   |   0.930014   |     -      |     -     |   23.05  \n",
      "   5    |   140   |   0.886802   |     -      |     -     |   23.08  \n",
      "   5    |   160   |   0.810469   |     -      |     -     |   23.04  \n",
      "   5    |   180   |   0.825784   |     -      |     -     |   23.06  \n",
      "   5    |   200   |   0.842376   |     -      |     -     |   23.05  \n",
      "   5    |   220   |   1.022221   |     -      |     -     |   23.02  \n",
      "   5    |   240   |   0.832510   |     -      |     -     |   23.06  \n",
      "   5    |   260   |   0.984821   |     -      |     -     |   23.06  \n",
      "   5    |   280   |   0.924837   |     -      |     -     |   23.06  \n",
      "   5    |   300   |   0.970973   |     -      |     -     |   23.07  \n",
      "   5    |   308   |   1.058218   |     -      |     -     |   9.03   \n",
      "----------------------------------------------------------------------\n",
      "   5    |    -    |   0.921675  \n",
      "\n",
      "Epoch 5\n",
      "------------------------------------------------------------\n",
      "   6    |   20    |   0.916107   |     -      |     -     |   23.88  \n",
      "   6    |   40    |   0.825356   |     -      |     -     |   23.11  \n",
      "   6    |   60    |   0.815822   |     -      |     -     |   23.06  \n",
      "   6    |   80    |   0.906607   |     -      |     -     |   23.04  \n",
      "   6    |   100   |   0.804030   |     -      |     -     |   23.05  \n",
      "   6    |   120   |   0.852745   |     -      |     -     |   23.08  \n",
      "   6    |   140   |   0.817545   |     -      |     -     |   23.05  \n",
      "   6    |   160   |   1.028007   |     -      |     -     |   23.06  \n",
      "   6    |   180   |   0.843250   |     -      |     -     |   23.26  \n",
      "   6    |   200   |   0.834023   |     -      |     -     |   23.11  \n",
      "   6    |   220   |   0.843918   |     -      |     -     |   23.03  \n",
      "   6    |   240   |   0.847107   |     -      |     -     |   23.05  \n",
      "   6    |   260   |   0.838696   |     -      |     -     |   23.05  \n",
      "   6    |   280   |   0.911161   |     -      |     -     |   23.03  \n",
      "   6    |   300   |   0.858992   |     -      |     -     |   23.07  \n",
      "   6    |   308   |   1.121921   |     -      |     -     |   9.06   \n",
      "----------------------------------------------------------------------\n",
      "   6    |    -    |   0.869770  \n",
      "\n",
      "Epoch 6\n",
      "------------------------------------------------------------\n",
      "   7    |   20    |   0.779912   |     -      |     -     |   23.87  \n",
      "   7    |   40    |   0.814767   |     -      |     -     |   23.05  \n",
      "   7    |   60    |   0.911266   |     -      |     -     |   23.05  \n",
      "   7    |   80    |   0.894243   |     -      |     -     |   23.04  \n",
      "   7    |   100   |   0.687463   |     -      |     -     |   23.07  \n",
      "   7    |   120   |   0.777425   |     -      |     -     |   23.07  \n",
      "   7    |   140   |   0.870529   |     -      |     -     |   23.05  \n",
      "   7    |   160   |   0.875182   |     -      |     -     |   23.04  \n",
      "   7    |   180   |   0.681328   |     -      |     -     |   23.09  \n",
      "   7    |   200   |   0.858552   |     -      |     -     |   23.06  \n",
      "   7    |   220   |   0.758623   |     -      |     -     |   23.03  \n",
      "   7    |   240   |   0.768696   |     -      |     -     |   23.06  \n",
      "   7    |   260   |   0.848708   |     -      |     -     |   23.06  \n",
      "   7    |   280   |   0.787561   |     -      |     -     |   23.06  \n",
      "   7    |   300   |   0.901143   |     -      |     -     |   23.02  \n",
      "   7    |   308   |   0.860701   |     -      |     -     |   9.08   \n",
      "----------------------------------------------------------------------\n",
      "   7    |    -    |   0.815448  \n",
      "\n",
      "Epoch 7\n",
      "------------------------------------------------------------\n",
      "   8    |   20    |   0.837445   |     -      |     -     |   23.85  \n",
      "   8    |   40    |   0.702725   |     -      |     -     |   23.06  \n",
      "   8    |   60    |   0.651503   |     -      |     -     |   23.06  \n",
      "   8    |   80    |   0.653163   |     -      |     -     |   23.04  \n",
      "   8    |   100   |   0.768082   |     -      |     -     |   23.08  \n",
      "   8    |   120   |   0.527789   |     -      |     -     |   23.06  \n",
      "   8    |   140   |   0.832592   |     -      |     -     |   23.06  \n",
      "   8    |   160   |   0.830771   |     -      |     -     |   23.04  \n",
      "   8    |   180   |   0.713010   |     -      |     -     |   23.25  \n",
      "   8    |   200   |   0.720380   |     -      |     -     |   23.08  \n",
      "   8    |   220   |   0.642210   |     -      |     -     |   23.03  \n",
      "   8    |   240   |   0.712912   |     -      |     -     |   23.09  \n",
      "   8    |   260   |   0.661497   |     -      |     -     |   23.06  \n",
      "   8    |   280   |   0.787069   |     -      |     -     |   23.05  \n",
      "   8    |   300   |   0.643078   |     -      |     -     |   23.05  \n",
      "   8    |   308   |   0.630611   |     -      |     -     |   9.05   \n",
      "----------------------------------------------------------------------\n",
      "   8    |    -    |   0.710572  \n",
      "\n",
      "Epoch 8\n",
      "------------------------------------------------------------\n",
      "   9    |   20    |   0.659007   |     -      |     -     |   23.84  \n",
      "   9    |   40    |   0.633706   |     -      |     -     |   23.05  \n",
      "   9    |   60    |   0.633304   |     -      |     -     |   23.07  \n",
      "   9    |   80    |   0.595837   |     -      |     -     |   23.06  \n",
      "   9    |   100   |   0.544559   |     -      |     -     |   23.02  \n",
      "   9    |   120   |   0.609396   |     -      |     -     |   23.06  \n",
      "   9    |   140   |   0.559516   |     -      |     -     |   23.04  \n",
      "   9    |   160   |   0.542541   |     -      |     -     |   23.04  \n",
      "   9    |   180   |   0.650025   |     -      |     -     |   23.09  \n",
      "   9    |   200   |   0.648188   |     -      |     -     |   23.15  \n",
      "   9    |   220   |   0.545953   |     -      |     -     |   23.06  \n",
      "   9    |   240   |   0.643096   |     -      |     -     |   23.02  \n",
      "   9    |   260   |   0.666119   |     -      |     -     |   23.04  \n",
      "   9    |   280   |   0.670785   |     -      |     -     |   23.07  \n",
      "   9    |   300   |   0.634585   |     -      |     -     |   23.03  \n",
      "   9    |   308   |   0.621501   |     -      |     -     |   9.07   \n",
      "----------------------------------------------------------------------\n",
      "   9    |    -    |   0.616063  \n",
      "\n",
      "Epoch 9\n",
      "------------------------------------------------------------\n",
      "  10    |   20    |   0.561420   |     -      |     -     |   23.87  \n",
      "  10    |   40    |   0.736341   |     -      |     -     |   23.02  \n",
      "  10    |   60    |   0.587953   |     -      |     -     |   23.10  \n",
      "  10    |   80    |   0.532512   |     -      |     -     |   23.06  \n",
      "  10    |   100   |   0.619453   |     -      |     -     |   23.04  \n",
      "  10    |   120   |   0.541737   |     -      |     -     |   23.06  \n",
      "  10    |   140   |   0.551446   |     -      |     -     |   23.05  \n",
      "  10    |   160   |   0.572294   |     -      |     -     |   23.05  \n",
      "  10    |   180   |   0.580538   |     -      |     -     |   23.05  \n",
      "  10    |   200   |   0.502971   |     -      |     -     |   23.06  \n",
      "  10    |   220   |   0.558357   |     -      |     -     |   23.06  \n",
      "  10    |   240   |   0.552955   |     -      |     -     |   23.03  \n",
      "  10    |   260   |   0.614679   |     -      |     -     |   23.07  \n",
      "  10    |   280   |   0.577757   |     -      |     -     |   23.06  \n",
      "  10    |   300   |   0.546597   |     -      |     -     |   23.05  \n",
      "  10    |   308   |   0.514182   |     -      |     -     |   9.06   \n",
      "----------------------------------------------------------------------\n",
      "  10    |    -    |   0.574159  \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# %%time\n",
    "model = train(model, optimizer, scheduler, loss_function, epochs,train_dataloader, device, clip_value=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7ec523e",
   "metadata": {
    "papermill": {
     "duration": 0.153585,
     "end_time": "2022-03-15T07:49:07.201681",
     "exception": false,
     "start_time": "2022-03-15T07:49:07.048096",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "16db1c11",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-15T07:49:07.511518Z",
     "iopub.status.busy": "2022-03-15T07:49:07.510777Z",
     "iopub.status.idle": "2022-03-15T07:49:07.964408Z",
     "shell.execute_reply": "2022-03-15T07:49:07.963814Z"
    },
    "papermill": {
     "duration": 0.610193,
     "end_time": "2022-03-15T07:49:07.964547",
     "exception": false,
     "start_time": "2022-03-15T07:49:07.354354",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from numba import cuda \n",
    "# device = cuda.get_current_device()\n",
    "# device.reset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9ad09af3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-15T07:49:08.481823Z",
     "iopub.status.busy": "2022-03-15T07:49:08.481016Z",
     "iopub.status.idle": "2022-03-15T07:49:08.492010Z",
     "shell.execute_reply": "2022-03-15T07:49:08.492993Z"
    },
    "papermill": {
     "duration": 0.277075,
     "end_time": "2022-03-15T07:49:08.493214",
     "exception": false,
     "start_time": "2022-03-15T07:49:08.216139",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def pearson(X,Y):\n",
    "    print(X,Y)\n",
    "    xm = torch.mean(X.float())\n",
    "    ym = torch.mean(Y.float())\n",
    "    print(xm, ym)\n",
    "    num = 0.0\n",
    "    deno1 = 0.0\n",
    "    deno2 = 0.0\n",
    "    deno = 0.0\n",
    "    n = X.shape[0]\n",
    "    for i in range(n):\n",
    "        num += (X[i]-xm)*(Y[i]-ym)\n",
    "        deno1 += (X[i]-xm)*(X[i]-xm)\n",
    "        deno2 += (Y[i]-ym)*(Y[i]-ym)\n",
    "    deno = deno1*deno2\n",
    "    deno = torch.sqrt(deno)\n",
    "    return (num/deno).item()\n",
    "\n",
    "from scipy.stats import pearsonr\n",
    "\n",
    "def evaluate(model, loss_function, test_dataloader, device):\n",
    "    model.eval()\n",
    "    test_loss, test_pearson, test_mse = [], [], []\n",
    "    for batch in test_dataloader:\n",
    "        batch_inputs1, batch_inputs2, batch_masks1, batch_masks2, batch_targets = tuple(b.to(device) for b in batch)\n",
    "        batch_inputs1 = batch_inputs1.squeeze(1)\n",
    "        batch_inputs2 = batch_inputs2.squeeze(1)\n",
    "        batch_masks1 = batch_masks1.squeeze(1)\n",
    "        batch_masks2 = batch_masks2.squeeze(1)\n",
    "        with torch.no_grad():\n",
    "            outputs = model(batch_inputs1, batch_inputs2, batch_masks1, batch_masks2)   \n",
    "\n",
    "        loss = loss_function(outputs, batch_targets)\n",
    "        test_loss.append(loss.item())\n",
    "        preds = outputs.cpu()\n",
    "        targs = batch_targets.cpu()\n",
    "        # print(preds, targs)\n",
    "        pearson_score = pearson(preds, targs)\n",
    "        MSE = np.square(np.subtract(preds, targs)).mean()\n",
    "        test_pearson.append(pearson_score)\n",
    "        test_mse.append(MSE)\n",
    "    return test_loss, test_pearson, test_mse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "566bcdbc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-15T07:49:08.861807Z",
     "iopub.status.busy": "2022-03-15T07:49:08.860985Z",
     "iopub.status.idle": "2022-03-15T07:50:26.026901Z",
     "shell.execute_reply": "2022-03-15T07:50:26.027455Z"
    },
    "papermill": {
     "duration": 77.324028,
     "end_time": "2022-03-15T07:50:26.027650",
     "exception": false,
     "start_time": "2022-03-15T07:49:08.703622",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/torch/nn/modules/loss.py:528: UserWarning: Using a target size (torch.Size([16])) that is different to the input size (torch.Size([16, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n",
      "  return F.mse_loss(input, target, reduction=self.reduction)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[2.0929],\n",
      "        [2.0202],\n",
      "        [3.7972],\n",
      "        [1.6334],\n",
      "        [3.4155],\n",
      "        [2.6441],\n",
      "        [1.6453],\n",
      "        [2.0940],\n",
      "        [4.0106],\n",
      "        [2.2975],\n",
      "        [3.2330],\n",
      "        [2.0919],\n",
      "        [2.1183],\n",
      "        [3.5080],\n",
      "        [3.5897],\n",
      "        [2.0931]]) tensor([1.0000, 1.0000, 3.0000, 1.0000, 1.0000, 4.0000, 3.0000, 1.0000, 3.5000,\n",
      "        4.0000, 1.0000, 1.0000, 1.0000, 4.0000, 1.0000, 2.0000],\n",
      "       dtype=torch.float64)\n",
      "tensor(2.6428) tensor(2.0312)\n",
      "tensor([[2.0928],\n",
      "        [2.4836],\n",
      "        [3.7770],\n",
      "        [3.6024],\n",
      "        [2.0930],\n",
      "        [1.5255],\n",
      "        [3.6439],\n",
      "        [2.0931],\n",
      "        [2.8795],\n",
      "        [3.6223],\n",
      "        [2.2961],\n",
      "        [3.7054],\n",
      "        [1.5123],\n",
      "        [2.8212],\n",
      "        [3.6037],\n",
      "        [1.7311]]) tensor([1.0000, 3.0000, 4.0000, 1.0000, 4.0000, 1.0000, 2.0000, 1.0000, 1.0000,\n",
      "        3.0000, 3.5000, 3.0000, 1.0000, 2.0000, 4.0000, 1.0000],\n",
      "       dtype=torch.float64)\n",
      "tensor(2.7177) tensor(2.2188)\n",
      "tensor([[2.0764],\n",
      "        [3.7826],\n",
      "        [2.0928],\n",
      "        [2.0931],\n",
      "        [3.9264],\n",
      "        [2.0927],\n",
      "        [4.0708],\n",
      "        [3.6908],\n",
      "        [1.9144],\n",
      "        [3.4679],\n",
      "        [3.7089],\n",
      "        [3.6773],\n",
      "        [3.1300],\n",
      "        [3.8400],\n",
      "        [2.6509],\n",
      "        [3.2568]]) tensor([4., 3., 1., 4., 1., 3., 3., 3., 4., 4., 4., 3., 3., 1., 2., 4.],\n",
      "       dtype=torch.float64)\n",
      "tensor(3.0920) tensor(2.9375)\n",
      "tensor([[4.0208],\n",
      "        [2.8997],\n",
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      "       dtype=torch.float64)\n",
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      "       dtype=torch.float64)\n",
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      "       dtype=torch.float64)\n",
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      "       dtype=torch.float64)\n",
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      "       dtype=torch.float64)\n",
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      "       dtype=torch.float64)\n",
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      "       dtype=torch.float64)\n",
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      "       dtype=torch.float64)\n",
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      "       dtype=torch.float64)\n",
      "tensor(2.8863) tensor(2.8125)\n",
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      "       dtype=torch.float64)\n",
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      "       dtype=torch.float64)\n",
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      "        [3.9821]]) tensor([2., 1., 1., 1., 2., 4., 2., 1., 1., 2., 1., 4., 1., 4., 1., 4.],\n",
      "       dtype=torch.float64)\n",
      "tensor(2.9319) tensor(2.)\n",
      "tensor([[2.2952],\n",
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      "       dtype=torch.float64)\n",
      "tensor(2.9333) tensor(2.7188)\n",
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      "       dtype=torch.float64)\n",
      "tensor(3.0490) tensor(3.1562)\n",
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      "       dtype=torch.float64)\n",
      "tensor(2.7309) tensor(2.5625)\n",
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      "       dtype=torch.float64)\n",
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      "       dtype=torch.float64)\n",
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      "       dtype=torch.float64)\n",
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      "       dtype=torch.float64)\n",
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      "       dtype=torch.float64)\n",
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      "       dtype=torch.float64)\n",
      "tensor(3.2109) tensor(2.8750)\n",
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      "       dtype=torch.float64)\n",
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      "       dtype=torch.float64)\n",
      "tensor(3.2206) tensor(2.3125)\n",
      "tensor([[3.1513],\n",
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      "       dtype=torch.float64)\n",
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      "       dtype=torch.float64)\n",
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      "       dtype=torch.float64)\n",
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      "       dtype=torch.float64)\n",
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      "       dtype=torch.float64)\n",
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      "       dtype=torch.float64)\n",
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      "       dtype=torch.float64)\n",
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      "       dtype=torch.float64)\n",
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      "       dtype=torch.float64)\n",
      "tensor(2.3542) tensor(2.1250)\n",
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      "       dtype=torch.float64)\n",
      "tensor(3.1290) tensor(2.6250)\n",
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      "       dtype=torch.float64)\n",
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      "       dtype=torch.float64)\n",
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      "       dtype=torch.float64)\n",
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      "       dtype=torch.float64)\n",
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      "       dtype=torch.float64)\n",
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      "       dtype=torch.float64)\n",
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      "       dtype=torch.float64)\n",
      "tensor(2.9356) tensor(2.3125)\n",
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      "        [1.5079]]) tensor([3., 4., 4., 2., 4., 3., 3., 4., 4., 4., 3., 1., 1., 4., 3., 4.],\n",
      "       dtype=torch.float64)\n",
      "tensor(2.8548) tensor(3.1875)\n",
      "tensor([[2.6895],\n",
      "        [4.0576],\n",
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      "        [3.3846],\n",
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      "        [3.1593]]) tensor([3.5000, 3.0000, 1.0000, 1.0000, 1.0000, 3.0000, 3.0000, 2.0000, 4.0000,\n",
      "        1.0000, 4.0000, 2.0000, 1.0000, 4.0000, 1.0000, 1.0000],\n",
      "       dtype=torch.float64)\n",
      "tensor(2.9527) tensor(2.2188)\n",
      "tensor([[3.7836],\n",
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      "        [2.3452]]) tensor([2.5000, 4.0000, 3.0000, 1.0000, 2.0000, 4.0000, 1.0000, 1.0000, 2.0000,\n",
      "        3.0000, 4.0000, 2.0000, 2.0000, 3.0000, 4.0000, 2.0000],\n",
      "       dtype=torch.float64)\n",
      "tensor(2.7188) tensor(2.5312)\n",
      "tensor([[2.3520],\n",
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      "       dtype=torch.float64)\n",
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      "       dtype=torch.float64)\n",
      "tensor(2.9340) tensor(2.6250)\n",
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      "       dtype=torch.float64)\n",
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      "       dtype=torch.float64)\n",
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      "       dtype=torch.float64)\n",
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      "       dtype=torch.float64)\n",
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      "       dtype=torch.float64)\n",
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      "       dtype=torch.float64)\n",
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      "       dtype=torch.float64)\n",
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      "       dtype=torch.float64)\n",
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      "       dtype=torch.float64)\n",
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      "       dtype=torch.float64)\n",
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      "       dtype=torch.float64)\n",
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      "       dtype=torch.float64)\n",
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      "        [2.5734]]) tensor([1., 3., 3., 4., 1., 3., 4., 1., 1., 3., 4., 3., 4., 4., 1., 4.],\n",
      "       dtype=torch.float64)\n",
      "tensor(3.0117) tensor(2.7500)\n",
      "tensor([[4.2212],\n",
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      "        [3.3146]]) tensor([4., 1., 2., 1., 1., 1., 3., 2., 3., 3., 1., 4., 1., 4., 2., 1.],\n",
      "       dtype=torch.float64)\n",
      "tensor(2.9778) tensor(2.1250)\n",
      "tensor([[3.7239],\n",
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      "        [2.0903],\n",
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      "        [1.5013]]) tensor([4.0000, 1.0000, 3.0000, 2.5000, 4.0000, 1.0000, 3.0000, 4.0000, 1.0000,\n",
      "        2.0000, 4.0000, 1.5000, 1.0000, 4.0000, 2.0000, 2.0000],\n",
      "       dtype=torch.float64)\n",
      "tensor(2.8304) tensor(2.5000)\n",
      "tensor([[3.1581],\n",
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      "        [2.9368]]) tensor([4.0000, 3.0000, 1.0000, 3.0000, 3.0000, 1.0000, 2.0000, 1.5000, 1.0000,\n",
      "        1.0000, 3.0000, 1.0000, 4.0000, 1.0000, 1.0000, 3.0000],\n",
      "       dtype=torch.float64)\n",
      "tensor(2.7916) tensor(2.0938)\n",
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      "        [2.5844]]) tensor([4.0000, 2.0000, 1.0000, 1.0000, 3.0000, 2.0000, 3.0000, 4.0000, 3.5000,\n",
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      "       dtype=torch.float64)\n",
      "tensor(2.4335) tensor(2.6562)\n",
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      "       dtype=torch.float64)\n",
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      "        [4.0169]]) tensor([4.0000, 4.0000, 1.0000, 2.0000, 3.5000, 4.0000, 2.0000, 1.0000, 4.0000,\n",
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      "       dtype=torch.float64)\n",
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      "       dtype=torch.float64)\n",
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      "       dtype=torch.float64)\n",
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      "       dtype=torch.float64)\n",
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      "       dtype=torch.float64)\n",
      "tensor(2.9639) tensor(2.4375)\n",
      "tensor([[3.1640],\n",
      "        [2.0918],\n",
      "        [2.0928],\n",
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      "        [2.2862],\n",
      "        [3.8370],\n",
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      "        [2.9375],\n",
      "        [2.0959]]) tensor([1.0000, 1.0000, 4.0000, 2.0000, 4.0000, 3.0000, 2.0000, 2.0000, 4.0000,\n",
      "        3.5000, 2.0000, 3.0000, 3.0000, 1.0000, 2.0000, 2.5000],\n",
      "       dtype=torch.float64)\n",
      "tensor(2.8320) tensor(2.5000)\n",
      "tensor([[3.4189],\n",
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      "        [3.1360],\n",
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      "        [2.7280]]) tensor([2., 4., 4., 1., 2., 2., 2., 1., 1., 3., 1., 2., 4., 4., 4., 3.],\n",
      "       dtype=torch.float64)\n",
      "tensor(3.2053) tensor(2.5000)\n",
      "tensor([[3.2598],\n",
      "        [2.2198],\n",
      "        [2.6689],\n",
      "        [1.8886],\n",
      "        [3.3867],\n",
      "        [2.1138],\n",
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      "        [3.1149],\n",
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      "        [2.0975]]) tensor([1.0000, 4.0000, 3.0000, 3.0000, 2.0000, 1.0000, 1.5000, 1.0000, 4.0000,\n",
      "        2.0000, 4.0000, 3.0000, 2.0000, 1.0000, 1.0000, 2.0000],\n",
      "       dtype=torch.float64)\n",
      "tensor(2.7765) tensor(2.2188)\n",
      "tensor([[3.5498],\n",
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      "        [2.0931]]) tensor([3.0000, 2.5000, 2.0000, 2.0000, 2.0000, 4.0000, 4.0000, 4.0000, 3.0000,\n",
      "        4.0000, 2.0000, 4.0000, 4.0000, 1.0000, 1.0000, 2.0000],\n",
      "       dtype=torch.float64)\n",
      "tensor(2.7417) tensor(2.7812)\n",
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      "        [1.7043]]) tensor([4.0000, 1.0000, 2.0000, 3.0000, 3.5000, 1.0000, 3.0000, 2.0000, 2.0000,\n",
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      "       dtype=torch.float64)\n",
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      "        [3.7715]]) tensor([2., 1., 1., 3., 4., 2., 2., 4., 4., 4., 2., 1., 1., 4., 2., 3.],\n",
      "       dtype=torch.float64)\n",
      "tensor(2.9509) tensor(2.5000)\n",
      "tensor([[3.9404],\n",
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      "        [2.8008]]) tensor([4.0000, 3.0000, 4.0000, 2.0000, 4.0000, 1.0000, 1.0000, 1.5000, 3.0000,\n",
      "        1.0000, 1.0000, 1.0000, 2.0000, 4.0000, 1.0000, 1.0000],\n",
      "       dtype=torch.float64)\n",
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      "       dtype=torch.float64)\n",
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      "tensor([[3.8463],\n",
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      "        [4.1407]]) tensor([3.0000, 3.0000, 3.0000, 1.0000, 4.0000, 1.0000, 3.0000, 2.0000, 3.5000,\n",
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      "       dtype=torch.float64)\n",
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      "       dtype=torch.float64)\n",
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      "       dtype=torch.float64)\n",
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      "       dtype=torch.float64)\n",
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      "       dtype=torch.float64)\n",
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      "        [3.2192]]) tensor([4.0000, 1.0000, 2.0000, 1.0000, 2.0000, 3.0000, 4.0000, 1.0000, 3.6667,\n",
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      "       dtype=torch.float64)\n",
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      "       dtype=torch.float64)\n",
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      "       dtype=torch.float64)\n",
      "tensor(3.0670) tensor(2.6250)\n",
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      "        [3.5943]]) tensor([2.0000, 2.0000, 2.0000, 1.0000, 4.0000, 3.0000, 4.0000, 3.0000, 4.0000,\n",
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      "       dtype=torch.float64)\n",
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      "        [2.8014]]) tensor([1.0000, 1.0000, 3.5000, 4.0000, 4.0000, 4.0000, 3.5000, 1.0000, 1.0000,\n",
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      "       dtype=torch.float64)\n",
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      "        [3.2301]]) tensor([4.0000, 3.0000, 4.0000, 1.0000, 3.0000, 4.0000, 3.0000, 2.0000, 1.0000,\n",
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      "       dtype=torch.float64)\n",
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      "       dtype=torch.float64)\n",
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      "        [4.1017],\n",
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      "       dtype=torch.float64)\n",
      "tensor(2.8169) tensor(2.4375)\n",
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      "       dtype=torch.float64)\n",
      "tensor(2.6649) tensor(1.9375)\n",
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      "       dtype=torch.float64)\n",
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      "       dtype=torch.float64)\n",
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      "       dtype=torch.float64)\n",
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      "       dtype=torch.float64)\n",
      "tensor(2.9200) tensor(2.3750)\n",
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      "       dtype=torch.float64)\n",
      "tensor(2.6847) tensor(2.0625)\n",
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      "       dtype=torch.float64)\n",
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      "       dtype=torch.float64)\n",
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      "       dtype=torch.float64)\n",
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      "       dtype=torch.float64)\n",
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      "       dtype=torch.float64)\n",
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      "       dtype=torch.float64)\n",
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      "       dtype=torch.float64)\n",
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      "        [3.5833]]) tensor([3., 1., 1., 1., 3., 4., 1., 3., 2., 1., 1., 4., 4., 2., 2., 3.],\n",
      "       dtype=torch.float64)\n",
      "tensor(2.7820) tensor(2.2500)\n",
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      "        [2.6821]]) tensor([2.0000, 3.0000, 4.0000, 3.0000, 1.0000, 2.0000, 3.0000, 1.0000, 4.0000,\n",
      "        3.5000, 2.0000, 3.0000, 1.0000, 4.0000, 3.5000, 4.0000],\n",
      "       dtype=torch.float64)\n",
      "tensor(2.7990) tensor(2.7500)\n",
      "tensor([[2.5253],\n",
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      "        [2.0848]]) tensor([1.0000, 1.0000, 1.0000, 2.0000, 3.0000, 1.0000, 3.0000, 1.0000, 4.0000,\n",
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      "       dtype=torch.float64)\n",
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      "        [1.8359]]) tensor([2.0000, 3.0000, 4.0000, 2.0000, 2.0000, 1.3333, 2.0000, 2.0000, 1.0000,\n",
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      "       dtype=torch.float64)\n",
      "tensor(3.2201) tensor(2.6458)\n",
      "tensor([[3.3550],\n",
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      "       dtype=torch.float64)\n",
      "tensor(2.7352) tensor(2.4375)\n",
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      "       dtype=torch.float64)\n",
      "tensor(2.5127) tensor(2.6250)\n",
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      "       dtype=torch.float64)\n",
      "tensor(2.9710) tensor(2.5625)\n",
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      "        [2.0964]]) tensor([3.0000, 3.0000, 3.5000, 3.0000, 4.0000, 4.0000, 1.0000, 1.0000, 4.0000,\n",
      "        1.0000, 3.0000, 1.0000, 3.5000, 4.0000, 1.0000, 1.0000],\n",
      "       dtype=torch.float64)\n",
      "tensor(2.5264) tensor(2.5625)\n",
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      "       dtype=torch.float64)\n",
      "tensor(2.7819) tensor(1.9375)\n",
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      "       dtype=torch.float64)\n",
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      "       dtype=torch.float64)\n",
      "tensor(2.8614) tensor(2.6875)\n",
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      "       dtype=torch.float64)\n",
      "tensor(2.6718) tensor(2.5000)\n",
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      "       dtype=torch.float64)\n",
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      "       dtype=torch.float64)\n",
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      "       dtype=torch.float64)\n",
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      "       dtype=torch.float64)\n",
      "tensor(3.0035) tensor(3.)\n",
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      "       dtype=torch.float64)\n",
      "tensor(2.7499) tensor(2.8750)\n",
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      "       dtype=torch.float64)\n",
      "tensor(2.6432) tensor(2.0625)\n",
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      "       dtype=torch.float64)\n",
      "tensor(2.6018) tensor(2.3125)\n",
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      "        [3.4288]]) tensor([3.0000, 1.0000, 4.0000, 4.0000, 1.0000, 2.0000, 1.0000, 1.0000, 2.0000,\n",
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      "       dtype=torch.float64)\n",
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      "tensor([[2.3698],\n",
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      "       dtype=torch.float64)\n",
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      "        [2.3151]]) tensor([1., 1., 1., 4., 4., 4., 1., 2., 4., 4., 4., 1., 3., 4., 3., 2.],\n",
      "       dtype=torch.float64)\n",
      "tensor(3.3381) tensor(2.6875)\n",
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      "       dtype=torch.float64)\n",
      "tensor(2.8809) tensor(2.6250)\n",
      "tensor([[2.3969],\n",
      "        [2.7418],\n",
      "        [2.8819]]) tensor([1., 4., 3.], dtype=torch.float64)\n",
      "tensor(2.6735) tensor(2.6667)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/torch/nn/modules/loss.py:528: UserWarning: Using a target size (torch.Size([3])) that is different to the input size (torch.Size([3, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n",
      "  return F.mse_loss(input, target, reduction=self.reduction)\n"
     ]
    }
   ],
   "source": [
    "test_loss, test_pearson, test_mse = evaluate(model, loss_function, test_dataloader, device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4ed7b43c",
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    "execution": {
     "iopub.execute_input": "2022-03-15T07:50:26.537765Z",
     "iopub.status.busy": "2022-03-15T07:50:26.536693Z",
     "iopub.status.idle": "2022-03-15T07:50:26.541243Z",
     "shell.execute_reply": "2022-03-15T07:50:26.540810Z"
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     "status": "completed"
    },
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Pearson Score is 0.2879457348488807\n",
      "Test MSE Score is 2.163341587406707\n"
     ]
    }
   ],
   "source": [
    "print(f\"Test Pearson Score is {np.sum(test_pearson)/len(test_pearson)}\")\n",
    "print(f\"Test MSE Score is {np.sum(test_mse)/len(test_mse)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a7c64e40",
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