06-train_SIMPLE_CNN_RNN_without_text_content.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "606fa719",
   "metadata": {},
   "source": [
    "# Train SIMPLE_CNN_RNN_without_text_content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a8a5bd39",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.nn.utils import clip_grad_norm_\n",
    "\n",
    "from tqdm import tqdm\n",
    "\n",
    "from rouge_score import rouge_scorer\n",
    "\n",
    "from utils.GloveMgr import GloveMgr\n",
    "from utils.Dataset import Dataset\n",
    "from utils.DataLoader import DataLoader\n",
    "from utils.preprocess_df import preprocess_df\n",
    "from utils.accuracy_nb_sent_per_doc import accuracy_nb_sent_per_doc_fn\n",
    "from utils.accuracy_prop_sent_per_doc import accuracy_prop_sent_per_doc_fn\n",
    "from utils.split_all_docs import split_all_docs\n",
    "\n",
    "\n",
    "from time import time\n",
    "\n",
    "import json\n",
    "import argparse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "818410ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "def is_notebook() -> bool:\n",
    "    try:\n",
    "        shell = get_ipython().__class__.__name__\n",
    "        if shell == 'ZMQInteractiveShell':\n",
    "            return True   # Jupyter notebook or qtconsole\n",
    "        elif shell == 'TerminalInteractiveShell':\n",
    "            return False  # Terminal running IPython\n",
    "        else:\n",
    "            return False  # Other type (?)\n",
    "    except NameError:\n",
    "        return False      # Probably standard Python interpreter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "690fa032",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_rows', None)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "b2636e0b",
   "metadata": {},
   "source": [
    "## (Hyper-)parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6e7aad39",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parse args if script mode\n",
    "parser = argparse.ArgumentParser(description='extractive summary')\n",
    "\n",
    "parser.add_argument('-is_graphic',type=int,default=1,choices=[0,1])\n",
    "parser.add_argument('-gpu_num',type=int,default=0)\n",
    "parser.add_argument('-batch_size',type=int,default=32)\n",
    "parser.add_argument('-epochs',type=int,default=100)\n",
    "\n",
    "args = None\n",
    "\n",
    "if is_notebook():\n",
    "    args = parser.parse_args(\"\")\n",
    "else:\n",
    "    args = parser.parse_args()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "824eacf5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "parse:\n",
      "is_graphic: True\n",
      "cuda_num: 0\n",
      "epochs 100\n",
      "batch_size 32\n"
     ]
    }
   ],
   "source": [
    "# parameters\n",
    "is_graphic = args.is_graphic != 0\n",
    "cuda_num = args.gpu_num\n",
    "embed_name = \"glove.6B.100\"\n",
    "\n",
    "# hyper-parameters\n",
    "vocab_size = 150000\n",
    "batch_size = args.batch_size\n",
    "epochs = args.epochs\n",
    "learning_rate = 1e-3\n",
    "early_stopping = 3\n",
    "model_name = \"06-train_SIMPLE_CNN_RNN_without_text_content\"\n",
    "sub_folder_name = \"model_name__{}__time__{}__embed_name__{}__lr__{}__batch_size__{}__vocab_size__{}__cuda_num__{}\".format(model_name, time(), embed_name, learning_rate, batch_size, vocab_size, cuda_num)\n",
    "checkpoints_folder = \"./checkpoints/\" + sub_folder_name\n",
    "average_number_of_sentences_per_document = 3\n",
    "\n",
    "# print\n",
    "print(\"parse:\")\n",
    "print(\"is_graphic:\", is_graphic)\n",
    "print(\"cuda_num:\", cuda_num)\n",
    "print(\"epochs\", epochs)\n",
    "print(\"batch_size\", batch_size)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "c8ddd4d5",
   "metadata": {},
   "source": [
    "## PyTorch initialisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d09bc3ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of available GPUs: 6\n",
      "GPU 0: NVIDIA GeForce GTX 1080 Ti\n",
      "GPU 1: NVIDIA GeForce GTX 1080 Ti\n",
      "GPU 2: NVIDIA GeForce GTX 1080 Ti\n",
      "GPU 3: NVIDIA GeForce GTX 1080\n",
      "GPU 4: NVIDIA GeForce GTX 1080\n",
      "GPU 5: NVIDIA GeForce GTX 1080\n"
     ]
    }
   ],
   "source": [
    "# Check if a GPU is available\n",
    "if torch.cuda.is_available():\n",
    "    # Display the number of available GPUs\n",
    "    print(f\"Number of available GPUs: {torch.cuda.device_count()}\")\n",
    "    # Display the name of each GPU\n",
    "    for i in range(torch.cuda.device_count()):\n",
    "        print(f\"GPU {i}: {torch.cuda.get_device_name(i)}\")\n",
    "else:\n",
    "    print(\"No GPU available.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b0c5786e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cuda', index=0)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if torch.cuda.is_available():  \n",
    "  dev = \"cuda:\" + str(cuda_num) \n",
    "else:  \n",
    "  dev = \"cpu\" \n",
    "\n",
    "device = torch.device(dev)\n",
    "device"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "3393fe4e",
   "metadata": {},
   "source": [
    "## Chargement des données"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "97644745",
   "metadata": {},
   "outputs": [],
   "source": [
    "glovemgr = GloveMgr(\"./data/glove.6B/glove.6B.100d.txt\", vocab_size=vocab_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fe745c8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = pd.read_json(\"./data/cnn_dailymail/train.json\")\n",
    "train_dataset = Dataset(preprocess_df(df_train, glovemgr=glovemgr, is_sep_n=False, remove_stop_word=True, stemming=False, trunc_sent=50, padding_sent=50, trunc_doc=100, doc_column_name=\"article\", labels_column_name=\"labels\"))\n",
    "train_iter = DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f8b39f3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_val = pd.read_json(\"./data/cnn_dailymail/val.json\")\n",
    "val_dataset = Dataset(preprocess_df(df_val, glovemgr=glovemgr, is_sep_n=False, remove_stop_word=True, stemming=False, trunc_sent=50, padding_sent=50, trunc_doc=100, doc_column_name=\"article\", labels_column_name=\"labels\"))\n",
    "val_iter = DataLoader(dataset=val_dataset, batch_size=1, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0f086978",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test = pd.read_json(\"./data/cnn_dailymail/test.json\")\n",
    "test_dataset = Dataset(preprocess_df(df_test, glovemgr=glovemgr, is_sep_n=False, remove_stop_word=True, stemming=False, trunc_sent=50, padding_sent=50, trunc_doc=100, doc_column_name=\"article\", labels_column_name=\"labels\"))\n",
    "test_iter = DataLoader(dataset=test_dataset, batch_size=1, shuffle=False)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "fc41eb07",
   "metadata": {},
   "source": [
    "## Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9683721f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from models.BasicModel import BasicModel\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torch.autograd import Variable\n",
    "\n",
    "class SIMPLE_CNN_RNN(BasicModel):\n",
    "    def __init__(self, device, vocab_size, word_embed = None, dim_word_embed = 100):\n",
    "        super(SIMPLE_CNN_RNN, self).__init__(device)\n",
    "\n",
    "        self.device = device\n",
    "\n",
    "        # 10: relative position range size, with segment size = 10\n",
    "        self.rel_pos_emb = nn.Embedding(11, 100)\n",
    "        self.abs_pos_emb = nn.Embedding(100, 100)\n",
    "\n",
    "        self.Wabs_pos = nn.Linear(100,1,bias=False)\n",
    "        self.Wrel_pos = nn.Linear(100,1,bias=False)\n",
    "        self.bias = nn.Parameter(torch.empty(1).uniform_(-0.1, 0.1))\n",
    "\n",
    "    def avg_pool1d(self,x,seq_lens):\n",
    "        out = []\n",
    "        for index,t in enumerate(x):\n",
    "            if seq_lens[index] == 0:\n",
    "                t = t[:1]\n",
    "            else:\n",
    "                t = t[:seq_lens[index],:]\n",
    "            t = torch.t(t).unsqueeze(0)\n",
    "            out.append(torch.avg_pool1d(t,t.size(2)))\n",
    "        \n",
    "        out = torch.cat(out).squeeze(2)\n",
    "        return out\n",
    "\n",
    "    def forward(self, arr_x, doc_lens):\n",
    "        probs = []\n",
    "\n",
    "        max_doc_lens = max(doc_lens)\n",
    "\n",
    "        # Compute position embedding\n",
    "        abs_poses = torch.tensor([[position] for position in range(max_doc_lens)], dtype=torch.long)\n",
    "        abs_poses = abs_poses.to(self.device)\n",
    "        abs_poses = self.abs_pos_emb(abs_poses).squeeze(1)\n",
    "        aps = self.Wabs_pos(abs_poses)\n",
    "\n",
    "        # Compute relative position embedding\n",
    "        rel_poses = torch.tensor([[int(round(position / 10))] for position in range(max_doc_lens)], dtype=torch.long)\n",
    "        rel_poses = rel_poses.to(self.device)\n",
    "        rel_poses = self.rel_pos_emb(rel_poses).squeeze(1)\n",
    "        rps = self.Wrel_pos(rel_poses)\n",
    "\n",
    "        zeros = torch.zeros((len(doc_lens), 1)).to(self.device)\n",
    "\n",
    "        # for each document, compute probabilities\n",
    "        for position in range(max_doc_lens):\n",
    "            prob = zeros + aps[position] + rps[position] + self.bias\n",
    "            prob = torch.sigmoid(prob)\n",
    "            probs.append(prob)\n",
    "\n",
    "        probs = torch.cat(probs).squeeze()\n",
    "        if len(probs.shape) == 0:\n",
    "            probs = probs.unsqueeze(0)\n",
    "        probs_filtered = []\n",
    "        for idx, doc_len in enumerate(doc_lens):\n",
    "            prob = probs[idx::len(doc_lens)]\n",
    "            prob = prob[:doc_len]\n",
    "            probs_filtered.append(prob)\n",
    "        probs_filtered = torch.cat(probs_filtered).squeeze()\n",
    "        if len(probs_filtered.shape) == 0:\n",
    "            probs_filtered = probs_filtered.unsqueeze(0)\n",
    "        #probs = probs.to(self.device)\n",
    "        return probs_filtered\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "380f8ba1",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = SIMPLE_CNN_RNN(device=device, vocab_size=vocab_size, word_embed=glovemgr.getEmbeddings())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2ae1a236",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SIMPLE_CNN_RNN(\n",
       "  (rel_pos_emb): Embedding(11, 100)\n",
       "  (abs_pos_emb): Embedding(100, 100)\n",
       "  (Wabs_pos): Linear(in_features=100, out_features=1, bias=False)\n",
       "  (Wrel_pos): Linear(in_features=100, out_features=1, bias=False)\n",
       ")"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4bc411ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "loss_fn = nn.BCELoss()\n",
    "optimizer = torch.optim.Adamax(model.parameters(),lr=learning_rate)\n",
    "scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.5)\n",
    "mae_fn = nn.L1Loss()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "1852182a",
   "metadata": {},
   "source": [
    "## Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5c803c4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.exists(checkpoints_folder):\n",
    "    os.makedirs(checkpoints_folder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c6618af5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_val[\"doc_splitted\"] = split_all_docs(df_val[\"article\"], is_sep_n = False)\n",
    "val_set = df_val\n",
    "scorer = rouge_scorer.RougeScorer(['rouge1', 'rouge2', 'rougeL'], use_stemmer=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0991d3c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 1: 100%|██████████| 4/4 [00:00<00:00,  5.82batch/s, accuracy=0.616, loss=0.9, mae=0.543]  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1 : val loss = 0.888, val mae = 0.543, val accuracy = 0.685, r1 = 0.254, r2 = 0.056, rL = 0.153\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 2: 100%|██████████| 4/4 [00:00<00:00, 13.22batch/s, accuracy=0.613, loss=0.839, mae=0.524]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2 : val loss = 0.848, val mae = 0.530, val accuracy = 0.685, r1 = 0.254, r2 = 0.056, rL = 0.154\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 3: 100%|██████████| 4/4 [00:00<00:00, 12.01batch/s, accuracy=0.616, loss=0.809, mae=0.515]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3 : val loss = 0.829, val mae = 0.524, val accuracy = 0.685, r1 = 0.255, r2 = 0.055, rL = 0.154\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 4: 100%|██████████| 4/4 [00:00<00:00, 11.83batch/s, accuracy=0.616, loss=0.796, mae=0.51] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 4 : val loss = 0.820, val mae = 0.520, val accuracy = 0.685, r1 = 0.254, r2 = 0.055, rL = 0.152\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 5: 100%|██████████| 4/4 [00:00<00:00, 12.08batch/s, accuracy=0.612, loss=0.788, mae=0.507]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 5 : val loss = 0.815, val mae = 0.519, val accuracy = 0.685, r1 = 0.254, r2 = 0.055, rL = 0.153\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 6: 100%|██████████| 4/4 [00:00<00:00, 12.56batch/s, accuracy=0.607, loss=0.787, mae=0.506]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 6 : val loss = 0.813, val mae = 0.518, val accuracy = 0.685, r1 = 0.254, r2 = 0.055, rL = 0.153\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 7: 100%|██████████| 4/4 [00:00<00:00, 11.82batch/s, accuracy=0.612, loss=0.784, mae=0.505]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 7 : val loss = 0.812, val mae = 0.517, val accuracy = 0.685, r1 = 0.254, r2 = 0.055, rL = 0.153\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 8: 100%|██████████| 4/4 [00:00<00:00, 11.88batch/s, accuracy=0.607, loss=0.784, mae=0.506]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 8 : val loss = 0.811, val mae = 0.517, val accuracy = 0.685, r1 = 0.254, r2 = 0.055, rL = 0.153\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 9: 100%|██████████| 4/4 [00:00<00:00, 12.00batch/s, accuracy=0.61, loss=0.784, mae=0.506] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 9 : val loss = 0.811, val mae = 0.517, val accuracy = 0.685, r1 = 0.254, r2 = 0.055, rL = 0.153\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 10: 100%|██████████| 4/4 [00:00<00:00, 11.92batch/s, accuracy=0.609, loss=0.782, mae=0.505]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 10 : val loss = 0.811, val mae = 0.517, val accuracy = 0.685, r1 = 0.254, r2 = 0.055, rL = 0.153\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 11: 100%|██████████| 4/4 [00:00<00:00, 12.52batch/s, accuracy=0.608, loss=0.785, mae=0.506]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 11 : val loss = 0.811, val mae = 0.517, val accuracy = 0.685, r1 = 0.254, r2 = 0.055, rL = 0.153\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 12: 100%|██████████| 4/4 [00:00<00:00, 11.98batch/s, accuracy=0.606, loss=0.788, mae=0.507]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 12 : val loss = 0.811, val mae = 0.517, val accuracy = 0.685, r1 = 0.254, r2 = 0.055, rL = 0.153\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 13: 100%|██████████| 4/4 [00:00<00:00, 11.81batch/s, accuracy=0.61, loss=0.783, mae=0.505] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 13 : val loss = 0.811, val mae = 0.517, val accuracy = 0.685, r1 = 0.254, r2 = 0.055, rL = 0.153\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 14: 100%|██████████| 4/4 [00:00<00:00, 11.98batch/s, accuracy=0.609, loss=0.785, mae=0.506]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 14 : val loss = 0.811, val mae = 0.517, val accuracy = 0.685, r1 = 0.254, r2 = 0.055, rL = 0.153\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 15: 100%|██████████| 4/4 [00:00<00:00, 12.46batch/s, accuracy=0.603, loss=0.789, mae=0.508]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 15 : val loss = 0.811, val mae = 0.517, val accuracy = 0.685, r1 = 0.254, r2 = 0.055, rL = 0.153\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 16: 100%|██████████| 4/4 [00:00<00:00, 11.93batch/s, accuracy=0.611, loss=0.785, mae=0.506]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 16 : val loss = 0.811, val mae = 0.517, val accuracy = 0.685, r1 = 0.254, r2 = 0.055, rL = 0.153\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 17: 100%|██████████| 4/4 [00:00<00:00, 11.76batch/s, accuracy=0.605, loss=0.783, mae=0.505]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 17 : val loss = 0.811, val mae = 0.517, val accuracy = 0.685, r1 = 0.254, r2 = 0.055, rL = 0.153\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 18: 100%|██████████| 4/4 [00:00<00:00, 12.00batch/s, accuracy=0.61, loss=0.783, mae=0.505] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 18 : val loss = 0.811, val mae = 0.517, val accuracy = 0.685, r1 = 0.254, r2 = 0.055, rL = 0.153\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 19: 100%|██████████| 4/4 [00:00<00:00, 12.01batch/s, accuracy=0.609, loss=0.787, mae=0.507]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 19 : val loss = 0.811, val mae = 0.517, val accuracy = 0.685, r1 = 0.254, r2 = 0.055, rL = 0.153\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 20: 100%|██████████| 4/4 [00:00<00:00, 12.13batch/s, accuracy=0.608, loss=0.782, mae=0.504]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 20 : val loss = 0.811, val mae = 0.517, val accuracy = 0.685, r1 = 0.254, r2 = 0.055, rL = 0.153\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 21: 100%|██████████| 4/4 [00:00<00:00, 11.97batch/s, accuracy=0.609, loss=0.781, mae=0.504]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 21 : val loss = 0.811, val mae = 0.517, val accuracy = 0.685, r1 = 0.254, r2 = 0.055, rL = 0.153\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 22: 100%|██████████| 4/4 [00:00<00:00, 11.80batch/s, accuracy=0.606, loss=0.786, mae=0.507]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 22 : val loss = 0.811, val mae = 0.517, val accuracy = 0.685, r1 = 0.254, r2 = 0.055, rL = 0.153\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 23: 100%|██████████| 4/4 [00:00<00:00, 11.84batch/s, accuracy=0.615, loss=0.783, mae=0.505]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 23 : val loss = 0.811, val mae = 0.517, val accuracy = 0.685, r1 = 0.254, r2 = 0.055, rL = 0.153\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 24: 100%|██████████| 4/4 [00:00<00:00, 12.11batch/s, accuracy=0.603, loss=0.783, mae=0.505]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 24 : val loss = 0.811, val mae = 0.517, val accuracy = 0.685, r1 = 0.254, r2 = 0.055, rL = 0.153\n",
      "Training duration = 23.70528745651245\n"
     ]
    }
   ],
   "source": [
    "arr_train_loss = []\n",
    "arr_train_mae = []\n",
    "arr_train_acc = []\n",
    "arr_val_loss = []\n",
    "arr_val_mae = []\n",
    "arr_val_acc = []\n",
    "\n",
    "t1 = time()\n",
    "for epoch in range(1, epochs+1):\n",
    "    # train 1 epoch\n",
    "    model.train()\n",
    "    nb_batch_train = 0\n",
    "    total_train_loss = 0\n",
    "    total_train_mae = 0\n",
    "    total_train_acc = 0\n",
    "    with tqdm(train_iter, unit=\"batch\", total=len(train_iter)) as tepoch:\n",
    "        for batch in tepoch:\n",
    "            tepoch.set_description(f\"Epoch {epoch}\")\n",
    "            features = []\n",
    "            doc_lens = []\n",
    "\n",
    "            for j in range(batch_size):\n",
    "                doc_lens.append(len(batch[j][\"docs\"]))\n",
    "                features = features + batch[j][\"docs\"]\n",
    "            \n",
    "            features = torch.tensor(features, dtype=torch.long).to(device)\n",
    "\n",
    "            targets = [torch.tensor(batch[j][\"labels\"], dtype=torch.float) for j in range(batch_size)]\n",
    "            targets = torch.cat(targets)\n",
    "            targets = targets.to(device)\n",
    "            \n",
    "            probs = model(features, doc_lens)\n",
    "            \n",
    "            loss = loss_fn(probs, targets)\n",
    "            optimizer.zero_grad()\n",
    "            loss.backward()\n",
    "            clip_grad_norm_(model.parameters(), 1.0)\n",
    "            optimizer.step()\n",
    "            nb_batch_train += 1\n",
    "            total_train_loss += loss.item()\n",
    "            total_train_mae += mae_fn(probs, targets).item()\n",
    "            total_train_acc += accuracy_prop_sent_per_doc_fn(probs=probs.cpu().detach().numpy(), targets=targets.cpu().detach().numpy(), doc_lens=doc_lens)\n",
    "            tepoch.set_postfix(loss=total_train_loss/nb_batch_train, mae=total_train_mae/nb_batch_train, accuracy=total_train_acc/nb_batch_train)\n",
    "\n",
    "    # Save model\n",
    "    model.save(checkpoints_folder + \"/\" + model_name + \"-\" + str(epoch) + \".pt\")\n",
    "\n",
    "    # Show train and val score\n",
    "    model.eval()\n",
    "    nb_batch_val = 0\n",
    "    total_val_loss = 0\n",
    "    total_val_mae = 0\n",
    "    total_val_acc = 0\n",
    "\n",
    "    total_r1 = 0\n",
    "    total_r2 = 0\n",
    "    total_rl = 0\n",
    "\n",
    "    for i, batch in enumerate(val_iter):\n",
    "        features = []\n",
    "        doc_lens = []\n",
    "\n",
    "        for j in range(1):\n",
    "            doc_lens.append(len(batch[j][\"docs\"]))\n",
    "            features = features + batch[j][\"docs\"]\n",
    "            \n",
    "        features = torch.tensor(features, dtype=torch.long).to(device)\n",
    "\n",
    "        targets = [torch.tensor(batch[j][\"labels\"], dtype=torch.float) for j in range(1)]\n",
    "        targets = torch.cat(targets)\n",
    "        targets = targets.to(device)\n",
    "        \n",
    "        y_pred = model(features, doc_lens)\n",
    "\n",
    "        loss = loss_fn(y_pred, targets)\n",
    "\n",
    "        nb_batch_val += 1\n",
    "        total_val_loss += loss.item()\n",
    "\n",
    "        doc = val_set[\"doc_splitted\"].iloc[i]\n",
    "        summaries = val_set[\"highlights\"].iloc[i]\n",
    "\n",
    "        indices = torch.argsort(y_pred, descending=True)\n",
    "\n",
    "        y_pred_thresh = []\n",
    "\n",
    "        txt = \"\"\n",
    "\n",
    "        for j in range(min(len(doc), y_pred.shape[0])):\n",
    "            txt = txt + \". \" + doc[indices[j]]\n",
    "            y_pred_thresh.append(indices[j])\n",
    "            if len(txt) >= len(summaries):\n",
    "                break\n",
    "\n",
    "        y_pred_thresh.sort()\n",
    "\n",
    "        txt = \"\"\n",
    "\n",
    "        for j in y_pred_thresh:\n",
    "            txt = txt + \". \" + doc[j]\n",
    "\n",
    "        n = min(len(txt), len(summaries))\n",
    "\n",
    "        while n < len(txt) and txt[n].isalnum():\n",
    "            n += 1\n",
    "\n",
    "        txt = txt[:n]\n",
    "\n",
    "        #assert len(txt) - len(summaries) <= 20\n",
    "\n",
    "        scores = scorer.score(summaries, txt)\n",
    "        total_r1 += scores[\"rouge1\"].fmeasure\n",
    "        total_r2 += scores[\"rouge2\"].fmeasure\n",
    "        total_rl += scores[\"rougeL\"].fmeasure\n",
    "\n",
    "        probs = y_pred.tolist() # compute_probs(y_pred)\n",
    "        probs_tensor = y_pred #torch.tensor(probs, dtype=torch.float).to(device)\n",
    "\n",
    "        total_val_mae += mae_fn(probs_tensor, targets).item()\n",
    "        total_val_acc += accuracy_nb_sent_per_doc_fn(probs=probs, targets=targets.cpu().detach().numpy(), doc_lens=doc_lens, average_number_of_sentences_per_document=average_number_of_sentences_per_document)\n",
    "\n",
    "    arr_train_loss.append(total_train_loss / nb_batch_train)\n",
    "    arr_train_mae.append(total_train_mae / nb_batch_train)\n",
    "    arr_train_acc.append(total_train_acc / nb_batch_train)\n",
    "\n",
    "    arr_val_loss.append(total_val_loss / nb_batch_val)\n",
    "    arr_val_mae.append(total_val_mae / nb_batch_val)\n",
    "    arr_val_acc.append(total_val_acc / nb_batch_val)\n",
    "    \n",
    "    print(\"Epoch {} : val loss = {:.3f}, val mae = {:.3f}, val accuracy = {:.3f}, r1 = {:.3f}, r2 = {:.3f}, rL = {:.3f}\".format(epoch, total_val_loss / nb_batch_val, total_val_mae / nb_batch_val, total_val_acc / nb_batch_val, total_r1 / nb_batch_val, total_r2 / nb_batch_val, total_rl / nb_batch_val))\n",
    "\n",
    "    if len(arr_val_loss) >= early_stopping+1:\n",
    "        if min(arr_val_loss[-early_stopping:]) >= arr_val_loss[-(early_stopping+1)]:\n",
    "            break\n",
    "\n",
    "    # update lr\n",
    "    scheduler.step()\n",
    "\n",
    "t2 = time()\n",
    "print(\"Training duration =\", t2-t1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2f39ceed",
   "metadata": {},
   "outputs": [],
   "source": [
    "training_metrics = {}\n",
    "training_metrics[\"duration\"]   = t2 - t1\n",
    "training_metrics[\"train_loss\"] = arr_train_loss\n",
    "training_metrics[\"train_mae\"]  = arr_train_mae\n",
    "training_metrics[\"train_acc\"]  = arr_train_acc\n",
    "training_metrics[\"val_loss\"]   = arr_val_loss\n",
    "training_metrics[\"val_mae\"]    = arr_val_mae\n",
    "training_metrics[\"val_acc\"]    = arr_val_acc\n",
    "\n",
    "# Save to file in JSON format\n",
    "\n",
    "with open(checkpoints_folder + \"/training_metrics.json\", 'w') as fp:\n",
    "    json.dump(training_metrics, fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8cd8fbee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# draw Losses\n",
    "if is_graphic:\n",
    "    plt.plot(list(range(1, len(arr_train_loss) + 1)), arr_train_loss, label=\"train\")\n",
    "    plt.plot(list(range(1, len(arr_val_loss) + 1)), arr_val_loss, label=\"val\")\n",
    "\n",
    "    plt.title(\"Loss\")\n",
    "    plt.xlabel(\"epoch\")\n",
    "    plt.ylabel(\"loss\")\n",
    "    plt.legend(loc=\"upper left\")\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8bda887e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# draw mae\n",
    "if is_graphic:\n",
    "    plt.plot(list(range(1, len(arr_train_mae) + 1)), arr_train_mae, label=\"train\")\n",
    "    plt.plot(list(range(1, len(arr_val_mae) + 1)), arr_val_mae, label=\"val\")\n",
    "\n",
    "    plt.title(\"MAE\")\n",
    "    plt.xlabel(\"epoch\")\n",
    "    plt.ylabel(\"mae\")\n",
    "    plt.legend(loc=\"upper right\")\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1cc21154",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# draw Accuracies\n",
    "if is_graphic:\n",
    "    plt.plot(list(range(1, len(arr_train_acc) + 1)), arr_train_acc, label=\"train\")\n",
    "    plt.plot(list(range(1, len(arr_val_acc) + 1)), arr_val_acc, label=\"val\")\n",
    "\n",
    "    plt.title(\"Accuracy\")\n",
    "    plt.xlabel(\"epoch\")\n",
    "    plt.ylabel(\"accuracy\")\n",
    "    plt.legend(loc=\"upper left\")\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "0133ff45",
   "metadata": {},
   "source": [
    "# Eval model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e095b271",
   "metadata": {},
   "outputs": [],
   "source": [
    "from utils.split_all_docs import split_all_docs\n",
    "from rouge_score import rouge_scorer\n",
    "from utils.threshold_probs import threshold_probs_by_prop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "273c6eb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = SIMPLE_CNN_RNN(device=device, vocab_size=vocab_size, word_embed=glovemgr.getEmbeddings())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "4fc41994",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_epoch = np.argmin(np.array(arr_val_loss)) + 1\n",
    "model.load(checkpoints_folder + \"/\" + model_name + \"-\" + str(best_epoch) + \".pt\")\n",
    "model.to(device)\n",
    "model.eval()\n",
    "best_epoch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3edbae7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_set = df_test\n",
    "test_set[\"doc_splited\"] = split_all_docs(test_set[\"article\"], False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "bb8ab07a",
   "metadata": {},
   "outputs": [],
   "source": [
    "scorer = rouge_scorer.RougeScorer(['rouge1', 'rouge2', 'rougeL'], use_stemmer=True)\n",
    "criterion = nn.BCELoss()\n",
    "mae_fn = nn.L1Loss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "67659b8a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Eval model: 100%|██████████| 100/100 [00:00<00:00, 117.14batch/s, accuracy=0.664, mae=0.514, rouge1=0.273, rouge2=0.0646, rougeL=0.167]\n"
     ]
    }
   ],
   "source": [
    "arr_rouge1 = []\n",
    "arr_rouge2 = []\n",
    "arr_rougeL = []\n",
    "arr_loss = []\n",
    "mae = []\n",
    "accuracy = []\n",
    "\n",
    "idx = 0\n",
    "with tqdm(test_iter, unit=\"batch\", total=len(test_iter)) as tepoch:\n",
    "    for batch in tepoch:\n",
    "        tepoch.set_description(\"Eval model\")\n",
    "        features = []\n",
    "        doc_lens = []\n",
    "\n",
    "        for j in range(1):\n",
    "            doc_lens.append(len(batch[j][\"docs\"]))\n",
    "            features = features + batch[j][\"docs\"]\n",
    "                \n",
    "        features = torch.tensor(features, dtype=torch.long).to(device)\n",
    "\n",
    "        targets = [torch.tensor(batch[j][\"labels\"], dtype=torch.float) for j in range(1)]\n",
    "        targets = torch.cat(targets)\n",
    "        targets = targets.to(device)\n",
    "\n",
    "        y_pred = model(features, doc_lens)\n",
    "    \n",
    "        loss = criterion(y_pred, targets)\n",
    "\n",
    "        arr_loss.append(loss.item())\n",
    "\n",
    "        doc = test_set[\"doc_splited\"].iloc[idx]\n",
    "        summaries = test_set[\"highlights\"].iloc[idx]\n",
    "\n",
    "        probs = np.array(y_pred.tolist()) # compute_probs(y_pred)\n",
    "        probs_tensor = y_pred #torch.tensor(probs, dtype=torch.float).to(device)\n",
    "\n",
    "        #probs = threshold_probs_by_nb(probs=probs, doc_lens=[probs.shape[0]], average_number_of_sentences_per_document=average_number_of_sentences_per_document)\n",
    "        #probs = threshold_probs_by_prop(probs=probs, doc_lens=[probs.shape[0]], average_proportion_of_sentences_per_document=average_proportion_of_sentences_per_document)\n",
    "        indices = torch.argsort(y_pred, descending=True)\n",
    "\n",
    "        y_pred_thresh = []\n",
    "\n",
    "        txt = \"\"\n",
    "\n",
    "        for i in range(min(len(doc), y_pred.shape[0])):\n",
    "            txt = txt + \". \" + doc[indices[i]]\n",
    "            y_pred_thresh.append(indices[i])\n",
    "            if len(txt) >= len(summaries):\n",
    "                break\n",
    "\n",
    "        y_pred_thresh.sort()\n",
    "\n",
    "        txt = \"\"\n",
    "\n",
    "        for i in y_pred_thresh:#range(min(len(doc), y_pred.shape[0])):\n",
    "            txt = txt + \". \" + doc[i]\n",
    "\n",
    "        n = min(len(txt), len(summaries))\n",
    "\n",
    "        while n < len(txt) and txt[n].isalnum():\n",
    "            n += 1\n",
    "\n",
    "        txt = txt[:n]\n",
    "\n",
    "        #assert len(txt) - len(summaries) <= 20\n",
    "\n",
    "        scores = scorer.score(summaries, txt)\n",
    "        arr_rouge1.append(scores[\"rouge1\"].fmeasure)\n",
    "        arr_rouge2.append(scores[\"rouge2\"].fmeasure)\n",
    "        arr_rougeL.append(scores[\"rougeL\"].fmeasure)\n",
    "\n",
    "        mae.append(mae_fn(probs_tensor, targets).item())\n",
    "\n",
    "        accuracy.append(accuracy_nb_sent_per_doc_fn(probs=probs, targets=targets.cpu().detach().numpy(), doc_lens=[len(probs)], average_number_of_sentences_per_document=average_number_of_sentences_per_document))\n",
    "        #accuracy.append(accuracy_prop_sent_per_doc_fn(probs=probs, targets=targets.cpu().detach().numpy(), doc_lens=[len(probs)], average_proportion_of_sentences_per_document=average_proportion_of_sentences_per_document))\n",
    "\n",
    "        tepoch.set_postfix(accuracy = np.mean(accuracy), mae = np.mean(mae), rouge1 = np.mean(arr_rouge1), rouge2 = np.mean(arr_rouge2), rougeL = np.mean(arr_rougeL))\n",
    "        idx += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "5c1fd15f",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_metrics = {}\n",
    "test_metrics[\"accuracy\"] = np.mean(accuracy)\n",
    "test_metrics[\"mae\"]      = np.mean(mae)\n",
    "test_metrics[\"rouge1\"]   = np.mean(arr_rouge1)\n",
    "test_metrics[\"rouge2\"]   = np.mean(arr_rouge2)\n",
    "test_metrics[\"rougeL\"]   = np.mean(arr_rougeL)\n",
    "\n",
    "# Save to file in JSON format\n",
    "\n",
    "with open(checkpoints_folder + \"/test_metrics.json\", 'w') as fp:\n",
    "    json.dump(test_metrics, fp)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "496646f2",
   "metadata": {},
   "source": [
    "### Lead-3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "4c96cdd0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Lead-3: 100%|██████████| 100/100 [00:00<00:00, 196.65batch/s, rouge1=0.4, rouge2=0.165, rougeL=0.249] \n"
     ]
    }
   ],
   "source": [
    "arr_rouge1 = []\n",
    "arr_rouge2 = []\n",
    "arr_rougeL = []\n",
    "mae = []\n",
    "accuracy = []\n",
    "\n",
    "idx = 0\n",
    "with tqdm(test_iter, unit=\"batch\", total=len(test_iter)) as tepoch:\n",
    "    for batch in tepoch:\n",
    "        tepoch.set_description(\"Lead-3\")\n",
    "        doc = test_set[\"doc_splited\"].iloc[idx]\n",
    "\n",
    "        txt = \"\"\n",
    "\n",
    "        for i in range(min(len(doc), 3)):\n",
    "            txt = txt + doc[i]\n",
    "\n",
    "        summaries = test_set[\"highlights\"].iloc[idx]\n",
    "\n",
    "        n = min(len(txt), len(summaries))\n",
    "\n",
    "        while n < len(txt) and txt[n].isalnum():\n",
    "            n += 1\n",
    "\n",
    "        txt = txt[:n]\n",
    "\n",
    "        scores = scorer.score(summaries, txt)\n",
    "        arr_rouge1.append(scores[\"rouge1\"].fmeasure)\n",
    "        arr_rouge2.append(scores[\"rouge2\"].fmeasure)\n",
    "        arr_rougeL.append(scores[\"rougeL\"].fmeasure)\n",
    "\n",
    "        idx += 1\n",
    "\n",
    "        tepoch.set_postfix(rouge1 = np.mean(arr_rouge1), rouge2 = np.mean(arr_rouge2), rougeL = np.mean(arr_rougeL))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78a3c5d9",
   "metadata": {},
   "source": [
    "### First n char"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a4c96c70",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "First-n-char': 100%|██████████| 100/100 [00:00<00:00, 189.10batch/s, rouge1=0.406, rouge2=0.168, rougeL=0.252]\n"
     ]
    }
   ],
   "source": [
    "arr_rouge1 = []\n",
    "arr_rouge2 = []\n",
    "arr_rougeL = []\n",
    "mae = []\n",
    "accuracy = []\n",
    "\n",
    "idx = 0\n",
    "with tqdm(test_iter, unit=\"batch\", total=len(test_iter)) as tepoch:\n",
    "    for batch in tepoch:\n",
    "        tepoch.set_description(\"First-n-char'\")\n",
    "        doc = test_set[\"doc_splited\"].iloc[idx]\n",
    "\n",
    "        txt = \"\"\n",
    "\n",
    "        for i in range(len(doc)):\n",
    "            txt = txt + doc[i]\n",
    "\n",
    "        summaries = test_set[\"highlights\"].iloc[idx]\n",
    "\n",
    "        n = min(len(txt), len(summaries))\n",
    "\n",
    "        while n < len(txt) and txt[n].isalnum():\n",
    "            n += 1\n",
    "\n",
    "        txt = txt[:n]\n",
    "\n",
    "        scores = scorer.score(summaries, txt)\n",
    "        arr_rouge1.append(scores[\"rouge1\"].fmeasure)\n",
    "        arr_rouge2.append(scores[\"rouge2\"].fmeasure)\n",
    "        arr_rougeL.append(scores[\"rougeL\"].fmeasure)\n",
    "\n",
    "        idx += 1\n",
    "\n",
    "        tepoch.set_postfix(rouge1 = np.mean(arr_rouge1), rouge2 = np.mean(arr_rouge2), rougeL = np.mean(arr_rougeL))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e4f4796c",
   "metadata": {},
   "outputs": [
    {
     "ename": "SystemExit",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "An exception has occurred, use %tb to see the full traceback.\n",
      "\u001b[0;31mSystemExit\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/raoufdine/anaconda3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:3465: UserWarning: To exit: use 'exit', 'quit', or Ctrl-D.\n",
      "  warn(\"To exit: use 'exit', 'quit', or Ctrl-D.\", stacklevel=1)\n"
     ]
    }
   ],
   "source": [
    "# Does not excute this cell if you want to execute the following cells.\n",
    "import sys\n",
    "sys.exit()"
   ]
  }
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