BBC_sentence_extraction.ipynb

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            "width": null
          }
        },
        "a1fb2236ca9e476690ba3d175643e280": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        }
      }
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/RanaBan/processing_docs/blob/master/BBC_sentence_extraction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "twlgwJ9kq4UT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0e757b05-3140-4a5b-d526-58a72bac7399"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "import pandas as pd\n",
        "from tqdm import tqdm\n",
        "import numpy as np\n",
        "import re\n",
        "import nltk\n",
        "nltk.download('punkt')\n",
        "from nltk import word_tokenize, sent_tokenize\n",
        "\n",
        "import spacy\n",
        "nlp = spacy.load(\"en_core_web_sm\")\n",
        "\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3vDHHcwysxWE",
        "outputId": "c46b2ce5-e51a-4366-c40d-dd80882379e4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Document wise sentence groups and their labels based on the summaries"
      ],
      "metadata": {
        "id": "ej4XWKClajid"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "path = '/content/drive/MyDrive/Research/GridSearch_GCN_on_DisasterData/BBC News Summary/'\n",
        "dirs = ['business', 'entertainment', 'politics', 'sport', 'tech']\n",
        "texts, summaries = [], []\n",
        "for adir in dirs:\n",
        "    text_dir = os.fsencode(path+'News Articles/'+adir)\n",
        "    filenames = os.listdir(text_dir)\n",
        "    filenames = [str(s).split('\\'')[1] for s in filenames]\n",
        "    filenames = sorted(filenames)\n",
        "    print(text_dir, ':', len(filenames))\n",
        "\n",
        "    for filenm in filenames:\n",
        "        thefile = path+'News Articles/'+adir+'/'+filenm\n",
        "\n",
        "        with open(thefile, 'rb') as fp:\n",
        "            temp = fp.readlines()\n",
        "            texts.append(''.join([t.decode('ISO-8859-1') for t in temp]))\n",
        "#   =================================================\n",
        "    summ_dir = os.fsencode(path+'Summaries/'+adir)\n",
        "    filenames = os.listdir(summ_dir)\n",
        "    filenames = [str(s).split('\\'')[1] for s in filenames]\n",
        "    filenames = sorted(filenames)\n",
        "    print(summ_dir, ':', len(filenames))\n",
        "\n",
        "    for filenm in filenames:\n",
        "        thefile = path+'Summaries/'+adir+'/'+filenm\n",
        "        with open(thefile, 'rb') as fp:\n",
        "            temp = fp.readlines()\n",
        "            summaries.append(''.join([t.decode('ISO-8859-1') for t in temp]))"
      ],
      "metadata": {
        "id": "fPE3UkinrP32",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0478a1dd-ab55-4aef-8d09-71b30c2b36a0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "b'/content/drive/MyDrive/Research/GridSearch_GCN_on_DisasterData/BBC News Summary/News Articles/business' : 510\n",
            "b'/content/drive/MyDrive/Research/GridSearch_GCN_on_DisasterData/BBC News Summary/Summaries/business' : 510\n",
            "b'/content/drive/MyDrive/Research/GridSearch_GCN_on_DisasterData/BBC News Summary/News Articles/entertainment' : 386\n",
            "b'/content/drive/MyDrive/Research/GridSearch_GCN_on_DisasterData/BBC News Summary/Summaries/entertainment' : 386\n",
            "b'/content/drive/MyDrive/Research/GridSearch_GCN_on_DisasterData/BBC News Summary/News Articles/politics' : 417\n",
            "b'/content/drive/MyDrive/Research/GridSearch_GCN_on_DisasterData/BBC News Summary/Summaries/politics' : 417\n",
            "b'/content/drive/MyDrive/Research/GridSearch_GCN_on_DisasterData/BBC News Summary/News Articles/sport' : 511\n",
            "b'/content/drive/MyDrive/Research/GridSearch_GCN_on_DisasterData/BBC News Summary/Summaries/sport' : 511\n",
            "b'/content/drive/MyDrive/Research/GridSearch_GCN_on_DisasterData/BBC News Summary/News Articles/tech' : 401\n",
            "b'/content/drive/MyDrive/Research/GridSearch_GCN_on_DisasterData/BBC News Summary/Summaries/tech' : 401\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def treat_num(s):\n",
        "    if '.' in str(s.group(0)):\n",
        "        return re.sub('\\.', '_', str(s.group(0)))\n",
        "    else:\n",
        "        return re.sub(',', '', str(s.group(0)))\n",
        "\n",
        "document_wise_sentences, summary_sentences, labels = [], [], []\n",
        "for text, summ in tqdm(zip(texts, summaries)):\n",
        "    text = re.sub('\\n+', '. ', text)\n",
        "    text = re.sub('\\.\\.', '.', text)\n",
        "    text = re.sub(r'[^0-9a-zA-Z\\.]+', ' ', text)\n",
        "    text = re.sub(r'[0-9]+\\.[0-9]+', treat_num, text)\n",
        "    temp_sentences = [re.sub('\\.', '', s).strip() for s in sent_tokenize(text)]\n",
        "    temp_sentences = [s for s in temp_sentences if s]\n",
        "\n",
        "    document_wise_sentences.append(temp_sentences)\n",
        "\n",
        "    summ = re.sub(r'[^0-9a-zA-Z\\.]+', ' ', summ)\n",
        "    summ = re.sub(r'[0-9]+\\.[0-9]+', treat_num, summ)\n",
        "    summ = re.sub(r'\\.', '. ', summ)\n",
        "\n",
        "    temp_summ_sentences = [re.sub('\\.', '', s).strip() for s in sent_tokenize(summ)]\n",
        "    summary_sentences.append(temp_summ_sentences)\n",
        "    labeling = lambda s : 1 if s.strip() in temp_summ_sentences else 0\n",
        "#     summary_sentences_in_text = [labeling(s) for s in sent_tokenize(summ)]\n",
        "    labels.append([labeling(s) for s in temp_sentences])"
      ],
      "metadata": {
        "id": "avdeTEGUrQqu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b9246de7-8705-460d-cd2d-69334261476d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "2225it [00:02, 923.27it/s] \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Dataset for the predictive modeling"
      ],
      "metadata": {
        "id": "UkoiPmMea_x9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "document_wise_sent_count = [len(document) for document in document_wise_sentences]\n",
        "words_per_sentences = []\n",
        "for document in document_wise_sentences:\n",
        "  for sentence in document:\n",
        "    words_per_sentences.append(len(word_tokenize(sentence)))"
      ],
      "metadata": {
        "id": "cw4P8xeCt6Ht"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('number of sentences in the whole dataset:', len(words_per_sentences))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S_OKTrC5wicl",
        "outputId": "a873f9d3-cf29-4b91-9ee7-37ac2699c975"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "number of sentences in the whole dataset: 43538\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.hist(document_wise_sent_count)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "ykfNaNtKy4jL",
        "outputId": "e2843558-576a-4173-f009-9e7a581db512"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(words_per_sentences)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "jlYsnExSy-er",
        "outputId": "50668949-ac9e-4dbc-bcc3-744b6704c5b1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "max_sents = 25\n",
        "max_tokens = 20"
      ],
      "metadata": {
        "id": "MLR6vjz10mO6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Revised documents and labels\n",
        "# MAXIMUM 25 SENTENCES PER DOCUMENT IS ALLOWED\n",
        "document_wise_sentences = [doc[:min(len(doc), max_sents)] for doc in document_wise_sentences]\n",
        "document_wise_sent_count = [len(document) for document in document_wise_sentences]"
      ],
      "metadata": {
        "id": "vy9i3XXvoJmW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for d in range(len(document_wise_sent_count)):\n",
        "  if document_wise_sent_count[d] is None:\n",
        "    print(d)"
      ],
      "metadata": {
        "id": "Ug3Quq4nIWCU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "labels = [l[:c] for l, c in zip(labels, document_wise_sent_count)]"
      ],
      "metadata": {
        "id": "NvMHMj3GpOHb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "# device = torch.device('cpu')\n",
        "device"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P-8_K-yozZGU",
        "outputId": "5f5ca1c2-9d5c-4386-af02-7a7121d02e61"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "device(type='cuda')"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "first_labels = torch.zeros((len(labels), max_sents))\n",
        "# second_labels = torch.zeros((len(labels), max_sents))\n",
        "\n",
        "for i, t in enumerate(labels):\n",
        "  l = min(len(t), max_sents)\n",
        "  for j, a_label in enumerate(torch.Tensor(t[:l])):\n",
        "    first_labels[i][j] = a_label #[:max_sents] # LABELS FOR 25 SENTENCES\n",
        "    # second_labels[i][j] = a_label"
      ],
      "metadata": {
        "id": "p8j93WqczpM0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "first_labels[:2]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Cs5itMAe1b3z",
        "outputId": "4c764a6f-e0e8-403a-f402-eccbf4e3018e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0., 1., 0., 1., 1., 0., 0., 1., 1., 0., 0., 1., 0., 1., 0., 1., 0., 0.,\n",
              "         0., 0., 0., 0., 0., 0., 0.],\n",
              "        [0., 1., 0., 0., 1., 0., 1., 1., 0., 1., 0., 0., 0., 0., 1., 0., 0., 0.,\n",
              "         0., 0., 0., 0., 0., 0., 0.]])"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def clean(text, title=True):\n",
        "    # new line characters\n",
        "    text = re.sub('\\n ','',str(text))\n",
        "    text = re.sub('\\n',' ',text)\n",
        "\n",
        "    # apostrophes\n",
        "    text = re.sub(\"'s\", '', text)\n",
        "\n",
        "    # multiple white-space\n",
        "    text = re.sub('  +', ' ', text)\n",
        "\n",
        "    # references to outside text\n",
        "    text = re.sub(\"[\\(\\[].*?[\\)\\]]\", \"\", text)\n",
        "\n",
        "    # html/xml tags\n",
        "    text = re.sub('<[^<]+>', \"\",text)\n",
        "\n",
        "    # url\n",
        "    text = re.sub(r'^https?:\\/\\/.*[\\r\\n]*', '', text, flags=re.MULTILINE)\n",
        "\n",
        "    # Only alpha-numeric allowed\n",
        "    text = re.sub('[^0-9a-zA-Z]+', ' ', text)\n",
        "    return text\n",
        "######### Tokenize #########\n",
        "def tokenize(text):\n",
        "    text = clean(text)\n",
        "    text = \" \".join([tok.lemma_.strip() for tok in nlp(text)]) ## LEMMATIZED\n",
        "    return [tok.text for tok in nlp.tokenizer(text)]\n",
        "######### TOKEN TO INDEX DICTIONARY #########\n",
        "def token_to_index(sentences):\n",
        "    tok2indx = {} # {'<sos>':1, '<eos>':2}\n",
        "    for doc in tqdm(sentences):\n",
        "#         tokenized = tokenize(''.join(doc))\n",
        "        tokenized = tokenize(clean(doc))\n",
        "        for tok in tokenized:\n",
        "            if tok not in tok2indx.keys():\n",
        "                tok2indx[tok] = len(tok2indx)+1\n",
        "    tok2indx['unk'] = 0\n",
        "    return tok2indx\n",
        "\n",
        "dataset_sentences = []\n",
        "for s in document_wise_sentences:\n",
        "  dataset_sentences.extend(s)\n",
        "tok2indx = token_to_index(dataset_sentences)\n",
        "indx2tok = {v: k for k, v in tok2indx.items()}"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zErguJJfCews",
        "outputId": "f7f60042-b551-4c6a-8f6c-2455d43f8800"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 39077/39077 [05:11<00:00, 125.60it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def build_vocab_and_encoding(doc_list, tok2indx):\n",
        "    encoded_documents = []\n",
        "    for sentences in tqdm(doc_list):\n",
        "        enc_doc = []\n",
        "#         sentences = sent_tokenize(doc)\n",
        "#         print(len(sentences))##\n",
        "        for s in range(max_sents):\n",
        "            encoded_sent = torch.zeros(max_tokens, dtype=torch.int32)\n",
        "            ## EACH SENTENCE IS REPRESENTED WITH 15 TOKEN IDs\n",
        "            if s<len(sentences):\n",
        "                sent = sentences[s]\n",
        "#                 print(sent)##\n",
        "                tokenized = tokenize(sent)\n",
        "#                 print(len(tokenized))##\n",
        "                length = min(max_tokens, len(tokenized))\n",
        "#                 encoded_sent[0] = tok2indx['<sos>']\n",
        "                for i in range(length):                              #(1, length+1):\n",
        "                    encoded_sent[i] = tok2indx[tokenized[i]]\n",
        "            enc_doc.append(encoded_sent)\n",
        "#         print(len(enc_doc))\n",
        "        encoded_documents.append(torch.cat(enc_doc))\n",
        "#         print(encoded_documents[-1].shape)\n",
        "\n",
        "    return torch.stack(encoded_documents)\n",
        "encoded_documents = build_vocab_and_encoding(document_wise_sentences, tok2indx)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zNquJFnIUlWb",
        "outputId": "1b39b8f2-1460-41dc-a892-60433b0b54d6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 2225/2225 [05:05<00:00,  7.28it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "encoded_documents[0][:max_tokens*document_wise_sent_count[0]]\n",
        "# torch.zeros(max_tokens, dtype=torch.int32)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p2UC_weRinXa",
        "outputId": "654f471f-5bfe-4003-ba63-67a8ef48c7a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              "         34,  55, 221,  37,  66, 222, 185,  61, 225,  67,  21, 226,  34,  69],\n",
              "       dtype=torch.int32)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# The Bipertite graph based encoder-decoder model"
      ],
      "metadata": {
        "id": "QaP3zNBybOQV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Pytorch geometric for graph\n",
        "!pip install --quiet torch_geometric\n",
        "import torch\n",
        "from torch_geometric.data import Data\n",
        "from torch_geometric.loader import DataLoader\n",
        "import torch.nn.functional as F\n",
        "from torch_geometric.nn import GCNConv"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T5VC4clYaZI7",
        "outputId": "4e8b7f56-2cdb-4697-eb21-c67b9a0f6daf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/661.6 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m368.6/661.6 kB\u001b[0m \u001b[31m11.0 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m661.6/661.6 kB\u001b[0m \u001b[31m13.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for torch_geometric (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def build_graph(encoded_documents=encoded_documents,\n",
        "                document_wise_sent_count=document_wise_sent_count,\n",
        "                max_tokens=max_tokens):\n",
        "    graphs = []\n",
        "    for i, doc in tqdm(enumerate(encoded_documents)):\n",
        "        src, dst = [], []\n",
        "#         edg = []\n",
        "        sent_tokens = torch.reshape(doc[:max_tokens*document_wise_sent_count[i]],\\\n",
        "                                 (document_wise_sent_count[i], max_tokens))\n",
        "\n",
        "\n",
        "        word_tokens = []\n",
        "        for token in doc[:max_tokens*document_wise_sent_count[i]]:\n",
        "          if token not in word_tokens:\n",
        "            word_tokens.append(int(token))\n",
        "\n",
        "        token_id = {} # will have {key:token_no_given_by_tokenization, value:serial_of_the_token_in_that_document}\n",
        "        for id, token in enumerate(word_tokens):\n",
        "          token_id[token] = id+1\n",
        "        id+=1\n",
        "        sent_ids = [id+k for k in range(document_wise_sent_count[i])]\n",
        "\n",
        "        for s_id, sent in zip(sent_ids, sent_tokens):\n",
        "            for j, token in enumerate(sent):\n",
        "                src.append(token_id[int(token)]) #word)\n",
        "                dst.append(s_id)\n",
        "        # n tokens in token_id and m sentences in sent_ids store integers 1 to n and n+1 to m representing tokens and sentences\n",
        "\n",
        "        edge_index = torch.tensor([src, dst], dtype=torch.long)\n",
        "        x = torch.tensor(encoded_documents[i][:max_tokens*document_wise_sent_count[i]], dtype=torch.float)\n",
        "        # x = torch.tensor(word_tokens, dtype=torch.float)\n",
        "        graphs.append(Data(x=x, edge_index=edge_index, y=[first_labels[i]],#, second_labels[i]],\\\n",
        "#                            edge_attr=edg,\n",
        "                           sent_count=document_wise_sent_count[i]))\n",
        "    return graphs\n",
        "graphs = build_graph()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q0SE2D1vcEjB",
        "outputId": "34c8f4a2-323d-4185-e76c-62fa1fce5775"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "0it [00:00, ?it/s]<ipython-input-19-6551e2f74a6b>:30: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  x = torch.tensor(encoded_documents[i][:max_tokens*document_wise_sent_count[i]], dtype=torch.float)\n",
            "2225it [05:13,  7.11it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for g in range(len(graphs)):\n",
        "  if graphs[g].sent_count is None:\n",
        "    print(g)"
      ],
      "metadata": {
        "id": "ViOVM_lwJtP-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "# graphs1 = graphs\n",
        "random.shuffle(graphs)"
      ],
      "metadata": {
        "id": "b-uCf4HzcW13"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_length = int(len(document_wise_sent_count)*0.8)\n",
        "val_length = int(len(document_wise_sent_count)*0.1)\n",
        "\n",
        "train_data, validate_data, test_data = graphs[:train_length],\\\n",
        "graphs[train_length:train_length+val_length], graphs[train_length+val_length:]"
      ],
      "metadata": {
        "id": "QYZcv5XScgVw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch.nn.functional as F\n",
        "!pip install -q pytorch-lightning\n",
        "import pytorch_lightning as pl\n",
        "from pytorch_lightning.callbacks import ModelCheckpoint\n",
        "from pytorch_lightning.loggers import TensorBoardLogger"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WKsPExqOcqeq",
        "outputId": "d3df34f4-5fde-4286-c768-ba3b87962aa5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m727.7/727.7 kB\u001b[0m \u001b[31m12.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m805.2/805.2 kB\u001b[0m \u001b[31m53.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "BATCH_SIZE = 1\n",
        "input_dim = len(tok2indx)   # size of the vocabulary 45085\n",
        "emb_dim = 128               # Embedding of each word\n",
        "hid_dim = 128               # The dimention of each hiddenstate comming out from a time step\n",
        "n_layers = 5                # LSTM layers\n",
        "dropout = 0.5\n",
        "num_node_features = emb_dim*2"
      ],
      "metadata": {
        "id": "ouAl8Qmac6AL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class Encoder(nn.Module):\n",
        "    def __init__(self):\n",
        "        super().__init__()\n",
        "\n",
        "        self.hid_dim = hid_dim\n",
        "        self.n_layers = n_layers\n",
        "\n",
        "        self.embedding = nn.Embedding(input_dim, emb_dim, device=device)\n",
        "        self.rnn = nn.LSTM(emb_dim, self.hid_dim, self.n_layers, bidirectional=True, dropout = dropout,\\\n",
        "                           batch_first=True, device=device)\n",
        "        self.dropout = nn.Dropout(dropout)\n",
        "\n",
        "    def forward(self, X):\n",
        "\n",
        "        embedded = self.embedding(X).to(device)\n",
        "        embedded = self.dropout(embedded)\n",
        "\n",
        "        outputs, _ = self.rnn(embedded)\n",
        "        outputs = torch.relu(outputs)\n",
        "\n",
        "        return outputs"
      ],
      "metadata": {
        "id": "pUiAMRp1dN9j"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# enc = Encoder()\n",
        "# output = enc(torch.unsqueeze(train_data[0].x, 0).to(torch.int64).to(device))\n",
        "print(output.shape)\n",
        "# torch.reshape(x, (sc, max_tokens, emb_dim*2))\n",
        "print(torch.reshape(output, (train_data[0].sent_count, max_tokens, emb_dim*2)).shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y5RY6q6gD8Em",
        "outputId": "e0eec15b-784b-4e91-a4a7-24779ec43ff2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([1, 220, 256])\n",
            "torch.Size([11, 20, 256])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_dl = DataLoader(train_data, batch_size=BATCH_SIZE, shuffle=True, num_workers=8)\n",
        "val_dl = DataLoader(validate_data, batch_size=BATCH_SIZE, shuffle=True, num_workers=8)\n",
        "test_dl = DataLoader(test_data, batch_size=BATCH_SIZE, num_workers=8)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f-XOfNlZdeaT",
        "outputId": "73bb7e0f-d8f2-4ad0-f63d-d40267be53ff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:560: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def sent_class_loss_function(predictions, y, sent_count):\n",
        "    # losses = torch.zeros(len(y))\n",
        "    loss = nn.BCELoss()\n",
        "    predictions = predictions.float()\n",
        "    y = y.float()\n",
        "    l = loss(predictions[:sent_count[0]], y[:sent_count[0]])\n",
        "    return l\n",
        "\n",
        "class GCN_Model(pl.LightningModule):\n",
        "    def __init__(self, bias=True):\n",
        "        super().__init__()\n",
        "        self.encoder = Encoder()\n",
        "        self.conv1 = GCNConv(num_node_features, 128)\n",
        "        self.conv2 = GCNConv(128, 64)\n",
        "\n",
        "        # self.dropout = nn.Dropout(dropout)\n",
        "        self.loss_fun = nn.BCELoss()\n",
        "        self.fc_impact1 = nn.Linear(64, 16)\n",
        "        # self.fc_location1 = nn.Linear(64, 16)\n",
        "\n",
        "        self.fc_impact2 = nn.Linear(16, 25)\n",
        "        # self.fc_location2 = nn.Linear(16, 25)\n",
        "\n",
        "    def forward(self, X, imp_y=None, sc=None): #loc_y=None,\n",
        "#         x, edge_index, edge_attr = X\n",
        "        x, edge_index = X\n",
        "        ### Encoder ###\n",
        "        x = torch.unsqueeze(x, 0)\n",
        "        x = self.encoder(x.to(torch.int64))\n",
        "        x = F.dropout(x, p=0.5, training=self.training)\n",
        "\n",
        "        x = x.squeeze()\n",
        "#         x = torch.reshape(x, (max_sents, max_tokens, emb_dim*2))\n",
        "        x = torch.reshape(x, (sc, max_tokens, emb_dim*2)) # sc is sentence count\n",
        "#         x = x[:sc]\n",
        "        temp = torch.mean(x, 1)    # sentence wise mean\n",
        "        x = torch.cat((temp, torch.reshape(x, (x.shape[0]*max_tokens, emb_dim*2))), 0)\n",
        "                       # sent emb followed by word emb\n",
        "\n",
        "        ### ------- ###\n",
        "        x = self.conv1(x, edge_index)#, edge_weight=edge_attr)\n",
        "        x = F.elu(x)\n",
        "        x = F.dropout(x, p=0.5, training=self.training)\n",
        "\n",
        "        x = self.conv2(x, edge_index)#, edge_weight=edge_attr)\n",
        "        x = F.elu(x)\n",
        "        x = F.dropout(x, p=0.5, training=self.training)\n",
        "        x = torch.mean(x, 0)\n",
        "\n",
        "        imp_x = F.elu(self.fc_impact1(x))\n",
        "        imp_x = F.dropout(imp_x, p=0.5, training=self.training)\n",
        "        impact_pred = F.sigmoid(self.fc_impact2(imp_x))\n",
        "\n",
        "        # loc_x = F.elu(self.fc_location1(x))\n",
        "        # loc_x = F.dropout(loc_x, p=0.5, training=self.training)\n",
        "        # location_pred = F.sigmoid(self.fc_location2(loc_x))\n",
        "\n",
        "        predictions = impact_pred #, location_pred)\n",
        "\n",
        "        if imp_y is not None:\n",
        "          imp_loss = sent_class_loss_function(impact_pred, imp_y, sc)\n",
        "          # loc_loss = sent_class_loss_function(location_pred, loc_y, sc)\n",
        "\n",
        "          # loss = (imp_loss + loc_loss)/2\n",
        "          loss = imp_loss\n",
        "          return loss, predictions\n",
        "        return predictions\n",
        "\n",
        "    def training_step(self, batch, batch_idx):\n",
        "        x = (batch.x, batch.edge_index)#, batch.edge_attr)\n",
        "        imp_y = batch.y[0]\n",
        "        # loc_y = batch.y[1]\n",
        "        sc = batch.sent_count #batch.y[2]\n",
        "        loss, pred_classes = self(x, imp_y, sc) #loc_y,\n",
        "\n",
        "        self.log('train_loss', loss, prog_bar = True, logger = True)\n",
        "        return loss\n",
        "\n",
        "    def validation_step(self, batch, batch_idx):\n",
        "        # print(batch.y)\n",
        "        x = (batch.x, batch.edge_index)#, batch.edge_attr) # batch.kwargs['sent_count']\n",
        "        imp_y = batch.y[0]\n",
        "        # loc_y = batch.y[1]\n",
        "        sc = batch.sent_count #batch.y[2]\n",
        "        loss, pred_classes = self(x, imp_y, sc) #loc_y,\n",
        "\n",
        "        self.log('val_loss', loss, prog_bar = True, logger = True)\n",
        "        return loss\n",
        "\n",
        "    def test_step(self, batch, batch_idx):\n",
        "        x = (batch.x, batch.edge_index)#, batch.edge_attr)\n",
        "        imp_y = batch.y[0]\n",
        "        # loc_y = batch.y[1]\n",
        "        sc = batch.sent_count #batch.y[2]\n",
        "        loss, pred_classes = self(x, imp_y, sc) #loc_y,\n",
        "\n",
        "        self.log('val_loss', loss, prog_bar = True, logger = True)\n",
        "        return loss, pred_classes\n",
        "\n",
        "    def configure_optimizers(self):\n",
        "        return torch.optim.AdamW(self.parameters(), lr = 0.001, weight_decay = 0.001)"
      ],
      "metadata": {
        "id": "9n18pTJddfZN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = GCN_Model()\n",
        "\n",
        "checkpoint_callback = ModelCheckpoint(\n",
        "    dirpath = path+'BBC_model_instances',\n",
        "    filename = path+'best-checkpoint-BBCdata',\n",
        "    save_top_k = 1,\n",
        "    verbose = True,\n",
        "    monitor = 'val_loss',\n",
        "    mode = 'min'\n",
        ")\n",
        "\n",
        "logger = TensorBoardLogger('model_logs_word2sent', name = 'GCN-model-word2sent')\n",
        "\n",
        "trainer = pl.Trainer(logger = logger, callbacks = checkpoint_callback, max_epochs = 30, accelerator=\"auto\")#gpus = 1)#, progress_bar_refresh_rate = 20)\n",
        "\n",
        "# train_dataset = MyDataset(train_x, train_y)\n",
        "# val_dataset = MyDataset(validate_x, validate_y)\n",
        "\n",
        "trainer.fit(model, train_dl, val_dl)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
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        },
        "id": "jintOCdldfkm",
        "outputId": "d6419938-3f6a-4e14-8100-495bfd524d3e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:GPU available: True (cuda), used: True\n",
            "INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
            "INFO:pytorch_lightning.utilities.rank_zero:IPU available: False, using: 0 IPUs\n",
            "INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
            "INFO:pytorch_lightning.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "INFO:pytorch_lightning.callbacks.model_summary:\n",
            "  | Name       | Type    | Params\n",
            "---------------------------------------\n",
            "0 | encoder    | Encoder | 4.3 M \n",
            "1 | conv1      | GCNConv | 32.9 K\n",
            "2 | conv2      | GCNConv | 8.3 K \n",
            "3 | loss_fun   | BCELoss | 0     \n",
            "4 | fc_impact1 | Linear  | 1.0 K \n",
            "5 | fc_impact2 | Linear  | 425   \n",
            "---------------------------------------\n",
            "4.4 M     Trainable params\n",
            "0         Non-trainable params\n",
            "4.4 M     Total params\n",
            "17.524    Total estimated model params size (MB)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Sanity Checking: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "dd70b8fdaebf457dae18c31304bb1e88"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/utilities/data.py:76: UserWarning: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 500. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`.\n",
            "  warning_cache.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/utilities/data.py:76: UserWarning: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 340. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`.\n",
            "  warning_cache.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Training: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6e5694b6599c47ba943c9f898f2fbf02"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b1e93bc1ceb2426f81eadfc646cdac9a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/utilities/data.py:76: UserWarning: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 400. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`.\n",
            "  warning_cache.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/utilities/data.py:76: UserWarning: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 380. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`.\n",
            "  warning_cache.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/utilities/data.py:76: UserWarning: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 240. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`.\n",
            "  warning_cache.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/utilities/data.py:76: UserWarning: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 320. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`.\n",
            "  warning_cache.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/utilities/data.py:76: UserWarning: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 260. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`.\n",
            "  warning_cache.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/utilities/data.py:76: UserWarning: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 160. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`.\n",
            "  warning_cache.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/utilities/data.py:76: UserWarning: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 220. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`.\n",
            "  warning_cache.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/utilities/data.py:76: UserWarning: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 420. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`.\n",
            "  warning_cache.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/utilities/data.py:76: UserWarning: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 460. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`.\n",
            "  warning_cache.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/utilities/data.py:76: UserWarning: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 180. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`.\n",
            "  warning_cache.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/utilities/data.py:76: UserWarning: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 360. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`.\n",
            "  warning_cache.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/utilities/data.py:76: UserWarning: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 300. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`.\n",
            "  warning_cache.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/utilities/data.py:76: UserWarning: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 280. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`.\n",
            "  warning_cache.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/utilities/data.py:76: UserWarning: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 440. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`.\n",
            "  warning_cache.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/utilities/data.py:76: UserWarning: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 200. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`.\n",
            "  warning_cache.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/utilities/data.py:76: UserWarning: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 480. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`.\n",
            "  warning_cache.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/utilities/data.py:76: UserWarning: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 100. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`.\n",
            "  warning_cache.warn(\n",
            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 0, global step 1780: 'val_loss' reached 0.62785 (best 0.62785), saving model to '/content/drive/MyDrive/Research/GridSearch_GCN_on_DisasterData/BBC News Summary/best-checkpoint-BBCdata-v1.ckpt' as top 1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cde26267ca7d4fc0849927e0d902cb61"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 1, global step 3560: 'val_loss' reached 0.62716 (best 0.62716), saving model to '/content/drive/MyDrive/Research/GridSearch_GCN_on_DisasterData/BBC News Summary/best-checkpoint-BBCdata-v1.ckpt' as top 1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a12f45c3bb774b6fb6e185eb754ee7cc"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 2, global step 5340: 'val_loss' reached 0.62577 (best 0.62577), saving model to '/content/drive/MyDrive/Research/GridSearch_GCN_on_DisasterData/BBC News Summary/best-checkpoint-BBCdata-v1.ckpt' as top 1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e7982a8a6fc44e698323d0f4fb36e8d9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 3, global step 7120: 'val_loss' was not in top 1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "55c2a583854b46b2b910eb16a928990b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 4, global step 8900: 'val_loss' was not in top 1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation: 0it [00:00, ?it/s]"
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            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "bd0c4e77e06a432f8ede62febed80285"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 5, global step 10680: 'val_loss' was not in top 1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation: 0it [00:00, ?it/s]"
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            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "aecb5e62728649f490e8c835a7296196"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 6, global step 12460: 'val_loss' was not in top 1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            "application/vnd.jupyter.widget-view+json": {
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            }
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 7, global step 14240: 'val_loss' was not in top 1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            "application/vnd.jupyter.widget-view+json": {
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            }
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 8, global step 16020: 'val_loss' was not in top 1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 9, global step 17800: 'val_loss' was not in top 1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 10, global step 19580: 'val_loss' was not in top 1\n"
          ]
        },
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          "output_type": "display_data",
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        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 11, global step 21360: 'val_loss' was not in top 1\n"
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 12, global step 23140: 'val_loss' was not in top 1\n"
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 13, global step 24920: 'val_loss' was not in top 1\n"
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 14, global step 26700: 'val_loss' was not in top 1\n"
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 15, global step 28480: 'val_loss' was not in top 1\n"
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        {
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          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 16, global step 30260: 'val_loss' was not in top 1\n"
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            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 17, global step 32040: 'val_loss' was not in top 1\n"
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            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 18, global step 33820: 'val_loss' was not in top 1\n"
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          "metadata": {}
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        {
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          "name": "stderr",
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            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 19, global step 35600: 'val_loss' was not in top 1\n"
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          "name": "stderr",
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            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 20, global step 37380: 'val_loss' was not in top 1\n"
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            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 21, global step 39160: 'val_loss' was not in top 1\n"
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          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 25, global step 46280: 'val_loss' was not in top 1\n"
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          "metadata": {}
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          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 26, global step 48060: 'val_loss' was not in top 1\n"
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          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 27, global step 49840: 'val_loss' was not in top 1\n"
          ]
        },
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          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 28, global step 51620: 'val_loss' was not in top 1\n"
          ]
        },
        {
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          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 29, global step 53400: 'val_loss' was not in top 1\n"
          ]
        },
        {
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        {
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          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 30, global step 55180: 'val_loss' was not in top 1\n"
          ]
        },
        {
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          "metadata": {}
        },
        {
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          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 31, global step 56960: 'val_loss' was not in top 1\n"
          ]
        },
        {
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        {
          "output_type": "stream",
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          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 32, global step 58740: 'val_loss' was not in top 1\n"
          ]
        },
        {
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 33, global step 60520: 'val_loss' was not in top 1\n"
          ]
        },
        {
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 34, global step 62300: 'val_loss' was not in top 1\n"
          ]
        },
        {
          "output_type": "display_data",
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 35, global step 64080: 'val_loss' was not in top 1\n"
          ]
        },
        {
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          "data": {
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          "output_type": "stream",
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          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 36, global step 65860: 'val_loss' was not in top 1\n"
          ]
        },
        {
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              "Validation: 0it [00:00, ?it/s]"
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          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 37, global step 67640: 'val_loss' was not in top 1\n"
          ]
        },
        {
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              "Validation: 0it [00:00, ?it/s]"
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          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 38, global step 69420: 'val_loss' was not in top 1\n"
          ]
        },
        {
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        },
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          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 39, global step 71200: 'val_loss' was not in top 1\n"
          ]
        },
        {
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        },
        {
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          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 40, global step 72980: 'val_loss' was not in top 1\n"
          ]
        },
        {
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        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 41, global step 74760: 'val_loss' was not in top 1\n"
          ]
        },
        {
          "output_type": "display_data",
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        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 42, global step 76540: 'val_loss' was not in top 1\n"
          ]
        },
        {
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        },
        {
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          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 43, global step 78320: 'val_loss' was not in top 1\n"
          ]
        },
        {
          "output_type": "display_data",
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        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 44, global step 80100: 'val_loss' was not in top 1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 45, global step 81880: 'val_loss' was not in top 1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation: 0it [00:00, ?it/s]"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 46, global step 83660: 'val_loss' was not in top 1\n"
          ]
        },
        {
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              "Validation: 0it [00:00, ?it/s]"
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        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 47, global step 85440: 'val_loss' was not in top 1\n"
          ]
        },
        {
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              "Validation: 0it [00:00, ?it/s]"
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 48, global step 87220: 'val_loss' was not in top 1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "de9baed233614874853b407ff3061d8e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:Epoch 49, global step 89000: 'val_loss' was not in top 1\n",
            "INFO:pytorch_lightning.utilities.rank_zero:`Trainer.fit` stopped: `max_epochs=50` reached.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%load_ext tensorboard\n",
        "%tensorboard --logdir 'model_logs'"
      ],
      "metadata": {
        "id": "Bi2-7IBlrE_E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.freeze()\n",
        "trainer.test(model, test_dl)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226,
          "referenced_widgets": [
            "18c1c647c15b422b9b7443cfde657709",
            "13d26d0e527e41ce86d94a6279cd0051",
            "65d47f5a14a143999804f30bcd9f4266",
            "3eda46b31f6e4a089612f476a5bf4610",
            "8ea25fc21cf941f5b262a5de802caf78",
            "044117ec6ade419793afbf6e77cb7fbc",
            "bed693066ec74564907629e7a6b57094",
            "edf797d1e0654b048f61235a265b74f2",
            "de52b82edc1041609345ef6a52bac8d4",
            "c924ba1fc69b46cb89a7cfe09b100a0f",
            "a1fb2236ca9e476690ba3d175643e280"
          ]
        },
        "id": "-dDt1QX_1iEz",
        "outputId": "7630b817-5808-427b-e7c7-dd694c660100"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Testing: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "18c1c647c15b422b9b7443cfde657709"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/utilities/data.py:76: UserWarning: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 140. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`.\n",
            "  warning_cache.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1m       Test metric       \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      DataLoader 0       \u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
              "│\u001b[36m \u001b[0m\u001b[36m        val_loss         \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   1.0196049213409424    \u001b[0m\u001b[35m \u001b[0m│\n",
              "└───────────────────────────┴───────────────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\">        Test metric        </span>┃<span style=\"font-weight: bold\">       DataLoader 0        </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
              "│<span style=\"color: #008080; text-decoration-color: #008080\">         val_loss          </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    1.0196049213409424     </span>│\n",
              "└───────────────────────────┴───────────────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'val_loss': 1.0196049213409424}]"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "device = torch.device('cpu')\n",
        "true_imp_y, pred_imp_y, sent_counts = [], [], []#, [] true_loc_y, pred_loc_y,, []\n",
        "with torch.no_grad():\n",
        "    for batch in tqdm(test_dl):#dataloader:\n",
        "        # x = (batch.x.to(device), batch.edge_index.to(device)#, batch.edge_attr.to(device))\n",
        "        x = (batch.x, batch.edge_index)\n",
        "        imp_y = batch.y[0].to(device)\n",
        "        # loc_y = batch.y[1].to(device)\n",
        "        sc = batch.sent_count #batch.y[2]\n",
        "\n",
        "        y_hat = model(X=x, sc=sc)\n",
        "\n",
        "        true_imp_y.append(imp_y)\n",
        "        # true_loc_y.append(loc_y)\n",
        "        pred_imp_y.append(y_hat)\n",
        "        # pred_loc_y.append(y_hat[1])\n",
        "        # docs.append(X[0])\n",
        "        sent_counts.append(sc)"
      ],
      "metadata": {
        "id": "oW_4cBc21neD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f18a2b93-83f6-4527-dd46-8b5d01132f66"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r  0%|          | 0/223 [00:00<?, ?it/s]/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:560: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n",
            "100%|██████████| 223/223 [00:09<00:00, 24.72it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def rounding(pred, threshold = 0.5):\n",
        "  prd = torch.zeros(pred.shape)\n",
        "  for j, a in enumerate(pred):\n",
        "    if a>threshold:\n",
        "      prd[j] = 1\n",
        "    else:\n",
        "      prd[j] = 0\n",
        "  return prd"
      ],
      "metadata": {
        "id": "2waoBSaKXj8M"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rounding(torch.Tensor(pred_imp_y[0]))\n",
        "# len(pred_imp_y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0F4RlADDZNtl",
        "outputId": "08afd177-206a-4787-da76-754ee622df85"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([0., 0., 0., 1., 1., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.,\n",
              "        0., 0., 0., 0., 0., 0., 0.])"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def dice_coefficient(p, y):\n",
        "    smoothing = 0.0001\n",
        "    return ((2*torch.sum(torch.abs(p * y)))+smoothing)/(torch.sum(torch.abs(p))+torch.sum(torch.abs(y))+smoothing)"
      ],
      "metadata": {
        "id": "6eYc2FnIYg-h"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "imp_magnitude = torch.zeros(len(true_imp_y)-1)\n",
        "count = 0\n",
        "for i in range(len(true_imp_y)-1):\n",
        "    imp_magnitude[i] = dice_coefficient(pred_imp_y[i][:sent_counts[i]], true_imp_y[i][:sent_counts[i]])\n",
        "\n",
        "print('Dice score: ', torch.mean(imp_magnitude))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JYTuePfEYomY",
        "outputId": "627fa745-7936-4881-9c04-d36f72888395"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor(0.4533)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class_correct_predictions = np.zeros(len(true_imp_y))\n",
        "for i in range(len(true_imp_y)):\n",
        "\n",
        "    k = true_imp_y[i][:sent_counts[i]] + rounding(pred_imp_y[i], 0.7)[:sent_counts[i]]\n",
        "    class_correct_predictions[i] = sum([1 if k[j] in [0, 2] else 0 for j in range(len(k))])/sent_counts[i]\n",
        "class_correct_predictions = np.array(class_correct_predictions)\n",
        "print('Accuracy: ', np.mean(class_correct_predictions))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JuNfkWbLY09z",
        "outputId": "3cebdf33-9e7a-46a1-caab-44aeebb5cde6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6425812303218071"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import f1_score\n",
        "# loc = np.zeros((len(pred_loc_y), len(pred_loc_y[0])))\n",
        "imp = np.zeros((len(pred_imp_y), len(pred_imp_y[0])))\n",
        "for i in range(len(pred_imp_y)):\n",
        "  # loc[i] = np.array(rounding(pred_loc_y[i], 0.45))\n",
        "  imp[i] = np.array(rounding(pred_imp_y[i], 0.45))\n",
        "\n",
        "# loc_y_np = np.zeros((len(true_loc_y), len(true_loc_y[0])))\n",
        "imp_y_np = np.zeros((len(true_imp_y), len(true_imp_y[0])))\n",
        "for i in range(len(pred_imp_y)):\n",
        "  # loc_y_np[i] = np.array(true_loc_y[i])\n",
        "  imp_y_np[i] = np.array(true_imp_y[i])\n",
        "\n",
        "# print('Location:', f1_score(loc_y_np, np.round(loc), average='samples'))\n",
        "print('F-measure score: ', f1_score(imp_y_np, np.round(imp), average='samples'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CkgBGTVhbWYW",
        "outputId": "cf30c827-83f1-4340-a9f8-4c687a3b9b31"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Impact: 0.33766753334253924\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "NLKCavMccPF5"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}