{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "nuclear-dream",
      "metadata": {
        "id": "nuclear-dream"
      },
      "source": [
        "# Eyettention"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Configuration**"
      ],
      "metadata": {
        "id": "g-FLEOyvYJUG"
      },
      "id": "g-FLEOyvYJUG"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "neutral-montreal",
      "metadata": {
        "id": "neutral-montreal"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "#configuration\n",
        "#English\n",
        "cf = {\"model_pretrained\": \"bert-base-cased\",\n",
        "      \"atten_type\": 'local-g',\n",
        "      #batch computation for training (CELER dataset)\n",
        "      \"max_sn_len\": 24,\n",
        "      \"max_sn_token\": 35,\n",
        "      \"max_sp_len\": 52\n",
        "     }\n",
        "\n",
        "#Chinese\n",
        "#cf = {\"model_pretrained\": \"bert-base-chinese\",\n",
        "      #\"atten_type\": 'local-g',\n",
        "      #batch computation for training (BSC dataset)\n",
        "      #\"max_sn_len\": 27,\n",
        "      #\"max_sp_len\": 40\n",
        "     #"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install --upgrade transformers==4.27.0"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uhLxInukW82r",
        "outputId": "76c33a63-4d13-4496-976d-126fe4ebb1b0"
      },
      "id": "uhLxInukW82r",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: transformers==4.27.0 in /usr/local/lib/python3.10/dist-packages (4.27.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers==4.27.0) (3.12.2)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.11.0 in /usr/local/lib/python3.10/dist-packages (from transformers==4.27.0) (0.16.4)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers==4.27.0) (1.23.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers==4.27.0) (23.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers==4.27.0) (6.0.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers==4.27.0) (2023.6.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers==4.27.0) (2.31.0)\n",
            "Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /usr/local/lib/python3.10/dist-packages (from transformers==4.27.0) (0.13.3)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers==4.27.0) (4.66.1)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.11.0->transformers==4.27.0) (2023.6.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.11.0->transformers==4.27.0) (4.7.1)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers==4.27.0) (3.2.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers==4.27.0) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers==4.27.0) (2.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers==4.27.0) (2023.7.22)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# You can also just download the file model.py, which is only used in this tutorial\n",
        "!git clone https://github.com/aeye-lab/Eyettention"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8-8qDf4EXljd",
        "outputId": "97cffda3-ffde-4231-c80b-0bfbc9f98a68"
      },
      "id": "8-8qDf4EXljd",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "fatal: destination path 'Eyettention' already exists and is not an empty directory.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Load the model and download pre-trained weights**"
      ],
      "metadata": {
        "id": "jbqmeRpRY5Sg"
      },
      "id": "jbqmeRpRY5Sg"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "stylish-member",
      "metadata": {
        "id": "stylish-member",
        "outputId": "2307c733-ee9e-4ffb-92ef-f9d973f53fd6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "keeping Bert with pre-trained weights\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at bert-base-cased were not used when initializing BertModel: ['cls.predictions.transform.LayerNorm.bias', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.decoder.weight']\n",
            "- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        }
      ],
      "source": [
        "from Eyettention import model\n",
        "import torch\n",
        "from torch.utils import model_zoo\n",
        "\n",
        "#DEVICE = 'cuda'\n",
        "DEVICE = 'cpu'\n",
        "\n",
        "pretrained=True\n",
        "\n",
        "#load model\n",
        "dnn = model.Eyettention(cf)\n",
        "\n",
        "dnn.eval()",
        "\n",
        "if pretrained:\n",
        "    dnn.load_state_dict(model_zoo.load_url('https://github.com/aeye-lab/Eyettention/releases/download/v1.0/Eyettention_english.pth', map_location=torch.device('cpu')))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Tokenization**\n",
        "<br>\n",
        "**Introduction**: https://huggingface.co/learn/nlp-course/chapter2/4?fw=pt"
      ],
      "metadata": {
        "id": "f0DvCfMrZMYg"
      },
      "id": "f0DvCfMrZMYg"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "severe-findings",
      "metadata": {
        "id": "severe-findings"
      },
      "outputs": [],
      "source": [
        "from transformers import BertTokenizerFast\n",
        "#initialize tokenizer\n",
        "tokenizer = BertTokenizerFast.from_pretrained(cf['model_pretrained'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "funded-validity",
      "metadata": {
        "id": "funded-validity",
        "outputId": "fbc6c028-3049-42ab-8187-6034e787c59d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['[CLS]', 'He', 'said', \"BankEast's\", 'offer', 'appears', 'to', 'be', '\"attractive', 'to', 'the', \"bank's\", 'shareholders.\"', '[SEP]']\n"
          ]
        }
      ],
      "source": [
        "sentence = 'He said BankEast\\'s offer appears to be \"attractive to the bank\\'s shareholders.\"'\n",
        "#add special tokens\n",
        "text = ('[CLS]' + ' ' + sentence + ' ' + '[SEP]').split()\n",
        "print(text)\n",
        "tokens = tokenizer(text, add_special_tokens=False, max_length=cf['max_sn_token'], padding='max_length', is_split_into_words=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "vulnerable-figure",
      "metadata": {
        "id": "vulnerable-figure",
        "outputId": "9dcba3a3-b8b8-4d77-bed9-3b6059f9cea8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['[CLS]', 'He', 'said', 'Bank', '##E', '##ast', \"'\", 's', 'offer', 'appears', 'to', 'be', '\"', 'attractive', 'to', 'the', 'bank', \"'\", 's', 'shareholders', '.', '\"', '[SEP]']\n"
          ]
        }
      ],
      "source": [
        "print(tokenizer.tokenize(text, add_special_tokens=False, is_split_into_words=True))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "inappropriate-marketplace",
      "metadata": {
        "id": "inappropriate-marketplace",
        "outputId": "6ca3b171-cd18-4a82-8770-871fd6fad840",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'input_ids': [101, 1124, 1163, 2950, 2036, 12788, 112, 188, 2906, 2691, 1106, 1129, 107, 8394, 1106, 1103, 3085, 112, 188, 16741, 119, 107, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}\n"
          ]
        }
      ],
      "source": [
        "print(tokens)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "intelligent-express",
      "metadata": {
        "id": "intelligent-express",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3a185313-5e28-450b-cd8c-4b885bac07f0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0, 1, 2, 3, 3, 3, 3, 3, 4, 5, 6, 7, 8, 8, 9, 10, 11, 11, 11, 12, 12, 12, 13, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]\n"
          ]
        }
      ],
      "source": [
        "#use offset mapping to assign a word index to each token.\n",
        "#index start from 0, CLS -> 0 and SEP -> last index\n",
        "word_ids = tokens.word_ids()\n",
        "word_ids = [val if val is not None else np.nan for val in word_ids]\n",
        "print(word_ids)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Prepare features**"
      ],
      "metadata": {
        "id": "Zz6pcnFualMb"
      },
      "id": "Zz6pcnFualMb"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "specified-stomach",
      "metadata": {
        "id": "specified-stomach"
      },
      "outputs": [],
      "source": [
        "def compute_word_length(txt):\n",
        "    txt_word_len = [len(t) for t in txt[1:-1]]\n",
        "    #pad nan for CLS and SEP tokens\n",
        "    txt_word_len = [np.nan] + txt_word_len + [np.nan]\n",
        "    #length of a punctuation is 0, plus an epsilon to avoid division output inf\n",
        "    arr = np.array(txt_word_len).astype('float64')\n",
        "    arr[arr==0] = 1/(0+0.5)\n",
        "    arr[arr!=0] = 1/(arr[arr!=0])\n",
        "    return arr\n",
        "\n",
        "def pad_seq(seqs, max_len, dtype=np.int32, fill_value=np.nan):\n",
        "    padded = np.full((len(seqs), max_len), fill_value=fill_value, dtype=dtype)\n",
        "    for i, seq in enumerate(seqs):\n",
        "        padded[i, :len(seq)] = seq\n",
        "    return padded"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "proprietary-efficiency",
      "metadata": {
        "id": "proprietary-efficiency",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a2b21281-ff87-4757-d3ff-88b9702436de"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[       nan 0.5        0.25       0.1        0.2        0.14285715\n",
            "  0.5        0.5        0.09090909 0.5        0.33333334 0.16666667\n",
            "  0.07142857        nan        nan        nan        nan        nan\n",
            "         nan        nan        nan        nan        nan        nan]]\n"
          ]
        }
      ],
      "source": [
        "#compute the word length feature\n",
        "text_word_len = [compute_word_length(txt) for txt in [text]]\n",
        "text_word_len = pad_seq(text_word_len, cf['max_sn_len'], fill_value=np.nan, dtype=np.float32)\n",
        "print(text_word_len)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "severe-industry",
      "metadata": {
        "id": "severe-industry",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "93474602-f510-4841-c612-dca9de230a9b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-08-16 12:55:49--  https://github.com/aeye-lab/Eyettention/releases/download/v1.0/feature_norm_celer.pickle\n",
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            "Resolving objects.githubusercontent.com (objects.githubusercontent.com)... 185.199.108.133, 185.199.111.133, 185.199.109.133, ...\n",
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            "Length: 364 [application/octet-stream]\n",
            "Saving to: ‘feature_norm_celer.pickle’\n",
            "\n",
            "\r          feature_n   0%[                    ]       0  --.-KB/s               \rfeature_norm_celer. 100%[===================>]     364  --.-KB/s    in 0s      \n",
            "\n",
            "2023-08-16 12:55:49 (26.5 MB/s) - ‘feature_norm_celer.pickle’ saved [364/364]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "#Download precomputed sn_word_len mean and std (from CELER dataset) for feature normalization\n",
        "import pickle\n",
        "!wget https://github.com/aeye-lab/Eyettention/releases/download/v1.0/feature_norm_celer.pickle\n",
        "path = 'feature_norm_celer.pickle'\n",
        "file_to_read = open(path, \"rb\")\n",
        "loaded_dictionary = pickle.load(file_to_read)\n",
        "sn_word_len_mean = loaded_dictionary['sn_word_len_mean']\n",
        "sn_word_len_std = loaded_dictionary['sn_word_len_std']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "dried-biodiversity",
      "metadata": {
        "id": "dried-biodiversity"
      },
      "outputs": [],
      "source": [
        "#normalize word length feature\n",
        "text_word_len = (text_word_len - sn_word_len_mean)/sn_word_len_std\n",
        "text_word_len = np.nan_to_num(text_word_len)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "according-decimal",
      "metadata": {
        "id": "according-decimal",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e88d7af1-1d6a-4d65-86cc-f6bb50dd3c2a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-14-b209a673cadd>:4: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:245.)\n",
            "  word_len = torch.tensor([text_word_len.squeeze()]).to(DEVICE)\n"
          ]
        }
      ],
      "source": [
        "tokens['input_ids'] = torch.tensor([tokens['input_ids']]).to(DEVICE)\n",
        "tokens['attention_mask'] = torch.tensor([tokens['attention_mask']]).to(DEVICE)\n",
        "word_ids = torch.tensor([word_ids]).to(DEVICE)\n",
        "word_len = torch.tensor([text_word_len.squeeze()]).to(DEVICE)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Generate synthetic scanpath**"
      ],
      "metadata": {
        "id": "Nrm-Rq4Ya2TX"
      },
      "id": "Nrm-Rq4Ya2TX"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "novel-monaco",
      "metadata": {
        "id": "novel-monaco",
        "outputId": "8169a529-a161-4e48-d324-0c4399d8ff70",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LabelEncoder()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LabelEncoder()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LabelEncoder</label><div class=\"sk-toggleable__content\"><pre>LabelEncoder()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "#prepare the saccade range classes for model predictions, cf[\"max_sn_len\"]-1 -> stop class\n",
        "le = LabelEncoder()\n",
        "le.fit(np.append(np.arange(-cf[\"max_sn_len\"]+3, cf[\"max_sn_len\"]-1), cf[\"max_sn_len\"]-1))\n",
        "#le.classes_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "mathematical-dimension",
      "metadata": {
        "id": "mathematical-dimension"
      },
      "outputs": [],
      "source": [
        "syn_scanpath, density_pred = dnn.scanpath_generation(sn_emd=tokens['input_ids'],\n",
        "                                                     sn_mask=tokens['attention_mask'],\n",
        "                                                     word_ids_sn=word_ids,\n",
        "                                                     sn_word_len = word_len,\n",
        "                                                     le=le,\n",
        "                                                     max_pred_len=50)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "bacterial-cardiff",
      "metadata": {
        "id": "bacterial-cardiff"
      },
      "outputs": [],
      "source": [
        "def post_process_scanpath(syn_scanpath, sn_len):\n",
        "    syn_scanpath = syn_scanpath.detach().to('cpu').numpy()\n",
        "    max_sp_len = syn_scanpath.shape[1]\n",
        "\n",
        "    #Find the number \"sn_len+1\" -> the stop class\n",
        "    stop_indx = []\n",
        "    for i in range(syn_scanpath.shape[0]):\n",
        "        stop = np.where(syn_scanpath[i,:]==(sn_len[i]+1))[0]\n",
        "        if len(stop)==0:#no stop class can be find -> exceeds the maximum length of the generated scanpath\n",
        "            stop_indx.append(max_sp_len-1)\n",
        "        else:\n",
        "            stop_indx.append(stop[0])\n",
        "\n",
        "    #Truncating data after the stop class\n",
        "    syn_scanpath_cut = [syn_scanpath[i][1:stop_indx[i]] for i in range(syn_scanpath.shape[0])]\n",
        "    return syn_scanpath_cut"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "southern-cycling",
      "metadata": {
        "id": "southern-cycling",
        "outputId": "32c519fa-5251-4869-fc78-242bae3cc5f3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ 1  2  2  3  5  3  5  7  9  8  9 10 11 12]\n"
          ]
        }
      ],
      "source": [
        "sn_len = (torch.max(torch.nan_to_num(word_ids), dim=1)[0]+1-2).detach().to('cpu').numpy()\n",
        "syn_scanpath = post_process_scanpath(syn_scanpath, sn_len)\n",
        "print(syn_scanpath[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "imposed-passing",
      "metadata": {
        "id": "imposed-passing",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2400559b-3d51-461b-8df2-19f1ccc621da"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['He', 'said', 'said', \"BankEast's\", 'appears', \"BankEast's\", 'appears', 'be', 'to', '\"attractive', 'to', 'the', \"bank's\", 'shareholders.\"']\n"
          ]
        }
      ],
      "source": [
        "fixated_word = [text[idx] for idx in syn_scanpath[0]]\n",
        "print(fixated_word)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Prediction visualization**"
      ],
      "metadata": {
        "id": "Q9ojBr5rba-R"
      },
      "id": "Q9ojBr5rba-R"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "dental-knock",
      "metadata": {
        "id": "dental-knock"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "def visualize_distribution(syn_scanpath, density_pred, timestep, text):\n",
        "    #visualize density map\n",
        "    plt.figure(figsize = (25,5))\n",
        "    sac_range = np.append(np.arange(-cf[\"max_sn_len\"]+3, cf[\"max_sn_len\"]-1), cf[\"max_sn_len\"]-1)\n",
        "\n",
        "    #current gaze position\n",
        "    cur_loc = syn_scanpath[timestep]\n",
        "    #convert the saccade range classes to the target word indexes\n",
        "    target_loc = sac_range + cur_loc\n",
        "\n",
        "    prob_valid = density_pred[np.where((0 <= target_loc) & (target_loc <= (len(text)-2)))]#exclude SEP token\n",
        "    prob_valid = np.append(prob_valid, density_pred[-1])\n",
        "\n",
        "    plt.subplot(2, 1, 2)\n",
        "    sns.heatmap(prob_valid[np.newaxis, :], annot=np.array(text)[np.newaxis,:], fmt=\"\", linewidths=2, cmap=\"YlGnBu\", xticklabels=False, yticklabels=False, cbar=True, annot_kws={\"fontsize\":15}, cbar_kws = dict(use_gridspec=False,location=\"top\"))\n",
        "    y = 3\n",
        "    for past_ts in range(timestep):\n",
        "        dy = 0\n",
        "        dx = syn_scanpath[past_ts+1] - syn_scanpath[past_ts]\n",
        "        #if refixation, display on next line\n",
        "        if dx <= 0:\n",
        "            dy = -0.5\n",
        "            plt.arrow(syn_scanpath[past_ts]+0.5, y, dx, dy, color='b', ec ='blue', head_width=0.2)\n",
        "            y = y + dy\n",
        "        else:\n",
        "            plt.arrow(syn_scanpath[past_ts]+0.5, y, dx, dy, color='b', ec ='blue', head_width=0.2)\n",
        "\n",
        "    plt.arrow(syn_scanpath[timestep]+0.5, y, (syn_scanpath[timestep+1]-syn_scanpath[timestep]), 0, color='r', ec ='red', head_width=0.2)\n",
        "    plt.xlim([0, len(text)])\n",
        "    plt.ylim([0, 3.5])\n",
        "    ax = plt.gca()\n",
        "    ax.axes.get_xaxis().set_visible(False)\n",
        "    ax.axes.get_yaxis().set_visible(False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "cross-commercial",
      "metadata": {
        "id": "cross-commercial",
        "outputId": "66a6e06d-7473-412f-9dba-10b9db130b9a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 130
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2500x500 with 2 Axes>"
            ],
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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sample_index=0\n",
        "timestep = 5\n",
        "visualize_distribution(syn_scanpath=syn_scanpath[sample_index],\n",
        "                       density_pred=density_pred[timestep][sample_index].detach().numpy(),\n",
        "                       timestep=timestep,\n",
        "                       text=text)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "orange-persian",
      "metadata": {
        "id": "orange-persian",
        "outputId": "df44ad8a-8a17-4f20-9c1d-3dea8be063a7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 130
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2500x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sample_index=0\n",
        "timestep = 8\n",
        "visualize_distribution(syn_scanpath=syn_scanpath[sample_index],\n",
        "                       density_pred=density_pred[timestep][sample_index].detach().numpy(),\n",
        "                       timestep=timestep,\n",
        "                       text=text)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Assignments**\n",
        "<br>\n",
        "Generate synthetic scanpaths for the following sentences:\n",
        "<br>\n",
        "1. Dole took many odd-jobs around Russell as a boy, and would later work as a soda jerk in the local drug store.\n",
        "<br>\n",
        "2. Dole studied Law at KU, but his studies were interrupted by the start of World War II.\n",
        "<br>\n",
        "3. He is of three quarters Irish and one quarter French descent."
      ],
      "metadata": {
        "id": "LQew7DVKcQ_t"
      },
      "id": "LQew7DVKcQ_t"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "preceding-phenomenon",
      "metadata": {
        "id": "preceding-phenomenon"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.9"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}