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      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4SKmiZrJAIrl"
      },
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      "source": [
        "!pip install transformers==4.30 > null\n",
        "!pip install -U sentence-transformers > null\n",
        "!pip install scikit-learn > null\n",
        "!pip install accelerate -U > null"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **HIPAA Dataset**"
      ],
      "metadata": {
        "id": "VE2gYtzIU-av"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import gdown\n",
        "\n",
        "url = 'https://drive.google.com/file/d/1W_XbXGcCumLIBGuAJkCRZ6dEa6APgrds/view?usp=sharing'\n",
        "gdown.download(url, fuzzy=True)\n",
        "\n",
        "!unzip HIPAA-original-json.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hV4FGL42BAOX",
        "outputId": "6818c19a-b85b-4f76-cec8-c6d337a7d0d1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1W_XbXGcCumLIBGuAJkCRZ6dEa6APgrds\n",
            "To: /content/HIPAA-original-json.zip\n",
            "100%|██████████| 78.1k/78.1k [00:00<00:00, 34.4MB/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  HIPAA-original-json.zip\n",
            "   creating: HIPAA-original-json/\n",
            "  inflating: HIPAA-original-json/ALLRequirements.json  \n",
            "  inflating: HIPAA-original-json/ALLTraces.json  \n",
            "  inflating: HIPAA-original-json/RegulatoryCodes.json  \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "\n",
        "requirements = {}\n",
        "regulations = {}\n",
        "alltraces = {}\n",
        "\n",
        "addr = \"HIPAA-original-json/ALLRequirements.json\"\n",
        "with open(addr, 'r') as myfile:\n",
        "    json_data = json.load(myfile)\n",
        "artifacts = json_data[\"artifacts\"][\"artifact\"]\n",
        "for tmp in artifacts:\n",
        "  requirements[tmp['art_id']] = tmp['art_title']\n",
        "\n",
        "\n",
        "addr = \"HIPAA-original-json/ALLTraces.json\"\n",
        "with open(addr, 'r') as myfile:\n",
        "    json_data = json.load(myfile)\n",
        "traces = json_data[\"traces\"]\n",
        "cnt = 0\n",
        "for trace in traces:\n",
        "  req_id = trace['requirement-id']\n",
        "  reg_id = trace['regulatory-code']\n",
        "  if req_id not in list(alltraces.keys()): alltraces[req_id] = []\n",
        "  alltraces[req_id].append(reg_id)\n",
        "\n",
        "addr = \"HIPAA-original-json/RegulatoryCodes.json\"\n",
        "with open(addr, 'r') as myfile:\n",
        "    json_data = json.load(myfile)\n",
        "regulatory_codes = json_data[\"artifacts\"][\"artifact\"]\n",
        "for tmp in regulatory_codes:\n",
        "  regulations[tmp['art_id']] = tmp['art_title']"
      ],
      "metadata": {
        "id": "Rg_cIUsfGay6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('total number of requirements: ', len(list(requirements.keys())))\n",
        "print('total number of regulations: ', len(list(regulations.keys())))\n",
        "print('total number of requirements that have links to regulations: ', len(list(alltraces.keys())))\n",
        "print('number of all links existed: ', len(traces))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D5V4Pm8U58bA",
        "outputId": "4315ea93-b0ab-44d3-c44e-560edea93480"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "total number of requirements:  1891\n",
            "total number of regulations:  10\n",
            "total number of requirements that have links to regulations:  230\n",
            "number of all links existed:  243\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train, test = [], []\n",
        "split = 0.8\n",
        "regulation_cnt = {}\n",
        "\n",
        "## positive samples --> requirements that have at least one link to a regulation\n",
        "for _id in list(alltraces.keys()):\n",
        "  for _reg_id in alltraces[_id]:\n",
        "    regulation_cnt[_reg_id] = regulation_cnt.get(_reg_id, 0) + 1\n",
        "print(regulation_cnt)\n",
        "\n",
        "for reg in list(regulation_cnt.keys()):\n",
        "  n = regulation_cnt[reg]\n",
        "  split_cnt = int(split * n)\n",
        "  c = 0\n",
        "  for req_id in list(alltraces.keys()):\n",
        "    regs = alltraces[req_id]\n",
        "    if reg in regs:\n",
        "      if c > split_cnt:\n",
        "        test.append({'requirement': requirements[req_id], 'regulation': regulations[reg], 'label': 'linked'})\n",
        "      else:\n",
        "        train.append({'requirement': requirements[req_id], 'regulation': regulations[reg], 'label': 'linked'})\n",
        "      c += 1\n",
        "\n",
        "print('Number of Positive Instances:')\n",
        "print('number of instances in train: ', len(train))\n",
        "print('number of instances in test: ', len(test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0Cx302Tu9BOx",
        "outputId": "f2c2aa34-a5d5-4a78-ba60-ccad2789cea9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'AC': 53, 'AL': 10, 'AUD': 86, 'PA': 42, 'SED': 7, 'TED': 5, 'EAP': 4, 'IC': 18, 'TS': 7, 'UUI': 11}\n",
            "Number of Positive Instances:\n",
            "number of instances in train:  200\n",
            "number of instances in test:  43\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "\n",
        "alltraces_keys = list(alltraces.keys())\n",
        "regulations_texts = list(regulations.values())\n",
        "N = len(train) + len(test)\n",
        "cnt = 0\n",
        "candidates = []\n",
        "for _id in list(requirements.keys()):\n",
        "  if _id not in alltraces_keys:\n",
        "    if cnt > N: break\n",
        "    cnt += 1\n",
        "    _index = random.randint(0, len(regulations_texts)-1)\n",
        "    candidates.append({'requirement': requirements[req_id], 'regulation': regulations_texts[_index], 'label': 'not_linked'})\n",
        "\n",
        "split_point = int(0.8 * len(candidates))\n",
        "train.extend(candidates[:split_point])\n",
        "test.extend(candidates[split_point:])\n",
        "print('Number of Instances:')\n",
        "print('number of instances in train: ', len(train))\n",
        "print('number of instances in test: ', len(test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ylRbrWnJBBLP",
        "outputId": "9b2f7330-a09e-4d7d-ca33-f9e2a35f57b9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of Instances:\n",
            "number of instances in train:  395\n",
            "number of instances in test:  92\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(train[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ICQruMU0S2un",
        "outputId": "3017652f-e222-4d4f-fc80-43e9cbed8f1c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'requirement': 'System will implement access control list mechanism to obtain information security. ACL system will be derived from the hierarchy in hospital / healthcare environments', 'regulation': 'Access Control. Implement technical policies and procedures for electronic information systems that maintain electronic protected health information to allow access only to those persons or software programs that have been granted access rights as specified in ? 164.308(a)(4).', 'label': 'linked'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Multi-Label and Multi-Class Classification**"
      ],
      "metadata": {
        "id": "CnMNt-cTVLo3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from torch.utils.data import DataLoader\n",
        "import torch.nn.functional as F\n",
        "import torch.nn as nn\n",
        "import torch\n",
        "\n",
        "from transformers import AutoTokenizer\n",
        "from transformers import AutoModelForSequenceClassification\n",
        "from transformers import TrainingArguments, Trainer\n",
        "from transformers import get_scheduler\n",
        "\n",
        "from sklearn.metrics import accuracy_score, recall_score, precision_score, f1_score, roc_auc_score, confusion_matrix, multilabel_confusion_matrix\n",
        "\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "UMyWw8s0a919"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "max_length = 128\n",
        "batch_size = 16\n",
        "gradient_acc_steps = 2\n",
        "epoch_nums = 5\n",
        "model_name = \"bert-base-cased\""
      ],
      "metadata": {
        "id": "jnA3HwR6dYbl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import BertForSequenceClassification\n",
        "\n",
        "model = BertForSequenceClassification.from_pretrained(model_name, problem_type=\"multi_label_classification\", num_labels=len(list(regulations.keys()))+1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136,
          "referenced_widgets": [
            "f82e88def0fa47858a2ca65e6f58a5b8",
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            "e8770611ab48468981403ec9adb663e4"
          ]
        },
        "id": "6s5hv9DcVCjY",
        "outputId": "2814ad43-7a95-452b-9910-75eb62db5eeb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f82e88def0fa47858a2ca65e6f58a5b8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/436M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "807758bd241e4a7b8264573a3b95c6d6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-cased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def prepare_dataset():\n",
        "  reversed_regulations = {}\n",
        "  allregs = list(regulations.values())\n",
        "  for idx, reg in enumerate(allregs):\n",
        "    reversed_regulations[reg] = idx\n",
        "  reversed_regulations['else'] = len(allregs)\n",
        "\n",
        "  train_texts, train_labels, test_texts, test_labels = [], [], [], []\n",
        "\n",
        "  duplicate = []\n",
        "  for idx in range(len(train)):\n",
        "    req = train[idx]['requirement']\n",
        "    reg = train[idx]['regulation']\n",
        "    if req in duplicate:\n",
        "      _index = train_texts.index(req)\n",
        "      train_labels[_index][reversed_regulations[reg]] = 1\n",
        "    else:\n",
        "      duplicate.append(req)\n",
        "      train_texts.append(train[idx]['requirement'])\n",
        "      if train[idx]['label'] == 'not_linked': reg = 'else'\n",
        "      y = np.zeros(len(allregs)+1)\n",
        "      y[reversed_regulations[reg]] = 1\n",
        "      train_labels.append(y)\n",
        "\n",
        "  duplicate = []\n",
        "  for idx in range(len(test)):\n",
        "    req = test[idx]['requirement']\n",
        "    reg = test[idx]['regulation']\n",
        "    if req in duplicate:\n",
        "      _index = test_texts.index(req)\n",
        "      test_labels[_index][reversed_regulations[reg]] = 1\n",
        "    else:\n",
        "      duplicate.append(req)\n",
        "      test_texts.append(test[idx]['requirement'])\n",
        "      if test[idx]['label'] == 'not_linked': reg = 'else'\n",
        "      y = np.zeros(len(allregs)+1)\n",
        "      y[reversed_regulations[reg]] = 1\n",
        "      test_labels.append(y)\n",
        "\n",
        "  return train_texts, train_labels, test_texts, test_labels, reversed_regulations\n",
        "\n",
        "class REDataset(torch.utils.data.Dataset):\n",
        "    def __init__(self, encodings, labels):\n",
        "        self.encodings = encodings\n",
        "        self.labels = labels\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}\n",
        "        item['labels'] = torch.tensor(self.labels[idx])\n",
        "        return item\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.labels)\n",
        "\n",
        "# def compute_metrics(p):\n",
        "#     pred, labels = p\n",
        "#     pred = np.argmax(pred, axis=1)\n",
        "\n",
        "#     accuracy = accuracy_score(y_true=labels, y_pred=pred)\n",
        "#     recall = recall_score(y_true=labels, y_pred=pred)\n",
        "#     precision = precision_score(y_true=labels, y_pred=pred)\n",
        "#     f1 = f1_score(y_true=labels, y_pred=pred)\n",
        "\n",
        "#     return {\"accuracy\": accuracy, \"precision\": precision, \"recall\": recall, \"f1\": f1}\n",
        "\n",
        "def multi_label_metrics(predictions, labels, threshold=0.5):\n",
        "    sigmoid = torch.nn.Sigmoid()\n",
        "    probs = sigmoid(torch.Tensor(predictions))\n",
        "\n",
        "    y_pred = np.zeros(probs.shape)\n",
        "    y_pred[np.where(probs >= threshold)] = 1\n",
        "\n",
        "    y_true = labels\n",
        "    f1_micro_average = f1_score(y_true=y_true, y_pred=y_pred, average='micro')\n",
        "    recall = recall_score(y_true=y_true, y_pred=y_pred, average = 'micro')\n",
        "    precision = precision_score(y_true=y_true, y_pred=y_pred, average = 'micro')\n",
        "    roc_auc = roc_auc_score(y_true, y_pred, average = 'micro')\n",
        "\n",
        "    matrix = multilabel_confusion_matrix(y_true=y_true, y_pred=y_pred)\n",
        "    tp_tn = sum([sum(a[i][2-i-1] for i in range(2)) for a in matrix])\n",
        "    N = sum([np.sum(a) for a in matrix])\n",
        "    accuracy = tp_tn / N\n",
        "\n",
        "    # return as dictionary\n",
        "    metrics = {'recall': recall,\n",
        "               'precision': precision,\n",
        "               'f1': f1_micro_average,\n",
        "               'roc_auc': roc_auc,\n",
        "               'accuracy': accuracy}\n",
        "    return metrics\n",
        "\n",
        "def compute_metrics(p):\n",
        "    preds = p.predictions[0] if isinstance(p.predictions,\n",
        "            tuple) else p.predictions\n",
        "    result = multi_label_metrics(\n",
        "        predictions=preds,\n",
        "        labels=p.label_ids)\n",
        "    return result"
      ],
      "metadata": {
        "id": "9YtcO983dcaI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "train_texts, train_labels, test_texts, test_labels, reversed_regulations = prepare_dataset()\n",
        "train_encodings = tokenizer(train_texts, truncation=True, padding=True, max_length=max_length)\n",
        "test_encodings = tokenizer(test_texts, truncation=True, padding=True, max_length=max_length)\n",
        "\n",
        "# convert our tokenized data into a torch Dataset\n",
        "train_dataset = REDataset(train_encodings, train_labels)\n",
        "test_dataset = REDataset(test_encodings, test_labels)\n",
        "# valid_dataset = REDataset(valid_encodings, valid_labels)\n",
        "\n",
        "#warmap_step\n",
        "warmup_steps = int(((len(train_dataset) / (batch_size * gradient_acc_steps)) * epoch_nums) / 10)\n",
        "\n",
        "id2label = {0: \"NEGATIVE\", 1: \"POSITIVE\"}\n",
        "label2id = {\"NEGATIVE\": 0, \"POSITIVE\": 1}\n",
        "\n",
        "training_args = TrainingArguments(\n",
        "    output_dir='/',\n",
        "    num_train_epochs=epoch_nums,\n",
        "    per_device_train_batch_size = batch_size,\n",
        "    gradient_accumulation_steps = gradient_acc_steps,\n",
        "    # per_device_eval_batch_size= batch_size*2 ,\n",
        "    # evaluation_strategy=\"epoch\",\n",
        "    disable_tqdm = False,\n",
        "    # save_steps=500,\n",
        "    # eval_steps=100,\n",
        "    # load_best_model_at_end=True,\n",
        "    warmup_steps=warmup_steps,\n",
        "    weight_decay=0.01,\n",
        "    logging_steps = 8,\n",
        "    # fp16 = False,\n",
        "    ignore_data_skip = True,\n",
        "    # logging_dir=logging_dir,\n",
        "    # dataloader_num_workers = 4,\n",
        "    run_name = 'classifier',\n",
        "    learning_rate=1e-5\n",
        ")\n",
        "\n",
        "\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=train_dataset,\n",
        "    eval_dataset=test_dataset,\n",
        "    compute_metrics=compute_metrics,\n",
        ")"
      ],
      "metadata": {
        "id": "qcz45rh-aNSa",
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          "base_uri": "https://localhost:8080/",
          "height": 113,
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      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/49.0 [00:00<?, ?B/s]"
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/213k [00:00<?, ?B/s]"
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    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "pl0FGAFvkcjZ",
        "outputId": "dd881fab-85b6-4207-b6f9-6909f62aa8a4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='30' max='30' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [30/30 00:16, Epoch 5/5]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.708700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16</td>\n",
              "      <td>0.610600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>24</td>\n",
              "      <td>0.587700</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=30, training_loss=0.6239731391270955, metrics={'train_runtime': 17.1476, 'train_samples_per_second': 55.401, 'train_steps_per_second': 1.75, 'total_flos': 52729249395600.0, 'train_loss': 0.6239731391270955, 'epoch': 5.0})"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.evaluate()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 211
        },
        "id": "8NZpE1S1keN-",
        "outputId": "d48674fd-af3e-4d7a-93e0-42e26a24d819"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='6' max='6' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [6/6 00:00]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'eval_loss': 0.5669102755787178,\n",
              " 'eval_recall': 0.4230769230769231,\n",
              " 'eval_precision': 0.22,\n",
              " 'eval_f1': 0.28947368421052627,\n",
              " 'eval_roc_auc': 0.6137941006362059,\n",
              " 'eval_accuracy': 0.2394678492239468,\n",
              " 'eval_runtime': 0.1521,\n",
              " 'eval_samples_per_second': 269.539,\n",
              " 'eval_steps_per_second': 39.445,\n",
              " 'epoch': 5.0}"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# predictions = trainer.predict(test_dataset)\n",
        "# preds = torch.softmax(torch.tensor(predictions.predictions),0)\n",
        "# preds = torch.argmax(preds, dim=1)\n",
        "# print(predictions.metrics)\n",
        "# print('The accuracy of the mode ({}) is: {}'.format(model_name, predictions.metrics['test_accuracy']))"
      ],
      "metadata": {
        "id": "nHDIGlmGhAT3"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}