Evaluation.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZUhGFH3PRMKz"
      },
      "source": [
        "# Evaluation of model"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Global Variables"
      ],
      "metadata": {
        "id": "W--e6ihrKrD8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "MODEL=\"bert-base-uncased\""
      ],
      "metadata": {
        "id": "d28sFueR3U9k"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8ker_mgZI8WP"
      },
      "source": [
        "### Installation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ag2obpM48IRq",
        "outputId": "4b0ec9a1-dc6f-42df-9129-27ee8ea965b7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting datasets\n",
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            "\u001b[?25hCollecting yarl<2.0,>=1.0\n",
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            "Installing collected packages: multidict, frozenlist, yarl, urllib3, asynctest, async-timeout, aiosignal, fsspec, aiohttp, xxhash, responses, huggingface-hub, datasets\n",
            "  Attempting uninstall: urllib3\n",
            "    Found existing installation: urllib3 1.24.3\n",
            "    Uninstalling urllib3-1.24.3:\n",
            "      Successfully uninstalled urllib3-1.24.3\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\u001b[0m\n",
            "Successfully installed aiohttp-3.8.1 aiosignal-1.2.0 async-timeout-4.0.2 asynctest-0.13.0 datasets-2.1.0 frozenlist-1.3.0 fsspec-2022.3.0 huggingface-hub-0.5.1 multidict-6.0.2 responses-0.18.0 urllib3-1.25.11 xxhash-3.0.0 yarl-1.7.2\n"
          ]
        }
      ],
      "source": [
        "!pip install datasets"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_psZV3q7pDSU",
        "outputId": "c86e2c4e-ea21-4772-ccb2-b44b90e6a4ce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting transformers\n",
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            "Installing collected packages: pyyaml, tokenizers, sacremoses, transformers\n",
            "  Attempting uninstall: pyyaml\n",
            "    Found existing installation: PyYAML 3.13\n",
            "    Uninstalling PyYAML-3.13:\n",
            "      Successfully uninstalled PyYAML-3.13\n",
            "Successfully installed pyyaml-6.0 sacremoses-0.0.49 tokenizers-0.12.1 transformers-4.18.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### All Imports"
      ],
      "metadata": {
        "id": "zRUKouJ3KSd6"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "T7xim5QEF5vd"
      },
      "outputs": [],
      "source": [
        "from datasets import load_dataset\n",
        "import os\n",
        "import re\n",
        "import numpy as np \n",
        "from sklearn.metrics import accuracy_score\n",
        "import transformers\n",
        "from transformers import BertTokenizer, BertModel\n",
        "from transformers import AutoModelForSequenceClassification\n",
        "from transformers import TFAutoModelForSequenceClassification\n",
        "from transformers import AutoTokenizer\n",
        "import torch\n",
        "from torch import cuda\n",
        "from tqdm import tqdm_notebook as tqdm"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7FGuxfTEFurq"
      },
      "source": [
        "### Emotion Dataset from Huggingface"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84,
          "referenced_widgets": [
            "c1462e2c66be4471bb79697c8e7120dc",
            "510dc80e6bfa4220927e48b1728fdfb3",
            "2a71b8570438402486c72a04e78ed6d4",
            "89b27764b7bf4ffc90ee725c64dbc638",
            "dcf89970abff46f9862a255ed928a7da",
            "083784299b834fd79a31fd93bd88d8c7",
            "d193b39ed72640b1ba85b485fcc3d011",
            "44cf39f768fe4894ac619ab37fc3b9e5",
            "a61499fbfa6e421f8d7ec8965903cd22",
            "d2e0518d221c45b98c84aac328f21373",
            "59953ebfa62a431fa86531bdd8c9d1fb"
          ]
        },
        "id": "autb5_cl2zY3",
        "outputId": "0ad4d318-e3a7-46ac-ce75-d461beab3955"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Using custom data configuration default\n",
            "Reusing dataset emotion (/root/.cache/huggingface/datasets/emotion/default/0.0.0/348f63ca8e27b3713b6c04d723efe6d824a56fb3d1449794716c0f0296072705)\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c1462e2c66be4471bb79697c8e7120dc",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/3 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "dataset=load_dataset('emotion')\n",
        "#dataset = load_dataset(\"sem_eval_2018_task_1\",'subtask5.english')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l3mbI_KSA6gI"
      },
      "source": [
        "### Twitter Dataset for our Analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kIXH7P8dBDfu",
        "outputId": "9f05c402-fed3-45df-c4d0-cd493b7f1c0b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "import pandas as pd\n",
        "dataset=pd.read_csv('/content/drive/MyDrive/Colab Notebooks/covid19_tweets.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yhZYMwRhBTZC"
      },
      "outputs": [],
      "source": [
        "X = dataset['text'].values"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r6-gz2ixF8zc"
      },
      "source": [
        "### TweetEval Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424,
          "referenced_widgets": [
            "f75ec208ca544b27ac124c47e546d72b",
            "b1d6fe3281f9458dbed6bef2616b4b63",
            "1cafcf8c81fd4c6796b2129c23436399",
            "96a1ce5cb636447bb866c488eebe315b",
            "d6208e010f34498cb842e5c14476d6bd",
            "742007fbae6c468688a53aef9003eb71",
            "202b95c1584f46c6be463bd4631b625e",
            "337ecd2560364009820d21fbb477615f",
            "0bc783f98e8a449788cedb9086d047fd",
            "98e05b2fad324454b362aa8974ffcdae",
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        },
        "id": "TjsT4o_lGJB_",
        "outputId": "450ef8e2-b6b4-4366-db5c-6d8ac2dd513b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/2.37k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f75ec208ca544b27ac124c47e546d72b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading metadata:   0%|          | 0.00/5.17k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "308edfb364704660861a7fdd0df989cc"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset tweet_eval/emotion (download: 472.47 KiB, generated: 511.52 KiB, post-processed: Unknown size, total: 984.00 KiB) to /root/.cache/huggingface/datasets/tweet_eval/emotion/1.1.0/12aee5282b8784f3e95459466db4cdf45c6bf49719c25cdb0743d71ed0410343...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "287c70a49a054288945e57b5a7fed684"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "01e909731eb14f369f16ad4bebfd369a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "48db665547c848eb81eda9592181f690"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6c162a77bb444e02b0574ad022d156ba"
            }
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          "metadata": {}
        },
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "b090df7226234c7f9529afa12966d8e0"
            }
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          "metadata": {}
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2ac526f79db44b75bf9b75d3897870de"
            }
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          "metadata": {}
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "03997602cbf747e2910a8aa8e5eb8c24"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "79a77b94215e46eab209189171819153"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/3257 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9a166d6785e84f0ba6ae1030f63aaf8d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d3f38cc4954a44b296c227f1c3fe49e4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation split:   0%|          | 0/374 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8e31d09f48884e83a02038f590024db9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset tweet_eval downloaded and prepared to /root/.cache/huggingface/datasets/tweet_eval/emotion/1.1.0/12aee5282b8784f3e95459466db4cdf45c6bf49719c25cdb0743d71ed0410343. Subsequent calls will reuse this data.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d3accda7ad0d4c86b3b98486cbab4be7"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "task='emotion'\n",
        "dataset=load_dataset('tweet_eval',task)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "10054882"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "train_data=pd.DataFrame(dataset['train'])\n",
        "test_data=pd.DataFrame(dataset['test'])\n",
        "#val_data=pd.DataFrame(dataset['validation'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "od6diAMhbFwp"
      },
      "outputs": [],
      "source": [
        "train_X=train_data['text'].values\n",
        "len(train_X)\n",
        "train_labels=train_data['label'].values\n",
        "from sklearn import preprocessing\n",
        "le = preprocessing.LabelEncoder()\n",
        "ohe = preprocessing.OneHotEncoder()\n",
        "le.fit(train_labels) \n",
        "y_train = le.transform(train_labels).reshape(-1, 1) # this is magic\n",
        "ohe.fit(y_train)\n",
        "train_y = ohe.transform(y_train).todense()\n",
        "del y_train\n",
        "del train_labels\n",
        "del train_data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "O0pr_9pU9Mbo"
      },
      "outputs": [],
      "source": [
        "test_X=test_data['text'].values\n",
        "test_labels=test_data['label'].values\n",
        "le = preprocessing.LabelEncoder()\n",
        "ohe = preprocessing.OneHotEncoder()\n",
        "le.fit(test_labels) \n",
        "y_test = le.transform(test_labels).reshape(-1, 1) # this is magic\n",
        "ohe.fit(y_test)\n",
        "test_y = ohe.transform(y_test).todense()\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "del y_test\n",
        "del test_labels\n",
        "del test_data\n",
        "\n"
      ],
      "metadata": {
        "id": "Q01C6h7V1U3j"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lit6LeDFZu8q"
      },
      "source": [
        "## train data for semeval 2018 task 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZV67UBH-5sfi"
      },
      "outputs": [],
      "source": [
        "#train_X=train_data['Tweet'] \n",
        "train_y=train_data.drop(columns=['ID','Tweet'])\n",
        "train_X=dataset['train']['Tweet']\n",
        "del train_data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aanjh66iH6yg"
      },
      "outputs": [],
      "source": [
        "train_y=train_y.astype(int)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PaajOl4W5v8j"
      },
      "outputs": [],
      "source": [
        "\n",
        "#test_X=test_data['Tweet']\n",
        "test_y=test_data.drop(columns=['ID','Tweet'])\n",
        "test_X=dataset['test']['Tweet']\n",
        "del test_data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CFJJtdUoH5zi"
      },
      "outputs": [],
      "source": [
        "test_y=test_y.astype(int)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SVQKSVEJDMGw",
        "outputId": "8c23dff6-20ca-48b8-b5be-fd5ab91d11aa"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "6838\n",
            "6838\n"
          ]
        }
      ],
      "source": [
        "print(len(train_X))\n",
        "print(len(train_y))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a-U2zVZhH0U4"
      },
      "source": [
        "## Model Init"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ga-9fRrpIMbZ"
      },
      "source": [
        "### Device Selection: CPU or Cuda"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "4LaHQhnlRMK5",
        "outputId": "3591bab3-391a-4c1c-a88d-cb6dffdfcc97"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'cuda'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "device = 'cuda' if cuda.is_available() else 'cpu'\n",
        "device"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "awy3tUD2RMLI"
      },
      "source": [
        "### Torch Datasets\n",
        "\n",
        "- takes in inputs and outputs/labels\n",
        "- interfaces with tokenizer\n",
        "- handles batching"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HYxVIYT8RMLJ"
      },
      "outputs": [],
      "source": [
        "def preprocess(text):\n",
        "    new_text = []\n",
        "    for t in text.split(\" \"):\n",
        "        t = '@user' if t.startswith('@') and len(t) > 1 else t\n",
        "        t = 'http' if t.startswith('http') else t\n",
        "        new_text.append(t)\n",
        "    return \" \".join(new_text)\n",
        "\n",
        "class MultiLabelDataset(torch.utils.data.Dataset):\n",
        "\n",
        "    def __init__(self, text, labels, tokenizer, max_len):\n",
        "        self.tokenizer = tokenizer\n",
        "        self.text = text\n",
        "        self.targets = labels\n",
        "        self.max_len = max_len\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.text)\n",
        "\n",
        "    def __getitem__(self, index):\n",
        "        text = self.text[index]\n",
        "        inputs = self.tokenizer.encode_plus(\n",
        "            text,\n",
        "            None,\n",
        "            add_special_tokens=True,\n",
        "            max_length=self.max_len,\n",
        "            pad_to_max_length=True,\n",
        "            truncation=True,\n",
        "            return_token_type_ids=True\n",
        "        )\n",
        "        ids = inputs['input_ids']\n",
        "        mask = inputs['attention_mask']\n",
        "        token_type_ids = inputs[\"token_type_ids\"]\n",
        "\n",
        "\n",
        "        return {\n",
        "            'ids': torch.tensor(ids, dtype=torch.long),\n",
        "            'mask': torch.tensor(mask, dtype=torch.long),\n",
        "            'token_type_ids': torch.tensor(token_type_ids, dtype=torch.long),\n",
        "            'targets': torch.tensor(self.targets[index], dtype=torch.long)\n",
        "        }"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-m3w1JTzRMLK"
      },
      "source": [
        "### Bert Class\n",
        "\n",
        "- first \"layer\" is a pre-trained BERT model\n",
        "- you can add whatever layers you want after that"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "37qPTfHSRMLK"
      },
      "outputs": [],
      "source": [
        "class BERTClass(torch.nn.Module):\n",
        "    def __init__(self, NUM_OUT):\n",
        "        super(BERTClass, self).__init__()\n",
        "                   \n",
        "        self.l1 = BertModel.from_pretrained(MODEL)\n",
        "#         self.pre_classifier = torch.nn.Linear(768, 256)\n",
        "        self.classifier = torch.nn.Linear(768, NUM_OUT)\n",
        "#         self.dropout = torch.nn.Dropout(0.5)\n",
        "        self.softmax = torch.nn.Softmax(dim=1)\n",
        "\n",
        "    def forward(self, input_ids, attention_mask, token_type_ids):\n",
        "        output_1 = self.l1(input_ids=input_ids, attention_mask=attention_mask)\n",
        "        hidden_state = output_1[0]\n",
        "        pooler = hidden_state[:, 0]\n",
        "#         pooler = self.pre_classifier(pooler)\n",
        "#         pooler = torch.nn.Tanh()(pooler)\n",
        "#         pooler = self.dropout(pooler)\n",
        "        output = self.classifier(pooler)\n",
        "        output = self.softmax(output)\n",
        "        return output"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vXziYRKERMLL"
      },
      "source": [
        "### Helpful Functions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hgq_pJK6M6-D"
      },
      "source": [
        "\n",
        "Loss\n",
        "\n",
        "- This task is binary, so it uses binary crossentropy loss\n",
        "- Tasks with more labels will use categorical crossentropy\n",
        "- Tasks that don't have labels, but rather have distributions should use KL divergence\n",
        "- Tasks that don't have distributions should use something like RMSE loss\n",
        "\n",
        "Train\n",
        "\n",
        "- Steps through the data batch by batch\n",
        "- grabs ids, masks, and token_type_ids which are required inputs for BERT\n",
        "- inputs are passed through the model, compared to targets, computes loss function, backprops\n",
        "\n",
        "Validation\n",
        "\n",
        "- Takes a model, passes inputs\n",
        "- Need to use the targets from here because they are potentially shuffled!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KZCqrV_gRMLL"
      },
      "outputs": [],
      "source": [
        "def loss_fn(outputs, targets):\n",
        "    #print(outputs,targets)\n",
        "    return torch.nn.CrossEntropyLoss()(outputs, targets)\n",
        "\n",
        "def train(model, training_loader, optimizer):\n",
        "    model.train()\n",
        "    for data in tqdm(training_loader):\n",
        "        ids = data['ids'].to(device, dtype = torch.long)\n",
        "        mask = data['mask'].to(device, dtype = torch.long)\n",
        "        token_type_ids = data['token_type_ids'].to(device, dtype = torch.long)\n",
        "        targets = data['targets'].to(device, dtype = torch.float)\n",
        "\n",
        "        outputs = model(ids, mask, token_type_ids)\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "        loss = loss_fn(outputs, targets)\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "    return outputs\n",
        "    \n",
        "def validation(model, testing_loader):\n",
        "    model.eval()\n",
        "    fin_targets=[]\n",
        "    fin_outputs=[]\n",
        "    with torch.no_grad():\n",
        "        for data in tqdm(testing_loader):\n",
        "            targets = data['targets']\n",
        "            ids = data['ids'].to(device, dtype = torch.long)\n",
        "            mask = data['mask'].to(device, dtype = torch.long)\n",
        "            token_type_ids = data['token_type_ids'].to(device, dtype = torch.long)\n",
        "            outputs = model(ids, mask, token_type_ids)\n",
        "            outputs = torch.sigmoid(outputs).cpu().detach()\n",
        "            fin_outputs.extend(outputs)\n",
        "            fin_targets.extend(targets)\n",
        "    return torch.stack(fin_outputs), torch.stack(fin_targets)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1zHJeAPWRMLM"
      },
      "source": [
        "### Preprocessing Data\n",
        "\n",
        "- Converts a raw string to the ids, masks, and token_type_ids"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sIJ_R6A-D8d4",
        "outputId": "620f5517-378a-4125-d5f1-2d155eaaf00f"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2269: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  FutureWarning,\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "{'input_ids': [101, 1030, 5310, 2632, 7405, 3406, 1024, 10506, 2097, 15697, 5018, 2454, 1999, 2254, 1010, 2178, 3263, 1999, 1996, 2621, 1998, 3488, 2000, 3288, 6752, 2072, 2011, 2418, 1000, 102, 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, 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, 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], '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, 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, 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, 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, 1, 1, 1, 1, 1, 1, 1, 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, 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, 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]}"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "i=0\n",
        "for txt in train_X:\n",
        "  train_X[i]=preprocess(txt)\n",
        "  i+=1"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yYt6iGifRMLN"
      },
      "source": [
        "### Training setup\n",
        "\n",
        "- hyperparameters\n",
        "- setup dataset\n",
        "- setup parameters\n",
        "- setup dataloader"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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          "metadata": {}
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      "source": [
        "MAX_LEN = 128\n",
        "BATCH_SIZE = 64\n",
        "EPOCHS = 3\n",
        "NUM_OUT = 4 \n",
        "LEARNING_RATE = 2e-05\n",
        "\n",
        "tokenizer=BertTokenizer.from_pretrained(MODEL)\n",
        "training_data = MultiLabelDataset(train_X, torch.from_numpy(train_y), tokenizer, MAX_LEN)\n",
        "test_data = MultiLabelDataset(test_X, torch.from_numpy(test_y), tokenizer, MAX_LEN)\n",
        "\n",
        "train_params = {'batch_size': BATCH_SIZE,\n",
        "                'shuffle': True,\n",
        "                'num_workers': 0\n",
        "                }\n",
        "\n",
        "test_params = {'batch_size': BATCH_SIZE,\n",
        "                'shuffle': True,\n",
        "                'num_workers': 0\n",
        "                }    \n",
        "\n",
        "training_loader = torch.utils.data.DataLoader(training_data, **train_params)\n",
        "testing_loader = torch.utils.data.DataLoader(test_data, **test_params)"
      ]
    },
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        "outputId": "77d7d490-a904-428d-98a1-efc4ce3e19e0"
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      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.dense.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",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:7: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
            "  import sys\n"
          ]
        },
        {
          "output_type": "display_data",
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            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2269: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  FutureWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:40: 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",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:28: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n"
          ]
        },
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            "accuracy on test set 0.7543983110485574\n"
          ]
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            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:7: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
            "  import sys\n"
          ]
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            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2269: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  FutureWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:40: 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",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:28: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n"
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            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:7: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
            "  import sys\n"
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            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2269: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  FutureWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:40: 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",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:28: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n"
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            "text/plain": [
              "  0%|          | 0/23 [00:00<?, ?it/s]"
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            "accuracy on test set 0.8029556650246306\n"
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      "source": [
        "model = BERTClass(NUM_OUT)\n",
        "model.to(device)    \n",
        "\n",
        "optimizer = torch.optim.Adam(params =  model.parameters(), lr=LEARNING_RATE)\n",
        "\n",
        "for epoch in range(EPOCHS):\n",
        "    loss = train(model, training_loader, optimizer)\n",
        "   # print(f'Epoch: {epoch}, Loss:  {loss.item()}')  \n",
        "    guess, targs = validation(model, testing_loader)\n",
        "    guesses = torch.max(guess, dim=1)\n",
        "    targets = torch.max(targs, dim=1)\n",
        "    print('accuracy on test set {}'.format(accuracy_score(guesses.indices, targets.indices)))"
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