BERT_for_Humor_detection.ipynb

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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FZfkW4tqjJ81",
        "outputId": "f4f540ca-d51f-4468-ac2b-0190ebb22c4a"
      },
      "source": [
        "! pip install contractions"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting contractions\n",
            "  Downloading contractions-0.0.58-py2.py3-none-any.whl (8.0 kB)\n",
            "Collecting textsearch>=0.0.21\n",
            "  Downloading textsearch-0.0.21-py2.py3-none-any.whl (7.5 kB)\n",
            "Collecting pyahocorasick\n",
            "  Downloading pyahocorasick-1.4.2.tar.gz (321 kB)\n",
            "\u001b[K     |████████████████████████████████| 321 kB 4.7 MB/s \n",
            "\u001b[?25hCollecting anyascii\n",
            "  Downloading anyascii-0.3.0-py3-none-any.whl (284 kB)\n",
            "\u001b[K     |████████████████████████████████| 284 kB 48.4 MB/s \n",
            "\u001b[?25hBuilding wheels for collected packages: pyahocorasick\n",
            "  Building wheel for pyahocorasick (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyahocorasick: filename=pyahocorasick-1.4.2-cp37-cp37m-linux_x86_64.whl size=85442 sha256=7b0331a7952ea0d96590ea9498e0dab494f4ce52c223c9941e4c53f6ec03700e\n",
            "  Stored in directory: /root/.cache/pip/wheels/25/19/a6/8f363d9939162782bb8439d886469756271abc01f76fbd790f\n",
            "Successfully built pyahocorasick\n",
            "Installing collected packages: pyahocorasick, anyascii, textsearch, contractions\n",
            "Successfully installed anyascii-0.3.0 contractions-0.0.58 pyahocorasick-1.4.2 textsearch-0.0.21\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
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        },
        "id": "YGmbv8bajdoV",
        "outputId": "c438ae72-2119-4e3d-b5d4-03bb8e8ee87c"
      },
      "source": [
        "! pip install transformers"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting transformers\n",
            "  Downloading transformers-4.12.5-py3-none-any.whl (3.1 MB)\n",
            "\u001b[K     |████████████████████████████████| 3.1 MB 5.3 MB/s \n",
            "\u001b[?25hRequirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (2019.12.20)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers) (4.62.3)\n",
            "Collecting tokenizers<0.11,>=0.10.1\n",
            "  Downloading tokenizers-0.10.3-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (3.3 MB)\n",
            "\u001b[K     |████████████████████████████████| 3.3 MB 35.8 MB/s \n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers) (3.4.0)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (1.19.5)\n",
            "Collecting pyyaml>=5.1\n",
            "  Downloading PyYAML-6.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (596 kB)\n",
            "\u001b[K     |████████████████████████████████| 596 kB 45.4 MB/s \n",
            "\u001b[?25hRequirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from transformers) (21.3)\n",
            "Collecting sacremoses\n",
            "  Downloading sacremoses-0.0.46-py3-none-any.whl (895 kB)\n",
            "\u001b[K     |████████████████████████████████| 895 kB 38.9 MB/s \n",
            "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers) (2.23.0)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from transformers) (4.8.2)\n",
            "Collecting huggingface-hub<1.0,>=0.1.0\n",
            "  Downloading huggingface_hub-0.1.2-py3-none-any.whl (59 kB)\n",
            "\u001b[K     |████████████████████████████████| 59 kB 6.1 MB/s \n",
            "\u001b[?25hRequirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub<1.0,>=0.1.0->transformers) (3.10.0.2)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->transformers) (3.0.6)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->transformers) (3.6.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2021.10.8)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2.10)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (1.24.3)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (7.1.2)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.15.0)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.1.0)\n",
            "Installing collected packages: pyyaml, tokenizers, sacremoses, huggingface-hub, 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 huggingface-hub-0.1.2 pyyaml-6.0 sacremoses-0.0.46 tokenizers-0.10.3 transformers-4.12.5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VnYYCK7HM-iV"
      },
      "source": [
        "# Imports"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PmVL0PINNAaF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "059b6f9e-719a-4887-b6c0-43b0a6465d19"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "# import contractions\n",
        "import nltk\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "from nltk.corpus import stopwords, wordnet\n",
        "import pickle\n",
        "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
        "from tqdm.notebook import tqdm\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn import metrics\n",
        "from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn import svm\n",
        "\n",
        "\n",
        "# bert imports\n",
        "import torch\n",
        "from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler\n",
        "# from keras.preprocessing.sequence import pad_sequences\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.model_selection import KFold\n",
        "from transformers import BertTokenizer, BertConfig\n",
        "from transformers import AdamW, BertForSequenceClassification\n",
        "from tqdm.notebook import tqdm, trange\n",
        "import pandas as pd\n",
        "import io\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pickle\n",
        "import nltk\n",
        "import gc\n",
        "import os\n",
        "# from BertForHumorDetection import BertForHumorDetection\n",
        "import sklearn\n",
        "import time\n",
        "import csv\n",
        "from sklearn.metrics import recall_score, precision_score, f1_score, accuracy_score, roc_auc_score\n",
        "from datetime import datetime\n",
        "\n",
        "import nltk\n",
        "\n",
        "nltk.download('punkt')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DQb6fxi9mqlO"
      },
      "source": [
        "#GPU Info"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "amcpmV7Amh0f",
        "outputId": "aff2645a-42b1-41d8-9def-012afdc70db8"
      },
      "source": [
        "print('========================== GPU INFO ==========================')\n",
        "os.system('nvcc --version')\n",
        "print('--------------------------------------------------------------')\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "n_gpu = torch.cuda.device_count()\n",
        "print('Is CUDA available: ', torch.cuda.is_available())\n",
        "print('N_GPU: ', n_gpu)\n",
        "print('GPU: ', torch.cuda.get_device_name(0))\n",
        "print('--------------------------------------------------------------')\n",
        "print('Monitor GPU by running following command in new terminal: watch -n 0.5 nvidia-smi')\n",
        "print('Monitor system resources by running commands like free, top and htop in new terminal.')\n",
        "print('--------------------------------------------------------------')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "========================== GPU INFO ==========================\n",
            "--------------------------------------------------------------\n",
            "Is CUDA available:  True\n",
            "N_GPU:  1\n",
            "GPU:  Tesla K80\n",
            "--------------------------------------------------------------\n",
            "Monitor GPU by running following command in new terminal: watch -n 0.5 nvidia-smi\n",
            "Monitor system resources by running commands like free, top and htop in new terminal.\n",
            "--------------------------------------------------------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v1FLsqlqNyFY"
      },
      "source": [
        "# Setup"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IEESeXr1SQDz",
        "outputId": "39bea842-a0ea-43c6-ac11-f6e3129ee254"
      },
      "source": [
        "# nltk.download('punkt') #required for tokenization"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "00p45KIcSXBj",
        "outputId": "e7ea0f11-2fa0-4d3a-aaef-55df2a1ffcd4"
      },
      "source": [
        "# nltk.download('averaged_perceptron_tagger') #required for lemmatization"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
            "[nltk_data]     /root/nltk_data...\n",
            "[nltk_data]   Unzipping taggers/averaged_perceptron_tagger.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i4Mg6pqVSbHq",
        "outputId": "8a115a3b-a1d2-42bd-c48a-205ee5e7cee5"
      },
      "source": [
        "# nltk.download('wordnet') #required for lemmatization"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/wordnet.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8UYlqtq8MjYf",
        "outputId": "bc7917a7-51e3-4e26-ec77-5f52b4ddb66f"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ce4Xc9n3k6Sc"
      },
      "source": [
        "# Constants"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9e4irrLFk30m"
      },
      "source": [
        "MODEL_TYPE = 'bert-base-uncased'\n",
        "LOGTIME = datetime.now().strftime('%Y-%m-%d-%H-%M-%S')\n",
        "LR = 1e-5\n",
        "MAX_SENTENCE_LEN = 20 #60 # twice longest sentence\n",
        "MAX_NUM_SENTENCES = 5 #10 \n",
        "MAX_SEQUENCE_LEN = 100 #120 # twice longest sequence\n",
        "LOG_FIELDS = ['fold_id','fold_dur','epoch_id','fold_dur','batch_id','batch_dur','batch_size','loss','precision','recall','accuracy','f1','roc_auc']\n",
        "LOG_PER_N_BATCH = 30\n",
        "training_sample_count = 60000"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nHXSq3i9lGYo"
      },
      "source": [
        "# Directories"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AVt_l8JOlD_7"
      },
      "source": [
        "DRIVE_DIR = os.path.join('/','content', 'drive', 'MyDrive')  # './' for storing CWD\n",
        "# DRIVE_DIR = os.path.join('content', 'drive', 'MyDrive')\n",
        "# PROJECT_REL_DIR = os.path.join('NLP project', 'humor-detection-2', 'humor-detection')\n",
        "PROJECT_REL_DIR = os.path.join('NLP project', 'humor-detection')\n",
        "PROJECT_DIR = os.path.join(DRIVE_DIR, PROJECT_REL_DIR)\n",
        "\n",
        "# PROJECT_DIR = './' #uncomment to absolute paths\n",
        "\n",
        "MODELS_REL_DIR = 'models'\n",
        "MODELS_DIR = os.path.join(PROJECT_DIR, MODELS_REL_DIR)\n",
        "if not os.path.exists(MODELS_DIR):\n",
        "    os.makedirs(MODELS_DIR)\n",
        "\n",
        "LOGS_REL_DIR = 'logs'\n",
        "LOGS_DIR = os.path.join(PROJECT_DIR, LOGS_REL_DIR)\n",
        "if not os.path.exists(LOGS_DIR):\n",
        "    os.makedirs(LOGS_DIR)\n",
        "\n",
        "LOG_FILE = os.path.join(LOGS_DIR, 'bert-%s.log'%(LOGTIME))\n",
        "LOG_FIELDS = ['fold_id','fold_dur','epoch_id','fold_dur','batch_id','batch_dur','batch_size','loss','precision','recall','accuracy','f1','roc_auc']\n",
        "\n",
        "DATA_REL_DIR = 'data'\n",
        "DATA_DIR = os.path.join(PROJECT_DIR, DATA_REL_DIR)\n",
        "if not os.path.exists(DATA_DIR):\n",
        "    os.makedirs(DATA_DIR)\n",
        "\n",
        "\n",
        "with open(LOG_FILE, 'a') as csvfile:\n",
        "    csvwriter = csv.writer(csvfile) \n",
        "    csvwriter.writerow(LOG_FIELDS) "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rh4kFmh7M78M"
      },
      "source": [
        "# Load data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "KLFhdwEyMw7P",
        "outputId": "e9678056-85a6-4af3-cdb1-6e1b85abd6f5"
      },
      "source": [
        "df_train = pd.read_csv('/content/drive/MyDrive/NLP project/humor-detection/data/train.csv')\n",
        "df_train"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>text</th>\n",
              "      <th>humor</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Joe biden rules out 2020 bid: 'guys, i'm not r...</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Watch: darvish gave hitter whiplash with slow ...</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>What do you call a turtle without its shell? d...</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5 reasons the 2016 election feels so personal</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Pasco police shot mexican migrant from behind,...</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>159995</th>\n",
              "      <td>Watch muhammad ali pull the greatest prank on ...</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>159996</th>\n",
              "      <td>Some days the problem is i care too much... to...</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>159997</th>\n",
              "      <td>If someone doesn't realize their blinker is st...</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>159998</th>\n",
              "      <td>I'm going out with a girl i met online who's a...</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>159999</th>\n",
              "      <td>‘people who work in the system become desensit...</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>160000 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                     text  humor\n",
              "0       Joe biden rules out 2020 bid: 'guys, i'm not r...  False\n",
              "1       Watch: darvish gave hitter whiplash with slow ...  False\n",
              "2       What do you call a turtle without its shell? d...   True\n",
              "3           5 reasons the 2016 election feels so personal  False\n",
              "4       Pasco police shot mexican migrant from behind,...  False\n",
              "...                                                   ...    ...\n",
              "159995  Watch muhammad ali pull the greatest prank on ...  False\n",
              "159996  Some days the problem is i care too much... to...   True\n",
              "159997  If someone doesn't realize their blinker is st...   True\n",
              "159998  I'm going out with a girl i met online who's a...   True\n",
              "159999  ‘people who work in the system become desensit...  False\n",
              "\n",
              "[160000 rows x 2 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "RvBy6klikhV0",
        "outputId": "8b6935d9-9b45-4d63-8eb8-34bff4cbd9fd"
      },
      "source": [
        "# f = open(os.path.join(DATA_DIR,'train_contractions_lemmatization'),'rb')\n",
        "# df_train = pickle.load(f)\n",
        "# f.close()\n",
        "# df_train"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>text</th>\n",
              "      <th>humor</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Joe biden rule out 2020 bid : 'guys , I be not...</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Watch : darvish give hitter whiplash with slow...</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>What do you call a turtle without it she will ...</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5 reason the 2016 election feel so personal</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Pasco police shot mexican migrant from behind ...</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>159995</th>\n",
              "      <td>Watch muhammad ali pull the great prank on uns...</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>159996</th>\n",
              "      <td>Some day the problem be i care too much ... to...</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>159997</th>\n",
              "      <td>If someone do not realize their blinker be sti...</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>159998</th>\n",
              "      <td>I be go out with a girl i met online who be a ...</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>159999</th>\n",
              "      <td>‘ people who work in the system become desensi...</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>160000 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                     text  humor\n",
              "0       Joe biden rule out 2020 bid : 'guys , I be not...  False\n",
              "1       Watch : darvish give hitter whiplash with slow...  False\n",
              "2       What do you call a turtle without it she will ...   True\n",
              "3             5 reason the 2016 election feel so personal  False\n",
              "4       Pasco police shot mexican migrant from behind ...  False\n",
              "...                                                   ...    ...\n",
              "159995  Watch muhammad ali pull the great prank on uns...  False\n",
              "159996  Some day the problem be i care too much ... to...   True\n",
              "159997  If someone do not realize their blinker be sti...   True\n",
              "159998  I be go out with a girl i met online who be a ...   True\n",
              "159999  ‘ people who work in the system become desensi...  False\n",
              "\n",
              "[160000 rows x 2 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "YVdOFFt40GL1",
        "outputId": "757a06fa-7055-463c-effe-f223a45ed051"
      },
      "source": [
        "df_test = pd.read_csv(os.path.join(DATA_DIR,'dev.csv'))\n",
        "df_test"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>text</th>\n",
              "      <th>humor</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>What kind of cat should you take into the  des...</td>\n",
              "      <td>True</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Remember when people used to have to be in sha...</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Pizza is always good. - everyone we'll see abo...</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>What's 6 inches long hard, bent, and in my pan...</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Black teen's response to violence in his commu...</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39995</th>\n",
              "      <td>Conor maynard seamlessly fits old-school r&amp;b h...</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39996</th>\n",
              "      <td>How to you make holy water? you boil the hell ...</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39997</th>\n",
              "      <td>How many optometrists does it take to screw in...</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39998</th>\n",
              "      <td>Mcdonald's will officially kick off all-day br...</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39999</th>\n",
              "      <td>An irish man walks on the street and ignores a...</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>40000 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                    text  humor\n",
              "0      What kind of cat should you take into the  des...   True\n",
              "1      Remember when people used to have to be in sha...   True\n",
              "2      Pizza is always good. - everyone we'll see abo...   True\n",
              "3      What's 6 inches long hard, bent, and in my pan...   True\n",
              "4      Black teen's response to violence in his commu...  False\n",
              "...                                                  ...    ...\n",
              "39995  Conor maynard seamlessly fits old-school r&b h...  False\n",
              "39996  How to you make holy water? you boil the hell ...   True\n",
              "39997  How many optometrists does it take to screw in...   True\n",
              "39998  Mcdonald's will officially kick off all-day br...  False\n",
              "39999  An irish man walks on the street and ignores a...   True\n",
              "\n",
              "[40000 rows x 2 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_eIKLUyiNYjZ"
      },
      "source": [
        "# Preprocess data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FxlCDjKbTLiL"
      },
      "source": [
        "**1. Expand contractions**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "GYx_H0LFTJYY",
        "outputId": "3444ba71-a87d-4542-caec-ec8151256cf2"
      },
      "source": [
        "# df_train.text = df_train.text.apply(lambda sequence: contractions.fix(sequence))\n",
        "# # contractions.fix(\"I hadn't done that\")\n",
        "# df_train"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<style scoped>\n",
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              "    .dataframe tbody tr th {\n",
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>text</th>\n",
              "      <th>humor</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Joe biden rules out 2020 bid: 'guys, I am not ...</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Watch: darvish gave hitter whiplash with slow ...</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>What do you call a turtle without its she will...</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5 reasons the 2016 election feels so personal</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Pasco police shot mexican migrant from behind,...</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>159995</th>\n",
              "      <td>Watch muhammad ali pull the greatest prank on ...</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>159996</th>\n",
              "      <td>Some days the problem is i care too much... to...</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>159997</th>\n",
              "      <td>If someone does not realize their blinker is s...</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>159998</th>\n",
              "      <td>I am going out with a girl i met online who is...</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>159999</th>\n",
              "      <td>‘people who work in the system become desensit...</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>160000 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                     text  humor\n",
              "0       Joe biden rules out 2020 bid: 'guys, I am not ...  False\n",
              "1       Watch: darvish gave hitter whiplash with slow ...  False\n",
              "2       What do you call a turtle without its she will...   True\n",
              "3           5 reasons the 2016 election feels so personal  False\n",
              "4       Pasco police shot mexican migrant from behind,...  False\n",
              "...                                                   ...    ...\n",
              "159995  Watch muhammad ali pull the greatest prank on ...  False\n",
              "159996  Some days the problem is i care too much... to...   True\n",
              "159997  If someone does not realize their blinker is s...   True\n",
              "159998  I am going out with a girl i met online who is...   True\n",
              "159999  ‘people who work in the system become desensit...  False\n",
              "\n",
              "[160000 rows x 2 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "phCO6Y-QYfwa"
      },
      "source": [
        "**#TODO**: Explore if contractions expansions is a good pre processing idea. \n",
        "For example, in below example 'shell' becomes 'she will' making no sense of humor."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "xE5NVFTXYP5R",
        "outputId": "685f280f-23c6-4376-d7f1-c5613e9830b8"
      },
      "source": [
        "contractions.fix('What do you call a turtle without its shell? dead.')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'What do you call a turtle without its she will? dead.'"
            ]
          },
          "execution_count": 47,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "spFKxW7FRF9j"
      },
      "source": [
        "**2. Lemmatization with nltk**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 475,
          "referenced_widgets": [
            "42c1eed2a70d48f6ae5c5ad2dbd95f33",
            "b285c14f4c734bdd8f26442d4af704a5",
            "43db7294dc6e46088ba8640f4e38bbd8",
            "e62688a627424b598959b8029283e976",
            "2bbd96a7cff04ad987de63046ca8b167",
            "80a07aaf255240208c873fdbe4c0306b",
            "e62bdbe5f98c4d3a9dd97613d468c6ee",
            "1a22caaf64d941ab9b136e5c9f89b4f1",
            "bbb091d68130468b9afaf5d96196aebb",
            "1ccc202735544cc3805f81da6ae26617",
            "df980837d3804864ab604709daa2431f"
          ]
        },
        "id": "SDKI-w-CPiNT",
        "outputId": "eef3d11b-0a76-4340-d86e-4e74ba50dc4a"
      },
      "source": [
        "# tqdm.pandas() # https://stackoverflow.com/a/34365537/1317018 call tqdm.pandas() and call 'progress_xyz()' instead of any pandas methods `xyz()`  \n",
        "\n",
        "# def get_wordnet_pos(word):\n",
        "#     \"\"\"Map POS tag to first character lemmatize() accepts\"\"\"\n",
        "#     tag = nltk.pos_tag([word])[0][1][0].upper()\n",
        "#     tag_dict = {\"J\": wordnet.ADJ,\n",
        "#                 \"N\": wordnet.NOUN,\n",
        "#                 \"V\": wordnet.VERB,\n",
        "#                 \"R\": wordnet.ADV}\n",
        "\n",
        "#     return tag_dict.get(tag, wordnet.NOUN)\n",
        "\n",
        "# lemmatizer = WordNetLemmatizer()\n",
        "\n",
        "# df_train.text = df_train.text.progress_apply(lambda sequence: ' '.join([lemmatizer.lemmatize(word, get_wordnet_pos(word)) for word in nltk.word_tokenize(sequence)]))\n",
        "\n",
        "# # ' '.join([lemmatizer.lemmatize(word, get_wordnet_pos(word)) for word in nltk.word_tokenize('I dont think you get what I am saying')])\n",
        "\n",
        "# df_train"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "42c1eed2a70d48f6ae5c5ad2dbd95f33",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/160000 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-10-579ee92b2735>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0mlemmatizer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mWordNetLemmatizer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0mdf_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtext\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprogress_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0msequence\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m' '\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlemmatizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlemmatize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mword\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mget_wordnet_pos\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mword\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mword\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnltk\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mword_tokenize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msequence\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;31m# ' '.join([lemmatizer.lemmatize(word, get_wordnet_pos(word)) for word in nltk.word_tokenize('I dont think you get what I am saying')])\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tqdm/std.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(df, func, *args, **kwargs)\u001b[0m\n\u001b[1;32m    812\u001b[0m                 \u001b[0;31m# on the df using our wrapper (which provides bar updating)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    813\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 814\u001b[0;31m                     \u001b[0;32mreturn\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf_function\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwrapper\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    815\u001b[0m                 \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    816\u001b[0m                     \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/series.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, func, convert_dtype, args, **kwds)\u001b[0m\n\u001b[1;32m   4211\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4212\u001b[0m                 \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobject\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4213\u001b[0;31m                 \u001b[0mmapped\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap_infer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconvert\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconvert_dtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4214\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4215\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmapped\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmapped\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/lib.pyx\u001b[0m in \u001b[0;36mpandas._libs.lib.map_infer\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tqdm/std.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    807\u001b[0m                     \u001b[0;31m# take a fast or slow code path; so stop when t.total==t.n\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    808\u001b[0m                     \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtotal\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtotal\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 809\u001b[0;31m                     \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    810\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    811\u001b[0m                 \u001b[0;31m# Apply the provided function (in **kwargs)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-10-579ee92b2735>\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(sequence)\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0mlemmatizer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mWordNetLemmatizer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0mdf_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtext\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprogress_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0msequence\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m' '\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlemmatizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlemmatize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mword\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mget_wordnet_pos\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mword\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mword\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnltk\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mword_tokenize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msequence\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;31m# ' '.join([lemmatizer.lemmatize(word, get_wordnet_pos(word)) for word in nltk.word_tokenize('I dont think you get what I am saying')])\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-10-579ee92b2735>\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0mlemmatizer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mWordNetLemmatizer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0mdf_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtext\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprogress_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0msequence\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m' '\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlemmatizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlemmatize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mword\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mget_wordnet_pos\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mword\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mword\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnltk\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mword_tokenize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msequence\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;31m# ' '.join([lemmatizer.lemmatize(word, get_wordnet_pos(word)) for word in nltk.word_tokenize('I dont think you get what I am saying')])\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-10-579ee92b2735>\u001b[0m in \u001b[0;36mget_wordnet_pos\u001b[0;34m(word)\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mget_wordnet_pos\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mword\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0;34m\"\"\"Map POS tag to first character lemmatize() accepts\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m     \u001b[0mtag\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnltk\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpos_tag\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mword\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m     tag_dict = {\"J\": wordnet.ADJ,\n\u001b[1;32m      7\u001b[0m                 \u001b[0;34m\"N\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mwordnet\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNOUN\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/nltk/tag/__init__.py\u001b[0m in \u001b[0;36mpos_tag\u001b[0;34m(tokens, tagset, lang)\u001b[0m\n\u001b[1;32m    131\u001b[0m     \u001b[0;34m:\u001b[0m\u001b[0mrtype\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    132\u001b[0m     \"\"\"\n\u001b[0;32m--> 133\u001b[0;31m     \u001b[0mtagger\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_tagger\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlang\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    134\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0m_pos_tag\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtokens\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtagset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtagger\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    135\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/nltk/tag/__init__.py\u001b[0m in \u001b[0;36m_get_tagger\u001b[0;34m(lang)\u001b[0m\n\u001b[1;32m     95\u001b[0m         \u001b[0mtagger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0map_russian_model_loc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     96\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 97\u001b[0;31m         \u001b[0mtagger\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPerceptronTagger\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     98\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mtagger\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     99\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/nltk/tag/perceptron.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, load)\u001b[0m\n\u001b[1;32m    135\u001b[0m         '''\n\u001b[1;32m    136\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mAveragedPerceptron\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 137\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtagdict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    138\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclasses\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    139\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mload\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0mpsfNFMajuf"
      },
      "source": [
        "**#TODO** Analyze if lemmatization is good idea.\n",
        "After analyzing different humors and their lemmatized versions, its seems so. \n",
        "But it was taking time to execute."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YCJf9MkqXqEc",
        "outputId": "60424a6a-8d35-45d9-e53c-b9a02a9e4972"
      },
      "source": [
        "pd.set_option('display.max_colwidth', None) # avoid pringing ... for long sequences\n",
        "df_train.iloc[[30]].text"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "30    Ugh, i just spilled red wine all over the inside of my tummy.\n",
              "Name: text, dtype: object"
            ]
          },
          "execution_count": 62,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "c73d7hL1ZA66",
        "outputId": "880206dd-6698-4eaa-915e-4134fdd3d91a"
      },
      "source": [
        "' '.join([lemmatizer.lemmatize(word, get_wordnet_pos(word)) for word in nltk.word_tokenize(str(df_train.iloc[[30]].text))])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'30 Ugh , i just spill red wine all over the inside of my tummy . Name : text , dtype : object'"
            ]
          },
          "execution_count": 63,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iJj0iprJmaAC"
      },
      "source": [
        "#BertForHumorDetection"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RDsByQfNmYv2"
      },
      "source": [
        "from transformers import BertPreTrainedModel, BertConfig, BertModel\n",
        "import torch\n",
        "from torch import nn\n",
        "\n",
        "# config = BertConfig() # print(config) to see settings\n",
        "# config.output_hidden_states = False # Set to True to obtain hidden states\n",
        "# bert_model = BertModel.from_pretrained('bert-base-uncased', config=config)\n",
        "# config\n",
        "\n",
        "\n",
        "# class BertForHumorDetection(BertPreTrainedModel):\n",
        "class BertForHumorDetection(nn.Module):\n",
        "\n",
        "    def __init__(self, MAX_NUM_SENTENCES, MAX_SENTENCE_LENGTH, MAX_SEQUENCE_LENGTH, bert_config=None):\n",
        "        # super().__init__(bert_config)\n",
        "        super().__init__()\n",
        "        self.num_labels = bert_config.num_labels\n",
        "        # self.config = bert_config\n",
        "\n",
        "        self.config = bert_config if bert_config else BertConfig() \n",
        "        self.config.output_hidden_states = False # Set to True to obtain hidden states\n",
        "        self.bert_model = BertModel.from_pretrained('bert-base-uncased', config=self.config)\n",
        "        # self.bert = BertModel(config=bert_config)\n",
        "\n",
        "        self.sentence_pool = nn.AvgPool1d(kernel_size=MAX_SENTENCE_LENGTH, stride=1)\n",
        "        self.sequence_pool = nn.AvgPool1d(kernel_size=MAX_SEQUENCE_LENGTH, stride=1)\n",
        "        # self.intermediate_features_nn = []\n",
        "        self.intermediate_features_nn = nn.ModuleList()\n",
        "\n",
        "        #extracting intermeriate features of individual sentences\n",
        "        for _ in range(MAX_NUM_SENTENCES):\n",
        "            sentence_stack = nn.Sequential(\n",
        "                # nn.AvgPool1d(kernel_size=20, stride=1),\n",
        "                nn.Linear(768, 32),\n",
        "                nn.ReLU(),\n",
        "                nn.Dropout(p=0.3),\n",
        "                nn.Linear(32,8),\n",
        "                nn.ReLU(),\n",
        "            )\n",
        "            self.intermediate_features_nn.append(sentence_stack)\n",
        "        \n",
        "        #extracting intermediate features of whole sequence\n",
        "        self.sequence_stack = nn.Sequential(\n",
        "            # nn.AvgPool1d(kernel_size=100, stride=1),\n",
        "            nn.Linear(768, 256),\n",
        "            nn.ReLU(),\n",
        "            nn.Dropout(p=0.2),\n",
        "            nn.Linear(256,64),\n",
        "            nn.ReLU(),\n",
        "        )\n",
        "        self.intermediate_features_nn.append(self.sequence_stack)\n",
        "\n",
        "        self.binary_classfier = nn.Sequential(\n",
        "            nn.Linear(MAX_NUM_SENTENCES*8+64, 512),\n",
        "            nn.ReLU(),\n",
        "            nn.Dropout(p=0.2),\n",
        "            nn.Linear(512,256),\n",
        "            nn.ReLU(),\n",
        "\n",
        "            nn.Linear(256,1),\n",
        "            nn.Sigmoid()\n",
        "        )\n",
        "\n",
        "    def forward(self, X):\n",
        "\n",
        "        intermediate_features = []\n",
        "        for i, layer in enumerate(self.intermediate_features_nn[:-1]): #avoid sequence layer with -1\n",
        "            \n",
        "            # use when data is in numpy array format and not a torch tensor\n",
        "            # sentence_embedding = self.bert_model(input_ids = torch.tensor(X[3*i], dtype=torch.long)\n",
        "            #                 , attention_mask = torch.tensor(X[3*i+1], dtype=torch.float)\n",
        "            #                 , token_type_ids = torch.tensor(X[3*i+2], dtype=torch.long)\n",
        "            #                 , return_dict = False)[0]\n",
        "\n",
        "            # use when data is alreadu in torch tensor format\n",
        "            sentence_embedding = self.bert_model(input_ids = X[3*i]\n",
        "                            , attention_mask = X[3*i+1]\n",
        "                            , token_type_ids = X[3*i+2]\n",
        "                            , return_dict = False)[0]\n",
        "   \n",
        "            sentence_embedding_p = sentence_embedding.permute(0,2,1)\n",
        "            output = self.sentence_pool(sentence_embedding_p)\n",
        "            output = output.squeeze()\n",
        "            sentence_features = layer(output)\n",
        "            intermediate_features.append(sentence_features)\n",
        "\n",
        "        # use when data is in numpy array format and not a torch tensor\n",
        "        # sequence_embedding = self.bert_model(input_ids = torch.tensor(X[-3], dtype=torch.long)\n",
        "        #                 , attention_mask = torch.tensor(X[-2], dtype=torch.float)\n",
        "        #                 , token_type_ids = torch.tensor(X[-1], dtype=torch.long)\n",
        "        #                 , return_dict = False)[0]\n",
        "\n",
        "        # use when data is alreadu in torch tensor format\n",
        "        sequence_embedding = self.bert_model(input_ids = X[-3]\n",
        "                        , attention_mask = X[-2]\n",
        "                        , token_type_ids = X[-1]\n",
        "                        , return_dict = False)[0]\n",
        "        \n",
        "        sequence_embedding_p = sequence_embedding.permute(0,2,1)\n",
        "        output = self.sequence_pool(sequence_embedding_p)\n",
        "        output = output.squeeze()\n",
        "        sequence_features = self.sequence_stack(output)\n",
        "        intermediate_features.append(sequence_features)\n",
        "        concated_intermediate_features = torch.cat(intermediate_features, dim=1)\n",
        "        return self.binary_classfier(concated_intermediate_features)\n",
        "        pass\n",
        "        \n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kw2yUPdfnR8v"
      },
      "source": [
        "#Helpers"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "te0X8GZ4me0w"
      },
      "source": [
        "def generate_log_record(fold_id, fold_start_time \n",
        "                        , epoch_id, epoch_start_time\n",
        "                        , batch_id, batch_start_time\n",
        "                        , loss\n",
        "                        , batch_size\n",
        "                        , f1, accuracy, recall, precision, roc_auc\n",
        "                        , fold_end_time=None, epoch_end_time=None, batch_end_time=None\n",
        "                        ):\n",
        "    '''\n",
        "    'fold_id','fold_dur','epoch_id','fold_dur','batch_id','batch_dur','batch_size','loss','precision','recall','accuracy','f1','roc_auc'\n",
        "    '''\n",
        "    fold_dur = fold_end_time - fold_start_time if fold_end_time else 0\n",
        "    epoch_dur = epoch_end_time - epoch_start_time if epoch_end_time else 0\n",
        "    batch_dur = batch_end_time - batch_start_time if batch_end_time else 0\n",
        "    return [fold_id, fold_dur, epoch_id, epoch_dur, batch_id, batch_dur, batch_size, loss.item()\n",
        "            , precision, recall, accuracy, f1, roc_auc\n",
        "            ]\n",
        "\n",
        "\n",
        "def add_to_bert_input(bert_input, input_dict, i, pad_length):\n",
        "    input_dict['input_ids'].extend([tokenizer.pad_token_id for _ in range(pad_length)])\n",
        "    input_dict['attention_mask'].extend([0 for _ in range(pad_length)])\n",
        "    input_dict['token_type_ids'].extend([0 for _ in range(pad_length)])\n",
        "\n",
        "    bert_input[i].append(input_dict['input_ids'])\n",
        "    i+=1\n",
        "    bert_input[i].append(input_dict['attention_mask'])\n",
        "    i+=1\n",
        "    bert_input[i].append(input_dict['token_type_ids'])\n",
        "    i+=1\n",
        "\n",
        "    return bert_input\n",
        "\n",
        "def save_best_model(epoch_id, colbert_model, optimizer, loss):\n",
        "    torch.save({\n",
        "            'epoch': epoch_id,\n",
        "            'model_state_dict': colbert_model.state_dict(),\n",
        "            'optimizer_state_dict': optimizer.state_dict(),\n",
        "            'loss': loss,\n",
        "            # }, '/content/drive/MyDrive/NLP project/humor-detection-2/humor-detection/models/model-%s-%s.pth'%(fold_id, epoch))\n",
        "            }, os.path.join(MODELS_DIR,'best_model.pth'))\n",
        "\n",
        "# def validate(colbert_model, X_test,  Y_test, batch_size):\n",
        "#     '''\n",
        "#     Performs validation. Logs metrics. Returns best accuracy yet.\n",
        "#     '''\n",
        "#     colbert_model.eval()\n",
        "#     with torch.no_grad():\n",
        "\n",
        "#         Y_hat = np.array([], dtype=float)\n",
        "    \n",
        "#         for i in trange(0,len(X_test[0]), batch_size, desc='batch'):\n",
        "\n",
        "#             torch.cuda.empty_cache()\n",
        "#             gc.collect()\n",
        "\n",
        "#             X_test_batch = [X_test[j][i:i+batch_size] for j in range(len(X_test))]\n",
        "#             move_to_cuda(X_test_batch)\n",
        "\n",
        "#             Y_test_pred_batch = colbert_model(X_test_batch)\n",
        "\n",
        "#             Y_hat = np.append(Y_hat, Y_test_pred_batch.squeeze().cpu().detach().numpy())\\\n",
        "\n",
        "#         Y_hat = np.array([1  if yi > 0.5 else 0 for yi in np.array(Y_hat)])\n",
        "\n",
        "#         accuracy = accuracy_score(Y_test, Y_hat)\n",
        "#         f1 = f1_score(Y_test, Y_hat)\n",
        "#         precision = precision_score(Y_test, Y_hat)\n",
        "#         recall = recall_score(Y_test, Y_hat)\n",
        "#         roc_auc = roc_auc_score(Y_test, Y_hat)\n",
        "        \n",
        "#         print(\"Fold: \", fold_id , \"Epoch: \", epoch_id, \", Accuracy: \", accuracy)\n",
        "\n",
        "#         return precision, recall, accuracy, f1, roc_auc\n",
        "\n",
        "def validate(colbert_model, X_test,  Y_test, batch_size):\n",
        "    '''\n",
        "    Performs validation. Logs metrics. Returns best accuracy yet.\n",
        "    print_cr_cm : print classification report and confusion matrix\n",
        "    '''\n",
        "    colbert_model.eval()\n",
        "    with torch.no_grad():\n",
        "\n",
        "        Y_hat = np.array([], dtype=float)\n",
        "\n",
        "        # permutation = torch.randperm(X_test[0].shape[0])\n",
        "\n",
        "        # Set our model to training mode and not to evaluation mode\n",
        "    \n",
        "        for i in trange(0,len(X_test[0]), batch_size, desc='batch'):\n",
        "\n",
        "            torch.cuda.empty_cache()\n",
        "            gc.collect()\n",
        "\n",
        "            # indices = permutation[i:i+batch_size]\n",
        "            # print(\"X fold and Y fold\", len(X_test_fold), len(Y_test_fold))\n",
        "            X_test_batch = [X_test[j][i:i+batch_size] for j in range(len(X_test))]\n",
        "            move_to_cuda(X_test_batch)\n",
        "\n",
        "            Y_test_pred_batch = colbert_model(X_test_batch)\n",
        "\n",
        "            Y_hat = np.append(Y_hat, Y_test_pred_batch.squeeze().cpu().detach().numpy())\n",
        "            # Y_test_fold_hat.extend(Y_test_pred_batch.squeeze().cpu().detach().numpy()) \n",
        "            # Y_test_batch = torch.tensor(Y_test_fold[indices], dtype=torch.float32).to(device)\n",
        "\n",
        "        Y_hat = np.array([1  if yi > 0.5 else 0 for yi in np.array(Y_hat)])\n",
        "\n",
        "        # if(print_cr_cm):\n",
        "        cm = confusion_matrix(Y_test, Y_hat)\n",
        "        cr = metrics.classification_report(Y_test, Y_hat)\n",
        "        #     disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=['Non humor', 'Humor'])\n",
        "        #     disp.plot(cmap='Blues')\n",
        "        #     plt.show()\n",
        "\n",
        "        accuracy = accuracy_score(Y_test, Y_hat)\n",
        "        f1 = f1_score(Y_test, Y_hat)\n",
        "        precision = precision_score(Y_test, Y_hat)\n",
        "        recall = recall_score(Y_test, Y_hat)\n",
        "        roc_auc = roc_auc_score(Y_test, Y_hat)\n",
        "        \n",
        "        # print(\"Fold: \", fold_id , \"Epoch: \", epoch_id, \", Accuracy: \", accuracy)\n",
        "\n",
        "        return precision, recall, accuracy, f1, roc_auc, cm, cr\n",
        "\n",
        "  \n",
        "\n",
        "def move_to_cuda(X):\n",
        "    for i in range(0, MAX_NUM_SENTENCES*3+3, 3):\n",
        "        X[i] = torch.tensor(X[i],dtype=torch.long).to(device)\n",
        "        X[i+1] = torch.tensor(X[i+1],dtype=torch.float).to(device)\n",
        "        X[i+2] = torch.tensor(X[i+2],dtype=torch.long).to(device)\n",
        "\n",
        "def prepare_bert_input(df, MAX_SENTENCE_LEN, MAX_NUM_SENTENCES, MAX_SEQUENCE_LEN, nltk_tokenizer, tokenizer):\n",
        "    sequences = df\n",
        "    bert_input = [[] for _ in range(MAX_NUM_SENTENCES*3 +3)]\n",
        "    \n",
        "    \n",
        "    for sequence in tqdm(sequences): #TODO debug check if indeed iterating over sequence\n",
        "        sentences = nltk_tokenizer.tokenize(sequence)\n",
        "\n",
        "        i = 0\n",
        "\n",
        "        for j in range(MAX_NUM_SENTENCES):\n",
        "            sentence = sentences[j] if j<len(sentences) else ''\n",
        "            input_dict = tokenizer(sentence, padding=\"max_length\", max_length=MAX_SENTENCE_LEN, truncation=True)\n",
        "\n",
        "            # keep them on CPU\n",
        "            bert_input[i].append(input_dict['input_ids'])\n",
        "            i+=1\n",
        "            bert_input[i].append(input_dict['attention_mask'])\n",
        "            i+=1\n",
        "            bert_input[i].append(input_dict['token_type_ids'])\n",
        "            i+=1\n",
        "            \n",
        "        input_dict = tokenizer(sequence, padding=\"max_length\", max_length=MAX_SEQUENCE_LEN, truncation=True)  \n",
        "        bert_input[i].append(input_dict['input_ids'])\n",
        "        i+=1\n",
        "        bert_input[i].append(input_dict['attention_mask'])\n",
        "        i+=1\n",
        "        bert_input[i].append(input_dict['token_type_ids'])\n",
        "        i+=1\n",
        "\n",
        "    for i in range((MAX_NUM_SENTENCES*3)+3):\n",
        "        bert_input[i] = np.asarray(bert_input[i], dtype=np.int32)\n",
        "\n",
        "        \n",
        "    return bert_input"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wDcBCOdQnWPn"
      },
      "source": [
        "# Some variables"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 185,
          "referenced_widgets": [
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            "685394cde142482090f44961c8d0e6cb"
          ]
        },
        "id": "16mCsNUSnTa4",
        "outputId": "9ecaf846-538f-4d72-f38a-2b77203635c3"
      },
      "source": [
        "config = BertConfig() \n",
        "config.output_hidden_states = False\n",
        "\n",
        "tokenizer = BertTokenizer.from_pretrained(MODEL_TYPE)\n",
        "nltk_tokenizer = nltk.data.load('tokenizers/punkt/english.pickle') # to split sequence into senteces\n",
        "\n",
        "X_train = prepare_bert_input(df_train[:training_sample_count].text.values, MAX_SENTENCE_LEN, MAX_NUM_SENTENCES, MAX_SEQUENCE_LEN, nltk_tokenizer, tokenizer)\n",
        "Y_train = np.array(df_train.humor)\n",
        "\n",
        "X_test = prepare_bert_input(df_test.text.values, MAX_SENTENCE_LEN, MAX_NUM_SENTENCES, MAX_SEQUENCE_LEN, nltk_tokenizer, tokenizer)\n",
        "Y_test = np.array(df_test.humor)\n",
        "\n",
        "colbert_model = BertForHumorDetection(bert_config=config, MAX_NUM_SENTENCES=MAX_NUM_SENTENCES, MAX_SENTENCE_LENGTH=MAX_SENTENCE_LEN, MAX_SEQUENCE_LENGTH=MAX_SEQUENCE_LEN)\n",
        "colbert_model.to(device)\n",
        "optimizer = torch.optim.Adam(colbert_model.parameters(), lr=LR)\n",
        "loss_f = torch.nn.BCELoss()\n",
        "\n",
        "kfold = KFold(n_splits=5, shuffle=True, random_state=1)\n",
        "\n",
        "fold_id = 0\n",
        "fold_start_time = time.time()\n",
        "fold_duration = 0\n",
        "\n",
        "batch_size = 16\n",
        "best_f1 = 0"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e6817a2b371b4c65a4ea03f40d698ff4",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/60000 [00:00<?, ?it/s]"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "bdcd60fec1ba447cb8a1d4d3bd850ab3",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/40000 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "720783c0df304d4fadc0cc76042dfe14",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/420M [00:00<?, ?B/s]"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.decoder.weight', 'cls.predictions.transform.dense.weight', 'cls.seq_relationship.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.bias']\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"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rHun_U7UpG41"
      },
      "source": [
        "#Train"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
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        },
        "id": "4j0nVbkxpHpi",
        "outputId": "897b372c-8651-491f-f03b-0d04b9122779"
      },
      "source": [
        "for train_index, test_index in tqdm(kfold.split(df_train.text[:training_sample_count]), desc='k-fold'):\n",
        "    # print('Fold>>> ', fold)\n",
        "    fold_id+=1\n",
        "\n",
        "    X_train_fold = [np.array(X_train[i][train_index]) for i in range(len(X_train))]\n",
        "    #Y_train_fold = torch.tensor(Y_train[train_index], dtype=torch.float32).to(device)\n",
        "    Y_train_fold = Y_train[train_index]\n",
        "\n",
        "    X_test_fold = [np.array(X_train[i][test_index]) for i in range(len(X_train))]\n",
        "    #Y_train_fold = torch.tensor(Y_train[train_index], dtype=torch.float32).to(device)\n",
        "    Y_test_fold = Y_train[test_index]\n",
        "\n",
        "    for epoch_id in trange(3,desc='epoch'):\n",
        "\n",
        "        epoch_start_time = time.time()\n",
        "        epoch_duration = 0\n",
        "\n",
        "        permutation = torch.randperm(X_train_fold[0].shape[0])\n",
        "\n",
        "        # Set our model to training mode and not to evaluation mode\n",
        "\n",
        "        batch_id = 0\n",
        "        for i in trange(0,len(X_train_fold[0]), batch_size, desc='batch'):\n",
        "\n",
        "            batch_id += 1\n",
        "            batch_duration = 0\n",
        "            batch_start_time = time.time()\n",
        "\n",
        "            colbert_model.train()  # TODO should this be outsize of for? inside epoch?\n",
        "\n",
        "            # print('Epoch>>> ', epoch)\n",
        "\n",
        "            torch.cuda.empty_cache()\n",
        "            gc.collect()\n",
        "\n",
        "            # indices = permutation[i:i+batch_size]\n",
        "            # X_train_batch = X_train_fold[i:i+batch_size] #np.array(X_train_fold)[indices]\n",
        "            X_train_batch = [X_train_fold[j][i:i+batch_size] for j in range(len(X_train_fold))]\n",
        "            move_to_cuda(X_train_batch)\n",
        "            Y_train_batch = torch.tensor(Y_train_fold[i:i+batch_size], dtype=torch.float32).to(device)\n",
        "\n",
        "            optimizer.zero_grad() #TODO\n",
        "\n",
        "            Y_hat_train_batch = colbert_model(X_train_batch)\n",
        "            loss = loss_f(Y_hat_train_batch.squeeze(), Y_train_batch)\n",
        "            loss.backward()\n",
        "            optimizer.step() #TODO\n",
        "\n",
        "            if batch_id % LOG_PER_N_BATCH == 0:\n",
        "                precision, recall, accuracy, f1, roc_auc, _, _ = validate(colbert_model, X_test_fold,  Y_test_fold, batch_size)\n",
        "                log_record = generate_log_record(fold_id, fold_start_time,epoch_id,epoch_start_time,batch_id,batch_start_time, loss, batch_size, f1, accuracy, recall, precision, roc_auc, batch_end_time=time.time())\n",
        "                with open(LOG_FILE, 'a') as csvfile:\n",
        "                    csvwriter = csv.writer(csvfile)                         \n",
        "                    csvwriter.writerow(log_record)\n",
        "                if best_f1 < f1:\n",
        "                    best_f1 = f1\n",
        "                    save_best_model(epoch_id, colbert_model, optimizer, loss)\n",
        "\n",
        "        precision, recall, accuracy, f1, roc_auc, _, _ = validate(colbert_model, X_test_fold,  Y_test_fold, batch_size)\n",
        "        log_record = generate_log_record(fold_id, fold_start_time,epoch_id,epoch_start_time,batch_id,batch_start_time, loss, batch_size, f1, accuracy, recall, precision, roc_auc, epoch_end_time=time.time(), batch_end_time=time.time())\n",
        "        with open(LOG_FILE, 'a') as csvfile:\n",
        "            csvwriter = csv.writer(csvfile)                         \n",
        "            csvwriter.writerow(log_record)\n",
        "        if best_f1 < f1:\n",
        "            best_f1 = f1\n",
        "            save_best_model(epoch_id, colbert_model, optimizer, loss)\n",
        "        \n",
        "print('Finished training')"
      ],
      "execution_count": null,
      "outputs": [
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              "k-fold: 0it [00:00, ?it/s]"
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              "epoch:   0%|          | 0/3 [00:00<?, ?it/s]"
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              "batch:   0%|          | 0/3000 [00:00<?, ?it/s]"
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        {
          "output_type": "stream",
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          "text": [
            "Fold:  2 Epoch:  0 , Accuracy:  0.724\n"
          ]
        },
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          "text": [
            "Fold:  2 Epoch:  0 , Accuracy:  0.9255\n"
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          "text": [
            "Fold:  2 Epoch:  0 , Accuracy:  0.9445\n"
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            "Fold:  2 Epoch:  0 , Accuracy:  0.9506666666666667\n"
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            "Fold:  2 Epoch:  0 , Accuracy:  0.9551666666666667\n"
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            "Fold:  2 Epoch:  0 , Accuracy:  0.9678333333333333\n"
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            "Fold:  2 Epoch:  0 , Accuracy:  0.9688333333333333\n"
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            "Fold:  2 Epoch:  0 , Accuracy:  0.9574166666666667\n"
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            "Fold:  2 Epoch:  0 , Accuracy:  0.97025\n"
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            "Fold:  2 Epoch:  0 , Accuracy:  0.97975\n"
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            "Fold:  2 Epoch:  0 , Accuracy:  0.97975\n"
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            "Fold:  2 Epoch:  0 , Accuracy:  0.9771666666666666\n"
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        {
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    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Orr8cGAf1pXf"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WhVRRqGdqvV3"
      },
      "source": [
        "# Load saved model and evaluate"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tuhOVoqCqyCK",
        "outputId": "e73532c1-4e22-4fab-c516-6c6d5ea77d38"
      },
      "source": [
        "colbert_model_loaded = BertForHumorDetection(MAX_NUM_SENTENCES, MAX_SENTENCE_LEN, MAX_SEQUENCE_LEN, config)\n",
        "\n",
        "colbert_model_loaded.to(device)\n",
        "colbert_model_loaded.load_state_dict(torch.load(os.path.join(MODELS_DIR,'the_best_bert_model.pth'), map_location=torch.device('cpu'))['model_state_dict'])\n",
        "colbert_model_loaded.eval()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.decoder.weight', 'cls.predictions.transform.dense.weight', 'cls.seq_relationship.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.bias']\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"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "BertForHumorDetection(\n",
              "  (bert_model): BertModel(\n",
              "    (embeddings): BertEmbeddings(\n",
              "      (word_embeddings): Embedding(30522, 768, padding_idx=0)\n",
              "      (position_embeddings): Embedding(512, 768)\n",
              "      (token_type_embeddings): Embedding(2, 768)\n",
              "      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "      (dropout): Dropout(p=0.1, inplace=False)\n",
              "    )\n",
              "    (encoder): BertEncoder(\n",
              "      (layer): ModuleList(\n",
              "        (0): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (1): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (2): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (3): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (4): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (5): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (6): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (7): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (8): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (9): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (10): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (11): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (pooler): BertPooler(\n",
              "      (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "      (activation): Tanh()\n",
              "    )\n",
              "  )\n",
              "  (sentence_pool): AvgPool1d(kernel_size=(20,), stride=(1,), padding=(0,))\n",
              "  (sequence_pool): AvgPool1d(kernel_size=(100,), stride=(1,), padding=(0,))\n",
              "  (intermediate_features_nn): ModuleList(\n",
              "    (0): Sequential(\n",
              "      (0): Linear(in_features=768, out_features=32, bias=True)\n",
              "      (1): ReLU()\n",
              "      (2): Dropout(p=0.3, inplace=False)\n",
              "      (3): Linear(in_features=32, out_features=8, bias=True)\n",
              "      (4): ReLU()\n",
              "    )\n",
              "    (1): Sequential(\n",
              "      (0): Linear(in_features=768, out_features=32, bias=True)\n",
              "      (1): ReLU()\n",
              "      (2): Dropout(p=0.3, inplace=False)\n",
              "      (3): Linear(in_features=32, out_features=8, bias=True)\n",
              "      (4): ReLU()\n",
              "    )\n",
              "    (2): Sequential(\n",
              "      (0): Linear(in_features=768, out_features=32, bias=True)\n",
              "      (1): ReLU()\n",
              "      (2): Dropout(p=0.3, inplace=False)\n",
              "      (3): Linear(in_features=32, out_features=8, bias=True)\n",
              "      (4): ReLU()\n",
              "    )\n",
              "    (3): Sequential(\n",
              "      (0): Linear(in_features=768, out_features=32, bias=True)\n",
              "      (1): ReLU()\n",
              "      (2): Dropout(p=0.3, inplace=False)\n",
              "      (3): Linear(in_features=32, out_features=8, bias=True)\n",
              "      (4): ReLU()\n",
              "    )\n",
              "    (4): Sequential(\n",
              "      (0): Linear(in_features=768, out_features=32, bias=True)\n",
              "      (1): ReLU()\n",
              "      (2): Dropout(p=0.3, inplace=False)\n",
              "      (3): Linear(in_features=32, out_features=8, bias=True)\n",
              "      (4): ReLU()\n",
              "    )\n",
              "    (5): Sequential(\n",
              "      (0): Linear(in_features=768, out_features=256, bias=True)\n",
              "      (1): ReLU()\n",
              "      (2): Dropout(p=0.2, inplace=False)\n",
              "      (3): Linear(in_features=256, out_features=64, bias=True)\n",
              "      (4): ReLU()\n",
              "    )\n",
              "  )\n",
              "  (sequence_stack): Sequential(\n",
              "    (0): Linear(in_features=768, out_features=256, bias=True)\n",
              "    (1): ReLU()\n",
              "    (2): Dropout(p=0.2, inplace=False)\n",
              "    (3): Linear(in_features=256, out_features=64, bias=True)\n",
              "    (4): ReLU()\n",
              "  )\n",
              "  (binary_classfier): Sequential(\n",
              "    (0): Linear(in_features=104, out_features=512, bias=True)\n",
              "    (1): ReLU()\n",
              "    (2): Dropout(p=0.2, inplace=False)\n",
              "    (3): Linear(in_features=512, out_features=256, bias=True)\n",
              "    (4): ReLU()\n",
              "    (5): Linear(in_features=256, out_features=1, bias=True)\n",
              "    (6): Sigmoid()\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467,
          "referenced_widgets": [
            "e23758c9112a4ba8b2503d7fbec9ee8c",
            "40e0b2d800bb4b958bd86ba4eb548b44",
            "39e8e44e6c8b42aba7ab082ac8401664",
            "0b2c24b1bce74426863e902f39037ea8",
            "046c791e21ac4425880b328c855e0a48",
            "77a0206d0f2e4ec59d758e475018e8ad",
            "36e332ab0ed347a6aa3089d2694f2357",
            "ba2f13453f9d4d4595221d0cba61a329",
            "4f2edbff5c9b4902878fc1f7db751330",
            "84bdba0094bb4c54a18eff9dc9bb535f",
            "e61c1d8ddffb4eb4a430a194d4ad0459"
          ]
        },
        "id": "-TLeH0yCs71L",
        "outputId": "eb1403c3-821d-415a-a10a-7109089caba8"
      },
      "source": [
        "#validate(colgpt2_model_loaded, X_test,  Y_test, batch_size, True)  #TODO run on full test dataset, but it takes hours to execute\n",
        "\n",
        "_, _, _, _, _,cm, cr = validate(colbert_model_loaded, [x_test_comp[:160] for x_test_comp in X_test], Y_test[:160], batch_size)  #TODO run on full test dataset\n",
        "print(cr)\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=['Non humor', 'Humor'])\n",
        "disp.plot(cmap='Blues')\n",
        "plt.show()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e23758c9112a4ba8b2503d7fbec9ee8c",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "batch:   0%|          | 0/10 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "       False       0.97      0.99      0.98        79\n",
            "        True       0.99      0.98      0.98        81\n",
            "\n",
            "    accuracy                           0.98       160\n",
            "   macro avg       0.98      0.98      0.98       160\n",
            "weighted avg       0.98      0.98      0.98       160\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2Aa8eVesPAYw"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}