Salinan_dari_(Used)_XLNet_Model.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "Salinan dari (Used) XLNet Model.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "pgbAFX4vJ4Vi"
      ]
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ER_tseZEFvMA"
      },
      "source": [
        "https://towardsdatascience.com/multi-label-text-classification-with-xlnet-b5f5755302df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BfpRxgVNI8VK"
      },
      "source": [
        "#Preprocess Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kIlGolHqnmp-",
        "outputId": "9629ba72-f412-4f70-ba8a-8916b5809ded"
      },
      "source": [
        "!pip install transformers\n",
        "!pip install sentencepiece"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: transformers in /usr/local/lib/python3.7/dist-packages (4.11.3)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.7/dist-packages (from transformers) (6.0)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from transformers) (21.0)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (1.19.5)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers) (4.62.3)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from transformers) (4.8.1)\n",
            "Requirement already satisfied: sacremoses in /usr/local/lib/python3.7/dist-packages (from transformers) (0.0.46)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers) (3.3.0)\n",
            "Requirement already satisfied: tokenizers<0.11,>=0.10.1 in /usr/local/lib/python3.7/dist-packages (from transformers) (0.10.3)\n",
            "Requirement already satisfied: huggingface-hub>=0.0.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (0.0.19)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers) (2.23.0)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (2019.12.20)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from huggingface-hub>=0.0.17->transformers) (3.7.4.3)\n",
            "Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->transformers) (2.4.7)\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: 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: 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: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2021.5.30)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.0.1)\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: sentencepiece in /usr/local/lib/python3.7/dist-packages (0.1.96)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XhLMBl6gntH5"
      },
      "source": [
        "import os\n",
        "import math\n",
        "import re\n",
        "\n",
        "import torch\n",
        "from torch.nn import BCEWithLogitsLoss\n",
        "from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler\n",
        "from transformers import AdamW, XLNetTokenizer, XLNetModel, XLNetLMHeadModel, XLNetConfig\n",
        "from keras.preprocessing.sequence import pad_sequences\n",
        "from sklearn.model_selection import train_test_split\n",
        "import sentencepiece\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from tqdm import tqdm, trange\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RikVjq7Jnv0Z",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8dd0e306-61f6-4d0f-8284-9d1de7f50efc"
      },
      "source": [
        "print(\"GPU Available: {}\".format(torch.cuda.is_available()))\n",
        "n_gpu = torch.cuda.device_count()\n",
        "print(\"Number of GPU Available: {}\".format(n_gpu))\n",
        "print(\"GPU: {}\".format(torch.cuda.get_device_name(0)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GPU Available: True\n",
            "Number of GPU Available: 1\n",
            "GPU: Tesla K80\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H5x-AZDIi0Ob"
      },
      "source": [
        "train = pd.read_csv(r\"https://raw.githubusercontent.com/RussellDash332/CS3244-Twemoji/main/Datasets/train_text_emoji_clean.csv\")\n",
        "test = pd.read_csv(r\"https://raw.githubusercontent.com/RussellDash332/CS3244-Twemoji/main/Datasets/test_text_emoji_clean.csv\")\n",
        "valid = pd.read_csv(r\"https://raw.githubusercontent.com/RussellDash332/CS3244-Twemoji/main/Datasets/valid_text_emoji_clean.csv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EiYY-vz50-GU"
      },
      "source": [
        "def to_columns(label):\n",
        "    if i == label:\n",
        "        return 1\n",
        "    else:\n",
        "        return 0"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qZa801vZ13aT"
      },
      "source": [
        "for i in range(12):\n",
        "    train[i] = train['labels'].apply(to_columns)\n",
        "    test[i] = test['labels'].apply(to_columns)\n",
        "    valid[i] = valid['labels'].apply(to_columns)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gMKPWSIo2Z6d"
      },
      "source": [
        "train = train.drop(\"labels\", axis = 1)\n",
        "test = test.drop(\"labels\", axis = 1)\n",
        "valid = valid.drop(\"labels\", axis = 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wd5zlGsYo8Aw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 641
        },
        "outputId": "1c261266-740e-43fc-a111-dd3c27c0ef77"
      },
      "source": [
        "fig, ax = plt.subplots(figsize=(20, 10));\n",
        "train[range(12)].hist(ax=ax);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: UserWarning: To output multiple subplots, the figure containing the passed axes is being cleared\n",
            "  \n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 12 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qd0hXPPdk-f1"
      },
      "source": [
        "import spacy\n",
        "\n",
        "nlp = spacy.load('en', disable=['parser', 'ner'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kn43oMzhn3e3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "42196153-e341-404e-f8e8-30c87a635c0d"
      },
      "source": [
        "sentence = \"The striped bats are hanging on their feet for best\"\n",
        "\n",
        "# Parse the sentence using the loaded 'en' model object `nlp`\n",
        "doc = nlp(sentence)\n",
        "\n",
        "# Extract the lemma for each token and join\n",
        "\" \".join([token.lemma_ for token in doc])\n",
        "#> 'the strip bat be hang on -PRON- foot for good'"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'the stripe bat be hang on -PRON- foot for good'"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eS95ouhr26DN"
      },
      "source": [
        "def preprocess_text(sen):\n",
        "    sentence = nlp(sen)\n",
        "    sentence = \" \".join([token.lemma_ for token in sentence])\n",
        "\n",
        "    sentence = re.sub(\"-PRON-\", \"\", sentence)\n",
        "\n",
        "    # Remove punctuations and numbers\n",
        "    sentence = re.sub('[^a-zA-Z]', ' ', sentence)\n",
        "\n",
        "    # Removing multiple spaces\n",
        "    sentence = re.sub(r'\\s+', ' ', sentence)\n",
        "\n",
        "    sentence = sentence.lower()\n",
        "\n",
        "    return sentence.strip()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TsKF_TSeWE6a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f82660ba-7b53-4111-c891-074080ba14d6"
      },
      "source": [
        "print(preprocess_text(\"I've known that you could've done something better than this-- ''' :)) THEY ARE called AppLes fruits oranges can't you see?\"))\n",
        "print(preprocess_text(\"I'll go there in a few seconds.. don't wait for me\"))\n",
        "print(preprocess_text(\"better best\"))\n",
        "print(preprocess_text(\"what's the point?\"))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "have know that could have do something well than this they are call apples fruit orange can not see\n",
            "will go there in a few second do not wait for\n",
            "better best\n",
            "what be the point\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lBhrpe3JYE80"
      },
      "source": [
        "train[\"tweets\"] = train[\"tweets\"].apply(preprocess_text)\n",
        "test[\"tweets\"] = test[\"tweets\"].apply(preprocess_text)\n",
        "valid[\"tweets\"] = valid[\"tweets\"].apply(preprocess_text)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4mpGax5fpwWf"
      },
      "source": [
        "def plot_sentence_embeddings_length(text_list, tokenizer):\n",
        "    tokenized_texts = list(map(lambda t: tokenizer.tokenize(t), text_list))\n",
        "    tokenized_texts_len = list(map(lambda t: len(t), tokenized_texts))\n",
        "    fig, ax = plt.subplots(figsize=(8, 5));\n",
        "    ax.hist(tokenized_texts_len, bins=40);\n",
        "    ax.set_xlabel(\"Length of Tweets Embeddings\");\n",
        "    ax.set_ylabel(\"Number of Tweets\");\n",
        "    return\n",
        "\n",
        "tokenizer = XLNetTokenizer.from_pretrained(\"xlnet-base-cased\", do_lower_case=True)\n",
        "tokenizer(\"Don't you love 🤗 Transformers? We sure do.\", return_tensors = \"pt\")\n",
        "\n",
        "train_text_list = train[\"tweets\"].values\n",
        "test_text_list = test[\"tweets\"].values\n",
        "valid_text_list = valid[\"tweets\"].values"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qAnE08YitXR8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "outputId": "693b740d-c0c5-41ea-c6d4-3b6f0147dac5"
      },
      "source": [
        "plot_sentence_embeddings_length(train_text_list, tokenizer)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jFUAhPLQtZWa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "outputId": "6dd3b758-d183-47d5-886d-155f5900c209"
      },
      "source": [
        "plot_sentence_embeddings_length(test_text_list, tokenizer)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jj03cHJptoUD"
      },
      "source": [
        "def tokenize_inputs(text_list, tokenizer, num_embeddings=512):\n",
        "    \"\"\"\n",
        "    Tokenizes the input text input into ids. Appends the appropriate special\n",
        "    characters to the end of the text to denote end of sentence. Truncate or pad\n",
        "    the appropriate sequence length.\n",
        "    \"\"\"\n",
        "    # tokenize the text, then truncate sequence to the desired length minus 2 for\n",
        "    # the 2 special characters\n",
        "    tokenized_texts = list(map(lambda t: tokenizer.tokenize(t)[:num_embeddings-2], text_list))\n",
        "    # convert tokenized text into numeric ids for the appropriate LM\n",
        "    input_ids = [tokenizer.convert_tokens_to_ids(x) for x in tokenized_texts]\n",
        "    # append special token \"<s>\" and </s> to end of sentence\n",
        "    input_ids = [tokenizer.build_inputs_with_special_tokens(x) for x in input_ids]\n",
        "    # pad sequences\n",
        "    input_ids = pad_sequences(input_ids, maxlen=num_embeddings, dtype=\"long\", truncating=\"post\", padding=\"post\")\n",
        "    return input_ids"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CSjif6Wht3_r",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7a2e3b9f-5d89-4eff-ad7c-23385fbfcb7c"
      },
      "source": [
        "# create input id tokens\n",
        "train_input_ids = tokenize_inputs(train_text_list, tokenizer, num_embeddings=31) # since most of the tweets have less than 40 sub-words\n",
        "print(train_input_ids)\n",
        "print(train_input_ids[0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[  297    39   102 ...     0     0     0]\n",
            " [   17 18535    47 ...     0     0     0]\n",
            " [   39 24361    93 ...     0     0     0]\n",
            " ...\n",
            " [11752 22268   112 ...     0     0     0]\n",
            " [  805 16395    44 ...     0     0     0]\n",
            " [  112    50    39 ...     0     0     0]]\n",
            "[ 297   39  102 1127   17  155 7290    4    3    0    0    0    0    0\n",
            "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
            "    0    0    0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IxvpZtPvuRp7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6956a236-03f4-465b-8d95-d0d386c441fc"
      },
      "source": [
        "print(tokenizer.decode([297, 39, 102, 1127, 17, 155, 1284, 180, 7290, 4, 3]))\n",
        "print(tokenizer.decode([4, 3, 4, 3]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "school be so dead o pron god<sep><cls>\n",
            "<sep><cls><sep><cls>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "loQBUF8Kuz4b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9576e418-c6d3-4818-bebf-7ae159a6ba0f"
      },
      "source": [
        "test_input_ids = tokenize_inputs(test_text_list, tokenizer, num_embeddings=31) # since most of the tweets have less than 50 sub-words\n",
        "print(test_input_ids)\n",
        "print(test_input_ids[0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[   18  3580    39 ...     0     0     0]\n",
            " [11508    39   830 ...     0     0     0]\n",
            " [   17   150    64 ...     0     0     0]\n",
            " ...\n",
            " [   17   150   112 ...     0     0     0]\n",
            " [  338  1816    48 ...     0     0     0]\n",
            " [ 5422   171   129 ...     0     0     0]]\n",
            "[  18 3580   39  102 1429   17  150  197   17 4925  529 7144    4    3\n",
            "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
            "    0    0    0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mwIVIRdTvifn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8b917bb1-5a20-4eef-cd2e-82f2f30e9cca"
      },
      "source": [
        "valid_input_ids = tokenize_inputs(valid_text_list, tokenizer, num_embeddings=31) # since most of the tweets have less than 50 sub-words\n",
        "print(valid_input_ids)\n",
        "print(valid_input_ids[0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[   17 11709 16247 ...     0     0     0]\n",
            " [   39  1127     4 ...     0     0     0]\n",
            " [ 2514  1615    18 ...     0     0     0]\n",
            " ...\n",
            " [ 4489   125   759 ...     0     0     0]\n",
            " [   71   166  1620 ...     0     0     0]\n",
            " [   17   150   564 ...     0     0     0]]\n",
            "[   17 11709 16247  2035  6231    76     4     3     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "siLisD2ZuGHx"
      },
      "source": [
        "def create_attn_masks(input_ids):\n",
        "    \"\"\"\n",
        "    Create attention masks to tell model whether attention should be applied to\n",
        "    the input id tokens. Do not want to perform attention on padding tokens.\n",
        "    \"\"\"\n",
        "    # Create attention masks\n",
        "    attention_masks = []\n",
        "\n",
        "    # Create a mask of 1s for each token followed by 0s for padding\n",
        "    for seq in input_ids:\n",
        "        seq_mask = [float(i>0) for i in seq]\n",
        "        attention_masks.append(seq_mask)\n",
        "    return attention_masks\n",
        "    \n",
        "train_attention_masks = create_attn_masks(train_input_ids)\n",
        "test_attention_masks = create_attn_masks(test_input_ids)\n",
        "valid_attention_masks = create_attn_masks(valid_input_ids)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DB3aeUjcuwwT"
      },
      "source": [
        "# add input ids and attention masks to the dataframe\n",
        "train[\"features\"] = train_input_ids.tolist()\n",
        "train[\"masks\"] = train_attention_masks"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w596yG5tvy1y"
      },
      "source": [
        "test[\"features\"] = test_input_ids.tolist()\n",
        "test[\"masks\"] = test_attention_masks"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rKUHFon9vzab"
      },
      "source": [
        "valid[\"features\"] = valid_input_ids.tolist()\n",
        "valid[\"masks\"] = valid_attention_masks"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xjg5uu3tvGUD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "f414f3a9-d8cd-4089-a19e-efdf7edd7825"
      },
      "source": [
        "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>annotations</th>\n",
              "      <th>tweets</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>10</th>\n",
              "      <th>11</th>\n",
              "      <th>features</th>\n",
              "      <th>masks</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>😂</td>\n",
              "      <td>school be so dead o god</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>[297, 39, 102, 1127, 17, 155, 7290, 4, 3, 0, 0...</td>\n",
              "      <td>[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>😂</td>\n",
              "      <td>lol have be tell be bad than but i do not beli...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>[17, 18535, 47, 39, 759, 39, 948, 100, 57, 17,...</td>\n",
              "      <td>[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>😂</td>\n",
              "      <td>be excite to hear that shit be go rattle soul</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>[39, 24361, 93, 22, 1388, 29, 7341, 39, 216, 2...</td>\n",
              "      <td>[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>😂</td>\n",
              "      <td>damn alicia know everything even indirect tweet</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>[7757, 1296, 25360, 175, 931, 176, 16094, 1929...</td>\n",
              "      <td>[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>😂</td>\n",
              "      <td>that sound like everything</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>[29, 1224, 115, 931, 4, 3, 0, 0, 0, 0, 0, 0, 0...</td>\n",
              "      <td>[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52879</th>\n",
              "      <td>🙃</td>\n",
              "      <td>or that picture be just cute to</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>[49, 29, 1986, 39, 125, 10920, 22, 4, 3, 0, 0,...</td>\n",
              "      <td>[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52880</th>\n",
              "      <td>🙃</td>\n",
              "      <td>be tow wit some gas still hate</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>[39, 22, 694, 16395, 106, 1256, 194, 4177, 4, ...</td>\n",
              "      <td>[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52881</th>\n",
              "      <td>🙃</td>\n",
              "      <td>yeah kinda do</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>[11752, 22268, 112, 4, 3, 0, 0, 0, 0, 0, 0, 0,...</td>\n",
              "      <td>[1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52882</th>\n",
              "      <td>🙃</td>\n",
              "      <td>nothing wit youuuu raymundo</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>[805, 16395, 44, 660, 660, 660, 17, 2563, 98, ...</td>\n",
              "      <td>[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52883</th>\n",
              "      <td>🙃</td>\n",
              "      <td>do not be that person who park right next to w...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>[112, 50, 39, 29, 601, 61, 2133, 203, 244, 22,...</td>\n",
              "      <td>[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>52884 rows × 16 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      annotations  ...                                              masks\n",
              "0               😂  ...  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, ...\n",
              "1               😂  ...  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, ...\n",
              "2               😂  ...  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, ...\n",
              "3               😂  ...  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, ...\n",
              "4               😂  ...  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, ...\n",
              "...           ...  ...                                                ...\n",
              "52879           🙃  ...  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, ...\n",
              "52880           🙃  ...  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, ...\n",
              "52881           🙃  ...  [1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...\n",
              "52882           🙃  ...  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, ...\n",
              "52883           🙃  ...  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, ...\n",
              "\n",
              "[52884 rows x 16 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VyS6M3gowVyh"
      },
      "source": [
        "X_train = train[\"features\"].values.tolist()\n",
        "X_valid = valid[\"features\"].values.tolist()\n",
        "\n",
        "train_masks = train[\"masks\"].values.tolist()\n",
        "valid_masks = valid[\"masks\"].values.tolist()\n",
        "\n",
        "label_cols = range(12)\n",
        "Y_train = train[label_cols].values.tolist()\n",
        "Y_valid = valid[label_cols].values.tolist()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NA5ehvAfnwrr"
      },
      "source": [
        "Y_test = test[label_cols].values.tolist()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PG1CJF3UvSUK"
      },
      "source": [
        "# Convert all of our input ids and attention masks into \n",
        "# torch tensors, the required datatype for our model\n",
        "\n",
        "X_train = torch.tensor(X_train)\n",
        "X_valid = torch.tensor(X_valid)\n",
        "\n",
        "Y_train = torch.tensor(Y_train, dtype=torch.float32)\n",
        "Y_valid = torch.tensor(Y_valid, dtype=torch.float32)\n",
        "\n",
        "train_masks = torch.tensor(train_masks, dtype=torch.long)\n",
        "valid_masks = torch.tensor(valid_masks, dtype=torch.long)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jKR-GOyVwwsa"
      },
      "source": [
        "# Select a batch size for training\n",
        "batch_size = 32\n",
        "\n",
        "# Create an iterator of our data with torch DataLoader. This helps save on \n",
        "# memory during training because, unlike a for loop, \n",
        "# with an iterator the entire dataset does not need to be loaded into memory\n",
        "\n",
        "train_data = TensorDataset(X_train, train_masks, Y_train)\n",
        "train_sampler = RandomSampler(train_data)\n",
        "train_dataloader = DataLoader(train_data,\\\n",
        "                              sampler=train_sampler,\\\n",
        "                              batch_size=batch_size)\n",
        "\n",
        "validation_data = TensorDataset(X_valid, valid_masks, Y_valid)\n",
        "validation_sampler = SequentialSampler(validation_data)\n",
        "validation_dataloader = DataLoader(validation_data,\\\n",
        "                                   sampler=validation_sampler,\\\n",
        "                                   batch_size=batch_size)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JSwkW3nXJIT2"
      },
      "source": [
        "# Model Building"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lmkTbnpKxKGE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e603b751-f036-4a9f-ba99-cafff8e814d5"
      },
      "source": [
        "#config = XLNetConfig()\n",
        "        \n",
        "class XLNetForMultiLabelSequenceClassification(torch.nn.Module):\n",
        "  \n",
        "  def __init__(self, num_labels=2):\n",
        "    super(XLNetForMultiLabelSequenceClassification, self).__init__()\n",
        "    self.num_labels = num_labels\n",
        "    self.xlnet = XLNetModel.from_pretrained('xlnet-base-cased')\n",
        "    self.classifier = torch.nn.Linear(768, num_labels)\n",
        "\n",
        "    torch.nn.init.xavier_normal_(self.classifier.weight)\n",
        "\n",
        "  def forward(self, input_ids, token_type_ids=None,\\\n",
        "              attention_mask=None, labels=None):\n",
        "    # last hidden layer\n",
        "    last_hidden_state = self.xlnet(input_ids=input_ids,\\\n",
        "                                   attention_mask=attention_mask,\\\n",
        "                                   token_type_ids=token_type_ids)\n",
        "    # pool the outputs into a mean vector\n",
        "    mean_last_hidden_state = self.pool_hidden_state(last_hidden_state)\n",
        "    logits = self.classifier(mean_last_hidden_state)\n",
        "        \n",
        "    if labels is not None:\n",
        "      loss_fct = BCEWithLogitsLoss()\n",
        "      loss = loss_fct(logits.view(-1, self.num_labels),\\\n",
        "                      labels.view(-1, self.num_labels))\n",
        "      return loss\n",
        "    else:\n",
        "      return logits\n",
        "    \n",
        "  def freeze_xlnet_decoder(self):\n",
        "    \"\"\"\n",
        "    Freeze XLNet weight parameters. They will not be updated during training.\n",
        "    \"\"\"\n",
        "    for param in self.xlnet.parameters():\n",
        "      param.requires_grad = False\n",
        "    \n",
        "  def unfreeze_xlnet_decoder(self):\n",
        "    \"\"\"\n",
        "    Unfreeze XLNet weight parameters. They will be updated during training.\n",
        "    \"\"\"\n",
        "    for param in self.xlnet.parameters():\n",
        "      param.requires_grad = True\n",
        "    \n",
        "  def pool_hidden_state(self, last_hidden_state):\n",
        "    \"\"\"\n",
        "    Pool the output vectors into a single mean vector \n",
        "    \"\"\"\n",
        "    last_hidden_state = last_hidden_state[0]\n",
        "    mean_last_hidden_state = torch.mean(last_hidden_state, 1)\n",
        "    return mean_last_hidden_state\n",
        "    \n",
        "model = XLNetForMultiLabelSequenceClassification(num_labels=len(Y_train[0]))\n",
        "#model = torch.nn.DataParallel(model)\n",
        "#model.cuda()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at xlnet-base-cased were not used when initializing XLNetModel: ['lm_loss.weight', 'lm_loss.bias']\n",
            "- This IS expected if you are initializing XLNetModel 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 XLNetModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QRcsZgDOxsT7"
      },
      "source": [
        "optimizer = AdamW(model.parameters(), lr=2e-5, weight_decay=0.01, correct_bias=False)\n",
        "#scheduler = WarmupLinearSchedule(optimizer, warmup_steps=num_warmup_steps, t_total=num_total_steps)  # PyTorch scheduler"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QFbCTEppJSdO"
      },
      "source": [
        "## Model Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VJNy6veuw8mv"
      },
      "source": [
        "def train(model, num_epochs,\\\n",
        "          optimizer,\\\n",
        "          train_dataloader, valid_dataloader,\\\n",
        "          model_save_path,\\\n",
        "          train_loss_set=[], valid_loss_set = [],\\\n",
        "          lowest_eval_loss=None, start_epoch=0,\\\n",
        "          device=\"cpu\"\n",
        "          ):\n",
        "  \"\"\"\n",
        "  Train the model and save the model with the lowest validation loss\n",
        "  \"\"\"\n",
        "\n",
        "  model.to(device)\n",
        "\n",
        "  # trange is a tqdm wrapper around the normal python range\n",
        "  for i in trange(num_epochs, desc=\"Epoch\"):\n",
        "    # if continue training from saved model\n",
        "    actual_epoch = start_epoch + i\n",
        "\n",
        "    # Training\n",
        "\n",
        "    # Set our model to training mode (as opposed to evaluation mode)\n",
        "    model.train()\n",
        "\n",
        "    # Tracking variables\n",
        "    tr_loss = 0\n",
        "    num_train_samples = 0\n",
        "\n",
        "    # Train the data for one epoch\n",
        "    for step, batch in enumerate(train_dataloader):\n",
        "      # Add batch to GPU\n",
        "      batch = tuple(t.to(device) for t in batch)\n",
        "      # Unpack the inputs from our dataloader\n",
        "      b_input_ids, b_input_mask, b_labels = batch\n",
        "      # Clear out the gradients (by default they accumulate)\n",
        "      optimizer.zero_grad()\n",
        "      # Forward pass\n",
        "      loss = model(b_input_ids, attention_mask=b_input_mask, labels=b_labels)\n",
        "      # store train loss\n",
        "      tr_loss += loss.item()\n",
        "      num_train_samples += b_labels.size(0)\n",
        "      # Backward pass\n",
        "      loss.backward()\n",
        "      # Update parameters and take a step using the computed gradient\n",
        "      optimizer.step()\n",
        "      #scheduler.step()\n",
        "\n",
        "    # Update tracking variables\n",
        "    epoch_train_loss = tr_loss/num_train_samples\n",
        "    train_loss_set.append(epoch_train_loss)\n",
        "\n",
        "    print(\"Train loss: {}\".format(epoch_train_loss))\n",
        "\n",
        "    # Validation\n",
        "\n",
        "    # Put model in evaluation mode to evaluate loss on the validation set\n",
        "    model.eval()\n",
        "\n",
        "    # Tracking variables \n",
        "    eval_loss = 0\n",
        "    num_eval_samples = 0\n",
        "\n",
        "    # Evaluate data for one epoch\n",
        "    for batch in valid_dataloader:\n",
        "      # Add batch to GPU\n",
        "      batch = tuple(t.to(device) for t in batch)\n",
        "      # Unpack the inputs from our dataloader\n",
        "      b_input_ids, b_input_mask, b_labels = batch\n",
        "      # Telling the model not to compute or store gradients,\n",
        "      # saving memory and speeding up validation\n",
        "      with torch.no_grad():\n",
        "        # Forward pass, calculate validation loss\n",
        "        loss = model(b_input_ids, attention_mask=b_input_mask, labels=b_labels)\n",
        "        # store valid loss\n",
        "        eval_loss += loss.item()\n",
        "        num_eval_samples += b_labels.size(0)\n",
        "\n",
        "    epoch_eval_loss = eval_loss/num_eval_samples\n",
        "    valid_loss_set.append(epoch_eval_loss)\n",
        "\n",
        "    print(\"Valid loss: {}\".format(epoch_eval_loss))\n",
        "\n",
        "    if lowest_eval_loss == None:\n",
        "      lowest_eval_loss = epoch_eval_loss\n",
        "      # save model\n",
        "      save_model(model, model_save_path, actual_epoch,\\\n",
        "                 lowest_eval_loss, train_loss_set, valid_loss_set)\n",
        "    else:\n",
        "      if epoch_eval_loss < lowest_eval_loss:\n",
        "        lowest_eval_loss = epoch_eval_loss\n",
        "        # save model\n",
        "        save_model(model, model_save_path, actual_epoch,\\\n",
        "                   lowest_eval_loss, train_loss_set, valid_loss_set)\n",
        "    print(\"\\n\")\n",
        "\n",
        "  return model, train_loss_set, valid_loss_set\n",
        "\n",
        "\n",
        "def save_model(model, save_path, epochs, lowest_eval_loss, train_loss_hist, valid_loss_hist):\n",
        "  \"\"\"\n",
        "  Save the model to the path directory provided\n",
        "  \"\"\"\n",
        "  model_to_save = model.module if hasattr(model, 'module') else model\n",
        "  checkpoint = {'epochs': epochs, \\\n",
        "                'lowest_eval_loss': lowest_eval_loss,\\\n",
        "                'state_dict': model_to_save.state_dict(),\\\n",
        "                'train_loss_hist': train_loss_hist,\\\n",
        "                'valid_loss_hist': valid_loss_hist\n",
        "               }\n",
        "  torch.save(checkpoint, save_path)\n",
        "  print(\"Saving model at epoch {} with validation loss of {}\".format(epochs,\\\n",
        "                                                                     lowest_eval_loss))\n",
        "  return\n",
        "  \n",
        "def load_model(save_path):\n",
        "  \"\"\"\n",
        "  Load the model from the path directory provided\n",
        "  \"\"\"\n",
        "  checkpoint = torch.load(save_path)\n",
        "  model_state_dict = checkpoint['state_dict']\n",
        "  model = XLNetForMultiLabelSequenceClassification(num_labels=model_state_dict[\"classifier.weight\"].size()[0])\n",
        "  model.load_state_dict(model_state_dict)\n",
        "\n",
        "  epochs = checkpoint[\"epochs\"]\n",
        "  lowest_eval_loss = checkpoint[\"lowest_eval_loss\"]\n",
        "  train_loss_hist = checkpoint[\"train_loss_hist\"]\n",
        "  valid_loss_hist = checkpoint[\"valid_loss_hist\"]\n",
        "  \n",
        "  return model, epochs, lowest_eval_loss, train_loss_hist, valid_loss_hist"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H8CbXK7KyFiU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "59fc1eeb-280d-4a96-aa60-fe4aa7d7cb3a"
      },
      "source": [
        "num_epochs=7\n",
        "\n",
        "cwd = os.getcwd()\n",
        "model_save_path = output_model_file = os.path.join(cwd, \"xlnet_tweets.bin\")\n",
        "model, train_loss_set, valid_loss_set = train(model=model,\\\n",
        "                                              num_epochs=num_epochs,\\\n",
        "                                              optimizer=optimizer,\\\n",
        "                                              train_dataloader=train_dataloader,\\\n",
        "                                              valid_dataloader=validation_dataloader,\\\n",
        "                                              model_save_path=model_save_path,\\\n",
        "                                              device=\"cuda\")\n",
        "\n",
        "# Model overfits when epoch >=3, but more number of correct predictions for test data compared to epoch = 2\n",
        "# Epoch:   0%|          | 0/5 [00:00<?, ?it/s]Train loss: 0.01796796278462626\n",
        "# Valid loss: 0.017568994314693265\n",
        "# Epoch:  20%|██        | 1/5 [25:29<1:41:59, 1529.99s/it]Saving model at epoch 0 with validation loss of 0.017568994314693265\n",
        "\n",
        "\n",
        "# Train loss: 0.016354909224060857\n",
        "# Valid loss: 0.016553795436609904\n",
        "# Epoch:  40%|████      | 2/5 [51:01<1:16:33, 1531.01s/it]Saving model at epoch 1 with validation loss of 0.016553795436609904\n",
        "\n",
        "\n",
        "# Train loss: 0.015297270020661714\n",
        "# Epoch:  60%|██████    | 3/5 [1:16:25<50:55, 1527.55s/it]Valid loss: 0.0169240757001789\n",
        "\n",
        "\n",
        "# Train loss: 0.01408364675511861\n",
        "# Epoch:  80%|████████  | 4/5 [1:41:53<25:27, 1527.79s/it]Valid loss: 0.017551086031044796\n",
        "\n",
        "\n",
        "# Train loss: 0.012696515599432199\n",
        "# Epoch: 100%|██████████| 5/5 [2:07:19<00:00, 1527.87s/it]Valid loss: 0.018607749649668708"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rEpoch:   0%|          | 0/7 [00:00<?, ?it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train loss: 0.008732781574669932\n",
            "Valid loss: 0.008485114984073375\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rEpoch:  14%|█▍        | 1/7 [13:09<1:18:58, 789.71s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving model at epoch 0 with validation loss of 0.008485114984073375\n",
            "\n",
            "\n",
            "Train loss: 0.007933024304064687\n",
            "Valid loss: 0.007711087938462509\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rEpoch:  29%|██▊       | 2/7 [26:17<1:05:41, 788.34s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving model at epoch 1 with validation loss of 0.007711087938462509\n",
            "\n",
            "\n",
            "Train loss: 0.007486590551066332\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rEpoch:  43%|████▎     | 3/7 [39:19<52:23, 785.82s/it]  "
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Valid loss: 0.007816424204938693\n",
            "\n",
            "\n",
            "Train loss: 0.007091111253399539\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rEpoch:  57%|█████▋    | 4/7 [52:22<39:13, 784.50s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Valid loss: 0.008010660856575333\n",
            "\n",
            "\n",
            "Train loss: 0.006620362956378637\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rEpoch:  71%|███████▏  | 5/7 [1:05:24<26:07, 783.75s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Valid loss: 0.008250404313714026\n",
            "\n",
            "\n",
            "Train loss: 0.006102429197240494\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rEpoch:  86%|████████▌ | 6/7 [1:18:34<13:05, 785.75s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Valid loss: 0.00872923785059435\n",
            "\n",
            "\n",
            "Train loss: 0.0055558715004660735\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch: 100%|██████████| 7/7 [1:31:44<00:00, 786.34s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Valid loss: 0.009352353171224844\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yiqq4vv2yqmG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "outputId": "473f3d1c-2506-46ff-aae9-8da063bb2752"
      },
      "source": [
        "# Plot loss\n",
        "num_epochs = np.arange(len(train_loss_set))\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(10, 5));\n",
        "ax.plot(num_epochs, np.array(train_loss_set), label=\"Train Loss\")\n",
        "ax.plot(num_epochs, np.array(valid_loss_set), 'g-', label=\"Valid Loss\")\n",
        "#ax1.plot(episode_record, lose_record, 'r-', label=\"Lose %\")\n",
        "ax.set_xlabel(\"Number of Epochs\")\n",
        "ax.set_ylabel(\"Loss\")\n",
        "ax.set_title(\"Loss vs Number of Epochs\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Loss vs Number of Epochs')"
            ]
          },
          "metadata": {},
          "execution_count": 102
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "12PYfqX3QrE-"
      },
      "source": [
        "## Get Predictions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ExDSnDRq4Gi5"
      },
      "source": [
        "# cwd = os.getcwd()\n",
        "# model_save_path = output_model_file = os.path.join(cwd, \"xlnet_tweets.bin\")\n",
        "# model, start_epoch, lowest_eval_loss, train_loss_hist, valid_loss_hist = load_model(model_save_path)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oX8X7oh643AI"
      },
      "source": [
        "# model = model.eval()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ey9FMYJxQqGs"
      },
      "source": [
        "def generate_predictions(model, df, num_labels, device=\"cpu\", batch_size=32):\n",
        "  num_iter = math.ceil(df.shape[0]/batch_size)\n",
        "  \n",
        "  pred_probs = np.array([]).reshape(0, num_labels)\n",
        "  \n",
        "  model.to(device)\n",
        "  model.eval()\n",
        "  \n",
        "  for i in range(num_iter):\n",
        "    df_subset = df.iloc[i*batch_size:(i+1)*batch_size,:]\n",
        "    X = df_subset[\"features\"].values.tolist()\n",
        "    masks = df_subset[\"masks\"].values.tolist()\n",
        "    X = torch.tensor(X)\n",
        "    masks = torch.tensor(masks, dtype=torch.long)\n",
        "    X = X.to(device)\n",
        "    masks = masks.to(device)\n",
        "    with torch.no_grad():\n",
        "      logits = model(input_ids=X, attention_mask=masks)\n",
        "      logits = logits.sigmoid().detach().cpu().numpy()\n",
        "      pred_probs = np.vstack([pred_probs, logits])\n",
        "  \n",
        "  return pred_probs"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wo0YyAH6XGTh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "37122e13-65dd-4855-a166-add3eba6b048"
      },
      "source": [
        "num_labels = len(label_cols)\n",
        "pred_probs = generate_predictions(model, test, num_labels, device=\"cuda\", batch_size=32)\n",
        "pred_probs"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[5.02958536e-01, 6.47765160e-01, 3.25229950e-02, ...,\n",
              "        4.47309692e-04, 2.30057049e-04, 1.25071257e-02],\n",
              "       [2.89852172e-01, 1.17401555e-01, 9.93184093e-03, ...,\n",
              "        4.44879949e-01, 6.97182342e-02, 7.69844353e-02],\n",
              "       [3.45544107e-02, 4.23822820e-01, 6.08724542e-02, ...,\n",
              "        4.43179011e-02, 1.14077460e-02, 6.66763335e-02],\n",
              "       ...,\n",
              "       [7.17747748e-01, 2.69745857e-01, 7.64435797e-04, ...,\n",
              "        1.96924433e-03, 7.12163281e-04, 1.91143025e-02],\n",
              "       [3.47794453e-03, 1.91507686e-03, 2.03420204e-04, ...,\n",
              "        2.79090041e-03, 1.18797168e-03, 4.39668804e-01],\n",
              "       [3.73856612e-02, 1.46435993e-02, 2.45874114e-02, ...,\n",
              "        1.97567895e-01, 7.00027883e-01, 2.12773196e-02]])"
            ]
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U3brOKN0oIz2"
      },
      "source": [
        "# Y_test"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yioyfLIGndiS"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "y_pred_class = np.argmax(pred_probs,axis=1)\n",
        "y_test_class = np.argmax(Y_test, axis=1)\n",
        "conf = confusion_matrix(y_test_class, y_pred_class)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gl1xzDkGoc49",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "d5ee2569-a6a0-4b9d-c0e9-3e4630b164b5"
      },
      "source": [
        "import seaborn as sns\n",
        "\n",
        "sns.heatmap(conf, annot=True, fmt=\"d\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0d07a473d0>"
            ]
          },
          "metadata": {},
          "execution_count": 109
        },
        {
          "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": "yPg3bfY7ofSw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6d657899-0d93-420f-e129-6fe2c019e9a9"
      },
      "source": [
        "trace = np.trace(conf)\n",
        "\n",
        "print(\"\\nTrace of given 12x12 matrix:\")\n",
        "print(trace)\n",
        "print(trace/np.sum(conf))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Trace of given 12x12 matrix:\n",
            "3611\n",
            "0.26777901371894697\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A_74l13963tz"
      },
      "source": [
        "test_to_csv = test.reset_index()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uVAWKam2ado5"
      },
      "source": [
        "label_cols = [range(12)]\n",
        "\n",
        "for i in range(12):\n",
        "    test_to_csv[i] = pred_probs[:, i]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mF95PeFM_rDh"
      },
      "source": [
        "cwd = os.getcwd()\n",
        "pred_save_path = output_model_file = os.path.join(cwd, \"tweets_5_epoch_before_rounding.csv\")\n",
        "test_to_csv[list(range(12))].to_csv(pred_save_path, index=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tBnRrhxrjlEj"
      },
      "source": [
        "pred_probs = np.round(pred_probs)\n",
        "\n",
        "for i in range(12):\n",
        "    test_to_csv[i] = pred_probs[:, i]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "st8HbdIl8NLi"
      },
      "source": [
        "cwd = os.getcwd()\n",
        "pred_save_path = output_model_file = os.path.join(cwd, \"tweets_5_epoch_after_rounding.csv\")\n",
        "test_to_csv[list(range(12))].to_csv(pred_save_path, index=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lHrGjbXFJrq7"
      },
      "source": [
        "## Ignore This"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h8swEQ0HcU43"
      },
      "source": [
        "cwd = os.getcwd()\n",
        "test_prediction = pd.read_csv(os.path.join(cwd, \"tweets_5_epoch_after_rounding.csv\"))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J4zhMRm4dVdC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "19ba2758-fce5-4d20-a82e-a026da586836"
      },
      "source": [
        "test_prediction"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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            ],
            "text/plain": [
              "         0    1    2    3    4    5    6    7    8    9   10   11\n",
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              "...    ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...\n",
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              "\n",
              "[13485 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7fYQpDPydvwK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 641
        },
        "outputId": "b009e658-0716-4bb0-ec4c-38e35fc9bcd1"
      },
      "source": [
        "fig, ax = plt.subplots(figsize=(20, 10));\n",
        "test_prediction[list(str(x) for x in range(12))].hist(ax=ax);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: UserWarning: To output multiple subplots, the figure containing the passed axes is being cleared\n",
            "  \n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 12 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SlCWDJAheOut",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5400976e-96f8-4a20-972d-9b3b14bde0c6"
      },
      "source": [
        "count = 0\n",
        "index=[]\n",
        "for i in range(len(test)):\n",
        "    same = True\n",
        "    for j in range(12):\n",
        "        if int(test.iloc[i,2+j]) != int(test_prediction.iloc[i, j]):\n",
        "            same = False\n",
        "        if i==1:\n",
        "            print(test.iloc[i,2+j])\n",
        "            print(test_prediction.iloc[i,j])\n",
        "    if same:\n",
        "        index+=[i]\n",
        "        count+=1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1\n",
            "0.0\n",
            "0\n",
            "0.0\n",
            "0\n",
            "0.0\n",
            "0\n",
            "0.0\n",
            "0\n",
            "0.0\n",
            "0\n",
            "0.0\n",
            "0\n",
            "0.0\n",
            "0\n",
            "0.0\n",
            "0\n",
            "0.0\n",
            "0\n",
            "0.0\n",
            "0\n",
            "0.0\n",
            "0\n",
            "0.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1MK-8lsee67o",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "35ca485e-4a1a-41ae-ff83-b3e9e058f5c8"
      },
      "source": [
        "count"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2299"
            ]
          },
          "metadata": {},
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LH8iszu-gDnJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "63b338ae-f438-4f9f-cd0f-201ac019d051"
      },
      "source": [
        "test.iloc[index]"
      ],
      "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>annotations</th>\n",
              "      <th>tweets</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>10</th>\n",
              "      <th>11</th>\n",
              "      <th>features</th>\n",
              "      <th>masks</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>😂</td>\n",
              "      <td>lao if i ever take a nap with pant on always e...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>[2483, 155, 108, 17, 150, 545, 182, 24, 17, 12...</td>\n",
              "      <td>[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>😂</td>\n",
              "      <td>how many more time will make a fool of hilario...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>[160, 142, 70, 92, 53, 144, 24, 8956, 20, 2426...</td>\n",
              "      <td>[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>😂</td>\n",
              "      <td>during the rain delay have a dance off shit be...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>[181, 18, 2655, 4598, 47, 24, 2724, 177, 7341,...</td>\n",
              "      <td>[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>😂</td>\n",
              "      <td>hahaha football player la</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "      <td>[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70</th>\n",
              "      <td>😂</td>\n",
              "      <td>no not that s plug</td>\n",
              "      <td>1</td>\n",
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              "      <td>[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>13465</th>\n",
              "      <td>🙃</td>\n",
              "      <td>be leave to tahoe in hour and i do not even ha...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>[39, 755, 22, 17, 751, 10579, 25, 1671, 21, 17...</td>\n",
              "      <td>[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13474</th>\n",
              "      <td>🙃</td>\n",
              "      <td>guess ill make this drive</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>[3132, 4195, 144, 52, 1323, 4, 3, 0, 0, 0, 0, ...</td>\n",
              "      <td>[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13476</th>\n",
              "      <td>🙃</td>\n",
              "      <td>here come the pregnancy police because i have ...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>[193, 280, 18, 7532, 365, 149, 17, 150, 47, 24...</td>\n",
              "      <td>[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13477</th>\n",
              "      <td>🙃</td>\n",
              "      <td>have work every other day be not that bad i guess</td>\n",
              "      <td>0</td>\n",
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              "      <td>[47, 154, 300, 86, 191, 39, 50, 29, 948, 17, 1...</td>\n",
              "      <td>[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13479</th>\n",
              "      <td>🙃</td>\n",
              "      <td>i can not even go to the liquor store by and d...</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>[17, 150, 64, 50, 176, 216, 22, 18, 17243, 151...</td>\n",
              "      <td>[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2299 rows × 16 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      annotations  ...                                              masks\n",
              "16              😂  ...  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, ...\n",
              "44              😂  ...  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, ...\n",
              "48              😂  ...  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, ...\n",
              "50              😂  ...  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, ...\n",
              "70              😂  ...  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, ...\n",
              "...           ...  ...                                                ...\n",
              "13465           🙃  ...  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, ...\n",
              "13474           🙃  ...  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...\n",
              "13476           🙃  ...  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, ...\n",
              "13477           🙃  ...  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, ...\n",
              "13479           🙃  ...  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, ...\n",
              "\n",
              "[2299 rows x 16 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K5fcC9unAlhl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "b52ab3cd-9177-4001-b0c7-4f890887cc69"
      },
      "source": [
        "test_prediction.iloc[index]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "         0    1    2    3    4    5    6    7    8    9   10   11\n",
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              "\n",
              "[2299 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 122
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TDHUO050ksqA"
      },
      "source": [
        "count1 = 0\n",
        "index1=[]\n",
        "for i in range(len(test)):\n",
        "    isone = False\n",
        "    for j in range(12):\n",
        "        if int(test_prediction.iloc[i, j]) !=0:\n",
        "            isone = True\n",
        "    if isone:\n",
        "        index1+=[i]\n",
        "        count1+=1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CoqMApnFk-HG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cf97cd3e-37e2-46c8-b5d4-9b88a6276ddf"
      },
      "source": [
        "count1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6823"
            ]
          },
          "metadata": {},
          "execution_count": 124
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vmJCdXVhlEiI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "a560dec7-b80d-4128-95eb-6967c6073823"
      },
      "source": [
        "test.iloc[index1]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>annotations</th>\n",
              "      <th>tweets</th>\n",
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              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>10</th>\n",
              "      <th>11</th>\n",
              "      <th>features</th>\n",
              "      <th>masks</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
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              "      <th>7</th>\n",
              "      <td>😂</td>\n",
              "      <td>what s bracken</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>😂</td>\n",
              "      <td>i watch that shit so many time</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>😂</td>\n",
              "      <td>lebron be trash lead all player in point rebou...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>[17, 529, 2369, 253, 39, 10247, 575, 71, 928, ...</td>\n",
              "      <td>[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>😂</td>\n",
              "      <td>lao if i ever take a nap with pant on always e...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "      <td>[2483, 155, 108, 17, 150, 545, 182, 24, 17, 12...</td>\n",
              "      <td>[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>13480</th>\n",
              "      <td>🙃</td>\n",
              "      <td>the ball hate croatia suh</td>\n",
              "      <td>0</td>\n",
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              "      <td>[18, 1657, 4177, 17, 4736, 4807, 101, 17, 23, ...</td>\n",
              "      <td>[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13481</th>\n",
              "      <td>🙃</td>\n",
              "      <td>sexy bitch i hope bout</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>[12187, 15683, 17, 150, 937, 12108, 4, 3, 0, 0...</td>\n",
              "      <td>[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13482</th>\n",
              "      <td>🙃</td>\n",
              "      <td>i do not realize that tweet be private and no ...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>[17, 150, 112, 50, 3131, 29, 19291, 39, 804, 2...</td>\n",
              "      <td>[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13483</th>\n",
              "      <td>🙃</td>\n",
              "      <td>lookey annoy for no reason</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>[338, 1816, 48, 488, 117, 28, 116, 994, 4, 3, ...</td>\n",
              "      <td>[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13484</th>\n",
              "      <td>🙃</td>\n",
              "      <td>appreciate while last</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>[5422, 171, 129, 4, 3, 0, 0, 0, 0, 0, 0, 0, 0,...</td>\n",
              "      <td>[1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6823 rows × 16 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      annotations  ...                                              masks\n",
              "0               😂  ...  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, ...\n",
              "7               😂  ...  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...\n",
              "10              😂  ...  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, ...\n",
              "12              😂  ...  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, ...\n",
              "16              😂  ...  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, ...\n",
              "...           ...  ...                                                ...\n",
              "13480           🙃  ...  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, ...\n",
              "13481           🙃  ...  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, ...\n",
              "13482           🙃  ...  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, ...\n",
              "13483           🙃  ...  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, ...\n",
              "13484           🙃  ...  [1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...\n",
              "\n",
              "[6823 rows x 16 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 125
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pgbAFX4vJ4Vi"
      },
      "source": [
        "## Evaluation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XAzhQGAqKJTg"
      },
      "source": [
        "1. https://stats.stackexchange.com/questions/12702/what-are-the-measure-for-accuracy-of-multilabel-data\n",
        "\n",
        "2. https://en.wikipedia.org/wiki/Multi-label_classification#Statistics_and_evaluation_metrics\n",
        "\n",
        "3. https://medium.datadriveninvestor.com/a-survey-of-evaluation-metrics-for-multilabel-classification-bb16e8cd41cd"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "26Og1-35Ayn6"
      },
      "source": [
        "y_pred = test_prediction.values.tolist()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dJ-VOGmbFORx"
      },
      "source": [
        "y_true = test[list(range(12))].values.tolist()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lgyhXlsMFhWP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ee536cb3-8ea5-43ab-b24c-130168da7e83"
      },
      "source": [
        "from sklearn.metrics import hamming_loss, accuracy_score\n",
        "\n",
        "print(\"accuracy score: \" + str(accuracy_score(y_true, y_pred)))\n",
        "print(\"hamming loss: \" + str(hamming_loss(y_true, y_pred)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "accuracy score: 0.17048572487949573\n",
            "hamming loss: 0.09687924854776912\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "30CkIOdQH-6Z",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f980916e-c789-4fe3-f7e9-a8ac748b51f8"
      },
      "source": [
        "y_tru = test[list(range(12))].values\n",
        "y_pre = test_prediction.values\n",
        "\n",
        "def hamming_score(y_tru, y_pre, normalize=True, sample_weight=None):\n",
        "    '''\n",
        "    Compute the Hamming score (a.k.a. label-based accuracy) for the multi-label case\n",
        "    http://stackoverflow.com/q/32239577/395857\n",
        "    '''\n",
        "    acc_list = []\n",
        "    for i in range(y_tru.shape[0]):\n",
        "        set_true = set( np.where(y_tru[i])[0] )\n",
        "        set_pred = set( np.where(y_pre[i])[0] )\n",
        "        #print('\\nset_true: {0}'.format(set_true))\n",
        "        #print('set_pred: {0}'.format(set_pred))\n",
        "        tmp_a = None\n",
        "        if len(set_true) == 0 and len(set_pred) == 0:\n",
        "            tmp_a = 1\n",
        "        else:\n",
        "            tmp_a = len(set_true.intersection(set_pred))/\\\n",
        "                    float( len(set_true.union(set_pred)) )\n",
        "        #print('tmp_a: {0}'.format(tmp_a))\n",
        "        acc_list.append(tmp_a)\n",
        "    return np.mean(acc_list)\n",
        "\n",
        "if __name__ == \"__main__\":\n",
        "    print('Hamming score: {0}'.format(hamming_score(y_tru, y_pre))) # 0.375 (= (0.5+1+0+0)/4)\n",
        "\n",
        "    # For comparison sake:\n",
        "    import sklearn.metrics\n",
        "\n",
        "    # Subset accuracy\n",
        "    # 0.25 (= 0+1+0+0 / 4) --> 1 if the prediction for one sample fully matches the gold. 0 otherwise.\n",
        "    print('Subset accuracy/ Exact Match Ratio: {0}'.format(sklearn.metrics.accuracy_score(y_tru, y_pre, normalize=True, sample_weight=None)))\n",
        "\n",
        "    # Hamming loss (smaller is better)\n",
        "    # $$ \\text{HammingLoss}(x_i, y_i) = \\frac{1}{|D|} \\sum_{i=1}^{|D|} \\frac{xor(x_i, y_i)}{|L|}, $$\n",
        "    # where\n",
        "    #  - \\\\(|D|\\\\) is the number of samples  \n",
        "    #  - \\\\(|L|\\\\) is the number of labels  \n",
        "    #  - \\\\(y_i\\\\) is the ground truth  \n",
        "    #  - \\\\(x_i\\\\)  is the prediction.  \n",
        "    # 0.416666666667 (= (1+0+3+1) / (3*4) )\n",
        "    print('Hamming loss: {0}'.format(sklearn.metrics.hamming_loss(y_tru, y_pre))) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Hamming score: 0.17341490545050056\n",
            "Subset accuracy/ Exact Match Ratio: 0.17048572487949573\n",
            "Hamming loss: 0.09687924854776912\n"
          ]
        }
      ]
    }
  ]
}