Tweet_Extraction.ipynb

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      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/banagg/Kaggle-TSE/blob/master/Tweet_Extraction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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      "metadata": {
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      },
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        "import numpy as np \n",
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        "\n",
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        "\n",
        "from simpletransformers.question_answering import QuestionAnsweringModel"
      ],
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    },
    {
      "cell_type": "markdown",
      "metadata": {
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        "colab_type": "text"
      },
      "source": [
        "Add your saved Kaggle token in the files upload section below"
      ]
    },
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      "metadata": {
        "id": "8QU-ctPIBNR2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from google.colab import files\n",
        "files.upload()"
      ],
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      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
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          "height": 382
        },
        "outputId": "036e420b-4705-4320-89c9-892330eb845d"
      },
      "source": [
        "!mkdir -p ~/.kaggle\n",
        "!cp kaggle.json ~/.kaggle/\n",
        "!chmod 600 ~/.kaggle/kaggle.json\n",
        "!pip install -q kaggle\n",
        "!pip install -q kaggle-cli\n",
        "!kaggle competitions download -c tweet-sentiment-extraction\n",
        "!unzip train.csv.zip\n",
        "!ls"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[K     |████████████████████████████████| 81kB 7.3MB/s \n",
            "\u001b[K     |████████████████████████████████| 5.3MB 14.3MB/s \n",
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            "\u001b[K     |████████████████████████████████| 112kB 59.3MB/s \n",
            "\u001b[K     |████████████████████████████████| 112kB 64.0MB/s \n",
            "\u001b[?25h  Building wheel for kaggle-cli (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for pyperclip (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Warning: Looks like you're using an outdated API Version, please consider updating (server 1.5.6 / client 1.5.4)\n",
            "Downloading train.csv.zip to /content\n",
            "  0% 0.00/1.23M [00:00<?, ?B/s]\n",
            "100% 1.23M/1.23M [00:00<00:00, 82.0MB/s]\n",
            "Downloading test.csv to /content\n",
            "  0% 0.00/307k [00:00<?, ?B/s]\n",
            "100% 307k/307k [00:00<00:00, 102MB/s]\n",
            "Downloading sample_submission.csv to /content\n",
            "  0% 0.00/41.4k [00:00<?, ?B/s]\n",
            "100% 41.4k/41.4k [00:00<00:00, 41.6MB/s]\n",
            "Archive:  train.csv.zip\n",
            "  inflating: train.csv               \n",
            "kaggle.json  sample_submission.csv  train.csv\n",
            "sample_data  test.csv\t\t    train.csv.zip\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hoUun2PXCoQA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_df = pd.read_csv('train.csv')\n",
        "test_df = pd.read_csv('test.csv')\n",
        "# sub_df = pd.read_csv('sample_submission.csv')\n",
        "sub_df = pd.DataFrame(columns=['textID', 'selected_text'])\n",
        "sub_df['textID'] = list(train_df['textID'][21960:])\n",
        "!mkdir -p data\n",
        "\n",
        "train = np.array(train_df)\n",
        "test = train[21960:]\n",
        "train = train[:21960]\n",
        "# test = np.array(test_df)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AH1Ap6T4C7NR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def find_all(input_str, search_str):\n",
        "    l1 = []\n",
        "    length = len(input_str)\n",
        "    index = 0\n",
        "    while index < length:\n",
        "        i = input_str.find(search_str, index)\n",
        "        if i == -1:\n",
        "            return l1\n",
        "        l1.append(i)\n",
        "        index = i + 1\n",
        "    return l1\n",
        "\n",
        "def do_qa_train(train):\n",
        "\n",
        "    output = []\n",
        "    for line in train:\n",
        "        context = line[1]\n",
        "\n",
        "        qas = []\n",
        "        question = line[-1]\n",
        "        qid = line[0]\n",
        "        answers = []\n",
        "        answer = line[2]\n",
        "        if type(answer) != str or type(context) != str or type(question) != str:\n",
        "            continue\n",
        "        answer_starts = find_all(context, answer)\n",
        "        for answer_start in answer_starts:\n",
        "            answers.append({'answer_start': answer_start, 'text': answer.lower()})\n",
        "            break\n",
        "        qas.append({'question': question, 'id': qid, 'is_impossible': False, 'answers': answers})\n",
        "\n",
        "        output.append({'context': context.lower(), 'qas': qas})\n",
        "        \n",
        "    return output\n",
        "\n",
        "qa_train = do_qa_train(train)\n",
        "\n",
        "with open('data/train.json', 'w') as outfile:\n",
        "    json.dump(qa_train, outfile)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V-RI8OlLDvJk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def do_qa_test(test):\n",
        "    output = []\n",
        "    for line in test:\n",
        "        context = line[1]\n",
        "        qas = []\n",
        "        question = line[-1]\n",
        "        qid = line[0]\n",
        "        if type(context) != str or type(question) != str:\n",
        "            continue\n",
        "        answers = []\n",
        "        answers.append({'answer_start': 1000000, 'text': '__None__'})\n",
        "        qas.append({'question': question, 'id': qid, 'is_impossible': False, 'answers': answers})\n",
        "        output.append({'context': context.lower(), 'qas': qas})\n",
        "    return output\n",
        "\n",
        "qa_test = do_qa_test(test)\n",
        "\n",
        "with open('data/test.json', 'w') as outfile:\n",
        "    json.dump(qa_test, outfile, indent = 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jrBKskMXE51w",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_original_text(testStr, subStr):\n",
        "    testToken = []\n",
        "    charBreak = '.,!'\n",
        "    curStr = ''\n",
        "    strCheck = testStr.lower()\n",
        "    \n",
        "    for i in range(0, len(strCheck)):\n",
        "        if strCheck[i] == ' ' and len(curStr) != 0:\n",
        "            testToken.append({'str': curStr, 'start': i - len(curStr)})\n",
        "            curStr = ''\n",
        "        elif strCheck[i] in charBreak:\n",
        "            if len(curStr) > 0:\n",
        "                testToken.append({'str': curStr, 'start': i - len(curStr)})\n",
        "                curStr = ''\n",
        "            testToken.append({'str': strCheck[i], 'start': i})\n",
        "        elif strCheck[i] != ' ':\n",
        "            curStr += strCheck[i]\n",
        "    \n",
        "    if len(curStr) != 0:\n",
        "        testToken.append({'str': curStr, 'start': i - len(curStr) + 1})\n",
        "\n",
        "    subToken = []\n",
        "    curStr = ''\n",
        "    for i in range(0, len(subStr)):\n",
        "        if subStr[i] == ' ' and len(curStr) != 0:\n",
        "            subToken.append(curStr)\n",
        "            curStr = ''\n",
        "        elif subStr[i] in charBreak:\n",
        "            if len(curStr) > 0:\n",
        "                subToken.append(curStr)\n",
        "                curStr = ''\n",
        "            subToken.append(subStr[i])\n",
        "        elif subStr[i] != ' ':\n",
        "            curStr += subStr[i]\n",
        "    \n",
        "    if len(curStr) != 0:\n",
        "        subToken.append(curStr)\n",
        "    \n",
        "    cnt = 0\n",
        "    for i in range(0, len(testToken)):\n",
        "        if testToken[i]['str'] == subToken[cnt]:\n",
        "            if cnt == 0:\n",
        "                ans = i\n",
        "            cnt += 1\n",
        "        else:\n",
        "            cnt = 0\n",
        "            if testToken[i]['str'] == subToken[cnt]:\n",
        "                ans = i\n",
        "                cnt += 1\n",
        "        if cnt >= len(subToken):\n",
        "            break\n",
        "    \n",
        "    startInd = testToken[ans]['start']\n",
        "    endInd = testToken[ans + len(subToken) - 1]['start'] + len(subToken[-1])\n",
        "    \n",
        "    ansStr = testStr[startInd:endInd]\n",
        "    \n",
        "    return ansStr"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SOOivqA5Otbz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def length_check(str1, str2, ques):\n",
        "    if ques == 'positive':\n",
        "        return False\n",
        "    ratio = len(str2) / len(str1)\n",
        "    if (ques == 'neutral' and ratio < 1 and ratio > 0.94) or (ques == 'negative' and ratio < 1 and ratio > 0.91):\n",
        "        return True\n",
        "    return False"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zxhQNfm7D-Sg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435,
          "referenced_widgets": [
            "a6ded0a031ab49b38a809fe470f501b0",
            "a292903b17134fb58e00d03eede0ac23",
            "4521fcb78c4c4d3f8439e9a35e849d08",
            "56ef8d4dd7f14fb0813017fabf1a2941",
            "d7151ce3ede24573ab8fed00b982ebd9",
            "72cee7f9507148f18cf9bfd6fb6dd729",
            "cbee0427406446aa9bcccfddab9f6e57",
            "3221048d475a443987d719f35edff186",
            "66f02527ace248638c388a7caa130d7a",
            "6f53b8817f8a403cacd1156494c6f553",
            "2caa5694a189476d9ca1b54936993dc4",
            "a76c1cb6880e42ceb9ea54970d0316ea",
            "338eb5b859f04ce6bc7afab834a9f72c",
            "42e2e4e87a8546eeaf3813c120287f05",
            "8166863db32f48c4bfef56a74b162a2c",
            "dae6e53c63bd4f11927c1483e94db3e7",
            "b92ef62fea694bcaa627ba6eceb5aaf8",
            "68d65e615bf246d58e702b1de1d26ec6",
            "481b4b2de99242f08df9d676fe971540",
            "b5b065bc3f45429da6dd461c300cd2e9",
            "5128ea93353842cf80629b6d1b17f6ca",
            "13a9811e35894ce9be31bd8a9c7b028e",
            "e5695dd416414cd5bd3d7d6011e68366",
            "47c5558ecad44e1496eec65796dc3150",
            "de9896a563324304886ee5abb119c7d7",
            "be3098df39d24493b590341898dbdb59",
            "66fba5bc5a614492891c939f6dc81656",
            "0759362110f54535a066cbaf463a6603",
            "68c9efe4e92b48b1a7ddfde73ccf02b9",
            "6e31fc21e69b4291b931c3bfb90e4d6f",
            "00452670fd7c480580ee875d96d4dbe8",
            "191d0dfc65464f90bfea3c9b7d0d85c1",
            "b7027a359407444f99d730160ca2ccc7",
            "bb41c4e237a444c6ad59eae17248d6e5",
            "296f262603d842a08c95bd9f51a82594",
            "295cb40e9c994395853a484a6afdb885",
            "5e5d34df1b764275a55feb2e214a1e10",
            "9513cc0784ef416b8e260ed4e12c9864",
            "9bc11e930440459cad29c4e954f0debc",
            "8d566992baf74ac19e2da42fecf86afa",
            "2904c481c017401b98e5b53d68ea5c78",
            "ea520a45216e4565bda8ad5e2c674362",
            "f6a5fa5b21e749c5afbcfe9c14fec03b",
            "347ff10a59ea4c46942925ef13441263",
            "5cc1fbf8582a49ba8850a52989e6875a",
            "f5cb7ca3b9794ddca1e08a0831f000bf",
            "5c2236d965684bffb7851e57ab3bbc8a",
            "70fb3b5ccc8541509bb50f3398f12ddf",
            "838cf2abfd274b9e95a0cb5e814a07fc",
            "0483d60a00b545e485d17979ed6f0cae",
            "170f36e282c04fd0ad21bbc3b769b5f7",
            "f1d5fb19916f4052b398f36fbeee6bef",
            "68eac13456624bdcaa7606d54d4019a1",
            "5e76729292984a14aab7f446c085b350",
            "25dccc8647c446a7b9231d3c7bf9ce3a",
            "c037e8d553f043ca919c0f69bcc2b4bf"
          ]
        },
        "outputId": "d2369220-d45e-4d55-eb99-3ac89583c1e7"
      },
      "source": [
        "from simpletransformers.question_answering import QuestionAnsweringModel\n",
        "\n",
        "model = QuestionAnsweringModel('distilbert', \n",
        "                               'distilbert-base-uncased-distilled-squad', \n",
        "                               args={'reprocess_input_data': True,\n",
        "                                     'overwrite_output_dir': True,\n",
        "                                     'learning_rate': 5e-5,\n",
        "                                     'num_train_epochs': 3,\n",
        "                                     'max_seq_length': 192,\n",
        "                                     'doc_stride': 64,\n",
        "                                     'fp16': False,\n",
        "                                    },\n",
        "                              use_cuda=True)\n",
        "\n",
        "model.train_model('data/train.json')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a6ded0a031ab49b38a809fe470f501b0",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, description='Downloading', max=555, style=ProgressStyle(description_width=…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "66f02527ace248638c388a7caa130d7a",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, description='Downloading', max=265481570, style=ProgressStyle(description_…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b92ef62fea694bcaa627ba6eceb5aaf8",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, description='Downloading', max=231508, style=ProgressStyle(description_wid…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 21959/21959 [00:26<00:00, 838.05it/s]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "de9896a563324304886ee5abb119c7d7",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, description='Epoch', max=3, style=ProgressStyle(description_width='initial…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b7027a359407444f99d730160ca2ccc7",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, description='Current iteration', max=2745, style=ProgressStyle(description…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Running loss: 2.814143"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torch/optim/lr_scheduler.py:224: UserWarning: To get the last learning rate computed by the scheduler, please use `get_last_lr()`.\n",
            "  warnings.warn(\"To get the last learning rate computed by the scheduler, \"\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Running loss: 1.691472\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2904c481c017401b98e5b53d68ea5c78",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, description='Current iteration', max=2745, style=ProgressStyle(description…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Running loss: 0.833212\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "838cf2abfd274b9e95a0cb5e814a07fc",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, description='Current iteration', max=2745, style=ProgressStyle(description…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Running loss: 0.772044\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pblll930-rGG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84,
          "referenced_widgets": [
            "ef71e1ec45f448d2ad63cc69a8e83ca2",
            "20240eb31c974bf4aa175aff7137f448",
            "c47ae969ec584a6985afb94ae694c782",
            "b56c113610ad4cbbac98538ac6b00189",
            "847ab4061fde4c73a6b00c2c1b90d04b",
            "cc463024aa2246c1830b18f9691388d0",
            "e4c0ca55879d46899a3f9125f5ea11bd",
            "403995e1c82143baab3b1a2cc08f0b2a"
          ]
        },
        "outputId": "0a080032-48f3-4949-fa46-34f34b5d1196"
      },
      "source": [
        "predictions = model.predict(qa_test)\n",
        "predictions_df = pd.DataFrame.from_dict(predictions)\n",
        "\n",
        "sub_df['selected_text'] = predictions_df['answer']\n",
        "\n",
        "for i in range(0, len(sub_df['selected_text'])):\n",
        "    try:\n",
        "        sub_df['selected_text'][i] = get_original_text(test[i][1], sub_df['selected_text'][i].lower())\n",
        "    except Exception:\n",
        "        pass\n",
        "\n",
        "    if length_check(test[i][1].strip(), sub_df['selected_text'][i], test[i][-1]):\n",
        "        sub_df['selected_text'][i] = test[i][1].strip()\n",
        "\n",
        "sub_df.to_csv('submission.csv', index=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 5521/5521 [00:05<00:00, 1019.79it/s]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ef71e1ec45f448d2ad63cc69a8e83ca2",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, max=691), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "udg911PjOSR-",
        "colab_type": "text"
      },
      "source": [
        "To check the similarity score of the validation results, categorised on the basis of sentiment."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x6Uj0VvQLUWw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        },
        "outputId": "09410ded-6c9e-4692-911a-6e74788b1235"
      },
      "source": [
        "simd = {'positive': 0, 'negative': 0, 'neutral': 0, 'total': 0}\n",
        "simc = {'positive': 0, 'negative': 0, 'neutral': 0, 'total': 0}\n",
        "\n",
        "def jaccard(str1, str2): \n",
        "\ta = set(str1.lower().split()) \n",
        "\tb = set(str2.lower().split())\n",
        "\tc = a.intersection(b)\n",
        "\treturn float(len(c)) / (len(a) + len(b) - len(c))\n",
        "\n",
        "for i in range(0, len(test)):\n",
        "\tval = jaccard(test[i][2], sub_df['selected_text'][i])\n",
        "\tsimd[test[i][3]] += val\n",
        "\tsimc[test[i][3]] += 1\n",
        "\tsimd['total'] += val\n",
        "\tsimc['total'] += 1\n",
        "\n",
        "for key in simd.keys():\n",
        "\tprint(key, simd[key] / simc[key])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "positive 0.5006376358765963\n",
            "negative 0.5158188255709868\n",
            "neutral 0.9741384037120444\n",
            "total 0.7000443341633147\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y-5EH59f-4cR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from google.colab import files\n",
        "files.download('submission.csv') "
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}