Shallow_Models.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C3owPm5QFwib"
      },
      "source": [
        "### Libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q9R-C9XzSw4J",
        "outputId": "725bda09-7838-4ebe-c417-74360162e7d2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting datasets\n",
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            "Installing collected packages: multidict, frozenlist, yarl, asynctest, async-timeout, aiosignal, fsspec, aiohttp, xxhash, huggingface-hub, datasets\n",
            "Successfully installed aiohttp-3.8.1 aiosignal-1.2.0 async-timeout-4.0.2 asynctest-0.13.0 datasets-1.17.0 frozenlist-1.2.0 fsspec-2021.11.1 huggingface-hub-0.2.1 multidict-5.2.0 xxhash-2.0.2 yarl-1.7.2\n"
          ]
        }
      ],
      "source": [
        "!pip install datasets transformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7PhcSmvNFvQU"
      },
      "outputs": [],
      "source": [
        "# NLP\n",
        "# from datasets import load_dataset, list_datasets\n",
        "\n",
        "import spacy\n",
        "\n",
        "# ML\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.metrics import precision_score, recall_score, f1_score\n",
        "from sklearn.multiclass import OneVsRestClassifier\n",
        "from sklearn.preprocessing import MultiLabelBinarizer, StandardScaler\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.base import BaseEstimator, TransformerMixin\n",
        "from sklearn.svm import LinearSVC, SVC\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
        "from sklearn.dummy import DummyClassifier\n",
        "from joblib import dump, load\n",
        "from tqdm import tqdm\n",
        "\n",
        "# Data Manipultion\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "# Visualization\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from IPython.display import Audio, display\n",
        "\n",
        "from pathlib import Path\n",
        "import glob\n",
        "\n",
        "# DL\n",
        "# from transformers import DistilBertTokenizer, DistilBertModel, AutoModel, AutoTokenizer\n",
        "import torch\n",
        "from torch.utils.data import TensorDataset, DataLoader"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N-jSeVRXLfXM",
        "outputId": "1d393ce0-cc7e-40d0-a0c0-a1699345ca48"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive', force_remount=True)\n",
        "check = Path.cwd()/'gdrive/MyDrive/TMProject'\n",
        "files = list(check.glob(\"**/*.npy\"))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MLugR_nUGAc2"
      },
      "source": [
        "### Helper Functions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oqscjABrGEBK"
      },
      "outputs": [],
      "source": [
        "def paired_bootstrap_test(test_set, model1,model2, B, score,*args,**kwargs):\n",
        "    \"\"\"\n",
        "    Function to generate \\delta(x) and \\delta(x^{(i)}) for B bootstrap samples.\n",
        "    Reference: \n",
        "        Jurafsky, Daniel, and James H. Martin. \"Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition.\"\n",
        "    Params:\n",
        "    ------\n",
        "    test_set: np.ndarray, Array of test outputs.\n",
        "    model1: np.ndarray, Array of Model A's output.\n",
        "    model2: np.ndarray, Array of Model B's output.\n",
        "    B : int, No of Bootstrap's to be generated\n",
        "    score: Evaluation algorithm.\n",
        "    \"\"\"\n",
        "    N = test_set.shape[0]\n",
        "    score1 =  score(test_set, model1, *args,**kwargs)\n",
        "    score2 =  score(test_set, model2, *args,**kwargs)\n",
        "    delta = score1-score2\n",
        "    deltas = [] # for storing \\delta(x) of bootstraps\n",
        "    for boot in tqdm(range(B)):\n",
        "        ind = np.random.randint(low=0, high=N, size=N)\n",
        "        sampleY = test_set[ind,:]\n",
        "        sample1 = model1[ind,:]\n",
        "        sc1 = score(sampleY, sample1,*args,**kwargs)\n",
        "        sample2 = model2[ind,:]\n",
        "        sc2 = score(sampleY, sample2, *args, **kwargs)\n",
        "        delta_b = sc1 - sc2\n",
        "        deltas.append(delta_b)\n",
        "    deltas = np.array(deltas)\n",
        "    return (deltas, delta)\n",
        "\n",
        "def hypothesis_test(dx_i,dx, significance=0.05, hypothesis=\"\"):\n",
        "    \"\"\"\n",
        "    Implementation of paired-bootstrap test.\n",
        "    Reference: Berg-Kirkpatrick, et. al. \"An empirical investigation of statistical significance in nlp.\"\n",
        "    \"\"\"\n",
        "    p_value = np.mean(dx_i>= (2*dx))\n",
        "    if p_value<significance:\n",
        "        print(f\"We reject the null hypothesis {hypothesis} at a significance of {significance}\")\n",
        "    else:\n",
        "        print(f\"We fail to reject the Null Hypothesis {hypothesis} at a significance of {significance}\")\n",
        "    return p_value"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "r28MjWSvGZRc"
      },
      "outputs": [],
      "source": [
        "def get_data(*args, **kwargs):\n",
        "    \"\"\"Function to load Dataset\"\"\"\n",
        "    reuters = load_dataset(\"reuters21578\",\"ModApte\") # ModApte b'coz of \"A re-examination of text categorization methods\" paper\n",
        "    train = reuters['train'] # Same as paper\n",
        "    test = reuters['test'] # Same as paper\n",
        "    train.set_format(type = \"pandas\")\n",
        "    test.set_format(type = \"pandas\")   \n",
        "    df_train = train[:]\n",
        "    df_test = test[:]\n",
        "    # Get empty Indexes: required for dealing with Tensored datasets\n",
        "    empty_train = df_train.index[df_train.topics.str.len().eq(0)] \n",
        "    empty_test = df_test.index[df_test.topics.str.len().eq(0)]\n",
        "\n",
        "    df_train = df_train[~df_train.topics.str.len().eq(0)] # Drop Empty Topics\n",
        "    df_test = df_test[~df_test.topics.str.len().eq(0)] # Drop Empty Topics\n",
        "    cols =df_train.columns\n",
        "\n",
        "    df_train = df_train.drop([col for col in cols if col not in ['text', 'topics']], axis=1)\n",
        "    df_test = df_test.drop([col for col in cols if col not in ['text', 'topics']], axis=1)\n",
        "\n",
        "    X_train = df_train['text']\n",
        "    X_test = df_test['text']\n",
        "    mlb = MultiLabelBinarizer()\n",
        "    y_train = mlb.fit_transform(df_train.topics)\n",
        "    y_test = mlb.transform(df_test.topics)\n",
        "    return X_train,X_test,y_train,y_test, empty_train,empty_test\n",
        "\n",
        "def spacy_tokenizer(text):\n",
        "    \"\"\"SpaCy Tokenizer pipeline\"\"\"\n",
        "    doc = nlp(text, disable=['ner', 'tagger', 'parser'])\n",
        "    return [token.text for token in doc if not token.is_stop and not token.is_punct]\n",
        "\n",
        "# def allDone():\n",
        "#   display(Audio(url='https://sound.peal.io/ps/audios/000/000/537/original/woo_vu_luvub_dub_dub.wav', autoplay=True))\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9DKvQYZ8yDp5"
      },
      "source": [
        "### Data Loading"
      ]
    },
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            "d88437b3b6df4b96ba5b3a66167220fd",
            "efe890a202b34f1a9eec6d13d704f2c2",
            "bcb96f0e16824ef6ab4403f963faea97",
            "a4a1da4c39b54f13a4fcd3dfbd60bd11",
            "df08a4e7156d413c9e8039576c1be7b7",
            "28b202974dfa42e485c6ba2785fb2e4e",
            "978c428a188b4e5fabde4bd947d3ad57",
            "cc717dd3c2bd457c89cadc4eabfceabb",
            "9d1676684eb446b9897e3400da252436"
          ]
        },
        "id": "RuB422Usw9Fc",
        "outputId": "9f353731-edb3-43bd-d00e-4a1ddbd37aa3"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "55eb692f627d448ab8e1aa511a204196",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/4.17k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c1d7a797612745abbc581b9602de2feb",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/2.37k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset reuters21578/ModApte (download: 7.77 MiB, generated: 12.48 MiB, post-processed: Unknown size, total: 20.25 MiB) to /root/.cache/huggingface/datasets/reuters21578/ModApte/1.0.0/98a2ad6a0242627562db83992f9625261854c40a88619322596153a5a16a206c...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8a0db2267960486891e82d6931da9382",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/8.15M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d8e7d305234942f9b5b3860ecf5792ab",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "0 examples [00:00, ? examples/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4c95de8d5a1244908cedfcb32513ab88",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "0 examples [00:00, ? examples/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "06beee2a46a54d9786bcea16b9bdbf07",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "0 examples [00:00, ? examples/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset reuters21578 downloaded and prepared to /root/.cache/huggingface/datasets/reuters21578/ModApte/1.0.0/98a2ad6a0242627562db83992f9625261854c40a88619322596153a5a16a206c. Subsequent calls will reuse this data.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c089d0ab8364495a98bd518764178d1d",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/3 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/preprocessing/_label.py:876: UserWarning: unknown class(es) ['cottonseed', 'f-cattle', 'sfr'] will be ignored\n",
            "  \"unknown class(es) {0} will be ignored\".format(sorted(unknown, key=str))\n"
          ]
        }
      ],
      "source": [
        "xtrain,xtest,ytrain,ytest, _,_ = get_data()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aMILE-bFocir"
      },
      "outputs": [],
      "source": [
        "tfidf = TfidfVectorizer(analyzer='word', stop_words='english')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XD5HSCPpocdK"
      },
      "outputs": [],
      "source": [
        "train_tfidf = tfidf.fit_transform(xtrain)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oSZFrFedocaK"
      },
      "outputs": [],
      "source": [
        "test_tfidf = tfidf.transform(xtest)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R-hE7josyF8O"
      },
      "source": [
        "### DL based Data Loading"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "xtrain,xtest,ytrain, ytest, empty_train,empty_test = get_data()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 123,
          "referenced_widgets": [
            "40888b7422234d07a6480beb97944d5b",
            "cb8d969c1e2947e8b63e7a4c76bc6b9b",
            "708cfe342aa144a59e4cbfb5c260d6f3",
            "e63c9daeafcb4fac97e6ff0b5e5e80a2",
            "e899a9c111e442239bc7ef9692107278",
            "80aa60a38e96419d84d85211a519dff7",
            "d62a83b706ef4dccb9ce712e43d7a258",
            "2f73619dd3594fe2bbcb038cdb8f1a58",
            "858d78a275a44f60bb749b3d10ca950d",
            "4b7c7dfbbd4f4460837f31a92559d3fc",
            "7e5720d5c0e14e579744fab8a8ece447"
          ]
        },
        "id": "n3YLEDoplKee",
        "outputId": "a53a38c7-206f-456d-9f17-a70cd06ae854"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Reusing dataset reuters21578 (/root/.cache/huggingface/datasets/reuters21578/ModApte/1.0.0/98a2ad6a0242627562db83992f9625261854c40a88619322596153a5a16a206c)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "40888b7422234d07a6480beb97944d5b",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/3 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/preprocessing/_label.py:876: UserWarning: unknown class(es) ['cottonseed', 'f-cattle', 'sfr'] will be ignored\n",
            "  \"unknown class(es) {0} will be ignored\".format(sorted(unknown, key=str))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "xtrain_bert.shape, xtest_bert.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GUCdBYVw_6j-",
        "outputId": "30870c30-afed-41cc-962b-8c14ef1a1bba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((9603, 768), (3299, 768))"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "50cGr6InyIVY"
      },
      "outputs": [],
      "source": [
        "xtest_bert = np.load(check/'bert_embedded_test.npy')\n",
        "xtrain_bert = np.load(check/'bert_embedded_train.npy')\n",
        "\n",
        "# Filter empty values\n",
        "xtest_bert = np.delete(xtest_bert, empty_test, 0)\n",
        "xtrain_bert = np.delete(xtrain_bert, empty_train, 0)\n",
        "\n",
        "ytrain_dl = torch.from_numpy(ytrain)\n",
        "ytest_dl = torch.from_numpy(ytest)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "guLFo11CznGb"
      },
      "outputs": [],
      "source": [
        "# Model Definition\n",
        "dtree = DecisionTreeClassifier()\n",
        "rftree = RandomForestClassifier(n_estimators=10)\n",
        "adaboost = AdaBoostClassifier(n_estimators=10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xbgS6VcEznDD"
      },
      "outputs": [],
      "source": [
        "# Model wrt Multiclass\n",
        "clf_dtree = OneVsRestClassifier(dtree)\n",
        "clf_rf = OneVsRestClassifier(rftree,n_jobs=2)\n",
        "clf_adab = OneVsRestClassifier(adaboost, n_jobs=2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QjXXTlI4znAQ"
      },
      "outputs": [],
      "source": [
        "# # Model fitting\n",
        "clf_dtree.fit(xtrain_bert, ytrain)\n",
        "ytrain_dt = clf_dtree.predict(xtrain_bert)\n",
        "np.save(f\"{check}/ytrain_dt.npy\",ytrain_dt)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ePouxiYBzm9Z",
        "outputId": "26621b0d-efda-4562-baf4-3932ae98f850"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "OneVsRestClassifier(estimator=RandomForestClassifier(n_estimators=10), n_jobs=2)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "clf_rf.fit(xtrain_bert, ytrain)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FWVwI1o8zm6Q",
        "outputId": "84ca2118-f6ff-473c-d427-107af0fa2aae"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "OneVsRestClassifier(estimator=AdaBoostClassifier(n_estimators=10), n_jobs=2)"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "clf_adab.fit(xtrain_bert, ytrain)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3JW6AzP1zm3X"
      },
      "outputs": [],
      "source": [
        "ytrain_rf = clf_rf.predict(xtrain_bert)\n",
        "ytrain_adab = clf_adab.predict(xtrain_bert)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qC-uNUg9zm0l"
      },
      "outputs": [],
      "source": [
        "# Save others\n",
        "np.save(f\"{check}/ytrain_rf.npy\", ytrain_rf)\n",
        "np.save(f\"{check}/ytrain_adab.npy\", ytrain_adab)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D_v5UdDoiZ2A"
      },
      "source": [
        "### Modeling"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Parameter Setting"
      ],
      "metadata": {
        "id": "LChpQcrIbGb2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Params\n",
        "jobs=2\n",
        "neighbors = 45\n",
        "kernel='linear'\n",
        "cache = 200\n",
        "layers = (64,)"
      ],
      "metadata": {
        "id": "rjFIIbDabFTp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Baseline"
      ],
      "metadata": {
        "id": "ztPqwk8Fa9qU"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4vnf4DHeYvSH"
      },
      "outputs": [],
      "source": [
        "dummy_clf =  DummyClassifier(strategy = \"stratified\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gBcGTG1bH4r6"
      },
      "outputs": [],
      "source": [
        "dummy_pipe = Pipeline(steps= [('tf_idf', TfidfVectorizer(analyzer='word', stop_words='english')),(\"dummy\",dummy_clf)])\n",
        "\n",
        "dummy_ovr = OneVsRestClassifier(dummy_pipe)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "15R6-U4lISYz",
        "outputId": "191d292b-35c2-43e0-b2a2-2037cab12edf"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "OneVsRestClassifier(estimator=Pipeline(steps=[('tf_idf',\n",
              "                                               TfidfVectorizer(stop_words='english')),\n",
              "                                              ('dummy',\n",
              "                                               DummyClassifier(strategy='stratified'))]))"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ],
      "source": [
        "dummy_ovr.fit(xtrain,ytrain)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ytest_dummy = dummy_ovr.predict(xtest)\n",
        "ytrain_dummy = dummy_ovr.predict(xtrain)"
      ],
      "metadata": {
        "id": "HdUQIYSczpU8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(ytest_dummy.shape)\n",
        "print(ytrain_dummy.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-HwvrMVR2nGw",
        "outputId": "72af970a-9fb1-455f-932d-7773dcc4b713"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(3019, 115)\n",
            "(7775, 115)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.save(f\"{check}/ytrain_dummy.npy\",ytrain_dummy)\n",
        "np.save(f\"{check}/ytest_dummy.npy\",ytest_dummy)"
      ],
      "metadata": {
        "id": "f7JPjwN4zzus"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Shallow Models"
      ],
      "metadata": {
        "id": "F2W0OMnUbQER"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "K54PRvk-Bjrh"
      },
      "outputs": [],
      "source": [
        "# ## Naive Bayes\n",
        "nb_tfv = Pipeline(steps = [('tf_idf', TfidfVectorizer(analyzer='word', stop_words = stop_words)),('nb', MultinomialNB()) ]) \n",
        "\n",
        "# # # KNN\n",
        "knn_tfv = Pipeline(steps = [('tf_idf', TfidfVectorizer(analyzer='word', stop_words = stop_words)),('knn', KNeighborsClassifier(n_neighbors=neighbors, n_jobs=jobs))])\n",
        "\n",
        "# # # SVM\n",
        "svm_tfv = Pipeline(steps = [('tf_idf', TfidfVectorizer(analyzer='word', stop_words = stop_words)),('svm', SVC(kernel = kernel, cache_size=cache))])\n",
        "\n",
        "# # ## NNet\n",
        "# # # Feature scaling\n",
        "nn_tfv = Pipeline(steps = [('tf_idf', TfidfVectorizer(analyzer='word', stop_words = stop_words)),('nnet',MLPClassifier(hidden_layer_sizes=layers))])\n",
        "\n",
        "nb = MultinomialNB()\n",
        "knn = KNeighborsClassifier(n_neighbors=neighbors, n_jobs=2)\n",
        "svm = SVC(kernel=kernel)\n",
        "nn = MLPClassifier(hidden_layer_sizes=layers)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0zicOX0gp04A"
      },
      "outputs": [],
      "source": [
        "from pathlib import Path\n",
        "check = Path.cwd()/'gdrive/MyDrive/TMProject'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "S8HNZhTlo3Ks",
        "outputId": "1a90cb83-77d6-4940-a694-874136eb94d2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(7775, 115)\n",
            "(7775, 115)\n",
            "(7775, 115)\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:699: UserWarning: Training interrupted by user.\n",
            "  warnings.warn(\"Training interrupted by user.\")\n"
          ]
        }
      ],
      "source": [
        "for name, model in zip(['naive_bayes','knn','svm','nn'],[nb,knn,svm,nn]):\n",
        "    model = OneVsRestClassifier(model).fit(train_tfidf, ytrain)\n",
        "    y_train_pred = model.predict(train_tfidf)\n",
        "    print(y_train_pred.shape)\n",
        "    np.save(f\"{check}/{name}_train.npy\", y_train_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "O6GySsvo8Hrz"
      },
      "outputs": [],
      "source": [
        "# nb_tfv.fit(xtrain,ytrain)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qoN-abofBZ4a"
      },
      "outputs": [],
      "source": [
        "pipelines = [nb_tfv,knn_tfv,svm_tfv,nn_tfv]\n",
        "classifiers = [OneVsRestClassifier(pipeline, n_jobs=-1) for pipeline in pipelines]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "N2sHIydUyIPK"
      },
      "outputs": [],
      "source": [
        "for classifier in classifiers:\n",
        "    classifier.fit(xtrain,ytrain)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hbyIPhe-MfPO",
        "outputId": "1e906953-154f-4fe8-9353-53db2e2af791"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.2323908092348849\n",
            "0.22522842565063447\n",
            "0.49175651853534785\n",
            "0.39856147306801937\n"
          ]
        }
      ],
      "source": [
        "for preds in [y_pred_nb, y_pred_knn, y_pred_svm, y_pred_nn]:\n",
        "    score = f1_score(ytest, preds, average='macro', zero_division=True)\n",
        "    print(score)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uBZV2W3kXBb5",
        "outputId": "6a84244c-27d7-48b6-d80e-0aaabfd2a241"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.5319773127322511\n",
            "0.26601896830904465\n",
            "0.8120529599756506\n",
            "0.773371104815864\n"
          ]
        }
      ],
      "source": [
        "for preds in [y_pred_nb, y_pred_knn, y_pred_svm, y_pred_nn]:\n",
        "    score = f1_score(ytest, preds, average='micro', zero_division=True)\n",
        "    print(score)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        ""
      ],
      "metadata": {
        "id": "ADiAkhfvblDY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "cjwMApEhbl72"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MnQpxAyLriCi"
      },
      "source": [
        "### Creating BERT based features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LGBNcelvbc32"
      },
      "outputs": [],
      "source": [
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else 'cpu')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bIxEjICbuc9B",
        "outputId": "f1b34fa7-7318-4efd-f141-cf4a03342ecf"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertModel: ['vocab_layer_norm.weight', 'vocab_transform.weight', 'vocab_projector.bias', 'vocab_transform.bias', 'vocab_projector.weight', 'vocab_layer_norm.bias']\n",
            "- This IS expected if you are initializing DistilBertModel 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 DistilBertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        }
      ],
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained('distilbert-base-uncased')\n",
        "model = AutoModel.from_pretrained('distilbert-base-uncased', num).to(device)"
      ]
    },
    {
      "cell_type": "code",
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        },
        "id": "m97q6xuz70Pm",
        "outputId": "960a1ae1-773b-4384-f036-96b0d1c3c4d8"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "291dcefe950842dba6fd3ca98b3d5724",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/4.17k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4e1b822054894fce9003abd7cede76f4",
              "version_major": 2,
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            "text/plain": [
              "Downloading:   0%|          | 0.00/2.37k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading and preparing dataset reuters21578/ModApte (download: 7.77 MiB, generated: 12.48 MiB, post-processed: Unknown size, total: 20.25 MiB) to /root/.cache/huggingface/datasets/reuters21578/ModApte/1.0.0/98a2ad6a0242627562db83992f9625261854c40a88619322596153a5a16a206c...\n"
          ]
        },
        {
          "data": {
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              "model_id": "c1117a955b7348d4a19c6aee0f039f6c",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/8.15M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a96d9a235d014488916923630b0bc1e9",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "0 examples [00:00, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "49b93198fc2c480c9f102a6fc298e907",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "0 examples [00:00, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9ac7395c42f04a97a370838370d6b08d",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "0 examples [00:00, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Dataset reuters21578 downloaded and prepared to /root/.cache/huggingface/datasets/reuters21578/ModApte/1.0.0/98a2ad6a0242627562db83992f9625261854c40a88619322596153a5a16a206c. Subsequent calls will reuse this data.\n"
          ]
        },
        {
          "data": {
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              "model_id": "d6d9170c6e9945e7bdaaac7908bed75b",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/3 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "reuters = load_dataset(\"reuters21578\",\"ModApte\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IAM3IGZK91h-"
      },
      "outputs": [],
      "source": [
        "reuters.reset_format()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eUf5wLzv91ec"
      },
      "outputs": [],
      "source": [
        "def tokenize(batch):\n",
        "    return tokenizer(batch['text'], padding= True, truncation=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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        },
        "id": "_SeP7bXj91cF",
        "outputId": "3f97c5ef-30ec-48a6-b351-b2b6d4749d90"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "44183e01e03749ed8fd408f8f67e8eda",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?ba/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "93e7add478864aa4942cf6e3885b8b95",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?ba/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "816b75be411840518950ae8e47a1915e",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?ba/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "reuters_encoded =  reuters.map(tokenize, batched=True, batch_size=None)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "izZPJBqN91Wr"
      },
      "outputs": [],
      "source": [
        "def forward_pass(batch):\n",
        "    input_ids = torch.tensor(batch['input_ids']).to(device)\n",
        "    attention_mask = torch.tensor(batch['attention_mask']).to(device)\n",
        "    with torch.no_grad():\n",
        "        last_hidden_state = model(input_ids, attention_mask).last_hidden_state\n",
        "        last_hidden_state = last_hidden_state.cpu().numpy()\n",
        "    # Using Average of Unmasked hidden states for classification\n",
        "    lhs_shape = last_hidden_state.shape\n",
        "    bool_mask = ~np.array(batch['attention_mask']).astype(bool)\n",
        "    bool_mask = np.repeat(bool_mask, lhs_shape[-1], axis=-1)\n",
        "    bool_mask = bool_mask.reshape(lhs_shape)\n",
        "    masked_mean = np.ma.array(last_hidden_state, mask= bool_mask).mean(axis=1)\n",
        "    batch['hidden_state'] = masked_mean.data\n",
        "    return batch"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Y5i5bKLF91T5"
      },
      "outputs": [],
      "source": [
        "# reuters_encoded = reuters_encoded.map(forward_pass, batched=True, batch_size = 16)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FtDMUlRA91RP"
      },
      "outputs": [],
      "source": [
        "bert_emb_xtrain = np.array(reuters_encoded['train']['hidden_state'])\n",
        "bert_emb_xtest = np.array(reuters_encoded['test']['hidden_state'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "L8qBNXQK91Oh"
      },
      "outputs": [],
      "source": [
        "np.save(\"bert_embedded_test\", bert_emb_xtest)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ehl2AQFW91L6"
      },
      "outputs": [],
      "source": [
        "# topics = pd.Series(reuters['train']['topics'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3OH0RlDJa1kv"
      },
      "source": [
        "### BERT Feature Based Models\n",
        "\n",
        "- Decision Tree\n",
        "- Random FOrest\n",
        "- AdaBoost"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "xtrain,xtest,ytrain, ytest, empty_train,empty_test = get_data()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 123,
          "referenced_widgets": [
            "40888b7422234d07a6480beb97944d5b",
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          ]
        },
        "outputId": "a53a38c7-206f-456d-9f17-a70cd06ae854",
        "id": "WNHOoam6a1kw"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Reusing dataset reuters21578 (/root/.cache/huggingface/datasets/reuters21578/ModApte/1.0.0/98a2ad6a0242627562db83992f9625261854c40a88619322596153a5a16a206c)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "40888b7422234d07a6480beb97944d5b",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/3 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/preprocessing/_label.py:876: UserWarning: unknown class(es) ['cottonseed', 'f-cattle', 'sfr'] will be ignored\n",
            "  \"unknown class(es) {0} will be ignored\".format(sorted(unknown, key=str))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sV62n5Moa1kx"
      },
      "outputs": [],
      "source": [
        "xtest_bert = np.load(check/'bert_embedded_test.npy')\n",
        "xtrain_bert = np.load(check/'bert_embedded_train.npy')\n",
        "\n",
        "# Filter empty values\n",
        "xtest_bert = np.delete(xtest_bert, empty_test, 0)\n",
        "xtrain_bert = np.delete(xtrain_bert, empty_train, 0)\n",
        "\n",
        "ytrain_dl = torch.from_numpy(ytrain)\n",
        "ytest_dl = torch.from_numpy(ytest)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OiDKFtQda1ky"
      },
      "outputs": [],
      "source": [
        "# Model Definition\n",
        "dtree = DecisionTreeClassifier()\n",
        "rftree = RandomForestClassifier(n_estimators=10)\n",
        "adaboost = AdaBoostClassifier(n_estimators=10)\n",
        "\n",
        "# Model wrt Multiclass\n",
        "clf_dtree = OneVsRestClassifier(dtree)\n",
        "clf_rf = OneVsRestClassifier(rftree,n_jobs=2)\n",
        "clf_adab = OneVsRestClassifier(adaboost, n_jobs=2)\n",
        "\n",
        "# # Model fitting\n",
        "clf_dtree.fit(xtrain_bert, ytrain)\n",
        "clf_rf.fit(xtrain_bert, ytrain)\n",
        "clf_adab.fit(xtrain_bert, ytrain)\n",
        "\n",
        "# Model Prediction\n",
        "ytrain_dt = clf_dtree.predict(xtrain_bert)\n",
        "ytrain_rf = clf_rf.predict(xtrain_bert)\n",
        "ytrain_adab = clf_adab.predict(xtrain_bert)\n",
        "\n",
        "# Save Outputs\n",
        "np.save(f\"{check}/ytrain_dt.npy\",ytrain_dt)\n",
        "np.save(f\"{check}/ytrain_rf.npy\", ytrain_rf)\n",
        "np.save(f\"{check}/ytrain_adab.npy\", ytrain_adab)"
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