Classical_methods.ipynb

{
  "cells": [
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "zXujg-yEioz8",
        "outputId": "19e78490-45b7-4d93-f069-d214c01ff8fb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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            "\u001b[33mWARNING: You are using pip version 21.1.1; however, version 22.2.1 is available.\n",
            "You should consider upgrading via the '/Users/max/Desktop/Dissertation/implementations/Disso-COLD/proj_env/bin/python -m pip install --upgrade pip' command.\u001b[0m\n",
            "Note: you may need to restart the kernel to use updated packages.\n",
            "ERROR: unknown command \"transformers\"\n",
            "Note: you may need to restart the kernel to use updated packages.\n",
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            "\u001b[33mWARNING: You are using pip version 21.1.1; however, version 22.2.1 is available.\n",
            "You should consider upgrading via the '/Users/max/Desktop/Dissertation/implementations/Disso-COLD/proj_env/bin/python -m pip install --upgrade pip' command.\u001b[0m\n",
            "Note: you may need to restart the kernel to use updated packages.\n",
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            "\u001b[33mWARNING: You are using pip version 21.1.1; however, version 22.2.1 is available.\n",
            "You should consider upgrading via the '/Users/max/Desktop/Dissertation/implementations/Disso-COLD/proj_env/bin/python -m pip install --upgrade pip' command.\u001b[0m\n",
            "Note: you may need to restart the kernel to use updated packages.\n",
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            "\u001b[33mWARNING: You are using pip version 21.1.1; however, version 22.2.1 is available.\n",
            "You should consider upgrading via the '/Users/max/Desktop/Dissertation/implementations/Disso-COLD/proj_env/bin/python -m pip install --upgrade pip' command.\u001b[0m\n",
            "Note: you may need to restart the kernel to use updated packages.\n",
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            "\u001b[33mWARNING: You are using pip version 21.1.1; however, version 22.2.1 is available.\n",
            "You should consider upgrading via the '/Users/max/Desktop/Dissertation/implementations/Disso-COLD/proj_env/bin/python -m pip install --upgrade pip' command.\u001b[0m\n",
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            "\u001b[33mWARNING: You are using pip version 21.1.1; however, version 22.2.1 is available.\n",
            "You should consider upgrading via the '/Users/max/Desktop/Dissertation/implementations/Disso-COLD/proj_env/bin/python -m pip install --upgrade pip' command.\u001b[0m\n"
          ]
        }
      ],
      "source": [
        "%pip install sklearn\n",
        "%pip transformers\n",
        "%pip install huggingface\n",
        "%pip install torch \n",
        "%pip install pandas\n",
        "%pip install sklearn\n",
        "%pip install datasets\n",
        "%pip install transformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "XMkSgI1TlvyK"
      },
      "outputs": [],
      "source": [
        "import pickle\n",
        "\n",
        "#from sklearn.datasets import fetch_20newsgroups #encountered issues SSLCertificationError with this \n",
        "from datasets import load_dataset, load_metric\n",
        "import os\n",
        "import pandas as pd\n",
        "import re\n",
        "from bs4 import BeautifulSoup\n",
        "\n",
        "from spacy import tokenizer\n",
        "from spacy.lang.en import English\n",
        "from sklearn.naive_bayes import GaussianNB, BernoulliNB,MultinomialNB\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\n",
        "from sklearn import svm\n",
        "import numpy as np\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "id": "y0eXjeh_9WZB"
      },
      "outputs": [],
      "source": [
        "#Methods\n",
        "acc = load_metric('accuracy'); f1_score = load_metric('f1')\n",
        "\n",
        "def parse_html(text):\n",
        "    \"removes hyperlink from a piece of text\"\n",
        "    \n",
        "    soup = BeautifulSoup(text, \"html.parser\")\n",
        "    parsed_text = soup.get_text()\n",
        "    return parsed_text\n",
        "\n",
        "\n",
        "def spacy_preproc(text):\n",
        "\n",
        "    \"Removes stopwords and punctuation and\"\n",
        "    nlp = English()\n",
        "    tokenizer = nlp.tokenizer\n",
        "    refined_text = tokenizer(text)\n",
        "        \n",
        "    refined_text = [text for text in refined_text if not text.is_stop]\n",
        "    refined_text = [text for text in refined_text if not text.is_punct]\n",
        "    refined_text = [text for text in refined_text if not text.like_email]\n",
        "    refined_text = [text for text in refined_text if not text.like_url]\n",
        "    refined_text = [text.text for text in refined_text if not text.like_num]\n",
        "\n",
        "    text = \" \".join(refined_text[:])\n",
        "\n",
        "    return text\n",
        "\n",
        "def regex_preproc(text): \n",
        "\n",
        "    refined_text = re.sub(\"(\\n)\", \" \", text) #removes python newline \n",
        "    refined_text = re.sub(\"(\\')\", \"\" , refined_text) #removes backslash\n",
        "    refined_text = re.sub(\"(<\\W+(p >))\", \" \", refined_text)\n",
        "    refined_text = re.sub(\"(\\s+)\", \" \", refined_text) #removes whitespaces\n",
        "    #refined_text = re.sub(\"(&amp|>+|-+)\", \"\", refined_text)\n",
        "    #refined_text = re.sub(\"\\W*(@)\", \"\", refined_text) #removes emails \n",
        "    #refined_text = re.sub(\"(\\d+)\", \"\", refined_text) #removes digits\n",
        "\n",
        "    return refined_text\n",
        "                \n",
        "def preproc(text):\n",
        "  \"\"\"Preprocesses files\"\"\"\n",
        "\n",
        "  #output = parse_html(text)\n",
        "  temp_output = spacy_preproc(text)\n",
        "  final_output = regex_preproc(temp_output)\n",
        "\n",
        "  return final_output\n",
        "\n",
        "\n",
        "def prepare_corpus(path):\n",
        "    \"\"\"BY date\"\"\"\n",
        "    corpus = {}; temp = []; \n",
        "    for topic in os.listdir(path):\n",
        "        subfolder = path + '/' + topic\n",
        "        current = []\n",
        "        for doc in os.listdir(subfolder):\n",
        "            file = subfolder + '/' + doc\n",
        "            with open(file, 'r', encoding='utf-8', errors= 'ignore') as t:\n",
        "               #corpus.append((preproc(\" \".join(t.readlines())),topic),)\n",
        "               temp = \" \".join(t.readlines())\n",
        "            current.append(temp)\n",
        "        corpus[topic] = current\n",
        "    return corpus\n",
        "\n",
        "\n",
        "def extra_long_documents(text, chunk_size = 100):\n",
        "\n",
        "  \"\"\"\n",
        "  Takes in a long documents chunks them into segments of lenght 'chunk_size'\n",
        "  \"\"\"\n",
        "  doc_length = len(text); chunks = []\n",
        "  \n",
        "  if doc_length > chunk_size:\n",
        "    output = range(doc_length//chunk_size)\n",
        "\n",
        "    chunks = [text[:chunk_size*n] for n in output]\n",
        "    last_index = output[-1]*chunk_size\n",
        "\n",
        "    chunks.append(text[last_index:])\n",
        "  else :\n",
        "    chunks.append(text)\n",
        "  \n",
        "    return chunks \n",
        "\n",
        "def extract_features(text, min_df = 0.05 , max_df = 0.5, max_features = 1000, method = \"Tfidf\"):\n",
        "    \"\"\"Count represents the number of features to be chosen from tfidf while text represents text data\n",
        "    vectorizer is either count_vectorizer or tfidfvectorizer\"\"\"\n",
        "    \n",
        "    if method.lower() == \"tfidf\":\n",
        "        vectorizer = TfidfVectorizer(token_pattern = '[A-Za-z]+', min_df = float(min_df), max_df = float\n",
        "                                     (max_df), ngram_range = (1,2), max_features = max_features)\n",
        "    elif method.lower() == \"count\":\n",
        "        vectorizer = CountVectorizer(token_pattern = '[A-Za-z]+', min_df = float(min_df), max_df = float\n",
        "                                     (max_df), ngram_range = (1,2), max_features = max_features)\n",
        "    else:\n",
        "        print(\"Wrong method. Choose between tfidf and Count\")\n",
        "\n",
        "    vec = vectorizer.fit_transform(text)\n",
        "    X_features = vectorizer.get_feature_names()\n",
        "    print(vec.shape)\n",
        "    return vec, X_features\n",
        "\n",
        "#^\\s\n",
        "\n",
        "def accuracy(prediction, goldstandard):\n",
        "    acc = 0\n",
        "    for a,b in zip(prediction,goldstandard):\n",
        "        if a == b:\n",
        "            acc+=1\n",
        "        else:pass\n",
        "    return (acc/len(prediction))*100\n",
        "\n",
        "def compute_metrics(predictions,label):\n",
        "\n",
        "    f_1 = f1_score.compute(predictions = predictions, references = label, pos_label = 1)\n",
        "    acc_1 = acc.compute(predictions = predictions, references = label)\n",
        "    return acc_1['accuracy'], f_1['f1']\n"
      ]
    },
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            "9d924cbcbe7546c79cdddf11b95c84ef",
            "da1547cd7bb04052a9c76263f448f67b",
            "4b523650d57a4654ac9652cb25184404",
            "6682ac09581341a4a6f3464e6dcbf074",
            "6bd759ffaa34476d89e576613da87092",
            "e85731f112d748ac8733bd1d71a80233",
            "14002c159bec473d91af383c840ae2d7",
            "c6b772cd33024265b03b2751680e0a08",
            "10002d9a365549eab7f9e1bff2f9dcc1",
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            "9e6e80a3484c4717a11a67347e9ad78d",
            "db1acf5d7e8f4617af6d7f53415e1a4c",
            "08615ab2a7874f8485211bd6f875b774",
            "2b66f6a7691046aa9c00d6f85b526990",
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            "0af57705cbc34f2c8a81ae87523dc496",
            "37110828f01f4bf48daa282c4c2c8976"
          ]
        },
        "id": "GH9YoVTM9hN_",
        "outputId": "a302dff9-ecc6-4405-c089-25020075b28c"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Reusing dataset hyperpartisan_news_detection (/Users/max/.cache/huggingface/datasets/hyperpartisan_news_detection/bypublisher/1.0.0/7f4215b0474950ddf516e806400ab81d098b3da3b3a919a13cd1a4cf2c677012)\n",
            "100%|██████████| 2/2 [00:00<00:00, 20.29it/s]\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "dict_keys(['train', 'validation'])"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "hyperpartisan_dataset = load_dataset(\"hyperpartisan_news_detection\", \"bypublisher\") #Not stripped of headers and footers\n",
        "hyperpartisan_dataset.keys()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "id": "l39vRF-Z9jvb"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Loading cached shuffled indices for dataset at /Users/max/.cache/huggingface/datasets/hyperpartisan_news_detection/bypublisher/1.0.0/7f4215b0474950ddf516e806400ab81d098b3da3b3a919a13cd1a4cf2c677012/cache-66d6117f38d3c868.arrow\n",
            "Loading cached shuffled indices for dataset at /Users/max/.cache/huggingface/datasets/hyperpartisan_news_detection/bypublisher/1.0.0/7f4215b0474950ddf516e806400ab81d098b3da3b3a919a13cd1a4cf2c677012/cache-31fcbf3d65e8fea2.arrow\n"
          ]
        }
      ],
      "source": [
        "#change to bypublisher because byarticle has no test set.\n",
        "hyperpartisan_train = hyperpartisan_dataset['train']\n",
        "hyperpartisan_valid = hyperpartisan_dataset['validation']\n",
        "\n",
        "hyperpartisan_train = hyperpartisan_train.shuffle(seed = 32)\n",
        "hyperpartisan_valid = hyperpartisan_valid.shuffle(seed = 32)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "id": "3PMLv4Tn9ram"
      },
      "outputs": [],
      "source": [
        "#If hyperpartisan use 1\n",
        "#Else use 0\n",
        "int_labels_train = [1 if x == True else 0 for x in hyperpartisan_train['hyperpartisan']]\n",
        "int_labels_val = [1 if x == True else 0 for x in hyperpartisan_valid['hyperpartisan']]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8B_YaIM9-AsS"
      },
      "outputs": [],
      "source": [
        "processed_train = [regex_preproc(parse_html(text)) for text in hyperpartisan_train[\"text\"]]\n",
        "processed_test = [regex_preproc(parse_html(text)) for text in hyperpartisan_train[\"text\"]]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "id": "zvIZKEWfL1Rc"
      },
      "outputs": [
        {
          "ename": "NameError",
          "evalue": "name 'processed_train' is not defined",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[1;32m/Users/max/Desktop/Dissertation/implementations/Disso-COLD/Classical_methods.ipynb Cell 8\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/max/Desktop/Dissertation/implementations/Disso-COLD/Classical_methods.ipynb#ch0000007?line=0'>1</a>\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mopen\u001b[39m(\u001b[39m'\u001b[39m\u001b[39mprocessed_train.pkl\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mwb\u001b[39m\u001b[39m\"\u001b[39m) \u001b[39mas\u001b[39;00m out:\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/max/Desktop/Dissertation/implementations/Disso-COLD/Classical_methods.ipynb#ch0000007?line=1'>2</a>\u001b[0m   pickle\u001b[39m.\u001b[39mdump(processed_train, out)\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/max/Desktop/Dissertation/implementations/Disso-COLD/Classical_methods.ipynb#ch0000007?line=3'>4</a>\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mopen\u001b[39m(\u001b[39m'\u001b[39m\u001b[39mprocessed_test.pkl\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mwb\u001b[39m\u001b[39m\"\u001b[39m) \u001b[39mas\u001b[39;00m out_2:\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/max/Desktop/Dissertation/implementations/Disso-COLD/Classical_methods.ipynb#ch0000007?line=4'>5</a>\u001b[0m   pickle\u001b[39m.\u001b[39mdump(processed_test, out_2)\n",
            "\u001b[0;31mNameError\u001b[0m: name 'processed_train' is not defined"
          ]
        }
      ],
      "source": [
        "with open('processed_train.pkl', \"wb\") as out:\n",
        "  pickle.dump(processed_train, out)\n",
        "\n",
        "with open('processed_test.pkl', \"wb\") as out_2:\n",
        "  pickle.dump(processed_test, out_2)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UELm7N8GIJwJ",
        "outputId": "ea079aab-4988-48aa-8648-95359c159b2e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(600000, 981)\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "(600000, 981)"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "vec, feat = extract_features(processed_train, method = \"tfidf\")\n",
        "vec.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "owSaKYqKPjpx",
        "outputId": "f39fb567-a27d-427a-d8df-7fd09c778a46"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(600000, 1000)\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ]
        }
      ],
      "source": [
        "vec_test, feat_test = extract_features(hyperpartisan_valid['text'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d3Wa2UTWTwhq",
        "outputId": "15b190dc-4f9f-4857-c0b5-404c68dfa62e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(600000, 1000)"
            ]
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "vec_test.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "id": "af9js2UjYyoT"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/var/folders/3v/kl4z85b17kd84k1hy0srlb3w0000gn/T/ipykernel_28561/520196388.py:5: DeprecationWarning: Please use `csr_matrix` from the `scipy.sparse` namespace, the `scipy.sparse.csr` namespace is deprecated.\n",
            "  vec = pickle.load(ins)\n",
            "/var/folders/3v/kl4z85b17kd84k1hy0srlb3w0000gn/T/ipykernel_28561/520196388.py:8: DeprecationWarning: Please use `csr_matrix` from the `scipy.sparse` namespace, the `scipy.sparse.csr` namespace is deprecated.\n",
            "  vec_test = pickle.load(test_in)\n"
          ]
        }
      ],
      "source": [
        "features = \"features_mat_1000.pkl\"\n",
        "test_features = \"features_mat_1000_test.pkl\"\n",
        "\n",
        "with open (features, 'rb') as ins:\n",
        "  vec = pickle.load(ins)\n",
        "\n",
        "with open(test_features, \"rb\") as test_in:\n",
        "  vec_test = pickle.load(test_in)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "6sU93aPY-FX0"
      },
      "outputs": [
        {
          "ename": "NameError",
          "evalue": "name 'vec' is not defined",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[1;32m/Users/max/Desktop/Dissertation/implementations/Disso-COLD/Classical_methods.ipynb Cell 13\u001b[0m in \u001b[0;36m<cell line: 4>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/max/Desktop/Dissertation/implementations/Disso-COLD/Classical_methods.ipynb#ch0000012?line=0'>1</a>\u001b[0m \u001b[39m#Initialising multinomial naive bayes\u001b[39;00m\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/max/Desktop/Dissertation/implementations/Disso-COLD/Classical_methods.ipynb#ch0000012?line=2'>3</a>\u001b[0m mnb \u001b[39m=\u001b[39m {}; gnb \u001b[39m=\u001b[39m {} ; bnb \u001b[39m=\u001b[39m {}\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/max/Desktop/Dissertation/implementations/Disso-COLD/Classical_methods.ipynb#ch0000012?line=3'>4</a>\u001b[0m num_of_features \u001b[39m=\u001b[39m [item \u001b[39mfor\u001b[39;00m item \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(\u001b[39m100\u001b[39m, vec\u001b[39m.\u001b[39mshape[\u001b[39m1\u001b[39m], \u001b[39m100\u001b[39m)]\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/max/Desktop/Dissertation/implementations/Disso-COLD/Classical_methods.ipynb#ch0000012?line=5'>6</a>\u001b[0m MNB \u001b[39m=\u001b[39m MultinomialNB()\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/max/Desktop/Dissertation/implementations/Disso-COLD/Classical_methods.ipynb#ch0000012?line=6'>7</a>\u001b[0m BNB \u001b[39m=\u001b[39m BernoulliNB()\n",
            "\u001b[0;31mNameError\u001b[0m: name 'vec' is not defined"
          ]
        }
      ],
      "source": [
        "#Initialising multinomial naive bayes\n",
        "\n",
        "mnb = {}; gnb = {} ; bnb = {}\n",
        "num_of_features = [item for item in range(100, vec.shape[1], 100)]\n",
        "\n",
        "MNB = MultinomialNB()\n",
        "BNB = BernoulliNB()\n",
        "GNB = GaussianNB()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "for sub_set in num_of_features:\n",
        "\n",
        "  #Fitting features and output labels\n",
        "  MNB.fit(vec[:,:sub_set],int_labels_train)\n",
        "  BNB.fit(vec[:,:sub_set],int_labels_train)\n",
        "  GNB.fit(vec[:,:sub_set].toarray(), int_labels_train)\n",
        "\n",
        "  #assign test vector and predict \n",
        "  test_vec = vec_test[:,:sub_set]\n",
        "  pred_mnb = MNB.predict(test_vec)\n",
        "  pred_bnb = BNB.predict(test_vec)\n",
        "  pred_gnb = GNB.predict(test_vec.toarray())\n",
        "\n",
        "  #Score \n",
        "  mnb_acc, mnb_f1 = compute_metrics(pred_mnb, int_labels_val)\n",
        "  mnb['acc'] = mnb.get('acc', []); mnb['f1'] = mnb.get('f1', [])\n",
        "  mnb['acc'].append(mnb_acc); mnb['f1'].append(mnb_f1)\n",
        "\n",
        "  bnb_acc, bnb_f1 = compute_metrics(pred_bnb, int_labels_val)\n",
        "  bnb['acc'] = bnb.get('acc', []); bnb['f1'] = bnb.get('f1', [])\n",
        "  bnb['acc'].append(bnb_acc); bnb['f1'].append(bnb_f1)\n",
        "\n",
        "  gnb_acc, gnb_f1 = compute_metrics(pred_gnb, int_labels_val)\n",
        "  gnb['acc'] = gnb.get('acc', []); gnb['f1'] = gnb.get('f1', [])\n",
        "  gnb['acc'].append(gnb_acc); gnb['f1'].append(gnb_f1)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I7C7G-nYul0c",
        "outputId": "335f1ac7-32cc-401c-fd27-5eb646e625c5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[0.56871,\n",
              " 0.58562,\n",
              " 0.5791833333333334,\n",
              " 0.5601483333333334,\n",
              " 0.5577233333333333,\n",
              " 0.5279116666666667,\n",
              " 0.52252,\n",
              " 0.5267666666666667,\n",
              " 0.5193383333333333]"
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "mnb['acc']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 462
        },
        "id": "Mfj7GTkIVT99",
        "outputId": "7d1d1605-5049-4b88-f5f9-87b5e239802a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fb65651d090>"
            ]
          },
          "execution_count": 44,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 1080x504 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from IPython.core.pylabtools import figsize\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt \n",
        "\n",
        "sns.set_style(\"whitegrid\")\n",
        "sns.set_color_codes(palette = \"deep\")\n",
        "\n",
        "fig,ax = plt.subplots(2,1, sharex = True, figsize = (15,7))\n",
        "\n",
        "ax[0].plot(num_of_features,mnb['acc'])\n",
        "ax[0].plot(num_of_features,bnb['acc'])\n",
        "ax[0].plot(num_of_features,gnb['acc'])\n",
        "ax[0].legend(['Multinomial NB', 'Bernoulli NB', 'Gaussian NB'], )\n",
        "ax[0].title.set_text(\"Accuracy vs number of tfidf features\")\n",
        "\n",
        "plt.title(\"Naive Bayes vs number of features \")\n",
        "\n",
        "\n",
        "ax[1].plot(num_of_features,mnb['f1'])\n",
        "ax[1].plot(num_of_features,bnb['f1'])\n",
        "ax[1].plot(num_of_features,gnb['f1'])\n",
        "ax[1].title.set_text(\"F1_score vs number of tfidf features\")\n",
        "ax[1].legend(['Multinomial NB', 'Bernoulli NB', 'Gaussian NB'])\n",
        "#ax[1].title(\"Naive Bayes vs number of features\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HC1cwwbC-Krl"
      },
      "source": [
        "SVM"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WncIiUKc-MHz"
      },
      "outputs": [],
      "source": [
        "acc_svc = [];\n",
        "\n",
        "svc_classifier = svm.SVC(decision_function_shape='ovo')\n",
        "svc_classifier.fit(vec[:,:],int_labels_train)\n",
        "\n",
        "svc_prediction = svc_classifier.predict(test_vec[:,:])\n",
        "acc_svc.append(accuracy(svc_prediction, int_labels_val))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CFhPrA1Km6pZ"
      },
      "outputs": [],
      "source": [
        "plt.plot(acc_svc, num_of_features)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "20NEWSGROUP"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/Users/max/Desktop/Dissertation/implementations/Disso-COLD/proj_env/lib/python3.8/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
            "  from .autonotebook import tqdm as notebook_tqdm\n"
          ]
        }
      ],
      "source": [
        "from utils import PrepareCorpus"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [],
      "source": [
        "parentdir = \"Datasets\"\n",
        "\n",
        "\n",
        "train = parentdir+\"/20news-bydate\"+\"/20news-bydate-train\"\n",
        "test =  parentdir+\"/20news-bydate\"+ \"/20news-bydate-train\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "LvFHiwy9n9cx"
      },
      "outputs": [],
      "source": [
        "news_group_train = PrepareCorpus(train)\n",
        "news_group_test = PrepareCorpus(test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [],
      "source": [
        "news_group_train._corpus()\n",
        "news_group_test._corpus()\n",
        "\n",
        "news_group_train = news_group_train._corpus\n",
        "news_group_test = news_group_test._corpus"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "'From: hm@cs.brown.edu (Harry Mamaysky)\\n Subject: Heil Hernlem \\n In-Reply-To: hernlem@chess.ncsu.edu\\'s message of Wed, 14 Apr 1993 12:58:13 GMT\\n Organization: Dept. of Computer Science, Brown University\\n Lines: 24\\n \\n In article <1993Apr14.125813.21737@ncsu.edu> hernlem@chess.ncsu.edu (Brad Hernlem) writes:\\n \\n    Lebanese resistance forces detonated a bomb under an Israeli occupation\\n    patrol in Lebanese territory two days ago. Three soldiers were killed and\\n    two wounded. In \"retaliation\", Israeli and Israeli-backed forces wounded\\n    8 civilians by bombarding several Lebanese villages. Ironically, the Israeli\\n    government justifies its occupation in Lebanon by claiming that it is \\n    necessary to prevent such bombardments of Israeli villages!!\\n \\n    Congratulations to the brave men of the Lebanese resistance! With every\\n    Israeli son that you place in the grave you are underlining the moral\\n    bankruptcy of Israel\\'s occupation and drawing attention to the Israeli\\n    government\\'s policy of reckless disregard for civilian life.\\n \\n    Brad Hernlem (hernlem@chess.ncsu.EDU)\\n \\n Very nice. Three people are murdered, and Bradly is overjoyed. When I\\n hear about deaths in the middle east, be it Jewish or Arab deaths, I\\n feel sadness, and only hope that soon this all stops. Apparently, my\\n view point is not acceptable to people like you Bradly.\\n \\n Hernlem, you disgust me.\\n \\n Harry.\\n'"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "news_group_train['talk.politics.mideast'][0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'talk.politics.mideast': 0,\n",
              " 'rec.autos': 1,\n",
              " 'comp.sys.mac.hardware': 2,\n",
              " 'alt.atheism': 3,\n",
              " 'rec.sport.baseball': 4,\n",
              " 'comp.os.ms-windows.misc': 5,\n",
              " 'rec.sport.hockey': 6,\n",
              " 'sci.crypt': 7,\n",
              " 'sci.med': 8,\n",
              " 'talk.politics.misc': 9,\n",
              " 'rec.motorcycles': 10,\n",
              " 'comp.windows.x': 11,\n",
              " 'comp.graphics': 12,\n",
              " 'comp.sys.ibm.pc.hardware': 13,\n",
              " 'sci.electronics': 14,\n",
              " 'talk.politics.guns': 15,\n",
              " 'sci.space': 16,\n",
              " 'soc.religion.christian': 17,\n",
              " 'misc.forsale': 18,\n",
              " 'talk.religion.misc': 19}"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "id2label = {x:i for i,x in enumerate(news_group_train.keys())}\n",
        "id2label"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {},
      "outputs": [],
      "source": [
        "#Converting input into a dictionary \n",
        "\n",
        "news_group_train_modif = {}\n",
        "news_group_test_modif = {}\n",
        "\n",
        "news_group_train_modif['text'] = []; news_group_train_modif['labels'] = []\n",
        "news_group_test_modif['text'] = []; news_group_test_modif['labels'] = []\n",
        "\n",
        "for key,value in news_group_train.items():\n",
        "    list_of_strings = value\n",
        "    lab = id2label[key]\n",
        "    for text in list_of_strings:\n",
        "        news_group_train_modif['text'].append(text)\n",
        "        news_group_train_modif['labels'].append(lab)\n",
        "\n",
        "\n",
        "for key,value in news_group_test.items():\n",
        "    list_of_strings = value\n",
        "    lab = id2label[key]\n",
        "    for text in list_of_strings:\n",
        "        news_group_test_modif['text'].append(text)\n",
        "        news_group_test_modif['labels'].append(lab)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {},
      "outputs": [],
      "source": [
        "import json\n",
        "\n",
        "train_file = \"data_dict/20news_train.json\"\n",
        "test_file = \"data_dict/20news_test.json\"\n",
        "id_2_label = \"data_dict/id2label.json\"\n",
        "\n",
        "with open(train_file, \"w\") as ins_1:\n",
        "    json.dump(news_group_train_modif, ins_1)\n",
        "\n",
        "with open(test_file, \"w\") as ins_2:\n",
        "    json.dump(news_group_test_modif, ins_2)\n",
        "\n",
        "with open(id_2_label, \"w\") as ins_3:\n",
        "    json.dump(id2label, ins_3)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "CLEANING FILES "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {},
      "outputs": [],
      "source": [
        "clean = [preproc(x) for x in news_group_train_modif['text']]\n",
        "news_group_train_modif['text'] = clean"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {},
      "outputs": [],
      "source": [
        "clean = [preproc(x) for x in news_group_test_modif['text']]\n",
        "news_group_test_modif['text'] = clean"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/Users/max/Desktop/Dissertation/implementations/Disso-COLD/proj_env/lib/python3.8/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(11314, 224)\n",
            "(11314, 224)\n"
          ]
        }
      ],
      "source": [
        "train_news_vec, news_feat = extract_features(news_group_train_modif['text'], method = \"Tfidf\")\n",
        "test_news_vec, news_feat = extract_features(news_group_test_modif['text'], method = \"Tfidf\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {},
      "outputs": [
        {
          "ename": "NameError",
          "evalue": "name 'MNB' is not defined",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[1;32m/Users/max/Desktop/Dissertation/implementations/Disso-COLD/Classical_methods.ipynb Cell 33\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/max/Desktop/Dissertation/implementations/Disso-COLD/Classical_methods.ipynb#ch0000031?line=0'>1</a>\u001b[0m MNB\u001b[39m.\u001b[39mfit(train_news_vec, news_group_train_modif[\u001b[39m'\u001b[39m\u001b[39mlabels\u001b[39m\u001b[39m'\u001b[39m])\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/max/Desktop/Dissertation/implementations/Disso-COLD/Classical_methods.ipynb#ch0000031?line=1'>2</a>\u001b[0m GNB\u001b[39m.\u001b[39mfit(train_news_vec\u001b[39m.\u001b[39mtoarray(), news_group_train_modif[\u001b[39m'\u001b[39m\u001b[39mlabels\u001b[39m\u001b[39m'\u001b[39m])\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/max/Desktop/Dissertation/implementations/Disso-COLD/Classical_methods.ipynb#ch0000031?line=2'>3</a>\u001b[0m BNB\u001b[39m.\u001b[39mfit(train_news_vec, news_group_train_modif[\u001b[39m'\u001b[39m\u001b[39mlabels\u001b[39m\u001b[39m'\u001b[39m])\n",
            "\u001b[0;31mNameError\u001b[0m: name 'MNB' is not defined"
          ]
        }
      ],
      "source": [
        "MNB.fit(train_news_vec, news_group_train_modif['labels'])\n",
        "GNB.fit(train_news_vec.toarray(), news_group_train_modif['labels'])\n",
        "BNB.fit(train_news_vec, news_group_train_modif['labels'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 85,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(50.866183489482054, 43.07053208414354, 39.28760827293619)"
            ]
          },
          "execution_count": 85,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "mnb_news_pred = MNB.predict(test_news_vec)\n",
        "bnb_news_pred = BNB.predict(test_news_vec)\n",
        "gnb_news_pred = GNB.predict(test_news_vec.toarray())\n",
        "\n",
        "compute_metrics(mnb_news_pred, news_group_test_modif['labels']), compute_metrics(bnb_news_pred, news_group_test_modif['labels']), compute_metrics(gnb_news_pred, news_group_test_modif['labels'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([ 0,  0,  0, ..., 19, 19, 10])"
            ]
          },
          "execution_count": 40,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "svc_classifier = svm.SVC(decision_function_shape='ovo')\n",
        "svc_classifier.fit(train_news_vec, news_group_train_modif['labels'])\n",
        "\n",
        "pred = svc_classifier.predict(test_news_vec)\n",
        "pred"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "88.74845324376878"
            ]
          },
          "execution_count": 51,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "accuracy(pred, news_group_test_modif['labels'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'f1': 0.8875869105270722}"
            ]
          },
          "execution_count": 50,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "f1_score.compute(predictions = pred, references = news_group_test_modif['labels'], average = \"weighted\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
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      "source": []
    }
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