chapter07/Data_level_techniques_NLP.ipynb

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    "<a href=\"https://colab.research.google.com/github/packtpublishing/Machine-Learning-for-Imbalanced-Data/blob/main/chapter07/Data_level_techniques_NLP.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    "References: https://amitness.com/2020/05/data-augmentation-for-nlp/\n",
    "\n",
    "Dataset taken from: https://www.kaggle.com/datasets/team-ai/spam-text-message-classification?datasetId=2050&searchQuery=sampling\n",
    "\n",
    "Dataset License: CC0: Public Domain\n",
    "\n",
    "Some of the code is adapted from https://www.kaggle.com/code/jth359/imbalanced-target-variable-with-text-data"
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       "      <td>ham</td>\n",
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       "      <td>Ok lar... Joking wif u oni...</td>\n",
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       "      <td>spam</td>\n",
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      "text/plain": [
       "  Category                                            Message\n",
       "0      ham  Go until jurong point, crazy.. Available only ...\n",
       "1      ham                      Ok lar... Joking wif u oni...\n",
       "2     spam  Free entry in 2 a wkly comp to win FA Cup fina...\n",
       "3      ham  U dun say so early hor... U c already then say...\n",
       "4      ham  Nah I don't think he goes to usf, he lives aro..."
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "url = \"https://drive.google.com/file/d/1HQ4mqidhJKLhNEOd6agMDv_nl2caSlNB/view?usp=share_link\"\n",
    "url = \"https://drive.google.com/uc?id=\" + url.split(\"/\")[-2]\n",
    "df = pd.read_csv(url)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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    },
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ham     4825\n",
       "spam     747\n",
       "Name: Category, dtype: int64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Category\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "T--OlWOYZZVW"
   },
   "source": [
    "87% of our data is of class ham and 13% is of class spam\n",
    "\n",
    "For this notebook, I am going to be focusing on different techniques for handling imbalanced classes.  For this reason I am going to be using TF-IDF and a Logistic Regression Classifier for all of the different techniques."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "dIKRRDpnZZVW"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "# convert all text to lowercase\n",
    "df[\"Message\"] = df[\"Message\"].str.lower()\n",
    "\n",
    "# perform train test split\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    df[\"Message\"], df[\"Category\"], stratify=df[\"Category\"], random_state=11\n",
    ")\n",
    "\n",
    "# vectorize text using TFIDF\n",
    "tfidf = TfidfVectorizer()\n",
    "X_train_tfidf = tfidf.fit_transform(X_train)\n",
    "X_test_tfidf = tfidf.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "O2-v7l054xE_",
    "outputId": "84d13b68-bbc0-44c0-ebc7-d668c27409d0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4179\n",
      "Counter({'ham': 3619, 'spam': 560})\n"
     ]
    }
   ],
   "source": [
    "from collections import Counter\n",
    "\n",
    "print(len(X_train))\n",
    "print(Counter(y_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "E3ljL4jRZZVq"
   },
   "source": [
    "To begin we are starting with a logistic regression model where we do not do anything to the classes even though they are imbalanced\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "### Baseline Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "x1NtAAkrbIpq",
    "outputId": "68b3a34e-edb0-41d1-ca5b-8104efd442aa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         ham       0.97      1.00      0.98      1206\n",
      "        spam       0.99      0.78      0.87       187\n",
      "\n",
      "    accuracy                           0.97      1393\n",
      "   macro avg       0.98      0.89      0.93      1393\n",
      "weighted avg       0.97      0.97      0.97      1393\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "lr = LogisticRegression(random_state=42)\n",
    "lr.fit(X_train_tfidf, y_train)\n",
    "print(classification_report(y_test, lr.predict(X_test_tfidf)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mB427m9KZZV5"
   },
   "source": [
    "I see that I get a relatively low recall on the minority class `spam` of 0.85\n",
    "\n",
    "### Random Over Sampling\n",
    "\n",
    "Next I am going to try random over sampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "W-liL5SjZZV6",
    "outputId": "f2fd7667-ed12-4313-9f78-3b6dda57d3f0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ham     4825\n",
       "spam     747\n",
       "Name: Category, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Category\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "K_klKhxnZZV6",
    "outputId": "9f2a182a-f135-457c-cffd-361bc0859241"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ham     3619\n",
       "spam    3619\n",
       "Name: Category, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from imblearn.over_sampling import RandomOverSampler\n",
    "\n",
    "ros = RandomOverSampler()\n",
    "X_ros, y_ros = ros.fit_resample(X_train_tfidf, y_train)\n",
    "\n",
    "# check distribution after applying over sampling\n",
    "y_ros.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QJWv9WDkZZV6"
   },
   "source": [
    "Applying the same model with the over sampled data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "AnIJusFnbbXA",
    "outputId": "e7b850d5-4a1e-4e0c-bde5-19f7046a8026"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         ham       0.99      0.99      0.99      1206\n",
      "        spam       0.93      0.95      0.94       187\n",
      "\n",
      "    accuracy                           0.98      1393\n",
      "   macro avg       0.96      0.97      0.96      1393\n",
      "weighted avg       0.98      0.98      0.98      1393\n",
      "\n"
     ]
    }
   ],
   "source": [
    "lr = LogisticRegression(random_state=42)\n",
    "lr.fit(X_ros, y_ros)\n",
    "print(classification_report(y_test, lr.predict(X_test_tfidf)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Iyx03YM_ZZWN"
   },
   "source": [
    "F1-Score for the minority class went down to 0.88"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1t3pWHGgDact"
   },
   "source": [
    "I see that I get a relatively low recall on the minority class `spam` of 0.85\n",
    "\n",
    "### Random Underampling\n",
    "\n",
    "Next I am going to try random undersampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Z36iGq3uDac5",
    "outputId": "14c003ee-2826-450e-a276-e4b1ac795617"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ham     4825\n",
       "spam     747\n",
       "Name: Category, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Category\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "B0JYOBZ4Dac6",
    "outputId": "2034f5ad-b381-4385-f952-26be6b524b06"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ham     560\n",
       "spam    560\n",
       "Name: Category, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "\n",
    "ros = RandomUnderSampler()\n",
    "X_ros, y_ros = ros.fit_resample(X_train_tfidf, y_train)\n",
    "\n",
    "# check distribution after applying over sampling\n",
    "y_ros.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iMPL_Zf0Dac6"
   },
   "source": [
    "Applying the same model with the over sampled data"
   ]
  },
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   "execution_count": 13,
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    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "J4va9--JDac6",
    "outputId": "bc485a60-a781-41ac-c937-66728806066d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         ham       0.99      0.98      0.98      1206\n",
      "        spam       0.85      0.94      0.90       187\n",
      "\n",
      "    accuracy                           0.97      1393\n",
      "   macro avg       0.92      0.96      0.94      1393\n",
      "weighted avg       0.97      0.97      0.97      1393\n",
      "\n"
     ]
    }
   ],
   "source": [
    "lr = LogisticRegression(random_state=42)\n",
    "lr.fit(X_ros, y_ros)\n",
    "print(classification_report(y_test, lr.predict(X_test_tfidf)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qjn7egAKDac6"
   },
   "source": [
    "F1-Score for the minority class is 0.89"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NvOyzyIYZZZD"
   },
   "source": [
    "### Data Augmentation\n",
    "\n",
    "Now we will try translating the Spam Messages to another language and then translate them back to English.  The idea is that we will add a little noise by performing a translation.\n",
    "\n",
    "An example of this can be seen below"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fHzRm54iZZZE"
   },
   "source": [
    "Now lets see an example of this for a single message.  I am going to take a message, translate it to French, and then translate it back to English"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "twiZx2QKg1on"
   },
   "source": [
    "# Text augmentation using Back Translation using nlpaug library\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "wVobpUIJo9QW",
    "outputId": "d1d9d393-2f76-4316-9ae5-3b0f4718bb50"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Building wheels for collected packages: sacremoses\n",
      "  Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Created wheel for sacremoses: filename=sacremoses-0.0.53-py3-none-any.whl size=895239 sha256=523b2c06554c5fcc5cec9511beeb8c4a74e1b4061435b9e6913e776c62935e2c\n",
      "  Stored in directory: /root/.cache/pip/wheels/00/24/97/a2ea5324f36bc626e1ea0267f33db6aa80d157ee977e9e42fb\n",
      "Successfully built sacremoses\n",
      "Installing collected packages: tokenizers, safetensors, sacremoses, huggingface-hub, transformers, nlpaug\n",
      "Successfully installed huggingface-hub-0.16.4 nlpaug-1.1.11 sacremoses-0.0.53 safetensors-0.3.1 tokenizers-0.13.3 transformers-4.31.0\n"
     ]
    }
   ],
   "source": [
    "!pip install numpy requests nlpaug transformers sacremoses"
   ]
  },
  {
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    },
    "id": "H-tCwOfBo9QW",
    "outputId": "e7df2da0-2b9e-4fbe-b729-b74d07f17925"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3a96af77980248df8a4074f701bbe346",
       "version_major": 2,
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       "model_id": "92515dcf5b634097a088022d5fcc1d28",
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     "text": [
      "Some weights of FSMTForConditionalGeneration were not initialized from the model checkpoint at facebook/wmt19-en-de and are newly initialized: ['model.decoder.embed_positions.weight', 'model.encoder.embed_positions.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    },
    {
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       "model_id": "573ed321be6f4071a6fd0d2081c8ac51",
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     },
     "metadata": {},
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of FSMTForConditionalGeneration were not initialized from the model checkpoint at facebook/wmt19-de-en and are newly initialized: ['model.decoder.embed_positions.weight', 'model.encoder.embed_positions.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1b042a2a398240b193a76d2bca47f920",
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    {
     "data": {
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      "text/plain": [
       "Downloading (…)okenizer_config.json:   0%|          | 0.00/67.0 [00:00<?, ?B/s]"
      ]
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      ]
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      ]
     },
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    },
    {
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     },
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    {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "['The speedy brown fox leapt over the lazy dog']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nlpaug.augmenter.word as naw\n",
    "\n",
    "text = \"The quick brown fox jumped over the lazy dog\"\n",
    "back_translation_aug = naw.BackTranslationAug(\n",
    "    from_model_name=\"facebook/wmt19-en-de\", to_model_name=\"facebook/wmt19-de-en\"\n",
    ")\n",
    "back_translation_aug.augment(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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    },
    "id": "9Ce_Gzx6o9QW",
    "outputId": "8bcb8343-4481-489e-cfee-6128c5af76b7"
   },
   "outputs": [
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       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
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       "  </div>\n"
      ],
      "text/plain": [
       "     Category                                            Message\n",
       "0         ham  go until jurong point, crazy.. available only ...\n",
       "1         ham                      ok lar... joking wif u oni...\n",
       "2        spam  free entry in 2 a wkly comp to win fa cup fina...\n",
       "3         ham  u dun say so early hor... u c already then say...\n",
       "4         ham  nah i don't think he goes to usf, he lives aro...\n",
       "...       ...                                                ...\n",
       "5567     spam  this is the 2nd time we have tried 2 contact u...\n",
       "5568      ham               will ü b going to esplanade fr home?\n",
       "5569      ham  pity, * was in mood for that. so...any other s...\n",
       "5570      ham  the guy did some bitching but i acted like i'd...\n",
       "5571      ham                         rofl. its true to its name\n",
       "\n",
       "[5572 rows x 2 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
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   "metadata": {
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    },
    "id": "M3phi0mBo9QW",
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       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-dd2c6155-88ed-47be-977b-fcfe365abd94 button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-dd2c6155-88ed-47be-977b-fcfe365abd94');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "                                                Message Category\n",
       "1426          i'll be at mu in like  &lt;#&gt;  seconds      ham\n",
       "1247  i do know what u mean,  is the king of not hav...      ham"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train = pd.concat([X_train, y_train], axis=1)\n",
    "df_train.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "PJszLNGmo9QX",
    "outputId": "5ed320e4-8c53-404d-d3e2-04a3b005db06"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "recpt 1/3. you have ordered a ringtone. your order is being processed...   |    recpt 1 / 3. you have ordered a ringtone. Your order will be processed...\n",
      "3. you have received your mobile content. enjoy   |    3. You have received your mobile content. Enjoy\n",
      "show ur colours! euro 2004 2-4-1 offer! get an england flag & 3lions tone on ur phone! click on the following service message for info!   |    Euro 2004 2-4-1 Offer! Get an English flag & 3lions sound on your phone! Click on the following service message for more information!\n",
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      "todays vodafone numbers ending with 4882 are selected to a receive a £350 award. if your number matches call 09064019014 to receive your £350 award.   |    Today's Vodafone numbers ending in 4882 will be selected to receive a prize of £350. If your number matches 09064019014, you will receive your prize of £350.\n",
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      "free entry in 2 a wkly comp to win fa cup final tkts 21st may 2005. text fa to 87121 to receive entry question(std txt rate)t&c's apply 08452810075over18's   |    Free entry to 2 a wkly comp to win fa cup final tkts 21 May 2005. text fa to 87121 to receive entry question (std txt rate) t & c's apply 08452810075over18's\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "ignored",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-13-77505c416487>\u001b[0m in \u001b[0;36m<cell line: 10>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mmessage\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdf_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Category'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'spam'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Message'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m   \u001b[0mnew_msg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mback_translation_aug\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maugment\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m   \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'  |   '\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_msg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m   \u001b[0mtranslated_texts\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_msg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/nlpaug/base_augmenter.py\u001b[0m in \u001b[0;36maugment\u001b[0;34m(self, data, n, num_thread)\u001b[0m\n\u001b[1;32m     96\u001b[0m             \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'AbstSummAug'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'BackTranslationAug'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'ContextualWordEmbsAug'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'ContextualWordEmbsForSentenceAug'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     97\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maug_num\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 98\u001b[0;31m                     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maction_fx\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclean_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     99\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    100\u001b[0m                         \u001b[0maugmented_results\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/nlpaug/augmenter/word/back_translation.py\u001b[0m in \u001b[0;36msubstitute\u001b[0;34m(self, data, n)\u001b[0m\n\u001b[1;32m     68\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 70\u001b[0;31m         \u001b[0maugmented_text\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     71\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0maugmented_text\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     72\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/nlpaug/model/lang_models/machine_translation_transformers.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, texts, target_words, n)\u001b[0m\n\u001b[1;32m     37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtexts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_words\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 39\u001b[0;31m         \u001b[0msrc_translated_texts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtranslate_one_step_batched\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtexts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msrc_tokenizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msrc_model\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     40\u001b[0m         \u001b[0mtgt_translated_texts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtranslate_one_step_batched\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc_translated_texts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtgt_tokenizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtgt_model\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     41\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/nlpaug/model/lang_models/machine_translation_transformers.py\u001b[0m in \u001b[0;36mtranslate_one_step_batched\u001b[0;34m(self, data, tokenizer, model)\u001b[0m\n\u001b[1;32m     60\u001b[0m                 \u001b[0minput_ids\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattention_mask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     61\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 62\u001b[0;31m                 translated_ids_batch = model.generate(\n\u001b[0m\u001b[1;32m     63\u001b[0m                     \u001b[0minput_ids\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minput_ids\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattention_mask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattention_mask\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     64\u001b[0m                     \u001b[0mmax_length\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax_length\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py\u001b[0m in \u001b[0;36mdecorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    113\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdecorate_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    114\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mctx_factory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 115\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    116\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    117\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mdecorate_context\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self, inputs, generation_config, logits_processor, stopping_criteria, prefix_allowed_tokens_fn, synced_gpus, assistant_model, streamer, **kwargs)\u001b[0m\n\u001b[1;32m   1625\u001b[0m             )\n\u001b[1;32m   1626\u001b[0m             \u001b[0;31m# 13. run beam search\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1627\u001b[0;31m             return self.beam_search(\n\u001b[0m\u001b[1;32m   1628\u001b[0m                 \u001b[0minput_ids\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1629\u001b[0m                 \u001b[0mbeam_scorer\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py\u001b[0m in \u001b[0;36mbeam_search\u001b[0;34m(self, input_ids, beam_scorer, logits_processor, stopping_criteria, max_length, pad_token_id, eos_token_id, output_attentions, output_hidden_states, output_scores, return_dict_in_generate, synced_gpus, **model_kwargs)\u001b[0m\n\u001b[1;32m   2930\u001b[0m             \u001b[0mmodel_inputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprepare_inputs_for_generation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_ids\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mmodel_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2931\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2932\u001b[0;31m             outputs = self(\n\u001b[0m\u001b[1;32m   2933\u001b[0m                 \u001b[0;34m**\u001b[0m\u001b[0mmodel_inputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2934\u001b[0m                 \u001b[0mreturn_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1499\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1500\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1502\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1503\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/models/fsmt/modeling_fsmt.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input_ids, attention_mask, decoder_input_ids, decoder_attention_mask, head_mask, decoder_head_mask, cross_attn_head_mask, encoder_outputs, past_key_values, inputs_embeds, decoder_inputs_embeds, labels, use_cache, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m   1218\u001b[0m             \u001b[0muse_cache\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1219\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1220\u001b[0;31m         outputs = self.model(\n\u001b[0m\u001b[1;32m   1221\u001b[0m             \u001b[0minput_ids\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1222\u001b[0m             \u001b[0minputs_embeds\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs_embeds\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1499\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1500\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1502\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1503\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/models/fsmt/modeling_fsmt.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input_ids, attention_mask, decoder_input_ids, decoder_attention_mask, head_mask, decoder_head_mask, cross_attn_head_mask, encoder_outputs, past_key_values, use_cache, output_attentions, output_hidden_states, inputs_embeds, decoder_inputs_embeds, return_dict)\u001b[0m\n\u001b[1;32m   1124\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1125\u001b[0m         \u001b[0;31m# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1126\u001b[0;31m         decoder_outputs = self.decoder(\n\u001b[0m\u001b[1;32m   1127\u001b[0m             \u001b[0mdecoder_input_ids\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1128\u001b[0m             \u001b[0mencoder_outputs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1499\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1500\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1502\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1503\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/models/fsmt/modeling_fsmt.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input_ids, encoder_hidden_states, encoder_padding_mask, decoder_padding_mask, decoder_causal_mask, head_mask, inputs_embeds, cross_attn_head_mask, past_key_values, use_cache, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m    801\u001b[0m             \u001b[0mlayer_state\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpast_key_values\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mpast_key_values\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    802\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 803\u001b[0;31m             x, layer_self_attn, layer_past, layer_cross_attn = decoder_layer(\n\u001b[0m\u001b[1;32m    804\u001b[0m                 \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    805\u001b[0m                 \u001b[0mencoder_hidden_states\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1499\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1500\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1502\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1503\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/models/fsmt/modeling_fsmt.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x, encoder_hidden_states, encoder_attn_mask, layer_state, causal_mask, layer_head_mask, cross_attn_layer_head_mask, decoder_padding_mask, output_attentions)\u001b[0m\n\u001b[1;32m    651\u001b[0m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mactivation_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfc1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    652\u001b[0m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunctional\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdropout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mactivation_dropout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtraining\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtraining\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 653\u001b[0;31m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfc2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    654\u001b[0m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunctional\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdropout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdropout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtraining\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtraining\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    655\u001b[0m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresidual\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1499\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1500\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1502\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1503\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/linear.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    112\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    113\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 114\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinear\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbias\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    115\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    116\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mextra_repr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "import time\n",
    "import nlpaug.augmenter.word as naw\n",
    "\n",
    "back_translation_aug = naw.BackTranslationAug(\n",
    "    from_model_name=\"facebook/wmt19-en-de\", to_model_name=\"facebook/wmt19-de-en\"\n",
    ")\n",
    "translated_texts = []\n",
    "\n",
    "for message in df_train[df_train[\"Category\"] == \"spam\"][\"Message\"]:\n",
    "    new_msg = back_translation_aug.augment(message)[0]\n",
    "    print(message, \"  |   \", new_msg)\n",
    "    translated_texts.append(new_msg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "RnQV8AW2o9QX",
    "outputId": "e760711d-a3a2-41cd-a6b8-4eaa36bae783"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "301"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(translated_texts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "pnLrUjQjo9QX"
   },
   "outputs": [],
   "source": [
    "translations_df = pd.DataFrame({\"Message\": translated_texts, \"Category\": \"spam\"})\n",
    "df_train_translations = pd.concat([df_train, translations_df])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "uj2ET7meo9QX",
    "outputId": "2a2e3408-b14f-4699-88f2-6e5f87cfa3ff"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ham     3609\n",
       "spam     871\n",
       "Name: Category, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train_translations[\"Category\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "BqEPMf6Eo9QX"
   },
   "outputs": [],
   "source": [
    "df_train_translations[\"Message\"] = df_train_translations[\"Message\"].str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "BX4OCj13o9QX"
   },
   "outputs": [],
   "source": [
    "# perform TFIDF\n",
    "X_train_trans_tfidf = tfidf.transform(df_train_translations[\"Message\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yfasMdaeo9QX",
    "outputId": "15a4e0f6-4425-4878-8176-2a56e83bc95c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         ham       0.98      1.00      0.99      1216\n",
      "        spam       0.96      0.86      0.91       177\n",
      "\n",
      "    accuracy                           0.98      1393\n",
      "   macro avg       0.97      0.93      0.95      1393\n",
      "weighted avg       0.98      0.98      0.98      1393\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "\n",
    "lr = LogisticRegression()\n",
    "lr.fit(X_train_trans_tfidf, df_train_translations[\"Category\"])\n",
    "print(classification_report(y_test, lr.predict(X_test_tfidf)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FrIPJHVCdg7F"
   },
   "source": [
    "# Text augmentation using simple EDA: Easy Data Augmentation Techniques\n",
    "\n",
    "Code from paper authors: https://github.com/jasonwei20/eda_nlp\n",
    "\n",
    "Simple Data Augmentatons Techniques are:\n",
    "- SR : Synonym Replacement\n",
    "- RD : Random Deletion\n",
    "- RS : Random Swap\n",
    "- RI : Random Insertion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jYkthB-dSAHr"
   },
   "source": [
    "### EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "-GFA4OddSjXv"
   },
   "outputs": [],
   "source": [
    "%%capture\n",
    "!pip install numpy requests\n",
    "!pip install -U nltk\n",
    "import nltk\n",
    "nltk.download('wordnet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 112
    },
    "id": "dzBwyUSmSLTI",
    "outputId": "28b5eb5d-bf73-4bbe-f3ef-3cfd5362f55d"
   },
   "outputs": [
    {
     "data": {
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       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-4a3c1992-bc86-4fef-b8f3-16a506477dcc');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "                                                Message Category\n",
       "1280  waiting 4 my tv show 2 start lor... u leh stil...      ham\n",
       "1658  s:-)if we have one good partnership going we w...      ham"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train = pd.concat([X_train, y_train], axis=1)\n",
    "df_train.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "QvDcu7DobGZ0"
   },
   "outputs": [],
   "source": [
    "# Adapted from https://github.com/jasonwei20/eda_nlp\n",
    "\n",
    "\"\"\"\n",
    "Take an input sentence,\n",
    "an alpha_sr parameter (controls the number of synonym replacement operations)\n",
    "an alpha_ri parameter (controls the number of random insertion operations)\n",
    "an alpha_rs parameter (controls the number of random swap operations)\n",
    "an alpha_rd parameter (controls the number of random deletion operations)\n",
    "\"\"\"\n",
    "\n",
    "import random\n",
    "from nltk.corpus import wordnet\n",
    "\n",
    "\n",
    "def get_synonyms(word):\n",
    "    \"\"\"Get synonyms of a word from WordNet.\"\"\"\n",
    "    synonyms = wordnet.synsets(word)\n",
    "    if synonyms:\n",
    "        words = [lem.name() for lem in synonyms[0].lemmas()]\n",
    "        return words\n",
    "    else:\n",
    "        return []\n",
    "\n",
    "\n",
    "def replace_with_synonyms(message, alpha_sr=0.1):\n",
    "    \"\"\"\n",
    "    Randomly choose n words from the sentence that are not stop words.\n",
    "    Replace each of these words with one of its synonyms chosen at random.\n",
    "    \"\"\"\n",
    "    words = message.split(\" \")\n",
    "    num_words = len(words)\n",
    "    n = max(1, int(alpha_sr * num_words))\n",
    "    for _ in range(n):\n",
    "        word_to_replace = random.choice(words)\n",
    "        synonyms = get_synonyms(word_to_replace)\n",
    "        if synonyms:\n",
    "            words[words.index(word_to_replace)] = random.choice(synonyms)\n",
    "    return \" \".join(words)\n",
    "\n",
    "\n",
    "def random_deletion(message, alpha_rd=0.1):\n",
    "    \"\"\"\n",
    "    For each word in the sentence, randomly remove it with probability p\n",
    "    \"\"\"\n",
    "    words = message.split(\" \")\n",
    "    num_words = len(words)\n",
    "    n = max(1, int(alpha_rd * num_words))\n",
    "    for _ in range(n):\n",
    "        if len(words) > 1:\n",
    "            del words[random.randint(0, len(words) - 1)]\n",
    "    return \" \".join(words)\n",
    "\n",
    "\n",
    "def random_insertion(message, alpha_ri=0.1):\n",
    "    \"\"\"\n",
    "    Find a random synonym of a random word in the sentence that is not a stop word.\n",
    "    Insert that synonym into a random position in the sentence. Do this n times.\n",
    "    \"\"\"\n",
    "    words = message.split(\" \")\n",
    "    num_words = len(words)\n",
    "    n = max(1, int(alpha_ri * num_words))\n",
    "    for _ in range(n):\n",
    "        synonym_word = random.choice(words)\n",
    "        synonyms = get_synonyms(synonym_word)\n",
    "        if synonyms:\n",
    "            words.insert(random.randint(0, len(words)), random.choice(synonyms))\n",
    "    return \" \".join(words)\n",
    "\n",
    "\n",
    "def random_swap(message, alpha_rs=0.1):\n",
    "    \"\"\"\n",
    "    Randomly choose two words in the sentence and swap their positions.\n",
    "    Do this n times.\n",
    "    \"\"\"\n",
    "    words = message.split(\" \")\n",
    "    num_words = len(words)\n",
    "    n = max(1, int(alpha_rs * num_words))\n",
    "    for _ in range(n):\n",
    "        if len(words) > 1:\n",
    "            idx1, idx2 = random.sample(range(len(words)), 2)\n",
    "            words[idx1], words[idx2] = words[idx2], words[idx1]\n",
    "    return \" \".join(words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "WtjXPwEQbGWN",
    "outputId": "f363aa38-b4af-4f07-8e12-e0d55854b8a3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "augmented_texts = []\n",
    "for message in df_train[df_train[\"Category\"] == \"spam\"][\"Message\"]:\n",
    "    message = replace_with_synonyms(message)\n",
    "    message = random_deletion(message)\n",
    "    message = random_insertion(message)\n",
    "    augmented_text = random_swap(message)\n",
    "    print(message, \"  |   \", augmented_text)\n",
    "    augmented_texts.append(augmented_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "7rOqsPc-ca-U"
   },
   "outputs": [],
   "source": [
    "eda_df = pd.DataFrame({\"Message\": augmented_texts, \"Category\": \"spam\"})\n",
    "df_train_eda = pd.concat([df_train, eda_df])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
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    },
    "id": "M38D7gLnca-V",
    "outputId": "4ac2335f-835e-4b3b-db98-e088408d4322"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ham     3619\n",
       "spam    1120\n",
       "Name: Category, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train_eda[\"Category\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "s0sz_nH3ca-W",
    "outputId": "2b4559dc-2c30-4402-b2f7-888ac24ba0c0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         ham       0.98      0.99      0.99      1206\n",
      "        spam       0.96      0.90      0.93       187\n",
      "\n",
      "    accuracy                           0.98      1393\n",
      "   macro avg       0.97      0.95      0.96      1393\n",
      "weighted avg       0.98      0.98      0.98      1393\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "df_train_eda[\"Message\"] = df_train_eda[\"Message\"].str.lower()\n",
    "\n",
    "df_train_eda[\"Message\"] = df_train_eda[\"Message\"].fillna(\" \")\n",
    "\n",
    "# perform TFIDF\n",
    "X_train_eda_tfidf = tfidf.transform(df_train_eda[\"Message\"])\n",
    "lr = LogisticRegression()\n",
    "lr.fit(X_train_eda_tfidf, df_train_eda[\"Category\"])\n",
    "print(classification_report(y_test, lr.predict(X_test_tfidf)))"
   ]
  },
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   "execution_count": null,
   "metadata": {
    "id": "wXYRH2rOehkv"
   },
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   "source": []
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