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"DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } } } } } }, "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "yOukCKxmClnn", "outputId": "f33ee10f-c8d9-402a-dfb5-24906dc10aed" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Mounted at /content/drive\n" ] } ], "source": [ "from google.colab import drive\n", "drive.mount('/content/drive')" ] }, { "cell_type": "code", "source": [ "import nltk\n", "import pandas as pd\n", "import string\n", "\n", "# Download necessary text parsing datasets\n", "nltk.download('stopwords')\n", "nltk.download('punkt')\n", "\n", "from nltk.corpus import stopwords\n", "from nltk.tokenize import word_tokenize" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "UcY_W_EfDBAM", "outputId": "41091efa-8897-4b12-8450-54abd2271aa1" }, "execution_count": 2, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "[nltk_data] Downloading package stopwords to /root/nltk_data...\n", "[nltk_data] Unzipping corpora/stopwords.zip.\n", "[nltk_data] Downloading package punkt to /root/nltk_data...\n", "[nltk_data] Unzipping tokenizers/punkt.zip.\n" ] } ] }, { "cell_type": "code", "source": [ "# Load the CSV file from your Drive environment\n", "# Adjust the path string if the file is located within a specific subfolder\n", "file_path = '/content/drive/My Drive/spam.csv'\n", "\n", "# Read the file and handle format anomalies\n", "df = pd.read_csv(file_path, encoding='latin1')\n", "\n", "# Clean columns up by dropping extraneous delimiters if present\n", "df = df.iloc[:, [0, 1]]\n", "df.columns = ['label', 'message']\n", "df = df.dropna(subset=['message'])\n", "\n", "# Display dataframe structure confirmation\n", "print(df.head())" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "zJrR-grZDF2O", "outputId": "5f6c6595-0340-42e3-d0de-f22f7e3eb665" }, "execution_count": 5, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " label 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" ] } ] }, { "cell_type": "code", "source": [ "# Convert categorical classification target to binary values\n", "df['label_encoded'] = df['label'].map({'ham': 0, 'spam': 1})" ], "metadata": { "id": "qvPqX5S5DSN2" }, "execution_count": 9, "outputs": [] }, { "cell_type": "code", "source": [ "nltk.download('punkt_tab')\n", "\n", "stop_words = set(stopwords.words('english'))\n", "\n", "def preprocess_sms(text):\n", " # Convert text to uniform lowercase\n", " text = text.lower()\n", "\n", " # Strip common platform token noise\n", " text = text.replace('<#>', '')\n", "\n", " # Isolate independent word tokens\n", " tokens = word_tokenize(text)\n", "\n", " # Eliminate punctuation and filter standard stop words\n", " cleaned_tokens = [\n", " word for word in tokens\n", " if word not in string.punctuation and word not in stop_words\n", " ]\n", "\n", " return cleaned_tokens\n", "\n", "# Map processing across dataset\n", "df['cleaned_tokens'] = df['message'].apply(preprocess_sms)\n", "\n", "# Review progress sample\n", "print(df[['message', 'cleaned_tokens']].head())" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "iPu3aTblDcrm", "outputId": "5aed74e6-b5d1-4afa-926d-628b62577a90" }, "execution_count": 11, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "[nltk_data] Downloading package punkt_tab to /root/nltk_data...\n", "[nltk_data] Unzipping tokenizers/punkt_tab.zip.\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " message \\\n", "0 Go until jurong point, crazy.. Available only ... \n", "1 Ok lar... Joking wif u oni... \n", "2 Free entry in 2 a wkly comp to win FA Cup fina... \n", "3 U dun say so early hor... U c already then say... \n", "4 Nah I don't think he goes to usf, he lives aro... \n", "\n", " cleaned_tokens \n", "0 [go, jurong, point, crazy, .., available, bugi... \n", "1 [ok, lar, ..., joking, wif, u, oni, ...] \n", "2 [free, entry, 2, wkly, comp, win, fa, cup, fin... \n", "3 [u, dun, say, early, hor, ..., u, c, already, ... \n", "4 [nah, n't, think, goes, usf, lives, around, th... \n" ] } ] }, { "cell_type": "code", "source": [ "from sklearn.feature_extraction.text import TfidfVectorizer\n", "\n", "# Format tokens for scikit-learn ingestion\n", "df['cleaned_text_str'] = df['cleaned_tokens'].apply(lambda tokens: ' '.join(tokens))\n", "\n", "# Create the vectorization transformation matrix\n", "tfidf_vectorizer = TfidfVectorizer()\n", "X = tfidf_vectorizer.fit_transform(df['cleaned_text_str'])\n", "y = df['label_encoded'].values\n", "\n", "print(f\"Data Matrix Preprocessing Complete. Matrix shape: {X.shape}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "QVOSlfPhDl9-", "outputId": "0354f14d-b1fe-4c66-8cde-6d0f93c6692f" }, "execution_count": 12, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Data Matrix Preprocessing Complete. Matrix shape: (5572, 8606)\n" ] } ] }, { "cell_type": "markdown", "source": [ "+++++++++++++++++++++++++++++++++++======================================================================================" ], "metadata": { "id": "HJ-0WugiDzpI" } }, { "cell_type": "code", "source": [ "\n", "# Step 1: Separate 20% of the complete dataset for final testing.\n", "# The remaining 80% is held temporarily for further partitioning.\n", "X_temp, X_test, y_temp, y_test = train_test_split(\n", " X,\n", " y,\n", " test_size=0.20,\n", " random_state=42,\n", " stratify=y\n", ")\n", "\n", "# Step 2: Split the remaining temporary 80% into training and validation sets.\n", "# Since 10% of the total dataset is required for validation, and 10% is exactly\n", "# 12.5% of the temporary 80% subset (0.10 / 0.80 = 0.125), we set test_size=0.125.\n", "X_train, X_val, y_train, y_val = train_test_split(\n", " X_temp,\n", " y_temp,\n", " test_size=0.125,\n", " random_state=42,\n", " stratify=y_temp\n", ")\n", "\n", "# Print out the verified distribution matrices\n", "print(f\"Dataset Partitioning Summary:\")\n", "print(f\"-----------------------------\")\n", "print(f\"Training set features shape (70%): {X_train.shape}\")\n", "print(f\"Validation set features shape (10%): {X_val.shape}\")\n", "print(f\"Testing set features shape (20%): {X_test.shape}\")\n", "print(f\"\\nTraining labels count : {len(y_train)}\")\n", "print(f\"Validation labels count : {len(y_val)}\")\n", "print(f\"Testing labels count : {len(y_test)}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "O97yy6KWD3fC", "outputId": "f7852262-a61a-499b-d589-b806c56164d0" }, "execution_count": 16, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Dataset Partitioning Summary:\n", "-----------------------------\n", "Training set features shape (70%): (3899, 8606)\n", "Validation set features shape (10%): (558, 8606)\n", "Testing set features shape (20%): (1115, 8606)\n", "\n", "Training labels count : 3899\n", "Validation labels count : 558\n", "Testing labels count : 1115\n" ] } ] }, { "cell_type": "code", "source": [ "# 1. Format raw token lists back into standard sentence strings for sklearn\n", "df['cleaned_text_str'] = df['cleaned_tokens'].apply(lambda tokens: ' '.join(tokens))\n", "\n", "# 2. Extract split partitions using text strings instead of previous matrix elements\n", "# Re-run the exact 70/10/20 split on raw text strings\n", "X_str_temp, X_str_test, y_temp, y_test = train_test_split(\n", " df['cleaned_text_str'], y, test_size=0.20, random_state=42, stratify=y\n", ")\n", "X_str_train, X_str_val, y_train, y_val = train_test_split(\n", " X_str_temp, y_temp, test_size=0.125, random_state=42, stratify=y_temp\n", ")\n", "\n", "# 3. Initialize and fit TF-IDF vectorizer strictly on training text data\n", "tfidf_vectorizer = TfidfVectorizer(max_features=3000)\n", "X_train_tfidf = tfidf_vectorizer.fit_transform(X_str_train)\n", "\n", "# 4. Transform validation and test arrays using the learned training vocabulary\n", "X_val_tfidf = tfidf_vectorizer.transform(X_str_val)\n", "X_test_tfidf = tfidf_vectorizer.transform(X_str_test)\n", "\n", "print(\"--- TF-IDF Matrix Dimensions ---\")\n", "print(f\"Train shapes : {X_train_tfidf.shape}\")\n", "print(f\"Validation shapes : {X_val_tfidf.shape}\")\n", "print(f\"Test shapes : {X_test_tfidf.shape}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "CYOi52P7Ei72", "outputId": "d8c92ff3-df72-4e46-e0b2-cd5791d8be93" }, "execution_count": 17, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "--- TF-IDF Matrix Dimensions ---\n", "Train shapes : (3899, 3000)\n", "Validation shapes : (558, 3000)\n", "Test shapes : (1115, 3000)\n" ] } ] }, { "cell_type": "code", "source": [ "from sklearn.feature_extraction.text import CountVectorizer\n", "\n", "# Initialize and fit Bag of Words vectorizer strictly on training text data\n", "count_vectorizer = CountVectorizer(max_features=3000)\n", "X_train_bow = count_vectorizer.fit_transform(X_str_train)\n", "\n", "# Transform validation and test configurations using the training vocabulary matrix\n", "X_val_bow = count_vectorizer.transform(X_str_val)\n", "X_test_bow = count_vectorizer.transform(X_str_test)\n", "\n", "print(\"--- Bag of Words Matrix Dimensions ---\")\n", "print(f\"Train shapes : {X_train_bow.shape}\")\n", "print(f\"Validation shapes : {X_val_bow.shape}\")\n", "print(f\"Test shapes : {X_test_bow.shape}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "nLcNZHoiEoEx", "outputId": "ea419ce5-1b99-42bf-ccb5-e25b5c5d64bb" }, "execution_count": 19, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "--- Bag of Words Matrix Dimensions ---\n", "Train shapes : (3899, 3000)\n", "Validation shapes : (558, 3000)\n", "Test shapes : (1115, 3000)\n" ] } ] }, { "cell_type": "markdown", "source": [], "metadata": { "id": "5IIidvTyCy5b" } }, { "cell_type": "code", "source": [ "# Install sentence-transformers locally in the Colab session\n", "!pip install sentence-transformers -q\n", "\n", "from sentence_transformers import SentenceTransformer\n", "import numpy as np\n", "\n", "# Load a lightweight, highly efficient sentence encoder model\n", "embedding_model = SentenceTransformer('all-MiniLM-L6-v2')\n", "\n", "# Generate contextual numerical arrays from input text elements\n", "X_train_emb = embedding_model.encode(X_str_train.tolist(), show_progress_bar=False)\n", "X_val_emb = embedding_model.encode(X_str_val.tolist(), show_progress_bar=False)\n", "X_test_emb = embedding_model.encode(X_str_test.tolist(), show_progress_bar=False)\n", "\n", "print(\"--- Dense Embedding Matrix Dimensions ---\")\n", "print(f\"Train shapes : {X_train_emb.shape}\")\n", "print(f\"Validation shapes : {X_val_emb.shape}\")\n", "print(f\"Test shapes : {X_test_emb.shape}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 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