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"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "markdown",
"source": [
"# Naive Bayes"
],
"metadata": {
"id": "MSLznnwzyf30"
}
},
{
"cell_type": "markdown",
"source": [
"## Imports"
],
"metadata": {
"id": "JJocdeRnyi7R"
}
},
{
"cell_type": "code",
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import joblib\n",
"import time\n",
"import joblib\n",
"\n",
"import matplotlib.pyplot as plt\n",
"\n",
"from sklearn.naive_bayes import MultinomialNB\n",
"\n",
"from sklearn.metrics import (\n",
" accuracy_score,\n",
" precision_score,\n",
" recall_score,\n",
" f1_score,\n",
" roc_auc_score,\n",
" confusion_matrix,\n",
" classification_report,\n",
" ConfusionMatrixDisplay\n",
")"
],
"metadata": {
"id": "-JY-1VcXyfky"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"tfidf = joblib.load(\"/content/tfidf_vectorizer.pkl\")\n",
"\n",
"x_train = joblib.load(\"/content/x_train.pkl\")\n",
"x_test = joblib.load(\"/content/x_test.pkl\")\n",
"\n",
"y_train = joblib.load(\"/content/y_train.pkl\")\n",
"y_test = joblib.load(\"/content/y_test.pkl\")"
],
"metadata": {
"id": "X3Guyq-0yfi3"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"print(x_train.shape)\n",
"print(x_test.shape)\n",
"print(y_train.shape)\n",
"print(y_test.shape)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "8Kal3VcNyfgu",
"outputId": "be7488eb-f9d8-4fc9-aea5-68ca51143b6b"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"(1274150,)\n",
"(318538,)\n",
"(1274150,)\n",
"(318538,)\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"x_train_tf = tfidf.transform(x_train)\n",
"x_test_tf = tfidf.transform(x_test)"
],
"metadata": {
"id": "UMsjEOMHyfeq"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"print(x_train_tf.shape)\n",
"print(x_test_tf.shape)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "N2dKQjHCyfci",
"outputId": "efdf5778-1f8d-4e73-ca5e-660ebc1b7be8"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"(1274150, 50000)\n",
"(318538, 50000)\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"## Model"
],
"metadata": {
"id": "bfLrH0Xx1gsx"
}
},
{
"cell_type": "code",
"source": [
"# MultinomialNB(\n",
"# alpha=1.0, # Laplace smoothing\n",
"# fit_prior=True, # Learn class priors from the data\n",
"# class_prior=None, # Use learned priors\n",
"# force_alpha=True # Keep alpha exactly as specified\n",
"# )"
],
"metadata": {
"id": "KuB6E2rB1uwl"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"model_nb = MultinomialNB(\n",
" alpha=0.5,\n",
" fit_prior=True,\n",
" force_alpha=True\n",
")"
],
"metadata": {
"id": "NFcj_ni3yfaK"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"start = time.time()\n",
"model_nb.fit(x_train_tf, y_train)\n",
"end = time.time()\n",
"\n",
"print(f\"Training Time: {end-start:.2f} Seconds\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "oYdT3ympyfXq",
"outputId": "89e1009f-11b2-4bfd-d83b-a6df9ca16d6e"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Training Time: 0.47 Seconds\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"start = time.time()\n",
"y_pred = model_nb.predict(x_test_tf)\n",
"end = time.time()\n",
"\n",
"print(f\"Prediction Time: {end-start:.2f} Seconds\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "gX-AdTYuyfS6",
"outputId": "6247a45d-0a85-4b01-943c-848b6e7c58bb"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Prediction Time: 0.10 Seconds\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"y_prob = model_nb.predict_proba(x_test_tf)[:, 1]\n",
"print(y_prob)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "a09aIaMhyfQ-",
"outputId": "ea183e61-3c81-461f-ef09-3c6059485c6d"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[0.54817049 0.06469533 0.34074515 ... 0.2089416 0.17129996 0.54538567]\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"print(f\"Accuracy : {accuracy_score(y_test, y_pred):.4f}\")\n",
"print(f\"Precision: {precision_score(y_test, y_pred):.4f}\")\n",
"print(f\"Recall : {recall_score(y_test, y_pred):.4f}\")\n",
"print(f\"F1 Score : {f1_score(y_test, y_pred):.4f}\")\n",
"print(f\"ROC AUC : {roc_auc_score(y_test, y_prob):.4f}\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "QxxBaJYRyfO5",
"outputId": "1933723b-edbd-4c98-a0d9-b8f8f5d0b169"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Accuracy : 0.7783\n",
"Precision: 0.7758\n",
"Recall : 0.7828\n",
"F1 Score : 0.7793\n",
"ROC AUC : 0.8599\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# Classification Report\n",
"print(f\"Classification_Report: \\n \\n {classification_report(y_test, y_pred)}\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "1gzqBH3eyfMq",
"outputId": "84ccdb5e-ec88-4138-e893-29989ef7c057"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Classification_Report: \n",
" \n",
" precision recall f1-score support\n",
"\n",
" 0 0.78 0.77 0.78 159267\n",
" 1 0.78 0.78 0.78 159271\n",
"\n",
" accuracy 0.78 318538\n",
" macro avg 0.78 0.78 0.78 318538\n",
"weighted avg 0.78 0.78 0.78 318538\n",
"\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"ConfusionMatrixDisplay.from_predictions(\n",
" y_test,\n",
" y_pred,\n",
" cmap = \"Blues\"\n",
")\n",
"\n",
"plt.title(\"Naive Bayes (TF-IDF)\")\n",
"plt.show()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 472
},
"id": "FobX3vh5yfKe",
"outputId": "abad3989-abf5-48ee-b782-2ca46c080fc9"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 2 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"joblib.dump(model_nb, \"nb_model.pkl\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "K-dFZdMPyfIS",
"outputId": "8cf13008-1aa4-4f37-b38b-ea806e3f81a2"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['nb_model.pkl']"
]
},
"metadata": {},
"execution_count": 21
}
]
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "Rlef8KrxyfF6"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "XXxeY7UGyfDu"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "yO5A0pl4yfBe"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "ZYXAqhT6ye_H"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "DGPQS347ye6V"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "sN9W4zHDye4C"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "m916t_7Vye13"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "e7pQVYz4yezq"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "SGkiyLsFyexn"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "QdCjJEDfyevL"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "p_vI0y4yyesw"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "QSkaVZCQyeqz"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "83LWjID2yeoP"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "wrfh-veYyejx"
},
"execution_count": null,
"outputs": []
}
]
} |