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fake-news-detector-LSTM

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fake-news-detector-LSTM
File size: 25,955 Bytes 
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{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kimi\\AppData\\Local\\Temp\\ipykernel_6488\\2691833235.py:5: MatplotlibDeprecationWarning: The seaborn styles shipped by Matplotlib are deprecated since 3.6, as they no longer correspond to the styles shipped by seaborn. However, they will remain available as 'seaborn-v0_8-<style>'. Alternatively, directly use the seaborn API instead.\n",
      "  plt.style.use(\"seaborn-whitegrid\")\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "\n",
    "plt.style.use(\"seaborn-whitegrid\")\n",
    "\n",
    "version = 9\n",
    "\n",
    "# Read confusion matrix from CSV\n",
    "cm_df = pd.read_csv(\n",
    "    f\"./output/version_{version}/confusion_matrix_inference_{version}.csv\"\n",
    ")\n",
    "cm = cm_df.values\n",
    "\n",
    "# Plotting\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\")\n",
    "plt.title(\"Confusion Matrix (LSTM, Holdout Set)\")\n",
    "plt.ylabel(\"True label\")\n",
    "plt.xlabel(\"Predicted label\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "torch",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}