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Author: RamiyapriyaS-NIH

notebooks/FederatedLearning/data/client_0/train_data.csv

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(397, 32)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "def split_dataframe(df, n=2):\n",
+    "    length = len(df)\n",
+    "    indices = np.array_split(range(length), n)\n",
+    "    return [df.iloc[index] for index in indices]\n",
+    "\n",
+    "df = pd.read_csv(\"full_data.csv\")\n",
+    "split_dfs = split_dataframe(df)\n",
+    "\n",
+    "train_full = list()\n",
+    "val_full = list()\n",
+    "\n",
+    "for i, part_df in enumerate(split_dfs):\n",
+    "    train, val = train_test_split(part_df, test_size=0.3)\n",
+    "    \n",
+    "    train_full.append(train)\n",
+    "    val_full.append(val)\n",
+    "\n",
+    "    train.to_csv(os.path.join(f\"client_{i}\", \"train_data.csv\"), index=False)\n",
+    "    val.to_csv(os.path.join(f\"client_{i}\", \"val_data.csv\"), index=False)\n",
+    "    \n",
+    "train_full_df = pd.concat(train_full)\n",
+    "val_full_df = pd.concat(val_full)\n",
+    "\n",
+    "print(train_full_df.shape)\n",
+    "\n",
+    "train_full_df.to_csv(os.path.join(\"full_train_data.csv\"), index=False)\n",
+    "val_full_df.to_csv(os.path.join(\"full_val_data.csv\"), index=False)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "fedavg",
+   "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": "3.12.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}