{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "def generate_random_dna_sequence(length):\n",
    "    return ''.join(random.choices('ATGC', k=length))\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "# Generate 200 sequences of random DNA sequences with lengths ranging from 200 to 2000\n",
    "sequence_lengths = np.random.randint(200, 2001, size=200)\n",
    "dna_sequences = [generate_random_dna_sequence(length) for length in sequence_lengths]\n",
    "labels1 = np.random.randint(0, 2, size=200)\n",
    "labels2 = np.random.randint(0, 3, size=200)\n",
    "labels3 = np.random.randint(0, 5, size=200)\n",
    "\n",
    "# Create a DataFrame with the DNA sequences and random labels\n",
    "df_dna = pd.DataFrame({\n",
    "    'sequence': dna_sequences,\n",
    "    'label1': labels1,\n",
    "    'label2': labels2,\n",
    "    'label3': labels3\n",
    "})\n",
    "\n",
    "# Save to CSV\n",
    "csv_dna_path = \"/data/project/hf_tutorial/data/train.csv\"\n",
    "df_dna.to_csv(csv_dna_path, index=False)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Generate 200 sequences of random DNA sequences with lengths ranging from 200 to 2000\n",
    "sequence_lengths = np.random.randint(200, 2001, size=200)\n",
    "dna_sequences = [generate_random_dna_sequence(length) for length in sequence_lengths]\n",
    "labels1 = np.random.randint(0, 2, size=200)\n",
    "labels2 = np.random.randint(0, 3, size=200)\n",
    "labels3 = np.random.randint(0, 5, size=200)\n",
    "\n",
    "# Create a DataFrame with the DNA sequences and random labels\n",
    "df_dna = pd.DataFrame({\n",
    "    'sequence': dna_sequences,\n",
    "    'label1': labels1,\n",
    "    'label2': labels2,\n",
    "    'label3': labels3\n",
    "})\n",
    "\n",
    "# Save to CSV\n",
    "csv_dna_path = \"/data/project/hf_tutorial/data/eval.csv\"\n",
    "df_dna.to_csv(csv_dna_path, index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to generate a random string of a given length\n",
    "def generate_random_string(length):\n",
    "    return ''.join(random.choices('ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789', k=length))\n",
    "\n",
    "# Generate random strings for each label category\n",
    "label1_strings = {0: generate_random_string(2), 1: generate_random_string(2)}\n",
    "label2_strings = {i: generate_random_string(3) for i in range(3)}\n",
    "label3_strings = {i: generate_random_string(5) for i in range(5)}\n",
    "\n",
    "# Save each string to a separate text file\n",
    "label1_path = \"/data/project/hf_tutorial/data/label1.txt\"\n",
    "label2_path = \"/data/project/hf_tutorial/data/label2.txt\"\n",
    "label3_path = \"/data/project/hf_tutorial/data/label3.txt\"\n",
    "\n",
    "def save_label_strings(label_strings, path):\n",
    "    with open(path, 'w') as f:\n",
    "        for label, string in label_strings.items():\n",
    "            f.write(f\"{label}: {string}\\n\")\n",
    "\n",
    "save_label_strings(label1_strings, label1_path)\n",
    "save_label_strings(label2_strings, label2_path)\n",
    "save_label_strings(label3_strings, label3_path)\n"
   ]
  }
 ],
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