Initial commit

dataset_info.json

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+{
+    "description": "This dataset contains 2000 DNA sequences with associated labels. Each sequence varies in length from 200 to 10000. The labels represent different categories relevant to the sequences.",
+    "homepage": "https://example.com",
+    "license": "MIT",
+    "citation": "@article{example2024,...}",
+    "task_categories": ["seq-classification", "bioinformatics"],
+    "genmoe": ["DNA"],
+    "dataset_size": {
+        "num_samples": 2000,
+        "total_size_in_bytes": 1234567
+    },
+    "features": {
+        "sequence": {
+          "type": "string",
+          "description": "DNA sequence"
+        },
+        "label1": {
+          "type": "int",
+          "num_classes": 2,
+          "description": "Binary label"
+        },
+        "label2": {
+          "type": "int",
+          "num_classes": 3,
+          "description": "Ternary label"
+        },
+        "label3": {
+          "type": "int",
+          "num_classes": 5,
+          "description": "Quinary label"
+        }
+      },
+      
+    "version": "1.0.0",
+    "author": {
+        "name": "ljc",
+        "contact": "ljc@example.com"
+      }
+      
+
+  }
+  

label1.txt

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+0: ZH
+1: KD

label2.txt

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+0: 1gw
+1: ThD
+2: eLv

label3.txt

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+0: 8U2DE
+1: r9Vas
+2: 9aBBP
+3: brcap
+4: 8Qb6q

tutorial.ipynb

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+{
+ "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"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}