Add dataset

convert.ipynb

@@ -0,0 +1,76 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "import os\n",
+    "import pandas as pd\n",
+    "\n",
+    "x = open(\"./HumanEval.jsonl\")\n",
+    "entries = []\n",
+    "for line in x:\n",
+    "    contents = json.loads(line)\n",
+    "    entries.append(contents)\n",
+    "x.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'task_id': 'HumanEval/0', 'prompt': 'from typing import List\\n\\n\\ndef has_close_elements(numbers: List[float], threshold: float) -> bool:\\n    \"\"\" Check if in given list of numbers, are any two numbers closer to each other than\\n    given threshold.\\n    >>> has_close_elements([1.0, 2.0, 3.0], 0.5)\\n    False\\n    >>> has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3)\\n    True\\n    \"\"\"\\n', 'entry_point': 'has_close_elements', 'canonical_solution': '    for idx, elem in enumerate(numbers):\\n        for idx2, elem2 in enumerate(numbers):\\n            if idx != idx2:\\n                distance = abs(elem - elem2)\\n                if distance < threshold:\\n                    return True\\n\\n    return False\\n', 'test': \"\\n\\nMETADATA = {\\n    'author': 'jt',\\n    'dataset': 'test'\\n}\\n\\n\\ndef check(candidate):\\n    assert candidate([1.0, 2.0, 3.9, 4.0, 5.0, 2.2], 0.3) == True\\n    assert candidate([1.0, 2.0, 3.9, 4.0, 5.0, 2.2], 0.05) == False\\n    assert candidate([1.0, 2.0, 5.9, 4.0, 5.0], 0.95) == True\\n    assert candidate([1.0, 2.0, 5.9, 4.0, 5.0], 0.8) == False\\n    assert candidate([1.0, 2.0, 3.0, 4.0, 5.0, 2.0], 0.1) == True\\n    assert candidate([1.1, 2.2, 3.1, 4.1, 5.1], 1.0) == True\\n    assert candidate([1.1, 2.2, 3.1, 4.1, 5.1], 0.5) == False\\n\\n\"}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(entries[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = {\"source\": [], \"target\": [], \"program_id\": []}\n",
+    "\n",
+    "for entry in entries:\n",
+    "    data[\"source\"].append(entry[\"source\"])\n",
+    "    data[\"target\"].append(entry[\"target\"])\n",
+    "    data[\"program_id\"].append(entry[\"program_id\"])\n",
+    "\n",
+    "pd.DataFrame(data=data).to_csv(\"./HumanEval.csv\")"
+   ]
+  }
+ ],
+ "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": "3.11.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

convert.py

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+import json
+import os
+with open("HumanEval.jsonl") as f:
+    for line in f:
+        contents = json.loads(line)