Merge pull request #807 from olliestanley/745-augment-codet

745: Add notebooks for CodeT datasets augmentation

notebooks/data-augmentation/codet-data/Augment_CodeT_codegen.ipynb

@@ -0,0 +1,198 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## CodeT Code Generation Datasets\n",
+    "\n",
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/LAION-AI/Open-Assistant/blob/main/notebooks/data-augmentation/codet-data/Augment_CodeT_codegen.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This notebook contains code to parse CodeT code generation prompt and solution data and modify to `(prompt, solution)` pairs outputted in a `.jsonl` file.\n",
+    "\n",
+    "Requirements: `requests`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "from pathlib import Path\n",
+    "import requests\n",
+    "from typing import Dict, List, Tuple"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "DATA_FILES: List[str] = [\n",
+    "    \"HumanEval_for_code_generation.jsonl\",\n",
+    "    \"mbpp_sanitized_for_code_generation.jsonl\",\n",
+    "]\n",
+    "\n",
+    "OUT_FILES: List[str] = [\n",
+    "    \"HumanEval_codegen.jsonl\",\n",
+    "    \"mbpp_codegen.jsonl\",\n",
+    "]\n",
+    "\n",
+    "FILE_PATHS: List[Path] = [Path(f\"data/{data_file}\") for data_file in DATA_FILES]\n",
+    "\n",
+    "OUT_PATHS: List[Path] = [Path(f\"data/augmented/{out_file}\") for out_file in OUT_FILES]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def download_file(filename: str):\n",
+    "    url = f\"https://raw.githubusercontent.com/microsoft/CodeT/main/CodeT/data/dataset/{filename}\"\n",
+    "    response = requests.get(url)\n",
+    "    with open(f\"data/{filename}\", \"wb\") as f:\n",
+    "        f.write(response.content)\n",
+    "\n",
+    "\n",
+    "for filename in DATA_FILES:\n",
+    "    download_file(filename)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can find the docstring, use its contents as the instruction (prefixed with \"Write a function corresponding to the docstring:\") and then use the content prior to the docstring and the canonical solution as the response."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_docstring_indices(prompt_lines: List[str]) -> Tuple[int, int]:\n",
+    "    docstring_start, docstring_end = None, None\n",
+    "\n",
+    "    for i, line in enumerate(prompt_lines):\n",
+    "        if not (line.strip().startswith('\"\"\"') or line.strip().startswith(\"'''\")):\n",
+    "            continue\n",
+    "        if docstring_start:\n",
+    "            docstring_end = i\n",
+    "            break\n",
+    "        docstring_start = i\n",
+    "\n",
+    "    if docstring_end:\n",
+    "        return docstring_start, docstring_end\n",
+    "    raise ValueError(f\"No complete docstring found!\\n{prompt_lines}\")\n",
+    "\n",
+    "\n",
+    "def get_before(prompt_lines: List[str], before: int) -> List[str]:\n",
+    "    before_lines = prompt_lines[:before]\n",
+    "    return before_lines\n",
+    "\n",
+    "\n",
+    "def get_between(prompt_lines: List[str], start: int, end: int) -> List[str]:\n",
+    "    between_lines = prompt_lines[start:end]\n",
+    "    return between_lines"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_request_and_solution(sample: dict) -> Tuple[List[str], List[str]]:\n",
+    "    prompt = sample[\"prompt\"]\n",
+    "    prompt_lines = prompt.splitlines()\n",
+    "\n",
+    "    docstring_start, docstring_end = get_docstring_indices(prompt_lines)\n",
+    "\n",
+    "    # Extract prompt\n",
+    "    in_docstring = get_between(prompt_lines, docstring_start, docstring_end)\n",
+    "    if '\"\"\"' in in_docstring[0] or \"'''\" in in_docstring[0]:\n",
+    "        in_docstring[0] = in_docstring[0].replace('\"\"\"', \"\").replace(\"...\", \"\").strip()\n",
+    "    request = \"Write a Python function corresponding to the docstring: \" + \" \".join([p.strip() for p in in_docstring])\n",
+    "\n",
+    "    # Extract solution\n",
+    "    before_docstring = get_before(prompt_lines, docstring_start)\n",
+    "    after_docstring = sample[\"canonical_solution\"].splitlines()\n",
+    "    solution = before_docstring + after_docstring\n",
+    "    # Gets rid of consecutive empty lines\n",
+    "    solution = [v for i, v in enumerate(solution) if v != \"\" or v != solution[i - 1]]\n",
+    "    solution = \"\\n\".join(solution)\n",
+    "\n",
+    "    return request, solution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def process_file(file_path: Path, out_path: Path):\n",
+    "    lines = file_path.read_text().splitlines()\n",
+    "    samples = list(map(json.loads, lines))\n",
+    "\n",
+    "    output = []\n",
+    "    for sample in samples:\n",
+    "        prompt, solution = get_request_and_solution(sample)\n",
+    "        output.append({\"prompt\": prompt, \"solution\": solution})\n",
+    "\n",
+    "    with open(out_path, \"w\") as f:\n",
+    "        for sample in output:\n",
+    "            f.write(json.dumps(sample))\n",
+    "            f.write(\"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for file_path, out_path in zip(FILE_PATHS, OUT_PATHS):\n",
+    "    process_file(file_path, out_path)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.10.5 ('venv': venv)",
+   "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": "3.10.5"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "1f9a0efd3e4a33b8f30a65df6ca5a95cc3f93ce2f11519ee8c13fe711de61465"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

notebooks/data-augmentation/codet-data/Augment_CodeT_testgen.ipynb

@@ -0,0 +1,191 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## CodeT Test Generation Datasets\n",
+    "\n",
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/LAION-AI/Open-Assistant/blob/main/notebooks/data-augmentation/codet-data/Augment_CodeT_testgen.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This notebook contains code to parse CodeT test case generation prompt and solution data and modify to `(prompt, solution)` pairs outputted in a `.jsonl` file.\n",
+    "\n",
+    "Requirements: `requests`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "from pathlib import Path\n",
+    "import requests\n",
+    "from typing import List, Tuple"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "DATA_FILES: List[str] = [\n",
+    "    \"HumanEval_for_test_case_generation.jsonl\",\n",
+    "    \"mbpp_sanitized_for_test_case_generation.jsonl\",\n",
+    "]\n",
+    "\n",
+    "OUT_FILES: List[str] = [\n",
+    "    \"HumanEval_testgen.jsonl\",\n",
+    "    \"mbpp_testgen.jsonl\",\n",
+    "]\n",
+    "\n",
+    "FILE_PATHS: List[Path] = [Path(f\"data/{data_file}\") for data_file in DATA_FILES]\n",
+    "\n",
+    "OUT_PATHS: List[Path] = [Path(f\"data/augmented/{out_file}\") for out_file in OUT_FILES]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def download_file(filename: str):\n",
+    "    url = f\"https://raw.githubusercontent.com/microsoft/CodeT/main/CodeT/data/dataset/{filename}\"\n",
+    "    response = requests.get(url)\n",
+    "    with open(f\"data/{filename}\", \"wb\") as f:\n",
+    "        f.write(response.content)\n",
+    "\n",
+    "\n",
+    "for filename in DATA_FILES:\n",
+    "    download_file(filename)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_docstring_indices(prompt_lines: List[str]) -> Tuple[int, int]:\n",
+    "    docstring_start, docstring_end = None, None\n",
+    "\n",
+    "    for i, line in enumerate(prompt_lines):\n",
+    "        if not (line.strip().startswith('\"\"\"') or line.strip().startswith(\"'''\")):\n",
+    "            continue\n",
+    "        if docstring_start:\n",
+    "            docstring_end = i\n",
+    "            break\n",
+    "        docstring_start = i\n",
+    "\n",
+    "    if docstring_end:\n",
+    "        return docstring_start, docstring_end\n",
+    "    raise ValueError(f\"No complete docstring found!\\n{prompt_lines}\")\n",
+    "\n",
+    "\n",
+    "def get_between(prompt_lines: List[str], start: int, end: int) -> List[str]:\n",
+    "    between_lines = prompt_lines[start:end]\n",
+    "    return between_lines"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_request(sample: dict) -> List[str]:\n",
+    "    prompt = sample[\"prompt\"]\n",
+    "    prompt_lines = prompt.splitlines()\n",
+    "\n",
+    "    docstring_start, docstring_end = get_docstring_indices(prompt_lines)\n",
+    "\n",
+    "    # Extract prompt\n",
+    "    in_docstring = get_between(prompt_lines, docstring_start, docstring_end)\n",
+    "    if '\"\"\"' in in_docstring[0] or \"'''\" in in_docstring[0]:\n",
+    "        in_docstring[0] = in_docstring[0].replace('\"\"\"', \"\").replace(\"...\", \"\").strip()\n",
+    "    request = \"Write a test for a Python function with the following docstring: \" + \" \".join(\n",
+    "        [p.strip() for p in in_docstring]\n",
+    "    )\n",
+    "\n",
+    "    return request\n",
+    "\n",
+    "\n",
+    "def get_test_code(sample: dict) -> List[str]:\n",
+    "    test = sample[\"test\"]\n",
+    "    test_lines = test.splitlines()\n",
+    "    start = 0\n",
+    "    for i, line in enumerate(test_lines):\n",
+    "        if \"def check(\" in line:\n",
+    "            start = i\n",
+    "    return \"\\n\".join(test_lines[start:])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def process_file(file_path: Path, out_path: Path):\n",
+    "    lines = file_path.read_text().splitlines()\n",
+    "    samples = list(map(json.loads, lines))\n",
+    "\n",
+    "    output = []\n",
+    "    for sample in samples:\n",
+    "        prompt = get_request(sample)\n",
+    "        test = get_test_code(sample)\n",
+    "        output.append({\"prompt\": prompt, \"solution\": test})\n",
+    "\n",
+    "    with open(out_path, \"w\") as f:\n",
+    "        for sample in output:\n",
+    "            f.write(json.dumps(sample))\n",
+    "            f.write(\"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for file_path, out_path in zip(FILE_PATHS, OUT_PATHS):\n",
+    "    process_file(file_path, out_path)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.10.5 ('venv': venv)",
+   "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": "3.10.5"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "1f9a0efd3e4a33b8f30a65df6ca5a95cc3f93ce2f11519ee8c13fe711de61465"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

notebooks/data-augmentation/codet-data/README.md

@@ -0,0 +1,15 @@
+# CodeT Datasets
+
+This folder contains two notebooks.
+
+One will download the data used for Microsoft CodeT for tuning a model for
+Python code generation from function docstrings, augment the data into prompt
+and solution pairs and write them to `.jsonl` files.
+
+The other will download the data used for Microsoft CodeT for tuning a model for
+Python test generation from corresponding function docstrings, augment the data
+into prompt and solution pairs and write them to `.jsonl` files.
+
+## Requirements
+
+Both notebooks require the library `requests`.