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ought
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raft-submission

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raft-submission / {{cookiecutter.repo_name}} /cli.py

lewtun HF staff

Add time information for when submissions are evaluated (#1)

35094cb almost 2 years ago

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3.72 kB

	import datetime
	import re
	import subprocess
	from pathlib import Path

	import pandas as pd
	import typer
	from datasets import get_dataset_config_names, load_dataset

	CSV_SCHEMA = {
	"banking_77": (5000, 2),
	"overruling": (2350, 2),
	"semiconductor_org_types": (449, 2),
	"ade_corpus_v2": (5000, 2),
	"twitter_complaints": (3399, 2),
	"neurips_impact_statement_risks": (150, 2),
	"systematic_review_inclusion": (2243, 2),
	"terms_of_service": (5000, 2),
	"tai_safety_research": (1639, 2),
	"one_stop_english": (516, 2),
	"tweet_eval_hate": (2966, 2),
	}

	app = typer.Typer()


	def _update_submission_name(submission_name: str):
	replacement = ""
	with open("README.md", "r", encoding="utf-8") as f:
	lines = f.readlines()

	for line in lines:
	if line.startswith("submission_name:"):
	changes = re.sub(
	r"submission_name:.+", f"submission_name: {submission_name}", line
	)
	replacement += changes
	else:
	replacement += line

	with open("README.md", "w", encoding="utf-8") as f:
	f.write(replacement)


	@app.command()
	def validate():
	# TODO(lewtun): Consider using great_expectations for the data validation
	tasks = get_dataset_config_names("ought/raft")

	# Check that all the expected files exist
	prediction_files = list(Path("data").rglob("predictions.csv"))
	mismatched_files = set(tasks).symmetric_difference(
	set([f.parent.name for f in prediction_files])
	)
	if mismatched_files:
	raise ValueError(
	f"Incorrect number of files! Expected {len(tasks)} files, but got {len(prediction_files)}."
	)

	# Check all files have the expected shape (number of rows, number of columns)
	# TODO(lewtun): Add a check for the specific IDs per file
	shape_errors = []
	column_errors = []
	for prediction_file in prediction_files:
	df = pd.read_csv(prediction_file)
	incorrect_shape = df.shape != CSV_SCHEMA[prediction_file.parent.name]
	if incorrect_shape:
	shape_errors.append(prediction_file)
	incorrect_columns = sorted(df.columns) != ["ID", "Label"]
	if incorrect_columns:
	column_errors.append(prediction_file)

	if shape_errors:
	raise ValueError(f"Incorrect CSV shapes in files: {shape_errors}")

	if column_errors:
	raise ValueError(f"Incorrect CSV columns in files: {column_errors}")

	# Check we can load the dataset for each task
	load_errors = []
	for task in tasks:
	try:
	_ = load_dataset("../{{cookiecutter.repo_name}}", task)
	except Exception as e:
	load_errors.append(e)

	if load_errors:
	raise ValueError(f"Could not load predictions! Errors: {load_errors}")

	typer.echo("All submission files validated! ✨ 🚀 ✨")
	typer.echo("Now you can make a submission 🤗")


	@app.command()
	def submit(
	submission_name: str = typer.Option(
	..., prompt="Please provide a name for your submission, e.g. GPT-4 😁"
	)
	):
	subprocess.call("git pull origin main".split())
	_update_submission_name(submission_name)
	subprocess.call(["git", "add", "data/*predictions.csv", "README.md"])
	subprocess.call(["git", "commit", "-m", f"Submission: {submission_name} "])
	subprocess.call(["git", "push"])

	today = datetime.date.today()
	# MON = 0, SUN = 6 -> SUN = 0 .. SAT = 6
	idx = (today.weekday() + 1) % 7
	sun = today + datetime.timedelta(7 - idx)
	typer.echo("Submission successful! 🎉 🥳 🎉")
	typer.echo(f"Your submission will be evaluated on {sun:%A %d %B %Y} at 12:00 UTC ⏳")


	if __name__ == "__main__":
	app()