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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": (2244, 2),
"terms_of_service": (5000, 2),
"tai_safety_research": (1639, 2),
"one_stop_english": (518, 2),
"tweet_eval_hate": (2966, 2),
}
app = typer.Typer()
def _update_submission_name(submission_name: str):
replacement = ""
with open("README.md", "r") 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") 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 evaulated on {sun:%A %d %B %Y} ⏳")
if __name__ == "__main__":
app()
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