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import argparse
import json
import os
import re
from pathlib import Path
import pandas as pd
from zeno_client import ZenoClient, ZenoMetric
from lm_eval.utils import eval_logger
def parse_args():
parser = argparse.ArgumentParser(
description="Upload your data to the Zeno AI evaluation platform to visualize results. This requires a ZENO_API_KEY in your environment variables. The eleuther harness must be run with log_samples=True and an output_path set for data to be written to disk."
)
parser.add_argument(
"--data_path",
required=True,
help="Where to find the results of the benchmarks that have been run. Uses the name of each subfolder as the model name.",
)
parser.add_argument(
"--project_name",
required=True,
help="The name of the generated Zeno project.",
)
return parser.parse_args()
def main():
"""Upload the results of your benchmark tasks to the Zeno AI evaluation platform.
This scripts expects your results to live in a data folder where subfolders contain results of individual models.
"""
args = parse_args()
client = ZenoClient(os.environ["ZENO_API_KEY"])
# Get all model subfolders from the parent data folder.
models = [
os.path.basename(os.path.normpath(f))
for f in os.scandir(Path(args.data_path))
if f.is_dir()
]
assert len(models) > 0, "No model directories found in the data_path."
tasks = set(tasks_for_model(models[0], args.data_path))
for model in models: # Make sure that all models have the same tasks.
old_tasks = tasks.copy()
task_count = len(tasks)
model_tasks = tasks_for_model(model, args.data_path)
tasks.intersection(set(model_tasks))
if task_count != len(tasks):
eval_logger.warning(
f"All models must have the same tasks. {model} has tasks: {model_tasks} but have already recorded tasks: {old_tasks}. Taking intersection {tasks}"
)
assert (
len(tasks) > 0
), "Must provide at least one task in common amongst models to compare."
for task in tasks:
# Upload data for all models
for model_index, model in enumerate(models):
model_args = re.sub(
r"[\"<>:/\|\\?\*\[\]]+",
"__",
json.load(
open(Path(args.data_path, model, "results.json"), encoding="utf-8")
)["config"]["model_args"],
)
with open(
Path(args.data_path, model, f"{model_args}_{task}.jsonl"),
"r",
encoding="utf-8",
) as file:
data = json.loads(file.read())
configs = json.load(
open(Path(args.data_path, model, "results.json"), encoding="utf-8")
)["configs"]
config = configs[task]
if model_index == 0: # Only need to assemble data for the first model
metrics = []
for metric in config["metric_list"]:
metrics.append(
ZenoMetric(
name=metric["metric"],
type="mean",
columns=[metric["metric"]],
)
)
project = client.create_project(
name=args.project_name + (f"_{task}" if len(tasks) > 1 else ""),
view="text-classification",
metrics=metrics,
)
project.upload_dataset(
generate_dataset(data, config),
id_column="id",
data_column="data",
label_column="labels",
)
project.upload_system(
generate_system_df(data, config),
name=model,
id_column="id",
output_column="output",
)
def tasks_for_model(model: str, data_path: str):
"""Get the tasks for a specific model.
Args:
model (str): The name of the model.
data_path (str): The path to the data.
Returns:
list: A list of tasks for the model.
"""
dir_path = Path(data_path, model)
config = (
json.load(open(Path(dir_path, "results.json"), encoding="utf-8"))["configs"],
)
return list(config[0].keys())
def generate_dataset(
data,
config,
):
"""Generate a Zeno dataset from evaluation data.
Args:
data: The data to generate a dataset for.
config: The configuration of the task.
Returns:
pd.Dataframe: A dataframe that is ready to be uploaded to Zeno.
"""
ids = [x["doc_id"] for x in data]
labels = [x["target"] for x in data]
instance = [""] * len(ids)
if config["output_type"] == "loglikelihood":
instance = [x["arguments"][0][0] for x in data]
labels = [x["arguments"][0][1] for x in data]
elif config["output_type"] == "multiple_choice":
instance = [
x["arguments"][0][0]
+ "\n\n"
+ "\n".join([f"- {y[1]}" for y in x["arguments"]])
for x in data
]
elif config["output_type"] == "loglikelihood_rolling":
instance = [x["arguments"][0][0] for x in data]
elif config["output_type"] == "generate_until":
instance = [x["arguments"][0][0] for x in data]
return pd.DataFrame(
{
"id": ids,
"data": instance,
"input_len": [len(x) for x in instance],
"labels": labels,
"output_type": config["output_type"],
}
)
def generate_system_df(data, config):
"""Generate a dataframe for a specific system to be uploaded to Zeno.
Args:
data: The data to generate a dataframe from.
config: The configuration of the task.
Returns:
pd.Dataframe: A dataframe that is ready to be uploaded to Zeno as a system.
"""
ids = [x["doc_id"] for x in data]
system_dict = {"id": ids}
system_dict["output"] = [""] * len(ids)
if config["output_type"] == "loglikelihood":
system_dict["output"] = [
"correct" if x["filtered_resps"][0][1] is True else "incorrect"
for x in data
]
elif config["output_type"] == "multiple_choice":
system_dict["output"] = [
", ".join([str(y[0]) for y in x["filtered_resps"]]) for x in data
]
system_dict["num_answers"] = [len(x["filtered_resps"]) for x in data]
elif config["output_type"] == "loglikelihood_rolling":
system_dict["output"] = [str(x["filtered_resps"][0]) for x in data]
elif config["output_type"] == "generate_until":
system_dict["output"] = [str(x["filtered_resps"][0]) for x in data]
system_dict["output_length"] = [len(str(x["filtered_resps"][0])) for x in data]
metrics = {}
for metric in config["metric_list"]:
if "aggregation" in metric and metric["aggregation"] == "mean":
metrics[metric["metric"]] = [x[metric["metric"]] for x in data]
system_dict.update(metrics)
system_df = pd.DataFrame(system_dict)
return system_df
if __name__ == "__main__":
main()
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