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import gradio as gr
import pandas as pd
mase = pd.read_csv("results/results_mase.csv")
datasets = mase.dataset.unique()
frameworks = mase.framework.unique()
mase.set_index(["dataset", "framework"], inplace=True)
data = {"Dataset": datasets}
def mean(data, framework):
try:
return f"{round(mase.loc[data, framework].metric_error.mean(),3)} +/- {round(mase.loc[data, framework].metric_error.std(),3)}"
except KeyError:
return "n/a"
for framework in frameworks:
data.update({framework: [mean(dataset, framework) for dataset in datasets]})
df = pd.DataFrame(data=data)
with gr.Blocks() as demo:
gr.Markdown(
"""
# Time Series Forecasting Leaderboard
This is a leaderboard of the [MASE](https://huggingface.co/spaces/evaluate-metric/mase) metric for time series forecasting problem on the different open datasets and models.
The table is generated from the paper [AutoGluon–TimeSeries: AutoML for Probabilistic Time Series Forecasting](https://github.com/autogluon/autogluon) by Oleksandr Shchur, Caner Turkmen, Nick Erickson, Huibin Shen, Alexander Shirkov, Tony Hu, and Bernie Wang.
## MASE Metric
"""
)
gr.Dataframe(df)
if __name__ == "__main__":
demo.launch()
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