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import pandas as pd |
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import numpy as np |
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from datasets import load_dataset |
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from about import results_repo |
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from about import LB_COLS0 |
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def make_user_clickable(name): |
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link =f'https://huggingface.co/{name}' |
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return f'<a target="_blank" href="{link}" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">{name}</a>' |
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def make_tag_clickable(tag): |
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return f'<a target="_blank" href="{tag}" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">link</a>' |
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def fetch_dataset_df(): |
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dset = load_dataset(results_repo, split='train', download_mode="force_redownload") |
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full_df = dset.to_pandas() |
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assert all( |
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col in full_df.columns for col in LB_COLS0 |
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), f"Expected columns {LB_COLS0} not found in {full_df.columns}. Missing columns: {set(LB_COLS0) - set(full_df.columns)}" |
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df = full_df.copy() |
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df = df[df["user"] != "test"].copy() |
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df["submission_time"] = pd.to_datetime(df["submission_time"], errors="coerce") |
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df = df.dropna(subset=["submission_time"]) |
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latest = ( |
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df.sort_values("submission_time") |
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.drop_duplicates(subset=["endpoint", "user"], keep="last") |
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.sort_values(["endpoint", "user"]) |
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.reset_index(drop=True) |
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) |
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latest.rename(columns={"submission_time": "submission time"}, inplace=True) |
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return latest |
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def metrics_per_ep(pred, true): |
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from scipy.stats import spearmanr, kendalltau |
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from sklearn.metrics import mean_absolute_error, r2_score |
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mae = mean_absolute_error(true, pred) |
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rae = mae / np.mean(np.abs(true - np.mean(true))) |
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if np.nanstd(true) == 0: |
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r2=np.nan |
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else: |
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r2 = r2_score(true, pred) |
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spr, _ = spearmanr(true, pred) |
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ktau, _ = kendalltau(true, pred) |
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return mae, rae, r2, spr, ktau |
