leaderboard/plots.py [1390:1413]:
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            }
        )

    def assign_feas_bin(feas):
        if feas < 0.33:
            return "Low"
        elif feas < 0.66:
            return "Mid"
        else:
            return "High"

    df = pd.DataFrame(rows)
    df["feas_bin"] = df["lambda"].map(assign_feas_bin)
    diff_min = np.floor(df["diff"].min())
    diff_max = np.ceil(df["diff"].max())
    diff_scale = alt.Scale(domain=(diff_min, diff_max))

    disc_min = np.floor(df["disc"].min())
    disc_max = np.ceil(df["disc"].max())
    disc_scale = alt.Scale(domain=(disc_min, disc_max))

    ratio = 1.5
    points = (
        alt.Chart(df)
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leaderboard/visualize.py [158:181]:
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            }
        )

    def assign_feas_bin(feas):
        if feas < 0.33:
            return "Low"
        elif feas < 0.66:
            return "Mid"
        else:
            return "High"

    df = pd.DataFrame(rows)
    df["feas_bin"] = df["lambda"].map(assign_feas_bin)
    diff_min = np.floor(df["diff"].min())
    diff_max = np.ceil(df["diff"].max())
    diff_scale = alt.Scale(domain=(diff_min, diff_max))

    disc_min = np.floor(df["disc"].min())
    disc_max = np.ceil(df["disc"].max())
    disc_scale = alt.Scale(domain=(disc_min, disc_max))

    ratio = 1.5
    points = (
        alt.Chart(df)
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