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Figure 6 : Critical distance plot of the accuracy obtained withh each augmentationacross the four tested datasets

import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns

%matplotlib inline
%config InlineBackend.figure_format='retina'

import autorank
import pandas as pd

dat = {
    "mirage19": pd.read_parquet(
        "./campaigns/mirage19/augmentation-at-loading-no-dropout/minpkts10/campaign_summary/augment-at-loading/runsinfo_flowpic_dim_32.parquet"
    ),
    "mirage22_10": pd.read_parquet(
        "./campaigns/mirage22/augmentation-at-loading-no-dropout/minpkts10/campaign_summary/augment-at-loading/runsinfo_flowpic_dim_32.parquet"
    ),
    "mirage22_1000": pd.read_parquet(
        "./campaigns/mirage22/augmentation-at-loading-no-dropout/minpkts1000/campaign_summary/augment-at-loading/runsinfo_flowpic_dim_32.parquet"
    ),
    "utmobile19": pd.read_parquet(
        "./campaigns/utmobilenet21/augmentation-at-loading-no-dropout/minpkts10/campaign_summary/augment-at-loading/runsinfo_flowpic_dim_32.parquet"
    ),
}

def prepare_data(df):
    res = df[["hash", "aug_name", "seed", "split_index", "f1"]]
    res.loc[:, "id"] = (
        "split_index"
        + res.loc[:, "split_index"].astype(str)
        + "_seed"
        + res.loc[:, "seed"].astype(str)
    )
    res = res[["aug_name", "id", "f1"]]
    return res.sort_values(["aug_name", "id"])


def get_ranks(df):
    df1 = prepare_data(df)
    df1 = df1.pivot(columns="aug_name", index="id").reset_index(drop=True)
    df1.columns = df1.columns.get_level_values(1)
    new_df = pd.DataFrame(
        {
            "changertt": df1["changertt"].values,
            "colorjitter": df1["colorjitter"].values,
            "horizontalflip": df1["horizontalflip"].values,
            "noaug": df1["noaug"].values,
            "packetloss": df1["packetloss"].values,
            "rotate": df1["rotate"].values,
            "timeshift": df1["timeshift"].values,
        }
    )
    replacement = {
        "noaug": "No augmentation",
        "horizontalflip": "Horizontal flip",
        "rotate": "Rotate",
        "timeshift": "Time shift",
        "colorjitter": "Color jitter",
        "changertt": "Change RTT",
        "packetloss": "Packet Loss",
    }
    new_df = new_df.rename(columns=replacement).dropna()
    rankmat = new_df.rank(axis="columns", ascending=False)
    return rankmat


def get_pivoted_df(df):
    df1 = prepare_data(df)
    df1 = df1.pivot(columns="aug_name", index="id").reset_index(drop=True)
    df1.columns = df1.columns.get_level_values(1)
    new_df = pd.DataFrame(
        {
            "changertt": df1["changertt"].values,
            "colorjitter": df1["colorjitter"].values,
            "horizontalflip": df1["horizontalflip"].values,
            "noaug": df1["noaug"].values,
            "packetloss": df1["packetloss"].values,
            "rotate": df1["rotate"].values,
            "timeshift": df1["timeshift"].values,
        }
    )
    replacement = {
        "noaug": "No augmentation",
        "horizontalflip": "Horizontal flip",
        "rotate": "Rotate",
        "timeshift": "Time shift",
        "colorjitter": "Color jitter",
        "changertt": "Change RTT",
        "packetloss": "Packet Loss",
    }
    new_df = new_df.rename(columns=replacement).dropna()
    return new_df

fig, ax = plt.subplots(figsize=(4, 2.5))

perfs = pd.concat(
    [
        get_pivoted_df(dat["mirage19"]),
        get_pivoted_df(dat["mirage22_10"]),
        get_pivoted_df(dat["mirage22_1000"]),
        get_pivoted_df(dat["utmobile19"]),
    ]
)

autorank.plot_stats(autorank.autorank(perfs), ax=ax)
for obj in ax._children:
    if isinstance(obj, mpl.text.Text) and obj.get_text() in (
        "Change RTT",
        "Time shift",
    ):
        obj.set_fontweight("bold")

plt.savefig("augmentations_rank_comparison_together1.png", bbox_inches="tight", dpi=300)

/tmp/ipykernel_13329/2271513213.py:3: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  res.loc[:, "id"] = (
/tmp/ipykernel_13329/2271513213.py:3: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  res.loc[:, "id"] = (
/tmp/ipykernel_13329/2271513213.py:3: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  res.loc[:, "id"] = (
/tmp/ipykernel_13329/2271513213.py:3: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  res.loc[:, "id"] = (

png