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Table 3: (G0) Baseline ML performance without augmentation in a supervised setting.

import pandas as pd

df = pd.read_csv(
    "./campaigns/ucdavis-icdm19/xgboost/noaugmentation-flowpic/campaign_summary/noaugmentation-flowpic/summary_flowpic_dim_32.csv",
    header=[0, 1],
    index_col=[0, 1],
)

# reformatting
df_tmp = df["acc"][["mean", "ci95"]].round(2)
df_tmp.loc[["test-script", "test-human"]].droplevel(1, axis=0).astype(float).round(2)
mean ci95
test-script 96.80 0.37
test-human 73.65 2.14 
df = pd.read_csv(
    "./campaigns/ucdavis-icdm19/xgboost/noaugmentation-timeseries/campaign_summary/noaugmentation-timeseries/summary_max_n_pkts_10.csv",
    header=[0, 1],
    index_col=[0, 1],
)

# reformatting
df_tmp = df["acc"][["mean", "ci95"]].round(2)
df_tmp.loc[["test-script", "test-human"]].droplevel(1, axis=0).astype(float).round(2)
mean ci95
test-script 94.53 0.56
test-human 66.91 1.40