Table 7: Accuracy on 32x32 flowpic when enlarging the training set (w/o Dropout)

import itertools
import pathlib

import pandas as pd

RENAME = {
    "noaug": "No augmentation",
    "rotate": "Rotate",
    "horizontalflip": "Horizontal flip",
    "colorjitter": "Color jitter",
    "packetloss": "Packet loss",
    "timeshift": "Time shift",
    "changertt": "Change RTT",
}

folder = pathlib.Path("campaigns/ucdavis-icdm19/larger-trainset/")

df_sup = pd.read_csv(
    folder
    / "augmentation-at-loading/campaign_summary/augment-at-loading-larger-trainset/summary_flowpic_dim_32.csv",
    header=[0, 1],
    index_col=[0, 1],
)
df_sup = df_sup["acc"][["mean", "ci95"]]
df_sup.index.set_names(["test_split_name", "aug_name"], inplace=True)
df_sup = df_sup.reset_index().pivot(
    columns=["test_split_name"], index="aug_name", values=["mean", "ci95"]
)
df_sup.columns.set_names(["stat", "test_split_name"], inplace=True)
df_sup = df_sup.reorder_levels(["test_split_name", "stat"], axis=1)
df_sup = df_sup[
    list(itertools.product(["test-script", "test-human"], ["mean", "ci95"]))
]
df_sup = df_sup.rename(RENAME, axis=0).rename(RENAME, axis=1)
df_sup.index.set_names([""], inplace=True)
df_sup.columns.set_names(["", ""], inplace=True)
df_sup = df_sup.round(2)
df_sup = df_sup.loc[list(RENAME.values())]
df_sup.to_csv("table7_larger-trainset_augment-at-loading.csv")
df_sup

	test-script		test-human
	mean	ci95	mean	ci95
No augmentation	98.37	0.19	72.95	0.96
Rotate	98.47	0.25	73.73	1.09
Horizontal flip	98.20	0.15	74.58	1.16
Color jitter	98.63	0.21	72.47	1.02
Packet loss	98.63	0.19	73.43	1.25
Time shift	98.60	0.22	73.25	1.17
Change RTT	98.33	0.16	72.47	1.04

df_cl = pd.read_csv(
    folder
    / "simclr/campaign_summary/simclr-larger-trainset/summary_flowpic_dim_32.csv",
    header=[0, 1],
    index_col=[0, 1],
)
df_cl = df_cl["acc"][["mean", "ci95"]]
df_cl = df_cl.droplevel(1, axis=0).round(2)
df_cl = df_cl.loc[["test-script", "test-human"]]
df_cl.to_csv("table7_larger-trainset_simclr.csv")
df_cl

	mean	ci95
test-script	93.90	0.74
test-human	80.45	2.37