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plot_ood_sc_comp.py
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plot_ood_sc_comp.py
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import argparse
from utils.data_utils import DATA_NAME_MAPPING
import pandas as pd
pd.set_option("display.max_rows", 200,
"display.max_columns", 10)
import numpy as np
from argparse import ArgumentParser
import json
from utils.train_utils import get_filename
from utils.eval_utils import METRIC_NAME_MAPPING
import os
import matplotlib.pyplot as plt
import matplotlib as mpl
import seaborn as sns
parser = ArgumentParser()
parser.add_argument(
"config_path",
help="path to the experiment config file for this test script"
)
parser.add_argument(
"num_runs",
type=int,
help="number of independent runs to average over"
)
parser.add_argument(
"--results_path",
type=str,
default=None,
help=(
"directory where result .csv files are kept,"
"deduced from config by default"
)
)
parser.add_argument(
"--seeds",
default=None,
type=str,
help="string containing random seeds, overrides default 1 to num_runs."
)
args = parser.parse_args()
metrics = ["errFPR@95", " FPR@95"]
EVAL_MAPPING = {
"errROC": r"\%AUROC$\uparrow$",
"ROC": r"\%AUROC$\uparrow$",
"errFPR@95": r"FPR@95$\downarrow$",
"FPR@95": r"FPR@95$\downarrow$"
}
# load config
config = open(args.config_path)
config = json.load(config)
# list of seeds
seeds = [i for i in range(1, args.num_runs + 1)] if (
args.seeds is None
) else list(args.seeds)
# results path generated as results_savedir/arch_dataset
if args.results_path is not None:
results_path = args.results_path
else:
results_path = os.path.join(
config["test_params"]["results_savedir"],
get_filename(config, seed=None)
)
# metrics we care about
metrics_of_interest = [
"SIRC_MSP_z",
"SIRC_MSP_res",
"SIRC_MSP_knn",
"SIRC_doctor_z",
"SIRC_doctor_res",
"SIRC_doctor_knn",
# "SIRC_MSP_knn_res_z",
"SIRC_H_z",
"SIRC_H_res",
"SIRC_H_knn",
# "SIRC_doctor_knn_res_z",
# "SIRC_H_knn_res_z",
"confidence",
"doctor",
"entropy",
"feature_norm",
"residual",
"knn",
"max_logit",
"energy",
"gradnorm",
"vim",
"mahalanobis",
]
# reformatting function
def rearrange_df(df, cols_to_drop, datasets_to_drop=[]):
# get rid of specified colums
df.drop(cols_to_drop, axis=1, inplace=True, errors="ignore")
# get rid of certain data
data_cols_to_drop = [
col for col in df.columns
if
any(data_name in col for data_name in datasets_to_drop)
or "-c" in col
or "-r" in col
]
df.drop(data_cols_to_drop, axis=1, inplace=True, errors="ignore")
# drop shifted rows
df.dropna(axis=1, inplace=True)
df = df.transpose().reset_index(level=0)
df.columns = ["data-method", "performance"]
df[["data", "method"]] = df["data-method"].str.rsplit(
" ", 1, expand=True
)
df.drop("data-method", axis=1, inplace=True, errors="ignore")
df = df[["data", "method", "performance"]]
def clean_data_name(name: str):
for pattern in [
" PR", " ROC", "OOD ", " FPR@95", " errROC", " errFPR@95", "err@95"
]:
name = name.replace(pattern, "")
return name
# retain order of datasets
df["data"] = df["data"].apply(clean_data_name)
df["data"] = pd.Categorical(
df["data"],
categories=df["data"].unique(),
ordered=True
)
df = df.pivot(
index="method", columns="data", values="performance"
)
# nice names for the datasets
df.columns = [
col
if col != config["id_dataset"]["name"] else "ID\\xmark"
for col in df.columns
]
df.columns = [
DATA_NAME_MAPPING[col]
if col in DATA_NAME_MAPPING else col
for col in df.columns
]
return df
# get only 1st row, outputs as a series so need to do a bit of messing around
dfs = [
pd.DataFrame(
pd.read_csv(
os.path.join(
results_path, get_filename(config, seed=seed) + ".csv"
), # results_savedir/arch_dataset/arch_dataset_seed.csv
index_col=0
).iloc[0].drop(
["weights", "activations", "dataset", "precision"],
axis=0,
errors="ignore"
)
).transpose()
for seed in
seeds
]
# concatenate together
df = pd.concat(dfs)
# get mean and standard deviation over training runs
df = df.astype(float)
mean = df.groupby(df.index).mean()
std = df.groupby(df.index).std()
id_mean = mean[["top1", "top5", "nll"]]
id_std = std[["top1", "top5", "nll"]]
mean_std = pd.concat([mean, std])
dfs = []
for i in range(2):
res = df.groupby(df.index).mean()
mean = df.groupby(df.index).mean()
print("="*80)
cols_to_drop = [
col for col in res.columns
if metrics[i] not in col
] + ["seed"]
data_to_drop = [
# option to exclude certain datasets
]
res = rearrange_df(res, cols_to_drop, datasets_to_drop=data_to_drop)
mean = rearrange_df(mean, cols_to_drop, datasets_to_drop=data_to_drop)
# exclude MD column from mean
if i==0:
print(mean.columns)
mean = mean.loc[:, mean.columns != "ID\\xmark"].mean(axis=1)
res.insert(1,"OOD mean",mean)
else:
mean = mean.mean(axis=1)
res.insert(0,"OOD Detection mean",mean)
res = res.loc[metrics_of_interest]
res = res.transpose()
if not i:
res = res.loc[["ID\\xmark", "OOD mean"]]
else:
res = pd.DataFrame(res.loc["OOD Detection mean"]).transpose()
# make text nicer
res.columns = [
METRIC_NAME_MAPPING[colname]
if colname in METRIC_NAME_MAPPING else colname.replace("_", " ")
for colname in res.columns
]
dfs.append(res)
idx = res.index
res = pd.concat(dfs, axis=0)
# get rid of components
# keep only full detection methods
res = res.sub(res["MSP"], axis=0).drop(
["MSP", "Residual", "$||\\b z||_1$"], axis=1
)
x_labels = res.columns
res.index.name = "separation"
res.reset_index(inplace=True)
res.loc[len(res)] = 0.0
res.loc[len(res)] = 0.0
res.loc[len(res)] = 0.0
res.iloc[5, 0] = 1.0
res.iloc[4, 0] = 2.0
# this is just inserting a gap into the barplot
res = res.reindex([5, 0,1, 3,2,4])
res = res.melt(
id_vars=["separation"], var_name="method", value_name="$\\Delta$%FPR@95$\leftarrow$\n(from MSP baseline)"
)
def clean_strings(x):
mapping = {
"ID\\xmark": "ID✗|ID✓",
"OOD mean": "OOD|ID✓",
"OOD Detection mean": "OOD|ID"
}
if x in mapping:
return mapping[x]
elif type(x) == str:
return x.replace("\\b", "")
else:
return x
res["separation"] = res["separation"].apply(clean_strings)
res["method"] = res["method"].apply(clean_strings)
sns.set_theme()
fig, ax = plt.subplots(1,1,figsize=(15,3))
sns.barplot(
ax=ax,
data=res,
x="method",
y="$\\Delta$%FPR@95$\leftarrow$\n(from MSP baseline)",
hue="separation",
palette=["white","indianred", "yellowgreen", "white", "darkolivegreen",],
alpha=0.5,
)
h, l = ax.get_legend_handles_labels()
ax.legend(h[1:3] + [h[4]], l[1:3]+[l[4]])
sns.move_legend(
ax,
"lower center",
bbox_to_anchor=(.5, 0.95), ncol=3, title=None, frameon=False,
)
ax.get_yaxis().set_minor_locator(mpl.ticker.AutoMinorLocator())
ax.grid(visible=True, which='minor', lw=0.5)
ax.annotate(
'SIRC',
xy=(0.262, .55),
xytext=(0.262, .7),
xycoords='axes fraction',
ha='center', va='bottom',
arrowprops=dict(
arrowstyle='-[, widthB=21, lengthB=1.0',
lw=2,
color="slategrey"
),
color="slategrey"
)
x_labels = [clean_strings(lab) for lab in x_labels]
ax.set_xticklabels(["\n"*(i%2) + l for i,l in enumerate(x_labels)])
fig.tight_layout()
# specify filename
spec = get_filename(config, seed=None)
save_dir = os.path.join(config["test_params"]["results_savedir"], spec)
filename = get_filename(config, seed=config["seed"]) + \
f"_bar_ood_sc_full.pdf"
path = os.path.join(save_dir, filename)
fig.savefig(path)
print(f"figure saved to:\n{path}")