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table.py
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table.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
from models.model_utils import MODEL_NAME_MAPPING
import os
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(
"--std",
type=int,
default=1,
help=(
"whether to print stds or not"
)
)
parser.add_argument(
"--latex",
type=int,
default=1,
help=(
"whether to print datframe directly or to latex"
)
)
parser.add_argument(
"--seeds",
default=None,
type=str,
help="string containing random seeds, overrides default 1 to num_runs."
)
parser.add_argument(
"--problem",
default="SCOD",
choices=["SCOD", "OOD"],
type=str
)
args = parser.parse_args()
# decide how to bold metrics
if args.problem == "SCOD":
metrics = ["errROC", "errFPR@95"]
else:
metrics = [" ROC", " FPR@95"] # space needed
EVAL_MAPPING = {
"errROC": r"\%AUROC$\uparrow$",
"ROC": r"\%AUROC$\uparrow$",
"errFPR@95": r"\%FPR@95$\downarrow$",
"FPR@95": r"\%FPR@95$\downarrow$"
}
higher = [True, False]
# 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_H_z",
"SIRC_H_res",
"SIRC_H_knn",
"SIRC_MSP_knn_res_z",
"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)
and "fix" not in col
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
if args.latex:
df.columns = [
col
if col != config["id_dataset"]["name"] else "ID\\xmark"
for col in df.columns
]
else:
df.columns = [
col
if col != config["id_dataset"]["name"] else "ID - incorrect"
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 FP 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 into a big boi
df = pd.concat(dfs)
# get mean and standard deviation over 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])
# format
def mean_std_format(data):
"""Take array [mean, std] and return formatted string."""
data = np.array(data)
if args.std and args.latex:
return f"{data[0]:.2f} \scriptsize ±{2*data[1]:.1f}"
elif args.std:
return f"{data[0]:.2f} ± {2*data[1]:.2f}"
else:
return f"{data[0]:.2f}"
dfs = []
for i in range(2):
mean = df.groupby(df.index).mean()
res = pd.DataFrame(mean_std.apply(mean_std_format, axis=0)).transpose()
print("="*80)
cols_to_drop = [
col for col in res.columns
if metrics[i] not in col
] + ["seed"]
data_to_drop = [
# optionally exclude 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 args.problem == "SCOD":
print(mean.columns)
if args.latex:
mean = mean.loc[:, mean.columns != "ID\\xmark"].mean(axis=1)
else:
mean = mean.loc[:, mean.columns != "ID - incorrect"].mean(axis=1)
mean = mean.apply(lambda x: f"{x:.2f}")
res.insert(1,"OOD mean",mean)
else:
mean = mean.mean(axis=1)
mean = mean.apply(lambda x: f"{x:.2f}")
res.insert(0,"OOD mean",mean)
high = higher[i]
def bold_max(data):
data = list(data)
means = [float(value.split(" ", maxsplit=1)[0]) for value in data]
means = np.array(means)
ids = np.argsort(means)
if high:
idx1 = ids[-1]
idx2 = ids[-2]
idx3 = ids[-3]
else:
idx1 = ids[0]
idx2 = ids[1]
idx3 = ids[2]
data[idx1] = "\\textbf{"+ data[idx1] + "}"
data[idx2] = "\\underline{" + data[idx2] + "}"
data[idx3] = "\\underline{" + data[idx3] + "}"
return data
res = res.loc[metrics_of_interest]
# bold best, underline 2nd and 3rd best
if args.latex:
res = res.apply(bold_max, axis=0)
res = res.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
comb = pd.concat(dfs, keys=[EVAL_MAPPING[metric] for metric in metrics])
comb = comb.swaplevel().reindex(idx, level=0).transpose()
def tidy_idx_cls(df):
"""Make dataframe nicer for paper."""
df.columns = pd.MultiIndex.from_tuples(
[("\\textbf{" + x[0] + "}", x[1]) for x in df.columns]
)
# comb1 = pd.concat({config["model"]["model_type"]: comb1}, names=["model"])
df_idx = df.index.to_frame(name="\\textbf{Method}")
side_cat = []
for metric in df_idx.iloc[:,0]:
if "KNN," in metric:
side_cat.append("\\begin{sideways}\\textbf{SIRC+}\\end{sideways}")
elif "(" in metric:
side_cat.append("\\begin{sideways}\\textbf{SIRC}\\end{sideways}")
else:
side_cat.append("")
df_idx.insert(
0, "", side_cat
# [
# "\\begin{sideways}\\textbf{SIRC}\\end{sideways}"
# if "(" in metric else "" for metric in df_idx.iloc[:,0]
# ]
)
df.index = pd.MultiIndex.from_frame(df_idx)
df_idx = df.index.to_frame()
side_cat = []
df_idx.insert(
0, "\\textbf{Model}",
[
f'\\begin{{sideways}}\\shortstack[l]{{\\textbf{{{MODEL_NAME_MAPPING[config["model"]["model_type"]]}}} \\\\ ID \\%Error: {id_mean["top1"][0]:.2f}}}\\end{{sideways}}'
for metric in df_idx.iloc[:,0]
]
)
df.index = pd.MultiIndex.from_frame(df_idx)
print(
f"tables of results for {config['model']['model_type']}"
)
if config["id_dataset"]["name"] in ["imagenet200"] and args.latex:
# split in 2 for presentation purposes
data_names1 = [
"ID\\xmark", "OOD mean", 'Near-ImageNet-200',
'Caltech-45', 'Openimage-O', "iNaturalist"
]
data_names2 = [
"Textures", "SpaceNet", 'Colonoscopy',
'Colorectal', 'Noise', 'ImageNet-O'
]
comb1 = comb[data_names1]
comb2 = comb[data_names2]
tidy_idx_cls(comb1)
tidy_idx_cls(comb2)
print(comb1.style.to_latex(hrules=True, multicol_align="c"))
print("\n", 90*"=", "\n")
print(comb2.style.to_latex(hrules=True, multicol_align="c"))
print("\n", 90*"=", "\n")
print(comb1.iloc[:,:4].style.to_latex(hrules=True, multicol_align="c"))
else:
if args.latex:
tidy_idx_cls(comb)
print(comb.style.to_latex(hrules=True, multicol_align="c"))
else:
# print entire dataframe
with pd.option_context(
'display.max_rows', None, 'display.max_columns', None
):
print(comb)