"""
This script computes the median difference and confidence intervals of all techniques from the ablation study for
improving the masker evaluation metrics. The differences in the metrics are computed
for all images of paired models, that is those which only differ in the inclusion or
not of the given technique. Then, statistical inference is performed through the
percentile bootstrap to obtain robust estimates of the differences in the metrics and
confidence intervals. The script plots the summary for all techniques.
"""
print("Imports...", end="")
from argparse import ArgumentParser
import yaml
import numpy as np
import pandas as pd
import seaborn as sns
from scipy.special import comb
from scipy.stats import trim_mean
from tqdm import tqdm
from collections import OrderedDict
from pathlib import Path
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import matplotlib.transforms as transforms
# -----------------------
# ----- Constants -----
# -----------------------
dict_metrics = {
"names": {
"tpr": "TPR, Recall, Sensitivity",
"tnr": "TNR, Specificity, Selectivity",
"fpr": "FPR",
"fpt": "False positives relative to image size",
"fnr": "FNR, Miss rate",
"fnt": "False negatives relative to image size",
"mpr": "May positive rate (MPR)",
"mnr": "May negative rate (MNR)",
"accuracy": "Accuracy (ignoring may)",
"error": "Error",
"f05": "F05 score",
"precision": "Precision",
"edge_coherence": "Edge coherence",
"accuracy_must_may": "Accuracy (ignoring cannot)",
},
"key_metrics": ["error", "f05", "edge_coherence"],
}
dict_techniques = OrderedDict(
[
("pseudo", "Pseudo labels"),
("depth", "Depth (D)"),
("seg", "Seg. (S)"),
("spade", "SPADE"),
("dada_seg", "DADA (S)"),
("dada_masker", "DADA (M)"),
]
)
# Model features
model_feats = [
"masker",
"seg",
"depth",
"dada_seg",
"dada_masker",
"spade",
"pseudo",
"ground",
"instagan",
]
# Colors
crest = sns.color_palette("crest", as_cmap=False, n_colors=7)
palette_metrics = [crest[0], crest[3], crest[6]]
sns.palplot(palette_metrics)
# Markers
dict_markers = {"error": "o", "f05": "s", "edge_coherence": "^"}
def parsed_args():
"""
Parse and returns command-line args
Returns:
argparse.Namespace: the parsed arguments
"""
parser = ArgumentParser()
parser.add_argument(
"--input_csv",
default="ablations_metrics_20210311.csv",
type=str,
help="CSV containing the results of the ablation study",
)
parser.add_argument(
"--output_dir",
default=None,
type=str,
help="Output directory",
)
parser.add_argument(
"--dpi",
default=200,
type=int,
help="DPI for the output images",
)
parser.add_argument(
"--n_bs",
default=1e6,
type=int,
help="Number of bootrstrap samples",
)
parser.add_argument(
"--alpha",
default=0.99,
type=float,
help="Confidence level",
)
parser.add_argument(
"--bs_seed",
default=17,
type=int,
help="Bootstrap random seed, for reproducibility",
)
return parser.parse_args()
def trim_mean_wrapper(a):
return trim_mean(a, proportiontocut=0.2)
def find_model_pairs(technique, model_feats):
model_pairs = []
for mi in df.loc[df[technique]].model_feats.unique():
for mj in df.model_feats.unique():
if mj == mi:
continue
if df.loc[df.model_feats == mj, technique].unique()[0]:
continue
is_pair = True
for f in model_feats:
if f == technique:
continue
elif (
df.loc[df.model_feats == mj, f].unique()[0]
!= df.loc[df.model_feats == mi, f].unique()[0]
):
is_pair = False
break
else:
pass
if is_pair:
model_pairs.append((mi, mj))
break
return model_pairs
if __name__ == "__main__":
# -----------------------------
# ----- Parse arguments -----
# -----------------------------
args = parsed_args()
print("Args:\n" + "\n".join([f" {k:20}: {v}" for k, v in vars(args).items()]))
# Determine output dir
if args.output_dir is None:
output_dir = Path(os.environ["SLURM_TMPDIR"])
else:
output_dir = Path(args.output_dir)
if not output_dir.exists():
output_dir.mkdir(parents=True, exist_ok=False)
# Store args
output_yml = output_dir / "bootstrap_summary.yml"
with open(output_yml, "w") as f:
yaml.dump(vars(args), f)
# Read CSV
df = pd.read_csv(args.input_csv, index_col="model_img_idx")
# Build data set
dfbs = pd.DataFrame(columns=["diff", "technique", "metric"])
for technique in model_feats:
# Get pairs
model_pairs = find_model_pairs(technique, model_feats)
# Compute differences
for m_with, m_without in model_pairs:
df_with = df.loc[df.model_feats == m_with]
df_without = df.loc[df.model_feats == m_without]
for metric in dict_metrics["key_metrics"]:
diff = (
df_with.sort_values(by="img_idx")[metric].values
- df_without.sort_values(by="img_idx")[metric].values
)
dfm = pd.DataFrame.from_dict(
{"metric": metric, "technique": technique, "diff": diff}
)
dfbs = dfbs.append(dfm, ignore_index=True)
### Plot
# Set up plot
sns.reset_orig()
sns.set(style="whitegrid")
plt.rcParams.update({"font.family": "serif"})
plt.rcParams.update(
{
"font.serif": [
"Computer Modern Roman",
"Times New Roman",
"Utopia",
"New Century Schoolbook",
"Century Schoolbook L",
"ITC Bookman",
"Bookman",
"Times",
"Palatino",
"Charter",
"serif" "Bitstream Vera Serif",
"DejaVu Serif",
]
}
)
fig, axes = plt.subplots(
nrows=1, ncols=3, sharey=True, dpi=args.dpi, figsize=(9, 3)
)
metrics = ["error", "f05", "edge_coherence"]
dict_ci = {m: {} for m in metrics}
for idx, metric in enumerate(dict_metrics["key_metrics"]):
ax = sns.pointplot(
ax=axes[idx],
data=dfbs.loc[dfbs.metric.isin(["error", "f05", "edge_coherence"])],
order=dict_techniques.keys(),
x="diff",
y="technique",
hue="metric",
hue_order=[metric],
markers=dict_markers[metric],
palette=[palette_metrics[idx]],
errwidth=1.5,
scale=0.6,
join=False,
estimator=trim_mean_wrapper,
ci=int(args.alpha * 100),
n_boot=args.n_bs,
seed=args.bs_seed,
)
# Retrieve confidence intervals and update results dictionary
for line, technique in zip(ax.lines, dict_techniques.keys()):
dict_ci[metric].update(
{
technique: {
"20_trimmed_mean": float(
trim_mean_wrapper(
dfbs.loc[
(dfbs.technique == technique)
& (dfbs.metric == metrics[idx]),
"diff",
].values
)
),
"ci_left": float(line.get_xdata()[0]),
"ci_right": float(line.get_xdata()[1]),
}
}
)
leg_handles, leg_labels = ax.get_legend_handles_labels()
# Change spines
sns.despine(left=True, bottom=True)
# Set Y-label
ax.set_ylabel(None)
# Y-tick labels
ax.set_yticklabels(list(dict_techniques.values()), fontsize="medium")
# Set X-label
ax.set_xlabel(None)
# X-ticks
xticks = ax.get_xticks()
xticklabels = xticks
ax.set_xticks(xticks)
ax.set_xticklabels(xticklabels, fontsize="small")
# Y-lim
display2data = ax.transData.inverted()
ax2display = ax.transAxes
_, y_bottom = display2data.transform(ax.transAxes.transform((0.0, 0.02)))
_, y_top = display2data.transform(ax.transAxes.transform((0.0, 0.98)))
ax.set_ylim(bottom=y_bottom, top=y_top)
# Draw line at H0
y = np.arange(ax.get_ylim()[1], ax.get_ylim()[0], 0.1)
x = 0.0 * np.ones(y.shape[0])
ax.plot(x, y, linestyle=":", linewidth=1.5, color="black")
# Draw gray area
xlim = ax.get_xlim()
ylim = ax.get_ylim()
if metric == "error":
x0 = xlim[0]
width = np.abs(x0)
else:
x0 = 0.0
width = np.abs(xlim[1])
trans = transforms.blended_transform_factory(ax.transData, ax.transAxes)
rect = mpatches.Rectangle(
xy=(x0, 0.0),
width=width,
height=1,
transform=trans,
linewidth=0.0,
edgecolor="none",
facecolor="gray",
alpha=0.05,
)
ax.add_patch(rect)
# Legend
leg_handles, leg_labels = ax.get_legend_handles_labels()
leg_labels = [dict_metrics["names"][metric] for metric in leg_labels]
leg = ax.legend(
handles=leg_handles,
labels=leg_labels,
loc="center",
title="",
bbox_to_anchor=(-0.2, 1.05, 1.0, 0.0),
framealpha=1.0,
frameon=False,
handletextpad=-0.2,
)
# Set X-label (title) │
fig.suptitle(
"20 % trimmed mean difference and bootstrapped confidence intervals",
y=0.0,
fontsize="medium",
)
# Save figure
output_fig = output_dir / "bootstrap_summary.png"
fig.savefig(output_fig, dpi=fig.dpi, bbox_inches="tight")
# Store results
output_results = output_dir / "bootstrap_summary_results.yml"
with open(output_results, "w") as f:
yaml.dump(dict_ci, f)