/
plot_confidence_per_class.py
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plot_confidence_per_class.py
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import argparse
import json
from collections import defaultdict
from pathlib import Path
import matplotlib.pyplot as plt
import numpy as np
CB91_Blue = "#2CBDFE"
CB91_Green = "#47DBCD"
CB91_Pink = "#F3A0F2"
CB91_Purple = "#9D2EC5"
CB91_Violet = "#661D98"
CB91_Amber = "#F5B14C"
# color_list = [CB91_Purple, CB91_Green, CB91_Amber, CB91_Blue, CB91_Pink, CB91_Violet]
color_list = [CB91_Blue, CB91_Pink, CB91_Amber, CB91_Green, CB91_Pink, CB91_Violet]
plt.rcParams["axes.prop_cycle"] = plt.cycler(color=color_list)
plt.rcParams["font.family"] = "Times New Roman"
markers = ["x", "+", "o", "8", "s", "X", "D", "p", "P", "d"]
x_axis = [0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]
def get_ax_index(name):
if name == "entailment":
return 0
if name == "nsp":
return 1
if name == "rnsp":
return 2
if name == "qa":
return 3
if name == "qa_article":
return 3
if name == "xclass":
return 4
if name == "lotclass":
return 5
def normalize(scores, reference):
diff = reference - scores[5]
new_scores = [score + diff for score in scores]
return new_scores
def get_model_key(dataset, model, threshold, is_soft):
model_key = f"{dataset}_{model}_{float(threshold)}"
model_key = f"{model_key}_soft" if is_soft else model_key
return model_key
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("-d", "--dataset", help="Dataset")
args = parser.parse_args()
all_data = {}
models = ["entailment", "nsp", "rnsp", "qa", "xclass", "lotclass"]
for model in models:
data_file = f"data/{args.dataset}/preds_{model}.json"
with open(data_file) as rf:
model_data = json.load(rf)
classes = model_data["classes"]
data = {c: [] for c in classes}
for sample in model_data["data"]:
pred = sample["prediction"]
confidence = sample["confidence"][pred]
data[classes[pred]].append(confidence)
all_data[model] = data
# data.append((model, all, correct, wrong))
fig, ax = plt.subplots(1, 6, figsize=(20, 5))
# fig.suptitle(
# f"Pseudo-Label Confidence Distribution",
# fontweight="bold",
## pad=30,
# fontsize=20,
# )
# ax.set_ylim([0, 4])
# ax.set_xlabel("Pseudo label confidence", style="italic", fontsize=20, labelpad=10)
# ax.set_ylabel(f"Count", style="italic", fontsize=20, labelpad=10)
# ax.set_xticks(x_axis)
# ax.set_xticklabels(
# ["0", "0.1", "0.2", "0.3", "0.4", "0.5", "0.6", "0.7", "0.8", "0.9"]
# )
# ax.tick_params(axis="y", labelsize=15)
# ax.tick_params(axis="x", labelsize=15)
for i, (model, model_preds) in enumerate(all_data.items()):
to_plot = [np.array(v) for _, v in model_preds.items()]
ax[i].boxplot(to_plot, showfliers=False)
ax[i].set_xlabel(model, style="italic", fontsize=15)
ax[i].set_ylabel("Confidence", style="italic", fontsize=15)
ax[i].set_title(
"Confidence Distribution By Class",
style="italic",
fontsize=15,
fontweight="bold",
)
# Cleanup.
# ax.spines["top"].set_visible(False)
# ax.spines["bottom"].set_visible(False)
# ax.spines["right"].set_visible(False)
# ax.spines["left"].set_visible(False)
# ax.get_xaxis().tick_bottom()
# ax.get_yaxis().tick_left()
# ax.tick_params(
# axis="both",
# which="both",
# bottom="off",
# top="off",
# labelbottom="on",
# left="off",
# right="off",
# labelleft="on",
# size=5,
# )
name = f"{args.dataset}_box"
fig_name = f"{name.replace(' ', '_')}.svg"
fig.tight_layout()
fig.savefig(fig_name)
print(f"Saved as {fig_name}")