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import argparse
import os
import json
import numpy as np
import pandas as pd
import matplotlib as mpl
import seaborn as sns
def main():
datasets = ["mnist", "fmnist", "cifar10"]
selected_epochs_dict = {"mnist":[[1,2], [10,13], [16,20]],"fmnist":[[1,6],[25,30],[36,50]], "cifar10":[[1,24], [70,100],[160,200]]}
k_neighbors = [10,15,20]
col = np.array(["dataset", "method", "type", "hue", "k", "period", "eval"])
df = pd.DataFrame({}, columns=col)
for k in k_neighbors: # k neighbors
for i in range(len(datasets)): # dataset
dataset = datasets[i]
data = np.array([])
selected_epochs = selected_epochs_dict[dataset]
# load data from evaluation.json
# DVI
content_path = "/home/xianglin/projects/DVI_data/resnet18_{}".format(dataset)
for epoch_id in range(3):
stage_epochs = selected_epochs[epoch_id]
nn_train_list = list()
nn_test_list = list()
for epoch in stage_epochs:
eval_path = os.path.join(content_path, "Model", "Epoch_{}".format(epoch), "evaluation_step2_A.json")
with open(eval_path, "r") as f:
eval = json.load(f)
nn_train = round(eval["nn_train_{}".format(k)], 3)
nn_test = round(eval["nn_test_{}".format(k)], 3)
nn_train_list.append(nn_train)
nn_test_list.append(nn_test)
nn_train = sum(nn_train_list)/len(nn_train_list)
nn_test = sum(nn_test_list)/len(nn_test_list)
if len(data) == 0:
data = np.array([[dataset, "DVI", "Train", "DVI(Train)", "{}".format(k), "{}".format(str(epoch_id)), nn_train]])
else:
data = np.concatenate((data, np.array([[dataset, "DVI", "Train", "DVI(Train)", "{}".format(k), "{}".format(str(epoch_id)), nn_train]])), axis=0)
data = np.concatenate((data, np.array([[dataset, "DVI", "Test", "DVI(Test)", "{}".format(k), "{}".format(str(epoch_id)), nn_test]])), axis=0)
eval_path = "/home/xianglin/projects/DVI_data/resnet18_{}/Model/test_evaluation_tnn.json".format(dataset)
with open(eval_path, "r") as f:
eval = json.load(f)
for epoch_id in range(3):
stage_epochs = selected_epochs[epoch_id]
nn_train_list = list()
nn_test_list = list()
for epoch in stage_epochs:
nn_train = round(eval[str(k)]["nn_train"][str(epoch)], 3)
nn_test = round(eval[str(k)]["nn_test"][str(epoch)], 3)
nn_train_list.append(nn_train)
nn_test_list.append(nn_test)
nn_train = sum(nn_train_list)/len(nn_train_list)
nn_test = sum(nn_test_list)/len(nn_test_list)
data = np.concatenate((data, np.array([[dataset, "TimeVis", "Train", "TimeVis(Train)", "{}".format(k), "{}".format(str(epoch_id)), nn_train]])), axis=0)
data = np.concatenate((data, np.array([[dataset, "TimeVis", "Test", "TimeVis(Test)", "{}".format(k), "{}".format(str(epoch_id)), nn_test]])), axis=0)
eval_path = "/home/xianglin/projects/DVI_data/resnet18_{}/Model/test_evaluation_hybrid.json".format(dataset)
with open(eval_path, "r") as f:
eval = json.load(f)
for epoch_id in range(3):
stage_epochs = selected_epochs[epoch_id]
nn_train_list = list()
nn_test_list = list()
for epoch in stage_epochs:
nn_train = round(eval["nn_train"][str(epoch)][str(k)], 3)
nn_test = round(eval["nn_test"][str(epoch)][str(k)], 3)
nn_train_list.append(nn_train)
nn_test_list.append(nn_test)
nn_train = sum(nn_train_list)/len(nn_train_list)
nn_test = sum(nn_test_list)/len(nn_test_list)
data = np.concatenate((data, np.array([[dataset, "DeepDebugger", "Train", "DeepDebugger(Train)", "{}".format(k), "{}".format(str(epoch_id)), nn_train]])), axis=0)
data = np.concatenate((data, np.array([[dataset, "DeepDebugger", "Test", "DeepDebugger(Test)", "{}".format(k), "{}".format(str(epoch_id)), nn_test]])), axis=0)
df_tmp = pd.DataFrame(data, columns=col)
df = df.append(df_tmp, ignore_index=True)
df[["period"]] = df[["period"]].astype(int)
df[["k"]] = df[["k"]].astype(int)
df[["eval"]] = df[["eval"]].astype(float)
df.to_excel("./plot_results/nn.xlsx")
for k in k_neighbors:
df_tmp = df[df["k"] == k]
pal20c = sns.color_palette('tab20', 20)
sns.set_theme(style="whitegrid", palette=pal20c)
hue_dict = {
"DVI(Train)": pal20c[14],
"TimeVis(Train)": pal20c[16],
"DeepDebugger(Train)": pal20c[18],
"DVI(Test)": pal20c[15],
"TimeVis(Test)": pal20c[17],
"DeepDebugger(Test)": pal20c[19],
}
sns.palplot([hue_dict[i] for i in hue_dict.keys()])
axes = {'labelsize': 15,
'titlesize': 15,}
mpl.rc('axes', **axes)
mpl.rcParams['xtick.labelsize'] = 15
hue_list = ["DVI(Train)", "DVI(Test)", "TimeVis(Train)", "TimeVis(Test)", "DeepDebugger(Train)", "DeepDebugger(Test)"]
fg = sns.catplot(
x="period",
y="eval",
hue="hue",
hue_order=hue_list,
# order = [1, 2, 3, 4, 5],
# row="method",
col="dataset",
ci=0.001,
height=2.5, #2.65,
aspect=1.0,#3,
data=df_tmp,
kind="bar",
palette=[hue_dict[i] for i in hue_list],
legend=True
)
sns.move_legend(fg, "lower center", bbox_to_anchor=(.42, 0.92), ncol=3, title=None, frameon=False)
mpl.pyplot.setp(fg._legend.get_texts(), fontsize='15')
axs = fg.axes[0]
# max_ = df_tmp["eval"].max()
# min_ = df["eval"].min()
# axs[0].set_ylim(0., max_*1.1)
axs[0].set_title("MNIST(20)")
axs[1].set_title("FMNIST(50)")
axs[2].set_title("CIFAR-10(200)")
(fg.despine(bottom=False, right=False, left=False, top=False)
.set_xticklabels(['Early', 'Mid', 'Late'])
.set_axis_labels("", "")
)
# fg.fig.suptitle("NN preserving property")
fg.savefig(
"./plot_results/nn_{}.png".format(k),
dpi=300,
bbox_inches="tight",
pad_inches=0.0,
transparent=True,
)
if __name__ == "__main__":
main()
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