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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"]
selected_epochs_dict = {"mnist":[[1], [10], [15]],"fmnist":[[1],[25],[50]], "cifar10":[[1], [100],[199]]}
col = np.array(["dataset", "method", "type", "hue", "period", "eval"])
df = pd.DataFrame({}, columns=col)
for i in range(len(datasets)): # dataset
dataset = datasets[i]
data = np.array([])
selected_epochs = selected_epochs_dict[dataset]
# load data from evaluation.json
content_path = "/home/xianglin/projects/DVI_data/resnet18_{}".format(dataset)
for epoch_id in range(3):
stage_epochs = selected_epochs[epoch_id]
inv_acc_train_list = list()
inv_acc_test_list = list()
for epoch in stage_epochs:
eval_path = os.path.join(content_path, "Model", "Epoch_{}".format(epoch), "evaluation_id_parametricUmap_step2.json")
with open(eval_path, "r") as f:
eval = json.load(f)
inv_acc_train = round(eval["inv_acc_train"], 3)
inv_acc_test = round(eval["inv_acc_test"], 3)
inv_acc_train_list.append(inv_acc_train)
inv_acc_test_list.append(inv_acc_test)
inv_acc_train = sum(inv_acc_train_list)/len(inv_acc_train_list)
inv_acc_test = sum(inv_acc_test_list)/len(inv_acc_test_list)
if len(data)==0:
data = np.array([[dataset, "DVI", "Train", "DVI(Train)", "{}".format(str(epoch_id)), inv_acc_train]])
else:
data = np.concatenate((data, np.array([[dataset, "DVI", "Train", "DVI(Train)", "{}".format(str(epoch_id)), inv_acc_train]])), axis=0)
data = np.concatenate((data, np.array([[dataset, "DVI", "Test", "DVI(Test)", "{}".format(str(epoch_id)), inv_acc_test]])), axis=0)
# torch dvi
eval_path = "/home/xianglin/projects/DVI_data/resnet18_{}/Model/evaluation_singleDVI.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]
ppr_train_list = list()
ppr_test_list = list()
for epoch in stage_epochs:
ppr_train = round(eval["ppr_train"][str(epoch)], 3)
ppr_test = round(eval["ppr_test"][str(epoch)], 3)
ppr_train_list.append(ppr_train)
ppr_test_list.append(ppr_test)
ppr_train = sum(ppr_train_list)/len(ppr_train_list)
ppr_test = sum(ppr_test_list)/len(ppr_test_list)
data = np.concatenate((data, np.array([[dataset, "torch-DVI", "Train", "torch-DVI(Train)", "{}".format(str(epoch_id)), ppr_train]])), axis=0)
data = np.concatenate((data, np.array([[dataset, "torch-DVI", "Test", "torch-DVI(Test)", "{}".format(str(epoch_id)), ppr_test]])), axis=0)
eval_path = "/home/xianglin/projects/DVI_data/resnet18_{}/Model/test_evaluation_tnn_noB.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]
ppr_train_list = list()
ppr_test_list = list()
for epoch in stage_epochs:
ppr_train = round(eval["ppr_train"][str(epoch)], 3)
ppr_test = round(eval["ppr_test"][str(epoch)], 3)
ppr_train_list.append(ppr_train)
ppr_test_list.append(ppr_test)
ppr_train = sum(ppr_train_list)/len(ppr_train_list)
ppr_test = sum(ppr_test_list)/len(ppr_test_list)
data = np.concatenate((data, np.array([[dataset, "TimeVis", "Train", "TimeVis(Train)", "{}".format(str(epoch_id)), ppr_train]])), axis=0)
data = np.concatenate((data, np.array([[dataset, "TimeVis", "Test", "TimeVis(Test)", "{}".format(str(epoch_id)), ppr_test]])), axis=0)
eval_path = "/home/xianglin/projects/DVI_data/resnet18_{}/Model/evaluation_dd_noB.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]
ppr_train_list = list()
ppr_test_list = list()
for epoch in stage_epochs:
ppr_train = round(eval["ppr_train"][str(epoch)], 3)
ppr_test = round(eval["ppr_test"][str(epoch)], 3)
ppr_train_list.append(ppr_train)
ppr_test_list.append(ppr_test)
ppr_train = sum(ppr_train_list)/len(ppr_train_list)
ppr_test = sum(ppr_test_list)/len(ppr_test_list)
data = np.concatenate((data, np.array([[dataset, "DD", "Train", "DD(Train)", "{}".format(str(epoch_id)), ppr_train]])), axis=0)
data = np.concatenate((data, np.array([[dataset, "DD", "Test", "DD(Test)", "{}".format(str(epoch_id)), ppr_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/PPR.xlsx")
pal20c = sns.color_palette('tab20', 20)
sns.set_theme(style="whitegrid", palette=pal20c)
hue_dict = {
"DVI(Train)": pal20c[4],
"torch-DVI(Train)":pal20c[10],
"TimeVis(Train)": pal20c[6],
"DD(Train)": pal20c[8],
"DVI(Test)": pal20c[5],
"torch-DVI(Test)": pal20c[11],
"TimeVis(Test)": pal20c[7],
"DD(Test)": pal20c[9],
}
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)", "torch-DVI(Train)", "torch-DVI(Test)", "TimeVis(Train)", "TimeVis(Test)", "DD(Train)", "DD(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,
kind="bar",
palette=[hue_dict[i] for i in hue_list],
legend=True
)
sns.move_legend(fg, "lower center", bbox_to_anchor=(.43, 0.92), ncol=4, title=None, frameon=False)
mpl.pyplot.setp(fg._legend.get_texts(), fontsize='15')
axs = fg.axes[0]
max_ = df["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("Prediction Preserving property")
fg.savefig(
"./plot_results/noB_inv_accu.png",
dpi=300,
bbox_inches="tight",
pad_inches=0.0,
transparent=True,
)
if __name__ == "__main__":
main()
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