t-SNE.py

from sklearn.manifold import TSNE
import numpy as np
import matplotlib.pyplot as plt


data = np.load("_for_tsne_multi_gc.npy", allow_pickle=True)
tsne_exp_name = "multi_(gc)"


tsne_data = TSNE(n_components = 2).fit_transform(data.item().get('data'))


plt.scatter(tsne_data[:, 0], tsne_data[:, 1], c = data.item().get('gt_label')[:], s = 3)


plt.xlim(tsne_data[:, 0].min(), tsne_data[:, 0].max()) # 최소, 최대

plt.ylim(tsne_data[:, 1].min(), tsne_data[:, 1].max()) # 최소, 최대

plt.xlabel('t-SNE attribute1') # x축 이름

plt.ylabel('t-SNE attribute2') # y축 이름

plt.show() # 그래프 출력

plt.savefig(f"./{tsne_exp_name}.png", dpi = 600)

 

 

from sklearn.manifold import TSNE
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd

data = np.load("_for_tsne_multi_sp.npy", allow_pickle=True)
tsne_exp_name = "multi_(sp)"

#import pdb; pdb.set_trace()
#data = {}
#data['data'] = np.random.rand(30, 128)
#data['gt_label'] = np.random.randint(0, 20, size = (30, 1))

tsne_data = TSNE(n_components = 2).fit_transform(data.item().get('data'))
#tsne_data = TSNE(n_components = 2).fit_transform(data['data'])
import pdb; pdb.set_trace()

df = pd.DataFrame({'t-SNE attribute1' :tsne_data[:, 0], 't-SNE attribute2':tsne_data[:, 1],'label' : np.squeeze(data.item().get('gt_label')[:])})

palette = sns.color_palette(n_colors = 20)


sns.scatterplot(data = df, x = 't-SNE attribute1', y = 't-SNE attribute2', hue = 'label', palette = palette, size = 1)
plt.legend(bbox_to_anchor=(1.02, 10), loc='upper left', borderaxespad=0)
plt.show() # 그래프 출력

plt.savefig(f"./{tsne_exp_name}.png", dpi = 600)