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draw_tsne.py
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draw_tsne.py
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# -*- coding: utf-8 -*-
# @Time : 2020/9/5 0:10
# @Author : Hui Wang
from collections import defaultdict
import tqdm
import random
import numpy as np
from sklearn.manifold import TSNE
def train_tsne_(data_name, epoch=10):
emb = np.load(f'npys/{data_name}_{epoch}.npy') # id 的embeddding
lines = open(f'npys/{data_name}_t-SNE.txt').readlines()
# 每一行是
# label1: id1, id2, id4
# label2: id3, id5, id6
# label3: id7, id8, id9
all_items = []
for i, line in enumerate(lines):
lable, items = line.strip().split(':')
items = items.split(',')
for item in items:
all_items.append(int(item))
features = emb[all_items] # 取出所有待画id的embedding
tsne_output = TSNE(n_components=2, random_state=1024).fit_transform(features)
np.save(f'npys/{data_name}_{epoch}.npy', tsne_output)
# 先训练t-SNE
data_name = 'Movielens'
for epoch in [0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110]:
train_tsne_(data_name, epoch)
# 再画
import matplotlib
matplotlib.rcParams['pdf.fonttype'] = 42
matplotlib.rcParams['ps.fonttype'] = 42
import matplotlib.pyplot as plt
import os
plt.rcParams["font.family"] = "Times New Roman"
def get_target(data_name):
lines = open(f'npys/{data_name}_t-SNE.txt').readlines()
targets = []
for i, line in enumerate(lines):
lable, items = line.strip().split(':')
items = items.split(',')
targets.extend([int(i)] * len(items))
return np.array(targets)
def plot_4(data_name, epochs):
target = get_target(data_name)
colors = np.array(['tomato', 'blue', 'orange', 'green', 'purple', 'deepskyblue'])
colors = colors[target]
fontsize = 20
plt.figure(figsize=(20, 5))
plt.subplot(141)
plt.axis('off')
X_tsne_10 = np.load(f'npys/{data_name}_{epochs[0]}.npy')
plt.title("Start", fontsize=fontsize)
# plt.text(-28, 25, '10 epochs', fontsize=fontsize)
plt.scatter(X_tsne_10[:, 0], X_tsne_10[:, 1], c=colors)
# 刻度不可见
plt.xticks([])
plt.yticks([])
plt.subplot(142)
plt.axis('off')
X_tsne_40 = np.load(f'npys/{data_name}_{epochs[1]}.npy')
plt.title("After Ele. Course", fontsize=fontsize)
# plt.text(-39, 29, '40 epochs', fontsize=fontsize)
plt.scatter(X_tsne_40[:, 0], X_tsne_40[:, 1], c=colors)
plt.xticks([])
plt.yticks([])
plt.subplot(143)
plt.axis('off')
X_tsne_70 = np.load(f'npys/{data_name}_{epochs[2]}.npy')
plt.title("After Ele. Course", fontsize=fontsize)
# plt.text(-46, 27, '70 epochs', fontsize=fontsize)
plt.scatter(X_tsne_70[:, 0], X_tsne_70[:, 1], c=colors)
plt.xticks([])
plt.yticks([])
plt.subplot(144)
plt.axis('off')
X_tsne_100 = np.load(f'npys/{data_name}_{epochs[3]}.npy')
plt.title("After Finetune", fontsize=fontsize)
# plt.text(-42, 26, '100 epochs', fontsize=fontsize)
plt.scatter(X_tsne_100[:, 0], X_tsne_100[:, 1], c=colors)
plt.xticks([])
plt.yticks([])
plt.savefig(f'{data_name.lower()}_sne.pdf', format='pdf',
bbox_inches='tight', pad_inches=0.05, dpi=100)
plt.show()
data_name = 'Movielens'
epochs = [0, 20, 40, 60]
plot_4(data_name, epochs)