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import numpy as np
import pandas as pd
from openTSNE import TSNE
import plotly.graph_objs as go
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
from sklearn.decomposition import PCA
from scipy.optimize import linear_sum_assignment
class TSNE_Plot():
def __init__(self, sentence, embed, label = None, n_clusters :int = 3, n_annotation_positions:int = 20):
assert n_clusters > 0, "N must be greater than 0"
self.N = n_clusters
self.test_X = pd.DataFrame({"text": sentence, "embed": [np.array(i) for i in embed]})
self.test_y = pd.DataFrame({'label':label}) if label is not None else pd.DataFrame({"label": self.cluster()})
self.embed = self.calculate_tsne()
self.init_df()
self.n_annotation_positions = n_annotation_positions
self.show_sentence = []
self.random_sentence()
self.annotation_positions = []
self.get_annotation_positions()
self.mapping = {}
def cluster(self):
from sklearn.cluster import KMeans
n_components = min(50, len(self.test_X))
pca = PCA(n_components=n_components)
compact_embedding = pca.fit_transform(np.array(self.test_X["embed"].tolist()))
kmeans = KMeans(n_clusters=self.N)
kmeans.fit(compact_embedding)
labels = kmeans.labels_
return labels
def generate_colormap(self, n_labels):
#创建一个均匀分布的颜色映射
color_norm = mcolors.Normalize(vmin=0, vmax=len(n_labels) - 1)
# 使用 plt.cm 中预先定义的colormap,你可以自由选择其他colormap如"hsv", "hot", "cool", "viridis"等
scalar_map = plt.cm.ScalarMappable(norm=color_norm, cmap='jet')
colormap = {}
for label in range(len(n_labels)):
# 将颜色值转换为十六进制
color_hex = mcolors.to_hex(scalar_map.to_rgba(label))
colormap[n_labels[label]] = color_hex
return colormap
def divide_hex_color_by_half(self, hex_color):
if len(hex_color) > 0 and hex_color[0] == "#":
hex_color = hex_color[1:]
red_hex, green_hex, blue_hex = hex_color[0:2], hex_color[2:4], hex_color[4:6]
red_half = int(red_hex, 16) // 10 + (255-25)
green_half = int(green_hex, 16) // 10 + (255-25)
blue_half = int(blue_hex, 16) // 10 + (255-25)
half_hex_color = "#{:02x}{:02x}{:02x}".format(red_half, green_half, blue_half)
return half_hex_color
def get_annotation_positions(self):
min_x, max_x = self.df['x'].min()-1, self.df['x'].max()+2
n = self.n_annotation_positions
y_min, y_max = self.df['y'].min() * 3, self.df['y'].max() * 3
add = 0 if n % 2 == 0 else 1
y_values = np.linspace(y_min, y_max, n//2+add)
left_positions = [(min_x, y) for y in y_values]
right_positions = [(max_x, y) for y in y_values]
self.annotation_positions = left_positions + right_positions
def euclidean_distance(self, p1, p2):
return np.sqrt((p1[0] - p2[0])**2 + (p1[1] - p2[1])**2)
def map_points(self):
# Get points from the dataframe using the show_sentence indices
points1 = [(self.embed[i][0], self.embed[i][1]) for i in self.show_sentence]
# Create a distance matrix between the points
distance_matrix = np.zeros((len(points1), len(self.annotation_positions)))
for i, point1 in enumerate(points1):
for j, point2 in enumerate(self.annotation_positions):
distance_matrix[i, j] = self.euclidean_distance(point1, point2)
# Apply linear_sum_assignment to find the optimal mapping
row_ind, col_ind = linear_sum_assignment(distance_matrix)
for i, j in zip(row_ind, col_ind):
self.mapping[self.show_sentence[i]] = self.annotation_positions[j]
def show_text(self, show_sentence, text):
sentence = []
for i in range(len(text)):
if i in show_sentence:
s = text[i][:10] + "..." + text[i][-10:]
sentence.append(s)
else:
sentence.append("")
return sentence
def init_df(self):
X, Y = np.split(self.embed, 2, axis=1)
data = {
"x": X.flatten(),
"y": Y.flatten(),
}
self.df = pd.DataFrame(data)
def format_data(self):
sentence = self.show_text(self.show_sentence, self.test_X["text"])
X, Y = np.split(self.embed, 2, axis=1)
n = len(self.test_X)
data = {
"x": X.flatten(),
"y": Y.flatten(),
"label": self.test_y["label"],
"sentence" : sentence,
"size" : [20 if i in self.show_sentence else 10 for i in range(n)],
"pos" : [{"x_offset": self.mapping.get(i, (0, 0))[0], "y_offset": self.mapping.get(i, (0, 0))[1]} for i in range(n)],
"annotate" : [True if i in self.show_sentence else False for i in range(n)],
}
self.df = pd.DataFrame(data)
def calculate_tsne(self):
embed = np.array(self.test_X["embed"].tolist())
n_components = min(50, len(self.test_X))
pca = PCA(n_components=n_components)
compact_embedding = pca.fit_transform(embed)
tsne = TSNE(
perplexity=30,
metric="cosine",
n_jobs=8,
random_state=42,
verbose=False,
)
embedding_train = tsne.fit(compact_embedding)
embedding_train = embedding_train.optimize(n_iter=1000, momentum=0.8)
return embedding_train
def random_sentence(self):
#多次随机可能会影响可视化结果
n_samples = len(self.test_y)
show_sentence = []
while len(show_sentence) < self.n_annotation_positions:
show_sentence.append(np.random.randint(0, n_samples))
show_sentence = list(set(show_sentence))
# 确保每个标签至少有一个句子,用在show_sentence中最多的标签的句子来补充
label_count = self.test_y["label"].value_counts()
max_label = label_count.index[0]
max_count = label_count[0]
for i in range(max_count):
for j in range(len(label_count)):
if label_count[j] == i:
show_sentence.append(self.test_y[self.test_y["label"] == label_count.index[j]].index[0])
self.show_sentence = list(set(show_sentence))
def plot(self, return_fig=False):
min_x, max_x = self.df['x'].min()-1, self.df['x'].max()+2
fig = go.Figure()
fig = go.Figure(layout=go.Layout(
autosize=False, # 禁止图像自动调整大小
height=800, # 您可以根据需要调整这个值
width=1500, # 您可以根据需要调整这个值
# plot_bgcolor="#262626",
))
label_colors = self.generate_colormap(self.df['label'].unique())
line_legend_group = "lines"
# 为每个类别的点创建散点图
for label, color in label_colors.items():
mask = self.df["label"] == label
fig.add_trace(go.Scatter(x=self.df[mask]['x'], y=self.df[mask]['y'], mode='markers',
marker=dict(color=color, size=self.df[mask]['size']), # 添加 size 参数
showlegend=True, legendgroup=line_legend_group,
name = str(label))
)
# 为每个句子创建注释
for x, y, label, sentence, pos, annotate in zip(self.df.x, self.df.y, self.df.label, self.df.sentence, self.df.pos, self.df.annotate):
if not sentence:
continue
if not annotate:
continue
# pos在左边
criteria = (pos["x_offset"] - min_x) < 1e-2
sentence_annotation = dict(
x=pos["x_offset"],
y=pos["y_offset"],
xref="x",
yref="y",
text=sentence,
showarrow=False,
xanchor="right" if criteria else 'left',
yanchor='middle',
font=dict(color="black"),
bordercolor=label_colors.get(label, "black"),
borderpad=2,
bgcolor=self.divide_hex_color_by_half(label_colors.get(label, "black"))
)
fig.add_annotation(sentence_annotation)
x_start = x - 1 if criteria else x + 1
x_turn = x - 0.5 if criteria else x + 0.5
y_turn = y
fig.add_trace(go.Scatter(x=[pos["x_offset"], x_start, x_turn, x], y=[pos["y_offset"], pos["y_offset"], y_turn, y], mode='lines',
line=dict(color=label_colors.get(label, "black")), showlegend=False, legendgroup=line_legend_group))
# 取消坐标轴的数字
fig.update_xaxes(tickvals=[])
fig.update_yaxes(tickvals=[])
if not return_fig:
fig.show()
else:
return fig
def tsne_plot(self, n_sentence = 20, return_fig=False):
# 计算t-SNE,返回降维后的数据,每个元素为一个二维向量
embedding_train = self.calculate_tsne()
# 随机抽取显示文本, n为抽取的数量,show_sentence为一个列表,每个元素为显示文本的索引
if self.n_annotation_positions != min(n_sentence, len(self.test_y)):
self.n_annotation_positions = min(n_sentence, len(self.test_y))
self.random_sentence()
self.get_annotation_positions()
# find the optimal sentence positions
self.map_points()
# 格式化数据,输出为一个pandas的DataFrame,包含x, y, label, sentence, sentence_pos, size
# x, y为降维后的坐标,label为类别,sentence为显示的文本,sentence_pos为文本的位置("left", "right"),size为被选中文本的大小
self.format_data()
# self.df = self.df.sort_values('y').reset_index(drop=True)
if not return_fig:
# 绘制图像
self.plot()
else:
return self.plot(return_fig=return_fig) |