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only_for_test.py
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import matplotlib.pyplot as plt
def draw_ablation():
y = [-3.489840849906151 + 7, -6.055583702985592 + 7, -3.4844833779276714 + 7]
x = [1, 2, 3]
colors = ['red', 'green', 'blue']
labels = ['High', 'Low', 'High & Low']
bars = plt.bar(x, y, color=colors, tick_label=labels, width=0.5, bottom=-6.1)
for bar, value in zip(bars, y):
label_position = bar.get_height() + 0.01 # 标签位置稍高于柱子顶端
plt.text(bar.get_x() + bar.get_width() / 2, label_position, f'{value-6}',
ha='center', va='bottom' if value >= 0 else 'top', color='red')
plt.gca().set_yticks([])
plt.ylim(-6,0)
plt.xlabel('quality of concepts')
plt.ylabel('rate')
plt.title('quality of concepts vs rate')
plt.savefig('analysis/quality_of_concepts_vs_rate.png')
def draw_pretrain():
x = [1, 2, 3, 5, 10, 30, 100]
y = [-0.36985745073497855, 0.1528059057473291, 0.17563401899286446, 0.31169262944109755, 0.4044842154734013, 0.2554469305703446, 0.305586654884458]
plt.plot(x, y, marker='o')
plt.xlabel('Sample num')
plt.ylabel('Rate')
plt.title('Sample num vs Rate')
plt.savefig('analysis/sample_num_vs_rate.png')
def draw_tuning():
x = [1, 2, 3, 5, 10, 30]
y = [-0.36985745073497855, 0.5667115435722416, 0.6356761992706395, 0.7232023223967892, 0.6893943835866749, 0.698464191375792]
plt.plot(x, y, marker='o')
plt.xlabel('Sample num')
plt.ylabel('Rate')
plt.title('Tuning: Sample num vs Rate')
plt.savefig('analysis/tuning_sample_num_vs_rate.png')
def draw_training_process():
path = 'logs/10-tuning.log'
with open(path, 'r') as f:
lines = f.readlines()
all_scores = []
for line in lines:
if 'Epoch' in line:
all_scores.append(float(line.split(' ')[-1]))
all_scores = all_scores[:5]
x = list(range(0, len(all_scores)))
y = all_scores
plt.plot(x, y, marker='o')
plt.xlim(-0.2, 4.2)
plt.gca().set_xticks([0,1,2,3,4])
plt.xlabel('Epoch')
plt.ylabel('Rate')
plt.title('Training Process')
plt.savefig('analysis/training_process.png')
def draw_cross_entropy():
# 3D 表面（颜色图）演示绘制使用冷暖色图着色的 3D 表面。通过使用 antialiased=False 使表面变得不透明。
from matplotlib import cm
from matplotlib.ticker import LinearLocator
import numpy as np
fig, ax = plt.subplots(subplot_kw={"projection": "3d"})
# 构建数据
X = np.arange(0, 1, 0.02)
Y = np.arange(0, 1, 0.02)
X, Y = np.meshgrid(X, Y)
Z = X*Y + (1-X)*(1-Y)
# 绘制曲面图
# 绘制使用冷暖色图着色的 3D 表面。通过使用 antialiased=False 使表面变得不透明。
surf = ax.plot_surface(X, Y, Z, cmap=cm.coolwarm,
linewidth=0, antialiased=False)
# 定制z轴
ax.set_zlim(-1.01, 1.01)
ax.zaxis.set_major_locator(LinearLocator(10))
# A StrMethodFormatter is used automatically
ax.zaxis.set_major_formatter('{x:.02f}')
# 添加一个颜色条形图展示颜色区间
fig.colorbar(surf, shrink=0.5, aspect=5)
plt.savefig('analysis/cross_entropy.png')
def draw_cross_entropy_partial():
# 3D 表面（颜色图）演示绘制使用冷暖色图着色的 3D 表面。通过使用 antialiased=False 使表面变得不透明。
from matplotlib import cm
from matplotlib.ticker import LinearLocator
import numpy as np
fig, ax = plt.subplots(subplot_kw={"projection": "3d"})
# 构建数据
X = np.arange(0, 1, 0.02)
Y = np.arange(0, 1, 0.02)
X, Y = np.meshgrid(X, Y)
ZX = 2*Y - 1
ZY = 2*X - 1
Z = np.abs(ZX) + np.abs(ZY)
# 绘制曲面图
# 绘制使用冷暖色图着色的 3D 表面。通过使用 antialiased=False 使表面变得不透明。
surf = ax.plot_surface(X, Y, Z, cmap=cm.coolwarm,
linewidth=0, antialiased=False)
# 定制z轴
ax.set_zlim(0, 2.01)
ax.zaxis.set_major_locator(LinearLocator(10))
# A StrMethodFormatter is used automatically
ax.zaxis.set_major_formatter('{x:.02f}')
# 添加一个颜色条形图展示颜色区间
fig.colorbar(surf, shrink=0.5, aspect=5)
plt.savefig('analysis/cross_entropy_partial.png')
if __name__ == "__main__":
draw_pretrain()