python - Python Matplotlib Funcanimation 问题：难以在函数内对等高线图（contourf）进行动画处理
问题描述
我在 matplotlib.animation 中与 FuncAnimation 作斗争，我找不到任何与我的问题相似的示例或帖子（我的意思是，是的，有关于 funcAnimation 中使用的 contourf 的帖子，但在那些帖子中，他们成功删除了 PathCollection 对象但在我的情况下，有些东西不起作用）。
语境：
在一个关于 One-vs-All 概念（多个二元分类器）的学校项目中，我想实现一个函数来为具有 3 个 Axes 并包含多个 Line2D 对象、来自 scatter方法的 PathCollection 对象和来自 contourf 方法的 QuadContourSet 设置动画。
这是它的外观屏幕（当我在 One-vs-All 训练结束时绘制数据时获得）：
传奇：
- 左：（草药学）-（黑魔法防御术）位面的边界判定，
- 右上：每个二元分类器的损失函数，
- 右下：每个分类器的 Precision 和 Recall 指标。
方法：
我正在尝试使用来自 matplotlib.Animation 模块的 FuncAnimation 制作情节的动画版本。情节的动画版是我项目的一个额外功能，然后动画部分/核心是在函数中制作的，你可以在下面看到一个简化（一个简单的骨骼表示）：
def anim_visu(models, data):
# initialization of the figure and object representing the data
...
def f_anim():
# Function which update the data at each frames
visu = FuncAnimation(fig, f_anim, ...)
return fig
[...]
if __name__ == "__main__":
[...]
if bool_dynamic: # activation of the dynamic visualization
anim_visu(models, data)
这是一个最小的工作示例：
# =========================================================================== #
# |Importation des lib/packages| #
# =========================================================================== #
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from matplotlib.animation import FuncAnimation
from matplotlib.gridspec import GridSpec
dct_palet = {"C1":"dodgerblue",
"C2":"red",
"C3":"green",
"C4":"goldenrod"}
fps = 15
# =========================================================================== #
# | Definition des fonctions | #
# =========================================================================== #
def one_vs_all_prediction(classifiers:list, X:np.array) -> np.array:
"""
... Docstring ...
"""
preds = np.zeros((X.shape[0],1))
for clf in classifiers:
tmp = clf.predict(X)
mask = preds == 0
preds[mask] = tmp[mask]
return preds
def one_vs_all_class_onehot(class_pred:np.array):
"""
... Docstring ...
"""
house = {"C1":1., "C2":2., "C3":3., "C4":4.}
onehot_pred = np.chararray((class_pred.shape[0],1), itemsize=2)
for key, item in house.items():
mask = class_pred == item
onehot_pred[mask] = key
return onehot_pred
def do_animation(clfs:list, data:np.ndarray):
""" Core function for the animated vizualisation.
The function defines all the x/y_labels, the titles.
"""
global idx, cost_clf1, cost_clf2, cost_clf3, cost_clf4, \
met1_clf1, met1_clf2, met1_clf3, met1_clf4, \
met2_clf1, met2_clf2, met2_clf3, met2_clf4, \
boundary, axes, \
l_cost_clf1, l_cost_clf2, l_cost_clf3, l_cost_clf4, \
l_met1_clf1, l_met1_clf2, l_met1_clf3, l_met1_clf4, \
l_met2_clf1, l_met2_clf2, l_met2_clf3, l_met2_clf4
plt.style.use('seaborn-pastel')
# -- Declaring the figure and the axes -- #
fig = plt.figure(figsize=(15,9.5))
gs = GridSpec(2, 2, figure=fig)
axes = [fig.add_subplot(gs[:, 0]), fig.add_subplot(gs[0, 1]), fig.add_subplot(gs[1, 1])]
# --formatting the different axes -- #
axes[0].set_xlabel("X_1")
axes[0].set_ylabel("X_2")
axes[0].set_title("Decision boundary")
axes[1].set_xlabel("i: iteration")
axes[1].set_xlim(-10, 1000)
axes[1].set_ylim(-10, 350)
axes[1].set_ylabel(r"$\mathcal{L}_{\theta_0,\theta_1}$")
axes[1].grid()
axes[2].set_xlabel("i: iteration")
axes[2].set_ylabel("Scores (metric_1 & metric_2)")
axes[2].set_xlim(-10, 1000)
axes[2].set_ylim(0.0,1.01)
axes[2].grid()
# -- Reading min and max values along X dimensions-- #
X = data[:,0:2]
X = X.astype(np.float64)
Y = data[:,2].reshape(-1,1)
idx = np.array([0])
X_min, X_max = X[:,:2].min(axis=0), X[:,:2].max(axis=0)
# -- Generate a grid of points with distance h between them -- #
h = 0.01
XX_1, XX_2 = np.meshgrid(np.arange(X_min[0], X_max[0], h),
np.arange(X_min[1], X_max[1], h))
zeros_arr = np.zeros((XX_1.shape[0] * XX_1.shape[1], 1))
XX = np.c_[XX_1.ravel(), XX_2.ravel(),
zeros_arr.ravel(), zeros_arr.ravel(), zeros_arr.ravel()]
# -- Predict the function value for the whole grid -- #
preds = one_vs_all_prediction(clfs, XX)
Z = preds.reshape(XX_1.shape)
## Initialisation of the PathCollection for the Axes[0] objects
boundary = axes[0].contourf(XX_1, XX_2, Z, 3,
colors=["red", "green", "goldenrod", "dodgerblue"], alpha=0.5)
lst_colors = np.array([dct_palet[house] for house in data[:,2]])
raw_data = axes[0].scatter(X[:,0], X[:,1], c=lst_colors, edgecolor="k")
## Initialisation of the Line2D object for the Axes[1] objects
cost_clf1 = clfs[0].cost()
cost_clf2 = clfs[1].cost()
cost_clf3 = clfs[2].cost()
cost_clf4 = clfs[3].cost()
l_cost_clf1, = axes[1].plot(idx, cost_clf1,
ls='-', marker='o', ms=2, lw=1.2, color=dct_palet[clfs[0].house])
l_cost_clf2, = axes[1].plot(idx, cost_clf2,
ls='-', marker='o', ms=2, lw=1.2, color=dct_palet[clfs[1].house])
l_cost_clf3, = axes[1].plot(idx, cost_clf3,
ls='-', marker='o', ms=2, lw=1.2, color=dct_palet[clfs[2].house])
l_cost_clf4, = axes[1].plot(idx, cost_clf4,
ls='-', marker='o', ms=2, lw=1.2, color=dct_palet[clfs[3].house])
## Initialisation of the Line2D object for the Axes[2] objects
met1_clf1 = clfs[0].dummy_metric1()
met1_clf2 = clfs[1].dummy_metric1()
met1_clf3 = clfs[2].dummy_metric1()
met1_clf4 = clfs[3].dummy_metric1()
met2_clf1 = clfs[0].dummy_metric2()
met2_clf2 = clfs[1].dummy_metric2()
met2_clf3 = clfs[2].dummy_metric2()
met2_clf4 = clfs[3].dummy_metric2()
l_met1_clf1, = axes[2].plot(idx, met1_clf1,
ls='-', marker='o', ms=2, lw=1.2, color=dct_palet[clfs[0].house])
l_met1_clf2, = axes[2].plot(idx, met1_clf2,
ls='-', marker='o', ms=2, lw=1.2, color=dct_palet[clfs[1].house])
l_met1_clf3, = axes[2].plot(idx, met1_clf3,
ls='-', marker='o', ms=2, lw=1.2, color=dct_palet[clfs[2].house])
l_met1_clf4, = axes[2].plot(idx, met1_clf4,
ls='-', marker='o', ms=2, lw=1.2, color=dct_palet[clfs[3].house])
l_met2_clf1, = axes[2].plot(idx, met2_clf1,
ls='--', marker='o', ms=2, lw=1.2, color=dct_palet[clfs[0].house])
l_met2_clf2, = axes[2].plot(idx, met2_clf2,
ls='--', marker='o', ms=2, lw=1.2, color=dct_palet[clfs[1].house])
l_met2_clf3, = axes[2].plot(idx, met2_clf3,
ls='--', marker='o', ms=2, lw=1.2, color=dct_palet[clfs[2].house])
l_met2_clf4, = axes[2].plot(idx, met2_clf4,
ls='--', marker='o', ms=2, lw=1.2, color=dct_palet[clfs[3].house])
fig.canvas.mpl_connect('close_event', f_close)
anim_fig = FuncAnimation(fig, f_animate, fargs=(XX_1, XX_2, XX,), frames=int(1000/fps), repeat=False, cache_frame_data = False, blit=False)
plt.waitforbuttonpress()
return fig
def f_animate(i, XX_1, XX_2, XX):
"""
... Docstring ...
"""
global clfs, idx, \
cost_clf1, cost_clf2, cost_clf3, cost_clf4, \
met1_clf1, met1_clf2, met1_clf3, met1_clf4, \
met2_clf1, met2_clf2, met2_clf3, met2_clf4, \
boundary, axes, l_cost_clf1, l_cost_clf2, l_cost_clf3, l_cost_clf4, \
l_met1_clf1, l_met1_clf2, l_met1_clf3, l_met1_clf4, \
l_met2_clf1, l_met2_clf2, l_met2_clf3, l_met2_clf4
n_cycle = 100
clfs[0].fit(n_cycle)
clfs[1].fit(n_cycle)
clfs[2].fit(n_cycle)
clfs[3].fit(n_cycle)
idx = np.concatenate((idx, np.array([i * n_cycle])))
preds = one_vs_all_prediction(clfs, XX)
Z = preds.reshape(XX_1.shape)
cost_clf1 = np.concatenate((cost_clf1, clfs[0].cost()))
cost_clf2 = np.concatenate((cost_clf2, clfs[1].cost()))
cost_clf3 = np.concatenate((cost_clf3, clfs[2].cost()))
cost_clf4 = np.concatenate((cost_clf4, clfs[3].cost()))
tmp_met1_clf1 = clfs[0].dummy_metric1()
tmp_met1_clf2 = clfs[1].dummy_metric1()
tmp_met1_clf3 = clfs[2].dummy_metric1()
tmp_met1_clf4 = clfs[3].dummy_metric1()
tmp_met2_clf1 = clfs[0].dummy_metric2()
tmp_met2_clf2 = clfs[1].dummy_metric2()
tmp_met2_clf3 = clfs[2].dummy_metric2()
tmp_met2_clf4 = clfs[3].dummy_metric2()
met1_clf1 = np.concatenate((met1_clf1, tmp_met1_clf1))
met1_clf2 = np.concatenate((met1_clf2, tmp_met1_clf2))
met1_clf3 = np.concatenate((met1_clf3, tmp_met1_clf3))
met1_clf4 = np.concatenate((met1_clf4, tmp_met1_clf4))
met2_clf1 = np.concatenate((met2_clf1, tmp_met2_clf1))
met2_clf2 = np.concatenate((met2_clf2, tmp_met2_clf2))
met2_clf3 = np.concatenate((met2_clf3, tmp_met2_clf3))
met2_clf4 = np.concatenate((met2_clf4, tmp_met2_clf4))
# -- Plot the contour and training examples -- #
# Update the plot objects: remove the previous collections to save memory.
#l = len(boundary.collections)
for coll in boundary.collections:
# Remove the existing contours
boundary.collections.remove(coll)
boundary = axes[0].contourf(XX_1, XX_2, Z, 3, colors=["red", "green", "goldenrod", "dodgerblue"], alpha=0.5)
l_cost_clf1.set_data(idx, cost_clf1)
l_cost_clf2.set_data(idx, cost_clf2)
l_cost_clf3.set_data(idx, cost_clf3)
l_cost_clf4.set_data(idx, cost_clf4)
l_met1_clf1.set_data(idx, met1_clf1)
l_met1_clf2.set_data(idx, met1_clf2)
l_met1_clf3.set_data(idx, met1_clf3)
l_met1_clf4.set_data(idx, met1_clf4)
l_met2_clf1.set_data(idx, met2_clf1)
l_met2_clf2.set_data(idx, met2_clf2)
l_met2_clf3.set_data(idx, met2_clf3)
l_met2_clf4.set_data(idx, met2_clf4)
return boundary.collections, l_cost_clf1, l_cost_clf2, l_cost_clf3, l_cost_clf4, \
l_met1_clf1, l_met1_clf2, l_met1_clf3, l_met1_clf4, \
l_met2_clf1, l_met2_clf2, l_met2_clf3, l_met2_clf4
def f_close(event):
""" Functions called when the graphical window is closed.
It prints the last value of the theta vector and the last value of the
cost function.
"""
plt.close()
class DummyBinary():
def __init__(self, house, theta0, theta1, alpha=1e-3):
self.house = house
self.theta0 = theta0
self.theta1 = theta1
self.alpha = alpha
if self.house == "C1":
self.border_x = 6
self.border_y = 6
if self.house == "C2":
self.border_x = 6
self.border_y = 13
if self.house == "C3":
self.border_x = 13
self.border_y = 6
if self.house == "C4":
self.border_x = 13
self.border_y = 13
def fit(self, n_cycle:int):
for _ in range(n_cycle):
self.theta0 = self.theta0 + self.alpha * (self.border_x - self.theta0)
self.theta1 = self.theta1 + self.alpha * (self.border_y - self.theta1)
def cost(self) -> float:
cost = (self.theta0 - self.border_x)**2 + (self.theta1 - self.border_y)**2
return cost
def predict(self, X:np.array) -> np.array:
if self.house == 'C1':
mask = (X[:,0] < self.theta0) & (X[:,1] < self.theta1)
if self.house == 'C2':
mask = (X[:,0] < self.theta0) & (X[:,1] > self.theta1)
if self.house == 'C3':
mask = (X[:,0] > self.theta0) & (X[:,1] < self.theta1)
if self.house == 'C4':
mask = (X[:,0] > self.theta0) & (X[:,1] > self.theta1)
pred =np.zeros((X.shape[0], 1))
pred[mask] = int(self.house[1])
return pred
def dummy_metric1(self):
return np.array([0.5 * (self.theta0 / self.border_x + self.theta1 / self.border_y)])
def dummy_metric2(self):
return np.array([0.5 * ((self.theta0 / self.border_x)**2 + (self.theta1 / self.border_y)**2)])
# =========================================================================== #
# _________________________________ MAIN __________________________________ #
# =========================================================================== #
if __name__ == "__main__":
# -- Dummy data -- #
x1 = np.random.randn(60,1) * 2.5 + 3.5
x2 = np.random.randn(60,1) * 2.5 + 3.5
x3 = np.random.randn(60,1) * 2.5 + 15.5
x4 = np.random.randn(60,1) * 2.5 + 15.5
stud_house = 60 * ['C1'] + 60 * ['C2'] + 60 * ['C3'] + 60 * ['C4']
c_house = [dct_palet[house] for house in stud_house]
y1 = np.random.randn(60,1) * 2.5 + 3.5
y2 = np.random.randn(60,1) * 2.5 + 15.5
y3 = np.random.randn(60,1) * 2.5 + 3.5
y4 = np.random.randn(60,1) * 2.5 + 15.5
X = np.concatenate((x1, x2, x3, x4)) # shape: (240,1)
Y = np.concatenate((y1, y2, y3, y4)) # shape: (240,1)
data = np.concatenate((X, Y, np.array(stud_house).reshape(-1,1)), axis=1) # shape: (240,3)
clf1 = DummyBinary("C1", np.random.rand(1), np.random.rand(1))
clf2 = DummyBinary("C2", np.random.rand(1), np.random.rand(1))
clf3 = DummyBinary("C3", np.random.rand(1), np.random.rand(1))
clf4 = DummyBinary("C4", np.random.rand(1), np.random.rand(1))
clfs = [clf1, clf2, clf3, clf4]
## Visualize the raw dummy data.
#plt.scatter(X, Y, c=c_house, s=5)
#plt.show()
do_animation(clfs, data)
DummyBinary 类以简化的方式模仿我的 One-vs-All 类可以做什么。您可以在anim_visu和f_anim中看到一堆全局代码，这样代码“有效”，但我知道有一些非常错误的地方。
尝试：
- 没有全局变量，所有内容都传递给
f_animviafargs，但是当从 返回时f_anim，范围内变量的所有修改f_anim都丢失了（显然是正常行为）， - 移动 inside 的定义
f_anim，anim_visu创建f_anim一个inner_function。我没有足够的经验，所以我没有成功使它以这种方式工作，我注意到它可能看起来不可能修改anim_visu内部函数范围内声明的变量。 - 将我需要的所有变量声明为全局变量，它以某种方式工作，但正如您通过运行代码（在轴 [0] 中）所看到的，PathCollections 不会被清除/删除（尽管使用 循环
boundary.collections.remove(coll)）和数量轴 [0] 中的 PathCollection 似乎增加了，导致帧更新速度下降。
期待您的建议（以及我希望的解决方案+解释）。感谢您的时间和神经元。
解决方案
推荐阅读
- javascript - 为什么这个函数在调用它时记录未定义？
- entity-framework - 具有指定外键列名的 TPT
- sql - JPA/Hibernate 重叠 PK 和 FK 列
- python - 用于生成 SVG 线框球体的 Python 脚本
- javascript - 将所有对象对放入一个数组（Javascript）
- tkinter - 如何在 Tkinter 中并排放置窗口和顶层？
- python - 动态张量的堆栈
- python - 循环 HTML 以解析字典，但返回重复值
- javascript - 在父组件中提交表单之前，如何确保 axios.post 在子组件中执行？
- swift - 启用对数据的访问并在两种不同类型的视图上显示该数据