matplotlib 如何找到多项式回归线的“峰值”

r7xajy2e 于 2023-10-24 发布在其他

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是否可以找到使用Matplotlib计算的多项式回归线的峰值（顶点？）值（x，y）？
我在下面包括了我的基本设置（当然有更完整的数据集），以及实际回归线问题的屏幕截图。

import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from sklearn.preprocessing import PolynomialFeatures
from sklearn.pipeline import make_pipeline
from sklearn.linear_model import LinearRegression
degree=6
setX={'Fixation Duration': {0:1,1:2,2:3}}
setY={'Fixation Occurrences': {0:1,1:2,2:3}}    
X_gall=pd.DataFrame.from_dict(setX)
Y_gall=pd.DataFrame.from_dict(setY)
X_seqGall = np.linspace(X_gall.min(),X_gall.max(),300).reshape(-1,1)
polyregGall=make_pipeline(PolynomialFeatures(degree),LinearRegression())
polyregGall.fit(X_gall,Y_gall)
plt.scatter(X_gall,Y_gall, c="#1E4174", s=100.0, alpha=0.4)
plt.plot(X_seqGall,polyregGall.predict(X_seqGall),color="#1E4174", linewidth=4)
plt.show()

would like to find x,y values along red arrows

matplotlib

来源：https://stackoverflow.com/questions/73467511/how-to-find-the-peak-of-a-polynomial-regression-line

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按热度按时间

puruo6ea1#

您可以从基础绘图数据中找到最大值。
首先，让我们更改绘图命令以显式定义轴：

fig, ax = plt.subplots(figsize=(6,4))
_ = ax.scatter(X_gall,Y_gall, c="#1E4174", s=100.0, alpha=0.4)
poly = ax.plot(X_seqGall,polyregGall.predict(X_seqGall),color="#1E4174", linewidth=4)
plt.show()

现在您可以访问行数据：

lines = poly[0].axes.lines
for line in lines:
    max_y = np.max(line.get_ydata())
    print(f"Maximum y is: {max_y}")
    x_of_max_y = line.get_xdata()[np.argmax(line.get_ydata())]
    print(f"x value of maximum y is: {x_of_max_y}")

输出量：

Maximum y is: 3.1515605364361114
x value of maximum y is: 2.8127090301003346

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matplotlib 如何找到多项式回归线的“峰值”

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