无法在 Python 中使用散点图正确绘制回归线

Posted 2023-03-12

【中文标题】无法在 Python 中使用散点图正确绘制回归线

【英文标题】：Unable to plot regression line correctly with scatter plot in Python

【发布时间】：2018-11-10 14:22:37

【问题描述】：

我正在学习有关 EdX 的课程，该课程是在数据科学中使用 Python 进行编程。当使用给定函数绘制我的线性回归模型的结果时，图表似乎非常偏离，所有散点都聚集在底部，而回归线则居高不下。

我不确定是定义的函数drawLine 不正确还是我的建模过程有问题。

这里是定义的函数

def drawLine(model, X_test, y_test, title, R2):
    fig = plt.figure()
    ax = fig.add_subplot(111)
    ax.scatter(X_test, y_test, c='g', marker='o')
    ax.plot(X_test, model.predict(X_test), color='orange', linewidth=1, alpha=0.7)

    title += " R2: " + str(R2)
    ax.set_title(title)
    print(title)
    print("Intercept(s): ", model.intercept_)

    plt.show()

这是我写的代码

import pandas as pd
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from sklearn import linear_model
from sklearn.model_selection import train_test_split

matplotlib.style.use('ggplot') # Look Pretty

# Reading in data
X = pd.read_csv('Datasets/College.csv', index_col=0)

# Wrangling data
X.Private = X.Private.map('Yes':1, 'No':0)

# Splitting data
roomBoard = X[['Room.Board']]
accStudent = X[['Accept']]
X_train, X_test, y_train, y_test = train_test_split(roomBoard, accStudent, test_size=0.3, random_state=7)

# Training model
model = linear_model.LinearRegression()
model.fit(X_train, y_train)
score = model.score(X_test, y_test)

# Visualise results
drawLine(model, X_test, y_test, "Accept(Room&Board)", score)

我使用的数据可以在here找到

感谢您的宝贵时间。 感谢您提供任何帮助或建议。

【参考方案1】：

在你的 drawLine 函数中，我将 ax.scatter 更改为 plt.scatter。我还将roomBoard 和accStudent 更改为numpy 数组而不是pandas.Series。最后，我将您更新“私人”列的方式更改为

X.loc[:, "Private"] = X.Private.map('Yes':1, 'No':0)

Pandas docs 解释了我进行此更改的原因。其他小的变化是装饰性的。

我得到了以下工作：

import pandas as pd
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from sklearn import linear_model
from sklearn.model_selection import train_test_split

matplotlib.style.use('ggplot') # Look Pretty

# Reading in data
X = pd.read_csv('College.csv', index_col=0)

# Wrangling data
X.loc[:, "Private"] = X.Private.map('Yes':1, 'No':0)

# Splitting data
roomBoard = X.loc[:, 'Room.Board'].values.reshape((len(X),1))
accStudent = X.loc[:, 'Accept'].values.reshape((len(X),1))
X_train, X_test, y_train, y_test = train_test_split(roomBoard, accStudent, test_size=0.3, random_state=7)

# Training model
model = linear_model.LinearRegression()
model.fit(X_train, y_train)
score = model.score(X_test, y_test)

# Visualise results
def drawLine(model, X_test, y_test, title, R2):
    fig = plt.figure()
    ax = fig.add_subplot(111)
    plt.scatter(X_test, y_test, c='g', marker='o')
    y_pred =  model.predict(X_test)
    plt.plot(X_test, y_pred, color='orange', linewidth=1, alpha=0.7)

    title += " R2: " + str(R2)
    ax.set_title(title)
    print(title)
    print("Intercept(s): ", model.intercept_)

    plt.xticks(())
    plt.yticks(())

    plt.show()

drawLine(model, X_test, y_test, "Accept(Room&Board)", score)

【讨论】：

感谢您的帮助！我尝试一次进行一项更改，并意识到roomBoard 和accStudent 的类型似乎是创建正确图形最重要的。但是，我很确定在我的原始代码 roomBoard = X[['Room.Board']] 返回一个 pandas.DataFrame。你能告诉我为什么 numpy 2DArray 可以工作，而 DataFrame 不能吗？

Scikit-Learn Docs 说输入必须是一个numpy数组