Python 尝试使用sk learn绘制线性回归模型时遇到模糊的运行时警告

错误：

import pandas
import math
from csv import reader
import sys
import numpy as np
from pandas.plotting import scatter_matrix
import matplotlib.pyplot as plt
import scipy.optimize as opt
import warnings
from sklearn import model_selection
from sklearn import linear_model  


def fxn():
    warnings.warn("Runtime Warning",RuntimeWarning)

with warnings.catch_warnings():
    warnings.simplefilter("ignore")
    fxn()
def costcomp(X,y,theta):
    inner=np.power(((X*theta.T)-y),2)
    return np.sum(inner)/(2*len(X))


def gradient(theta,X,y,lr,itr):
    temp=np.matrix(np.zeros(theta.shape))
    parameters=int(theta.ravel().shape[1])
    cost=np.zeros(itr)
    for i in range(itr):
        err=(X*theta.T)-y
        for j in range(parameters):
            tem=np.multiply(err,X[:,j])
            temp[0,j]=theta[0,j]-((lr/len(X))*np.sum(tem))
        theta=temp
        cost[i]=costcomp(X,y,theta)

    return theta,cost


dataset = pandas.read_csv("PYTHONFINAL.csv",names=['Month','Year','Day','Time','SpeedLimit','Age','Accidents'])
dataset = (dataset- dataset.mean()) / dataset.std()  
dataset.insert(0, 'Ones', 1)

cols=dataset.shape[1]
X=dataset.iloc[:,0:cols-1]
y=dataset.iloc[:,cols-1:cols]
X = np.matrix(X.values)  
y = np.matrix(y.values)
theta = np.matrix(np.array([0,0,0,0,0,0,0]))
X_train, X_validation, y_train, y_validation = model_selection.train_test_split(X, y, test_size=0.20, random_state=7)
print(costcomp(X_train,y_train,theta))
learningrate=0.01
iterations=2000
grad,costf=gradient(theta,X_train,y_train,learningrate,iterations)
print(grad)
print(costcomp(X_train,y_train,grad))
fig, ax = plt.subplots(figsize=(12,8))  
ax.plot(np.arange(iterations),costf, 'r')  
ax.set_xlabel('Iterations')  
ax.set_ylabel('Cost')  
ax.set_title('Error vs. Training Epoch')

#           **PROBLEM STARTS HERE**

model = linear_model.LinearRegression()
model.fit(X, y)
x = np.array(X[:, 1].A1)  
f = model.predict(X).flatten()

plt.show()


x = np.linspace(dataset.Month.min(), dataset.Month.max(), 100)  
f = grad[0, 0] + (grad[0, 1] * x)

fig, ax = plt.subplots(figsize=(12,8))  
ax.plot(x, f, 'r', label='Prediction')  
ax.scatter(dataset.Month, dataset.Accidents, label='Traning Data')  
ax.legend(loc=2)  
ax.set_xlabel('Month')  
ax.set_ylabel('Accidents')  
ax.set_title('Predicted Accidents vs. Month')  

问题:

我一直想绘制一个图表，看看线性回归模型构建是否有效。因此，我想为我构建的模型和用于此程序的原始数据集绘制一个回归线图。我使用的数据集的头（20）值在所附图像中提供

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您可以使用此代码抑制这些特殊警告

Warning (from warnings module):
  File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/scipy/linalg/basic.py", line 1226
    warnings.warn(mesg, RuntimeWarning)
RuntimeWarning: internal gelsd driver lwork query error, required iwork dimension not returned. This is likely the result of LAPACK bug 0038, fixed in LAPACK 3.2.2 (released July 21, 2010). Falling back to 'gelss' driver.

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您可以使用此代码抑制这些特殊警告

Warning (from warnings module):
  File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/scipy/linalg/basic.py", line 1226
    warnings.warn(mesg, RuntimeWarning)
RuntimeWarning: internal gelsd driver lwork query error, required iwork dimension not returned. This is likely the result of LAPACK bug 0038, fixed in LAPACK 3.2.2 (released July 21, 2010). Falling back to 'gelss' driver.

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谢谢你，警告不再显示了，我可以从你那里得到更多的帮助吗？关于如何参照此数据集绘制预先发生的事故与实际发生的事故数据集。这将非常有帮助。谢谢你，警告不再显示了，我可以从你那里得到更多的帮助吗？关于如何参照此数据集绘制早期事故与实际事故数据集。这将非常有帮助。