如何将每个迭代Statsmodel保存为以后使用的文件?

Question

生成了以下表格:

import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
# Generate 'random' data
np.random.seed(0)
X = 2.5 * np.random.randn(10) + 1.5
res = 0.5 * np.random.randn(10)
y = 2 + 0.3 * X + res
Name = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J']
# Create pandas dataframe to store our X and y values
df = pd.DataFrame(
{'Name': Name,
'X': X,
'y': y})
# Show the dataframe
df

结果如下表:

<表类>名称Xytbody><<tr>5.9101313.845061B2.5003933.477255C3.9468453.564572D7.1022334.191507E6.1688954.072600F-0.9431951.883879G td>3.909606H1.1216072.233903我1.2419532.529120J2.5264962.330901

你只需要添加这个:model.save(f"model_{row_index}.pkl")在你的循环

存储训练好的模型:假设您对每个模型文件mf都有一些可用的命名过程，您可以使用pickle存储模型。

import statsmodels.api as sm
import pickle
# Train your model
model = sm.OLS(y, X).fit()
# Save the model to a file
with open('model.pickle', 'wb') as f:
pickle.dump(model, f)
# Load the model from the file
with open('model.pickle', 'rb') as f:
loaded_model = pickle.load(f)
print(loaded_model.summary())

给出如下输出:

OLS Regression Results                                
=======================================================================================
Dep. Variable:                      y   R-squared (uncentered):                   0.525
Model:                            OLS   Adj. R-squared (uncentered):              0.472
Method:                 Least Squares   F-statistic:                              9.931
Date:                Mon, 03 Apr 2023   Prob (F-statistic):                      0.0117
Time:                        12:42:57   Log-Likelihood:                         -20.560
No. Observations:                  10   AIC:                                      43.12
Df Residuals:                       9   BIC:                                      43.42
Df Model:                           1                                                  
Covariance Type:            nonrobust                                                  
==============================================================================
coef    std err          t      P>|t|      [0.025      0.975]
------------------------------------------------------------------------------
x1             0.8743      0.277      3.151      0.012       0.247       1.502
==============================================================================
Omnibus:                        1.291   Durbin-Watson:                   0.989
Prob(Omnibus):                  0.524   Jarque-Bera (JB):                0.937
Skew:                           0.637   Prob(JB):                        0.626
Kurtosis:                       2.209   Cond. No.                         1.00
==============================================================================
Notes:
[1] R² is computed without centering (uncentered) since the model does not contain a constant.
[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.

注意，为了简化，模型导入与您的稍有不同。但是，您应该能够以相同的方式存储和加载您的模型。

我不太确定我是否正确理解了你关于输出和绘图间距的问题。

用空格隔开摘要:也许只是添加空的print()语句?

图间距:你每次都在生成全新的情节，所以我不明白这个问题。请随时提供其他信息，我会回复你的。