我生成了下表：
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
得到下表：
| 姓名 | X | y |
|---|---|---|
| A | 5.910131 | 3.845061 |
| 乙 | 2.500393 | 3.477255 |
| C | 3.946845 | 3.564572 |
| D | 7.102233 | 4.191507 |
| E | 6.168895 | 4.072600 |
| F | -0.943195 | 1.883879 |
| G | 3.875221 | 3.909606 |
| H | 1.121607 | 2.233903 |
| 我 | 1.241953 | 2.529120 |
| J | 2.526496 | 2.330901 |
以下代码迭代一次排除一行，并构建一组回归图：
import statsmodels.formula.api as smf
import warnings
warnings.filterwarnings('ignore')
# Initialise and fit linear regression model using `statsmodels`
for row_index, row in df.iterrows():
# dataframe with all rows except for one
df_reduced = df[~(df.index == row_index)]
model = smf.ols('X ~ y', data=df_reduced)
model = model.fit()
intercept, slope = model.params
print(model.summary())
y1 = intercept + slope * df_reduced.y.min()
y2 = intercept + slope * df_reduced.y.max()
plt.plot([df_reduced.y.min(), df_reduced.y.max()], [y1, y2], label=row.Name, color='red')
plt.scatter(df_reduced.y, df_reduced.X)
plt.legend()
plt.savefig(f"All except {row.Name} analogue.pdf")
plt.show()
问题是，如何将正在生成的每个模型保存为一个文件，以便以后使用？在本示例中，应该至少生成 9 个回归模型。我想让它们每个都作为一个文件，也可以用一个名字来识别。
第二个问题是，如何在 matplotlib 的视觉生成中的每个模型摘要和绘图之间添加一个空格。
你只需要添加这个：
model.save(f"model_{row_index}.pkl")存储训练好的模型： 假设你有一些命名程序可用于每个模型文件 mf，你可以使用 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())
给出以下输出。
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.
请注意，出于简化目的，模型导入与您的略有不同。但是，您应该能够以相同的方式存储和加载模型。
我不完全确定我是否正确理解了您关于输出和绘图间距的问题。
间隔摘要： 也许只需添加 emtpy print() 语句？
间隔地块： 您每次都在生成全新的图，因此我不明白这个问题。请随时提供更多信息，我会尽快回复您。