将 cross_validation 算法翻译成 model_selection

Question

2016 年，我运行使用以下代码构建套索回归模型：

#Import required packages 
import pandas as pd
import numpy as np
import matplotlib as mpl
import matplotlib.pylab as plt
import matplotlib.pyplot as plp
import seaborn as sns
import statsmodels.formula.api as smf
from scipy import stats
from sklearn.cross_validation import train_test_split
from sklearn.linear_model import LassoLarsCV

# split data into train and test sets
pred_train, pred_test, tar_train, tar_test = train_test_split(predictors, target, test_size=.4, random_state=123)
#%
# specify the lasso regression model
model=LassoLarsCV(cv=10, precompute=False).fit(pred_train,tar_train)
#%
# print variable names and regression coefficients
dict(zip(predictors.columns, model.coef_))
#regcoef.to_csv('variable+regresscoef.csv')
#%%
# plot coefficient progression
m_log_alphas = -np.log10(model.alphas_)
ax = plt.gca()
plt.plot(m_log_alphas, model.coef_path_.T)
plt.axvline(-np.log10(model.alpha_), linestyle='--', color='k',
            label='alpha CV')
plt.ylabel('Regression Coefficients')
plt.xlabel('-log(alpha)')
plt.title('Regression Coefficients Progression for Lasso Paths')
#%
# plot mean square error for each fold
m_log_alphascv = -np.log10(model.cv_alphas_)
plt.figure()
plt.plot(m_log_alphascv, model.cv_mse_path_, ':')
plt.plot(m_log_alphascv, model.cv_mse_path_.mean(axis=-1), 'k',
         label='Average across the folds', linewidth=2)
plt.axvline(-np.log10(model.alpha_), linestyle='--', color='k',
            label='alpha CV')
plt.legend()
plt.xlabel('-log(alpha)')
plt.ylabel('Mean squared error')
plt.title('Mean squared error on each fold')
#%       
# MSE from training and test data
from sklearn.metrics import mean_squared_error
train_error = mean_squared_error(tar_train, model.predict(pred_train))
test_error = mean_squared_error(tar_test, model.predict(pred_test))
print ('training data MSE')
print(train_error)
print ('test data MSE')
print(test_error)
#%
# R-square from training and test data
rsquared_train=model.score(pred_train,tar_train)
rsquared_test=model.score(pred_test,tar_test)
print ('training data R-square')
print(rsquared_train)
print ('test data R-square')
print(rsquared_test)

现在我想再次运行它并收到以下警告：

DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved.

如何使用 model_selection 重写此代码？

Answer 1

我在这里看到的唯一使用 cross_validation 模块的是 train_test_split.

因此，只需将您的导入更改为：

from sklearn.cross_validation import train_test_split

至：

from sklearn.model_selection import train_test_split

一切顺利。

将 cross_validation 算法翻译成 model_selection

Translate cross_validation algorithm to model_selection

python

scikit-learn

cross-validation

sklearn-pandas