如何解决带有交叉验证的 statsmodels GLM 预测的形状未对齐错误
我遇到了一个问题,我在 statsmodels 中构建阶跃函数,同时首先使用交叉验证来确定理想的切割量。但是我遇到了一个问题,我就是不知道如何解决。
在我使用 Sklearn 的 KFold 函数添加交叉验证循环后,我开始收到错误:
ValueError: shapes (480,2) and (1,) not aligned: 2 (dim 1) != 1 (dim 0)
我不确定为什么现在会发生这种情况,就像在我开始使用交叉验证循环之前一样,它运行良好,没有任何问题。
如果有人可以查看我的代码块并指出此问题的根源,我将不胜感激。
进入前X_train和y_train的形状:
X_train: (2400,) y_train: (2400,)
代码:
import statsmodels.api as sm
from sklearn.model_selection import KFold
kf = KFold(n_splits=5,shuffle=True,random_state=1)
cuts = []
RMSE = []
for i in range(1,11):
cuts.append(i)
cross_val_rms = []
for train_index,test_index in kf.split(X_train):
train_x,test_x= X_train.iloc[train_index],X_train.iloc[test_index]
train_y,test_y= y_train.iloc[train_index],y_train.iloc[test_index]
df_cut,bins = pd.cut(train_x,i,retbins=True,right=True)
df_steps = pd.concat([train_x,df_cut,train_y],keys=['age','age_cuts','wage'],axis = 1)
df_steps_dummies = pd.get_dummies(df_cut)
GLM_fitted = sm.GLM(df_steps.wage,df_steps_dummies).fit()
bin_mapping = np.digitize(test_x,bins)
X_valid = pd.get_dummies(bin_mapping)
pred = GLM_fitted.predict(X_valid)
rms = np.sqrt(mean_squared_error(test_y,pred))
cross_val_rms.append(rms)
mean_rms = sum(cross_vall_rms)/len(cross_vall_rms)
RMSE.append(mean_rms)
cuts_df = pd.DataFrame()
cuts_df['Cuts'] = cuts
cuts_df['RMSE'] = RMSE
print('Cuts with lowest Root Mean Squared Error:',cuts_df.loc[cuts_df['RMSE'].idxmin],sep='\n')
错误:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-166-a9794538c3e5> in <module>()
21 bin_mapping = np.digitize(test_x,bins)
22 X_valid = pd.get_dummies(bin_mapping)
---> 23 pred = GLM_fitted.predict(X_valid)
24 rms = np.sqrt(mean_squared_error(test_y,pred))
25 cross_val_rms.append(rms)
1 frames
/usr/local/lib/python3.7/dist-packages/statsmodels/genmod/generalized_linear_model.py in predict(self,params,exog,exposure,offset,linear)
870 exog = self.exog
871
--> 872 linpred = np.dot(exog,params) + offset + exposure
873 if linear:
874 return linpred
<__array_function__ internals> in dot(*args,**kwargs)
ValueError: shapes (480,) not aligned: 2 (dim 1) != 1 (dim 0)
解决方法
我认为如果您解释您在回归中尝试做什么会有所帮助。你得到这个错误是因为如果你从训练折叠中得到 3 个 bin,这并不意味着你从测试折叠中得到了 3 个 bin,你可能会得到 2 个折叠,因为 1 个 bin 中没有值。
据我所知,您可以简单地先对值进行离散化,然后使用示例数据进行训练:
import numpy as np
import pandas as pd
import statsmodels.api as sm
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import KFold
X_train = pd.Series(np.random.uniform(0,1,2400))
y_train = pd.Series(np.random.uniform(0,2400))
然后
for i in range(2,11):
cross_val_rms = []
df_steps_dummies = pd.get_dummies(pd.cut(X_train,i))
for train_index,test_index in kf.split(X_train):
train_x,test_x= df_steps_dummies.iloc[train_index,:],df_steps_dummies.iloc[test_index,:]
train_y,test_y= y_train[train_index],y_train[test_index]
GLM_fitted = sm.GLM(train_y,train_x).fit()
pred = GLM_fitted.predict(test_x)
rms = np.sqrt(mean_squared_error(test_y,pred))
cross_val_rms.append(rms)
RMSE.append(np.array(cross_val_rms).mean())
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