错误:__ init __()获得了意外的关键字参数' n_splits'.
我将为加利福尼亚住房数据集执行 ShuffleSplit()方法(来源:代码如下:
I am going to perform ShuffleSplit() method for California housing dataset (Source: https://www.dcc.fc.up.pt/~ltorgo/Regression/cal_housing.html) to fit SGD regression.
However, the 'n_splits' error is occurred when method is applied.
The code is following:
from sklearn import cross_validation, grid_search, linear_model, metrics
import numpy as np
import pandas as pd
from sklearn.preprocessing import scale
from sklearn.cross_validation import ShuffleSplit
housing_data = pd.read_csv('cal_housing.csv', header = 0, sep = ',')
housing_data.fillna(housing_data.mean(), inplace=True)
df=pd.get_dummies(housing_data)
y_target = housing_data['median_house_value'].values
x_features = housing_data.drop(['median_house_value'], axis = 1)
from sklearn.cross_validation import train_test_split
from sklearn import model_selection
train_x, test_x, train_y, test_y = model_selection.train_test_split(x_features, y_target, test_size=0.2, random_state=4)
reg = linear_model.SGDRegressor(random_state=0)
cv = ShuffleSplit(n_splits = 10, test_size = 0.2, random_state = 0)
错误如下:
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-22-8f8760b04f8c> in <module>()
----> 1 cv = ShuffleSplit(n_splits = 10, test_size = 0.2, random_state = 0)
TypeError: __init__() got an unexpected keyword argument 'n_splits'
我用 0.18版本更新了scikit-learn.
I updated scikit-learn with 0.18 version.
Anaconda版本: 4.5.8
Anaconda version: 4.5.8
请您提供有关此问题的建议?
Could you please advise on this issue?
您正在混淆两个不同的模块.
You are mixing up two different modules.
在0.18之前，cross_validation用于ShuffleSplit.因此，不存在 n_splits . n 用于定义拆分次数
Before 0.18, cross_validation was used for ShuffleSplit. In that, n_splits was not present. n was used to define the number of splits
但是，由于您现在已更新到0.18，因此不推荐使用 cross_validation 和 grid_search ，而推荐使用model_selection.
But since you have updated to 0.18 now, cross_validation and grid_search has been deprecated in favor of model_selection.
此处的文档中提到了这些
所以代替这个:
from sklearn.cross_validation import ShuffleSplit
from sklearn.cross_validation import train_test_split
执行此操作:
from sklearn.model_selection import ShuffleSplit
fro
m sklearn.model_selection导入train_test_split
m sklearn.model_selection import train_test_split
然后您可以使用 n_splits .
cv = ShuffleSplit(n_splits = 10, test_size = 0.2, random_state = 0)