sklearn ShuffleSplit 出现“__init__() 获得参数 'n_splits' 的多个值”错误

我正进入（状态

init() 获得参数“n_splits”的多个值

该行的错误：

cv = ShuffleSplit(n_splits = 10, test_size = 0.2, random_state = 0)

在下面的代码中：

import matplotlib.pyplot as pl
import numpy as np
import sklearn.model_selection as curves
from sklearn.tree import DecisionTreeRegressor
from sklearn.model_selection import ShuffleSplit, train_test_split, learning_curve

def ModelLearning(X, y):
    """ Calculates the performance of several models with varying sizes of training data.
        The learning and testing scores for each model are then plotted. """

    # Create 10 cross-validation sets for training and testing
    cv = ShuffleSplit(n_splits = 10, test_size = 0.2, random_state = 0)

    # Generate the training set sizes increasing by 50
    train_sizes = np.rint(np.linspace(1, X.shape[0]*0.8 - 1, 9)).astype(int)

    # Create the figure window
    fig = pl.figure(figsize=(10,7))

    # Create three different models based on max_depth
    for k, depth in enumerate([1,3,6,10]):

        # Create a Decision tree regressor at max_depth = depth
        regressor = DecisionTreeRegressor(max_depth = depth)

        # Calculate the training and testing scores
        sizes, train_scores, test_scores = learning_curve(regressor, X, y, \
           train_sizes = train_sizes,  cv = cv, scoring = 'r2')

        # Find the mean and standard deviation for smoothing
        train_std = np.std(train_scores, axis = 1)
        train_mean = np.mean(train_scores, axis = 1)
        test_std = np.std(test_scores, axis = 1)
        test_mean = np.mean(test_scores, axis = 1)

        # Subplot the learning curve 
        ax = fig.add_subplot(2, 2, k+1)
        ax.plot(sizes, train_mean, 'o-', color = 'r', label = 'Training Score')
        ax.plot(sizes, test_mean, 'o-', color = 'g', label = 'Testing Score')
        ax.fill_between(sizes, train_mean - train_std, \
            train_mean + train_std, alpha = 0.15, color = 'r')
        ax.fill_between(sizes, test_mean - test_std, \
            test_mean + test_std, alpha = 0.15, color = 'g')

        # Labels
        ax.set_title('max_depth = %s'%(depth))
        ax.set_xlabel('Number of Training Points')
        ax.set_ylabel('Score')
        ax.set_xlim([0, X.shape[0]*0.8])
        ax.set_ylim([-0.05, 1.05])

    # Visual aesthetics
    ax.legend(bbox_to_anchor=(1.05, 2.05), loc='lower left', borderaxespad = 0.)
    fig.suptitle('Decision Tree Regressor Learning Performances', fontsize = 16, y = 1.03)
    fig.tight_layout()
    fig.show()

我知道此错误通常表示参数顺序不正确，但这应该是正确的。这是 sklearn 文档中的示例：

rs = ShuffleSplit(n_splits=3, test_size=.25, random_state=0)

我还尝试删除 n_splits 参数，因为 10 无论如何都是默认值：

cv = ShuffleSplit(test_size = 0.2, random_state = 0)

这会产生相同的错误。

我正在将代码从 python 2.7 转换为 3.5，并将早期版本的 sklearn 转换为 0.18.1，所以我可能错过了一些东西，但我不知道它可能是什么。调用 ShuffleSplit 的行中的参数似乎也是按顺序排列的：

大小，train_scores，test_scores = Learning_curve（回归量，X，y，\ train_sizes = train_sizes，cv = cv，评分 = 'r2')

调用该函数的 X 和 y 可以在 python 2.7 中使用，所以它们也应该没问题。

追溯：

TypeError                                 Traceback (most recent call last)
<ipython-input-33-191abc15bbd7> in <module>()
      1 # Produce learning curves for varying training set sizes and maximum depths
----> 2 vs.ModelLearning(features, prices)

E:\Python\machine-learning-master\projects\boston_housing\visuals.py in ModelLearning(X, y)
     21 
     22     # Create 10 cross-validation sets for training and testing
---> 23     cv = ShuffleSplit(n_splits = 10, test_size = 0.2, random_state = 0)
     24 
     25     # Generate the training set sizes increasing by 50

TypeError: __init__() got multiple values for argument 'n_splits'

代替：

from sklearn.model_selection import ShuffleSplit

Use:

from sklearn.cross_validation import ShuffleSplit

您可能会得到相同的错误StratifiedShuffleSplit,再次使用cross_validation not model_selection.