python – “__init __()得到参数’n_splits’的多个值”与sklea

init() got multiple values for argument ‘n_splits’

此行的错误：

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

在以下代码中：

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,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,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,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()

我知道这个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)

我也尝试删除n_splits参数,因为10是默认值：

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

这会产生相同的错误.

我将代码从python 2.7转换为3.5,从早期版本的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顺序的：

sizes,
train_sizes = train_sizes,scoring = ‘r2’)

调用该函数的X和y与python 2.7一起工作,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:Pythonmachine-learning-masterprojectsboston_housingvisuals.py in ModelLearning(X,y)
     21 
     22     # Create 10 cross-validation sets for training and testing
---> 23     cv = ShuffleSplit(n_splits = 10,random_state = 0)
     24 
     25     # Generate the training set sizes increasing by 50

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