我正在得到
init() 为参数 'n_splits' 获取了多个值
此行的错误：
cv = 随机拆分(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, 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，因为无论如何 10 都是默认值：
cv = 随机拆分(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，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，得分 = 'r2')
调用该函数的 X 和 y 适用于 python 2.7，因此它们也应该没问题。
追踪：
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, 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'
而不是：
from sklearn.model_selection import ShuffleSplit
用：
from sklearn.cross_validation import ShuffleSplit
您可以得到相同的错误StratifiedShuffleSplit，再次使用cross_validation不是model_selection.