【发布时间】：2017-07-14 15:01:09
【问题描述】：
我正在尝试在 Python 中使用 Sklearn 进行 k 折交叉验证，现在已经学习了两个教程，但我的代码无法运行验证。
每次我尝试做
cross_val_score(dt, x, y, cv=5)
我得到错误：
Traceback (most recent call last):
File "C:/Users/djsg38/Documents/CS6001-SpatialTemporal/HW2/main.py", line 573, in <module>
scores = cross_val_score(dt, x, y, cv=5)
File "C:\Python27\lib\site-packages\sklearn\model_selection\_validation.py", line 128, in cross_val_score
X, y, groups = indexable(X, y, groups)
File "C:\Python27\lib\site-packages\sklearn\utils\validation.py", line 206, in indexable
check_consistent_length(*result)
File "C:\Python27\lib\site-packages\sklearn\utils\validation.py", line 177, in check_consistent_length
lengths = [_num_samples(X) for X in arrays if X is not None]
File "C:\Python27\lib\site-packages\sklearn\utils\validation.py", line 116, in _num_samples
'estimator %s' % x)
TypeError: Expected sequence or array-like, got estimator Its official US President Barack Obama wants lawmakers weigh \
0 1 4 12 3 2 12 4 4 2
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2 1 0 4 0 0 0 0 0 0
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.. ... ... .. ... ... ... ... ... ...
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whether ... Heh heh funny disassociate personWere \
0 4 ... 0 0 0 0 0
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.. ... ... ... ... ... ... ...
70 0 ... 0 0 0 0 0
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81 3 ... 0 0 0 0 0
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88 0 ... 0 0 0 0 0
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92 0 ... 0 0 0 0 0
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94 1 ... 0 0 0 0 0
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96 0 ... 0 0 0 0 0
97 0 ... 0 0 0 0 0
98 1 ... 0 0 0 0 0
99 0 ... 1 1 1 1 1
therehighlightAs indepth umpireshighlightThe headhighlightTwo \
0 0 0 0 0
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.. ... ... ... ...
70 0 0 0 0
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99 1 1 1 1
disrespect
0 0
1 0
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.. ...
70 0
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[100 rows x 12993 columns]
这是我的代码：
def encode_target(df, target_column):
df_mod = df.copy()
targets = df_mod[target_column].unique()
map_to_int = {name: n for n, name in enumerate(targets)}
df_mod["Target"] = df_mod[target_column].replace(map_to_int)
return (df_mod, targets)
df = pd.read_csv("C:/Users/djsg38/Documents/CS6001- SpatialTemporal/HW2/finalCounts.csv")
df2, targets = encode_target(df, "MYLABEL")
features = list(df2.columns[:12338])
y = df2["TARGET"]
x = df2[features]
dt = DecisionTreeClassifier()
dt.fit(x, y)
scores = cross_val_score(dt, x, y, cv=5)
我的 DecisionTreeClassifier 似乎工作正常，当我将其输出为图像时，它看起来不错，但这里的问题在于最后一行。
附：我不确定是否有列限制？我遵循的经典示例使用了 Iris 数据集，因此有四列可以查看数据。不过，对我来说，我有 12,338 列数据（100 篇文章中每个唯一单词的字数）。
【问题讨论】：
-
首先，打印错误的所有堆栈跟踪。其次，我无法理解您的代码。
encode_target在做什么？您在dt中将df2[features]作为x传递，但在cross-val_score中以y的形式传递。 -
encode_target 根据 target_column 抓取数据帧的值，对我来说它是 'MYLABEL' - 这是我给它的标签。然后它抓取该列中的所有 UNIQUE 值并将其放入列表中。然后它枚举它们并为它们提供整数值，因为显然分类器只能处理整数。我不确定我能提供多少帮助，就像我在问题中所说的那样，我正在关注用于进行此分类的在线教程。但假设 y 值是“目标”处数据帧的所有特征，即整数映射。 x 值就是其他一切。
-
看到这个功能就明白了。您应该在
cross_val_score中使用与在dt.fit中使用的相同的 X、y -
您知道，阅读您的问题并就此向我提问，我意识到我没有使用相同的东西似乎很奇怪..我今天会尝试！我只是尽可能多地按照教程进行操作，而他们似乎就是这样做的。如果它仍然不起作用，我会更新。到目前为止，谢谢你，Vivek。
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另外请说明您使用的教程来源
标签： python scikit-learn cross-validation