pandas 获取 ValueError：endog 和 exog 的索引未对齐
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Getting ValueError: The indices for endog and exog are not aligned
提问by Sanoj
I am getting above error when I am running an iteration using FOR loop to build multiple models. First two models having similar data sets build fine. While building third model I am getting this error. The code where error is thrown is when I call sm.logit() using Statsmodel package of python:
当我使用 FOR 循环运行迭代以构建多个模型时，出现上述错误。前两个具有相似数据集的模型构建良好。在构建第三个模型时，我收到此错误。抛出错误的代码是当我使用 python 的 Statsmodel 包调用 sm.logit() 时：
y = y_mort.convert_objects(convert_numeric=True)
#Building Logistic model_LSVC
print("Shape of y:", y.shape, " &&Shape of X_selected_lsvc:", X.shape)
print("y values:",y.head())
logit = sm.Logit(y,X,missing='drop')
The error that appears:
出现的错误：
Shape of y: (9018,) &&Shape of X_selected_lsvc: (9018, 59)
y values: 0 0
1 1
2 0
3 0
4 0
Name: mort, dtype: int64
ValueError Traceback (most recent call last)
<ipython-input-8-fec746e2ee99> in <module>()
160 print("Shape of y:", y.shape, " &&Shape of X_selected_lsvc:", X.shape)
161 print("y values:",y.head())
--> 162 logit = sm.Logit(y,X,missing='drop')
163 # fit the model
164 est = logit.fit(method='cg')
D:\Anaconda3\lib\site-packages\statsmodels\discrete\discrete_model.py in __init__(self, endog, exog, **kwargs)
399
400 def __init__(self, endog, exog, **kwargs):
--> 401 super(BinaryModel, self).__init__(endog, exog, **kwargs)
402 if (self.__class__.__name__ != 'MNLogit' and
403 not np.all((self.endog >= 0) & (self.endog <= 1))):
D:\Anaconda3\lib\site-packages\statsmodels\discrete\discrete_model.py in __init__(self, endog, exog, **kwargs)
152 """
153 def __init__(self, endog, exog, **kwargs):
--> 154 super(DiscreteModel, self).__init__(endog, exog, **kwargs)
155 self.raise_on_perfect_prediction = True
156
D:\Anaconda3\lib\site-packages\statsmodels\base\model.py in __init__(self, endog, exog, **kwargs)
184
185 def __init__(self, endog, exog=None, **kwargs):
--> 186 super(LikelihoodModel, self).__init__(endog, exog, **kwargs)
187 self.initialize()
188
D:\Anaconda3\lib\site-packages\statsmodels\base\model.py in __init__(self, endog, exog, **kwargs)
58 hasconst = kwargs.pop('hasconst', None)
59 self.data = self._handle_data(endog, exog, missing, hasconst,
---> 60 **kwargs)
61 self.k_constant = self.data.k_constant
62 self.exog = self.data.exog
D:\Anaconda3\lib\site-packages\statsmodels\base\model.py in _handle_data(self, endog, exog, missing, hasconst, **kwargs)
82
83 def _handle_data(self, endog, exog, missing, hasconst, **kwargs):
---> 84 data = handle_data(endog, exog, missing, hasconst, **kwargs)
85 # kwargs arrays could have changed, easier to just attach here
86 for key in kwargs:
D:\Anaconda3\lib\site-packages\statsmodels\base\data.py in handle_data(endog, exog, missing, hasconst, **kwargs)
564 klass = handle_data_class_factory(endog, exog)
565 return klass(endog, exog=exog, missing=missing, hasconst=hasconst,
--> 566 **kwargs)
D:\Anaconda3\lib\site-packages\statsmodels\base\data.py in __init__(self, endog, exog, missing, hasconst, **kwargs)
74 # this has side-effects, attaches k_constant and const_idx
75 self._handle_constant(hasconst)
---> 76 self._check_integrity()
77 self._cache = resettable_cache()
78
D:\Anaconda3\lib\site-packages\statsmodels\base\data.py in _check_integrity(self)
450 (hasattr(endog, 'index') and hasattr(exog, 'index')) and
451 not self.orig_endog.index.equals(self.orig_exog.index)):
--> 452 raise ValueError("The indices for endog and exog are not aligned")
453 super(PandasData, self)._check_integrity()
454
ValueError: The indices for endog and exog are not aligned
The y matrix and X matrix have shape of (9018,),(9018, 59). Therefore any mismatch in dependent and independent variables doesn't appear. Any idea?
y 矩阵和 X 矩阵的形状为 (9018,),(9018, 59)。因此，不会出现因变量和自变量的任何不匹配。任何的想法？
回答by yper
Try converting yinto a list before the sm.Logit()line.
尝试将y转换为sm.Logit()行之前的列表。
y = list(y)
回答by Ashish Anand
The error message indicates that you have endog and exog with different shape. This is common error in python which can be easily solved by using 'reshape' function on dependent variable to align it with independent variable's shape.
错误消息表明您有不同形状的 endog 和 exog。这是python中的常见错误，可以通过对因变量使用“reshape”函数使其与自变量的形状对齐来轻松解决。
y_train.values.reshape(-1,1)
Above lines means:- We have provided column as 1 but rows as unknown i.e. we got a single column with as many rows as X.
以上几行表示：- 我们提供的列是 1，但行是未知的，即我们得到了一个与 X 行一样多的单列。
Lets take a example:-
让我们举个例子：-
z = np.array([[1, 2], [ 3, 4]])
print(z.shape) # (2, 2)
Now we will use reshape(-1,1) function on this array. We can see new array has 4 row and 1 column.
现在我们将在这个数组上使用 reshape(-1,1) 函数。我们可以看到新数组有 4 行和 1 列。
new_z= z.reshape(-1,1)
print(new_z) #array([[1],[2],[3], [4]])
print(new_z.shape) #(4, 1)
回答by Andreas Hsieh
Have you checked if you have Nanin your data? You can use np.isNan(X)and np.isNan(y). I saw you turned on the option dropso I suspect if you have Nanin your data then that will change the shape of your input.
您是否检查Nan过您的数据中是否有？您可以使用np.isNan(X)和np.isNan(y)。我看到你打开了这个选项，drop所以我怀疑Nan你的数据是否会改变你输入的形状。
