如何修复“发现样本数量不一致的输入变量：[100, 50]”错误？答案

【问题标题】：How to fix "Found input variables with inconsistent numbers of samples: [100, 50]" error?如何修复“发现样本数量不一致的输入变量：[100, 50]”错误？
【发布时间】：2021-03-25 12:22:21
【问题描述】：

我收到此错误但不知道如何解决它。我想为我的回归设置两个 x 变量，所以我将它们放在代码中。但是我收到了这个错误，不知道如何重塑我的数组来解决这个问题。

from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor
from sklearn.metrics import r2_score,mean_squared_error

X = maindf[['Graduate Degree','Asian American Population']].values.reshape(-1,1)
Y = maindf["Democrats 2016"].values.reshape(-1,1)
x_train, x_test, y_train, y_test, = train_test_split(X, Y,train_size=49, random_state=np.random)
DecisionTreeRegModel = DecisionTreeRegressor(max_depth=3).fit(x_train, y_train)
y_pred = DecisionTreeRegModel.predict(x_test)
from sklearn import tree

这是错误。

ValueError                                Traceback (most recent call last)
<ipython-input-85-9aaccff5b23d> in <module>
      5 X = maindf[['Graduate Degree','Asian American Population']].values.reshape(-1,1)
      6 Y = maindf["Democrats 2016"].values.reshape(-1,1)
----> 7 x_train, x_test, y_train, y_test, = train_test_split(X, Y,train_size=49, random_state=np.random)
      8 DecisionTreeRegModel = DecisionTreeRegressor(max_depth=3).fit(x_train, y_train)
      9 y_pred = DecisionTreeRegModel.predict(x_test)

~\anaconda3\lib\site-packages\sklearn\model_selection\_split.py in train_test_split(*arrays, **options)
   2125         raise TypeError("Invalid parameters passed: %s" % str(options))
   2126 
-> 2127     arrays = indexable(*arrays)
   2128 
   2129     n_samples = _num_samples(arrays[0])

~\anaconda3\lib\site-packages\sklearn\utils\validation.py in indexable(*iterables)
    291     """
    292     result = [_make_indexable(X) for X in iterables]
--> 293     check_consistent_length(*result)
    294     return result
    295 

~\anaconda3\lib\site-packages\sklearn\utils\validation.py in check_consistent_length(*arrays)
    254     uniques = np.unique(lengths)
    255     if len(uniques) > 1:
--> 256         raise ValueError("Found input variables with inconsistent numbers of"
    257                          " samples: %r" % [int(l) for l in lengths])
    258 

ValueError: Found input variables with inconsistent numbers of samples: [100, 50]

【问题讨论】：

您是否检查过X.shape 和y.shape 以了解为什么train_test_split 看到一个输入有100 行而另一个只有50 行？
旁白：推荐.to_numpy() 优于.values。见docs。

标签： python arrays numpy scikit-learn jupyter-notebook

【解决方案1】：

您不需要重塑您的预测变量，这样做会使您的矩阵变平，所以不要：

X = maindf[['Graduate Degree','Asian American Population']].values.reshape(-1,1)

做：

X = maindf[['Graduate Degree','Asian American Population']]

下面是使用示例数据集运行您的代码：

import numpy as np
import pandas as pd

from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor
from sklearn.metrics import r2_score,mean_squared_error

maindf = pd.DataFrame({'Graduate Degree':np.random.choice([0,1],100),
                      'Asian American Population':np.random.choice([0,1],100),
                      "Democrats 2016":np.random.choice([0,1],100)})

X = maindf[['Graduate Degree','Asian American Population']]
Y = maindf["Democrats 2016"].values.reshape(-1,1)
x_train, x_test, y_train, y_test, = train_test_split(X, Y,train_size=49, random_state=np.random)
DecisionTreeRegModel = DecisionTreeRegressor(max_depth=3).fit(x_train, y_train)
y_pred = DecisionTreeRegModel.predict(x_test)

【讨论】：