Python sklearn 问题：在进行回归时发现样本数量不一致的数组
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sklearn issue: Found arrays with inconsistent numbers of samples when doing regression
提问by pyman
this question seems to have been asked before, but I can't seem to comment for further clarification on the accepted answer and I couldn't figure out the solution provided.
这个问题似乎以前被问过，但我似乎无法评论以进一步澄清已接受的答案，我无法弄清楚提供的解决方案。
I am trying to learn how to use sklearn with my own data. I essentially just got the annual % change in GDP for 2 different countries over the past 100 years. I am just trying to learn using a single variable for now. What I am essentially trying to do is use sklearn to predict what the GDP % change for country A will be given the percentage change in country B's GDP.
我正在尝试学习如何将 sklearn 与我自己的数据一起使用。我基本上只是获得了过去 100 年中 2 个不同国家的 GDP 年度变化百分比。我现在只是想学习使用单个变量。我基本上想要做的是使用 sklearn 来预测 A 国的 GDP 百分比变化将与 B 国 GDP 的百分比变化有关。
The problem is that I receive an error saying:
问题是我收到一条错误消息：
ValueError: Found arrays with inconsistent numbers of samples: [ 1 107]
ValueError：发现样本数量不一致的数组：[ 1 107]
Here is my code:
这是我的代码：
import sklearn.linear_model as lm
import numpy as np
import scipy.stats as st
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
def bytespdate2num(fmt, encoding='utf-8'):#function to convert bytes to string for the dates.
strconverter = mdates.strpdate2num(fmt)
def bytesconverter(b):
s = b.decode(encoding)
return strconverter(s)
return bytesconverter
dataCSV = open('combined_data.csv')
comb_data = []
for line in dataCSV:
comb_data.append(line)
date, chngdpchange, ausgdpchange = np.loadtxt(comb_data, delimiter=',', unpack=True, converters={0: bytespdate2num('%d/%m/%Y')})
chntrain = chngdpchange[:-1]
chntest = chngdpchange[-1:]
austrain = ausgdpchange[:-1]
austest = ausgdpchange[-1:]
regr = lm.LinearRegression()
regr.fit(chntrain, austrain)
print('Coefficients: \n', regr.coef_)
print("Residual sum of squares: %.2f"
% np.mean((regr.predict(chntest) - austest) ** 2))
print('Variance score: %.2f' % regr.score(chntest, austest))
plt.scatter(chntest, austest, color='black')
plt.plot(chntest, regr.predict(chntest), color='blue')
plt.xticks(())
plt.yticks(())
plt.show()
What am I doing wrong? I essentially tried to apply the sklearn tutorial (They used some diabetes data set) to my own simple data. My data just contains the date, country A's % change in GDP for that specific year, and country B's % change in GDP for that same year.
我究竟做错了什么？我基本上尝试将 sklearn 教程（他们使用了一些糖尿病数据集）应用于我自己的简单数据。我的数据只包含日期，A 国在该特定年份的 GDP 变化百分比，以及 B 国在同一年的 GDP 变化百分比。
I tried the solutions hereand here (basically trying to find more out about the solution in the first link), but just receive the exact same error.
我在这里和这里尝试了解决方案（基本上是试图在第一个链接中找到有关解决方案的更多信息），但收到完全相同的错误。
Here is the full traceback in case you want to see it:
这是完整的回溯，以防您想看到它：
Traceback (most recent call last):
File "D:\My Stuff\Dropbox\Python\Python projects\test regression\tester.py", line 34, in <module>
regr.fit(chntrain, austrain)
File "D:\Programs\Installed\Python34\lib\site-packages\sklearn\linear_model\base.py", line 376, in fit
y_numeric=True, multi_output=True)
File "D:\Programs\Installed\Python34\lib\site-packages\sklearn\utils\validation.py", line 454, in check_X_y
check_consistent_length(X, y)
File "D:\Programs\Installed\Python34\lib\site-packages\sklearn\utils\validation.py", line 174, in check_consistent_length
"%s" % str(uniques))
ValueError: Found arrays with inconsistent numbers of samples: [ 1 107]
回答by IVlad
regr.fit(chntrain, austrain)
This doesn't look right. The first parameter to fitshould be an X, which refers to a feature vector. The second parameter should be a y, which is the correct answers (targets) vector associated with X.
这看起来不对。to 的第一个参数fit应该是 an X，它指的是一个特征向量。第二个参数应该是 a y，它是与 关联的正确答案（目标）向量X。
For example, if you have GDP, you might have:
例如，如果你有 GDP，你可能有：
X[0] = [43, 23, 52] -> y[0] = 5
# meaning the first year had the features [43, 23, 52] (I just made them up)
# and the change that year was 5
Judging by your names, both chntrainand austrainare feature vectors. Judging by how you load your data, maybe the last column is the target?
从你的名字来看，chntrain和austrain都是特征向量。从您加载数据的方式来看，也许最后一列是目标？
Maybe you need to do something like:
也许您需要执行以下操作：
chntrain_X, chntrain_y = chntrain[:, :-1], chntrain[:, -1]
# you can do the same with austrain and concatenate them or test on them if this part works
regr.fit(chntrain_X, chntrain_y)
But we can't tell without knowing the exact storage format of your data.
但是，如果不知道您数据的确切存储格式，我们就无法判断。
回答by qg_jinn
Try changing chntrainto a 2-D array instead of 1-D, i.e. reshape to (len(chntrain), 1).
尝试更改chntrain为二维数组而不是一维数组，即重塑为(len(chntrain), 1)。
For prediction, also change chntestto a 2-D array.
对于预测，也更改chntest为二维数组。
回答by Chang Men
In fit(X,y),the input parameter X is supposed to be a 2-D array. But if X in your data is only one-dimension, you can just reshape it into a 2-D array like this:regr.fit(chntrain_X.reshape(len(chntrain_X), 1), chntrain_Y)
在 fit(X,y) 中，输入参数 X 应该是一个二维数组。但是，如果数据中的 X 只是一维，则可以将其重塑为二维数组，如下所示：regr.fit(chntrain_X.reshape(len(chntrain_X), 1), chntrain_Y)
回答by bobo
I have been having similar problems to you and have found a solution.
我一直遇到与您类似的问题，并找到了解决方案。
Where you have the following error:
出现以下错误的地方：
ValueError: Found arrays with inconsistent numbers of samples: [ 1 107]
The [ 1 107] part is basically saying that your array is the wrong way around. Sklearn thinks you have 107 columns of data with 1 row.
[ 1 107] 部分基本上是说你的数组是错误的。Sklearn 认为您有 107 列数据和 1 行。
To fix this try transposing the X data like so:
要解决此问题，请尝试转置 X 数据，如下所示：
chntrain.T
The re-run your fit:
重新运行您的适合：
regr.fit(chntrain, austrain)
Depending on what your "austrain" data looks like you may need to transpose this too.
根据您的“austrain”数据的样子，您可能也需要转置它。
回答by Cloud Cho
You may use np.newaxisas well. The example can be X = X[:, np.newaxis]. I found the method at Logistic function
你也可以使用np.newaxis。该示例可以是X = X[:, np.newaxis]. 我在Logistic 函数中找到了方法
