import pearonr计算相关性时TypeError: No loop matching the specified signature and casting was found

学习《python深度学习(NDLewis)》第六章，代码出了一个bug，原代码为：

# 使用相关性函数，检查预测值和实际值之间的相关性
from scipy.stats.stats import pearsonr
correl = pearsonr(pred3_test,y_test)

print("Test correlation = ",correl[0])
print("Test correlation ^ 2 = ",correl[0] * correl[0])

其中计算 correl时，报了一个错误：

  File "D:\PyCharm\PyCharm 2019.2.3\workspace\Deepling\BostonHousePricePrediction.py", line 98, in 
    correl = pearsonr(pred3_test,y_test)
  File "D:\Anaconda\lib\site-packages\scipy\stats\stats.py", line 3410, in pearsonr
    xmean = x.mean(dtype=dtype)
  File "D:\Anaconda\lib\site-packages\numpy\core\_methods.py", line 75, in _mean
    ret = umr_sum(arr, axis, dtype, out, keepdims)
TypeError: No loop matching the specified signature and casting
was found for ufunc add

大概是说，在计算相关性时的x参数（这里指的是 pred3_test）的类型不匹配，搜了一下，大概其他人的解决办法是，转换一下类型：

x = x.astype('float64')

或者

x = np.arrary(x,dtype= 'float64')

结果bug还是没有解决，然后就根据提示出错的文件，去看一下源码：stats.py

    # dtype is the data type for the calculations.  This expression ensures
    # that the data type is at least 64 bit floating point.  It might have
    # more precision if the input is, for example, np.longdouble.
    dtype = type(1.0 + x[0] + y[0])

    if n == 2:
        return dtype(np.sign(x[1] - x[0])*np.sign(y[1] - y[0])), 1.0

    xmean = x.mean(dtype=dtype)
    ymean = y.mean(dtype=dtype)

    # By using `astype(dtype)`, we ensure that the intermediate calculations
    # use at least 64 bit floating point.
    xm = x.astype(dtype) - xmean
    ym = y.astype(dtype) - ymean

这里计算 x.mean时用的dtype 就在上面几行定义了。回到我自己的程序，输出这些类型看一下：

 

print(type(pred3_test))
print(type(pred3_test[0]))

发现两个都是：

应该是数据的维度有冗余，里面的放float64类型的数组外面多包裹了一个维度。去除多余的维度就可以了：

pred3_test = np.squeeze(pred3_test)
y_test = np.squeeze(y_test)

执行结果：

Test correlation =  0.8501812635529712
Test correlation ^ 2 =  0.7228081808965267