ValueError: operands could not be broadcast together with shapes - inverse_transform- Python

问题

I know ValueError question has been asked many times. I am still struggling to find an answer because I am using inverse_transform in my code.

Say I have an array a

a.shape
> (100,20)

and another array b

b.shape
> (100,3)

When I did a np.concatenate,

hat = np.concatenate((a, b), axis=1)

Now shape of hat is

hat.shape    
(100,23)

After this, I tried to do this,

inversed_hat = scaler.inverse_transform(hat)

When I do this, I am getting an error:

ValueError: operands could not be broadcast together with shapes (100,23) (25,) (100,23)

Is this broadcast error in inverse_transform? Any suggestion will be helpful. Thanks in advance!

回答1:

Although you didn't specify, I'm assuming you are using inverse_transform() from scikit learn's StandardScaler. You need to fit the data first.

import numpy as np
from sklearn.preprocessing import MinMaxScaler


In [1]: arr_a = np.random.randn(5*3).reshape((5, 3))

In [2]: arr_b = np.random.randn(5*2).reshape((5, 2))

In [3]: arr = np.concatenate((arr_a, arr_b), axis=1)

In [4]: scaler = MinMaxScaler(feature_range=(0, 1)).fit(arr)

In [5]: scaler.inverse_transform(arr)
Out[5]:
array([[ 0.19981115,  0.34855509, -1.02999482, -1.61848816, -0.26005923],
       [-0.81813499,  0.09873672,  1.53824716, -0.61643731, -0.70210801],
       [-0.45077786,  0.31584348,  0.98219019, -1.51364126,  0.69791054],
       [ 0.43664741, -0.16763207, -0.26148908, -2.13395823,  0.48079204],
       [-0.37367434, -0.16067958, -3.20451107, -0.76465428,  1.09761543]])

In [6]: new_arr = scaler.inverse_transform(arr)

In [7]: new_arr.shape == arr.shape
Out[7]: True

回答2:

It seems you are using pre-fit scaler object of sklearn.preprocessing. If it's true, according to me data that you have used for fitting is of dimension (x,25) whereas your data shape is of (x,23) dimension and thats the reason you are getting this issue.

来源：https://stackoverflow.com/questions/45847006/valueerror-operands-could-not-be-broadcast-together-with-shapes-inverse-trans