Preprocessing in scikit learn - single sample - Depreciation warning

Question

On a fresh installation of Anaconda under Ubuntu... I am preprocessing my data in various ways prior to a classification task using Scikit-Learn.

from sklear         


        

Answer 1

This might help

temp = ([[1,2,3,4,5,6,.....,7]])

Answer 2

.values.reshape(-1,1) will be accepted without alerts/warnings

.reshape(-1,1) will be accepted, but with deprecation war

Answer 3

Well, it actually looks like the warning is telling you what to do.

As part of sklearn.pipeline stages' uniform interfaces, as a rule of thumb:

• when you see X, it should be an np.array with two dimensions

• when you see y, it should be an np.array with a single dimension.

Here, therefore, you should consider the following:

temp = [1,2,3,4,5,5,6,....................,7]
# This makes it into a 2d array
temp = np.array(temp).reshape((len(temp), 1))
temp = scaler.transform(temp)

Answer 4

I faced the same issue and got the same deprecation warning. I was using a numpy array of [23, 276] when I got the message. I tried reshaping it as per the warning and end up in nowhere. Then I select each row from the numpy array (as I was iterating over it anyway) and assigned it to a list variable. It worked then without any warning.

array = []
array.append(temp[0])

Then you can use the python list object (here 'array') as an input to sk-learn functions. Not the most efficient solution, but worked for me.

Answer 5

You can always, reshape like:

temp = [1,2,3,4,5,5,6,7]

temp = temp.reshape(len(temp), 1)

Because, the major issue is when your, temp.shape is: (8,)

and you need (8,1)

Answer 6

Just listen to what the warning is telling you:

Reshape your data either X.reshape(-1, 1) if your data has a single feature/column and X.reshape(1, -1) if it contains a single sample.

For your example type(if you have more than one feature/column):

temp = temp.reshape(1,-1) 

For one feature/column:

temp = temp.reshape(-1,1)