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 sklearn import preprocessing  scaler   preprocessing MinMaxScaler   fit train  train   scaler transform train      test   scaler transform test    This all works fine but if I have a new sample  temp below  that I want to classify  and thus I want to preprocess in the same way then I get  temp    1 2 3 4 5 5 6                      7  temp   scaler transform temp    Then I get a deprecation warning     DeprecationWarning  Passing 1d arrays as data is deprecated in 0 17  and will raise ValueError in 0 19  Reshape your data either using  X reshape -1  1  if your data has a single feature or X reshape 1  -1  if it contains a single sample     So the question is how should I be rescaling a single sample like this   I suppose an alternative  not very good one  would be     temp    temp  temp  temp   scaler transform temp  temp   temp 0    But I m sure there are better ways

User · Answer

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

User · Answer

This might help      temp      1 2 3 4 5 6       7

User · Answer

values reshape -1 1  will be accepted without alerts warnings   reshape -1 1  will be accepted  but with deprecation war

User · Answer

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

User · Answer

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

User · Answer

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

[python] Preprocessing in scikit learn - single sample - Depreciation warning

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