python - Scaling data in scikit-learn SVM

13
votes

While libsvm provides tools for scaling data, with Scikit-Learn (which should be based upon libSVM for the SVC classifier) I find no way to scale my data.

Basically I want to use 4 features, of which 3 range from 0 to 1 and the last one is a "big" highly variable number.

If I include the fourth feature in libSVM (using the easy.py script which scales my data automatically) I get some very nice results (96% accuracy). If I include the fourth variable in Scikit-Learn the accuracy drops to ~78% - but if I exclude it, I get the same results I get in libSVM when excluding that feature. Therefore I am pretty sure it's a problem of missing scaling.

How do I replicate programmatically (i.e. without calling svm-scale) the scaling process of SVM?

2
pythonsvmlibsvmscikit-learn

2 Answers

9
votes

You have that functionality in sklearn.preprocessing:

>>> from sklearn import preprocessing
>>> X = [[ 1., -1.,  2.],
...      [ 2.,  0.,  0.],
...      [ 0.,  1., -1.]]
>>> X_scaled = preprocessing.scale(X)

>>> X_scaled                                          
array([[ 0.  ..., -1.22...,  1.33...],
       [ 1.22...,  0.  ..., -0.26...],
       [-1.22...,  1.22..., -1.06...]])

The data will then have zero mean and unit variance.

0
votes

You can also try StandardScalerfor datascaling :

from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
scaler.fit(Xtrain) # where X is your data to be scaled
Xtrain = scaler.transform(Xtrain)