Question

I am using scikit-learn for some data analysis, and my dataset has some missing values (represented by NA). I load the data in with genfromtxt with dtype='f8' and go about training my classifier.

The classification is fine on RandomForestClassifier and GradientBoostingClassifier objects, but using SVC from sklearn.svm causes the following error:

    probas = classifiers[i].fit(train[traincv], target[traincv]).predict_proba(train[testcv])
  File "C:\Python27\lib\site-packages\sklearn\svm\base.py", line 409, in predict_proba
    X = self._validate_for_predict(X)
  File "C:\Python27\lib\site-packages\sklearn\svm\base.py", line 534, in _validate_for_predict
    X = atleast2d_or_csr(X, dtype=np.float64, order="C")
  File "C:\Python27\lib\site-packages\sklearn\utils\validation.py", line 84, in atleast2d_or_csr
    assert_all_finite(X)
  File "C:\Python27\lib\site-packages\sklearn\utils\validation.py", line 20, in assert_all_finite
    raise ValueError("array contains NaN or infinity")
ValueError: array contains NaN or infinity

What gives? How can I make the SVM play nicely with the missing data? Keeping in mind that the missing data works fine for random forests and other classifiers..

Answer 1

You can do data imputation to handle missing values before using SVM.

EDIT: In scikit-learn, there’s a really easy way to do this, illustrated on this page.

(copied from page and modified)

>>> import numpy as np
>>> from sklearn.preprocessing import Imputer
>>> # missing_values is the value of your placeholder, strategy is if you'd like mean, median or mode, and axis=0 means it calculates the imputation based on the other feature values for that sample
>>> imp = Imputer(missing_values='NaN', strategy='mean', axis=0)
>>> imp.fit(train)
Imputer(axis=0, copy=True, missing_values='NaN', strategy='mean', verbose=0)
>>> train_imp = imp.transform(train)