使用 scikit-learn 的 Imputer 模块预测缺失值

Question

I am writing a very basic program to predict missing values in a dataset using scikit-learn's Imputer class.

I have made a NumPy array, created an Imputer object with strategy='mean' and performed fit_transform() on the NumPy array.

When I print the array after performing fit_transform(), the 'Nan's remain, and I dont get any prediction.

What am I doing wrong here? How do I go about predicting the missing values?

import numpy as np
from sklearn.preprocessing import Imputer

X = np.array([[23.56],[53.45],['NaN'],[44.44],[77.78],['NaN'],[234.44],[11.33],[79.87]])

print X

imp = Imputer(missing_values='NaN', strategy='mean', axis=0)
imp.fit_transform(X)

print X

Answer 1

Per the documentation , sklearn.preprocessing.Imputer.fit_transform returns a new array , it doesn't alter the argument array. The minimal fix is therefore:

X = imp.fit_transform(X)

Answer 2

After scikit-learn version 0.20 the usage of impute module was changed. Now, we can use imputer like;

from sklearn.impute import SimpleImputer
impute = SimpleImputer(missing_values=np.nan, strategy='mean')
impute.fit(X)
X=impute.transform(X)

Pay attention:

Instead of 'NaN' , np.nan is used

Don't need to use axis parameter

We can use imp or imputer instead of my impute variable

Answer 3

Note: Due to the change in the sklearn library 'NaN' has to be replaced with np.nan as shown below.

 from sklearn.preprocessing import Imputer
 imputer = Imputer(missing_values= np.nan,strategy='mean',axis=0)  
 imputer = imputer.fit(X[:,1:3])
 X[:,1:3]= imputer.transform(X[:,1:3])