Using sklearn voting ensemble with partial fit

Question

Can someone please tell how to use ensembles in sklearn using partial fit. I don\'t want to retrain my model. Alternatively, can we pass pre-trained models for ensembling ?

Answer 1

The Mlxtend library has an implementation works, you still need to call the fit function for the EnsembleVoteClassifier. Seems the fit function doesn't really modify any parameters rather checking the possible label values. In the example below, you have to give an array contains all the possible values appear in original y(in this case 1,2) to eclf2.fit It doesn't matter for X.

import numpy as np
from mlxtend.classifier import EnsembleVoteClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
import copy
clf1 = LogisticRegression(random_state=1)
clf2 = RandomForestClassifier(random_state=1)
clf3 = GaussianNB()
X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
y = np.array([1, 1, 1, 2, 2, 2])

for clf in (clf1, clf2, clf3):
    clf.fit(X, y)    
eclf2 = EnsembleVoteClassifier(clfs=[clf1, clf2, clf3],voting="soft",refit=False)
eclf2.fit(None,np.array([1,2]))
print(eclf2.predict(X))