majority_vote_classifier.py

from sklearn.base import BaseEstimator
from sklearn.base import ClassifierMixin
from sklearn.base import clone
from sklearn.externals import six
from sklearn.pipeline import _name_estimators
from sklearn.preprocessing import LabelEncoder

import numpy as np
import operator

class MajorityVoteClassifier(BaseEstimator, ClassifierMixin):
    def __init__(self, classifiers, vote = 'classlabel', weights = None):
        self.classifiers = classifiers
        self.named_classifiers = { key: value for key, value in _name_estimators(classifiers) }
        self.vote = vote
        self.weights = weights

    def fit(self, X, y):
        self.lablenc_ = LabelEncoder()
        self.lablenc_.fit(y)
        self.classes_ = self.lablenc_.classes_
        self.classifiers_ = []
        y_trans = self.lablenc_.transform(y)
        for clf in self.classifiers:
            fitted_clf = clone(clf).fit(X, y_trans)
            self.classifiers_.append(fitted_clf)
        return self

    def predict(self, X):
        if self.vote == 'probability':
            maj_vote = np.argmax(self.predict_proba(X), axis = 1)
        else: # 'classlabel' vote
            predictions = np.asarray([clf.predict(X) for clf in self.classifiers_]).T
            maj_vote = np.apply_along_axis(lambda x: np.argmax(np.bincount(x, weights = self.weights)), axis = 1, arr = predictions)
        maj_vote = self.lablenc_.inverse_transform(maj_vote)
        return maj_vote

    def predict_proba(self, X):
        probas = np.asarray([clf.predict_proba(X) for clf in self.classifiers_])
        return np.average(probas, axis = 0, weights = self.weights)

    def get_params(self, deep = True):
        if not deep:
            return super(MajorityVoteClassifier, self).get_params(deep = False)
        else:
            out = self.named_classifiers.copy()
            for name, step in six.iteritems(self.named_classifiers):
                for key, value in six.iteritems(step.get_params(deep = True)):
                    out['%s__%s' % (name, key)] = value
            return out