import numpy as np
from scipy import sparse
from sklearn import utils
from sklearn import linear_model
class NBSVMClassifier(linear_model.LogisticRegression):
def predict(self, X):
return super().predict(X.multiply(self.r_))
def predict_proba(self, X):
return super().predict_proba(X.multiply(self.r_))
def fit(self, X, y, sample_weight=None):
X, y = utils.check_X_y(X, y, accept_sparse='csr', order='C')
def pr(x, y_i, y):
p = x[y == y_i].sum(0)
return (p+1) / ((y==y_i).sum()+1)
self.r_ = sparse.csr_matrix(np.log(pr(X, 1, y) / pr(X, 0, y)))
return super().fit(X.multiply(self.r_), y, sample_weight)
