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nbsvm.py
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nbsvm.py
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# NBSVM implementation adapted from https://github.com/Joshua-Chin/nbsvm
import numpy as np
from scipy.sparse import spmatrix, coo_matrix
from sklearn.base import BaseEstimator
from sklearn.linear_model._base import LinearClassifierMixin, SparseCoefMixin
from sklearn.svm import LinearSVC
class NBSVM(BaseEstimator, LinearClassifierMixin, SparseCoefMixin):
def __init__(self, alpha=1, C=1, beta=0.25, fit_intercept=False, random_state=0, class_weight=None, max_iter = 1000):
self.alpha = alpha
self.C = C
self.beta = beta
self.fit_intercept = fit_intercept
self.class_weight = class_weight
self.max_iter = max_iter
self.random_state = random_state
def fit(self, X, y):
self.classes_ = np.unique(y)
if len(self.classes_) == 2:
coef_, intercept_ = self._fit_binary(X, y)
self.coef_ = coef_
self.intercept_ = intercept_
else:
coef_, intercept_ = zip(*[
self._fit_binary(X, y == class_)
for class_ in self.classes_
])
self.coef_ = np.concatenate(coef_)
self.intercept_ = np.array(intercept_).flatten()
return self
def _fit_binary(self, X, y):
y = np.array(y)
p = np.asarray(self.alpha + X[y == 1].sum(axis=0)).flatten()
q = np.asarray(self.alpha + X[y == 0].sum(axis=0)).flatten()
r = np.log(p/np.abs(p).sum()) - np.log(q/np.abs(q).sum())
b = np.log((y == 1).sum()) - np.log((y == 0).sum())
if isinstance(X, spmatrix):
indices = np.arange(len(r))
r_sparse = coo_matrix(
(r, (indices, indices)),
shape=(len(r), len(r))
)
X_scaled = X * r_sparse
else:
X_scaled = X * r
lsvc = LinearSVC(
C=self.C,
fit_intercept=self.fit_intercept,
max_iter=self.max_iter,
random_state=self.random_state,
class_weight=self.class_weight
).fit(X_scaled, y)
mean_mag = np.abs(lsvc.coef_).mean()
coef_ = (1 - self.beta) * mean_mag * r + \
self.beta * (r * lsvc.coef_)
intercept_ = (1 - self.beta) * mean_mag * b + \
self.beta * lsvc.intercept_
return coef_, intercept_