53 lines
1.5 KiB
Python
53 lines
1.5 KiB
Python
import numpy as np
|
|
from scipy.sparse import issparse, vstack
|
|
from sklearn.linear_model import LogisticRegression
|
|
from sklearn.model_selection import GridSearchCV
|
|
from sklearn.neighbors import KernelDensity
|
|
|
|
|
|
def logreg(Xtr, ytr, Xte):
|
|
# check "Direct Density Ratio Estimation for
|
|
# Large-scale Covariate Shift Adaptation", Eq.28
|
|
|
|
if issparse(Xtr):
|
|
X = vstack([Xtr, Xte])
|
|
else:
|
|
X = np.concatenate([Xtr, Xte])
|
|
|
|
y = [0] * Xtr.shape[0] + [1] * Xte.shape[0]
|
|
|
|
logreg = GridSearchCV(
|
|
LogisticRegression(),
|
|
param_grid={"C": np.logspace(-3, 3, 7), "class_weight": ["balanced", None]},
|
|
n_jobs=-1,
|
|
)
|
|
logreg.fit(X, y)
|
|
probs = logreg.predict_proba(Xtr)
|
|
prob_train, prob_test = probs[:, 0], probs[:, 1]
|
|
prior_train = Xtr.shape[0]
|
|
prior_test = Xte.shape[0]
|
|
w = (prior_train / prior_test) * (prob_test / prob_train)
|
|
return w
|
|
|
|
|
|
kdex2_params = {"bandwidth": np.logspace(-1, 1, 20)}
|
|
|
|
|
|
def kdex2_lltr(Xtr):
|
|
if issparse(Xtr):
|
|
Xtr = Xtr.toarray()
|
|
return GridSearchCV(KernelDensity(), kdex2_params).fit(Xtr).score_samples(Xtr)
|
|
|
|
|
|
def kdex2_weights(Xtr, Xte, log_likelihood_tr):
|
|
log_likelihood_te = (
|
|
GridSearchCV(KernelDensity(), kdex2_params).fit(Xte).score_samples(Xtr)
|
|
)
|
|
likelihood_tr = np.exp(log_likelihood_tr)
|
|
likelihood_te = np.exp(log_likelihood_te)
|
|
return likelihood_te / likelihood_tr
|
|
|
|
|
|
def get_acc(tr_preds, ytr, w):
|
|
return np.sum((1.0 * (tr_preds == ytr)) * w) / np.sum(w)
|