67 lines
1.8 KiB
Python
67 lines
1.8 KiB
Python
import numpy as np
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from scipy.sparse import issparse, vstack
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from sklearn.linear_model import LogisticRegression
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from sklearn.model_selection import GridSearchCV
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from sklearn.neighbors import KernelDensity
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from baselines import densratio
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from baselines.pykliep import DensityRatioEstimator
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def kliep(Xtr, ytr, Xte):
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kliep = DensityRatioEstimator()
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kliep.fit(Xtr, Xte)
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return kliep.predict(Xtr)
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def usilf(Xtr, ytr, Xte, alpha=0.0):
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dense_ratio_obj = densratio(Xtr, Xte, alpha=alpha, verbose=False)
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return dense_ratio_obj.compute_density_ratio(Xtr)
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def logreg(Xtr, ytr, Xte):
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# check "Direct Density Ratio Estimation for
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# Large-scale Covariate Shift Adaptation", Eq.28
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if issparse(Xtr):
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X = vstack([Xtr, Xte])
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else:
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X = np.concatenate([Xtr, Xte])
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y = [0] * Xtr.shape[0] + [1] * Xte.shape[0]
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logreg = GridSearchCV(
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LogisticRegression(),
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param_grid={"C": np.logspace(-3, 3, 7), "class_weight": ["balanced", None]},
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n_jobs=-1,
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)
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logreg.fit(X, y)
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probs = logreg.predict_proba(Xtr)
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prob_train, prob_test = probs[:, 0], probs[:, 1]
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prior_train = Xtr.shape[0]
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prior_test = Xte.shape[0]
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w = (prior_train / prior_test) * (prob_test / prob_train)
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return w
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kdex2_params = {"bandwidth": np.logspace(-1, 1, 20)}
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def kdex2_lltr(Xtr):
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if issparse(Xtr):
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Xtr = Xtr.toarray()
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return GridSearchCV(KernelDensity(), kdex2_params).fit(Xtr).score_samples(Xtr)
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def kdex2_weights(Xtr, Xte, log_likelihood_tr):
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log_likelihood_te = (
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GridSearchCV(KernelDensity(), kdex2_params).fit(Xte).score_samples(Xtr)
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)
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likelihood_tr = np.exp(log_likelihood_tr)
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likelihood_te = np.exp(log_likelihood_te)
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return likelihood_te / likelihood_tr
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def get_acc(tr_preds, ytr, w):
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return np.sum((1.0 * (tr_preds == ytr)) * w) / np.sum(w)
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