From adb41ffb35c71bfce4941e5a3ba52e256299e67a Mon Sep 17 00:00:00 2001
From: Lorenzo Volpi <lorenzo.volpi@outlook.com>
Date: Sun, 5 Nov 2023 14:17:03 +0100
Subject: [PATCH] baselines code updated

---
 baselines/impweight.py | 14 ++++++++++++++
 baselines/models.py    |  2 +-
 baselines/pykliep.py   |  4 +++-
 3 files changed, 18 insertions(+), 2 deletions(-)

diff --git a/baselines/impweight.py b/baselines/impweight.py
index 83e7f6e..f144bce 100644
--- a/baselines/impweight.py
+++ b/baselines/impweight.py
@@ -4,6 +4,20 @@ from sklearn.linear_model import LogisticRegression
 from sklearn.model_selection import GridSearchCV
 from sklearn.neighbors import KernelDensity
 
+from baselines import densratio
+from baselines.pykliep import DensityRatioEstimator
+
+
+def kliep(Xtr, ytr, Xte):
+    kliep = DensityRatioEstimator()
+    kliep.fit(Xtr, Xte)
+    return kliep.predict(Xtr)
+
+
+def usilf(Xtr, ytr, Xte, alpha=0.0):
+    dense_ratio_obj = densratio(Xtr, Xte, alpha=alpha, verbose=False)
+    return dense_ratio_obj.compute_density_ratio(Xtr)
+
 
 def logreg(Xtr, ytr, Xte):
     # check "Direct Density Ratio Estimation for
diff --git a/baselines/models.py b/baselines/models.py
index 001f02c..a0e8c35 100644
--- a/baselines/models.py
+++ b/baselines/models.py
@@ -123,7 +123,7 @@ if __name__ == "__main__":
 
     results = []
     for sample in protocol():
-        wx = iw.logreg(d.validation.X, d.validation.y, sample.X)
+        wx = iw.kliep(d.validation.X, d.validation.y, sample.X)
         test_preds = lr.predict(sample.X)
         estim_acc = np.sum((1.0 * (val_preds == d.validation.y)) * wx) / np.sum(wx)
         true_acc = metrics.accuracy_score(sample.y, test_preds)
diff --git a/baselines/pykliep.py b/baselines/pykliep.py
index b9ccedd..8c67ea4 100644
--- a/baselines/pykliep.py
+++ b/baselines/pykliep.py
@@ -74,7 +74,9 @@ class DensityRatioEstimator:
         #     X_test_shuffled = X_test.copy()
         X_test_shuffled = X_test.copy()
 
-        np.random.shuffle(X_test_shuffled)
+        X_test_index = np.arange(X_test_shuffled.shape[0])
+        np.random.shuffle(X_test_index)
+        X_test_shuffled = X_test_shuffled[X_test_index, :]
 
         j_scores = {}