From 23512565ac7e4092474ffa5444075d6e22c910ba Mon Sep 17 00:00:00 2001
From: 33798 <3379854453@qq.com>
Date: Sun, 31 Aug 2025 14:04:55 +0800
Subject: [PATCH 1/8] conformal selection

---
 examples/regression_selection_synthetic.py | 47 +++++++++++
 torchcp/regression/predictor/__init__.py   |  3 +-
 torchcp/regression/predictor/selection.py  | 94 ++++++++++++++++++++++
 torchcp/regression/score/__init__.py       |  3 +-
 torchcp/regression/score/res.py            | 20 +++++
 torchcp/regression/utils/metrics.py        | 14 ++++
 6 files changed, 179 insertions(+), 2 deletions(-)
 create mode 100644 examples/regression_selection_synthetic.py
 create mode 100644 torchcp/regression/predictor/selection.py
 create mode 100644 torchcp/regression/score/res.py

diff --git a/examples/regression_selection_synthetic.py b/examples/regression_selection_synthetic.py
new file mode 100644
index 0000000..6e853fc
--- /dev/null
+++ b/examples/regression_selection_synthetic.py
@@ -0,0 +1,47 @@
+# Copyright (c) 2023-present, SUSTech-ML.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+#
+
+
+import numpy as np
+import torch
+from sklearn.preprocessing import StandardScaler
+from torch.utils.data import TensorDataset
+from tqdm import tqdm
+import torch.nn as nn
+import torch.optim as optim
+
+from examples.regression_cqr_synthetic import prepare_dataset
+from torchcp.regression.predictor import Selector
+from torchcp.regression.score import RES
+from torchcp.regression.utils import build_regression_model
+
+if __name__ == "__main__":
+    # get dataloader
+    train_loader, cal_loader, test_loader = prepare_dataset(train_ratio=0.4, cal_ratio=0.2, batch_size=128)
+    # build regression model
+    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+    model = build_regression_model("NonLinearNet")(next(iter(train_loader))[0].shape[1], 1, 64, 0.5).to(device)
+
+    # train model
+    epochs = 100
+    criterion = nn.MSELoss()
+    lr = 0.01
+    optimizer = optim.Adam(model.parameters(), lr=lr)
+
+    for tmp_x, tmp_y in train_loader:
+        outputs = model(tmp_x.to(device))
+        loss = criterion(outputs, tmp_y.reshape(-1, 1).to(device))
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+
+    # Conformal Selection
+    thresholds = torch.ones(len(test_loader.dataset)) * 5
+
+    selector = Selector(score_function=RES(), model=model)
+    selector.calibrate(cal_loader)
+    print(selector.evaluate(test_loader, thresholds))
diff --git a/torchcp/regression/predictor/__init__.py b/torchcp/regression/predictor/__init__.py
index 7eb405f..714d8ed 100644
--- a/torchcp/regression/predictor/__init__.py
+++ b/torchcp/regression/predictor/__init__.py
@@ -9,4 +9,5 @@
 from .ensemble import EnsemblePredictor
 from .split import SplitPredictor
 from .agaci import AgACIPredictor
-from .cpd import ConformalPredictiveDistribution
\ No newline at end of file
+from .cpd import ConformalPredictiveDistribution
+from .selection import Selector
\ No newline at end of file
diff --git a/torchcp/regression/predictor/selection.py b/torchcp/regression/predictor/selection.py
new file mode 100644
index 0000000..262947b
--- /dev/null
+++ b/torchcp/regression/predictor/selection.py
@@ -0,0 +1,94 @@
+# Copyright (c) 2023-present, SUSTech-ML.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+#
+
+
+import torch
+
+from torchcp.regression.predictor.split import SplitPredictor
+from torchcp.regression.utils.metrics import Metrics
+from torchcp.utils.common import get_device
+
+
+class Selector(SplitPredictor):
+    """
+    Conformal Selection.
+
+    a screening procedure that aims to select candidates whose unobserved outcomes exceed user-specified value.
+
+    Args:
+        score_function (torchcp.regression.scores): A class that implements the score function.
+        model (torch.nn.Module): A PyTorch model capable of outputting quantile values.
+            The model should be an initialization model that has not been trained.
+        alpha (float, optional): The significance level. Default is 0.1.
+        device (torch.device, optional): The device on which the model is located. Default is None.
+
+    Reference:
+        Paper: Selection by Prediction with Conformal p-values (Jin et al., 2023)
+        Link: https://arxiv.org/pdf/2210.01408
+        Github: https://github.com/ying531/conformal-selection
+    """
+
+    def __init__(self, score_function, model, alpha=0.1, device=None):
+        super().__init__(score_function, model, alpha, device)
+        self._metric = Metrics()
+
+    def calibrate(self, cal_dataloader):
+        self._model.eval()
+        predicts_list, y_truth_list = [], []
+        with torch.no_grad():
+            for tmp_x, tmp_labels in cal_dataloader:
+                tmp_x, tmp_labels = tmp_x.to(self._device), tmp_labels.to(self._device)
+                tmp_predicts = self._model(tmp_x).detach()
+                predicts_list.append(tmp_predicts)
+                y_truth_list.append(tmp_labels)
+
+        predicts = torch.cat(predicts_list).float().to(self._device)
+        y_truth = torch.cat(y_truth_list).to(self._device)
+        self.cal_scores = self.score_function(predicts, y_truth)
+
+
+    def evaluate(self, data_loader, thresholds):
+        self._model.eval()
+        y_truth_list = []
+        predicts_list = []
+        with torch.no_grad():
+            for examples in data_loader:
+                tmp_x, tmp_labels = examples[0].to(self._device), examples[1].to(self._device)
+                tmp_predicts = self._model(tmp_x).detach()
+                predicts_list.append(tmp_predicts)
+                y_truth_list.append(tmp_labels)
+        predicts = torch.cat(predicts_list).float().to(self._device)
+        y_truth = torch.cat(y_truth_list).to(self._device)
+        scores = self.score_function(predicts, thresholds)
+
+        n_cal, n_test = self.cal_scores.shape[0], scores.shape[0]
+
+        # Compute p-values with tie-breaking
+        u = torch.rand(n_test)
+        count_less = (self.cal_scores.view(1, n_cal) < scores.view(n_test, 1)).sum(dim=1)
+        count_tie = (self.cal_scores.view(1, n_cal) == scores.view(n_test, 1)).sum(dim=1) + 1
+        p_values = (count_less + count_tie * u) / (n_cal + 1)
+        p_values= torch.sort(p_values)[0]
+
+        # Conduct BH procedure
+        k_range = torch.arange(1, n_test + 1, device=p_values.device)
+        thresholds = k_range * self.alpha / n_test
+        mask = p_values <= thresholds
+        k_star = torch.max(torch.where(mask, k_range, torch.zeros_like(k_range))) if mask.any() else 0
+        threshold = (k_star * self.alpha / n_test) if k_star > 0 else 0
+
+        # Get indices where p_values <= threshold
+        indices = torch.nonzero(p_values <= threshold, as_tuple=False).squeeze()
+
+        if indices.dim() == 0:
+            indices = indices.unsqueeze(0)
+
+        # Evaluation
+        res_dict = {"false_discovery_proportion": self._metric("false_discovery_proportion")(y_truth, thresholds,
+                                                                                             indices),
+                    "power": self._metric("power")(y_truth, thresholds, indices)}
+        return res_dict
diff --git a/torchcp/regression/score/__init__.py b/torchcp/regression/score/__init__.py
index a68921b..7964058 100644
--- a/torchcp/regression/score/__init__.py
+++ b/torchcp/regression/score/__init__.py
@@ -12,4 +12,5 @@
 from .cqrm import CQRM
 from .cqrr import CQRR
 from .r2ccp import R2CCP
-from .sign import Sign
\ No newline at end of file
+from .sign import Sign
+from .res import RES
\ No newline at end of file
diff --git a/torchcp/regression/score/res.py b/torchcp/regression/score/res.py
new file mode 100644
index 0000000..cd1667b
--- /dev/null
+++ b/torchcp/regression/score/res.py
@@ -0,0 +1,20 @@
+# Copyright (c) 2023-present, SUSTech-ML.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+#
+
+
+from torchcp.regression.score.base import BaseScore
+
+
+class RES(BaseScore):
+    """
+    RES score (Jin et al., 2023)
+    paper: https://arxiv.org/pdf/2210.01408
+    """
+    def __call__(self, predicts, y_truth):
+        if len(predicts.shape) == 2:
+            predicts = predicts.squeeze().view(-1)
+        return y_truth - predicts
diff --git a/torchcp/regression/utils/metrics.py b/torchcp/regression/utils/metrics.py
index 0303ea9..0e75665 100644
--- a/torchcp/regression/utils/metrics.py
+++ b/torchcp/regression/utils/metrics.py
@@ -71,6 +71,20 @@ def average_size(prediction_intervals):
     return average_size
 
 
+@METRICS_REGISTRY_REGRESSION.register()
+def false_discovery_proportion(y_truth, thresholds, indices):
+    false_positives = torch.sum(y_truth[indices] <= thresholds[indices])
+    fdp = false_positives / indices.shape[-1] if indices.shape[-1] > 0 else torch.tensor(0.)
+    return fdp.item()
+
+
+@METRICS_REGISTRY_REGRESSION.register()
+def power(y_truth, thresholds, indices):
+    true_positives = torch.sum(y_truth[indices] > thresholds[indices])
+    power = true_positives / torch.sum(y_truth > thresholds)
+    return power.item()
+
+
 class Metrics:
     def __call__(self, metric) -> Any:
         if metric not in METRICS_REGISTRY_REGRESSION.registered_names():

From 8451dd3cef565df9f1bc831f07bf5ec2de9fb27e Mon Sep 17 00:00:00 2001
From: 33798 <3379854453@qq.com>
Date: Sun, 31 Aug 2025 14:04:55 +0800
Subject: [PATCH 2/8] conformal selection

---
 examples/regression_selection_synthetic.py | 56 +++++++++++-----------
 torchcp/regression/predictor/selection.py  | 23 +++++++--
 torchcp/regression/utils/metrics.py        | 29 +++++++++++
 3 files changed, 76 insertions(+), 32 deletions(-)

diff --git a/examples/regression_selection_synthetic.py b/examples/regression_selection_synthetic.py
index 6e853fc..c00580f 100644
--- a/examples/regression_selection_synthetic.py
+++ b/examples/regression_selection_synthetic.py
@@ -15,33 +15,33 @@
 import torch.optim as optim
 
 from examples.regression_cqr_synthetic import prepare_dataset
-from torchcp.regression.predictor import Selector
-from torchcp.regression.score import RES
+from torchcp.regression.predictor import ConformalSelector
+from torchcp.regression.score import Sign
 from torchcp.regression.utils import build_regression_model
 
-if __name__ == "__main__":
-    # get dataloader
-    train_loader, cal_loader, test_loader = prepare_dataset(train_ratio=0.4, cal_ratio=0.2, batch_size=128)
-    # build regression model
-    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-    model = build_regression_model("NonLinearNet")(next(iter(train_loader))[0].shape[1], 1, 64, 0.5).to(device)
-
-    # train model
-    epochs = 100
-    criterion = nn.MSELoss()
-    lr = 0.01
-    optimizer = optim.Adam(model.parameters(), lr=lr)
-
-    for tmp_x, tmp_y in train_loader:
-        outputs = model(tmp_x.to(device))
-        loss = criterion(outputs, tmp_y.reshape(-1, 1).to(device))
-        optimizer.zero_grad()
-        loss.backward()
-        optimizer.step()
-
-    # Conformal Selection
-    thresholds = torch.ones(len(test_loader.dataset)) * 5
-
-    selector = Selector(score_function=RES(), model=model)
-    selector.calibrate(cal_loader)
-    print(selector.evaluate(test_loader, thresholds))
+
+# get dataloader
+train_loader, cal_loader, test_loader = prepare_dataset(train_ratio=0.4, cal_ratio=0.2, batch_size=128)
+# build regression model
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+model = build_regression_model("NonLinearNet")(next(iter(train_loader))[0].shape[1], 1, 64, 0.5).to(device)
+
+# train model
+epochs = 100
+criterion = nn.MSELoss()
+lr = 0.01
+optimizer = optim.Adam(model.parameters(), lr=lr)
+
+for tmp_x, tmp_y in train_loader:
+    outputs = model(tmp_x.to(device))
+    loss = criterion(outputs, tmp_y.reshape(-1, 1).to(device))
+    optimizer.zero_grad()
+    loss.backward()
+    optimizer.step()
+
+# Conformal Selection
+thresholds = torch.ones(len(test_loader.dataset)) * 5
+
+selector = ConformalSelector(score_function=Sign(), model=model)
+selector.calibrate(cal_loader)
+print(selector.evaluate(test_loader, thresholds))
diff --git a/torchcp/regression/predictor/selection.py b/torchcp/regression/predictor/selection.py
index 262947b..98bb3fa 100644
--- a/torchcp/regression/predictor/selection.py
+++ b/torchcp/regression/predictor/selection.py
@@ -13,7 +13,7 @@
 from torchcp.utils.common import get_device
 
 
-class Selector(SplitPredictor):
+class ConformalSelector(SplitPredictor):
     """
     Conformal Selection.
 
@@ -52,6 +52,24 @@ def calibrate(self, cal_dataloader):
 
 
     def evaluate(self, data_loader, thresholds):
+        """
+        Evaluate the performance of conformal selection on a test dataset by calculating false discovery proportion
+        (FDP) and power of the selection set.
+
+        Args:
+            data_loader (DataLoader): The DataLoader providing the test data batches.
+            thresholds (torch.Tensor): A tensor of user-defined thresholds.
+
+        Returns:
+            dict: A dictionary containing:
+                - "False discovery proportion": The FDP of the selection set.
+                - "Power": The power of the selection set.
+
+        Example::
+
+            >>> eval_results = selector.evaluate(test_loader, thresholds)
+            >>> print(eval_results)
+        """
         self._model.eval()
         y_truth_list = []
         predicts_list = []
@@ -84,9 +102,6 @@ def evaluate(self, data_loader, thresholds):
         # Get indices where p_values <= threshold
         indices = torch.nonzero(p_values <= threshold, as_tuple=False).squeeze()
 
-        if indices.dim() == 0:
-            indices = indices.unsqueeze(0)
-
         # Evaluation
         res_dict = {"false_discovery_proportion": self._metric("false_discovery_proportion")(y_truth, thresholds,
                                                                                              indices),
diff --git a/torchcp/regression/utils/metrics.py b/torchcp/regression/utils/metrics.py
index 0e75665..cdfae02 100644
--- a/torchcp/regression/utils/metrics.py
+++ b/torchcp/regression/utils/metrics.py
@@ -73,6 +73,21 @@ def average_size(prediction_intervals):
 
 @METRICS_REGISTRY_REGRESSION.register()
 def false_discovery_proportion(y_truth, thresholds, indices):
+    """
+    Conpute the false discovery proportion (the proportion of false discovery among all selected points) of the
+    selection set.
+
+    Args:
+        y_truth (torch.Tensor): A tensor of ground truth values.
+        thresholds (torch.Tensor): Tensor of user-defined thresholds.
+        indices (torch.Tensor): A tensor containing the indices of selected points.
+
+    Returns:
+        torch.Tensor: The false discovery proportion of the selection set.
+    """
+    if indices.dim() == 0:
+        indices = indices.unsqueeze(0)
+
     false_positives = torch.sum(y_truth[indices] <= thresholds[indices])
     fdp = false_positives / indices.shape[-1] if indices.shape[-1] > 0 else torch.tensor(0.)
     return fdp.item()
@@ -80,6 +95,20 @@ def false_discovery_proportion(y_truth, thresholds, indices):
 
 @METRICS_REGISTRY_REGRESSION.register()
 def power(y_truth, thresholds, indices):
+    """
+        Conpute the power (the proportion of desirable points that are correctly selected) of the selection set.
+
+        Args:
+            y_truth (torch.Tensor): A tensor of ground truth values.
+            thresholds (torch.Tensor): Tensor of user-defined thresholds.
+            indices (torch.Tensor): A tensor containing the indices of selected points.
+
+        Returns:
+            torch.Tensor: The power of the selection set.
+        """
+    if indices.dim() == 0:
+        indices = indices.unsqueeze(0)
+
     true_positives = torch.sum(y_truth[indices] > thresholds[indices])
     power = true_positives / torch.sum(y_truth > thresholds)
     return power.item()

From d19b5c9fbab9a6f4c10b583ab5b12eb002a7330c Mon Sep 17 00:00:00 2001
From: 33798 <3379854453@qq.com>
Date: Sun, 31 Aug 2025 15:28:22 +0800
Subject: [PATCH 3/8] conformal selection

---
 torchcp/regression/predictor/__init__.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/torchcp/regression/predictor/__init__.py b/torchcp/regression/predictor/__init__.py
index 714d8ed..0852cb7 100644
--- a/torchcp/regression/predictor/__init__.py
+++ b/torchcp/regression/predictor/__init__.py
@@ -10,4 +10,4 @@
 from .split import SplitPredictor
 from .agaci import AgACIPredictor
 from .cpd import ConformalPredictiveDistribution
-from .selection import Selector
\ No newline at end of file
+from .selection import ConformalSelector
\ No newline at end of file

From 18b88471f25e1369fec7c4b43b4333403399de50 Mon Sep 17 00:00:00 2001
From: 33798 <3379854453@qq.com>
Date: Sat, 18 Oct 2025 15:50:23 +0800
Subject: [PATCH 4/8] conformal selection

---
 examples/regression_selection_synthetic.py    | 13 ++--
 torchcp/classification/loss/__init__.py       |  1 -
 torchcp/classification/trainer/__init__.py    |  1 -
 torchcp/regression/predictor/__init__.py      |  1 -
 torchcp/regression/score/__init__.py          |  1 -
 torchcp/selection/__init__.py                 |  0
 torchcp/selection/score/__init__.py           | 10 +++
 torchcp/selection/score/clip.py               | 21 ++++++
 .../{regression => selection}/score/res.py    |  0
 torchcp/selection/selector/__init__.py        |  8 +++
 .../selector}/selection.py                    | 23 ++-----
 .../selection/testing_correction/__init__.py  | 10 +++
 torchcp/selection/testing_correction/base.py  | 27 ++++++++
 .../testing_correction/bh_procedure.py        | 49 ++++++++++++++
 torchcp/selection/utils/__init__.py           |  0
 torchcp/selection/utils/metric.py             | 64 +++++++++++++++++++
 torchcp/utils/metrics.py                      |  0
 17 files changed, 201 insertions(+), 28 deletions(-)
 create mode 100644 torchcp/selection/__init__.py
 create mode 100644 torchcp/selection/score/__init__.py
 create mode 100644 torchcp/selection/score/clip.py
 rename torchcp/{regression => selection}/score/res.py (100%)
 create mode 100644 torchcp/selection/selector/__init__.py
 rename torchcp/{regression/predictor => selection/selector}/selection.py (81%)
 create mode 100644 torchcp/selection/testing_correction/__init__.py
 create mode 100644 torchcp/selection/testing_correction/base.py
 create mode 100644 torchcp/selection/testing_correction/bh_procedure.py
 create mode 100644 torchcp/selection/utils/__init__.py
 create mode 100644 torchcp/selection/utils/metric.py
 create mode 100644 torchcp/utils/metrics.py

diff --git a/examples/regression_selection_synthetic.py b/examples/regression_selection_synthetic.py
index c00580f..252305a 100644
--- a/examples/regression_selection_synthetic.py
+++ b/examples/regression_selection_synthetic.py
@@ -6,18 +6,15 @@
 #
 
 
-import numpy as np
 import torch
-from sklearn.preprocessing import StandardScaler
-from torch.utils.data import TensorDataset
-from tqdm import tqdm
 import torch.nn as nn
 import torch.optim as optim
 
 from examples.regression_cqr_synthetic import prepare_dataset
-from torchcp.regression.predictor import ConformalSelector
-from torchcp.regression.score import Sign
 from torchcp.regression.utils import build_regression_model
+from torchcp.selection.score import RES
+from torchcp.selection.selector import ConformalSelector
+from torchcp.selection.testing_correction import BH_procedure
 
 
 # get dataloader
@@ -42,6 +39,6 @@
 # Conformal Selection
 thresholds = torch.ones(len(test_loader.dataset)) * 5
 
-selector = ConformalSelector(score_function=Sign(), model=model)
+selector = ConformalSelector(score_function=RES(), testing_correction=BH_procedure(), model=model)
 selector.calibrate(cal_loader)
-print(selector.evaluate(test_loader, thresholds))
+print(selector.select(test_loader, thresholds))
diff --git a/torchcp/classification/loss/__init__.py b/torchcp/classification/loss/__init__.py
index c163bbc..83cb900 100644
--- a/torchcp/classification/loss/__init__.py
+++ b/torchcp/classification/loss/__init__.py
@@ -8,5 +8,4 @@
 from .cd import CDLoss
 from .conftr import ConfTrLoss
 from .confts import ConfTSLoss
-from .uncertainty_aware import UncertaintyAwareLoss
 from .scpo import SCPOLoss
\ No newline at end of file
diff --git a/torchcp/classification/trainer/__init__.py b/torchcp/classification/trainer/__init__.py
index 52161e4..a3538ef 100644
--- a/torchcp/classification/trainer/__init__.py
+++ b/torchcp/classification/trainer/__init__.py
@@ -10,6 +10,5 @@
 from .confts_trainer import ConfTSTrainer
 from .model_zoo import TemperatureScalingModel
 from .ts_trainer import TSTrainer
-from .ua_trainer import UncertaintyAwareTrainer
 from .ordinal_trainer import OrdinalTrainer
 from .scpo_trainer import SCPOTrainer
\ No newline at end of file
diff --git a/torchcp/regression/predictor/__init__.py b/torchcp/regression/predictor/__init__.py
index 0852cb7..77bcbc3 100644
--- a/torchcp/regression/predictor/__init__.py
+++ b/torchcp/regression/predictor/__init__.py
@@ -10,4 +10,3 @@
 from .split import SplitPredictor
 from .agaci import AgACIPredictor
 from .cpd import ConformalPredictiveDistribution
-from .selection import ConformalSelector
\ No newline at end of file
diff --git a/torchcp/regression/score/__init__.py b/torchcp/regression/score/__init__.py
index 7964058..1519448 100644
--- a/torchcp/regression/score/__init__.py
+++ b/torchcp/regression/score/__init__.py
@@ -13,4 +13,3 @@
 from .cqrr import CQRR
 from .r2ccp import R2CCP
 from .sign import Sign
-from .res import RES
\ No newline at end of file
diff --git a/torchcp/selection/__init__.py b/torchcp/selection/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/torchcp/selection/score/__init__.py b/torchcp/selection/score/__init__.py
new file mode 100644
index 0000000..bb9d168
--- /dev/null
+++ b/torchcp/selection/score/__init__.py
@@ -0,0 +1,10 @@
+# Copyright (c) 2023-present, SUSTech-ML.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+#
+
+
+from .clip import CLIP
+from .res import RES
\ No newline at end of file
diff --git a/torchcp/selection/score/clip.py b/torchcp/selection/score/clip.py
new file mode 100644
index 0000000..889843a
--- /dev/null
+++ b/torchcp/selection/score/clip.py
@@ -0,0 +1,21 @@
+# Copyright (c) 2023-present, SUSTech-ML.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+#
+
+
+import torch
+from torchcp.regression.score.base import BaseScore
+
+
+class CLIP(BaseScore):
+    """
+    CLIP score (Jin et al., 2023), only apply to binary classification.
+    paper: https://arxiv.org/pdf/2210.01408
+    """
+    def __call__(self, predicts, y_truth, M=100):
+        if len(predicts.shape) == 2:
+            predicts = predicts.squeeze().view(-1)
+        return M * torch.max(predicts, 0) - predicts
diff --git a/torchcp/regression/score/res.py b/torchcp/selection/score/res.py
similarity index 100%
rename from torchcp/regression/score/res.py
rename to torchcp/selection/score/res.py
diff --git a/torchcp/selection/selector/__init__.py b/torchcp/selection/selector/__init__.py
new file mode 100644
index 0000000..5e15819
--- /dev/null
+++ b/torchcp/selection/selector/__init__.py
@@ -0,0 +1,8 @@
+# Copyright (c) 2023-present, SUSTech-ML.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+#
+
+from .selection import ConformalSelector
\ No newline at end of file
diff --git a/torchcp/regression/predictor/selection.py b/torchcp/selection/selector/selection.py
similarity index 81%
rename from torchcp/regression/predictor/selection.py
rename to torchcp/selection/selector/selection.py
index 98bb3fa..537b86a 100644
--- a/torchcp/regression/predictor/selection.py
+++ b/torchcp/selection/selector/selection.py
@@ -10,14 +10,12 @@
 
 from torchcp.regression.predictor.split import SplitPredictor
 from torchcp.regression.utils.metrics import Metrics
-from torchcp.utils.common import get_device
 
 
 class ConformalSelector(SplitPredictor):
     """
-    Conformal Selection.
-
-    a screening procedure that aims to select candidates whose unobserved outcomes exceed user-specified value.
+    Conformal Selection:
+        a screening procedure that aims to select candidates whose unobserved outcomes exceed user-specified value.
 
     Args:
         score_function (torchcp.regression.scores): A class that implements the score function.
@@ -32,10 +30,12 @@ class ConformalSelector(SplitPredictor):
         Github: https://github.com/ying531/conformal-selection
     """
 
-    def __init__(self, score_function, model, alpha=0.1, device=None):
+    def __init__(self, score_function, testing_correction, model, alpha=0.1, device=None):
         super().__init__(score_function, model, alpha, device)
+        self.testing_correction = testing_correction
         self._metric = Metrics()
 
+
     def calibrate(self, cal_dataloader):
         self._model.eval()
         predicts_list, y_truth_list = [], []
@@ -51,7 +51,7 @@ def calibrate(self, cal_dataloader):
         self.cal_scores = self.score_function(predicts, y_truth)
 
 
-    def evaluate(self, data_loader, thresholds):
+    def select(self, data_loader, thresholds):
         """
         Evaluate the performance of conformal selection on a test dataset by calculating false discovery proportion
         (FDP) and power of the selection set.
@@ -90,17 +90,8 @@ def evaluate(self, data_loader, thresholds):
         count_less = (self.cal_scores.view(1, n_cal) < scores.view(n_test, 1)).sum(dim=1)
         count_tie = (self.cal_scores.view(1, n_cal) == scores.view(n_test, 1)).sum(dim=1) + 1
         p_values = (count_less + count_tie * u) / (n_cal + 1)
-        p_values= torch.sort(p_values)[0]
-
-        # Conduct BH procedure
-        k_range = torch.arange(1, n_test + 1, device=p_values.device)
-        thresholds = k_range * self.alpha / n_test
-        mask = p_values <= thresholds
-        k_star = torch.max(torch.where(mask, k_range, torch.zeros_like(k_range))) if mask.any() else 0
-        threshold = (k_star * self.alpha / n_test) if k_star > 0 else 0
 
-        # Get indices where p_values <= threshold
-        indices = torch.nonzero(p_values <= threshold, as_tuple=False).squeeze()
+        indices = self.testing_correction(p_values, self.alpha)
 
         # Evaluation
         res_dict = {"false_discovery_proportion": self._metric("false_discovery_proportion")(y_truth, thresholds,
diff --git a/torchcp/selection/testing_correction/__init__.py b/torchcp/selection/testing_correction/__init__.py
new file mode 100644
index 0000000..a0f6b8e
--- /dev/null
+++ b/torchcp/selection/testing_correction/__init__.py
@@ -0,0 +1,10 @@
+# Copyright (c) 2023-present, SUSTech-ML.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+#
+
+
+from .base import Base
+from .bh_procedure import BH_procedure
\ No newline at end of file
diff --git a/torchcp/selection/testing_correction/base.py b/torchcp/selection/testing_correction/base.py
new file mode 100644
index 0000000..6f18b93
--- /dev/null
+++ b/torchcp/selection/testing_correction/base.py
@@ -0,0 +1,27 @@
+# Copyright (c) 2023-present, SUSTech-ML.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+#
+
+
+from abc import ABCMeta, abstractmethod
+import torch
+from tqdm import tqdm
+
+from torchcp.utils.common import get_device
+
+
+class Base(object):
+    """
+    Abstract base class for all multiple testing correction algorithms.
+    """
+    __metaclass__ = ABCMeta
+
+    def __init__(self) -> None:
+        pass
+
+    @abstractmethod
+    def __call__(self, p_values, alpha):
+        raise NotImplementedError
diff --git a/torchcp/selection/testing_correction/bh_procedure.py b/torchcp/selection/testing_correction/bh_procedure.py
new file mode 100644
index 0000000..66e1705
--- /dev/null
+++ b/torchcp/selection/testing_correction/bh_procedure.py
@@ -0,0 +1,49 @@
+# Copyright (c) 2023-present, SUSTech-ML.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+#
+
+
+import torch
+
+from torchcp.regression.score.base import BaseScore
+
+
+class BH_procedure(BaseScore):
+    """
+    Benjamini-Hochberg (BH) procedure:
+        finds a p-value threshold from a list of p-values to determine which null hypotheses to reject, given a target
+        FDR level 'alpha'.
+
+    References:
+        Paper: Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
+               (Benjamini and Hochberg, 1995)
+        Link: https://www.jstor.org/stable/2346101
+    """
+    def __init__(self):
+        super().__init__()
+
+    def __call__(self, p_values, alpha):
+        """
+        Apply the Benjamini-Hochberg procedure.
+
+        Args:
+            p_values (torch.Tensor): A 1D tensor of p-values.
+            alpha (float): The desired False Discovery Rate (FDR) level (e.g., 0.1).
+
+        Returns:
+            torch.Tensor: A 1D tensor of indices corresponding to the p-values (hypotheses) that are rejected.
+        """
+        p_values_sorted, _ = torch.sort(p_values)
+        n_test = p_values_sorted.shape[0]
+
+        k_range = torch.arange(1, n_test + 1, device=p_values_sorted.device)
+        thresholds = k_range * alpha / n_test
+        mask = p_values_sorted <= thresholds
+        k_star = torch.max(torch.where(mask, k_range, torch.zeros_like(k_range))) if mask.any() else 0
+        threshold = (k_star * alpha / n_test) if k_star > 0 else 0
+        indices = torch.nonzero(p_values <= threshold, as_tuple=False).squeeze()
+
+        return indices
diff --git a/torchcp/selection/utils/__init__.py b/torchcp/selection/utils/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/torchcp/selection/utils/metric.py b/torchcp/selection/utils/metric.py
new file mode 100644
index 0000000..5f71b09
--- /dev/null
+++ b/torchcp/selection/utils/metric.py
@@ -0,0 +1,64 @@
+# Copyright (c) 2023-present, SUSTech-ML.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+#
+
+from typing import Any
+
+import torch
+
+from torchcp.utils.registry import Registry
+
+METRICS_REGISTRY_REGRESSION = Registry("METRICS")
+
+
+@METRICS_REGISTRY_REGRESSION.register()
+def false_discovery_proportion(y_truth, thresholds, indices):
+    """
+    Conpute the false discovery proportion (the proportion of false discovery among all selected points) of the
+    selection set.
+
+    Args:
+        y_truth (torch.Tensor): A tensor of ground truth values.
+        thresholds (torch.Tensor): Tensor of user-defined thresholds.
+        indices (torch.Tensor): A tensor containing the indices of selected points.
+
+    Returns:
+        torch.Tensor: The false discovery proportion of the selection set.
+    """
+    if indices.dim() == 0:
+        indices = indices.unsqueeze(0)
+
+    false_positives = torch.sum(y_truth[indices] <= thresholds[indices])
+    fdp = false_positives / indices.shape[-1] if indices.shape[-1] > 0 else torch.tensor(0.)
+    return fdp.item()
+
+
+@METRICS_REGISTRY_REGRESSION.register()
+def power(y_truth, thresholds, indices):
+    """
+        Conpute the power (the proportion of desirable points that are correctly selected) of the selection set.
+
+        Args:
+            y_truth (torch.Tensor): A tensor of ground truth values.
+            thresholds (torch.Tensor): Tensor of user-defined thresholds.
+            indices (torch.Tensor): A tensor containing the indices of selected points.
+
+        Returns:
+            torch.Tensor: The power of the selection set.
+        """
+    if indices.dim() == 0:
+        indices = indices.unsqueeze(0)
+
+    true_positives = torch.sum(y_truth[indices] > thresholds[indices])
+    power = true_positives / torch.sum(y_truth > thresholds)
+    return power.item()
+
+
+class Metrics:
+    def __call__(self, metric) -> Any:
+        if metric not in METRICS_REGISTRY_REGRESSION.registered_names():
+            raise NameError(f"The metric: {metric} is not defined in TorchCP.")
+        return METRICS_REGISTRY_REGRESSION.get(metric)
\ No newline at end of file
diff --git a/torchcp/utils/metrics.py b/torchcp/utils/metrics.py
new file mode 100644
index 0000000..e69de29

From 63a0a2d144de8d0676cc4ad712abfb72415b9747 Mon Sep 17 00:00:00 2001
From: 33798 <3379854453@qq.com>
Date: Sat, 18 Oct 2025 15:50:23 +0800
Subject: [PATCH 5/8] conformal selection

---
 examples/regression_selection_synthetic.py    | 13 ++--
 torchcp/classification/loss/__init__.py       |  1 -
 torchcp/classification/trainer/__init__.py    |  1 -
 torchcp/regression/predictor/__init__.py      |  1 -
 torchcp/regression/score/__init__.py          |  1 -
 torchcp/selection/__init__.py                 |  0
 torchcp/selection/score/__init__.py           | 10 +++
 torchcp/selection/score/clip.py               | 21 ++++++
 .../{regression => selection}/score/res.py    |  0
 torchcp/selection/selector/__init__.py        |  8 +++
 .../selector}/selection.py                    | 23 ++-----
 .../selection/testing_correction/__init__.py  | 10 +++
 torchcp/selection/testing_correction/base.py  | 27 ++++++++
 .../testing_correction/bh_procedure.py        | 49 ++++++++++++++
 torchcp/selection/utils/__init__.py           |  0
 torchcp/selection/utils/metric.py             | 64 +++++++++++++++++++
 torchcp/utils/metrics.py                      |  0
 17 files changed, 201 insertions(+), 28 deletions(-)
 create mode 100644 torchcp/selection/__init__.py
 create mode 100644 torchcp/selection/score/__init__.py
 create mode 100644 torchcp/selection/score/clip.py
 rename torchcp/{regression => selection}/score/res.py (100%)
 create mode 100644 torchcp/selection/selector/__init__.py
 rename torchcp/{regression/predictor => selection/selector}/selection.py (81%)
 create mode 100644 torchcp/selection/testing_correction/__init__.py
 create mode 100644 torchcp/selection/testing_correction/base.py
 create mode 100644 torchcp/selection/testing_correction/bh_procedure.py
 create mode 100644 torchcp/selection/utils/__init__.py
 create mode 100644 torchcp/selection/utils/metric.py
 create mode 100644 torchcp/utils/metrics.py

diff --git a/examples/regression_selection_synthetic.py b/examples/regression_selection_synthetic.py
index c00580f..252305a 100644
--- a/examples/regression_selection_synthetic.py
+++ b/examples/regression_selection_synthetic.py
@@ -6,18 +6,15 @@
 #
 
 
-import numpy as np
 import torch
-from sklearn.preprocessing import StandardScaler
-from torch.utils.data import TensorDataset
-from tqdm import tqdm
 import torch.nn as nn
 import torch.optim as optim
 
 from examples.regression_cqr_synthetic import prepare_dataset
-from torchcp.regression.predictor import ConformalSelector
-from torchcp.regression.score import Sign
 from torchcp.regression.utils import build_regression_model
+from torchcp.selection.score import RES
+from torchcp.selection.selector import ConformalSelector
+from torchcp.selection.testing_correction import BH_procedure
 
 
 # get dataloader
@@ -42,6 +39,6 @@
 # Conformal Selection
 thresholds = torch.ones(len(test_loader.dataset)) * 5
 
-selector = ConformalSelector(score_function=Sign(), model=model)
+selector = ConformalSelector(score_function=RES(), testing_correction=BH_procedure(), model=model)
 selector.calibrate(cal_loader)
-print(selector.evaluate(test_loader, thresholds))
+print(selector.select(test_loader, thresholds))
diff --git a/torchcp/classification/loss/__init__.py b/torchcp/classification/loss/__init__.py
index c163bbc..83cb900 100644
--- a/torchcp/classification/loss/__init__.py
+++ b/torchcp/classification/loss/__init__.py
@@ -8,5 +8,4 @@
 from .cd import CDLoss
 from .conftr import ConfTrLoss
 from .confts import ConfTSLoss
-from .uncertainty_aware import UncertaintyAwareLoss
 from .scpo import SCPOLoss
\ No newline at end of file
diff --git a/torchcp/classification/trainer/__init__.py b/torchcp/classification/trainer/__init__.py
index 52161e4..a3538ef 100644
--- a/torchcp/classification/trainer/__init__.py
+++ b/torchcp/classification/trainer/__init__.py
@@ -10,6 +10,5 @@
 from .confts_trainer import ConfTSTrainer
 from .model_zoo import TemperatureScalingModel
 from .ts_trainer import TSTrainer
-from .ua_trainer import UncertaintyAwareTrainer
 from .ordinal_trainer import OrdinalTrainer
 from .scpo_trainer import SCPOTrainer
\ No newline at end of file
diff --git a/torchcp/regression/predictor/__init__.py b/torchcp/regression/predictor/__init__.py
index 0852cb7..77bcbc3 100644
--- a/torchcp/regression/predictor/__init__.py
+++ b/torchcp/regression/predictor/__init__.py
@@ -10,4 +10,3 @@
 from .split import SplitPredictor
 from .agaci import AgACIPredictor
 from .cpd import ConformalPredictiveDistribution
-from .selection import ConformalSelector
\ No newline at end of file
diff --git a/torchcp/regression/score/__init__.py b/torchcp/regression/score/__init__.py
index 7964058..1519448 100644
--- a/torchcp/regression/score/__init__.py
+++ b/torchcp/regression/score/__init__.py
@@ -13,4 +13,3 @@
 from .cqrr import CQRR
 from .r2ccp import R2CCP
 from .sign import Sign
-from .res import RES
\ No newline at end of file
diff --git a/torchcp/selection/__init__.py b/torchcp/selection/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/torchcp/selection/score/__init__.py b/torchcp/selection/score/__init__.py
new file mode 100644
index 0000000..bb9d168
--- /dev/null
+++ b/torchcp/selection/score/__init__.py
@@ -0,0 +1,10 @@
+# Copyright (c) 2023-present, SUSTech-ML.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+#
+
+
+from .clip import CLIP
+from .res import RES
\ No newline at end of file
diff --git a/torchcp/selection/score/clip.py b/torchcp/selection/score/clip.py
new file mode 100644
index 0000000..889843a
--- /dev/null
+++ b/torchcp/selection/score/clip.py
@@ -0,0 +1,21 @@
+# Copyright (c) 2023-present, SUSTech-ML.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+#
+
+
+import torch
+from torchcp.regression.score.base import BaseScore
+
+
+class CLIP(BaseScore):
+    """
+    CLIP score (Jin et al., 2023), only apply to binary classification.
+    paper: https://arxiv.org/pdf/2210.01408
+    """
+    def __call__(self, predicts, y_truth, M=100):
+        if len(predicts.shape) == 2:
+            predicts = predicts.squeeze().view(-1)
+        return M * torch.max(predicts, 0) - predicts
diff --git a/torchcp/regression/score/res.py b/torchcp/selection/score/res.py
similarity index 100%
rename from torchcp/regression/score/res.py
rename to torchcp/selection/score/res.py
diff --git a/torchcp/selection/selector/__init__.py b/torchcp/selection/selector/__init__.py
new file mode 100644
index 0000000..5e15819
--- /dev/null
+++ b/torchcp/selection/selector/__init__.py
@@ -0,0 +1,8 @@
+# Copyright (c) 2023-present, SUSTech-ML.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+#
+
+from .selection import ConformalSelector
\ No newline at end of file
diff --git a/torchcp/regression/predictor/selection.py b/torchcp/selection/selector/selection.py
similarity index 81%
rename from torchcp/regression/predictor/selection.py
rename to torchcp/selection/selector/selection.py
index 98bb3fa..537b86a 100644
--- a/torchcp/regression/predictor/selection.py
+++ b/torchcp/selection/selector/selection.py
@@ -10,14 +10,12 @@
 
 from torchcp.regression.predictor.split import SplitPredictor
 from torchcp.regression.utils.metrics import Metrics
-from torchcp.utils.common import get_device
 
 
 class ConformalSelector(SplitPredictor):
     """
-    Conformal Selection.
-
-    a screening procedure that aims to select candidates whose unobserved outcomes exceed user-specified value.
+    Conformal Selection:
+        a screening procedure that aims to select candidates whose unobserved outcomes exceed user-specified value.
 
     Args:
         score_function (torchcp.regression.scores): A class that implements the score function.
@@ -32,10 +30,12 @@ class ConformalSelector(SplitPredictor):
         Github: https://github.com/ying531/conformal-selection
     """
 
-    def __init__(self, score_function, model, alpha=0.1, device=None):
+    def __init__(self, score_function, testing_correction, model, alpha=0.1, device=None):
         super().__init__(score_function, model, alpha, device)
+        self.testing_correction = testing_correction
         self._metric = Metrics()
 
+
     def calibrate(self, cal_dataloader):
         self._model.eval()
         predicts_list, y_truth_list = [], []
@@ -51,7 +51,7 @@ def calibrate(self, cal_dataloader):
         self.cal_scores = self.score_function(predicts, y_truth)
 
 
-    def evaluate(self, data_loader, thresholds):
+    def select(self, data_loader, thresholds):
         """
         Evaluate the performance of conformal selection on a test dataset by calculating false discovery proportion
         (FDP) and power of the selection set.
@@ -90,17 +90,8 @@ def evaluate(self, data_loader, thresholds):
         count_less = (self.cal_scores.view(1, n_cal) < scores.view(n_test, 1)).sum(dim=1)
         count_tie = (self.cal_scores.view(1, n_cal) == scores.view(n_test, 1)).sum(dim=1) + 1
         p_values = (count_less + count_tie * u) / (n_cal + 1)
-        p_values= torch.sort(p_values)[0]
-
-        # Conduct BH procedure
-        k_range = torch.arange(1, n_test + 1, device=p_values.device)
-        thresholds = k_range * self.alpha / n_test
-        mask = p_values <= thresholds
-        k_star = torch.max(torch.where(mask, k_range, torch.zeros_like(k_range))) if mask.any() else 0
-        threshold = (k_star * self.alpha / n_test) if k_star > 0 else 0
 
-        # Get indices where p_values <= threshold
-        indices = torch.nonzero(p_values <= threshold, as_tuple=False).squeeze()
+        indices = self.testing_correction(p_values, self.alpha)
 
         # Evaluation
         res_dict = {"false_discovery_proportion": self._metric("false_discovery_proportion")(y_truth, thresholds,
diff --git a/torchcp/selection/testing_correction/__init__.py b/torchcp/selection/testing_correction/__init__.py
new file mode 100644
index 0000000..a0f6b8e
--- /dev/null
+++ b/torchcp/selection/testing_correction/__init__.py
@@ -0,0 +1,10 @@
+# Copyright (c) 2023-present, SUSTech-ML.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+#
+
+
+from .base import Base
+from .bh_procedure import BH_procedure
\ No newline at end of file
diff --git a/torchcp/selection/testing_correction/base.py b/torchcp/selection/testing_correction/base.py
new file mode 100644
index 0000000..6f18b93
--- /dev/null
+++ b/torchcp/selection/testing_correction/base.py
@@ -0,0 +1,27 @@
+# Copyright (c) 2023-present, SUSTech-ML.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+#
+
+
+from abc import ABCMeta, abstractmethod
+import torch
+from tqdm import tqdm
+
+from torchcp.utils.common import get_device
+
+
+class Base(object):
+    """
+    Abstract base class for all multiple testing correction algorithms.
+    """
+    __metaclass__ = ABCMeta
+
+    def __init__(self) -> None:
+        pass
+
+    @abstractmethod
+    def __call__(self, p_values, alpha):
+        raise NotImplementedError
diff --git a/torchcp/selection/testing_correction/bh_procedure.py b/torchcp/selection/testing_correction/bh_procedure.py
new file mode 100644
index 0000000..66e1705
--- /dev/null
+++ b/torchcp/selection/testing_correction/bh_procedure.py
@@ -0,0 +1,49 @@
+# Copyright (c) 2023-present, SUSTech-ML.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+#
+
+
+import torch
+
+from torchcp.regression.score.base import BaseScore
+
+
+class BH_procedure(BaseScore):
+    """
+    Benjamini-Hochberg (BH) procedure:
+        finds a p-value threshold from a list of p-values to determine which null hypotheses to reject, given a target
+        FDR level 'alpha'.
+
+    References:
+        Paper: Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
+               (Benjamini and Hochberg, 1995)
+        Link: https://www.jstor.org/stable/2346101
+    """
+    def __init__(self):
+        super().__init__()
+
+    def __call__(self, p_values, alpha):
+        """
+        Apply the Benjamini-Hochberg procedure.
+
+        Args:
+            p_values (torch.Tensor): A 1D tensor of p-values.
+            alpha (float): The desired False Discovery Rate (FDR) level (e.g., 0.1).
+
+        Returns:
+            torch.Tensor: A 1D tensor of indices corresponding to the p-values (hypotheses) that are rejected.
+        """
+        p_values_sorted, _ = torch.sort(p_values)
+        n_test = p_values_sorted.shape[0]
+
+        k_range = torch.arange(1, n_test + 1, device=p_values_sorted.device)
+        thresholds = k_range * alpha / n_test
+        mask = p_values_sorted <= thresholds
+        k_star = torch.max(torch.where(mask, k_range, torch.zeros_like(k_range))) if mask.any() else 0
+        threshold = (k_star * alpha / n_test) if k_star > 0 else 0
+        indices = torch.nonzero(p_values <= threshold, as_tuple=False).squeeze()
+
+        return indices
diff --git a/torchcp/selection/utils/__init__.py b/torchcp/selection/utils/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/torchcp/selection/utils/metric.py b/torchcp/selection/utils/metric.py
new file mode 100644
index 0000000..5f71b09
--- /dev/null
+++ b/torchcp/selection/utils/metric.py
@@ -0,0 +1,64 @@
+# Copyright (c) 2023-present, SUSTech-ML.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+#
+
+from typing import Any
+
+import torch
+
+from torchcp.utils.registry import Registry
+
+METRICS_REGISTRY_REGRESSION = Registry("METRICS")
+
+
+@METRICS_REGISTRY_REGRESSION.register()
+def false_discovery_proportion(y_truth, thresholds, indices):
+    """
+    Conpute the false discovery proportion (the proportion of false discovery among all selected points) of the
+    selection set.
+
+    Args:
+        y_truth (torch.Tensor): A tensor of ground truth values.
+        thresholds (torch.Tensor): Tensor of user-defined thresholds.
+        indices (torch.Tensor): A tensor containing the indices of selected points.
+
+    Returns:
+        torch.Tensor: The false discovery proportion of the selection set.
+    """
+    if indices.dim() == 0:
+        indices = indices.unsqueeze(0)
+
+    false_positives = torch.sum(y_truth[indices] <= thresholds[indices])
+    fdp = false_positives / indices.shape[-1] if indices.shape[-1] > 0 else torch.tensor(0.)
+    return fdp.item()
+
+
+@METRICS_REGISTRY_REGRESSION.register()
+def power(y_truth, thresholds, indices):
+    """
+        Conpute the power (the proportion of desirable points that are correctly selected) of the selection set.
+
+        Args:
+            y_truth (torch.Tensor): A tensor of ground truth values.
+            thresholds (torch.Tensor): Tensor of user-defined thresholds.
+            indices (torch.Tensor): A tensor containing the indices of selected points.
+
+        Returns:
+            torch.Tensor: The power of the selection set.
+        """
+    if indices.dim() == 0:
+        indices = indices.unsqueeze(0)
+
+    true_positives = torch.sum(y_truth[indices] > thresholds[indices])
+    power = true_positives / torch.sum(y_truth > thresholds)
+    return power.item()
+
+
+class Metrics:
+    def __call__(self, metric) -> Any:
+        if metric not in METRICS_REGISTRY_REGRESSION.registered_names():
+            raise NameError(f"The metric: {metric} is not defined in TorchCP.")
+        return METRICS_REGISTRY_REGRESSION.get(metric)
\ No newline at end of file
diff --git a/torchcp/utils/metrics.py b/torchcp/utils/metrics.py
new file mode 100644
index 0000000..e69de29

From 9241334350a43c07ea81738cb4383e896381213e Mon Sep 17 00:00:00 2001
From: 33798 <3379854453@qq.com>
Date: Sat, 18 Oct 2025 16:08:52 +0800
Subject: [PATCH 6/8] delete FDR and power from regression\utils\metrics.py

---
 torchcp/regression/utils/metrics.py | 43 -----------------------------
 1 file changed, 43 deletions(-)

diff --git a/torchcp/regression/utils/metrics.py b/torchcp/regression/utils/metrics.py
index cdfae02..0303ea9 100644
--- a/torchcp/regression/utils/metrics.py
+++ b/torchcp/regression/utils/metrics.py
@@ -71,49 +71,6 @@ def average_size(prediction_intervals):
     return average_size
 
 
-@METRICS_REGISTRY_REGRESSION.register()
-def false_discovery_proportion(y_truth, thresholds, indices):
-    """
-    Conpute the false discovery proportion (the proportion of false discovery among all selected points) of the
-    selection set.
-
-    Args:
-        y_truth (torch.Tensor): A tensor of ground truth values.
-        thresholds (torch.Tensor): Tensor of user-defined thresholds.
-        indices (torch.Tensor): A tensor containing the indices of selected points.
-
-    Returns:
-        torch.Tensor: The false discovery proportion of the selection set.
-    """
-    if indices.dim() == 0:
-        indices = indices.unsqueeze(0)
-
-    false_positives = torch.sum(y_truth[indices] <= thresholds[indices])
-    fdp = false_positives / indices.shape[-1] if indices.shape[-1] > 0 else torch.tensor(0.)
-    return fdp.item()
-
-
-@METRICS_REGISTRY_REGRESSION.register()
-def power(y_truth, thresholds, indices):
-    """
-        Conpute the power (the proportion of desirable points that are correctly selected) of the selection set.
-
-        Args:
-            y_truth (torch.Tensor): A tensor of ground truth values.
-            thresholds (torch.Tensor): Tensor of user-defined thresholds.
-            indices (torch.Tensor): A tensor containing the indices of selected points.
-
-        Returns:
-            torch.Tensor: The power of the selection set.
-        """
-    if indices.dim() == 0:
-        indices = indices.unsqueeze(0)
-
-    true_positives = torch.sum(y_truth[indices] > thresholds[indices])
-    power = true_positives / torch.sum(y_truth > thresholds)
-    return power.item()
-
-
 class Metrics:
     def __call__(self, metric) -> Any:
         if metric not in METRICS_REGISTRY_REGRESSION.registered_names():

From 10cb8ab82fe21cb08f454dccdcc41250b439e6f2 Mon Sep 17 00:00:00 2001
From: 33798 <3379854453@qq.com>
Date: Sat, 18 Oct 2025 16:13:27 +0800
Subject: [PATCH 7/8] style: rename selection\utils\metrics.py

---
 torchcp/selection/utils/{metric.py => metrics.py} | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename torchcp/selection/utils/{metric.py => metrics.py} (100%)

diff --git a/torchcp/selection/utils/metric.py b/torchcp/selection/utils/metrics.py
similarity index 100%
rename from torchcp/selection/utils/metric.py
rename to torchcp/selection/utils/metrics.py

From 6ee10927bb4689edbc637e62d41f6a60a633d9a4 Mon Sep 17 00:00:00 2001
From: 33798 <3379854453@qq.com>
Date: Sat, 18 Oct 2025 16:17:22 +0800
Subject: [PATCH 8/8] style: rename selection\selector\conformal_selector.py

---
 torchcp/selection/selector/__init__.py                          | 2 +-
 .../selection/selector/{selection.py => conformal_selector.py}  | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)
 rename torchcp/selection/selector/{selection.py => conformal_selector.py} (98%)

diff --git a/torchcp/selection/selector/__init__.py b/torchcp/selection/selector/__init__.py
index 5e15819..556c204 100644
--- a/torchcp/selection/selector/__init__.py
+++ b/torchcp/selection/selector/__init__.py
@@ -5,4 +5,4 @@
 # LICENSE file in the root directory of this source tree.
 #
 
-from .selection import ConformalSelector
\ No newline at end of file
+from .conformal_selector import ConformalSelector
\ No newline at end of file
diff --git a/torchcp/selection/selector/selection.py b/torchcp/selection/selector/conformal_selector.py
similarity index 98%
rename from torchcp/selection/selector/selection.py
rename to torchcp/selection/selector/conformal_selector.py
index 537b86a..81efaae 100644
--- a/torchcp/selection/selector/selection.py
+++ b/torchcp/selection/selector/conformal_selector.py
@@ -9,7 +9,7 @@
 import torch
 
 from torchcp.regression.predictor.split import SplitPredictor
-from torchcp.regression.utils.metrics import Metrics
+from torchcp.selection.utils.metrics import Metrics
 
 
 class ConformalSelector(SplitPredictor):