Format

examples/list_resources.py

@@ -1,4 +1,5 @@
 """Example on how to list all resources available in `detectors` package."""
+
 import detectors
 
 if __name__ == "__main__":

scripts/parse_arxiv.py

@@ -1,6 +1,7 @@
 """Requirements
     - feedparser installed: pip install feedparser
 """
+
 import argparse
 import json
 import logging

scripts/push_model_to_hf_hub.py

@@ -4,6 +4,7 @@
 - Jinja2 installed
 - Git LFS installed
 """
+
 import argparse
 import json
 import logging

src/detectors/aggregations/combine_and_conquer.py

@@ -25,7 +25,15 @@ def p_value_fn(test_statistic: np.ndarray, X: np.ndarray, w=None):
     y_ecdf = np.concatenate([np.arange(1, X.shape[0] + 1).reshape(-1, 1) / X.shape[0]] * X.shape[1], axis=1)
     if w is not None:
         y_ecdf = y_ecdf * w.reshape(1, -1)
-    return np.concatenate(list(map(lambda xx: np.interp(*xx).reshape(-1, 1), zip(test_statistic.T, X.T, y_ecdf.T))), 1)
+    return np.concatenate(
+        list(
+            map(
+                lambda xx: np.interp(*xx).reshape(-1, 1),
+                zip(test_statistic.T, X.T, y_ecdf.T),
+            )
+        ),
+        1,
+    )
 
 
 def fisher_method(p_values: np.ndarray):
@@ -153,7 +161,10 @@ def simes_tau_method(p_values: np.ndarray):
     Returns:
         np.ndarray (n,): combined p-values
     """
-    tau = np.min(np.sort(p_values, axis=1) / np.arange(1, p_values.shape[1] + 1) * p_values.shape[1], 1)
+    tau = np.min(
+        np.sort(p_values, axis=1) / np.arange(1, p_values.shape[1] + 1) * p_values.shape[1],
+        1,
+    )
     return tau
 
 
@@ -169,6 +180,7 @@ def geometric_mean_tau_method(p_values: np.ndarray):
     tau = np.prod(p_values, axis=1) ** (1 / p_values.shape[1])
     return tau
 
+
 def rho(p_values):
     k = p_values.shape[1]
     phi = stats.norm.ppf(p_values)
@@ -178,7 +190,8 @@ def rho(p_values):
 def hartung(p_values, r):
     k = p_values.shape[1]
     t = stats.norm.ppf(p_values)
-    return np.sum(t, axis=1) / np.sqrt((1 - r) * k + r * k**2
+    return np.sum(t, axis=1) / np.sqrt((1 - r) * k + r * k**2)
+
 
 def get_combine_p_values_fn(method_name: str):
     method_name = method_name.lower()
@@ -202,4 +215,13 @@ def get_combine_p_values_fn(method_name: str):
         raise NotImplementedError(f"method {method_name} not implemented")
 
 
-ensemble_names = ["fisher", "stouffer", "tippet", "wilkinson", "edgington", "pearson", "simes", "geometric_mean"]
+ensemble_names = [
+    "fisher",
+    "stouffer",
+    "tippet",
+    "wilkinson",
+    "edgington",
+    "pearson",
+    "simes",
+    "geometric_mean",
+]

src/detectors/config.py

@@ -10,6 +10,7 @@
 - `CHECKPOINTS_DIR`: The directory where the checkpoints are stored.
 - `RESULTS_DIR`: The directory where the results are stored.
 """
+
 import os
 
 HOME = os.path.dirname(os.path.dirname(os.path.dirname(__file__)))

src/detectors/data/__init__.py

@@ -1,6 +1,7 @@
 """
 Datasets module.
 """
+
 import logging
 import os
 from enum import Enum

src/detectors/data/imagenetlt.py

@@ -1,4 +1,5 @@
 """From https://github.com/KaihuaTang/Long-Tailed-Recognition.pytorch/blob/master/classification/data/dataloader.py"""
+
 import os
 
 import numpy as np

src/detectors/eval.py

@@ -1,6 +1,7 @@
 """
 Module containing evaluation metrics.
 """
+
 from typing import Dict, Union
 
 import numpy as np

src/detectors/methods/__init__.py

@@ -1,6 +1,7 @@
 """
 Detection methods.
 """
+
 import logging
 import types
 from enum import Enum

src/detectors/methods/templates.py

@@ -1,6 +1,7 @@
 """
 Generalized detection methods templates.
 """
+
 import logging
 from abc import ABC, abstractmethod
 from typing import Dict, List, Optional

src/detectors/models/densenet.py

@@ -1,4 +1,5 @@
 """Densenet models for CIFAR10, CIFAR100 and SVHN datasets."""
+
 import timm
 import timm.models
 import torch

src/detectors/models/dino.py

@@ -3,6 +3,7 @@
 https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
 https://github.com/facebookresearch/dino/
 """
+
 import math
 from functools import partial
 

src/detectors/models/resnet.py

@@ -1,4 +1,5 @@
 """ResNet models for CIFAR10, CIFAR100, and SVHN datasets."""
+
 import logging
 
 import timm

src/detectors/models/vgg.py

@@ -1,4 +1,5 @@
 """VGG models for CIFAR10, CIFAR100 and SVHN datasets."""
+
 import timm
 import timm.models
 import torch

src/detectors/models/vit.py

@@ -1,4 +1,5 @@
 """Finetuned ViT models for CIFAR10, CIFAR100, and SVHN datasets."""
+
 import timm
 import timm.models
 import torch

src/detectors/pipelines/__init__.py

@@ -1,6 +1,7 @@
 """
 Pipeline module.
 """
+
 from enum import Enum
 from typing import Any, List, Optional, Tuple
 

src/detectors/pipelines/base.py

@@ -1,4 +1,5 @@
 """Base abstract pipeline class."""
+
 import logging
 from typing import Any, Dict
 

src/detectors/pipelines/ood.py

@@ -1,6 +1,7 @@
 """
 OOD Pipelines.
 """
+
 import logging
 import time
 from typing import Any, Callable, Dict, List, Literal, Tuple, Union

src/detectors/trainer.py

@@ -1,4 +1,5 @@
 """Trainer for classification models."""
+
 import json
 import logging
 import os