Disagreement for auroc1 with sklearn

[crazyn2]

🐛 Describe the bug

Torcheval gives a different answer from sklearn for roc auc

mse_roc_auc_trh=0.553, mse_roc_auc_sk=0.596

here is my code repository https://github.com/crazyn2/mini-ad please set download=True in datamodules/cifar10.py and run this command

python main/cifar10/msd/aev1v3msdv1.py --seed 0 --pre_epochs 200 --progress_bar --visual --epochs 20 --normal_class 1 --log_path /home/zby/Workspaces/mini-ad --batch_size 100 --n_trials 2 --sampler random --monitor mse

Versions

PyTorch version: 2.1.1+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.3 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: Could not collect
CMake version: version 3.22.1
Libc version: glibc-2.35

Python version: 3.11.5 (main, Sep 11 2023, 13:54:46) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-6.2.0-39-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 11.5.119
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: 
GPU 0: NVIDIA GeForce RTX 3080 Ti
GPU 1: NVIDIA GeForce RTX 3080 Ti
GPU 2: NVIDIA GeForce RTX 3090

Nvidia driver version: 535.129.03
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architecture:                       x86_64
CPU op-mode(s):                     32-bit, 64-bit
Address sizes:                      46 bits physical, 48 bits virtual
Byte Order:                         Little Endian
CPU(s):                             24
On-line CPU(s) list:                0-23
Vendor ID:                          GenuineIntel
Model name:                         Intel(R) Core(TM) i9-10920X CPU @ 3.50GHz
CPU family:                         6
Model:                              85
Thread(s) per core:                 2
Core(s) per socket:                 12
Socket(s):                          1
Stepping:                           7
CPU max MHz:                        4800.0000
CPU min MHz:                        1200.0000
BogoMIPS:                           6999.82
Flags:                              fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cdp_l3 invpcid_single ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req avx512_vnni md_clear flush_l1d arch_capabilities
Virtualization:                     VT-x
L1d cache:                          384 KiB (12 instances)
L1i cache:                          384 KiB (12 instances)
L2 cache:                           12 MiB (12 instances)
L3 cache:                           19.3 MiB (1 instance)
NUMA node(s):                       1
NUMA node0 CPU(s):                  0-23
Vulnerability Gather data sampling: Mitigation; Microcode
Vulnerability Itlb multihit:        KVM: Mitigation: VMX disabled
Vulnerability L1tf:                 Not affected
Vulnerability Mds:                  Not affected
Vulnerability Meltdown:             Not affected
Vulnerability Mmio stale data:      Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Retbleed:             Mitigation; Enhanced IBRS
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass:    Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1:           Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:           Mitigation; Enhanced IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Mitigation; TSX disabled

Versions of relevant libraries:
[pip3] flake8==6.0.0
[pip3] kmeans-pytorch==0.3
[pip3] mypy-extensions==1.0.0
[pip3] numpy==1.26.2
[pip3] pytorch-lightning==2.0.9.post0
[pip3] torch==2.1.1
[pip3] torch-tb-profiler==0.4.3
[pip3] torchaudio==2.1.0
[pip3] torchdata==0.7.1
[pip3] torcheval==0.0.7
[pip3] torchmetrics==1.2.1
[pip3] torchsummary==1.5.1
[pip3] torchtext==0.16.1
[pip3] torchvision==0.16.1
[pip3] triton==2.1.0
[conda] blas                      1.0                         mkl    defaults
[conda] kmeans-pytorch            0.3                      pypi_0    pypi
[conda] mkl                       2023.1.0         h213fc3f_46344    defaults
[conda] mkl-service               2.4.0           py311h5eee18b_1    defaults
[conda] mkl_fft                   1.3.8           py311h5eee18b_0    defaults
[conda] mkl_random                1.2.4           py311hdb19cb5_0    defaults
[conda] numpy                     1.26.2          py311h08b1b3b_0    defaults
[conda] numpy-base                1.26.2          py311hf175353_0    defaults
[conda] pytorch-cuda              12.1                 ha16c6d3_5    pytorch
[conda] pytorch-lightning         2.0.9.post0              pypi_0    pypi
[conda] pytorch-mutex             1.0                        cuda    pytorch
[conda] torch                     2.1.1                    pypi_0    pypi
[conda] torch-tb-profiler         0.4.3                    pypi_0    pypi
[conda] torchaudio                2.1.0               py311_cu121    pytorch
[conda] torchdata                 0.7.1                    pypi_0    pypi
[conda] torcheval                 0.0.7                    pypi_0    pypi
[conda] torchmetrics              1.2.1                    pypi_0    pypi
[conda] torchsummary              1.5.1                    pypi_0    pypi
[conda] torchtext                 0.16.1                   pypi_0    pypi
[conda] torchvision               0.16.1                   pypi_0    pypi
[conda] triton                    2.1.0                    pypi_0    pypi

[bobakfb]

Hey @crazyn2 thanks for the post. We do have tests against Sklearn that are passing and have passed a bunch of times since auroc landed. Can you give a specific set of inputs that yields a different output?

[crazyn2]

Thank you for your prompt response. I've provide my code repository of github https://github.com/crazyn2/mini-ad which contained the whole procedures including dataset inputs ? If you have time, you can reproduce the result utilizing those codes.

[crazyn2]

I've identified the reason for the discrepancy in AUC-ROC values with sklearn. I realized that I forgot to reset the metric class in my loop. Apologies for any inconvenience.