Closed gounley closed 1 year ago
Hi @gounley thanks for bringing this up. I'm not able to replicate this locally on my end. Can you try and see if using torcheval-nightly also results in the discrepancy?
Nightly (2023.8.20) gives me the same incorrect result.
if you create a new environment and install torcheval-nightly, does it still produce the same incorrect result?
I made a new environment on a different machine and the result is still the same.
Versions: Collecting environment information... PyTorch version: 2.0.1 Is debug build: False CUDA used to build PyTorch: None ROCM used to build PyTorch: N/A
OS: macOS 13.5.1 (arm64) GCC version: Could not collect Clang version: Could not collect CMake version: Could not collect Libc version: N/A
Python version: 3.11.4 (main, Jul 5 2023, 08:40:20) [Clang 14.0.6 ] (64-bit runtime) Python platform: macOS-13.5.1-arm64-arm-64bit Is CUDA available: False CUDA runtime version: No CUDA CUDA_MODULE_LOADING set to: N/A GPU models and configuration: No CUDA Nvidia driver version: No CUDA cuDNN version: No CUDA HIP runtime version: N/A MIOpen runtime version: N/A Is XNNPACK available: True
CPU: Apple M2
Versions of relevant libraries: [pip3] numpy==1.25.2 [pip3] torch==2.0.1 [pip3] torchaudio==2.0.2 [pip3] torcheval-nightly==2023.8.20 [pip3] torchmetrics==1.1.0 [pip3] torchvision==0.15.2 [conda] numpy 1.25.2 pypi_0 pypi [conda] torch 2.0.1 pypi_0 pypi [conda] torchaudio 2.0.2 pypi_0 pypi [conda] torcheval-nightly 2023.8.20 pypi_0 pypi [conda] torchmetrics 1.1.0 pypi_0 pypi [conda] torchvision 0.15.2 pypi_0 pypi
Ah ok, I found the error - it was in our test environment. I'll close the issue.
🐛 Describe the bug
Torcheval gives a different answer from sklearn and torchmetrics for macro MulticlassF1Score when there are classes in the target that are not included in the prediction:
Example:
Output:
Expectation is that the torcheval result would match torchmetrics and sklearn, which are consistent with calculating this by hand.
Package versions: torch : 2.1.0a0+gita014d1b torchmetrics: 1.0.3 torcheval : 0.0.6 sklearn : 1.2.2
Versions
Collecting environment information... PyTorch version: 2.1.0a0+gita014d1b Is debug build: False CUDA used to build PyTorch: N/A ROCM used to build PyTorch: 5.4.22801-aaa1e3d8
OS: SUSE Linux Enterprise Server 15 SP4 (x86_64) GCC version: (SUSE Linux) 7.5.0 Clang version: 15.0.0 (324a8e7de6a18594c06a0ee5d8c0eda2109c6ac6) CMake version: version 3.20.4 Libc version: glibc-2.31
Python version: 3.9.16 (main, Jan 11 2023, 16:05:54) [GCC 11.2.0] (64-bit runtime) Python platform: Linux-5.14.21-150400.24.46_12.0.83-cray_shasta_c-x86_64-with-glibc2.31 Is CUDA available: True CUDA runtime version: Could not collect CUDA_MODULE_LOADING set to: LAZY GPU models and configuration: Nvidia driver version: Could not collect cuDNN version: Could not collect HIP runtime version: 5.4.22801 MIOpen runtime version: 2.19.0 Is XNNPACK available: True
CPU: Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Address sizes: 48 bits physical, 48 bits virtual Byte Order: Little Endian CPU(s): 128 On-line CPU(s) list: 0-127 Vendor ID: AuthenticAMD Model name: AMD EPYC 7763 64-Core Processor CPU family: 25 Model: 1 Thread(s) per core: 2 Core(s) per socket: 64 Socket(s): 1 Stepping: 1 BogoMIPS: 4890.91 Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 invpcid_single hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 invpcid cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr rdpru wbnoinvd amd_ppin arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold v_vmsave_vmload vgif v_spec_ctrl umip pku ospke vaes vpclmulqdq rdpid overflow_recov succor smca Virtualization: AMD-V L1d cache: 2 MiB (64 instances) L1i cache: 2 MiB (64 instances) L2 cache: 32 MiB (64 instances) L3 cache: 256 MiB (8 instances) NUMA node(s): 4 NUMA node0 CPU(s): 0-15,64-79 NUMA node1 CPU(s): 16-31,80-95 NUMA node2 CPU(s): 32-47,96-111 NUMA node3 CPU(s): 48-63,112-127 Vulnerability Itlb multihit: Not affected Vulnerability L1tf: Not affected Vulnerability Mds: Not affected Vulnerability Meltdown: Not affected Vulnerability Mmio stale data: Not affected Vulnerability Retbleed: Not affected Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization Vulnerability Spectre v2: Mitigation; Retpolines, IBPB conditional, IBRS_FW, STIBP always-on, RSB filling, PBRSB-eIBRS Not affected Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Not affected
Versions of relevant libraries: [pip3] mypy-extensions==1.0.0 [pip3] numpy==1.24.0 [pip3] torch==2.1.0a0+gita014d1b [pip3] torcheval==0.0.6 [pip3] torchmetrics==1.0.3 [pip3] torchtnt==0.2.0 [conda] blas 1.0 mkl [conda] mkl 2023.1.0 h6d00ec8_46342 [conda] mkl-service 2.4.0 py39h5eee18b_1 [conda] mkl_fft 1.3.6 py39h417a72b_1 [conda] mkl_random 1.2.2 py39h417a72b_1 [conda] numpy 1.22.4 pypi_0 pypi [conda] torch 2.1.0a0+gita014d1b pypi_0 pypi [conda] torcheval 0.0.6 pypi_0 pypi [conda] torchmetrics 1.0.3 pypi_0 pypi [conda] torchtnt 0.2.0 pypi_0 pypi