Open
Description
Describe the bug
When fitting on constant regression target, errors
Steps/Code to Reproduce
import numpy as np
from tabpfn import TabPFNRegressor
# Set random seed for reproducibility
np.random.seed(42)
# Create a synthetic dataset with constant targets
n_samples = 100
n_features = 10
constant_value = 5.0
# Generate random features
X = np.random.normal(size=(n_samples, n_features))
# Create constant target values
y = np.ones(n_samples) * constant_value
# Split data into training and testing sets
n_train = int(0.8 * n_samples)
X_train, X_test = X[:n_train], X[n_train:]
y_train, y_test = y[:n_train], y[n_train:]
print(f"Training with {n_train} samples, testing with {n_samples - n_train} samples")
print(f"Target values are constant: {constant_value}")
# Initialize and train the tabPFN regressor
model = TabPFNRegressor(device='cpu')
# Train the model
model.fit(X_train, y_train)
# Make predictions
y_pred = model.predict(X_test)
# Print a few predictions to see the results
print(f"First 5 predictions: {y_pred[:5]}")
print(f"Expected value: {constant_value}")
Expected Results
No error is thrown, predict constant value
Actual Results
AssertionError Traceback (most recent call last)
in <cell line: 0>()
28
29 # Train the model
---> 30 model.fit(X_train, y_train)
31
32 # Make predictions
3 frames
/content/tabpfn/src/tabpfn/model/bar_distribution.py in init(self, borders, ignore_nan_targets)
38 full_width = self.bucket_widths.sum()
39
---> 40 assert (1 - (full_width / (self.borders[-1] - self.borders[0]))).abs() < 1e-2, (
41 f"diff: {full_width - (self.borders[-1] - self.borders[0])} with"
42 f" {full_width} {self.borders[-1]} {self.borders[0]}"
AssertionError: diff: 0.0 with 0.0 5.0 5.0
Versions
PyTorch version: 2.6.0+cu124
CUDA used to build PyTorch: 12.4
ROCM used to build PyTorch: N/A
OS: Ubuntu 22.04.4 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: 14.0.0-1ubuntu1.1
CMake version: version 3.31.6
Libc version: glibc-2.35
Python version: 3.11.11 (main, Dec 4 2024, 08:55:07) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-6.1.85+-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 12.5.82
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA A100-SXM4-40GB
Nvidia driver version: 550.54.15
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.9.2.1
/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.2.1
/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.2.1
/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.2.1
/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.2.1
/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.2.1
/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.2.1
/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.2.1
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): 12
On-line CPU(s) list: 0-11
Vendor ID: GenuineIntel
Model name: Intel(R) Xeon(R) CPU @ 2.20GHz
CPU family: 6
Model: 85
Thread(s) per core: 2
Core(s) per socket: 6
Socket(s): 1
Stepping: 7
BogoMIPS: 4400.29
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm mpx avx512f avx512dq rdseed adx smap clflushopt clwb avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves arat avx512_vnni md_clear arch_capabilities
Hypervisor vendor: KVM
Virtualization type: full
L1d cache: 192 KiB (6 instances)
L1i cache: 192 KiB (6 instances)
L2 cache: 6 MiB (6 instances)
L3 cache: 38.5 MiB (1 instance)
NUMA node(s): 1
NUMA node0 CPU(s): 0-11
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit: Not affected
Vulnerability L1tf: Not affected
Vulnerability Mds: Not affected
Vulnerability Meltdown: Not affected
Vulnerability Mmio stale data: Vulnerable
Vulnerability Reg file data sampling: Not affected
Vulnerability Retbleed: Vulnerable
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass: Vulnerable
Vulnerability Spectre v1: Vulnerable: __user pointer sanitization and usercopy barriers only; no swapgs barriers
Vulnerability Spectre v2: Vulnerable; IBPB: disabled; STIBP: disabled; PBRSB-eIBRS: Vulnerable; BHI: Vulnerable (Syscall hardening enabled)
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Vulnerable
Dependency Versions:
--------------------
tabpfn: 2.0.6
torch: 2.6.0+cu124
numpy: 1.26.4
scipy: 1.14.1
pandas: 2.2.2
scikit-learn: 1.5.2
typing_extensions: 4.12.2
einops: 0.8.1
huggingface-hub: 0.28.1