⚡ TabPFN: Foundation Model for Tabular Data ⚡
This report presents the forensic synthetic code analysis of PriorLabs/TabPFN, a Python project with 7,600 GitHub stars. SynthScan v2.0 examined 80,459 lines of code across 241 source files, recording 1457 pattern matches distributed across 17 syntactic categories. The overall adjusted score of 25.0 places this repository in the Moderate AI signal band.
The scanner applied 160+ deterministic lexical heuristics, multi-line block detectors, abstract syntax tree depth profilers, and a cross-file Jaccard similarity matrix to construct a statistically normalised synthetic code estimate. All matches are individually weighted by severity coefficient and contextual multiplier before summation, and the resulting headline score is temporally discounted to account for the repository's development history relative to the commercial emergence of large language model coding tooling (November 2022 onward).
This chart maps the temporal evolution of the adjusted synthetic code score across successive scan runs. An upward trajectory indicates ongoing incorporation of AI-generated code or expanding LLM-assisted scaffolding; a stable or declining trajectory may reflect active human refactoring, code removal, or the adoption of stricter authorship policies. The dashed secondary line (right axis) independently tracks total raw pattern hit count, which can diverge from the normalised score when codebase size changes significantly between scans.
Classifies detected patterns by their diagnostic confidence and structural impact. CRITICAL patterns (coefficient 10) represent definitive synthetic signatures — hallucinated imports, explicit LLM attribution metadata — virtually never produced by human authors. HIGH (5) indicates strong structural tells such as cross-file repetition or cross-linguistic idioms. MEDIUM (2) covers recognisable conversational padding and AI-specific vocabulary. LOW (1) captures subtle indicators like tautological comments and generic boilerplate that require density to carry independent signal.
This horizontal bar chart decomposes the repository's raw synthetic code score by top-level directory, allowing you to pinpoint precisely which modules or components carry the highest AI authorship density. Directories with disproportionately high scores relative to their size warrant targeted manual review: concentrated AI signatures often trace back to mass-generated configuration layers, auto-ported test suites, LLM-scaffolded boilerplate classes, or entire subsystems authored under heavy copilot assistance. Use this view to prioritise your human code-review effort.
The scanner identified 1457 distinct pattern matches across 17 syntactic categories. Each entry below represents a discrete location in the source code where the engine recorded a statistically significant AI authorship indicator. Expand any category row to inspect the individual file paths, line numbers, code snippets, and the lexical context (CODE, COMMENT, or STRING) in which each match was detected.
Reading the findings table: The Severity column indicates the diagnostic confidence level (CRITICAL / HIGH / MEDIUM / LOW). The Context column identifies whether the match occurred inside executable code, an inline comment, or a string literal — comment-context matches receive a ×1.5 weight because LLMs systematically over-annotate. The ⚡ bolt icon marks clustered matches: three or more patterns within a 10-line window, each receiving an additional ×1.5 density multiplier as dense clusters constitute far stronger evidence of synthetic authorship than isolated hits.
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | tests/test_inference.py | 103 | def reset_save_peak_mem_factor(self, factor: int | None = None) -> None: | CODE |
| LOW | tests/test_inference.py | 146 | def reset_save_peak_mem_factor(self, factor: int | None = None) -> None: | CODE |
| LOW | tests/test_inference.py | 225 | def reset_save_peak_mem_factor(self, factor: int | None = None) -> None: | CODE |
| LOW | tests/test_inference.py | 229 | def test__cache_preprocessing__result_equal_in_serial_and_in_parallel() -> None: | CODE |
| LOW | tests/test_inference.py | 282 | def test__cache_preprocessing__with_outlier_removal() -> None: | CODE |
| LOW | tests/test_inference.py | 336 | def test__on_demand__result_equal_in_serial_and_in_parallel() -> None: | CODE |
| LOW | tests/test_inference.py | 399 | def test__iter_outputs__task_type_forwarded( | CODE |
| LOW | tests/test_inference.py | 443 | def _create_test_ensemble_configs( | CODE |
| LOW | tests/test_inference.py | 492 | def test__explicit_kv_cache__produces_outputs() -> None: | CODE |
| LOW | tests/test_inference.py | 541 | def test__explicit_kv_cache__keep_on_device_reuses_tensors(device: str) -> None: | CODE |
| LOW | tests/test_inference.py | 614 | def test__public_keep_cache_on_device__controls_cache_placement( | CODE |
| LOW | tests/test_inference.py | 676 | def test__init__mps_target_device__applies_mps_linear_bug_workaround( | CODE |
| LOW | tests/test_inference.py | 732 | def test__to__mps_target_device__applies_mps_linear_bug_workaround( | CODE |
| LOW | tests/test_inference.py | 784 | def test__kv_cache_chunking__matches_unchunked( | CODE |
| LOW | tests/test_inference.py | 854 | def test__kv_cache_chunking__train_embeddings_not_duplicated( | CODE |
| LOW | tests/test_consistency.py | 51 | def _get_tiny_classification_data() -> tuple[np.ndarray, np.ndarray, np.ndarray]: | CODE |
| LOW | tests/test_consistency.py | 68 | def _get_tiny_regression_data() -> tuple[np.ndarray, np.ndarray, np.ndarray]: | CODE |
| LOW | tests/test_consistency.py | 84 | def _get_iris_multiclass_data() -> tuple[np.ndarray, np.ndarray, np.ndarray]: | CODE |
| LOW | tests/test_consistency.py | 245 | def _get_current_platform_string() -> str: | CODE |
| LOW | tests/test_consistency.py | 271 | def test__fit_predict__predictions_match_reference( | CODE |
| LOW | tests/test_consistency.py | 314 | def _load_reference_predictions_or_fail(test_name: str) -> np.ndarray: | CODE |
| LOW | tests/test_consistency.py | 324 | def save_reference_predictions() -> None: | CODE |
| LOW | tests/test_utils.py | 21 | def test__infer_devices__auto__cuda_and_mps_not_available__selects_cpu( | CODE |
| LOW | tests/test_utils.py | 30 | def test__infer_devices__auto__single_cuda_gpu_available__selects_it( | CODE |
| LOW | tests/test_utils.py | 41 | def test__infer_devices__auto__multiple_cuda_gpus_available__selects_all( | CODE |
| LOW | tests/test_utils.py | 57 | def test__infer_devices__auto__cuda_and_mps_available_but_excluded__selects_cpu( | CODE |
| LOW | tests/test_utils.py | 68 | def test__infer_devices__auto__mps_available_but_torch_too_old__selects_cpu( | CODE |
| LOW | tests/test_utils.py | 77 | def test__infer_devices__device_specified__selects_it( | CODE |
| LOW | tests/test_utils.py | 88 | def test__infer_devices__multiple_devices_specified___selects_them( | CODE |
| LOW | tests/test_utils.py | 101 | def test__infer_devices__device_specified_twice__raises() -> None: | CODE |
| LOW | tests/test_utils.py | 109 | def test__infer_devices__mps_specified_but_torch_too_old__raises( | CODE |
| LOW | tests/test_utils.py | 199 | def test_balance_probas_by_class_counts(): | CODE |
| LOW | tests/test_utils.py | 215 | def test__translate_probs_across_borders__matches_unchunked(batch: int) -> None: | CODE |
| LOW | tests/test_utils.py | 232 | def test__translate_probs_across_borders__forces_chunking( | CODE |
| LOW | tests/test_estimators.py | 32 | def test__to__before_fit__does_not_crash( | CODE |
| LOW | tests/test_estimators.py | 50 | def test__to__between_fit_and_predict__does_not_crash( | CODE |
| LOW | tests/test_estimators.py | 68 | def test__to__between_fits__outputs_equal( | CODE |
| LOW | tests/test_estimators.py | 105 | def test__to__after_fit__no_tensors_left_on_old_device( | CODE |
| LOW | tests/test_estimators.py | 128 | def test__to__after_fit_and_predict__no_tensors_left_on_old_device( | CODE |
| LOW | tests/test_notebook_installs.py | 96 | def test_notebook_resolves_latest_pinned_packages( | CODE |
| LOW | tests/test_save_load_fitted_model.py | 31 | def _make_classification_data_with_categoricals() -> tuple[np.ndarray, np.ndarray]: | CODE |
| LOW | tests/test_save_load_fitted_model.py | 48 | def _assert_roundtrip_predictions( | CODE |
| LOW | tests/test_save_load_fitted_model.py | 95 | def test__save_and_load_twice__predictions_equal_to_before_save( | CODE |
| LOW | tests/test_save_load_fitted_model.py | 134 | def test__save_fit_state__does_not_move_live_estimator_to_cpu( | CODE |
| LOW | tests/test_save_load_fitted_model.py | 156 | def test__save_fit_state__keeps_tabpfn_fit_parent_name(tmp_path: Path) -> None: | CODE |
| LOW | tests/test_save_load_fitted_model.py | 176 | def test_saving_unfitted_model_raises_error(tmp_path: Path) -> None: | CODE |
| LOW | tests/test_save_load_fitted_model.py | 183 | def test__load_regressor_state_in_classifier__raises_error(tmp_path: Path) -> None: | CODE |
| LOW | tests/test_save_load_fitted_model.py | 196 | def test__load_classifier_state_in_regressor__raises_error(tmp_path: Path) -> None: | CODE |
| LOW | tests/test_save_load_fitted_model.py | 209 | def _init_and_save_unique_checkpoint( | CODE |
| LOW | tests/test_save_load_fitted_model.py | 224 | def test_saving_and_loading_model_with_weights(tmp_path: Path) -> None: | CODE |
| LOW | tests/test_save_load_fitted_model.py | 263 | def test_saving_and_loading_multiple_models_with_weights( | CODE |
| LOW | tests/test_save_load_fitted_model.py | 310 | def test_saving_and_loading_with_tuning_config( | CODE |
| LOW | tests/test_save_load_fitted_model.py | 358 | def test__save_and_load_fit_with_cache__predictions_equal( | CODE |
| LOW | tests/test_save_load_fitted_model.py | 408 | def test__save_and_load_fit_with_cache_twice__predictions_equal( | CODE |
| LOW | tests/test_inference_config.py | 21 | def test__save_and_load__loaded_value_equal_to_saved() -> None: | CODE |
| LOW | tests/test_inference_config.py | 34 | def test__override_with_user_input__dict_of_overrides__sets_values_correctly() -> None: | CODE |
| LOW | tests/test_inference_config.py | 57 | def test__override_with_user_input__config_override__replaces_entire_config() -> None: | CODE |
| LOW⚡ | tests/test_inference_config.py | 127 | def test__classifier_get_inference_config__before_fit__returns_config() -> None: | CODE |
| LOW⚡ | tests/test_inference_config.py | 136 | def test__classifier_get_inference_config__returns_deepcopy() -> None: | CODE |
| LOW⚡ | tests/test_inference_config.py | 145 | def test__classifier_get_inference_config__with_override__applies_override() -> None: | CODE |
| 812 more matches not shown… | ||||
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| MEDIUM⚡ | tests/test_finetuning_logging.py | 15 | # --------------------------------------------------------------------------- | COMMENT |
| MEDIUM⚡ | tests/test_finetuning_logging.py | 17 | # --------------------------------------------------------------------------- | COMMENT |
| MEDIUM⚡ | tests/test_browser_auth.py | 56 | # --------------------------------------------------------------------------- | COMMENT |
| MEDIUM⚡ | tests/test_browser_auth.py | 58 | # --------------------------------------------------------------------------- | COMMENT |
| MEDIUM⚡ | tests/test_browser_auth.py | 108 | # --------------------------------------------------------------------------- | COMMENT |
| MEDIUM⚡ | tests/test_browser_auth.py | 110 | # --------------------------------------------------------------------------- | COMMENT |
| MEDIUM⚡ | tests/test_browser_auth.py | 131 | # --------------------------------------------------------------------------- | COMMENT |
| MEDIUM⚡ | tests/test_browser_auth.py | 133 | # --------------------------------------------------------------------------- | COMMENT |
| MEDIUM⚡ | tests/test_browser_auth.py | 591 | # --------------------------------------------------------------------------- | COMMENT |
| MEDIUM⚡ | tests/test_browser_auth.py | 593 | # --------------------------------------------------------------------------- | COMMENT |
| MEDIUM | tests/test_browser_auth.py | 26 | # --------------------------------------------------------------------------- | COMMENT |
| MEDIUM | tests/test_browser_auth.py | 28 | # --------------------------------------------------------------------------- | COMMENT |
| MEDIUM | tests/test_browser_auth.py | 235 | # --------------------------------------------------------------------------- | COMMENT |
| MEDIUM | tests/test_browser_auth.py | 237 | # --------------------------------------------------------------------------- | COMMENT |
| MEDIUM | tests/test_browser_auth.py | 358 | # --------------------------------------------------------------------------- | COMMENT |
| MEDIUM | tests/test_browser_auth.py | 360 | # --------------------------------------------------------------------------- | COMMENT |
| MEDIUM | tests/test_browser_auth.py | 405 | # --------------------------------------------------------------------------- | COMMENT |
| MEDIUM | tests/test_browser_auth.py | 407 | # --------------------------------------------------------------------------- | COMMENT |
| MEDIUM⚡ | tests/test_regressor_interface.py | 1031 | # --------------------------------------------------------------------------- | COMMENT |
| MEDIUM⚡ | tests/test_regressor_interface.py | 1033 | # --------------------------------------------------------------------------- | COMMENT |
| MEDIUM⚡ | tests/test_finetuning_classifier.py | 391 | # ============================================================================= | COMMENT |
| MEDIUM⚡ | tests/test_finetuning_classifier.py | 393 | # ============================================================================= | COMMENT |
| MEDIUM⚡ | tests/test_finetuning_classifier.py | 570 | # ============================================================================= | COMMENT |
| MEDIUM⚡ | tests/test_finetuning_classifier.py | 572 | # ============================================================================= | COMMENT |
| MEDIUM⚡ | tests/test_finetuning_classifier.py | 932 | # ============================================================================= | COMMENT |
| MEDIUM⚡ | tests/test_finetuning_classifier.py | 934 | # ============================================================================= | COMMENT |
| MEDIUM | tests/test_finetuning_classifier.py | 99 | # ============================================================================= | COMMENT |
| MEDIUM | tests/test_finetuning_classifier.py | 101 | # ============================================================================= | COMMENT |
| MEDIUM⚡ | tests/test_classifier_interface.py | 1387 | # ============================================================================= | COMMENT |
| MEDIUM⚡ | tests/test_classifier_interface.py | 1389 | # ============================================================================= | COMMENT |
| MEDIUM | …ch_preprocessing/test_gpu_preprocessing_consistency.py | 39 | # --------------------------------------------------------------------------- | COMMENT |
| MEDIUM | …ch_preprocessing/test_gpu_preprocessing_consistency.py | 41 | # --------------------------------------------------------------------------- | COMMENT |
| MEDIUM | …ch_preprocessing/test_gpu_preprocessing_consistency.py | 231 | # --------------------------------------------------------------------------- | COMMENT |
| MEDIUM | …ch_preprocessing/test_gpu_preprocessing_consistency.py | 233 | # --------------------------------------------------------------------------- | COMMENT |
| MEDIUM | …ch_preprocessing/test_gpu_preprocessing_consistency.py | 258 | # --------------------------------------------------------------------------- | COMMENT |
| MEDIUM | …ch_preprocessing/test_gpu_preprocessing_consistency.py | 260 | # --------------------------------------------------------------------------- | COMMENT |
| MEDIUM | tests/test_architectures/test_mlx_backend.py | 175 | # --------------------------------------------------------------------------- | COMMENT |
| MEDIUM | tests/test_architectures/test_mlx_backend.py | 177 | # --------------------------------------------------------------------------- | COMMENT |
| MEDIUM⚡ | tests/test_architectures/test_mlx_backend.py | 248 | # --------------------------------------------------------------------------- | COMMENT |
| MEDIUM⚡ | tests/test_architectures/test_mlx_backend.py | 250 | # --------------------------------------------------------------------------- | COMMENT |
| MEDIUM | tests/test_architectures/test_attention_backends.py | 61 | # --------------------------------------------------------------------- | COMMENT |
| MEDIUM | tests/test_architectures/test_attention_backends.py | 63 | # --------------------------------------------------------------------- | COMMENT |
| MEDIUM | tests/test_architectures/test_attention_backends.py | 128 | # --------------------------------------------------------------------- | COMMENT |
| MEDIUM | tests/test_architectures/test_attention_backends.py | 130 | # --------------------------------------------------------------------- | COMMENT |
| MEDIUM | examples/finetune_classifier.py | 41 | # ============================================================================= | COMMENT |
| MEDIUM | examples/finetune_classifier.py | 47 | # ============================================================================= | COMMENT |
| MEDIUM⚡ | examples/sagemaker.py | 45 | # --------------------------------------------------------------------------- | COMMENT |
| MEDIUM⚡ | examples/sagemaker.py | 47 | # --------------------------------------------------------------------------- | COMMENT |
| MEDIUM⚡ | examples/sagemaker.py | 57 | # --------------------------------------------------------------------------- | COMMENT |
| MEDIUM⚡ | examples/sagemaker.py | 59 | # --------------------------------------------------------------------------- | COMMENT |
| MEDIUM | examples/sagemaker.py | 74 | # --------------------------------------------------------------------------- | COMMENT |
| MEDIUM | examples/sagemaker.py | 76 | # --------------------------------------------------------------------------- | COMMENT |
| MEDIUM⚡ | examples/sagemaker.py | 353 | # --------------------------------------------------------------------------- | COMMENT |
| MEDIUM⚡ | examples/sagemaker.py | 355 | # --------------------------------------------------------------------------- | COMMENT |
| MEDIUM | examples/finetune_regressor.py | 38 | # ============================================================================= | COMMENT |
| MEDIUM | examples/finetune_regressor.py | 44 | # ============================================================================= | COMMENT |
| MEDIUM | .github/workflows/ci.yml | 162 | # ------------------------------------------------------------------- | COMMENT |
| MEDIUM | .github/workflows/ci.yml | 164 | # ------------------------------------------------------------------- | COMMENT |
| MEDIUM | .github/workflows/ci.yml | 209 | # ------------------------------------------------------------------- | COMMENT |
| MEDIUM | .github/workflows/ci.yml | 211 | # ------------------------------------------------------------------- | COMMENT |
| 42 more matches not shown… | ||||
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| HIGH | …torch_preprocessing/test_torch_quantile_transformer.py | 0 | test with 3d tensor (t, b, h) shape commonly used in tabpfn. | STRING |
| HIGH | …t_torch_preprocessing/test_torch_soft_clip_outliers.py | 0 | test with 3d tensor (t, b, h) shape commonly used in tabpfn. | STRING |
| HIGH | …test_torch_preprocessing/test_torch_standard_scaler.py | 0 | test with 3d tensor (t, b, h) shape commonly used in tabpfn. | STRING |
| HIGH | tests/test_architectures/test_tabpfn_v2.py | 0 | kv-cache inference must match the standard (full train+test) forward. | STRING |
| HIGH | tests/test_architectures/test_tabpfn_v2_6.py | 0 | kv-cache inference must match the standard (full train+test) forward. | STRING |
| HIGH | tests/test_architectures/test_tabpfn_v2_5.py | 0 | kv-cache inference must match the standard (full train+test) forward. | STRING |
| HIGH | tests/test_architectures/test_tabpfn_v2.py | 0 | the cache path also returns matching embeddings dicts. | STRING |
| HIGH | tests/test_architectures/test_tabpfn_v2_6.py | 0 | the cache path also returns matching embeddings dicts. | STRING |
| HIGH | tests/test_architectures/test_tabpfn_v2_5.py | 0 | the cache path also returns matching embeddings dicts. | STRING |
| HIGH | src/tabpfn/architectures/tabpfn_v2_6.py | 0 | number of key/value heads to use for per-column-inter-row attention. | STRING |
| HIGH | src/tabpfn/architectures/tabpfn_v2_5.py | 0 | number of key/value heads to use for per-column-inter-row attention. | STRING |
| HIGH | src/tabpfn/architectures/tabpfn_v2.py | 0 | number of key/value heads to use for per-column-inter-row attention. | STRING |
| HIGH | src/tabpfn/architectures/tabpfn_v2_6.py | 0 | if > 1, the features will be grouped into groups of this size and the attention is across groups. | STRING |
| HIGH | src/tabpfn/architectures/tabpfn_v2_5.py | 0 | if > 1, the features will be grouped into groups of this size and the attention is across groups. | STRING |
| HIGH | src/tabpfn/architectures/tabpfn_v2.py | 0 | if > 1, the features will be grouped into groups of this size and the attention is across groups. | STRING |
| HIGH | src/tabpfn/architectures/tabpfn_v2_6.py | 0 | base class for the between-features and between-rows attention layers. | STRING |
| HIGH | src/tabpfn/architectures/tabpfn_v2_5.py | 0 | base class for the between-features and between-rows attention layers. | STRING |
| HIGH | src/tabpfn/architectures/tabpfn_v2.py | 0 | base class for the between-features and between-rows attention layers. | STRING |
| HIGH | src/tabpfn/architectures/tabpfn_v2_6.py | 0 | construct a new instance. args: embedding_size: the size of the input embedding. num_heads: the number of heads to use. | STRING |
| HIGH | src/tabpfn/architectures/tabpfn_v2_5.py | 0 | construct a new instance. args: embedding_size: the size of the input embedding. num_heads: the number of heads to use. | STRING |
| HIGH | src/tabpfn/architectures/tabpfn_v2.py | 0 | construct a new instance. args: embedding_size: the size of the input embedding. num_heads: the number of heads to use. | STRING |
| HIGH | src/tabpfn/architectures/tabpfn_v2_6.py | 0 | computes the attention between features of a single row. this is standard multi-head self-attention, where all features | STRING |
| HIGH | src/tabpfn/architectures/tabpfn_v2_5.py | 0 | computes the attention between features of a single row. this is standard multi-head self-attention, where all features | STRING |
| HIGH | src/tabpfn/architectures/tabpfn_v2.py | 0 | computes the attention between features of a single row. this is standard multi-head self-attention, where all features | STRING |
| HIGH | src/tabpfn/architectures/tabpfn_v2_6.py | 0 | forward pass for along-row attention between features. args: x_brse: the input tensor of shape (br, c, e), where: - br: | STRING |
| HIGH | src/tabpfn/architectures/tabpfn_v2_5.py | 0 | forward pass for along-row attention between features. args: x_brse: the input tensor of shape (br, c, e), where: - br: | STRING |
| HIGH | src/tabpfn/architectures/tabpfn_v2.py | 0 | forward pass for along-row attention between features. args: x_brse: the input tensor of shape (br, c, e), where: - br: | STRING |
| HIGH | src/tabpfn/architectures/tabpfn_v2_6.py | 0 | computes the attention between cells of a single column. this is multi-head attention featuring: - an implicit mask: the | STRING |
| HIGH | src/tabpfn/architectures/tabpfn_v2_5.py | 0 | computes the attention between cells of a single column. this is multi-head attention featuring: - an implicit mask: the | STRING |
| HIGH | src/tabpfn/architectures/tabpfn_v2.py | 0 | computes the attention between cells of a single column. this is multi-head attention featuring: - an implicit mask: the | STRING |
| HIGH | src/tabpfn/architectures/tabpfn_v2_6.py | 0 | forward pass for attention between cells of a single column. args: x_bcre: the input tensor of shape (bc, r, e), where: | STRING |
| HIGH | src/tabpfn/architectures/tabpfn_v2_5.py | 0 | forward pass for attention between cells of a single column. args: x_bcre: the input tensor of shape (bc, r, e), where: | STRING |
| HIGH | src/tabpfn/architectures/tabpfn_v2.py | 0 | forward pass for attention between cells of a single column. args: x_bcre: the input tensor of shape (bc, r, e), where: | STRING |
| HIGH | src/tabpfn/architectures/tabpfn_v2_6.py | 0 | layernorm that maintains fp16 precision in autocast mode. pytorch autocast runs layernorm in fp32, which has bad effects | STRING |
| HIGH | src/tabpfn/architectures/tabpfn_v2_5.py | 0 | layernorm that maintains fp16 precision in autocast mode. pytorch autocast runs layernorm in fp32, which has bad effects | STRING |
| HIGH | src/tabpfn/architectures/tabpfn_v2.py | 0 | layernorm that maintains fp16 precision in autocast mode. pytorch autocast runs layernorm in fp32, which has bad effects | STRING |
| HIGH | src/tabpfn/architectures/tabpfn_v2_6.py | 0 | a block of one column-wise, one row-wise attention layer and an mlp layer. | STRING |
| HIGH | src/tabpfn/architectures/tabpfn_v2_5.py | 0 | a block of one column-wise, one row-wise attention layer and an mlp layer. | STRING |
| HIGH | src/tabpfn/architectures/tabpfn_v2.py | 0 | a block of one column-wise, one row-wise attention layer and an mlp layer. | STRING |
| HIGH | src/tabpfn/architectures/tabpfn_v2_6.py | 0 | tabpfnblock constructor. args: emsize: the input embedding size. nhead: the number of query attention heads to use. dim_ | STRING |
| HIGH | src/tabpfn/architectures/tabpfn_v2_5.py | 0 | tabpfnblock constructor. args: emsize: the input embedding size. nhead: the number of query attention heads to use. dim_ | STRING |
| HIGH | src/tabpfn/architectures/tabpfn_v2.py | 0 | tabpfnblock constructor. args: emsize: the input embedding size. nhead: the number of query attention heads to use. dim_ | STRING |
| HIGH | src/tabpfn/architectures/tabpfn_v2_6.py | 0 | compute one column-wise, one row-wise attention, and an mlp layer. uses post-norm. b: batch size r: number of rows / ite | STRING |
| HIGH | src/tabpfn/architectures/tabpfn_v2_5.py | 0 | compute one column-wise, one row-wise attention, and an mlp layer. uses post-norm. b: batch size r: number of rows / ite | STRING |
| HIGH | src/tabpfn/architectures/tabpfn_v2.py | 0 | compute one column-wise, one row-wise attention, and an mlp layer. uses post-norm. b: batch size r: number of rows / ite | STRING |
| HIGH | src/tabpfn/architectures/tabpfn_v2_6.py | 0 | move all cached tensors to the given device. returns a new cache. | STRING |
| HIGH | src/tabpfn/architectures/tabpfn_v2_5.py | 0 | move all cached tensors to the given device. returns a new cache. | STRING |
| HIGH | src/tabpfn/architectures/tabpfn_v2.py | 0 | move all cached tensors to the given device. returns a new cache. | STRING |
| HIGH | src/tabpfn/architectures/tabpfn_v2_6.py | 0 | perform a forward pass. see modelinterface.forward() for the full docstring of the shared arguments. in addition to thos | STRING |
| HIGH | src/tabpfn/architectures/tabpfn_v2_5.py | 0 | perform a forward pass. see modelinterface.forward() for the full docstring of the shared arguments. in addition to thos | STRING |
| HIGH | src/tabpfn/architectures/tabpfn_v2.py | 0 | perform a forward pass. see modelinterface.forward() for the full docstring of the shared arguments. in addition to thos | STRING |
| HIGH | src/tabpfn/architectures/tabpfn_v2_6.py | 0 | parse the config dict into a tabpfnv2config, return unused keys. args: config: config dict to parse. this function shoul | STRING |
| HIGH | src/tabpfn/architectures/tabpfn_v2_5.py | 0 | parse the config dict into a tabpfnv2config, return unused keys. args: config: config dict to parse. this function shoul | STRING |
| HIGH | src/tabpfn/architectures/tabpfn_v2.py | 0 | parse the config dict into a tabpfnv2config, return unused keys. args: config: config dict to parse. this function shoul | STRING |
| HIGH | src/tabpfn/architectures/tabpfn_v2_6.py | 0 | construct tabpfnv2 based on the given config. this factory method implements the interface defined in tabpfn.architectur | STRING |
| HIGH | src/tabpfn/architectures/tabpfn_v2_5.py | 0 | construct tabpfnv2 based on the given config. this factory method implements the interface defined in tabpfn.architectur | STRING |
| HIGH | src/tabpfn/architectures/tabpfn_v2.py | 0 | construct tabpfnv2 based on the given config. this factory method implements the interface defined in tabpfn.architectur | STRING |
| HIGH | src/tabpfn/finetuning/finetuned_regressor.py | 0 | fine-tune the tabpfn model on the provided training data. args: x: the training input samples of shape (n_samples, n_fea | STRING |
| HIGH | src/tabpfn/finetuning/finetuned_base.py | 0 | fine-tune the tabpfn model on the provided training data. args: x: the training input samples of shape (n_samples, n_fea | STRING |
| HIGH | src/tabpfn/finetuning/finetuned_classifier.py | 0 | fine-tune the tabpfn model on the provided training data. args: x: the training input samples of shape (n_samples, n_fea | STRING |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | tests/test_debug_versions.py | 3 | CODE | |
| LOW | tests/test_inference.py | 5 | CODE | |
| LOW | tests/test_consistency.py | 26 | CODE | |
| LOW | tests/test_utils.py | 3 | CODE | |
| LOW | tests/test_estimators.py | 5 | CODE | |
| LOW | tests/conftest.py | 5 | CODE | |
| LOW | tests/test_notebook_installs.py | 16 | CODE | |
| LOW | tests/quick_test.py | 9 | CODE | |
| LOW | tests/test_save_load_fitted_model.py | 5 | CODE | |
| LOW | tests/test_inference_config.py | 5 | CODE | |
| LOW | tests/test_visualisation.py | 3 | CODE | |
| LOW | tests/test_parallel_execute.py | 5 | CODE | |
| LOW | tests/test_finetuning_regressor.py | 13 | CODE | |
| LOW | tests/test_finetuning_logging.py | 5 | CODE | |
| LOW | tests/test_ft_utils.py | 3 | CODE | |
| LOW | tests/utils.py | 3 | CODE | |
| LOW | tests/test_config.py | 3 | CODE | |
| LOW | tests/test_model_loading.py | 3 | CODE | |
| LOW | tests/test_browser_auth.py | 5 | CODE | |
| LOW | tests/test_regressor_interface.py | 3 | CODE | |
| LOW | tests/test_download_fallbacks.py | 3 | CODE | |
| LOW | tests/test_inference_tuning.py | 3 | CODE | |
| LOW | tests/test_finetuning_classifier.py | 12 | CODE | |
| LOW | tests/test_checkpoint.py | 3 | CODE | |
| LOW | tests/test_settings.py | 3 | CODE | |
| LOW | tests/test_classifier_interface.py | 3 | CODE | |
| LOW | …st_preprocessing/test_adaptive_quantile_transformer.py | 3 | CODE | |
| LOW | …preprocessing/test_encode_categorical_features_step.py | 5 | CODE | |
| LOW | …ocessing/test_nan_handling_polynomial_features_step.py | 5 | CODE | |
| LOW | tests/test_preprocessing/test_modality_detection.py | 5 | CODE | |
| LOW | tests/test_preprocessing/test_power_transformer.py | 3 | CODE | |
| LOW | tests/test_preprocessing/test_data_cleaning.py | 5 | CODE | |
| LOW | …reprocessing/test_reshape_feature_distribution_step.py | 5 | CODE | |
| LOW | tests/test_preprocessing/test_add_svd_features_step.py | 5 | CODE | |
| LOW | tests/test_preprocessing/test_kdi_transformer.py | 3 | CODE | |
| LOW | …sts/test_preprocessing/test_feature_name_uniqueness.py | 13 | CODE | |
| LOW | tests/test_preprocessing/test_preprocessing_steps.py | 3 | CODE | |
| LOW | tests/test_preprocessing/test_label_encoder.py | 5 | CODE | |
| LOW | tests/test_preprocessing/test_ops.py | 5 | CODE | |
| LOW | tests/test_preprocessing/test_inf_handling.py | 13 | CODE | |
| LOW | …st_preprocessing/test_add_fingerprint_features_step.py | 5 | CODE | |
| LOW | …s/test_preprocessing/test_differentiable_znorm_step.py | 3 | CODE | |
| LOW | tests/test_preprocessing/test_datamodel.py | 5 | CODE | |
| LOW | tests/test_preprocessing/test_ensemble.py | 3 | CODE | |
| LOW | …st_preprocessing/test_remove_constant_features_step.py | 5 | CODE | |
| LOW | tests/test_preprocessing/test_pipeline_consistency.py | 16 | CODE | |
| LOW | …ts/test_preprocessing/test_sklearn_estimator_checks.py | 5 | CODE | |
| LOW | …torch_preprocessing/test_torch_quantile_transformer.py | 5 | CODE | |
| LOW | …orch_preprocessing/test_torch_shuffle_features_step.py | 5 | CODE | |
| LOW | …ts/test_torch_preprocessing/test_pipeline_interface.py | 5 | CODE | |
| LOW | …t_torch_preprocessing/test_torch_soft_clip_outliers.py | 5 | CODE | |
| LOW | …est_torch_preprocessing/test_torch_squashing_scaler.py | 5 | CODE | |
| LOW | …ch_preprocessing/test_gpu_preprocessing_consistency.py | 11 | CODE | |
| LOW | tests/test_torch_preprocessing/test_torch_svd.py | 5 | CODE | |
| LOW | …test_torch_preprocessing/test_torch_standard_scaler.py | 5 | CODE | |
| LOW | tests/test_architectures/test_mlx_backend.py | 10 | CODE | |
| LOW | tests/test_architectures/test_tabpfn_v2.py | 4 | CODE | |
| LOW | tests/test_architectures/test_tabpfn_v2_6.py | 5 | CODE | |
| LOW | tests/test_architectures/test_tabpfn_v3.py | 5 | CODE | |
| LOW | tests/test_architectures/test_bar_distribution.py | 3 | CODE | |
| 212 more matches not shown… | ||||
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| MEDIUM | tests/test_model_loading.py | 528 | # Create a fake checkpoint to simulate a successful download | COMMENT |
| MEDIUM | tests/test_regressor_interface.py | 459 | # Create a simple preprocessing pipeline | COMMENT |
| MEDIUM | tests/test_regressor_interface.py | 684 | # Create a CPU model | COMMENT |
| MEDIUM | tests/test_regressor_interface.py | 725 | # Create a CPU model | COMMENT |
| MEDIUM | tests/test_regressor_interface.py | 773 | # Create a TabPFNRegressor with fixed random state for reproducibility | COMMENT |
| MEDIUM | tests/test_regressor_interface.py | 790 | # Create a new dataset with added constant features | COMMENT |
| MEDIUM | tests/test_finetuning_classifier.py | 1211 | # Initialize two classifiers with the necessary modes | COMMENT |
| MEDIUM | tests/test_finetuning_classifier.py | 1379 | # Create a larger dataset where columns have distinct mean values | COMMENT |
| MEDIUM⚡ | tests/test_classifier_interface.py | 933 | # Create a TabPFNClassifier with fixed random state for reproducibility | COMMENT |
| MEDIUM⚡ | tests/test_classifier_interface.py | 941 | # Create a new dataset with added constant features | COMMENT |
| MEDIUM | tests/test_classifier_interface.py | 505 | # Create a subset of X to match the length of y_imbalanced | COMMENT |
| MEDIUM | tests/test_classifier_interface.py | 698 | # Create an IMBALANCED dataset | COMMENT |
| MEDIUM | tests/test_classifier_interface.py | 740 | # Create a simple preprocessing pipeline | COMMENT |
| MEDIUM | tests/test_classifier_interface.py | 977 | # Create a DataFrame with text and NA values | COMMENT |
| MEDIUM | tests/test_preprocessing/test_add_svd_features_step.py | 239 | # Create an invalid enum value by bypassing the enum | COMMENT |
| MEDIUM | examples/tabpfn_with_tuning.py | 38 | # Initialize a classifier with tuning and fit | COMMENT |
| MEDIUM | examples/tabpfn_with_tuning.py | 46 | # Initialize a classifier with tuning and fit | COMMENT |
| MEDIUM | examples/tabpfn_for_binary_classification.py | 20 | # Initialize a classifier | COMMENT |
| MEDIUM | examples/tabpfn_for_multiclass_classification.py | 23 | # Initialize a classifier | COMMENT |
| MEDIUM | src/tabpfn/regressor.py | 1262 | # Create a dictionary of outputs with proper typing via TypedDict | COMMENT |
| MEDIUM | src/tabpfn/misc/_sklearn_compat.py | 31 | # The following code does not depend on the sklearn version | COMMENT |
| MEDIUM | src/tabpfn/architectures/tabpfn_v2_6.py | 1239 | # The following class imputation is performed for backwards compatibility. | COMMENT |
| MEDIUM | src/tabpfn/architectures/tabpfn_v2_5.py | 1361 | # The following class imputation is performed for backwards compatibility. | COMMENT |
| MEDIUM | …tabpfn/preprocessing/torch/torch_soft_clip_outliers.py | 61 | # Create a clean copy with outliers masked as NaN | COMMENT |
| MEDIUM | src/tabpfn/preprocessing/torch/ops.py | 131 | # Create a mask to zero out the padding positions. | COMMENT |
| MEDIUM | …reprocessing/steps/encode_categorical_features_step.py | 201 | # Create a column transformer | COMMENT |
| MEDIUM | …reprocessing/steps/encode_categorical_features_step.py | 250 | # Create a column transformer | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| HIGH | examples/sagemaker.py | 87 | Serialize TabPFN input data and parameters into a JSON request body. This utility converts NumPy arrays and configu | STRING |
| HIGH | examples/sagemaker.py | 173 | Invoke the TabPFN SageMaker endpoint with tabular data. This helper prepares the payload for a TabPFN 2.5 model, se | STRING |
| HIGH | src/tabpfn/classifier.py | 942 | Predict probabilities for several independent datasets in one pass. Each ``(X_train, y_train, X_test)`` triple | STRING |
| HIGH | src/tabpfn/utils.py | 228 | Infer whether fp16 inference should be enabled. Args: devices: The devices to validate against. ena | STRING |
| HIGH | src/tabpfn/architectures/tabpfn_v2_6.py | 1050 | Parse the config dict into a TabPFNV2Config, return unused keys. Args: config: Config dict to parse. This f | STRING |
| HIGH | src/tabpfn/architectures/interface.py | 117 | Parses the given config dict into ArchitectureConfig or a subclass. This config will then be passed to get_arch | STRING |
| HIGH | src/tabpfn/architectures/tabpfn_v2_5.py | 1073 | Parse the config dict into a TabPFNV2Config, return unused keys. Args: config: Config dict to parse. This f | STRING |
| HIGH | src/tabpfn/architectures/tabpfn_v2.py | 1088 | Parse the config dict into a TabPFNV2Config, return unused keys. Args: config: Config dict to parse. This f | STRING |
| HIGH | src/tabpfn/finetuning/data_util.py | 340 | Retrieves, splits, and preprocesses the dataset config at the index. Performs train/test splitting and applies | STRING |
| HIGH | src/tabpfn/finetuning/train_util.py | 241 | Create and initialize AdamW optimizer, optionally loading from checkpoint. Args: model_parameters: Iterator | STRING |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | tests/test_consistency.py | 48 | logger = logging.getLogger(__name__) | CODE |
| LOW | tests/conftest.py | 17 | def set_global_seed() -> None: | CODE |
| LOW | tests/test_preprocessing/test_pipeline_consistency.py | 34 | logger = logging.getLogger(__name__) | CODE |
| LOW | src/tabpfn/__init__.py | 58 | __all__ = [ | CODE |
| LOW⚡ | src/tabpfn/browser_auth.py | 38 | logger = logging.getLogger(__name__) | CODE |
| LOW | src/tabpfn/model_loading.py | 50 | logger = logging.getLogger(__name__) | CODE |
| LOW | src/tabpfn/inference.py | 193 | def _set_models(self, models: list[Architecture]) -> None: | CODE |
| LOW | src/tabpfn/inference.py | 259 | def _set_models(self, models: list[Architecture]) -> None: | CODE |
| LOW | src/tabpfn/inference.py | 297 | def _set_models(self, models: list[Architecture]) -> None: | CODE |
| LOW | src/tabpfn/inference.py | 1296 | def set_dtype(self, dtype: torch.dtype) -> None: | CODE |
| LOW | …bpfn/architectures/shared/workaround_mps_linear_bug.py | 28 | logger = logging.getLogger(__name__) | CODE |
| LOW | src/tabpfn/finetuning/finetuned_regressor.py | 27 | logger = logging.getLogger(__name__) | CODE |
| LOW | src/tabpfn/finetuning/finetuned_regressor.py | 415 | def _setup_estimator(self) -> None: | CODE |
| LOW | src/tabpfn/finetuning/finetuned_regressor.py | 424 | def _setup_batch(self, batch: RegressorBatch) -> None: # type: ignore[override] | CODE |
| LOW | src/tabpfn/finetuning/_torch_compat.py | 53 | __all__ = [ | CODE |
| LOW | src/tabpfn/finetuning/__init__.py | 15 | __all__ = [ | CODE |
| LOW | src/tabpfn/finetuning/train_util.py | 25 | logger = logging.getLogger(__name__) | CODE |
| LOW | src/tabpfn/finetuning/finetuned_base.py | 57 | logger = logging.getLogger(__name__) | CODE |
| LOW | src/tabpfn/finetuning/finetuned_base.py | 439 | def _setup_estimator(self) -> None: | CODE |
| LOW | src/tabpfn/finetuning/finetuned_base.py | 444 | def _setup_batch(self, batch: ClassifierBatch | RegressorBatch) -> None: | CODE |
| LOW | src/tabpfn/finetuning/finetuned_classifier.py | 27 | logger = logging.getLogger(__name__) | CODE |
| LOW | src/tabpfn/finetuning/finetuned_classifier.py | 221 | def _setup_estimator(self) -> None: | CODE |
| LOW | src/tabpfn/finetuning/finetuned_classifier.py | 228 | def _setup_batch(self, batch: ClassifierBatch) -> None: # type: ignore[override] | CODE |
| LOW | src/tabpfn/visualisation/__init__.py | 7 | __all__ = ["plot_regression_distribution"] | CODE |
| LOW | src/tabpfn/preprocessing/configs.py | 184 | __all__ = [ | CODE |
| LOW | src/tabpfn/preprocessing/presets.py | 85 | __all__ = [ | CODE |
| LOW | src/tabpfn/preprocessing/__init__.py | 26 | __all__ = [ | CODE |
| LOW | src/tabpfn/preprocessing/torch/__init__.py | 29 | __all__ = [ | CODE |
| LOW | …bpfn/preprocessing/steps/differentiable_z_norm_step.py | 48 | __all__ = [ | CODE |
| LOW | …eprocessing/steps/reshape_feature_distribution_step.py | 725 | __all__ = [ | CODE |
| LOW | …reprocessing/steps/encode_categorical_features_step.py | 455 | __all__ = [ | CODE |
| LOW | …c/tabpfn/preprocessing/steps/safe_power_transformer.py | 207 | __all__ = [ | CODE |
| LOW | …n/preprocessing/steps/add_fingerprint_features_step.py | 158 | __all__ = [ | CODE |
| LOW | src/tabpfn/preprocessing/steps/__init__.py | 36 | __all__ = [ | CODE |
| LOW | …n/preprocessing/steps/adaptive_quantile_transformer.py | 185 | __all__ = [ | CODE |
| LOW | src/tabpfn/preprocessing/steps/kdi_transformer.py | 165 | __all__ = [ | CODE |
| LOW | …fn/preprocessing/steps/squashing_scaler_transformer.py | 371 | __all__ = ["SquashingScaler"] | STRING |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | …reprocessing/test_reshape_feature_distribution_step.py | 335 | CODE | |
| LOW | tests/test_preprocessing/test_ensemble.py | 824 | CODE | |
| LOW | src/tabpfn/classifier.py | 523 | CODE | |
| LOW | src/tabpfn/classifier.py | 1618 | CODE | |
| LOW | src/tabpfn/model_loading.py | 198 | CODE | |
| LOW | src/tabpfn/model_loading.py | 225 | CODE | |
| LOW | src/tabpfn/model_loading.py | 318 | CODE | |
| LOW | src/tabpfn/model_loading.py | 607 | CODE | |
| LOW | src/tabpfn/utils.py | 271 | CODE | |
| LOW | src/tabpfn/regressor.py | 510 | CODE | |
| LOW | src/tabpfn/regressor.py | 1294 | CODE | |
| LOW | src/tabpfn/misc/debug_versions.py | 188 | CODE | |
| LOW | src/tabpfn/misc/debug_versions.py | 269 | CODE | |
| LOW | src/tabpfn/misc/debug_versions.py | 347 | CODE | |
| LOW | src/tabpfn/architectures/tabpfn_v2_6.py | 696 | CODE | |
| LOW | src/tabpfn/architectures/interface.py | 43 | CODE | |
| LOW | src/tabpfn/architectures/tabpfn_v3.py | 1732 | CODE | |
| LOW | src/tabpfn/architectures/tabpfn_v3.py | 2121 | CODE | |
| LOW | src/tabpfn/architectures/tabpfn_v2_5.py | 698 | CODE | |
| LOW | src/tabpfn/architectures/tabpfn_v2.py | 794 | CODE | |
| LOW | src/tabpfn/finetuning/finetuned_base.py | 607 | CODE | |
| LOW | src/tabpfn/preprocessing/clean.py | 104 | CODE | |
| LOW | …eprocessing/steps/reshape_feature_distribution_step.py | 188 | CODE | |
| LOW | …eprocessing/steps/reshape_feature_distribution_step.py | 265 | CODE | |
| LOW | …n/preprocessing/steps/add_fingerprint_features_step.py | 68 | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| HIGH | src/tabpfn/browser_auth.py | 629 | ' 4. Set the environment variable: export TABPFN_TOKEN="<your-api-key>"\n' | CODE |
| HIGH | src/tabpfn/browser_auth.py | 631 | ' import os; os.environ["TABPFN_TOKEN"] = "<your-api-key>"' | CODE |
| HIGH | src/tabpfn/errors.py | 49 | ' export TABPFN_TOKEN="<your-api-key>"\n' | CODE |
| HIGH | src/tabpfn/errors.py | 51 | ' import os; os.environ["TABPFN_TOKEN"] = "<your-api-key>"' | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| MEDIUM | pyproject.toml | 151 | # "DTZ", # One day I should know how to utilize timezones and dates... | COMMENT |
| MEDIUM | tests/test_finetuning_classifier.py | 1187 | n_total = 50 # Increased slightly for more robust testing | CODE |
| MEDIUM | …t_torch_preprocessing/test_torch_soft_clip_outliers.py | 58 | """Test that two-pass approach produces more robust bounds.""" | STRING |
| MEDIUM | src/tabpfn/inference_tuning.py | 263 | """Selects the robust optimal threshold for the given metric. | STRING |
| LOW⚡ | src/tabpfn/parallel_execute.py | 85 | # If we only have one device then just use the current thread to avoid overhead. | COMMENT |
| MEDIUM | src/tabpfn/preprocessing/configs.py | 70 | "robust", # a standard sklearn robust scaler | CODE |
| MEDIUM | src/tabpfn/preprocessing/steps/preprocessing_helpers.py | 116 | # TODO: Could utilize the categorical dtype values directly instead of "auto" | COMMENT |
| MEDIUM | …fn/preprocessing/steps/squashing_scaler_transformer.py | 88 | r"""Perform robust centering and scaling followed by soft clipping. | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | tests/test_preprocessing/test_pipeline_consistency.py | 413 | except Exception as e: | CODE |
| MEDIUM⚡ | examples/sagemaker.py | 348 | print(f"Error parsing JSON response: {e}") | CODE |
| LOW | src/tabpfn/classifier.py | 773 | except Exception as e: # noqa: BLE001 | CODE |
| LOW⚡ | src/tabpfn/browser_auth.py | 137 | except Exception: | CODE |
| LOW⚡ | src/tabpfn/browser_auth.py | 207 | except Exception: | CODE |
| LOW | src/tabpfn/browser_auth.py | 166 | except Exception as exc: | CODE |
| LOW | src/tabpfn/browser_auth.py | 331 | except Exception: # noqa: BLE001 | CODE |
| LOW | src/tabpfn/browser_auth.py | 507 | except Exception: | CODE |
| LOW | src/tabpfn/model_loading.py | 301 | except Exception as e: # noqa: BLE001 | CODE |
| LOW | src/tabpfn/model_loading.py | 361 | except Exception: # noqa: BLE001 | CODE |
| LOW | src/tabpfn/model_loading.py | 366 | except Exception as e: # noqa: BLE001 | CODE |
| LOW | src/tabpfn/model_loading.py | 513 | except Exception as e: # noqa: BLE001 | CODE |
| LOW | src/tabpfn/model_loading.py | 524 | except Exception as e: # noqa: BLE001 | CODE |
| LOW | src/tabpfn/model_loading.py | 552 | except Exception as e: # noqa: BLE001 | CODE |
| LOW | src/tabpfn/preprocessing/steps/kdi_transformer.py | 161 | except Exception: # noqa: BLE001 | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | examples/finetune_classifier.py | 41 | # ============================================================================= | COMMENT |
| LOW | examples/sagemaker.py | 61 | # | COMMENT |
| LOW | examples/finetune_regressor.py | 41 | # | COMMENT |
| LOW | .github/workflows/claude-code-review.yml | 21 | COMMENT | |
| LOW | .github/workflows/claude-code-review.yml | 81 | # "Enable debug logging" ticked. We normally keep this off to avoid writing | COMMENT |
| LOW | .github/workflows/assign-pr-reviewer.yml | 1 | # When a PR is opened from a fork or by dependabot, request a review from a randomly | COMMENT |
| LOW | src/tabpfn/classifier.py | 1041 | f"not supported); got train shapes {sorted(train_shapes)} and test " | COMMENT |
| LOW | src/tabpfn/misc/_sklearn_compat.py | 1001 | COMMENT | |
| LOW | src/tabpfn/preprocessing/clean.py | 141 | isinstance(col, (int, np.integer)) for col in X.columns.tolist() | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | src/tabpfn/classifier.py | 209 | CODE | |
| LOW | src/tabpfn/regressor.py | 220 | CODE | |
| LOW | src/tabpfn/misc/_sklearn_compat.py | 448 | CODE | |
| LOW | src/tabpfn/misc/_sklearn_compat.py | 502 | CODE | |
| LOW | src/tabpfn/architectures/tabpfn_v3.py | 1732 | CODE | |
| LOW | src/tabpfn/finetuning/finetuned_regressor.py | 313 | CODE | |
| LOW | src/tabpfn/finetuning/finetuned_base.py | 299 | CODE | |
| LOW | src/tabpfn/finetuning/finetuned_classifier.py | 131 | CODE | |
| LOW | src/tabpfn/preprocessing/ensemble.py | 88 | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | src/tabpfn/model_loading.py | 308 | # Check if this is an authentication/gating error | COMMENT |
| LOW | src/tabpfn/utils.py | 203 | # Check if the function is available in torch | COMMENT |
| LOW | src/tabpfn/preprocessing/steps/preprocessing_helpers.py | 56 | # Check if there is a single transformer, of subtype OneToOneFeatureMixin | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | tests/test_parallel_execute.py | 15 | def test_function(device: torch.device) -> int: # noqa: ARG001 | CODE |
| LOW | tests/test_parallel_execute.py | 42 | def test_function(device: torch.device) -> int: # noqa: ARG001 | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | tests/test_finetuning_classifier.py | 1420 | # Step 3: Validate that columns are in the same order across all batches. | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | tests/test_finetuning_classifier.py | 1420 | # Step 3: Validate that columns are in the same order across all batches. | COMMENT |