Error code:   JWTInvalidSignature
Exception:    InvalidSignatureError
Message:      Signature verification failed
Traceback:    Traceback (most recent call last):
                File "/src/libs/libapi/src/libapi/jwt_token.py", line 286, in validate_jwt
                  decoded = jwt.decode(
                      jwt=token,
                  ...<2 lines>...
                      options=options,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 368, in decode
                  decoded = self.decode_complete(
                      jwt,
                  ...<8 lines>...
                      leeway=leeway,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 265, in decode_complete
                  decoded = self._jws.decode_complete(
                      jwt,
                  ...<3 lines>...
                      detached_payload=detached_payload,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 270, in decode_complete
                  self._verify_signature(
                  ~~~~~~~~~~~~~~~~~~~~~~^
                      signing_input,
                      ^^^^^^^^^^^^^^
                  ...<4 lines>...
                      options=merged_options,
                      ^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 417, in _verify_signature
                  raise InvalidSignatureError("Signature verification failed")
              jwt.exceptions.InvalidSignatureError: Signature verification failed

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Tabnetics Validation Runs

Per-run, per-dataset, per-seed, per-method experimental results from the tabnetics automated tabular-classification pipeline across validation campaigns val-18 through val-21.

Statistic	Value
Rows	140,403
Columns	176
Campaigns	val-18, val-19, val-20, val-21
Unique datasets	63
Unique pipeline profiles	278
Seeds	11, 23, 37, 42, 59, 67, 73, 89, 97
Winner rows	56,702
Classifier-candidate rows	83,701

Row types

Every row represents one (dataset, seed, classifier) trial:

winner — the pipeline's final selected classifier for that (dataset, seed, profile) run. These rows carry full holdout metrics (accuracy, balanced_accuracy, macro_f1, hybrid_score, roc_auc, etc.) plus timing and feature-selection details from the CSV results.
classifier_candidate — a non-winning classifier from the model cross-validation stage. These rows carry the CV score (model_cv_score) and train-test gap (model_cv_train_test_gap) but not holdout metrics, since only the winner was evaluated on the held-out test set.

Key columns

Identity & metadata

Column	Description
`campaign`	Validation campaign (val-18 through val-21)
`profile`	Pipeline profile name
`dataset_shard`	Dataset shard identifier (ds0, ds1, etc.)
`run_timestamp`	Run timestamp
`dataset_id`	OpenML / internal dataset identifier
`dataset_name`	Human-readable dataset name
`tier` / `effective_tier`	Dataset complexity tier
`domain`	Dataset domain
`seed`	Random seed
`row_type`	`winner` or `classifier_candidate`
`is_winner`	Boolean flag

Performance metrics (winner rows)

Column	Description
`accuracy`	Holdout accuracy
`balanced_accuracy`	Holdout balanced accuracy
`macro_f1`	Holdout macro-F1
`hybrid_score`	Composite hybrid score
`roc_auc`	ROC-AUC (various curve types)

Model selection (all rows)

Column	Description
`model`	Classifier name
`model_cv_score`	Cross-validation score during model selection
`model_cv_train_test_gap`	CV train-test gap (overfitting indicator)

Feature selection

Column	Description
`selection_strategy`	FS strategy used
`n_features_selected`	Number of features after selection
`n_portfolio_candidates`	Size of the FS portfolio
`fs_method_preset`	FS method preset name

Timing

Column	Description
`fs_time_sec`	Feature-selection wall time
`dist_time_sec`	Distribution fitting wall time
`classification_stage2_wall_sec`	Classification stage-2 wall time

Configuration flags (`cfg_*` columns)

~40 boolean/string configuration flags from each run's metadata, prefixed with cfg_. These capture the exact pipeline settings for reproducibility. The raw JSON is also available in config_flags_json.

Usage

from datasets import load_dataset

ds = load_dataset("klokedm/tabnetics-runs", split="train")
df = ds.to_pandas()

# Winners only
winners = df[df["row_type"] == "winner"]

# All classifier candidates for a specific dataset
cands = df[(df["dataset_name"] == "wdbc") & (df["seed"] == 42)]

# Compare campaigns
df.groupby("campaign")["balanced_accuracy"].mean()

Source

Built with scripts/build_hf_runs_dataset.py.

Library: tabnetics on PyPI · GitHub · Documentation

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