unknown error with dataset "openml__Census-Income__168340"

[duncanmcelfresh]

here is the output written to log file:

run_experiment: model_name: LinearModel
run_experiment: dataset_name: openml__Census-Income__168340
run_experiment: env_name: sklearn
run_experiment: instance_name: all-datasets-b-0-13
run_experiment: experiment_name: all-datasets-b
run_experiment: config_file: /home/shared/tabzilla/TabSurvey/tabzilla_experiment_config.yml
launching instance all-datasets-b-0-13...
Created [https://www.googleapis.com/compute/v1/projects/research-collab-naszilla/zones/us-central1-a/instances/all-datasets-b-0-13].
NAME                 ZONE           MACHINE_TYPE  PREEMPTIBLE  INTERNAL_IP  EXTERNAL_IP    STATUS
all-datasets-b-0-13  us-central1-a  n1-highmem-2               10.128.0.5   34.173.66.211  RUNNING
successfully created instance: all-datasets-b-0-13
Warning: Permanently added 'compute.6541935476525142947' (ECDSA) to the list of known hosts.
ENV_NAME: sklearn
MODEL_NAME: LinearModel
DATASET_NAME: openml__Census-Income__168340
EXPERIMENT_NAME: all-datasets-b
CONFIG_FILE: /home/shared/tabzilla/TabSurvey/tabzilla_experiment_config.yml
no change     /opt/conda/condabin/conda
no change     /opt/conda/bin/conda
no change     /opt/conda/bin/conda-env
no change     /opt/conda/bin/activate
no change     /opt/conda/bin/deactivate
no change     /opt/conda/etc/profile.d/conda.sh
no change     /opt/conda/etc/fish/conf.d/conda.fish
no change     /opt/conda/shell/condabin/Conda.psm1
no change     /opt/conda/shell/condabin/conda-hook.ps1
no change     /opt/conda/lib/python3.7/site-packages/xontrib/conda.xsh
no change     /opt/conda/etc/profile.d/conda.csh
no change     /home/duncan/.bashrc
No action taken.
running experiment with model LinearModel on dataset openml__Census-Income__168340 in env sklearn

ARGS: Namespace(experiment_config='/home/shared/tabzilla/TabSurvey/tabzilla_experiment_config.yml', dataset_dir='./datasets/openml__Census-Income__168340', model_name
='LinearModel')
EXPERIMENT ARGS: Namespace(experiment_config='/home/shared/tabzilla/TabSurvey/tabzilla_experiment_config.yml', output_dir='./results/', use_gpu=False, gpu_ids=[0], da
ta_parallel=True, n_random_trials=30, hparam_seed=0, n_opt_trials=0, batch_size=128, val_batch_size=256, early_stopping_rounds=20, epochs=500, logging_period=100, exp
eriment_time_limit=36000, trial_time_limit=7200)
evaluating 30 random hyperparameter samples...
A new study created in memory with name: no-name-a98b6db2-0fcb-4510-8c2a-4de575fb8c32
ESC[32m[I 2022-11-03 04:33:32,195]ESC[0m A new study created in memory with name: no-name-a98b6db2-0fcb-4510-8c2a-4de575fb8c32ESC[0m
/opt/conda/envs/sklearn/lib/python3.10/site-packages/optuna/study/study.py:393: FutureWarning: `n_jobs` argument has been deprecated in v2.7.0. This feature will be r
emoved in v4.0.0. See https://github.com/optuna/optuna/releases/tag/v2.7.0.
  warnings.warn(
/opt/conda/envs/sklearn/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/opt/conda/envs/sklearn/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
Trial 1 finished with value: 0.907014396743351 and parameters: {}. Best is trial 1 with value: 0.907014396743351.
ESC[32m[I 2022-11-03 04:41:02,125]ESC[0m Trial 1 finished with value: 0.907014396743351 and parameters: {}. Best is trial 1 with value: 0.907014396743351.ESC[0m
Trial 0 finished with value: 0.907014396743351 and parameters: {}. Best is trial 1 with value: 0.907014396743351.
ESC[32m[I 2022-11-03 04:41:06,122]ESC[0m Trial 0 finished with value: 0.907014396743351 and parameters: {}. Best is trial 1 with value: 0.907014396743351.ESC[0m
Trial 2 finished with value: 0.907014396743351 and parameters: {}. Best is trial 1 with value: 0.907014396743351.
Trial 3 finished with value: 0.907014396743351 and parameters: {}. Best is trial 1 with value: 0.907014396743351.
ESC[32m[I 2022-11-03 04:48:11,421]ESC[0m Trial 2 finished with value: 0.907014396743351 and parameters: {}. Best is trial 1 with value: 0.907014396743351.ESC[0m
ESC[32m[I 2022-11-03 04:48:14,990]ESC[0m Trial 3 finished with value: 0.907014396743351 and parameters: {}. Best is trial 1 with value: 0.907014396743351.ESC[0m
Trial 4 finished with value: 0.907014396743351 and parameters: {}. Best is trial 1 with value: 0.907014396743351.
:

[duncanmcelfresh]

can't tell what the issue is at first glance... need to look into it.

[duncanmcelfresh]

closing, no longer relevant