Test Regression Age Random ForestFailed

CLI Command (Copy-Paste Ready)

OMP_NUM_THREADS=1 OPENBLAS_NUM_THREADS=1 MKL_NUM_THREADS=1 VECLIB_MAXIMUM_THREADS=1 NUMEXPR_NUM_THREADS=1 /opt/ml-env/bin/python /app/scripts/ml_pipeline.py --config /app/public/ml_results/67/job_config.json

{"success": false, "job_id": 67, "error": "Shape of passed values is (40, 0), indices imply (40, 10)", "traceback": "Traceback (most recent call last):\n  File \"/app/scripts/ml_pipeline.py\", line 1482, in run_from_json\n    results = run_nested_validation(\n        X,\n    ...<7 lines>...\n        input_data.get('job_id')\n    )\n  File \"/app/scripts/ml_pipeline.py\", line 559, in run_nested_validation\n    X_train_processed, X_test_processed, transformers = apply_data_treatments(\n                                                        ~~~~~~~~~~~~~~~~~~~~~^\n        X_train_fold, X_test_fold, treatment\n        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n    )\n    ^\n  File \"/app/scripts/ml_pipeline.py\", line 270, in apply_data_treatments\n    X_train = pd.DataFrame(X_train_imputed, columns=cols, index=X_train.index)\n  File \"/opt/ml-env/lib/python3.13/site-packages/pandas/core/frame.py\", line 827, in __init__\n    mgr = ndarray_to_mgr(\n        data,\n    ...<4 lines>...\n        typ=manager,\n    )\n  File \"/opt/ml-env/lib/python3.13/site-packages/pandas/core/internals/construction.py\", line 336, in ndarray_to_mgr\n    _check_values_indices_shape_match(values, index, columns)\n    ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/ml-env/lib/python3.13/site-packages/pandas/core/internals/construction.py\", line 420, in _check_values_indices_shape_match\n    raise ValueError(f\"Shape of passed values is {passed}, indices imply {implied}\")\nValueError: Shape of passed values is (40, 0), indices imply (40, 10)\n"}

/opt/ml-env/lib/python3.13/site-packages/sklearn/impute/_base.py:653: UserWarning: Skipping features without any observed values: ['1_methylguanosine (µM)' '1_methylinosine (µM)'
 '1_methylnicotinamide (µM)' '1_methyluric_acid (µM)'
 '1h_indole_4_carboxaldehyde (µM)' '2_5_furandicarboxylic_acid (µM)'
 '2_aminopimelic_acid (µM)' '2_hydroxy_2_methylbutyric_acid (µM)'
 '2_hydroxy_3_methylvaleric_acid_2_hydroxy_4_methylpentanoic_acid (µM)'
 '2_hydroxybutyric_acid (µM)']. At least one non-missing value is needed for imputation with strategy='mean'.
  warnings.warn(
/opt/ml-env/lib/python3.13/site-packages/sklearn/impute/_base.py:653: UserWarning: Skipping features without any observed values: ['1_methylguanosine (µM)' '1_methylinosine (µM)'
 '1_methylnicotinamide (µM)' '1_methyluric_acid (µM)'
 '1h_indole_4_carboxaldehyde (µM)' '2_5_furandicarboxylic_acid (µM)'
 '2_aminopimelic_acid (µM)' '2_hydroxy_2_methylbutyric_acid (µM)'
 '2_hydroxy_3_methylvaleric_acid_2_hydroxy_4_methylpentanoic_acid (µM)'
 '2_hydroxybutyric_acid (µM)']. At least one non-missing value is needed for imputation with strategy='mean'.
  warnings.warn(