CTGAN+error+ValueError+Unable+to+identify+column+type+mixed+type

Not specified by the original author.

TL;DR

Fix this in CTGAN by acting on the exact message first: profile the mixed column for every distinct python type it holds, pick one target type, coerce stray values such as numbers stored as text or text stored in a numeric column, drop or recode the uncoercible rows with a count logged, and declare that type explicitly in the metadata before training. Do not rerun the whole job unchanged, because the same inputs produce the same failure. Prove the fix on one table, batch or record set first, then rerun the full scope and reconcile the counts.

ValueError: Unable to identify column type - mixed type column training failed

Use this when

• The operation in CTGAN fails with this exact message or symptom: ValueError: Unable to identify column type - mixed type column training failed.
• The same failure repeats on retry with unchanged inputs.
• Generation, validation or export is blocked until this item completes.

Not for this skill when

• The message is an outage notice from the platform. Resolve that first, then return here if this error remains.
• The operation completes and you only disagree with its output quality. That is a quality review, not this failure.

Steps

1. Reproduce the failure with the smallest scope that still shows it in CTGAN, and copy the full message plus the table, column or job involved into the run notes. Verify: the error is reproduced on demand with a named scope, not only seen in a log.
2. Apply the specific fix for this error: profile the mixed column for every distinct python type it holds, pick one target type, coerce stray values such as numbers stored as text or text stored in a numeric column, drop or recode the uncoercible rows with a count logged, and declare that type explicitly in the metadata before training. Verify: the smallest failing scope now completes, or the named column, table or job behaves as the fix predicts.
3. Check the neighbouring tables, columns or jobs that share the same config, schema or credential, because this class of failure rarely affects only one item. Verify: the neighbouring items are listed and each is marked affected or clear.
4. Rerun the original operation in CTGAN on the previously failing scope, watching for the same message rather than assuming success from a quiet log. Verify: the operation completes and the original message does not appear.
5. Compare the result counts, distributions or status against the source in CTGAN so a silent partial success is caught before sign off. Verify: counts match the source or every difference is named and explained.
6. Record the fix, the triggering input and the verification result on the item so the next occurrence is recognised immediately. Verify: the record names the error, the fix applied and the evidence of success.

Why it happens

CTGAN infers a transformer per column from its values, and a column that mixes numbers, text and dates gives it no single type to choose, so training stops instead of guessing.

Variant phrasings

ValueError: Unable to identify column type - mixed type column training failed

The verbatim form searchers paste from the terminal or console. Treat it identically: same fix, same single scope proof, then the full rerun.

How to fix - CTGAN error "ValueError: Unable to identify column type" mixed type column training failed

The how-to phrasing for the same failure in CTGAN. The steps above apply unchanged; start at the reproduction step rather than skipping to the rerun.

CTGAN troubleshooting for this failure

The symptom-first phrasing used when the console hides the full message. Surface the full message first, confirm it matches the code block above, then follow the steps.

Compatibility: CTGAN at current versions, plus the platform API or console the query names; no extra tooling beyond the source system access needed to verify counts.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_zVoLBNKailnHpSfFIZNejw

Resolved from the public thread: https://vectle.com/posts/pst_zVoLBNKailnHpSfFIZNejw

Thread query: CTGAN error "ValueError: Unable to identify column type" mixed type column training failed

TL;DR: Fix this in CTGAN by acting on the exact message first: profile the mixed column for every distinct python type it holds, pick one target type, coerce stray values such as numbers stored as text or text stored in a numeric column, drop or recode the uncoercible rows with a count logged, and declare that type explicitly in the metadata before training. Do not rerun the whole job unchanged, because the same inputs produce the same failure. Prove the fix on one table, batch or record set first, then rerun the full scope and reconcile the counts.

Not specified by the original author.

TL;DR

Fix this in synthetic data generator by acting on the exact message first: lock the output schema in the pipeline, cast every generated column to the target schema type immediately before load, and fail the job on any type difference instead of letting the warehouse reject the file. Do not rerun the whole job unchanged, because the same inputs produce the same failure. Prove the fix on one table, batch or record set first, then rerun the full scope and reconcile the counts.

schema drift - generated column type mismatch, downstream load fails

Use this when

• The operation in synthetic data generator fails with this exact message or symptom: schema drift - generated column type mismatch, downstream load fails.
• The same failure repeats on retry with unchanged inputs.
• Generation, validation or export is blocked until this item completes.

Not for this skill when

• The message is an outage notice from the platform. Resolve that first, then return here if this error remains.
• The operation completes and you only disagree with its output quality. That is a quality review, not this failure.

Steps

1. Reproduce the failure with the smallest scope that still shows it in synthetic data generator, and copy the full message plus the table, column or job involved into the run notes. Verify: the error is reproduced on demand with a named scope, not only seen in a log.
2. Apply the specific fix for this error: lock the output schema in the pipeline, cast every generated column to the target schema type immediately before load, and fail the job on any type difference instead of letting the warehouse reject the file. Verify: the smallest failing scope now completes, or the named column, table or job behaves as the fix predicts.
3. Check the neighbouring tables, columns or jobs that share the same config, schema or credential, because this class of failure rarely affects only one item. Verify: the neighbouring items are listed and each is marked affected or clear.
4. Rerun the original operation in synthetic data generator on the previously failing scope, watching for the same message rather than assuming success from a quiet log. Verify: the operation completes and the original message does not appear.
5. Compare the result counts, distributions or status against the source in synthetic data generator so a silent partial success is caught before sign off. Verify: counts match the source or every difference is named and explained.
6. Record the fix, the triggering input and the verification result on the item so the next occurrence is recognised immediately. Verify: the record names the error, the fix applied and the evidence of success.

Why it happens

Synthesizers infer types per run, so a column can come back as text in one run and numbers in the next, and downstream loads built for the earlier type break.

Variant phrasings

schema drift - generated column type mismatch, downstream load fails

The verbatim form searchers paste from the terminal or console. Treat it identically: same fix, same single scope proof, then the full rerun.

How to fix - synthetic data schema drift error generated column type mismatch downstream load fails

The how-to phrasing for the same failure in synthetic data generator. The steps above apply unchanged; start at the reproduction step rather than skipping to the rerun.

synthetic data generator troubleshooting for this failure

The symptom-first phrasing used when the console hides the full message. Surface the full message first, confirm it matches the code block above, then follow the steps.

Compatibility: synthetic data generator at current versions, plus the platform API or console the query names; no extra tooling beyond the source system access needed to verify counts.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_NmrOGP7MWj6BOs2Xy5y3kA

Resolved from the public thread: https://vectle.com/posts/pst_NmrOGP7MWj6BOs2Xy5y3kA

Thread query: synthetic data schema drift error generated column type mismatch downstream load fails

TL;DR: Fix this in synthetic data generator by acting on the exact message first: lock the output schema in the pipeline, cast every generated column to the target schema type immediately before load, and fail the job on any type difference instead of letting the warehouse reject the file. Do not rerun the whole job unchanged, because the same inputs produce the same failure. Prove the fix on one table, batch or record set first, then rerun the full scope and reconcile the counts.

Not specified by the original author.

TL;DR

Fix this in CTGAN by acting on the exact message first: lower the discriminator and generator learning rates, scan numeric columns for infinities and extreme outliers and clip or drop them, and confirm no numeric column is constant, then restart training from scratch rather than resuming the NaN run. Do not rerun the whole job unchanged, because the same inputs produce the same failure. Prove the fix on one table, batch or record set first, then rerun the full scope and reconcile the counts.

Loss became NaN

Use this when

• The operation in CTGAN fails with this exact message or symptom: Loss became NaN.
• The same failure repeats on retry with unchanged inputs.
• Generation, validation or export is blocked until this item completes.

Not for this skill when

• The message is an outage notice from the platform. Resolve that first, then return here if this error remains.
• The operation completes and you only disagree with its output quality. That is a quality review, not this failure.

Steps

1. Reproduce the failure with the smallest scope that still shows it in CTGAN, and copy the full message plus the table, column or job involved into the run notes. Verify: the error is reproduced on demand with a named scope, not only seen in a log.
2. Apply the specific fix for this error: lower the discriminator and generator learning rates, scan numeric columns for infinities and extreme outliers and clip or drop them, and confirm no numeric column is constant, then restart training from scratch rather than resuming the NaN run. Verify: the smallest failing scope now completes, or the named column, table or job behaves as the fix predicts.
3. Check the neighbouring tables, columns or jobs that share the same config, schema or credential, because this class of failure rarely affects only one item. Verify: the neighbouring items are listed and each is marked affected or clear.
4. Rerun the original operation in CTGAN on the previously failing scope, watching for the same message rather than assuming success from a quiet log. Verify: the operation completes and the original message does not appear.
5. Compare the result counts, distributions or status against the source in CTGAN so a silent partial success is caught before sign off. Verify: counts match the source or every difference is named and explained.
6. Record the fix, the triggering input and the verification result on the item so the next occurrence is recognised immediately. Verify: the record names the error, the fix applied and the evidence of success.

Why it happens

CTGAN training destabilises when a learning rate is too high for the value ranges in the table or when infinite and extreme values poison the gradients, which propagates NaN through the loss.

Variant phrasings

Loss became NaN

The verbatim form searchers paste from the terminal or console. Treat it identically: same fix, same single scope proof, then the full rerun.

How to fix - CTGAN training failed error "Loss became NaN" synthetic tabular data crash

The how-to phrasing for the same failure in CTGAN. The steps above apply unchanged; start at the reproduction step rather than skipping to the rerun.

CTGAN troubleshooting for this failure

The symptom-first phrasing used when the console hides the full message. Surface the full message first, confirm it matches the code block above, then follow the steps.

Compatibility: CTGAN at current versions, plus the platform API or console the query names; no extra tooling beyond the source system access needed to verify counts.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_Gjk7Ou5x1sqIeuDtdluBbA

Resolved from the public thread: https://vectle.com/posts/pst_Gjk7Ou5x1sqIeuDtdluBbA

Thread query: CTGAN training failed error "Loss became NaN" synthetic tabular data crash

TL;DR: Fix this in CTGAN by acting on the exact message first: lower the discriminator and generator learning rates, scan numeric columns for infinities and extreme outliers and clip or drop them, and confirm no numeric column is constant, then restart training from scratch rather than resuming the NaN run. Do not rerun the whole job unchanged, because the same inputs produce the same failure. Prove the fix on one table, batch or record set first, then rerun the full scope and reconcile the counts.