Troubleshooting Watson OpenScale

When I use AutoAI, why am I getting an error about mismatched data?

You receive an error message about mismatched data when using AutoAI for binary classification. Note that AutoAI is only supported in IBM Watson OpenScale for IBM Cloud Pak for Data.

For binary classification type, AutoAI automatically sets the data type of the prediction column to boolean.

To fix this, implement one of the following solutions:

• Change the label column values in the training data to integer values, such as 0 or 1 depending on the outcome.
• Change the label column values in the training data to string value, such as A and B.

Why am I getting errors during model configuration?

The following error messages appear when you are configuring model details: Field feature_fields references column <name>, which is missing in input_schema of the model. Feature not found in input schema.

The preceding messages while completing the Model details section during configuration indicate a mismatch between the model input schema and the model training data schema:

To fix the issue, you must determine which of the following conditions is causing the error and take corrective action: If you use IBM watsonx.ai Runtime as your machine learning provider and the model type is XGBoost/scikit-learn refer to the watsonx.ai Runtime Python SDK documentation for important information about how to store the model. To generate the drift detection model, you must use scikit-learn version 0.20.2 in notebooks. For all other cases, you must ensure that the training data column names match with the input schema column names.

Why are my class labels missing when I use XGBoost?

Native XGBoost multiclass classification does not return class labels.

By default, for binary and multiple class models, the XGBoost framework does not return class labels.

For XGBoost binary and multiple class models, you must update the model to return class labels.

Why are the payload analytics not displaying properly?

Payload analytics does not display properly and the following error message displays: AIQDT0044E Forbidden character " in column name <column name>

For proper processing of payload analytics, Watson OpenScale does not support column names with double quotation marks (") in the payload. This affects both scoring payload and feedback data in CSV and JSON formats.

Remove double quotation marks (") from the column names of the payload file.

Error: An error occurred while computing feature importance

You receive the following error message during processing: Error: An error occurred while computing feature importance.

Having an equals sign (=) in the column name of a dataset causes an issue with explainability.

Remove the equals sign (=) from the column name and send the dataset through processing again.

Why are some of my active debias records missing?

Active debias records do not reach the payload logging table.

When you use the active debias API, there is a limit of 1000 records that can be sent at one time for payload logging.

To avoid loss of data, you must use the active debias API to score in chunks of 1000 records or fewer.

For more information, see Reviewing debiased transactions.

Watson OpenScale does not show any available schemas

When a user attempts to retrieve schema information for Watson OpenScale, none are available. After attempting directly in DB2, without reference to Watson OpenScale, checking what schemas are available for the database userid also returns none.

Insufficient permissions for the database userid is causing database connection issues for Watson OpenScale.

Make sure the database user has the correct permissions needed for Watson OpenScale.

A monitor run fails with an OutOfResources exception error message

You receive an OutOfResources exception error message.

Although there's no longer a limit on the number of rows you can have in the feedback payload, scoring payload, or business payload tables. The 50,000 limit now applies to the number of records you can run through the quality and bias monitors each billing period.

After you reach your limit, you must either upgrade to a Standard plan or wait for the next billing period.

Missing deployments

A deployed model does not show up as a deployment that can be selected to create a subscription.

There are different reasons that a deployment does not show up in the list of available deployed models. If the model is not a supported type of model because it uses an unsupported algorithm or framework, it won't appear. Your machine learning provider might not be configured properly. It could also be that there are issues with permissions.

Use the following steps to resolve this issue:

1. Check that the model is a supported type. Not sure? For more information, see Supported machine learning engines, frameworks, and models.
2. Check that a machine learning provider exists in the Watson OpenScale configuration for the specific deployment space. For more information, see Deployment spaces.
3. Check that the CP4D admin user has permission to access the deployment space.

"Failure converting response to expected model EntityStreamSizeException: actual entity size (Some(8644836)) exceeded content length limit (8388608 bytes)! You can configure this by setting akka.http.[server|client].parsing.max-content-length or calling HttpEntity.withSizeLimit before materializing the dataBytes stream".

oc patch woservice <release name> -p '{"spec": {"datamart": {"additional_jvm_options":"-Dakka.http.client.parsing.max-content-length=100m"} }}' --type=merge

Attribute name "table name" contains invalid character(s) among " ,;{}()\\n\\t=". Please use alias to rename it.;

db2 update db cfg using DFT_EXTENT_SZ 32