A class to build custom conditions for DataFrame assertions and alerting.
A Condition
is a class for building custom data quality checks. Simply create a condition, and after the run is processed your conditions will be evaluated. Integrate with email or slack to have condition results alerting via a Run Report. Use Conditions to answer questions such as “Is the average confidence for my training data below 0.25” or “Has over 20% of my inference data drifted”.
You can build a Run Report
that will evaluate all conditions after a run is processed.
You can also build and evaluate conditions by accessing the processed DataFrame.
A Condition
is defined as:
To gain an intuition for what can be accomplished, consider the following examples:
By adding filters, you can further narrow down the scope of the condition. If the aggregate function is “pct”, you don’t need to specify a metric, as the filters will determine the percentage of data.
Complex conditions can be built when the filter has a different metric than the metric used in the condition.
You can also call conditions directly, which will assert its truth against a DataFrame.
The available aggregate functions are:
The available operators are:
The metric must be the name of a column in the DataFrame. Threshold is a numeric value for comparison in the Condition.
Alerting via email, slack in development. Please reach out to Galileo at team@rungalileo.io for more information.
A class to build custom conditions for DataFrame assertions and alerting.
A Condition
is a class for building custom data quality checks. Simply create a condition, and after the run is processed your conditions will be evaluated. Integrate with email or slack to have condition results alerting via a Run Report. Use Conditions to answer questions such as “Is the average confidence for my training data below 0.25” or “Has over 20% of my inference data drifted”.
You can build a Run Report
that will evaluate all conditions after a run is processed.
You can also build and evaluate conditions by accessing the processed DataFrame.
A Condition
is defined as:
To gain an intuition for what can be accomplished, consider the following examples:
By adding filters, you can further narrow down the scope of the condition. If the aggregate function is “pct”, you don’t need to specify a metric, as the filters will determine the percentage of data.
Complex conditions can be built when the filter has a different metric than the metric used in the condition.
You can also call conditions directly, which will assert its truth against a DataFrame.
The available aggregate functions are:
The available operators are:
The metric must be the name of a column in the DataFrame. Threshold is a numeric value for comparison in the Condition.
Alerting via email, slack in development. Please reach out to Galileo at team@rungalileo.io for more information.