This action runs Whylog constraints on a static dataset.
Required Name of file holding JSON-encoded constraints. Constraints assert that a logged value or summary statistic is within an expected range. Each constraint is bound to a column in the data, and each column may have multiple constraints. The standard boolean comparison operators are supported -- LT, LE, EQ, NE, GE, GT
For example,
{
"valueConstraints": {
"loan_amnt": {
"constraints": [
{
"value": 548250.0,
"op": "LT"
},
{
"value": 2500.0,
"op": "LT",
"verbose": true
}
]
}
},
"summaryConstraints": {
"annual_inc": {
"constraints": [
{
"firstField": "min",
"value": 0.0,
"op": "GE"
}
]
}
}
}
Constraints may have an optional name to make them easier to identify.
The name has no significance beyond labelling the constraint for reporting. If not provided, a label is automatically constructed.
Constraints may also be marked 'verbose' which will log every failure.
INFO - value constraint value GT 2500.0 failed on value 2500.0
Verbose logging helps identify why a constraint is failing to validate, but can be very chatty if there are a lot of failures.
Constraints are divided into two categories; value constraints and summary constraints. Value constraints are applied to every value that is logged for a feature. At a minimum, Value constraints must specify a comparison operator and a literal value. e.g.
{
"op": "GT",
"value": 4000.0
},
{
"op": "LT",
"value": 50000.0,
"name": "Must not exceed",
"verbose": true
},
Summary constraints are applied to Whylogs feature summaries, They compare fields of the summary to static literals or to another field in the summary, e.g.
Required File holding feature data. Format is anything that pandas package can load, but CSV works well.
uses: whylogs-actions/action@v1 with: constraintsfile: 'constraints.json' datafile: 'lending_club_1000.csv'