Evaluation¶

Once our JSON Logic expression has been typechecked [1], it can be evaluated using the utility evaluate() function:

from jsonlogic import JSONLogicExpression, Operator
from jsonlogic.evaluation import evaluate
from jsonlogic.operators import operator_registry

expr = JSONLogicExpression.from_json({">": [{"var": "my_int"}, 2]})

root_op = expr.as_operator_tree(operator_registry)

return_value = evaluate(
    root_op,
    data={"my_int": 1},
    data_schema=None,
    settings={  # Optional
        "variable_casts": {...},
    },
)

assert return_value is False

This function returns the evaluated expression result. Because the implementation of the typechecking functionnality is based on the JSON Schema specification, we assume the provided data argument is JSON data. When string variables are of a specific format, The data_schema argument is used to know what is the corresponding format:

expr = JSONLogicExpression.from_json({
    ">": [
        {"var": "a_date_var"},
        "2020-01-01",
    ]
})

root_op = expr.as_operator_tree(operator_registry)

return_value = evaluate(
    root_op,
    data={"a_date_var": "2024-01-01"},
    data_schema={  # Use the same schema used during typechecking.
        "type": "object",
        "properties": {
            "a_date_var": {"type": "string", "format": "date"},
        },
    },
    settings={
        "literal_casts": [date.fromisoformat],
    },
)

assert return_value is True

During evaluation, variables are resolved and casted to a specific type (see resolve_variable()) according to the provided JSON Schema.

Note

If you are dealing with already converted data, you can pass None to the data_schema argument. This way, no variable cast will be performed during evaluation.

Evaluation settings¶

Most of the available evaluation settings are analogous to the typechecking settings. You can refer to the API documentation of the EvaluationSettings class for more details.

footnotes