LinkML Validator

The LinkML Validator is a library for performing validation on data objects that conform to a given LinkML schema.

The Validator is initialized using a LinkML schema YAML, and is designed to allow for flexible validation where each type of validation is done by a plugin.

For example, JSONSchema validation is performed by JsonSchemaValidationPlugin.

Motivation

The LinkML Validator is built with the following goals in mind:

• the Validator should respond with parseable validation messages
• the Validator should not break the validation process even if one object from a list of objects fail validation
• the Validator should provide the ability to perform more than one type of validation on an object

Installation

python setup.py install

To install development dependencies (like pytest, mkdocs, etc.):

pip install -e ".[dev]"

Running the LinkML Validator via CLI

To run the LinkML Validator,

linkml-validator --inputs <INPUT JSON> \
    --schema <SCHEMA YAML> \
    --output <OUTPUT>

You can pass filepath or a URL that points to the LinkML schema YAML.

Input data as a dictionary of objects

The input JSON can be a dictionary of objects keyed by the object type.

{
    "<OBJECT_TYPE>": [
        {

        }
    ]
}

Where the <OBJECT_TYPE> is the pythonic representation of a class defined in the schema YAML.

For example, consider examples/example_data1.json:

{
    "NamedThing": [
        {
            "id": "obj1",
            "name": "Object 1",
            "type": "X"
        },
        {
            "id": "obj2",
            "name": "Object 2",
            "type": "Y"
        }
    ]
}

In the above example, the NamedThing is the target_class, which is the pythonic representation of the class named thing as defined in the examples/example_schema.yaml.

You can run the validator on the above data as follows:

linkml-validator --inputs examples/example_data1.json \
    --schema examples/example_schema.yaml \
    --output examples/example_data1_validation_report.json

Input data as an array of objects

The input JSON can also be an array of objects:

[
    {},
    {}
]

In this case, one must also specify the object type via --target-class argument in the CLI.

For example, consider examples/example_data2.json:

[
    {
        "id": "obj1",
        "name": "Object 1",
        "type": "X"
    },
    {
        "id": "obj2",
        "name": "Object 2",
        "type": "Y"
    }
]

You can run the validator on the above data as follows:

linkml-validator --inputs examples/example_data2.json \
    --schema examples/example_schema.yaml \
    --output examples/example_data2_validation_report.json \
    --target-class NamedThing

Running selected plugins

To run only certain plugins as part of the validation,

linkml-validator --inputs data.json \
    --schema schema.yaml \
    --output validation_results.json \
    --plugins JsonSchemaValidationPlugin

To perform strict validation,

linkml-validator --inputs data.json \
    --schema schema.yaml \
    --output validation_results.json \
    --plugins JsonSchemaValidationPlugin \
    --strict

Under normal (default) mode, the validator will run all the checks defined in all referenced plugins on a given object.

When in strict mode, the validator will stop the validation for an object if even one of the plugins report a failed validation.

Running your own plugins with the Validator (via CLI)

To run your custom plugin as part of the validation,

linkml-validator --inputs data.json \
    --schema schema.yaml \
    --output validation_results.json \
    --plugins JsonSchemaValidationPlugin \
    --plugins <CUSTOM_PLUGIN_CLASS>

where <CUSTOM_PLUGIN_CLASS> the reference to a custom plugin class.

Note: The custom plugin class must be a subclass of linkml_validator.plugins.base.BasePlugin and must implement all the methods defined in BasePlugin class.

Using LinkML Validator as a module

You can use the linkml_validator.validator.Validator class directly in your codebase to perform validation on objects that you are working with.

The following code snippet provides a quick way of instantiating the Validator class and performing validation on an object:

from linkml_validator.validator import Validator

data_obj = {
    "id": "obj1",
    "name": "Object 1",
    "type": "X"
}
validator = Validator(schema="examples/example_schema.yaml")
validator.validate(obj=data_obj, target_class="NamedThing")

Note: The above code makes the assumption that there is a class named thing defined in the examples/example_schema.yaml and that NamedThing is its Pythonic representation.

You can also provide your own custom plugin class to run with the Validator,

from linkml_validator.validator import Validator
from linkml_validator.plugins.base import BasePlugin
from linkml_validator.models import ValidationResult

class MyCustomPlugin(BasePlugin):
    NAME = "MyCustomPlugin"

    def __init__(self, schema: str, **kwargs) -> None:
        super().__init__(schema)

    def process(self, obj: dict, **kwargs) -> ValidationResult:
        # Add your custom logic for processing and validating the incoming object
        valid = False
        print("In MyCustomPlugin.process method")
        result = ValidationResult(
            plugin_name=self.NAME,
            valid=valid,
            validation_messages=[]
        )
        return result

data_obj = {
    "id": "obj1",
    "name": "Object 1",
    "type": "X"
}
validator = Validator(schema="examples/example_schema.yaml", plugins=[{"plugin_class": "MyCustomPlugin", "args": {}])
validator.validate(obj=data_obj, target_class="NamedThing")