# LinkML Validator
[](https://github.com/linkml/linkml-validator/actions/workflows/run-tests.yml)
[](https://img.shields.io/pypi/v/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](linkml_validator/plugins/jsonschema_validation.py).
## 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
```sh
python setup.py install
```
To install development dependencies (like `pytest`, `mkdocs`, etc.):
```sh
pip install -e ".[dev]"
```
## Running the LinkML Validator via CLI
To run the LinkML Validator,
```sh
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.
```json
{
"<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](examples/example_data1.json):
```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](examples/example_schema.yaml).
You can run the validator on the above data as follows:
```sh
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:
```json
[
{},
{}
]
```
In this case, one must also specify the object type via `--target-class` argument in the CLI.
For example, consider [examples/example_data2.json](examples/example_data2.json):
```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:
```sh
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,
```sh
linkml-validator --inputs data.json \
--schema schema.yaml \
--output validation_results.json \
--plugins JsonSchemaValidationPlugin
```
To perform strict validation,
```sh
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,
```sh
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:
```py
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](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,
```py
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")
```
Raw data
{
"_id": null,
"home_page": "https://github.com/linkml/linkml-validator",
"name": "linkml-validator",
"maintainer": "",
"docs_url": null,
"requires_python": ">=3.8",
"maintainer_email": "",
"keywords": "",
"author": "Deepak Unni",
"author_email": "deepak.unni3@gmail.com",
"download_url": "https://files.pythonhosted.org/packages/d6/d7/2b67879fdd0b77e2451e073ac62d1284b6e88c85ceb919dbad2bf487d6b9/linkml_validator-0.4.5.tar.gz",
"platform": null,
"description": "# LinkML Validator\n\n[](https://github.com/linkml/linkml-validator/actions/workflows/run-tests.yml)\n[](https://img.shields.io/pypi/v/linkml-validator)\n\nThe LinkML Validator is a library for performing validation on data objects that\nconform to a given LinkML schema.\n\nThe Validator is initialized using a LinkML schema YAML, and is designed to allow\nfor flexible validation where each type of validation is done by a plugin.\n\nFor example, JSONSchema validation is performed by\n[JsonSchemaValidationPlugin](linkml_validator/plugins/jsonschema_validation.py).\n\n## Motivation\n\nThe LinkML Validator is built with the following goals in mind:\n- the Validator should respond with parseable validation messages\n- the Validator should not break the validation process even if one\nobject from a list of objects fail validation\n- the Validator should provide the ability to perform more than one\ntype of validation on an object\n\n\n\n## Installation\n\n```sh\npython setup.py install\n```\n\nTo install development dependencies (like `pytest`, `mkdocs`, etc.):\n\n```sh\npip install -e \".[dev]\"\n```\n\n## Running the LinkML Validator via CLI\n\nTo run the LinkML Validator,\n\n```sh\nlinkml-validator --inputs <INPUT JSON> \\\n --schema <SCHEMA YAML> \\\n --output <OUTPUT>\n```\n\nYou can pass filepath or a URL that points to the LinkML schema YAML.\n\n\n### Input data as a dictionary of objects\n\nThe input JSON can be a dictionary of objects keyed by the object type.\n\n```json\n{\n \"<OBJECT_TYPE>\": [\n {\n\n }\n ]\n}\n```\n\nWhere the `<OBJECT_TYPE>` is the pythonic representation of a class defined in the schema YAML.\n\nFor example, consider [examples/example_data1.json](examples/example_data1.json):\n\n```json\n{\n \"NamedThing\": [\n {\n \"id\": \"obj1\",\n \"name\": \"Object 1\",\n \"type\": \"X\"\n },\n {\n \"id\": \"obj2\",\n \"name\": \"Object 2\",\n \"type\": \"Y\"\n }\n ]\n}\n```\n\nIn the above example, the `NamedThing` is the `target_class`, which is the pythonic\nrepresentation of the class `named thing` as defined in the\n[examples/example_schema.yaml](examples/example_schema.yaml).\n\nYou can run the validator on the above data as follows:\n\n```sh\nlinkml-validator --inputs examples/example_data1.json \\\n --schema examples/example_schema.yaml \\\n --output examples/example_data1_validation_report.json\n```\n\n\n### Input data as an array of objects\n\nThe input JSON can also be an array of objects:\n\n```json\n[\n {},\n {}\n]\n```\n\nIn this case, one must also specify the object type via `--target-class` argument in the CLI.\n\nFor example, consider [examples/example_data2.json](examples/example_data2.json):\n\n```json\n[\n {\n \"id\": \"obj1\",\n \"name\": \"Object 1\",\n \"type\": \"X\"\n },\n {\n \"id\": \"obj2\",\n \"name\": \"Object 2\",\n \"type\": \"Y\"\n }\n]\n```\n\nYou can run the validator on the above data as follows:\n\n```sh\nlinkml-validator --inputs examples/example_data2.json \\\n --schema examples/example_schema.yaml \\\n --output examples/example_data2_validation_report.json \\\n --target-class NamedThing\n```\n\n\n## Running selected plugins\n\nTo run only certain plugins as part of the validation,\n\n```sh\nlinkml-validator --inputs data.json \\\n --schema schema.yaml \\\n --output validation_results.json \\\n --plugins JsonSchemaValidationPlugin\n```\n\nTo perform strict validation,\n\n```sh\nlinkml-validator --inputs data.json \\\n --schema schema.yaml \\\n --output validation_results.json \\\n --plugins JsonSchemaValidationPlugin \\\n --strict\n```\n\nUnder normal (default) mode, the validator will run all the checks defined in all\nreferenced plugins on a given object.\n\nWhen in strict mode, the validator will stop the validation for an object if even one\nof the plugins report a failed validation.\n\n## Running your own plugins with the Validator (via CLI)\n\nTo run your custom plugin as part of the validation,\n\n```sh\nlinkml-validator --inputs data.json \\\n --schema schema.yaml \\\n --output validation_results.json \\\n --plugins JsonSchemaValidationPlugin \\\n --plugins <CUSTOM_PLUGIN_CLASS>\n```\nwhere `<CUSTOM_PLUGIN_CLASS>` the reference to a custom plugin class.\n\n**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.\n\n\n## Using LinkML Validator as a module\n\nYou can use the `linkml_validator.validator.Validator` class directly in your codebase\nto perform validation on objects that you are working with.\n\nThe following code snippet provides a quick way of instantiating the Validator class\nand performing validation on an object:\n\n```py\nfrom linkml_validator.validator import Validator\n\ndata_obj = {\n \"id\": \"obj1\",\n \"name\": \"Object 1\",\n \"type\": \"X\"\n}\nvalidator = Validator(schema=\"examples/example_schema.yaml\")\nvalidator.validate(obj=data_obj, target_class=\"NamedThing\")\n```\n\n**Note:** The above code makes the assumption that there is a class `named thing` defined\nin the [examples/example_schema.yaml](examples/example_schema.yaml) and that `NamedThing`\nis its Pythonic representation.\n\n\nYou can also provide your own custom plugin class to run with the Validator,\n\n```py\nfrom linkml_validator.validator import Validator\nfrom linkml_validator.plugins.base import BasePlugin\nfrom linkml_validator.models import ValidationResult\n\nclass MyCustomPlugin(BasePlugin):\n NAME = \"MyCustomPlugin\"\n\n def __init__(self, schema: str, **kwargs) -> None:\n super().__init__(schema)\n\n def process(self, obj: dict, **kwargs) -> ValidationResult:\n # Add your custom logic for processing and validating the incoming object\n valid = False\n print(\"In MyCustomPlugin.process method\")\n result = ValidationResult(\n plugin_name=self.NAME,\n valid=valid,\n validation_messages=[]\n )\n return result\n\ndata_obj = {\n \"id\": \"obj1\",\n \"name\": \"Object 1\",\n \"type\": \"X\"\n}\nvalidator = Validator(schema=\"examples/example_schema.yaml\", plugins=[{\"plugin_class\": \"MyCustomPlugin\", \"args\": {}])\nvalidator.validate(obj=data_obj, target_class=\"NamedThing\")\n\n```\n\n\n\n",
"bugtrack_url": null,
"license": "BSD3",
"summary": "LinkML Validator",
"version": "0.4.5",
"split_keywords": [],
"urls": [
{
"comment_text": "",
"digests": {
"md5": "7c14e8848bf595b56a4bd4b3bec4ae73",
"sha256": "7a9799830cba3e1df203cde86263d8ea85f9ed9f6a45a218e3f752483abac030"
},
"downloads": -1,
"filename": "linkml_validator-0.4.5-py3-none-any.whl",
"has_sig": false,
"md5_digest": "7c14e8848bf595b56a4bd4b3bec4ae73",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.8",
"size": 14320,
"upload_time": "2022-12-06T16:08:44",
"upload_time_iso_8601": "2022-12-06T16:08:44.803740Z",
"url": "https://files.pythonhosted.org/packages/87/e1/4a9473997ae94d899008b9ae0c5da0702c7dfdc9ee1dd53c0a00c86873cf/linkml_validator-0.4.5-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"md5": "5a07b109dd32dc34e1dd8f048e49fbe2",
"sha256": "bce72053c1593a678ccefd28ed5cf4b39a878e78da884b5b41fef65610a23867"
},
"downloads": -1,
"filename": "linkml_validator-0.4.5.tar.gz",
"has_sig": false,
"md5_digest": "5a07b109dd32dc34e1dd8f048e49fbe2",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.8",
"size": 12126,
"upload_time": "2022-12-06T16:08:46",
"upload_time_iso_8601": "2022-12-06T16:08:46.038304Z",
"url": "https://files.pythonhosted.org/packages/d6/d7/2b67879fdd0b77e2451e073ac62d1284b6e88c85ceb919dbad2bf487d6b9/linkml_validator-0.4.5.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2022-12-06 16:08:46",
"github": true,
"gitlab": false,
"bitbucket": false,
"github_user": "linkml",
"github_project": "linkml-validator",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"requirements": [],
"lcname": "linkml-validator"
}
JSON
