def validate(data_asset,
expectation_suite=None,
data_asset_name=None,
data_context=None,
data_asset_type=None,
*args,
**kwargs):
"""Validate the provided data asset using the provided expectation suite"""
if expectation_suite is None and data_context is None:
raise ValueError(
"Either an expectation suite or a DataContext is required for validation."
)
if expectation_suite is None:
logger.info("Using expectation suite from DataContext.")
# Allow data_context to be a string, and try loading it from path in that case
if isinstance(data_context, string_types):
data_context = DataContext(data_context)
expectation_suite = data_context.get_expectation_suite(data_asset_name)
else:
if data_asset_name in expectation_suite:
logger.info("Using expectation suite with name %s" %
expectation_suite["data_asset_name"])
else:
logger.info("Using expectation suite with no data_asset_name")
# If the object is already a Dataset type, then this is purely a convenience method
# and no conversion is needed
if isinstance(data_asset, dataset.Dataset) and data_asset_type is None:
return data_asset.validate(expectation_suite=expectation_suite,
data_context=data_context,
*args,
**kwargs)
elif data_asset_type is None:
# Guess the GE data_asset_type based on the type of the data_asset
if isinstance(data_asset, pd.DataFrame):
data_asset_type = dataset.PandasDataset
# Add other data_asset_type conditions here as needed
# Otherwise, we will convert for the user to a subclass of the
# existing class to enable new expectations, but only for datasets
if not isinstance(data_asset, (dataset.Dataset, pd.DataFrame)):
raise ValueError(
"The validate util method only supports dataset validations, including custom subclasses. For other data asset types, use the object's own validate method."
)
if not issubclass(type(data_asset), data_asset_type):
if isinstance(data_asset, (pd.DataFrame)) and issubclass(
data_asset_type, dataset.PandasDataset):
pass # This is a special type of allowed coercion
else:
raise ValueError(
"The validate util method only supports validation for subtypes of the provided data_asset_type."
)
data_asset_ = _convert_to_dataset_class(data_asset, data_asset_type,
expectation_suite)
return data_asset_.validate(*args, data_context=data_context, **kwargs)
def validate(
data_asset,
expectation_suite=None,
data_asset_name=None,
expectation_suite_name=None,
data_context=None,
data_asset_class_name=None,
data_asset_module_name="great_expectations.dataset",
data_asset_class=None,
*args,
**kwargs,
):
"""Validate the provided data asset. Validate can accept an optional data_asset_name to apply, data_context to use
to fetch an expectation_suite if one is not provided, and data_asset_class_name/data_asset_module_name or
data_asset_class to use to provide custom expectations.
Args:
data_asset: the asset to validate
expectation_suite: the suite to use, or None to fetch one using a DataContext
data_asset_name: the name of the data asset to use
expectation_suite_name: the name of the expectation_suite to use
data_context: data context to use to fetch an an expectation suite, or the path from which to obtain one
data_asset_class_name: the name of a class to dynamically load a DataAsset class
data_asset_module_name: the name of the module to dynamically load a DataAsset class
data_asset_class: a class to use. overrides data_asset_class_name/ data_asset_module_name if provided
*args:
**kwargs:
Returns:
"""
# Get an expectation suite if not provided
if expectation_suite is None and data_context is None:
raise ValueError(
"Either an expectation suite or a DataContext is required for validation."
)
if expectation_suite is None:
logger.info("Using expectation suite from DataContext.")
# Allow data_context to be a string, and try loading it from path in that case
if isinstance(data_context, str):
from great_expectations.data_context import DataContext
data_context = DataContext(data_context)
expectation_suite = data_context.get_expectation_suite(
expectation_suite_name=expectation_suite_name
)
else:
if isinstance(expectation_suite, dict):
expectation_suite = expectationSuiteSchema.load(expectation_suite)
if data_asset_name is not None:
raise ValueError(
"When providing an expectation suite, data_asset_name cannot also be provided."
)
if expectation_suite_name is not None:
raise ValueError(
"When providing an expectation suite, expectation_suite_name cannot also be provided."
)
logger.info(
"Validating data_asset_name %s with expectation_suite_name %s"
% (data_asset_name, expectation_suite.expectation_suite_name)
)
# If the object is already a DataAsset type, then this is purely a convenience method
# and no conversion is needed; try to run validate on the given object
if data_asset_class_name is None and data_asset_class is None:
return data_asset.validate(
expectation_suite=expectation_suite,
data_context=data_context,
*args,
**kwargs,
)
# Otherwise, try to convert and validate the dataset
if data_asset_class is None:
verify_dynamic_loading_support(module_name=data_asset_module_name)
data_asset_class = load_class(data_asset_class_name, data_asset_module_name)
import pandas as pd
from great_expectations.dataset import Dataset, PandasDataset
if data_asset_class is None:
# Guess the GE data_asset_type based on the type of the data_asset
if isinstance(data_asset, pd.DataFrame):
data_asset_class = PandasDataset
# Add other data_asset_type conditions here as needed
# Otherwise, we will convert for the user to a subclass of the
# existing class to enable new expectations, but only for datasets
if not isinstance(data_asset, (Dataset, pd.DataFrame)):
raise ValueError(
"The validate util method only supports dataset validations, including custom subclasses. For other data "
"asset types, use the object's own validate method."
)
if not issubclass(type(data_asset), data_asset_class):
if isinstance(data_asset, pd.DataFrame) and issubclass(
data_asset_class, PandasDataset
):
pass # This is a special type of allowed coercion
else:
raise ValueError(
"The validate util method only supports validation for subtypes of the provided data_asset_type."
)
data_asset_ = _convert_to_dataset_class(
data_asset, dataset_class=data_asset_class, expectation_suite=expectation_suite
)
return data_asset_.validate(*args, data_context=data_context, **kwargs)