MixedNulls#

class MixedNulls[source]#

Search for various types of null values, including string representations of null.

Parameters

null_string_listIterable[str] , default: None: List of strings to be considered alternative null representations
check_nanbool , default: True: Whether to add to null list to check also NaN values
columnsUnion[Hashable, List[Hashable]] , default: None: Columns to check, if none are given checks all columns except ignored ones.
ignore_columnsUnion[Hashable, List[Hashable]] , default: None: Columns to ignore, if none given checks based on columns variable
n_top_columnsint , optional: amount of columns to show ordered by feature importance (date, index, label are first)
aggregation_method: t.Optional[str], default: ‘max’: Argument for the reduce_output functionality, decides how to aggregate the vector of per-feature scores into a single aggregated score. The aggregated score value is between 0 and 1 for all methods. Possible values are: ‘l3_weighted’: Default. L3 norm over the ‘per-feature scores’ vector weighted by the feature importance, specifically, sum(FI * PER_FEATURE_SCORES^3)^(1/3). This method takes into account the feature importance yet puts more weight on the per-feature scores. This method is recommended for most cases. ‘l5_weighted’: Similar to ‘l3_weighted’, but with L5 norm. Puts even more emphasis on the per-feature scores and specifically on the largest per-feature scores returning a score closer to the maximum among the per-feature scores. ‘weighted’: Weighted mean of per-feature scores based on feature importance. ‘max’: Maximum of all the per-feature scores. None: No averaging. Return a dict with a per-feature score for each feature.
n_samplesint , default: 10_000_000: number of samples to use for this check.
random_stateint, default: 42: random seed for all check internals.

__init__(null_string_list: Optional[Iterable[str]] = None, check_nan: bool = True, columns: Optional[Union[Hashable, List[Hashable]]] = None, ignore_columns: Optional[Union[Hashable, List[Hashable]]] = None, n_top_columns: int = 10, aggregation_method: Optional[str] = 'max', n_samples: int = 10000000, random_state: int = 42, **kwargs)[source]#

__new__(*args, **kwargs)#

Methods

`MixedNulls.add_condition`(name, ...)	Add new condition function to the check.
`MixedNulls.add_condition_different_nulls_less_equal_to`([...])	Add condition - require column's number of different null values to be less or equal to threshold.
`MixedNulls.clean_conditions`()	Remove all conditions from this check instance.
`MixedNulls.conditions_decision`(result)	Run conditions on given result.
`MixedNulls.config`([include_version, ...])	Return check configuration (conditions' configuration not yet supported).
`MixedNulls.feature_reduce`(...)	Return an aggregated drift score based on aggregation method defined.
`MixedNulls.from_config`(conf[, version_unmatch])	Return check object from a CheckConfig object.
`MixedNulls.from_json`(conf[, version_unmatch])	Deserialize check instance from JSON string.
`MixedNulls.greater_is_better`()	Return True if the check reduce_output is better when it is greater.
`MixedNulls.metadata`([with_doc_link])	Return check metadata.
`MixedNulls.name`()	Name of class in split camel case.
`MixedNulls.params`([show_defaults])	Return parameters to show when printing the check.
`MixedNulls.reduce_output`(check_result)	Return an aggregated drift score based on aggregation method defined.
`MixedNulls.remove_condition`(index)	Remove given condition by index.
`MixedNulls.run`(dataset[, model, ...])	Run check.
`MixedNulls.run_logic`(context, dataset_kind)	Run check.
`MixedNulls.to_json`([indent, ...])	Serialize check instance to JSON string.

Examples#

ColumnsInfo.to_json

MixedNulls.add_condition