etiq.etiq_dataissues package

Submodules

etiq.etiq_dataissues.core module

etiq.etiq_dataissues.core.get_abstractdataset_issues(base_dataset: AbstractDataset, comparison_dataset: AbstractDataset, search_for_missing_features: bool = True, search_for_unknown_features: bool = True, identical_feature_filter: List[str] | None = None, range_feature_filter: List[str] | None = None, missing_category_feature_filter: List[str] | None = None, unknown_category_feature_filter: List[str] | None = None) → List[DataIssue]

etiq.etiq_dataissues.core.get_data_issues(base_dataset: DataFrame, comparison_dataset: DataFrame, categorical_features: List[str] | None = None, continuous_features: List[str] | None = None, search_for_missing_features: bool = True, search_for_unknown_features: bool = True, identical_feature_filter: List[str] | None = None, range_feature_filter: List[str] | None = None, missing_category_feature_filter: List[str] | None = None, unknown_category_feature_filter: List[str] | None = None) → List[DataIssue]

etiq.etiq_dataissues.data_issues module

class etiq.etiq_dataissues.data_issues.DataIssue(name: str, feature: str, value: str | float, segment: str)

Bases: object

A simple data class to represent a data issue e.g. “unknown_category” found by a pipeline the data

feature: str

name: str

segment: str

value: str | float

class etiq.etiq_dataissues.data_issues.DataIssueBuilder

Bases: object

A builder for the Issue class

add_feature_name(issue_name: str)

add_issue_name(issue_name: str)

add_segment(asegment_name: str)

add_value(value: str | float)

build() → DataIssue

Builds and returns an Issue object

Returns:: An issue built using data and parameters passed to the builder.
Return type:: DataIssue

reset()

Reset the builder.

Returns:: This instance of the builder with data reset
Return type:: DataIssueBuilder

etiq.etiq_dataissues.utils module

etiq.etiq_dataissues.utils.categorical_feature_missing(base_dataset: DataFrame, comparison_dataset: DataFrame, afeature: str) → List[Any]

etiq.etiq_dataissues.utils.categorical_feature_unrecognized(base_dataset: DataFrame, comparison_dataset: DataFrame, afeature: str) → List[Any]

etiq.etiq_dataissues.utils.continuous_feature_above_base_max(base_dataset: DataFrame, comparison_dataset: DataFrame, afeature: str) → float

etiq.etiq_dataissues.utils.continuous_feature_below_base_min(base_dataset: DataFrame, comparison_dataset: DataFrame, afeature: str) → float

etiq.etiq_dataissues.utils.guess_cont_cat_col(data=None, names_col=None)

Function attempts to guess which columns have categorical data and which ones have continuous data

Parameters:

data (numpy array or pandas Dataframe) – input data
names_col (numpy array of strings) – a list of all the column names in data

Returns:

A tuple of Numpy arrays. The first array has column names that we assume are categorical features. The second array has column names that we assume are continuous features.

test to implemnet: there is no column name that is not categorized as continuous or categorical

etiq.etiq_dataissues.utils.identical_values(base_dataset: DataFrame, comparison_dataset: DataFrame, afeature: str) → bool

etiq.etiq_dataissues.utils.missing_features(base_dataset: DataFrame, comparison_dataset: DataFrame) → List[str]

etiq.etiq_dataissues.utils.unknown_features(base_dataset: DataFrame, comparison_dataset: DataFrame) → List[str]

Module contents

class etiq.etiq_dataissues.DataIssue(name: str, feature: str, value: str | float, segment: str)

Bases: object

A simple data class to represent a data issue e.g. “unknown_category” found by a pipeline the data

feature: str

name: str

segment: str

value: str | float

class etiq.etiq_dataissues.DataIssueBuilder

Bases: object

A builder for the Issue class

add_feature_name(issue_name: str)

add_issue_name(issue_name: str)

add_segment(asegment_name: str)

add_value(value: str | float)

build() → DataIssue

Builds and returns an Issue object

Returns:: An issue built using data and parameters passed to the builder.
Return type:: DataIssue

reset()

Reset the builder.

Returns:: This instance of the builder with data reset
Return type:: DataIssueBuilder

etiq.etiq_dataissues.get_abstractdataset_issues(base_dataset: AbstractDataset, comparison_dataset: AbstractDataset, search_for_missing_features: bool = True, search_for_unknown_features: bool = True, identical_feature_filter: List[str] | None = None, range_feature_filter: List[str] | None = None, missing_category_feature_filter: List[str] | None = None, unknown_category_feature_filter: List[str] | None = None) → List[DataIssue]

etiq.etiq_dataissues.get_data_issues(base_dataset: DataFrame, comparison_dataset: DataFrame, categorical_features: List[str] | None = None, continuous_features: List[str] | None = None, search_for_missing_features: bool = True, search_for_unknown_features: bool = True, identical_feature_filter: List[str] | None = None, range_feature_filter: List[str] | None = None, missing_category_feature_filter: List[str] | None = None, unknown_category_feature_filter: List[str] | None = None) → List[DataIssue]