load_atherosclerosis

getml.datasets.load_atherosclerosis(roles: bool = True, as_pandas: bool = False, as_dict: bool = False) → Union[Tuple[Union[DataFrame, DataFrame], ...], Dict[str, Union[DataFrame, DataFrame]]][source]

Binary classification dataset on the lethality of atherosclerosis

The atherosclerosis dataset is a medical dataset from the the CTU Prague Relational Learning Repository. It contains information from an longitudal study on 1417 middle-aged men obeserved over the course of 20 years. After preprocessing, it consists of 2 tables with 76 and 66 columns:

  • population: Data on the study’s participants

  • contr: Data on control dates

The population table is split into a training (70%), a testing (15%) set and a validation (15%) set.

Args:
as_pandas (bool):

Return data as pandas.DataFrame s

roles (bool):

Return data with roles set

as_dict (bool):

Return data as dict with df.name s as keys and df s as values.

Returns:
tuple:

Tuple containing (sorted alphabetically by df.name`s) the data as :class:`~getml.DataFrame s or pandas.DataFrame s (if as_pandas is True) or

dict:

if as_dict is True: Dictionary containing the data as DataFrame s or pandas.DataFrame s (if as_pandas is True). The keys correspond to the name of the DataFrame on the engine.

The following DataFrames are returned:

  • population

  • contr

Examples:
>>> population, contr = getml.datasets.load_atherosclerosis()
>>> type(population)
... getml.data.data_frame.DataFrame

For an full analysis of the atherosclerosis dataset including all necessary preprocessing steps please refer to getml-examples.

Note:

Roles can be set ad-hoc by supplying the respective flag. If roles is False, all columns in the returned DataFrames s have roles unused_string or unused_float. This dataset contains no units. Before using them in an analysis, a data model needs to be constructed using Placeholder s.