Skip to content

data_feature

OpenMLDataFeature

Data Feature (a.k.a. Attribute) object.

Parameters:

Name Type Description Default
index int

The index of this feature

 required
name str

Name of the feature

 required
data_type str

can be nominal, numeric, string, date (corresponds to arff)

 required
nominal_values list(str)

list of the possible values, in case of nominal attribute

 required
number_missing_values int

Number of rows that have a missing value for this feature.

 required
ontologies list(str)

list of ontologies attached to this feature. An ontology describes the concept that are described in a feature. An ontology is defined by an URL where the information is provided.

 None
Source code in openml/datasets/data_feature.py 
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
class OpenMLDataFeature:
    """
    Data Feature (a.k.a. Attribute) object.

    Parameters
    ----------
    index : int
        The index of this feature
    name : str
        Name of the feature
    data_type : str
        can be nominal, numeric, string, date (corresponds to arff)
    nominal_values : list(str)
        list of the possible values, in case of nominal attribute
    number_missing_values : int
        Number of rows that have a missing value for this feature.
    ontologies : list(str)
        list of ontologies attached to this feature. An ontology describes the
        concept that are described in a feature. An ontology is defined by an
        URL where the information is provided.
    """

    LEGAL_DATA_TYPES: ClassVar[Sequence[str]] = ["nominal", "numeric", "string", "date"]

    def __init__(  # noqa: PLR0913
        self,
        index: int,
        name: str,
        data_type: str,
        nominal_values: list[str],
        number_missing_values: int,
        ontologies: list[str] | None = None,
    ):
        if not isinstance(index, int):
            raise TypeError(f"Index must be `int` but is {type(index)}")

        if data_type not in self.LEGAL_DATA_TYPES:
            raise ValueError(
                f"data type should be in {self.LEGAL_DATA_TYPES!s}, found: {data_type}",
            )

        if data_type == "nominal":
            if nominal_values is None:
                raise TypeError(
                    "Dataset features require attribute `nominal_values` for nominal "
                    "feature type.",
                )

            if not isinstance(nominal_values, list):
                raise TypeError(
                    "Argument `nominal_values` is of wrong datatype, should be list, "
                    f"but is {type(nominal_values)}",
                )
        elif nominal_values is not None:
            raise TypeError("Argument `nominal_values` must be None for non-nominal feature.")

        if not isinstance(number_missing_values, int):
            msg = f"number_missing_values must be int but is {type(number_missing_values)}"
            raise TypeError(msg)

        self.index = index
        self.name = str(name)
        self.data_type = str(data_type)
        self.nominal_values = nominal_values
        self.number_missing_values = number_missing_values
        self.ontologies = ontologies

    def __repr__(self) -> str:
        return "[%d - %s (%s)]" % (self.index, self.name, self.data_type)

    def __eq__(self, other: Any) -> bool:
        return isinstance(other, OpenMLDataFeature) and self.__dict__ == other.__dict__

    def _repr_pretty_(self, pp: pretty.PrettyPrinter, cycle: bool) -> None:  # noqa: FBT001, ARG002
        pp.text(str(self))