Values (Value)

class Orange.data.Value

Contains a value of a variable. Value can be discrete, continuous or of some other type, like discrete or continuous distribution, or a string.

Values are not assigned as references. For instance, when taking a value from a data instance (e.g. value = data[2]), the result is a copy of the value; changing it does not affect the original data instance.

value

The actual value.

• If the Value is numeric, value is a number.
• If it is discrete and the variable descriptor is present (variable is not None), value is a string with the symbolic value (as retrieved from Orange.feature.Discrete.values).
• If it is discrete and the variable descriptor is unknown, value is a string with the number representing the value index, enclosed in brackets.
• If the value is missing, value is a string '?', '~' or '.' for don’t know, don’t care and other, respectively.

svalue

Stores a value that corresponds to a variable that is neither Orange.feature.Discrete nor Orange.feature.Continuous or a distribution of a discrete or continuous value.

This attribute is most often used for values of Orange.feature.StringVariable; in that case svalue is an instance of Orange.data.StringValue. Distributions are seldom used; when svalue contains a distribution, it is represented with an instance of Orange.statistics.distribution.Discrete or Orange.statistics.distribution.Continuous.

variable

Descriptor related to the value. Can be None.

var_type

Read-only descriptor that gives the variable type. Can be Orange.feature.Type.Discrete, Orange.feature.Type.Continuous, Orange.feature.Type.String or Orange.feature.Type.Other.

value_type

Tells whether the value is known (Orange.data.Value.Regular) or not (Orange.data.Value.DC. Orange.data.Value.DK).

__init__(variable[, value])

Construct a value with the given descriptor. Value can be any object that the descriptor understands. For discrete variables, this can be a string or an index (an integer number), for continuous it can be a string or a number. If the value is omitted, it is set to unknown (Orange.data.Value.DK).

import Orange
v = Orange.feature.Discrete("fruit", values=["plum", "orange", "apple"])
an_apple = Orange.data.Value(v, "apple")
another_apple = Orange.data.Value(v, 2)
unknown_fruit = Orange.data.Value(v)

v2 = Orange.feature.Continuous("iq")
genius = Orange.data.Value(v2, 180)
troll = Orange.data.Value(v2, "42")
stranger = Orange.data.value(v2)

Parameters:

• variable – variable descriptor
• value (int, float or string, or another type accepted by descriptor) – A value

__init__(value)

Construct either a discrete value, if the argument is an integer, or a continuous one, if the argument is a floating-point number. Descriptor is set to None.

Parameters:value (int or float) – A value

native()

Return the value in a “native” Python form: strings for discrete and undefined values and floating-point numbers for continuous values.

is_DC()

Return True if value is “don’t care”.

is_DK()

Return True if value is “don’t know”.

is_special()

Return True if value is either “don’t know” or “don’t care”.

Casting and comparison of values

Discrete and continuous values can be cast to Python types int, float, long, to strings and to boolean values. A value is considered true if it is not undefined. Continuous values support arithmetic operations.

Values can be compared with each other or with ordinary numbers. All discrete variables are treated as ordinal; values are compared by their respective indices and not in alphabetical order of their symbolic representations. When comparing values corresponding to different descriptors, Orange checks whether the order is unambiguous. Here are two such values:

deg3 = Orange.feature.Discrete(
    "deg3", values=["little", "medium", "big"])
deg4 = Orange.feature.Discrete(
    "deg4", values=["tiny", "little", "big", "huge"])
val3 = Orange.data.Value(deg3)
val4 = Orange.data.Value(deg4)
val3.value = "medium"
val4.value = "little"

Given this order, “medium” and “little” can be compared since it is known, from deg3, that “little” is less than “medium”.

val3.value = "medium"
val4.value = "huge"

These two values cannot be compared since they do not appear in the same variable. (Orange cannot use transitivity to conclude that medium is less than huge since medium is less than big and big is less than huge.)

Two values also cannot be compared when they have different order in the two variables.