.. currentmodule:: Orange.data ############################### Domain description (``domain``) ############################### Description of a domain stores a list of features, class(es) and meta attribute descriptors. A domain descriptor is attached to all tables in Orange to assign names and types to the corresponding columns. Columns in the :obj:`Orange.data.Table` have the roles of attributes (features, independent variables), class(es) (targets, outcomes, dependent variables) and meta attributes; in parallel to that, the domain descriptor stores their corresponding descriptions in collections of variable descriptors of type :obj:`Orange.data.Variable`. Domain descriptors are also stored in predictive models and other objects to facilitate automated conversions between domains, as described below. Domains are most often constructed automatically when loading the data or wrapping the numpy arrays into Orange's :obj:`~Orange.data.Table`. :: >>> from Orange.data import Table >>> iris = Table("iris") >>> iris.domain [sepal length, sepal width, petal length, petal width | iris] .. autoclass:: Orange.data.Domain .. attribute:: attributes A tuple of descriptors (instances of :class:`Orange.data.Variable`) for attributes (features, independent variables). :: >>> iris.domain.attributes (ContinuousVariable('sepal length'), ContinuousVariable('sepal width'), ContinuousVariable('petal length'), ContinuousVariable('petal width')) .. attribute:: class_var Class variable if the domain has a single class; `None` otherwise. :: >>> iris.domain.class_var DiscreteVariable('iris') .. attribute:: class_vars A tuple of descriptors for class attributes (outcomes, dependent variables). :: >>> iris.domain.class_vars (DiscreteVariable('iris'),) .. attribute:: variables A list of attributes and class attributes (the concatenation of the above). :: >>> iris.domain.variables (ContinuousVariable('sepal length'), ContinuousVariable('sepal width'), ContinuousVariable('petal length'), ContinuousVariable('petal width'), DiscreteVariable('iris')) .. attribute:: metas List of meta attributes. .. attribute:: anonymous `True` if the domain was constructed when converting numpy array to :class:`Orange.data.Table`. Such domains can be converted to and from other domains even if they consist of different variable descriptors for as long as their number and types match. .. automethod:: Domain.__init__ The following script constructs a domain with a discrete feature *gender* and continuous feature *age*, and a continuous target *salary*. :: >>> from Orange.data import Domain, DiscreteVariable, ContinuousVariable >>> domain = Domain([DiscreteVariable.make("gender"), ... ContinuousVariable.make("age")], ... ContinuousVariable.make("salary")) >>> domain [gender, age | salary] This constructs a new domain with some features from the Iris dataset and a new feature *color*. :: >>> new_domain = Domain(["sepal length", ... "petal length", ... DiscreteVariable.make("color")], ... iris.domain.class_var, ... source=iris.domain) >>> new_domain [sepal length, petal length, color | iris] .. automethod:: from_numpy :: >>> import numpy as np >>> from Orange.data import Domain >>> X = np.arange(20, dtype=float).reshape(5, 4) >>> Y = np.arange(5, dtype=int) >>> domain = Domain.from_numpy(X, Y) >>> domain [Feature 1, Feature 2, Feature 3, Feature 4 | Class 1] .. automethod:: __getitem__ :: >>> iris.domain[1:3] (ContinuousVariable('sepal width'), ContinuousVariable('petal length')) .. automethod:: __len__ .. automethod:: __contains__ :: >>> "petal length" in iris.domain True >>> "age" in iris.domain False .. automethod:: index :: >>> iris.domain.index("petal length") 2 .. automethod:: has_discrete_attributes :: >>> iris.domain.has_discrete_attributes() False >>> iris.domain.has_discrete_attributes(include_class=True) True .. automethod:: has_continuous_attributes :: >>> iris.domain.has_continuous_attributes() True Domain conversion ################# Domain descriptors also convert data instances between different domains. In a typical scenario, we may want to discretize some continuous data before inducing a model. Discretizers (:mod:`Orange.preprocess`) construct a new data table with attribute descriptors (:class:`Orange.data.variable`), that include the corresponding functions for conversion from continuous to discrete values. The trained model stores this domain descriptor and uses it to convert instances from the original domain to the discretized one at prediction phase. In general, instances are converted between domains as follows. - If the target attribute appears in the source domain, the value is copied; two attributes are considered the same if they have the same descriptor. - If the target attribute descriptor defines a function for value transformation, the value is transformed. - Otherwise, the value is marked as missing. An exception to this rule are domains in which the anonymous flag is set. When the source or the target domain is anonymous, they match if they have the same number of variables and types. In this case, the data is copied without considering the attribute descriptors.