HierarchicalPolarsEager

class HierarchicalPolarsEager(is_univariate=None, is_equally_spaced=None, is_equal_length=None, is_empty=None, is_one_series=None, is_one_panel=None, has_nans=None, n_instances=None, n_panels=None, n_features=None, feature_names=None, dtypekind_dfip=None, feature_kind=None)[source]

Data type: polars DataFrame frame based specification of hierarchical series.

Name: "polars_hierarchical"

Short description: A polars.DataFrame where hierarchy is represented using explicit columns instead of a traditional index, and time is recorded in a dedicated column.

Long description: The "polars_hierarchical" mtype is a concrete specification of the Hierarchical scitype, which represents a hierarchically structured collection of time series.

An object obj: polars.DataFrame follows the specification iff:

structure convention: obj must have at least three index columns, where:
- The first n-1 index columns define the hierarchy.
- The last index column represents time.
- All index columns must be explicitly named following the pattern __index__*, such as __index__0, __index__1, …, __index__N-1.
hierarchy level: rows with the same values in the hierarchy columns belong to the same hierarchy unit, while different hierarchy values correspond to different hierarchy units.
hierarchy: the hierarchy structure is explicitly encoded in columns rather than an index.
time index: the last column in the index set is interpreted as a time column. It must be monotonically increasing within each hierarchy unit.
time points: rows with the same value in the time column correspond to the same time point.
variables: columns excluding the hierarchy and time columns correspond to variables.
variable names: column names are taken from obj.columns.

Capabilities: * can represent multivariate hierarchical series. * can represent unequally spaced hierarchical series. * can represent unequally supported hierarchical series. * cannot represent hierarchical series with different sets of variables. * can represent missing values.

Parameters:

is_univariate: bool: True iff table has one variable
is_equally_spacedbool: True iff series index is equally spaced
is_equal_length: bool: True iff all series in panel are of equal length
is_empty: bool: True iff table has no variables or no instances
is_one_series: bool: True iff there is only one series in the hierarchical collection.
is_one_panel: bool: True iff there is only one flat panel in the hierarchical collection, i.e., the collection has a flat hierarchy, plus additional hierarchy levels that have only a single value.
has_nans: bool: True iff the table contains NaN values
n_instances: int: number of instances in the hierarchical collection
n_panels: int: number of flat panels in the hierarchical collection
n_features: int: number of variables in table
feature_names: list of int or object: names of variables in table
dtypekind_dfip: list of DtypeKind enum: list of DtypeKind enum values for each feature in the panel, following the data frame interface protocol
feature_kind: list of str: list of feature kind strings for each feature in the panel, coerced to FLOAT or CATEGORICAL type

Methods

`__call__`(obj[, return_metadata, var_name, ...])	Check if obj is of this data type.
`check`(obj[, return_metadata, var_name, ...])	Check if obj is of this data type.
`clone`()	Obtain a clone of the object with same hyper-parameters and config.
`clone_tags`(estimator[, tag_names])	Clone tags from another object as dynamic override.
`create_test_instance`([parameter_set])	Construct an instance of the class, using first test parameter set.
`create_test_instances_and_names`([parameter_set])	Create list of all test instances and a list of names for them.
`get`(key[, default])	Get attribute by key.
`get_class_tag`(tag_name[, tag_value_default])	Get class tag value from class, with tag level inheritance from parents.
`get_class_tags`()	Get class tags from class, with tag level inheritance from parent classes.
`get_config`()	Get config flags for self.
`get_param_defaults`()	Get object's parameter defaults.
`get_param_names`([sort])	Get object's parameter names.
`get_params`([deep])	Get a dict of parameters values for this object.
`get_tag`(tag_name[, tag_value_default, ...])	Get tag value from instance, with tag level inheritance and overrides.
`get_tags`()	Get tags from instance, with tag level inheritance and overrides.
`get_test_params`([parameter_set])	Return testing parameter settings for the skbase object.
`is_composite`()	Check if the object is composed of other BaseObjects.
`load_from_path`(serial)	Load object from file location.
`load_from_serial`(serial)	Load object from serialized memory container.
`reset`()	Reset the object to a clean post-init state.
`save`([path, serialization_format])	Save serialized self to bytes-like object or to (.zip) file.
`set_config`(**config_dict)	Set config flags to given values.
`set_params`(**params)	Set the parameters of this object.
`set_random_state`([random_state, deep, ...])	Set random_state pseudo-random seed parameters for self.
`set_tags`(**tag_dict)	Set instance level tag overrides to given values.