ScitypeTable

class ScitypeTable(is_univariate=None, is_empty=None, has_nans=None, n_instances=None, n_features=None, feature_names=None, dtypekind_dfip=None, feature_kind=None)[source]

Data Frame or Table data type.

The Table data type is an abstract data type (= scitype).

It represents a row- and column-indexed 2D table of data, commonly referred to as “data frame”.

Formally, an abstract Table object has:

an index \(i_1, \dots, i_T\), with \(i_i\) being any hashable type
values \(x_1, \dots, x_T\), with \(x_i\), taking values in an abstract typed data frame row domain \(\mathcal{Y}\), i.e., vectors with entries being numbers (float, integer) or categorical, always the same type at the same entry

The value \(x_i\) is interpreted to be an “observation” or “instance” at index \(i_i\).

The indices \(i_i\) are assumed distinct, but not necessarily ordered.

Concrete types implementing the Table data type must specify:

features: how the dimensions of \(\mathcal{Y}\) are represented
feature names: optional, names of the column dimensions
instances: how the value “observed at” an index is represented
instance index: how the instance index is represented

Concrete implementations may implement only sub-cases of the full abstract type.

Parameters:

is_univariate: bool: True iff table has one variable
is_empty: bool: True iff table has no variables or no instances
has_nans: bool: True iff the table contains NaN values
n_instances: int: number of instances/rows in the table
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 table, following the data frame interface protocol. In same order as feature_names.
feature_kind: list of str: list of feature-kind strings for each feature in the table, coerced to "FLOAT" or "CATEGORICAL" type string. In same order as feature_names.

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.