TableNp2D

class TableNp2D(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 type: 2D np.ndarray based specification of data frame table.

Name: "numpy2D"

Short description:

a 2D numpy array representing tabular data, with rows = instances, cols = features

Long description:

The "numpy2D" mtype is a concrete specification that implements the Table scitype, i.e., the abstract type of tabular data.

An object obj: numpy.ndarray follows the specification iff:

structure convention: obj.shape is (n_instances, n_features)
features: columns of obj correspond to different features
feature names: feature names are integers 0, 1, …, n_features-1
instances: rows of obj correspond to different instances

Capabilities:

can represent multivariate data
can represent missing values

Parameters are inferred by check.

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 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.