explabox.explore

Functions/classes for exploring your data (dataset descriptives).

class explabox.explore.Dataset(instances, labels, type='dataset', subtype=None, callargs=None, **kwargs)

Bases: MetaInfo

Digestible for dataset.

Examples

Construct a dataset with 5 instances and get instance 2 through 4:

>>> dataset = Dataset(instances, ['positive', 'negative', 'positive', 'neutral', 'positive'])
>>> dataset[2:4]

Get the first instance:

>>> dataset.head(n=1)

Randomly sample two instances:

>>> dataset.sample(n=2, seed=0)

Get all instances in the dataset labelled as ‘positive’:

>>> dataset.filter('positive')

Parameters:

instances (_type_) – Instances.
labels (Sequence[LT]) – Ground-truth labels (annotated).
type (str, optional) – Type description. Defaults to “dataset”.
subtype (Optional[str], optional) – Subtype description. Defaults to None.
callargs (Optional[dict], optional) – Call arguments for reproducibility. Defaults to None.

property content: Content as dictionary.

property data: Get data property.

filter(indexer)

Filter dataset by label, filter function or boolean list/array.

Examples

Filter by label ‘positive’:

>>> dataset.filter('positive')

Filter if ‘@’ character in data:

>>> dataset.filter(lambda data, label: '@' in data)

Filter if ‘@’ character not in instance and label in (‘neutral’, ‘negative’):

>>> def filter_fn(instance):
...     return ('@' not in instance['data'] and
...             instance['label'] in (frozenset({'neutral'}), frozenset({'negative'})))
>>> dataset.filter(filter_fn)

Filter by boolean sequence (should be equal length to the number of instances):

>>> dataset.filter([True] * len(dataset))

Parameters:: indexer (Union[Callable[[dict], bool], Callable[[DT, LT], bool], Sequence[bool], LT]) – Filter to apply.
Raises:: ValueError – Boolean array should be equal length to number of instances.
Returns:: Filtered dataset.
Return type:: Dataset

get_by_index(index)

Get item(s) by integer index.

Return type:: Dataset

get_by_key(index)

Get item(s) by key.

Return type:: Dataset

head(n=10)

Get the first n elements in the dataset.

Parameters:: n (int, optional) – Number of elements >= 0. Defaults to 10.
Raises:: ValueError – n should be >= 0.
Returns:: First n elements.
Return type:: Dataset

property instances: Get instances property

property keys: Get keys property

property labels: Get labels property.

sample(n=1, seed=None)

Get a random sample of size n.

Parameters:

n (int, optional) – Number of elements >= 0. Defaults to 1.
seed (int, optional) – Seed for reproducibility; if None it takes a random seed. Defaults to None.

Raises:

ValueError – n should be >= 0.

Returns:

Random subsample.

Return type:

Dataset

tail(n=10)

Get the last n elements in the dataset.

Parameters:: n (int, optional) – Number of elements >= 0. Defaults to 10.
Raises:: ValueError – n should be >= 0.
Returns:: Last n elements.
Return type:: Dataset

class explabox.explore.Descriptives(labels, label_counts, tokenized_lengths, type='descriptives', callargs=None, **kwargs)

Bases: MetaInfo

Digestible for descriptive statistics.

Parameters:

labels (Sequence[LT]) – Names of labels.
label_counts (Dict[str, Dict[LT, int]]) – Counts per label per split.
tokenized_lengths (dict) – Descriptive statistics for lengths of tokenized instances.
type (str, optional) – Type description. Defaults to “descriptives”.
subtype (Optional[str], optional) – Subtype description. Defaults to None.
callargs (Optional[dict], optional) – Call arguments for reproducibility. Defaults to None.

property content: Content as dictionary.

class explabox.explore.Explorer(data=None, ingestibles=None, **kwargs)

Bases: Readable, IngestiblesMixin

The Explorer explores your data by providing descriptive statistics.

The Explorer requires ‘data’ defined. It is included in the Explabox under the .explore property.

Examples

Get dataset descriptives:

>>> from explabox.explore import Explorer
>>> explorer = Explorer(data=data)
>>> explorer()

Show the first 10 instances of the test split

>>> from explabox.explore import Explorer
>>> explorer = Explorer(data=data)
>>> explorer.instances(split="test")[:10]

Parameters:

data (Optional[Environment], optional) – Data for ingestibles. Defaults to None.
ingestibles (Optional[Ingestible], optional) – Ingestible. Defaults to None.

descriptives(**kwargs)

Describe features such as the amount per label for the train, test and model predictions and text data specific features such as the maximum/minimum/mean amount of words in a sample and the standard deviation.

Returns:: Descriptive statistics of each split.
Return type:: Descriptives

instances(split='test', **kwargs)

Get the instances of the given split.

Parameters:: split (str, optional) – Split to select. Defaults to “test”.
Returns:: Instances in the split.
Return type:: Dataset