pycircular.datasets package

Submodules

pycircular.datasets.base module

Base IO code for all datasets https://github.com/scikit-learn/scikit-learn/blob/56057c9630dd13f3c61fbb4c7debdff6ba8e9e8c/sklearn/datasets/base.py

class pycircular.datasets.base.Bunch(**kwargs)

Bases: dict

Container object for datasets: dictionary-like object that exposes its keys as attributes.

Methods

clear()
copy()
fromkeys(iterable[, value])	Create a new dictionary with keys from iterable and values set to value.
get(key[, default])	Return the value for key if key is in the dictionary, else default.
items()
keys()
pop(key[, default])	If key is not found, default is returned if given, otherwise KeyError is raised
popitem(/)	Remove and return a (key, value) pair as a 2-tuple.
setdefault(key[, default])	Insert key with a value of default if key is not in the dictionary.
update([E, ]**F)	If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]
values()

pycircular.datasets.base.load_transactions()

Load and return the transactions’ dataset (classification).

The bank transactions is an easily transformable transactional dataset.

Returns

dataBunch

Dictionary-like object, the interesting attributes are: ‘data’, the data to learn, ‘target’, the classification labels, meaning of the features, and ‘DESCR’, the full description of the dataset.

References

1

A. Correa Bahnsen, A. Stojanovic, D.Aouada, B, Ottersten, “Improving Credit Card Fraud Detection with Calibrated Probabilities”, in Proceedings of the fourteenth SIAM International Conference on Data Mining, 677-685, 2014.

Examples

>>> from pycircular.datasets import load_transactions
>>> data = load_transactions()
>>> data.data.head()

Module contents

The pycircular.datasets module includes utilities to load datasets, including methods to load and fetch popular reference datasets. It also features some artificial data generators.

pycircular.datasets.load_transactions()

Load and return the transactions’ dataset (classification).

The bank transactions is an easily transformable transactional dataset.

Returns

dataBunch

Dictionary-like object, the interesting attributes are: ‘data’, the data to learn, ‘target’, the classification labels, meaning of the features, and ‘DESCR’, the full description of the dataset.

References

1

A. Correa Bahnsen, A. Stojanovic, D.Aouada, B, Ottersten, “Improving Credit Card Fraud Detection with Calibrated Probabilities”, in Proceedings of the fourteenth SIAM International Conference on Data Mining, 677-685, 2014.

Examples

>>> from pycircular.datasets import load_transactions
>>> data = load_transactions()
>>> data.data.head()