STAGGER¶
STAGGER concepts stream generator.
This generator is an implementation of the dara stream with abrupt concept drift, as described in 1.
The STAGGER concepts are boolean functions f
with three features describing objects: size (small, medium and large), shape (circle, square and triangle) and colour (red, blue and green).
f
options:
-
True
if the size is small and the color is red. -
True
if the color is green or the shape is a circle. -
True
if the size is medium or large
Concept drift can be introduced by changing the classification function. This can be done manually or using ConceptDriftStream
.
One important feature is the possibility to balance classes, which means the class distribution will tend to a uniform one.
Parameters¶
-
classification_function (int) – defaults to
0
Classification functions to use. From 0 to 2.
-
seed (int) – defaults to
None
If int,
seed
is used to seed the random number generator; If RandomState instance,seed
is the random number generator; If None, the random number generator is theRandomState
instance used bynp.random
. -
balance_classes (bool) – defaults to
False
Whether to balance classes or not. If balanced, the class distribution will converge to an uniform distribution.
Attributes¶
-
desc
Return the description from the docstring.
Examples¶
>>> from river import synth
>>> dataset = synth.STAGGER(classification_function = 2, seed = 112,
... balance_classes = False)
>>> for x, y in dataset.take(5):
... print(x, y)
{'size': 0, 'color': 0, 'shape': 2} 0
{'size': 1, 'color': 0, 'shape': 1} 1
{'size': 0, 'color': 0, 'shape': 0} 0
{'size': 1, 'color': 2, 'shape': 0} 1
{'size': 1, 'color': 0, 'shape': 2} 1
Methods¶
generate_drift
Generate drift by switching the classification function at random.
take
Iterate over the k samples.
Parameters
- k (int)
Notes¶
The sample generation works as follows: The 3 attributes are generated with the random number generator. The classification function defines whether to classify the instance as class 0 or class 1. Finally, data is balanced, if this option is set by the user.
References¶
-
Schlimmer, J. C., & Granger, R. H. (1986). Incremental learning from noisy data. Machine learning, 1(3), 317-354. ↩