Sine¶
Sine generator.
This generator is an implementation of the dara stream with abrupt concept drift, as described in Gama, Joao, et al. 1.
It generates up to 4 relevant numerical features, that vary from 0 to 1, where only 2 of them are relevant to the classification task and the other 2 are optionally added by as noise. A classification function is chosen among four options:
-
SINE1
. Abrupt concept drift, noise-free examples. It has two relevant attributes. Each attributes has values uniformly distributed in [0, 1]. In the first context all points below the curve \(y = sin(x)\) are classified as positive. -
Reversed SINE1
. The reversed classification ofSINE1
. -
SINE2
. The same two relevant attributes. The classification function is \(y < 0.5 + 0.3 sin(3 \pi x)\). -
Reversed SINE2
. The reversed classification ofSINE2
.
Concept drift can be introduced by changing the classification function. This can be done manually or using ConceptDriftStream
.
Two important features are the possibility to balance classes, which means the class distribution will tend to a uniform one, and the possibility to add noise, which will, add two non relevant attributes.
Parameters¶
-
classification_function
Type → int
Default →
0
Classification functions to use. From 0 to 3.
-
seed
Type → int | None
Default →
None
Random seed for reproducibility.
-
balance_classes
Type → bool
Default →
False
Whether to balance classes or not. If balanced, the class distribution will converge to an uniform distribution.
-
has_noise
Type → bool
Default →
False
Adds 2 non relevant features to the stream.
Attributes¶
-
desc
Return the description from the docstring.
Examples¶
from river.datasets import synth
dataset = synth.Sine(classification_function = 2, seed = 112,
balance_classes = False, has_noise = True)
for x, y in dataset.take(5):
print(x, y)
{0: 0.4812, 1: 0.6660, 2: 0.6198, 3: 0.6994} 1
{0: 0.9022, 1: 0.7518, 2: 0.1625, 3: 0.2209} 0
{0: 0.4547, 1: 0.3901, 2: 0.9629, 3: 0.7287} 0
{0: 0.4683, 1: 0.3515, 2: 0.2273, 3: 0.6027} 0
{0: 0.9238, 1: 0.1673, 2: 0.4522, 3: 0.3447} 0
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 two 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 and noise is added, if these options are set by the user.
The generated sample will have 2 relevant features, and an additional
two noise features if has_noise
is set.
-
Gama, Joao, et al.'s 'Learning with drift detection.' Advances in artificial intelligence-SBIA 2004. Springer Berlin Heidelberg, 2004. 286-295." ↩