Hyperplane¶
Hyperplane stream generator.
Generates a problem of prediction class of a rotation hyperplane. It was used as testbed for CVFDT and VFDT in 1.
A hyperplane in d-dimensional space is the set of points \(x\) that satisfy
where \(x_i\) is the i-th coordinate of \(x\).
-
Examples for which \(\sum^{d}_{i=1} w_i x_i > w_0\), are labeled positive.
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Examples for which \(\sum^{d}_{i=1} w_i x_i \leq w_0\), are labeled negative.
Hyperplanes are useful for simulating time-changing concepts because we can change the orientation and position of the hyperplane in a smooth manner by changing the relative size of the weights. We introduce change to this dataset by adding drift to each weighted feature \(w_i = w_i + d \sigma\), where \(\sigma\) is the probability that the direction of change is reversed and \(d\) is the change applied to each example.
Parameters¶
-
seed
Type → int | None
Default →
None
Random seed for reproducibility.
-
n_features
Type → int
Default →
10
The number of attributes to generate. Higher than 2.
-
n_drift_features
Type → int
Default →
2
The number of attributes with drift. Higher than 2.
-
mag_change
Type → float
Default →
0.0
Magnitude of the change for every example. From 0.0 to 1.0.
-
noise_percentage
Type → float
Default →
0.05
Percentage of noise to add to the data. From 0.0 to 1.0.
-
sigma
Type → float
Default →
0.1
Probability that the direction of change is reversed. From 0.0 to 1.0.
Attributes¶
-
desc
Return the description from the docstring.
Examples¶
from river.datasets import synth
dataset = synth.Hyperplane(seed=42, n_features=2)
for x, y in dataset.take(5):
print(x, y)
{0: 0.2750, 1: 0.2232} 0
{0: 0.0869, 1: 0.4219} 1
{0: 0.0265, 1: 0.1988} 0
{0: 0.5892, 1: 0.8094} 0
{0: 0.3402, 1: 0.1554} 0
Methods¶
take
Iterate over the k samples.
Parameters
- k — 'int'
Notes¶
The sample generation works as follows: The features are generated with the random number generator, initialized with the seed passed by the user. Then the classification function decides, as a function of the sum of the weighted features and the sum of the weights, whether the instance belongs to class 0 or class 1. The last step is to add noise and generate drift.
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G. Hulten, L. Spencer, and P. Domingos. Mining time-changing data streams. In KDD'01, pages 97-106, San Francisco, CA, 2001. ACM Press. ↩