Agrawal stream generator.
The generator was introduced by Agrawal et al. 1, and was a common source of data for early work on scaling up decision tree learners. The generator produces a stream containing nine features, six numeric and three categorical. There are 10 functions defined for generating binary class labels from the features. Presumably these determine whether the loan should be approved. Classification functions are listed in the original paper 1.
Feature | Description | Values
salary| salary | uniformly distributed from 20k to 150k
commission| commission | 0 if
salary< 75k else uniformly distributed from 10k to 75k
age| age | uniformly distributed from 20 to 80
elevel| education level | uniformly chosen from 0 to 4
car| car maker | uniformly chosen from 1 to 20
zipcode| zip code of the town | uniformly chosen from 0 to 8
hvalue| house value | uniformly distributed from 50k x zipcode to 100k x zipcode
hyears| years house owned | uniformly distributed from 1 to 30
loan| total loan amount | uniformly distributed from 0 to 500k
classification_function (int) – defaults to
The classification function to use for the generation. Valid values are from 0 to 9.
seed (int) – defaults to
seedis used to seed the random number generator; If RandomState instance,
seedis the random number generator; If None, the random number generator is the
RandomStateinstance used by
balance_classes (bool) – defaults to
If True, the class distribution will converge to a uniform distribution.
perturbation (float) – defaults to
The probability that noise will happen in the generation. Each new sample will be perturbed by the magnitude of
perturbation. Valid values are in the range [0.0 to 1.0].
Return the description from the docstring.
>>> from river import synth >>> dataset = synth.Agrawal( ... classification_function=0, ... seed=42 ... ) >>> dataset Synthetic data generator <BLANKLINE> Name Agrawal Task Binary classification Samples ∞ Features 9 Outputs 1 Classes 2 Sparse False <BLANKLINE> Configuration ------------- classification_function 0 seed 42 balance_classes False perturbation 0.0 >>> for x, y in dataset.take(5): ... print(list(x.values()), y) [68690.2154, 81303.5729, 62, 4, 6, 2, 419982.4410, 11, 433088.0728] 1 [98144.9515, 0, 43, 2, 1, 7, 266488.5281, 6, 389.3829] 0 [148987.502, 0, 52, 3, 11, 8, 79122.9140, 27, 199930.4858] 0 [26066.5362, 83031.6639, 34, 2, 11, 6, 444969.2657, 25, 23225.2063] 1 [98980.8307, 0, 40, 0, 6, 1, 1159108.4298, 28, 281644.1089] 0
Generate drift by switching the classification function randomly.
Iterate over the k samples.
- k (int)
The sample generation works as follows: The 9 features are generated
with the random generator, initialized with the seed passed by the
user. Then, the classification function decides, as a function of all
the attributes, whether to classify the instance as class 0 or class
1. The next step is to verify if the classes should be balanced, and
if so, balance the classes. Finally, add noise if
perturbation > 0.0.