Agrawal¶
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 ifsalary
< 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
Parameters¶
-
classification_function
Type → int
Default →
0
The classification function to use for the generation. Valid values are from 0 to 9.
-
seed
Type → int | None
Default →
None
Random seed for reproducibility.
-
balance_classes
Type → bool
Default →
False
If True, the class distribution will converge to a uniform distribution.
-
perturbation
Type → float
Default →
0.0
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].
Attributes¶
-
desc
Return the description from the docstring.
Examples¶
from river.datasets 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)
[103125.4837, 0, 21, 2, 8, 3, 319768.9642, 4, 338349.7437] 1
[135983.3438, 0, 25, 4, 14, 0, 423837.7755, 7, 116330.4466] 1
[98262.4347, 0, 55, 1, 18, 6, 144088.1244, 19, 139095.3541] 0
[133009.0417, 0, 68, 1, 14, 5, 233361.4025, 7, 478606.5361] 1
[63757.2908, 16955.9382, 26, 2, 12, 4, 522851.3093, 24, 229712.4398] 1
Methods¶
generate_drift
Generate drift by switching the classification function randomly.
take
Iterate over the k samples.
Parameters
- k — 'int'
Notes¶
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.