Binarizer¶
Binarizes the data to 0 or 1 according to a threshold.
Parameters¶
-
threshold
Default →
0.0
Values above this are replaced by 1 and the others by 0.
-
dtype
Default →
<class 'bool'>
The desired data type to apply.
Examples¶
import river
import numpy as np
rng = np.random.RandomState(42)
X = [{'x1': v, 'x2': int(v)} for v in rng.uniform(low=-4, high=4, size=6)]
binarizer = river.preprocessing.Binarizer()
for x in X:
binarizer.learn_one(x)
print(binarizer.transform_one(x))
{'x1': False, 'x2': False}
{'x1': True, 'x2': True}
{'x1': True, 'x2': True}
{'x1': True, 'x2': False}
{'x1': False, 'x2': False}
{'x1': False, 'x2': False}
Methods¶
learn_one
Update with a set of features x
.
A lot of transformers don't actually have to do anything during the learn_one
step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the learn_one
can override this method.
Parameters
- x — 'dict'
transform_one
Transform a set of features x
.
Parameters
- x — 'dict'
Returns
dict: The transformed values.