RobustScaler¶
Scale features using statistics that are robust to outliers.
This Scaler removes the median and scales the data according to the interquantile range.
Parameters¶
-
with_centering
Default →
True
Whether to centre the data before scaling.
-
with_scaling
Default →
True
Whether to scale data to IQR.
-
q_inf
Default →
0.25
Desired inferior quantile, must be between 0 and 1.
-
q_sup
Default →
0.75
Desired superior quantile, must be between 0 and 1.
Attributes¶
-
median (dict)
Mapping between features and instances of
stats.Quantile
(0.5)`. -
iqr (dict)
Mapping between features and instances of
stats.IQR
.
Examples¶
from pprint import pprint
import random
from river import preprocessing
random.seed(42)
X = [{'x': random.uniform(8, 12)} for _ in range(5)]
pprint(X)
[{'x': 10.557707},
{'x': 8.100043},
{'x': 9.100117},
{'x': 8.892842},
{'x': 10.945884}]
scaler = preprocessing.RobustScaler()
for x in X:
scaler.learn_one(x)
print(scaler.transform_one(x))
{'x': 0.0}
{'x': -1.0}
{'x': 0.0}
{'x': -0.12449923287875722}
{'x': 1.1086595155704708}
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.