PolynomialExtender¶
Polynomial feature extender.
Generate features consisting of all polynomial combinations of the features with degree less than or equal to the specified degree.
Be aware that the number of outputted features scales polynomially in the number of input features and exponentially in the degree. High degrees can cause overfitting.
Parameters¶
-
degree
Default →
2
The maximum degree of the polynomial features.
-
interaction_only
Default →
False
If
True
then only combinations that include an element at most once will be computed. -
include_bias
Default →
False
Whether or not to include a dummy feature which is always equal to 1.
-
bias_name
Default →
bias
Name to give to the bias feature.
Examples¶
from river import feature_extraction as fx
X = [
{'x': 0, 'y': 1},
{'x': 2, 'y': 3},
{'x': 4, 'y': 5}
]
poly = fx.PolynomialExtender(degree=2, include_bias=True)
for x in X:
print(poly.transform_one(x))
{'x': 0, 'y': 1, 'x*x': 0, 'x*y': 0, 'y*y': 1, 'bias': 1}
{'x': 2, 'y': 3, 'x*x': 4, 'x*y': 6, 'y*y': 9, 'bias': 1}
{'x': 4, 'y': 5, 'x*x': 16, 'x*y': 20, 'y*y': 25, 'bias': 1}
X = [
{'x': 0, 'y': 1, 'z': 2},
{'x': 2, 'y': 3, 'z': 2},
{'x': 4, 'y': 5, 'z': 2}
]
poly = fx.PolynomialExtender(degree=3, interaction_only=True)
for x in X:
print(poly.transform_one(x))
{'x': 0, 'y': 1, 'z': 2, 'x*y': 0, 'x*z': 0, 'y*z': 2, 'x*y*z': 0}
{'x': 2, 'y': 3, 'z': 2, 'x*y': 6, 'x*z': 4, 'y*z': 6, 'x*y*z': 12}
{'x': 4, 'y': 5, 'z': 2, 'x*y': 20, 'x*z': 8, 'y*z': 10, 'x*y*z': 40}
Polynomial features are typically used for a linear model to capture interactions between features. This may done by setting up a pipeline, as so:
from river import datasets
from river import evaluate
from river import linear_model as lm
from river import metrics
from river import preprocessing as pp
dataset = datasets.Phishing()
model = (
fx.PolynomialExtender() |
pp.StandardScaler() |
lm.LogisticRegression()
)
metric = metrics.Accuracy()
evaluate.progressive_val_score(dataset, model, metric)
Accuracy: 88.88%
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.