SelectKBest

Removes all but the $k$ highest scoring features.

Parameters

• similarity

  Type → stats.base.Bivariate

• k

  Default → 10

  The number of features to keep.

• use_abs

  Type → bool

  Default → False

  A boolean indicating whether to rank features based on the absolute value of their scores. This is particularly useful when the similarity metric can produce negative values, such as Pearson correlation. Defaults to False.

Attributes

• similarities (dict)

  The similarity instances used for each feature.

• leaderboard (dict)

  The actual similarity measures.

Examples

from pprint import pprint
from river import feature_selection
from river import stats
from river import stream
from sklearn import datasets

X, y = datasets.make_regression(
    n_samples=100,
    n_features=10,
    n_informative=2,
    random_state=42
)

selector = feature_selection.SelectKBest(
    similarity=stats.PearsonCorr(),
    k=2
)

for xi, yi, in stream.iter_array(X, y):
    selector.learn_one(xi, yi)

pprint(selector.leaderboard)

Counter({9: 0.7898,
        7: 0.5444,
        8: 0.1062,
        2: 0.0638,
        4: 0.0538,
        5: 0.0271,
        1: -0.0312,
        6: -0.0657,
        3: -0.1501,
        0: -0.1895})

selector.transform_one(xi)

{7: -1.2795, 9: -1.8408}

import random

random.seed(42)
X_abs = [[random.random() for _ in range(3)] for _ in range(100)]
y_abs = [
    0.6 * x[0]
    - 0.9 * x[1]
    + 0.1 * x[2]
    + random.gauss(0, 0.1)
    for x in X_abs
]

selector_no_abs = feature_selection.SelectKBest(
    stats.PearsonCorr(),
    k=1,
    use_abs=False
)
for xi, yi in stream.iter_array(X_abs, y_abs):
    selector_no_abs.learn_one(xi, yi)
pprint(selector_no_abs.leaderboard)

Counter({0: 0.5683236302249015,
         2: -0.09937590098236333,
         1: -0.7655616041162767})

selector_no_abs.transform_one({i: v for i, v in enumerate(X_abs[-1])})

{0: 0.009669699608339966}

selector_with_abs = feature_selection.SelectKBest(
    stats.PearsonCorr(),
    k=1,
    use_abs=True
)
for xi, yi in stream.iter_array(X_abs, y_abs):
    selector_with_abs.learn_one(xi, yi)
selector_with_abs.transform_one({i: v for i, v in enumerate(X_abs[-1])})

{1: 0.07524386007376704}

Methods

learn_one

Update with a set of features x and a target y.

Parameters

• x — 'dict[base.typing.FeatureName, Any]'
• y — 'base.typing.Target'

transform_one

Transform a set of features x.

Parameters

• x — 'dict[base.typing.FeatureName, Any]'

Returns

dict[base.typing.FeatureName, Any]: The transformed values.