KNNRegressor

K-Nearest Neighbors regressor.

This non-parametric regression method keeps track of the last window_size training samples. Predictions are obtained by aggregating the values of the closest n_neighbors stored samples with respect to a query sample.

Parameters

• n_neighbors

  Type → int

  Default → 5

  The number of nearest neighbors to search for.

• window_size

  Type → int

  Default → 1000

  The maximum size of the window storing the last observed samples.

• aggregation_method

  Type → str

  Default → mean

  The method to aggregate the target values of neighbors. | 'mean' | 'median' | 'weighted_mean'

• min_distance_keep

  Type → float

  Default → 0.0

  The minimum distance (similarity) to consider adding a point to the window. E.g., a value of 0.0 will add even exact duplicates.

• distance_func

  Type → DistanceFunc | None

  Default → None

  An optional distance function that should accept an a=, b=, and any custom set of kwargs. If not defined, the Minkowski distance is used with p=2 (Euclidean distance). See the example section for more details.

Examples

from river import datasets
from river import evaluate
from river import metrics
from river import neighbors
from river import preprocessing

dataset = datasets.TrumpApproval()

model = neighbors.KNNRegressor(window_size=50)
evaluate.progressive_val_score(dataset, model, metrics.RMSE())

RMSE: 1.427746

When defining a custom distance function you can rely on functools.partial to set default parameter values. For instance, let's use the Manhattan function instead of the default Euclidean distance:

import functools
from river.utils.math import minkowski_distance
model = (
    preprocessing.StandardScaler() |
    neighbors.KNNRegressor(
        window_size=50,
        distance_func=functools.partial(minkowski_distance, p=1)
    )
)
evaluate.progressive_val_score(dataset, model, metrics.RMSE())

RMSE: 1.460385

Methods

learn_one

Fits to a set of features x and a real-valued target y.

Parameters

• x — 'dict'
• y — 'base.typing.RegTarget'

Returns

Regressor: self

predict_one

Predict the output of features x.

Parameters

• x — 'dict'

Returns

base.typing.RegTarget: The prediction.