macrosynergy.learning.forecasting.neighbors#

class KNNClassifier(n_neighbors='sqrt', weights='uniform')[source]#

Bases: ClassifierMixin, BaseEstimator

fit(X, y)[source]#

Fit method.

Parameters:

  • X (pd.DataFrame or np.ndarray) – The input feature matrix.

  • y (pd.Series or np.ndarray) – The target variable.

Returns:

The fitted model.

Return type:

self

predict(X)[source]#

Predict method.

Parameters:

X (pd.DataFrame or np.ndarray) – The input feature matrix.

Returns:

The predicted values.

Return type:

np.ndarray

predict_proba(X)[source]#

Predict probability method.

Parameters:

X (pd.DataFrame or np.ndarray) – The input feature matrix.

Returns:

The predicted probabilities.

Return type:

np.ndarray

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → KNNClassifier#

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to score.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

sample_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in score.

Returns:

self – The updated object.

Return type:

object

Submodules#