oraclesai.classification.GeographicalClassifier

class GeographicalClassifier(global_model=None, model_cls=None, spatial_weights_definition=None, bandwidth=None, fixed=True, local_weight=0.25, **kwargs)

Geographical classification algorithm. It uses a global model and multiple local models to perform classification.

Parameters:

  • global_model – A scikit-learn estimator instance, default=None. A trained model used as global model. Local models will be of the same type as this model. Required when model_cls is None.

  • model_cls – Class of scikit-learn estimator, default=None. Type of the global model and local models. When model_cls is provided (instead of global_model), a global model will be trained.Required when global_model=None. model_cls creation parameters are specified as kwargs.

  • spatial_weights_definition – SpatialWeightsDefinition, default=None. Spatial relationship specification. These criteria are used to group data into neighborhoods and train local models.

  • bandwidth – int or float, default=None. Distance (fixed=True) or number of nearest neighbors (fixed=False). bandwidth + fixed is another way to set the spatial relationship specification. It is ignored if spatial_weights_definition was set.

  • fixed – bool, default=True. True if bandwidth represents a distance. False for number of nearest neighbors.

  • local_weight – float (0.0 to 1.0), default=0.25. Weight associated to the local models predictions.

  • kwargs – Additional parameters for the inner models created with parameter model_cls.

Methods

__init__([global_model, model_cls, ...])

add_metric_info(metric_name, result[, ...])

fit(X, y[, geometries, crs, ...])

Trains a geographical classification model.

get_params([deep])

Get parameters for this estimator.

predict(X[, y, geometries, crs])

Predict the target class for X using the global model and the local models that are closer to geometries.

predict_proba(X[, y, geometries, crs])

Predict the probability of each class for X using the global model and the local models that are closer to geometries.

score(X, y[, geometries, crs])

Compute the F1 score on the given test data and labels.

set_fit_input(X[, geometries, y, ...])

set_params(**params)

Set the parameters of this estimator.

Attributes

metrics_info

predict_input_info

predict_output_info