gwlearn.base.BaseClassifier

class gwlearn.base.BaseClassifier(model, bandwidth, fixed=False, kernel='bisquare', n_jobs=-1, fit_global_model=True, measure_performance=True, strict=False, keep_models=False, temp_folder=None, batch_size=None, min_proportion=0.2, **kwargs)

Generic geographically weighted modelling meta-class

NOTE: local models leave out focal, unlike in traditional approaches. This allows assessment of geographically weighted metrics on unseen data without a need for train/test split, hence providing value for all samples. This is needed for futher spatial analysis of the model performance (and generalises to models that do not support OOB scoring).

Parameters:

modelmodel class

Scikit-learn model class

bandwidthint | float

bandwidth value consisting of either a distance or N nearest neighbors

fixedbool, optional

True for distance based bandwidth and False for adaptive (nearest neighbor) bandwidth, by default False

kernelstr, optional

type of kernel function used to weight observations, by default “bisquare”

n_jobsint, optional

The number of jobs to run in parallel. -1 means using all processors by default -1

fit_global_modelbool, optional

Determines if the global baseline model shall be fitted alognside the geographically weighted, by default True

strictbool | None, optional

Do not fit any models if at least one neighborhood has invariant y, by default None. None is treated as False but provides a warning if there are invariant models.

keep_modelsbool | str | Path, optional

Keep all local models (required for prediction), by default False. Note that for some models, like random forests, the objects can be large. If string or Path is provided, the local models are not held in memory but serialized to the disk from which they are loaded in prediction.

temp_folderstr, optional

Folder to be used by the pool for memmapping large arrays for sharing memory with worker processes, e.g., /tmp. Passed to joblib.Parallel.

**kwargs

Additional keyword arguments passed to model initialisation

__init__(model, bandwidth, fixed=False, kernel='bisquare', n_jobs=-1, fit_global_model=True, measure_performance=True, strict=False, keep_models=False, temp_folder=None, batch_size=None, min_proportion=0.2, **kwargs)

Methods

__init__(model, bandwidth[, fixed, kernel, ...])
fit(X, y, geometry)	Fit the geographically weighted model
predict(X, geometry)
predict_proba(X, geometry)	Predict probabiliies using the ensemble of local models

fit(X, y, geometry)

Fit the geographically weighted model

Parameters:

Xpd.DataFrame

Independent variables

ypd.Series

Dependent variable

geometrygpd.GeoSeries

Geographic location

predict(X, geometry)

predict_proba(X, geometry)

Predict probabiliies using the ensemble of local models

For any given location, this uses the

Parameters:

Xpd.DataFrame

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geometrygpd.GeoSeries

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Returns:

pd.DataFrame

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Raises:

NotImplementedError

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