gwlearn.linear_model.GWLinearRegression

class gwlearn.linear_model.GWLinearRegression(bandwidth=None, fixed=False, kernel='bisquare', include_focal=True, geometry=None, graph=None, n_jobs=-1, fit_global_model=True, measure_performance=True, keep_models=False, temp_folder=None, batch_size=None, **kwargs)

Geographically weighted linear regression

Parameters:

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 | Callable, optional

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

include_focalbool, optional

Include focal in the local model training. Excluding it 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 further spatial analysis of the model performance (and generalises to models that do not support OOB scoring). However, it leaves out the most representative sample. By default True

geometrygpd.GeoSeries, optional

Geographic location of the observations in the sample. Used to determine the spatial interaction weight based on specification by bandwidth, fixed, kernel, and include_focal keywords. Either geometry or graph need to be specified. To allow prediction, it is required to specify geometry.

graphGraph, optional

Custom libpysal.graph.Graph object encoding the spatial interaction between observations in the sample. If given, it is used directly and bandwidth, fixed, kernel, and include_focal keywords are ignored. Either geometry or graph need to be specified. To allow prediction, it is required to specify geometry. Potentially, both can be specified where graph encodes spatial interaction between observations in geometry.

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 alongside the geographically weighted, by default True

measure_performancebool, optional

Calculate performance metrics for the model. If True, measures accuracy score, precision, recall, balanced accuracy, and F1 scores (based on focal prediction, pooled local predictions and individual local predictions). By default True

strictbool | None, optional

Do not fit any models if at least one neighborhood has invariant y, by default False. 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 | None, 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, by default None

batch_sizeint | None, optional

Number of models to process in each batch. Specify batch_size if your models do not fit into memory. By default None

verbosebool, optional

Whether to print progress information, by default False

**kwargs

Additional keyword arguments passed to sklearn.linear_model.LinearRegression initialisation

Attributes:

local_coef_pd.DataFrame

Local coefficient of the features in the decision function for each feature at each location

local_intercept_pd.Series

Local intercept values at each location

__init__(bandwidth=None, fixed=False, kernel='bisquare', include_focal=True, geometry=None, graph=None, n_jobs=-1, fit_global_model=True, measure_performance=True, keep_models=False, temp_folder=None, batch_size=None, **kwargs)

Methods

__init__([bandwidth, fixed, kernel, ...])
fit(X, y)	Fit the geographically weighted model
get_metadata_routing()	Get metadata routing of this object.
get_params([deep])	Get parameters for this estimator.
score(X, y[, sample_weight])	Return coefficient of determination on test data.
set_fit_request(*[, geometry])	Configure whether metadata should be requested to be passed to the fit method.
set_params(**params)	Set the parameters of this estimator.
set_score_request(*[, sample_weight])	Configure whether metadata should be requested to be passed to the score method.

fit(X, y)

Fit the geographically weighted model

Parameters:

Xpd.DataFrame

Independent variables

ypd.Series

Dependent variable

geometrygpd.GeoSeries

Geographic location

set_score_request(*, sample_weight='$UNCHANGED$')

Configure whether metadata should be requested to be passed to the score method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the 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.

Added in version 1.3.

Parameters:

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for sample_weight parameter in score.

Returns:

selfobject

The updated object.