gwlearn.base.BaseRegressor

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

Generic geographically weighted regression meta-class

TODO:

• tvalues & adj_alpha & critical_t val

• predict

• performance measurements

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 | 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 False

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, by default True. If True, measures R2 and adjusted R2.

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

random_stateint | None, optional

Random seed for reproducibility, by default None

verbosebool, optional

Whether to print progress information, by default False

**kwargs

Additional keyword arguments passed to model initialisation

Attributes:

pred_pd.Series

Focal predictions for each location.

resid_pd.Series

Residuals for each location (y - pred_).

RSS_pd.Series

Residual sum of squares for each location.

TSS_pd.Series

Total sum of squares for each location.

y_bar_pd.Series

Weighted mean of y for each location.

local_r2_pd.Series

Local R2 for each location.

focal_r2_float

Global R2 for focal predictions.

score_float

Alias for focal_r2_ (global R2 for focal predictions).

focal_adj_r2_float

Adjusted R2 for focal predictions.

hat_values_pd.Series

Hat values for each location (diagonal elements of hat matrix).

effective_df_float

Effective degrees of freedom (sum of hat values).

log_likelihood_float

Global log likelihood of the model.

aic_float

Akaike information criterion of the model.

aicc_float

Corrected Akaike information criterion to account for model complexity (smaller bandwidths).

bic_float

Bayesian information criterion.

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

Methods

__init__(model, *[, bandwidth, fixed, ...])
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 the coefficient of determination of the prediction.
set_fit_request(*[, geometry])	Request metadata passed to the fit method.
set_params(**params)	Set the parameters of this estimator.
set_score_request(*[, sample_weight])	Request metadata 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='$UNCHANGED$')

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.

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

Metadata routing for sample_weight parameter in score.

Returns:

selfobject

The updated object.