import geopandas as gpd
import sklearn
from geodatasets import get_path
from sklearn.model_selection import GridSearchCV

from gwlearn.linear_model import GWLinearRegression

sklearn.set_config(enable_metadata_routing=True)

gdf = gpd.read_file(get_path("geoda.guerry"))

gwlr = GWLinearRegression(fixed=False, keep_models=True)
gwlr.set_fit_request(geometry=True)
gwlr.set_predict_proba_request(geometry=True)
gwlr.set_predict_request(geometry=True)
gwlr.set_score_request(geometry=True)

gs = GridSearchCV(gwlr, {'bandwidth': [15, 25]}, cv=2)
gs.fit(
    gdf[['Crm_prp', 'Litercy', 'Donatns', 'Lottery']],
    gdf["Suicids"],
    geometry=gdf.representative_point()
)

gs.cv_results_

{'mean_fit_time': array([0.082605  , 0.07042313]),
 'std_fit_time': array([0.01016295, 0.0070281 ]),
 'mean_score_time': array([0.01314092, 0.02728152]),
 'std_score_time': array([3.19480896e-05, 1.44383907e-02]),
 'param_bandwidth': masked_array(data=[15, 25],
              mask=[False, False],
        fill_value=999999),
 'params': [{'bandwidth': 15}, {'bandwidth': 25}],
 'split0_test_score': array([0.25171764, 0.25787701]),
 'split1_test_score': array([-0.6960835 , -0.19733769]),
 'mean_test_score': array([-0.22218293,  0.03026966]),
 'std_test_score': array([0.47390057, 0.22760735]),
 'rank_test_score': array([2, 1], dtype=int32)}