.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "examples/08_krige_cv.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. .. rst-class:: sphx-glr-example-title .. _sphx_glr_examples_08_krige_cv.py: Krige CV -------- Searching for optimal kriging parameters with cross validation .. GENERATED FROM PYTHON SOURCE LINES 8-79 .. rst-class:: sphx-glr-script-out .. code-block:: none Fitting 5 folds for each of 8 candidates, totalling 40 fits n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging best_score R² = -0.040 best_params = {'method': 'universal', 'variogram_model': 'spherical'} CV results:: - mean_test_score : [-0.16726998 -0.16726998 -0.17326498 -0.15370651 -0.05679591 -0.05679591 -0.05369984 -0.04040341] - mean_train_score : [1. 1. 1. 1. 1. 1. 1. 1.] - param_method : ['ordinary' 'ordinary' 'ordinary' 'ordinary' 'universal' 'universal' 'universal' 'universal'] - param_variogram_model : ['linear' 'power' 'gaussian' 'spherical' 'linear' 'power' 'gaussian' 'spherical'] Fitting 5 folds for each of 8 candidates, totalling 40 fits n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging n_closest_points will be ignored for UniversalKriging best_score R² = -0.097 best_params = {'method': 'universal3d', 'variogram_model': 'power'} CV results:: - mean_test_score : [-0.25558626 -0.25299609 -0.27331333 -0.26203314 -0.11692599 -0.09740764 -0.15082839 -0.12514965] - mean_train_score : [1. 1. 1. 1. 1. 1. 1. 1.] - param_method : ['ordinary3d' 'ordinary3d' 'ordinary3d' 'ordinary3d' 'universal3d' 'universal3d' 'universal3d' 'universal3d'] - param_variogram_model : ['linear' 'power' 'gaussian' 'spherical' 'linear' 'power' 'gaussian' 'spherical'] | .. code-block:: Python import numpy as np from sklearn.model_selection import GridSearchCV from pykrige.rk import Krige # 2D Kring param opt param_dict = { "method": ["ordinary", "universal"], "variogram_model": ["linear", "power", "gaussian", "spherical"], # "nlags": [4, 6, 8], # "weight": [True, False] } estimator = GridSearchCV(Krige(), param_dict, verbose=True, return_train_score=True) # dummy data X = np.random.randint(0, 400, size=(100, 2)).astype(float) y = 5 * np.random.rand(100) # run the gridsearch estimator.fit(X=X, y=y) if hasattr(estimator, "best_score_"): print("best_score R² = {:.3f}".format(estimator.best_score_)) print("best_params = ", estimator.best_params_) print("\nCV results::") if hasattr(estimator, "cv_results_"): for key in [ "mean_test_score", "mean_train_score", "param_method", "param_variogram_model", ]: print(" - {} : {}".format(key, estimator.cv_results_[key])) # 3D Kring param opt param_dict3d = { "method": ["ordinary3d", "universal3d"], "variogram_model": ["linear", "power", "gaussian", "spherical"], # "nlags": [4, 6, 8], # "weight": [True, False] } estimator = GridSearchCV(Krige(), param_dict3d, verbose=True, return_train_score=True) # dummy data X3 = np.random.randint(0, 400, size=(100, 3)).astype(float) y = 5 * np.random.rand(100) # run the gridsearch estimator.fit(X=X3, y=y) if hasattr(estimator, "best_score_"): print("best_score R² = {:.3f}".format(estimator.best_score_)) print("best_params = ", estimator.best_params_) print("\nCV results::") if hasattr(estimator, "cv_results_"): for key in [ "mean_test_score", "mean_train_score", "param_method", "param_variogram_model", ]: print(" - {} : {}".format(key, estimator.cv_results_[key])) .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 1.267 seconds) .. _sphx_glr_download_examples_08_krige_cv.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: 08_krige_cv.ipynb <08_krige_cv.ipynb>` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: 08_krige_cv.py <08_krige_cv.py>` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: 08_krige_cv.zip <08_krige_cv.zip>` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_