Krige CV

Searching for optimal kriging parameters with cross validation

Out:

Fitting 3 folds for each of 8 candidates, totalling 24 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
best_score R² = -0.030
best_params =  {'method': 'ordinary', 'variogram_model': 'power'}

CV results::
 - mean_test_score : [-0.03194734 -0.03036039 -0.0730884  -0.12390364 -0.03437615 -0.04016295
 -0.08348872 -0.12087894]
 - 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 3 folds for each of 8 candidates, totalling 24 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
best_score R² = 0.001
best_params =  {'method': 'universal3d', 'variogram_model': 'gaussian'}

CV results::
 - mean_test_score : [-0.15076042 -0.15101485 -0.15294586 -0.15537616 -0.03021721 -0.0499446
  0.00147667  0.0005714 ]
 - 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']

import numpy as np
from pykrige.rk import Krige
from pykrige.compat import GridSearchCV


# 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)

# 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)

# 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]))

Total running time of the script: ( 0 minutes 2.881 seconds)