# coding: utf-8 """Classification Kriging.""" import numpy as np from pykrige.compat import Krige, validate_sklearn, check_sklearn_model validate_sklearn() from sklearn.metrics import accuracy_score from sklearn.svm import SVC from sklearn.preprocessing import OneHotEncoder from scipy.linalg import helmert class ClassificationKriging: """ An implementation of Simplicial Indicator Kriging applied to classification ilr transformed residuals. Parameters ---------- classification_model: machine learning model instance from sklearn method: str, optional type of kriging to be performed variogram_model: str, optional variogram model to be used during Kriging n_closest_points: int number of closest points to be used during Ordinary Kriging nlags: int see OK/UK class description weight: bool see OK/UK class description verbose: bool
# coding: utf-8 from pykrige.compat import validate_sklearn validate_sklearn() from pykrige.ok import OrdinaryKriging from pykrige.uk import UniversalKriging from pykrige.ok3d import OrdinaryKriging3D from pykrige.uk3d import UniversalKriging3D from sklearn.base import RegressorMixin, BaseEstimator from sklearn.svm import SVR from sklearn.metrics import r2_score krige_methods = {'ordinary': OrdinaryKriging, 'universal': UniversalKriging, 'ordinary3d': OrdinaryKriging3D, 'universal3d': UniversalKriging3D } threed_krige = ('ordinary3d', 'universal3d') def validate_method(method): if method not in krige_methods.keys(): raise ValueError('Kriging method must be ' 'one of {}'.format(krige_methods.keys())) class Krige(RegressorMixin, BaseEstimator): """ A scikit-learn wrapper class for Ordinary and Universal Kriging. This works with both Grid/RandomSearchCv for finding the best Krige parameters combination for a problem.