# 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.
