Add ClassificationKriging

README.rst

@@ -74,6 +74,7 @@ Kriging algorithms
 * ``OrdinaryKriging3D``: 3D ordinary kriging
 * ``UniversalKriging3D``: 3D universal kriging
 * ``RegressionKriging``: An implementation of Regression-Kriging
+* ``ClassificationKriging``: An implementation of Simplicial Indicator Kriging
 
 
 Wrappers
@@ -109,6 +110,17 @@ the ``OrdinaryKriging`` or the ``UniversalKriging`` class, and performs a correc
 A demonstration of the regression kriging is provided in the
 `corresponding example <http://pykrige.readthedocs.io/en/latest/examples/07_regression_kriging2d.html#sphx-glr-examples-07-regression-kriging2d-py>`_.
 
+Classification Kriging
+------------------
+
+`Simplifical Indicator kriging <https://www.sciencedirect.com/science/article/abs/pii/S1002070508600254>`_ can be performed
+with `pykrige.rk.ClassificationKriging <http://pykrige.readthedocs.io/en/latest/examples/10_classification_kriging2d.html>`_.
+This class takes as parameters a scikit-learn classification model, and details of either either
+the ``OrdinaryKriging`` or the ``UniversalKriging`` class, and performs a correction steps on the ML classification prediction.
+
+A demonstration of the classification kriging is provided in the
+`corresponding example <http://pykrige.readthedocs.io/en/latest/examples/10_classification_kriging2d.html#sphx-glr-examples-10-classification-kriging2d-py>`_.
+
 
 License
 ^^^^^^^

examples/10_classification_kriging2d.py

@@ -0,0 +1,49 @@
+"""
+Classification kriging
+------------------
+
+An example of classification kriging
+"""
+
+import sys
+
+from sklearn.svm import SVC
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.linear_model import LogisticRegression
+from sklearn.datasets import fetch_california_housing
+from sklearn.preprocessing import KBinsDiscretizer
+
+from pykrige.rk import ClassificationKriging
+from pykrige.compat import train_test_split
+
+svc_model = SVC(C=0.1, gamma="auto", probability=True)
+rf_model = RandomForestClassifier(n_estimators=100)
+lr_model = LogisticRegression(max_iter=10000)
+
+models = [svc_model, rf_model, lr_model]
+
+try:
+    housing = fetch_california_housing()
+except PermissionError:
+    # this dataset can occasionally fail to download on Windows
+    sys.exit(0)
+
+# take the first 5000 as Kriging is memory intensive
+p = housing["data"][:5000, :-2]
+x = housing["data"][:5000, -2:]
+target = housing["target"][:5000]
+discretizer = KBinsDiscretizer(encode='ordinal')
+target = discretizer.fit_transform(target.reshape(-1, 1))
+
+p_train, p_test, x_train, x_test, target_train, target_test = train_test_split(
+    p, x, target, test_size=0.3, random_state=42
+)
+
+for m in models:
+    print("=" * 40)
+    print("regression model:", m.__class__.__name__)
+    m_ck = ClassificationKriging(classification_model=m, n_closest_points=10)
+    m_ck.fit(p_train, x_train, target_train)
+    print("Classification Score: ",
+          m_ck.classification_model.score(p_test, target_test))
+    print("CK score: ", m_ck.score(p_test, x_test, target_test))