Add ClassificationKriging

README.rst

@@ -111,11 +111,11 @@ 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
+This class takes as parameters a scikit-learn classification model, and details of 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

examples/10_classification_kriging2d.py

@@ -13,7 +13,7 @@
 from sklearn.datasets import fetch_california_housing
 from sklearn.preprocessing import KBinsDiscretizer
 
-from pykrige.rk import ClassificationKriging
+from pykrige.ck import ClassificationKriging
 from pykrige.compat import train_test_split
 
 svc_model = SVC(C=0.1, gamma="auto", probability=True)
@@ -41,7 +41,7 @@
 
 for m in models:
     print("=" * 40)
-    print("regression model:", m.__class__.__name__)
+    print("classification 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: ",

pykrige/ck.py

@@ -0,0 +1,290 @@
+# coding: utf-8
+from pykrige.compat import Krige, validate_sklearn, check_sklearn_model
+
+validate_sklearn()
+
+from sklearn.metrics import r2_score, accuracy_score
+from sklearn.svm import SVC
+from sklearn.preprocessing import OneHotEncoder
+import numpy as np
+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
+        see OK/UK class description
+    exact_values : bool
+        see OK/UK class description
+    variogram_parameters : list or dict
+        see OK/UK class description
+    variogram_function : callable
+        see OK/UK class description
+    anisotropy_scaling : tuple
+        single value for 2D (UK/OK) and two values in 3D (UK3D/OK3D)
+    anisotropy_angle : tuple
+        single value for 2D (UK/OK) and three values in 3D (UK3D/OK3D)
+    enable_statistics : bool
+        see OK class description
+    coordinates_type : str
+        see OK/UK class description
+    drift_terms : list of strings
+        see UK/UK3D class description
+    point_drift : array_like
+        see UK class description
+    ext_drift_grid : tuple
+        Holding the three values external_drift, external_drift_x and
+        external_drift_z for the UK class
+    functional_drift : list of callable
+        see UK/UK3D class description
+    """
+
+    def __init__(
+        self,
+        classification_model=SVC(),
+        method="ordinary",
+        variogram_model="linear",
+        n_closest_points=10,
+        nlags=6,
+        weight=False,
+        verbose=False,
+        exact_values=True,
+        pseudo_inv=False,
+        pseudo_inv_type="pinv",
+        variogram_parameters=None,
+        variogram_function=None,
+        anisotropy_scaling=(1.0, 1.0),
+        anisotropy_angle=(0.0, 0.0, 0.0),
+        enable_statistics=False,
+        coordinates_type="euclidean",
+        drift_terms=None,
+        point_drift=None,
+        ext_drift_grid=(None, None, None),
+        functional_drift=None,
+    ):
+        check_sklearn_model(classification_model, task="classification")
+        self.classification_model = classification_model
+        self.n_closest_points = n_closest_points
+        self._kriging_kwargs = dict(
+            method=method,
+            variogram_model=variogram_model,
+            nlags=nlags,
+            weight=weight,
+            n_closest_points=n_closest_points,
+            verbose=verbose,
+            exact_values=exact_values,
+            pseudo_inv=pseudo_inv,
+            pseudo_inv_type=pseudo_inv_type,
+            variogram_parameters=variogram_parameters,
+            variogram_function=variogram_function,
+            anisotropy_scaling=anisotropy_scaling,
+            anisotropy_angle=anisotropy_angle,
+            enable_statistics=enable_statistics,
+            coordinates_type=coordinates_type,
+            drift_terms=drift_terms,
+            point_drift=point_drift,
+            ext_drift_grid=ext_drift_grid,
+            functional_drift=functional_drift,
+        )
+
+    def fit(self, p, x, y):
+        """
+        Fit the classification method and also krige the residual.
+
+        Parameters
+        ----------
+        p: ndarray
+            (Ns, d) array of predictor variables (Ns samples, d dimensions)
+            for classification
+        x: ndarray
+            ndarray of (x, y) points. Needs to be a (Ns, 2) array
+            corresponding to the lon/lat, for example 2d classification kriging.
+            array of Points, (x, y, z) pairs of shape (N, 3) for 3d kriging
+        y: ndarray
+            array of targets (Ns, )
+        """
+        self.classification_model.fit(p, y.ravel())
+        print("Finished learning classification model")
+        self.classes_ = self.classification_model.classes_
+
+        self.krige = []
+        for i in range(len(self.classes_) - 1):
+            self.krige.append(Krige(**self._kriging_kwargs))
+
+        ml_pred = self.classification_model.predict_proba(p)
+        ml_pred_ilr = ilr_transformation(ml_pred)
+
+        self.onehotencode = OneHotEncoder(categories=[self.classes_])
+        y_ohe = np.array(self.onehotencode.fit_transform(y).todense())
+        y_ohe_ilr = ilr_transformation(y_ohe)
+
+        for i in range(len(self.classes_) - 1):
+            self.krige[i].fit(x=x, y=y_ohe_ilr[:, i] - ml_pred_ilr[:, i])
+
+        print("Finished kriging residuals")
+
+    def predict(self, p, x, **kwargs):
+        """
+        Predict.
+
+        Parameters
+        ----------
+        p: ndarray
+            (Ns, d) array of predictor variables (Ns samples, d dimensions)
+            for classification
+        x: ndarray
+            ndarray of (x, y) points. Needs to be a (Ns, 2) array
+            corresponding to the lon/lat, for example.
+            array of Points, (x, y, z) pairs of shape (N, 3) for 3d kriging
+
+        Returns
+        -------
+        pred: ndarray
+            The expected value of ys for the query inputs, of shape (Ns,).
+
+        """
+
+        ml_pred = self.classification_model.predict_proba(p)
+        ml_pred_ilr = ilr_transformation(ml_pred)
+
+        pred_proba_ilr = self.krige_residual(x, **kwargs) + ml_pred_ilr
+        pred_proba = inverse_ilr_transformation(pred_proba_ilr)
+
+        return np.argmax(pred_proba, axis=1)
+
+    def krige_residual(self, x, **kwargs):
+        """
+        Calculate the residuals.
+
+        Parameters
+        ----------
+        x: ndarray
+            ndarray of (x, y) points. Needs to be a (Ns, 2) array
+            corresponding to the lon/lat, for example.
+
+        Returns
+        -------
+        residual: ndarray
+            kriged residual values
+        """
+
+        krig_pred = [
+            self.krige[i].predict(x=x, **kwargs) for i in range(len(self.classes_) - 1)
+        ]
+
+        return np.vstack(krig_pred).T
+
+    def score(self, p, x, y, sample_weight=None, **kwargs):
+        """
+        Overloading default classification score method.
+
+        Parameters
+        ----------
+        p: ndarray
+            (Ns, d) array of predictor variables (Ns samples, d dimensions)
+            for classification
+        x: ndarray
+            ndarray of (x, y) points. Needs to be a (Ns, 2) array
+            corresponding to the lon/lat, for example.
+            array of Points, (x, y, z) pairs of shape (N, 3) for 3d kriging
+        y: ndarray
+            array of targets (Ns, )
+        """
+        return accuracy_score(
+            y_pred=self.predict(p, x, **kwargs), y_true=y, sample_weight=sample_weight
+        )
+
+
+def closure(data, k=1.0):
+    """Apply closure to data, sample-wise.
+    Adapted from https://github.com/ofgulban/compoda.
+
+    Parameters
+    ----------
+    data : 2d numpy array, shape [n_samples, n_measurements]
+        Data to be closed to a certain constant. Do not forget to deal with
+        zeros in the data before this operation.
+    k : float, positive
+        Sum of the measurements will be equal to this number.
+
+    Returns
+    -------
+    data : 2d numpy array, shape [n_samples, n_measurements]
+        Closed data.
+
+    Reference
+    ---------
+    [1] Pawlowsky-Glahn, V., Egozcue, J. J., & Tolosana-Delgado, R.
+        (2015). Modelling and Analysis of Compositional Data, pg. 9.
+        Chichester, UK: John Wiley & Sons, Ltd.
+        DOI: 10.1002/9781119003144
+    """
+
+    return k * data / np.sum(data, axis=1)[:, np.newaxis]
+
+
+def ilr_transformation(data):
+    """Isometric logratio transformation (not vectorized).
+    Adapted from https://github.com/ofgulban/compoda.
+
+    Parameters
+    ----------
+    data : 2d numpy array, shape [n_samples, n_coordinates]
+        Barycentric coordinates (closed) in simplex space.
+
+    Returns
+    -------
+    out : 2d numpy array, shape [n_samples, n_coordinates-1]
+        Coordinates in real space.
+
+    Reference
+    ---------
+    [1] Pawlowsky-Glahn, V., Egozcue, J. J., & Tolosana-Delgado, R.
+        (2015). Modelling and Analysis of Compositional Data, pg. 37.
+        Chichester, UK: John Wiley & Sons, Ltd.
+        DOI: 10.1002/9781119003144
+    """
+    data = data + np.finfo(float).eps
+
+    return np.einsum("ij,jk->ik", np.log(data), -helmert(data.shape[1]).T)
+
+
+def inverse_ilr_transformation(data):
+    """Inverse isometric logratio transformation (not vectorized).
+    Adapted from https://github.com/ofgulban/compoda.
+
+    Parameters
+    ----------
+    data : 2d numpy array, shape [n_samples, n_coordinates]
+        Isometric log-ratio transformed coordinates in real space.
+
+    Returns
+    -------
+    out : 2d numpy array, shape [n_samples, n_coordinates+1]
+        Barycentric coordinates (closed) in simplex space.
+
+    Reference
+    ---------
+    [1] Pawlowsky-Glahn, V., Egozcue, J. J., & Tolosana-Delgado, R.
+        (2015). Modelling and Analysis of Compositional Data, pg. 37.
+        Chichester, UK: John Wiley & Sons, Ltd.
+        DOI: 10.1002/9781119003144
+    """
+    data = data + np.finfo(float).eps
+
+    return closure(np.exp(np.einsum("ij,jk->ik", data, -helmert(data.shape[1] + 1))))