| import numpy as np |
| from scipy.spatial.distance import cdist |
|
|
| from ..base import Base |
| from ..utils.distance import euclidean, haversine |
|
|
|
|
| class SpatialAverage(Base): |
| """ |
| Class to interpolate by fitting a XGBoost Regressor to given |
| data. |
| Note that radius you specify must be in kilometres if you are passing latitude and longitude as inputs |
| """ |
|
|
| def __init__( |
| self, |
| radius=100, |
| resolution="standard", |
| coordinate_type="Euclidean", |
| **kwargs |
| ): |
| super().__init__(resolution, coordinate_type) |
| self.radius = radius |
| if self.coordinate_type == "Geographic": |
| self.distance = haversine |
| elif self.coordinate_type == "Euclidean": |
| self.distance = euclidean |
| else: |
| raise NotImplementedError( |
| "Only Geographic and Euclidean Coordinates are available" |
| ) |
|
|
| def _fit(self, X, y): |
| """Function for fitting. |
| This function is not supposed to be called directly. |
| """ |
| self.X = X |
| self.y = y |
| return self |
|
|
| def _predict_grid(self, x1lim, x2lim): |
| """Function for grid interpolation. |
| This function is not supposed to be called directly. |
| """ |
| |
| x1min, x1max = x1lim |
| x2min, x2max = x2lim |
|
|
| |
| x1 = np.linspace(x1min, x1max, self.resolution) |
| x2 = np.linspace(x2min, x2max, self.resolution) |
| X1, X2 = np.meshgrid(x1, x2) |
| return self._predict(np.asarray([X1.ravel(), X2.ravel()]).T) |
|
|
| def _predict(self, X): |
| """Function for interpolation on specific points. |
| This function is not supposed to be called directly. |
| """ |
| return self._average(X) |
|
|
| def _average(self, X): |
| dist = self.distance(X, self.X) |
| mask = self.radius >= dist |
| return (self.y * mask).sum(axis=1) / mask.sum(axis=1) |
|
|
