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