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import numpy as np
from ..base import Base
class CustomInterpolator(Base):
"""
Class to interpolate by fitting a sklearn type Regressor to
the given data.
Parameters
----------
regressor: class definition,
This variable is used to pass in the Regressor we would like
to use for interpolation. The regressor sould be sklearn type
regressor. Example from sklearn.ensemble -> RandomForestRegressor
reg_kwargs: dict, optional
This is a dictionary that is passed into the Regressor initialization.
Use this to change the behaviour of the passed regressor. Default = empty dict
Attributes
----------
reg : object
Object of the `regressor` class passed.
"""
def __init__(
self, regressor, resolution="standard", coordinate_type="Euclidean"
):
super().__init__(resolution, coordinate_type)
self.reg = regressor
def _fit(self, X, y):
"""Function for fitting.
This function is not supposed to be called directly.
"""
self.reg.fit(X, y)
return self
def _predict_grid(self, x1lim, x2lim):
"""Function for grid interpolation.
This function is not supposed to be called directly.
"""
# getting the boundaries for interpolation
x1min, x1max = x1lim
x2min, x2max = x2lim
# building the grid
x1 = np.linspace(x1min, x1max, self.resolution)
x2 = np.linspace(x2min, x2max, self.resolution)
X1, X2 = np.meshgrid(x1, x2)
return self.reg.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.reg.predict(X)
def __repr__(self):
return self.__class__.__name__ + "." + self.reg.__class__.__name__
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