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import numpy as np
from ..idw.idw import IDW
from ..utils.distance import haversine, euclidean
class SpatialFeatures:
"""Generate spatial features from N-closest locations
Args:
n_closest : 'N' closest locations
idw : To use idw output as one of the feature
idw_exponent : Exponent to be used in idw (if idw is False, ignore)
coordinate_type : 'Eucleadian' or 'Geographic' (if idw is False, ignore)
resolution : 'low', 'standard' or 'high' (if idw is False, ignore)
"""
def __init__(
self,
n_closest: int = 5,
idw: bool = True,
idw_exponent: float = 2,
coordinate_type: str = "Euclidean",
resolution: str = "standard",
) -> None:
self.n_closest = n_closest
self.idw = idw
self.idw_exponent = idw_exponent
self.coordinate_type = coordinate_type
self.resolution = resolution
if self.coordinate_type == "Eucledian":
self.distance = euclidean
elif self.coordinate_type == "Geographic":
self.distance = haversine
else:
raise NotImplementedError(
'"'
+ self.coordinate_type
+ '" is not implemented yet or invalid'
)
def fit(self, X: np.ndarray, y: np.ndarray) -> object:
"""[summary]
Args:
X : Reference X data (longitude, latitude, time, ...)
y : Reference y data
Returns:
self
"""
self.X = X
self.y = y
def transform(self, X: np.ndarray) -> np.ndarray:
"""Transform features
Args:
X (np.ndarray): (longitude, latitude, time, ...)
Raises:
Exception: If not already fitted
Returns:
np.ndarray: Transformed features
"""
try:
self.X
except AttributeError:
raise Exception("Not fitted yet. first call the 'fit' method")
Xflag = False
if np.all(X == self.X):
Xflag = True
F = (
np.empty(
(X.shape[0], (X.shape[1] - 3) + self.n_closest * 2 + self.idw)
)
* np.nan
)
for t in np.unique(X[:, 2]): # Iterating over time
mask = X[:, 2] == t # rows with time t
trn_mask = self.X[:, 2] == t
X_local = X[mask]
self_X_local = self.X[trn_mask]
lonlat = X_local[:, :2] # locs
self_lonlat = self_X_local[:, :2] # Reference locs
dst = self.distance(lonlat, self_lonlat)
if Xflag:
idx = dst.argsort()[:, 1 : self.n_closest + 1]
else:
idx = dst.argsort()[:, : self.n_closest]
# Feature set 1: closest distances
f1 = dst[np.arange(lonlat.shape[0])[:, None], idx]
self_y_local = self.y[trn_mask] # Train obs
ymat = self_y_local[:, None].repeat(lonlat.shape[0], 1).T
# Feature set 2: closest observations
f2 = ymat[np.arange(lonlat.shape[0])[:, None], idx]
if self.idw:
def for_each_row(i):
i = i[0]
model = IDW(exponent=self.idw_exponent)
model.resolution = self.resolution
model.coordinate_type = self.coordinate_type
model.fit(self_lonlat[idx[i]], self_y_local[idx[i]])
return model.predict(lonlat[i][None, :])
# Feature set 3: IDW observation
f3 = np.apply_along_axis(
for_each_row,
axis=1,
arr=np.arange(lonlat.shape[0]).reshape(-1, 1),
)
F[mask] = np.concatenate([X_local[:, 3:], f1, f2, f3], axis=1)
else:
F[mask] = np.concatenate([X_local[:, 3:], f1, f2], axis=1)
return F
def fit_transform(self, X: np.ndarray, y: np.ndarray):
self.fit(X, y)
return self.transform(X)
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