from .metrics import *
[docs]def w_inverse_LMAE(actual, predicted):
"""
Inverse Local MAE
"""
return 1/mean_absolute_error(actual, predicted)
[docs]def w_inverse_log_LMAE(actual, predicted):
"""
Inverse Log Local MAE
"""
return np.log(max(abs(actual-predicted))/mean_absolute_error(actual, predicted))
[docs]def get_k_nearest_neighbors(point, data, k, metric):
"""
Get the k nearest neighbors of a point in a dataset
"""
distances = metric(point, data)
return distances.argsort()[:k]
[docs]def get_k_nearest_neighbors_weights(point, data, k, metric, weights):
"""
Get the k nearest neighbors of a point in a dataset weighing the neighbors
"""
distances = metric(point, data)
return distances.argsort()[:k], weights(distances)
[docs]def predict_inverse_LMAE(point, data, k, metric):
"""
Predict the target value of a point using the inverse LMAE
"""
neighbors = get_k_nearest_neighbors(point, data, k, metric)
return np.average(data[neighbors], axis=0, weights=w_inverse_LMAE)
[docs]def error_bias(data, k, metric):
"""
Calculate the bias of the error
"""
error_bias = []
for i in range(len(data)):
neighbors = get_k_nearest_neighbors(data[i], data, k, metric)
error_bias.append(np.sum(data[neighbors] - data[i])/k)
return error_bias