Using this example, I created a precision-recall AUC eval metric for Catboost. However, I need some guidance on how to make it compatible with the class_weights argument in which I will be passing a list (example: [625.0, 0.500400320256205]) because I have a large class imbalance. Below is my custom eval metric:
from sklearn.metrics import average_precision_score
import numpy as np
from scipy.special import expit
# define pr-AUC custom eval metric
class PrecisionRecallAUC:
# define a static method to use in evaluate method
@staticmethod
def get_pr_auc(y_true, y_pred):
# fit predictions to logistic sigmoid function
y_pred = expit(y_pred).astype(float)
# actual values should be 1 or 0 integers
y_true = y_true.astype(int)
# calculate average precision
flt_pr_auc = average_precision_score(y_true=y_true,
y_score=y_pred)
return flt_pr_auc
# define a function to tell catboost that greater is better (or not)
def is_max_optimal(self):
# greater is better
return True
# get the score
def evaluate(self, approxes, target, weight):
# make sure length of approxes == 1
assert len(approxes) == 1
# make sure length of target is the same as predictions
assert len(target) == len(approxes[0])
# set target to integer and save as y_true
y_true = np.array(target).astype(int)
# save predictions
y_pred = approxes[0]
# generate score
score = self.get_pr_auc(y_true=y_true, y_pred=y_pred)
return score, 1
# return score
def get_final_error(self, error, weight):
return error
How do I use my list of class weights in this custom eval metric?
Thank you in advance.
