this will be a long question. I’m trying to define my own custom objective function I want the XGBClassifier, so I run
from xgboost import XGBClassifier
the documentation of xgboost says:
A custom objective function can be provided for the objective parameter. In this case, it should have the signature
objective(y_true, y_pred) -> grad, hess :
y_true: array_like of shape [n_samples], The target values
y_pred: array_like of shape [n_samples], The predicted values
grad: array_like of shape [n_samples], The value of the gradient for each sample point.
hess: array_like of shape [n_samples], The value of the second derivative for each sample point
Now, I’ve coded this custom:
def guess_averse_loss(y_true, y_pred):
y_true = y_true.astype(int)
y_pred = y_pred.astype(int)
... stuffs ...
return grad, hess
everything is compatible with the previous documentation.
If I run:
classifier=XGBClassifier(eval_metric=custom_weighted_accuracy,objective=guess_averse_loss,**params_common_model)
classifier.train(X_train, y_train)
(where custom_weighted_accuracy is a custom metric defined by me following the documentation of scikitlearn) I get the error:
-> first_term = np.multiply(cost_matrix[y_true, y_pred], np.exp(y_pred - y_true))
IndexError: shape mismatch: indexing arrays could not be broadcast together with shapes (4043,) (4043,5)
So, y_pred enters the function as a matrix (n_samples x n_classes) where the element ij is the probability that the sample i belongs to the class j.
Then, I modify the line as
first_term = np.multiply(cost_matrix[y_true, np.argmax(y_pred, axis=1)],np.exp(np.argmax(y_pred, axis=1) - y_true))
so it passes from a matrix to an array, This leads to the error:
unknown custom metric
so it seems that the problem now is the metric.
I try to remove the custom obj function using the default one and another error comes:
XGBoostError: Check failed: in_gpair->Size() % ngroup == 0U (3 vs. 0) : must have exactly ngroup * nrow gpairs
WHAT CAN I DO???
You read what I've tried, I'm excepting some suggestion to solve this problems
