"""Mean Squared Log Error for regression"""
import typing
import numpy as np
from h2oaicore.metrics import CustomScorer
from sklearn.metrics import mean_squared_log_error
class MyMeanSquaredLogError(CustomScorer):
_description = "My Mean Squared Error Scorer for Regression."
_regression = True
_maximize = False
_perfect_score = 0
_display_name = "MSLE"
_supports_sample_weight = False
def score(self,
actual: np.array,
predicted: np.array,
sample_weight: typing.Optional[np.array] = None,
labels: typing.Optional[np.array] = None,
**kwargs) -> float:
if not ((actual >= 0).all() and (predicted >= 0).all()):
return 1e36
return mean_squared_log_error(actual, predicted)
