Python library including algorithms for optimization problems like weighted blending, hyperparameter tuning and more.
from sklearn.metrics import mean_absolute_error from mlopt import BlendingTransformer labels = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9] predictions_model_1 = [0.11, 0.19, 0.25, 0.37, 0.55, 0.62, 0.78, 0.81, 0.94] predictions_model_2 = [0.07, 0.21, 0.29, 0.33, 0.53, 0.54, 0.74, 0.74, 0.91] predictions_blended = [predictions_model_1, predictions_model_2] blender = BlendingTransformer(metric=mean_absolute_error, maximize=False) blender.fit(y=labels, X=predictions_blended) weights = blender.weights score = blender.score print('MAE 1: {:0.3f}'.format(mean_absolute_error(labels, predictions_model_1))) print('MAE 2: {:0.3f}'.format(mean_absolute_error(labels, predictions_model_2))) print('Optimized blending weights: ', weights) print('MAE blended: {:0.3f}'.format(score))
