| from sklearn.linear_model import Ridge, Lasso | |
| from utils import seqs_to_onehot, seqs_to_georgiev | |
| from predictors.base_predictors import BaseRegressionPredictor, BaseGPPredictor | |
| class OnehotRidgePredictor(BaseRegressionPredictor): | |
| """Simple one hot encoding + ridge regression.""" | |
| def __init__(self, dataset_name, reg_coef=1.0, **kwargs): | |
| super(OnehotRidgePredictor, self).__init__( | |
| dataset_name, reg_coef, Ridge, **kwargs) | |
| def seq2feat(self, seqs): | |
| return seqs_to_onehot(seqs) | |
| class OnehotLassoPredictor(BaseRegressionPredictor): | |
| """Simple one hot encoding + lasso regression.""" | |
| def __init__(self, dataset_name, reg_coef=1.0, **kwargs): | |
| super(OnehotLassoPredictor, self).__init__( | |
| dataset_name, reg_coef, Lasso, **kwargs) | |
| def seq2feat(self, seqs): | |
| return seqs_to_onehot(seqs) | |
| class OnehotGPPredictor(BaseGPPredictor): | |
| def seq2feat(self, seqs): | |
| return seqs_to_onehot(seqs) | |
| class GeorgievRidgePredictor(BaseRegressionPredictor): | |
| """Georgiev encoding + ridge regression.""" | |
| def __init__(self, dataset_name, reg_coef=1.0, **kwargs): | |
| super(GeorgievRidgePredictor, self).__init__( | |
| dataset_name, reg_coef, Ridge, **kwargs) | |
| def seq2feat(self, seqs): | |
| return seqs_to_georgiev(seqs) | |