Source code for traveltimes_prediction.models.algorithms.ridge_wrapper

from sklearn.linear_model import Ridge
from sklearn.preprocessing import StandardScaler
import numpy as np

from ..base_model import BaseModel


[docs]class RidgeWrapper(Ridge, BaseModel): """ Class - wrapper for RidgeRegression. """ name='Ridge' def __init__(self, **kwargs): """ Constructor. :param dict kwargs: """ super().__init__(**kwargs) self.scaler = StandardScaler()
[docs] def fit(self, X, y, sample_weight=None): """ Method for prediction. :param numpy.ndarray X: :param numpy.ndarray y: :param numpy.ndarray sample_weight: :return: object - self """ self._get_descriptors(X) self.scaler = self.scaler.fit(X) X = self.scaler.transform(X) self.min_y_train = np.percentile(y, 10) super().fit(X, y, sample_weight) return self
[docs] def predict(self, X): """ Method for prediction. :param numpy.ndarray X: :return: list """ X = self._impute_prediction_sample(X=X) X = self.scaler.transform(X) pred = super().predict(X=X) return self._coerce(pred)
[docs] def dump(self): """ Method for dumping of the estimator. :return: dict """ d = dict() d['model'] = dict() d['model']['coef_'] = self.coef_.tolist() d['model']['intercept_'] = self.intercept_.tolist() d['model']['n_iter_'] = self.n_iter_.tolist() if self.n_iter_ is not None else "" d['model']['min_y_train'] = self.min_y_train d['model']['scaler'] = dict() d['model']['scaler']['scale_'] = self.scaler.scale_.tolist() d['model']['scaler']['mean_'] = self.scaler.mean_.tolist() d['model']['scaler']['var_'] = self.scaler.var_.tolist() d['model']['scaler']['n_samples_seen_'] = self.scaler.n_samples_seen_ d['model']['_median_imputer'] = self._median_imputer.tolist() d['model_type'] = self.name return d
@staticmethod
[docs] def load(model): """ Method for loading of the estimator - recreating it from dumped dict. :param dict model: :return: instance of RidgeWrapper """ inst = RidgeWrapper() inst.coef_ = np.array(model['coef_']) inst.intercept_ = np.array(model['intercept_']) inst.n_iter_ = np.array(model['n_iter_']) if model['n_iter_'] != "" else None inst.min_y_train = model['min_y_train'] inst.scaler.scale_ = np.array(model['scaler']['scale_']) inst.scaler.mean_ = np.array(model['scaler']['mean_']) inst.scaler.var_ = np.array(model['scaler']['var_']) inst.scaler.n_samples_seen_d = model['scaler']['n_samples_seen_'] inst._median_imputer = np.array(model['_median_imputer']) return inst