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