Source code for traveltimes_prediction.models.algorithms.elastic_net_wrapper
from sklearn.linear_model import ElasticNet
from sklearn.preprocessing import StandardScaler
import numpy as np
from ..base_model import BaseModel
[docs]class ElasticNetWrapper(ElasticNet, BaseModel):
"""
Class - wrapper for ElasticNet.
"""
name='ElasticNet'
def __init__(self, **kwargs):
"""
Constructor.
:param dict kwargs:
"""
super().__init__(**kwargs)
self.scaler = StandardScaler()
[docs] def fit(self, X, y, sample_weigth=None):
"""
Method for fitting of the estimator.
:param numpy.ndarray X: Matrix of input features.
:param numpy.ndarray y: Vector of ground truths for X.
:param numpy.ndarray sample_weigth:
: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)
self.max_y_train = max(y)
# ElasticNet requires Fortran style arrays
X = np.asfortranarray(X)
y = np.asfortranarray(y)
super().fit(X, y)
return self
[docs] def predict(self, X):
"""
Method for prediction.
:param numpy.ndarray X: Data, which should be used as input for the estimation of predicted value.
:return: np.ndarray
"""
X = self._impute_prediction_sample(X=X)
X = self.scaler.transform(X)
X = np.asfortranarray(X)
pred = super().predict(X=X)
return self._coerce(pred)
[docs] def dump(self):
"""
Method for dumping of the model providing descriptors of model in dict.
: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_
d['model']['min_y_train'] = self.min_y_train
d['model']['max_y_train'] = self.max_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 model - recreating it from dumped dict.
:param dict model: dumped model
:return: ElasticNetWrapper
"""
inst = ElasticNetWrapper()
inst.coef_ = np.array(model['coef_'])
inst.intercept_ = np.array(model['intercept_'])
inst.n_iter_ = model['n_iter_']
inst.min_y_train = model['min_y_train']
inst.max_y_train = model['max_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