class_docstr: Estimator for learning linear regressors by AdaGrad.
  
  Solves the following objective:
  
  .. code-block::
  
      minimize_w  1 / n_samples * \sum_i loss(w^T x_i, y_i)
                  + alpha * l1_ratio * ||w||_1
                  + alpha * (1 - l1_ratio) * 0.5 * ||w||^2_2
class_docstr_tok: ["Estimator","for","learning","linear","regressors","by","AdaGrad",".","Solves","the","following","objective",":","..","code-block",":",":","minimize_w","1","\/","n_samples","*","\\sum_i","loss","(","w^T","x_i",",","y_i",")","+","alpha","*","l1_ratio","*","||w||_1","+","alpha","*","(","1","-","l1_ratio",")","*","0.5","*","||w||^2_2"]
code: def _get_loss(self):
      losses = {
          "squared": SquaredLoss(),
          "huber": Huber(self.epsilon),
          "epsilon_insensitive": EpsilonInsensitive(self.epsilon),
          "absolute": EpsilonInsensitive(0),
      }
      return losses[self.loss]
  
imports: import numpy
  from sklearn.utils import check_random_state
  from .base import BaseClassifier, BaseRegressor
  from .dataset_fast import get_dataset
  from .adagrad_fast import _adagrad_fit
  from .sgd_fast import ModifiedHuber
  from .sgd_fast import Hinge
  from .sgd_fast import SmoothHinge
  from .sgd_fast import SquaredHinge
  from .sgd_fast import Log
  from .sgd_fast import SquaredLoss
  from .sgd_fast import EpsilonInsensitive
  from .sgd_fast import Huber
label_desc: Use image node_id 2 for calling the AdaGradRegressor obj's underlying member method code with example usage: obj._get_loss() and returns: losses