class_docstr: Cox regression learner, using the partial Cox likelihood for
  proportional risks, with many choices of penalization.
  
  Note that this learner does not have predict functions
  
  Parameters
  ----------
  C : `float`, default=1e3
      Level of penalization
  
  penalty : {'none', 'l1', 'l2', 'elasticnet', 'tv', 'binarsity'}, default='l2'
      The penalization to use. Default is 'l2', namely Ridge penalization
  
  solver : {'gd', 'agd'}, default='agd'
      The name of the solver to use.
  
  warm_start : `bool`, default=False
      If true, learning will start from the last reached solution
  
  step : `float`, default=None
      Initial step size used for learning. Used when solver is 'gd' or
      'agd'.
  
  tol : `float`, default=1e-5
      The tolerance of the solver (iterations stop when the stopping
      criterion is below it). By default the solver does ``max_iter``
      iterations
  
  max_iter : `int`, default=100
      Maximum number of iterations of the solver
  
  verbose : `bool`, default=True
      If `True`, we verbose things, otherwise the solver does not
      print anything (but records information in history anyway)
  
  print_every : `int`, default=10
      Print history information when ``n_iter`` (iteration number) is
      a multiple of ``print_every``
  
  record_every : `int`, default=10
      Record history information when ``n_iter`` (iteration number) is
      a multiple of ``record_every``
  
  Other Parameters
  ----------------
  elastic_net_ratio : `float`, default=0.95
      Ratio of elastic net mixing parameter with 0 <= ratio <= 1.
      For ratio = 0 this is ridge (L2 squared) regularization
      For ratio = 1 this is lasso (L1) regularization
      For 0 < ratio < 1, the regularization is a linear combination
      of L1 and L2.
      Used in 'elasticnet' penalty
  
  random_state : int seed, RandomState instance, or None (default)
      The seed that will be used by stochastic solvers. Used in 'sgd',
      'svrg', and 'sdca' solvers
  
  blocks_start : `numpy.array`, shape=(n_features,), default=None
      The indices of the first column of each binarized feature blocks. It
      corresponds to the ``feature_indices`` property of the
      ``FeaturesBinarizer`` preprocessing.
      Used in 'binarsity' penalty
  
  blocks_length : `numpy.array`, shape=(n_features,), default=None
      The length of each binarized feature blocks. It corresponds to the
      ``n_values`` property of the ``FeaturesBinarizer`` preprocessing.
      Used in 'binarsity' penalty
  
  Attributes
  ----------
  coeffs : np.array, shape=(n_features,)
      The learned coefficients of the model
code: def __init__(
      self,
      penalty="l2",
      C=1e3,
      solver="agd",
      step=None,
      tol=1e-5,
      max_iter=100,
      verbose=False,
      warm_start=False,
      print_every=10,
      record_every=10,
      elastic_net_ratio=0.95,
      random_state=None,
      blocks_start=None,
      blocks_length=None,
  ):
      self._actual_kwargs = CoxRegression.__init__.actual_kwargs
      LearnerOptim.__init__(
          self,
          penalty=penalty,
          C=C,
          solver=solver,
          step=step,
          tol=tol,
          max_iter=max_iter,
          verbose=verbose,
          warm_start=warm_start,
          print_every=print_every,
          record_every=record_every,
          sdca_ridge_strength=0,
          elastic_net_ratio=elastic_net_ratio,
          random_state=random_state,
          blocks_start=blocks_start,
          blocks_length=blocks_length,
      )
      self.coeffs = None
  
file_path: tick/tick/survival/cox_regression.py
imports: import numpy
  from tick.base import actual_kwargs
  from tick.preprocessing.utils import safe_array
  from .model_coxreg_partial_lik import ModelCoxRegPartialLik
  from tick.base.learner import LearnerOptim
label_desc: Use image node_id 1 to create a new CoxRegression object from inherited base classes: LearnerOptim with example: obj = CoxRegression(penalty, C, solver, step, tol, max_iter, verbose, warm_start, print_every, record_every, elastic_net_ratio, random_state, blocks_start, blocks_length)