class_docstr: Partial likelihood of the Cox regression model (proportional
  hazards).
  This class gives first order information (gradient and loss) for
  this model.
  
  Attributes
  ----------
  features : `numpy.ndarray`, shape=(n_samples, n_features), (read-only)
      The features matrix
  
  times : `numpy.ndarray`, shape = (n_samples,), (read-only)
      Obverved times
  
  censoring : `numpy.ndarray`, shape = (n_samples,), (read-only)
      Boolean indicator of censoring of each sample.
      ``True`` means true failure, namely non-censored time
  
  n_samples : `int` (read-only)
      Number of samples
  
  n_features : `int` (read-only)
      Number of features
  
  n_failures : `int` (read-only)
      Number of true failure times
  
  n_coeffs : `int` (read-only)
      Total number of coefficients of the model
  
  censoring_rate : `float`
      The censoring_rate (percentage of ???)
  
  Notes
  -----
  There is no intercept in this model
docstr: Set the data into the model object
  
  Parameters
  ----------
  features : `numpy.ndarray`, shape=(n_samples, n_features)
      The features matrix
  
  times : `numpy.array`, shape = (n_samples,)
      Observed times
  
  censoring : `numpy.array`, shape = (n_samples,)
      Indicator of censoring of each sample.
      ``True`` means true failure, namely non-censored time.
      dtype must be unsigned short
  
  Returns
  -------
  output : `ModelCoxRegPartialLik`
      The current instance with given data
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code: def fit(
      self, features: np.ndarray, times: np.array, censoring: np.array
  ) -> Model:
      """Set the data into the model object
  
      Parameters
      ----------
      features : `numpy.ndarray`, shape=(n_samples, n_features)
          The features matrix
  
      times : `numpy.array`, shape = (n_samples,)
          Observed times
  
      censoring : `numpy.array`, shape = (n_samples,)
          Indicator of censoring of each sample.
          ``True`` means true failure, namely non-censored time.
          dtype must be unsigned short
  
      Returns
      -------
      output : `ModelCoxRegPartialLik`
          The current instance with given data
      """
      # The fit from Model calls the _set_data below
      return Model.fit(self, features, times, censoring)
  
file_path: tick/tick/survival/model_coxreg_partial_lik.py
imports: import numpy
  from tick.base_model import Model, ModelFirstOrder
  from tick.preprocessing.utils import safe_array
  from .build.survival import ModelCoxRegPartialLikDouble
  from .build.survival import ModelCoxRegPartialLikFloat
label_desc: Use image node_id 2 for calling the ModelCoxRegPartialLik obj's underlying member method code with example usage: obj.fit(features, times, censoring) and returns: Model