class_docstr: Theil-Sen Estimator: robust multivariate regression model.
  
  The algorithm calculates least square solutions on subsets with size
  n_subsamples of the samples in X. Any value of n_subsamples between the
  number of features and samples leads to an estimator with a compromise
  between robustness and efficiency. Since the number of least square
  solutions is "n_samples choose n_subsamples", it can be extremely large
  and can therefore be limited with max_subpopulation. If this limit is
  reached, the subsets are chosen randomly. In a final step, the spatial
  median (or L1 median) is calculated of all least square solutions.
  
  Read more in the :ref:`User Guide <theil_sen_regression>`.
  
  Parameters
  ----------
  fit_intercept : boolean, optional, default True
      Whether to calculate the intercept for this model. If set
      to false, no intercept will be used in calculations.
  
  copy_X : boolean, optional, default True
      If True, X will be copied; else, it may be overwritten.
  
  max_subpopulation : int, optional, default 1e4
      Instead of computing with a set of cardinality 'n choose k', where n is
      the number of samples and k is the number of subsamples (at least
      number of features), consider only a stochastic subpopulation of a
      given maximal size if 'n choose k' is larger than max_subpopulation.
      For other than small problem sizes this parameter will determine
      memory usage and runtime if n_subsamples is not changed.
  
  n_subsamples : int, optional, default None
      Number of samples to calculate the parameters. This is at least the
      number of features (plus 1 if fit_intercept=True) and the number of
      samples as a maximum. A lower number leads to a higher breakdown
      point and a low efficiency while a high number leads to a low
      breakdown point and a high efficiency. If None, take the
      minimum number of subsamples leading to maximal robustness.
      If n_subsamples is set to n_samples, Theil-Sen is identical to least
      squares.
  
  max_iter : int, optional, default 300
      Maximum number of iterations for the calculation of spatial median.
  
  tol : float, optional, default 1.e-3
      Tolerance when calculating spatial median.
  
  random_state : RandomState or an int seed, optional, default None
      A random number generator instance to define the state of the
      random permutations generator.
  
  n_jobs : integer, optional, default 1
      Number of CPUs to use during the cross validation. If ``-1``, use
      all the CPUs.
  
  verbose : boolean, optional, default False
      Verbose mode when fitting the model.
  
  Attributes
  ----------
  coef_ : array, shape = (n_features)
      Coefficients of the regression model (median of distribution).
  
  intercept_ : float
      Estimated intercept of regression model.
  
  breakdown_ : float
      Approximated breakdown point.
  
  n_iter_ : int
      Number of iterations needed for the spatial median.
  
  n_subpopulation_ : int
      Number of combinations taken into account from 'n choose k', where n is
      the number of samples and k is the number of subsamples.
  
  References
  ----------
  - Theil-Sen Estimators in a Multiple Linear Regression Model, 2009
    Xin Dang, Hanxiang Peng, Xueqin Wang and Heping Zhang
    http://home.olemiss.edu/~xdang/papers/MTSE.pdf
code: def _check_subparams(self, n_samples, n_features):
      n_subsamples = self.n_subsamples
  
      if self.fit_intercept:
          n_dim = n_features + 1
      else:
          n_dim = n_features
  
      if n_subsamples is not None:
          if n_subsamples > n_samples:
              raise ValueError(
                  "Invalid parameter since n_subsamples > "
                  "n_samples ({0} > {1}).".format(
                      n_subsamples, n_samples
                  )
              )
          if n_samples >= n_features:
              if n_dim > n_subsamples:
                  plus_1 = "+1" if self.fit_intercept else ""
                  raise ValueError(
                      "Invalid parameter since n_features{0} "
                      "> n_subsamples ({1} > {2})."
                      "".format(plus_1, n_dim, n_samples)
                  )
          else:  # if n_samples < n_features
              if n_subsamples != n_samples:
                  raise ValueError(
                      "Invalid parameter since n_subsamples != "
                      "n_samples ({0} != {1}) while n_samples "
                      "< n_features.".format(n_subsamples, n_samples)
                  )
      else:
          n_subsamples = min(n_dim, n_samples)
  
      if self.max_subpopulation <= 0:
          raise ValueError(
              "Subpopulation must be strictly positive "
              "({0} <= 0).".format(self.max_subpopulation)
          )
  
      all_combinations = max(1, np.rint(binom(n_samples, n_subsamples)))
      n_subpopulation = int(
          min(self.max_subpopulation, all_combinations)
      )
  
      return n_subsamples, n_subpopulation
  
imports: from __future__ import division, print_function, absolute_import
  import warnings
  from itertools import combinations
  import numpy
  from scipy import linalg
  from scipy.special import binom
  from scipy.linalg.lapack import get_lapack_funcs
  from .base import LinearModel
  from ..base import RegressorMixin
  from ..utils import check_random_state
  from ..utils import check_X_y, _get_n_jobs
  from ..utils.random import choice
  from ..externals.joblib import Parallel, delayed
  from ..externals.six.moves import xrange
  from ..exceptions import ConvergenceWarning
label_desc: Use image node_id 2 for calling the TheilSenRegressor obj's underlying member method code with example usage: obj._check_subparams(n_samples, n_features) and returns: n_subsamples,  n_subpopulation