class_docstr: The base graph selection object.
  
  This object defines the structures that all submodular selection algorithms
  should follow if they operate on a graph, such as pairwise similarity 
  measurements. All algorithms will have the same public methods and the same 
  attributes.
  
  NOTE: All ~pairwise~ values in your data must be positive for these
  selection methods to work.
  
  This implementation allows users to pass in either their own symmetric
  square matrix of similarity values, or a data matrix as normal and a function
  that calculates these pairwise values.
  
  Parameters
  ----------
  n_samples : int
          The number of samples to return.
  
  metric : str
          The method for converting a data matrix into a square symmetric matrix
          of pairwise similarities. If a string, can be any of the metrics
          implemented in sklearn (see https://scikit-learn.org/stable/modules/
          generated/sklearn.metrics.pairwise_distances.html), including
          "precomputed" if one has already generated a similarity matrix. Note
          that sklearn calculates distance matrices whereas apricot operates on
          similarity matrices, and so a distances.max() - distances transformation
          is performed on the resulting distances. For backcompatibility,
          'corr' will be read as 'correlation'.
  
  initial_subset : list, numpy.ndarray or None
          If provided, this should be a list of indices into the data matrix
          to use as the initial subset, or a group of examples that may not be
          in the provided data should beused as the initial subset. If indices, 
          the provided array should be one-dimensional. If a group of examples,
          the data should be 2 dimensional.
  
  optimizer : string or optimizers.BaseOptimizer, optional
          The optimization approach to use for the selection. Default is
          'two-stage', which makes selections using the naive greedy algorithm
          initially and then switches to the lazy greedy algorithm. Must be
          one of
  
                  'naive' : the naive greedy algorithm
                  'lazy' : the lazy (or accelerated) greedy algorithm
                  'approximate-lazy' : the approximate lazy greedy algorithm
                  'two-stage' : starts with naive and switches to lazy
                  'stochastic' : the stochastic greedy algorithm
                  'greedi' : the GreeDi distributed algorithm
                  'bidirectional' : the bidirectional greedy algorithm
  
          Default is 'naive'.
  
  optimizer_kwds : dict or None
          A dictionary of arguments to pass into the optimizer object. The keys
          of this dictionary should be the names of the parameters in the optimizer
          and the values in the dictionary should be the values that these
          parameters take. Default is None.
  
  n_neighbors : int or None
          When constructing a similarity matrix, the number of nearest neighbors
          whose similarity values will be kept. The result is a sparse similarity
          matrix which can significantly speed up computation at the cost of
          accuracy. Default is None.
  
  reservoir : numpy.ndarray or None
          The reservoir to use when calculating gains in the sieve greedy
          streaming optimization algorithm in the `partial_fit` method.
          Currently only used for graph-based functions. If a numpy array
          is passed in, it will be used as the reservoir. If None is passed in,
          will use reservoir sampling to collect a reservoir. Default is None.
  
  max_reservoir_size : int 
          The maximum size that the reservoir can take. If a reservoir is passed
          in, this value is set to the size of that array. Default is 1000.
  
  n_jobs : int
          The number of threads to use when performing computation in parallel.
          Currently, this parameter is exposed but does not actually do anything.
          This will be fixed soon.
  
  random_state : int or RandomState or None, optional
          The random seed to use for the random selection process. Only used
          for stochastic greedy.
  
  verbose : bool
          Whether to print output during the selection process.
  
  Attributes
  ----------
  n_samples : int
          The number of samples to select.
  
  metric : callable
          A function that takes in a data matrix and converts it to a square
          symmetric matrix.
  
  ranking : numpy.array int
          The selected samples in the order of their gain.
  
  gains : numpy.array float
          The gain of each sample in the returned set when it was added to the
          growing subset. The first number corresponds to the gain of the first
          added sample, the second corresponds to the gain of the second added
          sample, and so forth.
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code: def _calculate_gains(self, X_pairwise):
      super(BaseGraphSelection, self)._calculate_gains(X_pairwise)
  
imports: import numpy
  from tqdm import tqdm
  from ..optimizers import BaseOptimizer
  from ..optimizers import NaiveGreedy
  from ..optimizers import LazyGreedy
  from ..optimizers import ApproximateLazyGreedy
  from ..optimizers import TwoStageGreedy
  from ..optimizers import StochasticGreedy
  from ..optimizers import BidirectionalGreedy
  from ..optimizers import GreeDi
  from ..optimizers import SieveGreedy
  from ..optimizers import OPTIMIZERS
  from ..utils import PriorityQueue
  from ..utils import check_random_state
  from ..utils import _calculate_pairwise_distances
  from scipy.sparse import csr_matrix
label_desc: Use image node_id 5 for calling the BaseGraphSelection obj's underlying member method code with example usage: obj._calculate_gains(X_pairwise) without return types