class_docstr: Base class for symbolic regression / classification estimators.
  
  Warning: This class should not be used directly.
  Use derived classes instead.
class_docstr_tok: ["Base","class","for","symbolic","regression","\/","classification","estimators",".","Warning",":","This","class","should","not","be","used","directly",".","Use","derived","classes","instead","."]
code: def __init__(
      self,
      *,
      population_size=1000,
      hall_of_fame=None,
      n_components=None,
      generations=20,
      tournament_size=20,
      stopping_criteria=0.0,
      const_range=(-1.0, 1.0),
      init_depth=(2, 6),
      init_method="half and half",
      function_set=("add", "sub", "mul", "div"),
      transformer=None,
      metric="mean absolute error",
      parsimony_coefficient=0.001,
      p_crossover=0.9,
      p_subtree_mutation=0.01,
      p_hoist_mutation=0.01,
      p_point_mutation=0.01,
      p_point_replace=0.05,
      max_samples=1.0,
      class_weight=None,
      feature_names=None,
      warm_start=False,
      low_memory=False,
      n_jobs=1,
      verbose=0,
      random_state=None,
  ):
      self.population_size = population_size
      self.hall_of_fame = hall_of_fame
      self.n_components = n_components
      self.generations = generations
      self.tournament_size = tournament_size
      self.stopping_criteria = stopping_criteria
      self.const_range = const_range
      self.init_depth = init_depth
      self.init_method = init_method
      self.function_set = function_set
      self.transformer = transformer
      self.metric = metric
      self.parsimony_coefficient = parsimony_coefficient
      self.p_crossover = p_crossover
      self.p_subtree_mutation = p_subtree_mutation
      self.p_hoist_mutation = p_hoist_mutation
      self.p_point_mutation = p_point_mutation
      self.p_point_replace = p_point_replace
      self.max_samples = max_samples
      self.class_weight = class_weight
      self.feature_names = feature_names
      self.warm_start = warm_start
      self.low_memory = low_memory
      self.n_jobs = n_jobs
      self.verbose = verbose
      self.random_state = random_state
  
imports: import itertools
  from abc import ABCMeta, abstractmethod
  from time import time
  from warnings import warn
  import numpy
  from joblib import Parallel, delayed
  from scipy.stats import rankdata
  from sklearn.base import BaseEstimator
  from sklearn.base import RegressorMixin, TransformerMixin, ClassifierMixin
  from sklearn.exceptions import NotFittedError
  from sklearn.utils import compute_sample_weight
  from sklearn.utils.validation import check_array, _check_sample_weight
  from sklearn.utils.multiclass import check_classification_targets
  from ._program import _Program
  from .fitness import _fitness_map, _Fitness
  from .functions import _function_map, _Function, sig1
  from .utils import _partition_estimators
  from .utils import check_random_state
label_desc: Use image node_id 1 to create a new BaseSymbolic object from inherited base classes: BaseEstimator with example: obj = BaseSymbolic()