class_docstr: Configuration class for a selection.
  
  Attributes:
      method:
          The method to use for selection, one of CORESET, RANDOM, CORAL, ACTIVE_LEARNING
      n_samples:
          The maximum number of samples to be chosen by the selection
          including the samples in the preselected tag. One of the stopping
          conditions.
      min_distance:
          The minimum distance of samples in the chosen set, one of the
          stopping conditions.
      name:
          The name of this selection, defaults to a name consisting of all
          other attributes and the datetime. A new tag will be created in the
          web-app under this name.
  
  Examples:
      >>> # select 100 images with CORESET selection
      >>> config = SelectionConfig(method=SamplingMethod.CORESET, n_samples=100)
      >>>
      >>> # give your selection a name
      >>> config = SelectionConfig(method=SamplingMethod.CORESET, n_samples=100, name='my-selection')
      >>>
      >>> # use minimum distance between samples as stopping criterion
      >>> config = SelectionConfig(method=SamplingMethod.CORESET, n_samples=-1, min_distance=0.1)
class_docstr_tok: ["Configuration","class","for","a","selection",".","Attributes",":","method",":","The","method","to","use","for","selection",",","one","of","CORESET",",","RANDOM",",","CORAL",",","ACTIVE_LEARNING","n_samples",":","The","maximum","number","of","samples","to","be","chosen","by","the","selection","including","the","samples","in","the","preselected","tag",".","One","of","the","stopping","conditions",".","min_distance",":","The","minimum","distance","of","samples","in","the","chosen","set",",","one","of","the","stopping","conditions",".","name",":","The","name","of","this","selection",",","defaults","to","a","name","consisting","of","all","other","attributes","and","the","datetime",".","A","new","tag","will","be","created","in","the","web-app","under","this","name",".","Examples",":",">",">",">","#","select","100","images","with","CORESET","selection",">",">",">","config","=","SelectionConfig","(","method=SamplingMethod.CORESET",",","n_samples=100",")",">",">",">",">",">",">","#","give","your","selection","a","name",">",">",">","config","=","SelectionConfig","(","method=SamplingMethod.CORESET",",","n_samples=100",",","name='my-selection","'",")",">",">",">",">",">",">","#","use","minimum","distance","between","samples","as","stopping","criterion",">",">",">","config","=","SelectionConfig","(","method=SamplingMethod.CORESET",",","n_samples=-1",",","min_distance=0.1",")"]
code: def __init__(
      self,
      method: SamplingMethod = SamplingMethod.CORESET,
      n_samples: int = 32,
      min_distance: float = -1,
      name: str = None,
  ):
      raise_active_learning_deprecation_warning()
      self.method = method
      self.n_samples = n_samples
      self.min_distance = min_distance
      if name is None:
          date_time = datetime.now().strftime("%m_%d_%Y__%H_%M_%S")
          name = f"{self.method}_{self.n_samples}_{self.min_distance}_{date_time}"
      self.name = name
  
imports: import warnings
  from datetime import datetime
  from lightly.active_learning import raise_active_learning_deprecation_warning
  from lightly.openapi_generated.swagger_client.models.sampling_method import SamplingMethod
label_desc: Use image node_id 1 to create a new SelectionConfig object  with example: obj = SelectionConfig(method, n_samples, min_distance, name)