docstr: Returns table of metrics available.
  
  
  Example
  -------
  >>> from pycaret.datasets import get_data
  >>> jewellery = get_data('jewellery')
  >>> from pycaret.clustering import *
  >>> exp_name = setup(data = jewellery)
  >>> all_metrics = get_metrics()
  
  
  reset: bool, default = False
      If True, will reset all changes made using add_metric() and get_metric().
  
  
  include_custom: bool, default = True
      Whether to include user added (custom) metrics or not.
  
  
  raise_errors: bool, default = True
      If False, will suppress all exceptions, ignoring models
      that couldn't be created.
  
  
  Returns:
      pandas.DataFrame
docstr_tok: ["Returns","table","of","metrics","available",".","Example","--","--","--","-",">",">",">","from","pycaret.datasets","import","get_data",">",">",">","jewellery","=","get_data","(","'jewellery","'",")",">",">",">","from","pycaret.clustering","import","*",">",">",">","exp_name","=","setup","(","data","=","jewellery",")",">",">",">","all_metrics","=","get_metrics","(",")","reset",":","bool",",","default","=","False","If","True",",","will","reset","all","changes","made","using","add_metric","(",")","and","get_metric","(",")",".","include_custom",":","bool",",","default","=","True","Whether","to","include","user","added","(","custom",")","metrics","or","not",".","raise_errors",":","bool",",","default","=","True","If","False",",","will","suppress","all","exceptions",",","ignoring","models","that","could","n't","be","created",".","Returns",":","pandas.DataFrame"]
code: def get_metrics(
      self,
      reset: bool = False,
      include_custom: bool = True,
      raise_errors: bool = True,
  ) -> pd.DataFrame:
      """
      Returns table of metrics available.
  
  
      Example
      -------
      >>> from pycaret.datasets import get_data
      >>> jewellery = get_data('jewellery')
      >>> from pycaret.clustering import *
      >>> exp_name = setup(data = jewellery)
      >>> all_metrics = get_metrics()
  
  
      reset: bool, default = False
          If True, will reset all changes made using add_metric() and get_metric().
  
  
      include_custom: bool, default = True
          Whether to include user added (custom) metrics or not.
  
  
      raise_errors: bool, default = True
          If False, will suppress all exceptions, ignoring models
          that couldn't be created.
  
  
      Returns:
          pandas.DataFrame
  
      """
  
      if reset and not self._setup_ran:
          raise ValueError("setup() needs to be ran first.")
  
      np.random.seed(self.seed)
  
      if reset:
          self._all_metrics = self._get_metrics(
              raise_errors=raise_errors
          )
  
      metric_containers = self._all_metrics
      rows = [v.get_dict() for k, v in metric_containers.items()]
  
      df = pd.DataFrame(rows)
      df.set_index("ID", inplace=True, drop=True)
  
      if not include_custom:
          df = df[df["Custom"] is False]
  
      return df
  
imports: from typing import Any, List, Optional, Tuple, Union
  import numpy
  import pandas
  import pycaret.internal.patches.sklearn
  import pycaret.internal.patches.yellowbrick
  import pycaret.internal.persistence
  import pycaret.internal.preprocess
  from pycaret.containers.metrics.clustering import get_all_metric_containers
  from pycaret.containers.models.clustering import get_all_model_containers
  from pycaret.internal.logging import get_logger
  from pycaret.internal.pycaret_experiment.unsupervised_experiment import _UnsupervisedExperiment
  from pycaret.utils.generic import MLUsecase
label_desc: Use image node_id 7 for calling the ClusteringExperiment obj's underlying member method code with example usage: obj.get_metrics(reset, include_custom, raise_errors) and returns: df