docstr: Returns the positive class name in binary classification. Useful for computing metrics such as F1 which require a positive and negative class.
  In binary classification, :class:`TabularPredictor.predict_proba()` returns the estimated probability that each row belongs to the positive class.
  Will print a warning and return None if called when `predictor.problem_type != 'binary'`.
  
  Returns
  -------
  The positive class name in binary classification or None if the problem is not binary classification.
code: def positive_class(self):
      """
      Returns the positive class name in binary classification. Useful for computing metrics such as F1 which require a positive and negative class.
      In binary classification, :class:`TabularPredictor.predict_proba()` returns the estimated probability that each row belongs to the positive class.
      Will print a warning and return None if called when `predictor.problem_type != 'binary'`.
  
      Returns
      -------
      The positive class name in binary classification or None if the problem is not binary classification.
      """
      if not self.is_fit:
          if self._positive_class is not None:
              return self._positive_class
          raise AssertionError(
              "Predictor must be fit to return positive_class."
          )
      if self.problem_type != BINARY:
          logger.warning(
              f"Warning: Attempted to retrieve positive class label in a non-binary problem. Positive class labels only exist in binary classification. Returning None instead. self.problem_type is '{self.problem_type}' but positive_class only exists for '{BINARY}'."
          )
          return None
      return self.label_cleaner.cat_mappings_dependent_var[1]
  
imports: from __future__ import annotations
  import copy
  import json
  import logging
  import time
  from collections.abc import Iterable
  from typing import List, Union
  import numpy
  import pandas
  from pandas import DataFrame, Series
  from sklearn.metrics import classification_report
  from autogluon.core.constants import AUTO_WEIGHT, BALANCE_WEIGHT, BINARY, MULTICLASS, QUANTILE, REGRESSION
  from autogluon.core.data.label_cleaner import LabelCleaner, LabelCleanerMulticlassToBinary
  from autogluon.core.learner import AbstractLearner
  from autogluon.core.metrics import Scorer, confusion_matrix, get_metric
  from autogluon.core.models.greedy_ensemble.ensemble_selection import EnsembleSelection
  from autogluon.core.utils import augment_rare_classes, compute_weighted_metric, extract_column, get_leaderboard_pareto_frontier, get_pred_from_proba, get_pred_from_proba_df, infer_problem_type
  from autogluon.features.generators import PipelineFeatureGenerator
label_desc: Use image node_id 8 for calling the AbstractTabularLearner obj's underlying member method code with example usage: obj.positive_class() and returns: self, None, self