class_docstr: Return the PPS (Predictive Power Score) of all features in relation to the label.
  
  The PPS represents the ability of a feature to single-handedly predict another feature or label.
  A high PPS (close to 1) can mean that this feature's success in predicting the label is actually due to data
  leakage - meaning that the feature holds information that is based on the label to begin with.
  
  Uses the ppscore package - for more info, see https://github.com/8080labs/ppscore
  
  Parameters
  ----------
  ppscore_params : dict , default: None
      dictionary of additional parameters for the ppscore.predictors function
  n_top_features : int , default: 5
      Number of features to show, sorted by the magnitude of difference in PPS
  n_samples : int , default: 100_000
      number of samples to use for this check.
  random_state : int , default: None
      Random state for the ppscore.predictors function
code: def __init__(
      self,
      ppscore_params: t.Optional[t.Dict[t.Any, t.Any]] = None,
      n_top_features: int = 5,
      n_samples: int = 100_000,
      random_state: t.Optional[int] = None,
      **kwargs,
  ):
      super().__init__(**kwargs)
      self.ppscore_params = ppscore_params or {}
      self.n_top_features = n_top_features
      self.n_samples = n_samples
      self.random_state = random_state
  
file_path: deepchecks/deepchecks/tabular/checks/data_integrity/feature_label_correlation.py
imports: import typing
  import deepchecks.ppscore
  from deepchecks.core import CheckResult, ConditionCategory, ConditionResult
  from deepchecks.core.check_utils.feature_label_correlation_utils import get_pps_figure, pd_series_to_trace
  from deepchecks.tabular import Context, SingleDatasetCheck
  from deepchecks.tabular.utils.messages import get_condition_passed_message
  from deepchecks.tabular.utils.task_type import TaskType
  from deepchecks.utils.strings import format_number
  from deepchecks.utils.typing import Hashable
label_desc: Use image node_id 1 to create a new FeatureLabelCorrelation object from inherited base classes: SingleDatasetCheck with example: obj = FeatureLabelCorrelation(ppscore_params, n_top_features, n_samples, random_state)