class_docstr: Return the PPS (Predictive Power Score) of all properties in relation to the label.
  
  The PPS represents the ability of a feature to single-handedly predict another feature or label.
  In this check, we specifically use it to assess the ability to predict the label by a text property (e.g.
  text length, language etc.).
  A high PPS (close to 1) can mean that there's a bias in the dataset, as a single property can predict the label
  successfully, using simple classic ML algorithms - meaning that a deep learning algorithm may accidentally learn
  these properties instead of more accurate complex abstractions.
  
  Uses the ppscore package - for more info, see https://github.com/8080labs/ppscore
  
  Parameters
  ----------
  properties_to_ignore: Optional[List[str]], default: None
      List of properties to ignore in the check.
  properties_to_include: Optional[List[str]], default: None
      List of properties to include in the check. If None, all properties will be included.
  ppscore_params : dict , default: None
      dictionary of additional parameters for the ppscore.predictors function
  n_top_properties : int , default: 5
      Number of properties 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,
      properties_to_ignore: t.Optional[t.List[str]] = None,
      properties_to_include: t.Optional[t.List[str]] = None,
      ppscore_params: t.Optional[t.Dict[t.Any, t.Any]] = None,
      n_top_properties: int = 5,
      n_samples: int = 100_000,
      **kwargs,
  ):
      super().__init__(**kwargs)
      if (
          properties_to_ignore is not None
          and properties_to_include is not None
      ):
          raise DatasetValidationError(
              "Cannot use both properties_to_ignore and properties_to_include arguments."
          )
      self.properties_to_ignore = properties_to_ignore
      self.properties_to_include = properties_to_include
      self.ppscore_params = ppscore_params or {}
      self.n_top_properties = n_top_properties
      self.n_samples = n_samples
  
file_path: deepchecks/deepchecks/nlp/checks/data_integrity/property_label_correlation.py
imports: import typing
  import pandas
  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.core.errors import DatasetValidationError
  from deepchecks.nlp import Context, SingleDatasetCheck
  from deepchecks.nlp.task_type import TaskType
  from deepchecks.tabular.utils.messages import get_condition_passed_message
  from deepchecks.utils.strings import format_number
  from deepchecks.utils.typing import Hashable
label_desc: Use image node_id 1 to create a new PropertyLabelCorrelation object from inherited base classes: SingleDatasetCheck with example: obj = PropertyLabelCorrelation(properties_to_ignore, properties_to_include, ppscore_params, n_top_properties, n_samples)