class_docstr: Calculate label drift between train dataset and test dataset, using statistical measures.
  
  Check calculates a drift score for the label in test dataset, by comparing its distribution to the train
  dataset.
  
  For numerical columns, we use the Kolmogorov-Smirnov statistic.
  See https://en.wikipedia.org/wiki/Kolmogorov%E2%80%93Smirnov_test
  We also support Earth Mover's Distance (EMD).
  See https://en.wikipedia.org/wiki/Wasserstein_metric
  
  For categorical distributions, we use the Cramer's V.
  See https://en.wikipedia.org/wiki/Cram%C3%A9r%27s_V
  We also support Population Stability Index (PSI).
  See https://www.lexjansen.com/wuss/2017/47_Final_Paper_PDF.pdf.
  
  For categorical labels, it is recommended to use Cramer's V, unless your variable includes categories with a
  small number of samples (common practice is categories with less than 5 samples).
  However, in cases of a variable with many categories with few samples, it is still recommended to use Cramer's V.
  
  **Note:** In case of highly imbalanced classes, it is recommended to use Cramer's V, together with setting
  the ``balance_classes`` parameter to ``True``.
  
  Parameters
  ----------
  margin_quantile_filter: float, default: 0.025
      float in range [0,0.5), representing which margins (high and low quantiles) of the distribution will be filtered
      out of the EMD calculation. This is done in order for extreme values not to affect the calculation
      disproportionally. This filter is applied to both distributions, in both margins.
  min_category_size_ratio: float, default 0.01
      minimum size ratio for categories. Categories with size ratio lower than this number are binned
      into an "Other" category. Ignored if balance_classes=True.
  max_num_categories_for_drift: int, default: None
      Only for classification. Max number of allowed categories. If there are more,
      they are binned into an "Other" category.
  max_num_categories_for_display: int, default: 10
      Max number of categories to show in plot.
  show_categories_by: str, default: 'largest_difference'
      Specify which categories to show for categorical features' graphs, as the number of shown categories is limited
      by max_num_categories_for_display. Possible values:
      - 'train_largest': Show the largest train categories.
      - 'test_largest': Show the largest test categories.
      - 'largest_difference': Show the largest difference between categories.
  numerical_drift_method: str, default: "KS"
      decides which method to use on numerical variables. Possible values are:
      "EMD" for Earth Mover's Distance (EMD), "KS" for Kolmogorov-Smirnov (KS).
  categorical_drift_method: str, default: "cramers_v"
      decides which method to use on categorical variables. Possible values are:
      "cramers_v" for Cramer's V, "PSI" for Population Stability Index (PSI).
  balance_classes: bool, default: False
      If True, all categories will have an equal weight in the Cramer's V score. This is useful when the categorical
      variable is highly imbalanced, and we want to be alerted on changes in proportion to the category size,
      and not only to the entire dataset. Must have categorical_drift_method = "cramers_v".
      If True, the variable frequency plot will be created with a log scale in the y-axis.
  ignore_na: bool, default False
      For categorical columns only. If True, ignores nones for categorical drift. If False, considers none as a
      separate category. For numerical columns we always ignore nones.
  min_samples : int , default: 10
      Minimum number of samples required to calculate the drift score. If there are not enough samples for either
      train or test, the check will raise a ``NotEnoughSamplesError`` exception.
  n_samples : int , default: 100_000
      Number of samples to use for drift computation and plot.
  random_state : int , default: 42
      Random seed for sampling.
docstr: Return label drift score.
docstr_tok: ["Return","label","drift","score","."]
code: def reduce_output(
      self, check_result: CheckResult
  ) -> Dict[str, float]:
      """Return label drift score."""
      return {"Label Drift Score": check_result.value["Drift score"]}
  
file_path: deepchecks/deepchecks/tabular/checks/train_test_validation/label_drift.py
imports: from typing import Dict
  from deepchecks.core import CheckResult
  from deepchecks.core.reduce_classes import ReduceLabelMixin
  from deepchecks.tabular import Context, TrainTestCheck
  from deepchecks.tabular.utils.task_type import TaskType
  from deepchecks.utils.abstracts.label_drift import LabelDriftAbstract
label_desc: Use image node_id 3 for calling the LabelDrift obj's underlying member method code with example usage: obj.reduce_output(check_result) and returns: dict