class_docstr: BaseAnalyzer Class, used by ModelAnalyzer and PredictionsAnalyzer.
  
  BaseAnalyzer class is the common base class for the ModelAnalyzer
  and PredictionsAnalyzer classes.
  
  :param dataset:  A matrix of feature vector examples
      (# examples x # features).
  :type dataset: numpy.ndarray or list[][] or pandas.DataFrame
  :param true_y: The true labels for the provided dataset.
  :type true_y: numpy.ndarray or list[] or pandas.Series
  :param feature_names: Feature names.
  :type feature_names: numpy.ndarray or list[]
  :param categorical_features: The categorical feature names.
  :type categorical_features: list[str]
  :param model_task: Optional parameter to specify whether the model
      is a classification or regression model. In most cases, the
      type of the model can be inferred based on the shape of the
      output, where a classifier has a predict_proba method and
      outputs a 2 dimensional array, while a regressor has a
      predict method and outputs a 1 dimensional array.
  :type model_task: str
  :param metric: The metric name to evaluate at each tree node or
      heatmap grid.  Currently supported classification metrics
      include 'error_rate', 'recall_score' for binary
      classification and 'micro_recall_score' or
      'macro_recall_score' for multiclass classification,
      'precision_score' for binary classification and
      'micro_precision_score' or 'macro_precision_score'
      for multiclass classification, 'f1_score' for binary
      classification and 'micro_f1_score' or 'macro_f1_score'
      for multiclass classification, and 'accuracy_score'.
      Supported regression metrics include 'mean_absolute_error',
      'mean_squared_error', 'r2_score', and 'median_absolute_error'.
  :type metric: str
  :param classes: The class names.
  :type classes: numpy.ndarray or list[]
docstr: Computes the matrix filter (aka heatmap) json.
  
  :param features: One or two feature names to compute the heatmap.
  :type features: list
  :param dataset: The dataset on which matrix view needs to be computed.
      The dataset should have the feature columns and the columns
      'true_y' and 'index'. The 'true_y' column should have the true
      target values corresponding to the test data. The 'index'
      column should have the indices. If the analyzer is of type
      PredictionsAnalyzer, then the dataset should include the column
      'pred_y' which will hold the predictions.
  :type dataset: pd.DataFrame
  :param quantile_binning: If true, use quantile binning for the
      heatmap. By default uses equal width binning.
  :type quantile_binning: bool
  :param num_bins: The number of bins per feature in the heatmap.
  :type num_bins: int
  :return: The heatmap in json representation.
  :rtype: dict
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code: def compute_matrix_on_dataset(
      self,
      features,
      dataset,
      quantile_binning=False,
      num_bins=BIN_THRESHOLD,
  ):
      """Computes the matrix filter (aka heatmap) json.
  
      :param features: One or two feature names to compute the heatmap.
      :type features: list
      :param dataset: The dataset on which matrix view needs to be computed.
          The dataset should have the feature columns and the columns
          'true_y' and 'index'. The 'true_y' column should have the true
          target values corresponding to the test data. The 'index'
          column should have the indices. If the analyzer is of type
          PredictionsAnalyzer, then the dataset should include the column
          'pred_y' which will hold the predictions.
      :type dataset: pd.DataFrame
      :param quantile_binning: If true, use quantile binning for the
          heatmap. By default uses equal width binning.
      :type quantile_binning: bool
      :param num_bins: The number of bins per feature in the heatmap.
      :type num_bins: int
      :return: The heatmap in json representation.
      :rtype: dict
      """
      return _compute_matrix_on_dataset(
          self, features, dataset, quantile_binning, num_bins
      )
  
imports: import logging
  from abc import ABC, abstractmethod
  import numpy
  import pandas
  from sklearn.feature_selection import mutual_info_classif, mutual_info_regression
  from erroranalysis._internal.constants import ErrorCorrelationMethods, MatrixParams, Metrics, ModelTask, RootKeys, metric_to_display_name
  from erroranalysis._internal.matrix_filter import compute_matrix
  from erroranalysis._internal.matrix_filter import compute_matrix_on_dataset
  from erroranalysis._internal.metrics import metric_to_func
  from erroranalysis._internal.process_categoricals import process_categoricals
  from erroranalysis._internal.surrogate_error_tree import compute_error_tree
  from erroranalysis._internal.surrogate_error_tree import compute_error_tree_on_dataset
  from erroranalysis._internal.utils import generate_random_unique_indexes
  from erroranalysis._internal.version_checker import check_pandas_version
  from erroranalysis.error_correlation_methods import compute_ebm_global_importance, compute_gbm_global_importance
  from erroranalysis.report import ErrorReport
label_desc: Use image node_id 14 for calling the BaseAnalyzer obj's underlying member method code with example usage: obj.compute_matrix_on_dataset(features, dataset, quantile_binning, num_bins) and returns: _compute_matrix_on_dataset