class_docstr: Search class for learning tree related graph structure. The algorithms
  supported are Chow-Liu and Tree-augmented naive bayes (TAN).
  
  Chow-Liu constructs the maximum-weight spanning tree with mutual information
  score as edge weights.
  
  TAN is an extension of Naive Bayes classifier to allow a tree structure over
  the independent variables to account for interaction.
  
  Parameters
  ----------
  data: pandas.DataFrame object
      dataframe object where each column represents one variable.
  
  root_node: str, int, or any hashable python object, default is None.
      The root node of the tree structure. If None then root node is auto-picked
      as the node with the highest sum of edge weights.
  
  n_jobs: int (default: -1)
      Number of jobs to run in parallel. `-1` means use all processors.
  
  References
  ----------
  [1] Chow, C. K.; Liu, C.N. (1968), "Approximating discrete probability
      distributions with dependence trees", IEEE Transactions on Information
      Theory, IT-14 (3): 462–467
  
  [2] Friedman N, Geiger D and Goldszmidt M (1997). Bayesian network classifiers.
      Machine Learning 29: 131–163
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docstr: Helper function to TAN (Tree Augmented Naive Bayes) algorithm for
  estimating tree structure from given data. Refer to
  pgmpy.estimators.TreeSearch for more details. This function returns the
  edge weights matrix.
  
  Parameters
  ----------
  data: pandas.DataFrame object
      dataframe object where each column represents one variable.
  
  class_node: str
      The class node for TAN. The edge weight is computed as I(X, Y | class_node).
  
  edge_weights_fn: str or function (default: mutual_info)
      Method to use for computing edge weights. Options are:
          1. 'mutual_info': Mutual Information Score.
          2. 'adjusted_mutual_info': Adjusted Mutual Information Score.
          3. 'normalized_mutual_info': Normalized Mutual Information Score.
          4. function(array[n_samples,], array[n_samples,]): Custom function.
  
  n_jobs: int (default: -1)
      Number of jobs to run in parallel. `-1` means use all processors.
  
  show_progress: boolean
      If True, shows a progress bar for the running algorithm.
  
  Returns
  -------
  weights: numpy 2D array, shape = (n_columns, n_columns)
      symmetric matrix where each element represents an edge weight.
  
  Examples
  --------
  >>> import numpy as np
  >>> import pandas as pd
  >>> from pgmpy.estimators import TreeSearch
  >>> values = pd.DataFrame(np.random.randint(low=0, high=2, size=(1000, 5)),
  ...                       columns=['A', 'B', 'C', 'D', 'E'])
  >>> est = TreeSearch(values, root_node='B')
  >>> model = est.estimate(estimator_type='tan')
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code: def _get_conditional_weights(
      data,
      class_node,
      edge_weights_fn="mutual_info",
      n_jobs=-1,
      show_progress=True,
  ):
      """
      Helper function to TAN (Tree Augmented Naive Bayes) algorithm for
      estimating tree structure from given data. Refer to
      pgmpy.estimators.TreeSearch for more details. This function returns the
      edge weights matrix.
  
      Parameters
      ----------
      data: pandas.DataFrame object
          dataframe object where each column represents one variable.
  
      class_node: str
          The class node for TAN. The edge weight is computed as I(X, Y | class_node).
  
      edge_weights_fn: str or function (default: mutual_info)
          Method to use for computing edge weights. Options are:
              1. 'mutual_info': Mutual Information Score.
              2. 'adjusted_mutual_info': Adjusted Mutual Information Score.
              3. 'normalized_mutual_info': Normalized Mutual Information Score.
              4. function(array[n_samples,], array[n_samples,]): Custom function.
  
      n_jobs: int (default: -1)
          Number of jobs to run in parallel. `-1` means use all processors.
  
      show_progress: boolean
          If True, shows a progress bar for the running algorithm.
  
      Returns
      -------
      weights: numpy 2D array, shape = (n_columns, n_columns)
          symmetric matrix where each element represents an edge weight.
  
      Examples
      --------
      >>> import numpy as np
      >>> import pandas as pd
      >>> from pgmpy.estimators import TreeSearch
      >>> values = pd.DataFrame(np.random.randint(low=0, high=2, size=(1000, 5)),
      ...                       columns=['A', 'B', 'C', 'D', 'E'])
      >>> est = TreeSearch(values, root_node='B')
      >>> model = est.estimate(estimator_type='tan')
      """
      # Step 0: Check for edge weight computation method
      if edge_weights_fn == "mutual_info":
          edge_weights_fn = mutual_info_score
      elif edge_weights_fn == "adjusted_mutual_info":
          edge_weights_fn = adjusted_mutual_info_score
      elif edge_weights_fn == "normalized_mutual_info":
          edge_weights_fn = normalized_mutual_info_score
      elif not callable(edge_weights_fn):
          raise ValueError(
              f"edge_weights_fn should either be 'mutual_info', 'adjusted_mutual_info', "
              f"'normalized_mutual_info'or a function of form fun(array, array). Got: f{edge_weights_fn}"
          )
  
      # Step 1: Compute edge weights for a fully connected graph.
      n_vars = len(data.columns)
      pbar = combinations(data.columns, 2)
      if show_progress and config.SHOW_PROGRESS:
          pbar = tqdm(
              pbar,
              total=(n_vars * (n_vars - 1) / 2),
              desc="Building tree",
          )
  
      def _conditional_edge_weights_fn(u, v):
          """
          Computes the conditional edge weight of variable index u and v conditioned on class_node
          """
          cond_marginal = (
              data.loc[:, class_node].value_counts() / data.shape[0]
          )
          cond_edge_weight = 0
          for index, marg_prob in cond_marginal.items():
              df_cond_subset = data[data.loc[:, class_node] == index]
              cond_edge_weight += marg_prob * edge_weights_fn(
                  df_cond_subset.loc[:, u], df_cond_subset.loc[:, v]
              )
          return cond_edge_weight
  
      vals = Parallel(n_jobs=n_jobs)(
          delayed(_conditional_edge_weights_fn)(u, v) for u, v in pbar
      )
      weights = np.zeros((n_vars, n_vars))
      indices = np.triu_indices(n_vars, k=1)
      weights[indices] = vals
      weights.T[indices] = vals
  
      return weights
  
file_path: pgmpy/pgmpy/estimators/TreeSearch.py
imports: from itertools import combinations
  import networkx
  import numpy
  import pandas
  from joblib import Parallel, delayed
  from sklearn.metrics import adjusted_mutual_info_score, mutual_info_score, normalized_mutual_info_score
  from tqdm.auto import tqdm
  from pgmpy import config
  from pgmpy.base import DAG
  from pgmpy.estimators import StructureEstimator
label_desc: Use image node_id 4 for calling the TreeSearch obj's underlying member method code with example usage: obj._get_conditional_weights(data, class_node, edge_weights_fn, n_jobs, show_progress) and returns: weights, cond_edge_weight