Tree Search

class pgmpy.estimators.TreeSearch(data, root_node=None, n_jobs=- 1, **kwargs)

static chow_liu(data, root_node, edge_weights_fn='mutual_info', n_jobs=- 1, show_progress=True)

Chow-Liu algorithm for estimating tree structure from given data. Refer to pgmpy.estimators.TreeSearch for more details.

Parameters

• data (pandas.DataFrame object) – dataframe object where each column represents one variable.

• root_node (str, int, or any hashable python object.) – The root node of the tree structure.

• n_jobs (int (default: -1)) – Number of jobs to run in parallel. -1 means use all processors.

• 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.

• show_progress (boolean) – If True, shows a progress bar for the running algorithm.

Returns

model – The estimated model structure.

Return type

pgmpy.base.DAG instance

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='chow-liu')

estimate(estimator_type='chow-liu', class_node=None, edge_weights_fn='mutual_info', show_progress=True)

Estimate the DAG structure that fits best to the given data set without parametrization.

Parameters

• estimator_type (str (chow-liu | tan)) – The algorithm to use for estimating the DAG.

• class_node (string, int or any hashable python object. (optional)) – Optional if estimator_type = ‘tan’. If None then class node is auto-picked as the node with the second highest sum of edge weights.

• edge_weights_fn (str or function (default: mutual info)) – Method to use for computing edge weights. By default Mutual Info Score is used.

• show_progress (boolean) – If True, shows a progress bar for the running algorithm.

Returns

model – The estimated model structure.

Return type

pgmpy.base.DAG instance

Examples

>>> import numpy as np
>>> import pandas as pd
>>> import networkx as nx
>>> import matplotlib.pyplot as plt
>>> 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='chow-liu')
>>> nx.draw_circular(model, with_labels=True, arrowsize=20, arrowstyle='fancy',
...                  alpha=0.3)
>>> plt.show()
>>> est = TreeSearch(values)
>>> model = est.estimate(estimator_type='chow-liu')
>>> nx.draw_circular(model, with_labels=True, arrowsize=20, arrowstyle='fancy',
...                  alpha=0.3)
>>> plt.show()
>>> est = TreeSearch(values, root_node='B')
>>> model = est.estimate(estimator_type='tan', class_node='A')
>>> nx.draw_circular(model, with_labels=True, arrowsize=20, arrowstyle='fancy',
...                  alpha=0.3)
>>> plt.show()
>>> est = TreeSearch(values)
>>> model = est.estimate(estimator_type='tan')
>>> nx.draw_circular(model, with_labels=True, arrowsize=20, arrowstyle='fancy',
...                  alpha=0.3)
>>> plt.show()