Exhaustive Search

class pgmpy.estimators.ExhaustiveSearch(data, scoring_method=None, use_cache=True, **kwargs)

Search class for exhaustive searches over all DAGs with a given set of variables. Takes a StructureScore-Instance as parameter; estimate finds the model with maximal score.

Parameters:

• data (pandas DataFrame object) – dataframe object where each column represents one variable. (If some values in the data are missing the data cells should be set to numpy.nan. Note that pandas converts each column containing numpy.nan`s to dtype `float.)

• scoring_method (Instance of a StructureScore-subclass (K2Score is used as default)) – An instance of K2Score, BDeuScore, BicScore or ‘AICScore’. This score is optimized during structure estimation by the estimate-method.

• state_names (dict (optional)) – A dict indicating, for each variable, the discrete set of states (or values) that the variable can take. If unspecified, the observed values in the data set are taken to be the only possible states.

• use_caching (boolean) – If True, uses caching of score for faster computation. Note: Caching only works for scoring methods which are decomposable. Can give wrong results in case of custom scoring methods.

all_dags(nodes=None)

Computes all possible directed acyclic graphs with a given set of nodes, sparse ones first. 2**(n*(n-1)) graphs need to be searched, given n nodes, so this is likely not feasible for n>6. This is a generator.

Parameters:

nodes (list of nodes for the DAGs (optional)) – A list of the node names that the generated DAGs should have. If not provided, nodes are taken from data.

Returns:

dags – Generator that yields all acyclic nx.DiGraphs, ordered by number of edges. Empty DAG first.

Return type:

Generator object for nx.DiGraphs

Examples

>>> import pandas as pd
>>> from pgmpy.estimators import ExhaustiveSearch
>>> s = ExhaustiveSearch(pd.DataFrame(data={'Temperature': [23, 19],
                                            'Weather': ['sunny', 'cloudy'],
                                            'Humidity': [65, 75]}))
>>> list(s.all_dags())
[<networkx.classes.digraph.DiGraph object at 0x7f6955216438>,
 <networkx.classes.digraph.DiGraph object at 0x7f6955216518>,
....
>>> [dag.edges() for dag in s.all_dags()]
[[], [('Humidity', 'Temperature')], [('Humidity', 'Weather')],
[('Temperature', 'Weather')], [('Temperature', 'Humidity')],
....
[('Weather', 'Humidity'), ('Weather', 'Temperature'), ('Temperature', 'Humidity')]]

all_scores()

Computes a list of DAGs and their structure scores, ordered by score.

Returns:

A list of (score, dag) pairs – A list of (score, dag)-tuples, where score is a float and model a acyclic nx.DiGraph. The list is ordered by score values.

Return type:

list

Examples

>>> import pandas as pd
>>> import numpy as np
>>> from pgmpy.estimators import ExhaustiveSearch, K2Score
>>> # create random data sample with 3 variables, where B and C are identical:
>>> data = pd.DataFrame(np.random.randint(0, 5, size=(5000, 2)), columns=list('AB'))
>>> data['C'] = data['B']
>>> searcher = ExhaustiveSearch(data, scoring_method=K2Score(data))
>>> for score, model in searcher.all_scores():
...   print("{0}        {1}".format(score, model.edges()))
-24234.44977974726      [('A', 'B'), ('A', 'C')]
-24234.449760691063     [('A', 'B'), ('C', 'A')]
-24234.449760691063     [('A', 'C'), ('B', 'A')]
-24203.700955937973     [('A', 'B')]
-24203.700955937973     [('A', 'C')]
-24203.700936881774     [('B', 'A')]
-24203.700936881774     [('C', 'A')]
-24203.700936881774     [('B', 'A'), ('C', 'A')]
-24172.952132128685     []
-16597.30920265254      [('A', 'B'), ('A', 'C'), ('B', 'C')]
-16597.30920265254      [('A', 'B'), ('A', 'C'), ('C', 'B')]
-16597.309183596342     [('A', 'B'), ('C', 'A'), ('C', 'B')]
-16597.309183596342     [('A', 'C'), ('B', 'A'), ('B', 'C')]
-16566.560378843253     [('A', 'B'), ('C', 'B')]
-16566.560378843253     [('A', 'C'), ('B', 'C')]
-16268.324549347722     [('A', 'B'), ('B', 'C')]
-16268.324549347722     [('A', 'C'), ('C', 'B')]
-16268.324530291524     [('B', 'A'), ('B', 'C')]
-16268.324530291524     [('B', 'C'), ('C', 'A')]
-16268.324530291524     [('B', 'A'), ('C', 'B')]
-16268.324530291524     [('C', 'A'), ('C', 'B')]
-16268.324530291524     [('B', 'A'), ('B', 'C'), ('C', 'A')]
-16268.324530291524     [('B', 'A'), ('C', 'A'), ('C', 'B')]
-16237.575725538434     [('B', 'C')]
-16237.575725538434     [('C', 'B')]

estimate()

Estimates the DAG structure that fits best to the given data set, according to the scoring method supplied in the constructor. Exhaustively searches through all models. Only estimates network structure, no parametrization.

Returns:

Estimated Model – A DAG with maximal score.

Return type:

pgmpy.base.DAG

Examples

>>> import pandas as pd
>>> import numpy as np
>>> from pgmpy.estimators import ExhaustiveSearch
>>> # create random data sample with 3 variables, where B and C are identical:
>>> data = pd.DataFrame(np.random.randint(0, 5, size=(5000, 2)), columns=list('AB'))
>>> data['C'] = data['B']
>>> est = ExhaustiveSearch(data)
>>> best_model = est.estimate()
>>> best_model
<pgmpy.base.DAG.DAG object at 0x7f695c535470>
>>> best_model.edges()
[('B', 'C')]