ApproxInference#

class pgmpy.inference.ApproxInference(model)[source]#

Bases: object

Initializes the Approximate Inference class.

Parameters:

model: Instance of pgmpy.models.DiscreteBayesianNetwork or pgmpy.models.DynamicBayesianNetwork

Examples

>>> from pgmpy.example_models import load_model
>>> model = load_model("bnlearn/alarm")
>>> infer = ApproxInference(model)

get_distribution(samples, variables, state_names=None, joint=True)[source]#

Computes distribution of variables from given data samples.

Parameters:

samples: pandas.DataFrame: A dataframe of samples generated from the model.
variables: list (array-like): A list of variables whose distribution needs to be computed.
state_names: dict (default: None): A dict of state names for each variable in variables in the form {variable_name: list of states}. If None, inferred from the data but is possible that the final distribution misses some states.
joint: boolean: If joint=True, computes the joint distribution over variables. Else, returns a dict with marginal distribution of each variable in variables.

map_query(variables, n_samples=10000, samples=None, evidence=None, virtual_evidence=None, state_names=None, show_progress=True, seed=None)[source]#

Finds the most probable state in the joint distribution of variables. Calculates the result by generating samples and calculating most probable states based on the probabilities.

Parameters:

variables: list: List of variables for which the probability distribution needs to be calculated.
n_samples: int: The number of samples to generate for computing the distributions. Higher n_samples results in more accurate results at the cost of more computation time.
samples: pd.DataFrame (default: None): If provided, uses these samples to compute the distribution instead of generating samples. samples must conform with the provided evidence and virtual_evidence.
evidence: dict (default: None): The observed values. A dict key, value pair of the form {var: state_name}.
virtual_evidence: list (default: None): A list of pgmpy.factors.discrete.TabularCPD representing the virtual/soft evidence.
state_names: dict (default: None): A dict of state names for each variable in variables in the form {variable_name: list of states}. If None, inferred from the data but is possible that the final distribution misses some states.
show_progress: boolean (default: True): If True, shows a progress bar when generating samples.
seed: int (default: None): Sets the seed for the random generators.

Returns:

MAP values: dict: The most probable state of provided variables given the evidence.

Examples

>>> from pgmpy.example_models import load_model
>>> from pgmpy.inference import ApproxInference
>>> from pgmpy.factors.discrete import State, TabularCPD
>>> model = load_model("bnlearn/alarm")
>>> infer = ApproxInference(model)
>>> print(infer.map_query(variables=["HISTORY", "CVP"]))
{'HISTORY': 'FALSE', 'CVP': 'NORMAL'}
>>> virtual_evidence_history = TabularCPD(
...     variable="HISTORY",
...     variable_card=2,
...     values=[[0.99], [0.01]],
...     state_names={"HISTORY": ["TRUE", "FALSE"]},
... )
>>> evidence = {"CVP": "NORMAL"}
>>> print(
...     infer.map_query(
...         variables=["HISTORY"],
...         evidence=evidence,
...         virtual_evidence=[virtual_evidence_history],
...     )
... )
{'HISTORY': 'TRUE'}

query(variables, n_samples=10000, samples=None, evidence=None, virtual_evidence=None, joint=True, state_names=None, show_progress=True, seed=None)[source]#

Method for doing approximate inference based on sampling in Bayesian Networks and Dynamic Bayesian Networks.

Parameters:

variables: list: List of variables for which the probability distribution needs to be calculated.
n_samples: int: The number of samples to generate for computing the distributions. Higher n_samples results in more accurate results at the cost of more computation time.
samples: pd.DataFrame (default: None): If provided, uses these samples to compute the distribution instead of generating samples. samples must conform with the provided evidence and virtual_evidence.
evidence: dict (default: None): The observed values. A dict key, value pair of the form {var: state_name}.
virtual_evidence: list (default: None): A list of pgmpy.factors.discrete.TabularCPD representing the virtual/soft evidence.
state_names: dict (default: None): A dict of state names for each variable in variables in the form {variable_name: list of states}. If None, inferred from the data but is possible that the final distribution misses some states.
show_progress: boolean (default: True): If True, shows a progress bar when generating samples.
seed: int (default: None): Sets the seed for the random generators.

Returns:

Probability distribution: pgmpy.factors.discrete.TabularCPD: The queried probability distribution.

Examples

>>> from pgmpy.example_models import load_model
>>> from pgmpy.inference import ApproxInference
>>> model = load_model("bnlearn/alarm")
>>> infer = ApproxInference(model)
>>> infer.query(variables=["HISTORY"])
<DiscreteFactor representing phi(HISTORY:2) at 0x...>
>>> infer.query(variables=["HISTORY", "CVP"], joint=True)
<DiscreteFactor representing phi(HISTORY:2, CVP:3) at 0x...>
>>> infer.query(
...     variables=["HISTORY", "CVP"], joint=False
... )
{'HISTORY': <DiscreteFactor representing phi(HISTORY:2) at 0x...>,
 'CVP': <DiscreteFactor representing phi(CVP:3) at 0x...>}