FisherC

class pgmpy.metrics.FisherC(ci_test=None, compute_rmsea=False, show_progress=True)

Bases: _BaseUnsupervisedMetric

Returns a p-value for testing whether the given data is faithful to the model structure’s constraints.

Each missing edge in a model structure implies a CI statement. This test uses constructs implied CIs such that they are independent of each other, run statistical tests for each of them on the data, and finally combines them using the Fisher’s method.

Parameters:

ci_test: str or callable

The CI test to use for statistical testing. Can be a string name of any test in pgmpy.ci_tests (e.g. "chi_square", "pearsonr") or a callable.

compute_rmsea: bool (default: False)

While calculating Fisher C statistic if RMSEA value required should be included in method call as True. Returns a tuple of (p-value, rmsea) if True otherwise only the p-value.

show_progress: bool (default: True)

Whether to show the progress of testing.

Returns:

float (default): The p-value for the fit of the model structure to the data. A low

p-value (e.g. <0.05) represents that the model structure doesn’t fit the data well. This is returned if the compute_rmsea parameter is False.

tuple: A (float, float) tuple packing p-value and rmsea value is returned if RMSEA

computation is necessary, i.e., compute_rmsea is True in the method call

Examples

>>> from pgmpy.example_models import load_model
>>> model = load_model("bnlearn/cancer")
>>> df = model.simulate(int(1e3))
>>> fisher_c = FisherC(ci_test="chi_square", compute_rmsea=False)
>>> fisher_c(X=df, causal_graph=model)
0.7504