Data Structures
Data structures.
- class VermaConstraint(lhs_cfactor: Expression, lhs_expr: Expression, rhs_cfactor: Expression, rhs_expr: Expression, variables: Tuple[Variable, ...])[source]
Represent a Verma constraint.
Create new instance of VermaConstraint(lhs_cfactor, lhs_expr, rhs_cfactor, rhs_expr, variables)
- lhs_cfactor: Expression
Alias for field number 0
- lhs_expr: Expression
Alias for field number 1
- rhs_cfactor: Expression
Alias for field number 2
- rhs_expr: Expression
Alias for field number 3
- classmethod from_element(element) VermaConstraint [source]
Extract content from each element in the vector returned by verma.constraint.
- Parameters:
element – An element in the vector returned by verma.constraint
- Returns:
A Verma constraint tuple for the given element
See also
Extracting from R objects https://rpy2.github.io/doc/v3.4.x/html/vector.html#extracting-items
- class DSeparationJudgement(separated: bool, left: Variable, right: Variable, conditions: Tuple[Variable, ...])[source]
Record if a left/right pair are d-separated given the conditions.
By default, acts like a boolean, but also caries evidence graph.
- classmethod create(left: Variable, right: Variable, conditions: Iterable[Variable] | None = None, *, separated: bool = True) DSeparationJudgement [source]
Create a d-separation judgement in canonical form.
- test(df: DataFrame, *, boolean: bool = False, method: Literal['pearson', 'chi-square', 'cressie_read', 'freeman_tuckey', 'g_sq', 'log_likelihood', 'modified_log_likelihood', 'power_divergence', 'neyman'] | None = None, significance_level: float | None = None, _method_checked: bool = False) bool | CITestTuple [source]
Test for conditional independence, given some data.
- Parameters:
df – A dataframe.
boolean – Should results be returned as a pre-cutoff boolean?
method – Conditional independence from
pgmpy
to use. If none, defaults topgmpy.estimators.CITests.cressie_read()
.significance_level – The statistical tests employ this value for comparison with the p-value of the test to determine the independence of the tested variables. If none, defaults to 0.01. Only applied if
boolean=True
.
- Returns:
Tests the null hypothesis that X is independent of Y given Zs. If
boolean=False
, returns a three-tuple of chi, dof, p_value. Ifboolean=True
, make sure you also setsignificance_level=0.05
or your preferred value, then returns simply a boolean if the test fails.- Raises:
ValueError – if any parts of the judgement aren’t in the dataframe’s columns